Semi‐automated background removal limits data loss and normalizes imaging mass cytometry data
Cytometry Part A, vol. 99, pp. 1187–1197
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.
Authors 5
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Marieke E. Ijsselsteijn Aachen Department of Pathology Leiden University Medical Center Leiden The Netherlands Department of Pathology, Leiden University Medical Center
Leiden University Medical Center
Affiliation as printed
Department of Pathology Leiden University Medical Center Leiden The Netherlands
Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands
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Antonios Somarakis Aachen Department of Radiology Leiden University Medical Center Leiden The Netherlands Department of Radiology, Leiden University Medical Center
Leiden University Medical Center
Affiliation as printed
Department of Radiology Leiden University Medical Center Leiden The Netherlands
Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
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Boudewijn P. F. Lelieveldt Aachen Department of Radiology Leiden University Medical Center Leiden The Netherlands Department of Radiology, Leiden University Medical Center
Leiden University Medical Center
Affiliation as printed
Department of Radiology Leiden University Medical Center Leiden The Netherlands
Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
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Leiden University Medical Center · Delft University of Technology
Affiliation as printed
Computer Graphics and Visualization, EEMCS, TU Delft Delft The Netherlands
Leiden Computational Biology Center Leiden University Medical Center Leiden The Netherlands
Computer Graphics and Visualization, EEMCS, TU Delft, Delft, The Netherlands
Leiden Computational Biology Center, Leiden University Medical Center, Leiden, The Netherlands
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Noel F.C.C. de Miranda corresponding Aachen Department of Pathology Leiden University Medical Center Leiden The Netherlands Department of Pathology, Leiden University Medical Center
Leiden University Medical Center
Affiliation as printed
Department of Pathology Leiden University Medical Center Leiden The Netherlands
Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands
Cited by 50 stored of 47
Cited by patents worldwide 1 (Lens.org)
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