SC2Spa: a deep learning based approach to map transcriptome to spatial origins at cellular resolution
Abstract Background Understanding cellular heterogeneity within tissues hinges on knowledge of their spatial context. However, it is still challenging to accurately map cells to their spatial coordinates. Results We present SC2Spa, a deep learning-based approach that learns intricate spatial relatio...
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| Format: | Article |
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BMC
2025-06-01
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| Series: | BMC Bioinformatics |
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| Online Access: | https://doi.org/10.1186/s12859-025-06173-6 |
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| author | Linbu Liao Esha Madan António M. Palma Hyobin Kim Amit Kumar Praveen Bhoopathi Robert Winn Jose Trevino Paul Fisher Cord Herbert Brakebusch Gahyun Kim Junil Kim Rajan Gogna Kyoung Jae Won |
| author_facet | Linbu Liao Esha Madan António M. Palma Hyobin Kim Amit Kumar Praveen Bhoopathi Robert Winn Jose Trevino Paul Fisher Cord Herbert Brakebusch Gahyun Kim Junil Kim Rajan Gogna Kyoung Jae Won |
| author_sort | Linbu Liao |
| collection | DOAJ |
| description | Abstract Background Understanding cellular heterogeneity within tissues hinges on knowledge of their spatial context. However, it is still challenging to accurately map cells to their spatial coordinates. Results We 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. Conclusions SC2Spa is a robust and accurate approach to provide single cells with their spatial location and identify spatially meaningful genes. |
| format | Article |
| id | doaj-art-06952bec3ebf48d1bb2009f49b7ec212 |
| institution | Kabale University |
| issn | 1471-2105 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj-art-06952bec3ebf48d1bb2009f49b7ec2122025-08-20T03:25:19ZengBMCBMC Bioinformatics1471-21052025-06-0126111910.1186/s12859-025-06173-6SC2Spa: a deep learning based approach to map transcriptome to spatial origins at cellular resolutionLinbu Liao0Esha Madan1António M. Palma2Hyobin Kim3Amit Kumar4Praveen Bhoopathi5Robert Winn6Jose Trevino7Paul Fisher8Cord Herbert Brakebusch9Gahyun Kim10Junil Kim11Rajan Gogna12Kyoung Jae Won13Biotech Research and Innovation Centre (BRIC), University of CopenhagenDepartment of Surgery, School of Medicine, Virginia Commonwealth UniversityDepartment of Surgery, School of Medicine, Virginia Commonwealth UniversityDepartment of Bioinformatics, Soongsil UniversityMassey Cancer Center, Virginia Commonwealth UniversityMassey Cancer Center, Virginia Commonwealth UniversityDepartment of Surgery, School of Medicine, Virginia Commonwealth UniversityDepartment of Surgery, School of Medicine, Virginia Commonwealth UniversityMassey Cancer Center, Virginia Commonwealth UniversityBiotech Research and Innovation Centre (BRIC), University of CopenhagenDepartment of Bioinformatics, Soongsil UniversityDepartment of Bioinformatics, Soongsil UniversityDepartment of Surgery, School of Medicine, Virginia Commonwealth UniversityDepartment of Computational Biomedicine, Cedars-Sinai Medical CenterAbstract Background Understanding cellular heterogeneity within tissues hinges on knowledge of their spatial context. However, it is still challenging to accurately map cells to their spatial coordinates. Results We 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. Conclusions SC2Spa is a robust and accurate approach to provide single cells with their spatial location and identify spatially meaningful genes.https://doi.org/10.1186/s12859-025-06173-6Spatial transcriptomicsSpatial inferenceSpatial mappingDeep learningSpatially variable genes |
| spellingShingle | Linbu Liao Esha Madan António M. Palma Hyobin Kim Amit Kumar Praveen Bhoopathi Robert Winn Jose Trevino Paul Fisher Cord Herbert Brakebusch Gahyun Kim Junil Kim Rajan Gogna Kyoung Jae Won SC2Spa: a deep learning based approach to map transcriptome to spatial origins at cellular resolution BMC Bioinformatics Spatial transcriptomics Spatial inference Spatial mapping Deep learning Spatially variable genes |
| title | SC2Spa: a deep learning based approach to map transcriptome to spatial origins at cellular resolution |
| title_full | SC2Spa: a deep learning based approach to map transcriptome to spatial origins at cellular resolution |
| title_fullStr | SC2Spa: a deep learning based approach to map transcriptome to spatial origins at cellular resolution |
| title_full_unstemmed | SC2Spa: a deep learning based approach to map transcriptome to spatial origins at cellular resolution |
| title_short | SC2Spa: a deep learning based approach to map transcriptome to spatial origins at cellular resolution |
| title_sort | sc2spa a deep learning based approach to map transcriptome to spatial origins at cellular resolution |
| topic | Spatial transcriptomics Spatial inference Spatial mapping Deep learning Spatially variable genes |
| url | https://doi.org/10.1186/s12859-025-06173-6 |
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