Automated cell type annotation and exploration of single-cell signaling dynamics using mass cytometry
Dimitrios Kleftogiannnis,
Sonia Gavasso,
Benedicte Sjo Tislevoll,
Nisha van der Meer,
Inga K.F. Motzfeldt,
Monica Hellesøy,
Stein-Erik Gullaksen,
Emmanuel Griessinger,
Oda Fagerholt,
Andrea Lenartova,
Yngvar Fløisand,
Jan Jacob Schuringa,
Bjørn Tore Gjertsen,
Inge Jonassen
Affiliations
Dimitrios Kleftogiannnis
Department of Informatics, Computational Biology Unit, University of Bergen, 5020 Bergen, Norway; Centre for Cancer Biomarkers (CCBIO), Department of Clinical Science, University of Bergen, 5021 Bergen, Norway; Neuro-SysMed Centre of Clinical Treatment Research, Department of Clinical Science, University of Bergen, 5021 Bergen, Norway; Corresponding author
Sonia Gavasso
Neuro-SysMed Centre of Clinical Treatment Research, Department of Clinical Science, University of Bergen, 5021 Bergen, Norway
Benedicte Sjo Tislevoll
Centre for Cancer Biomarkers (CCBIO), Department of Clinical Science, University of Bergen, 5021 Bergen, Norway
Nisha van der Meer
Department of Experimental Hematology, University Medical Center Groningen, University of Groningen, 9713 Groningen, the Netherlands
Inga K.F. Motzfeldt
Department of Medicine, Hematology Section, Haukeland University Hospital, Helse Bergen HF, 5021 Bergen, Norway
Monica Hellesøy
Department of Medicine, Hematology Section, Haukeland University Hospital, Helse Bergen HF, 5021 Bergen, Norway
Stein-Erik Gullaksen
Department of Medicine, Hematology Section, Haukeland University Hospital, Helse Bergen HF, 5021 Bergen, Norway
Emmanuel Griessinger
Department of Experimental Hematology, University Medical Center Groningen, University of Groningen, 9713 Groningen, the Netherlands
Oda Fagerholt
Centre for Cancer Biomarkers (CCBIO), Department of Clinical Science, University of Bergen, 5021 Bergen, Norway
Andrea Lenartova
Department of Hematology, Oslo University Hospital, 4950 Oslo, Norway
Yngvar Fløisand
Department of Hematology, Oslo University Hospital, 4950 Oslo, Norway
Jan Jacob Schuringa
Department of Experimental Hematology, University Medical Center Groningen, University of Groningen, 9713 Groningen, the Netherlands
Bjørn Tore Gjertsen
Centre for Cancer Biomarkers (CCBIO), Department of Clinical Science, University of Bergen, 5021 Bergen, Norway; Department of Medicine, Hematology Section, Haukeland University Hospital, Helse Bergen HF, 5021 Bergen, Norway
Inge Jonassen
Department of Informatics, Computational Biology Unit, University of Bergen, 5020 Bergen, Norway; Centre for Cancer Biomarkers (CCBIO), Department of Clinical Science, University of Bergen, 5021 Bergen, Norway
Summary: 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.
