A new automated flow cytometry data analysis approach for the diagnostic screening of neoplastic B-cell disorders in peripheral blood samples with absolute lymphocytosis

Flow cytometric analysis of myelodysplasia: Pre-analytical and technical issues-Recommendations from the European LeukemiaNet

BACKGROUND

Flow cytometry (FCM) aids the diagnosis and prognostic stratification of patients with suspected or confirmed myelodysplastic syndrome (MDS). Over the past few years, significant progress has been made in the FCM field concerning technical issues (including software and hardware) and pre-analytical procedures.

METHODS

Recommendations are made based on the data and expert discussions generated from 13 yearly meetings of the European LeukemiaNet international MDS Flow working group.

RESULTS

We report here on the experiences and recommendations concerning (1) the optimal methods of sample processing and handling, (2) antibody panels and fluorochromes, and (3) current hardware technologies.

CONCLUSIONS

These recommendations will support and facilitate the appropriate application of FCM assays in the diagnostic workup of MDS patients. Further standardization and harmonization will be required to integrate FCM in MDS diagnostic evaluations in daily practice.

From big flow cytometry datasets to smart diagnostic strategies: The EuroFlow approach

The rise in the analytical speed of mutiparameter flow cytometers made possible by the introduction of digital instruments, has brought up the possibility to manage progressively higher number of parameters simultaneously on significantly greater numbers of individual cells. This has led to an exponential increase in the complexity and volume of flow cytometry data generated about cells present in individual samples evaluated in a single measurement. This increase demands for new developments in flow cytometry data analysis, graphical representation, and visualization and interpretation tools to address the new big data challenges, i.e. processing data files of ≥10-25 parameters per cell in samples with >5-10 million cells (= up to 250 million data points per cell sample) obtained in a few minutes. Here, we present a comprehensive review of some of the tools developed by the EuroFlow consortium for processing flow cytometric big data files in diagnostic laboratories, particularly focused on automated EuroFlow approaches for: i) identification of all cell populations coexisting in a sample (automated gating); ii) smart classification of aberrant cell populations in routine diagnostics; iii) automated reporting; together with iv) new tools developed to visualize n-dimensional data in 2-dimensional plots to support expert-guided automated data analysis. The concept of using reference data bases implemented into software programs, in combination with multivariate statistical analysis pioneered by EuroFlow, provides an innovative, highly efficient and fast approach for diagnostic screening, classification and monitoring of patients with distinct hematological and immune disorders, as well as other diseases.

A Method for the Interpretation of Flow Cytometry Data Using Genetic Algorithms

Background:

Flow cytometry analysis is the method of choice for the differential diagnosis of hematologic disorders. It is typically performed by a trained hematopathologist through visual examination of bidimensional plots, making the analysis time-consuming and sometimes too subjective. Here, a pilot study applying genetic algorithms to flow cytometry data from normal and acute myeloid leukemia subjects is described.

Subjects and Methods:

Initially, Flow Cytometry Standard files from 316 normal and 43 acute myeloid leukemia subjects were transformed into multidimensional FITS image metafiles. Training was performed through introduction of FITS metafiles from 4 normal and 4 acute myeloid leukemia in the artificial intelligence system.

Results:

Two mathematical algorithms termed 018330 and 025886 were generated. When tested against a cohort of 312 normal and 39 acute myeloid leukemia subjects, both algorithms combined showed high discriminatory power with a receiver operating characteristic (ROC) curve of 0.912.

Conclusions:

The present results suggest that machine learning systems hold a great promise in the interpretation of hematological flow cytometry data.

A Monoclonal Antibody That Discriminates Between SNAP-Tagged and CLIP-Tagged Proteins

SNAP-tag technology allows recombinant proteins to be covalently labeled to O(6)-benzylguanine (BG)-modified substrates with 1:1 stoichiometry. By attaching according fluorophores, this method is ideally suited for in vitro and in vivo imaging, as well as protein interaction analyses. Fluorophores modified with BG react with the SNAP-tag, whereas those modified with O(2)-benzylcytosine (BC) conjugate to a more recent derivative known as the CLIP-tag. The orthogonal substrate specificity of the SNAP- and CLIP-tags extends the range of applications by allowing double labeling. We previously developed a monoclonal antibody (mAb) that recognizes both tags. In this study, we describe a new mAb, which is specific for the SNAP-tag alone. Therefore, this mAb allows discrimination between SNAP- and CLIP-tags within a broad range of immunological methods, including enzyme-linked immunosorbent assays, western blotting, flow cytometry, and immunohistochemistry.

Standardizing Flow Cytometry Immunophenotyping Analysis from the Human ImmunoPhenotyping Consortium

Standardization of immunophenotyping requires careful attention to reagents, sample handling, instrument setup, and data analysis, and is essential for successful cross-study and cross-center comparison of data. Experts developed five standardized, eight-color panels for identification of major immune cell subsets in peripheral blood. These were produced as pre-configured, lyophilized, reagents in 96-well plates. We present the results of a coordinated analysis of samples across nine laboratories using these panels with standardized operating procedures (SOPs). Manual gating was performed by each site and by a central site. Automated gating algorithms were developed and tested by the FlowCAP consortium. Centralized manual gating can reduce cross-center variability, and we sought to determine whether automated methods could streamline and standardize the analysis. Within-site variability was low in all experiments, but cross-site variability was lower when central analysis was performed in comparison with site-specific analysis. It was also lower for clearly defined cell subsets than those based on dim markers and for rare populations. Automated gating was able to match the performance of central manual analysis for all tested panels, exhibiting little to no bias and comparable variability. Standardized staining, data collection, and automated gating can increase power, reduce variability, and streamline analysis for immunophenotyping.

Computationally efficient multidimensional analysis of complex flow cytometry data using second order polynomial histograms

Many methods have been described for automated clustering analysis of complex flow cytometry data, but so far the goal to efficiently estimate multivariate densities and their modes for a moderate number of dimensions and potentially millions of data points has not been attained. We have devised a novel approach to describing modes using second order polynomial histogram estimators (SOPHE). The method divides the data into multivariate bins and determines the shape of the data in each bin based on second order polynomials, which is an efficient computation. These calculations yield local maxima and allow joining of adjacent bins to identify clusters. The use of second order polynomials also optimally uses wide bins, such that in most cases each parameter (dimension) need only be divided into 4-8 bins, again reducing computational load. We have validated this method using defined mixtures of up to 17 fluorescent beads in 16 dimensions, correctly identifying all populations in data files of 100,000 beads in <10 s, on a standard laptop. The method also correctly clustered granulocytes, lymphocytes, including standard T, B, and NK cell subsets, and monocytes in 9-color stained peripheral blood, within seconds. SOPHE successfully clustered up to 36 subsets of memory CD4 T cells using differentiation and trafficking markers, in 14-color flow analysis, and up to 65 subpopulations of PBMC in 33-dimensional CyTOF data, showing its usefulness in discovery research. SOPHE has the potential to greatly increase efficiency of analysing complex mixtures of cells in higher dimensions.

Screening bone marrow samples for abnormal lymphoid populations and myelodysplasia-related features with one 10-color 14-antibody screening tube

BACKGROUND

We have designed one-tube 14-antibody 10-color flow cytometry (FCM) panel that would provide maximum information on lymphoid and myeloid cell subsets in bone marrow aspirates (BMA) from patients with cytopenia(s).

SAMPLES AND METHODS

BMA from 8 normal donors, from 286 non-malignant hospital controls, 92 myelodysplastic syndromes (MDS), 47 myeloproliferative neoplasms (MPN), and from 14 MDS/MPN patients were investigated. One tube 14-monoclonal antibody (MAb) 10-fluorochrome panel included: kappa+CD4 FITC, Lambda+CD8 PE, CD3 + CD14 ECD, CD34 APC, CD20+CD56 PC7, CD10 APC-A750, CD19 APC-A700, CD33 PC5.5, CD5 PB, and CD45 KO. Kappa/lambda expression was evaluated separately in CD19+, CD10+ and CD5+ B-cells. CD4+CD3+, CD8+CD3+, CD5+CD3+ T-lymphocyte subsets were enumerated. Blasts were evaluated using CD45/SSC and CD34 gating. The FCM score for MDS (sc. Ogata score) included CD34+ myeloblast and B-progenitor cluster size, myeloblast/lymphocyte CD45 expression, and granulocyte/lymphocyte SSC ratio.

RESULTS

Abnormal lymphoid populations or increased plasma cells were found in 18 patients (4%). A 43/92 BMA from MDS and 7/14 from MDS/MPN patients had score >2. Score >2 had 92.5% positive predictive value for MDS/MDS-MPN diagnosis. Negative predictive value for MDS/MDS-MPN was 83% for scores under 3 and 88% for scores under 2. All but two of normal/hospital control samples had FCM score <3 (99%). Differences in scores between MDS & MDS/MPN and the control groups were statistically significant (P < 0.0001).

CONCLUSIONS

Our one-tube FCM panel can be easily applied to screening for aberrant lymphoid populations and myelodysplasia-related features. MDS scores >2 are highly indicative of MDS or MDS-MPN.

CD200 has an important role in the differential diagnosis of mature B-cell neoplasms by multiparameter flow cytometry

BACKGROUND

Multiparameter flow cytometry is a useful tool for the diagnostic evaluation of mature B-cell neoplasms (MBN). Recently, it has been shown that CD200 may improve the distinction between chronic lymphocytic leukemia (CLL; CD200+) and mantle cell lymphoma (MCL; CD200-), but the role of CD200 expression in atypical CLL and other MBN remains to be established.

METHODS

To address this issue, we investigated the expression of CD200 in 159 consecutive cases of MBN.

RESULTS

CD200 was strongly expressed in CLL and was revealed to be an excellent marker to distinguish CLL from MCL, even in cases of atypical CLL. However, lack of CD200 was not an exclusive finding of MCL, being also observed in other MBNs. Furthermore, CD200 was highly expressed in hairy cell leukemia, being useful in the differential diagnosis of lymphomas with villous lymphocytes. Herein, we propose an algorithm to classify CD5+ MBNs based on the expression of CD200, CD11c, heavy chain immunoglobulins, and Matutes score.

CONCLUSIONS

These results expand the understanding of the CD200 expression in MBNs, giving further support for the inclusion of this marker in the routine investigation by flow cytometry.

Computational analysis optimizes the flow cytometric evaluation for lymphoma

BACKGROUND

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.

DESIGN

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.

RESULTS

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.

CONCLUSION

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.

EuroFlow standardization of flow cytometer instrument settings and immunophenotyping protocols

The EU-supported EuroFlow Consortium aimed at innovation and standardization of immunophenotyping for diagnosis and classification of hematological malignancies by introducing 8-color flow cytometry with fully standardized laboratory procedures and antibody panels in order to achieve maximally comparable results among different laboratories. This required the selection of optimal combinations of compatible fluorochromes and the design and evaluation of adequate standard operating procedures (SOPs) for instrument setup, fluorescence compensation and sample preparation. Additionally, we developed software tools for the evaluation of individual antibody reagents and antibody panels. Each section describes what has been evaluated experimentally versus adopted based on existing data and experience. Multicentric evaluation demonstrated high levels of reproducibility based on strict implementation of the EuroFlow SOPs and antibody panels. Overall, the 6 years of extensive collaborative experiments and the analysis of hundreds of cell samples of patients and healthy controls in the EuroFlow centers have provided for the first time laboratory protocols and software tools for fully standardized 8-color flow cytometric immunophenotyping of normal and malignant leukocytes in bone marrow and blood; this has yielded highly comparable data sets, which can be integrated in a single database.

EuroFlow antibody panels for standardized n-dimensional flow cytometric immunophenotyping of normal, reactive and malignant leukocytes

Most consensus leukemia & lymphoma antibody panels consist of lists of markers based on expert opinions, but they have not been validated. Here we present the validated EuroFlow 8-color antibody panels for immunophenotyping of hematological malignancies. The single-tube screening panels and multi-tube classification panels fit into the EuroFlow diagnostic algorithm with entries defined by clinical and laboratory parameters. The panels were constructed in 2-7 sequential design-evaluation-redesign rounds, using novel Infinicyt software tools for multivariate data analysis. Two groups of markers are combined in each 8-color tube: (i) backbone markers to identify distinct cell populations in a sample, and (ii) markers for characterization of specific cell populations. In multi-tube panels, the backbone markers were optimally placed at the same fluorochrome position in every tube, to provide identical multidimensional localization of the target cell population(s). The characterization markers were positioned according to the diagnostic utility of the combined markers. Each proposed antibody combination was tested against reference databases of normal and malignant cells from healthy subjects and WHO-based disease entities, respectively. The EuroFlow studies resulted in validated and flexible 8-color antibody panels for multidimensional identification and characterization of normal and aberrant cells, optimally suited for immunophenotypic screening and classification of hematological malignancies.

Technologies supporting analytical cytology: clinical, research and drug discovery applications

The tools and techniques developed for analytical cytology have become invaluable in expanding the development of cancer screening programs and biomarker discovery for personalized medicine. Detecting cellular, molecular, and functional changes of diseased tissue as defined by quantitative analytical methodologies has enhanced the field of medical diagnostics and prognostics. The focus of this review is to outline applications and recent technical advances in flow cytometry, laser scanning cytometry, image cytometry, and quantitative image analysis, as they pertain to clinical, research, and drug discovery applications.

Leukoflow: multiparameter extended white blood cell differentiation for routine analysis by flow cytometry

Differential white blood cell count (dWBC) is a frequently used diagnostic tool. For most patient samples an automated blood counter produces a five-part differential count. If this dWBC does not meet pre-set criteria, microscopic dWBC is performed. Microscopy is labor intensive and requires sustained training of technicians. Inter-observer variation and statistical variation are significant, due to limited numbers of cells counted. Flow cytometry is a candidate reference method for dWBC. Advantages are immunological definitions and large number of measured cells. Our goal was to replace (part of) the microscopic dWBC by a flow cytometric dWBC, that gives additional information on blasts, myeloid precursors, and lymphocyte subsets. We designed a cocktail of antibodies (CD4, CD14, CD34, CD16, CD56, CD19, CD45, CD138, CD3, and CD71) combined with a gating strategy and flow cytometric protocol for easy identification of leukocyte populations. This assay, called Leukoflow, requires low sample volume, has few manual handling steps, and a potential turn-around-time shorter than 2 h. We determine percentages and absolute concentrations of at least 13 different cell populations. For quantification of normoblasts a second flow cytometric staining was designed. We compared microscopic dWBC with that of the automated blood counter and Leukoflow for normal and abnormal blood samples. Leukoflow results correlate well with the automated blood counter for leukocytes, neutrophils, eosinophils, monocytes, and lymphocytes. Correlation with manual dWBC is lower. Blast counts reported by Leukoflow suffer less from inter-observer variation compared to manual dWBC. In addition to microscopic or cytometric dWBC-techniques T-lymphocytes, CD4-T-lymphocytes, B-lymphocytes, NK-cells, myeloid progenitors, plasma cells, and blasts are determined by Leukoflow. These populations give potential useful clinical information and are subject for future studies focusing on the additional clinical value. Leukoflow is a highly interesting and promising technique for clinical laboratories.

Harmonization of light scatter and fluorescence flow cytometry profiles obtained after staining peripheral blood leucocytes for cell surface-only versus intracellular antigens with the Fix & Perm reagent

Staining for intracellular markers with the Fix & Perm reagent is associated with variations in the scatter properties of leucocytes, limiting automated analysis of flow cytometry (FCM) data. Here, we investigated those variables significantly contributing to changes in the light scatter, autofluorescence, and bcl2 staining characteristics of peripheral blood (PB) leucocytes, after fixation with Fix & Perm. Our major aim was to evaluate a new mathematical approach for automated harmonization of FCM data from datafiles corresponding to aliquots of a sample treated with cell-surface-only versus Fix & Perm intracellular staining techniques. Overall, neither the anticoagulant used nor sample storage for <24 h showed significant impact on the light scatter and fluorescence properties of PB leucocytes; similarly, the duration of the fixation period (once >15 min were used) had a minimum impact on the FCM properties of PB leucocytes. Conversely, changes in cell/protein concentrations and the fixative/sample (vol/vol) ratio had a clear impact on the light scatter features of some populations of leucocytes. Accordingly, lower cell/protein concentrations were associated with lower scatter values, particularly for the neutrophils. Such changes could be partially corrected through the use of higher fixative to sample volume ratios. Despite the variable changes detected between aliquots of the same sample treated with cell surface-only versus intracellular staining procedures, the new mathematical approach here proposed and evaluated for automated harmonization of common parameters in both datafiles, could correct the FCM profiles of leucocytes derived from cells undergoing conventional fixation/permeabilization procedures, and made them indistinguishable from those corresponding to aliquots of the same sample treated with cell-surface-only staining techniques.

A software framework enabling analysis of plate-based flow cytometry data for high-throughput screening

Flow cytometry (FCM) is an important technology with a broad spectrum of applications ranging from basic research to clinical diagnostics. In a typical FCM experiment, thousands of cells are queried with respect to size, shape, and abundance of multiple cell surface antigens. Recent advances in FCM techniques and instrumentation have enabled researchers to raise the throughput of experimentation dramatically. However, data analysis has remained a time-consuming activity requiring significant manual intervention for gating as well as for overall data reduction and interpretation. Presented in this article is a novel, algorithmically flexible, internally developed, software framework for the analysis of plate-based FCM data for high-throughput screening (HTS). Utilizing a post-treatment pooling strategy, >87,000 individual wells representing over 240,000 compounds were automatically gated, percent of control (POC) calculated, results assembled, deconvolved, and sorted, allowing researchers to visually assess wells of interest in minutes.

A survey of flow cytometry data analysis methods

Flow cytometry (FCM) is widely used in health research and in treatment for a variety of tasks, such as in the diagnosis and monitoring of leukemia and lymphoma patients, providing the counts of helper-T lymphocytes needed to monitor the course and treatment of HIV infection, the evaluation of peripheral blood hematopoietic stem cell grafts, and many other diseases. In practice, FCM data analysis is performed manually, a process that requires an inordinate amount of time and is error-prone, nonreproducible, nonstandardized, and not open for re-evaluation, making it the most limiting aspect of this technology. This paper reviews state-of-the-art FCM data analysis approaches using a framework introduced to report each of the components in a data analysis pipeline. Current challenges and possible future directions in developing fully automated FCM data analysis tools are also outlined.

Guidelines for an integrated diagnostic approach of chronic lymphoproliferative disorders in the routine laboratory of haematology in Belgium

This paper summarizes the minimal workout of chronic lymphoproliferative disorders in a routine laboratory of haematology as recommended by a team of experienced laboratory supervisors in Belgium, taking into account the specific organisation of healthcare in Belgium, the innovations in the field of molecular analyses and related reimbursement. The starting point was essentially based upon clinical and/or haematological indications and it is emphasized that conclusions should be drawn in close dialogue with the clinician and experts in cytogenetics and histopathology. Reports made in the laboratory should be based upon an integration of cytomorphological, immunophenotypical and molecular data. These guidelines are not intended to be used as universal 'diagnostic pathways', but should be useful in developing local diagnostic pathways. It is well understood that this consensus, being valid anno 2009, may rapidly change with new technologies being introduced and new targets discovered.

A probabilistic approach for the evaluation of minimal residual disease by multiparameter flow cytometry in leukemic B-cell chronic lymphoproliferative disorders

Multiparameter flow cytometry has become an essential tool for monitoring response to therapy in hematological malignancies, including B-cell chronic lymphoproliferative disorders (B-CLPD). However, depending on the expertise of the operator minimal residual disease (MRD) can be misidentified, given that data analysis is based on the definition of expert-based bidimensional plots, where an operator selects the subpopulations of interest. Here, we propose and evaluate a probabilistic approach based on pattern classification tools and the Bayes theorem, for automated analysis of flow cytometry data from a group of 50 B-CLPD versus normal peripheral blood B-cells under MRD conditions, with the aim of reducing operator-associated subjectivity. The proposed approach provided a tool for MRD detection in B-CLPD by flow cytometry with a sensitivity of < or =8 x 10(-5) (median of < or =2 x 10(-7)). Furthermore, in 86% of B-CLPD cases tested, no events corresponding to normal B-cells were wrongly identified as belonging to the neoplastic B-cell population at a level of < or =10(-7). Thus, this approach based on the search for minimal numbers of neoplastic B-cells similar to those detected at diagnosis could potentially be applied with both a high sensitivity and specificity to investigate for the presence of MRD in virtually all B-CLPD. Further studies evaluating its efficiency in larger series of patients, where reactive conditions and non-neoplastic disorders are also included, are required to confirm these results.

Automated high-dimensional flow cytometric data analysis

Flow cytometric analysis allows rapid single cell interrogation of surface and intracellular determinants by measuring fluorescence intensity of fluorophore-conjugated reagents. The availability of new platforms, allowing detection of increasing numbers of cell surface markers, has challenged the traditional technique of identifying cell populations by manual gating and resulted in a growing need for the development of automated, high-dimensional analytical methods. We present a direct multivariate finite mixture modeling approach, using skew and heavy-tailed distributions, to address the complexities of flow cytometric analysis and to deal with high-dimensional cytometric data without the need for projection or transformation. We demonstrate its ability to detect rare populations, to model robustly in the presence of outliers and skew, and to perform the critical task of matching cell populations across samples that enables downstream analysis. This advance will facilitate the application of flow cytometry to new, complex biological and clinical problems.

Analysis of flow cytometry data using an automatic processing tool

In spite of recent advances in flow cytometry technology, most cytometry data is still analyzed manually which is labor-intensive for large datasets and prone to bias and inconsistency. We designed an automatic processing tool (APT) to rapidly and consistently define and describe cell populations across large datasets. Image processing, smoothing, and clustering algorithms were used to generate an expert system that automatically reproduces the functionality of commercial manual cytometry processing tools. The algorithms were developed using a dataset collected from CMV-infected infants and combined within a graphical user interface, to create the APT. The APT was used to identify regulatory T-cells in HIV-infected adults, based on expression of FOXP3. Results from the APT were compared directly with the manual analyses of five immunologists and showed close agreement, with a concordance correlation coefficient of 0.96 (95% CI 0.91-0.98). The APT was well accepted by users and able to process around 100 data files per hour. By applying consistent criteria to all data generated by a study, the APT can provide a level of objectivity that is difficult to match using conventional manual analysis.

Generation of flow cytometry data files with a potentially infinite number of dimensions

Immunophenotypic characterization of B-cell chronic lymphoproliferative disorders (B-CLPD) is associated with the use of increasingly larger panels of multiple combinations of 3 to > or =6 monoclonal antibodies (Mab), data analysis being separately performed for each of the different stained sample aliquots. Here, we describe and validate an automated method for calculation of flow cytometric data from several multicolor stainings of the same cell sample--i.e., the merging of data from different aliquots stained with partially overlapping combinations of Mab reagents (focusing on > or =1 cell populations)--into one data file as if it concerned a single "super" multicolor staining. Evaluation of the performance of the method described was done in a group of 60 B-CLPD studied at diagnosis with 18 different reagents in a panel containing six different 3- and 4-color stainings, which systematically contained CD19 for the identification of B-cells. Our results show a high degree of correlation and agreement between originally measured and calculated data about cell surface stainings, providing a basis for the use of this approach for the generation of flow cytometric data files containing information about a virtually infinite number of stainings for each individual cellular event measured in a sample, using a limited number of fluorochrome stainings.

Mixture modeling approach to flow cytometry data

Flow Cytometry has become a mainstay technique for measuring fluorescent and physical attributes of single cells in a suspended mixture. These data are reduced during analysis using a manual or semiautomated process of gating. Despite the need to gate data for traditional analyses, it is well recognized that analyst-to-analyst variability can impact the dataset. Moreover, cells of interest can be inadvertently excluded from the gate, and relationships between collected variables may go unappreciated because they were not included in the original analysis plan. A multivariate non-gating technique was developed and implemented that accomplished the same goal as traditional gating while eliminating many weaknesses. The procedure was validated against traditional gating for analysis of circulating B cells in normal donors (n = 20) and persons with Systemic Lupus Erythematosus (n = 42). The method recapitulated relationships in the dataset while providing for an automated and objective assessment of the data. Flow cytometry analyses are amenable to automated analytical techniques that are not predicated on discrete operator-generated gates. Such alternative approaches can remove subjectivity in data analysis, improve efficiency and may ultimately enable construction of large bioinformatics data systems for more sophisticated approaches to hypothesis testing.

A multidimensional classification approach for the automated analysis of flow cytometry data

We describe an automated multidimensional approach for the analysis of flow cytometry data based on pattern classification. Flow cytometry is a widely used technique both for research and clinical purposes where it has become essential for the diagnosis and follow up of a wide spectrum of diseases, such as HIV-infection and neoplastic disorders. Flow cytometry data sets are composed of quite a large number of observations that can be viewed as elements of a n-dimensional space. The aim of the analysis of such data files is typically to classify groups of cellular events as specific populations with biological meaning. Despite significant improvements in data acquisition capabilities of flow cytometers, data analysis is still based on bi-dimensional strategies which were defined a long time ago. These are strongly dependent on the expertise of an expert operator, this approach being relatively subjective and potentially leading to unreliable results. Automated analysis of flow cytometry data is an essential step to improve reproducibility of the results. The proposed automated analysis was implemented on peripherial blood lymphocyte subsets from 307 samples stained and prepared in an identical way and it was capable of identifying all cell subsets present in each sample studied that could also be detected in the same data files by an expert operator. A highly significant correlation was found between the results obtained by an expert operator using a conventional manual method of analysis and those obtained using the implemented automated approach.

“6 markers/5 colors” extended white blood cell differential by flow cytometry

Electronic white blood cell (WBC) differential by standard cytology (hematology analyzer and visual inspection of blood smears) is limited to five types and identification of abnormal cells is only qualitative, often problematic, poorly reproducible, and labour costing. We present our results on WBC differential by flow cytometry (FCM) with a 6 markers, 5 colors CD36-FITC/CD2-PE+CRTH2-PE/CD19-ECD/CD16-Cy5/CD45-Cy7 combination, on 379 subjects, with detection of 12 different circulating cell types, among them 11 were quantified. Detection of quantitative abnormalities of whole leucocytes, neutrophils, eosinophils, basophils, monocytes, or lymphocytes was comparable by FCM and by standard cytology in terms of sensitivity and specificity. FCM was better than standard cytology in detection and quantification of circulating blast cells or immature granulocytes, with a first lineage orientation in the former case. All cases of lymphocytosis, with lineage assignment, were detected by FCM. FCM identified a group of patients with excess of CD16pos monocytes as those having an inflammatory syndrome. WBC differential by FCM is at least as reliable as by standard cytology. FCM superiority consists in identification and systematic quantification of parameters that cannot be assessed by standard cytology such as lineage orientation of blast cells or lymphocytes, and expression of markers of interest such as CD16 on inflammatory monocytes.

Discriminant function analysis as decision support system for the diagnosis of acute leukemia with a minimal four color screening panel and multiparameter flow cytometry immunophenotyping

Despite several recommendations for standardization of multiparameter flow cytometry (MFC) the number, specificity and combinations of reagents used by diagnostic laboratories for the diagnosis and classification of acute leukemias (AL) are still very diverse. Furthermore, the current diagnostic interpretation of flow cytometry readouts is influenced arbitrarily by individual experience and knowledge. We determined the potential value of a minimal four-color combination panel of 13 monoclonal antibodies (mAbs) with a CD45/sideward light scatter-gating strategy for a standardized MFC immunophenotyping of the clinically most relevant subgroups of AL. Bone marrow samples from 155 patients with acute myeloid leukemia (AML, n=79), B-cell precursor acute lymphoblastic leukemia (BCP-ALL, n=29), T-cell precursor acute lymphoblastic leukemia (T-ALL, n=12) and normal bone marrow donors (NBMD, n=35) were analyzed. A knowledge-based learning algorithm was generated by comparing the results of the minimal panel with the actual diagnosis, using discriminative function analysis. Correct classification of the test sample according to lineage, that is, BCP-ALL, T-ALL, AML and differentiation of NBMD was achieved in 97.2% of all cases with only six of the originally applied 13 mAbs of the panel. This provides evidence that discriminant function analysis can be utilized as a decision support system for interpretation of flow cytometry readouts.