Intracellular spatial transcriptomic analysis toolkit (InSTAnT)

Imaging-based spatial transcriptomics technologies such as Multiplexed error-robust fluorescence in situ hybridization (MERFISH) can capture cellular processes in unparalleled detail. However, rigorous and robust analytical tools are needed to unlock their full potential for discovering subcellular biological patterns. We present Intracellular Spatial Transcriptomic Analysis Toolkit (InSTAnT), a computational toolkit for extracting molecular relationships from spatial transcriptomics data at single molecule resolution. InSTAnT employs specialized statistical tests and algorithms to detect gene pairs and modules exhibiting intriguing patterns of co-localization, both within individual cells and across the cellular landscape. We showcase the toolkit on five different datasets representing two different cell lines, two brain structures, two species, and three different technologies. We perform rigorous statistical assessment of discovered co-localization patterns, find supporting evidence from databases and RNA interactions, and identify associated subcellular domains. We uncover several cell type and region-specific gene co-localizations within the brain. Intra-cellular spatial patterns discovered by InSTAnT mirror diverse molecular relationships, including RNA interactions and shared sub-cellular localization or function, providing a rich compendium of testable hypotheses regarding molecular functions.

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Publication Details

Journal
Nature Communications
Published
2024-09-06
DOI
https://doi.org/10.1038/s41467-024-49457-w
Citations
11
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
2.57

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article

Intracellular spatial transcriptomic analysis toolkit (InSTAnT)

Hee‐Sun Han, Marisa Asadian, Alex W. Schrader, Bhavay Aggarwal et al.
11 citations
Nature Communications
Single-cell and spatial transcriptomics
2.57
article

Intracellular spatial transcriptomic analysis toolkit (InSTAnT)

Hee‐Sun Han, Marisa Asadian, Alex W. Schrader, Bhavay Aggarwal, Ali Ebrahimpour Boroojeny, You Jin Song, Sihai Dave Zhao, Anurendra Kumar, Saurabh Sinha, JuYeon Lee
article en
11 citations

Abstract

Imaging-based spatial transcriptomics technologies such as Multiplexed error-robust fluorescence in situ hybridization (MERFISH) can capture cellular processes in unparalleled detail. However, rigorous and robust analytical tools are needed to unlock their full potential for discovering subcellular biological patterns. We present Intracellular Spatial Transcriptomic Analysis Toolkit (InSTAnT), a computational toolkit for extracting molecular relationships from spatial transcriptomics data at single molecule resolution. InSTAnT employs specialized statistical tests and algorithms to detect gene pairs and modules exhibiting intriguing patterns of co-localization, both within individual cells and across the cellular landscape. We showcase the toolkit on five different datasets representing two different cell lines, two brain structures, two species, and three different technologies. We perform rigorous statistical assessment of discovered co-localization patterns, find supporting evidence from databases and RNA interactions, and identify associated subcellular domains. We uncover several cell type and region-specific gene co-localizations within the brain. Intra-cellular spatial patterns discovered by InSTAnT mirror diverse molecular relationships, including RNA interactions and shared sub-cellular localization or function, providing a rich compendium of testable hypotheses regarding molecular functions.

Nature CommunicationsVol. 15(1)
Georgia Institute of Technology (US), University of Illinois Urbana-Champaign (US), The Wallace H. Coulter Department of Biomedical Engineering (US)
Georgia Institute of Technology, National Institutes of Health, Carl R. Woese Institute for Genomic Biology
Openalex Percentile: Top 10%
Single-cell and spatial transcriptomics
2.57
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