AutoSpill is a principled framework that simplifies the analysis of multichromatic flow cytometry data.
AutoSpill is a principled framework that simplifies the analysis of multichromatic flow cytometry data.
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Autospill是一个原则性的框架，可简化多种流式细胞仪数据的分析。
DOI：
10.1038/s41467-021-23126-8
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发表时间：
2021-05-17
影响因子：
16.6
通讯作者：
Liston A
中科院分区：
文献类型：
--
作者：
Roca CP;Burton OT;Gergelits V;Prezzemolo T;Whyte CE;Halpert R;Kreft Ł;Collier J;Botzki A;Spidlen J;Humblet-Baron S;Liston A
流式细胞仪中的补偿是基于荧光的流式细胞仪数据分析中不可避免的挑战。即使是光谱细胞仪的出现也不能回避溢出问题，光谱解混是这种系统的固有部分。单色质控品溢出系数的计算自成立以来基本保持不变，并且在处理高参数流式细胞术的能力方面越来越受到限制。在这里，我们提出了AutoSpill，一种计算溢出系数的替代方法。该方法结合了自动门控的细胞，计算一个初始的溢出矩阵的基础上强大的线性回归，迭代细化，以减少错误。此外，通过将其作为未染色对照中的内源性染料处理，可以补偿自发荧光。AutoSpill使用单色质控品，与常用流式细胞仪软件兼容。AutoSpill允许更简单、更强大的工作流程，同时降低高参数流式细胞术中的补偿误差幅度。流式细胞术允许同时定量细胞内和细胞上的许多标志物，但这些数据的分析是复杂的。在这里，作者提出了AutoSpill，这是一个通过自动化部分分析和需要更少控制来促进此类数据分析的框架。
Compensating in flow cytometry is an unavoidable challenge in the data analysis of fluorescence-based flow cytometry. Even the advent of spectral cytometry cannot circumvent the spillover problem, with spectral unmixing an intrinsic part of such systems. The calculation of spillover coefficients from single-color controls has remained essentially unchanged since its inception, and is increasingly limited in its ability to deal with high-parameter flow cytometry. Here, we present AutoSpill, an alternative method for calculating spillover coefficients. The approach combines automated gating of cells, calculation of an initial spillover matrix based on robust linear regression, and iterative refinement to reduce error. Moreover, autofluorescence can be compensated out, by processing it as an endogenous dye in an unstained control. AutoSpill uses single-color controls and is compatible with common flow cytometry software. AutoSpill allows simpler and more robust workflows, while reducing the magnitude of compensation errors in high-parameter flow cytometry. Flow cytometry allows the simultaneous quantification of many markers in and on a cell, but the analysis of such data is complicated. Here, the authors propose AutoSpill, a framework that facilitates the analysis of such data by automating parts of the analysis and requiring fewer controls.
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影响因子：
5.5
作者：
Mitchell, Andrew J.;Pradel, Lydie C.;Chimini, Giovanna
通讯作者：
Chimini, Giovanna
影响因子：
3.7
作者：
Nguyen, Richard;Perfetto, Stephen;Mahnke, Yolanda D.;Chattopadhyay, Pratip;Roederer, Mario
通讯作者：
Roederer, Mario
影响因子：
16.8
作者：
Bendall SC;Nolan GP;Roederer M;Chattopadhyay PK
通讯作者：
Chattopadhyay PK
影响因子：
3.7
作者：
Hanley, Mary Beth;Lomas, Woodrow;Park, Emily
通讯作者：
Park, Emily
影响因子：
2.2
作者：
Li, Fuqiang;Yang, Mingjie;Wang, Lai;Williamson, Ian;Tian, Fang;Qin, Minghui;Shah, Prediman K.;Sharifi, Behrooz G.
通讯作者：
Sharifi, Behrooz G.