Assessment of Automated Flow Cytometry Data Analysis Tools within Cell and Gene Therapy Manufacturing.
Assessment of Automated Flow Cytometry Data Analysis Tools within Cell and Gene Therapy Manufacturing.
复制标题
细胞和基因治疗制造中自动流式细胞术数据分析工具的评估。
DOI：
10.3390/ijms23063224
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发表时间：
2022-03-17
影响因子：
5.6
通讯作者：
Petzing J
中科院分区：
文献类型：
--
作者：
Cheung M;Campbell JJ;Thomas RJ;Braybrook J;Petzing J
流式细胞术广泛用于细胞和基因疗法的制造中以测量和鉴定细胞。传统的手动数据分析在很大程度上依赖于操作者的判断，这是一个主要的变化来源，可能会对患者治疗的质量和预测潜力产生不利影响。计算工具有能力最大限度地减少流式细胞术数据分析中的操作员差异和偏倚;然而，在许多情况下，对这些技术的信心尚未完全建立，反映在监管问题方面。在这里，我们采用了包含分离的受控群体特征和正态/偏态分布的合成流式细胞术数据集，以研究六种细胞群体识别工具的准确性和再现性，每种工具都实现了不同的无监督聚类算法：Flock2、flowMeans、FlowSOM、PhenoGraph、SPADE 3和SWIFT（分别为基于密度、k均值、自组织映射、k最近邻、确定性k均值和基于模型的聚类）。我们发现，从软件分析相同的参考合成数据集的输出变化很大，准确性恶化的集群分离指数福尔斯低于零。因此，随着集群开始合并，flowMeans和Flock 2软件平台比其他平台更难以识别目标集群。此外，偏斜细胞群的存在导致SWIFT的性能较差，尽管相比之下FlowSOM、PhenoGraph和SPADE 3相对不受影响。这些发现说明了如何利用新型流式细胞术合成数据集来验证一系列自动化细胞鉴定方法，从而提高自动化细胞表征和计数的数据质量的置信度。
Flow cytometry is widely used within the manufacturing of cell and gene therapies to measure and characterise cells. Conventional manual data analysis relies heavily on operator judgement, presenting a major source of variation that can adversely impact the quality and predictive potential of therapies given to patients. Computational tools have the capacity to minimise operator variation and bias in flow cytometry data analysis; however, in many cases, confidence in these technologies has yet to be fully established mirrored by aspects of regulatory concern. Here, we employed synthetic flow cytometry datasets containing controlled population characteristics of separation, and normal/skew distributions to investigate the accuracy and reproducibility of six cell population identification tools, each of which implement different unsupervised clustering algorithms: Flock2, flowMeans, FlowSOM, PhenoGraph, SPADE3 and SWIFT (density-based, k-means, self-organising map, k-nearest neighbour, deterministic k-means, and model-based clustering, respectively). We found that outputs from software analysing the same reference synthetic dataset vary considerably and accuracy deteriorates as the cluster separation index falls below zero. Consequently, as clusters begin to merge, the flowMeans and Flock2 software platforms struggle to identify target clusters more than other platforms. Moreover, the presence of skewed cell populations resulted in poor performance from SWIFT, though FlowSOM, PhenoGraph and SPADE3 were relatively unaffected in comparison. These findings illustrate how novel flow cytometry synthetic datasets can be utilised to validate a range of automated cell identification methods, leading to enhanced confidence in the data quality of automated cell characterisations and enumerations.
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影响因子：
64.5
作者：
Levine JH;Simonds EF;Bendall SC;Davis KL;Amir el-AD;Tadmor MD;Litvin O;Fienberg HG;Jager A;Zunder ER;Finck R;Gedman AL;Radtke I;Downing JR;Pe'er D;Nolan GP
通讯作者：
Nolan GP
影响因子：
9.8
作者：
Bhattacharya S;Dunn P;Thomas CG;Smith B;Schaefer H;Chen J;Hu Z;Zalocusky KA;Shankar RD;Shen-Orr SS;Thomson E;Wiser J;Butte AJ
通讯作者：
Butte AJ
影响因子：
3.7
作者：
Aghaeepour, Nima;Nikolic, Radina;Hoos, Holger H.;Brinkman, Ryan R.
通讯作者：
Brinkman, Ryan R.
影响因子：
48
作者：
Aghaeepour N;Finak G;FlowCAP Consortium;DREAM Consortium;Hoos H;Mosmann TR;Brinkman R;Gottardo R;Scheuermann RH
通讯作者：
Scheuermann RH
影响因子：
3
作者：
Maecker, HT;Rinfret, A;Sekaly, RP
通讯作者：
Sekaly, RP