- Open Access
Exhaustive expansion: A novel technique for analyzing complex data generated by higher-order polychromatic flow cytometry experiments
© Siebert et al; licensee BioMed Central Ltd. 2010
- Received: 29 April 2010
- Accepted: 30 October 2010
- Published: 30 October 2010
The complex data sets generated by higher-order polychromatic flow cytometry experiments are a challenge to analyze. Here we describe Exhaustive Expansion, a data analysis approach for deriving hundreds to thousands of cell phenotypes from raw data, and for interrogating these phenotypes to identify populations of biological interest given the experimental context.
We apply this approach to two studies, illustrating its broad applicability. The first examines the longitudinal changes in circulating human memory T cell populations within individual patients in response to a melanoma peptide (gp100209-2M) cancer vaccine, using 5 monoclonal antibodies (mAbs) to delineate subpopulations of viable, gp100-specific, CD8+ T cells. The second study measures the mobilization of stem cells in porcine bone marrow that may be associated with wound healing, and uses 5 different staining panels consisting of 8 mAbs each.
In the first study, our analysis suggests that the cell surface markers CD45RA, CD27 and CD28, commonly used in historical lower order (2-4 color) flow cytometry analysis to distinguish memory from naïve and effector T cells, may not be obligate parameters in defining central memory T cells (TCM). In the second study, we identify novel phenotypes such as CD29+CD31+CD56+CXCR4+CD90+Sca1-CD44+, which may characterize progenitor cells that are significantly increased in wounded animals as compared to controls.
Taken together, these results demonstrate that Exhaustive Expansion supports thorough interrogation of complex higher-order flow cytometry data sets and aids in the identification of potentially clinically relevant findings.
- Long Term Memory
- Compartment Syndrome
- Bone Marrow Stem Cell
- Central Memory
- Stem Cell Phenotype
Flow cytometry (FCM) is a powerful technology with major scientific and public health relevance. FCM can be used to collect multiple simultaneous light scatter and antigen specific fluorescence measurements on cells as each cell is excited by multiple lasers and emitted fluorescence signals are passed along an array of detectors. This technology permits characterization of various cell subpopulations in complex mixtures of cells. Using new higher-order multiparameter FCM techniques we can simultaneously identify T and B cell subsets, stem cells, and specific cell surface antigens, cytokines, chemokines, and phosphorylated proteins produced by these cells. Higher order FCM allows us to measure at least 17 parameters per cell , at rates as high as 20,000-50,000 cells per second.
Increasing sophistication in FCM, coupled with the inherent complex dimensionality of clinical and translational experiments, leads to data analysis bottlenecks. While the literature documents a long history of automated approaches to gating events within a single sample [2–4], the gated data remains complex, with readouts for tens to hundreds of phenotypes per sample, multiple samples per patient, and multiple cohorts per study. Unfortunately, there is a paucity of proven analytical approaches that provide meaningful biological insight in the face of such complex data sets.
Furthermore, interpretation of results from higher order experiments may be biased by historical results from simpler lower order experiments. Marincola  suggests that modern high-throughput tools, coupled with high-throughput analysis, provide a more unbiased opportunity to reevaluate the basis of human disease, while advocates of cytomics [6, 7] observe that exhaustive bioinformatics data extraction avoids the inadvertent loss of information associated with a priori hypotheses. Fundamentally, these authors underscore the distinction between inductive (hypothesis-generating) and deductive (hypothesis-driven) reasoning. This distinction is clearly applicable to the interpretation of higher-order multiparameter flow cytometry data. Herein, we apply a powerful inductive data analysis approach to two distinctly different studies in order to demonstrate its broad applicability. The first study examines human memory T cell responses to a melanoma peptide cancer vaccine, while the second inspects porcine stem cell phenotypes associated with wound healing.
In a previously described melanoma booster vaccine study , we used 8-color FCM to characterize the phenotypes of viable (7AAD-) melanoma antigen-specific (gp100 tetramer+) CD8+ T cells collected from peripheral blood. Memory and effector T cell subpopulations responding to vaccine antigen were characterized using 5 additional monoclonal antibodies (mAbs) specific for CCR7, CD45RA, CD57, CD27, and CD28. Samples were collected from 7 donors at 3 time points: after (post) the initial vaccine regimen (PIVR); at a long term memory (LTM) time point collected 18 to 24 months after the end of vaccine administration; and after two boosting vaccines (P2B). Phenotypes for TCM have been described based on lower-order 3-4 color staining with different combinations of the above antibodies, with data suggesting a consensus TCM phenotype of CCR7+CD45RA-CD57-CD27+CD28+. We demonstrated that LTM gp100-specific CD8+ T cells were enriched for this consensus phenotype . We also described a gp100-specific TCM subset that retained CD45RA expression (CCR7+CD45RA+CD57-CD27+CD28+), which we termed TCMRA, and which may represent a TCM precursor population similar to that described in the mouse . Although this consensus phenotype has previously been used to primarily define naïve T cells, it clearly characterized a subpopulation of antigen-educated (i.e. gp100 tetramer positive) long term memory CD8+ T cells in the melanoma vaccine study. This phenotype signature may delineate a TCM precursor population that arises shortly after antigen activation of naïve T cells. Thus, studies in the mouse demonstrate that tumor-specific TCM and similar putative TCM precursors, referred to as central memory stem cells (TSCM), which may derive from early daughter cell division after antigen stimulation of naïve T cells, express elevated levels of proliferation, enhanced survival in vivo, and superior CTL function compared to effector or effector-memory (TEM) T cells . However, the origin of TCM and TSCM precursors remains controversial, since other data supports the hypotheses that such memory subpopulations may also develop from effector and effector-memory T cells . Controversy aside, enhanced proliferative and survival properties characteristic of memory T cells have been correlated with anti-tumor responses in mice and humans receiving adoptive T cell-based therapies . Thus, the use of higher-order flow cytometry and comprehensive multiparameter data analysis could facilitate the identification and expansion of TCM and TCM precursor subpopulations (i.e. TSCM) for more effective cancer immunotherapy regimens. However, such a therapeutic strategy would depend on first demonstrating memory T cell functional properties by sorted cells exhibiting such putative memory phenotype signatures.
Five monoclonal antibody panels for stem cell study.
Combinations of positive/negative phenotypes in a 5-marker panel.
Number of markers
Number of +/- gates given M markers
Number of combinations of M markers in a 5 marker panel (C)
Number of gates times number
(G × C)
20 = 1
No markers specified
21 = 2
A, B, C, D, E
22 = 4
AB, AC, AD, AE, BC, BD, BE, CD, CE, DE
23 = 8
ABC, ABD, ABE, ACD, ACE, ADE, BCD, BCE, BDE, CDE
24 = 16
ABCD, ABCE, ABDE, ACDE, BCDE
25 = 32
TOTAL = 243
Since we could not manually analyze data from hundreds to thousands of phenotypes efficiently, we first identified numerically interesting phenotypes by computing metrics for all derived sets. For example, in the melanoma vaccine study, the middle of three time points represented a long term memory time point, collected 18 to 24 months after exposure to the vaccine antigen. Consequently, one feature of interest was the delineation of phenotypes that peaked at this long term memory time point. In the wound healing study, since there were both wounded animals and control animals, we could identify phenotypes in which the expression levels for the wounded animals were greater than the levels for the control animals. In each case, simple visualizations, such as those presented in the Results, illustrated the patterns of response and helped us vet the numerically interesting phenotypes for biological relevance. In both studies we identified results with possible important clinical implications that would have been very difficult to find using standard analytical techniques. Using Exhaustive Expansion we were able to define a putative minimum obligate phenotype for central memory T cells, and delineate multiple bone-marrow-derived putative myogenic MSC subpopulations that may be mobilized in response to myonecrotic injury.
Melanoma Vaccine Study
The clinical trial protocol and the flow cytometry staining and analysis procedures used to acquire data in this study have been described in detail elsewhere [8, 33]. Briefly, early stage melanoma patients were vaccinated every second or every third week over six months with a modified, HLA-A2 restricted melanoma associated peptide, gp100209-2M. Leukophereses were collected before the vaccine regimen, after (post) the initial vaccine regimen (PIVR); at a long term memory (LTM) time point 18-24 months later; and following two additional boosting vaccines (P2B) given at one month intervals following the LTM leukopak collection. The protocol was reviewed by NCI's CTEP and approved by the Providence Health System institutional review board. All patients gave written informed consent. Cryopreserved PBMCs from PIVR, LTM and P2B time points were stained simultaneously with gp100 tetramers and with mAbs specific for CD8β, CCR7, CD45RA, CD57, CD27, CD28, and with 7AAD to discriminate live from dead cells. All samples were analyzed on a 9 color Beckman Cyan ADP flow cytometer. Viable lymphocytes were gated for positive CD8β and gp100 tetramer staining, and gp100-specific CD8β+ T cells were further interrogated for expression of the remaining five cell surface markers (CCR7, CD45RA, CD57, CD27, and CD28) to determine their subphenotypes. At least 5,000 gp100-specific CD8β+ T cells were collected per sample. All data was acquired in FCS format (Summit 4.2) and analyzed using the FCOM format of Winlist 5.0 Software (Verity House Software). "Fluorescence minus one" (FMO) controls were used to define positive and negative histogram staining regions for each fluorescent variable.
Porcine Stem Cell Study
All protocols were approved by the IACUC of Legacy Research and Technology Center. A bilateral compartment syndrome injury was produced in the anterior tibialis muscles by infusing porcine plasma directly into the muscles. A standardized bone marrow collection procedure was used as previously described , with bone marrow harvested from the tibia of anesthetized swine. Bone marrow was transferred to an automated cell processing system, BioSafe SEPAX cell separating system (Biosafe SA, Bern, Switzerland), within 60 minutes of collection, and mononuclear cells were isolated. Each sample was divided into 5 aliquots, which were stained for surface marker expression as summarized in Table 1. All samples were acquired using a BD™ LSR II flow cytometer.
To identify ckit (a.k.a stem cell factor (SCF)) expression, a porcine SCF ligand conjugated with biotin, kindly provided by Dr. Christene Huang (Transplantation Biology Research Center at Massachusetts General Hospital), was used together with a streptavidin-PE (Jackson Immunoresearch, West Grove, PA) for secondary binding. The antibodies for the other markers were all commercial monoclonal antibodies which were specific for porcine antigens or were anti-human or anti-mouse which cross react with the designated epitopes in swine: CD29-FITC, CD146-FITC and CD105 (GeneTex Inc., Irvine, CA), CD90-APC and CD44-APC-Cy7 (BioLegend, San Diego, CA), CD56-PE-TR (Invitrogen, Carlsbad, CA), Sca-1-Alexa Fluor 700 (Sca-1-AF700), CXCR4-PE-Cy7 (eBioscience, San Diego, CA), CD31-PE (AbD Serotec, Raleigh, NC), CD144-PE (Santa Cruz Biotechnology, Santa Cruz, CA), and VEGFR2-APC (R&D Systems, Minneapolis, MN). The anti-CD105 antibody was conjugated with Pacific Blue using a monoclonal antibody labeling kit (Invitrogen, Carlsbad, CA), following manufacturer's protocol.
Systems and Software
Representative input and output for the "Expander" program.
CCR7+CD45+CD57-CD27+CD28-, panel, EA02, LTM,2.48
CCR7+CD45+CD57-CD27+CD28+, panel, EA02, LTM,5.41
CCR7+CD45+CD57+CD27-CD28-, panel, EA02, LTM,1.47
CCR7+CD45+CD57+CD27-CD28+, panel, EA02, LTM,0.22
CCR7+CD45+CD57+CD27+CD28-, panel, EA02, LTM,0.34
CCR7+CD45+CD57+CD27+CD28+, panel, EA02, LTM,1.34
panel, EA02, LTM,+++++,1.34
panel, EA02, LTM,++++-,0.34
panel, EA02, LTM,++++.,1.68
panel, EA02, LTM,+++-+,0.22
panel, EA02, LTM,+++--,1.47
panel, EA02, LTM,+++-.,1.69
panel, EA02, LTM,+++.+,1.56
panel, EA02, LTM,+++.-,1.81
In the melanoma vaccine study, the Wilcoxon signed-rank test was used to identify either increased expression between time points or decreased expression between time points, depending on the pair of time points under consideration. The p-values were then used to screen populations for biologically meaningful results. These p-values provided a simple, well-understood metric to encapsulate the differences between the two time points. An alternative metric, such as 4 of 7 donors showing at least a 5% change between time points, would have been more verbose and would have required more detailed justification. In the porcine wound healing study, the Wilcoxon rank sum test was used to identify phenotypes in which the wounded cohort showed a greater change from baseline than did the control cohort.
In both studies, standard FCM analysis software was used to establish positive and negative gates based on the use of "fluorescence-minus-one" (FMO) controls for the included markers. In the case of the 5 memory markers used in the melanoma vaccine study, 32 (25) sets were subsequently generated using WinList's™ (http://www.vsh.com) FCOM function. Such combination gates also can be generated with other flow cytometry analytical software such as FlowJo (http://www.flowjo.com) and FCS Express (http://www.denovosoftware.com). The gating strategy for this study is illustrated in Figure 1. By inspecting a series of two-dimensional scatter plots, positive and negative gating boundaries were set, dividing the cells into subpopulations. Each of the 4 quadrants in dot plots 1 through 4 illustrates the frequencies of phenotypes of gp100 tetramer+ CD8+ T cells that are defined by positive and negative combinations of CCR7, CD45RA, CD57, CD27, and CD28.
Next we derived the percentage of cells in the more comprehensive analysis of all 243 (35) possible phenotypes, as defined by 0, 1, 2,... 5 parameters, using a custom Java program as described in the Methods. We utilize a shorthand notation for phenotypes by introducing a placeholder (".") to represent an unspecified parameter. These concepts are also illustrated in Figure 1, in which the callout table shows the shorthand notation for 2 populations specified by 5 markers, CCR7+CD45RA-CD57-CD27+CD28+ (+--++) and CCR7+CD45RA-CD57-CD27+CD28- (+--+-). The table also shows the notation for the 4 marker phenotype (+--+.) resulting from the summation of the frequencies of the two 5 marker phenotypes. Notice that CD28 assumes 3 values, "+", "-", and ".". The phenotype +--+. represents the combination or union of two subphenotypes or subsets (+--++ and +--+-), Hereafter, subphenotype signatures will be referred to as either sets or phenotypes.
The universal set (.....) contains 100% of the cells in the population of interest (e.g. viable, antigen-positive, CD8+ cells), and thus serves as an internal control. All other sets are proper subsets of the universal set. As presented here, Exhaustive Expansion applies to binary classification systems (e.g. positive and negative gating), but extension to n-ary classification systems (e.g. dim, intermediate, bright) is possible. After derivation of frequencies for all sets, data was loaded into a relational database (MySQL) and analyzed with SQL statements and graphing utilities.
Melanoma Vaccine Study
Average CV Suggests Stable CD27, CD28, and CD45RA Expression Over Time
Peak Finding Algorithm Highlights Central-Memory-Like Phenotype
Arguably, in situations of acute primary antigen challenge, such as the gp-100 vaccine regimen, central memory phenotypes (TCM) should be more predominant 18 to 24 months after antigen exposure, represented by a peak frequency at time point B (LTM). Both effector and early and late stage effector memory phenotypes should be more predominant after recent secondary antigen exposure, represented by an increase in these phenotypes (and a concomitant decrease in TCM) following boosting immunizations at time point C (P2B). Thus, to identify specific patterns of longitudinal changes, we computed p-values (Wilcoxon signed-rank test, a paired test) between pairs of time points for each phenotype.
To identify the TCM peaks, we looked for phenotypes that showed a statistically significant increase from A to B, and a concomitant decrease from B to C. Twenty three sets met these criteria with p-values less than 0.05. Eleven sets met these criteria with p-values less than 0.01. We inspected the longitudinal profiles for all 11 sets to verify the presence of reasonable peaks. We did not correct for multiple comparisons because we simply used the p-values as a numeric indicator of changes across the population, giving us direction for visual inspection. Furthermore, we did not make family-wide conclusions about the statistical significance of the peaks. We call the algorithm used in this analysis a "peak finding algorithm." A similar approach could be used to find valleys.
One of the phenotypes identified by the peak-finding algorithm was CCR7+CD57-CD27+CD28+ (+.-++), in which CD45RA is unspecified, and therefore includes both the CD45RA+ putative TCM precursor phenotype (TCMRA) and the CD45RA- TCM phenotype. The longitudinal profile for this set is shown in Figure 4C, and shows that 6 of 7 patients clearly peak at time point B. If the basic assumption that circulating gp100 specific CD8+ T cells which are maintained 1-2 years after initial antigen exposure are both TCM and TCMRA is correct, this data confirms that CD45RA staining may not be obligate in identifying all long term central memory T cell subpopulations. This interpretation is reinforced by the donor-level consistency in CD45RA expression over time as illustrated in Figure 2. Fundamentally, if 3 donors (e.g. EA02, EA07, EA29) have relatively consistently high/intermediate frequencies of CD45RA staining over time, they are unlikely to show a peak in the 5-marker consensus phenotype characterized by negative expression of CD45RA at the LTM time point when frequencies of central memory subpopulations should be elevated. Similarly, CD27+ and CD28+ staining may not be obligate descriptors for TCM/TCMRA subpopulations since staining frequencies for both remain relatively stable (low average CVs - Figure 2) over time, and may simply reflect memory T-cell redistribution between TEM and TCM/TCMRA phenotype compartments. Concomitant CCR7+CD57- staining may prove to be a more definitive minimal obligate phenotype signature for TCM/TCMRA subpopulations. This is suggested by the observations that 6 of 7 patients show CCR7+CD57- peaks at LTM (Figure 4C), and that 7 of the 9 sets in Figure 3 are subsets of the CCR7+CD57- (+.-..) phenotype.
Porcine Stem Cell Study
Screening of Thousands of Subpopulations Identifies Novel Stem Cell Phenotype
In the porcine wound-healing study, Exhaustive Expansion was applied to 5 different 8-parameter data sets generated using WinList's FCOM function, after setting positive and negative staining regions for each marker with FMO controls. This resulted in delineation of 6,561 (38) sets per sample per panel. Next, we computed changes from baseline (e.g. week 1 results minus week 0 results) for all phenotypes for all donors for weeks 1 through 4. We did not see clear kinetic changes in this data over the 4 week period, perhaps because these changes occurred much earlier, during the interval between week 0 and week 1, when no samples were drawn. Thus, to look for changes from baseline across the time frame of the study, we averaged the change from baseline data for each donor for each cell population over the 4 observations made in week 1 through week 4. Hereafter, we refer to this metric as the average delta value.
Additionally, we defined a process control range, based on analysis of 6 aliquots from a single animal drawn at a single point in time. For each phenotype, the process control range was defined as the maximum frequency value of the 6 replicates minus the minimum frequency value. This provided a conservative approach to quantifying the precision of our assay, and allowed us to focus on phenotypes with readouts exceeding the process control range.
23 CD29+CXCR4+ subsets showing significant differences between wounded and control animals.
Relative Set Name
Absolute Set Name
Here we have applied Exhaustive Expansion to two very different translational studies to demonstrate its broad application and utility. In each analysis, we generated all possible cell sets for each sample. Then we identified interesting sets based on coefficients of variation and long term memory peaks in the melanoma vaccine study, and separation between test and control cohorts in the wound healing study.
Analysis of data from multiparameter flow cytometry experiments consists of two main activities with well defined separation of concerns. First, events are gated into cell sets of interest using either manual or automatic techniques. Second, summary statistics describing these sets of cells are analyzed to identify meaningful experimental results. Exhaustive Expansion touches on both of these activities. In the case where positive/negative boundaries can be established for multiple markers, our Expander logic allows us to define a large number of supersets by exhaustively combining constituent subsets. Next, we identify features of interest such as Average CV, peaks, and separation between control and test cohorts. Such numeric features can be sorted and filtered, and illustrated with simple graphs. Importantly, these features are calculated for all phenotypes, thereby allowing systematic and relatively unbiased interrogation of the data. Additionally, the use of powerful mature software tools such as Java, MySQL, and R provides us with the flexibility to pursue the data analysis as suggested by the data itself and the underlying science.
For example, while we used a statistical test to quantify peaks in the melanoma study, we could have defined peaks based on an average fold change between time points (e.g. greater than 3), or on a criteria such as at least 4 donors showing at least a 5 percentage point change between time points. Alternatively, we could identify all phenotypes with a larger change than that shown by a predicted consensus phenotype. Or if we were interested in rare events, we could select sets in which less than 2 cells at baseline expanded to more than 20 cells after treatment. When a filter identifies many sets, the filter can be made more stringent. Alternatively, filters can identify a specific number or percentage of sets, such as the 10 sets with the largest average fold changes between two time points. Additionally, sets can be sorted on numeric characteristics such as fold change, p-value, or Average CV. This allows us to inspect sets ranked from largest to smallest fold change, for example, and perhaps further refine a threshold criteria based on some meaningful feature in the data. All of these numeric thresholds can and should be adjusted based on experimental conditions, assay precision, and the biological questions under investigation.
Adoptive transfer of tumor specific T cells in cancer immunotherapy translational studies has previously emphasized the transfer of highly differentiated, end stage effector T cells from in vitro IL-2 supported expansion cultures. More recently, compelling data from mouse tumor models suggests that tumor specific TCM and very early TCM precursors, referred to as central memory stem cells (TSCM), express elevated proliferation potential, enhanced long term survival in vivo, and give rise to activated CTLs in vivo with superior cytolytic activity compared to effector memory (TEM) or effector (TEFF) T cells from in vitro expansion cultures . Adoptive transfer immunotherapy strategies based on the in vitro expansion of TCM and TSCM subpopulations may offer significant clinical advantage in treating cancer patients if the human phenotype signatures for TCM and TSCM can be identified, and rapid efficient recovery procedures are developed to recover memory cells for subsequent in vitro expansion [38–40].
Previously, in a clinical study of long term tumor specific T cell memory function in melanoma patients, we elucidated the multiparameter phenotype of tumor specific TCM (CCR7+CD45RA-CD57-CD27+CD28+), and a second potentially early TCM precursor which we referred to as TCMRA (CCR7+CD45RA+CD57-CD27+CD28+) . Gp100-specific TCMRA shares its phenotype with naïve CD8+ T cells, and thus may be similar to the TSCM subset described in the mouse. Sorting strategies to select for these highly defined putative central memory populations could thus be implemented prior to cytokine-mediated in vitro expansion and adoptive transfer. However, recovery strategies based on a more simple minimal obligate phenotype signature would facilitate the more rapid, efficient recovery of larger numbers of cells using bulk techniques such as magnetic bead separation. Exhaustive Expansion identified a possible minimal obligate TCM/TCMRA phenotype (CCR7+CD57-: Figure 4) that was common to 7/8 of the CCR7+ CD45RA-CD57-CD27+CD28+ supersets that showed frequency peaks at LTM (Figure 3). This putative minimal obligate TCM/TCMRA phenotype signature may thus facilitate the recovery of TCM/TCMRA T cells, and cells from the intermediate stages of the TCMRA to TCM to TEM differentiation pathway represented by the other superset phenotypes in Figure 3. Clearly, additional experiments, including functional assays, are required to validate the hypothesis that CCR7+CD57- is a minimal obligate phenotype for TCM.
A second somewhat unexpected outcome of Exhaustive Expansion of the melanoma specific CD8+ T cell memory response was the suggestion that the combined frequency of tumor-specific T cells which express either the TCM or TEM phenotypes may not change appreciably over the course of the primary antigen challenge, long term memory maintenance, and following boosting immunization. The frequencies of gp100 specific T cells expressing key individual identifiers for the resolution of TCM and early TEM cells, such as CD45RA, CD27 and CD28, did not change appreciably across all three time points in the study (Figure 2). This may be explained in part by the observation that TCM and TEM phenotypes share the CD45RA-CD27+CD28+ signature [8, 35, 36]. The expression stability for each individual marker may suggest that, although cells may transition between the TCM and TEM phenotype compartments due to homeostasis-driven or antigen-stimulated proliferation, the overall combined frequency of the TCM plus TEM memory T cell pool as a fraction of all antigen specific T cells remains relatively constant. Thus, absolute numbers of cells in each compartment, and even the ratio of the frequency of cells with each phenotype, can fluctuate; but the total combined memory T cell frequency (i.e. TCM + TEM) may remain relatively stable after primary immunization. This observation has important implications for the optimal design of primary immunization strategies in both infectious disease and cancer vaccine settings.
In the stem cell study, 8 color staining panels that included mAbs previously employed in lower-order panels to delineate mesenchymal cells (CD29, CD90, and CD44), primitive pluripotent stem cells (ckit, CXCR4, and Sca-1), differentiated myoblasts (CD56 and CXCR4), and vascular-relative cells (CD146, CD31, CD144, CD105, and VEGFR2) were used to more comprehensively characterize significant changes in bone-morrow-derived putative mesenchymal progenitor cell populations following myonecrotic injury. Our data analysis technique allowed us to identify novel populations by focusing on phenotypes that showed both statistically significant differences between wounded and control animals and credible readouts above the process control range.
Studies have demonstrated that injection of bone marrow stem cells into ischemic muscle can reduce the damage to the muscle and the loss of muscle function . Bone marrow contains stem and progenitor cells which can differentiate into specific cell types such as myoblasts, chondrocytes, and endothelial cells in vitro and in vivo. The role of bone-marrow-derived mesenchymal stem cells (MSCs) to directly reconstitute myoblast formation in vivo in damaged muscle is controversial since their main role may be that of augmenting the myogenic potential of resident muscle MSCs referred to as satellite cells . In vitro, bone marrow cells acquire tissue-specific phenotypes when co-cultured with specialized cell types or tissue-derived extracts . These potentially multipotent cells may be mobilized in the bone marrow and recruited into muscle tissue where they mitigate tissue damage following acute myonecrotic injury. Our results show that cell surface markers can be used to comprehensively track bone marrow phenotype changes associated with muscle injury in porcine compartment syndrome, which are significantly different between the control and wounded groups. Moreover, our results demonstrate that we can detect multiple putative stem and progenitor phenotypes. The large majority of these 23 phenotype subpopulations (20/23) appear to share a common minimum obligate phenotype signature (e.g. CD29+CXCR4+CD90+: Table 4), expressing markers reported to be characteristic of MSC-derived myogenic cells [25, 37, 43]. However, there may already be lineage-specific heterogeneity expressed by these MSC-like subpopulations in the bone marrow, since approximately half (10/23) expressed the endothelial differentiation marker CD31  and an equal number (11/23) expressed the CD56 marker more commonly associated with regenerating muscle fibers and satellite cells. Lineage-specific commitment can be tested by culturing such sorted MSC subsets under lineage-promoting culture conditions . Based on the results presented here, the identification of bone marrow subpopulations by multiparameter FCM might be used to further sort or purify cell sets for autologous cell therapy to regenerate muscle, nerve and vascular tissues in compartment syndrome or other extremity injuries.
There are limitations to this work. First, from a biological perspective, both studies were performed with a small number of subjects. Additional experiments, including correlated memory T cell and MSC functional assays, are needed to validate the hypotheses generated by this work. Second, from an assay perspective, the analytical approach described here more readily supports those circumstances where orthogonal boundary gates (e.g. positive and negative regions) can be established. Third, from a process control perspective, the process control samples used to identify phenotypes of interest were analyzed on three consecutive days. Controls analyzed over the duration of the study would more accurately calibrate the precision of the assay. Fourth, from a computational perspective, there are practical limits to the scalability of the algorithm. Applying Exhaustive Expansion to an experiment in which there were 10 variable markers would result in a manageable 310 = 59,049 possible phenotypes, while 20 variable markers would result in a challenging 320 = 3,486,784,401 possible phenotypes.
While there is no way to alter the exponential increase in number of phenotypes as a function of the number of markers, it is unlikely that millions or billions of phenotypes would be meaningful, whether due to experimental noise (e.g. too few events to be adequately precise) or underlying biology. Thus, the phenotype search space would be pruned to a more reasonable number of phenotypes. Specific strategies for pruning the search space are beyond the scope of this work, but the general approach would mitigate the scalability impacts of the exponential increase, further extending the applicability of Exhaustive Expansion.
Furthermore, Exhaustive Expansion adds immediate value to contemporary experimental strategies and paves the way for the practical use of increasing numbers of markers. For example, one experimental design commonly published in contemporary literature uses a single fluorophore marker dump channel to exclude certain cells (e.g. CD14+, CD19+ and dead cells), two markers to identify lineage of interest (e.g. CD3 and CD4 or CD8), and another 5 markers to identify functional sets of interest (CD107a, IFN-γ, IL-2, MIP1β, and TNF-α) [31, 32, 46]. Using this experimental approach, 3 of the 8 total fluorophores are required to identify the parent population, while the other 5 can be considered variable identifiers of subphenotypes of interest. This construct leads to 31 sets of interest (25 - 1, since the universal set is excluded). In comparison, we have demonstrated that we can analyze over 32,000 sets, generated by 5 different panels of 8 variable markers. Additionally our approach recognizes that potential sets of interest are both those defined by all variable markers, and those defined by subsets of variable markers. Thus, our approach is readily applicable to contemporary flow cytometry experimental strategies, providing both support for an increasing number of variable markers and exhaustive interrogation of phenotypes defined by combinations of these markers.
In conclusion, we have demonstrated that Exhaustive Expansion is a valuable technique for analyzing higher order polychromatic FCM data sets. Exhaustive Expansion consists of:
generating data for all possible 0- to N-parameter sets;
creating appropriate data visualizations;
identifying numerically interesting sets, using such metrics as CVs and p-values; and
inspecting the numerically interesting sets for correlative analysis of clinically or biologically meaningful results.
This approach allows us to screen hundreds to thousands of phenotypes for biological responses. Use of free, widely available, and mature software components gives us the flexibility to pursue the data analysis in directions indicated by the data itself and the associated science. Our techniques are straightforward, yet highlight intriguing results when executed exhaustively across the entire data space. They support inductive reasoning by highlighting all cell subpopulations that meet appropriate numerical criteria. In both studies discussed here, our analysis provided the foundation for a refined understanding of complex phenotypes, and allowed for the development of new hypotheses pertaining to the identification and recovery of potentially important myogenic MSC progenitor cells, and tumor antigen-specific CD8+ TCM and TCM precursor populations for future clinical studies.
Funding support was received from NIH (1R21-CS82614-01 and RA21-CA099265-02), the M. J. Murdock Charitable Trust, and the Chiles Foundations.
- Perfetto SP, Chattopadhyay PK, Roederer M: Seventeen-colour flow cytometry: unravelling the immune system. Nat Rev Immunol. 2004, 4: 648-655. 10.1038/nri1416.PubMedView ArticleGoogle Scholar
- Pyne S, Hu X, Wang K, Rossin E, Lin T, Maier LM, Baecher-Allan C, McLachlan GJ, Tamayo P, Hafler DA, De Jager PL, Mesirov JP: Automated high-dimensional flow cytometric data analysis. Proc Natl Acad Sci USA. 2009, 106: 8519-8524. 10.1073/pnas.0903028106.PubMedPubMed CentralView ArticleGoogle Scholar
- Lo K, Brinkman RR, Gottardo R: Automated gating of flow cytometry data via robust model-based clustering. Cytometry A. 2008, 73: 321-332.PubMedView ArticleGoogle Scholar
- Murphy RF: Automated identification of subpopulations in flow cytometric list mode data using cluster analysis. Cytometry. 1985, 6: 302-309. 10.1002/cyto.990060405.PubMedView ArticleGoogle Scholar
- Marincola FM: In support of descriptive studies; relevance to translational research. J Transl Med. 2007, 5: 21-10.1186/1479-5876-5-21.PubMedPubMed CentralView ArticleGoogle Scholar
- Valet G: Cytomics as a new potential for drug discovery. Drug Discov Today. 2006, 11: 785-791. 10.1016/j.drudis.2006.07.003.PubMedView ArticleGoogle Scholar
- Valet G, Leary JF, Tárnok A: Cytomics--new technologies: towards a human cytome project. Cytometry A. 2004, 59: 167-171. 10.1002/cyto.a.20047.PubMedView ArticleGoogle Scholar
- Walker EB, Haley D, Petrausch U, Floyd K, Miller W, Sanjuan N, Alvord G, Fox BA, Urba WJ: Phenotype and functional characterization of long-term gp100-specific memory CD8+ T cells in disease-free melanoma patients before and after boosting immunization. Clin Cancer Res. 2008, 14: 5270-5283. 10.1158/1078-0432.CCR-08-0022.PubMedPubMed CentralView ArticleGoogle Scholar
- Gattinoni L, Zhong X, Palmer DC, Ji Y, Hinrichs CS, Yu Z, Wrzesinski C, Boni A, Cassard L, Garvin LM, Paulos CM, Muranski P, Restifo NP: Wnt signaling arrests effector T cell differentiation and generates CD8+ memory stem cells. Nat Med. 2009, 15: 808-813. 10.1038/nm.1982.PubMedPubMed CentralView ArticleGoogle Scholar
- Berger C, Jensen MC, Lansdorp PM, Gough M, Elliott C, Riddell SR: Adoptive transfer of effector CD8+ T cells derived from central memory cells establishes persistent T cell memory in primates. J Clin Invest. 2008, 118: 294-305. 10.1172/JCI32103.PubMedPubMed CentralView ArticleGoogle Scholar
- Gattinoni L, Powell DJ, Rosenberg SA, Restifo NP: Adoptive immunotherapy for cancer: building on success. Nat Rev Immunol. 2006, 6: 383-393. 10.1038/nri1842.PubMedPubMed CentralView ArticleGoogle Scholar
- Hassan HT, El-Sheemy M: Adult bone-marrow stem cells and their potential in medicine. J R Soc Med. 2004, 97: 465-471. 10.1258/jrsm.97.10.465.PubMedPubMed CentralView ArticleGoogle Scholar
- Gourgiotis S, Villias C, Germanos S, Foukas A, Ridolfini MP: Acute limb compartment syndrome: a review. J Surg Educ. 2007, 64: 178-186. 10.1016/j.jsurg.2007.03.006.PubMedView ArticleGoogle Scholar
- Ferrari G, Cusella-De Angelis G, Coletta M, Paolucci E, Stornaiuolo A, Cossu G, Mavilio F: Muscle regeneration by bone marrow-derived myogenic progenitors. Science. 1998, 279: 1528-1530. 10.1126/science.279.5356.1528.PubMedView ArticleGoogle Scholar
- Fukada S, Miyagoe-Suzuki Y, Tsukihara H, Yuasa K, Higuchi S, Ono S, Tsujikawa K, Takeda S, Yamamoto H: Muscle regeneration by reconstitution with bone marrow or fetal liver cells from green fluorescent protein-gene transgenic mice. J Cell Sci. 2002, 115: 1285-1293.PubMedGoogle Scholar
- Corbel SY, Lee A, Yi L, Duenas J, Brazelton TR, Blau HM, Rossi FMV: Contribution of hematopoietic stem cells to skeletal muscle. Nat Med. 2003, 9: 1528-1532. 10.1038/nm959.PubMedView ArticleGoogle Scholar
- Umemura T, Nishioka K, Igarashi A, Kato Y, Ochi M, Chayama K, Yoshizumi M, Higashi Y: Autologous bone marrow mononuclear cell implantation induces angiogenesis and bone regeneration in a patient with compartment syndrome. Circ J. 2006, 70: 1362-1364. 10.1253/circj.70.1362.PubMedView ArticleGoogle Scholar
- Tateishi-Yuyama E, Matsubara H, Murohara T, Ikeda U, Shintani S, Masaki H, Amano K, Kishimoto Y, Yoshimoto K, Akashi H, Shimada K, Iwasaka T, Imaizumi T: Therapeutic angiogenesis for patients with limb ischaemia by autologous transplantation of bone-marrow cells: a pilot study and a randomised controlled trial. Lancet. 2002, 360: 427-435. 10.1016/S0140-6736(02)09670-8.PubMedView ArticleGoogle Scholar
- Herrera MB, Bruno S, Buttiglieri S, Tetta C, Gatti S, Deregibus MC, Bussolati B, Camussi G: Isolation and characterization of a stem cell population from adult human liver. Stem Cells. 2006, 24: 2840-2850. 10.1634/stemcells.2006-0114.PubMedView ArticleGoogle Scholar
- Dicker A, Le Blanc K, Aström G, van Harmelen V, Götherström C, Blomqvist L, Arner P, Rydén M: Functional studies of mesenchymal stem cells derived from adult human adipose tissue. Exp Cell Res. 2005, 308: 283-290. 10.1016/j.yexcr.2005.04.029.PubMedView ArticleGoogle Scholar
- Wilson A, Oser GM, Jaworski M, Blanco-Bose WE, Laurenti E, Adolphe C, Essers MA, Macdonald HR, Trumpp A: Dormant and self-renewing hematopoietic stem cells and their niches. Ann N Y Acad Sci. 2007, 1106: 64-75. 10.1196/annals.1392.021.PubMedView ArticleGoogle Scholar
- Pitchford SC, Furze RC, Jones CP, Wengner AM, Rankin SM: Differential mobilization of subsets of progenitor cells from the bone marrow. Cell Stem Cell. 2009, 4: 62-72. 10.1016/j.stem.2008.10.017.PubMedView ArticleGoogle Scholar
- Miller RJ, Banisadr G, Bhattacharyya BJ: CXCR4 signaling in the regulation of stem cell migration and development. J Neuroimmunol. 2008, 198: 31-38. 10.1016/j.jneuroim.2008.04.008.PubMedPubMed CentralView ArticleGoogle Scholar
- Zheng B, Cao B, Crisan M, Sun B, Li G, Logar A, Yap S, Pollett JB, Drowley L, Cassino T, Gharaibeh B, Deasy BM, Huard J, Péault B: Prospective identification of myogenic endothelial cells in human skeletal muscle. Nat Biotechnol. 2007, 25: 1025-1034. 10.1038/nbt1334.PubMedView ArticleGoogle Scholar
- Cerletti M, Jurga S, Witczak CA, Hirshman MF, Shadrach JL, Goodyear LJ, Wagers AJ: Highly efficient, functional engraftment of skeletal muscle stem cells in dystrophic muscles. Cell. 2008, 134: 37-47. 10.1016/j.cell.2008.05.049.PubMedPubMed CentralView ArticleGoogle Scholar
- Crisan M, Yap S, Casteilla L, Chen C, Corselli M, Park TS, Andriolo G, Sun B, Zheng B, Zhang L, Norotte C, Teng P, Traas J, Schugar R, Deasy BM, Badylak S, Buhring H, Giacobino J, Lazzari L, Huard J, Péault B: A perivascular origin for mesenchymal stem cells in multiple human organs. Cell Stem Cell. 2008, 3: 301-313. 10.1016/j.stem.2008.07.003.PubMedView ArticleGoogle Scholar
- Middleton J, Americh L, Gayon R, Julien D, Mansat M, Mansat P, Anract P, Cantagrel A, Cattan P, Reimund J, Aguilar L, Amalric F, Girard J: A comparative study of endothelial cell markers expressed in chronically inflamed human tissues: MECA-79, Duffy antigen receptor for chemokines, von Willebrand factor, CD31, CD34, CD105 and CD146. J Pathol. 2005, 206: 260-268. 10.1002/path.1788.PubMedView ArticleGoogle Scholar
- Ingram DA, Mead LE, Tanaka H, Meade V, Fenoglio A, Mortell K, Pollok K, Ferkowicz MJ, Gilley D, Yoder MC: Identification of a novel hierarchy of endothelial progenitor cells using human peripheral and umbilical cord blood. Blood. 2004, 104: 2752-2760. 10.1182/blood-2004-04-1396.PubMedView ArticleGoogle Scholar
- Garlanda C, Dejana E: Heterogeneity of endothelial cells. Specific markers. Arterioscler Thromb Vasc Biol. 1997, 17: 1193-1202.PubMedView ArticleGoogle Scholar
- Lugli E, Pinti M, Nasi M, Troiano L, Ferraresi R, Mussi C, Salvioli G, Patsekin V, Robinson JP, Durante C, Cocchi M, Cossarizza A: Subject classification obtained by cluster analysis and principal component analysis applied to flow cytometric data. Cytometry A. 2007, 71: 334-344.PubMedView ArticleGoogle Scholar
- Casazza JP, Betts MR, Price DA, Precopio ML, Ruff LE, Brenchley JM, Hill BJ, Roederer M, Douek DC, Koup RA: Acquisition of direct antiviral effector functions by CMV-specific CD4+ T lymphocytes with cellular maturation. J Exp Med. 2006, 203: 2865-2877. 10.1084/jem.20052246.PubMedPubMed CentralView ArticleGoogle Scholar
- Betts MR, Nason MC, West SM, De Rosa SC, Migueles SA, Abraham J, Lederman MM, Benito JM, Goepfert PA, Connors M, Roederer M, Koup RA: HIV nonprogressors preferentially maintain highly functional HIV-specific CD8+ T cells. Blood. 2006, 107: 4781-4789. 10.1182/blood-2005-12-4818.PubMedPubMed CentralView ArticleGoogle Scholar
- Smith JW, Walker EB, Fox BA, Haley D, Wisner KP, Doran T, Fisher B, Justice L, Wood W, Vetto J, Maecker H, Dols A, Meijer S, Hu H, Romero P, Alvord WG, Urba WJ: Adjuvant Immunization of HLA-A2-Positive Melanoma Patients With a Modified gp100 Peptide Induces Peptide-Specific CD8+ T-Cell Responses. J Clin Oncol. 2003, 21: 1562-1573. 10.1200/JCO.2003.09.020.PubMedView ArticleGoogle Scholar
- Swindle MM: Swine in the laboratory. 2007, CRC PressView ArticleGoogle Scholar
- Romero P, Zippelius A, Kurth I, Pittet MJ, Touvrey C, Iancu EM, Corthesy P, Devevre E, Speiser DE, Rufer N: Four functionally distinct populations of human effector-memory CD8+ T lymphocytes. J Immunol. 2007, 178: 4112-4119.PubMedView ArticleGoogle Scholar
- Takata H, Takiguchi M: Three memory subsets of human CD8+ T cells differently expressing three cytolytic effector molecules. J Immunol. 2006, 177: 4330-4340.PubMedView ArticleGoogle Scholar
- Perez AL, Bachrach E, Illigens BMW, Jun SJ, Bagden E, Steffen L, Flint A, McGowan FX, Del Nido P, Montecino-Rodriguez E, Tidball JG, Kunkel LM: CXCR4 enhances engraftment of muscle progenitor cells. Muscle Nerve. 2009, 40: 562-572. 10.1002/mus.21317.PubMedPubMed CentralView ArticleGoogle Scholar
- Palmer DC, Restifo NP: Suppressors of cytokine signaling (SOCS) in T cell differentiation, maturation, and function. Trends Immunol. 2009, 30: 592-602. 10.1016/j.it.2009.09.009.PubMedPubMed CentralView ArticleGoogle Scholar
- Geginat J, Sallusto F, Lanzavecchia A: Cytokine-driven proliferation and differentiation of human naive, central memory, and effector memory CD4(+) T cells. J Exp Med. 2001, 194: 1711-1719. 10.1084/jem.194.12.1711.PubMedPubMed CentralView ArticleGoogle Scholar
- Klebanoff CA, Gattinoni L, Torabi-Parizi P, Kerstann K, Cardones AR, Finkelstein SE, Palmer DC, Antony PA, Hwang ST, Rosenberg SA, Waldmann TA, Restifo NP: Central memory self/tumor-reactive CD8+ T cells confer superior antitumor immunity compared with effector memory T cells. Proc Natl Acad Sci USA. 2005, 102: 9571-9576. 10.1073/pnas.0503726102.PubMedPubMed CentralView ArticleGoogle Scholar
- da Silva Meirelles L, Chagastelles PC, Nardi NB: Mesenchymal stem cells reside in virtually all post-natal organs and tissues. J Cell Sci. 2006, 119: 2204-2213. 10.1242/jcs.02932.PubMedView ArticleGoogle Scholar
- Sherwood RI, Christensen JL, Conboy IM, Conboy MJ, Rando TA, Weissman IL, Wagers AJ: Isolation of adult mouse myogenic progenitors: functional heterogeneity of cells within and engrafting skeletal muscle. Cell. 2004, 119: 543-554. 10.1016/j.cell.2004.10.021.PubMedView ArticleGoogle Scholar
- Zuk PA, Zhu M, Ashjian P, De Ugarte DA, Huang JI, Mizuno H, Alfonso ZC, Fraser JK, Benhaim P, Hedrick MH: Human adipose tissue is a source of multipotent stem cells. Mol Biol Cell. 2002, 13: 4279-4295. 10.1091/mbc.E02-02-0105.PubMedPubMed CentralView ArticleGoogle Scholar
- Uezumi A, Ojima K, Fukada S, Ikemoto M, Masuda S, Miyagoe-Suzuki Y, Takeda S: Functional heterogeneity of side population cells in skeletal muscle. Biochem Biophys Res Commun. 2006, 341: 864-873. 10.1016/j.bbrc.2006.01.037.PubMedView ArticleGoogle Scholar
- Illa I, Leon-Monzon M, Dalakas MC: Regenerating and denervated human muscle fibers and satellite cells express neural cell adhesion molecule recognized by monoclonal antibodies to natural killer cells. Ann Neurol. 1992, 31: 46-52. 10.1002/ana.410310109.PubMedView ArticleGoogle Scholar
- Precopio ML, Betts MR, Parrino J, Price DA, Gostick E, Ambrozak DR, Asher TE, Douek DC, Harari A, Pantaleo G, Bailer R, Graham BS, Roederer M, Koup RA: Immunization with vaccinia virus induces polyfunctional and phenotypically distinctive CD8(+) T cell responses. J Exp Med. 2007, 204: 1405-1416. 10.1084/jem.20062363.PubMedPubMed CentralView ArticleGoogle Scholar
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