Analysis Of Flow Cytometry Data Research Articles

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.

FlowFP: A Bioconductor Package for Fingerprinting Flow Cytometric Data

A new software package called flowFP for the analysis of flow cytometry data is introduced. The package, which is tightly integrated with other Bioconductor software for analysis of flow cytometry, provides tools to transform raw flow cytometry data into a form suitable for direct input into conventional statistical analysis and empirical modeling software tools. The approach of flowFP is to generate a description of the multivariate probability distribution function of flow cytometry data in the form of a “fingerprint.” As such, it is independent of a presumptive functional form for the distribution, in contrast with model-based methods such as Gaussian Mixture Modeling. FlowFP is computationally efficient and able to handle extremely large flow cytometry data sets of arbitrary dimensionality. Algorithms and software implementation of the package are described. Use of the software is exemplified with applications to data quality control and to the automated classification of Acute Myeloid Leukemia.

Scalable analysis of flow cytometry data using R/Bioconductor

Flow cytometry is one of the fundamental research tools available to the life scientist. The ability to observe multidimensional changes in protein expression and activity at single-cell resolution for a large number of cells provides a unique perspective on the behavior of cell populations. However, the analysis of complex multidimensional data is one of the obstacles for wider use of polychromatic flow cytometry. Recent enhancements to an open-source platform-R/Bioconductor-enable the graphical and data analysis of flow cytometry data. Prior examples have focused on high-throughput applications. To facilitate wider use of this platform for flow cytometry, the analysis of a dataset, obtained following isolation of CD4(+)CD62L(+) T cells from Balb/c splenocytes using magnetic microbeads, is presented as a form of tutorial. A common workflow for analyzing flow cytometry data was presented using R/Bioconductor. In addition, density function estimation and principal component analysis are provided as examples of more complex analyses. The compendium presented here is intended to help illuminate a path for inquisitive readers to explore their own data using R/Bioconductor (available as Supporting Information).

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.

Quality Control of Flow Cytometry Data Analysis for Evaluation of Minimal Residual Disease in Bone Marrow From Acute Leukemia Patients During Treatment

Low levels of leukemia cells in the bone marrow, minimal residual disease (MRD), are considered to be a powerful indicator of treatment response in acute lymphatic leukemia (ALL). A Nordic quality assurance program, aimed on standardization of the flow cytometry MRD analysis, has been established before implementation of MRD at cutoff level 10 as one of stratifying parameters in next Nordic Society of Pediatric Hematology and Oncology (NOPHO) treatment program for ALL. In 4 quality control (QC) rounds 15 laboratories determined the MRD levels in 48 follow-up samples from 12 ALL patients treated according to NOPHO 2000. Analysis procedures were standardized. For each QC round a compact disc containing data in list-mode files was sent out and results were submitted to a central laboratory. At cutoff level 10, which will be applied for clinical decisions, laboratories obtained a high concordance (91.6%). If cutoff level 10 was applied, the concordance would be lower (85.3%). The continuing standardization resulted in better concordance in QC3 and QC4 compared with QC1 and QC2. The concordance was higher in precursor B as compared with T-cell ALL. We conclude that after standardization, flow cytometry MRD detection can be reliably applied in international, multicenter treatment protocols.

FlowClust: a Bioconductor package for automated gating of flow cytometry data

BackgroundAs a high-throughput technology that offers rapid quantification of multidimensional characteristics for millions of cells, flow cytometry (FCM) is widely used in health research, medical diagnosis and treatment, and vaccine development. Nevertheless, there is an increasing concern about the lack of appropriate software tools to provide an automated analysis platform to parallelize the high-throughput data-generation platform. Currently, to a large extent, FCM data analysis relies on the manual selection of sequential regions in 2-D graphical projections to extract the cell populations of interest. This is a time-consuming task that ignores the high-dimensionality of FCM data.ResultsIn view of the aforementioned issues, we have developed an R package called flowClust to automate FCM analysis. flowClust implements a robust model-based clustering approach based on multivariate t mixture models with the Box-Cox transformation. The package provides the functionality to identify cell populations whilst simultaneously handling the commonly encountered issues of outlier identification and data transformation. It offers various tools to summarize and visualize a wealth of features of the clustering results. In addition, to ensure its convenience of use, flowClust has been adapted for the current FCM data format, and integrated with existing Bioconductor packages dedicated to FCM analysis.ConclusionflowClust addresses the issue of a dearth of software that helps automate FCM analysis with a sound theoretical foundation. It tends to give reproducible results, and helps reduce the significant subjectivity and human time cost encountered in FCM analysis. The package contributes to the cytometry community by offering an efficient, automated analysis platform which facilitates the active, ongoing technological advancement.

An improved ontological representation of dendritic cells as a paradigm for all cell types.

BackgroundRecent increases in the volume and diversity of life science data and information and an increasing emphasis on data sharing and interoperability have resulted in the creation of a large number of biological ontologies, including the Cell Ontology (CL), designed to provide a standardized representation of cell types for data annotation. Ontologies have been shown to have significant benefits for computational analyses of large data sets and for automated reasoning applications, leading to organized attempts to improve the structure and formal rigor of ontologies to better support computation. Currently, the CL employs multiple is_a relations, defining cell types in terms of histological, functional, and lineage properties, and the majority of definitions are written with sufficient generality to hold across multiple species. This approach limits the CL's utility for computation and for cross-species data integration.ResultsTo enhance the CL's utility for computational analyses, we developed a method for the ontological representation of cells and applied this method to develop a dendritic cell ontology (DC-CL). DC-CL subtypes are delineated on the basis of surface protein expression, systematically including both species-general and species-specific types and optimizing DC-CL for the analysis of flow cytometry data. We avoid multiple uses of is_a by linking DC-CL terms to terms in other ontologies via additional, formally defined relations such as has_function.ConclusionThis approach brings benefits in the form of increased accuracy, support for reasoning, and interoperability with other ontology resources. Accordingly, we propose our method as a general strategy for the ontological representation of cells. DC-CL is available from .

Recent Bioinformatics Advances in the Analysis of High Throughput Flow Cytometry Data

Recent Bioinformatics Advances in the Analysis of High Throughput Flow Cytometry Data

WebFlow: A Software Package for High-Throughput Analysis of Flow Cytometry Data

Flow cytometry has emerged as a powerful tool for quantitative, single-cell analysis of both surface markers and intracellular antigens, including phosphoproteins and kinase signaling cascades, with the flexibility to process hundreds of samples in multiwell plate format. Quantitative flow cytometric analysis is being applied in many areas of biology, from the study of immunology in animal models or human patients to high-content drug screening of pharmacologically active compounds. However, these experiments generate thousands of data points per sample, each with multiple measured parameters, leading to data management and analysis challenges. We developed WebFlow (http://webflow.stanford.edu), a web server-based software package to manage, analyze, and visualize data from flow cytometry experiments. WebFlow is accessible via standard web browsers and does not require users to install software on their personal computers. The software enables plate-based annotation of large data sets, which provides the basis for exploratory data analysis tools and rapid visualization of multiple different parameters. These tools include custom user-defined statistics to normalize data to other wells or other channels, as well as interactive, user-selectable heat maps for viewing the underlying single-cell data. The web-based approach of WebFlow allows for sharing of data with collaborators or the general public. WebFlow provides a novel platform for quantitative analysis of flow cytometric data from high-throughput drug screening or disease profiling experiments.

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.

Natural polyploidy inVanilla planifolia(Orchidaceae)

Vanilla planifolia accessions cultivated in Reunion Island display important phenotypic variation, but little genetic diversity is demonstrated by AFLP and SSR markers. This study, based on analyses of flow cytometry data, Feulgen microdensitometry data, chromosome counts, and stomatal length measurements, was performed to determine whether polyploidy could be responsible for some of the intraspecific phenotypic variation observed. Vanilla planifolia exhibited an important variation in somatic chromosome number in root cells, as well as endoreplication as revealed by flow cytometry. Nevertheless, the 2C-values of the 50 accessions studied segregated into three distinct groups averaging 5.03 pg (for most accessions), 7.67 pg (for the 'Stérile' phenotypes), and 10.00 pg (for the 'Grosse Vanille' phenotypes). For the three groups, chromosome numbers varied from 16 to 32, 16 to 38, and 22 to 54 chromosomes per cell, respectively. The stomatal length showed a significant variation from 37.75 microm to 48.25 microm. Given that 2C-values, mean chromosome numbers, and stomatal lengths were positively correlated and that 'Stérile' and 'Grosse Vanille' accessions were indistinguishable from 'Classique' accessions using molecular markers, the occurrence of recent autotriploid and autotetraploid types in Reunion Island is supported. This is the first report showing evidence of a recent autopolyploidy in V. planifolia contributing to the phenotypic variation observed in this species.

Quantitative Flow Cytometry Immunophenotypic Data in Myelodysplastic Syndromes (MDS)

Background: Recent studies using qualitative analysis of flow cytometry data have demonstrated various im- munophenotypic abnormalities associated with myelodysplastic syndromes (MDS). However, there are limited reports as- sessing the ability of quantitative immunophenotypic analysis to discriminate MDS from other cytopenic conditions. Design: Using flow cytometry, we studied 37 bone marrow specimens from 23 patients with MDS and 14 cytopenic pa- tients with non-clonal hematologic disorders. Samples were analyzed quantitatively for percentages of T-cells, B-cells, NK cells, granulocytes, monocytes, blasts, erythroid precursors, and plasma cells; CD4:CD8 ratio; % granulocyte subsets; % CD56+ monocytes; and % erythroid precursor subsets. Results: Quantitative analysis of immunophenotypic data in MDS patients compared to controls showed decreases in total granulocytes (p=0.037) and more mature subsets of CD11b + CD16 bright granulocytes (p=0.0046) and CD10 + granulocytes (p=0.022). MDS patients also showed a trending increase in subset percentage of CD56+monocytes (p=0.056). Using re- ceiver operating characteristic (ROC) analysis, cut-off values for these parameters favoring a diagnosis of MDS were identified as follows: total granulocytes 10%. Subsequently, a scoring system was proposed whereby a score of one was assigned for the presence of each quantitative abnormality. Using this system on the original study population, the pres- ence of at least two abnormalities (score 2) revealed optimal sensitivity (69.6%) and specificity (71.4%) for a diagnosis of MDS. Conclusion: These findings suggest that quantitative analysis of immunophenotypic data can be complementary to quali- tative interpretation. These data may be useful for distinguishing MDS from non-clonal cytopenic disorders and warrant prospective study in additional MDS patients.

Gating-ML: XML-based gating descriptions in flow cytometry.

The lack of software interoperability with respect to gating due to lack of a standardized mechanism for data exchange has traditionally been a bottleneck, preventing reproducibility of flow cytometry (FCM) data analysis and the usage of multiple analytical tools. To facilitate interoperability among FCM data analysis tools, members of the International Society for the Advancement of Cytometry (ISAC) Data Standards Task Force (DSTF) have developed an XML-based mechanism to formally describe gates (Gating-ML). Gating-ML, an open specification for encoding gating, data transformations and compensation, has been adopted by the ISAC DSTF as a Candidate Recommendation. Gating-ML can facilitate exchange of gating descriptions the same way that FCS facilitated for exchange of raw FCM data. Its adoption will open new collaborative opportunities as well as possibilities for advanced analyses and methods development. The ISAC DSTF is satisfied that the standard addresses the requirements for a gating exchange standard.

Flow cytometry histograms: Transformations, resolution, and display

Flow cytometry data analysis routinely includes the use of one- or two-parameter histograms to visualize the data. These histograms have traditionally been plotted with either a linear or logarithmic scale. However, the recent trend of performing the logarithmic conversion in software has made apparent some limitations of the traditional visual presentation of logarithmic data. This review discusses the mathematics of presenting data on a histogram and emphasizes the difference between scaling and binning. The review introduces the concept of an effective resolution to describe how the bin width changes in a variable bin-width histogram. The change in effective resolution is used to explain the commonly observed valley and picket fencing artifacts. These result from the effective resolution of the display histogram being too high for the data being presented. Recently, several different binning transformations have been described that are becoming more popular because they allow one to view a large dynamic range of data on a single plot, while allowing the display of negative data values. While each of the transforms is based upon different equations, they all exhibit very similar properties. All of the transforms bin the data logarithmically at high channel values and linearly at low channel values. The linear scaling of the lower channels serves to limit the effective resolution of the histogram, thus minimizing the valley and picket fencing artifacts. The newer transformations are not without their own limitations and recommendations for the appropriate manner of presenting flow cytometry data using these newer transformations are discussed.

Parametric analysis of flow-cytometry data

A parametric approach to the analysis of the data of flow cytometry is considered. In the framework of this approach, two models are proposed and are compared with the use of a criterion based on the analysis of the tails of distribution functions. Some properties of the estimates related to this criterion are also proved. In particular, it is shown that the power of test tends to unity as the number of observations in a sample increases.

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.

Automated gating of flow cytometry data via robust model‐based clustering

The capability of flow cytometry to offer rapid quantification of multidimensional characteristics for millions of cells has made this technology indispensable for health research, medical diagnosis, and treatment. However, the lack of statistical and bioinformatics tools to parallel recent high-throughput technological advancements has hindered this technology from reaching its full potential. We propose a flexible statistical model-based clustering approach for identifying cell populations in flow cytometry data based on t-mixture models with a Box-Cox transformation. This approach generalizes the popular Gaussian mixture models to account for outliers and allow for nonelliptical clusters. We describe an Expectation-Maximization (EM) algorithm to simultaneously handle parameter estimation and transformation selection. Using two publicly available datasets, we demonstrate that our proposed methodology provides enough flexibility and robustness to mimic manual gating results performed by an expert researcher. In addition, we present results from a simulation study, which show that this new clustering framework gives better results in terms of robustness to model misspecification and estimation of the number of clusters, compared to the popular mixture models. The proposed clustering methodology is well adapted to automated analysis of flow cytometry data. It tends to give more reproducible results, and helps reduce the significant subjectivity and human time cost encountered in manual gating analysis.

Using flowViz to visualize flow cytometry data

Automated analysis of flow cytometry (FCM) data is essential for it to become successful as a high throughput technology. We believe that the principles of Trellis graphics can be adapted to provide useful visualizations that can aid such automation. In this article, we describe the R/Bioconductor package flowViz that implements such visualizations. flowViz is available as an R package from the Bioconductor project: http://bioconductor.org

A flow cytometer for the measurement of Raman spectra

Multiparameter measurements in flow cytometry are limited by the broad emission spectra of fluorescent labels. By contrast, Raman spectra are notable for their narrow spectral features. To increase the multiparameter analysis capabilities of flow cytometry, we investigated the possibility of measuring Raman signals in a flow cytometry-based system. We constructed a Raman Spectral Flow Cytometer, substituting a spectrograph and CCD detector for the traditional mirrors, optical filters, and photomultiplier tubes. Excitation at 633 nm was provided by a HeNe laser, and forward-angle light scatter is used to trigger acquisition of complete spectra from individual particles. Microspheres were labeled with nanoparticle surface enhanced Raman scattering (SERS) tags and measured using the RSFC. Fluorescence and Raman spectra from labeled microspheres were acquired using the Raman Spectral Flow Cytometer. SERS spectral intensities were dependent on integration time, laser power, and detector pixel binning. Spectra from particles labeled with one each of four different SERS tags could be distinguished by either a virtual bandpass approach using commercial flow cytometry data analysis software or by principal component analysis. Raman flow cytometry opens up new possibilities for highly multiparameter and multiplexed measurements of cells and other particles using a simple optical design and a single detector and light source.

Relative Value of CD38 and ZAP-70 Versus Immunoglobulin Mutation Status in Predicting Early Disease Progression in Chronic Lymphocytic Leukemia.

We assessed the relative value of CD38 in predicting the need for early treatment in 307 patients with chronic lymphocytic leukemia (CLL) previously characterized for ZAP-70 and immunoglobulin heavy-chain-variable region gene (IgVH) mutational status (NEJM 2004, 351;9;893–901). Recursive partitioning by maximally selected chi-square analyses of flow cytometry and clinical data identified the optimal cut-off for designating a CLL sample as CD38+ was at 34%, which was highly similar to the conventional cut-off of 30%. Since these cut-offs identified highly similar cohorts of CD38+ CLL patients (e.g. 32.2% (99/307) vs. 35.5% (109/307)) we chose to use 30% as the cut-off for this analysis. We used the logrank test to compare CD38 expression-status to the time from diagnosis to therapy (TTT), initiated per established NCI-Working Group guidelines. Patients with CLL cells that were CD38-negative by this criterion had a median TTT of 7.8 years; whereas, patients with CD38+ CLL cells had a median TTT of 3.7 years (p<0.0001). There was a significant association between expression of CD38 and ZAP-70 or use of unmutated IgVH genes (p < 0.0001). The median level of CD38 among cases that used mutated IgVH was 4.0%, compared to 27.4% among cases that used unmutated IgVH (p < 0.0001). The median level of CD38 among ZAP-70-negative cases was 5.3% (range from 0.1% to 99.5%), compared to 27.2% among ZAP-70-positive cases (range from 0.1% to 99.4%) (p < 0.0001).We explored the TTT based only on flow cytometric parameters (CD38 and ZAP-70) and investigated whether addition of mutation status significantly altered our predictions. For most patients, the knowledge of CD38 and ZAP-70 provided a reliable means for predicting the TTT and the knowledge of the IgVH mutation status did not substantially alter the prediction. Only in 41 cases (14% of the total), where the CLL cells were CD38+ but negative for ZAP-70, was the prediction significantly improved by inclusion of IgVH mutation status. The median TTT of this group of 41 patients was 7.8 years. Addition of mutation status identified a subgroup of 20 patients with unmutated IgVH genes for whom median TTT was estimated at 4.6 years; 21 patients with mutated IgVH genes had a median TTT of 8.4 years. For patients with CLL cells that were CD38-negative and either negative or positive ZAP-70, stratification via IgVH mutation status did not identify subgroups within each of these categories that had significantly different median TTT. We conclude that knowledge of CD38 and ZAP-70, as assessed by the CRC, can reliably predict TTT in most patients with CLL. For the 14% of cases that are CD38+ but negative for ZAP-70, determining the IgVH mutation status may provide added value in predicting the time from diagnosis to initial therapy.