Cytometry: Today’s technology and tomorrow’s horizons

ImmunoTechnology Section, VRC, NIAID, NIH, 40 Convent Dr., Room 5509, Bethesda, MD 20817, United States
Methods (Impact Factor: 3.22). 07/2012; 57(3):251–258. DOI: 10.1016/j.ymeth.2012.02.009

ABSTRACT Flow cytometry has been the premier tool for single cell analysis since its invention in the 1960s. It has maintained this position through steady advances in technology and applications, becoming the main force behind interrogating the complexities of the immune system. Technology development was a three-pronged effort, including the hardware, reagents, and analysis algorithms to allow measurement of as many as 20 independent parameters on each cell, at tens of thousands of cells per second. In the coming years, cytometry technology will integrate with other techniques, such as transcriptomics, metabolomics, and so forth. Ongoing efforts are aimed at algorithms to analyse these aggregated datasaets over large numbers of samples. Here we review the development efforts heralding the next stage of flow cytometry.

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    ABSTRACT: Multi-parametric flow cytometry (FCM) represents an invaluable instrument to conduct single cell analysis and has significantly increased our understanding of the immune system. However, due to new techniques allowing us to measure an increased number of phenotypes within the immune system, FCM data analysis has become more complex and labor-intensive than previously. We have therefore developed a semi-automatic gating strategy (NetFCM) that uses clustering and principal component analysis (PCA) together with other statistical methods to mimic manual gating approaches. NetFCM is an online tool both for subset identification as well as for quantification of differences between samples. Additionally, NetFCM can classify and cluster samples based on multidimensional data. We tested the method using a data set of peripheral blood mononuclear cells collected from 23 HIV-infected individuals, which were stimulated with overlapping HIV Gag-p55 and CMV-pp65 peptides or medium alone (negative control). NetFCM clustered the virus-specific CD8+ T cells based on IFNγ and TNF responses into distinct compartments. Additionally, NetFCM was capable of identifying HIV- and CMV-specific responses corresponding to those obtained by manual gating strategies. These data demonstrate that NetFCM has the potential to identify relevant T cell populations by mimicking classical FCM data analysis and reduce the subjectivity and amount of time associated with such analysis. © 2014 International Society for Advancement of Cytometry
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    ABSTRACT: Foxp3 expression is a marker of regulatory T cells (Treg), but how early it is expressed in the thymus is still not fully defined. In this study, we examined Foxp3 expression in double-negative (DN) CD4(-)CD8(-) T cell precursors in the thymus by flow cytometry. By increasing the number of collected cells from the conventional 10(4) cells up to more than 10(6) cells during flow cytometry, we found that DN cells exhibited higher Foxp3 expression than double-positive (DP) CD4(+)CD8(+) and single-positive (SP) CD4(+) or CD8(+) (SP) T cells. CD44(+) expression positively correlated with Foxp3 in thymic DN cells. Furthermore, TCR-β(-)CD25(+) DN cells exhibited the highest frequency of Foxp3-expressing cells. Almost all Foxp3(+) cells expressed CD25in DN cells. These results suggest that Foxp3 expression in DN cells can directly be detected by flow cytometry and it was positively corelated with CD25 and CD44 in DN cells.
    Scientific Reports 07/2014; 4:5781. DOI:10.1038/srep05781 · 5.08 Impact Factor
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    ABSTRACT: Phenotypically identical cells can dramatically vary with respect to behavior during their lifespan and this variation is reflected in their molecular composition such as the transcriptomic landscape. Single-cell transcriptomics using next-generation transcript sequencing (RNA-seq) is now emerging as a powerful tool to profile cell-to-cell variability on a genomic scale. Its application has already greatly impacted our conceptual understanding of diverse biological processes with broad implications for both basic and clinical research. Different single-cell RNA-seq protocols have been introduced and are reviewed here-each one with its own strengths and current limitations. We further provide an overview of the biological questions single-cell RNA-seq has been used to address, the major findings obtained from such studies, and current challenges and expected future developments in this booming field.
    Nucleic Acids Research 07/2014; 42(14). DOI:10.1093/nar/gku555 · 8.81 Impact Factor