{"doi":"10.1101/2020.08.29.272831","title":"Deep learning and alignment of spatially-resolved whole transcriptomes of single cells in the mouse brain with Tangram","abstract":"Charting a biological atlas of an organ, such as the brain, requires us to spatially-resolve whole transcriptomes of single cells, and to relate such cellular features to the histological and anatomical scales. Single-cell and single-nucleus RNA-Seq (sc/snRNA-seq) can map cells comprehensively 5,6 , but relating those to their histological and anatomical positions in the context of an organ’s common coordinate framework remains a major challenge and barrier to the construction of a cell atlas 7–10 . Conversely, Spatial Transcriptomics allows for in-situ measurements 11–13 at the histological level, but at lower spatial resolution and with limited sensitivity. Targeted in situ technologies 1–3 solve both issues, but are limited in gene throughput which impedes profiling of the entire transcriptome. Finally, as samples are collected for profiling, their registration to anatomical atlases often require human supervision, which is a major obstacle to build pipelines at scale. Here, we demonstrate spatial mapping of cells, histology, and anatomy in the somatomotor area and the visual area of the healthy adult mouse brain. We devise Tangram, a method that aligns snRNA-seq data to various forms of spatial data collected from the same brain region, including MERFISH 1 , STARmap 2 , smFISH 3 , and Spatial Transcriptomics 4 (Visium), as well as histological images and public atlases. Tangram can map any type of sc/snRNA-seq data, including multi-modal data such as SHARE-seq data 5 , which we used to reveal spatial patterns of chromatin accessibility. We equipped Tangram with a deep learning computer vision pipeline, which allows for automatic identification of anatomical annotations on histological images of mouse brain. By doing so, Tangram reconstructs a genome-wide, anatomically-integrated, spatial map of the visual and somatomotor area with ∼30,000 genes at single-cell resolution, revealing spatial gene expression and chromatin accessibility patterning beyond current limitation of in-situ technologies.","journal":null,"year":2020,"id":1982,"datarank":1.939784605988428,"base_score":3.6109179126442243,"endowment":3.6109179126442243,"self_citation_contribution":0.5416376868966337,"citation_network_contribution":1.3981469190917943,"self_endowment_contribution":0.5416376868966337,"citer_contribution":1.3981469190917943,"corpus_percentile":64.11716842961758,"corpus_rank":442,"citation_count":36,"citer_count":29,"citers_with_citation_signal":26,"citers_with_endowment":26,"datacite_reuse_total":0,"is_dataset":true,"is_dataset_confidence":0.5027,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-08-30","fair_score":28.3333,"fair_percentile":10.004397537379068,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":3611,"name":"Gabriele Scalia","orcid":"0000-0003-3305-9220","position":1,"is_corresponding":false},{"id":3612,"name":"Lorenzo Buffoni","orcid":"0000-0002-8191-3375","position":2,"is_corresponding":false},{"id":3613,"name":"Raghav Avasthi","orcid":"0000-0001-8861-6448","position":3,"is_corresponding":false},{"id":3614,"name":"Ziqing Lu","orcid":"0000-0002-0921-6807","position":4,"is_corresponding":false},{"id":3615,"name":"Aman Sanger","orcid":"0000-0002-6643-0316","position":5,"is_corresponding":false},{"id":3616,"name":"Neriman Tokcan","orcid":"0000-0001-7676-5534","position":6,"is_corresponding":false},{"id":3617,"name":"Charles R. Vanderburg","orcid":"0000-0001-8979-5054","position":7,"is_corresponding":false},{"id":3618,"name":"Åsa Segerstolpe","orcid":"0000-0002-3700-9661","position":8,"is_corresponding":false},{"id":58951,"name":"Maitham Naeemi","orcid":"0000-0001-9139-3548","position":9,"is_corresponding":false},{"id":3629,"name":"Inbal Avraham‐Davidi","orcid":"0000-0001-7118-9179","position":10,"is_corresponding":false},{"id":483,"name":"Sanja Vicković","orcid":"0000-0003-0985-9885","position":11,"is_corresponding":false},{"id":3621,"name":"Mor Nitzan","orcid":"0000-0003-0074-9196","position":12,"is_corresponding":false},{"id":3622,"name":"Sai Ma","orcid":"0000-0002-9785-7929","position":13,"is_corresponding":false},{"id":3630,"name":"Jason D. Buenrostro","orcid":"0000-0001-9958-3987","position":14,"is_corresponding":false},{"id":3625,"name":"Nik Bear Brown","orcid":"0000-0001-6270-7536","position":15,"is_corresponding":false},{"id":3626,"name":"Duccio Fanelli","orcid":"0000-0001-8545-9424","position":16,"is_corresponding":false},{"id":3627,"name":"Xiaowei Zhuang","orcid":"0000-0002-6034-7853","position":17,"is_corresponding":false},{"id":3628,"name":"Evan Z. Macosko","orcid":"0000-0002-2794-5165","position":18,"is_corresponding":false},{"id":29633,"name":"Prisca Liberali","orcid":"0000-0003-0695-6081","position":19,"is_corresponding":false},{"id":3610,"name":"Tommaso Biancalani","orcid":"0000-0001-9104-9755","position":0,"is_corresponding":true}],"reference_count":45,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-03-01T18:20:47.508186Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":"green","license":"cc-by-nc","views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":37.0,"fair_a":58.0,"fair_i":5.0,"fair_r":13.3333,"fair_zscore":-1.5267,"fair_rationale":{"fair_score":28.33,"has_llm":true,"dimensions":{"F":{"name":"Findable","score":37.0,"criteria":[{"key":"f_has_doi","label":"Has a persistent DOI","kind":"deterministic","weight":1.0,"fraction":1.0,"signal":"DOI present","rationale":null},{"key":"f_repository_presence","label":"Indexed in repositories / literature DBs","kind":"deterministic","weight":1.0,"fraction":0.0,"signal":"datacite=0, pmcid=False, pmid=False","rationale":null},{"key":"f_persistent_ids","label":"Resolvable scholarly identifiers (OpenAlex)","kind":"deterministic","weight":0.5,"fraction":0.0,"signal":"no OpenAlex id","rationale":null},{"key":"f_metadata_richness","label":"Rich, machine-readable metadata","kind":"llm","weight":1.0,"fraction":0.25,"signal":null,"rationale":"The paper mentions using standard abbreviations and nature of data (e.g., scRNA-seq, MERFISH), but provides no explicit machine-readable metadata or structured metadata description."}]},"A":{"name":"Accessible","score":58.0,"criteria":[{"key":"a_open_access","label":"Open Access / files deposited","kind":"deterministic","weight":1.5,"fraction":1.0,"signal":"Open Access","rationale":null},{"key":"a_retrievable","label":"Free full text retrievable","kind":"deterministic","weight":1.0,"fraction":0.0,"signal":"0 OA location(s)","rationale":null},{"key":"a_access_protocol","label":"Clear data/code access protocol","kind":"llm","weight":1.0,"fraction":0.5,"signal":null,"rationale":"The paper describes the method 'Tangram' and its capabilities, but does not state where the code, data, or trained models are deposited or how to access them."}]},"I":{"name":"Interoperable","score":5.0,"criteria":[{"key":"i_linked_data","label":"Linked datasets / DataCite relations","kind":"deterministic","weight":1.0,"fraction":0.0,"signal":"linked_datasets=0, datacite=0","rationale":null},{"key":"i_standard_ids","label":"References data via standard accessions","kind":"deterministic","weight":1.0,"fraction":0.0,"signal":"accessions=0, trials=0","rationale":null},{"key":"i_standards","label":"Standard formats, vocabularies & identifiers","kind":"llm","weight":1.0,"fraction":0.25,"signal":null,"rationale":"While it mentions common data types (e.g., MERFISH, smFISH), no standard formats, vocabularies, or persistent identifiers are explicitly used or referenced."}]},"R":{"name":"Reusable","score":13.33,"criteria":[{"key":"r_license","label":"Clear, open reuse license","kind":"deterministic","weight":1.5,"fraction":0.0,"signal":"no license","rationale":null},{"key":"r_downloads","label":"Demonstrated reuse (downloads)","kind":"deterministic","weight":0.5,"fraction":0.0,"signal":"downloads=0","rationale":null},{"key":"r_version","label":"Versioned / maintained","kind":"deterministic","weight":0.5,"fraction":0.0,"signal":"no version chain","rationale":null},{"key":"r_dataset","label":"Classified as a data resource","kind":"deterministic","weight":0.5,"fraction":1.0,"signal":"is_dataset","rationale":null},{"key":"r_reusability","label":"Data-availability statement, license & reproducibility","kind":"llm","weight":2.0,"fraction":0.0,"signal":null,"rationale":"No data-availability statement, license, or reproducibility details are provided; the paper only describes the method's results."}]}},"suggestions":["Provide a data-availability statement listing repository (e.g., GEO, Figshare) for the snRNA-seq and spatial data.","Deposit the Tangram source code in a public repository (e.g., GitHub) with a license (e.g., MIT, GPL) and version identifier.","Use standardized file formats (e.g., HDF5 for spatial data, BED/GFF for genomic coordinates) and metadata schemas (e.g., EBI SCXA).","Assign persistent identifiers (e.g., DOI, accession numbers) to datasets and mention them explicitly in the paper."],"model":"deepseek/deepseek-v4-flash","agent_version":"fair_agent_v2","fulltext_source":"abstract_only"},"fair_model":"deepseek/deepseek-v4-flash","fair_agent_version":"fair_agent_v2","fair_fulltext_source":"abstract_only","fair_has_llm":true,"fair_computed_at":"2026-06-18T00:44:19.042355Z","clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}