Personalized cytomic assessment of vascular health: Evaluation of the vascular health profile in diabetes mellitus

Abstract

Background: An inexpensive and accurate blood test does not currently exist that can evaluate the cardiovascular health of a patient. This study evaluated a novel high dimensional flow cytometry approach in combination with cytometric fingerprinting (CF), to comprehensively enumerate differentially expressed subsets of pro-angiogenic circulating progenitor cells (CPCs), involved in the repair of vasculature, and microparticles (MPs), frequently involved in inflammation and thrombosis. CF enabled discovery of a unique pattern, involving both MPs and CPCs and generated a personalized signature of vascular health, the vascular health profile (VHP).

Methods: Levels of CPCs and MPs were measured with a broad panel of cell surface markers in a population with atherosclerosis and type 2 diabetes mellitus (DM) and age-similar Healthy controls (HC) using an unbiased computational approach, termed CF.

Results: Circulating hematopoietic stem and progenitor cell (CHSPCAng) levels were detected at significantly lower concentrations in DM (P < 0.001), whereas levels of seven phenotypically distinct MPs were present at significantly higher concentrations in DM patients and one MP subset was present at significantly lower concentration in DM patients. Collectively, the combination of CHSPC(Ang) and MP levels was more informative than any one measure alone.

Conclusions: This work provides the basis for a personalized cytomic vascular health profile that may be useful for a variety of applications including drug development, clinical risk assessment and companion diagnostics.

Fig. 1

Automated gating strategy for CPC analysis. As indicated in this representative example, a sequential gating strategy for CPCs (A) consisted of gating viable events (negative for Propidium Iodide detected on the PE channel), small cells, singlet events, and finally events that are negative for the lineage markers (CD3, CD19, or CD33) and dim to negative for CD45. Gating was fully automatic and was applied to each sample individually with no operator intervention. Panel B shows the resulting bivariate distributions for the remaining markers after gating.

Fig. 2

Size gating for MP analysis. A: Using 0.3, 1, and 3-micron size calibration beads, a kernel density estimate was computed with SSCW as the parameter. Peaks were automatically detected (red dots) and the peak corresponding to the 1-micron bead was used as the size gate for all samples run on that day (dashed vertical line). The dashed line represents the size cutoff and all events in sample tubes run on the same day with a SSCW signal less than the value were included in the analysis. B: A bivariate plot showing SSCA versus SSCW for the same bead data shown in Panel A. This plot shows that resolution of the three beads is better in SSCW compared with SSCA (the same was also true for SSCH and all of the FSC parameters). [Color figure can be viewed in the online issue which is available at wileyonlinelibrary.com.]

Fig. 3

The subset of CHSPC^Ang determined by CF to be present at significantly lower concentration in DM compared with HC. The individually gated HC data sets are aggregated and displayed as the colored distributions in four bivariate plots using biexponential transformation. Events in the fingerprint bin that was discovered by CF to be more strongly expressed in HC as compared with DM (P < 0.001) are shown as black dots. The thresholds for positive expression of three of the four markers shown (CD31, CD34, and CD133) were determined for each individual sample using FMO controls (no FMO control was performed for CD45), and their means (solid black lines) and standard deviations (dot-dashed lines enclosing gray region) are shown. It is notable that the differentially expressed subset does not comprise the entire set of cells that positively express these three markers as determined by the FMO controls.

Fig. 4

MP subsets present at different concentrations in DM compared with HCCF analysis of MP distributions led to the discovery of eight populations that are differentially expressed between HC and DM. Events in differentially expressed bins, aggregated into distinct phenotypes (panels A–H) 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 (Supporting Information Fig. 2). Above each panel the pheno-type 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.

Fig. 5

Combining CHSPC^Ang and MP measures. In the upper panel, the vertical axis represents the ratio of CD31⁺/CD41a⁺ bright to dim MP subsets. The horizontal axis represents CHSPC^Ang-Rel as described in the Table 2. Both measures are standardized by dividing by the median of the HC group and then transforming logarithmically. 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.