• flow cytometry;
  • lymphoma;
  • CD38;
  • computational analysis


  1. Top of page
  2. Abstract


Although many clinical laboratories are adopting higher color flow cytometric assays, the approach to optimizing panel design and data analysis is often traditional and subjective. In order to address the question “What is the best flow cytometric strategy to reliably distinguish germinal center B-cell lymphoma (GC-L) from germinal center hyperplasia (GC-H)?” we applied a computational tool that identifies target populations correlated with a desired outcome, in this case diagnosis.


Cases of GC-H and GC-L evaluated by flow cytometric immunophenotyping using CD45, CD20, kappa, lambda, CD19, CD5, CD10, CD38, were analyzed with flowType and RchyOptimyx to construct cellular hierarchies that best distinguished the two diagnostic groups.


The population CD5−CD19+CD10+CD38− had the highest predictive power. Manual reanalysis confirmed significantly higher CD10+/CD38−B-cells in GC-L (median 12.44%, range 0.74–63.29, n = 52) than GC-H (median 0.24%, 0.03–4.49, n = 48, P = 0.0001), but was not entirely specific. Difficulties encountered using this computational approach included the presence of CD10+ granulocytes, continuously variable B-cell expression of CD38, more variable intensity antigen staining in GC-L and inability to assess the contribution of light chain restriction.


Computational analysis with construction of cellular hierarchies related to diagnosis helped guide manual analysis of high dimensional flow cytometric data. This approach highlighted the diagnostic utility of CD38 expression in the evaluation of B-cells with a CD10+ GC phenotype. In contrast to computational analysis of non-neoplastic cell populations, evaluation of neoplastic cells must be able to take into consideration increased variability in antigen expression. © 2013 International Clinical Cytometry Society

The distinction between germinal center B-cell lymphoma (GC-L), such as follicular lymphoma, and germinal center lymphoid hyperplasia (GC-H) can be challenging. Although for some specimens a diagnosis can be made readily using conventional histologic evaluation, for others additional phenotypic or genotypic evaluation is required. Flow cytometric immunophenotyping is a rapid technique that can assist with this distinction, but has some limitations ([1]). Identification of a population of cells with immunoglobulin light chain restriction by flow cytometry provides strong support for a diagnosis of lymphoma, but can occasionally be present in florid GC-H ([2]). In addition, light chain restriction can easily be overlooked if the abnormal population is small, the lymphoma cells lack surface immunoglobulin expression, or the assay is limited by non-specific staining. Although bcl-2 protein overexpression can be identified by immunohistochemistry or flow cytometry in many cases of GC-L, some cases are bcl-2 negative ([3, 4]). In addition, bcl-2 staining of GC B-cells can be difficult to distinguish from that of T-cells and plasma cells by single color immunohistochemistry and although flow cytometric analysis can isolate the B-cells of interest, it requires cell permeabilization, is prone to non-specific staining, and can be difficult to interpret. Abnormal increased or decreased expression of the surface antigens CD19, CD20, and CD10 can be found in many cases of follicular lymphoma, but evaluation for each antigen in isolation lacks sensitivity for the diagnosis of lymphoma ([5, 6]). The availability of higher color flow cytometric assays facilitates the analysis of multiple antigens simultaneously, but raises questions about which antigens to evaluate and how best to analyze the data. In order to address the question “What is the best flow cytometric analysis strategy to distinguish GC-L from reactive lymphoid tissue using the 8-color antibody combination: anti-kappa, anti-lambda, CD19, CD20, CD10, CD5, CD38, and CD45 antibodies?” we applied the computational tools flowType and RchyOptimyx (cellular hieraRCHY OPTIMization), which will be briefly summarized in the discussion section of the manuscript ([7, 8]). This computational analysis highlighted the diagnostic utility of identifying CD10 positive, CD38 negative B-cells in the distinction between GC-L and GC-H. In addition, this study revealed some of the limiting factors that must be considered when applying computational analysis to clinical data sets.


  1. Top of page
  2. Abstract

Lymphoid tissue biopsy specimens with the following features were identified from the pathology reports at the University of Pittsburgh Medical Center (UPMC; University of Pittsburgh Institutional Review Board IRB proposal PRO11060224):

  • Flow cytometric immunophenotyping using an 8-color B-cell tube containing CD45 V500/CD20 V450/kappa fluorescein isothiocyanate (FITC)/lambda phycoerythrin (PE)/CD19 PE-Cy7/CD5 PerCP-Cy5.5/CD10 allophycocyanin (APC)/CD38 APC-H7 (BD Bioscience, San Jose, CA).
  • Flow cytometric immunophenotyping using a tube containing Bcl-2 FITC/CD10 PE/CD20 PerCP-Cy5.5 (BD Bioscience, San Jose, CA).
  • Presence of a population of CD10 positive germinal center B-cells.
  • Diagnosis of reactive changes, including hyperplastic follicles (GC-H), or GC-L, confirmed by review of all diagnostic materials and using the criteria outlined in the 2008 WHO classification ([9]).

Using these criteria, the following specimens were identified: GC-H (n = 48, 25 females, 23 males, median age 40 years) and GC-L (n = 52, 29 females, 23 males, median age 70 years). Cases of GC-L included follicular lymphoma, Grade 1–2 (n = 34) and follicular lymphoma, Grade 3A and/or diffuse large B-cell lymphoma (n = 18).

Flow cytometric immunophenotyping was performed on cells extracted by manual disaggregation. Viability was determined by Trypan Blue exclusion and ranged from 66 to 99% viable (median 84%). A suspension of 5 × l05 cells/tube in phosphate buffer saline (PBS) containing 0.1% sodium azide and 2% fetal bovine serum was incubated with the 8-color surface antibody combination for 15–30 min at 4°C. Lysis was performed using ammonium chloride, and followed by washing with PBS. Stained cells were fixed with 2% formaldehyde. For the bcl-2 tube cells were fixed and permeabilized (Fix and Perm Kit, Life Technologies, NY) as previously described ([3]). Acquisition for both tubes was performed on the same day as staining using a BD FACS Canto II flow cytometer (BD Bioscience, San Jose, CA), and collection of 30,000 events. To ensure consistency of results instrument setup was standardized using target CST beads (BD Bioscience, San Jose, CA) and voltages were monitored with Levey–Jennings plots, settings were cloned between instruments, instrument spectral compensation was set up using Compbeads (BD Biosciences) and lot-to-lot reagent checks were performed.

Data are publicly available through FlowRepository (

The original manual analysis was performed in the UPMC clinical flow cytometry laboratory using FACS DIVA software (BD Bioscience, San Jose, CA), with a template that includes the following steps: exclusion of doublets using a plot of forward light scatter (FSC)-area versus FSC-height, exclusion of debris using a plot of CD45 versus side light scatter (SSC) by gating on cells with low SSC and staining for CD45, identify B-cells through expression of CD19 and/or CD20; identify the following subsets: CD38+(bright) plasma cells, CD10 positive GC B-cells, CD5 positive B-cells; evaluate each subset for immunoglobulin light chain restriction; evaluate each subset visually for altered expression of CD19, CD20, or CD10. A separate tube evaluating bcl-2 expression was either ordered up-front or added if the 8-color B-cell tube identified a CD10 positive germinal center B-cell population, but did not adequately distinguish between GC-H and GC-L. The results of this conventional flow cytometric analysis for the 8-color B-cell tube and a separate flow cytometric tube containing Bcl-2 FITC/CD10 PE/CD20 PerCP-Cy5.5 were reviewed.

Computational analysis was performed on de-identified flow cytometric data as previously described ([8]). Briefly, logical transformation of immunophenotypic data was performed and gates were determined for each marker so as to partition positive and negative cell populations ([8]). Using this data, all possible phenotypes were extracted using flowType ([7, 8]). Receiver operating characteristic (ROC) analysis was then performed to identify the phenotypes associated with a statistically significant difference between GC-H and GC-L and those with the strongest predictive power were selected for further analysis. The phenotypes selected were analyzed using RchyOptimyx to identify their most important parent populations.

Manual re-analysis of the flow cytometric data was then performed using FACS DIVA software (BD Bioscience, San Jose, CA) to further explore the cell populations identified by computational analysis: CD10+, CD10+CD38(−), CD19+CD10+CD38(−), CD5(-)CD19+CD10+CD38(−); CD10+CD38(−), CD10+CD38(−)CD45(−), CD19+CD10+CD38(−)CD45(−); Lambda+CD10(−), Kappa+CD10−, Lambda+Kappa+CD10(−), Lambda+Kappa+CD10(−)CD38(−), Lambda+Kappa+CD10(−)CD20+. The proportion of events and median fluorescence intensity (MFI) was determined for each population identified in order to illustrate features previously reported using current manual methods and compare those with the computational results.


  1. Top of page
  2. Abstract

Original Manual Flow Cytometric Analysis

Manual analysis performed in the clinical flow cytometry laboratory revealed immunoglobulin light chain restriction, bcl-2 expression, and aberrant weak intensity expression of CD19, CD20, and/or bright intensity CD10, in the CD10 positive germinal center B-cells present in some GC-L specimens, but not GC-H (Table 1).

Table 1. Diagnostic Utility of Parameters Identified by Conventional Flow Cytometric Analysis
ParameterGC-hyperplasia (n=48)GC-lymphoma (n=52)Diagnostic utility
Ig light chain restriction047 (90.4%)Sensitivity 90.4%
Specificity 100%
Aberrant CD19022 (42.3%)Sensitivity 42%
Specificity 100%
Bcl-2+ GC B-cells045 (86.5%)Sensitivity 86.5%
Specificity 100%
Aberrant CD2006 (11.5%)Sensitivity 11.5%
Specificity 100%
Aberrant CD1004 (7.7%)Sensitivity 7.7%
Specificity 100%
Aberrant CD19, CD20 or CD10025 (48.0%)Sensitivity 52.1%
Specificity 100%
Population CD38- GC B-cells11 (22.9%)39 (75.0%)Sensitivity 75%
Specificity 75%
Aberrant CD19, CD20 or CD10 or population CD38- GC B-cells11 (22.9%)42 (80.8%)Sensitivity 87.5%
Specificity 75%

Automated Computational Flow Cytometric Analysis

A total of 5,660 phenotypes were identified and those with the strongest predictive power were selected and arranged into a hierarchy (Fig. 1). The phenotypes most capable of discriminating between GC-H and GC-L specimens demonstrated a range of AUC, including CD10+ (higher proportion in GC-L, AUC 0.893), kappa+, lambda+, CD10−, CD20+ (fewer CD10 negative B-cells in GC-L, AUC 0.917), CD5−CD19+CD10+CD38− (higher proportion in GC-L, AUC 0.985), as demonstrated by the colors in Figure 1.


Figure 1. Computational analysis identifies cellular hierarchies with a high predictive power for distinguishing germinal center hyperplasia and germinal center lymphoma. The color of the nodes indicates the predictive power of the phenotype, as measured by AUC (area under ROC curve).

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Manual Re-analysis of Flow Cytometric Data

Refined manual analysis, focusing on the cell populations identified by computational analysis, confirmed the presence of a higher proportion of CD10 positive cells in GC-L, but demonstrated significant overlap with the proportion seen in GC-H, limiting the diagnostic utility of this feature: GC-L median 82.15%, range 14.5–99.3%; GC-H median 8.20%, range 2.1–58.1%). In addition, review of one GC-H case with a large number of CD10 positive events (67.2%) revealed that these events represented neutrophils, rather than germinal center B-cells. This case highlights problems that can be encountered when evaluating each marker in isolation, rather than as part of a multiparameter approach, such as evaluating CD10 expression on B-cells identified with CD20 and/or CD19. Plots of CD10 versus CD38 also revealed a small number of spurious events, with apparent very bright staining for CD38 and CD10 in a diagonal pattern, which likely represent aggregates from the CD38 APC-H7 reagent. Although this artifact was easy to recognize on review of dot-plots, they could potentially mislead the computational analysis.

A new manual clinical analysis template was created with focus on CD10+CD38(−) events as follows: singlets identified using FSC-area versus FSC-height, SSC(low)CD45+(bright) lymphoid cells, CD19+CD20+ B-cells, CD10+ B-cells, CD10+CD38+ B-cells, CD10+CD38− B-cells, Kappa+ and Lambda+ CD10(−), and CD10+ B-cells. Re-analysis using this template confirmed the presence of a significantly higher proportion of CD10 positive, CD38 negative B-cells in GC-L (median 12.44%, range 0.74–63.29, n = 52) than GC-H (median 0.24%, 0.03–4.49, n = 48) (Mann–Whitney P = 0.0001). On visual inspection, a discrete population of CD10 positive, CD38 negative events was present in 11/48 (22.9%) GC-H specimens and 39/52 (75%) GC-L specimens. Therefore, although visual identification of a population of CD10 positive, CD38 negative events had a higher sensitivity for the identification of GC-L than aberrant antigen expression of CD19, CD20, or CD10, it had limited specificity (Table 1 and Fig. 2). However, identification of a discrete population of CD10 positive, CD38 negative B-cells in three of five cases of GC-L that lacked light chain restriction (i.e., were surface immunoglobulin negative) could potentially be used to prompt additional investigation. Consistency of staining was confirmed by lack of a statistically significant difference between CD38 MFI of CD10 negative germinal center B-cells in GC-L and GC-H.


Figure 2. Example phenotypes identified in germinal center hyperplasia and lymphoma. Dark blue events = B-cells identified with staining for CD19 and CD20. Green events = CD10 positive B-cells. Red events = CD10+CD38(−). GC-H Case 56 (upper row) displays many CD10+ B-cells (58.1% total) that demonstrate typical staining for CD38 and polytypic staining for lambda and kappa immunoglobulin light chains. GC-H Case 31 (center row) displays small population of CD10 positive events with typical expression of CD19 and CD20, and polytypic staining for lambda and kappa immunoglobulin light chains, but partial lack of staining for CD38. GC-L Case 77 (lower row) demonstrates a large population of CD10 positive B-cells with decreased intensity staining for CD19 and CD20, lambda light chain restriction and lack of staining for CD38. Note the presence of a few events with apparent very bright, diagonal, staining for CD10 and CD38 that likely represent reagent aggregates.

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Review of manual analysis also highlighted limitations in the computational analysis that related to the partition of positive and negative cell events. Although there were discrete CD5, CD10, CD19, and CD20 positive and negative populations in most specimens, GC-L demonstrated more variable intensity of antigen expression than GC-H (Fig. 3). Indeed, as reported previously, there were significant differences in the MFI for CD19 (lower in GC-L), CD20 (lower in GC-L), CD10 (higher in GC-L), and CD38 (lower in GC-L) (Table 2 and Fig. 3). Therefore, when a single threshold value was applied to partition positive and negative cell populations, some GC-L cells with weak intensity expression of CD19 were considered “negative.” However, our computational approach, with a fixed threshold value, was not an effective way of identifying alterations in intensity. In addition, some other antigens did not demonstrate discrete positive and negative populations. For example, CD45 staining was uniform and of relatively weak intensity and interpreted as “negative” by computational analysis but was considered “positive” by manual analysis because of the expected staining of leukocytes. Although this difference in interpretation did not affect the analysis because CD45 staining was similar in lymphoma and reactive cases, it could potentially mask detection of loss of staining in other data sets. In contrast, CD38 demonstrated continuously variable intensity of staining. Therefore, the threshold chosen by computational analysis divided cells into those with relatively higher and lower intensity staining for CD38, with lower intensity staining that crossed the threshold being classified as “negative.” Repeat computational analysis utilizing the MFI for CD19 and CD38, rather than the percent of each cell population, did not improve the ROC values (data not shown).


Figure 3. Variation in germinal center B-cell antigen expression in hyperplasia and lymphoma. MFI indicated for CD10 positive B-cells in GC-H and GC-L.

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Table 2. Comparison of germinal center B-cell antigen expression in hyperplasia and lymphoma
ParameterGC-hyperplasia MFI (n=48)GC-lymphoma MFI (n = 52)Statistical significance
  1. MFI = Median fluorescence intensity of CD10 positive B-cells.

CD199,818 (3,023 – 24,129)4,811 (1,074 – 14,025)P <0.0001
CD2016,862 (1,483 – 35,149)7,700 (312 – 20,938)P <0.0001
CD10885 (513 – 2,187)2,048 (424 – 10,830)P <0.0001
CD381,648 (169 – 2,972)503 (12 – 1,684)P <0.0001

Review of the manual analysis for kappa and lambda highlighted some difficulties encountered by the computational analysis. The computational approach applied did not effectively assess for immunoglobulin light chain restriction because it evaluated each phenotype independently (e.g., kappa+/lambda−, lambda+/kappa−), rather than identifying a dominant population of B-cells expressing one light chain, either kappa or lambda, along with lack of another population of B-cells expressing the other light chain. In addition, there was poor separation of surface immunoglobulin light chain positive and negative events, in part due to cytophilic antibody staining of non-B-cells, which likely led to the identification of events apparently positive for both kappa and lambda. Interestingly, the population “kappa+, lambda+, CD10−, CD20+” was identified as one of the key discriminators between GC-L and GC-H, and appeared to relate to the higher prevalence of non-germinal center B-cells, without dominant light chain expression, in GC-H.


  1. Top of page
  2. Abstract

Advances in instrumentation and reagent technologies have led to the widespread use of high-level multicolor flow cytometry. However, effective strategies for storing, representing, and interpreting the increasingly complex data have been lacking. Recognition of this need has led to the recent development of many automated gating tools, some of which are now employed in research applications and to support high-throughput technology ([10-12]). There is increasing interest in employing these tools in clinical flow cytometry laboratories to support the use of higher color systems, larger panels of reagents, and more sophisticated analysis strategies with complex hierarchical gating ([13, 14]). Manual analysis of flow cytometric data remains one of the largest variables in flow cytometric immunophenotyping ([15]) often relies on personal experience, is time intense, error prone, and difficult to standardize. Automated analysis is poised not only to remove the burden of manual gating ([16]), but also to take the next step and identify biological changes associated with disease. However, these tools have the potential to also assist with manual analysis by maximizing the information obtained from a single multicolor tube, evaluating the relative importance of information in reaching a final interpretation, suggesting optimized gating strategies and potentially decreasing the size of marker panels ([13, 14]).

In the current study, we used computational analysis to identify features that can best distinguish germinal center lymphoma from reactive germinal center cells. The utility of our computational pipeline has previously been demonstrated using 13-color flow cytometric data from T-cell subset evaluation of HIV positive subjects ([7, 8]). This pipeline consists of two independent open-source tools. In this setting, flowType was used to identify surrogate cell surface marker phenotypes that could overcome the need to detect intracellular markers and RchyOptimyx was used to simplify the gating strategy for the identified phenotypes (e.g., using CD45RO− CCR5− CCR7+ instead of the more complicated CD28+ CD45RO− CD57− CCR5− CD27+ CCR7+) and summarize a large list of phenotypes to identify three strong predictors of progression to AIDS ([7]). In the current study, we utilized RchyOptimyx to objectively identify the optimal analysis strategy for the 8-color B-cell directed antibody combination: anti-kappa, anti-lambda, CD19, CD20, CD10, CD5, CD38, and CD45. Application of this computational tool highlighted the diagnostic utility of identifying CD38 negative, CD10 positive germinal center B-cells in association with lymphoma, a feature not emphasized in the original manual analysis.

CD38 is a glycoprotein that is expressed by precursor B-cells, germinal center B-cells, and plasma cells, but is absent from naïve B-cells and memory B-cells ([17-19]). Its expression appears to be under tight control, with B-cells undergoing synchronous gain and loss of CD38 and CD10 expression as they enter and exit the germinal center ([20]). Non-neoplastic B-cells with a CD38 negative, CD10 positive phenotype are not well recognized and therefore, the presence of this phenotype in GC-L may reflect aberrant antigen expression. This interpretation is supported by a previous study that reported significantly lower CD38 expression by the neoplastic cells in follicular lymphoma, as determined by MFI, when compared with reactive germinal center B-cells ([21]). However, in both the previous and current study, evaluation of CD38 expression alone was insufficient to establish a diagnosis of lymphoma ([21]). Although revision of the manual analysis strategy in the current study to emphasize CD38 negative, CD10 positive B-cells identified most of the cases of germinal center lymphoma, a small population of cells with this phenotype was also identified in some cases of reactive hyperplasia. The identification of a small population of CD38 negative, CD10 positive B-cells in reactive lymphoid tissue suggests that in GC-L this phenotype may represent expansion of this subset rather than aberrant antigen expression. It will be of interest to explore additional phenotypic findings than can assist in the distinction between normal and neoplastic CD10 positive, CD38 negative B-cells, and determine whether non-neoplastic B-cells with this phenotype reflects a transitional stage from naïve or immature/transitional B-cells to germinal center B-cells, or germinal center B-cells to memory B-cells ([19]).

The main utility of the computational strategy utilized in this study was the ability to identify phenotypes that were associated with an outcome, in this case diagnosis, and thereby assess the relative utility of different antibodies and analysis strategies. However, although the computational analysis employed in the current study highlighted a cell population that had not been emphasized in the manual analysis, it did not supersede any of the existing analysis strategies. One factor that contributed to the decision to retain the other analysis components is the low tolerance for any false positive or false negative results in the clinical diagnostic setting. In addition, some of the diagnostically useful parameters utilized in the conventional analysis could not be identified with the computational analysis because of independent consideration of each marker and use of fixed thresholds. For example, the independent partition of each marker into positive and negative cell populations led to difficulty in distinguishing CD10 staining of granulocytes from that of germinal center B-cells, and could have been minimized by initial gating on B-cells or possibly use of side light scatter to gate out granulocytes. Similarly, this strategy could not distinguish B-cells and non-B-cell cytophilic staining for kappa and lambda, and was unable to assess for immunoglobulin light chain restriction, i.e., more homogeneous staining of B-cells for one light chain only. Other strategies for automated analysis of flow cytometric data, such as vector quantization, dimension reduction, and clustering algorithms have the advantage of utilizing multiple parameters simultaneously to identify and evaluate populations of cells, as shown in the referenced examples ([22-24]). Multiparametric approaches, such as these, or an ensemble of computational strategies, as highlighted through the FlowCAP challenges, might be more successful at population identification and characterization, and serve as a better comparison with manual analysis ([16]). However, these multivariate approaches are limited in their ability to incorporate biological guidance for identifying cell populations, and often require a complex and subjective meta-clustering step for matching high-dimensional cell populations across different patients ([14, 25]). Another challenge in the design of automated computational strategies is the presence of technical and biologic variability in the data. Although the flow cytometric data utilized in the current study was generated employing clinical laboratory procedures to ensure consistency of data, there were some artifacts, such as the presence of non-specific antibody staining, doublet formation, and reagent aggregates, which could mislead automated computational analysis. As the development of clinically applicable computational tools progresses, it will be important to address these issues using controlled clinical data sets with associated outcome data, such as the one we describe here.


  1. Top of page
  2. Abstract
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