The brain is the most heterogeneous and complex tissue in the body.Previous studies have shown that immune cells are essential functional components in both healthy and pathological brains.Cytometry by the time of fli...The brain is the most heterogeneous and complex tissue in the body.Previous studies have shown that immune cells are essential functional components in both healthy and pathological brains.Cytometry by the time of flight(CyTOF)is a high-dimensional single-cell detection technology that allows measurements of up to 100 cell markers with a small number of samples.This technique enables the identification and characterization of various cell types at the single-cell level under steady-state and diseased brain conditions.This review outlines three major advantages of the CyTOF technique compared with the traditional flow cytometry approach.We also discuss CyTOF applications in brain immune cell component research in both healthy and pathological brains.展开更多
目的·使用质谱流式细胞技术(cytometry by time-of-flight,CyTOF)分析乳腺癌患者肿瘤组织的多种抗原,探究其与乳腺癌微环境、乳腺癌患者预后的关系。方法·使用Maxpar^(■)Panel Designer v2.0.1软件结合相关抗原蛋白及组织细...目的·使用质谱流式细胞技术(cytometry by time-of-flight,CyTOF)分析乳腺癌患者肿瘤组织的多种抗原,探究其与乳腺癌微环境、乳腺癌患者预后的关系。方法·使用Maxpar^(■)Panel Designer v2.0.1软件结合相关抗原蛋白及组织细胞标志物设计Panel。使用Maxpar X8抗体标记试剂盒将相关镧系(Ln)金属同位素与Panel中的蛋白抗体连接后,采用成像质谱流式细胞染色(imaging mass cytometry staining,IMC)法对乳腺癌组织芯片进行染色。在Hyperion成像系统中观察,得到Panel中多种蛋白标志物的表达和空间定位。使用R语言对原始数据进行数据归一化、去噪和降噪、补偿校正以及数据转换,再进行降维处理。通过聚类算法进行细胞亚群注释。使用空间邻域分析,解析乳腺癌微环境中的各类细胞和临床意义。结果·将金属标签与相应的抗原抗体连接后,染色效果良好,可用于IMC染色。在乳腺癌组织芯片中,根据现有的26种标志物可以将乳腺癌微环境分成9种细胞类型,共410000个细胞。在配对的肿瘤组织和癌旁组织中,乳腺癌微环境主要由B细胞、CD4^(+)T细胞、CD8^(+)T细胞、上皮细胞、内皮细胞、巨噬细胞、肌上皮细胞、中性粒细胞、成纤维细胞组成。其中,巨噬细胞和CD4^(+)T细胞在肿瘤组织与癌旁组织中的数量差异有统计学意义(均P<0.05)。在乳腺癌组织中鉴定出15种特征性细胞邻域,其中CD8^(+)T细胞和巨噬细胞与肿瘤细胞空间共定位邻域,与患者生存期延长显著相关(P=0.011,P<0.001)。结论·CyTOF对于大批量检测多种抗原在组织中的表达有重要作用,可以在微观角度上分析乳腺癌组织与乳腺癌微环境的关系。在乳腺癌微环境中,CD8^(+)T细胞和巨噬细胞的表达量较高与乳腺癌患者的良好预后相关。展开更多
Cells are inherently heterogeneous to achieve a diverse spectrum of biological functions.To understand the underlying protein machinery that achieves these fascinating functions,it is important to develop advanced ana...Cells are inherently heterogeneous to achieve a diverse spectrum of biological functions.To understand the underlying protein machinery that achieves these fascinating functions,it is important to develop advanced analytical methods that can profile proteins in their native environment,as protein expression,aggregation,degradation,and regulation define both normal physiology as well as pathogenicity.Genome and transcriptome sequencing have seen major advances at the single-cell levels,but comprehensive proteomic profiling is still challenging.The conventional proteomic methods,such as enzyme-linked immunosorbent assay(ELISA),western blot,and protein chips,can characterize biomarkers of interest.Still,these ensemble techniques are unsuitable for single-cell studies.Increasing evidence has shown the significance of in situ,sensitive,quantitative,and multiplexed profiling of biomarkers in single cells for diagnosis and treatment guidance.Here,we review the recent development of advanced imaging and spectroscopy techniques,including mass cytometry,immunofluorescence,and surfaceenhanced Raman spectrometry(SERS)for single-cell proteomic imaging.We also provide our view on the challenges and the outlook.展开更多
Background:Mass cytometry(CyTOF)gives unprecedented opportunity to simultaneously measure up to 40 proteins in single cells,with a theoretical potential to reach 100 proteins.This high-dimensional single-cell informat...Background:Mass cytometry(CyTOF)gives unprecedented opportunity to simultaneously measure up to 40 proteins in single cells,with a theoretical potential to reach 100 proteins.This high-dimensional single-cell information can be very useful in dissecting mechanisms of cellular activity.In particular,measuring abundances of signaling proteins like phospho-proteins can provide detailed information on the dynamics of single-cell signaling processes.However,computational analysis is required to reconstruct such networks with a mechanistic model.Methods:We propose our Mass cytometry Signaling Network Analysis Code(McSNAC),a new software capable of reconstructing signaling networks and estimating their kinetic parameters from CyTOF data.McSNAC approximates signaling networks as a network of first-order reactions between proteins.This assumption often breaks down as signaling reactions can involve binding and unbinding,enzymatic reactions,and other nonlinear constructions.Furthermore,McSNAC may be limited to approximating indirect interactions between protein species,as cytometry experiments are only able to assay a small fraction of protein species involved in signaling.Results:We carry out a series of in silico experiments here to show(1)McSNAC is capable of accurately estimating the ground-truth model in a scalable manner when given data originating from a first-order system;(2)McSNAC is capable of qualitatively predicting outcomes to perturbations of species abundances in simple second-order reaction models and in a complex in silico nonlinear signaling network in which some proteins are unmeasured.Conclusions:These findings demonstrate that McSNAC can be a valuable screening tool for generating models of signaling networks from time-stamped CyTOF data.展开更多
基金supported by the Beijing Natural Science Foundation(7214269)the National Natural Science Foundation of China(82001248)the Peking University Third Hospital Talent Program C(BYSYZD2019047)。
文摘The brain is the most heterogeneous and complex tissue in the body.Previous studies have shown that immune cells are essential functional components in both healthy and pathological brains.Cytometry by the time of flight(CyTOF)is a high-dimensional single-cell detection technology that allows measurements of up to 100 cell markers with a small number of samples.This technique enables the identification and characterization of various cell types at the single-cell level under steady-state and diseased brain conditions.This review outlines three major advantages of the CyTOF technique compared with the traditional flow cytometry approach.We also discuss CyTOF applications in brain immune cell component research in both healthy and pathological brains.
文摘目的·使用质谱流式细胞技术(cytometry by time-of-flight,CyTOF)分析乳腺癌患者肿瘤组织的多种抗原,探究其与乳腺癌微环境、乳腺癌患者预后的关系。方法·使用Maxpar^(■)Panel Designer v2.0.1软件结合相关抗原蛋白及组织细胞标志物设计Panel。使用Maxpar X8抗体标记试剂盒将相关镧系(Ln)金属同位素与Panel中的蛋白抗体连接后,采用成像质谱流式细胞染色(imaging mass cytometry staining,IMC)法对乳腺癌组织芯片进行染色。在Hyperion成像系统中观察,得到Panel中多种蛋白标志物的表达和空间定位。使用R语言对原始数据进行数据归一化、去噪和降噪、补偿校正以及数据转换,再进行降维处理。通过聚类算法进行细胞亚群注释。使用空间邻域分析,解析乳腺癌微环境中的各类细胞和临床意义。结果·将金属标签与相应的抗原抗体连接后,染色效果良好,可用于IMC染色。在乳腺癌组织芯片中,根据现有的26种标志物可以将乳腺癌微环境分成9种细胞类型,共410000个细胞。在配对的肿瘤组织和癌旁组织中,乳腺癌微环境主要由B细胞、CD4^(+)T细胞、CD8^(+)T细胞、上皮细胞、内皮细胞、巨噬细胞、肌上皮细胞、中性粒细胞、成纤维细胞组成。其中,巨噬细胞和CD4^(+)T细胞在肿瘤组织与癌旁组织中的数量差异有统计学意义(均P<0.05)。在乳腺癌组织中鉴定出15种特征性细胞邻域,其中CD8^(+)T细胞和巨噬细胞与肿瘤细胞空间共定位邻域,与患者生存期延长显著相关(P=0.011,P<0.001)。结论·CyTOF对于大批量检测多种抗原在组织中的表达有重要作用,可以在微观角度上分析乳腺癌组织与乳腺癌微环境的关系。在乳腺癌微环境中,CD8^(+)T细胞和巨噬细胞的表达量较高与乳腺癌患者的良好预后相关。
基金supported by the National Key R&D Program of China(2019YFA0210103)the National Natural Science Foundation of China(61905122,22174070,21775075,21977053)+2 种基金the Natural Science Foundation of Jiangsu Province(BK20190735)the Research Start-up Fund of Nanjing University of Posts and Telecommunications(NY220149,NY219006)the Fundamental Research Funds for the Central Universities,Nankai University(2122018165)。
文摘Cells are inherently heterogeneous to achieve a diverse spectrum of biological functions.To understand the underlying protein machinery that achieves these fascinating functions,it is important to develop advanced analytical methods that can profile proteins in their native environment,as protein expression,aggregation,degradation,and regulation define both normal physiology as well as pathogenicity.Genome and transcriptome sequencing have seen major advances at the single-cell levels,but comprehensive proteomic profiling is still challenging.The conventional proteomic methods,such as enzyme-linked immunosorbent assay(ELISA),western blot,and protein chips,can characterize biomarkers of interest.Still,these ensemble techniques are unsuitable for single-cell studies.Increasing evidence has shown the significance of in situ,sensitive,quantitative,and multiplexed profiling of biomarkers in single cells for diagnosis and treatment guidance.Here,we review the recent development of advanced imaging and spectroscopy techniques,including mass cytometry,immunofluorescence,and surfaceenhanced Raman spectrometry(SERS)for single-cell proteomic imaging.We also provide our view on the challenges and the outlook.
文摘Background:Mass cytometry(CyTOF)gives unprecedented opportunity to simultaneously measure up to 40 proteins in single cells,with a theoretical potential to reach 100 proteins.This high-dimensional single-cell information can be very useful in dissecting mechanisms of cellular activity.In particular,measuring abundances of signaling proteins like phospho-proteins can provide detailed information on the dynamics of single-cell signaling processes.However,computational analysis is required to reconstruct such networks with a mechanistic model.Methods:We propose our Mass cytometry Signaling Network Analysis Code(McSNAC),a new software capable of reconstructing signaling networks and estimating their kinetic parameters from CyTOF data.McSNAC approximates signaling networks as a network of first-order reactions between proteins.This assumption often breaks down as signaling reactions can involve binding and unbinding,enzymatic reactions,and other nonlinear constructions.Furthermore,McSNAC may be limited to approximating indirect interactions between protein species,as cytometry experiments are only able to assay a small fraction of protein species involved in signaling.Results:We carry out a series of in silico experiments here to show(1)McSNAC is capable of accurately estimating the ground-truth model in a scalable manner when given data originating from a first-order system;(2)McSNAC is capable of qualitatively predicting outcomes to perturbations of species abundances in simple second-order reaction models and in a complex in silico nonlinear signaling network in which some proteins are unmeasured.Conclusions:These findings demonstrate that McSNAC can be a valuable screening tool for generating models of signaling networks from time-stamped CyTOF data.
