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Though a powerful technique, conventional flow cytometric protocols are highly susceptible to and in fact require the operator to make subjective decisions in the process of data acquisition and interpretation (1, 2). As additional fluorescence values are increasingly added to define more cell parameters, the advent of polychromatic flow cytometry (PFC) emerged to implement the rigorous controls and standards necessary to discriminate multiparametric data (for more detailed information on the technical advances, see the Supporting Information and Table S1) (1). Nowhere is this level of scrutiny of greater concern than in rare and dim event analysis where great care must be taken to ensure the reliability of collected data (3). Besides occurring in frequencies on the cusp of reproducible detection (0.001–0.1%), cells of endothelial and hematopoietic origin proposed to be engaged in angiogenesis are often discriminated using a combination of antigens with low, dull, or a continuum of cell surface expression (4, 5). Limitations of conventional flow cytometry, compounded by antigen promiscuity between the endothelial and hematopoietic lineages has necessitated consideration of the newer methods and approaches of PFC in defining the various circulating cell subsets involved in neoangiogenesis (6–10). For example, current debate often centers on CD45 expression in putative endothelial progenitor cell (EPC) subsets (11, 12). Previously, the distinction between a CD34+ progenitor cell that expressed CD45 versus one that was CD34+ and CD45− was deemed critical for the discrimination of myeloid cells that mimicked endothelial morphology in culture (colony forming unit-Hill; CFU-Hill) versus bona fide endothelial cells found in blood that form vessels upon implantation following in vitro expansion (endothelial colony forming cells; ECFCs). Although the original putative EPCs were first described as CD45−CD34+AC133+KDR+ cells, recent use of flow cytometers with more channels of resolution have since revealed that the EPCs described as CD45− may in fact be CD45dim (11). However, these debates exist because the conventional flow cytometry methods employed to analyze these cell populations have lacked the controls necessary to accurately depict the rare event and low antigen expression profiles desired (13).
A PFC protocol was applied to discriminate previously undetected phenotypic and functional heterogeneity within the commonly referenced circulating progenitor cells (CPCs) (14). Our PFC profile utilized a panel of antigens including rodent monoclonal antibodies to human CD45, CD34, CD31, and AC133, reagents to exclude false positive events, and methods to improve data analysis. Using PFC, the CPC subset identified as CD45dimCD34+CD31+ and heterogeneous in AC133 expression is now reported to be comprised of circulating hematopoietic stem and progenitor cells (CHSPCs) that engraft in NOD/SCID mice of which a subset display proangiogenic tumor growth promoting activity in vivo. Thus, the application of PFC techniques and approaches to the EPC field have permitted clarification of the cells involved in neoangiogenesis and may now permit broader application of these cells to therapeutic and diagnostic applications.
MATERIALS AND METHODS
Peripheral blood (PB) samples (16–32 ml) were collected from 20 healthy adult donors (10 male and 10 female, age range 20–40 years) and umbilical cord blood (CB) samples (20–100 ml) were collected from 15 full-term newborns. The Institutional Review Board at the Indiana University School of Medicine approved all protocols, informed consent was obtained from adult donors, and cord blood collection was deemed exempt. Granulocyte colony stimulating factor (G-CSF) mobilized peripheral blood (mPB) CD34+ cells were kindly provided through a Program of Excellence in Gene Therapy grant from Shelly Heimfeld at the Fred Hutchinson Cancer Research Centre, Seattle, WA.
Clinical Samples and Subject Characteristics
PB samples (16 ml) were collected from 9 patients (pts) with peripheral artery disease (PAD) (6 male and 3 female, age range 50–81) along with age and gender matched controls. PAD pts ranged in Rutherford class 1–6, with comorbidity related to coronary artery disease (CAD) in 4 patients and chronic obstructive pulmonary disease (COPD) in 3 pts.
Isolation of Mononuclear Cells
Blood was diluted 2:1 with phosphate buffered saline without calcium or magnesium (PBS, Invitrogen, Grand Island, NY) and underlaid with Ficoll (GE Healthcare). Mononuclear cells (MNCs) were isolated by centrifuging blood at 740g for 30 min at room temperature. The MNCs were removed and washed two times in PBS without calcium or magnesium (Invitrogen, Grand Island, NY) with 2% fetal bovine serum (FBS, Hyclone, Logan, UT). Cells were counted on a hemacytometer.
A total of 107 PB MNCs were suspended in 720 μl PBS with 2% FBS and incubated for 10 min at 4°C with 180 μl human Fc blocking reagent (Miltenyi Biotec). Cells were aliquoted into 9 tubes and stained with the following antibodies. Subsequently, 100 μl of the cell suspension was distributed into nine sample tubes with the following pre-titered antibodies: (1) unstained; (2) Isotypes: 4 μl IgG FITC, 15 μl IgG PE, 10 μl IgG APC, 10 μl IgG PECy5.5, 5 μl IgG APC-AF750 and 4 μl IgG PacB; (3) FITC FMO: 15 μl CD34 PE, 10 μl AC133 APC, 10 μl CD14 PECy5.5, 5 μl CD45 APC-AF750, 4 μl glyA PacB and 1 μl ViViD; (4) PE FMO: 4 μl CD31 FITC, 10 μl AC133 APC, 10 μl CD14 PECy5.5, 5 μl CD45 APC-AF750, 4μl glyA PacB and 1 μl ViViD; (5) APC FMO: 4 μl CD31 FITC, 15 μl CD34 PE, 10 μl CD14 PECy5.5, 5 μl CD45 APC-AF750, 4 μl glyA PacB and 1 μl ViViD; (6) PECy5.5 FMO: 4 μl CD31 FITC, 15 μl CD34 PE, 10 μl AC133 APC, 5 μl CD45 APC-AF750, 4 μl glyA PacB and 1 μl ViViD; (7) APC-AF750 FMO: 4 μl CD31 FITC, 15 μl CD34 PE, 10 μl AC133 APC, 10 μl CD14 PECy5.5, 4 μl glyA PacB and 1 μl ViViD; (8) V450 Channel FMO: 4 μl CD31 FITC, 15 μl CD34 PE, 10 μl AC133 APC, 10 μl CD14 PECy5.5, and 5 μl CD45 APC-AF750; and (9) full panel: 4 μl CD31 FITC, 15 μl CD34 PE, 10 μl AC133 APC, 10 μl CD14 PECy5.5, 5 μl CD45 APC-AF750, 4 μl glyA PacB and 1 μl ViViD (six-antibody/viability marker panel). Table S2 is included in the Supporting Information to better illustrate the staining panel. Cells were incubated with antibodies for 30 min at 4°C, washed twice in PBS with 2% FBS, and fixed in 300 μl 1% paraformaldehyde (Sigma Aldrich, St. Louis, MO). Additionally, anti-mouse Ig BD CompBeads (BD Biosciences, Bedford, MA) were stained with each of the individual test antibodies to serve as single-color compensation controls. Prior to use, each lot of antibody was individually titered as previously described (17) to determine the optimal staining concentration. In some experiments, cells were incubated with either glyA PacB or ViViD to determine the individual contribution of RBCs or dead/apoptotic cells, respectively.
Flow Cytometry Acquisition and Sorting
Stained fixed MNC samples were acquired on a BD LSRII flow cytometer (BD, Franklin Lakes, NJ) equipped with a 405-nm violet laser, 488-nm blue laser, and 633-nm red laser (for filter specifications see Supporting Information Table S3) (14). Prior to acquiring any data, photomultiplier tube (PMT) voltages were calibrated to the highest signal to background ratio as previously described (18). At least 300,000 events were acquired for each sample. Data was acquired uncompensated, exported as FCS 3.0 files and analyzed using FlowJo software, version 8.7.3 (Tree Star, Ashland, OR).
For sorting, unfixed MNC samples were run on a BD FACSAria flow cytometer (BD) equipped with a 407-nm violet laser, 488-nm blue laser, and 630-nm red laser. PMT voltages were also calibrated and using single stained compensation beads a compensation matrix was created. Cells were collected into sterile PBS supplemented with 2% FBS. Data was exported as FCS 3.0 files and also analyzed using FlowJo software, version 8.7.3 (Tree Star).
To assess the hematopoietic progenitor colony forming potential of CD31+CD34brightCD45dimCD133+ cells from PB, 500 freshly sorted cells or 10,000 MNCs were suspended in 0.66–1.0% agar (Becton Dickinson) in the presence of 1,000 U/ml human interleukin (IL)-1α, 200 U/ml human IL-3, 100 ng/ml human macrophage colony stimulating factor (M-CSF), and 100 ng/ml human stem cell factor (SCF) (all from Peprotech, Rocky Hill, NJ) as previously described (19). Cells were plated in 35-mm Petri dishes in triplicate and scored for low proliferative potential-colony forming cells (LPP) and high proliferative potential-colony forming cells (HPP-CFCs) on Day 14. Sorted subpopulations were also assayed for the presence of multipotential granulocyte, erythroid, macrophage, megakaryocyte progenitors (i.e., CFU-GEMMs) using MethoCult® GF H4434, Complete Methylcellulose Kit (StemCell Technologies, Vancouver, BC, Canada) according to the manufacturer's protocol.
Matrigel Tube Forming Assays
To assess the presence of functional endothelial cells within subpopulations of PB, freshly sorted CD31+CD34brightCD45dimCD133+ cells (3,500–5,000 per well) were seeded onto Matrigel-coated (BD Biosciences) 96-well plates as previously described (6, 20). Wells were examined by visual microscopy every 2 hours for capillary-like tube formation. Early passage cultured CB ECFCs were used as a positive control.
For preamplification, 1,000–3,000 sorted cells were first lysed and reverse transcribed exactly according to the manufacturer's protocol using the TaqMan PreAmp Cells-to-CT Kit (Applied Biosystems, Foster City, CA). All reverse transcription (RT) reactions were performed for 60 min at 37°C and 5 min at 95°C. Pooled cDNA was preamplified with TaqMan Gene Expression Assays (Applied Biosystems) with primers for CD34, CD45, CD31, AC133, and ACTB (β-actin, Applied Biosystems). Preamplification was performed for 10 min at 95°C, 10 cycles of 15 sec at 95°C and 4 min at 60°C. A quantitative real-time PCR (qPCR) analysis was performed with the ABI PRISM 7500 sequence detection system (Applied Biosystems). Cycling conditions consisted of a 2-min hold at 50°C for uracilo-N-glycosylase degradation, 10 min hold at 95°C for enzyme activation, 40 cycles of 15 sec denaturation at 95°C, 1 min annealing and elongation at 60°C. Relative quantification of triplicate samples was performed using the delta-CT method and expressed as fold increase relative to ACTB.
Cellular content of fluorescence activated cell sorting (FACS) derived CPC populations was evaluated by counting, cytospin preparation and Wright-Giemsa staining as previously described (21). Identification of cell types was done by visual inspection under 100× magnification and photomicrographs of cytospins were taken with an Olympus DP20 on an Olympus BX50 microscope.
NOD/SCID mice, 6–8-weeks old, were housed according to protocols approved by the Laboratory Animal Research Facility and adhered strictly to National Institutes of Health guidelines and protocols were approved by Indiana University Animal Care and Use Board.
Transplantation of NOD/SCID Mice
All animals were given a sublethal dose of 300cGy total body irradiation 4 hours before transplantation. The mPB CD34+ cells (105 per mouse), sorted CD31+CD34bright CD45dimAC133+ subpopulation (i.e., CPCs) from mPB CD34+ cells (105 per mouse), or cells not contained in the CD31+CD34brightCD45dimAC133+ sort gate (i.e., non-CPCs) (2.5 × 104 per mouse) were resuspended in PBS and transplanted by tail vein injection. To assess engraftment, mice were sacrificed 8–12 weeks after transplantation and both femurs were flushed with a total of 2 × 106 cells collected and stained with anti-human CD45 and CD34 antibodies. Approximately 500,000 events per sample were collected on a BD LSRII flow cytometer. Analysis was performed with FlowJo software version 8.7.3.
Determination of Human CPC Function in a Melanoma Xenograft Model
NOD.CB17-Prkdcscid/J (NOD/SCID) mice were subcutaneously injected with 2 × 106 C32 human melanoma cells (ATCC) and tumor growth monitored. Once tumors reached ∼50 mm3, mice were injected with 5 × 104 AC133+ CPCs, AC133− CPCs (non-CPCs), bulk CD34+ cells, or vehicle control (PBS). Tumor growth was monitored by caliper and the volume determined by the following formula: mm3 = (width)2 × length × 0.5. The fold increase in tumor growth was determined by comparing tumor volume over time to the base line tumor volume. At the end of the experiment, mice were euthanized, tumors harvested, and the weight of each tumor determined. Data are presented as the mean ± SEM. Statistical significance was determined using a two-sided student's t-test to calculate p values.
Statistical analysis was performed using GraphPad Prism software, version 5.01 for Windows (GraphPad Software, San Diego, CA). Data was tested for normality using the D'Agostino-Pearson normality test (alpha = 0.05), and normal data sets were compared using two-tailed Student's t test or one-way ANOVA.
Frequency Analysis and Characterization of CD31+CD34brightCD45dimAC133+ cells
We performed a similar methodological comparison of the flow cytometry methods to enumerate the CD31+CD34brightCD45dimAC133+ putative CPCs (4, 5, 12, 14, 22). We initially isolated PB MNC samples from 10 healthy, young adult volunteers and stained the cells with the six monoclonal antibodies (CD34, CD45, CD31, AC133, glyA, and CD14) and the viability marker (ViViD) with “fluorescence minus one” (FMO) controls or isotype controls as described (4, 14). Stained samples were acquired on a digital BD LSRII flow cytometer and assessed for CD31+CD34brightCD45dimAC133+ (CPCs) events using two different analysis schemas as shown in Figure 1. First, stained MNCs were analyzed in conventional logarithmic dot plots (Figs. 1a–1d) (4) and CPC identification was determined by placement of population gates, which were based on isotype controls exactly as previously described for conventional flow cytometry (Figs. 1a–1d) (4). Data were manually compensated using singly stained cell controls as reported (4). In contrast, MNCs from the same donor were analyzed in biexponential contour plots to identify CD31+CD34brightCD45dimAC133+ cells after exclusion of contaminating monocytes, red blood cells (RBCs), and dead cells (Figs. 1e–1i) (14). Before analysis, automated compensation was applied based on single-color bead controls. To objectively identify CPCs, regional gates were applied based on the use of proper FMO gating controls as previously described (14).
To compare the reproducibility and margin of error between the two methods, PB MNCs were harvested, and CPCs were identified utilizing the methods outlined in Figure 1. In samples analyzed using the four-antibody panel and logarithmic dot plots (Figs. 1a–1d), 0.290 ± 0.218% (mean ± SD, n = 10, range 0.170–0.900) of gated MNCs were CD31+CD34brightCD45dimAC133+. In comparison, when cell preparations from the same donors were analyzed using biexponential contour plots and FMO gating controls with the exclusion of monocytes, RBCs, and dead/apoptotic cells (Figs. 1e–1i), CD31+CD34brightCD45dimAC133+ cells constituted only 0.134 ± 0.0347% (mean ± SD, n = 10, range 0.0800–0.200; logarithmic method vs. biexponential method, P = 0.0380 by two-tailed, unpaired Student's t test) of viable glyA−CD14− cells. Examination of Figure 1g reveals a distinct CD31+CD34brightCD45dimAC133− population that is nearly indistinguishable from the neighboring CD31+CD34bright CD45dimAC133+ population in Figure 1b. Exclusion of the unwanted myelo-erythroid events, the use of contour plots and biexponential display for data depiction, and selection of an antigen panel for maximal resolution of dull populations has culminated in the enhanced resolution depicted in Figure 1g, facilitating the detection of previously unreported heterogeneity within the putative CPC population (14). Moreover, Figure 1c depicts a CD45bright population (dark blue) due to monocyte and dead cell contamination that has been eliminated in the equivalent PFC plot (Fig. 1h). FMO gating control analysis provides the only objective means of distinguishing between a broad negative population and an adjacent dull positive subset and in this case, confirms the visually apparent demarcation of AC133 expression (14). The combined frequencies of the CD31+CD34brightCD45dimAC133− and CD31+CD34brightCD45dimAC133+ populations illustrated in Figure 1g, confirm the observed frequency gated by conventional methodology in Figure 1b, 0.310 ± 0.024% (mean ± SD, n = 10, range 0.270–0.400; logarithmic method vs. biexponential method, P = 0.7973 by two-tailed, unpaired Student's t test). To confirm the antigen profile, we performed quantitative RT-qPCR and demonstrated that FACS purified and isolated CPCs as gated in Figure 1g transcribe mRNA for the cell surface antigens CD34, CD31, and AC133 (Fig. 2a). Importantly, CPCs also transcribe mRNA for CD45 (Fig. 2a). Thus, the putative CPC population enumerated by conventional flow cytometry methods is comprised of two phenotypically distinct cellular subsets. Application of PFC analysis for CPC enumeration reveals heterogeneity (14) and produces a smaller range of values with a lower standard deviation, which is critical for clinical comparative studies.
Though previous reports of heterogeneous CPCs found correlations with extent of tumor progression and CVD risk, the functional identity of these progenitors is unknown (23). Therefore, we performed experiments to determine the functional phenotype of CPCs in human PB identified with PFC. To better ascertain the identity of the CPCs, we isolated, pelleted, resuspended, and deposited the CPCs onto slides and performed Wright-Giemsa staining on the CD31+ CD34brightCD45dimAC133+ cells. Remarkably, morphological analysis revealed hematopoietic blast cells or progenitor cells, which potentially represent hematopoietic stem and progenitor cells (HSPCs) (Fig. 2b). Based on expression of multiple hematopoietic cell surface antigens (CD34, CD45, and AC133) and cellular morphology via immunocytochemistry, we next tested whether CPCs displayed functional properties of primitive HSPCs or EPCs in colony forming assays. Both CD31+CD34brightCD45dimAC133− and CD31+CD34bright CD45dimAC133+ CPC populations formed primitive hematopoietic progenitor cell colonies including LPP-CFCs, HPP-CFCs, and CFU-GEMMs (Fig. 2c) at a frequency range of 1:7–1:20 in each population. Neither population of CPCs yielded ECFCs in established in vitro clonal assays or formed capillary-like tubes with lumens in Matrigel™ and neither population formed vessels in vivo.
As both CPC populations formed primitive multilineage hematopoietic cell colonies and expressed HSPC antigens, we tested whether these populations in fact contained NOD/SCID engrafting hematopoietic stem cells (HSCs). NOD/SCID mice were sublethally irradiated and subsequently transplanted intravenously with purified mPB CD34+ cells (positive control for HSPCs), CD31+CD34brightCD45dimAC133+ or CD31+CD34brightCD45dimAC133− CPCs. Mice were sacrificed after 8–12 weeks, and human cell engraftment was measured in the mouse bone marrow (BM) by the presence of human CD45+ cells using species-specific monoclonal antibodies. Transplanted mouse BM was also analyzed for the presence of human CD19, CD33, and CD34 expressing cells; markers used to determine multilineage potential of engrafted human cells in NOD/SCID mice. Strikingly, mice transplanted with either CD31+CD34brightCD45dimAC133+ or CD31+CD34bright CD45dimAC133− CPCs demonstrated multilineage engraftment, which is the hallmark of transplantable NOD/SCID repopulating HSPCs (Table 1). Thus, the previously identified human CPCs are comprised of a heterogenous mixture of HSPCs and would be best described as circulating HSPCs (CHSPCs) rather than the less descript term CPCs.
Table 1. Engraftment analysis of NOD/SCID mice transplanted with mPB CD34+ cells, sorted CPCs, or sorted non-CPCs
% of CD45+ cells that are CD34+
% of CD45+ cells that are CD33+
% of CD45+ cells that are CD19+
CPC Mouse 1
CPC Mouse 2
CPC Mouse 3
non-CPC Mouse 1
non-CPC Mouse 2
mPB CD34+ Mouse 1
mPB CD34+ Mouse 2
mPB CD34+ Mouse 3
mPB CD34+ Mouse 4
CHSPC Heterogeneity Corresponds with Disparate Angiogenic Potential
As the CD34+ cells, CD31+CD34brightCD45dimAC133+ CHSPCs, or CD31+CD34brightCD45dimAC133− CHSPCs all showed similar rates of NOD/SCID engraftment, we wanted to compare their capacity for promoting angiogenesis in an in vivo model. Thus, NOD/SCID mice bearing human melanoma xenografts were intravenously injected with equal numbers of CB CD34+ cells, CD31+CD34brightCD45dimAC133+ CHSPCs or CD31+CD34brightCD45dimAC133− CHSPCs and tumor growth was monitored over time in each cohort (Fig. 3a). Surprisingly, mice injected with CD31+CD34bright CD45dimAC133+ CHSPCs demonstrated a 23.12 ± 0.15% (mean ± SEM, n = 8, range 18.35–29.00) fold increase in tumor growth as compared to tumor bearing animals treated with CD31+CD34brightCD45dimAC133− CHSPCs (7.20 ± 0.15% mean ± SEM n = 8, range 5.31–9.87), the parental population of CD34+ cells (5.98 ± 0.23% mean ± SEM n = 8, range 4.99–7.59) and PBS control (9.17 ± 0.14% mean ± SEM n = 8, range 5.30–11.96; AC133+ CHSPCs vs. PBS, P = 0.001 by two-tailed, unpaired Student's t test). When the explanted tumors were removed from the host mice in each cohort and weighed, animals injected with the CD31+CD34brightCD45dimAC133+ CHSPCs tumors were significantly heavier (0.89 ± 0.04 g mean ± SEM n = 8) than the tumors removed from animals that were injected with PBS (0.51 ± 0.06 g mean ± SEM n = 8) or CD34+ cells (0.53 ± 0.06 g mean ± SEM n = 8; AC133+ CHSPCs vs. PBS or CD34+ cells, P < 0.001) (Fig. 3b). Furthermore, it was apparent that the animals injected with the CD31+CD34brightCD45dimAC133+ CHSPCs displayed a greater tumor vasculature that may be the most plausible explanation for the enhanced tumor volume measured in this group of animals. Though the transplanted CD31+CD34brightCD45dimAC133+ CHSPCs promoted tumor angiogenesis and tumor growth, we found no evidence of human CD31, CD33, or CD34 expressing cells/endothelium in the murine tumor vessels. Thus, CD31+CD34bright CD45dimAC133+ CHSPCs are proangiogenic and accelerate tumor growth in a statistically significant manner and are functionally distinct from the nonangiogenic CD31+ CD34brightCD45dimAC133− CHSPCs. Moreover, lineage phenotyping of proangiogenic and nonangiogenic CHSPCs revealed further heterogeneity (Fig. 3c). Proangiogenic CHSPCs expressed a preponderance of myeloid cell surface markers (CD11b, CD13, CD33) while the nonangiogenic CHSPCs displayed more lymphoid cell surface markers (CD3, CD4, CD7, CD10, CD56) (Fig. 3c). This data demonstrates that both proangiogenic and nonangiogenic CHSPCs are not ECFCs or CPCs with endothelial potential but are comprised of hematopoietic progenitor cells, myeloblasts, and HSCs with NOD/SCID repopulating ability.
Ratio of Circulating Progenitor Subsets Denotes Disease State in Peripheral Arterial Disease Patients
The disparate angiogenic potential of the two CHSPC subsets (as functionally determined in the tumor xenograft model) observed within the putative CPC population led us to hypothesize that these two progenitor fractions may regulate different aspects of vascular homeostasis. To test this hypothesis, we measured the frequency of the proangiogenic CHSPCs versus the nonangiogenic CHSPCs in a patient population with vascular dysfunction leading to PAD. In patients with diagnosed PAD, we have identified a significant decrease in the ratio of proangiogenic CHSPCs to nonangiogenic CHSPCs (0.79 ± 0.16050% mean ± SEM, n = 9, range 0.140–1.52) as compared to age and gender matched control subjects (1.81 ± 0.09433% mean ± SEM, n = 9, range 1.231–2.130; healthy vs. PAD, P = 0.0001 by two-tailed, unpaired Student's t test) (Figs. 4a–4c). Interestingly, patients with PAD and healthy controls were indistinguishable when the total CHSPC population (CD31+CD34brightCD45dimAC133+/−) was enumerated and compared. These data suggest that future studies to define biologic changes in the proangiogenic fraction of CHSPCs may be informative as to deficiencies they may exhibit in vascular repair in subjects with PAD.
A PFC protocol (14) was utilized to isolate certain putative hematopoietic subsets now found to be comprised of CHSPCs which are defined by cell surface antigen expression, colony assay, morphologic analysis, and in vivo function. The circulating hematopoietic cells identified are validated herein as cells that function in neoangiogenesis and serve as potential biomarkers of CVD or tumor progression. Another reported protocol for CPC enumeration that correlates with tumor progression risk is now clarified to identify hematopoietic progenitor cells, myeloblasts and engrafting HSCs (4). Thus, we present data that provides an analytical method for enumerating circulating blood cells that participate in new blood vessel formation at homeostasis and in subjects with abnormal cardiovascular health.
Confusion around the function of, EPCs, circulating endothelial progenitors (CEPs), and CPCs in vascular repair and regeneration at homeostasis or in response to injury or disease is linked to lack of consensus regarding quantitative measures to isolate each cell type using in vitro colony assays, immunomagnetic separation (IMS), or conventional flow cytometry approaches (11, 24, 25). More recently, the heterogeneous nature of EPC populations (to include nonendothelial precursors) has been recognized [reviewed in (24)] and an even broader term, CPC, is now used to encompass circulating cells with proangiogenic activity (4, 26). Use of the term CPC, without functional validation of the cell types comprising this fraction has not been helpful in understanding the mechanisms of cellular action purported to emerge from these flow cytometry “events.” It is widely recognized that a new approach in defining the parameters and properties of cells involved in neoangiogenesis is required for the field to make advancements in clinical treatments (12).
Many different proposals for using conventional flow cytometry approaches to identify EPCs and CPCs are published [reviewed in (24)]. The CPC population identified using the conventional flow cytometry approach (4) and the novel CHSPC population isolated using the PFC protocol (14) are now demonstrated to be comprised of hematopoietic cells at different stages of differentiation. All observed cells belong to the HSPC pool, a significant proportion of which display in vitro hematopoietic colony forming cell activity and others engraft in immunodeficient mice. Hematopoietic cells are known to participate in angiogenesis (27). Consistent with their biological function, it is not surprising then that increased concentrations of CPCs correlate with risk for tumor recurrence and patient responsiveness to antiangiogenic therapies (23).
Use of the PFC protocol has permitted a clear distinction between CHSPCs with proangiogenic function and those lacking in angiogenic supportive activity, based upon AC133 expression (14). AC133 is proposed as a marker for circulating EPCs and has been used in combination with CD34 and/or CD31 and/or KDR as a biomarker in patients with CVD, cancer, sepsis, or renal failure (28, 29). The PFC approach (14) permits isolation of CHSPC subsets based upon AC133 expression and only the CD31+CD34brightCD45dimAC133+ CHSPC subset possesses proangiogenic activity in promoting angiogenesis and human melanoma tumor growth in an immunodeficient mouse explant model system. This particular CHSPC subset was enriched in cells displaying a variety of myeloid cell surface antigens in addition to displaying in vitro and in vivo HSPC functions. This subset did not display any vasculogenic ability in vivo when examined for the presence of human endothelium within the explanted human tumors within the immunodeficient mice. Thus, the proangiogenic CHSPC appears to be enriched in proangiogenic functions but lacks postnatal vasculogenic activity. Future examination of these cells to identify the specific molecular pathways for promoting tumor angiogenesis may be insightful and permit a better understanding of mechanisms for blocking tumor angiogenesis. It will also be important to understand whether the CHSPCs are specifically recruited to the tumor site and differentiate into myeloid cells such as Tie2+ monocytes that are known to promote tumor angiogenesis (10).
An interesting insight into the variance of circulating concentrations of proangiogenic and nonangiogenic CHSPCs was observed in patients with PAD. A significant decrease in the ratio of proangiogenic to nonangiogenic CHSPCs was measured in the bloodstream of patients with PAD as compared to healthy control subjects. Future studies to evaluate potential differences in the gene expression and function of the CHSPCs in normal subjects and those with PAD at various stages of their disease may permit a new insight into whether or not this cell population contributes to PAD disease progression.
In summary our PFC method has now been shown to identify subsets of circulating cells in human PB which promote angiogenesis. Use of this method for prospective identification of these cells should facilitate human clinical studies and functional biological characteristics of each defined cellular subset.
The authors thank Janice Walls for her expert administrative assistance in preparation of this article. They also acknowledge the assistance and state-of-the-art facilities from Sue Rice at the Flow Cytometry Core at the Indiana University Simon Cancer Center.