Cell Segmentation-free Inference of Cell Types from in Situ Transcriptomics Data

Overview

Journal Nat Commun

Specialty Biology

Date 2021 Jun 11

PMID 34112806

Citations 49

Authors

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

Naveed Ishaque

Affiliations

Soon will be listed here.

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.

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