Dataset Information

GenePattern flow cytometry suite.

ABSTRACT:

Background

Traditional flow cytometry data analysis is largely based on interactive and time consuming analysis of series two dimensional representations of up to 20 dimensional data. Recent technological advances have increased the amount of data generated by the technology and outpaced the development of data analysis approaches. While there are advanced tools available, including many R/BioConductor packages, these are only accessible programmatically and therefore out of reach for most experimentalists. GenePattern is a powerful genomic analysis platform with over 200 tools for analysis of gene expression, proteomics, and other data. A web-based interface provides easy access to these tools and allows the creation of automated analysis pipelines enabling reproducible research.

Results

In order to bring advanced flow cytometry data analysis tools to experimentalists without programmatic skills, we developed the GenePattern Flow Cytometry Suite. It contains 34 open source GenePattern flow cytometry modules covering methods from basic processing of flow cytometry standard (i.e., FCS) files to advanced algorithms for automated identification of cell populations, normalization and quality assessment. Internally, these modules leverage from functionality developed in R/BioConductor. Using the GenePattern web-based interface, they can be connected to build analytical pipelines.

Conclusions

GenePattern Flow Cytometry Suite brings advanced flow cytometry data analysis capabilities to users with minimal computer skills. Functionality previously available only to skilled bioinformaticians is now easily accessible from a web browser.

SUBMITTER: Spidlen J

PROVIDER: S-EPMC3717030 | biostudies-literature | 2013 Jul

REPOSITORIES: biostudies-literature

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Publications

GenePattern flow cytometry suite.

Spidlen Josef J Barsky Aaron A Breuer Karin K Carr Peter P Nazaire Marc-Danie MD Hill Barbara Allen BA Qian Yu Y Liefeld Ted T Reich Michael M Mesirov Jill P JP Wilkinson Peter P Scheuermann Richard H RH Sekaly Rafick-Pierre RP Brinkman Ryan R RR

Source code for biology and medicine 20130703 1

<h4>Background</h4>Traditional flow cytometry data analysis is largely based on interactive and time consuming analysis of series two dimensional representations of up to 20 dimensional data. Recent technological advances have increased the amount of data generated by the technology and outpaced the development of data analysis approaches. While there are advanced tools available, including many R/BioConductor packages, these are only accessible programmatically and therefore out of reach for mo ...[more]

PMID: 23822732

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Project description: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.

Project description:Tumorigenic breast cancer cells characterized by CD44 expression and low or undetectable CD24 levels (CD44+/CD24-/low) may be resistant to chemotherapy and therefore responsible for cancer relapse. Paired breast cancer core biopsies before and after neoadjuvant chemotherapy or lapatinib were obtained and as single cell suspensions stained using antibodies against CD24, CD44, and lineage markers, and then analyzed by flow cytometry. Mammosphere (MS) formation in culture was compared before and after treatment. Global gene expression differences between cancer cells bearing CD44+/CD24-/low cells and all other sorted cells, and between cancer MS and the primary bulk invasive cancers were analyzed. We report that CD44+/CD24-/low tumorigenic breast cancer cells were intrinsically chemoresistant â chemotherapy led to increased CD44+/CD24-/low cells, increased self-renewal capacity on MS assays, and enhanced tumorigeneicity in immunocompromised SCID/Beige mice. Conversely, in patients with HER2 overexpressing tumors, the EGFR/HER2 tyrosine kinase inhibitor, lapatinib decreased CD44+/CD24-/low cells, with the majority of these patients after conventional therapy achieving pathologic complete response, a validated surrogate marker for long-term survival. Gene transcription pathways that underlie chemoresistant, MS-forming CD44+/CD24-/low cells involve genes belonging to stem cell self-renewal, Wnt signaling, and early development pathways. Experiment Overall Design: Human breast tumor samples were sorted using flow cytometry to select for cells that were CD44+ and CD24-. Gene expression profiles of these cells were compared with profiles of the other sorted cells (CD24+ and CD44-/CD24-). Experiment Overall Design: Core biopsies of primary breast tumors were taken and placed immediately in cold RPMI-1640 supplemented with 10% heat-inactivated newborn calf serum (HINCS, Invitrogen, Carlsbad, CA). Within an hour, the samples were minced and then digested in 10-15 mL of MEGM with 250-300 units/mL collagenase at 370C. The samples were filtered, washed, and then subjected to hypotonic shock to lyse red blood cells. About 106 single cells were re-suspended, incubated for 15 min at 40C with anti-CD44 (APC), anti-CD24 (FITC), and anti-lineage cocktail antibodies (PE-conjugated anti-CD2, CD3, CD10, CD16, CD18, CD31 and CD 140B) (Pharmingen, San Diego, CA) using the manufacturerâs suggested concentrations. The cells were then washed twice, re-suspended with the viability dye propidium iodide, and analyzed using Dako MoFlo flow cytometry. Side- and forward- scatter were used to eliminate debris and cell doublets, and the Lin- cells were further analyzed by CD44 and CD24 markers.

Dataset Information

GenePattern flow cytometry suite.

Background

Results

Conclusions

Publications

GenePattern flow cytometry suite.

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