Evidence map›Paper›PMID 40457183›Full record

ArticleBMC bioinformatics2025

SC2Spa: a deep learning based approach to map transcriptome to spatial origins at cellular resolution.

Linbu Liao, Esha Madan, António M Palma, Hyobin Kim, Amit Kumar, Praveen Bhoopathi, Robert Winn, Jose Trevino, Paul Fisher, Cord Herbert Brakebusch and 4 more

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Article in BMC bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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4 · The record

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5 · Who and what money

Authors and funding

14 authors.

Linbu LiaoBiotech Research and Innovation Centre (BRIC), University of Copenhagen, Ole Maaløes Vej 5, 2200, Copenhagen, Denmark.
Esha MadanDepartment of Surgery, School of Medicine, Virginia Commonwealth University, 1200 E Broad St., P.O. Box 980011, Richmond, VA, 23298, USA.
António M PalmaDepartment of Surgery, School of Medicine, Virginia Commonwealth University, 1200 E Broad St., P.O. Box 980011, Richmond, VA, 23298, USA.
Hyobin KimDepartment of Bioinformatics, Soongsil University, 369 Sangdo-Ro, Dongjak-Gu, Seoul, 06978, Republic of Korea.
Amit KumarMassey Cancer Center, Virginia Commonwealth University, Richmond, VA, 23298, USA.
Praveen BhoopathiMassey Cancer Center, Virginia Commonwealth University, Richmond, VA, 23298, USA.
Robert WinnDepartment of Surgery, School of Medicine, Virginia Commonwealth University, 1200 E Broad St., P.O. Box 980011, Richmond, VA, 23298, USA.
Jose TrevinoDepartment of Surgery, School of Medicine, Virginia Commonwealth University, 1200 E Broad St., P.O. Box 980011, Richmond, VA, 23298, USA.
Paul FisherMassey Cancer Center, Virginia Commonwealth University, Richmond, VA, 23298, USA.
Cord Herbert BrakebuschBiotech Research and Innovation Centre (BRIC), University of Copenhagen, Ole Maaløes Vej 5, 2200, Copenhagen, Denmark.
Gahyun KimDepartment of Bioinformatics, Soongsil University, 369 Sangdo-Ro, Dongjak-Gu, Seoul, 06978, Republic of Korea.
Junil KimDepartment of Bioinformatics, Soongsil University, 369 Sangdo-Ro, Dongjak-Gu, Seoul, 06978, Republic of Korea.
Rajan GognaDepartment of Surgery, School of Medicine, Virginia Commonwealth University, 1200 E Broad St., P.O. Box 980011, Richmond, VA, 23298, USA. rajangogna@gmail.com.
Kyoung Jae WonDepartment of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA. kyoungjae.won@cshs.org.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundUnderstanding cellular heterogeneity within tissues hinges on knowledge of their spatial context. However, it is still challenging to accurately map cells to their spatial coordinates.

resultsWe present SC2Spa, a deep learning-based approach that learns intricate spatial relationships from spatial transcriptomics (ST) data. Benchmarking tests show that SC2Spa outperformed other predictors and accurately detected tissue architecture from transcriptome. SC2Spa successfully mapped single cell RNA sequencing (scRNA-seq) to Visium assay, providing an approach to enhance the resolution for low resolution ST data. Our test showed that SC2Spa performs well for various ST technologies and robust to spatial resolution. In addition, SC2Spa can suggest spatially variable genes that cannot be identified from previous approaches.

conclusionsSC2Spa is a robust and accurate approach to provide single cells with their spatial location and identify spatially meaningful genes.

Indexed as

Deep LearningGene Expression ProfilingSingle-Cell AnalysisTranscriptomeAnimalsHumansRNA-SeqSequence Analysis, RNADeep learningSpatial inferenceSpatially variable genesSpatial mappingSpatial transcriptomics

Identifiers

PMID40457183
PMCPMC12131412

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.