Classifications

• • 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
  • G01N33/6893—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids related to diseases not provided for elsewhere
• • 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
  • G01N15/1456—Optical investigation techniques, e.g. flow cytometry without spatial resolution of the texture or inner structure of the particle, e.g. processing of pulse signals
  • G01N15/1459—Optical investigation techniques, e.g. flow cytometry without spatial resolution of the texture or inner structure of the particle, e.g. processing of pulse signals the analysis being performed on a sample stream
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N2800/00—Detection or diagnosis of diseases
  • G01N2800/32—Cardiovascular disorders

Definitions

• Cardiovascular disease is the leading cause of death in the United States; every 39 seconds an adult dies from heart attack, stroke or other cardiovascular disease ("High blood pressure and cholesterol out of control in the US.” Centers for Disease Control Web Site. http://www.cdc.gov/Features/Vitalsigns/CardiovascularDisease/. Updated January 31 , 201 1 Accessed February 6, 2012).
• the prevalence of CVD in the USA is already very high (36.9% of adults or about 81 million people) and is projected to increase by about 10% over the next 20 years, and by 2030 it is estimated that over 40% of adults (approximately 1 16 million people) will have one or more forms of CVD (Heidenreich et al., 201 1 ,
• Circulation 123:933-944 Long before symptoms are clinically evident, nascent vascular disease begins as a dysfunction of endothelial cells. Symptomatic, clinical CVD events generally occur when atherosclerosis progresses to a point where obstructed blood flow causes ischemia, or when a thrombus forms from an existing plaque due to rupture or erosion (Heidenreich et al., 201 1 , Circulation 123:933-944).
• cardiovascular risk is typically assessed by several circulating biomarkers, such as high-sensitivity C-reactive protein (hsCRP) and fibrinogen (Ridker, 2003, Circulation 107:363-369).
• hsCRP high-sensitivity C-reactive protein
• fibrinogen fibrinogen
• the Evaluation of Genomic Applications in Practice and Prevention Working Group found insufficient evidence to recommend testing for the 9p21 genetic variant or 57 other variants in 28 genes to assess risk for CVD in the general population, specifically heart disease and stroke.
• the EWG put forth that the magnitude of the net health benefit from the use of any of these genomic markers alone or in combination is negligible (Evaluation of Genomic Applications in Practice Prevention Working Group. 2010. Recommendations from the EGAPP Working Group: Genomic profiling to assess cardiovascular risk to improve cardiovascular health. Journal 12:839-843 810.1097/GIM.1090bl013e3181 fl 872cl090).
• PCs progenitor cells
• EPCs endothelial progenitor cells
• MPs microparticles
• the present invention relates to a method of determining vascular health in a subject.
• the method includes the steps of obtaining a biological sample from the subject, obtaining microparticle data based on the level of at least one set of microparticles in the biological sample, obtaining progenitor cell data based on the level of at least one set of progenitor cells in the biological sample, generating a cytometric fingerprint of the biological sample based on the microparticle and progenitor cell data, and determining the vascular health of the subject based on the generated cytometric fingerprint.
• the level of the at least one set of microparticles is measured by flow cytometry.
• the biological sample is a sample of whole blood.
• the biological sample is a plasma sample.
• the subject is a subject with psoriasis.
• the subject is a subject with lupus.
• the subject is a subject with known CVD risk factors.
• the subject is a subject with no known CVD risk factors.
• the subject is a diabetic subject.
• the subject is a Type 1 diabetic subject.
• the subject is a Type 2 diabetic subject.
• the progenitor cells are endothelial progenitor cells (EPCs).
• the subject when the level of at least one of the microparticle sets is up-regulated, the subject is at risk of cardiovascular disease or vascular dysfunction, or of progressing cardiovascular disease or vascular dysfunction. In another embodiment, when the level of at least one of the progenitor cell sets is down-regulated, the subject is at risk of cardiovascular disease or vascular dysfunction, or of progressing cardiovascular disease or vascular dysfunction. In another embodiment, when the level of at least one of the microparticle sets is up-regulated and the level of at least one of the progenitor cell sets is down-regulated, the subject is at risk of cardiovascular disease or vascular dysfunction, or of progressing cardiovascular disease or vascular dysfunction.
• the present invention also relates to a method for determining a risk associated with cardiovascular disease or vascular dysfunction in a subject.
• the method includes the steps of obtaining a biological sample from the subject, obtaining microparticle data based on the level of at least one set of microparticies in the biological sample, obtaining progenitor cell data based on the level of at least one set of progenitor cells in the biological sample, generating a cytometric fingerprint of the biological sample based on the microparticle and progenitor cell data, and determining the risk associated with cardiovascular disease or vascular dysfunction of the subject based on the generated cytometric fingerprint.
• the level of the at least one set of microparticies is measured by flow cytometry.
• the biological sample is a plasma sample.
• the biological sample is a sample of whole blood.
• the subject is a subject with psoriasis.
• the subject is a subject with lupus.
• the subject is a subject with known CVD risk factors.
• the subject is a subject with no known CVD risk factors.
• the subject is a diabetic subject.
• the subject is a Type 1 diabetic subject.
• the subject is a Type 2 diabetic subject.
• the progenitor cells are endothelial progenitor cells (EPCs).
• the generated cytometric fingerprint indicates a cellular damage. In another embodiment, the generated cytometric fingerprint indicates the integrity of endothelium, a loss of endothelial repair capacity, or a combination thereof. In another embodiment, the method further comprises generating a cytometric fingerprint of a healthy control sample derived from one or more individuals, and comparing the generated cytometric fingerprint of the subject's biological sample to the cytometric fingerprint of the healthy control sample.
• FIG. 1 is a schematic illustrating a high throughput flow cytometry assay for the determination of a vascular health profile.
• a blood sample is partitioned into peripheral blood mononuclear cells (PBMCs) and plasma.
• PBMCs peripheral blood mononuclear cells
• Progenitor cells are identified in PBMCs, while microparticles are identified in the plasma.
• FIG 2 is an illustration of FSC/SSC threshold optimization on Canto A.
• FSC and SSC thresholds were determined by 0.3 ⁇ beads (Row A) or 0.3, 1 and 3 ⁇ beads (Row B on FFC/SSC contour plot and C on SSC-W histogram) passing through the machine at medium rate with different FSC or SSC threshold.
• Columns D and E showed better resolutions of 3 beads on both FFC/SSC contour plot and SSC-W histogram plot with FSC threshold set to 5000 or SSC threshold to 200 (column D), and only SSC threshold set to 200 and FSC threshold off (column E). More background noise was acquired with FSC threshold set to 200 or SSC threshold to 200 (Column F).
• FIG 3 is an illustration of FSC/SSC PMT determination on Canto A.
• double-filtered PBS and 0.3um beads were used to set up the FSC, SSC PMT. Less than 10 events per second was accepted when double-filtered PBS passing through the machine at medium flow rate.
• the threshold of FSC and SSC were set to 5000 and 200, respectively.
• FSC and SSC PMT were determined by the same sample running on the machine using different SSC voltage. More MPs were lost using SSC 300 and 325 and more background noise was acquired on SSC 375 and 400. SSC voltage of 350 was accepted in this study. SSC voltage of 400 data was not shown here because of too much background noise.
• FIG 4 is an illustration of window extension determination. As depicted in Figure 4, FACS Canto A Window Extension (WE) was determined by 0.3, 1 and 3 ⁇ beads running at medium rate form WE 0 to 7. WE 0.2 was chosen for its better resolution of 3 beads and low background noise on SSC-W histogram plots.
• WE FACS Canto A Window Extension
• Figure 5 is a comparison of window extension 0.2 and 7 in sample detection on Canto A.
• PFP was stained with FITC-Annexin (or CD31 ), Percp-Cy5.5- CD41 , PE-CD 105 (or CD 144), APC-CD64 and run on WE 0.2 or 7.
• the acquisition was stopped when a fixed number of 3 ⁇ beads (100,000) were counted.
• Row A was gated on less than 1 ⁇ on dot plot with WE set to 0.2.
• Row B was gated on less than 1 ⁇ on dot plot with WE set to 7.
• Row C was gated on less than ⁇ ⁇ with WE 0.2 and D gated on less than ⁇ ⁇ with WE 7 on SSC-W histogram plots. More background noise and fewer positive particles were collected using WE 7 (Row B and Row D).
• Figure 6 is an illustration of gating strategy. As depicted in Figure 6, 0.3, 1 and 3 ⁇ beads were used to estimate MPs size (A and B on Canto A and C on Gallios). MPs were gated on less than 1 ⁇ (D gated on SSC-W histogram and E on FSC/SSC dot plot on Canto and F on Gallios). For the Canto A setting, forward and side scatter thresholds were set to 5000 and 200, respectively, and the window extension was set to 0.2. For the Gallios setting, the discriminator value for FS was set to 1 and the Forward Scatter Collection Angle was W2.
• Figure 7 is an illustration of the optimization of antibodies. As depicted in Figure 7, the same volume of antibodies used for MP detection in 500 ⁇ 1 of double filtered PBS was run on Canto A. Unfiltered antibodies (Row A) showed more false positive events than double- filtered antibodies (Row B). All reagents used for MPs detection should be double-filtered through 0.1-0.22 ⁇ low.protein binding filter to remove the antibody aggregates and background noise from running buffer.
• FIG 8 is a further illustration of the optimization of antibodies.
• 50 ⁇ 1 of PFP were labeled with unfiltered antibodies (Rows A and C) and double- filtered antibodies (Rows B and D). Rows A and B were gated on less than 1 ⁇ on SSC-W histogram plot. Rows C and D were gated on less than ⁇ ⁇ on FSC and SSC dot plot. Pre- filtering antibodies helps to reduce the false positive particles by removing the aggregation of antibodies.
• Figure 9 is a representation of MP detection on both Canto A and Gallios. As depicted in Figure 9, MPs were detected on both Canto and Gallios. MPs were gated on less than 1 ⁇ on both Canto (Row A) and Gallios (Row B). Positive MPs were determined based on fluorescence minutes one (FMO) tubes.
• Figure 10 is a comparison of BD Canto A and BC Gallios. As depicted in Figure 10, computed Spearman rank correlations were used to compare the MP counts between the two platforms, most correlations exceeded 0.8 with the exception of CD105(+) which demonstrated a correlation of 0.6 (P ⁇ 0.05). MPs counts on Gallios were two times greater than on Canto A. Row A shows the correlations of the two platforms, while Row B shows the comparison of MPs number on the two platforms.
• Figure 1 1 is an illustration of gating strategy for MP analysis.
• MPs were identified by first gating on the PI region on the FSC/SSC plot defined by calibrator beads of less than 1 ⁇ (A and B).
• the origin of the microparticles was determined by coexpression of Annexin-V and CD 144 for endothelial derived MPs (C), Annexin-V and CD41 for platelet- derived MPs (D), Annexin-V and CDI4 for monocyte-derived MPs (E).
• Figure 12 is an illustration of scatter plots (with median lines) showing low density lipoprotein levels (panel A) and EPO level (panel B) compared to statin use.
• LDL low density lipoprotein cholesterol
• EPO erythropoietin
• H healthy
• ES early stage diabetes
• LT long-term diabetes.
• Figure 13 is an illustration of scatter plots (with median lines) and significance of CD34+ PCs, nM of PS+ MPs by plate-based assay and ratio of nM of PS+ MPs/CD34+ PCs.
• P calculated by W. H, healthy; ES, early stage diabetes; LT, long-term diabetes; MP,, microparticle; PC, progenitor cell.
• Figure 14 is an illustration of median levels of ELISA plate MPs, flow cytometry measured CD34 cells and ratio. Insert table shows comparison with "non cell” atherosclertoic biomarkers.
• FIG 15 is an illustration of gating strategy for EPC analysis.
• a sequential gating strategy for EPCs consisted of gating (a) viable events (upper left panel), below the red dashed line, (b) cells in a size region consistent with lymphocytes (upper right panel), inside the red oval, (c) singlet events (lower left panel), inside the black polygon, and finally (d) events that are negative for the lineage markers CD3, CD19 or CD33 and dim to negative for CD45 (lower right panel), inside the lower left quadrant shown in color.
• the viability marker used was Propidium Iodide, detected on the PE-A channel (upper left panel). Gating was fully automatic and was applied with no operator intervention to each sample individually.
• Figure 16 is an illustration of raw (ungated) distribution of microparticles. The distribution of microparticles in an ungated, arbitrarily chosen sample is shown. All pairwise combinations of the 7 fluorescence parameters plus Side Scatter Width are shown.
• Figure 17 is also an illustration of raw (ungated) distribution of microparticles. The same sample depicted in Figure 16 is shown after gating for particles below 1 ⁇ .
• Figure 18 is an illustration of univariate microparticle distributions. Individual microparticle data sets were aggregated. The distribution of events with respect to each of the fluorescence parameters was plotted (x-axes) with respect to Side Scatter (y-axes).
• Figure 19 is another illustration of raw (ungated) distribution of microparticles. The same sample, depicted in Figures 16 and 17, is shown after gating for particles below 1 ⁇ and for particles expressing at least one marker at a level above the threshold for positive expression (as shown in Figure 18).
• Figure 20 is an illustration of the subset of EPCs determined by cytometric fingerprinting to be present at significantly lower concentration in DM compared with HC.
• the individual HC data sets are aggregated and displayed as the colored distributions in three bivariate plots using biexponential transformation. Events in the fingerprint bin that was discovered by CF as more strongly expressed in HC as compared with DM (P ⁇ 0.001) are shown as black dots.
• the thresholds for positive expression of each of the markers shown (CD31 , CD24 and CD 133) were determined for each individual sample using Fluorescence Minus One (FMO) controls, and their means (solid black lines) and standard deviations (dot- dashed lines enclosing gray region) are shown.
• FMO Fluorescence Minus One
• FIG 21 is an illustration of MP subsets present at different concentrations in DM compared with HC.
• CF analysis of MP distributions led to the discovery of 8 populations that are differentially expressed between HC and DM.
• Events in differentially expressed bins are shown as black dots superimposed on the aggregate (shown as colored distributions) of all of the individual DM data sets.
• Black lines represent the thresholds for positive expression determined individually for each parameter (see Figure 18).
• Above each panel the phenotype of the differentially expressed subset is given. Inside each panel the cohort in which the subset is more highly expressed (either DM or HC) is shown.
• Figure 22 is an illustration of combining EPC and MP measures.
• the vertical axis represents the ratio of MP subsets CD31 bright /CD41 brigh ' to CD31 dim /CD41 dim .
• the horizontal axis represents EPC Rel as described in the text. Both measures are standardized by dividing by the median among the HC group and logarithmically transformed. DM subjects are plotted as red dots, while HC subjects are plotted as blue dots.
• the lower two panels independently depict the two measures as box plots, in which the median is indicated by the horizontal bar, the boxes extend from the first to the third quartiles, and the whiskers extend to no more than 1.5 times the interquartile range.
• FIG 23 is an illustration of Fluorescence Minus One (FMO) analysis of the VEGF- R2 reagent.
• FMO control tubes were prepared by staining cells with all of the markers in the panel except one. Shown are the VEGF-R2 FMO distributions for 3 samples chosen arbitrarily.
• FMO thresholds were determined by first finding the boundary of the main negative cluster in the 2D kernel density estimate for the distribution of VEGF-R2 vs Side Scatter Area (red ovals), and then finding the horizontal tangent to this boundary (red dashed lines). Notice that in these samples there are significant numbers of events above the ⁇ FMO threshold. Moreover, these events frequently appear to form clusters well removed from the negative population.
• Figure 24 is an illustration of the differential expression of microparticle phenotypes. Shown are boxplots representing differential expression between Diabetic Mellitus and Healthy Control of the microparticle subsets described in Table 7. Each box plot shows the median and first and third quartiles of the class-specific distributions.
• the present invention relates to a system and method of profiling vascular health.
• the systems and methods of the present invention include a cell-based assay for assessing cardiovascular status based on the measurement of a various PCs (such as EPCs) and MPs.
• PCs such as EPCs
• MPs MPs.
• the systems and methods utilize a broad and comprehensive cell surface marker panel with an unbiased analysis scheme using cytometric fingerprinting to evaluate differences between patients with diabetes mellitus (DM) and healthy controls (HC).
• DM diabetes mellitus
• HC healthy controls
• an element means one element or more than one element.
• “About” as used herein when referring to a measurable value such as an amount, a temporal duration, and the like, is meant to encompass variations of ⁇ 20% or ⁇ 10%, more preferably ⁇ 5%, even more preferably ⁇ 1%, and still more preferably ⁇ 0.1 % from the specified value, as such variations are appropriate to perform the disclosed methods.
• abnormal when used in the context of subjects, organisms, tissues, cells or components thereof, refers to those subjects, organisms, tissues, cells or components thereof that differ in at least one observable or detectable characteristic (e.g., age, treatment, time of day, etc.) from those subjects, organisms, tissues, cells or components thereof that display the "normal” (expected) respective characteristic. Characteristics which are normal or expected for one cell or tissue type, might be abnormal for a different cell or tissue type.
• assessing includes any form of measurement, and includes determining if an element is present or not.
• determining includes any form of measurement, and includes determining if an element is present or not.
• evaluating includes any form of measurement, and includes determining if an element is present or not.
• assessing and “assaying” are used interchangeably and include quantitative and qualitative determinations. Assessing may be relative or absolute. “Assessing the presence of includes determining the amount of something present, and/or determining whether it is present or absent.
• biomarker is a biological entity such as a cell or group of cells or a fragment or fragments thereof, a protein or a fragment thereof, including a polypeptide or peptide that may be isolated from, or measured in or on the biological sample, which is differentially present in a sample taken from a subject having established or potentially clinically significant CVD as compared to a comparable sample taken from an apparently normal subject that does not have CVD.
• a biomarker can be an intact cell or molecule, or it can be a portion thereof that may be partially functional or recognized, for example, by a specific binding protein or other detection method.
• a biomarker is considered to be informative if a measurable aspect of the biomarker is associated with the presence or risk of CVD in a subject in comparison to a predetermined value or a reference profile from a control population.
• a measurable aspect may include, for example, the presence, absence, amount, or concentration of the biomarker, or a portion thereof, in the biological sample, and/or its presence as a part of a profile of more than one biomarker.
• a measurable aspect of a biomarker is also referred to as a feature.
• a feature may be a ratio or other such mathematically defined relationship of two or more measurable aspects of biomarkers.
• a biomarker profile comprises at least one measurable feature, and may comprise two, three, four, five, 10, 20, 30 or any number of features.
• the biomarker profile may also comprise at least one measurable aspect of at least one feature relative to at least one external or internal standard.
• cytometric fingerprint refers to a representation of the multivariate probability distribution of a plurality of cells, microparticles or other objects as measured in a flow cytometer.
• each cell or microparticle is typically characterized by not less than two measured variables, and often by as many as twenty or more measured variables.
• the flow cytomtetric measurement of a plurality of cells can thus be characterized by a distribution in a hyperspace defined by as many dimensions as the number of measurement variables.
• a cytometric fingerprint is a compact representation of this multivariate probability distribution in the form of a vector of numbers, each number representing the density of the distribution function in a particular sub-region of the multivariate space.
• cardiovascular disease generally refers to heart and blood vessel diseases, including atherosclerosis, coronary heart disease, cerebrovascular disease, and peripheral vascular disease. Cardiovascular disorders are acute manifestations of CVD and include myocardial infarction, stroke, angina pectoris, transient ischemic attacks, and congestive heart failure. Cardiovascular disease, including atherosclerosis, usually results from the build-up of fatty material, inflammatory cells, extracellular matrix and plaque.
• data in relation to one or more biomarkers, or the term “biomarker data” generally refers to data reflective of the absolute and/or relative abundance (level) of a product of a biomarker in a sample.
• dataset in relation to one or more biomarkers refers to a set of data representing levels of each of one or more biomarker products of a panel of biomarkers in a reference population of subjects.
• a dataset can be used to generate a formula/classifier of the invention. According to one embodiment the dataset need not comprise data for each biomarker product of the panel for each individual of the reference population.
• the "dataset" when used in the context of a dataset to be applied to a formula can refer to data representing levels of products of each biomarker for each individual in one or more reference populations, but as would be understood can also refer to data representing levels of products of each biomarker for 99%, 95%, 90%, 85%, 80%, 75%, 70% or less of the individuals in each of said one or more reference populations and can still be useful for purposes of applying to a formula.
• Diabetes mellitus is a severe, chronic form of diabetes caused by insufficient production of insulin and resulting in abnormal metabolism of carbohydrates, fats, and proteins.
• the disease is characterized by increased sugar levels in the blood and urine, excessive thirst, frequent urination, acidosis, and wasting.
• the condition is exacerbated by obesity and an inactive lifestyle.
• This disease often has no symptoms, is usually diagnosed by tests that indicate glucose intolerance, and is treated with changes in diet and an exercise regimen.
• Diabetes mellitus is associated with high risk of cardiovascular complications including diseases of coronary, peripheral, and carotid arteries.
• DM was used as a model system of vascular disease and results of the vascular health profile of the present invention from a diabetic cohort was compared to age and gender-similar healthy controls (HC) to discover biologically informative markers to aid in detection and treatment of vascular complications.
• HC age and gender-similar healthy controls
• a “disease” is a state of health of an animal wherein the animal cannot maintain homeostasis, and wherein if the disease is not ameliorated then the animal's health continues to deteriorate.
• a disorder in an animal is a state of health in which the animal is able to maintain homeostasis, but in which the animal's state of health is less favorable than it would be in the absence of the disorder. Left untreated, a disorder does not necessarily cause a further decrease in the animal's state of health.
• a “formula,” “algorithm,” or “model” is any mathematical equation, algorithmic, analytical or programmed process, or statistical technique that takes one or more continuous or categorical inputs (or “parameters”) and calculates an output value, sometimes referred to as an "index” or “index value.”
• “formulas” include sums, ratios, and regression operators, such as coefficients or exponents, biomarker value transformations and normalizations (including, without limitation, those normalization schemes based on clinical parameters, such as gender, age, or ethnicity), rules and guidelines, statistical classification models, and neural networks trained on historical populations.
• CVD markers and other biomarkers are linear and non-linear equations and statistical classification analyses to determine the relationship between levels of CVD markers detected in a subject sample.
• structural statistical classification algorithms, and methods of risk index construction utilizing pattern recognition features, including established techniques such as cross-correlation, Principal Components Analysis (PCA), factor rotation, Logistic Regression (LogReg), Linear Discriminant Analysis (LDA), Support Vector Machines (SVM), Random Forest (RF), Partial Least Squares, Sparse Partial Least Squares, Flexible Discriminant Analysis, Recursive Partitioning Tree (RPART), as well as other related decision tree classification techniques, Nearest Shrunken Centroids (SC), stepwise model selection procedures, Kth-Nearest Neighbor, Boosting or Boosted Tree, Decision Trees, Neural Networks, Bayesian Networks, Support Vector Machines, and Hidden Markov Models, and others.
• Other techniques may be used in survival and time to event hazard analysis, including Cox
• “Increased risk of developing CVD” is used herein to refer to an increase in the likelihood or possibility of a subject developing CVD. This risk can be assessed relative to an individual's own risk, or with respect to a reference population that does not have clinical evidence of CVD. The reference population may be representative of the individual with regard to approximate age, age group and/or gender.
• “Increased risk of progressing CVD” is used herein to refer to an increase in the likelihood or possibility of a subject having CVD to have progressing CVD. This risk can be assessed relative to an individual's own risk, or with respect to a reference population that does not have clinical evidence of CVD. The reference population may be representative of the individual with regard to approximate age, age group and/or gender.
• an "instructional material” includes a publication, a recording, a diagram, or any other medium of expression which can be used to communicate the usefulness of a compound, composition, vector, biomarker or delivery system of the invention in the kit for effecting determining or assessing risk of the various diseases or disorders recited herein.
• the instructional material of the kit of the invention can, for example, be affixed to a container which contains the identified compound, composition, vector, biomarker or delivery system of the invention or be shipped together with a container which contains the identified compound, composition, vector, biomarker or delivery system.
• the instructional material can be shipped separately from the container with the intention that the instructional material and the compound, composition, vector, biomarker or delivery system be used cooperatively by the recipient.
• the "level" of one or more biomarkers means the absolute or relative amount or concentration of the biomarker in the sample.
• Measurement or “measurement,” or alternatively “detecting” or “detection,” means assessing the presence, absence, quantity or amount (which can be an effective amount) of either a given substance within a clinical or subject-derived sample, including the derivation of qualitative or quantitative concentration levels of such substances, or otherwise evaluating the values or categorization of a subject's clinical parameters.
• MPs are 0.1 - ⁇ ⁇ plasma particles shed from eukaryotic cells that are formed by exocytic budding due to activation or apoptosis, and are indicative of cell damage.
• MPs may be endothelial MPs (EMPs), platelet MPs (PMPs) and/or monocyte MPs (MMPs).
• EMPs endothelial MPs
• PMPs platelet MPs
• MMPs monocyte MPs
• MPs contain miRNA, proteins and other antigens from their parent cell and are often pro-coagulative and pro-inflammatory.
• patient refers to any animal, or cells thereof whether in vitro or in situ, amenable to the methods described herein.
• the patient, subject or individual is a human.
• the term "predetermined value” refers to the amount of one or more biomarkers in biological samples obtained from the general population or from a select population of subjects.
• the select population may be comprised of apparently healthy subjects, such as individuals who have not previously had any sign or symptoms indicating the presence of CVD.
• the predetermined value may be comprised of subjects having established CVD.
• the predetermined value may be comprised of subjects having DM.
• the predetermined value can be a cut-off value, or a range. The predetermined value can be established based upon comparative
• PC Progenitor cell
• PC may include any type of PC understood by those skilled in the art, including proangiogenic cells (PACs), endothelial progenitor cells (EPCs) and circulating hematopoietic stem and progenitor cells (CHSPCs).
• PACs proangiogenic cells
• EPCs endothelial progenitor cells
• CHSPCs circulating hematopoietic stem and progenitor cells
• sample or “biological sample” as used herein means a biological material isolated from an individual.
• the biological sample may contain any biological material suitable for detecting the desired biomarkers, and may comprise cellular and/or non-cellular material obtained from the individual.
• range format is merely for convenience and brevity and should not be construed as an inflexible limitation on the scope of the invention. Accordingly, the description of a range should be considered to have specifically disclosed all the possible subranges as well as individual numerical values within that range. For example, description of a range such as from 1 to 6 should be considered to have specifically disclosed subranges such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6 etc., as well as individual numbers within that range, for example, 1 , 2, 2.7, 3, 4, 5, 5.3, 6 and any whole and partial increments therebetween. This applies regardless of the breadth of the range.
• vascular health also known as 'cytomics', integrates the biologic consequences of environmental and genetic cardiovascular risk factors. There is an unmet clinical need to develop such assays that could be used routinely to guide medical therapy and risk assessment.
• the systems and methods of the present invention provide a comprehensive insight into vascular health by using pattern discovery computational methods to analyze characteristics of several targets, including populations of vascular microparticles (recently identified as robust biomarkers of vascular health) and endothelial progenitor cells. For example, asymptomatic patients can be evaluated for cardiovascular risk, and symptomatic patients can be monitored longitudinally. These capabilities realize the main goals of personalized medicine.
• the systems and methods of the present invention include an early diagnostic test that provides a measure of cardiovascular health prior to overt CVD, and an assessment of therapeutic interventions. It should be appreciated that the present invention may be used for the measure of any parameter of cardiovascular health, and may be suitable for the detection and determination of risk for any CVD as would be understood by those skilled in the art.
• diabetes mellitus is associated with high risk of cardiovascular complications including diseases of coronary, peripheral, and carotid arteries.
• Patients with type 2 DM have a 2- to 4-fold increase in the risk of CAD and PAD (Beckman et al., 2002, JAMA 287:2570-2581 ).
• CAD and PAD Bacillus et al., 2002, JAMA 287:2570-2581 .
• blood samples from patients with long-term type 2 DM and with clinically apparent atherosclerosis will have an abnormal vascular health profile different from non-diabetic control subjects.
• DM was used as a model system of vascular disease and results of the vascular health profile of the present invention from a diabetic cohort was compared to age and gender-similar healthy controls (HC) to discover biologically informative markers to aid in detection and treatment of vascular complications.
• HC age and gender-similar healthy controls
• PCs progenitor cells
• PACs PACs, EPCs and/or CHSPCs and any other progenitor cell type associated with CVD.
• CHSPCs progenitor cells
• These cells are mediators of reparative capacity, and while a precise phenotypic definition of such cells has yet to be determined, they are thought to participate in angiogenesis either through structural development or paracrine action to support vascular growth.
• EPCs are bone marrow-derived cells that mobilize into circulation in response to endogenous (e.g., from ischemic tissue, tumor cells) or exogenous (e.g., statins) signals.
• endogenous e.g., from ischemic tissue, tumor cells
• exogenous e.g., statins
• the physiological function of EPCs contributes to vascular homeostasis, which is crucial to prevent the pathogenesis of various diseases with vascular injury (Mobius- Winkler et al., 2009, Cytometry Part A 75A:25-37).
• EPCs with flow cytometry include CD133, CD34 and VEGF-R2 (also called KDR) (Mobius- Winkler et al., 2009, Cytometry Part A 75A:25-37; Hirschi et al., 2008, Arterioscler Thromb Vase Biol. 28: 1584- 1595; Khan et al., 2005, Cytometry B Clin Cytom 64: 1-8).
• KDR VEGF-R2
• CHSPCs circulating hematopoietic stem and progenitor cells
• PCs can be measured by any suitable method understood by those skilled in the art.
• the cells may be measured by a high throughput method.
• the cells are measured by flow cytometry.
• the cells are measured by an ELISA based technique.
• the cells are measured by a plate based capture assay.
• the cells measured by flow cytometry are represented using cytometric fingerprinting.
• the cells are measured by a plurality of methods in any combination. For example, flow cytometry, an ELISA based technique, and a plate based capture assay or any combination thereof may be used.
• MPs are 0.1 - ⁇ ⁇ plasma particles shed from eukaryotic cells that are formed by exocytic budding due to activation or apoptosis, and are indicative of cell damage.
• MPs may be endothelial MPs (EMPs), platelet MPs (PMPs) and/or monocyte MPs (MMPs).
• MPs contain miRNA, proteins and other antigens from their parent cell and are often pro-coagulative and pro-inflammatory.
• the role of MPs in coagulation and inflammation is an important part of atherosclerotic pathophysiology, making MPs especially attractive as potential biomarkers of vascular health. Indeed, many studies have demonstrated elevated cell-specific MPs in conditions of vascular dysfunction (Tushuizen et al., 201 1 , Arterioscler Thromb Vase Biol 31 :4-9).
• MPs can prevent apoptosis in their parent cell by 'exporting' pro-apoptotic compounds such as Caspase 3, thereby lowering cytosolic levels (Hussein et al., 2007, Thromb Haemost 98(5): 1096-107). Still, MPs are significantly elevated in patients with acute coronary syndromes compared to patients with stable anginal symptoms (Bernal-Mizrachi et al., 2003, American Heart Journal 145:962-970), and are a robust predictor of secondary myocardial infarction or death (Sinning et al., 201 1 , European Heart Journal 32:2034-2041). They are also elevated following acute ischemic
• MP presence, number, and type may be used as biomarkers and/or as a component of the sensitive and specific vascular health profile assay of the present invention, with clinical utility in predicting atherosclerotic risk in asymptomatic patients.
• MPs can be measured by any suitable method understood by those skilled in the art.
• MPs may be measured by a high throughput method.
• the cells are measured by flow cytometry, as demonstrated in the various Examples.
• flow cytometry there is no limitation to the number and type of surface markers used to characterize a MP or population of MPs, as would be understood by those skilled in the art.
• Cytometric Fingerprinting (Rogers et al., 2008, Cytometry Part A 73A:430-441 ; Rogers & Holyst, 2009, Adv Bioinformatics: 1 3947) (CF) provides a means to rapidly analyze high-dimensional, high-content flow cytometry data without investigator or system bias. CF breaks a multivariate distribution into a large number of non-overlapping regions, referred to herein as "bins," that fully span the space, resulting every event recorded in the dataset is found in one of the bins.
• CF assigns each event to a bin, counts the number of events in each bin, and represents the full multivariate distribution for a sample as a flattened vector, referred to herein as a "fingerprint," of the number of events per bin.
• the fingerprint may be regarded as a complete micro-gating of the data, with each gate, or bin, being tagged.
• the multivariate probability distribution functions for multiple samples can be compared using straightforward statistical analysis methods. For example, one can search for bins that are significantly up-regulated and/or down-regulated in a group of samples as compared with another group of samples using methods similar to those now routinely employed for the analysis of gene expression data (Boscolo et al., 2008, IEEE/ ACM Transactions on 5(1): 15-24). Therefore, the use of CF not only enables a "datamining" approach to the analysis of flow cytometric data, whereby disease-related or treatment-related alterations of the multivariate distributions are discovered directly from the data, but also builds a bridge to integrative analysis with other technologies (e.g., Doring et. al.
• the present invention utilizes an array of cell-based biomarkers in the determination of vascular health of a subject.
• the method can be generally described as shown in Figure 1 , wherein a biological sample, such as blood, is collected from a subject.
• a biological sample such as blood
• the biological sample of the subject and used in performance of the methods described herein may be blood, sera, plasma, or any other suitable fluid, tissue or cellular sample as would be understood by those skilled in the art.
• at least one PC and at least one MP is measured by a high throughput method, such as flow cytometry.
• a cytometric fingerprinting of the flow cytometry data is performed to categorize the data into a plurality of categories without investigator or system bias, such that vascular health profile of the identified biomarkers is obtained.
• vascular health profile of the identified biomarkers is obtained.
• significantly up-regulated and/or down-regulated biomarkers in a group of samples as compared with another group of samples can be identified and used in the determination or prediction of CVD or risk of increasing or progressing CVD in the subject.
• the present invention relates to a method of determining vascular health in a subject.
• the method includes the steps of obtaining a biological sample from the subject, obtaining microparticle data based on the level of at least one set of microparticles in the biological sample, obtaining progenitor cell data based on the level of at least one set of progenitor cells in the biological sample, generating a cytometric fingerprint of the biological sample based on the microparticle and progenitor cell data, and determining the vascular health of the subject based on the generated cytometric fingerprint.
• a comprehensive panel was employed, in which cells were first selected based on size. Then, cells belonging to the mature hematopoietic lineage were removed by gating out CD3 + , CD19 + , CD33 + and CD45 b ⁇ 8h, cells. Then, using cytometric fingerprinting, the remaining population was subdivided and subjected to statistical evaluation, without any predetermined bias, to discover if there were populations of cells differentially expressed between the DM patients and HC.
• the invention relates to methods for determining the risk of cardiovascular disease incidence or vascular dysfunction in a diabetic subject by measuring the relationship of microparticles to progenitor cells. In one embodiment, the method determines or is predictive of an increased risk of developing CVD. In another embodiment, the method determines or is predictive of an increased risk of progressing CVD.
• the method includes the steps of obtaining a biological sample from the subject, obtaining microparticle data based on the level of at least one set of microparticles in the biological sample, obtaining progenitor cell data based on the level of at least one set of progenitor cells in the biological sample, generating a cytometric fingerprint of the biological sample based on the microparticle and progenitor cell data, and determining the risk associated with cardiovascular disease or vascular dysfunction of the subject based on the generated cytometric fingerprint.
• the invention includes a method for determining the vascular health in a subject, where the method includes the steps of obtaining a biological sample from the subject, and determining the type and/or level of microparticles relative to the level of progenitor cells in the sample, wherein the relative level of microparticles to progenitor cells indicates a risk associated with cardiovascular disease or vascular dysfunction in the subject, thereby determining vascular health in said subject.
• the invention includes a method for determining the risk associated with cardiovascular disease or vascular dysfunction in a diabetic subject, the method including the steps of obtaining a biological sample from said subject, determining the level of microparticles relative to the level of progenitor cells in the sample, wherein the relative level of microparticles to progenitor cells indicates the risk associated with cardiovascular disease or vascular dysfunction in said subject.
• the diabetic subject is a Type 1 diabetic subject. In another embodiment, the diabetic subject is a Type 2 diabetic subject.
• the invention includes a method for determining diabetes associated risk in a subject, the method comprising the steps of obtaining a biological sample from the subject, determining the level of microparticles relative to the level of progenitor cells in said sample, wherein the relative level of microparticles to progenitor cells indicates a risk associated with cardiovascular disease or vascular dysfunction in the subject, thereby determining diabetes associated risk in the subject.
• the methods of the invention include the step of determining the level of microparticles relative to the level of PACs or EPCs.
• the relative level of microparticles to PCs, PACs or EPCs indicates cellular damage. In another embodiment, the relative level of
• microparticles to PCs, PACs or EPCs indicates the integrity of endothelium, a loss of endothelial repair capacity, or a combination thereof.
• the comparison of levels of microparticles and progenitor cells is a ratio of microparticles to progenitor cells. In some embodiments, this ratio can be directly associated with aortic pulse wave velocity (aPWV), providing a functional link between plasma cholesterol levels, MPs, PACs, endothelial injury, and arterial stiffness.
• aPWV aortic pulse wave velocity
• FCM flow cytometry
• MPs microparticles
• This technology enables measurement of thousands of MPs in one sample, with the simultaneous determination of multiple markers identifying various MP subsets.
• the very small size of MPs (0.1 to 1.0 ⁇ ) makes their detection at the limit of size resolution of standard flow cytometers.
• accurate detection requires extraordinar attention to detail in specimen preparation, sample acquisition, and data analysis.
• the detection of MPs in human plasma in patients was optimized using the BD Biosciences FACS Canto A and Beckman Coulter Gallios.
• Example 1 The following materials and methods were used in Example 1 :
• PFP Platelet free plasma
• FSC and SSC thresholds were determined by 0.3 ⁇ beads (Row A) or 0.3, 1 and 3 ⁇ beads (Row B on FFC/SSC contour plot and C on SSC-W histogram) passing through the machine at medium rate with different FSC or SSC threshold.
• Columns D and E showed better resolutions of 3 beads on both FFC/SSC contour plot and SSC-W histogram plot with FSC threshold set to 5000 or SSC threshold to 200 (column D), and only SSC threshold set to 200 and FSC threshold off (column E). More background noise was acquired with FSC threshold set to 200 or SSC threshold to 200 (Column F).
• 0.3 ⁇ beads were missing with FSC threshold set to 200 and SSC off (column G). Side scatter is a better parameter for small particles than forward scatter.
• FSC and SSC threshold were set to 5000 and 200 for its better resolution of 3 mixture beads on both FSC/SSC contour and SSC- W histogram plots in this study (column D).
• double-filtered PBS and 0.3um beads were used to set up the FSC, SSC PMT. Less than 10 events per second was accepted when double-filtered PBS passing through the machine at medium flow rate.
• the threshold of FSC and SSC were set to 5000 and 200, respectively.
• FSC and SSC PMT were determined by the same sample running on the machine using different SSC voltage. More MPs were lost using SSC 300 and 325 and more background noise was acquired on SSC 375 and 400. SSC voltage of 350 was accepted in this study. SSC voltage of 400 data was not shown here because of too much background noise.
• FACS Canto A Window Extension was determined by 0.3, 1 and 3 ⁇ beads running at medium rate form WE 0 to 7.
• WE 0.2 was chosen for its better resolution of 3 beads and low background noise on SSC-W histogram plots.
• PFP was stained with FITC-Annexin (or CD31), Percp- Cy5.5-CD41, PE-CD105 (or CD 144), APC-CD64 and run on WE 0.2 or 7.
• the acquisition was stopped when a fixed number of 3 ⁇ beads (100,000) were counted.
• Row A was gated on less than 1 ⁇ on dot plot with WE set to 0.2.
• Row B was gated on less than 1 ⁇ on dot plot with WE set to 7.
• Row C was gated on less than ⁇ ⁇ with WE 0.2 and D gated on less than ⁇ ⁇ with WE 7 on SSC-W histogram plots. More background noise and fewer positive particles were collected using WE 7 (Row B and Row D).
• PFP 50 ⁇ 1 of PFP were labeled with unfiltered antibodies (Rows A and C) and double-filtered antibodies (Rows B and D). Rows A and B were gated on less than ⁇ ⁇ on SSC-W histogram plot. Rows C and D were gated on less than ⁇ ⁇ on FSC and SSC dot plot. Pre-filtering antibodies helps to reduce the false positive particles by removing the aggregation of antibodies.
• MPs were detected on both Canto and Gallios. MPs were gated on less than ⁇ ⁇ on both Canto (Row A) and Gallios (Row B). Positive MPs were determined based on fluorescence minutes one (FMO) tubes.
• the reagents including buffer and antibodies
• the values obtained on the Gallios and FACSCanto were correlative, and the Gallios detected larger numbers of events in the region of interest.
• Example 2 Relationship of Microparticles to Progenitor Cells as a Measure of Vascular Health in a Diabetic Population
• ES Early Stage
• LT Long Term
• Age related "Healthy” (H) subjects were recruited into the study on the basis of having no prior or current history of diabetic- or cardiovascular-related conditions and were not taking any type of CV-related medication including statins or medication for hyperlipidemia, hypertension or diabetes.
• statins or medication for hyperlipidemia, hypertension or diabetes Four (36%) of the ES group and 14 (67%) of the LT group were receiving statin medication, along with a range of other medications to treat diabetes, hypertension, and other conditions.
• Blood was collected in heparinized (Baxter) syringes for cell and soluble protein analysis (30 mL), SodiumCitrate for microparticles (10 mL) and EDTA for lipid analysis (10 mL). Demographic data, medical/medication history, physical examination, and vital signs were recorded for each subject.
• FITC-antiCD31 PECAM
• PE-anti-CDI33 Mertenyi Biotec
• PerCP-Cy5.5- anti-CD3,-CDI9,-CD33 Becton Dickinson
• APC-H7 anti-CD45 Becton Dickinson
• PE- Cy7-anti-CD34 Becton Dickinson
• APC-anti-VEGF-R2 R&D Systems
• PC Progenitor cell
• MMP microparticles
• Platelet-poor plasma was obtained from citrated blood within an hour after blood collection in order to isolate MPs. Whole blood was centrifuged at 1500g for 15 min, supernatant collected, and PPP obtained by centrifugation at 13,500g for 5 min at room temperature. For each subject, PPP was aliquoted into separate tubes and stored at -80°C until subsequent use. All samples used were subjected to only one freeze-thaw cycle. Flow Cytometry for MPs
• PPP was incubated with a mixture of Annexin-V (FITC), PECY5-CD41a, APC-CD14 (BD Biosciences) and PE-CD144 (R&D System) in IX BD annexin-V binding buffer (10 mM Hepes, pH 7.4, 140 mM NaCl, and 2.5 mM CaC12) (BD Biosciences) for 30 min at RT in darkness, then I X BD annexin-V binding buffer was added to make total volume of 1 mL/tube.
• the negative control was prepared as PPP stained with Annexin-V (FITC) and same amount of matched isotype control antibodies in calcium-free binding buffer.
• a PI region ( ⁇ 1 l m) on FSC-H and SSC-H scatter (log scale) was defined by calibrator beads (Fig. 1 1 A).
• the number of MPs per 1 L was determined using the PI region and also 6-l m microsphere beads (Bacteria Counting Kit, Invitrogen) to determine volume of sample (1 L) analyzed (Fig. 1 I B).
• the number of MPs stained with each specific Ab and AnnexinV was analyzed and determined using FACSDiva software (BD Biosciences) and expressed as MPs/1 L.
• Soluble proteins were determined from citrated plasma by electro-chemiluminescant detection using commercially tested kits, as per the manufacturer's instructions.
• Meso-Scale Discovery multiplex kits including the Vascular Injury II assay kit (CAT # Kl 1 136C-1) was used to measure SAA, CRP, VCAMl , and ICAM l .
• IL6, IL8, TNFa, and ILlb were measured using the Human Pro-Inflammatory Base Kit (KI5025A-5).
• the Human Hypoxia Assay (CAT# KI5122C-1) measured VEGF, IGFBF-1 and EPO.
• Plasminogen activator inhibitor (PAI-1 ) was detected with the Imubind kit from AmericanDiagnostica.
• Stromal cell-derived factor 1 (SDF-1 ) was measured by R&D systems (CAT# DY350).
• ILl b, SAA, 1L6, and IL8 were below detection with these assays.
• HbAl c was performed using the Primus boronate affinity HPLC method (Primus Corporation, Kansas City, MO) according to the manufacture's protocol.
• HDL was performed using an enzymatic in-vitro assay for the direct quantitative determination of human HDL cholesterol on Roche automated clinical chemistry analyzers following the manufacture's protocol. Triglyceride and cholesterol were performed using VITROS TRIG slides and VITROS chemistry products calibrator kit 2 on VITROS chemistry systems (VITROS 950 Chemistry System). LDL was calculated by the Friedewald Equation.
• CBC Complete Blood Count
• WBC White Blood Count
• LT diabetics receiving statins had reduced LDL levels compared to healthy individuals, indicating that without such therapy LDL levels in this cohort would have been substantially higher.
• HDL and Triglycerides did not differ between the three groups.
• LDL levels were controlled in the LT group systolic blood pressure was higher (P ⁇ 0.01) in LT versus H groups.
• P ⁇ 0.01 systolic blood pressure
• the monocyte and lymphocyte counts were statistically unchanged among the three groups. Red blood cell count was not altered between ES and H groups but decreased significantly in the LT group.
• hemoglobin levels were significantly lower in the LT versus H group (Table 2).
• HDL (mg dL) 55 ⁇ 11 44 ⁇ 13 51 ⁇ 19 NS
• Lymphocyte Count ( ⁇ / ⁇ ) 1557 ⁇ 522 2074 ⁇ 920 1660 ⁇ 634 NS
• Hemoglobin (g/dU 13.92 ⁇ 1.58 13.46 ⁇ 1.83 12.22 ⁇ 1.45°
• Data are means ⁇ SD.
• KW was used to test significance between all three groups for measured values.
• post-hoc analyses between patient groups used the two sample Wiicoxon test. Comparisons for gender data used the two-sided exact Pearson Chi Square test for comparisons of the three groups and for the post-hoc analyses. The characteristics with zero healthy patients had that as a group requirement, thus comparisons to that group were not analyzed.
• the flow cytometry method could detect differences with onset of disease, H vs. ES, whereas the plate-based method detected changes with disease duration, ES and LT.
• the differing resolution between the two assays may be due to their separate readouts; cytometry (AnnV+ MPs) counts the number of PS+ MPs whereas the plate captures PS+ MPs and quantifies using a prothrombinase assay. Nonetheless, both assays detected differences between the H and LT.
• CD144+ MPs 204 (93-41 1 ) 453 (187-616) 571 (207-892)° 0.03*
• CD14 + MPs 120 (68-347) 340 (178-469) 230 (77-488) 0.15
• ICAMl (pg mL) 1.4 ⁇ 0.5 2.7 ⁇ 1.8' 1.6 ⁇ 0.1 " 0.382
• VCAM1 (pg mL) 2.43 ⁇ 0.9 3.58 ⁇ 2.01 3.22 ⁇ 1.31 0.071
• P ⁇ 0.01 H healthy; ES, early stage diabetes; LJ, long-term diabetes. Post-hoc comparison significance levels (not adjusted for multiple comparisons). ⁇ P ⁇ 0.01 LT s. H, *P ⁇ 0.05 ES vs. H. 'P ⁇ 0.05 LT vs. ES.
• CAD coronary artery disease
• Example 1 As demonstrated in Example 1 , significant alterations in both PCs/PACs and cell derived MPs in Type 2 diabetes and also with duration of disease were identified. This is illustrative of the value of using the ratio of MPs/PCs, which encompasses two biologically relevant markers that impact functionally on disease progression. Further, it shows that this ratio may be more informative than many individual standard protein biomarkers commonly used to stratify individuals at heightened cardiovascular risk. From a clinical standpoint, the results from these studies indicate that a single platform high throughput, multiplexed flow cytometry assay for hematopoietic progenitors and plate-based assay for MPs is a feasible and cost effective method to identify those individuals at highest risk for cardiovascular events.
• Example 3 Study of MPs and PCs in Diabetes Mellitus (DM)
• EPCs endothelial progenitor cells
• ES 'Early Stage
• LT Long Term
• H Age related "Healthy” subjects were recruited into the study on the basis of having no prior or current history of diabetic or cardiovascular disease and were not taking any type of CV related medication, including statins. Only non-smokers were permitted to participate in the study. Each subject donated approximately 50 mL of venous blood. All subjects fasted the night before the visit and blood samples were drawn at approximately 8am in the morning.
• Cell derived MPs and PCs were identified and quantified in each subject group according to cell surface markers. Using flow cytometry, CD133 and/or CD34 expression defined PCs, whereas the additional surface expression of VEGF-R2 defined EPCs. MPs, initially defined by size ( ⁇ lum using flow cytometry) from platelet free plasma (PFP) were further characterized by cell type using single marker surface expression. Endothelial, platelet and monocyte MPs were determined by surface expression of CD 144, 41 and 14 respectively. The anionic phopholipid, phosphatidylserine, detected on the outer leaflet of the plasma membrane on many cell derived MPs was determined by AnnV binding via flow cytometry.
• Cytometric Fingerprinting expresses the multivariate probability distribution functions corresponding to list-mode data as a "flattened", computationally-efficient fingerprint representation that facilitates quantitative comparisons of samples.
• experimental and synthetic data were generated to act as reference sets for evaluating CF. Without the introduction of prior knowledge, CF was able to "discover" the location and concentration of spiked cells in un-gated analyses over a concentration range covering four orders of magnitude, to a lower limit on the order of 10 spiked events in a background of 100,000 events.
• the samples (e.g., platelet free plasma samples) tested by flow cytometry can also be tested by Enzyme Linked Immunosorbent Assay (ELISA).
• ELISA samples can be serially diluted with 1 % EDT A/saline.
• a pre-titrated amount (5 ug/ml in PBS) of Annexin V (BD Biosciences) can be added to each well of a 96-well microtiter plate as the capture reagent (to target the phosphatidylserine on MP surface) and incubated for 18 h at 4°C.
• Plates can be washed and PFP serially diluted samples can be added to the wells incubated for 18 h at 25°C on a plate shaker (200 r.p.m.). After washing, pre-titrated amounts of biotinylated antibody (CD 144, 14,4 la) can added to each well and incubated for 2 h at 25°C on the plate shaker. Following washing, peroxidase-conjugated avidin can be added to each well. Each well may be subsequently washed and then incubated with peroxidase substrate solution for 20 min at room temperature. After this incubation, stop solution can be added to each well, and the absorbance can be measured with an EIA reader at a wavelength of 450 run.
• biotinylated antibody CD 144, 14,4 la
• peroxidase-conjugated avidin can be added to each well.
• Each well may be subsequently washed and then incubated with peroxidase substrate solution for 20 min at room temperature.
• the specificity of the assay is the ability to assess unequivocally the binding of MP particles to annexin- V.
• the accuracy of the analytical procedure expresses the closeness of agreement between the value which is accepted either as a conventional true value or an accepted reference value and the value found.
• the ELISA assay is not a quantitative assay, while the flow cytometry assay is. Results on the two platforms can be compared.
• the precision of an analytical procedure expresses the closeness of agreement between a series of measurements obtained from multiple sampling of the same homogeneous sample under the prescribed conditions. Precision may be considered at three levels: repeatability, intermediate precision and reproducibility. Repeatability or intra-assay precision is most relevant to this study.
• the linearity of an analytical procedure is its ability to obtain the results which are directly proportional to the concentration of analyte in the sample.
• the detection limit of an individual analytical procedure is the lowest amount of analyte in a sample which can be detected but not necessarily quantified as an exact value.
• the range.of an analytical procedure is the interval between the upper and lower concentration of analyte in the sample for which it has been demonstrated that the analytical procedure has a suitable level of precision, accuracy and linearity. The limit and range again can be assessed by comparing the data with the flow cytometry data.
• Example 6 Study of MPs and EPCs in Patients with Diabetes Mellitus and Atherosclerosis This study employed a novel method of scientific discovery based on a broad and comprehensive cell surface marker panel with an unbiased analysis scheme using cytometric fingerprinting to evaluate differences between the DM patients and HC. Unlike other studies, which only observe levels of either MPs or EPCs, by obtaining both MP and EPC samples, this study observed the balance of how vascular dysfunction, through the levels of MP, and reparative capacity, through the levels of EPC, interact.
• BD CompBeads anti-mouse IgG and negative control, Cat# 552843.
• 8 peak fluorescent calibration beads (Spherotech, cat# RCP- 30-SA) were run before and after acquisition each day for normalization between days. All acquisition occurred on a BD FACS Canto A analytical flow cytometer and stopped after at least 200,000 lymphocytes were counted.
• FSC-A Forward Scatter Area
• SSC-A Side Scatter Area
• a binning model was constructed using the method flowFPModel with default resolution based on the aggregate of all gated events from healthy control subjects using the four measured fluorescence parameters not used in the gating (PE-Cy7-CD34, PE-CD133, APC-VEGF-R2 and FITC- CD31). The resulting binning model contained 1024 bins. Fingerprints were then generated using the method flowFP for all of the samples from both DM and HC subjects.
• Relative event counts in each bin were computed by dividing the number of events in the bin by the number of events in the small cell gate (generally regarded as representing the number of lymphocytes measured in the flow cytometer). Absolute event counts were obtained by multiplying the relative event counts by the ALC laboratory result expressed as 1000's of lymphocytes per ⁇ ! of whole blood. Finally, bins were compared between DM and HC samples using the Wilcoxon test, and P-values were corrected for multiple comparisons using the Benjamimi-Hochberg correction. P-values ⁇ 0.05 were considered significant.
• Platelet-poor plasma was obtained using centrifugation from blood collected in a sodium citrate tube. Within an hour after blo.od collection, in order to isolate MPs as previously described (Curtis et al., 2010, Cytometry B Clin Cytom 78(5):329-37), whole blood was centrifuged at 2,500g for 15 minutes at room temperature. The PPP was carefully moved to a new tube and mixed gently. Fresh samples were then analyzed via flow cytometry.
• 50 ⁇ 1 PPP was labeled with 2.5 ⁇ 1 FITC-Annexin-V (BD Bioscience Cat# 556570), 2.5 ⁇ 1 PE-CD144 (BD Bioscience Cat# 560410, clone 55-7H1), 0.75 ⁇ 1 Percp-Cy5.5-CD64 (BD Bioscience Cat# 561 194, Clone 10.1), 0.75 ⁇ 1 AF647-CD105 (BD Bioscience Cat# 561439, clone 266), 0.75 ⁇ 1 APC-H7-CD41a (BD Bioscience Cat# 561422, clone HIP8), 2.5 ⁇ 1 PE-Cy7-CD31 (Biolegend Cat#3031 18, clone WM59) and 0.75 ⁇ 1 BV421 -CD3 (Biolegend Cat# 300433, Clone UCHT1 ) for 30 minutes at room temperature in the dark.
• the antibodies were double-filtered before labeling with a 0.1 ⁇ low protein binding filter (Millipore, Cat# SLVV033RS).
• Annexin Buffer (l OmM Hepes, pH 7.4, 140 mM NaCl, and 2.5 mM CaCh) was added to each tube to make the total volume 500 ⁇ 1.
• the Annexin Buffer was double-filtered by 0.22 micro filter followed by 0.1 ⁇ filter.
• the BD FACSCanto A cytometer was calibrated daily with Cell Tracker Beads (BD) using Diva Software version 6.1.2. Forward and side scatter threshold, photomultiplier tube (PMT) voltage and window extension (WE) were optimized to detect 0.1-1.0 ⁇ particles using 0.3, 1.0 and 3.0 ⁇ calibration beads. Beads of known size (0.3 ⁇ , ⁇ . ⁇ and 3.0 ⁇ ) were used for the estimation of MP size.
• the acquisition was stopped when a fixed number of 3.0 ⁇ beads (20,000) were counted resulting in 82 thousand to 2.2 million MPs per sample. Compensation tubes were also run using PPP, BD CompBead (BD Bioscience Cat# 552843), and were stained using the same reagents as were used in the sample tubes.
• the R environment for statistical computing was used for analysis of the flow cytometry data.
• the flowCore package (38) was used for reading files, compensation and gating.
• FlowFP (Rogers et al., 2008, Cytometry Part A 73A:430-441 ) was used for
• Cytometric Fingerprinting Analysis.
• the list mode data were read in untransformed linear coordinates.
• Digital compensation was applied based on the spillover matrix determined by the Diva acquisition software and stored in the FCS header.
• Data were gated on Side Scatter Width ( Figures 16 and 17) as a relative measure of particle size to eliminate all events larger than ⁇ ⁇ as determined by the size calibration beads collected each day.
• Thresholds for positive expression of markers were identified by examining the kernel density estimates of the univariate distributions of all events captured ( Figure 18).
• the thresholds for fluorescence markers without clear separation in the kernel density estimate between the positive and negative populations were increased by various factors between about 1.4-to 2.5-fold and the results were shown to be stable, demonstrating that the choice of thresholds did not materially affect the results.
• the fluorescence data were biexponentially transformed and events that expressed none of the markers in the panel were presumed to be debris and were gated out (Figure 19). Fingerprinting analysis was carried out on resulting distributions using the R package flowFP (Holyst, H.A., and Rogers, W.T. 2009. FlowFP: Fingerprinting for Flow Cytometry. Bioconductor Package version 1.12.1.
• a phenotypic subset determined to be differentially expressed between DM as compared with HC can be comprised of one or more bins adjacent in multivariate space, all of which may fall on the same side of each parameter's threshold for positive expression.
• High-sensitivity C-reactive protein was measured using a laser-based
• EPC and MP counts were compared between DM and HC groups using Wilcoxon rank-sum tests.
• Multivariable linear regression models were used to estimate adjusted differences in EPC and MP counts between groups. Adjustment variables were selected based on a stepwise model-selection procedure based on the Akaike information criterion (AIC), for which a variable that reduced the AIC was retained. Variables evaluated were: age, gender, race, current exercise, and body mass index. Because EPC and MP counts were positively skewed, a log transformation was applied such that the exponentiated regression coefficient for DM versus HC quantified the ratio in the average count between DM and HC groups.
• the DM group was somewhat older than the HC group, as shown in Table 6.
• Body mass index, 30 (27, 36) 24(23, ⁇ 31 (27, 36) 24 (23, ⁇ kg/m 2 26) 0.001 26) 0.001
• Gender differed slightly between the DM and HC with -60% females in HC group compared to ⁇ 40% females in DM group. There were also higher numbers of African Americans in the DM group compared to the HC group. In addition, there was a higher proportion of smoking, lower proportion reporting exercise, and higher average BMI in the DM group. As expected, HbAi c was elevated in the DM group, as was blood pressure;
• levels of HDL were lower in the DM group compared to the HC group.
• Levels of high-sensitivity CRP were higher in DM than HC in both the MP and EPC cohorts (Haffner, 2006, Am J Cardiol 97(2A):3A- 1 1 A). While most of the DM patients were on antiplatelet and/or statin drugs, few of the controls were on preventative antiplatelet medications.
• EPCs phenotypic subset
• This subset had the phenotype CD34 + /CD3 l + /CD133 brigh, /CD45 dim"ne8a,ive ( Figure 20), and was lower on average in DM patients compared with HC.
• the relative event count (EPC rel ) of this subset was significantly different between DM and HC, even after adjustment for covariates as discussed above.
• FIG. 21 shows a subset of MPs that are positive for CD41. These are not all of the MPs that are positive for CD41 , but rather those events that are positive for CD41 while also being negative for all other markers in the panel and that fall into fingerprint bins that were significantly differently populated between the DM and HC cohorts.
• TMP T-lymphocyte MPs
• EMP Endothelial MP
• Another significant subset is comprised of events that are Annexin V + , which was up-regulated in DM. While the Annexin V single positive subset signifies an apoptotic MP, it is not a marker that is specific for the parent cell type.
• Another subset that was increased was the CD31 + phenotype. While this subset had a p-value >0.05, it was significant after adjustment for confounding variables. PE-CAMl (CD31) marker alone is not specific for one type of cell.
• the last 4 subsets of MP that were found to be significant were all platelet MP (PMP) and were positive for CD41 (and some for CD31 as well).
• the CD41 single positive population and Annexin V7CD31 + /CD41 + triple positive subsets were both marginally significant (P ⁇ 0.05) and up-regulated in DM patients.
• Neither the CD41 + nor the Annexin V + /CD31 + /CD41 + subsets were significant after adjustment for confounding variables.
• Epc Rel 6.1 (4.0, 9.9) 12 (7.5, 18) ⁇ 0.65 (0.49,
• Annexin7CD31 + /CD41 + 4.9 (1.9, 13) 3.2 (1.6, 6.6) 0.031 1.4 (0.88, 2.4)
• ne g aiive /CD 1 33 bright EpCs up _ regu i ated j n Hc compared to DM.
• EPCs as a biomarker is significant as it is directly involved in pathological processes in the cardiovascular system as opposed to other commonly used biomarkers, which respond non-specifically to the underlying condition.
• EPCs In the case of DM patients vs HC, there was one population of EPCs, with a phenotype of CD31 + /CD34 + /CD45 dim" ncga,lv 7CD133 + that was upregulated in the control group.
• This EPC population is similar to the circulating hematopoietic stem and progenitor cells (CHSPC) population described by Estes et. Al. (Estes et al., 2010, Cytometry Part A 77A:831-839) and shown in Figure 20.
• CHSPC circulating hematopoietic stem and progenitor cells
• EPCs are a heterogeneous population whose specific phenotypic definition remains controversial.
• EPC phenotypes described in the literature will include a stem cell marker such as CD34, an immaturity marker such as CD 133, and an endothelial marker such as VEGF-R2 ( DR) (Mobius- Winkler et al., 2009, Cytometry Part A 75A:25-37). '
• VEGF-R2 was found to not be a useful marker in analysis (Estes, M.L., Mund, J. A., Ingram, D.A., and Case, J. 2001.
• the panel in the present study contained an additional endothelial marker (CD31), which was also positively expressed in the informative phenotype, supporting a similar general phenotype of immature (CD 133), stem (CD34), and endothelial (CD31) markers that are accepted to be expressed on EPCs.
• CD31 endothelial marker
• the population found to be differentially expressed in the present study expresses the same markers found in an Estes et. al. study (Estes et al., 2010, Cytometry Part A 77A:831 - 839) that were shown to be pro-angiogenic.
• Estes et. al. an in vivo tumor model in mice that showed cells with the same phenotype as the EPCs in this study resulted in a significant increase in tumor growth, indicating that these cells are involved in neo-angiogenesis.
• CD34 + /CD133 + /CD1 17 + (CD1 17, c-Kit, is a stem cell marker), were found to produce increased angiogenesis in ischemic tissue resulting in 3-5 fold higher capillary numbers in infarct zones in rats after 2 weeks as opposed to more mature vascular endothelia not expressing CD 133 (Kocher et al., 2001 , Nat Med 7:430-436).
• the present study did not include CD1 17, however, it is likely due to the two other markers that the two populations overlap and share similar function. Therefore, the unbiased results of our study are consistent with other studies in showing that pro-angiogenic EPCs are differentially expressed between an atherosclerotic population and HC.
• Platelet MPs are known to have both beneficial and detrimental effects on vascular health (Tushuizen et al., 201 1, Arterioscler Thromb Vase Biol 31 :4-9). These claims are supported by the present study as multiple distinct PMP populations were significantly different between the two populations, three of which were up-regulated in DM patients and the other up-regulated in HC. Omoto et. al. (Omoto et al., 1999, Nephron 81 :271-277) found that PMPs are significantly up-regulated in type 2 DM patients with nephropathy compared to DM patients without complications, suggesting that PMPs are involved in activity leading to the kidney dysfunction. Furthermore, Tan et. al.
• Endothelial MPs like PMPs, are elevated in DM patients and it has been theorized that EMPs are associated with vascular dysfunction and, are a sign of cellular apoptosis, and therefore reflecting vascular wall damage (Chironi et al., 2009, Cell Tissue Res 335: 143- 151 ).
• EMP levels were negatively correlated with flow-mediated dilation (FMD) indicating that EMPs are associated with endothelial dysfunction (Feng et al., 2010, Atherosclerosis 208:5).
• FMD flow-mediated dilation
• EMPs were significantly higher in patients with coronary artery disease than in controls (Bernal-Mizrachi et al., 2003, American Heart Journal 145:962-970).
• the CD105 + population subset that was discovered in this study likely corresponds to EMPs, which has been shown in numerous studies to be a marker for disease and vascular dysfunction. Therefore the results presented herein are in concordance with previous work on EMPs showing that DM patients with clinical atherosclerosis have higher levels of EMPs than HC.
• TMP CD3 + T-cell MP
• TMPs impaired acetylcholine-induced relaxation of aortic rings in similar concentrations to humans (Martin et al., 2004, Circulation 109: 1653- 1659).
• TMPs also have been shown to produce endothelial dysfunction in response to flow and chemical stimuli (Martin et al., 2004, Circulation 109: 1653- 1659).
• Annexin V* cells bind to phosphatidylserine, which is a marker of apoptosis.
• apoptotic MPs found in plaques account for almost all of the TF (tissue factor) activity of the plaque extracts. This indicates that the MPs may play a role in the initiation of the coagulation cascade (Mallat et al., 1999, Circulation 99:348-353).
• MPs positive for Annexin V are significantly upregulated in patients with acute coronary syndrome compared to patients with stable angina (Mallat et al., 2000, Circulation 101 :841-843).
• Annexin V* MPs reflects a worsening of the atherosclerotic condition of patients.
• a study limitation was that the prothrombinase assay on Annexin V* MP was done separately from the flow cytometry, therefore these cells could be of any origin.
• the present study supports these findings as the Annexin V* population subset was significantly up-regulated in DM patients.
• cytometric fingerprinting and a broad assay allowed us to remove any unintentional gating biases to find numerous populations that were differentially expressed in the populations.
• the methods used involved rigorous flow cytometry protocol involving: careful standardization of instruments over time using bead standards with additional residual instrument variation mathematically corrected using the bead standards, use of FMO controls for positivity, biexponential transforming, and use of digital instrumentation.
• DM patients and HC were consecutively recruited as opposed to retrospectively.

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