Semi-automated background removal limits data loss and normalizes imaging mass cytometry data.
Semi-automated background removal limits data loss and normalizes imaging mass cytometry data.
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DOI：
10.1002/cyto.a.24480
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发表时间：
2021-12
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影响因子：
--
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成像质谱细胞术（IMC）允许检测多种抗原（约40种标记物）与空间信息相结合，使其成为评估复杂生物系统的独特工具。由于其广泛的可用性和保留的组织形态，福尔马林固定的石蜡包埋（FFPE）组织通常是IMC研究的首选材料。然而，由于组织处理（包括固定）的变化，FFPE组织之间的抗体性能和信噪比可能存在显著差异。与由免疫检测程序中的差异引起的批次效应相反，组织处理中的变化难以控制。我们研究了免疫检测相关的信号强度波动对IMC分析和表型鉴定的影响，在12个结直肠癌组织的队列中。此外，我们探索了不同的归一化策略，并提出了一个工作流程，使用公开可用的工具通过半自动背景去除来归一化IMC数据。该工作流程可直接应用于先前采集的数据集，并显著提高IMC数据的质量，从而支持多个样品的分析和比较。
Imaging mass cytometry (IMC) allows the detection of multiple antigens (approximately 40 markers) combined with spatial information, making it a unique tool for the evaluation of complex biological systems. Due to its widespread availability and retained tissue morphology, formalin‐fixed, paraffin‐embedded (FFPE) tissues are often a material of choice for IMC studies. However, antibody performance and signal to noise ratios can differ considerably between FFPE tissues as a consequence of variations in tissue processing, including fixation. In contrast to batch effects caused by differences in the immunodetection procedure, variations in tissue processing are difficult to control. We investigated the effect of immunodetection‐related signal intensity fluctuations on IMC analysis and phenotype identification, in a cohort of 12 colorectal cancer tissues. Furthermore, we explored different normalization strategies and propose a workflow to normalize IMC data by semi‐automated background removal, using publicly available tools. This workflow can be directly applied to previously acquired datasets and considerably improves the quality of IMC data, thereby supporting the analysis and comparison of multiple samples.
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影响因子：
12.3
作者：
Carpenter AE;Jones TR;Lamprecht MR;Clarke C;Kang IH;Friman O;Guertin DA;Chang JH;Lindquist RA;Moffat J;Golland P;Sabatini DM
通讯作者：
Sabatini DM
DOI：
10.1126/science.1198704
发表时间：
2011-05-06
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Bendall SC;Simonds EF;Qiu P;Amir el-AD;Krutzik PO;Finck R;Bruggner RV;Melamed R;Trejo A;Ornatsky OI;Balderas RS;Plevritis SK;Sachs K;Pe'er D;Tanner SD;Nolan GP
通讯作者：
Nolan GP
影响因子：
48
作者：
Giesen, Charlotte;Wang, Hao A. O.;Bodenmiller, Bernd
通讯作者：
Bodenmiller, Bernd
影响因子：
6.6
作者：
O'Hurley G;Sjöstedt E;Rahman A;Li B;Kampf C;Pontén F;Gallagher WM;Lindskog C
通讯作者：
Lindskog C
影响因子：
6.4
作者：
Walker, R. A.
通讯作者：
Walker, R. A.