Semi‐automated background removal limits data loss and normalizes imaging mass cytometry data. Issue 12 (10th July 2021)

• Record Type: Journal Article
• Authors: Ijsselsteijn, Marieke E.; Somarakis, Antonios; Lelieveldt, Boudewijn P. F.; Höllt, Thomas; de Miranda, Noel F. C. C.
• Other Names: Moore Jonni guestEditor.; Tárnok Attila guestEditor.
• Is Part Of: Cytometry. Volume 99:Issue 12(2021)
• Page Start: 1187
• Page End: 1197
• Publication Date: 2021-07-10
• Subjects: background removal -- CyTOF -- imaging mass cytometry -- multiplex immunophenotyping; Flow cytometry -- Periodicals; Imaging systems in biology -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnostic imaging -- Periodicals; 571.605
• DOI: 10.1002/cyto.a.24480
• Languages: English
• ISSNs: 1552-4922

Abstract

Abstract: 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.