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Multinode acoustic focusing for parallel flow cytometry.

ABSTRACT: Flow cytometry can simultaneously measure and analyze multiple properties of single cells or particles with high sensitivity and precision. Yet, conventional flow cytometers have fundamental limitations with regards to analyzing particles larger than about 70 ?m, analyzing at flow rates greater than a few hundred microliters per minute, and providing analysis rates greater than 50,000 per second. To overcome these limits, we have developed multinode acoustic focusing flow cells that can position particles (as small as a red blood cell and as large as 107 ?m in diameter) into as many as 37 parallel flow streams. We demonstrate the potential of such flow cells for the development of high throughput, parallel flow cytometers by precision focusing of flow cytometry alignment microspheres, red blood cells, and the analysis of a CD4+ cellular immunophenotyping assay. This approach will have significant impact toward the creation of high throughput flow cytometers for rare cell detection applications (e.g., circulating tumor cells), applications requiring large particle analysis, and high volume flow cytometry.

SUBMITTER: Piyasena ME

PROVIDER: S-EPMC3325784 | biostudies-other | 2012 Feb

REPOSITORIES: biostudies-other

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Multinode acoustic focusing for parallel flow cytometry.

Piyasena Menake E ME Austin Suthanthiraraj Pearlson P PP Applegate Robert W RW Goumas Andrew M AM Woods Travis A TA López Gabriel P GP Graves Steven W SW

Analytical chemistry 20120130 4

Flow cytometry can simultaneously measure and analyze multiple properties of single cells or particles with high sensitivity and precision. Yet, conventional flow cytometers have fundamental limitations with regards to analyzing particles larger than about 70 μm, analyzing at flow rates greater than a few hundred microliters per minute, and providing analysis rates greater than 50,000 per second. To overcome these limits, we have developed multinode acoustic focusing flow cells that can position ...[more]

PMID: 22239072

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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.

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Multinode acoustic focusing for parallel flow cytometry.

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Multinode acoustic focusing for parallel flow cytometry.

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