Protocol for identification and computational analysis of human natural killer cells using flow cytometry and R.
Protocol for identification and computational analysis of human natural killer cells using flow cytometry and R.
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DOI：
10.1016/j.xpro.2023.102044
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发表时间：
2023-03-17
期刊：
影响因子：
--
通讯作者：
Reeves, R. Keith
中科院分区：
文献类型：
--
作者：
Kroll, Kyle;Reeves, R. Keith
鉴定差异蛋白表达通常用于描述来自不同样本群的自然杀伤（NK）细胞。本协议描述了NK细胞分析的关键步骤：使用流动门控识别人类NK细胞，从FlowJo导出数据，在R中加载数据，使用均匀流形逼近和投影进行降维和可视化，以及使用CyotGLMM进行广义线性建模。这些分析可以帮助产生潜在的感兴趣的生物标志物，以识别不同年龄、治疗组和其他组的NK细胞。有关该协议的使用和执行的完整细节，请参阅Kroll等人（2022）。计算分析检查NK细胞，HIV和衰老的交叉点利用可与现有数据一起使用的标准流式细胞术分析工作流程在R中轻松应用计算分析流式细胞术数据轻量级代码，可在本地硬件上运行，无需云计算出版商注：进行任何实验方案都需要遵守当地机构的实验室安全和伦理指导方针。鉴定差异蛋白表达通常用于描述来自不同样本群的自然杀伤（NK）细胞。本协议描述了NK细胞分析的关键步骤：使用流动门控识别人类NK细胞，从FlowJo导出数据，在R中加载数据，使用均匀流形逼近和投影进行降维和可视化，以及使用CyotGLMM进行广义线性建模。这些分析可以帮助产生潜在的感兴趣的生物标志物，以识别不同年龄、治疗组和其他组的NK细胞。
Identifying differential protein expression is routinely used to delineate natural killer (NK) cells from various sample cohorts. This protocol describes key steps for NK cell analysis: identifying human NK cells using flow gating, data export from FlowJo, data loading in R, dimensionality reduction and visualization with Uniform Manifold Approximation and Projection, and generalized linear modeling with CyotGLMM. These analyses can help generate potential biomarkers of interest to identify NK cells across aging, treatment groups, and others. For complete details on the use and execution of this protocol, please refer to Kroll et al. (2022). Computational analyses to examine the intersection of NK cells, HIV, and aging Utilize standard flow cytometry analysis workflows that can be used with existing data Easy application of computational analyses in R to flow cytometry data Lightweight code that can be run on local hardware, no need for cloud computing Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. Identifying differential protein expression is routinely used to delineate natural killer (NK) cells from various sample cohorts. This protocol describes key steps for NK cell analysis: identifying human NK cells using flow gating, data export from FlowJo, data loading in R, dimensionality reduction and visualization with Uniform Manifold Approximation and Projection, and generalized linear modeling with CyotGLMM. These analyses can help generate potential biomarkers of interest to identify NK cells across aging, treatment groups, and others.
影响因子：
14.3
作者：
Kroll, Kyle W.;Shah, Spandan, V;Lucar, Olivier A.;Premeaux, Thomas A.;Shikuma, Cecilia M.;Corley, Michael J.;Mosher, Matthew;Woolley, Griffin;Bowler, Scott;Ndhlovu, Lishomwa C.;Reeves, R. Keith
通讯作者：
Reeves, R. Keith
影响因子：
9.9
作者：
Valcour, V;Shikuma, C;Sacktor, N
通讯作者：
Sacktor, N
影响因子：
3
作者：
Seiler C;Ferreira AM;Kronstad LM;Simpson LJ;Le Gars M;Vendrame E;Blish CA;Holmes S
通讯作者：
Holmes S