WO2011068917A1 - Habilitation d'un échantillon pour une analyse protéique ou peptidique - Google Patents

Habilitation d'un échantillon pour une analyse protéique ou peptidique Download PDF

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Publication number
WO2011068917A1
WO2011068917A1 PCT/US2010/058623 US2010058623W WO2011068917A1 WO 2011068917 A1 WO2011068917 A1 WO 2011068917A1 US 2010058623 W US2010058623 W US 2010058623W WO 2011068917 A1 WO2011068917 A1 WO 2011068917A1
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Prior art keywords
protein
rabbit
analyte
biological sample
normalization
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PCT/US2010/058623
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English (en)
Inventor
Virginia Espina
Antonella Chiechi
Lance Liotta
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George Mason Intellectual Properties, Inc.
Istituto Superiore Di Sanita
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Publication of WO2011068917A1 publication Critical patent/WO2011068917A1/fr

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids

Definitions

  • FIGURE 1 depicts an image of a reverse phase protein microarray
  • FIGURE 3 shows the effect of array heat treatment on various
  • FIGURE 4 panels A-D, are four graphs depicting the results of a run to run reproducibility analysis of the anti-ssDNA antibody binding reaction in 4 different arrays and staining runs as per an aspect of an embodiment of the invention.
  • FIGURE 5 is a side by side comparison of three methods of protein normalization as per at least one aspect of an embodiment of the invention.
  • FIGURE 6 shows two graphs allowing for an identification of
  • FIGURE 7 is a graph showing a concordance between stable molecules in renal cell carcinoma samples procured by laser capture microdissection (LCM) as per at least one aspect of an embodiment of the invention.
  • FIGURE 8 is a graph showing a lack of concordance between a stable protein and a less stable protein as per at least one aspect of an embodiment of the invention.
  • FIGURE 9 is a graph allowing for the selection of the most stable protein for a given data set as per at least one aspect of an embodiment of the invention.
  • FIGURE 10 is a graph depicting a room temperature time course of snap frozen human uterine tissue to assess fluctuations in variable proteins over time as per at least one aspect of an embodiment of the invention.
  • FIGURE 12 is a flow diagram of a method for analyzing biological sample representing at least one aspect of an embodiment of the invention.
  • FIGURE 13 is a block diagram of a diagnostic for analyzing a biological sample as per at least one aspect of an embodiment of the invention. DETAILED DESCRIPTION OF EMBODIMENTS
  • Embodiments relate to processes to determine the suitability of a
  • a biological sample may be procured.
  • a biological sample may include one or more of cells, tissue, bone, serum and/or body fluid.
  • procuring a biological sample may include one or more of performing a biopsy, drawing fluids and/or unpacking biological samples, for example receiving a biological sample from a remote location that is packaged and/or preparing a stored sample for analysis.
  • a biological sample may be analyzed, for example for suitability, diagnostic value normalization, diagnosis and/or treatment of a condition.
  • a biological sample may be analyzed after procurement.
  • the time period between procuring a biological sample and analyzing a biological sample may be any period, for example from about 1 minute to about 4 days. In embodiments, the time period between procuring a biological sample and analyzing a biological sample may be more than one day.
  • a process to determine the suitability of a biological sample may include measuring one or more diagnostic peptide analytes and/or one or more normalizing analytes from a biological sample.
  • measuring one or more diagnostic peptide analytes and/or one or more normalizing analytes may be accomplished by one or more of polyacrylamide gel electrophoresis, Coomassie Blue staining, gas chromatography/mass spectrometry, mass spectrometry, UV absorption, flourimetry, immunodetection, enzyme linked immunosorbent assay (ELISA), autoradiography, light absorption, mass determination, chromatography, fluorescence atomic force microscopy radioisotope detection, Sypro Ruby Protein staining, and reverse phase protein microarrays.
  • one or more diagnostic peptide analytes and/or one or more normalizing analytes may include an amino acid and/or two amino acids bonded by a peptide bond.
  • one or more diagnostic peptide analytes and/or one or more normalizing analytes may include one or more cellular protein analytes, extracellular protein analytes, post-translationally modified proteins, cleaved proteins, phosphorylated proteins, glycosylated proteins and/or membrane-bound protein analytes.
  • one or more diagnostic peptide analytes and/or one or more normalizing analytes may include one or more cytoplasmic proteins organelle compartment proteins, nuclear proteins, mitochondrial proteins, ribosomal proteins, cytoskeletal proteins, housekeeping proteins, golgi proteins, endoplasmic reticulum proteins, single stranded DNA, RNA, double stranded DNA, nucleic acid antigens and/or lysosomal element proteins.
  • at least one or more normalizing analytes may be identified in Example Table 5.
  • one or more diagnostic peptide analytes and/or one or more normalizing analytes may fluctuate. In embodiments, one or more normalizing analytes may fluctuate less than one or more diagnostic peptide analytes. In embodiments, one or more diagnostic peptide analytes may fluctuate less than one or more peptide analytes within, for example, a standard deviation of 1.5. In embodiments, one or more diagnostic peptide analytes and/or one or more normalizing analytes may fluctuate as a result of one or more factors. In embodiments, one or more factors, which may induce fluctuation, may include changes in pressure, changes in temperature, changes in humidity, the passage of time and/or exposure to environmental conditions. In embodiments, a fluctuation of one or more diagnostic peptide analytes and/or one or more normalizing analytes may occur in a positive and/or a negative manner, for example upregulated and/or downregulated.
  • fluctuation of one or more diagnostic peptide analytes and/or one or more normalizing analytes may include one ore more of a change in affinity, change in concentration, post-translational modification, cleavage, denaturization and/or enzymatic activity.
  • a process to determine the suitability of a biological sample may include calculating a suitability score.
  • a suitability score may be employed to determine the suitability of a biological sample, for example for analysis.
  • a suitability score may be calculated by comparing a measure of one or more diagnostic peptide analytes to a measure of one or more normalizing analyte.
  • comparing the measure of one or more diagnostic peptide analytes to the measure of one or more normalizing analytes may include calculating a ratio, calculating a geometric mean of a multitude of normalizing analytes, calculating a sum of a multitude of normalizing analytes, performing a mathematical operation and/or performing a statistical operation.
  • a process to determine the suitability of a biological sample may include comparing a suitability score with a predetermined threshold value.
  • a threshold value may be a known good suitability score from a known good biological sample and/or a composite suitability score derived from a multitude of biological samples.
  • a known good (acceptable for analysis) biological sample may include a biological sample at approximately the time of procurement and/or a reference biological sample.
  • a process to determine the suitability of a biological sample may include measuring one or more diagnostic peptide analytes 1110 and/or one or more normalizing analytes 1120 from a biological sample.
  • one or more diagnostic peptide analytes and/or one or more normalizing analytes may fluctuate.
  • a process to determine the suitability of a biological sample may include calculating a suitability score 1130.
  • a process to determine the suitability of a biological sample may include comparing a suitability score with a predetermined threshold value 1140.
  • an analysis on a biomarker of a biological sample may be performed to correlate the analysis to a disease state 1150, for example for treatment and/or diagnosis, once the sample is qualified as suitable.
  • a process to normalize a diagnostic value of a protein analyte in tissue may include determining a diagnostic value by measuring one or more diagnostic peptide analytes in a biological sample.
  • a suitability score may be specific for the type of tissue or class of sample being measured.
  • a process to normalize a diagnostic value may include determining a normalization value by measuring one or more normalization analytes in a biological sample.
  • a process to normalize a diagnostic value may include determining a suitability score by comparing a diagnostic value and a normalization value.
  • a combination of one or more normalization analytes may be used to normalize a diagnostic value, and/or may meet a preselected level of precision.
  • a preselected level of precision may be based on any criteria, for example a precision greater than the precision of measuring the total biological sample protein content of one or more normalization analytes.
  • a preselected level of precision may be the standard deviation below 10% of the mean of a combination of one or more normalization analytes.
  • a preselected level of precision may be the standard deviation below 20% of the mean of a combination of one or more normalization analytes.
  • one or more diagnostic peptide analytes may be normalized using a combination of one or more normalization analytes, for example for a specific class of biological samples.
  • a biological sample may be a multitude of biological samples.
  • a process to normalize a diagnostic value may include ranking at least two combinations of one or more normalization analytes, for example based upon variability of a suitability score for two or more of a multitude of biological samples.
  • a process to normalize a diagnostic value may include determining a diagnostic value by measuring one or more diagnostic peptide analytes in a biological sample 1210.
  • a process to normalize a diagnostic value may include determining a normalization value by measuring one or more normalization analytes in a biological sample 1120.
  • a process to normalize a diagnostic value may include determining a suitability score by comparing a diagnostic value and a normalization value such that a combination of one or more normalization analytes meet a preselected level of precision 1230.
  • a process to normalize a diagnostic value may include determining the suitability of a biological sample by comparing a suitability score with a
  • an analysis on a biomarker of a biological sample may be performed to correlate the analysis to a disease state 1150, for example for treatment and/or diagnosis.
  • a kit may be manufactured and/or provided to accomplish any analysis in accordance with aspects of embodiments.
  • a kit may be manufactured and/or provided to determine the suitability of a biological sample.
  • a kit may include a first measurement system configured to measure one or more diagnostic peptide analytes from a biological sample.
  • one or more diagnostic peptide analytes may fluctuate, for example after procurement of a biological sample.
  • a kit may include a second measurement system configured to measure one or more normalizing analytes from a biological sample.
  • one or more normalizing analytes may fluctuate, for example less than one or more diagnostic peptide analytes.
  • a kit may include a report generator configured to calculate and/or report a suitability score.
  • a suitability score may determine the suitability of a biological sample for analysis, for example by comparing the measure of one or more diagnostic peptide analytes to the measure of one or more normalizing analytes.
  • a kit may be manufactured and/or produced to normalize a diagnostic value, and to qualify a specimen in order to diagnose a condition and/or to treat a condition. [0029] According to embodiments, any process and/or kit in accordance with aspects of embodiments may be used to diagnose and/or treat a condition.
  • a method to diagnose and/or treat a condition may include normalizing a diagnostic value and/or determining the suitability of a biological sample in accordance with aspects of embodiments.
  • a method to diagnose and/or treat a condition may include performing an analysis on the biological sample.
  • an analysis on a suitable biological sample may include analyzing the biological sample for a biomarker.
  • an analysis on a biological sample, for example biomarker analysis may be correlated to a disease state for diagnosis and/or treatment.
  • performing an analysis of protein biomarkers within a sample judged suitable for analysis may include determining the presence of a biomarker, absence of a biomarker, concentration of a biomarker, change in the biomarker and/or activity of the biomarker.
  • correlating an analysis of a biomarker to a disease state may include determining whether a subject is in a disease state and/or prone to a disease state. In embodiments, for example where a biomarker has been analyzed and determined to be overexpressed and/or
  • a suitable treatment may be developed and deployed to inhibit the biomarker in a subject.
  • a suitable treatment may be developed and deployed to supplement the biomarker in a subject.
  • a suitable treatment may be developed to mitigate and/or slow the progression towards a disease state.
  • a kit 1300 may be manufactured and/or provided to determine the suitability of a biological sample.
  • a kit 1300 may include diagnostic peptide measurement system 1310 configured to measure one or more diagnostic peptide analytes 1315 from biological sample 1340.
  • a kit 1300 may include normalizing analyte measurement system 1320 configured to measure one or more normalizing analytes 1325 from biological sample 1340.
  • a kit 1300 may include report generator 1303 configured to calculate and/or report 1330 suitability score 1335.
  • a single sample, or a plurality of samples may be used together with any kit, including a kit 1300, in accordance with any aspect of embodiments.
  • protein polypeptide
  • polypeptide refers to a chain of amino acids that are connected by a peptide (- NHCO-) bond.
  • protein and polypeptide include chains of amino acids having a length of at least two amino acids.
  • Bio sample refers to cells, tissues, specimens, cell components, body fluids, pathology specimens, and tissue from a living organism. This tissue may be diseased, cancerous, or normal.
  • diagnosis peptide refers to a peptide, polypeptide, or protein to be analyzed.
  • threshold value refers to an empirically determined suitability score for a known good biological sample.
  • known good biological sample refers to a sample to be used as a reference for comparison to other samples.
  • normalizing analyte refers to an analyte against which the diagnostic analyte can be compared.
  • the normalizing analyte may also be an analyte which is known to be less labile in procured biological samples.
  • sample refers to biological material taken from a biological organism. At least one aspect of aspect of an embodiment of the invention includes samples of biological material which have been preserved or not preserved. The methods of tissue preparation are well-known to one of ordinary skill in the art.
  • Samples may also originate from the bone marrow, kidney, liver, brain, pancreas, gallbladder, large intestine, small intestine, lung, heart, lymph nodes, spleen, breast, ovary, neck, head, esophagus, throat, and colon. Samples may also originate from mesodermal tissue, ectodermal tissue, endodermal tissue.
  • analyte refers to a composition of matter amenable to analysis.
  • measuring refers at least to measuring with polyacrylamide gel electrophoresis, Coomassie Blue staining, gas chromatography/mass spectrometry, mass spectrometry, UV absorption, flourimetry, immunodetection, enzyme linked immunosorbent assay (ELISA), autoradiography, light absorption, mass determination, chromatography, fluorescence atomic force microscopy, radioisotope detection, Sypro Ruby Protein staining, and reverse phase protein microarrays.
  • antibody refers to proteins, polypeptides, or fragments thereof capable of acting as a ligand.
  • Antibodies include IgG, IgE, IgA, IgM, antibody fragments, humanized antibodies, chimeric antibodies, single chain antibodies, double chain antibodies, monoclonal antibodies, polyclonal antibodies, antibody heavy chain fragments, and antibody light chain fragments.
  • reference biological sample refers to a sample known to be of a certain quality.
  • Biospecimens are defined as tissue or body fluid samples from animals including humans that contain cellular elements.
  • statistical operation refers to arithmetic operations pertaining to the collection, interpretation, explanation, analysis and presentation of data.
  • tissue refers to tissue originating from a biological organism.
  • a biological sample may originate from a subject or patient.
  • the terms subject and patient are used interchangeably herein.
  • the methods of at least one aspect of an embodiment of the invention may find use in treatment and experimental settings including use in biological organisms.
  • the biological organisms include land and sea animals.
  • Land animals includes at least chordates, rodents, mammals, humans, livestock, dogs, cats, swine, poultry, primates, horses, birds, reptiles, and mice.
  • ssDNA single-stranded DNA refers to polymeric molecules of adenosine, guanine, cytosine, uracil, inosine, and thymidine.
  • Reverse-Phase Protein Micro- Array is a technology platform designed for quantitative, multiplexed analysis of specific forms of cellular proteins from a limited amount of sample.
  • This class of array can be used to interrogate different kinds of sample: tissue, cells, serum or body fluids.
  • the array is composed of a glass slide with a nitrocellulose layer on the top. Protein samples are deposited on nitrocellulose and then protein expression levels are detected by immunostaining using specific antibodies.
  • the conventional data analysis procedure for RPMA employs a normalization step based on total protein. Nevertheless total protein is not an accurate reference for some kind of samples such as blood contaminated tissues.
  • DNA normalization data can be normalized on the real tumor cell number constituting the sample.
  • DNA is normally present in tissue and cell sample lysates and reagents used for protein extraction are not able to destroy DNA or remove it from the lysate. So, it is not necessary to make any change in extraction procedure to keep DNA in sample lysates.
  • ssDNA single stranded DNA
  • ssDNA can be detected on RPMA using commercial anti-ssDNA antibodies by the same immunostaining procedure as used for protein detection. Moreover ssDNA staining can be done in the same staining run together with the end-point proteins avoiding staining run variability. Spotting known amounts of ssDNA spiked into BSA on RPMA, a calibration curve for ssDNA staining could be done and relative intensity of unknown samples could be matched on this curve. In this way we could do a more precise normalization even when the ssDNA amount in unknown samples is not in the antibody linear range of reaction.
  • ssDNA staining has been demonstrated to be specific, to not cross-react with RNA and to be directly proportional to DNA amount and cell number.
  • ssDNA normalization methods have a lower variability across printing run compared to total protein normalization
  • ssDNA content on the RPMA corresponds to cell number
  • ssDNA detection is not affected by red blood cell contamination of samples
  • ssDNA antibody specifically binds ssDNA and not dsDNA or RNA.
  • RPMA technology is being applied in clinical trials to predict pharmacological tractability of the patients and individual response to treatments.
  • ssDNA normalization applied to RPMA could improve the precision, sensitivity and accuracy between and within arrays of this technique.
  • This technique could be applied to any laboratory researcher using the RPMA technology and/or potentially applicable to other multiplexed platforms.
  • This invention addresses a critical need to reduce sources of variability in the study of specimens which contain contaminating substances such as erythrocytes (red blood cells), hemoglobin, serum, or other proteins.
  • erythrocytes red blood cells
  • hemoglobin hemoglobin
  • serum or other proteins.
  • RPMA was normalized to the total protein in the sample. This may be inaccurate because it doesn't necessarily reflect the total number of cells from which the source material was derived and is subject to interference by extra-cellular protein.
  • ssDNA provides a measurement of total cell number and is independent of total protein content of the sample.
  • the level of a protein analyte measured in a cellular tissue ideally should reflect the physiologic state of the tissue at the time it was procured from the patient.
  • the level of the protein analyte can also be influenced by reactive changes in the tissue post procurement, specimen handling, specimen treatment, or specimen storage conditions. For example, tissue following procurement is hypoxic, nutrient deprived, and metabolically stressed. As the tissue remains outside its host, wide fluctuations both positive and negative can occur in the protein analytes, particularly in post translationally modified forms of protein analytes (Espina 2010). Such fluctuations render the tissue specimen unsuitable for use because the variability caused by ex vivo reactive changes, handling, or treatment, can mask the actual value of the protein analyte that existed at the time of tissue procurement.
  • embodiment of the invention provides an improved means of determining whether the magnitude of the protein fluctuations induced in a biospecimen occurring after procurement have reached an unacceptable level. This means that the tissue can not be used for analysis because a protein analyte value has a high likelihood of being a false positive or a false negative.
  • At least one aspect of aspect of an embodiment of the invention provides a scoring system that compares one or more labile protein analytes that fluctuate due to post procurement effects against one or more analytes that are relatively stable over the post procurement time period.
  • At least aspect of an embodiment of the invention also provides a means of discovering which analytes should be used to derive the score. It also provides a new set of previously unknown reference analytes that are stable over a post procurement period.
  • This new class of reference analytes is derived from cellular organelle compartments. These analytes are derived from the nuclear compartment, the mitochondrial compartment, the ribosomal compartment, the cytoplasmic compartment, and the cytoskeletal compartment. A combination of reference protein analytes from these different compartments is shown to provide the least variable baseline for a diagnostic assay using tissue biospecimens.
  • the type of protein peptides that fluctuate in a tissue post procurement may be different depending on the type of tissue or class of sample. For example the peptides that fluctuated within 30 minutes post procurement of brain tissue may be quite different from those of colon or breast tissue.
  • At least one aspect of aspect of an embodiment of the invention addresses an unmet need to normalize protein analytes in cellular tissue.
  • Tissue is highly heterogenous in regard to cellular and extracellular elements, biologic state, disease state, originating organ, and level of contamination by blood. Due to this heterogeneity, the unknown content of cellular and extracellular components, including blood and water, the level of an analyte cannot simply be normalized to total tissue volume or to total amount of protein content.
  • tissue samples are quite different from plasma or serum in which an analyte can measured per unit volume of sample: for example nanograms of PSA per mL.
  • At least one aspect of aspect of an embodiment of the invention provides a means to determine the optimal normalization reference analytes for tissue protein analytes, given a set or class of particular specimens selected for analysis.
  • the reference analytes are derived from at least two organelle compartments including the nuclear compartment, the
  • At least one aspect of an embodiment of the invention provides a method for determining the optimum normalization score by the steps of a) measuring the set of candidate reference analytes from the aforementioned cellular compartments, and b) determining the combination of these reference candidate analytes that exhibit the least variability within the context of the specimen set desired for analysis.
  • At least one aspect of an embodiment of the invention provides an improved rapid means of identifying highly variable, and highly stable cellular derived protein analytes in tissue samples.
  • the most stable protein analytes are those with the least variability across treatment conditions, specimen type, or over time after the specimen is procured from the patient.
  • the most variable analytes are those with the highest variability across treatment conditions, specimen type, or over time after the specimen is procured from the patient. Variability is determined by any standard statistical measure of variance including standard deviation, standard error of the mean, or other suitable measures of fluctuation of values over a distribution of values.
  • At least one aspect of aspect of an embodiment of the invention also provides improved classes of analytes that can be used for cellular protein sample normalization that are derived from separate cellular organelle compartments. These analytes are derived from the nuclear compartment, the mitochondrial compartment, the ribosomal compartment, and the cytoskeletal compartment.
  • At least one aspect of aspect of an embodiment of the invention further provides a means for scoring the suitability of a cellular biospecimen for analysis.
  • the score is a ratio between one or more analyte that is found to be highly variable over a desired time period following tissue procurement, compared to one or more reference proteins that do not change over time after tissue procurement.
  • At least one aspect of aspect of an embodiment of the invention provides a new set of analytes that represent stable proteins that do not fluctuate over a typical period of time after procurement such as between 1 minute and 4 days or between 10 and 90 minutes. Such stable proteins provide critical baselines against which to measure labile proteins indicative of tissue deterioration.
  • a further aim of at least aspect of an embodiment of the invention is to employ normalization of tissue protein analytes based on ssDNA in combination with one or more analytes derived from cellular organelle compartments such as the mitochondria, the ribosome, the lysosome, or the cytoskeletal compartment.
  • the new normalization methods are shown in at least aspect of an embodiment of the invention to be superior to conventional normalization methods using total specimen protein content.
  • the example embodiments provided for in at least one aspect of the invention demonstrate their use in determining the most stable analytes among seven (Glyceraldehyde 3-phosphate dehydrogenase (GAPDH), alpha/beta-tubulin, mitochondrial ribosomal protein Ll l (MRPL11), ribosomal protein L13a (RPL13a), beta-actin, total protein, and ssDNA) within different tissue types or under different conditions.
  • Glyceraldehyde 3-phosphate dehydrogenase Glyceraldehyde 3-phosphate dehydrogenase (GAPDH), alpha/beta-tubulin, mitochondrial ribosomal protein Ll l (MRPL11), ribosomal protein L13a (RPL13a), beta-actin, total protein, and ssDNA
  • RPMA Microarray
  • RPMA is a technology platform designed for quantitative, multiplexed analysis of specific forms of cellular proteins from a limited amount of sample.
  • This class of array can be used to interrogate different kinds of sample: tissue, cells, serum or body fluids.
  • the array is composed of a glass slide with a nitrocellulose layer on the top. Protein samples are deposited on nitrocellulose and then protein expression levels are detected by immunostaining using specific validated antibodies directed against target proteins.
  • the relative intensity value represents the amount of total target protein in each sample.
  • a normalization factor is required to correct for specimen variability in total cellular content. This data normalization may be performed by a total protein staining using a general protein staining dye such as Sypro Ruby dye: the relative intensity for the target protein for each dot is divided by the relative intensity for total protein of the correspondent dot.
  • RPMA technique is applied to cell lines or laser capture microdissected tissues.
  • Contaminating total protein from blood will contaminate the cells in the tissue thus reducing the accuracy of a measurement derived from cells in the tissue. Since blood contamination is mostly represented by red blood cells (RBCs) that are devoid of nucleus or by abundant serum proteins, a good alternative to total protein can be found in DNA. Using the DNA content, data can be normalized on the real cell number constituting the sample.
  • RBCs red blood cells
  • the contamination can be represented by proteins present in the extra cellular matrix (ECM) or stroma.
  • ECM extra cellular matrix
  • reference proteins derived from cellular organelles can be used to represent the true cellular content because they are derived from the inside of living cells and are not components of the extracellular matrix.
  • sample lysates were used protein solutions obtained from a known number of cells from 2 different commercial cell lines, RPMI 8226 and U266 (ATCC, Manassas, VA).
  • the cells were maintained in Dulbecco's Modified Eagle's Medium (ATCC, Manassas, VA), at 37°C in a cell incubator with 5% C02 and 70% humidity. Cells were counted in a haemacytometer, then a known number of cells was incubated in protein extraction buffer for 10 minutes and dry-heated at 100°C for 5 minutes.
  • RPMA RPMA were printed with whole cell and tissue protein lysates, ssDNA control and RNA control, in duplicate or triplicate. Briefly, the lysates were printed on glass-backed nitrocellulose array slides (SCHOTT Nexterion, Germany) using an Aushon 2470 arrayer equipped with 350- ⁇ pins (Aushon Biosystems, Billerica, MA). Each lysate was printed in a 2-fold dilution curve. Known calibrator lysates were printed in a 2-fold dilution curve. At the end of the printing run, the slides were alternatively baked for 2h at 80°C in oven and then stored or directly stored. All the slides were then stored with desiccant (Drierite, W. A. Hammond, Xenia, OH) at -20°C prior to use.
  • desiccant Drierite, W. A. Hammond, Xenia, OH
  • the slides were blocked (I-Block, Applied Biosystems, Foster City, CA) for 2h before immunostaining. Immunostaining was performed on an automated slide stainer according to the manufacturer's instructions (Autostainer catalyzed signal amplification (CSA) kit, Dako, Carpinteria, CA). Each array was probed with a single polyclonal or monoclonal primary antibody for 30 min.
  • Anti Akt S473 antibody, anti ⁇ -actin antibody were purchased by Cell Signaling Technology (Danvers,MA); anti ssDNA antibody was purchased by IBL (Immuno-Biological Laboratories Co, Gunma, Japan).
  • the negative control slides were incubated with the only antibody diluent (Dako, Carpinteria, CA). Secondary antibody was goat anti-rabbit IgG heavy + light (1:7500) (Vector Laboratories, Burlingame, CA) or rabbit anti-mouse IgG (1:10) (Dako, Carpinteria, CA). Subsequent protein detection was amplified via horseradish peroxidase-mediated biotinyl tyramide with chromogenic detection
  • the immunostained slides were scanned by Umax 2100XL using the following settings: white 255, black 0, middle tone 1.37.
  • the image capture of total protein slides was carried out using NovaRay (Alpha Innotech, San Leonardo, CA). Spot intensity was analyzed by Image Quant v5.2 software (Molecular Dynamics).
  • At least one aspect of aspect of an embodiment of the invention are directed toward quantitation of protein biomarkers in tissue biospecimens.
  • Biospecimens are defined as tissue samples from animals including humans that contain cellular elements. They can be procured by surgical excision, needle biopsy, aspiration, dissection or other suitable means. Proteins are defined as amino acid containing peptides, or polypeptide proteins with or without posttranslational modification such as phosphorylation, gylcosylation, etc. At least one aspect of aspect of an embodiment of the invention are directed toward tissue biospecimen protein analytes useful in medical diagnosis, individualization of therapy, or prediction of outcome. The protein analytes are derived from cellular elements of tissue, residing within or outside the cell membrane.
  • the level of a protein analyte measured in a cellular tissue ideally should reflect the physiologic state of the tissue at the time it was procured from the patient.
  • the level of the protein analyte can also be influenced by reactive changes in the tissue post procurement, specimen handling, specimen treatment, or specimen storage conditions. For example, tissue following procurement is hypoxic, nutrient deprived, and metabolically stressed. As the tissue remains outside its host, wide fluctuations both positive and negative can occur in the protein analytes, particularly in post translationally modified forms of protein analytes (Espina 2010).
  • At least one aspect of an embodiment of the invention provides an improved means of determining whether the magnitude of the protein fluctuations induced in a biospecimen occurring after procurement have reached an unacceptable level. This means that the tissue can not be used for analysis because a protein analyte value has a high likelihood of being a false positive or a false negative.
  • At least one aspect of aspect of an embodiment of the invention provides a scoring system that compares one or more labile protein analytes that fluctuate due to post procurement effects against one or more analytes that are relatively stable over the post procurement time period.
  • At least one aspect of aspect of an embodiment of the invention also provides a means of discovering which analytes should be used to derive the score. It also provides a new set of previously unknown reference analytes that are stable over a post procurement period. This new class of reference analytes is derived from cellular organelle compartments. These analytes are derived from the nuclear compartment, the mitochondrial compartment, the ribosomal compartment, and the cytoskeletal compartment. A combination of reference protein analytes from these different compartments is shown to provide the least variable baseline for a diagnostic assay using tissue biospecimens.
  • At least one aspect of aspect of an embodiment of the invention addresses an unmet need to normalize protein analytes in cellular tissue.
  • Tissue is highly heterogeneous in regard to cellular and extracellular elements, biologic state, disease state, originating organ, and level of contamination by blood. Due to this
  • tissue samples are quite different from plasma or serum in which an analyte can measured per unit volume of sample: for example nanograms of PSA per mL.
  • tissue samples are quite different from plasma or serum in which an analyte can measured per unit volume of sample: for example nanograms of PSA per mL.
  • At least one aspect of an embodiment of the invention provides a means to determine the optimal normalization reference analytes for tissue protein analytes, given a set or class of particular specimens selected for analysis.
  • the reference analytes are derived from at least two organelle compartments including the nuclear compartment, the mitochondrial compartment, the ribosomal compartment, and the cytoskeletal compartment.
  • At least one aspect of an embodiment of the invention provides a method for determining the optimum normalization score by the steps of a) measuring the set of candidate reference analytes from the aforementioned cellular compartments, and b) determining the combination of these reference candidate analytes that exhibit the least variability within the context of the specimen set desired for analysis.
  • arrays were printed with ssDNA, Ribosomal RNA, BSA, several amounts of ssDNA spiked- in BSA, several amounts of Ribosomal RNA spiked-in BSA, tissue protein lysates, lysates from a known number of cells, tissue and cell protein lysates after DNA precipitation by ethanol.
  • the arrays were then probed with the anti-ssDNA antibody.
  • the relative intensity signal was detectable only in those samples containing ssDNA. No reaction was revealed in RNA samples either alone or spiked-in BSA; no reaction was revealed in BSA samples and tissue and cell samples previously treated with ethanol. [See Figure 1A] Moreover the reaction showed an increasing relative intensity directly correlated to the increasing amount of ssDNA in the spiked-in samples and directly correlated to the cell number in the cell lysate samples. [See Figure IB].
  • FIGURE 1 depicts an image of a reverse phase protein microarray
  • RPMA probed with anti-ssDNA.
  • the antibody is sensitive and specific for single stranded DNA (ssDNA) alone (1) or in a mixture of protein and ssDNA (4). There is minimal evidence of cross-reactivity with RNA (2), proteins, such as Bovine Serum Albumin (BSA) (3), or with RNA in BSA (5).
  • BSA Bovine Serum Albumin
  • the ssDNA antibody does not appear to cross-react with DNA in a protein preparation after DNA precipitation (6).
  • Anti-ssDNA array staining requires heat treatment of the array prior to staining.
  • reverse phase protein microarrays were either heated at 80°C for 2 hours (baked) or frozen at -20°C (not baked) prior to staining with total protein, anti-AKT Ser473, or anti- ⁇ Actin.
  • RPMI 8226 myeloma cells were used as an example nucleated cell.
  • RMPI 8226 cells were mixed in different proportions with red blood cells collected in an EDTA blood collection tube from a volunteer donor. RMPI cells were combined with RBC in two different ratios: RPMI:RBC 10: 1 or RPMI:RBC 1 : 10. The amount of total protein detected across the samples was greater than the amount of ⁇ Actin or ssDNA.
  • FIGURE 3 shows the effect of array heat treatment on various
  • Anti-ssDNA array staining requires heat treatment of the array prior to staining.
  • reverse phase protein microarrays were either heated at 80°C for 2 hours (baked) or frozen at -20°C (not baked) prior to staining with total protein, anti-AKT Ser473, or anti- ⁇ Actin.
  • FIGURE 5 is a side by side comparison of three methods of protein normalization.
  • Total protein, ⁇ -actin and ssDNA normalization is compared in experiments with contaminated samples.
  • ssDNA normalization is essentially unaffected by contaminating red blood cell/plasma/serum proteins.
  • Example reverse phase protein microarray data is shown which is normalized using 3 different methods for two different proteins. Bone marrow core biopsy samples consisting of red blood cells, osteoblasts /osteoclasts, and hematopoietic cells, as well as plasma proteins were stained with two different proteins (protein 1 or protein 2) and spot intensities were quantified. The spot intensity data was normalized using one of three methods: total protein, ⁇ Actin, or ssDNA.
  • geNORM [17] and NormFinder [18], a Visual Basic Applet for Microsoft Excel designed to calculate pairwise variations for internal reference genes used for real-time polymerase chain reaction (RT-PCR) normalization.
  • RT-PCR real-time polymerase chain reaction
  • geNORM calculates the stability of each internal reference protein as well as how many proteins are necessary for the calculation of a normalization factor.
  • geNorm ranks the proteins by stability and calculates the normalization factor, which is the geometric mean of expression levels of the proteins needed for a good normalization for each sample.
  • Vjk st.dev(Ajk) (the standard deviation of the array).
  • the pairwise variations were calculated between the normalization factor calculated using n and n-1 proteins, and when the pairwise variation was smallest, usually when n proteins was 3 or 4, one would know in the future to only use n-1 proteins.
  • the protein-stability measure Mj (Vjkl + Vjk2 + . . . + Vjkn)/(n-l), which is the arithmetic mean of the pairwise variations.
  • FIGURE 6 shows two graphs allowing for an identification of
  • RPMAs Reverse phase microarrays
  • RPMA constructed with human melanoma tissue lysates were probed with antibodies to either ⁇ tubulin, ⁇ Actin, Ribosomal Protein L13a (L13a), Mitochondrial
  • Ribosomal Protein Lll (MRPL11), ssDNA, GAPDH, ERK, AKT, Cytokeratin 7, Cytokeratin 5/6, or stained with a total protein stain (Sypro Ruby Blot Stain).
  • the most stabile proteins identified using an algorithm were Ribosomal Protein L13a, MRPL11 and ⁇ Actin.
  • the optimal number of proteins for normalization was identified as 2-3 (pairwise variation score of 0.019).
  • FIGURE 7 is a graph showing a concordance between stable molecules in renal cell carcinoma samples procured by laser capture microdissection (LCM). An algorithm was used to identify molecules (proteins or DNA) that changed the least compared to proteins that changed the most. ssDNA and Ribosomal Protein L13a were identified in this tissue set as highly stable molecules.
  • FIGURE 8 is a graph showing a lack of concordance between a stable protein and a less stable protein. Renal cell carcinoma cells were procured by laser capture microdissection and analyzed by reverse phase protein microarray. An algorithm was used to identify molecules (proteins or DNA) that changed the most compared to proteins that changed the least. ssDNA was identified in this tissue set as a highly stable molecule, whereas ⁇ Actin was identified as identified as a least stable molecule. A lack of concordance was noted between the stabile and least stable molecules.
  • FIGURE 9 is a graph allowing for the selection of the most stable protein for a given data set.
  • FIGURE 10 is a graph depicting a room temperature time course of snap frozen human uterine tissue to assess fluctuations in variable proteins over time. An algorithm was used to identify proteins that did not change over time within the data set. Two highly stable proteins, ⁇ Tubulin and Ribosomal Protein LI 3a, were identified as the best candidates. Individual cell signaling kinase protein data was normalized to the sum of ⁇ Tubulin and Ribosomal Protein L13a data for each time point. The normalized data was used to assess the fluctuations over time in variable cell signaling proteins.
  • VEGFR 2 (Yl 175) (19A10) RmAb
  • CD24 (GPI-linked surface mucin) Ab-2 (SN3b) Mouse
  • HBB Hemoglobin, beta
  • HDAC1 Histone Deacetylase 1
  • HDAC3 Histone Deacetylase 3
  • HDAC4 Histone Deacetylase 4
  • Protein Phosphatase 1 Beta (EP1804Y) RmAb
  • VDAC1 N-18

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Abstract

L'invention porte sur un procédé de détermination de la conformité d'un échantillon biologique. Une valeur de diagnostic est déterminée par mesure d'analytes peptidiques de diagnostic provenant d'un échantillon biologique. Une valeur de normalisation est déterminée par mesure d'analytes de normalisation provenant d'un échantillon biologique. La notation de conformité est déterminée par comparaison de la valeur de diagnostic à la valeur de normalisation de telle sorte que la combinaison de la normalisation connexe satisfait un niveau de précision présélectionné.
PCT/US2010/058623 2009-12-01 2010-12-01 Habilitation d'un échantillon pour une analyse protéique ou peptidique WO2011068917A1 (fr)

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WO2016149316A1 (fr) * 2015-03-16 2016-09-22 Waters Technologies Corporation Systèmes, dispositifs et procédés d'étalonnage d'analytes par seuil et quantification au moyen de l'étalonnage d'analytes par seuil

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
GB2513352A (en) * 2013-04-24 2014-10-29 Randox Lab Ltd Identification of novel normalisation proteins
WO2016149316A1 (fr) * 2015-03-16 2016-09-22 Waters Technologies Corporation Systèmes, dispositifs et procédés d'étalonnage d'analytes par seuil et quantification au moyen de l'étalonnage d'analytes par seuil
US10794881B2 (en) 2015-03-16 2020-10-06 Waters Technologies Corporation Systems, devices, and methods for threshold analyte calibration and quantitation using threshold analyte calibration

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