Reference-based cell type matching of in situ image-based spatial transcriptomics data on primary visual cortex of mouse brain

Yun Zhang, Jeremy A. Miller, Jeongbin Park, Boudewijn P. Lelieveldt, Tamim Abdelaal, Oleh Dzyubachyk, Ed S. Lein, Richard H. Scheuermann*, More Authors

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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Abstract

With the advent of multiplex fluorescence in situ hybridization (FISH) and in situ RNA sequencing technologies, spatial transcriptomics analysis is advancing rapidly, providing spatial location and gene expression information about cells in tissue sections at single cell resolution. Cell type classification of these spatially-resolved cells can be inferred by matching the spatial transcriptomics data to reference atlases derived from single cell RNA-sequencing (scRNA-seq) in which cell types are defined by differences in their gene expression profiles. However, robust cell type matching of the spatially-resolved cells to reference scRNA-seq atlases is challenging due to the intrinsic differences in resolution between the spatial and scRNA-seq data. In this study, we systematically evaluated six computational algorithms for cell type matching across four image-based spatial transcriptomics experimental protocols (MERFISH, smFISH, BaristaSeq, and ExSeq) conducted on the same mouse primary visual cortex (VISp) brain region. We find that many cells are assigned as the same type by multiple cell type matching algorithms and are present in spatial patterns previously reported from scRNA-seq studies in VISp. Furthermore, by combining the results of individual matching strategies into consensus cell type assignments, we see even greater alignment with biological expectations. We present two ensemble meta-analysis strategies used in this study and share the consensus cell type matching results in the Cytosplore Viewer (https://viewer.cytosplore.org) for interactive visualization and data exploration. The consensus matching can also guide spatial data analysis using SSAM, allowing segmentation-free cell type assignment.

Original languageEnglish
Article number9567
Number of pages17
JournalScientific Reports
Volume13
Issue number1
DOIs
Publication statusPublished - 2023

Funding

The work was supported by the Chan Zuckerberg Initiative DAF, an advised fund of the Silicon Valley Community Foundation (2017-174399, 2018-182730); the U.S. National Institutes of Health (RF1MH123220); the Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO) Gravitation (NWO: 024.004.012); NWO TTW project 3DOMICS (NWO: 17126); the Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government MSIT (No.2020-2-01450, Artificial Intelligence Convergence Research Center, Pusan National University); and the National Research Foundation of Korea (NRF) grant funded by the Korea government MSIT (No. 2022R1F1A1076160). The funding bodies had no role in the design or conclusions of this study.

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