Computational flow cytometry: helping to make sense of high-dimensional immunology data

Saeys, Yvan; Gassen, Sofie Van; Lambrecht, Bart N.

doi:10.1038/nri.2016.56

Cited by 421 publications

(416 citation statements)

References 84 publications

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“…Currently, most flow cytometry data is analyzed manually: 2D-plots are inspected visually and cells are assigned to subpopulations based on current knowledge. Over the past decade computational knowledge and power has increased and several automated visualization, clustering and analysis tools have been developed [52,53]. Implementation could decrease the time and expertise needed for analysis and more importantly the subjectivity that is inherent to manual analysis.…”

Section: Implementation In Routine Practicementioning

confidence: 99%

Clinical Implication of Multi-Parameter Flow Cytometry in Myelodysplastic Syndromes

Duetz¹,

Westers²,

Loosdrecht

2018

Pathobiology

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Myelodysplastic syndromes (MDS) are a challenging group of diseases for clinicians and researchers, as both disease course and pathobiology are highly heterogeneous. In (suspected) MDS patients, multi-parameter flow cytometry can aid in establishing diagnosis, risk stratification and choice of therapy. This review addresses the developments and future directions of multi-parameter flow cytometry scores in MDS. Additionally, we propose an integrated diagnostic algorithm for suspected MDS.

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Section: Implementation In Routine Practicementioning

confidence: 99%

Clinical Implication of Multi-Parameter Flow Cytometry in Myelodysplastic Syndromes

Duetz¹,

Westers²,

Loosdrecht

2018

Pathobiology

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“…A higher resolution and more advanced flow data analysis can be achieved by referencing other publications [10,11]. gated for further analysis.…”

Section: Resuspend Cells In 100 μLmentioning

confidence: 99%

Analysis of S1P Receptor Expression by Uterine Immune Cells Using Standardized Multi-parametric Flow Cytometry

Zhang¹,

Bang²,

Lye

2017

Methods in Molecular Biology

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Flow cytometry is a powerful tool for phenotypic and functional analyses of single immune cells. The increasing capability of flow cytometry technology has driven a more detailed understanding of immune cell subsets and functions in complex cellular systems such as the developing human decidua/placenta. We propose a standardized procedure for the isolation and analysis of human decidual natural killer (dNK) cells and this method can be extended to investigation of other uterine lymphocytes. Here this platform is used to examine the expression of sphingosine-1-phosphate (S1P) receptor and functional growth factors by dNK cells.

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“…New computational techniques that use dimension reduction to aid in the analysis of high-dimensional cytometry data have been developed recently (reviewed in Refs. 1–3). One such dimension-reduction algorithm is referred to as visualization of t -distributed stochastic neighbor embedding (viSNE) (4) or t -distributed stochastic neighbor embedding ( t SNE) (5).…”

mentioning

confidence: 99%

Using Visualization of t-Distributed Stochastic Neighbor Embedding To Identify Immune Cell Subsets in Mouse Tumors

Acuff

Linden

2017

The Journal of Immunology

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High-dimensional flow cytometry is proving to be valuable for the study of subtle changes in tumor-associated immune cells. As flow panels become more complex, detection of minor immune cell populations by traditional gating using biaxial plots, or identification of populations that display small changes in multiple markers, may be overlooked. Visualization of t-distributed stochastic neighbor embedding (viSNE) is an unsupervised analytical tool designed to aid the analysis of high-dimensional cytometry data. In this study we use viSNE to analyze the simultaneous binding of 15 fluorophore-conjugated Abs and one cell viability probe to immune cells isolated from syngeneic mouse MB49 bladder tumors, spleens, and tumor-draining lymph nodes to identify patterns of anti-tumor immune responses. viSNE maps identified populations in multidimensional space of known immune cells, including T cells, B cells, eosinophils, neutrophils, dendritic cells, and NK cells. Based on the expression of CD86 and programmed cell death protein 1, CD8+ T cells were divided into distinct populations. Additionally, both CD8+ T cells and CD8+ dendritic cells were identified in the tumor microenvironment. Apparent differences between splenic and tumor polymorphonuclear cells/granulocytic myeloid-derived suppressor cells are due to the loss of CD44 upon enzymatic digestion of tumors. In conclusion, viSNE is a valuable tool for high-dimensional analysis of immune cells in tumor-bearing mice, which eliminates gating biases and identifies immune cell subsets that may be missed by traditional gating.

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Computational flow cytometry: helping to make sense of high-dimensional immunology data

Cited by 421 publications

References 84 publications

Clinical Implication of Multi-Parameter Flow Cytometry in Myelodysplastic Syndromes

Clinical Implication of Multi-Parameter Flow Cytometry in Myelodysplastic Syndromes

Analysis of S1P Receptor Expression by Uterine Immune Cells Using Standardized Multi-parametric Flow Cytometry

Using Visualization of t-Distributed Stochastic Neighbor Embedding To Identify Immune Cell Subsets in Mouse Tumors

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