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

Inaccurate cell segmentation has been the major problem for cell-type identification and tissue characterization of the in situ spatially resolved transcriptomics data. Here we show a robust cell segmentation-free computational framework (SSAM), for identifying cell types and tissue domains in 2D an...

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Main 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
Format:	Article
Language:	English
Published:	Nature Publishing Group 2021-06-01
Series:	Nature Communications
Online Access:	https://doi.org/10.1038/s41467-021-23807-4

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spelling	doaj-a175c0b017934a918d3257520493c81c2021-06-13T11:14:58ZengNature Publishing GroupNature Communications2041-17232021-06-0112111310.1038/s41467-021-23807-4Cell segmentation-free inference of cell types from in situ transcriptomics dataJeongbin Park0Wonyl Choi1Sebastian Tiesmeyer2Brian Long3Lars E. Borm4Emma Garren5Thuc Nghi Nguyen6Bosiljka Tasic7Simone Codeluppi8Tobias Graf9Matthias Schlesner10Oliver Stegle11Roland Eils12Naveed Ishaque13Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Digital Health CenterDepartment of Computer Science, Boston UniversityBerlin Institute of Health at Charité – Universitätsmedizin Berlin, Digital Health CenterAllen Institute for Brain ScienceDivision of molecular neurobiology, Department of medical biochemistry and biophysics, Karolinska InstitutetAllen Institute for Brain ScienceAllen Institute for Brain ScienceAllen Institute for Brain ScienceDivision of molecular neurobiology, Department of medical biochemistry and biophysics, Karolinska InstitutetBerlin Institute of Health at Charité – Universitätsmedizin Berlin, Digital Health CenterBioinformatics and Omics Data Analytics, German Cancer Research Center (DKFZ)Division of Computational Genomics and System Genetics, German Cancer Research Center (DKFZ)Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Digital Health CenterBerlin Institute of Health at Charité – Universitätsmedizin Berlin, Digital Health CenterInaccurate cell segmentation has been the major problem for cell-type identification and tissue characterization of the in situ spatially resolved transcriptomics data. Here we show a robust cell segmentation-free computational framework (SSAM), for identifying cell types and tissue domains in 2D and 3D.https://doi.org/10.1038/s41467-021-23807-4
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	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
spellingShingle	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 Cell segmentation-free inference of cell types from in situ transcriptomics data Nature Communications
author_facet	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
author_sort	Jeongbin Park
title	Cell segmentation-free inference of cell types from in situ transcriptomics data
title_short	Cell segmentation-free inference of cell types from in situ transcriptomics data
title_full	Cell segmentation-free inference of cell types from in situ transcriptomics data
title_fullStr	Cell segmentation-free inference of cell types from in situ transcriptomics data
title_full_unstemmed	Cell segmentation-free inference of cell types from in situ transcriptomics data
title_sort	cell segmentation-free inference of cell types from in situ transcriptomics data
publisher	Nature Publishing Group
series	Nature Communications
issn	2041-1723
publishDate	2021-06-01
description	Inaccurate cell segmentation has been the major problem for cell-type identification and tissue characterization of the in situ spatially resolved transcriptomics data. Here we show a robust cell segmentation-free computational framework (SSAM), for identifying cell types and tissue domains in 2D and 3D.
url	https://doi.org/10.1038/s41467-021-23807-4
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Cell segmentation-free inference of cell types from in situ transcriptomics data

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