{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T03:52:34Z","timestamp":1785815554322,"version":"3.56.0"},"reference-count":33,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2024,5,26]],"date-time":"2024-05-26T00:00:00Z","timestamp":1716681600000},"content-version":"vor","delay-in-days":3,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"National Science and Technology Innovation 2030 Major Program","award":["2021ZD0204400"],"award-info":[{"award-number":["2021ZD0204400"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,5,23]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Limited gene capture efficiency and spot size of spatial transcriptome (ST) data pose significant challenges in cell-type characterization. The heterogeneity and complexity of cell composition in the mammalian brain make it more challenging to accurately annotate ST data from brain. Many algorithms attempt to characterize subtypes of neuron by integrating ST data with single-nucleus RNA sequencing (snRNA-seq) or single-cell RNA sequencing. However, assessing the accuracy of these algorithms on Stereo-seq ST data remains unresolved. Here, we benchmarked 9 mapping algorithms using 10 ST datasets from four mouse brain regions in two different resolutions and 24 pseudo-ST datasets from snRNA-seq. Both actual ST data and pseudo-ST data were mapped using snRNA-seq datasets from the corresponding brain regions as reference data. After comparing the performance across different areas and resolutions of the mouse brain, we have reached the conclusion that both robust cell-type decomposition and SpatialDWLS demonstrated superior robustness and accuracy in cell-type annotation. Testing with publicly available snRNA-seq data from another sequencing platform in the cortex region further validated our conclusions. Altogether, we developed a workflow for assessing suitability of mapping algorithm that fits for ST datasets, which can improve the efficiency and accuracy of spatial data annotation.<\/jats:p>","DOI":"10.1093\/bib\/bbae250","type":"journal-article","created":{"date-parts":[[2024,5,26]],"date-time":"2024-05-26T03:41:00Z","timestamp":1716694860000},"source":"Crossref","is-referenced-by-count":11,"title":["Benchmarking mapping algorithms for cell-type annotating in mouse brain by integrating single-nucleus RNA-seq and Stereo-seq data"],"prefix":"10.1093","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2739-3548","authenticated-orcid":false,"given":"Quyuan","family":"Tao","sequence":"first","affiliation":[{"name":"College of Life Sciences, University of Chinese Academy of Sciences , Beijing 100049 , China"},{"name":"BGI Research , Hangzhou 310012 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiheng","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Neurobiology and Department of Neurology of Second Affiliated Hospital, Zhejiang University School of Medicine , Hangzhou 310058 , China"},{"name":"NHC and CAMS Key Laboratory of Medical Neurobiology , MOE Frontier Center of Brain Science and Brain-machine Integration, School of Brain Science and Brain Medicine, , Hangzhou 310058 , China"},{"name":"Zhejiang University , MOE Frontier Center of Brain Science and Brain-machine Integration, School of Brain Science and Brain Medicine, , Hangzhou 310058 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-9534-0303","authenticated-orcid":false,"given":"Youzhe","family":"He","sequence":"additional","affiliation":[{"name":"College of Life Sciences, University of Chinese Academy of Sciences , Beijing 100049 , China"},{"name":"BGI Research , Hangzhou 310012 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ting","family":"Luo","sequence":"additional","affiliation":[{"name":"BGI Research , Hangzhou 310012 , China"},{"name":"BGI Research , Shenzhen 518103 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoming","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Neurobiology and Department of Neurology of Second Affiliated Hospital, Zhejiang University School of Medicine , Hangzhou 310058 , China"},{"name":"NHC and CAMS Key Laboratory of Medical Neurobiology , MOE Frontier Center of Brain Science and Brain-machine Integration, School of Brain Science and Brain Medicine, , Hangzhou 310058 , China"},{"name":"Zhejiang University , MOE Frontier Center of Brain Science and Brain-machine Integration, School of Brain Science and Brain Medicine, , Hangzhou 310058 , China"},{"name":"Research Units for Emotion and Emotion disorders, Chinese Academy of Medical Sciences , Beijing 100730 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Han","sequence":"additional","affiliation":[{"name":"BGI Research , Hangzhou 310012 , China"},{"name":"BGI Research , Shenzhen 518103 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2024,5,25]]},"reference":[{"key":"2024052603404611600_ref1","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1038\/nn.4216","article-title":"Adult mouse cortical cell taxonomy revealed by single cell transcriptomics","volume":"19","author":"Tasic","year":"2016","journal-title":"Nat Neurosci"},{"key":"2024052603404611600_ref2","doi-asserted-by":"crossref","first-page":"1138","DOI":"10.1126\/science.aaa1934","article-title":"Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq","volume":"347","author":"Zeisel","year":"2015","journal-title":"Science"},{"key":"2024052603404611600_ref3","doi-asserted-by":"crossref","first-page":"534","DOI":"10.1038\/s41592-022-01409-2","article-title":"Museum of spatial transcriptomics","volume":"19","author":"Moses","year":"2022","journal-title":"Nat Methods"},{"key":"2024052603404611600_ref4","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1038\/s41586-021-03634-9","article-title":"Exploring tissue architecture using spatial transcriptomics","volume":"596","author":"Rao","year":"2021","journal-title":"Nature"},{"key":"2024052603404611600_ref5","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1038\/s41593-020-00787-0","article-title":"Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex","volume":"24","author":"Maynard","year":"2021","journal-title":"Nat Neurosci"},{"key":"2024052603404611600_ref6","doi-asserted-by":"crossref","first-page":"1463","DOI":"10.1126\/science.aaw1219","article-title":"Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution","volume":"363","author":"Rodriques","year":"2019","journal-title":"Science"},{"key":"2024052603404611600_ref7","doi-asserted-by":"crossref","first-page":"aaa6090","DOI":"10.1126\/science.aaa6090","article-title":"RNA imaging. Spatially resolved, highly multiplexed RNA profiling in single cells","volume":"348","author":"Chen","year":"2015","journal-title":"Science"},{"key":"2024052603404611600_ref8","doi-asserted-by":"crossref","DOI":"10.1126\/science.aat5691","article-title":"Three-dimensional intact-tissue sequencing of single-cell transcriptional states","volume":"361","author":"Wang","year":"2018","journal-title":"Science"},{"key":"2024052603404611600_ref9","doi-asserted-by":"crossref","first-page":"1777","DOI":"10.1016\/j.cell.2022.04.003","article-title":"Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays","volume":"185","author":"Chen","year":"2022","journal-title":"Cell"},{"key":"2024052603404611600_ref10","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1038\/s41586-019-1049-y","article-title":"Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH","volume":"568","author":"Eng","year":"2019","journal-title":"Nature"},{"key":"2024052603404611600_ref11","doi-asserted-by":"crossref","first-page":"932","DOI":"10.1038\/s41592-018-0175-z","article-title":"Spatial organization of the somatosensory cortex revealed by osmFISH","volume":"15","author":"Codeluppi","year":"2018","journal-title":"Nat Methods"},{"key":"2024052603404611600_ref12","doi-asserted-by":"crossref","first-page":"1665","DOI":"10.1016\/j.cell.2020.10.026","article-title":"High-spatial-resolution multi-omics sequencing via deterministic barcoding in tissue","volume":"183","author":"Liu","year":"2020","journal-title":"Cell"},{"key":"2024052603404611600_ref13","doi-asserted-by":"crossref","first-page":"3559","DOI":"10.1016\/j.cell.2021.05.010","article-title":"Microscopic examination of spatial transcriptome using Seq-scope","volume":"184","author":"Cho","year":"2021","journal-title":"Cell"},{"key":"2024052603404611600_ref14","doi-asserted-by":"crossref","first-page":"857","DOI":"10.1038\/nmeth.2563","article-title":"In situ sequencing for RNA analysis in preserved tissue and cells","volume":"10","author":"Ke","year":"2013","journal-title":"Nat Methods"},{"key":"2024052603404611600_ref15","doi-asserted-by":"crossref","first-page":"eabp9444","DOI":"10.1126\/science.abp9444","article-title":"Single-cell Stereo-seq reveals induced progenitor cells involved in axolotl brain regeneration","volume":"377","author":"Wei","year":"2022","journal-title":"Science"},{"key":"2024052603404611600_ref16","doi-asserted-by":"crossref","first-page":"3726","DOI":"10.1016\/j.cell.2023.06.009","article-title":"Single-cell spatial transcriptome reveals cell-type organization in the macaque cortex","volume":"186","author":"Chen","year":"2023","journal-title":"Cell"},{"key":"2024052603404611600_ref17","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1038\/s41587-021-01139-4","article-title":"Cell2location maps fine-grained cell types in spatial transcriptomics","volume":"40","author":"Kleshchevnikov","year":"2022","journal-title":"Nat Biotechnol"},{"key":"2024052603404611600_ref18","doi-asserted-by":"crossref","first-page":"1360","DOI":"10.1038\/s41587-022-01272-8","article-title":"DestVI identifies continuums of cell types in spatial transcriptomics data","volume":"40","author":"Lopez","year":"2022","journal-title":"Nat Biotechnol"},{"key":"2024052603404611600_ref19","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1038\/s41587-021-00830-w","article-title":"Robust decomposition of cell type mixtures in spatial transcriptomics","volume":"40","author":"Cable","year":"2022","journal-title":"Nat Biotechnol"},{"key":"2024052603404611600_ref20","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1186\/s13059-021-02362-7","article-title":"SpatialDWLS: accurate deconvolution of spatial transcriptomic data","volume":"22","author":"Dong","year":"2021","journal-title":"Genome Biol"},{"key":"2024052603404611600_ref21","doi-asserted-by":"crossref","first-page":"2975","DOI":"10.1038\/s41467-019-10802-z","article-title":"Accurate estimation of cell-type composition from gene expression data","volume":"10","author":"Tsoucas","year":"2019","journal-title":"Nat Commun"},{"key":"2024052603404611600_ref22","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1186\/s13059-021-02286-2","article-title":"Giotto: a toolbox for integrative analysis and visualization of spatial expression data","volume":"22","author":"Dries","year":"2021","journal-title":"Genome Biol"},{"key":"2024052603404611600_ref23","doi-asserted-by":"crossref","first-page":"3573","DOI":"10.1016\/j.cell.2021.04.048","article-title":"Integrated analysis of multimodal single-cell data","volume":"184","author":"Hao","year":"2021","journal-title":"Cell"},{"key":"2024052603404611600_ref24","doi-asserted-by":"crossref","first-page":"1352","DOI":"10.1038\/s41592-021-01264-7","article-title":"Deep learning and alignment of spatially resolved single-cell transcriptomes with tangram","volume":"18","author":"Biancalani","year":"2021","journal-title":"Nat Methods"},{"key":"2024052603404611600_ref25","doi-asserted-by":"crossref","first-page":"7640","DOI":"10.1038\/s41467-022-35288-0","article-title":"Spatial-ID: a cell typing method for spatially resolved transcriptomics via transfer learning and spatial embedding","volume":"13","author":"Shen","year":"2022","journal-title":"Nat Commun"},{"key":"2024052603404611600_ref26","doi-asserted-by":"crossref","first-page":"7419","DOI":"10.1038\/s41467-022-35094-8","article-title":"A unified computational framework for single-cell data integration with optimal transport","volume":"13","author":"Cao","year":"2022","journal-title":"Nat Commun"},{"key":"2024052603404611600_ref27","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbad533","article-title":"SPANN: annotating single-cell resolution spatial transcriptome data with scRNA-seq data","volume":"25","author":"Yuan","year":"2024","journal-title":"Brief Bioinform"},{"key":"2024052603404611600_ref28","doi-asserted-by":"crossref","first-page":"662","DOI":"10.1038\/s41592-022-01480-9","article-title":"Benchmarking spatial and single-cell transcriptomics integration methods for transcript distribution prediction and cell type deconvolution","volume":"19","author":"Li","year":"2022","journal-title":"Nat Methods"},{"key":"2024052603404611600_ref29","doi-asserted-by":"crossref","first-page":"1548","DOI":"10.1038\/s41467-023-37168-7","article-title":"A comprehensive benchmarking with practical guidelines for cellular deconvolution of spatial transcriptomics","volume":"14","author":"Li","year":"2023","journal-title":"Nat Commun"},{"key":"2024052603404611600_ref30","doi-asserted-by":"crossref","DOI":"10.1101\/2023.12.03.569501","article-title":"Spatially resolved molecular and cellular atlas of the mouse brain","author":"Han","year":"2023"},{"key":"2024052603404611600_ref31","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/j.cell.2021.12.022","article-title":"Vision-dependent specification of cell types and function in the developing cortex","volume":"185","author":"Cheng","year":"2022","journal-title":"Cell"},{"key":"2024052603404611600_ref32","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbac245","article-title":"A comprehensive comparison on cell-type composition inference for spatial transcriptomics data","volume":"23","author":"Chen","year":"2022","journal-title":"Brief Bioinform"},{"key":"2024052603404611600_ref33","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image quality assessment: from error visibility to structural similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans Image Process"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/25\/4\/bbae250\/57908425\/bbae250.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/25\/4\/bbae250\/57908425\/bbae250.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,26]],"date-time":"2024-05-26T03:41:23Z","timestamp":1716694883000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbae250\/7682297"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,23]]},"references-count":33,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,5,23]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbae250","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2024,7]]},"published":{"date-parts":[[2024,5,23]]},"article-number":"bbae250"}}