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Solid state yellow and orange lasers for flow cytometry.

ABSTRACT: Diode and DPSS lasers emitting a variety of wavelengths are now commonly incorporated into flow cytometers, greatly increasing our capacity to excite a wide variety of fluorochromes. Until recently, however, virtually no practical technology existed for generating yellow or orange laser light for flow cytometry that was compatible with smaller instrumentation. In this study, we evaluate several new solid state laser systems that emit from the 570 to 600 nm as excitation sources for flow cytometry. DPSS 580, 589, and 592 nm sources were integrated into a cuvette-based flow cytometer (BD LSR II) and a stream-in-air cell sorter (FACSVantage DiVa), and used to excite a variety of yellow, orange, and red excited fluorochromes, including Texas Red, APC, and its tandem conjugates, and the genetically encoded red fluorescent protein HcRed and the more recently developed Katushka. All laser sources were successfully incorporated into the indicated flow cytometry platforms. The yellow and orange sources (particularly 592 nm) were ideal for exciting Texas Red, and provided excitation of APC and its tandems that was comparable to a traditional red laser source, albeit at higher power levels than red sources. Yellow and orange laser light was optimal for exciting HcRed and Katushka. Practical yellow and orange laser sources are now available for flow cytometry. This technology fills an important gap in the laser wavelengths available for flow, now almost any fluorochrome requiring visible light excitation can be accommodated.

SUBMITTER: Kapoor V

PROVIDER: S-EPMC7410328 | biostudies-literature | 2008 Jun

REPOSITORIES: biostudies-literature

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Solid state yellow and orange lasers for flow cytometry.

Kapoor Veena V Karpov Vladimir V Linton Claudette C Subach Fedor V FV Verkhusha Vladislav V VV Telford William G WG

Cytometry. Part A : the journal of the International Society for Analytical Cytology 20080601 6

Diode and DPSS lasers emitting a variety of wavelengths are now commonly incorporated into flow cytometers, greatly increasing our capacity to excite a wide variety of fluorochromes. Until recently, however, virtually no practical technology existed for generating yellow or orange laser light for flow cytometry that was compatible with smaller instrumentation. In this study, we evaluate several new solid state laser systems that emit from the 570 to 600 nm as excitation sources for flow cytometr ...[more]

PMID: 18449918

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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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Solid state yellow and orange lasers for flow cytometry.

Publications

Solid state yellow and orange lasers for flow cytometry.

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