MXPA01010970A - Phenotype and biological marker identification system. - Google Patents

Abstract

A phenotyping system for obtaining multiple parameters of an organism in order to full characterize said organism. Said phenotype comprising the results of at least 20 assays relating to cell populations and/or cell associated molecules, the results of at least 20 assays relating to soluble factor and clinical parameters.

Description

SYSTEM PE IDENTIFICATION OF BIOLOGICAL MARKERS AND PHENOTYPE Scope of the Invention The present invention provides a system for identifying biological marker and phenotype and methods for identifying and using novel patterns of biological markers relating to diseases, disease progression, response to therapy and normal biological functions. The discovery and use of novel patterns of biological markers will result in a better effective development in cost of drugs, including the improvement of the selection of patients in clinical trials and the identification of therapeutic agents with increased safety and efficacy greatly. The information of phenotype and biological markers can also be used in diagnostic applications.

BACKGROUND OF THE INVENTION As a result of recent innovations in drug discovery including combinatorial chemistry, genetics and high-throughput screening, the number of candidate drugs available for clinical testing exceeds the development and economic capacity of the pharmaceutical industries. In 1998, the world's leading pharmaceutical and biotechnology companies spent more than $ 50 billion on research and development, more than a third of which was spent directly on clinical development. As a result of a number of factors, including increased competition and pressure from health organizations and other sponsors, the pharmaceutical industry is looking to increase quality, including the safety and efficacy of new drugs brought to market and improve efficiency of clinical development.

Recent innovations in drug discovery, therefore, have contributed to making clinical trials a bottleneck. The numbers of therapeutic targets being identified and leading compounds being generated far exceed the ability of pharmaceutical companies to conduct the clinical tests as they are currently being done. In addition, as the industry currently estimates that the average cost of developing a new drug is $ 500 million, it is prohibitively expensive to develop all potential candidate drugs.

The pharmaceutical industry is being forced to seek equivalent technological improvements in drug development. Clinical trials are still very expensive and very risky, and decision-making is often based on highly subjective analyzes. As a result, it is often difficult to determine the patient population for which a drug is most effective, the appropriate dose for a given drug and the potential side effects associated with its use. Not only does this lead to more flaws in clinical development, it can also lead to approved products that can be dosed, improperly prescribed or cause dangerous side effects. With an increasing number of drugs in their facilities, pharmaceutical companies require technologies to identify objective measures of the safety and efficacy profile of a candidate drug before the process of drug development.

An attempt to deal with the masses of information and technologies is to separate from traditional methods of drug identification and development.

As a variety of different analytical, clinical and information management technologies are continuously advancing, it may be possible to develop a phenotype for an individual or population that allows for an unprecedented systematic evaluation of such an individual or population. The phenotype for a given individual includes, in theory, all the measurable characteristics of an individual at all points in time. One use of such phenotype information is the identification of biological markers.

Biological markers are characterized in that when measured or evaluated they have, inter alia, a discrete relationship or correlation as an indicator of normal biological processes, pathogenic processes or pharmacological responses to a therapeutic intervention. The pharmacological responses to the intervention Therapeutic measures include, but are not limited to, responses to general intervention (eg, efficacy), response to intervention dose, lateral effect profiles of the intervention, and pharmacokinetic properties. The answers can be correlated with either beneficial or adverse (for example, toxic) changes. Biomarkers include patterns of cells or molecules that change in association with a pathological process and have a diagnostic and / or prognostic value. Biological markers can include levels of cell populations and their associated molecules, levels of soluble factors, levels of other molecules, genotypic information, gene expression levels, genetic mutations, and genetic parameters that can be correlated with the presence and / or progression of the disease.

In contrast to such points of clinical extremes as measures of disease progression or recurrence or quality of life (which typically take a long time to determine), biomarkers can provide a more rapid and quantitative measure of a clinical profile of a drug. . The single biological markers currently used in both clinical practice and drug development include cholesterol, prostate specific antigen ("PSA"), CD4 T cells and viral RNA. Despite the well-known correlation between high cholesterol and heart disease, despite prostate cancer, and diminished CD4 positive T cells and viral RNA in AIDS, biological markers correlated with many other diseases have yet to be identified. As a result, although government agencies and pharmaceutical companies are increasingly seeking to develop biomarkers for the use of clinical trials, the use of biomarkers in drug development has been limited to date.

Although there are many potential biological markers, there is a limited technology that is able to bypass the vast amount of information necessary to establish the correlation of biological markers with normal biological processes, diseases, disease progression and response to therapy. The Phenotyping requires the instrumentation and testing required to measure hundreds of thousands of parameters, a computer system to allow these data to be accessed easily, programs to correlate information patterns with clinical data and the ability to use the information resulting in the development process. of the drug The present invention provides such technology.

SUMMARY OF THE INVENTION The present invention relates to phenotyping an organism or a class or subclass of organisms. The present invention also includes the identification of biological markers that are measured and evaluated as an indicator of normal biological processes, pathogenic processes or pharmacological responses to a therapeutic intervention. This invention includes technology capable of providing quantitative, sensitive, reproducible and rapid measurements of multiple and diverse biological markers that could adequately profile a phenotype of an organism or a disease status of a patient and response to therapy. In addition, because blood is the only tissue richest in information, and easily accessible for testing, the invention focuses on identifying the biological parameters of small blood samples. The invention includes a multidisciplinary format comprising three main elements: instrumentation, test development and clinical information.

BRIEF DESCRIPTION OF THE FIGURES FIG. 1 is a schematic representation of the types of information that are assimilated to obtain a modality of an identification system of a biological marker.

Figure 2 depicts a schematic representation of an improved MLSC instrument of the invention (called "SurroScan" instrumentation).

Figure 3 illustrates the integrated information infrastructure for analyzing the data obtained in the present invention.

Figures 4A-C describe the results obtained in Example 1 showing that CD27 + and CD27"CD28" T cells can vary between samples Blood samples from three different donors (Figures 4A, B and C) were stained with Cy5 anti -CD27 and Cy5.t ant¡-CD8.

Figures 5A and B describe measurements of robust cells with MLSC of 2 colors. Figure 5A demonstrates the consistency of CD8 T cell counts from 6 different capillaries. Cy5.5 anti-CD8 was combined with a different antibody conjugate Cy5 for each of the capillaries (anti-CD3, CD25, CD27, CD4RA, CD62L, CD69). Fifty different blood samples were analyzed.

The graphs of tables and bars show that the distribution of cell counts are very similar for each capillary. Paired linear regression also shows a high degree of consistency for these trials (data not shown). The Rgura 5 shows the consistency of two measurements of B cells, one with Cy5.5 anti-CD20 and one with 0 5.5 anti-CD19. The 95% confidence interval (dotted line) of the linear regression includes a slope of 1 and the adjustment has a correlation coefficient of 0.97.

Figure 6 shows a classification matrix comparing CD8 T and CD4 T cells in samples of RA patients and blood bank samples.

Figures 7A and B show the results of a three-color cell assay on a SurroScan instrument.

Figures 8A-C show the results of the dyeing of intracellular molecules as measured with the MLSC technology.

Figures 9A-C show the results of a 3-channel detection analysis using MLSC technology.

DETAILED DESCRIPTION OF THE INVENTION The present invention is directed to the phenotyping of an organism or a class or subclass of organisms. In theory, the phenotyping of an organism includes obtaining all the measurable characteristics of that individual, past and present. While the complete phenotyping of any organism is not practical or even possible, the phenotyping discovered and described here provides an unprecedented amount of an unprecedented number of parameter types or characteristics to provide a source of information that will allow the analysis of normal biological functions , diseases, progression of diseases and changes associated with virtually any disturbance to the organism.

One utility of the phenotyping system taught by the present invention is towards the identification of biological markers for normal biological processes, diseases or medical conditions. In order to realize this aspect of the present invention, it is necessary to have i) biological information of a population of individuals, ii) an adequate amount of data of each individual, preferably obtained by multiple sampling in time, and iii) a system storage and retrieval of information that a) can comprehensively incorporate a wide variety of types of information and b) can perform a significant correlation analysis of disparate data types. Figure 1 illustrates information that is useful for creating a biological marker system.

The disease and the progression of the disease involve the complex interaction of genetic and environmental factors. The present invention has the potential to identify and trace changes in the patterns of biological markers that reflect the genetic and environmental factors of small blood samples. In addition, the present invention helps decipher the components of the susceptibility of the disease, progression of the disease and response to therapy.

The present invention is capable of verifying cells, proteins, organic molecules, genotype, soluble factors, clinical and environmental factors, all of which have been used as biological markers in the development of drugs and as markers of diseases. Examples of a known biological marker include verification for decreases in CD4 positive T cells and levels of viral RNA in AIDS, elevated cholesterol levels as an accepted biological marker for heart disease and changing levels of PSA as a marker for protein found in the blood of patients with prostate cancer.

Since biological characteristics or parameters that can be discovered to be a biological marker or part of a marker "cluster" are often not predictable, it is essential that the appropriate database contains information regarding as many parameters as possible. .

The present invention extends to a phenotype of a given organism, methods for assembling such a phenotype and methods for using such a phenotype. The phenotype of an organism or class or subclass or organisms comprises a large collection of data related to the organism or class or subclass of organisms. The novel aspect of the present invention is based on the disparate nature of the data and the amount of data of each of the various categories of data available in an organism. A phenotype can only reach its full utility if the data that define the phenotype are extensive. For example, a phenotype for a human patient that contains a standard blood profile and clinical factors routinely obtained from a physical examination can not provide enough information to fully exploit such a phenotype. Although the trials involved and the data obtained are within the scientific and clinical capabilities of the technique, obtain all the information of a single organism, is a novel task. Although the management and maintenance of a phenotype lends itself to computerization, a given phenotype can be maintained in traditional formats. The manipulation of phenotypes to identify a biological marker or to observe the effect of a disturbance in the organism is, of course, greatly simplified by the use of computer analysis via a computer. As described above, the complete phenotyping of an organism would literally include thousands or possibly millions of data points. In the preferred aspects of this invention, a phenotype comprises more than 40 biological parameters, more preferably, more than 100 parameters, and even more preferably, more than 200 different parameters, and in some cases, more than 300 parameter parameters . The phenotype must contain biological parameters that include information on cell assays, soluble factor assays and clinical information. In the preferred embodiment, the results of at least 20 cell assays that incorporate measurements of at least 20 populations of cells and / or molecules associated with cells and the results of at least 20 assays of soluble factors are included in the phenotype, together with the clinical information In the most preferred embodiments, the results of at least 40 cell assays incorporating measurements from at least 40 populations of cells and / or molecules associated with cells and at least 40 assays of soluble factors, preferably with an extensive set of parameters, are included. clinical and environmental In the preferred embodiments of the invention, more than 20 clinical parameters are included, preferably more than 40 and in some cases, more than 60 clinical parameters.

A rich and easily accessible source of biological information for a patient is blood. At present, there are more than 200 discrete leukocyte cell surface antigens, identified, with the identified antibodies. In addition, there are literally thousands of proteins and other soluble factors and small molecules that can be identified in the blood. The problem, therefore, is not finding enough information content in the blood, but efficiently extracting all available information from limited amounts of blood.

Many levels of biological markers can vary widely from individual to individual. In many cases, such variations can be random, but this is not always the case. For example, in some situations, the basal levels may be specific to the individual, and only by taking multiple readings from an individual would it be possible to identify a biological marker. Although it may not be likely that a baseline is established for a healthy individual, it can be a valuable information gained from the variations over time in a given individual, who has a disease or medical condition. For example, a patient with rheumatoid arthritis may show interesting variations when taking medicine or not, or when exhibiting a severe flare-up of symptoms. If such longitudinal correlations exist, a review of the longitudinal data of other similarly situated patients could confirm the valuable biological markers associated with the disease. When there are longitudinal data over an extended period of time, the number of individuals needed for the analysis to be statistically significant may be relatively small.

An additional application of the present invention is to verify the dose response studies. In this modality, a population of individuals is evaluated before and after the administration of a drug and after increasing the doses of drug. In this modality, the selected population can be of healthy individuals, and the extreme point of the response to the anticipated biological dose is the profile of toxicity or lateral effects. In modalities where individuals have a particular disease or medical condition, the markers can be identified by efficiency, along with the negative effects of the drug.

By evaluating the information of the individuals before and after the administration of drugs, it will be possible to identify markers or groupings of markers associated with the administration and response to the drug. In some situations, such markers could be used as an endpoint for clinical studies. For example, in contrast to such clinical endpoints, such as measures of disease progression or recurrence or quality of life (which typically take a long time to evaluate), biological markers can provide a more rapid and quantitative measure of a clinical profile of a drug.

In other embodiments of the present invention, longitudinal studies of individuals receiving a drug or treatment for the prevention or treatment of a disease or medical condition, could constitute the population of individuals that is evaluated. By correlating the biological indicators of individuals before they receive treatment with subsequent clinical observations, it will be possible to identify biological markers associated with those members of a population of potential patients who would benefit most from treatment therapy. In such a way, expensive treatments may be limited to the subpopulation of patients who would most likely benefit from the treatment.

Another application of the present invention is the use of biological markers to identify patients who have very early clinical signs of a disease. This would be extremely valuable for a multitude of disease states where a patient may have "subclinical" signs and symptoms that are not severe enough to be taken to the doctor's office. However, if a patient has a marker, which was discovered in his blood and he was advised to seek medical attention, his "subclinical" signs can be identified as his earliest phenotypic presentation of a disease. For many diseases, it is extremely advantageous to diagnose a disease as soon as possible, so that therapeutic drugs can be initiated, and generally lead to a reduced morbidity and mortality of that disease entity for that individual. One possible scenario would be if a patient could take a blood sample to see if it has a biological marker for Rheumatoid Arthritis. If the marker was present, you could seek treatment during the "subclinical" stage, where there could only be a feeling of warmth in your joints, instead of waiting until you have pain, swelling and deformity in the joints. That individual will probably have a better long-term result term for Rheumatoid Arthritis, compared to someone who waits until he has a later stage of the disease before seeking treatment.

The present invention is directed to the phenotyping of an organism or class or subclass of organisms. The phenotype is made of data from a large number of data categories. The main categories of data included within the scope of this invention are i) levels of cell populations including their molecules associated with cells in the biological fluid, ii) levels of factors soluble in the biological fluid, iii) dosage of the drug and pharmacokinetics (measurement of a 10 drug and its metabolites in a body) and iv) clinical parameters. Additional categories of data may include, but are not limited to, i) levels of small molecule compounds in the biological fluid, ii) genotype information with respect to the individual, including levels of genetic makeup and gene expression (mRNA) or transcripts) of the individual, and iii) data obtained from tests of the 15 components of urine. In certain modalities, the data categories may include images such as x-rays, CAT scans of the brain or body, or MRI, or information obtained from biopsies, EKGs, stress tests, endoscopies, ultrasound exams, laparascopic procedure, ortroscopic surgeries , PET scans, any other measure of an individual's condition. 20 In the preferred modality, the clinical parameters included in the base of Data of the present invention may include, but is not limited to, age on ™ individual, gender, weight, height, body type, medical history (including comorbidities, medication, etc.), manifestations and categorization of 25 the disease or medical condition (if any) and other standard clinical observations made by a doctor. Also included among the clinical parameters, would be environmental factors and family history.

The clinical parameters could be further characterized by the source 30 from which the information is obtained. The clinical parameters obtained from the patient may include information that the patient provides via a questionnaire, such as the WOMAC for osteoarthritis, and the Health Assessment Questionnaire for Rheumatoid Arthritis, which can be filled out in the doctor's office.

Similarly, electronic or network-based questionnaires targeting all of a patient's current clinical symptoms could be completed by the patient prior to their clinical visit. Information obtained by a nurse would include vital signs, information from a variety of tests, including allergy tests, pulmonary function tests, thallium-tension tests, or ECG tests. Clinical parameters collected from a physician include a detailed history of previous illnesses, surgeries, hospitalizations, medications, reactions to medications, family history, social history, alcohol / drug / cigar history, as well as other behaviors that could put the patient at a high risk for HIV or Hepatitis. A thorough medical examination is also carried out by the clinical staff and is a crucial component of the patient's clinical parameters.

In the preferred embodiment, the levels of cell populations and their associated molecules are identified by cytometry by microvolume laser scanning. Such data can also be obtained by flow cytometry, but the volume of blood needed to perform the flow cytometry assays places a serious limit on the number of assays that can be performed on the blood taken from a given individual at a time. In addition, the preparation of the sample required to perform the flow cytometry tests is time consuming, expensive and may interfere with the measurement result.

The levels of soluble factors can be measured by any suitable technique. In the preferred embodiment, the levels of soluble factors are measured by standard immunoassay techniques, such as ELISA techniques. In an alternative modality, microvolume laser scanning cytometry is used to obtain levels of soluble factors. Soluble factors can be detected by immunoassays such as MLSC, ELISA, etc., spectrometry mass, 2D gel electrophoresis, combinations of mass spectrometry and immunosorption, chemical tests. In the preferred embodiment, cell populations are detected by MLSC assays and soluble factors are detected by immunoassays or mass spectrometry.

The invention includes improved instrumentation for rapid, reproducible and quantitative evaluation of the biological parameters of a small amount of blood; high sensitivity compatible assays with improved, miniaturized instrumentation for the detection of hundreds of biological parameters in the blood; a broad clinical strategy to collect an extensive medical information content of patients that are followed over time; programs, databases and data extraction tools to correlate the patterns of the parameters with the normal biological functions, specific diseases, disease progression and response to therapy; databases of clinical data and biological markers in collaboration with academic centers and clinical research institutes for use in drug development; development of diagnostic tests using patented marker patterns and the ability to improve the efficiency of drug development by enabling more informed decisions by choosing lead compounds and identifying patients who are more likely to benefit from a given therapy.

The unique ability to phenotype an organism and to make rapid and reproducible measurements of a large number of biological parameters is essential for the present invention to identify new patterns of biological markers of small blood samples. Statistical analyzes to date have shown that assays for the numbers of different subsets of cells or cell populations are quantitative and highly reproducible. The present technology, which uses small volumes of blood and requires limited handling of patient samples, has distinct advantages over other commercially available measurement technologies.

The invention also includes studies of patient populations related to a particular disease. These studies are based on statistical analyzes of disease patterns and require the collection of large numbers of blood samples from affected individuals. In addition, the present invention has utility in the phenotyping and identification of biological markers in plants and animals and to aid in preclinical studies.

Definitions As used herein, the term "phenotype" or "phenotyping" refers to a collection comprising a substantial subset of all measurable characteristics of an organism. Such characteristics or parameters include, but are not limited to, levels of cell populations and their associated molecules, levels of soluble factors, levels of other molecules, genotype information, gene expression levels, genetic mutations, and clinical parameters. Such characteristics or parameters include all historical data and present data. For example, a complete phenotype of an organism includes all features measurable in the present tense, as well as all such characteristics at all past time points. In addition to the technically measurable characteristics, the phenotype can include the feelings or emotions of the organism (in the case where the organism is a human, the phenotype includes the mental state of the individual, for example, depression, pain, agitation, mental illness, dependencies chemical), diet and changes in diet, injuries, related history, sexual practices, socioeconomic status.

As used herein, the term "organism" refers to all plants, animals, viruses and extra-terrestrial materials. Included within this definition, but not limited in any way, are humans, mice, rats, rabbits, companion animals, natural and genetically engineered plants, and genetically engineered and natural animals.

A given phenotype may include a compilation of the characteristics of a single organism or a class or subclass of organisms. For example, the phenotypic data can be obtained from a single male individual who has been diagnosed with cancer, before and after the therapeutic intervention, a group of males between the ages of 15 and 55, or a group of males between the ages of 15 and 55 diagnosed with cancer. In this way, the phenotype can be specific to a given individual, or it can represent the average or typical condition of a combined group of individuals.

The phenotype of an individual organism or group of organisms can be used for a variety of purposes. In the broader scheme of the invention, the phenotype is observed longitudinally and is evaluated after some disturbance to the organism. For example, comparing the phenotype of an individual before and after exhibiting asthma symptoms could be used to identify biological markers associated with asthma. In another example, the phenotype of an individual having asthma can be compared to the phenotype of a population of normal adults. In another example, the phenotype of a naturally occurring plant can be compared to the phenotype of a genetically altered plant to determine what measurable characteristics are altered by the introduction of the genetic alteration. A further example of the use of phenotyping information would be to periodically check the welfare state of a patient of an individual and to track the measures of biological aging processes. The potential uses for complete phenotypic data for an organism are almost infinite.

The present invention includes phenotypes for an organism or class or subclass of organisms, methods for obtaining such phenotypes and methods for using such phenotypes, including the identification of biological markers.

As used here, the term "biological marker" or "marker" or "biomarker" means a characteristic or parameter that is measured and evaluated as an indicator of normal and abnormal biological processes, pathogenic processes or pharmacological responses for a therapeutic intervention. Pharmacological responses to therapeutic intervention include, but are not limited to, response to general intervention (eg, efficiency), response to dose to intervention, profiles of side effects of the intervention, and pharmacokinetic properties. The answer can be correlated with efficient or adverse (eg, toxic) changes. Biomarkers include patterns or sets of cells or molecules that change in association with a pathological process and have a diagnostic and / or prognostic value.

Biomarkers include, but are not limited to, levels of cell populations and their associated molecules, levels of soluble factors, levels of other molecules, levels of gene expression (mRNA or transcripts), genetic mutations, and clinical parameters that can be correlated. with the presence and progression of the disease, normal biological processes and response to therapy. The simple biological markers currently used in clinical practice and in the development of drugs include cholesterol, PSA, CD4 T cells, and viral RNA. Unlike the well-known correlations between high cholesterol and heart disease, PSA and prostate cancer, and CD4 positive T cells and viral RNA and AIDS, biological markers correlated with at least other diseases have yet to be identified. As a result, although government agencies and pharmaceutical companies are increasingly seeking the development of biomarkers for use in clinical trials, the use of biomarkers in drug development has been limited to date.

As a non-limiting example, it is often thought that biological markers have discrete relationships with normal biological status, a disease or medical condition, for example, high cholesterol correlates with an increased risk of heart disease, PSA levels elevated, correlate with an increased risk of prostate cancer, T cells Reduced CD4, and an increased viral RNA, correlate with the presence / progression of AIDS. However, it is very likely that useful markers for a variety of diseases or medical conditions may consist of significantly non-complex patterns. For example, it can be discovered that the decreased levels of one or more specific cell surface antigens in a specific cell type, when found together with elevated levels of one or more soluble factors - cytokines, perhaps - - is indicative of a disease particular autoimmune. Therefore, for the purposes of this invention, a biological marker can refer to a pattern of a number of indicators.

As used herein, the term "biological marker identification system" means a system for obtaining information from a patient population and assimilating the information in a manner that allows the correlation of the data and the identification of biological markers. A biological marker identification system comprises an integrated database comprising a plurality of data categories, data from a plurality of individuals corresponding to each of the data categories, and processing means to correlate the data within the data categories, where the correlation analysis of the data categories can be done to identify the category or categories of data, where the individuals who have the disease or medical condition can be differentiated from those individuals who do not have the disease or condition medical, where the category or categories identified are markers of said disease or medical condition. In addition, markers can be identified by comparing the data in several categories of data for a single individual at different time points, for example, before and after the administration of a drug.

As used herein, the term "data category" means any type of measurement that can be discerned about an organism. Examples of data categories useful in the present invention include, but are not limited to, numbers and types of cell populations and their associated molecules in the fluid biological of an organism, numbers and types of factors soluble in the biological fluid of an organism, information associated with a clinical parameter of an organism, cellular volumetric counts per ml of biological fluid of an organism, numbers and types of small molecules in the fluid biological of an individual, genomic information associated with the DNA of an organism and gene expression levels. For example, a single category of data could represent the concentration of IL-1 in the blood of an organism. In addition, a category of data could be the level of a drug or its metabolites in the blood or urine. An additional example of a data category would be the absolute CD4 T cell count. The number or information assigned to an organism or class or subclass of organisms at any given moment in time, comprises, in part, the phenotype of that organism.

As used herein, the term "biological fluid" means any biological substance, including but not limited to, blood (including whole blood, leukocytes prepared by lysis of red blood cells, peripheral blood mononuclear cells, plasma, and serum) , sputum, urine, semen, cerebrospinal fluid, bronchial aspirate, sweat, feces, synovial fluid and complete or manipulated tissue. The biological fluid typically contains cells and their associated molecules, soluble factors, small molecules and other substances. Blood is the preferred biological fluid in this invention for numerous reasons. First, it is readily available and can be extracted multiple times. The blood is replenished, in part, by the progenitors in the marrow, over time. Blood is responsible for antigenic challenges and has a memory of antigenic challenges. The blood is located centrally, recirculates and potentially reports changes through the body. Blood contains numerous cell populations, including surface molecules, internal molecules, and secreted molecules associated with individual cells. The blood also contains soluble factors that are both themselves, such as cytokines, antibodies, acute phase proteins, etc., as foreign, such as chemicals and products of infectious diseases.

As used herein, the term "cell population" means a set of cells with common characteristics. Characteristics may include the presence and level of a, two three or more molecules associated with cells, size, etc. One, two or more molecules associated with cells can define a cell population. In general, some molecules associated with additional cells can be used for a subset of a cell population. A cell population is identified at the level of the population and not at the level of the protein. A cell population can be defined by one, two or more molecules. Any cell population is a potential marker.

As used herein, the term "molecule associated with a cell" means any molecule associated with a cell. This includes, but is not limited to: 1) intrinsic cell surface molecules such as proteins, glycoproteins, lipids, and glycolipids; 2) extrinsic cell surface molecules, such as cytokines bound to their receptors, immunoglobulin bound to Fc receptors, foreign antigen bound to B cell or T cell receptors and self-antibodies bound to self antigens; 3) intrinsic internal molecules such as cytoplasmic proteins, carbohydrates, lipids and mRNA, and nuclear protein and DNA (including genomic and somatic nucleic acids); and 4) extrinsic internal molecules such as viral proteins and nucleic acid. The molecule associated with a preferred cell is typically a protein on the surface of the cell. As an example, there are hundreds of proteins or cell surface antigens of leukocytes, including leukocyte differentiation antigens (including CD antigens, currently up to CD166, see, Leucocyte Typing VI, Kishimoto, T. et al .. ED, 1997), antigen receptors (such as the B cell receptor and the T cell receptor), and a major histocompatibility complex. Each of these classes covers a vast number of proteins. A list of exemplary cell surface proteins is provided in Table 1, which is merely an illustration of a vast number of cell surface proteins and is by no means intended to be a complete list.

As used herein, the term "soluble factor" means any measurable component of a biological fluid or tissue that is not a cell population or molecule associated with a cell. The soluble factor includes, but is not limited to, soluble proteins, carbohydrates, lipids, lipoproteins, steroids, other small molecules, including metal, inorganic, ionic and metalorganic species and complexes of any of the above components, eg, cytokines and receptor soluble; antibodies and antigens; and a drug complexed with anything. The soluble factors can be their own, such as cytokines, antibodies, acute phase proteins, etc., and foreign factors, such as chemicals, infectious disease products and intestinal flora and fauna. Soluble factors can be intrinsic, that is, produced by the organism, or extrinsic factors such as a virus, drug or environmental toxin. Soluble factors can be compounds of small molecules, such as prostaglandins, vitamins, metabolites (such as iron, sugars, amino acids, etc.), drugs and drug metabolites. A list of exemplary soluble proteins is provided in Table 6, which is merely an illustration of the vast number of soluble proteins and is by no means intended to be a complete list.

For the purposes of this invention, the soluble factors may be known or unknown entities. A variety of techniques are available where a given species may be identifiable, but the chemical identity of the species is unknown. In the present invention, the chemical identity of the soluble factor need not be known or known at the time when the assay is performed to determine its presence or absence.

As used herein, the term "small molecule" or "organic molecule" or "small organic molecule" means a soluble factor or factor associated with a cell having a molecular weight in the range of 18 to 10,000. Small molecules may include, but are not limited to, prostaglandins, vitamins, metabolites (such as iron, sugars, amino acids, etc.), drugs and drug metabolites.

As used herein, the term "disease or medical condition" means an interruption, cessation, disorder or change in the functions, systems or organs of the body. Examples of the disease or medical condition include, but are not limited to, immune and inflammatory conditions, cancer, cardiovascular disease, infectious diseases, psychiatric conditions, obesity, and other diseases. By way of illustration, immune and inflammatory conditions include autoimmune diseases, which also include rheumatoid arthritis (RA), multiple sclerosis (MS), diabetes, etc.

As used herein, the term "disturbance" means an external or internal measurable event that may occur in an organism. A simple example would be the administration of a therapeutic people to an individual, or an individual who was healthy and developed asthma. In this application, a disturbance may also include differences between an individual or groups of organisms that are being compared. For example, a population of animals can be considered normal, and its phenotype is compared to the phenotype of a similar animal, but altered genetically. The individual genetically altered animal is disturbed in the sense that its genetic alteration was disturbed from the normal one. In many cases, the disturbance is not a single event that occurs at a discrete point in time. The disturbance may occur over an extended period of time, and / or may be clinical or intermittent.

As used herein, the term "clinical parameter" means information that is obtained that may be relevant to a disease or medical condition. Such information may be provided by the patient or by a physician or scientific observer. Examples of clinical parameters for humans include, but are not limited to, age, gender, weight, height, body type, medical history, ethnicity, family history, genetic factors, environmental factors, manifestation and categorization of the disease or condition medical, and any results of a clinical laboratory test, such as blood pressure, MRI, x-rays, etc.

The clinical parameters can also be characterized by the source of information that is obtained. The clinical parameters obtained from the patient can include information that the patient provides via a questionnaire such as the WOMAC for osteoarthritis, and the Health Assessment Questionnaire for Rheumatoid Arthritis, which can be filled out in paper in the doctor's office. Similarly, an electronic or network-based questionnaire that addresses all of the patient's current symptoms may be completed by the patient before a clinical visit. Information obtained by a nurse can include vital signs, information from a variety of tests including allergy tests, pulmonary function test, thallium-tension test, or ECG tests. The clinical parameters collected by the doctor include a detailed history of previous illnesses, surgeries, hospitalizations, medications, reactions to medications, family history, social history, alcohol / drug / cigarette history, as well as other behaviors that could put a patient with a high risk for HIV or Hepatitis. A thorough physical examination is also performed by clinical staff and is a crucial component of a patient's clinical parameters.

As used herein, the term "genotype information" means any data related to the genetic constitution of organisms, genetic mutations, expression of the gene, e.g., mRNA or transcription levels, and any other measure or parameter associated with the material genetic of the organism.

As used herein, the term "clinical endpoint" means a characteristic or variable that measures how the patient feels, functions or survives. There are several mechanisms that are commonly used to measure how a patient feels, or works with a specific disease, and often include validated clinical questionnaires. These can be self-administered, such as the Beck Depression Questionnaire or the International Prostate Questionnaire, to determine if changes in urination are due to prostatic hypertrophy. V. obstruction of the exit of the bladder. These tools can be given by a health care provider, who is judging features such as facial expression, inability of the patient to sit more than 10 minutes, level of agitation, etc., while the Carroll Questionnaire is completed to determine if a patient is maniac. And finally, in the case of a psychiatric illness, typically patients who are admitted for a hospitalization for an acute exacerbation of their disease, will be observed without being noticed by the clinical staff, to notice their ability to function in a variety of settings, including group interactions or lunch. These "clinical extreme points" are highly variable by disease entity and therefore, the tools used to characterize these extreme points are quite broad.

As used herein, the term "Microvolume Laser Scan Cytometry" or "MLSC" means a method for detecting the presence of a component in a small volume of a sample, using a fluorescently labeled detection molecule, and subjecting the sample to an optical scan, where the fluorescent emission is recorded. The MLSC system has several key features that distinguish it from other technologies: 1) only small amounts of blood (5-50 ml) are required for many trials; 2) Absolute cell counts (cells / ml) are obtained; and, 3) the assay can be run either directly on whole blood or on purified white blood cells. The implementation of this technology will facilitate the measurement of several hundred different cell populations from a single blood collection. The MLSC technology is described in the North American Patents Numbers 5, 547,849 and 5,556,764 and in Dietz et al. (Cytometry 23: 177-186 (1996)), and the provisional patent application entitled "Confocal Time-Resolved Fluorescence Spectroscopy System Laser-Scanner" (United States Provisional Application Number 60 / 144,798, filed July 21, 1999) , and the commonly owned, possessed patent filed concurrently with the present application, entitled "System for Microvolume Laser Scanning Cytometry", each of which is incorporated herein in its entirety. The cytometry by laser exploration with microvolume capillaries, provides a powerful method to verify fluorescently labeled cells in whole blood, processed blood, and other fluids. The present invention further provides the MLSC technology to improve the ability of the MLSC instrument to make simultaneous measurements of multiple biological markers of a small amount of blood. An improved SurroScan optical system scheme is shown in Figure 2.

As used herein, the term "tag" means any entity or species, including but not limited to an atom, a molecule, a molecule fragment or a functional group; a particle or combination of particles; a single or a sequence of electromagnetic pulses; or any other form of matter associated with, attached to (either covalently or non-covalently), or otherwise connected to a component of a biological system (a molecule or collection of molecules such as a cell, a cation, an anion, an atom, or any supramolecular assembly, including but not limited to non-covalent complexes between biological molecules) that is used to identify, quantify, associate, recognize, follow, mark, distinguish, see, name, track, or otherwise distinguish ( hereinafter I / Q) said component.

Labels are often extrinsic, that is, they are not part of the component under investigation. For example, a fluorescent stained molecule is often used as a label, either for screening, quantification or both. Likewise, the use of biotin or streptavidin as a label, linked to a secondary species such as an enzyme by ELISA, is very widespread. Other forms of labels include, but are not limited to, isotopic mass labels for protein I / Q, mass spectrometry, Raman active labels for I / Q by Raman spreading, particulate labels for I / Q by light scattering, fluorescence, agglutination, energy transfer, and a variety of other detection mechanisms, including surface plasmon resonance.

In this regard, there is almost an infinite number of particulate labels, only a small number of which has previously been used. Since the science of nanoparticles is in its infancy (as organic chemistry was two centuries ago), one can anticipate that the complexity of the particulate labels will reach molecular complexity. In other words, we hope that the particulate labels can rival the organic molecules currently used as point labels in combinatorial chemistry, in other words, thousands to hundreds of thousands or even millions of uniquely identifiable labels. We anticipate, in addition, that such labels will become small enough to allow all intracellular measurements. For example, there are now approximately half a dozen different luminescent semiconductor point nanoparticles, each one fluorescing at a different wavelength. In theory, one could anticipate the production of thousands or millions of such orthogonal nanoparticulate optical labels, although the detection mechanism may or may not involve fluorescence (or even other optical methods).

The same can be said of supramolecular science, and of supramolecular labels. We anticipate that molecular mounts held together by non-covalent forces could eventually find use as labels. In addition, labels could comprise individual molecules associated either covalently or non-covalently with biological components. For example, one could imagine using electrochemically active redox labels to uniquely identify components. If one has 10 different molecules, each with a different redox potential, and each prefunctionalized to react with a particular biological component, then one could carry out an I / Q with multiplexed tags, using the detection of the redox potential as the characteristic. of identification. This is identical to the strategy currently used with fluorescence, with the redox "space" used instead of the "wavelength" space.

Note also that a label can be a functional group, such as a carboxylate, an amine, a sugar, etc., or even a spin associated with a molecule. For example, we anticipate the possibility that two samples may be mixed together, with each sample having one or more cores imparted with a particular sequence of electromagnetic pulses (of the type typically used in high-field NMR). In addition, we anticipate that the pulses for two samples would last long enough to be compared using a detection method. In particular, we foresee the possibility that the signatures for the two samples would cancel all species where the concentrations are identical, leaving behind a signal only for those species where the concentrations in the two samples are not identical.

It should be clear to a person skilled in the art that there is no functional difference between the labels, as defined above, and the "reporters" or "reporter molecules," as is typically used in the chemical and biological literature. Similarly, a "detection molecule" as defined below, can itself be a label (for example, when the I / Q is based on the mass, such as a microbalance of quartz crystal or inert piezobio sensors).

As used herein, the term "detection molecule" means any molecule or molecular assembly capable of binding to a molecule or other species of interest, including but not limited to a molecule associated with a cell, a soluble factor, or a small molecule or organic molecule. The preferred detection molecules are antibodies. The antibodies can be monoclonal or polyclonal.

Note, however, that as new types of detection molecules have been discovered and popularized, they can certainly be used. For example, aptamers are increasingly being used for molecular recognition, and organic chemists have now synthesized a large number of molecular receptors. Finally, these could be used as detection molecules, either by themselves or in association with a label.

As used herein, the terms "dye", "fluorophore", "fluorescent dye" are used interchangeably to mean a molecule capable of fluorescing under excitation by a laser. The dye is typically linked directly to a detection molecule in the present invention, although the Indirect linkage is also encompassed here. Many dyes are well known in the art, and include, but are not limited to those shown in Table 2. In certain preferred embodiments, fluorophores are used, which can be excited in the red region (> 600 nm) of the spectrum. Two red dyes, Cy5 and Cy5.5, are typically used. They have emission peaks of 665 and 695 nanometers, respectively, and can be easily coupled to antibodies. Both can be excited at 633 nm with a helium-neon laser. The sets of 3 red dyes that can be used include Cy5, Cy5.5 and Cy7 or Cy5, Cy5.5 and Cy7 APC. See Mujumdar et al., Bioconjugate Chemistry, 7: 356 (1996); U.S. Patent No. 5,268,486; Beavis et al., Cytometry, 24: 390 (1996); Roderser et al., Cytometry, 24: 191 (1996); and U.S. Patent No. 5,714,386. Additional novel dyes useful for labeling or for detection purposes within the present invention are described in the Commonly Owned United States Provisional Application, Number 60 / 142,477, filed July 6, 1999, entitled "Bridged Fluorescent Dyes, Their Preparation and Their Use in Assays, "incorporated herein in its entirety by reference.

As used herein, the term "animal model" refers to any experimental animal system, in which diseases or conditions with a pathology and progression similar to diseases or human medical conditions may develop. Suitable animal systems include, but are not limited to, rats, mice, rabbits, and primates. In some cases, the disease arises spontaneously in the animal model. In other cases, the induction of the disease in the animal model may result from exposure to them conditions - for example, infection with a pathogen, exposure to a toxin, or a particular diet - that cause the disease in humans. Alternatively, the disease or condition can be induced in the animal model with agents that mimic the human disease or medical condition, even if the current initiator of the disease or human medical condition is unknown. The disease or medical condition can also be induced through the use of surgical techniques. The genetic manipulation of experimental animal model systems provides an additional tool for the development of animal models, either alone or in combination with the other methods of induction of the disease.

Preclinical Applications of Phenotyping Currently, much effort is directed towards the identification and analysis of biological markers in humans. However, it would be desirable to have a method for identifying and analyzing biological markers in experimental animal systems. For example, biological markers of the progression of a particular human disease can be identified in the device (1) according to experimentally induced animal model of that disease, for example, the adjuvant rat model of arthritis (reviewed in Philippe, et al. American Journal of Physiology 273: R1550-56 (1997)). Using the identified markers, the efficiency of the experimental therapies could be determined in the animal model. Therapies that have a highly specific effect on the expression of biological markers in animals, markers which are for the prognosis or diagnosis of the same disease in humans, can therefore be identified without conducting early clinical trials - and for so risky - in humans. Alternatively, novel biological markers can be identified in experimental animal models of the human disease, and then experiments can be performed to determine whether the same markers, or their human counterparts, are prognostic or diagnostic of the same disease or medical condition in humans. In some cases, biological markers identified in humans can be used to facilitate Preclinical tests, where animal models can be evaluated by the corresponding biological markers. The present invention provides methods and instruments for developing such analyzes.

In a series of embodiments of the invention, the state of biological markers is studied in an animal model of a human disease. Currently there are many of these models and many more are being developed, using a variety of different techniques to induce the specific disease. In each case, the biological markers of interest can be initially identified in the preferred modalities using MLSC. The identified markers can be studied using MLSC to determine the response of the animal to a therapeutic candidate. Because MLSC-based assays typically only require small volumes of biological fluid, the MLSC is uniquely suited for use in animal model systems (especially in rat and mouse), where only limited amounts of fluid can be used. obtained from an animal without sacrificing it. In particular, the use of the MLSC will allow multiple point analyzes of an experimental animal to determine the pharmacokinetics of a candidate therapeutician.

In some embodiments of the invention, animal homologs of known and newly identified human markers of a particular disease are studied in an experimentally induced animal model of that disease. In many cases, the animal homologs of human molecules will be easily known and characterized. For example, through extensive study, much is known about proteins that behave similarly in mice and in humans. The identification of animal homologs of previously unknown human biomarkers, and the preparation of reagents that can bind them, can be achieved through the use of standard molecular biology techniques, well known in the art.

In other embodiments of the invention, novel biological markers - for example, a pattern of the expression of proteins associated with a known cell, previously unknown - can be observed initially in an animal model of the human disease. In this modality, the relevance of the markers identified with the progression or development of the human disease can be determined by identifying the human homologs of the biological markers, and then studying their expression in humans suffering from the disease of interest. If the animal biological markers identified seem to be relevant to the human disease, then they can serve: 1) as the basis of new diagnostic and prognostic tests for the disease in humans; and 2) as a means to evaluate the specificity and efficacy of the candidate therapeutic agents in the animal model of the disease.

In one embodiment of the present invention, new and improved animal models can be developed based on biological markers identified in humans. For example, using the biological marker identification system of the present invention, it can be found that for a given disease or medical condition, that the level of a soluble factor given in the serum is greatly increased, while the level of a certain cell population decreases. Based on this information, animal models can be adjusted - for example, by the use of genetic eliminators of homologous factors - to better simulate the disease in animal serum.

The phenotyping system of the present invention may also be useful in the identification of new or improved animal models. For example, by phenotyping a number of genetically altered animals, a more complete picture of the manifestations of the genetic alterations can be recognized. The use of this knowledge can be useful in the identification of new or improved animal models. For example, it may be possible to create a number of genetically deleted mice that all appear to simulate a chosen human disease state. However, phenotyping each of the Several suppressions, as well as humans suffering from the disease, will be possible to identify the animal model that more closely mimics the human disease.

The present invention can be used in any animal model of a human disease. By way of illustration only, the present invention can be used to identify and analyze biological markers in animal models of many aspects of cardiovascular disease, including hypertension, atherosclerosis, cardiac hypertrophy, atherogenesis, and thrombosis. Many animal models of cardiac congestive failure and hypertrophy are currently being developed, and a number is reviewed in: Carmeliet, Artherosclerosis, 144: 163-93 (1999); Young et al., Molecular Basis of Cardiovascular Disease, 37-85 (K.R. Chien, Editor) (1999); Hasenfuss, Cardiovasc. Res. 39: 60-76 (1998); Krege et al, Fundam. Clin. Cardiol. 26: 271-92 (1996); Liao et al., Am. J. Therap. 4: 149-58 (1997); and Becker et al., Hypertension 27: 495-501 (1996). The following is a partial list of some animal models of cardiovascular disease: The rat JCR: LA-cp model of human cardiovascular disease can be used to identify and study biomarkers that correlate with insulin resistance, vascular disease, and cardiovascular disease. O'Brien et al. Dog. J. Physiol. Pharmacol. 76: 72-76 (1998).

Animal models of insulin-dependent diabetes have been used to study the development of ischemic heart disease in the diabetic population. Reviewed in: Pierce et al., Can. J. Physiol. 75: 343-50 (1997).

The infection of mice, rabbits and monkeys with Chlamydia pneumonia has been used to investigate the role of pathogens in the development of asthma and cardiovascular disease in humans, as reviewed in: Saikku et al, Artherosclerosis, 140 (Suppl. , S17-S19 (1998).

Varieties of spontaneously hypertensive rats (SHR), and SHR strains that carry a portion of chromosome 13 (including the renin gene) of normotensive rats (SHR.BN-Ren) can be used to investigate the interaction between high blood pressure and blood pressure. dyslipidemia in cardiovascular disease. St. Lezin et al, Hypertension, 31: 373-377 (1998).

Hypertrophic cardiomyopathy that occurs spontaneously in Landrace pigs may be a useful model of cardiovascular disease in humans. Chiu et al., Cardiovasc. Pathol. 8: 169-75 (1999).

Varieties of cardiomyopathic hamsters can be used to investigate the role of natriuretic peptides in the brain and atrial (BNP and ANP) in human cardiovascular disease. Tamura et al., J. Clin. Invest. 94: 1059-68 (1994).

Varieties of hypertensive and atherogenic rats have been used as models for studying the effect of salt on diet, proteins and lipids in the pathogenesis of human cardiovascular disease. Reviewed in: Yamori et al., Nutritional Prevention of Cardiovascular Disease (1984), Published by: Academic Press, Orlando, Fia.

The models in rats, guinea pigs, rabbits, dogs, sheep and baboons of preeclampsia have been used for the study of the pathophysiology of this hypertensive disorder of human pregnancy. Revived in: Hypertension in Pregnancy 12: 413-37 (1993).

In other embodiments, the present invention is used to identify and analyze biological markers in animal models of inflammatory diseases such as arthritis and multiple sclerosis.

When used to select candidate therapeutic agents, the present invention has a number of significant advantages over more traditional selection methodologies. First, clinical trials come at a relatively late stage in the development of therapy, at which point it is known that the therapeutic agent has a highly specific effect on the expression of analogous animal biological markers; this minimizes the risks for clinical participants. Second, using experimental animal models to analyze patterns of expression of the biological marker means that only relatively small quantities of the potential therapeutic agent need to be synthesized initially, thus reducing the cost of developing the therapeutic agent.

In other embodiments, the methods and systems of the present invention are used to identify markers of the disease or medical condition in animals for veterinary purposes. The identified markers can then be used to select candidate therapeutic agents, directed against that disease or condition. This modality can be applied to domesticated animals, cattle and plants.

Instrumentation Any suitable means to obtain data that meets the requirements of the data categories is within the scope of the invention. In the preferred embodiments, Microvolume Laser Scanning Cytometry ("MLSC") is used to obtain the data for the molecules associated with cells and the cell type count. In some modalities, the MLSC technology is used with a point-based capture system or with several types of enzyme-linked immunosorbent assays (such as ELISA) to obtain data on soluble proteins. Another preferred means of obtaining data for compounds, particularly small molecules, includes the use of mass spectrometry. The MLSC technology used in this invention is a powerful method for verifying fluorescently labeled cells and proteins soluble in the blood. This technology is currently used in clinical laboratories for the identification of one or two cell markers for diagnostic applications.

The present invention uses the MLSC to facilitate the identification of biological parameters. In one embodiment, the present invention improves the MLSC technology, improving the ability of the MLSC instrument to make simultaneous measurements of multiple characteristics or biological parameters of a small amount of blood.

Specific improvements achieved with the instrument of the invention (called "SurroScan instrument") include the following: 1) two additional fluorescent color channels that allow the simultaneous detection and measurement of up to four fluorescent colors; 2) higher laser excitation energy that improves sensitivity and performance; 3) disposable capillaries that allow more testing per patient sample, using less blood per test; 4) improved programs and an integration system that automates sample measurements and data analysis; 5) the capacity of SurroScan instruments is expanded to handle larger volumes of patient samples, for the creation of databases and the discovery of the biological marker.

Design of the MicroVolume Laser Scanning Cytometry System fMLSC) The MLSC technology is described in U.S. Patent Nos. 5,547,849 and 5,556,764 and in Dietz et al. (Cytometry 23: 177-186 (1996)), each of which is incorporated here in its entirety. The Imagn 2000 system, commercially available from Biometric Imaging Inc., is an example of an MLSC system. Cytometry with laser scanning with microvolume capillaries provides a powerful method to verify fluorescently labeled cells in whole blood, processed blood, and other fluids. The present invention also improves the MLSC technology, improving the capacity of the MLSC instrument to make simultaneous measurements of multiple biological markers from a small amount of blood. An improved SurroScan optical system scheme is shown in Figure 2. The preferred MLSC instrument for use in the present invention is described in United States Provisional Patent Application No. 60 / 144,798, filed on July 21, 1999, entitled "System for Microvolume Laser Scanning Cytometry" and in the commonly owned utility application filed concurrently with the present invention, entitled "System for Microvolume Laser Scanning Cytometry". Both of these applications are incorporated herein by reference in their entirety.

One embodiment of the improved optical configurations are shown in Figure 2. A capillary array 10 contains samples for analysis. In the preferred embodiment, collimated excitation light is provided by one or more lasers. In particularly preferred embodiments, the excitation light of 633nm is provided by a He-Ne 11 laser. This wavelength avoids the problems associated with the autofluorescence of the biological materials. The laser power is increased from 3 to 17 mW. The power of the larger laser has two potential advantages, increased sensitivity and increased scanning speed. The collimated laser light is deflected by a dichroic excitation filter 12. With the reflection, the light falls on a scanning mirror driven by a galvanometer 13. The scanning mirror can rapidly oscillate in a fixed range of angles through the galvanometer. example +/- 2.5 degrees. The scanning mirror reflects the incident light in two transmitting lenses 14 and 15 that form the image of the scanning mirror in the entrance pupil of the microscope objective 16. This optical configuration converts a specific scanned angle in the mirror to a field position specific in the focus of the microscope's objective. The angular sweep of +/- degrees results in a 1mm wide scan at the target's focus. The relationship between the angle of exploration and the position of the field is essentially linear in its configuration and over this range of angles. In addition, the objective of the microscope focuses the incoming collimated beam at a point in the plane of focus of the objective. The diameter of the point, which adjusts the optical resolution, is determined by the diameter of the collimated beam and the focal length of the objective.

Fluorescent samples placed in the trajectory of the swept excitation beam, emits light displaced by estokesios (fluorescence unit). This light is collected by the target and collimated. This collimated light emerges from the two transmission lenses 14 and 15 until it collimates and collides with the scanning mirror that reflects it and does not explore it. The light shifted by stokesios (fluorescence unit), then passes through a dichroic excitation filter (which reflects a light of shorter wavelength and allows light of longer wavelength to pass through) and then through a first long-pass filter 17 that also serves to filter any reflected excitation light.

The improved instrument of the present invention then uses a series of additional dichroic filters to separate the light shifted by stokesios (fluorescence unit) in four different emission bands. A first fluorescent dichroic filter 18 divides the two bluer fluorescent colors of the two redder colors. The two bluer colors are then focused on a first aperture 19 via a first focusing lens 20, in order to significantly reduce any fluorescent signal out of focus. After passing through the opening, a second fluorescent dichroic filter 21 further separates the individual blue colors from one another. The individual blue colors are brought to two separate photomultipliers 22 and 23. The two reddest colors are focused on a second aperture 24 via a second long pass filter 25, a mirror 26, and a second focus lens 27, after being divided from the two bluer colors by the first fluorescent dichroic 28. After passing through the aperture 24, the two reddest colors are separated from each other by a third fluorescent dichroic 28. The individual red colors are then brought to the photomultipliers 29 and 30. In this way, four separate fluorescent signals can be transmitted simultaneously from the sample held in the capillary to individual photomultipliers. This improvement, for the first time Once, it allows four separate analyzes to be verified simultaneously. Each photomultiplier generates an electronic current in response to the incoming fluorescent photon flux. These individual currents are converted to voltages separated by one or more preamplifiers in the electronic detection elements. The voltages are sampled at regular intervals by an analog-to-digital converter, in order to determine the intensity values of the pixels for the scanned image. The four channels of the present invention are called channels 0, 1, 2, and 3.

The new optical array has four detection channels to allow the simultaneous measurement of up to 4 fluorescently labeled molecules. In a preferred embodiment, multiple color tests are used. Typically, 3 or more fluorescent colors are used in each assay. Under circumstances where combinations of appropriate dyes are available, the instrument is capable of supporting 4-color tests.

An XY translational stage is used to move an array of capillaries relative to the optical system. The translation stage of the SurroScan system supports 2 arrays, each of which has the imprint of a 96-well plate. The capillary arrangements have been designed to have 32 fixed capillaries each and with a separation that is compatible with multiple channel pipettes. The operator is able to load two plates of 32 capillaries at once. No operator intervention is needed while the plates are scanned and the images are processed. As an alternative, 16 individual capillaries designed for the Imagn 2000 (VC120) are loaded onto alternative supports.

The image processing program accommodates images with either 2, 3, or 4 colors of fluorescent dyes. The program automatically identifies and parameterizes the particles detected in any of the individual colors. The measured parameters that describe each particle are stored in a format of List mode, which is done with the conventional cytometry analysis program, such as FlowJo.

A new disposable cartridge design containing capillary arrays has been developed and is described in the United States Provisional Patent Applications, (Provisional US Application No. 60 / 130,876, entitled, "Disposable Optical Cuvette Cartridge", filed on April 23, 1999, United States Provisional Application Number 60 / 130,918, entitled "Spectrophotometric Analysis System Employing a Disposable Optical Cuvette Cartridge", filed on April 23, 1999; and United States Provisional Application Number 60 / 130,875, entitled "Vacuum Chuck for Thin Film Optical Cuvette Cartridge", filed on April 23, 1999), and the commonly owned utility application filed on April 20, 2000, entitled "Disposal Optical Cuvette Cartridge ", which are incorporated here as a reference in its entirety. This capillary cartridge is used in Examples 5, 7 and 8. The design currently in use, called Flex-32, contains 32 capillaries. The filled holes in the FLEX32 plates have the same 9 mm spacing as the 96-well plates and the multi-channel pipetting devices. It is constructed of 2 layers of mylar walled together with a layer of double-stick adhesive, which is cut with a matrix to define the internal dimensions of the capillary. The resulting cartridge can be manufactured at low cost at high volumes. The cartridge is flexible, which allows it to be held on a flat base plate optically, by vacuum pressure, removing the requirements for planarity in the manufacturing process. The capillary spacing was designed to retain compatibility with multi-channel microplate pipettes and robotic devices.

Cellular Assays The invention includes cellular assays, many of which are based on anti-convolutions, which are compatible with the instrumentation, preferably MLSC instrumentation and which are capable of measuring from hundreds to thousands of cell populations and their molecules associated with single cells. 10 mL blood tube.

In a preferred embodiment, any type of detection molecule and assay format compatible with MLSC is encompassed in this invention, including, but not limited to, cell surface proteins that include activation and adhesion markers, intracellular molecules, assays to distinguish changes in the activation states of the cells, assays to concentrate and identify rare white cells, assays for use with whole blood, and assays for the detection of soluble factors, such as proteins in the blood.

As with flow cytometry, fluorophore-labeled antibodies specific for cell surface antigens are used to identify, characterize and enumerate specific populations. The reaction can be done in whole blood. In general, there is no need to wash the reagent, the quantitative dilution of the blood-antibody mixture is usually sufficient for the preparation of the mixture. The antibody-antibody mixture is loaded into an optical quality capillary of known volume and analyzed with a laser-based fluorescent imaging instrument. In order to operate on whole blood, fluorophores that can be excited in the red region (> 600 nm) of the spectrum were used. The white cells of the purified blood can be analyzed with the instrument. In contrast to flow cytometry, the laser scans stationary cells instead of cells that flow through the laser. A point of the small cylindrical laser is scanned through the capillary in one direction, while the capillary is moved relative to the optical system in a second direction. Photomultiplier tubes are used to detect the fluorescent signal. An image processing program is used to analyze the image and identify and list the cells of interest.

This MLSC method allows obtaining absolute cell counts from hundreds of different cell populations from a single blood tube. For a set of antibodies from 100 different antigens, there are approximately 5000 possible combinations of 2 colors and approximately 162,000 possible combinations of 3 colors, (n combinations for a time, nCr = n! / R! (Nr)!), So that needed Be careful to develop the most appropriate set of 100 or more trials. The ability of multiple colors allows more cell populations to be identified with a given amount of blood than with the original 2-color system. As an example, multiplexing the reagents, all populations identified in the two 2-color assays can be identified in a 3-color assay. For example, it is possible to test CD3, CD4 and CD8 in one capillary instead of CD3 and CD4 in one capillary and CD3 and CD8 in another. More importantly, unique cell populations can be defined by simultaneous expression of three or more antigens. For example, CD8 T cells can be classified into subsets in 4 different populations based on the differential expression of CD45RA and CD62L.

Immunoassay Procedures Immunoassays can be run in a variety of formats, and any suitable format is provided in the present invention. Subsequently, two examples are given. The MLSC system can be used with microsphere-based immunoassays. In this sandwich assay, the microsphere is used as a solid support for a specific anti-trap of the analyte. The analyte of a biological fluid is bound to the microsphere coated with antibody and detected with a second antibody, which is directly labeled with a fluorescent molecule, such as Cy5, and which binds to a distinctive epitope on the analyte. A protocol that utilizes aminopears and a heterobifunctional crosslinker to covalently bind antibodies via its hinge region works well in multiple assays. It is possible to distinguish pearls (ranging from 3 to 20 microns) with the MLSC instrument and the current program. By coupling different capture antibodies to microspheres of different sizes, it is possible to multiplex immunoassays in a single capillary. The internal indicator dyes can also be used to distinguish microspheres and facilitate multiplexing.

The immunoassays for soluble factors discussed in Example 5 are all chemiluminescent-based sandwich ELISA. Microtiter plates are coated with capture antibodies specific for analyte of interest and blocked. The biological fluid containing the analyte is added, incubated and then washed. The biotinylated antibody specific for a second epitope of the same analyte is added, incubated and washed followed by an avidin-alkaline phosphatase conjugate. The level of the analyte is revealed with a chemiluminescent alkaline phosphatase substrate. The plates are read in a Wallac Victor2 luminometer or similar instrument.

Design and implementation for a robust panel of cell assays to aid in the discovery of biological markers for diseases or medical conditions. The MLSC system is designed to allow rapid staining of cells using minimal amounts of blood. Reagents directed against markers of different cell surface antigens are developed, which when combined, can identify hundreds of different cell populations. The strategy for the development of reagents and combination is discussed later.

A set of monoclonal antibody reagents are used, which are suitable to develop more than 100 cell assays. To date, many (approximately 120) different monoclonal antibodies directed against numerous (approximately 80) different cell surface antigens have been successfully identified and tested in the 2-color MLSC instrument. Small organic dyes such as Cy5 and Cy5.5 easily attach to the amino groups of antibodies using one-step NHS chemistry and well-established procedures. Preferred dye to antibody ratios have been determined for Cy5, Cy5.5, and Cy7 reagents, and generally in the range of one to four. Fluorochromes of the protein, such as APC, are linked to the sulfhydryl groups of a moderately reduced antibody in a 3-step procedure using the heterobifunctional crosslinking reagent SMCC. The preparation of reagents containing other fluorophores is also possible. The preparation of the conjugates of Cy7-APC and (Cy7-APC) -antibody for flow cytometry applications have been previously described. The chemistry of The antibody-fluorophore coupling is the same as for APC. All protein-protein conjugates are purified by traditional means, such as, for example, gel filtration in an Akta FPLC. Fluorescent microspheres can also be investigated. The antibodies are coupled with 2-step carbodiamide chemistry for carboxylated microspheres.

The reagents of new monoclonal antibodies are titrated both in whole blood and in red cells of lysed blood. The specificity of the reagent, and the lack of a non-specific binding, is confirmed with appropriate counting dyes. The analysis is done with any appropriate program, including the FlowJo cytometry program (Treestar, Inc. available as an Internet download at http://www.treestar.com/ flowjo /). From titration of the optimum amount of each reagent per assay (typically 0.01 to 2 μg / ml) and preliminary analyzes, the criterion is determined. In the preferred embodiment, all tests are conducted in a homogeneous mode (without washing). This generally requires that each antibody reagent have a titration point of < . 1 μg / ml so that the fluorescent background is not too high. A potential difficulty may be that a particular reagent may not be easy to conjugate or may have a titration point that is too high. It is usually possible to replace a second monoclonal antibody in the same antigen. There is also a risk that some individual antigens may not be measurable during the time course of the study. Typically, multiple antibodies of each category of antigen are evaluated.

Typically, a panel of approximately 50-100 (or greater) cell assays is developed to verify a disease or medical condition. Such assays allow enumerating hundreds of different cell populations. As an example, it is possible to verify the cellular parameters immune and inflammatory potentially significant for rheumatoid arthritis (RA). For RA populations, cell surface antigens are evaluated for use, they can be divided into different subsets based on the types of cellular antigens recognized. As an illustration, antigens found in major leukocyte subtypes including T cells, B cells, antigen presenting cells, NK cells, and granulocytes, as well as the relevant receptors and structures found in these cells, are included . These may include activation molecules, co-stimulatory molecules, adhesion molecules, antigen receptors, cytokine receptors, etc. A representative, but not exhaustive, list of the antigens that can be evaluated for RA is provided in Table 1.

Formats of the Cellular Assays The cell assays described above are designed in either of two formats, whole blood or blood lysed with RBC. In preferred embodiments, the assays are done in whole blood or in a blood format lysed with RBC. Minimal manipulation ensures that the most accurate cell counts are obtained (cell / μl of blood). In addition, only small amounts of blood are required per test, so many assays can be performed from a single tube of blood. However, for some cell populations an alternative test format, blood lysed with RBC, will be preferable. These include particular pairs of antigen-antibody for which the soluble factors (free Ig, soluble cytokine receptors, etc.) contained in the serum, interfere with cell tagging, and cell populations that are present in very low frequency. This method is useful for activated cells expressing CD25 or CD69, which are essentially undetectable in whole blood from normal individuals, but which are increased tenfold in the lysate format and have been shown to increase in several autoimmune states. Improved detection of other minor cell populations such as NK cells has also been demonstrated, and should be proved particularly useful in the analyzes. As an example, for a panel of 96 trials, it is estimated that 64 will be made in whole blood, 32 in lysed blood. An alternative sample processing may include, the preparation of PBMC by Ficoll gradient, ex vivo stimulation with polyclonal or antigen activators.

The combination of antibody reagents is important for the identification of novel cell populations that may contribute to pathogenesis, or be a marker for diseases or medical conditions, such as autoimmune diseases. For example, it is known that adhesion molecules can be differentially expressed in T cells even though they are involved in the autoimmune process. In addition, several studies have indicated that there may be an increase in the number of CD4 T cells in memory in patients with autoimmune disease. With the assays of the present invention, it is possible to search simultaneously for different levels of adhesion molecules (eg, CDlla +), specifically on a subset of memory T cells (eg CD45RO +) of the restricted line HLA class II- (e.g. CD4 +). This would increase the ability to identify relevant disease-related cell populations. The ability of multiple colors also allows to look for novel populations of cells by choosing combinations of antigens not typically found together with a given cell type or marker, found in the same cell type at different stages of ontogeny.

Determination of Appropriate Fluorescent Dyes As indicated above, any suitable fluorescent dye is within the scope of the present invention. Two commonly used dyes are cyanine stains Cy5 (em 667) and Cy5.5 (em 703). Typically, a single dichroic filter is used to divide the emission signal at 685 nm. More than two filters will be required when more than two dyes are used. The dyes are evaluated to determine their compatibility in the MLSC system. As an example, a variety of dyes were evaluated to determine an appropriate total 3-color set (see table 2). The parameters to be considered when evaluating the dyes include 1) spectral separation of the 3 dyes, 2) signal to noise ratio as a function of the laser power, 3) suitability of the available filters, 4) ease of conjugation, and 5) specificity of the resulting antibody-fluorophore conjugates. The Cy5 and the APC are appropriate for the first color and the Cy5.5 is appropriate for the second color. Several potential dyes are appropriate for the third color. The Cy7-APC is expected to be suitable for the MLSC system. The preliminary results with the Imagn 2000 system, show that this dye is detectable in the wavelength channel, (> 685 nm) and distinctive of both the Cy5 and the APC. The emission spectrum indicates that the overlap with Cy5.5 should not be a problem given the appropriate filters for the new instrument. Fluorescent microspheres offer a wide variety of alternative colors and have been used successfully in some cytometry applications. The conjugation methods will be used, which will minimize the non-specific binding that occasionally occurs with the microsphere reagents. Typically, each of the fluorophores is evaluated in the context of the fluorophore-antibody conjugates using few selected antibodies, for example anti-CD3, anti-CD4 and anti-CD20.

Soluble Factor Methods There are a large number of bioanalyses carried out by means other than fluorescence. Prominent among these is mass spectrometry, which has quickly become the tool of choice for the detailed identification and analysis of polypeptides and proteins. There are two widely used methods for the introduction of the biomolecular sample in mass spectrometry: electro-ionization ionization (ESI) and matrix-assisted laser desorption / ionization (MALDI). MALDI-TOF is currently used successfully for the analysis of proteins, polypeptides and other macromolecules. Although the introduction of an organic matrix to transfer energy to the analyte has advanced tremendously, the field of mass spectrometry by desorption, MALDI-TOF still has some limits. For example, the detection of small molecules is not practical due to the presence of background ions of the matrix. In such cases, mass spectrometry by ESI or even gas chromatography (GC) can be used to detect or profile.

The complexity of molecular structures and the heterogeneous nature of proteins requires the need for multidimensional separation techniques. A of the main areas for this, for example, is the development of two-dimensional gel electrophoresis using polyacrylamide as the gel matrix. The gel is modified in terms of crosslinking, addition of detergents, immobilization of enzymes or antibodies (affinity electrophoresis) or substrates (zymography) and the pH gradient. This technique is used for the characterization of proteins in terms of structural modifications, activities, pl values, and molecular weights.

Another area is the development of multidimensional chromatographic methods also referred to as separation techniques with ions. Advantages include the ability to more accurately quantitate the analyte and better compatibility with online detection methods such as laser-induced fluorescence or mass spectrography. Till the date, usually two separation systems are chosen so that they are orthogonal and lead to a better peak capacity (resolution). The main technical problems are the integration of the various separation techniques with the detection system in terms of maintaining the resolution with the transfer to the second dimension, the compatibility of the mobile phases with the detection system, for example, salts and detergents in the eluent that are incompatible with the mass spectrometry by electrobraking. Naylor J. Chromatogr. A1996, 744 237-78; Jorgenson Anal.

Chem 1997, 69, 1518-1524.

The chemical derivatization can be used selectively to activate the components in a mixture that is not sufficiently ionized to provide an ESI mass spectrum. For example, sterols that typically lack acidic or basic residues that do not ionize under electrorhose conditions have been coupled with ferrocene carboxylic acids, the electrochemical nature facilitates ionization. Anal. Chem. 1994, 66, 209-212. The derivation can also serve as a point to differentiate between stereoisomers (sobaric species) using different fragmentation patterns in the ions derived from the originals. The MSn capacity in a mass spectrometer that captures ions of quadrupole, for example, has been used to distinguish monosaccharides from hexosamine, glucosamine, galactosamine and mannosamine derivatives with CoCl2 (DAP) 2Cl, where DAP is a diaminopropane. Anal. Chem. 1999, 71, 4142-4147.

The affinity-based separation followed by mass spectrometric detection is of clinical interest, since it allows for analysis of complex molecules in biological fluids, such as blood and urine with little or no sample preparation. Ciphergen technology (desorption ionization with surface enriched laser (SELDI), a variation of the MALDI), is based on this principle. The Ciphergen offers 5-6 different surfaces on which the protein and / or small molecules are applied, and then washed with increased rigor. Since each combination of surface / rigor leads to a different absorption profile, the technique provides means to analyze a complex mixture.

Clinical and Computer Ducks The identification and correlation of biological markers with clinical measurements requires the integration of vast quantities of biological and medical data and a search engine that makes such data accessible and usable. The instrumentation and assays developed in the present invention have the ability to identify hundreds to thousands of independent markers of a small blood sample. The present invention includes the development of a broad clinical strategy for collecting extensive medical information from patients who are followed at the time of disease progression and response to therapy. In addition, the present invention includes programs, databases and data extraction tools for correlating the patterns of markers with specific diseases, disease progression, and responses to therapy, including, but not limited to, databases of clinical trials and information, data conversion and statistical analysis tools, and prototypes of medical questionnaires. The information system of this invention is designed to use a common language and common formats for the introduction of disparate types of data and is structured for data extraction purposes.

The universal medical language that will probably become widely used in the following several years is the SnoMed-RT. This language will be easily adaptable with the current information system of the present invention. Similarly, the present invention is adaptable in that as other languages or technologies become available, they can also be incorporated into the 10 database. An example would be the eventual development of tools to integrate digital x-rays, mammograms, or a virtual colonoscopy that yields a C.T. scan. of the abdomen. The technical challenges in developing a computer system capable of handling the vast amounts of biological and clinical information necessary to correlate biological markers with disease, 15 include the modeling and integration of a number of diverse, complex, and often incompatible information sources, adapting to rapid advances in scientific and medical knowledge and methods, and developing a user-friendly interconnection, an appropriate format and tools powerful search The computer system provided by the present invention complies with 20 these technical challenges.

^ Data Analysis ™ The data produced from the cellular analyzes include the number of cells per μl of whole blood for each identified population, the average intensity 25 of dyeing for molecule associated with a cell, which gives an estimate of the antigen density for a given population, the average cell size, and the expression levels of a particular molecule. Each number will be analyzed, because as explained above, both the number of real cells and the expression levels of a particular molecule can vary in a state of 30 disease given. To identify markers (cell counts or intensity of staining, levels of soluble factors), associated with clinical variables categorized (such as the diagnosis of a disease), a variety of discriminating techniques are used. To identify markers associated with continuously assessed clinical variables (such as levels of soluble factors), a variety of regression techniques are used. For discriminant and regression analyzes, variable selection and cross-validation are used to identify those markers that are most closely associated with the clinical variable of interest. Where appropriate, demographic and clinical variables (such as age, gender, concomitant drugs), and genetic parameters are included as covariates in the models. These techniques are applied in data analysis, typically using statistical analysis packages SAS Statistics and Statistica.

The infrastructure architecture of the integrated computer (see Figure 3) of the present invention comprises a structure with multiple rows. The lowest level consists of a set of data sources. The first source comprises the scientific data including, but not limited to, the cell assay data and the soluble factor assay data. The second structure can be semi-structured data, which are in a combined form of textual and tabulated data, which describe the protocols for the development of the trial and the protocols for the execution of the clinical studies. The structure can be encoded as a definition of the type of data (DTD), which define labels that serve for the indexation of information and consultation, as well as a representation of the selective information in the browsers of the network. DTD tags also define an information exchange model that allows sharing high-level electronic information with other parties. The third source of data is the clinical data collected and restructured to meet the requirements of the clinical study. Clinical questionnaires that are optimized to maximize, under time constraints, the collection of useful and reliable information from patients, are used to gather the information and to provide the necessary quality control. If necessary, the questionnaires will be in multiple languages, and adapted to physical challenges (for example, inability to use a computer keyboard), which respondents may have to face. The technology of choice for these data sources can be XML. In addition, the clinical information collection system also includes non-text input media. A respondent can interact via visual and graphic representations to provide health-related information by indicating the images of the human anatomy to indicate a problem without having to articulate it. Other means, for example, a simple measure of vital capacity and Fevlsec. in asthmatic patients or with emphysema or verification devices to detect and / or correct cardiac arrhythmias, etc., they could also be used for the introduction. The fourth data source is the instrumentation data that contains all the relevant parameter settings required for the execution of the scientific tests in a combination of different instruments, such as Imagn, SurroScan and the ELISA plate reader.

Additionally, the data can be collected and recorded in lists. In the list form, the measured values for each individual cell are recorded. This facilitates the identification and analysis of individual cell populations that express a complex set of different molecules. Alternative analysis schemes are easily explored, facilitating the optimal data analysis. In the same way, the complete set of patient data (cell populations, soluble factors, medical history, clinical parameters, etc.) can be stored in lists for each sample of the patient.

As indicated in Figure 3, these data sources are integrated and stored using a common scheme. This scheme coordinates the interpretation of the information of the constituent data sources. The interpretation is in a way that is independent of the physical or logical storage details of each of the constituent data sources. The common scheme provides that data sources can be added or modified over time (change management) without significantly affecting the toolset or interface of the user who finally uses the collected data. The common scheme provides a buffer between the data sources that change and the application programs that use the collected data and the knowledge derived from the data. Similarly, if in the future additional instrumentation, for example NMR, the generation of additional data is included, they can be added without affecting the organization of the warehouse that already exists.

The scheme is enhanced with an ontology of common concepts and their relationships in the areas of immunology and related clinics. The ontology will be used by the data extraction tools and by the interconnection of the user to help in the interpretation of data sources and for the specification of data extraction tasks. The ontology will also be used in the verification of the collected clinical data.

The program tools team includes programs for statistical analysis, data extraction and visualization of results. A result of the analysis by the toolkit programs provides a set of rules related to a set of conditions to a set of consequences. These rules are applied to a statistically significant portion of the underlying data and are in the form: if condl and cond2 and ... and condN then consequencel, consequence2, ...

For example, when applied to the cellular data source and the clinical data source, the tool team can derive relationships between the cell assay and previously unknown measurements of soluble factor. The results of the analysis by the tool team are recycled to the users and to the database to be reused in the future. The architecture aims to improve the process of knowledge discovery, storing the experience of accumulated discoveries and integrating this experience for continuous improvement.

Other tools for data extraction include methods for grouping them into highly dimensional data. It is intended that these tools increase and replace the existing method of manual collection as currently used. Unlike the current cytometry program, which considers a trial for one patient at a time, the cytometry tools of the present invention can examine the data in the list mode through an assay for multiple patient samples for the purpose of to determine the optimal set of circumscribed population (floodgates). The system is coupled with a multi-dimensional display system that simultaneously projects the calculated groups into selected subsets of two-dimensional and three-dimensional views.

The final row is a user interface. This part of the system is used for user interaction and is used to plan and execute tasks related to clinical studies. The tasks supported at the user interface level include discovery of the knowledge of the study data, planning of the clinical studies, protocol planning and evaluation and development of the trial.

The user interface will accept requests for information in a uniform manner. You can combine a graphical interface and you can "extract" the information from an abstract concept level to the stored details.

It can allow information requests that include both data and text (for example, documentation pertaining to a trial protocol planning), and can allow interaction over a network.

EXAMPLES Example One Use of the present invention to identify biological markers for Rheumatoid Arthritis The present invention can be used to identify biological markers for rheumatoid arthritis (RA). Cytometry by laser scanning of Microvolume (MLSC), is used to help create data to identify biological markers for RA. Efforts to discover markers are focused on easily accessible biological fluids, most particularly blood. A two-color instrument, and antibody-based assays, have demonstrated the potential of this technique to identify and enumerate records of different cell populations with only a small amount of whole blood. Multi-parameter cell analyzes, in combination with multiple assays for soluble factors, small molecules and an extensive clinical database, is a powerful tool for the discovery of future biomarkers. Such markers have the potential to lead to new and more effective ways to predict and verify the activity of the disease and the response to therapy.

Rheumatoid Arthritis is a chronic inflammatory disorder of the small joints, which also has pronounced systemic consequences. Although the etiology of the disease is unknown, its pathology evolves with common characteristics over time. Early events appear to include an inflammatory response initiated by unknown mediators. Activated CD4 + T cells appear to amplify and perpetuate inflammation. The presence of activated T cells can induce the activation of polyclonal B cells and the production of Rheumatoid Factor (RF). Tissue damage is exacerbated, releasing autoantigens, and the degree of response of T cells is widened. Eventually, the constant inflammatory medium can lead to the transformation of synovial fibroblasts, leading to the destructive potential that is independent of T cells and macrophages. The pro-inflammatory cytokines, produced mainly by the macrophages in the joint and the inducing cytokines, such as IL-6, are systemically active, present in the serum and increase the hepatic synthesis of the acute phase proteins. Through the various stages of the disease, there are changes in the molecules and cells in the synovium and blood, which have the potential to be markers of the disease. Blood, because of its easy accessibility and circulation through the body, provides an attractive window to verify the activity of the disease and is thus, the main objective of this invention. The present invention is useful for identifying biological markers of diagnostic and prognostic value for Rheumatoid Arthritis. Such markers are required to classify different forms of the disease, for example, to identify the subset of patients in whose joints erosion occurs more rapidly than in others. In addition, markers are critical to assess the effectiveness of intervention and development of early therapies, non-toxic and successful. Many investigations have been made of cells and soluble factors in the blood, synovium and urine, which are candidate markers for the disease. In general, one to several markers have been investigated at one time. Although some factors, such as rheumatoid factor and C reactive protein have been associated with RA, there is no consensus panel of specific markers for RA. There is a strong need to simultaneously evaluate multiple candidate markers. This is achieved with multiple trials and increasing the number of parameters (colors) that can be measured in a single trial.

The present invention is capable of developing a platform to identify the markers of a disease and apply them to the RA. In general, each test combination consists of one or more reagents to identify the major cell subsets (column on the left of Table 1). Some of these antigens, for example CD4, are the targets in multiple assays. The main markers are combined with different subset antibodies (right column of Table 1) in order to maximize information about the sample. The properties of the fiuorochromes and the target antigens are considered in the development of each test combination. For example, the brightest fluorochromes are used with less abundant antigens. For other tests, it is important to use reagents with the best spectral differences for certain objectives. In general, for each triplet of the antibody, the FlowJo program is used to analyze from 1 to 3 different combinations of 3 colors (for example, Cy5 CD3, Cy5.5 CD4, Cy7APC-CD45RA vs. Cy5 CD45RA, Cy5.5 CD4, Cy7APC-CD3) to determine the best combination to distinguish different cell populations.

The design of a panel of successful trials requires some empirical knowledge. The process is typically an iterative one, with each experiment being built on the previous one. As an example, the following is an overview of the candidate combinations with a potential value for the RA.

T cells. The main antigens evaluated in a panel of T cells include CD2, CD3, CD4, CD5, CD7, and CD8. Many kinds of molecules of these subpopulations of T cells can be investigated. These include surface antigens that help distinguish "candid" cells (CD45RA) vs. Memory (CD45RO, CD26), and antigens that play a role in activation (CD25, CD69, CD71, HLA class II) or costimulation (CD27, CD28). In addition, markers that may play a role in adherence to inflammatory sites are being tested (CD62L, CDlla / CD18, CD44, CD54, and CD58). T cell subpopulations based on the expression of aßTCR, ydTCR, and a Vß TCR gene panel are evaluated.

B cells. The major antigens evaluated in a panel of B cells include CD19, CD20, CD21, CD22, CD23, and CD72. In addition, several markers in these subsets of B cells, including markers that may indicate a more activated phenotype (CD40, CD80, CD86, HLA class II, CD5) and those that have been implicated in the establishment and adhesion of the lymphocyte (CD62L, CD44, CDlla / CD18), are analyzed. The IgM, IgG, and IgA receptors for specific antigens are also evaluated.

Cells that present antigens. Cells that present antigens are evaluated using markers for the major antigens CD13, CD14, CD15, and CD33. In addition, a variety of adhesion molecules (CDlla, CD18, CD29, CD44, CD54, CD58, CD62L) and co-stimulatory molecules are analyzed.

(CD80, CD86) in these cells. Other relevant receptors, including CD16 (Fc? RIII), CD32 (Fc? RII), and CD64 (FcyRI) are being tested.

Other types of cells and antigens. Only few studies have investigated the expression of NK markers and granulocyte markers in RA, and in general, these have given inconsistent results. The subpopulations of NK using the markers CD16, CD56, CD57, and NKB1 are analyzed. Granulocytes, including neutrophils and eosinophils, can be phenotyped using CD13, CD15, and CD16. A panel of adhesion molecules and receptors, similar to that described above, is used to further subclassify these populations.

There are many antigens whose expression has been associated with a more activated or memory phenotype, involved in adhesion or co-stimulation, or that has been shown to be the receptor of an important ligand. The examples are presented in Table 1. Several of these markers have been examined in various autoimmune conditions and the expression has been found to be variable. For example, T cells from RA patients show higher levels of LFA-1 adhesion receptor (CDlla / CD18), but no change in IL2 receptor (CD25) expression, which is normally increased in activated cells , or a marker for activation and co-stimulation (CD80).

Some examples that illustrate the kinds of indicators and cell populations that can be examined are discussed below.

T cells. There are several lines of evidence involving T cells in RA (Fox, D.A. (1997) Arthritis Rheum 40, 598-609). Such evidence includes the association of RA with MHC class II alleles that share a common sequence in the third hypervariable region (Weyand, CM and Goronzy, JJ. (1997) Ann NY Acad Sci 815, 353-6 and Weyand, CM and Goronzy, JJ (1997) Med Clin North Am 81, 29-55). Since CD4 + T cells recognize antigen bound to antigens MHC class II, the association of RA with the expression of specific molecules class II, involves a role of CD4 T cells in RA. In addition, studies in animal RA models, such as collagen-induced arthritis or adjuvant arthritis, have shown that T cells transferred from affected animals can induce synovitis in susceptible hosts. In addition, studies in patients with RA have shown that strategies that seek to eliminate T cells or interfere with the function of T cells can improve rheumatoid inflammation.

Perhaps more relevant to the present invention, examination of the phenotype of T cells, whether in the synovial fluid, the synovial tissue and / or the peripheral blood of patients with RA, has led to some interesting findings (Cush, JJ and L? Psky, PE (1991) Clin Orthop, 9-22). The increased numbers of activated T cells are detectable in the peripheral blood and synovial fluid of patients with RA. These T cells express CD3 and CD4 surface markers at a lower antigen density, compared to controls, similar to the levels observed in in vitro activated myogenic T cells (Luyten, F., Suykens, S., Veys, EM, Van Lerbeirghe, J., Ackerman, C, Mielants, H. and Verbruggen, G. (1986) J Rheumatol 13, 864-9). There is also a slightly diminished number of CD8 + cells in most active RA, causing an increase in the ratio CD4 / CD8. In addition, T cells from patients with RA express increased amounts of early activation marker CD69 (Pitzalis, C, Kingsley, G., Lanchbury, JS, Murphy, J. and Panayi, GS (1987) J Rheumatol 14, 662-6 ), increased numbers of CD4 + CD29 + and CD4 + CD45RO + memory cells, and an increased expression of MHC class II products (Pitzalis, C, Kingsley, G., Murphy, J. and Panayi, G. (1987) Clin Immunol Immunopathol 45, 252-8). The expression of CD44-dependent primary adhesion is strongly correlated with symptomatic concurrent disease in juvenile RA and systemic lupus erythematosus (Estess, P., DeGrendele, HC, Pascual, V. and Siegelman, MH (1998) J Clin Invest 102, 1173-82) and may be important in RA in adults. Some studies have shown increased numbers of TDR TCR cells and an expression increased HLA in these cells (Reme, T., Portier, M., Frayssinoux, F., Combe, B., Miossec, P., Favier, F. and Sany, J. (1990) Arthritis Rheum 33, 485- 92). An increase in CD8 + 57 + cells in the RA, sometimes associated with a restricted TCR, has also been reported (Morley, JK, Batliwalla, FM, Hingorani, R. and Gregersen, PK (1995) J Immunol 154, 6182 -90 and Serrano, D., Monteiro, J., Alien, SL, Kolitz, J., Schulman, P., Lichtman, SM, Buchbinder, A., Vinciguerra, VP, Chiorazzi, N. and Gregersen, PK (1997). ) J Immunol 158, 1482-9). Three-color assays can verify restricted Vß expression in this subset of specific T cells.

B cells. Phenotypic analyzes of B cells have also been performed in patients with RA. A subpopulation of B cells expressing the screened T cell marker CD5 has been shown to be elevated (Sowden, JA, Roberts-Thomson, PJ and Zola, H. (1987) Rheumatol Int 7, 255-9, Hardy, RR, Hayakawa, K., Shimizu, M., Yamasaki, K. and Kishimoto, T. (1987) Science 236, 81-3 and Casali, P., Burastero, S.E., Nakamura, M., Inghirami, G. and Notkins, A.L. (1987) Science 236, 77-81). This subset is also elevated in autoimmune mice, where IgM autoantibodies have been shown to be constitutively expressed (Hayakawa, K. and Hardy, R.R. (1988) Annu Rev Immunol 6, 197-218). In humans, however, CD5 + B cells do not produce autoantibodies preferentially (Suzuki, N., Sakane, T. and Engleman, EG (1990) J Clin Invest 85, 238-47) and the role of CD5 + B cells B in the pathogenesis of autoimmunity in humans is not yet clear, perhaps reflecting the presence of activated B cells (Wemer-Favre, C, Vischer, TL, Wohlwend, D. and Zubler, RH (1989) Eur J Immunol 19 , 1209-13). Circulating B cells from patients with RA also demonstrated an increased expression of HLA DR molecules, again indicative of an activated B cell phenotype (Eliaou, JF, Andary, M., Favier, F., Carayon, P., Poncelet, P ., Sany, J., Brochier, J. and Clot, J. (1988) Autoimmunity 1, 217-22). A three-color assay is able to verify the increased expression of HLA class II, specifically in CD5 + CD19 + B cells.

Cells that present antigens. Several cell types can serve as antigen presenting cells, including monocytes, macrophages, dendritic cells, B cells and other cells induced to express class II antigens. In general, these cells show an activated phenotype demonstrated by the increased expression levels of HLA class II antigens in patients with autoimmune disease (Lipsky, PE, Davis, LS, Cush, JJ, and Oppenheimer-Marks, N. (1989). ) Springer Semin Immunopathol 11, 123-62). Cells that present antigens are abundant in the synovial compartment (Viner, NJ. (1995) Br Med Bull 51, 359-67) and blood-derived macrophages have been associated with glycoprotein 39 cartilage expression in some studies (Kirkpatrick, RB, Emer ?, JG, Connor, JR, Dodds, R., Lysko, PG and Rosenberg, M. (1997) Exp Cell Res 237, 46-54).

Soluble factor assays. Soluble factor assays provide an additional battery of potential biomarkers. There are many important soluble factors that have been identified in patients with RA. These include levels of circulating cytokines such as TNFa and IL-6, cytokine receptors, chemokines, rheumatoid factors of different isotypes, immunoglobulin with different forms of glycosylation, hormones, acute-based proteins such as reactive protein C and amyloid serum A , and soluble adhesion molecules, as well as matrix metalloproteinases and their inhibitors. Many of these soluble factors are known to be present at varying levels in patients with RA at different stages of the disease (Choy, E.H. and Scott, D.L. (1995) Drugs 50, 15-25, Feldmann, M., Brennan, F.M. and Maini, R.N. (1996) Annu Rev Immunol 14, 397-440, and Wollheim, F.A. (1996) Apmis 104, 81-93). Therefore, assays can be conducted to measure these soluble factors and look for statistical correlations with the cell populations identified.

Medical Records. In addition to the soluble factors, the information in the patients' medical history is included in the database. Clinical parameters will include information on age, gender, stage of the disease, tests outside of the laboratory, such as ESR, previous therapy and any other drugs or concomitant therapies. This information is relevant for the evaluation. For example, it is known that immunosuppressive drugs, such as those frequently taken by patients with RA, can have a profound effect on the expression of antigens on the surface of the cell. Patients treated with methotrexate show a decrease in CD19 + and CD5 + 19 + B cells. Patients treated with cyclophosphamide show a decrease in activated T cells expressing CD25 or HLA DR. Patients treated with prednisone express several changes in the cell surface phenotype, including a decrease in CD3 + 25 + active T cells, a decrease in CD5 + 19 + B cells, and a decrease in CD16 + NK cells and CD56 + (Lacki, JK and Mackiewicz, SH (1997) Pol Arch Med Wewn 97, 134-43). Other clinical variables such as the duration of the disease may also be useful.

Distinction of patient populations. A review of the literature on cellular assays since it is related to the autoimmune disease reveals that there are apparently conflicting reports. For example, some reports indicate an increase in CD5 B cell levels (Markeljevic, J., Batinic, D., Uzarevic, B., Bozikov, J., Cikes, N., Babic-Naglic, D., Horvat, Z. and Marusic, M. (1994) J Rheumatol 21, 2225-30) in patients with RA, while other studies do not (Liu, S.T., Wang, C.R., Liu, M.F., Li, J.S., Lei, H.Y. and Chuang, C.Y. (1996) Clin Rheumatol 15, 250-3). These publications suggest that there may be other confounding factors, which have important applications for cellular phenotypes, and perhaps cellular function in patients with RA. Segregating patient populations selected for the study, based on the levels of soluble circulating factors in the serum, stage of the disease, and therapy, the apparent discrepancy with respect to the levels of CD5 + 19 + B cells in RA patients discussed above could be explained in part. For example, it is known that there is a significant correlation between the levels of the rheumatoid factor IgM and the percentage of CD5 B cells (Youinou, P., Mackenzie, L., Katsikis, P., Merdrignac, G., Isenberg, D.A., Tuaillon, N., Lamour, A., Le Goff, P., Jouquan, J., Drogou, A. and et al. (1990) Arthritis Rheum 33, 339-48). In addition, the level of rheumatoid factor IgA is associated with the level of CD5 B cells as well as CD4 + CD45 + T cells (Arinbjamarson, S., Jonsson, T., Steinsson, K., Sigfusson, A., Jonsson, H ., Geirsson, A., Thorsteinsson, J. and Valdimarsson, H. (1997) J Rheumatol 24, 269-74). The simultaneous measurement of multiple parameters increases the probability of identifying key variables to segregate patient groups.

This generic example illustrates that this invention is uniquely suited for identifying sets of biological markers to characterize diseases. MLSC technology, which requires only a very small sample volume, provides that numerous assays can be completed on a single blood sample and ensures that the maximum amount of biological information can be acquired. The identification system of biological markers can accommodate a mixture of types of assays, including whole blood and blood lysed with RBC, among others. The conducted tests are considered relevant for the clinical indication and allow a broad inspection. The relevant biological markers can be identified using the technology of the present invention.

Example Two Use of the present invention to identify biological markers for Multiple Sclerosis The present invention can be used to identify biological markers for Multiple Sclerosis (MS). The identification system of the biological marker is used to identify markers for Multiple Sclerosis. MS is an autoimmune inflammatory disease of the central nervous system. MS is clinically characterized by episodes of relapse and remission of neurological dysfunction. The etiology of the disease remains unknown, however, the presence of inflammatory cells in the brain, spinal cord, and cerebrospinal fluid implies that an immune attack against the CNS myelin is central to the pathogenesis of MS. The hallmark of the MS lesion is an area of demyelination called a plaque that can be found through the brain and the brain. spinal cord. Inflammatory cells are observed at the edges of the plaque and distributed through the white material. The major inflammatory cells include activated lymphocytes and monocyte-derived macrophages. CD4 T cells accumulate at the edges of the plaque; CD8 T cells are not found as frequently in active disease, but are present in previous lesions. The autoreactive T cells that recognize the myelin basic protein and other non-myelin autoantigens circulate in the blood with activation, can pass through the blood-brain barrier. The regulation of adhesion molecules, histocompatibility antigens and other markers of lymphocyte and monocyte activation (IL2R, FcR) are all connected to the process of activation and establishment. In addition, there is an increase in preinflammatory cytokines that serve to amplify the immune response. The immune response also includes pronounced stimulation of B cells. The autoantibodies produced can activate the complementary system and promote demyelination. Through various stages of the disease, there are changes in the molecules and cells in the CNS and in the blood that have potential to be markers of the disease.

The present invention can identify disease markers with diagnostic and prognostic value for Multiple Sclerosis. Such markers are valuable for classifying different forms of the disease, for example, to identify the subset of patients with relapsing-remitting disease who are more likely to develop those secondary progressive diseases.

In addition, the markers are valuable to evaluate the efficiency of the intervention and develop early therapies, non-toxic and successful. Many investigations have been made of cells and soluble factors in the blood, cerebrospinal fluid (CSF) and urine that are candidate markers for the disease. In general, one to several markers have been investigated at one time. Although some factors, such as oligoclonal immunoglobulin in CSF, have been associated with MS, there is no consensus panel of specific markers for MS. There is a great need to simultaneously evaluate multiple candidate markers.

T cells. There are several lines of evidence involving T cells in MS. Such evidence includes the association of MS with the MHC class II alleles (particularly HLA DR) (Hauser, SL, Fleischnick, E., Weiner, HL, Marcus, D., Awdeh, Z., Yunis, EJ., And Alper, CA (1989) Neurology 39, 275-7). Since CD4 + T cells recognize the antigen bound to MHC class II antigens, the association of MS with the expression of specific class II molecules implies a role for CD4 T cells in the MS. In addition, studies in animal models of MS such as experimental allergic mouse or rat encephalomyelitis have shown that CD4 T cells specific for myelin antigen can induce the disease when they are transferred in an approved manner to "candid" animals ( Cross, AH and Raine, CS (1990) J Neuroimmunol 28, 27-37 and Cross, AH, Cannella, B., Brosnan, CF and Raine, CS (1990) Lab Invest 63, 162-70). In addition, studies in patients with MS have shown that strategies that seek to eliminate T cells or interfere with the function of T cells may slow the progression of MS.

Perhaps more relevant to the present invention, studies examining the phenotype of T cells, either in the cerebrospinal fluid and / or peripheral blood of patients with MS have led to some interesting findings. There is a reduction in CD8 + T cells in the blood of patients with MS. The subset that shows the most marked decrease was the CD8 + CDllb + subset (Ilonen, J., Surcel, HM, Jagerroos, H., Nurmi, T. and Reunanen, M. (1990) Acta Neurol Scand 81, 128-30 y Oksaranta, O., Tarvonen, S., Ilonen, J., Poikonen, K., Reunanen, M., Panelius, M. and Salonen, R. (1996) Neurology 47, 1542-5). There is also an increase in activated T cells carrying the CD71 and CD25 markers, particularly in the active MS (Gene, K., Dona, DL and Reder, AT (1997) J Clin Invest 99, 2664-71 and Strauss, K ., Hulstaert, F., Deneys, V., Mazzon, AM, Hannet, I., De Bruyere, M., Reichert, T. and Sindic, CJ (1995) J Neuroimmunol 63, 133-42). In addition, most of the T cells in the cerebrospinal fluid and peripheral blood show a memory phenotype with high levels of CD45RO and CD29 in both populations of CD4 and CD8 T cells (Vrethem, M., Dahle, C, Ekerfelt , C, Forsberg, P., Danielsson, O. and Ernerudh, J. (1998) Neurol Scand Act 97, 215-20). This leads to a reduction in candid CD4 + CD45RA + T cells (Strauss, K., Hulstaert, F., Deneys, V., Mazzon, AM, Hannet, I., De Bruyere, M., Reichert, T. and Sindic , CJ (1995) J Neuroimmunol 63, 133-42) and CD8 + CD27-CD45RA + (Hintzen, RQ, Fiszer, U., Fredrikson, S., Rep, M., Polman, CH, van Lier, RA and Link, H. (1995) J Neuroimmunol 56, 99-105) in the peripheral circulation. A recent study has concluded that CD4 +, CD4 + SLAM +, and CD4 + CD7 + cells (preferably helper 1 cells that produce cytokine) are increased in patients with MS in relation to controls (Ferrante, P., Fusi, ML , Saresella, M., Caputo, D., Biasin, M., Trabattoni, D., Salvaggio, A., Clerici, E., de Vries, JE, Aversa, G., Cazzullo, CL and Clerici, M. ( 1998) J Immunol 160, 1514-21). In addition, some studies have shown a use of the biased TCR beta variable in the peripheral blood of patients with MS, indicative of a restricted TCR repertoire (Gran, B., Gestri, D., Sottini, A., Quiros Roldan, E. , Bettinardi, A., Signorini, S., Primi, D., Ballerini, C, Taiuti, R., Amaducci, L. and Massacesi, L. (1998) J Neuroimmunol 85, 22-32). A restricted pattern of gene rearrangements has also been described in the subset of T cells and d (Michalowska-Wender, G., Nowak, J. and Wender, M. (1998) Folia Neuropathol 36, 1-5).

B cells. Phenotypic analyzes of B cells have also been performed in patients with MS. A subpopulation of B cells expressing the markers of screened CD5 T cells has been shown to be elevated (Mix, E., Olsson, T., Corréale, J., Baig, S., Kostulas, V., Olsson, O. and Link, H. (1990) Clin Exp Immunol 79, 21-7). This subset is also elevated in autoimmune mice, where it has been shown to constitutively express autoantibody IgM autoantibodies (Hardy, RR, Hayakawa, K., Shimizu, M., Yamasaki, K. and Kishimoto, T. (1987) Science 236, 81- 3). In humans, however, CD5 + B cells do not, preferably, produce autoantibodies (Suzuki, N., Sakane, T. and Engleman, EG (1990) J Clin Invest 85, 238-47) and the role of cells B CD5 + in the pathogenesis of autoimmunity in humans is not yet clear, perhaps reflecting the presence of activated B cells (Wemer-Favre, C, Vischer, TL, Wohlwend, D. and Zubler, RH (1989) Eur J Immunol 19, 1209-13). Consistent with this conclusion, high levels of the CD45RO memory marker were found in the circulating CD20 + B cells of patients with MS (Yacyshyn, B., Meddings, J., Sadowski, D. and Bowen-Yacyshyn, MB (1996) Dig. Dis Sci 41, 2493-8). The number of circulating CD80 + B cells also increases significantly in patients with MS, with active disease, but is normal in stable MS (Gene, K., Dona, DL and Reder, AT (1997) J Clin Invest 99 , 2664-71).

Cells that present antigens. Several cell types can serve as antigen presenting cells, including monocytes, macrophages, dendritic cells, B cells and other cells induced to express class II antigens.

In general, these cells show an activated phenotype demonstrated by increased levels of HLA class II antigen expression in patients with active MS (Gene, K., Dona, D.L. and Reder, A.T. (1997) J Clin Invest 99, 2664-71). A recent study has also shown that monocytes expressing CD86 and CD95 (fas) are increased in MS, compared to healthy controls (Gene, K., Dona, DL and Reder, AT (1997) J Clin Invest 99, 2664- 71).

Other types of cells Only a few studies have looked for the expression of NK markers and granulocyte markers in MS. One study shows an increase in NK CD16 + NK cells in chronic, progressive MS (Kastrukoff, LF, Morgan, NG, Aziz, TM, Zecchini, D., Berkowitz, J. and Paty, DW (1988) J Neuroimmunol 20, 15-23).

Soluble factor assays. Soluble factor assays provide an additional battery of potential biomarkers. There are many important soluble factors that have been identified in patients with MS. For example, Apo Al / soluble Fas levels (Ferrante, P., Fusi, ML, Saresella, M., Caputo, D., Biasin, M., Trabattoni, D., Salvaggio, A., Clerici, E. , de Vries, JE, Aversa, G., Cazzullo, CL and Clerici, M. (1998) J Immunoi 160, 1514-21) are increased in acute MS, compared with the levels seen in patients with stable disease. or with healthy controls. In addition, the levels of soluble adhesion molecules, such as soluble intracellular adhesion molecule 1 (ICAM-1) (Giovannoni, G., Lai, M., Thorpe, J., Kidd, D., Chamoun, V. , Thompson, AJ., Miller, DH, Feldmann, M. and Thompson, EJ (1997) Neurology 48, 1557-65) and soluble E-selectin (Giovannoni, G., Thorpe, JW, Kidd, D., Kendall , BE, Moseley, IF, Thompson, AJ, Keir, G., Miller, DH, Feldmann, M. and Thompson, EJ (1996) J Neurol Neurosurg Psychiatry 60, 20-6) have been shown to be increased in patients with MS in different stages of the disease. Proinflammatory cytokines such as TNFa and IFN ?, are known to be present at varying levels in patients with MS at different stages of the disease (Navikas, V. and Link, H. (1996) J Neurosci Res 45, 322-33) . Other relevant proteins, such as cytokines and cytokine receptors, chemokines, matrix metalloproteinases and their inhibitors, neopterin, and myelin basic protein, have also been shown to be present at varying levels in patients with MS at different stages of the disease. and in healthy controls. Therefore, assays can be conducted to measure these soluble factors and look for statistical correlations with the cell populations identified.

Medical records and distinction of patient populations. In addition, the information of the soluble factors in the medical history of the patients will be included in the database. The clinical history will include information on age, gender, disease status, evidence outside the laboratory (magnetic resonance imaging, analysis of cerebrospinal fluid for oligoclonal immunoglobulin and evoked potential records), prior therapy, and any concomitant drugs or therapies. . This information is relevant to segregate the populations of the patients.

It is evident that the effects of the treatment play a role in the phenotype of the cells. Although patients with untreated MS have a large population of circulating CD3 + CD4 + CD8 + T cells, compared with healthy donors, this population of cells is reduced after treatment with corticosteroids [30]. In addition, the number of lymphocytes and monocytes CD71 + and HLA DR + is increased in the active MS. However, therapy with IFNß-lb reduces the number of activated HLADR +, CD71 + and CD25 + cells. In addition, although the number of circulating CD80 + B cells decreases, the number of CD86 + monocytes increases after therapy [14]. Other clinical variables, such as the duration of the disease, may be useful. For example, it has been shown that in patients with MS with restricted TCR Vβ repertoires, the average duration of the disease is shorter than in patients who do not have a restricted repertoire (Gran, B., Gestri, D., Sottini, A ., Quiros Roldan, E., Bettinardi, A., Signorini, S., Primi, D., Ballerini, C, Taiuti, R., Amaducci, L. and Massacesi, L. (1998) J Neuroimmunol 85, 22- 32).

Example Three Study comparing a patient with rheumatoid arthritis with a patient blood control In a pilot study, a panel of 40 2-color cell assays was prepared for Imagn 2000 and blood samples from approximately 50 donors were evaluated. Half of the samples come from the Stanford Blood Bank and half come from the Rheumatology Clinic at Stanford University. The study was designed to evaluate and develop the key components of the biomarker search engine of the invention: instruments, assays and analytical tools. It was not necessarily designed to elucidate biomarkers. All the tests were done in whole blood, without washing to remove unbound reagents. Thirty-eight of the assays comprised 27 different antibody reagents for 23 different cell surface antigens. Eighteen were conjugated to Cy5 and nine were conjugated to Cy5.5. Each of these cell assays comprises an antigen conjugated to each dye to constitute the combination of two colors. Two trials verified cell viability with a dye that intercalates DNA. Cellular assays allowed us to identify approximately 100 different populations Cells, including sets of T cells, B cells, NK cells, monocytes and granulocytes.

Methods Cellular Assays The panel of tests is shown in Table 3. Each reagent is tested and titrated before preparing the reagent combinations, in order to optimize the assay performance Sample Preparation For this study, all cell assays were applied to whole blood, in a homogeneous mode (without post-rinse washing). Aliquots (20 uL) of reagent combinations of fluorescently labeled antibody from a DNA dye were distributed with a multi-channel pipette of grids prepared in discrete wells of a microtiter plate. Complete blood or diluted whole blood (30 uL) was added with a multichannel pipette and the sample was mixed. The cells were incubated for 20 minutes, followed by the addition of 100 uL of diluent and mixing. A portion of each stained sample (50 uL, corresponding to 10 uL of blood) was added to the volumetric capillaries (VC120) and loaded onto a modified Imagn 2000 instrument. The explorations were started and executed without the intervention of the operator. The data files were transferred to the computer network and converted to the Standard Flow Cytometry format. The FlowJo cytometry program was used to identify cell populations and to obtain numerical values for cell counts (cells per uL), relative cell size, and relative fluorescent intensity, which is an estimate of the density of antigens for each cell population introduced (collected).

Soluble Factors Serum levels of reactive protein C were measured in Imagn 2000 with a pearl-based immunoassay. The beads coated with an antibody Anti-CRP were used to capture the analyte. An anti-CRP antibody conjugated with Cy5 was used to reveal the captured analyte.

Medical Information of the Patient An abbreviated medical history, including age, gender, parameters of disease severity, comorbidities and medications, was obtained from each patient. Data for blood samples were limited to age and gender.

Database and Statistics The data produced from the cell assays, soluble factor assay and medical records were combined into a single database. To identify potential biomarkers (cell counts or staining intensity, serum concentration) associated with categorical clinical variables (such as the diagnosis of the disease), a variety of discriminant techniques were used, including quadratic and linear discriminant analysis of Fisher, logistic regression and classification tree diagrams. To identify the markers associated with the continually evaluated clinical variables (such as erythrocyte sedimentation rate), we used a variety of regression techniques, including linear muitivariable regression and regression tree plots. For discriminant and regression analyzes, a selection of variables and sequential cross-validation was used to identify those markers that are most closely associated with the clinical variable of interest. Where appropriate, clinical and demographic variables (such as age, gender, and concomitant drugs) were included as covariates in the models. These techniques were implemented and applied using the statistical software packages SAS and Statistica.

Results Common analysis strategies can be used for most patient samples Although one of the disadvantages of studies of cell populations is often the variability between donor samples, identical analysis windows (gates) were used across all donors, for 95% of the cell populations analyzed. This demonstrates the robustness and consistency of these assays and cell analysis systems. The remaining 5% of the floodgates were adjusted to take into account the new populations that appear in certain donors or a reagent that seemed unreliable for a few donors. In the latter case, the problem reagent can be replaced with an improved version in future studies.

An example of a combination of 2 colors with variation between the donors is shown in Figure 4. The cells were stained with CD27 conjugated to Cy5 in combination with a CD8 conjugated to Cy5.5. This combination allows the CD8 + T cells (MHC class I restricted) to be verified, which are CD27 + cells (activated) and CD27"CD8", CD27 + (which are actually CD4 + T cells, MHC class II, activated) they are detected Although there is a variation among the donors, a single gates formation strategy can be impiemented. Three cell populations that differ among donors were identified. In Figure 4A, the majority of CD8 + T cells are CD27 negative. In Figure 4B, the majority of CD8 + cells are CD27 positive. Finally, in Figure 4C, the CD8 population is divided between those that are CD27 positive and those that are CD27 negative. FlowJo, our cytometry program, calculates the cell count for each of the populations in a gate. In addition, the average fluorescence intensity was obtained for each antigen. This is indicative of the density of antigens in the cell surface. The relative size of the cell was also obtained for each cell population. The numbers were compared and statistics were calculated for all donors. The differences shown here with respect to CD27 expression in CD8 + cells are typical of the kinds of changes that are observed when comparing patient and control populations in our clinical study.

Excellent correlation between the related measures Another metal of this initial study was to evaluate the robustness of the 2-color Imagn system and develop statistical tools. The study was designed so that several capillaries contained in the same antibody reagent, are conjugated either to the same or to an alternative dye. This allows the same measurements to be obtained anywhere 2-6 times from the same donor for the CD3, CD4, CD5, CD7, CD8, CD19, CD20 and CD27 antigens. The same cell populations were also measured using antibodies to different antigens found in them. For example, total T cells were enumerated using CD3 as well as CD5. B cells were enumerated using CD19, as well as CD20, etc. In this preliminary study, excellent consistency is observed between capillaries that contain an identical reagent and capillaries that contain different antibodies that stain similar cell populations. The correlation coefficients for the same antigen through different capillaries, average 0.94. The correlation coefficient was 0.97 for CD3 vs. CD5 and CD19 vs. CD20. The examples of these correlations are given in Figure 5 and Figure 6.

Differences are observed between the blood bank samples and the RA Several measured parameters were used to segregate general blood bank samples and samples from patients with RA, as shown in Table 4. The best single markers segregate exactly 80 to 86% of the sample (7 to 10 incorrect functions). Some, two pairs of cell populations secrete 90% of the samples, suggesting that the cell population sets may be more useful than the unique cell populations to segregate the populations of the patients.

Example Four Expanded RA Study This Example expands the measurement capabilities in an RA study. Cell populations and soluble factors from patients with rheumatoid arthritis (RA) were verified. Patients with RA are part of a clinical study, who receive methotrexate and either ARAVA or a placebo. Patients were longitudinally verified for approximately 2 months. At each point of time, the data of the cell population, data of the soluble factor and the clinical information were collected.

Cellular Assays Most cell assays are done in a whole blood format, as described in Example three. Minimal manipulations ensure that absolute cell counts are obtained (more accurate cells / μl of blood) and that only small amounts of blood are required per test (40 ul) so that many assays can be run from a single tube of blood. However, for some cell populations, an alternative assay format, blood lysed with RBC, is preferable.These include particular antigen-antibody pairs, for which soluble factors (free Ig, soluble cytokine receptors, etc.) contained in the serum, interfere with the labeling of cells and populations of cells that are present in a very low frequency.For example, the preparation of the sample lysed with RBC is useful for the activated cells that express CD25 or CD69, the which are essentially undetectable in whole blood of normal individuals, but are increased tenfold in the lysate format, and are likely to be increased in autoimmune states. Improved detection of other populations of minor cells, such as NK cells, has also been demonstrated.

For this protocol, cellular assays include a panel of 60 combinations of 2 colors comprising 46 assays in whole blood and 14 assays of whole blood lysed with RBC. A total of 39 different antibody reagents (30 conjugated to Cy5 and 9 conjugated to Cy5.5) were used, which target 35 different cell surface antigens. All tests are done in a homogenous way (without washing after dyeing). This panel test panel allows the identification of more than 150 different populations of cells The combinations of reagents and cell populations that can be identified are given in Table 5.

Soluble Factor Assays Serum aliquots are taken and frozen for each blood sample for subsequent measurement of multiple soluble factors. These include levels of circulating cytokines such as TNFa and IL-6, cytokine receptors, chemokines, rheumatoid factors (RF) of different isotypes, immunoglobulin, acute phase proteins such as reactive protein C and amyloid serum A, and soluble adhesion, as well as matrix metalloproteinases and their inhibitors. The initial panel of 22 tested soluble factors is shown in Table 6. Additional targets are also provided in Table 6. All assays are done in a sandwich ELISA format, using pairs of paired antibodies to ensure the required sensitivity and specificity .

Medical Information of the Patient A medical history with more specific disease-specific information is included, with each sample in the study.

Example Five Cellular Tests in a Four-Channel MLSC Instrument More tests, with more information content per test, can be run on the 4-channel SurroScan instrument. The tests are developed using combinations of 3-color reagents. Effective dye combinations include Cy5, Cy5.5 and Cy7 and Cy5, Cy5.5 and Cy7-APC allow the simultaneous and independent measurement of three target antigens. The combinations of three colors facilitate the acquisition of more information by capillary than the combinations of 2 colors by 1) eliminating redundancy (for example, measuring CD3, CD4 and CD8 in a capillary, instead of measuring CD3 + CD4 and CD3 + CD8 in two capillaries) and 2) identify new populations that are defined by the simultaneous expression of 3 antigens (for example, "candid" CD4 + T cells that express both CD45RA as CD62L). Given the appropriate fluorescent dyes, with a different emission spectrum, it is possible to simultaneously verify the additional target antigens in the fourth channel, or in some cases, in the existing channels. Figure 7 provides the results of a 3-color test on the SurroScan instrument.

The tests on the SurroScan instrument can be executed in capillary arrays that use approximately 1/3 less sample than the VC120 capillaries. For whole blood assays, it is possible to process 10 uL or less per 3-color assay, giving the potential of up to 1000 assays per 10 mL tube of blood. For blood samples used with RBC, with a 10-fold increase in leukocyte concentration, approximately 100 tests can be done by blood tube. A panel of 64 3-color assays, with 50 or more target antigens, is in development, using full and lysed blood formats. It should allow the identification of more than 200 cell populations.

Example Six Intracellular Staining Intracellular molecules can be measured with MLSC technology. PBMC was cultured for 5 hours in the presence of PHA and ionomycin. The cells were stained with Cy5.5 anti-CDd to identify the cytotoxic T cells, fixed, permeabilized, and stained with Cy5 anti-inerferon-gamma (IFN-γ) to detect the intracellular cytokine. The data in Figure 8 show that IFN-y is detected only in stimulated cells. A control reagent (MOPC) does not label the cells. Among CD8 T cells, 20% express IFN- and intracellular.

EXAMPLE Seven Biological Markers of Identification for the Treatment of Allergy and Asthma The present invention can be used for biological markers for allergic asthma. Asthma is a common chronic lung disease, from a uncertain etiology. It is characterized by an inflammation of the airways that leads to symptoms such as cough, wheezing, depressed chest and shortness of breath. It is thought that these clinical symptoms are due to a hyperresponsiveness of the airways and a long-term inflammatory process that causes the obstruction of the air flow. The disease causes extreme discomfort and can sometimes, be fatal with the absence of appropriate treatment. It is thought that the clinical manifestations of asthma result from the superimposition of a variety of environmental factors into genetic predispositions that increase the likelihood of developing asthma. Atopy, hypersensitivity to environmental allergens, is common in asthma, but not all atopic individuals develop asthma. The relative importance of allergic mechanisms is not completely understood. Corticosteroids (inhaled and systemic) are effective in asthma, but have been associated with perceived and actual side effects that limit their usefulness. A more complete understanding of the response to corticosteroids may allow the development of drugs with only local effects within the lungs or drugs that have beneficial effects without side effects.

A study has been designed to identify the biological markers of atopy, asthma and the response to corticosteroid therapy. Subjects are selected for four study groups of 20: 1) mild asthmatics that have given positive results to the test allergens on the skin, 2) mild asthmatics that have given a negative result to the test allergens on the skin, 3) no asthmatics who have tested positive for test allergens on the skin, and 4) non-asthmatics who have tested negative on skin allergens (healthy subjects). All eligible subjects are enrolled in a randomized, single-blind, placebo-controlled parallel study to investigate the effect of the drug prednisone on biomarkers after 3 days of declared treatment. Blood samples are taken before treatment on the morning of day 1 and 12 hours after the last dose in the morning of day 4.

Subjects underwent a rigorous selection, which includes detailed medical history and clinical tests for lung function and allergy. Mild asthmatics have a 1) FEVi predicted at 80%, 2) documented diagnosis of asthma or history of any of the following: cough, which worsens particularly at night, recurrent wheezing, recurrent shortness of breath, recurrent chest tightness and 3 ) a positive methacholine challenge test (Cockcroft DW, et al Clin Allergy 1977; 7: 235 and Juniper EF, et al Thorax 1984; 39: 556). Non-asthmatics have a 1) FF. predicted at 80%, 2) no asthma history and 3) a negative methacholine challenge test. Allergic subjects have a positive skin test on at least one of a panel of allergens.

Examples of clinical data include Hematology: white blood cell count (WBC), red cell count (RBC), hemoglobin (Hb), hematocrit (HCT), average cell volume (MCV), average cell hemoglobin (MCH), average cell hemoglobin concentration (MCHC), platelet count, neutrophil count, lymphocyte count, monocyte count, eosinophil count, basophil count and ESR - erythrocyte sedimentation rate; blood biochemistry: alkaline phosphatase, alanine transaminase, aspartate transaminase, gamma-glutamyl transpeptidase, albumin, total protein, total bilirubin, urea, creatinine, sodium, potassium, glucose; urinalysis: protein, glucose, ketones, bilirubin, blood, leukocytes; Hepatitis and HIV test: HIV I and II, Hepatitis B surface antigens, Hepatitis C antibody. All clinical records and test parameters will be included in a master database for statistical analyzes, evaluation as covariables and data extraction.

Atopic asthma is an immune disease mediated by IgE antibodies. Exposure to allergens causes B cells to synthesize IgE, which binds to the high-affinity receptor of mast cells residing in the airway mucosa. With re-exposure to the allergen, the antigen-antibody interactions on the surface of the mast cells, activates the release of the mediators of anaphylaxis stored in mast cell granules, including: histamine, tryptase, GD2, leukotriene C4 and D4, and platelet activation factor (PAF). These soluble factors induce the contraction of aerial smooth muscle and cause an immediate fall in FEVi. Re-exposure to allergens also leads to the synthesis and release of a variety of cytokines: IL-4, IL-5, GM-CSF, TNF-α, TGF-β from T cells and mast cells. These cytokines attract and activate B cells, which leads to the production of more IgE, and eosinophils and neutrophils, which produce the cationic eosinophilic protein (ECP), the main basic protein (MBP) and PAF. These factors cause edema, mucus hypersecretion, smooth muscle contraction and an increase in bronchial reactivity that is typically associated with the late asthmatic response, indicated by a fall in FEVi approximately 4-6 hours after exposure.

A wide panel of cell measurements and soluble factors is applied to the subject's blood samples in order to discover biomarkers. The study design provides information on the interindividual variability within the groups, and the differences between the groups in the expression of the marker. It is believed that intergroup differences (eg, allergic non-asthmatic versus non-allergic non-asthmatic) will be greater than interindividual variability within the groups. It is further believed that prednisone therapy will result in significant intraindividual changes in marker expression.

Cellular Assays A panel of 64 three-cell cellular assays for initial atopic asthma has been prepared and tested, focusing on the immune and inflammatory parameters in the blood. The panel is given in Table 7.

Soluble Factors The study also looks at a broad panel of soluble factors. The immunoassays, in the chemiluminescent sandwich ELISA format, are used for the following purposes: Cytokines, chemokines and their soluble receptors: IL-1 alpha, IL-1 beta, IL-1 RA, IL-1 sRI, IL-1 sRII, IL-2, IL-2sR, IL-3IL-4, IL-5 , IL-6, IL-6 sR, IL-8, IL-10, IL-12 p40, IL-12 p70, IL-13, IL-16, IL-17, MIF, MIP-1 alpha, MIP-1 beta, RANTES, sTNF alpha Rl *, sTNF alpha RII *, TGF beta, TNF alpha, alpha, TGF beta2, TGF beta3, Oncostatin M, M-CSF, GM-CSF, IGF-1, PDGF-BB, FGF-4 , FGF-6, FGF-7, Fas, VEGF, MCP-1, PF-4, EOTAXIN, IFN gamma, Immunoglobulin: IgAl Kappa, IgAl Lambda, IgAl, 2 Kappa, IgAl, 2 Lambda, IgA2 Kappa, IgA2 Lambda, Total IgE, IgGl Kappa, IgGl Lambda, IgGl total, IgG2 Kappa, IgG2 Lambda, total IgG2, IgG3 Kappa, IgG3 Lambda, total IgG3, IgG4 Kappa, IgG4 Lambda, total IgG4, total IgG, total IgG Kappa, total IgG Lambda, IgM Kappa, Lambda IgM, total IgM, RFIgA, RFIgG, RFIgM, total RF, acute phase proteins: CRP, SAA; matrix metalloproteinases and their inhibitors: MMP-3, MMP-9, T1MP-1, TIMP-2; Soluble adhesion molecules: sCD54 (ICAM-1), SCD62E, SCD62P.

Additional soluble factors, which are measured by immunoassays or by mass spectroscopy assay, include, but are not limited to, cytokines, chemokines, and their soluble receptors: IL-9, IL-11, IL-14, IL -15, IL-18, sCD23, eosinophil proteins: ECP, MBP, Immunoglobulin: IgE specific allergen, carbohydrate modified Ig; a variety of prostaglandins; a variety of leukotrienes, histamine.

The data produced from cell assays, soluble factor assays, medical records and selection marks are combined into a single database. To identify potential biomarkers (cell counts, antigen intensity in particular cell types, soluble factor concentrations, etc.) associated with categorical clinical variables (disease status, prednisone or placebo, before or after therapy) , a variety of discriminant and ANOVA techniques can be used. Where appropriate, cynical and demographic variables (such as age, gender, specific historical results) can be included as covariates in the models. The techniques can implemented with packages of statistical analysis programs such as SAS, Statistica, Statview or similar.

Example Eight Use of the present invention to identify biological markers after administration of aspirin The present invention can be used for biological markers to evaluate the effects of drug administration on cells and soluble factors to be performed on small blood samples. peripheral. It is hoped that these trials will make possible the analysis of the effects of different doses of drugs on cell markers and soluble in human peripheral blood. In this example, the widely used over-the-counter drug, aspirin (acetylsalicylic acid), is administered to human volunteers. Different doses of the drug will be administered orally, blood is drawn before and at several points in time after administration, and cell test panels and soluble factor are undertaken. Aspirin is expected to cause changes in the cellular and soluble components of the blood.

Aspirin is routinely used for two main indications: 1) to reduce the risk of coronary and cerebral thrombosis and 2) as an analgesic / anti-inflammatory agent. It is believed that the underlying mechanism of the first indication is the irreversible inhibition of the PGH-synthase enzyme in platelets.

A prostaglandin product of this enzyme in platelets is converted to thromboxane A2, which facilitates the aggregation of platelets and thrombosis. A side effect of prostaglandin synthesis is the generation of oxygen free radicals, which, in the presence of redox-oxidative metals, convert unsaturated fatty acids into aldehydes. A relatively stable product of lipid oxidation is malondialdehyde (MDA). This compound is routinely tested calorimetrically or fluorometrically after interaction with thiobarbituric acid (TBA). Aspirin, by inhibiting prostaglandin synthesis, is expected to decrease MDA levels in peripheral blood platelets.

This is a parameter that is expected to change after the administration of aspirin. Changes in other markers of platelet activation, such as changes in the expression of CD62P and CD63, can also occur.

Type E prostaglandins suppress lymphocyte activation and tumor necrosis factor a (TNF-a) production by cells of the monocyte-macrophage line. If there are some levels of lymphocyte activation and production of TNF-a in healthy normal people, these can be increased after treatment with aspirin and detectable in the peripheral blood. These are examples of the changes expected after the administration of aspirin; If many markers are tested, unexpected changes may also be found, and may prove to be more interesting than expected.

The study is designed to identify the effects of aspirin on blood parameters. The subjects are assigned randomly to aspirin administered orally, according to one of three dosage schemes. Group I, 1 dose (325 mg tablet) after breakfast, Group II, 2 doses (650 mg) after breakfast and Group III, 2 doses after breakfast and 2 doses after dinner (1300 mg total). There are 10-12 subjects per cohort. Blood samples are taken before, during and after the administration of aspirin. The program is given in Table 8. The subjects are healthy individuals aged 18-65 years, who are not taking another aspirin other than non-steroidal anti-inflammatory drugs, nor are they currently under care, requiring the use of anti-inflammatory drugs (steroidal or non-steroidal).

Cellular assays A panel of 42 three-cell cellular assays is used for the initial study, see Table 9. The panel includes immune and inflammatory parameters and contains some of the assays listed in Example 7. It also includes a series of assays for the function of platelets (1-17). These trials include direct measurements in diluted whole blood (WB, 1-9), as well as thrombin stimulation assays (TRT, 10-13) and stimulation controls (NTRT, 14-17).

Soluble Factors A broad panel of soluble factors as described in Example 7 will be part of this study. Additional measures include: von Willebrand factor, b-thromboglobulin, thromboxane B2, 6-keto PGF and malondialdehyde. The soluble factors will be measured from the plasma. In addition, some soluble factors (eg, MDA, prostaglandin leukotrienes) will be evaluated for stimulated samples and controls.

TABLE 1 Some cell surface antigens are in more than one category.

TABLE 2 Amershman, 2 Molecular Tests, 3 Multiple Vendors, 4 Pharmingen REF, 5 Diatron TABLE 3 Panel of essays TABLE 3 (CONTINUED) TABLE 3 (CONTINUED) TABLE 3 (CONTINUED) This is an example of possible cell populations to monitor, alternate or additional populations can be monitored.

TABLE 4 Analysis of linear discriminant of pilot study Better parameters to distinguish RA and blood bank samples in a group of data Samples, n = 51, Blood bank = 26, RA = 25 Most measurements have averages of 2 to 6 trials.

TABLE 5 Information on reagent combinations in panels for Pro-5003 9Í TABLE 5 (CONTINUED) TABLE 6 Soluble Factor Immunoassays S = soluble TABLE 7 TABLE 7 (CONTINUED) TABLE 7 (CONTINUED) TABLE 7 (CONTINUED) TABLE 8 Blood draws between 8 and 9 am each day. TABLE 9

Claims (71)

1. A biological marker identification system, characterized in that it comprises: a) an integrated database comprising a plurality of data category, said data categories comprising, i) levels of a plurality of populations of cells and / or molecules associated with cells in the biological fluid of an organism, and / or levels of a plurality of factors soluble in the biological fluid in an organism, and ii) information associated with a plurality of clinical parameters of an organism; b) data from a plurality of organisms corresponding to said category of data; and i) processing means to correlate the data within the data categories, where the correlation analysis of data categories can be done to identify the category or categories of data indicating normal biological processes, pathogenic processes, or pharmacological responses to the therapeutic intervention, where said category or identified categories are biological markers.

2. The biological marker identification system according to claim 1, characterized in that the data for the levels of the cell populations and / or the molecules associated with cells are obtained by cytometry by microvolume laser scanning.

3. The biological marker identification system according to claims 1 and 2, characterized in that it comprises at least 20 categories of level data of cell populations and / or molecules associated with cells.

4. The biological marker identification system according to claim 3, characterized in that it comprises at least 30 categories of level data of cell populations and / or molecules associated with cells.

5. The biological marker identification system according to claim 3, characterized in that it comprises at least 50 categories of level data of cell populations and / or molecules associated with cells.

6. The biological marker identification system according to claims 1-3, characterized in that the soluble factor is a soluble protein.

7. The biological marker identification system according to claim 1, characterized in that the soluble factor is a small molecule.

8. The biological marker identification system according to claim 1, characterized in that the data for the levels of soluble factors are obtained by cytometry by microvolume laser scanning.

9. The biological marker identification system according to claim 1, characterized in that the data for the levels of soluble factors are obtained by immunoassays.

10. The biological marker identification system according to claim 1, characterized in that it comprises at least 20 data categories of the soluble factor level.

11. The biological marker identification system according to claim 10, characterized in that it comprises at least 30 data categories of the soluble factor level.

12. The biological marker identification system according to claim 10, characterized in that it comprises at least 40 data categories of the soluble factor level.

13. The biological marker identification system according to claim 1, characterized in that the data of at least some of the organisms are included a plurality of times.

14. The biological marker identification system according to claim 1, characterized in that the data categories further include: iii) genotype information associated with an organism.

15. The biological marker identification system according to claim 1, characterized in that the data for the levels of soluble factors are obtained by mass spectrometry.

16. The biological marker identification system according to claim 1, characterized in that the information associated with the clinical parameters is selected from the group consisting of age, gender, weight, height, body type, medical history, family history, factors of the environment and manifestation and categorization of the disease or medical condition.

17. The biological marker identification system according to claim 1, characterized in that the data are obtained from organisms before and after the administration of a therapeutic treatment.

18. The biological marker identification system according to claim 1, characterized in that at least some of the data is obtained from an organism that has been previously diagnosed as having a predetermined medical condition or disease.

19. The biological marker identification system according to claim 1, characterized in that at least some of the data is obtained a plurality of times of an organism that has been previously diagnosed as having a predetermined medical condition or disease.

20. The biological marker identification system according to claims 18 and 19, characterized in that the disease or predetermined medical condition is rheumatoid arthritis.

21. The biological marker identification system according to claims 18 and 19, characterized in that the predetermined disease or medical condition is selected from the group consisting of rheumatoid arthritis, asthma, allergy and multiple sclerosis.

22. The biological marker identification system according to claim 1, characterized in that the data categories comprise levels of a plurality of populations of cells and / or molecules associated with cells in the biological fluid of an organism and levels of a plurality of factors soluble in the biological fluid of an organism.

23. A method for identifying a biological marker for a given disease or medical condition, characterized in that it comprises: correlating the information obtained from a plurality of organisms, at least some of the organisms have the disease or medical condition, where the information is associated with a plurality of data categories, and where the data categories comprise, i) levels of a plurality of populations of cells and / or molecules associated with cells in the biological fluid of an organism, and / or levels of a plurality of factors soluble in the biological fluid in an organism, and ii) information associated with a plurality of clinical parameters of an organism; identify a category of data where the organisms that have the disease or medical condition can be differentiated from those organisms that do not have the disease or medical condition, where the category identified is a biological marker for the disease.

24. The method for identifying a biological marker according to claim 23, characterized in that the data for the levels of the cell populations and / or the molecules associated with cells are obtained by cytometry by microvolume laser scanning.

25. The method for identifying a biological marker according to claim 23, characterized in that it comprises at least 20 categories of level data of cell populations and / or molecules associated with cells.

26. The method for identifying a biological marker according to claim 25, characterized in that it comprises at least 30 categories of level data of cell populations and / or molecules associated with cells.

27. The method for identifying a biological marker according to claim 25, characterized in that it comprises at least 40 categories of level data of cell populations and / or molecules associated with cells.

28. The method for identifying a biological marker according to claim 23, characterized in that the data for the levels of soluble factors are obtained by cytometry by microvolume laser scanning.

29. The method for identifying a biological marker according to claim 23, characterized in that it comprises at least 20 data categories of the soluble factor level.

30. The method for identifying a biological marker according to claim 29, characterized in that it comprises at least 30 data categories of the level of the soluble factor.

31. The method for identifying a biological marker according to claim 29, characterized in that it comprises at least 40 data categories of the soluble factor level.

32. The method for identifying a biological marker according to claim 23, characterized in that the data categories further include: üi) genotype information associated with any organism.

33. The method for identifying a biological marker according to claim 23, characterized in that the data for the levels of soluble factors are obtained by mass spectrometry.

34. The method for identifying a biological marker according to claim 23, characterized in that the data for the levels of soluble factors are obtained by immunoassays.

35. The method for identifying a biological marker according to claim 23, characterized in that the information associated with the clinical parameters is selected from the group consisting of age, gender, weight, height, body type, medical history, family history, factors of the environment and manifestation and categorization of the disease or medical condition.

36. The method for identifying a biological marker according to claim 23, characterized in that the disease is rheumatoid arthritis.

37. The method for identifying a biological marker according to claim 23, characterized in that the disease is selected from the group consisting of rheumatoid arthritis, asthma, allergy and multiple sclerosis.

38. A phenotype of an organism comprising a plurality of biological parameters, characterized in that it comprises: the results of at least 20 assays related to the populations of cells and / or molecules associated with cells; the results of at least 20 trials are related to soluble factors; and clinical parameters.

39. The phenotype according to claim 38, characterized in that it comprises the results of at least 40 assays that relate to the cell populations and / or molecules associated with cells.

40. The phenotype according to claim 38, characterized in that it comprises the results of at least 40 tests that are related to the soluble factors.

41. The phenotype according to claim 38, characterized in that it also comprises the information of the genotype of the organism.

42. A phenotype of a class or subclass of organisms comprising a plurality of biological parameters of a plurality of members of the class or subclass; where from each of the members the biological parameters comprise: i) the results of at least 20 assays that relate to cell populations and / or molecules associated with cells; ii) the results of at least 20 trials that relate to the soluble factors; and iii) clinical parameters.

43. The phenotype according to claim 42, characterized in that it comprises the results of at least 40 assays that relate to cell populations and / or molecules associated with cells.

44. The phenotype according to claim 42, characterized in that it comprises the results of at least 40 tests that are related to soluble factors.

45. The phenotype according to claim 42, characterized in that it also comprises the genotype information of each member.

46. A system for creating the phenotype of an organism, characterized in that it comprises: obtaining the biological parameters of the organism, comprising: a) the results of at least 20 assays that relate to cell populations and / or molecules associated with cells; the results of at least 20 trials that relate to the soluble factors; and clinical parameters; and introduce the biological parameters into an integrated database.

47. The system according to claim 46, characterized in that it comprises the results of at least 40 assays that relate to cell populations and / or molecules associated with cells.

48. The system according to claim 46, characterized in that it comprises the results of at least 40 tests that are related to the soluble factors.

49. The system according to claim 46, characterized in that the biological parameters further comprise the genotype information.

50. A method for evaluating the effect of a disturbance in an organism, characterized in that it comprises: i) obtaining the phenotype of the organism before and after the disturbance; Y ii) compare the information in the previous and subsequent phenotypes to identify the changed parameters; where the phenotypes are comprised of: a) the results of at least 20 assays that relate to the populations of cells and / or molecules associated with cells; the results of at least 20 trials that relate to the soluble factors; and clinical parameters.

51. The method according to claim 50, characterized in that the phenotypes comprise at least 40 assays that relate to cell populations and / or molecules associated with cells.

52. The method according to claim 50, characterized in that the phenotypes comprise at least 40 tests that are related to the soluble factors.

53. The method according to claim 50, characterized in that the phenotypes also comprise the information of the genotype of the organism.

54. A method for evaluating the effect of a disturbance in a class or subclass of organisms, characterized in that it comprises: i) obtaining the phenotype of a plurality of members of the class or subclass of organisms before and after the disturbance; i) compare the information of the previous and subsequent phenotype to identify the changed parameters; where the phenotypes are comprised of: a) the results of at least 20 assays that relate to the populations of cells and / or molecules associated with cells; b) the results of at least 20 trials that relate to the soluble factors; Y c) clinical parameters.

55. A method for evaluating the effect of a disturbance in an organism or class or subclass or organisms, characterized in that it comprises: i) obtaining the phenotype of a plurality of organisms that have not been affected by the disturbance and phenotype of one or more of the organisms that have been affected by the disturbance; and ii) compare the information in the phenotypes of the plurality of organisms that have not been affected by the disturbance with the phenotype of one or more organisms that have been affected by the disturbance to identify the changed parameters; where the phenotypes are comprised of: a) the results of at least 20 assays that relate to the populations of cells and / or molecules associated with cells; b) the results of at least 20 trials that relate to the soluble factors; and c) clinical parameters.

56. A system for the identification of biological markers of a disease or medical condition in an animal model of the disease or medical condition, characterized in that it comprises: a) an integrated database comprising a plurality of data categories, the data categories comprising i) levels of a plurality of populations of cells and / or molecules associated with cells in the biological fluid of an animal, and / or levels of a plurality of factors soluble in the biological fluid of an animal, and ii) information associated with a plurality of physical parameters of an animal; b) data of a plurality of animals that correspond to the data categories; and i) processing means to correlate the data within the data categories, where the correlation analysis of data categories can be done to identify the category or categories of data that indicate the normal biological processes, pathogenic processes or pharmacological responses to a therapeutic intervention candidate; where the category or categories identified are biological markers.

57. The biological marker identification system according to claim 56, characterized in that the data for the levels of cell populations and / or molecules associated with cells are obtained by microvolume laser scanning cytometry.

58. The biological marker identification system according to claims 56 and 57, characterized in that it comprises at least 20 categories of data of the level of cell populations and / or molecules associated with cells.

59. The biological marker identification system according to claim 58, characterized in that it comprises at least 40 categories of data of the level of cell populations and / or molecules associated with cells.

60. The biological marker identification system according to claim 56, characterized in that it comprises at least 20 data categories of the soluble factor level.

61. The biological marker identification system according to claim 56, characterized in that it comprises at least 40 data categories of the soluble factor level.

62. The biological marker identification system according to claim 56, characterized in that the data categories also include: phenotype information associated with an animal.

63. A method for identifying a biological marker for a disease or medical condition given in an animal model of the disease or medical condition, characterized in that it comprises: providing an animal model of the disease or medical condition; correlate the information obtained from a plurality of individual animals, at least some of the individual animals have the disease or medical condition, where the information is associated with a plurality of data categories, and where the data categories comprise, i) levels of a plurality of populations of cells and / or molecules associated with cells in the biological fluid of an individual, and / or levels of a plurality of factors soluble in the biological fluid of an individual animal; and?) information associated with a plurality of the physical parameters of an individual animal; identify a category of data in which the individual animals having the disease or medical condition can be differentiated from those individual animals that do not have the disease or medical condition, where the category identified is a biological marker for the disease in the animal model.

64. A method for identifying a biological marker for a disease or medical condition given in humans, characterized in that it comprises: providing an animal model of the disease or medical condition; identifying a biological marker for the disease or medical condition in the animal model of the disease or medical condition according to the method according to claim 63; and determine if the biological marker is a diagnosis or prognosis of the disease or medical condition in humans.

65. A method for testing a candidate therapeutic agent directed against a human disease or medical condition, the method is characterized in that it comprises: providing an animal model in the disease or medical condition; identifying at least one biological marker of the disease or medical condition in the animal model by the method according to claim 63; treat the animal model with the candidate therapeutic agent; and verify the response of biological markers in the animal model.

66. A method for verifying the results of a clinical study in humans, with a given disease or medical condition, characterized in that it comprises: evaluating the biological markers in humans, which are homologous to the biological markers identified in the animal models of the disease or medical condition .

67. A method for designing an improved animal model for a human disease or medical condition, characterized in that it comprises: identifying human biological markers related to the disease or medical condition; adjust the animal model to more accurately simulate the disease or medical condition by raising or lowering the levels of the animal homologs of the human biomarker.

68. A method for identifying an animal model of a disease or medical condition, characterized in that it comprises: i) obtaining the phenotype of a plurality of potential animal models of the disease or medical condition; ii) obtain the phenotype of the organism that has the disease or medical condition; ii) compare the phenotypes of the potential animal model with the phenotype of the organisms that have the disease or medical condition to identify the phenotype of the animal model that more closely simulates the phenotype of the organisms that have the disease or medical condition; where the phenotypes are comprised of: a) the results of at least 20 assays that relate to the populations of cells and / or molecules associated with cells; b) the results of at least 20 trials that relate to the soluble factors; and c) clinical parameters.

69. The phenotype according to claim 38, characterized in that the organism is selected from the group consisting of a human, an animal, a plant, and a virus.

70. The phenotype according to claim 42, characterized in that the class or subclass of organisms is selected from the group consisting of humans, animals, plants and viruses.

71. A method for evaluating the effects of a genetic alteration in a plant or animal, characterized in that it comprises: i) obtaining the phenotype of the plant or animal that has been genetically altered and the phenotype of the plant or animal not genetically altered; and ii) compare the information in the genetically altered or non-genetically altered phenotype to identify the changed parameters; where the phenotypes are comprised of: a) the results of at least 20 assays that relate to the populations of cells and / or molecules associated with cells; b) the results of at least 20 trials that relate to the soluble factors; and c) clinical parameters.