Automated cell type annotation and exploration of single-cell signaling dynamics using mass cytometry

• Published in: iScience Vol. 27; no. 7; p. 110261
• Main Authors: Kleftogiannis, Dimitrios, Gavasso, Sonia, Tislevoll, Benedicte Sjo, van der Meer, Nisha, Motzfeldt, Inga K.F., Hellesøy, Monica, Gullaksen, Stein-Erik, Griessinger, Emmanuel, Fagerholt, Oda, Lenartova, Andrea, Fløisand, Yngvar, Schuringa, Jan Jacob, Gjertsen, Bjørn Tore, Jonassen, Inge
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 19-07-2024; Elsevier
• Subjects: Bioinformatics; Cancer; Machine learning; Bioinformatics; Machine learning; Cancer
• ISSN: 2589-0042; 2589-0042
• DOI: 10.1016/j.isci.2024.110261

Mass cytometry by time-of-flight (CyTOF) is an emerging technology allowing for in-depth characterization of cellular heterogeneity in cancer and other diseases. Unfortunately, high-dimensional analyses of CyTOF data remain quite demanding. Here, we deploy a bioinformatics framework that tackles two fundamental problems in CyTOF analyses namely (1) automated annotation of cell populations guided by a reference dataset and (2) systematic utilization of single-cell data for effective patient stratification. By applying this framework on several publicly available datasets, we demonstrate that the Scaffold approach achieves good trade-off between sensitivity and specificity for automated cell type annotation. Additionally, a case study focusing on a cohort of 43 leukemia patients reported salient interactions between signaling proteins that are sufficient to predict short-term survival at time of diagnosis using the XGBoost algorithm. Our work introduces an automated and versatile analysis framework for CyTOF data with many applications in future precision medicine projects.
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•The Scaffold facilitates automated cell type annotation guided by a reference dataset•DREMI scores and the XGBoost algorithm predict survival in patients with leukemia•Signaling dynamics measured with CyTOF enhance standard risk-stratification methods•Potential to dissect rich single-cell data from CyTOF with machine learning
Bioinformatics; Cancer; Machine learning