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一种用于流式细胞术和质谱流式细胞术数据的可视化和定量分析以发现免疫特征及相关性的工作流程。

: A workflow for visual and quantitative analysis of flow and mass cytometry data to discover immune signatures and correlations.

作者信息

Beyrend Guillaume, Stam Koen, Höllt Thomas, Ossendorp Ferry, Arens Ramon

机构信息

Department of Immunohematology and Blood Transfusion, Leiden University Medical Center, Albinusdreef 2, Leiden 2333 ZA, The Netherlands.

Department of Parasitology, Leiden University Medical Center, Leiden, The Netherlands.

出版信息

Comput Struct Biotechnol J. 2018 Oct 24;16:435-442. doi: 10.1016/j.csbj.2018.10.004. eCollection 2018.

DOI:10.1016/j.csbj.2018.10.004
PMID:30450167
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6226576/
Abstract

Multi-parametric flow and mass cytometry allows exceptional high-resolution exploration of the cellular composition of the immune system. A large panel of computational tools have been developed to analyze the high-dimensional landscape of the data generated. Analysis frameworks such as FlowSOM or Cytosplore incorporate clustering and dimensionality reduction techniques and include algorithms allowing visualization of multi-parametric cytometric analysis. To additionally provide means to quantify specific cell clusters and correlations between samples, we developed an R-package, called , for further downstream analysis. Specifically, enables the visualization and quantification of cell clusters for an efficient discovery of cell populations associated with diseases or physiology. We used on mass and flow cytometry datasets based on the modulation of the immune system upon immunotherapy. With we rapidly generated visual representations of group-related immune cell clusters and showed correlations with the immune system composition. We discovered macrophage subsets that significantly decrease upon cancer immunotherapy and distinct prime-boost effects of prophylactic vaccines on the myeloid compartment. is a time-efficient tool for comprehensive cytometric analysis to reveal immune signatures and correlations. is available at Bioconductor.

摘要

多参数流式细胞术和质谱流式细胞术能够对免疫系统的细胞组成进行卓越的高分辨率探索。已经开发了大量计算工具来分析所生成数据的高维格局。诸如FlowSOM或Cytosplore等分析框架纳入了聚类和降维技术,并包括允许可视化多参数细胞分析的算法。为了额外提供量化特定细胞簇以及样本间相关性的方法,我们开发了一个名为 的R包,用于进一步的下游分析。具体而言, 能够对细胞簇进行可视化和量化,以高效发现与疾病或生理相关的细胞群体。我们基于免疫疗法对免疫系统的调节,将 应用于质谱流式细胞术和流式细胞术数据集。借助 ,我们迅速生成了与组相关的免疫细胞簇的视觉表示,并展示了与免疫系统组成的相关性。我们发现了在癌症免疫疗法后显著减少的巨噬细胞亚群,以及预防性疫苗对髓系区室的独特初免-加强效应。 是一种用于全面细胞分析以揭示免疫特征和相关性的省时工具。 可在生物导体(Bioconductor)上获取。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de5c/6226576/7860ba80147a/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de5c/6226576/df5242f88e77/ga1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de5c/6226576/e781d3af1027/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de5c/6226576/7c6f98d58f17/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de5c/6226576/7e02dedb2b37/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de5c/6226576/15bb9925dd24/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de5c/6226576/7860ba80147a/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de5c/6226576/df5242f88e77/ga1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de5c/6226576/e781d3af1027/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de5c/6226576/7c6f98d58f17/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de5c/6226576/7e02dedb2b37/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de5c/6226576/15bb9925dd24/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de5c/6226576/7860ba80147a/gr5.jpg

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