Dataset Information

How Clinical Flow Cytometry Rebooted Sepsis Immunology.

ABSTRACT: On May 2017, the World Health Organization (WHO) recognized sepsis as a global health priority by adopting a resolution to improve the prevention, diagnosis, and management of this deadly disease. While it has long been known that sepsis deeply perturbs immune homeostasis by inducing a tremendous systemic inflammatory response, pivotal observations based on clinical flow cytometry indicate that sepsis indeed initiates a more complex immune response that varies over time, with the concomitant occurrence of both pro- and anti-inflammatory mechanisms. As a resultant, some septic patients enter a stage of protracted immunosuppression. This paved the way for immunostimulation approaches in sepsis. Clinical flow cytometry permitted this evolution by drawing a new picture of pathophysiology and reshaping immune trajectories in patients. Additional information from cytometry by time of flight mass cytometry and other high-dimensional flow cytometry platform should rapidly enrich our understanding of this complex disease. This review reports on landmarks of clinical flow cytometry in sepsis and how this single-cell analysis technique permitted to breach the wall of decades of unfruitful anti-inflammatory-based clinical trials in sepsis. © 2019 International Society for Advancement of Cytometry.

SUBMITTER: Monneret G

PROVIDER: S-EPMC6461502 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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How Clinical Flow Cytometry Rebooted Sepsis Immunology.

Monneret Guillaume G Gossez Morgane M Aghaeepour Nima N Gaudilliere Brice B Venet Fabienne F

Cytometry. Part A : the journal of the International Society for Analytical Cytology 20190319 4

On May 2017, the World Health Organization (WHO) recognized sepsis as a global health priority by adopting a resolution to improve the prevention, diagnosis, and management of this deadly disease. While it has long been known that sepsis deeply perturbs immune homeostasis by inducing a tremendous systemic inflammatory response, pivotal observations based on clinical flow cytometry indicate that sepsis indeed initiates a more complex immune response that varies over time, with the concomitant occ ...[more]

PMID: 30887636

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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.

Dataset Information

How Clinical Flow Cytometry Rebooted Sepsis Immunology.

Publications

How Clinical Flow Cytometry Rebooted Sepsis Immunology.

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