WO2001008081A1 - Systeme de cytometrie par balayage, a laser de microvolume - Google Patents

Systeme de cytometrie par balayage, a laser de microvolume Download PDF

Info

Publication number
WO2001008081A1
WO2001008081A1 PCT/US2000/011133 US0011133W WO0108081A1 WO 2001008081 A1 WO2001008081 A1 WO 2001008081A1 US 0011133 W US0011133 W US 0011133W WO 0108081 A1 WO0108081 A1 WO 0108081A1
Authority
WO
WIPO (PCT)
Prior art keywords
particles
sample
pixel values
light
threshold
Prior art date
Application number
PCT/US2000/011133
Other languages
English (en)
Inventor
Louis J. Dietz
Ian Walton
Chih-Hua Chung
Scott Norton
James L. Winkler
Aaron B. Kantor
Byron Lee
Shalom Tsur
Original Assignee
Surromed, Inc.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Surromed, Inc. filed Critical Surromed, Inc.
Priority to CA002379836A priority Critical patent/CA2379836A1/fr
Priority to KR1020027000806A priority patent/KR20020013970A/ko
Priority to JP2001513096A priority patent/JP2003505707A/ja
Priority to NZ516637A priority patent/NZ516637A/en
Priority to EP00926370A priority patent/EP1203339A4/fr
Priority to MXPA01013398A priority patent/MXPA01013398A/es
Priority to AU44911/00A priority patent/AU4491100A/en
Publication of WO2001008081A1 publication Critical patent/WO2001008081A1/fr

Links

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
    • G01N15/10—Investigating individual particles
    • G01N15/14—Optical investigation techniques, e.g. flow cytometry
    • G01N15/1429—Signal processing
    • G01N15/1433—Signal processing using image recognition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/60—Type of objects
    • G06V20/69—Microscopic objects, e.g. biological cells or cellular parts
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
    • G01N15/10—Investigating individual particles
    • G01N15/14—Optical investigation techniques, e.g. flow cytometry
    • G01N15/1484—Optical investigation techniques, e.g. flow cytometry microstructural devices

Definitions

  • the present invention relates to the analysis of biological markers using Microvolume Laser Scanning Cytometry (MLSC).
  • MLSC Microvolume Laser Scanning Cytometry
  • the invention includes instrumentation for performing MLSC, a system for analysis of image data obtained from the instrumentation, and an informatics system for the coordinated analysis of biological marker data and medical information.
  • Biological markers are characteristics that when measured or evaluated have a discrete relationship or correlation as an indicator of normal biologic processes, pathogenic processes or pharmacologic responses to a therapeutic intervention.
  • Pharmacologic responses to therapeutic intervention include, but are not limited to, response to the intervention generally (e.g., efficacy), dose response to the intervention, side effect profiles of the intervention, and pharmacokinetic properties such as the rate of drug metabolism and the identity of the drug metabolites. Response may be correlated with either efficacious or adverse (e.g., toxic) changes.
  • Biological markers include patterns of cells or molecules that change in association with a pathological process and have diagnostic and/or prognostic value.
  • Bio markers may include levels of cell populations and their associated molecules, levels of soluble factors, levels of other molecules, gene expression levels, genetic mutations, and clinical parameters that can be correlated with the presence and/or progression of disease. In contrast to such clinical endpoints as disease progression or recurrence or quality of life measures (which typically take a long time to assess), biological markers may provide a more rapid and quantitative measurement of a drug's clinical profile.
  • Single biological markers currently used in both clinical practice and drug development include cholesterol, prostate specific antigen ("PSA"), CD4 T cells and viral RNA. Unlike the well known correlations between high cholesterol and heart disease, PSA and prostate cancer, and decreased CD4 positive T cells and viral RNA in AIDS, the biological markers correlated with most other diseases have yet to be identified. As a result, although both government agencies and pharmaceutical companies are increasingly seeking development of biological markers for use in clinical trials, the use of biological markers in drug development has been limited to date.
  • a biological fluid is contacted with one or more fluorescently-labeled detection molecules that can bind to specific molecules in that fluid.
  • the biological fluid is a blood sample
  • the detection molecule is a fluorescent dye-labeled antibody specific for a cell-associated molecule that is present on, or within, one or more sub-types of blood cell.
  • the labeled sample is then placed in a capillary tube, and the tube is mounted on a MLSC instrument. This instrument scans laser light through a microscope objective onto the blood sample. Fluorescent light emitted from the sample is collected by the microscope objective and passed to a series of photomultipliers where images of the sample in each fluorescent channel are formed.
  • the system then processes the raw image from each channel to identify cells, and then determines absolute cell counts and relative antigen density levels for each type of cell labeled with a fluorescent antibody.
  • Marker MLSC can also be used to quantitate soluble factors in biological fluids by using a microsphere-bound primary antibody to the factor along with a secondary fluorescently-labeled antibody to the factor. The factor thereby becomes bound to the microsphere, and the binding of the secondary antibody fluorescently labels the bound factor.
  • the system in this embodiment measures the fluorescent signal associated with each bead in the blood sample in order to determine the concentration of each soluble factor. It is possible to perform multiple assays in the same sample volume by using multiple bead types (each conjugated to a different primary antibody). In order to identify each bead type, the different beads can have distinct sizes or can have a different internal color, or each secondary antibody can be labeled with a different fluorophore.
  • any other detection molecule capable of binding specifically to a particular biological marker is contemplated.
  • various types of receptor molecules can be detected through their interaction with a fluorescently-labeled cognate ligand.
  • the raw data from the MLSC instrument is processed by image analysis software to produce data about the cell populations and soluble factors that were the subject of the assay. This data is then transferred to a database.
  • Other data that can be stored along with this cell population and soluble factor data for the purposes of establishing correlations between biological markers and diseases or medical conditions include: drug dosing and pharmacokinetics (measurement of the concentrations of a drug and its metabolites in a body); clinical parameters including, but not be limited to, the individual's age, gender, weight, height, body type, medical history (including co-morbidities, medication, etc.), manifestations and categorization of disease or medical condition (if any) and other standard clinical observations made by a physician.
  • Data may also include images such as x-ray photographs, brain scans, or MRIs, or information obtained from biopsies, EKGs, stress tests or any other measurement of an individual's condition.
  • An informatics system then a) compares the data with stored profiles (either from the same individual for disease progression or therapeutic evaluation purposes) and/or from other individuals (for disease diagnosis); and b) "mines" the data in order to derive new profiles.
  • the present invention provides an improved system for performing Microvolume Laser Scanning Cytometry (MLSC).
  • the system is termed the SurroScan system. It includes an improved MLSC instrument capable of working at variable scan rates and capable of simultaneously collecting data in four different fluorescent channels.
  • the invention includes an improved method for performing image processing on the raw data obtained from the MLSC instrument, and an improved method for working with this data in a relational database. The improvements described herein will greatly facilitate the construction and use of a rapid, multi-factorial disease database.
  • This database will allow users to a) compare blood profiles obtained with the laser scanning cytometer with stored profiles of individuals suffering from known diseases in order to obtain prognostic or diagnostic outcomes; and b) allow the user to rapidly build new prognostic and diagnostic profiles for particular diseases c) uncover new links between patterns of biological markers and disease in any organism.
  • FIGURE 1 illustrates the optical architecture of the MLSC instrument in one preferred embodiment of the invention.
  • FIGURE 2A is a partial circuit diagram of a switchable filter scheme.
  • FIGURE 2B is a partial circuit diagram of a switchable filter scheme.
  • FIGURE 3 is a flowchart of the Surrolmage process.
  • FIGURE 4 illustrates schematically one file storage embodiment contemplated by the instant invention.
  • N channels of data are stored in an interleave format into a binary file designated with the extension, *.sml.
  • the header was chosen to allow for a variety of data formats.
  • FIGURE 5 is a flowchart of the baseline analysis process.
  • FIGURE 6 is a flowchart of the cell detection process.
  • FIGURE 7 illustrates the noise analysis process.
  • FIGURE 8 is a flowchart of the MASK generation process.
  • FIGURE 9 is a flowchart illustrating the 8-point Connectivity Rule for finding cells.
  • FIGURE 10 illustrates some possible types of cell analysis contemplated by the instant invention.
  • FIGURE 11 is a plot comparing gaussian fit algorithm to diameter-moment calculation. Images: Each point is average diameter value of those particles detected from a 1000 particle (cell) artificial image with RMS noise equal to 250 counts.
  • FIGURE 12 is a flowchart of the informatics architecture of the SurroScan system. Detailed Description of the Invention DEFINITIONS
  • biological marker or “marker” or “biomarker” means a characteristic that is measured and evaluated as an indicator of normal biologic processes, pathogenic processes or pharmacologic responses to a therapeutic intervention.
  • Pharmacologic responses to therapeutic intervention include, but are not limited to, response to the intervention generally (e.g., efficacy), dose response to the intervention, side effect profiles of the intervention, and pharmacokinetic properties. Response may be correlated with either efficacious or adverse (e.g., toxic) changes.
  • Biological markers include patterns or ensembles of cells or molecules that change in association with a pathological process and have diagnostic and/or prognostic value.
  • Biological markers include, but are not limited to, cell population counts, and levels of associated molecules, levels of soluble factors, levels of other molecules, gene expression levels, genetic mutations, and clinical parameters that can be correlated with the presence and progression of disease, normal biologic processes and response to therapy.
  • Single biological markers currently used in both clinical practice and drug development 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, the biological markers correlated with most other diseases have yet to be identified.
  • the use of biological markers in drug development has been limited to date.
  • biological markers are often thought of as having discrete relationships with normal biological status, a disease or medical condition, e.g., high cholesterol correlates with an increased risk of heart disease, elevated PSA levels correlate with increased risk of prostate cancer, reduced CD4 T cells and increased viral RNA correlate with the presence/progression of AIDS.
  • useful markers for a variety of diseases or medical conditions may consist of significantly more complex patterns. For example, it could be discovered that lowered levels of one or more specific cell surface antigens on specific cell type(s) when found in conjunction with elevated levels of one or more soluble proteins - - cytokines, perhaps - - is indicative of a 7 particular auto-immune disease. Therefore, for the purposes of this invention, a biological marker may refer to a pattern of a number of indicators.
  • biological marker identification system means a system for obtaining information from a patient population and assimilating the information in a manner that enables the correlation of the data and the identification of biological markers.
  • a patient population can comprise any organism.
  • 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 said data categories, and processing means for correlating data within the data categories, wherein correlation analysis of data categories can be made to identify the data category or categories where individuals having said disease or medical condition may be differentiated from those individuals not having said disease or medical condition, wherein said identified category or categories are markers for said disease or medical condition.
  • markers may be identified by comparing data in various data categories for a single individual at different points of time, e.g., before and after the administration of a drug.
  • the MLSC system of the instant application termed the SurroScan system, is an example of a biological marker identification system.
  • data category means a type of measurement that can be discerned about an individual.
  • 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 biological fluid of an individual, numbers and types of soluble factors in the biological fluid of an individual, information associated with a clinical parameter of an individual, cell volumetric counts per ml of biological fluid of an individual, numbers and types of small molecules in the biological fluid of an individual, and genomic information associated with the DNA of an individual.
  • a single data category would represent the concentration of IL-1 in the blood of an individual.
  • a data category could be the level of a drug or its metabolites in blood or urine.
  • An additional example of a data category would be absolute CD4 T cell count.
  • 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, saliva, urine, semen, cerebrospinal fluid, bronchial aspirate, sweat, feces, synovial fluid, lymphatic fluid, tears, and macerated tissue obtained from any organism.
  • Biological fluid typically contains 8 cells and their associated molecules, soluble factors, small molecules and other substances. Blood is the preferred biological fluid in this invention for a number of reasons. First, it is readily available and can be drawn at multiple times. Blood replenishes, in part, from progenitors in the marrow over time.
  • Blood is responsive to antigenic challenges and has a memory of antigenic challenges. Blood is centrally located, recirculates and potentially reports on changes throughout the body. Blood contains numerous cell populations, including surface molecules, internal molecules, and secreted molecules associated with individual cells. Blood also contains soluble factors that are both self, such as cytokines, antibodies, acute phase proteins, etc., and foreign, such as chemicals and products of infectious diseases.
  • cell population means a set of cells with common characteristics. The characteristics may include the presence and level of one, two, three or more cell associated molecules, size, etc.
  • One, two or more cell associated molecules can define a cell population. In general some additional cell associated molecules can be used to further subset a cell population.
  • a cell population is identified at the population level and not at the protein level.
  • a cell population can be defined by one, two or more molecules. Any cell population is a potential marker.
  • cell associated molecule 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 auto-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 preferred cell associated molecule is typically a cell surface protein.
  • leukocyte cell surface proteins or antigens including leukocyte differentiation antigens (including CD antigens, currently through CD 166), antigen receptors (such as the B cell receptor and the T cell receptor), and major histocompatibility complex.
  • leukocyte differentiation antigens including CD antigens, currently through CD 166
  • antigen receptors such as the B cell receptor and the T cell receptor
  • major histocompatibility complex Each of these classes encompass a vast number of proteins.
  • soluble factor means any soluble molecule that is found in a biological fluid, typically blood.
  • Soluble factor includes, but is not limited to, soluble proteins, carbohydrates, lipids, lipoproteins, steroids, other small molecules, and complexes 9 of any of the preceding components e.g. cytokines and soluble receptor; antibodies and antigens; and a drug complexed to anything.
  • Soluble factors can be both self, such as cytokines, antibodies, acute phase proteins, etc., and foreign, such as chemicals and products of infectious diseases.
  • Soluble factors may be intrinsic, i.e. produced by the individual, or extrinsic such as a virus, drug or environmental toxin.
  • Soluble factors can be small molecule compounds such as prostaglandins, vitamins, metabolites (such as iron, sugars, amino acids, etc.), drugs and drug metabolites.
  • small molecule or "organic molecule” or “small organic molecule” means a soluble factor or cell associated factor having a molecular weight in the range of 2 to 2000.
  • Small molecules can include, but are not limited to, prostaglandins, vitamins, metabolites (such as iron, sugars, amino acids, etc.), drugs and drug metabolites.
  • the MLSC system is used to measure changes in the concentration of drugs and drug metabolites in biological fluids in tandem with other biological markers during a treatment regime.
  • disease or “medical condition” means an interruption, cessation, disorder or change of body functions, systems or organs in any organism.
  • diseases or medical conditions include, but are not limited to, immune and inflammatory conditions, cancer, cardiovascular disease, infectious diseases, psychiatric conditions, obesity, and other such diseases.
  • immune and inflammatory conditions include autoimmune diseases, which further include rheumatoid arthritis (RA), multiple sclerosis (MS), diabetes, etc.
  • clinical parameter means information that is obtained in a clinical setting that may be relevant to a disease or medical condition.
  • clinical parameters 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 disease or medical condition, and any result of a any clinical lab test, such as blood pressure, MRI, x-ray, etc.
  • the term “clinical endpoint” means a characteristic or variable that measures how a patient feels, functions, or survives.
  • MLC Microvolume Laser Scanning Cytometry
  • MLSC system 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 optical scanning where the fluorescence emission is recorded.
  • the MLSC 10 system has several key features that distinguish it from other technologies: 1) only small amounts of blood (5-50 ⁇ l) are required for many assays; 2) absolute cell counts (cells/ ⁇ l) are obtained; and, 3) the assay can be executed either directly on whole blood or on purified white blood cells. Implementation of this technology will facilitate measurement of several hundred different cell populations from a single harvesting of blood.
  • MLSC technology is described in United States Patent Numbers 5,547,849 and 5,556,764 and in Dietz et al.
  • Laser scanning cytometry with microvolume capillaries provides a powerful method for monitoring fluorescently labeled cells and molecules in whole blood, processed blood, and other fluids, including biological fluids.
  • the present invention further improves MLSC technology by improving the capacity of the MLSC instrument to do simultaneous measurement of multiple biological markers from a small quantity of blood.
  • the improved MLSC system of the instant invention is termed the "SurroScan system”.
  • detection molecule means any molecule capable of binding to a molecule of interest, particularly a protein.
  • Preferred detection molecules are antibodies.
  • the antibodies can be monoclonal or polyclonal.
  • the terms “dye”, “fluorophore”, “fluorescent dye”, “fluorescent label”, or “fluorescent group” are used interchangeably to mean a molecule capable of fluorescing under excitation by a laser.
  • the dye is typically directly linked to a detection molecule in the present invention, although indirect linkage is also encompassed herein.
  • Many dyes are well known in the art.
  • 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 readily coupled to antibodies.
  • the term "particle” means any macromolecular structure which is detected by MLSC in order to obtain information about a biological marker.
  • the particle to be detected is a cell; in other embodiments, the particle to be detected is an antibody-labeled bead.
  • the present invention provides an improved Microvolume Laser Scanning
  • MSC Cytometry
  • SurroScan System, termed the SurroScan system, or simply SurroScan.
  • Prior systems are described in United States Patent Numbers 5,547,849 and 5,556,764, United States Provisional Patent Application Serial No. 60/131,105 entitled “Biological Marker Identification System”, filed 26 April, 1999, United States Provisional Patent Application Serial No. 60/097,506, entitled "Laser-Scanner Confocal Time-Resolved Fluorescence
  • the improved MLSC system of the present invention comprises the following components:
  • an MLSC instrument including an electronic control system, for obtaining raw data from the analyte samples;
  • an image analysis system for collecting and enhancing raw data from the MLSC instrument;
  • the current invention provides significant improvements in several keys aspects of the operation of the MLSC system: a) the MLSC optics; b) the MLSC system control electronics; c) the image display and analysis algorithms; and d) the informatics architecture.
  • the instant invention also provides improved methods for image display and for data conversion to an industry standard Flow Cytometry Standard (.FCS file format).
  • the SurroScan system provides significant improvements in the optical architecture of MLSC instruments.
  • Previous MLSC instruments have typically been able to detect fluorescent signals in two channels, thereby limiting the number of analytes that can be 12 detected simultaneously in a single experiment.
  • simultaneous measurement of three or more antigens is needed to identify some cell populations, such as naive T cells that express CD4, CD45RA, and CD62L.
  • the improved SurroScan instruments of the instant invention are capable of detecting at least four separate fluorescent signals, thereby allowing the use of at least four separate fluorescent reagents in a single experiment.
  • One embodiment of the improved optical configuration is shown in FIGURE 1.
  • a capillary array 10 contains samples for analysis.
  • collimated excitation light is provided by one or more lasers.
  • excitation light of 633nm is provided by a He-Ne laser 11. This wavelength avoids problems associated with the autofluorescence of biological materials.
  • the power of the laser is increased from 3 to 17 mW. Higher laser power has two potential advantages, increased sensitivity and increased scanning speed.
  • the collimated laser light is deflected by an excitation dichroic filter 12. Upon reflection, the light is incident on a galvanometer-driven scan mirror 13. The scan mirror can be rapidly oscillated over a fixed range of angles by the galvanometer, e.g., +/- 2.5 degrees.
  • the scanning mirror reflects the incident light into two relay lenses 14 and 15 that image the scan mirror onto the entrance pupil of the microscope objective 16.
  • This optical configuration converts a specific scanned angle at the mirror to a specific field position at the focus of the microscope objective.
  • the +/- degree angular sweep results in a 1 mm scan width at the objective's focus.
  • the relationship between the scan angle and the field position is essentially linear in this configuration and over this range of angles.
  • the microscope objective focuses the incoming collimated beam to a spot at the objective's focus plane.
  • the spot diameter which sets the optical resolution, is determined by the diameter of the collimated beam and the focal length of the objective.
  • Fluorescence samples placed in the path of the swept excitation beam emit stokes- shifted light. This light is collected by the objective and collimated. This collimated light emerges from the two relay lenses 14 and 15 still collimated and impinges upon the scan mirror which reflects and descans it.
  • the stokes-shifted light then passes through a dichroic excitation filter (which reflects shorter wavelength light and allows longer wavelength light to pass through) and then through first long pass filter 17 that further serves to filter out any reflected excitation light. 13
  • the improved instrument of the instant invention uses a series of further dichroic filters to separate the stokes-shifted light into four different emission bands.
  • a first fluorescence dichroic 18 divides the two bluest fluorescence colors from the two reddest. The two bluest colors are then focussed onto first aperture 19 via a first focusing lens 20 in order to significantly reduce any out-of-focus fluorescence signal.
  • a second fluorescence dichroic 21 further separates the individual blue colors from one another. The individual blue colors are then parsed to two separate photomultipliers 22 and 23.
  • the two reddest colors are focused onto a second aperture 24 via a second long pass filter 25, a mirror 26, and a second focusing lens 27 after being divided from the two bluest colors by first fluorescence dichroic 28.
  • the reddest colors are separated from one another by third fluorescence dichroic 28.
  • the individual red colors are then parsed to photomultipliers 29 and 30. In this way, four separate fluorescence signals can be simultaneously transmitted from the sample held in the capillary to individual photomultipliers. This improvement, for the first time, allows four separate analytes to be monitored simultaneously.
  • Each photomultiplier • generates an electronic current in response to the incoming fluorescence photon flux.
  • the four channels of the instant invention are named channel 0, 1, 2, and 3.
  • Cy7 would not normally be considered by those skilled in the art to be useful in a He-Ne excitation system.
  • the present inventors have found that Cy7 can be adequately excited at 633 nm for enumerating specific cells in whole blood. This excitation likely results from the presence of a long excitation tail, as described in Mujumdar, R. B., L. A. Ernst, S. R. Mujumdar, C. J. Lewis, and A. S. Waggoner, 1993, Cyanine dye labeling reagents: sulfoindocyanine succinimidyl esters, Bioconjug Chem. 14
  • Cy7 is coupled to APC to make a tandem dye that can be excited at the APC excitation wavelength but emits at the Cy7 emission wavelength.
  • This tandem dye uses energy transfer from the donor (APC) to excite the acceptor (Cy7) as described in Beavis, A. J., and K. J. Pennline, 1996, Allo-7: a new fluorescent tandem dye for use in flow cytometry, Cytometry. 24:390-5; and in Roederer, M., A. B. Kantor, D. R. Parks, and L. A. Herzenberg, 1996, Cy7PE and Cy7APC: bright new probes for immunofluorescence, Cytometry, 24:191-7, both of which are incorporated herein by reference in their entirety.
  • more than one excitation wavelength is used.
  • Multiple excitation wavelengths can be obtained in at least three ways: (1) using an Ar-Kr laser as the excitation source with excitation wavelengths of 488nm, 568nm, and 647nm, and so can be used for triple excitation of three different fluorescent groups (e.g., fluorescein, rhodamine, and Texas Red ®) ; (2) using more than one laser source, each supplying a different wavelength of collimated excitation light; (3) using a laser capable of generating femto-second pulses, such as a Ti-S laser ( ⁇ 700nm excitation light) or a Nd:YLF laser (1047nm excitation light), for multiphoton fluorescence excitation.
  • the sample to be scanned is mounted on a stage that is automatically translatable in the X, Y and Z planes.
  • the galvanometer driven mirror scans the excitation beam in the Y axis; the stage moves the sample in X axis at a constant velocity.
  • the sample interval of each analog to digital converter multiplied by the swept beam rate determines the pixel spacing in the Y axis of the image.
  • the X stage scan speed divided by the line rate determines the pixel spacing in the X axis of the image.
  • the stage not only scans an individual sample in the X axis, but can also shuttle many samples to the microscope objective. In this way, many individual samples can be sequentially scanned by computer control without any operator intervention. This will 15 greatly increase the throughput of the instrument, and will make the instrument even more amenable to high-speed automated analyses of blood samples in a clinical setting.
  • the SurroScan MLSC stage holds one or more capillary arrays, each of which has the footprint of a 96- ell plate. Each capillary holds a sample to be analyzed. Disposable capillary arrays which have 32 fixed capillaries each and spacing that is compatible with multi-channel pipettes are described in Provisional United States Patent applications, Attorney Docket No.
  • the operator is able to load two plates of 32 capillaries at a time. No operator intervention is needed while the plates are scanned and the images are processed.
  • 16 individual capillaries designed for the Imagn 2000 (VC120) are loaded into alternative holders.
  • the Z motion of the stage provides a means to place each sample at the focus plane of the objective.
  • the Z motion can also be scanned to allow acquisition of a stack of focal plane images for each individual sample.
  • the optimal focus position for each sample can be determined from this scanned Z image, preferably by the computer control system in order to avoid the need for operator intervention.
  • the optimal focus can be determined for the two ends of the sample. While the sample is scanned in the X axis, the stage is moved at a constant velocity through the focus difference between the two ends, thus correcting for any tilt that may exist in the sample or fixture.
  • the scan rate of the laser beam determines the amount of time spent integrating the optical signal at each pixel; the longer the integration time, the better the signal to noise ratio.
  • the scan rate is also proportional to the throughput rate of the system.
  • variable scan speed system allows system sensitivity to be optimized for each individual sample.
  • some assays may involve the detection of analytes that are present at very low concentration in the sample.
  • the fluorescent signal relative to background noise from such low concentrations of analytes may be correspondingly low.
  • system sensitivity can be increased by scanning slowly, allowing more time to integrate the optical signal at each pixel. This results in a much improved signal to noise ratio.
  • some assays may involve the detection of much brighter fluorescent signals, possibly because of the relatively high concentration of the particular analyte to be detected in the sample.
  • variable scan speed system contemplated herein is a significant improvement over prior art fixed scan speed systems because it a) allows the signal to noise ratio for each analyte to be optimized, thereby collecting the highest quality data possible for each analyte; and b) allows the system to function at the most efficient throughput rate possible.
  • the scan rate can be varied by adjusting the scan rate of galvanometer-mounted mirror, and by adjusting the rate at which the stage moves in the X axis during sample imaging.
  • the SurroScan system also provides a novel switchable filter scheme that is incorporated into the analog processing circuitry.
  • Low-pass filters are commonly used to pass the signal of interest, and to reject unnecessary high frequency noise that is created by the measurement process.
  • the optimal filter bandwidth for each scan speed is different, and is usually proportional to the scan speed.
  • at least 2 bandwidths are provided for each channel by the switchable filters.
  • 4 bandwidths are provided.
  • FIGURES 2A and 2B show a circuit diagram for a switchable filter scheme that provides bandwidths of 4, 8, 12, and 16 kHz (corresponding to the optimal bandwidths for scan speeds of 64, 128, 192, and 256 Hz respectively).
  • such a filter bandwidth switching scheme is associated with each photomultiplier channel.
  • the present invention is a significant improvement over prior art MLSC systems because the system is optimized in two separate ways: 1) the scan speed of the 17 system is variable to optimize the signal to noise ratio; 2) the bandwidth of each analog filter at each signal channel is also varied to further optimize the signal to noise ratio.
  • This novel combination synergistically enhances the sensitivity and efficiency of the MLSC instrument and system.
  • the optimal scan speed and filter bandwidth of the SurroScan system are determined for each particular assay that is performed. These variables are stored in a clinical protocol database (see below) which can then automatically select these settings when an operator later chooses to run the same assay again. In this way, it is possible to have many different assays present on the same stage; the computer can automatically select the pre-determined optimal scan speed and filter settings for each sample. This advance will contribute greatly to the flexibility of the SurroScan system.
  • the present invention uses laser excitation of fluorophores that emit in the visible or near infrared part of the electromagnetic spectrum in order to detect particles.
  • the present invention also contemplates the use of other types of electromagnetic radiation and emission probes, such as infrared radiation.
  • the present invention contemplates the use of assemblies of probes, rather than just single probes.
  • the present invention also contemplates the use of light scattering modes other than fluorescence, including but not limited to, Raman scattering, Mie scattering, luminescence, and phosphorescence.
  • Image processing is a critical requirement for laser scanning cytometry.
  • An image processing program needs to handle multiple binary images, representing different spectral regions of a cell's or other particles fluorescence (channels); it needs to determine the background fluorescence level in each channel; the overall noise in each channel, such that it can enumerate cells or other particles from noise; it needs to ignore extraneous signals such as bubbles, dust particulates, and other "blob” or "grunge” sources; and it needs to characterize each recognized cell or particle to report parameters including, but not limited to, weighted flux, size, ellipticity, and ratios and correlations between the signal in other channels at the same location.
  • the SurroScan system includes an image processing and particle detection system, termed the Surrolmage system, that meets the above criteria and outputs the results of the analysis in a text list-mode format. 18
  • FIGURE 3 depicts a flowchart of the operations executed by the Surrolmage system. Note also, that in the enabling description that follows, the Surrolmage system is described in a cell-detection context. However, as described above, the Surrolmage system is capable of detecting any structure with predefined physical parameters, such as antibody-labeled beads.
  • the Surrolmage system is contemplated for use in any embodiment of MLSC described in the prior art, including, but not limited to, the embodiments described in United States Provisional Patent Application Serial No. 60/131,105 entitled "Biological Marker
  • a binary, interlaced format is used to store the image data. Any number of 16 bit data channels (images) can be interlaced in the format illustrated in FIGURE 4.
  • a channel image array is stored along each row, (Row 0: Col 0, Col 1, Col 2, ... , Col nCol ; Row 1 : ... to Row nRow) where nCol is typically 250 pixels, and nRow is typically 10000 pixels.
  • the SMI header as shown in FIGURE 4 has 28 bytes in the header with four bytes per descriptor. Each file descriptor is arranged in a low- high word format.
  • the "4 character descriptor" can be any four characters describing a unique image type, such as "SM01".
  • the system uses two bytes or 16 bits per pixel, thus each pixel can have any of 65536 values.
  • the field descriptor, "Bytes per pixel” allows flexibility to extend the image-type from WORD to float, or any other data format.
  • the "interleave" field gives one the option of writing channels in a sequential mode.
  • the scanning system gathers channel information sequentially, rather than concurrently, e.g., 19 storing all the data in channel 0 first, followed by channel 1, etc.
  • FIGURE 4 shows a graphical representation of the preferred file format.
  • the *.SM1 file is read into Surrolmage and each channel is stored in memory with handle descriptors.
  • the information about each channel of data is stored in a class designated Smlmagelnfo with the image handle property, him being a member of that structure.
  • Surrolmage is a command line executable. To run the program the following format can be used. If no parameters are given, the current parameter defaults are shown.
  • SMI input file Full path to *.sml file
  • LSM output file Optional full path designating *. Ism output location. If this parameter is omitted, then the same path as the *.sml including base name, *, is used.
  • optional parameter list Multiple parameters can be assigned, separated by a space. An example format is:
  • Optional parameters include, but are not limited to, the following: ThreshRatio Noise multiplicative factor used to determine cell detection threshold level.
  • iNumCorrelations Provide correlations out to iNumCorrelations number of channels.
  • UseBandPassForBlob 1 Use filtered image to detect cells (must be mutually exclusive to UsePeaksForBlobs
  • UsePeaksForBlobs l Use difference between center of 5x5 kernel and outer pixels to detect cells
  • UseFullPerimDetect l Use all outer perimeter pixels in conjunction with center to locate cells Blobarealo minimum cell diameter to detect.
  • MaxCellSize set diameter of cell to MaxCellSize is diameter > 20
  • BubbleThreshFactor -threshold*Noisefactor to be applied to median-subtracted source image for bubble detection.
  • NoiseFactor can be replaced with baseline value (see text).
  • NoiseFactor can be replaced with baseline value (see text).
  • MaskDilationPix final mask image is dilated MaskDilationPix pixels WriteRAWFiles Diagnostic: Boolean variable which indicates whether all intermediate image files should be written to the C:VA directory.
  • SameCellRadius Cells in alternate channels are considered the same cell if the distance between their centroids (in float format) are less than or equal to SameCellRadius.
  • NomCellMicrons The following three parameters determine the kernel size used for all cell calculations:
  • NomCellPix hypot( NomCellMicrons, BeamMicrons ) / MicronsPerPix;
  • MicronsPerPix iNomCellPix (int)(NomCellPix + 1.); iNomCellPix is (KernelSize -l)/2
  • the central routine in Surrolmage is designated, SMProcessImages().
  • the Surrolmage system performs a number of functions on each source image--/, e., the image from each channel-including, but not limited to, filtering, masking, locating blobs and bubbles, and establishing an initial cell list.
  • the central feature of the Surrolmage system is that each channel is analyzed independently, with no summing of the individual channels taking place. Briefly, the Surrolmage system performs a number of manipulations independently on each source image in order to remove noise and background features (such as bubbles and dirt) and enhance features with the spatial characteristics of the particles to be identified.
  • the system also determines a threshold for particle determination in each channel, and independently identifies and analyzes particles in each channel based on this threshold and on the particle parameters. The system then finds the same pixels in the remaining channels— where the particle was not detected because it was below the threshold for that channel— and measures the parameters of the particle in those channels also. In this way, the Surrolmage system collects data for each identified particle even in those channels where the particle was not originally identified.
  • the Surrolmage system starts by opening handles to a number of floating point images, used to store 1) filtered source images (application of convolution kernel) 2) median subtracted source images, and 3) work images, used for temporary storage.
  • a number of BYTE images are created to store thresholded versions of the above floating point images, including a MASK image which will be discussed later.
  • the routine preferably starts by performing a baseline analysis.
  • the statistical values can be stored globally including a boolean value, BaselineErrorFlag, which designates that the baseline has varied over a predefined limit (generally, max - min > 0.3 median.
  • FIGURE 5 depicts this process in flowchart format. 22
  • a 15x15 median kernel is then applied to each source image using a high-speed median algorithm designated TurboMedianQ.
  • the kernel operates by replacing the center pixel in the 15x15 kernel with the median value of all the pixels within the kernel.
  • the application of this median kernel to each pixel acts to "smooth" out gradual variations in pixel intensity that arise along the image in the y axis.
  • the primary role of the smoothing operation is to eliminate the intensity contributions due to cells, and in effect, get a background representation of the image.
  • the median image can then subtracted from the source image and stored in a global handle designated hlmbgnd. This image can be used later after the cell list has been generated to determine the cell parameters including, but not limited to, total flux, ellipticity, and cell diameter (also called fit area).
  • the multiple images are then convolved with a predefined kernel and stored in a global handle designated imBlobSrc.
  • convolution kernels are well known in the art.
  • the kernel structure chosen (the size of the kernel and the weighted values within the kernel) depend on the particle that is to be detected. For example, for blood cell determination, a 7x7 kernel is typically used as this kernel is approximately the size of a blood cell. For the purposes of this description, it will be assumed that the convolution kernel is a 7x7 kernel, but it is to be appreciated that other kernels will be useful in other embodiments.
  • the result of this convolution is a filtered image that enhances those features with predefined spatial components corresponding to the cell-types to be detected. A thresholded version of this image can be used for cell detection and in addition, for weighted flux calculations.
  • a "perimeter” method rather than the above-described convolution method, is used for the initial enhancement of those features with predefined spatial components corresponding to the cell-types to be detected.
  • the perimeter method creates a differential source image—a "difference" image— and can be performed in two different ways. In some embodiments of the perimeter method, every pixel is set to the smallest difference between it and the outer four pixels of a 7x7 kernel. In other embodiments, each pixel is set to the smallest difference between its value and all the outside pixels of a 7x7 kernel.
  • the use of these "difference" images, rather than convolved images can be designated through a boolean command line argument designated UsePeaksForBlobs. Again, the enhanced image is stored in the global handle imBlobSrc. 23
  • FIGURE 6 illustrates the use of the perimeter method and the convolution filter method in a flowchart format.
  • FIGURE 7 illustrates this process in flowchart format.
  • Each block is nCols wide (the full width of the image) and RowsPerNoiseBlock (a command line argument) long.
  • Each noise value for each block is stored in an array with ( t)(nRows/RowsPerNoiseBlock) elements.
  • This array is then multiplied by threshratio (a command line argument) and interpolated into a nRows length array that is used for thresholding.
  • the thresholding subroutine uses either the convolved image or the "difference" image to generate the thresholded BYTE image, imBlobSeg.
  • a subroutine called MaskGrungeAndBubbles(), is called before performing segmentation or cell-detection on imBlobSeg, if the source image is that associated with channel 0.
  • FIGURE 8 illustrates this subroutine in flowchart format.
  • channel 0 is used to find bubbles and blobs whose regions are added to a MASK image. This is because dirt in the sample tends to consistently emit into this channel, which corresponds to the shortest emission wavelength from the sample.
  • other channels one or more can be used for the MASK image.
  • the MASK byte image is appended to through three different conditions.
  • MaskGrungeAndBubblesQ tests these conditions. It uses the image, hlmbgnd, the median- subtracted source image, to apply the bubble and blob thresholds, BubbleThreshF actor and BlobThreshF actor (multiplied by the peak-peak noise value), respectively.
  • BubbleThreshF actor*p- pNoise bubbles are signified by the absence of background fluorescence
  • MaxBubblePix a blob detection is done using BlobThreshF actor*p- pNoise and MaxBlobPix.
  • the bubble and blob thresholding is based on a percentage of the average baseline value rather then a factor of the peak-peak noise level.
  • bubble and blob threshold levels are given by BubbleThreshF actor* BaseLine(y), and by BlobThreshF actor*BaseLine(y), respectively, 24 where BaseLine(y) is the median value of the baseline evaluated over the x range of pixels for a given y value (i.e. over the width of the capillary).
  • BaseLine(y) is the median value of the baseline evaluated over the x range of pixels for a given y value (i.e. over the width of the capillary).
  • the final addition to the mask is made based on the segmented filtered imBlobSeg image. It also uses the same threshratio as given in the command line, yet only adds to the mask if MaxBubblePix is exceeded.
  • An artifact of the convolution filter is that the rim of a bubble tends to be convolved into a ring that can be mistakenly identified as a cell. The dilation tends to suppress this error.
  • the cells in the imBlobSeg image are then tallied using a
  • FIGURE 9 illustrates this process in flowchart format. Any number of contiguous pixels is added to a cell list and basic parameters are determined for each. These include, but are not limited to, an index, maximum x and y pixel values, total number of pixels, a x-y cenfroid value based on the uniform thresholded cell region, and a weighted cenfroid that uses the same pixels which exceed threshold yet weights those positions with the pixel value in the source image. This cenfroid value is a floating point value used for all future calculations.
  • SMProcessImages is a histogram of the mask image to determine percentage of the image which are obscured due to each of the aforementioned factors (blobs, bubbles, and filter artifacts). An overall total image masked parameter is also calculated. This allows one to recalculate the volume of the capillary if a significant fraction is masked.
  • the MLSC system also stores parameters in the clinical protocol database for operation of the MLSC instrument e.g. scan speed, filter bandwidth value etc.
  • parameters in the clinical protocol database e.g. scan speed, filter bandwidth value etc.
  • the majority of cell analysis and file output in the Surrolmage system occur in the routine, WriteLsmFileQ .
  • the purpose of this routine is to 25 output a text-based list file of all the cell events detected in any channel.
  • the header portion of the *.LSM file contains image statistics (measured noise levels, mean, median, and standard deviation statistics on the baseline level, percentages of the image masked due to bubbles and blobs, and image creation dates), as well as overall cell statistics (number of cell detected in each channel, and minimum and maximum sizes). Even if only one channel has a "blob" that exceeds the threshold of detection for that given channel, cell characteristic information is output for all channels.
  • Table 1 lists cell data for two independent cell events.
  • the second cell was only detected in channel 0, yet parameters were still calculated for the same location in channel 1.
  • the data output in the *.LSM is completely sorted by y-centroid value. A description of how this data is generated in preferred embodiments from an individual channel cell list follows.
  • the routine begins by sorting the cell lists in each channel. Since the "FindCell" routine appends to the cell list any cell perimeter it locates first by "walking" in the y direction, it is not necessarily sorted by y-centroid value. Therefore, a bubble sort is used to generate this list (bubble sorts are the best sorting algorithm when a low number of rearrangements need to take place).
  • the next step is to create a general cell list that merges the cells in the channels and is also sorted by y-centroid.
  • the details of this routine are as follows. An index to the next available cell to be processed is created for each channel, called
  • CellFirstAvaillndexf Channel The routine loops over the channels to locate the cell with the lowest y-centroid value, which has yet to be printed. This cell index and its 26 corresponding channel number are then saved to a temporary set of variables.
  • a list, CellPrintListlndex [ChannelsMax] ' is created containing the indices of the cells in alternate channels whose cenfroid are within SameCellRadius of the previously located cell.
  • the routine loops through cells in all channels. However, if a cell in an alternate channel has already been "marked” as being analyzed, it skips and moves on to the rest of the cells in that specified channel.
  • the routine loops through all the channels and accesses the cenfroid value of those cells indexed in the CellPrintListlndex array. The routine then calculates the average cenfroid value in x and y between channels for the particular cell being evaluated. The result is rounded to the nearest whole pixel in X,Y and used to call another routine called AnalyzeCellf) that calculates the cell parameters in the 7x7 pixel region centered at X,Y. This routine is called in a loop over channel number.
  • the C++ cell structure AnalyzeCellQ fills is as follows:
  • AnalyzeCellQ begins by getting a pointer to the imBlobSrc image and relocating that pointer to the X,Y location of the cell.
  • One of the parameters passed to AnalyzeCellO besides, the X,Y location and the calling channel number, is a boolean flag indicating whether this particular channel was a "source" channel” (i.e. whether the cell was actually detected in this channel). If it is a source channel, then the location of the maximum value found in the 7x7 region-of-interest (ROI) in the imBlobSrc image is returned. If this mismatches the center X,Y location of the kernel, then a global parameter, nBlobsOffsetFromPeak, for this particular channel is incremented.
  • ROI region-of-interest
  • the weighted flux is calculated by simply evaluating the pixel value at the X,Y location in the imBlobSrc image. This pixel value represents a weighted sum of all the source image pixel values in the 7x7 region, weighted by a predefined 7x7 kernel given in Table 2 below.
  • FIGURE 10 illustrates this functional call in flowchart format.
  • ComputeMeanRadiusQ not only computes the mean diameter, but, since total flux is computed from the same median-subtracted image, hlmbgnd, it is also included in this routine. Recall, to derive hlmbgnd, a 15x15 pixel median filter was applied to the source image and the result was subtracted from the source image.
  • centroid value is first calculated (Note: this is different from the cenfroid value calculated to determine the cell's center, since this centroid is calculated from the pixels in the 7x7 square versus the previous centroid calculated from those pixels exceeding the threshold for that channel). Then, the distance of each pixel from the centroid is weighted against the pixel value, as mathematically shown by,
  • centroid values, C x and C y are given by, 28
  • P Xmtym is the value of the pixel at location x,y, and N is 49 for a 7x7 kernel.
  • This method-of-moment's algorithm for calculating small particle diameter was 5 found to provide better performance over a two-dimensional gaussian fit routine.
  • the gaussian fit routine as shown in FIGURE 11, suffers from a tendency to under-estimate the actual diameter for low intensity cells. This bias, which while found in the moment's algorithm, is much less pronounced.
  • the total flux is simply given by the denominator of Eqs. (1) and (2). If the total 10 flux is less than or equal to zero, which can happen in background subtracted images, then the sum is assigned the value 1.0 to prevent overflows, and mean diameter is set to 0.
  • Two other cell parameters evaluated in the PrintCellQ routine include the ratio and correlation values between the channels.
  • the ratio (see example in Table 1), is given by,
  • the Surrolmage system described herein is a substantial advancement over prior art systems for particle detection in the laser scanning cytometry context.
  • One such prior art system is described in United States Patent No. 5,556,764 (the '764 patent), incorporated herein by reference in its entirety.
  • the system described in the '764 patent first sums the images from the individual channels and then performs particle on the resulting composite image; the '764 system also does not perform any masking of blobs and bubbles.
  • the '764 system is designed to be very selective for the particular types of cells of interest in the assay, for example by detecting cells within a certain size range.
  • the present system is less restrictive, and thus detects more different types of cells.
  • the independent channel analysis coupled with the blob and bubble masking techniques described herein enable the Surrolmage to identify precisely, and collect data from, more true cells than the '764 system. Hence, the present system is more accurate and sensitive than prior art systems.
  • the Surrolmage system can readily be optimized for the detection of a variety of different cells with diverse morphologies and/or different patterns or intensities of cell-associated molecule fluorescence. Additionally, the Surrolmage system can be rapidly optimized for the detection of particles other than cells. For example, in some embodiments of the invention, the Surrolmage system is used to detect microbeads in capillaries, which microbeads bind to a particular reagent present in the blood. In contrast to the Surrolmage system, prior art systems are capable of detecting only certain cells, and cannot be re-configured for detection of other structures without significant operator intervention.
  • the parameters of the individual subroutines of the Surrolmage system can be rapidly changed to optimize detection of these particles. These parameters can be stored in a clinical protocol database (see below).
  • the Surrolmage system increases the flexibility of the MLSC system, allowing it to perform diverse assays without making compromises in sensitivity.
  • the present invention includes a novel informatics architecture that performs a number of critical functions.
  • the heart of the system is a relational database that is used to coordinate all of the information required to design multiparameter assays, control the measurement instrumentation, perform image and data analysis, and archive results.
  • the 30 system comprises a number of interlinked modules that perform discrete functions.
  • FIGURE 11 shows a flowchart representation of the way this system operates in preferred embodiments. Briefly, Instrument Control Software controls the SurroScan hardware (the MLSC instrument), thereby scanning the sample and producing raw image files (.SMI files). The .SMI files are then processed and enhanced by the Surrolmage Image Analysis Software (above).
  • This module enhances each image, determines the position and size of each cell (or fluorescent bead in some applications) in each image, and then calculates the fluorescent intensity of each cell (or bead) in each channel.
  • the resulting Surrolmage data is stored as a text file (.LSM file) and can then be converted to the industry standard .FCS format by the FCS Conversion Software, or to any other file format appropriate for subsequent analysis.
  • the Instrument Control Software, the Image Analysis Software and the FCS Conversion Software are all controlled by a Clinical Protocol Database which stores parameters for each type of assay used in the execution of a clinical protocol.
  • Such parameters include, but are not limited to, the scan speed of the MLSC instrument, the value of the filter bandwidth used in the MLSC instrument, and the kernel structures used in the Surrolmage system.
  • Data in the form, for example, of .FCS and .LSM files can then be exported to a server in order to further process the data using, for example, commercially- available Flow Jo software.
  • the data is also sent to an experimental data file server for archiving and periodic export to tertiary media, and also to a central database such as an Oracle database.
  • the central database is used, without limitation: to maintain the consistency of the clinical protocol database; as a central repository for instrument results, filenames, calibration information; to store cellular assay measurements and soluble factor measurements (whether obtained through the MLSC system or through conventional ELISA assays); and to maintain clinical questionnaire information.
  • the SurroScan informatics system is used in the following way for clinical studies (assuming the prior design of an appropriate relational database schema, and availability of a calibrated instrument). Firstly, the user defines the clinical study protocol, including information such as number and identity of patients, number of samples per patient etc. The clinical study may involve tens to hundreds of patients, and may last from weeks to months.
  • the user also defines the assay protocol, which defines in detail each of the assays that will be performed on each particular patient sample.
  • Each assay includes detailed identification and description for each of the reagents, including, but not limited to, fluorophore used, target molecules, dilution and fluorescence 31 compensation parameters. Sample preparation method and sample dilution are also included.
  • the protocol also includes the information required to automatically control the SurroScan instrument and the data analysis software. After the patient samples have been processed for each assay (which can be automated under control of the database) and loaded into measurement cartridges on the SurroScan, the user enters Protocol ID and Sample ID parameters into the scanner software, which then interrogates the database to determine the detailed scan parameters e.g. scan speed, filter bandwidth settings, stage translation speed etc.
  • FCS output files are further analyzed using commercially available FCS analysis software.
  • a summary of the FCS output data for each patient sample is then generated by the FCS software, and further processed to enable storage in a relational database.
  • the measurement results and patient clinical information are then further processed with various statistical and visualization methods to identify patterns and correlations that may indicate candidate biological markers.
  • Sample and assay information is associated with the data throughout the analysis, from raw image to list mode format to relational database.
  • the instant invention also contemplates the use of an image system to display graphically the enhanced data.
  • This system termed SurroView, displays the individual cells identified by the Surrolmage software; a box can be placed around each identified cell in order to distinguish bona fide cells from other cell-shaped spurious signals in the image.
  • the SurroView software is particularly useful for quickly diagnosing various types of system failure modes. It should be pointed out that during normal operation of the SurroScan instrument, it is not necessary that the operator ever see such images of cells.
  • Table 1 Example of list mode output. The data corresponds to a 2-channel scan.
  • imBlobSrc Convolution kernel used to create filtered image, imBlobSrc.
  • imBlobSrc is used both for cell detection and the evaluation of a cell's weighted flux.

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • General Physics & Mathematics (AREA)
  • Chemical & Material Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Data Mining & Analysis (AREA)
  • Multimedia (AREA)
  • General Engineering & Computer Science (AREA)
  • Evolutionary Biology (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Biomedical Technology (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Molecular Biology (AREA)
  • Evolutionary Computation (AREA)
  • Signal Processing (AREA)
  • Artificial Intelligence (AREA)
  • Dispersion Chemistry (AREA)
  • Analytical Chemistry (AREA)
  • Biochemistry (AREA)
  • Immunology (AREA)
  • Pathology (AREA)
  • Investigating, Analyzing Materials By Fluorescence Or Luminescence (AREA)
  • Investigating Or Analysing Biological Materials (AREA)

Abstract

L'invention porte sur un système intégré amélioré d'identification de marqueurs biologiques recourant à la MLSC (16) (microscopie à balayage à laser (11) de microvolume) pour mesurer les motifs d'expression de marqueurs biologiques dans des fluides (10) biologiques, et à une instrumentation améliorée pour exécuter la MLSC, ainsi qu'à des méthodes améliorées de détection des particules et d'analyse. Ledit système comporte une architecture informatique pour l'analyse des données obtenues par MLSC, en tandem avec celle d'autres informations à caractère médical.
PCT/US2000/011133 1999-07-21 2000-04-26 Systeme de cytometrie par balayage, a laser de microvolume WO2001008081A1 (fr)

Priority Applications (7)

Application Number Priority Date Filing Date Title
CA002379836A CA2379836A1 (fr) 1999-07-21 2000-04-26 Systeme de cytometrie par balayage, a laser de microvolume
KR1020027000806A KR20020013970A (ko) 1999-07-21 2000-04-26 마이크로볼륨 레이져 스캐닝 세포계측을 위한 시스템
JP2001513096A JP2003505707A (ja) 1999-07-21 2000-04-26 微小体積レーザ・スキャニング・サイトメトリのためのシステム
NZ516637A NZ516637A (en) 1999-07-21 2000-04-26 System for microvolume laser scanning cytometry
EP00926370A EP1203339A4 (fr) 1999-07-21 2000-04-26 Systeme de cytometrie par balayage, a laser de microvolume
MXPA01013398A MXPA01013398A (es) 1999-07-21 2000-04-26 Sistema para citometria de escaneo laser de microvolumen.
AU44911/00A AU4491100A (en) 1999-07-21 2000-04-26 System for microvolume laser scanning cytometry

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US14479899P 1999-07-21 1999-07-21
US60/144,798 1999-07-21

Publications (1)

Publication Number Publication Date
WO2001008081A1 true WO2001008081A1 (fr) 2001-02-01

Family

ID=22510184

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/US2000/011133 WO2001008081A1 (fr) 1999-07-21 2000-04-26 Systeme de cytometrie par balayage, a laser de microvolume

Country Status (8)

Country Link
EP (1) EP1203339A4 (fr)
JP (1) JP2003505707A (fr)
KR (1) KR20020013970A (fr)
AU (1) AU4491100A (fr)
CA (1) CA2379836A1 (fr)
MX (1) MXPA01013398A (fr)
NZ (1) NZ516637A (fr)
WO (1) WO2001008081A1 (fr)

Cited By (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2002090947A2 (fr) * 2001-05-07 2002-11-14 Deutsches Krebsforschungszentrum Stiftung D. Öffentl. Rechts Microscope pour analyse de la fluctuation de fluorescence et module de mesure ou module de balayage de la fluctuation de fluorescence et procede de mesure de la fluctuation de fluorescence ainsi que procede et dispositif d'alignement d'un microscope pour analyse de la fluctuation de fluorescence
WO2006125674A1 (fr) * 2005-05-25 2006-11-30 Stiftelsen Universitetsforskning Bergen Systeme de microscope et procede de criblage de medicaments, physiotherapies et biorisques
US7192778B2 (en) 1999-10-06 2007-03-20 Natan Michael J Surface enhanced spectroscopy-active composite nanoparticles
US7387880B2 (en) 2000-09-20 2008-06-17 Ppd Biomarker Discovery Sciences, Llc Method for monitoring resting and activated platelets in unfixed blood samples
US7572642B2 (en) 2001-04-18 2009-08-11 Ambrigen, Llc Assay based on particles, which specifically bind with targets in spatially distributed characteristic patterns
US8409863B2 (en) 2005-12-14 2013-04-02 Becton, Dickinson And Company Nanoparticulate chemical sensors using SERS
US8497131B2 (en) 1999-10-06 2013-07-30 Becton, Dickinson And Company Surface enhanced spectroscopy-active composite nanoparticles comprising Raman-active reporter molecules
US9297766B2 (en) 2001-01-26 2016-03-29 Becton, Dickinson And Company Method of tagging materials with surface-enhanced spectroscopy-active sandwich particles
CN113302492A (zh) * 2019-01-03 2021-08-24 彼克斯赛尔医疗科技有限公司 用于分析流体样品的系统和方法

Families Citing this family (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7796251B2 (en) * 2006-03-22 2010-09-14 Itt Manufacturing Enterprises, Inc. Method, apparatus and system for rapid and sensitive standoff detection of surface contaminants
JP4909254B2 (ja) * 2007-12-26 2012-04-04 日本電信電話株式会社 浮遊粒子状物質測定装置
US9470616B2 (en) * 2009-04-27 2016-10-18 E.I. Spectra, Llc Pipette instrument
US9746412B2 (en) 2012-05-30 2017-08-29 Iris International, Inc. Flow cytometer
CN116165124A (zh) * 2012-05-30 2023-05-26 艾瑞斯国际有限公司 流式细胞仪

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4786813A (en) * 1984-10-22 1988-11-22 Hightech Network Sci Ab Fluorescence imaging system
US5127730A (en) * 1990-08-10 1992-07-07 Regents Of The University Of Minnesota Multi-color laser scanning confocal imaging system
US5377003A (en) * 1992-03-06 1994-12-27 The United States Of America As Represented By The Department Of Health And Human Services Spectroscopic imaging device employing imaging quality spectral filters
US5456252A (en) * 1993-09-30 1995-10-10 Cedars-Sinai Medical Center Induced fluorescence spectroscopy blood perfusion and pH monitor and method
US5741411A (en) * 1995-05-19 1998-04-21 Iowa State University Research Foundation Multiplexed capillary electrophoresis system

Family Cites Families (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5107422A (en) * 1989-10-02 1992-04-21 Kamentsky Louis A Method and apparatus for measuring multiple optical properties of biological specimens
JPH0599912A (ja) * 1991-10-11 1993-04-23 Union Shokai:Kk アナログフイルター装置
DE69329554T2 (de) * 1992-02-18 2001-05-31 Neopath Inc Verfahren zur identifizierung von objekten unter verwendung von datenverarbeitungstechniken
US5889881A (en) * 1992-10-14 1999-03-30 Oncometrics Imaging Corp. Method and apparatus for automatically detecting malignancy-associated changes
US5556764A (en) * 1993-02-17 1996-09-17 Biometric Imaging, Inc. Method and apparatus for cell counting and cell classification
JP3474612B2 (ja) * 1993-12-03 2003-12-08 浜松ホトニクス株式会社 光検出装置
JPH08313348A (ja) * 1995-05-16 1996-11-29 Kanagawa Kagaku Gijutsu Akad 赤外光時間応答測定装置

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4786813A (en) * 1984-10-22 1988-11-22 Hightech Network Sci Ab Fluorescence imaging system
US5127730A (en) * 1990-08-10 1992-07-07 Regents Of The University Of Minnesota Multi-color laser scanning confocal imaging system
US5377003A (en) * 1992-03-06 1994-12-27 The United States Of America As Represented By The Department Of Health And Human Services Spectroscopic imaging device employing imaging quality spectral filters
US5456252A (en) * 1993-09-30 1995-10-10 Cedars-Sinai Medical Center Induced fluorescence spectroscopy blood perfusion and pH monitor and method
US5741411A (en) * 1995-05-19 1998-04-21 Iowa State University Research Foundation Multiplexed capillary electrophoresis system

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
See also references of EP1203339A4 *

Cited By (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8918161B2 (en) 1999-10-06 2014-12-23 Becton, Dickinson And Company Methods of use for surface enhanced spectroscopy-active composite nanoparticles
US7192778B2 (en) 1999-10-06 2007-03-20 Natan Michael J Surface enhanced spectroscopy-active composite nanoparticles
US7443489B2 (en) 1999-10-06 2008-10-28 Oxonica, Inc. Surface enhanced spectroscopy-active composite nanoparticles
US8497131B2 (en) 1999-10-06 2013-07-30 Becton, Dickinson And Company Surface enhanced spectroscopy-active composite nanoparticles comprising Raman-active reporter molecules
US9201013B2 (en) 1999-10-06 2015-12-01 Becton, Dickinson And Company Method for tagging material with surface-enhanced spectroscopy (SES)-active composite nanoparticles
US7387880B2 (en) 2000-09-20 2008-06-17 Ppd Biomarker Discovery Sciences, Llc Method for monitoring resting and activated platelets in unfixed blood samples
US9297766B2 (en) 2001-01-26 2016-03-29 Becton, Dickinson And Company Method of tagging materials with surface-enhanced spectroscopy-active sandwich particles
US7572642B2 (en) 2001-04-18 2009-08-11 Ambrigen, Llc Assay based on particles, which specifically bind with targets in spatially distributed characteristic patterns
WO2002090947A3 (fr) * 2001-05-07 2003-04-10 Deutsches Krebsforsch Microscope pour analyse de la fluctuation de fluorescence et module de mesure ou module de balayage de la fluctuation de fluorescence et procede de mesure de la fluctuation de fluorescence ainsi que procede et dispositif d'alignement d'un microscope pour analyse de la fluctuation de fluorescence
WO2002090947A2 (fr) * 2001-05-07 2002-11-14 Deutsches Krebsforschungszentrum Stiftung D. Öffentl. Rechts Microscope pour analyse de la fluctuation de fluorescence et module de mesure ou module de balayage de la fluctuation de fluorescence et procede de mesure de la fluctuation de fluorescence ainsi que procede et dispositif d'alignement d'un microscope pour analyse de la fluctuation de fluorescence
WO2006125674A1 (fr) * 2005-05-25 2006-11-30 Stiftelsen Universitetsforskning Bergen Systeme de microscope et procede de criblage de medicaments, physiotherapies et biorisques
US8409863B2 (en) 2005-12-14 2013-04-02 Becton, Dickinson And Company Nanoparticulate chemical sensors using SERS
CN113302492A (zh) * 2019-01-03 2021-08-24 彼克斯赛尔医疗科技有限公司 用于分析流体样品的系统和方法

Also Published As

Publication number Publication date
NZ516637A (en) 2003-08-29
AU4491100A (en) 2001-02-13
MXPA01013398A (es) 2003-03-27
EP1203339A1 (fr) 2002-05-08
KR20020013970A (ko) 2002-02-21
CA2379836A1 (fr) 2001-02-01
JP2003505707A (ja) 2003-02-12
EP1203339A4 (fr) 2006-09-13

Similar Documents

Publication Publication Date Title
US7336812B2 (en) System for microvolume laser scanning cytometry
US20220274109A1 (en) Single-use test device for imaging blood cells
JP4215397B2 (ja) 多重分析物診断システム
US8660332B2 (en) Blood and cell analysis using an imaging flow cytometer
US7634125B2 (en) Blood and cell analysis using an imaging flow cytometer
US6350613B1 (en) Determination of white blood cell differential and reticulocyte counts
US20030096324A1 (en) Methods for differential cell counts including related apparatus and software for performing same
WO2001008081A1 (fr) Systeme de cytometrie par balayage, a laser de microvolume
JP2005502872A (ja) 光バイオディスクシステムを使用した、核の形態に基づく白血球の型の識別および定量
US8060316B2 (en) Methods, data structures, and systems for classifying microparticles
Bachar et al. An artificial intelligence‐assisted diagnostic platform for rapid near‐patient hematology
CN112161913A (zh) 一种用于流式荧光分析系统的分析方法及设备
US11714043B2 (en) Specimen analysis system and specimen analysis method
US20190056384A1 (en) Single-use test device for imaging assay beads
JP2022039780A (ja) 測定方法、測定装置、及び測定プログラム
US11592367B2 (en) Method for analyzing and selecting a specific droplet among a plurality of droplets and associated apparatus
US20240029458A1 (en) A method for automated determination of platelet count based on microscopic images of peripheral blood smears
Norton et al. Cell enumeration and characterization in microvolume laser scanning cytometry: a multicolor image-processing package
Tárnok et al. Immunophenotyping using a laser scanning cytometer

Legal Events

Date Code Title Description
AK Designated states

Kind code of ref document: A1

Designated state(s): AE AG AL AM AT AU AZ BA BB BG BR BY CA CH CN CR CU CZ DE DK DM DZ EE ES FI GB GD GE GH GM HR HU ID IL IN IS JP KE KG KP KR KZ LC LK LR LS LT LU LV MA MD MG MK MN MW MX NO NZ PL PT RO RU SD SE SG SI SK SL TJ TM TR TT TZ UA UG UZ VN YU ZA ZW

AL Designated countries for regional patents

Kind code of ref document: A1

Designated state(s): GH GM KE LS MW SD SL SZ TZ UG ZW AM AZ BY KG KZ MD RU TJ TM AT BE CH CY DE DK ES FI FR GB GR IE IT LU MC NL PT SE BF BJ CF CG CI CM GA GN GW ML MR NE SN TD TG

121 Ep: the epo has been informed by wipo that ep was designated in this application
DFPE Request for preliminary examination filed prior to expiration of 19th month from priority date (pct application filed before 20040101)
WWE Wipo information: entry into national phase

Ref document number: PA/a/2001/013398

Country of ref document: MX

Ref document number: 200110397

Country of ref document: ZA

WWE Wipo information: entry into national phase

Ref document number: 516637

Country of ref document: NZ

WWE Wipo information: entry into national phase

Ref document number: 2379836

Country of ref document: CA

WWE Wipo information: entry into national phase

Ref document number: 1020027000806

Country of ref document: KR

WWE Wipo information: entry into national phase

Ref document number: IN/PCT/2002/89/KOL

Country of ref document: IN

WWE Wipo information: entry into national phase

Ref document number: 2000926370

Country of ref document: EP

ENP Entry into the national phase

Ref document number: 2002 2002104494

Country of ref document: RU

Kind code of ref document: A

WWP Wipo information: published in national office

Ref document number: 1020027000806

Country of ref document: KR

WWP Wipo information: published in national office

Ref document number: 2000926370

Country of ref document: EP

REG Reference to national code

Ref country code: DE

Ref legal event code: 8642

WWP Wipo information: published in national office

Ref document number: 516637

Country of ref document: NZ

WWG Wipo information: grant in national office

Ref document number: 516637

Country of ref document: NZ