A chromatic explosion: the development and future of multiparameter flow cytometry

Chattopadhyay, Pratip K.; Högerkorp, Carl-Magnus; Roederer, Mario

doi:10.1111/j.1365-2567.2008.02989.x

Cited by 152 publications

(121 citation statements)

References 76 publications

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“…Much waste could have been avoided if a strong NIH-sponsored programme, emulating the Histocompatibility Workshops, had coordinated the assembly and distribution of comprehensive collections of mono clonal antibody reagents. Such a need for community-based coordination may become even more important given the everincreasing capability of cytometry techniques for multiplex analyses 21,22 .…”

Section: Consortium Biology In Immunology?mentioning

confidence: 99%

Consortium biology in immunology: the perspective from the Immunological Genome Project

Benoist¹,

Lanier²,

Mérad³

et al. 2012

Nat Rev Immunol

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Section: Consortium Biology In Immunology?mentioning

confidence: 99%

Consortium biology in immunology: the perspective from the Immunological Genome Project

Benoist¹,

Lanier²,

Mérad³

et al. 2012

Nat Rev Immunol

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“…The continuing improvements in hardware and reagents widely available for flow cytometry have created a need for more efficient methods of data analysis of subsets within complex cell mixtures (2). We have estimated that at least 25 markers are needed to subdivide all the currently known functional subsets of CD41 T cells in blood.…”

Section: Discussionmentioning

confidence: 99%

“…A large number of alternative approaches to cluster analysis in multidimensional flow cytometry data have been reported, and have recently been reviewed (2,16). The most common approaches have been based on well-established kmeans clustering (18), but this requires an initial input of the number of clusters and is very sensitive to these initial conditions, as well as needing modifications to allow for nonGaussian data and nonspherical cluster shapes (16).…”

mentioning

confidence: 99%

Computationally efficient multidimensional analysis of complex flow cytometry data using second order polynomial histograms

et al. 2015

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Many methods have been described for automated clustering analysis of complex flow cytometry data, but so far the goal to efficiently estimate multivariate densities and their modes for a moderate number of dimensions and potentially millions of data points has not been attained. We have devised a novel approach to describing modes using second order polynomial histogram estimators (SOPHE). The method divides the data into multivariate bins and determines the shape of the data in each bin based on second order polynomials, which is an efficient computation. These calculations yield local maxima and allow joining of adjacent bins to identify clusters. The use of second order polynomials also optimally uses wide bins, such that in most cases each parameter (dimension) need only be divided into 4-8 bins, again reducing computational load. We have validated this method using defined mixtures of up to 17 fluorescent beads in 16 dimensions, correctly identifying all populations in data files of 100,000 beads in <10 s, on a standard laptop. The method also correctly clustered granulocytes, lymphocytes, including standard T, B, and NK cell subsets, and monocytes in 9-color stained peripheral blood, within seconds. SOPHE successfully clustered up to 36 subsets of memory CD4 T cells using differentiation and trafficking markers, in 14-color flow analysis, and up to 65 subpopulations of PBMC in 33-dimensional CyTOF data, showing its usefulness in discovery research. SOPHE has the potential to greatly increase efficiency of analysing complex mixtures of cells in higher dimensions. V C 2015 International Society for Advancement of Cytometry Key terms data analysis; clustering; high dimensions; complex data LYMPHOCYTES were originally viewed by light microscopy as small homogeneous round cells with minimal cytoplasm (1). Multiparameter flow cytometry, using currently available lasers, monoclonal antibodies and fluorochromes (2), has helped reveal the extremely complex heterogeneity of differentiated T and B cells with diverse immunological properties (3,4). It is possible to analyze 18 or more markers simultaneously on individual lymphocytes (2), and the development of additional labels will greatly increase this number. Study of 40 or more markers has already been achieved by the use of transition element isotopes as chelated antibody tags and mass cytometry, instead of fluorochromes (5,6).Addition of more parameters is an important goal of flow cytometric analysis of lymphocytes, because, paradoxically, instead of simply increasing complexity of the results, they can actually reveal important subsets with much greater clarity. This was originally best shown by the combination of 2 light scatter and 2 fluorescence parameters, CD45 and CD14, to clearly separate lymphocytes in blood from monocytes, granulocytes and red cell debris (7). In our experience, an important example is to first separate na€ ıve and memory T cells using CD45RA/CD45RO to measure expres-

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“…Flow cytometers are usually equipped with 2-3 lasers allowing excitation of 6 or more standard fluorochromes and the term multiparametric and/or polychromatic flow cytometry is used for this approach [7]. The classical immunophenotypisation identifies cells based on their size and granularity/ complexity as well as by the "visualization" of antigen-antibody binding.…”

Section: Flow Cytometry In Mgs -Past Present and Futurementioning

confidence: 99%

Immunophenotyping in Multiple Myeloma and Others Monoclonal Gammopathies

Říhová¹,

Raja²,

Leite³

et al. 2013

Multiple Myeloma - A Quick Reflection on the Fast Progress

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A chromatic explosion: the development and future of multiparameter flow cytometry

Cited by 152 publications

References 76 publications

Consortium biology in immunology: the perspective from the Immunological Genome Project

Consortium biology in immunology: the perspective from the Immunological Genome Project

Computationally efficient multidimensional analysis of complex flow cytometry data using second order polynomial histograms

Immunophenotyping in Multiple Myeloma and Others Monoclonal Gammopathies

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