Explainable multi-view framework for dissecting intercellular signaling from highly multiplexed spatial data

bioRxiv (Cold Spring Harbor Laboratory)

Abstract

Abstract The advancement of technologies to measure highly multiplexed spatial data requires the development of scalable methods that can leverage the spatial information. We present MISTy, a flexible, scalable and explainable machine learning framework for extracting interactions from any spatial omics data. MISTy builds multiple views focusing on different spatial or functional contexts to dissect different effects, such as those from direct neighbours versus those from distant cells. MISTy can be applied to different spatially resolved omics data with dozens to thousands of markers, without the need to perform cell-type annotation. We evaluate the performance of MISTy on an in silico dataset and demonstrate its applicability on three breast cancer datasets, two measured by imaging mass cytometry and one by Visium spatial transcriptomics. We show how we can estimate interactions coming from different spatial contexts that we can relate to tumor progression and clinical features. Our analysis also reveals that the estimated interactions in triple negative breast cancer are associated with clinical outcomes which could improve patient stratification. Finally, we demonstrate the flexibility of MISTy to integrate different kinds of views by modeling activities of pathways estimated from gene expression in a spatial context to analyse intercellular signaling.

Authors 5

1. Jovan Tanevski

   Heidelberg University · University Hospital Heidelberg · Jožef Stefan Institute · Jožef Stefan International Postgraduate School

   Affiliation as printed

   Department of Knowledge Technologies, Jožef Stefan Institute, Ljubljana, Slovenia

   Institute for Computational Biomedicine, Faculty of Medicine, Heidelberg University and Heidelberg University Hospital, Heidelberg, Germany

2. Ricardo O. Ramirez Flores

   Heidelberg University · University Hospital Heidelberg

   Affiliation as printed

   Institute for Computational Biomedicine, Faculty of Medicine, Heidelberg University and Heidelberg University Hospital, Heidelberg, Germany

3. Attila Gábor

   Heidelberg University · University Hospital Heidelberg

   Affiliation as printed

   Institute for Computational Biomedicine, Faculty of Medicine, Heidelberg University and Heidelberg University Hospital, Heidelberg, Germany

4. Denis Schapiro

   Broad Institute · Harvard University · Heidelberg University · University Hospital Heidelberg · Klarman Cell Observatory

   Affiliation as printed

   Institute for Computational Biomedicine and Institute of Pathology, Faculty of Medicine, Heidelberg University and Heidelberg University Hospital, Heidelberg, Germany

   Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA

   Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA

5. Julio Sáez-Rodríguez corresponding Aachen Joint Research Centre for Computational Biomedicine (JRC-COMBINE) Faculty of Medicine

   Heidelberg University · University Hospital Heidelberg · RWTH Aachen University

   Affiliation as printed

   Institute for Computational Biomedicine, Faculty of Medicine, Heidelberg University and Heidelberg University Hospital, Heidelberg, Germany

   Joint Research Centre for Computational Biomedicine (JRC-COMBINE), Faculty of Medicine, RWTH Aachen University, Aachen, Germany