Proposed new data file standard for flow cytometry, version FCS 3.0

Whole-brain tissue mapping toolkit using large-scale highly multiplexed immunofluorescence imaging and deep neural networks

Mapping biological processes in brain tissues requires piecing together numerous histological observations of multiple tissue samples. We present a direct method that generates readouts for a comprehensive panel of biomarkers from serial whole-brain slices, characterizing all major brain cell types, at scales ranging from subcellular compartments, individual cells, local multi-cellular niches, to whole-brain regions from each slice. We use iterative cycles of optimized 10-plex immunostaining with 10-color epifluorescence imaging to accumulate highly enriched image datasets from individual whole-brain slices, from which seamless signal-corrected mosaics are reconstructed. Specific fluorescent signals of interest are isolated computationally, rejecting autofluorescence, imaging noise, cross-channel bleed-through, and cross-labeling. Reliable large-scale cell detection and segmentation are achieved using deep neural networks. Cell phenotyping is performed by analyzing unique biomarker combinations over appropriate subcellular compartments. This approach can accelerate pre-clinical drug evaluation and system-level brain histology studies by simultaneously profiling multiple biological processes in their native anatomical context.

Data File Standard for Flow Cytometry, Version FCS 3.2

FCS 3.2 is a revision of the flow cytometry data standard based on a decade of suggested improvements from the community as well as industry needs to capture instrument conditions and measurement features more precisely. The unchanged goal of the standard is to provide a uniform file format that allows files created by one type of acquisition hardware and software to be analyzed by any other type. The standard retains the overall FCS file structure and most features of previous versions, but also contains a few changes that were required to support new types of data and use cases efficiently. These changes are incompatible with existing FCS file readers. Notably, FCS 3.2 supports mixed data types to, for example, allow FCS measurements that are intrinsically integers (e.g., indices or class assignments) or measurements that are commonly captured as integers (e.g., time ticks) to be more represented as integer values, while capturing other measurements as floating-point values in the same FCS data set. In addition, keywords explicitly specifying dyes, detectors, and analytes were added to avoid having to extract those heuristically and unreliably from measurement names. Types of measurements were formalized, several keywords added, others removed, or deprecated, and various aspects of the specification were clarified. A reference implementation of the cyclic redundancy check (CRC) calculation is provided in two programming languages since a correct CRC implementation was problematic for many vendors. © 2020 International Society for Advancement of Cytometry.

Robust FCS Parsing: Exploring 211,359 Public Files

When it comes to data storage, the field of flow cytometry is fairly standardized, thanks to the flow cytometry standard (FCS) file format. The structure of FCS files is described in the FCS specification. Software that strictly complies with the FCS specification is guaranteed to be interoperable (in terms of exchange via FCS files). Nowadays, software interoperability is crucial for eco system, as FCS files are frequently shared, and workflows rely on more than one piece of software (e.g., acquisition and analysis software). Ideally, software developers strictly follow the FCS specification. Unfortunately, this is not always the case, which resulted in various nonconformant FCS files being generated over time. Therefore, robust FCS parsers must be developed, which can handle a wide variety of nonconformant FCS files, from different resources. Development of robust FCS parsers would greatly benefit from a fully fledged set of testing files. In this study, readability of 211,359 public FCS files was evaluated. Each FCS file was checked for conformance with the FCS specification. For each data set, within each FCS file, validated parse results were obtained for the TEXT segment. Highly space efficient testing files were generated. FlowCore was benchmarked in depth, by using the validated parse results, the generated testing files, and the original FCS files. Robustness of FlowCore (as measured by testing against 211,359 files) was improved by re‐implementing the TEXT segment parser. Altogether, this study provides a comprehensive resource for FCS parser development, an in‐depth benchmark of FlowCore, and a concrete proposal for improving FlowCore. © 2020 The Authors. Cytometry Part A published by Wiley Periodicals LLC on behalf of International Society for Advancement of Cytometry.

Quantitation and Comparison of Phenotypic Heterogeneity Among Single Cells of Monoclonal Microbial Populations

Phenotypic heterogeneity within microbial populations arises even when the cells are exposed to putatively constant and homogeneous conditions. The outcome of this phenomenon can affect the whole function of the population, resulting in, for example, new "adapted" metabolic strategies and impacting its fitness at given environmental conditions. Accounting for phenotypic heterogeneity becomes thus necessary, due to its relevance in medical and applied microbiology as well as in environmental processes. Still, a comprehensive evaluation of this phenomenon requires a common and unique method of quantitation, which allows for the comparison between different studies carried out with different approaches. Consequently, in this study, two widely applicable indices for quantitation of heterogeneity were developed. The heterogeneity coefficient (HC) is valid when the population follows unimodal activity, while the differentiation tendency index (DTI) accounts for heterogeneity implying outbreak of subpopulations and multimodal activity. We demonstrated the applicability of HC and DTI for heterogeneity quantitation on stable isotope probing with nanoscale secondary ion mass spectrometry (SIP-nanoSIMS), flow cytometry, and optical microscopy datasets. The HC was found to provide a more accurate and precise measure of heterogeneity, being at the same time consistent with the coefficient of variation (CV) applied so far. The DTI is able to describe the differentiation in single-cell activity within monoclonal populations resolving subpopulations with low cell abundance, individual cells with similar phenotypic features (e.g., isotopic content close to natural abundance, as detected with nanoSIMS). The developed quantitation approach allows for a better understanding on the impact and the implications of phenotypic heterogeneity in environmental, medical and applied microbiology, microbial ecology, cell biology, and biotechnology.

Lossless Compression of Cytometric Data

Nowadays, most cytometrists apply lossless compression by storing their FCS files in ZIP archives. Unfortunately, ZIP only achieves modest space savings in cytometric data, due to DEFLATE being used as the underlying lossless compression algorithm (LCA). Presumably, other modern LCA can outperform DEFLATE, especially in terms of space savings. Twenty-one codecs (programs implementing LCA) were evaluated in 167,131 publicly available FCS files. Within floating-point data, as produced by modern instruments, most favorable compression ratios (CRs) were achieved by ZPAQ (median 0.469), BCM (median 0.523), and LZMA (median 0.545). In comparison, the DEFLATE-based codecs only achieved median CR of 0.728 under the most optimal conditions. By default, ZIP offers nine compression level (CL) settings, where lower ZIP-CL optimizes for time efficiency, while higher ZIP-CL optimizes for space efficiency. Interestingly, the third ZIP-CL already resulted in near optimal CR in 90% of the files with floating-point data, as produced by digital cytometers. LZMA is well established, widely supported, and actively maintained (in sharp contrast to ZPAQ and BCM) and therefore arguably the most attractive alternative for ZIP. Within floating-point data, by shifting from ZIP (under optimal conditions) to LZMA (at default settings), the median CR can be improved by 25%. Based on our results, cytometrists can benefit from state-of-the-art compression by choosing the appropriate codec for their situation. Our results are likely to speed-up the adaptation of modern codecs, as CR around 0.5 were beyond all expectations, and such space savings will benefit the field of cytometry. © 2019 International Society for Advancement of Cytometry.

beadplexr: reproducible and automated analysis of multiplex bead assays

Multiplex bead assays are an extension of the commonly used sandwich ELISA. The advantage over ELISA is that they make simultaneous evaluation of several analytes possible. Several commercial assay systems, where the beads are acquired on a standard flow cytometer, exist. These assay systems come with their own software tool for analysis and evaluation of the concentration of the analyzed analytes. However, these tools are either tied to particular commercial software or impose other limitations to their licenses, such as the number of events which can be analyzed. In addition, all these solutions are 'point and click' which potentially obscures the steps taken in the analysis. Here we present beadplexer, an open-source R-package for the reproducible analysis of multiplex bead assay data. The package makes it possible to automatically identify bead clusters, and provides functionality to easily fit a standard curve and calculate the concentrations of the analyzed analytes. beadplexer is available from CRAN and from https://gitlab.com/ustervbo/beadplexr.

flowIO: Flow cytometry standard conformance testing, editing, and export tool

The Flow Cytometry Standard (FCS) format is a widely accepted norm for storing Flow Cytometry (FCM) data. Its goal as a standard is to allow FCM data sharing and re-analysis. Over more than three decades of its existence FCS has evolved into a well-defined, flexible file format reflecting technical changes in the FCM field. Its flexibility as well as rising numbers of instrument vendors leads to suboptimal implementations of FCS in some cases. Such situations compromise the primary goal of the standard and hinder the ability to reproduce FCM analyses. It is further underlined by rapid rise of advanced FCM analyses, often carried out outside traditional software tools and heavily relying on standard data storage and presentation. We have developed flowIO, an R package which tests FCS file conformance with the standard as defined by International Society for Advancement of Cytometry (ISAC) normative. Along with the package we provide a web based application (also at http://bioinformin.cesnet.cz/flowIO/) allowing user friendly access to the conformance testing as well as FCS file editing and export for further analysis.

Toward deterministic and semiautomated SPADE analysis

SPADE stands for spanning-tree progression analysis for density-normalized events. It combines downsampling, clustering and a minimum-spanning tree to provide an intuitive visualization of high-dimensional single-cell data, which assists with the interpretation of the cellular heterogeneity underlying the data. SPADE has been widely used for analysis of high-content flow cytometry data and CyTOF data. The downsampling and clustering components of SPADE are both stochastic, which lead to stochasticity in the tree visualization it generates. Running SPADE twice on the same data may generate two different tree structures. Although they typically lead to the same biological interpretation of subpopulations present in the data, robustness of the algorithm can be improved. Another avenue of improvement is the interpretation of the SPADE tree, which involves visual inspection of multiple colored versions of the tree based on expression of measured markers. This is essentially manual gating on the SPADE tree and can benefit from automated algorithms. This article presents improvements of SPADE in both aspects above, leading to a deterministic SPADE algorithm and a software implementation for semiautomated interpretation. © 2017 International Society for Advancement of Cytometry.

FlowCal: A User-Friendly, Open Source Software Tool for Automatically Converting Flow Cytometry Data from Arbitrary to Calibrated Units

Flow cytometry is widely used to measure gene expression and other molecular biological processes with single cell resolution via fluorescent probes. Flow cytometers output data in arbitrary units (a.u.) that vary with the probe, instrument, and settings. Arbitrary units can be converted to the calibrated unit molecules of equivalent fluorophore (MEF) using commercially available calibration particles. However, there is no convenient, nonproprietary tool available to perform this calibration. Consequently, most researchers report data in a.u., limiting interpretation. Here, we report a software tool named FlowCal to overcome current limitations. FlowCal can be run using an intuitive Microsoft Excel interface, or customizable Python scripts. The software accepts Flow Cytometry Standard (FCS) files as inputs and is compatible with different calibration particles, fluorescent probes, and cell types. Additionally, FlowCal automatically gates data, calculates common statistics, and produces publication quality plots. We validate FlowCal by calibrating a.u. measurements of E. coli expressing superfolder GFP (sfGFP) collected at 10 different detector sensitivity (gain) settings to a single MEF value. Additionally, we reduce day-to-day variability in replicate E. coli sfGFP expression measurements due to instrument drift by 33%, and calibrate S. cerevisiae Venus expression data to MEF units. Finally, we demonstrate a simple method for using FlowCal to calibrate fluorescence units across different cytometers. FlowCal should ease the quantitative analysis of flow cytometry data within and across laboratories and facilitate the adoption of standard fluorescence units in synthetic biology and beyond.

flowAI: automatic and interactive anomaly discerning tools for flow cytometry data

Motivation: Flow cytometry (FCM) is widely used in both clinical and basic research to characterize cell phenotypes and functions. The latest FCM instruments analyze up to 20 markers of individual cells, producing high-dimensional data. This requires the use of the latest clustering and dimensionality reduction techniques to automatically segregate cell sub-populations in an unbiased manner. However, automated analyses may lead to false discoveries due to inter-sample differences in quality and properties.

Results: We present an R package, flowAI, containing two methods to clean FCM files from unwanted events: (i) an automatic method that adopts algorithms for the detection of anomalies and (ii) an interactive method with a graphical user interface implemented into an R shiny application. The general approach behind the two methods consists of three key steps to check and remove suspected anomalies that derive from (i) abrupt changes in the flow rate, (ii) instability of signal acquisition and (iii) outliers in the lower limit and margin events in the upper limit of the dynamic range. For each file analyzed our software generates a summary of the quality assessment from the aforementioned steps. The software presented is an intuitive solution seeking to improve the results not only of manual but also and in particular of automatic analysis on FCM data.

Availability and implementation: R source code available through Bioconductor: http://bioconductor.org/packages/flowAI/

Contacts:  mongianni1@gmail.com or Anis_Larbi@immunol.a-star.edu.sg

Supplementary information:  Supplementary data are available at Bioinformatics online.

Regulatory T Cells in Melanoma Revisited by a Computational Clustering of FOXP3+ T Cell Subpopulations

CD4⁺ T cells that express the transcription factor FOXP3 (FOXP3⁺ T cells) are commonly regarded as immunosuppressive regulatory T cells (Tregs). FOXP3⁺ T cells are reported to be increased in tumor-bearing patients or animals and are considered to suppress antitumor immunity, but the evidence is often contradictory. In addition, accumulating evidence indicates that FOXP3 is induced by antigenic stimulation and that some non-Treg FOXP3⁺ T cells, especially memory-phenotype FOXP3^low cells, produce proinflammatory cytokines. Accordingly, the subclassification of FOXP3⁺ T cells is fundamental for revealing the significance of FOXP3⁺ T cells in tumor immunity, but the arbitrariness and complexity of manual gating have complicated the issue. In this article, we report a computational method to automatically identify and classify FOXP3⁺ T cells into subsets using clustering algorithms. By analyzing flow cytometric data of melanoma patients, the proposed method showed that the FOXP3⁺ subpopulation that had relatively high FOXP3, CD45RO, and CD25 expressions was increased in melanoma patients, whereas manual gating did not produce significant results on the FOXP3⁺ subpopulations. Interestingly, the computationally identified FOXP3⁺ subpopulation included not only classical FOXP3^high Tregs, but also memory-phenotype FOXP3^low cells by manual gating. Furthermore, the proposed method successfully analyzed an independent data set, showing that the same FOXP3⁺ subpopulation was increased in melanoma patients, validating the method. Collectively, the proposed method successfully captured an important feature of melanoma without relying on the existing criteria of FOXP3⁺ T cells, revealing a hidden association between the T cell profile and melanoma, and providing new insights into FOXP3⁺ T cells and Tregs.

Computational prediction of manually gated rare cells in flow cytometry data

Rare cell identification is an interesting and challenging question in flow cytometry data analysis. In the literature, manual gating is a popular approach to distill flow cytometry data and drill down to the rare cells of interest, based on prior knowledge of measured protein markers and visual inspection of the data. Several computational algorithms have been proposed for rare cell identification. To compare existing algorithms and promote new developments, FlowCAP-III put forward one computational challenge that focused on this question. The challenge provided flow cytometry data for 202 training samples and two manually gated rare cell types for each training sample, roughly 0.02 and 0.04% of the cells, respectively. In addition, flow cytometry data for 203 testing samples were provided, and participants were invited to computationally identify the rare cells in the testing samples. Accuracy of the identification results was evaluated by comparing to manual gating of the testing samples. We participated in the challenge, and developed a method that combined the Hellinger divergence, a downsampling trick and the ensemble SVM. Our method achieved the highest accuracy in the challenge.

ISAC's Gating-ML 2.0 data exchange standard for gating description

The lack of software interoperability with respect to gating has traditionally been a bottleneck preventing the use of multiple analytical tools and reproducibility of flow cytometry data analysis by independent parties. To address this issue, ISAC developed Gating-ML, a computer file format to encode and interchange gates. Gating-ML 1.5 was adopted and published as an ISAC Candidate Recommendation in 2008. Feedback during the probationary period from implementors, including major commercial software companies, instrument vendors, and the wider community, has led to a streamlined Gating-ML 2.0. Gating-ML has been significantly simplified and therefore easier to support by software tools. To aid developers, free, open source reference implementations, compliance tests, and detailed examples are provided to stimulate further commercial adoption. ISAC has approved Gating-ML as a standard ready for deployment in the public domain and encourages its support within the community as it is at a mature stage of development having undergone extensive review and testing, under both theoretical and practical conditions.

Compound signature detection on LINCS L1000 big data

The Library of Integrated Network-based Cellular Signatures (LINCS) L1000 big data provide gene expression profiles induced by over 10 000 compounds, shRNAs, and kinase inhibitors using the L1000 platform. We developed csNMF, a systematic compound signature discovery pipeline covering from raw L1000 data processing to drug screening and mechanism generation. The csNMF pipeline demonstrated better performance than the original L1000 pipeline. The discovered compound signatures of breast cancer were consistent with the LINCS KINOMEscan data and were clinically relevant. The csNMF pipeline provided a novel and complete tool to expedite signature-based drug discovery leveraging the LINCS L1000 resources.

Flow cytometry bioinformatics

Flow cytometry bioinformatics is the application of bioinformatics to flow cytometry data, which involves storing, retrieving, organizing, and analyzing flow cytometry data using extensive computational resources and tools. Flow cytometry bioinformatics requires extensive use of and contributes to the development of techniques from computational statistics and machine learning. Flow cytometry and related methods allow the quantification of multiple independent biomarkers on large numbers of single cells. The rapid growth in the multidimensionality and throughput of flow cytometry data, particularly in the 2000s, has led to the creation of a variety of computational analysis methods, data standards, and public databases for the sharing of results. Computational methods exist to assist in the preprocessing of flow cytometry data, identifying cell populations within it, matching those cell populations across samples, and performing diagnosis and discovery using the results of previous steps. For preprocessing, this includes compensating for spectral overlap, transforming data onto scales conducive to visualization and analysis, assessing data for quality, and normalizing data across samples and experiments. For population identification, tools are available to aid traditional manual identification of populations in two-dimensional scatter plots (gating), to use dimensionality reduction to aid gating, and to find populations automatically in higher dimensional space in a variety of ways. It is also possible to characterize data in more comprehensive ways, such as the density-guided binary space partitioning technique known as probability binning, or by combinatorial gating. Finally, diagnosis using flow cytometry data can be aided by supervised learning techniques, and discovery of new cell types of biological importance by high-throughput statistical methods, as part of pipelines incorporating all of the aforementioned methods. Open standards, data, and software are also key parts of flow cytometry bioinformatics. Data standards include the widely adopted Flow Cytometry Standard (FCS) defining how data from cytometers should be stored, but also several new standards under development by the International Society for Advancement of Cytometry (ISAC) to aid in storing more detailed information about experimental design and analytical steps. Open data is slowly growing with the opening of the CytoBank database in 2010 and FlowRepository in 2012, both of which allow users to freely distribute their data, and the latter of which has been recommended as the preferred repository for MIFlowCyt-compliant data by ISAC. Open software is most widely available in the form of a suite of Bioconductor packages, but is also available for web execution on the GenePattern platform.

A likelihood approach to classifying fluorescent events collected by multicolor flow cytometry

Flow cytometry is an effective tool for enumerating fluorescently-labeled microbes recovered from natural environments. However, low signal strength and the presence of fluorescent, non-cellular particles complicate the separation of cellular events from noise. Existing classification methods rely on the arbitrary placement of noise thresholds, resulting in potentially high rates of misclassification of fluorescent cells, thus precluding the robust estimation of the proportions of classes of fluorescent cells. Here we present a method for objectively separating signal from noise. Rather than setting an arbitrary noise threshold, the Z-scoring approach uses the Gaussian distribution of signal strength (a) to locate noise threshold for individual fluorophores, (b) to predict the likelihood of different fluorescent genotypes in producing the signal observed, and (c) to normalize the fraction of cellular events count for each fluorescent cell class. The likelihood framework allows rejection of alternative genotypes, leading to robust and reliable classification of fluorescent cells. Use of Z-scoring in classification of cells expressing multiple fluorophores, use of spillover in actively scoring events, and the successful classification of multiple fluorophores using a single detector within a flow cytometer are discussed. A software package that performs Z-scoring for cells labeled with one or more fluorophores is described.

Computational analysis of high-throughput flow cytometry data

INTRODUCTION

Flow cytometry has been around for over 40 years, but only recently has the opportunity arisen to move into the high-throughput domain. The technology is now available and is highly competitive with imaging tools under the right conditions. Flow cytometry has, however, been a technology that has focused on its unique ability to study single cells and appropriate analytical tools are readily available to handle this traditional role of the technology.

AREAS COVERED

Expansion of flow cytometry to a high-throughput (HT) and high-content technology requires both advances in hardware and analytical tools. The historical perspective of flow cytometry operation as well as how the field has changed and what the key changes have been discussed. The authors provide a background and compelling arguments for moving toward HT flow, where there are many innovative opportunities. With alternative approaches now available for flow cytometry, there will be a considerable number of new applications. These opportunities show strong capability for drug screening and functional studies with cells in suspension.

EXPERT OPINION

There is no doubt that HT flow is a rich technology awaiting acceptance by the pharmaceutical community. It can provide a powerful phenotypic analytical toolset that has the capacity to change many current approaches to HT screening. The previous restrictions on the technology, based on its reduced capacity for sample throughput, are no longer a major issue. Overcoming this barrier has transformed a mature technology into one that can focus on systems biology questions not previously considered possible.

FCS 3.1 Implementation guidance

The Flow Cytometry Standard (FCS) format was developed back in 1984. Since then, FCS became the standard file format supported by all flow cytometry software and hardware vendors. Over the years, updates were incorporated to adapt to technological advancements in both flow cytometry and computing technologies. However, flexibility in how data may be stored in FCS has led to implementation difficulties for instrument vendors and third party software developers. In this technical note, we are providing implementation guidance and examples related to FCS 3.1, the latest version of the standard. By publishing this text, we intend to prevent potential compatibility issues that could be faced when implementing the FCS spillover and preferred display keywords that have arisen during discussions among some implementers.

Data File Standard for Flow Cytometry, version FCS 3.1

The flow cytometry data file standard provides the specifications needed to completely describe flow cytometry data sets within the confines of the file containing the experimental data. In 1984, the first Flow Cytometry Standard format for data files was adopted as FCS 1.0. This standard was modified in 1990 as FCS 2.0 and again in 1997 as FCS 3.0. We report here on the next generation flow cytometry standard data file format. FCS 3.1 is a minor revision based on suggested improvements from the community. The unchanged goal of the standard is to provide a uniform file format that allows files created by one type of acquisition hardware and software to be analyzed by any other type.The FCS 3.1 standard retains the basic FCS file structure and most features of previous versions of the standard. Changes included in FCS 3.1 address potential ambiguities in the previous versions and provide a more robust standard. The major changes include simplified support for international characters and improved support for storing compensation. The major additions are support for preferred display scale, a standardized way of capturing the sample volume, information about originality of the data file, and support for plate and well identification in high throughput, plate based experiments. Please see the normative version of the FCS 3.1 specification in Supporting Information for this manuscript (or at http://www.isac-net.org/ in the Current standards section) for a complete list of changes.

Publishing flow cytometry data

Cellular measurements by flow cytometric analysis constitute an important step toward understanding individual attributes within a population of cells. Assessing individual cells within a population by protein expression using fluorescently labeled antibodies and other fluorescent probes can identify cellular patterns. The technology for accurately identifying subtle changes in protein expression within a population of cells using a vast array of technology has resulted in controversy and questions regarding reproducibility, which can be explained at least in part by the absence of standard methods to facilitate comparison of flow cytometric data. The complexity of technological advancements and the need for improvements in biological resolution results in the generation of complex data that demands the use of minimum standards for their publication. Herein we present a summarized view for the inclusion of consistent flow cytometric experimental information as supplemental data. Four major points, experimental and sample information, data acquisition, analysis, and presentation are emphasized. Together, these guidelines will facilitate the review and publication of flow cytometry data that provide an accurate foundation for ongoing studies with this evolving technology.

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).

Toward the integration of cytomics and medicine

The integration of cytomics research and healthcare informatics will facilitate technology transfer and reduce medical costs. The CytometryML prototype of the Advanced Cytometry Standard (ACS) has the benefits of including microscopic image and flow list-mode data, being based on XML and thus is compatible with existing medical and scientific informatics standards, such as HL7, and employing a design based upon the Digital Imaging and Communications in Medicine (DICOM) standard. The reuse of the well tested DICOM model resulted in a great decrease in the design and documentation effort and increased probability of reliability. Schemas for flow cytometers and microscopes have been created. XML schemas for two related types of container (ZIP) files have been specified for a set of measurements. The series and instance containers respectively include the metadata that is constant and the metadata that is specific to an individual or small closely related group of measurements.

FuGEFlow: data model and markup language for flow cytometry

Background

Flow cytometry technology is widely used in both health care and research. The rapid expansion of flow cytometry applications has outpaced the development of data storage and analysis tools. Collaborative efforts being taken to eliminate this gap include building common vocabularies and ontologies, designing generic data models, and defining data exchange formats. The Minimum Information about a Flow Cytometry Experiment (MIFlowCyt) standard was recently adopted by the International Society for Advancement of Cytometry. This standard guides researchers on the information that should be included in peer reviewed publications, but it is insufficient for data exchange and integration between computational systems. The Functional Genomics Experiment (FuGE) formalizes common aspects of comprehensive and high throughput experiments across different biological technologies. We have extended FuGE object model to accommodate flow cytometry data and metadata.

Methods

We used the MagicDraw modelling tool to design a UML model (Flow-OM) according to the FuGE extension guidelines and the AndroMDA toolkit to transform the model to a markup language (Flow-ML). We mapped each MIFlowCyt term to either an existing FuGE class or to a new FuGEFlow class. The development environment was validated by comparing the official FuGE XSD to the schema we generated from the FuGE object model using our configuration. After the Flow-OM model was completed, the final version of the Flow-ML was generated and validated against an example MIFlowCyt compliant experiment description.

Results

The extension of FuGE for flow cytometry has resulted in a generic FuGE-compliant data model (FuGEFlow), which accommodates and links together all information required by MIFlowCyt. The FuGEFlow model can be used to build software and databases using FuGE software toolkits to facilitate automated exchange and manipulation of potentially large flow cytometry experimental data sets. Additional project documentation, including reusable design patterns and a guide for setting up a development environment, was contributed back to the FuGE project.

Conclusion

We have shown that an extension of FuGE can be used to transform minimum information requirements in natural language to markup language in XML. Extending FuGE required significant effort, but in our experiences the benefits outweighed the costs. The FuGEFlow is expected to play a central role in describing flow cytometry experiments and ultimately facilitating data exchange including public flow cytometry repositories currently under development.

flowCore: a Bioconductor package for high throughput flow cytometry

Background

Recent advances in automation technologies have enabled the use of flow cytometry for high throughput screening, generating large complex data sets often in clinical trials or drug discovery settings. However, data management and data analysis methods have not advanced sufficiently far from the initial small-scale studies to support modeling in the presence of multiple covariates.

Results

We developed a set of flexible open source computational tools in the R package flowCore to facilitate the analysis of these complex data. A key component of which is having suitable data structures that support the application of similar operations to a collection of samples or a clinical cohort. In addition, our software constitutes a shared and extensible research platform that enables collaboration between bioinformaticians, computer scientists, statisticians, biologists and clinicians. This platform will foster the development of novel analytic methods for flow cytometry.

Conclusion

The software has been applied in the analysis of various data sets and its data structures have proven to be highly efficient in capturing and organizing the analytic work flow. Finally, a number of additional Bioconductor packages successfully build on the infrastructure provided by flowCore, open new avenues for flow data analysis.

MIFlowCyt: the minimum information about a Flow Cytometry Experiment

A fundamental tenet of scientific research is that published results are open to independent validation and refutation. Minimum data standards aid data providers, users, and publishers by providing a specification of what is required to unambiguously interpret experimental findings. Here, we present the Minimum Information about a Flow Cytometry Experiment (MIFlowCyt) standard, stating the minimum information required to report flow cytometry (FCM) experiments. We brought together a cross-disciplinary international collaborative group of bioinformaticians, computational statisticians, software developers, instrument manufacturers, and clinical and basic research scientists to develop the standard. The standard was subsequently vetted by the International Society for Advancement of Cytometry (ISAC) Data Standards Task Force, Standards Committee, membership, and Council. The MIFlowCyt standard includes recommendations about descriptions of the specimens and reagents included in the FCM experiment, the configuration of the instrument used to perform the assays, and the data processing approaches used to interpret the primary output data. MIFlowCyt has been adopted as a standard by ISAC, representing the FCM scientific community including scientists as well as software and hardware manufacturers. Adoptionof MIFlowCyt by the scientific and publishing communities will facilitate third-party understanding and reuse of FCM data.

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.

Dial-in flow cytometry data analysis

As listmode data files continue to grow larger, access via any kind of network connections becomes more and more trouble because of the enormous traffic generated. The limited speed of transmission via modem makes analysis almost impossible. This unit presents a solution to these problems, one that involves installation at the central storage facility of a small computer program called a Web servlet. Operating in concert with a Web server, the servlet assists the analysis by extracting the display array from the data file and organizing its transmission over the network to a remote client program that creates the data display. The author discusses a recent implementation of this solution and the results for model transmission of two typical data files. The system greatly speeds access to remotely stored data yet retains the flexibility of manipulation expected with local access.

Flow: Statistics, visualization and informatics for flow cytometry

Flow is an open source software application for clinical and experimental researchers to perform exploratory data analysis, clustering and annotation of flow cytometric data. Flow is an extensible system that offers the ease of use commonly found in commercial flow cytometry software packages and the statistical power of academic packages like the R BioConductor project.

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 perspective for biomedical data integration: design of databases for flow cytometry

Background

The integration of biomedical information is essential for tackling medical problems. We describe a data model in the domain of flow cytometry (FC) allowing for massive management, analysis and integration with other laboratory and clinical information. The paper is concerned with the proper translation of the Flow Cytometry Standard (FCS) into a relational database schema, in a way that facilitates end users at either doing research on FC or studying specific cases of patients undergone FC analysis

Results

The proposed database schema provides integration of data originating from diverse acquisition settings, organized in a way that allows syntactically simple queries that provide results significantly faster than the conventional implementations of the FCS standard. The proposed schema can potentially achieve up to 8 orders of magnitude reduction in query complexity and up to 2 orders of magnitude reduction in response time for data originating from flow cytometers that record 256 colours. This is mainly achieved by managing to maintain an almost constant number of data-mining procedures regardless of the size and complexity of the stored information.

Conclusion

It is evident that using single-file data storage standards for the design of databases without any structural transformations significantly limits the flexibility of databases. Analysis of the requirements of a specific domain for integration and massive data processing can provide the necessary schema modifications that will unlock the additional functionality of a relational database.

Data standards for flow cytometry

Flow cytometry (FCM) is an analytical tool widely used for cancer and HIV/AIDS research, and treatment, stem cell manipulation and detecting microorganisms in environmental samples. Current data standards do not capture the full scope of FCM experiments and there is a demand for software tools that can assist in the exploration and analysis of large FCM datasets. We are implementing a standardized approach to capturing, analyzing, and disseminating FCM data that will facilitate both more complex analyses and analysis of datasets that could not previously be efficiently studied. Initial work has focused on developing a community-based guideline for recording and reporting the details of FCM experiments. Open source software tools that implement this standard are being created, with an emphasis on facilitating reproducible and extensible data analyses. As well, tools for electronic collaboration will assist the integrated access and comprehension of experiments to empower users to collaborate on FCM analyses. This coordinated, joint development of bioinformatics standards and software tools for FCM data analysis has the potential to greatly facilitate both basic and clinical research--impacting a notably diverse range of medical and environmental research areas.

A relational database for management of flow cytometry and ELISpot clinical trial data

BACKGROUND

Although relational databases are widely used in bioinformatics with deposited and finalized data, they have not received widespread usage among immunologists for managing raw laboratory data such as that generated by ELISpot or flow cytometry assays. Almost no published guidance exists for immunologists to design appropriate and useful data management systems.

METHODS

We describe the design and implementation of a Microsoft Access relational database used in a clinical trial in which the primary immunogenicity measures were ELISpot and intracellular cytokine staining.

RESULTS

Our data management system enabled us to perform sophisticated queries and to interpret our data as quantitatively as possible. It could easily be used without modification by other researchers using automated plate reading of ELISpot plates or four color flow cytometry.

CONCLUSION

We illustrate in detail the use of a flexible data management system for two of the most widely used immunological techniques. Minor modifications for more colors or other outputs can easily be implemented. Based on this example, other modifications could be easily envisaged for any other quantitative output.

Modelling data across labs, genomes, space and time

Logical models and physical specifications provide the foundation for storage, management and analysis of complex sets of data, and describe the relationships between measured data elements and metadata - the contextual descriptors that define the primary data. Here, we use imaging applications to illustrate the purpose of the various implementations of data specifications and the requirement for open, standardized, data formats to facilitate the sharing of critical digital data and metadata.

Automated in-silico detection of cell populations in flow cytometry readouts and its application to leukemia disease monitoring

Background

Identification of minor cell populations, e.g. leukemic blasts within blood samples, has become increasingly important in therapeutic disease monitoring. Modern flow cytometers enable researchers to reliably measure six and more variables, describing cellular size, granularity and expression of cell-surface and intracellular proteins, for thousands of cells per second. Currently, analysis of cytometry readouts relies on visual inspection and manual gating of one- or two-dimensional projections of the data. This procedure, however, is labor-intensive and misses potential characteristic patterns in higher dimensions.

Results

Leukemic samples from patients with acute lymphoblastic leukemia at initial diagnosis and during induction therapy have been investigated by 4-color flow cytometry. We have utilized multivariate classification techniques, Support Vector Machines (SVM), to automate leukemic cell detection in cytometry. Classifiers were built on conventionally diagnosed training data. We assessed the detection accuracy on independent test data and analyzed marker expression of incongruently classified cells. SVM classification can recover manually gated leukemic cells with 99.78% sensitivity and 98.87% specificity.

Conclusion

Multivariate classification techniques allow for automating cell population detection in cytometry readouts for diagnostic purposes. They potentially reduce time, costs and arbitrariness associated with these procedures. Due to their multivariate classification rules, they also allow for the reliable detection of small cell populations.

Statistical determination of threshold for cellular division in the CFSE-labeling assay

The combination of flow cytometry and carboxyl fluorescent succinimidyl ester (CFSE) labeling techniques has been widely used in the study of cellular proliferation, including measurement of the percentage of proliferated cells and the number of cell divisions undergone by proliferated cells. However, the smallest numbers that represent true cell division rather than experimental variation are not known. To define a threshold that separates true proliferation from experimental variation, we performed a large number of replicate CFSE labeling experiments using polyclonal stimulation, obtained the percentages of proliferated cells using ModFit software, and then analyzed these data using several statistical methods. Our results indicate that the threshold of proliferation lies between 0.071% (95% confidence) and 0.114% (99% confidence) of total CFSE-labeled cells under our laboratory conditions. We offer our methods presented here for other investigators to calculate a threshold in their own CFSE-labeling experiments.

Computer program for determining fluorescence resonance energy transfer efficiency from flow cytometric data on a cell-by-cell basis

The determination of fluorescence resonance energy transfer (FRET) with flow cytometry (FCET) is one of the most efficient tools to study the proximity relationships of cell membrane components in cell populations on a cell-by-cell basis. Because of the high amount of data and the relatively tedious calculations, this procedure should be assisted by powerful data processing software. The currently available programs are not able to fulfill this requirement. We developed a Windows-based program to calculate fluorescence resonance energy transfer efficiency values from list mode flow cytometry standard (FCS) files. This program displays the measured data in standard plots by generating one- and two-parameter histograms on linear or logarithmic scales. A graphical gating tool allows the user to select the desired cell population according to any combination of the parameter values. The program performs several statistical calculations, including mean, S.D., percent of the gated data. We have implemented two types of data sheet for FRET calculations to aid and guide the user during the analysis: one with population-mean-based autofluorescence correction and the other with spectrum-based cell-by-cell autofluorescence correction. In this paper, we describe the gating algorithms, the file opening procedure and the rules of gating. The structure of the program and a short description of the graphical user-interface (GUI) are also presented in this article.

An analytical system based on a compact flow cytometer for DNA fragment sizing and single-molecule detection

BACKGROUND

Previous reports have demonstrated accurate DNA fragment sizing of linear DNA fragments, from 564 to approximately 4 x 10(5) bp, in a flow system. B-phycoerythrin (B-PE), commonly used in conventional cytometric applications that require high-sensitivity, was the first fluorophore detected in flow at the single-molecule level.

METHODS

Dilute solutions of stained DNA fragments or B-PE were analyzed in a simplified, compact flow system, with enhanced performance and lower cost, utilizing a solid-state laser and a single-photon sensing avalanche photodiode detector (SSAPD). Extensive data processing and display software, developed specifically for the photon-counting data stream, extracts correlated height, width, and area features from bursts of photons due to discrete molecules passing through the sensing region in the flow channel.

RESULTS

DNA fragment sizing in flow has now been demonstrated for SYTOX-orange-stained fragments ranging in size over 3.4 orders of magnitude, from 125 to 5 x 10(5) bp. For Lambda bacteriophage DNA (lambda DNA; 48.5 kbp) a CV of 1.2 % has been achieved. Analysis of a femtomolar B-PE solution demonstrates that the bursts of photons from individual molecules can be baseline-resolved with 0.5 mW of laser power at a signal to noise ratio (SNR) of approximately 30, with approximately 100 photons detected from each molecule.

CONCLUSIONS

A compact, low-power, high-sensitivity system detects DNA fragments as small as 125 bp or individual B-PE molecules in a flowing liquid stream. Demonstrated linearity, sensitivity, and resolution indicate that <1.0 mW of laser power is optimal, permitting further miniaturization of the system and additional cost reduction. Comprehensive analytical software exploits the standard cytometric paradigm of multiple 2D graphs and gating to extract features from classes of individually analyzed biomolecules. This complete system is thus poised to engage high-sensitivity applications not amenable to conventional flow cytometric instrumentation.

CytometryML, an XML format based on DICOM and FCS for analytical cytology data

BACKGROUND

Flow Cytometry Standard (FCS) was initially created to standardize the software researchers use to analyze, transmit, and store data produced by flow cytometers and sorters. Because of the clinical utility of flow cytometry, it is necessary to have a standard consistent with the requirements of medical regulatory agencies.

METHODS

We extended the existing mapping of FCS to the Digital Imaging and Communications in Medicine (DICOM) standard to include list-mode data produced by flow cytometry, laser scanning cytometry, and microscopic image cytometry. FCS list-mode was mapped to the DICOM Waveform Information Object. We created a collection of Extensible Markup Language (XML) schemas to express the DICOM analytical cytologic text-based data types except for large binary objects. We also developed a cytometry markup language, CytometryML, in an open environment subject to continuous peer review.

RESULTS

The feasibility of expressing the data contained in FCS, including list-mode in DICOM, was demonstrated; and a preliminary mapping for list-mode data in the form of XML schemas and documents was completed. DICOM permitted the creation of indices that can be used to rapidly locate in a list-mode file the cells that are members of a subset. DICOM and its coding schemes for other medical standards can be represented by XML schemas, which can be combined with other relevant XML applications, such as Mathematical Markup Language (MathML).

CONCLUSIONS

The use of XML format based on DICOM for analytical cytology met most of the previously specified requirements and appears capable of meeting the others; therefore, the present FCS should be retired and replaced by an open, XML-based, standard CytometryML.

Web servlet-assisted, dial-in flow cytometry data analysis

BACKGROUND

The obvious benefits of centralized data storage notwithstanding, the size of modern flow cytometry data files discourages their transmission over commonly used telephone modem connections. The proposed solution is to install at the central location a web servlet that can extract compact data arrays, of a form dependent on the requested display type, from the stored files and transmit them to a remote client computer program for display.

METHODS

A client program and a web servlet, both written in the Java programming language, were designed to communicate over standard network connections. The client program creates familiar numerical and graphical display types and allows the creation of gates from combinations of user-defined regions. Data compression techniques further reduce transmission times for data arrays that are already much smaller than the data file itself.

RESULTS

For typical data files, network transmission times were reduced more than 700-fold for extraction of one-dimensional (1-D) histograms, between 18 and 120-fold for 2-D histograms, and 6-fold for color-coded dot plots. Numerous display formats are possible without further access to the data file.

CONCLUSIONS

This scheme enables telephone modem access to centrally stored data without restricting flexibility of display format or preventing comparisons with locally stored files.