Cell segmentation-free inference of cell types from in situ transcriptomics data

Jeongbin Park, Wonyl Choi, Sebastian Tiesmeyer, Brian Long, Lars E. Borm, Emma Garren, Thuc Nghi Nguyen, Bosiljka Tasic, Simone Codeluppi, Tobias Graf, Matthias Schlesner, Oliver Stegle, Roland Eils () and Naveed Ishaque ()
Additional contact information
Jeongbin Park: Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Digital Health Center
Wonyl Choi: Boston University
Sebastian Tiesmeyer: Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Digital Health Center
Brian Long: Allen Institute for Brain Science
Lars E. Borm: Karolinska Institutet
Emma Garren: Allen Institute for Brain Science
Thuc Nghi Nguyen: Allen Institute for Brain Science
Bosiljka Tasic: Allen Institute for Brain Science
Simone Codeluppi: Karolinska Institutet
Tobias Graf: Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Digital Health Center
Matthias Schlesner: Bioinformatics and Omics Data Analytics, German Cancer Research Center (DKFZ)
Oliver Stegle: Division of Computational Genomics and System Genetics, German Cancer Research Center (DKFZ)
Roland Eils: Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Digital Health Center
Naveed Ishaque: Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Digital Health Center

Nature Communications, 2021, vol. 12, issue 1, 1-13

Abstract: Abstract Multiplexed fluorescence in situ hybridization techniques have enabled cell-type identification, linking transcriptional heterogeneity with spatial heterogeneity of cells. However, inaccurate cell segmentation reduces the efficacy of cell-type identification and tissue characterization. Here, we present a method called Spot-based Spatial cell-type Analysis by Multidimensional mRNA density estimation (SSAM), a robust cell segmentation-free computational framework for identifying cell-types and tissue domains in 2D and 3D. SSAM is applicable to a variety of in situ transcriptomics techniques and capable of integrating prior knowledge of cell types. We apply SSAM to three mouse brain tissue images: the somatosensory cortex imaged by osmFISH, the hypothalamic preoptic region by MERFISH, and the visual cortex by multiplexed smFISH. Here, we show that SSAM detects regions occupied by known cell types that were previously missed and discovers new cell types.

Date: 2021
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DOI: 10.1038/s41467-021-23807-4

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