{"topic":"single-cell-spatial","sort":"top","limit":25,"offset":0,"count":25,"papers":[{"id":"doi:10.1038/nbt.4096","kind":"article","title":"Integrating single-cell transcriptomic data across different conditions, technologies, and species","authors":["Andrew Butler","Paul Hoffman","Peter Smibert","Efthymia Papalexi","Rahul Satija"],"author_count":5,"year":2018,"published":"2018-04-02","venue":"Nature Biotechnology","cited_by":13167,"doi":"10.1038/nbt.4096","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1038/nbt.4096","archive_url":"https://space-kitty.com/papers/doi:10.1038/nbt.4096","updated":null,"pmid":"29608179","pmcid":null,"openalex_id":"W2794480084","pdf_url":"https://www.biorxiv.org/content/biorxiv/early/2017/07/18/164889.full.pdf","abstract":null,"abstract_license":null,"license":"cc-by-nc-nd","is_open_access":true,"peer_reviewed":null,"categories":["Single-cell and spatial transcriptomics"],"details":null,"provenance":"openalex","attribution":"Metadata and citation counts: OpenAlex (CC0)."},{"id":"doi:10.1038/s41467-021-21246-9","kind":"article","title":"Inference and analysis of cell-cell communication using CellChat","authors":["Suoqin Jin","Christian Fernando Guerrero‐Juarez","Lihua Zhang","Ivan Chang","Raúl Ramos","Chen‐Hsiang Kuan","Peggy S. Myung","Maksim V. Plikus","Qing Hua Nie"],"author_count":9,"year":2021,"published":"2021-02-17","venue":"Nature Communications","cited_by":8376,"doi":"10.1038/s41467-021-21246-9","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1038/s41467-021-21246-9","archive_url":"https://space-kitty.com/papers/doi:10.1038/s41467-021-21246-9","updated":null,"pmid":"33597522","pmcid":null,"openalex_id":"W3132661792","pdf_url":"https://www.nature.com/articles/s41467-021-21246-9.pdf","abstract":"Understanding global communications among cells requires accurate representation of cell-cell signaling links and effective systems-level analyses of those links. We construct a database of interactions among ligands, receptors and their cofactors that accurately represent known heteromeric molecular complexes. We then develop CellChat, a tool that is able to quantitatively infer and analyze intercellular communication networks from single-cell RNA-sequencing (scRNA-seq) data. CellChat predicts major signaling inputs and outputs for cells and how those cells and signals coordinate for functions using network analysis and pattern recognition approaches. Through manifold learning and quantitative contrasts, CellChat classifies signaling pathways and delineates conserved and context-specific pathways across different datasets. Applying CellChat to mouse and human skin datasets shows its ability to extract complex signaling patterns. Our versatile and easy-to-use toolkit CellChat and a web-based Explorer ( http://www.cellchat.org/ ) will help discover novel intercellular communications and build cell-cell communication atlases in diverse tissues.","abstract_license":"CC-BY (publisher license, via OpenAlex)","license":"cc-by","is_open_access":true,"peer_reviewed":null,"categories":["Single-cell and spatial transcriptomics"],"details":null,"provenance":"openalex","attribution":"Abstract of \"Inference and analysis of cell-cell communication using CellChat\" by Suoqin Jin, Christian Fernando Guerrero‐Juarez, Lihua Zhang et al., https://doi.org/10.1038/s41467-021-21246-9, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1016/j.cell.2015.05.002","kind":"article","title":"Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets","authors":["Evan Z. Macosko","Anindita Basu","Rahul Satija","James C. Nemesh","Karthik Shekhar","Melissa Goldman","Itay Tirosh","Allison R. Bialas","Nolan Kamitaki","Emily M. Martersteck","John J. Trombetta","David A. Weitz","Joshua R. Sanes","Alex K. Shalek","Aviv Regev","Steven A. McCarroll"],"author_count":16,"year":2015,"published":"2015-05-01","venue":"Cell","cited_by":7108,"doi":"10.1016/j.cell.2015.05.002","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1016/j.cell.2015.05.002","archive_url":"https://space-kitty.com/papers/doi:10.1016/j.cell.2015.05.002","updated":null,"pmid":"26000488","pmcid":null,"openalex_id":"W2102212449","pdf_url":"http://www.cell.com/article/S0092867415005498/pdf","abstract":null,"abstract_license":null,"license":null,"is_open_access":true,"peer_reviewed":null,"categories":["Single-cell and spatial transcriptomics"],"details":null,"provenance":"openalex","attribution":"Metadata and citation counts: OpenAlex (CC0)."},{"id":"doi:10.1038/s41586-019-0969-x","kind":"article","title":"The single-cell transcriptional landscape of mammalian organogenesis","authors":["Junyue Cao","Malte Spielmann","Xiaojie Qiu","Xingfan Huang","Daniel M. Ibrahim","Andrew J. Hill","Fan Zhang","Stefan Mundlos","Lena Christiansen","Frank J. Steemers","Cole Trapnell","Jay Shendure"],"author_count":12,"year":2019,"published":"2019-02-20","venue":"Nature","cited_by":4481,"doi":"10.1038/s41586-019-0969-x","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1038/s41586-019-0969-x","archive_url":"https://space-kitty.com/papers/doi:10.1038/s41586-019-0969-x","updated":null,"pmid":"30787437","pmcid":null,"openalex_id":"W2916020270","pdf_url":null,"abstract":null,"abstract_license":null,"license":null,"is_open_access":true,"peer_reviewed":null,"categories":["Single-cell and spatial transcriptomics"],"details":null,"provenance":"openalex","attribution":"Metadata and citation counts: OpenAlex (CC0)."},{"id":"doi:10.1016/j.cell.2019.06.024","kind":"article","title":"An Integrative Model of Cellular States, Plasticity, and Genetics for Glioblastoma","authors":["Cyril Neftel","Julie Laffy","Mariella G. Filbin","Toshiro Hara","Marni E. Shore","Gilbert J. Rahme","Alyssa R. Richman","Dana Silverbush","McKenzie L. Shaw","Christine M. Hebert","John C. DeWitt","Simon Gritsch","Elizabeth M. Perez","L. Nicolas Gonzalez Castro","Xiaoyang Lan","Nicholas Druck","Christopher Rodman","Danielle Ashley Dionne","Alexander B. Kaplan","Mia Solana Bertalan","Julia L. Small","Kristine Pelton","Sarah Becker","Dennis M. Bonal","Quang‐Dé Nguyen"],"author_count":58,"year":2019,"published":"2019-07-18","venue":"Cell","cited_by":2844,"doi":"10.1016/j.cell.2019.06.024","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1016/j.cell.2019.06.024","archive_url":"https://space-kitty.com/papers/doi:10.1016/j.cell.2019.06.024","updated":null,"pmid":"31327527","pmcid":null,"openalex_id":"W2961217357","pdf_url":"http://www.cell.com/article/S0092867419306877/pdf","abstract":null,"abstract_license":null,"license":null,"is_open_access":true,"peer_reviewed":null,"categories":["Glioma Diagnosis and Treatment"],"details":null,"provenance":"openalex","attribution":"Metadata and citation counts: OpenAlex (CC0)."},{"id":"doi:10.1016/j.cell.2022.04.003","kind":"article","title":"Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays","authors":["Ao Chen","Sha Liao","Mengnan Cheng","Kailong Ma","Liang Li Wu","Yiwei Lai","Xiaojie Qiu","Jin Yang","Jiangshan Xu","Shijie Hao","Xin Wang","Huifang Lü","Xi Xian Chen","Xing Liu","Xin Huang","Li Zhao","Hong Yan","Yujia Jiang","Jian Peng","Shuai Liu","Mengzhe Shen","Chuanyu Liu","Quanshui Li","Yue Yuan","Xiaoyu Wei"],"author_count":69,"year":2022,"published":"2022-05-01","venue":"Cell","cited_by":1782,"doi":"10.1016/j.cell.2022.04.003","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1016/j.cell.2022.04.003","archive_url":"https://space-kitty.com/papers/doi:10.1016/j.cell.2022.04.003","updated":null,"pmid":"35512705","pmcid":null,"openalex_id":"W4229033508","pdf_url":"http://www.cell.com/article/S0092867422003993/pdf","abstract":"Spatially resolved transcriptomic technologies are promising tools to study complex biological processes such as mammalian embryogenesis. However, the imbalance between resolution, gene capture, and field of view of current methodologies precludes their systematic application to analyze relatively large and three-dimensional mid- and late-gestation embryos. Here, we combined DNA nanoball (DNB)-patterned arrays and in situ RNA capture to create spatial enhanced resolution omics-sequencing (Stereo-seq). We applied Stereo-seq to generate the mouse organogenesis spatiotemporal transcriptomic atlas (MOSTA), which maps with single-cell resolution and high sensitivity the kinetics and directionality of transcriptional variation during mouse organogenesis. We used this information to gain insight into the molecular basis of spatial cell heterogeneity and cell fate specification in developing tissues such as the dorsal midbrain. Our panoramic atlas will facilitate in-depth investigation of longstanding questions concerning normal and abnormal mammalian development.","abstract_license":"CC-BY (publisher license, via OpenAlex)","license":"cc-by","is_open_access":true,"peer_reviewed":null,"categories":["Single-cell and spatial transcriptomics"],"details":null,"provenance":"openalex","attribution":"Abstract of \"Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays\" by Ao Chen, Sha Liao, Mengnan Cheng et al., https://doi.org/10.1016/j.cell.2022.04.003, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1038/nature12787","kind":"article","title":"An atlas of active enhancers across human cell types and tissues","authors":["Robin Andersson","Claudia Gebhard","Irene Miguel-Escalada","Ilka Hoof","Jette Bornholdt","Mette Boyd","Yun Chen","Xiaobei Zhao","Christian Schmidl","Takahiro Suzuki","Evgenia Ntini","Erik Arner","Eivind Valen","Kang Li","Lucia Schwarzfischer","Dagmar Glatz","Johanna Raithel","Berit Lilje","Nicolas Rapin","Frederik Otzen Bagger","Mette Jørgensen","Peter Refsing Andersen","Nicolas Bertin","Owen J. L. Rackham","Alexander Maxwell Burroughs"],"author_count":50,"year":2014,"published":"2014-03-01","venue":"Nature","cited_by":2549,"doi":"10.1038/nature12787","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1038/nature12787","archive_url":"https://space-kitty.com/papers/doi:10.1038/nature12787","updated":null,"pmid":"24670763","pmcid":null,"openalex_id":"W2015845481","pdf_url":"https://www.nature.com/articles/nature12787.pdf","abstract":null,"abstract_license":null,"license":null,"is_open_access":true,"peer_reviewed":null,"categories":["Genomics and Chromatin Dynamics"],"details":null,"provenance":"openalex","attribution":"Metadata and citation counts: OpenAlex (CC0)."},{"id":"doi:10.1016/j.cell.2021.12.018","kind":"article","title":"Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches","authors":["Guilliams M","Bonnardel J","Haest B","Vanderborght B","Wagner C","Remmerie A","Bujko A","Martens L","Thoné T","Browaeys R","De Ponti FF","Vanneste B","Zwicker C","Svedberg FR","Vanhalewyn T","Gonçalves A","Lippens S","Devriendt B","Cox E","Ferrero G","Wittamer V","Willaert A","Kaptein SJF","Neyts J","Dallmeier K"],"author_count":38,"year":2022,"published":"2022-01-11","venue":"Cell","cited_by":886,"doi":"10.1016/j.cell.2021.12.018","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1016/j.cell.2021.12.018","archive_url":"https://space-kitty.com/papers/doi:10.1016/j.cell.2021.12.018","updated":null,"pmid":"35021063","pmcid":"PMC8809252","openalex_id":null,"pdf_url":"https://europepmc.org/articles/PMC8809252/pdf","abstract":"The liver is the largest solid organ in the body, yet it remains incompletely characterized. Here we present a spatial proteogenomic atlas of the healthy and obese human and murine liver combining single-cell CITE-seq, single-nuclei sequencing, spatial transcriptomics, and spatial proteomics. By integrating these multi-omic datasets, we provide validated strategies to reliably discriminate and localize all hepatic cells, including a population of lipid-associated macrophages (LAMs) at the bile ducts. We then align this atlas across seven species, revealing the conserved program of bona fide Kupffer cells and LAMs. We also uncover the respective spatially resolved cellular niches of these macrophages and the microenvironmental circuits driving their unique transcriptomic identities. We demonstrate that LAMs are induced by local lipid exposure, leading to their induction in steatotic regions of the murine and human liver, while Kupffer cell development crucially depends on their cross-talk with hepatic stellate cells via the evolutionarily conserved ALK1-BMP9/10 axis.","abstract_license":"CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)","license":"CC-BY","is_open_access":true,"peer_reviewed":null,"categories":[],"details":null,"provenance":"europepmc","attribution":"Abstract of \"Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches\" by Guilliams M, Bonnardel J, Haest B et al., https://doi.org/10.1016/j.cell.2021.12.018, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1038/s41588-021-00911-1","kind":"article","title":"A single-cell and spatially resolved atlas of human breast cancers","authors":["Sunny Z. Wu","Ghamdan Al‐Eryani","Daniel Lee Roden","Simon R. Junankar","Kate Harvey","Alma Andersson","Aatish Thennavan","Chenfei Wang","James Torpy","Nenad Bartoniček","Taopeng Wang","Ludvig Larsson","Dominik C. Kaczorowski","Neil Ira Weisenfeld","Cedric R. Uytingco","Jennifer G. Chew","Zachary Bent","Chia‐Ling Chan","Vikkitharan Gnanasambandapillai","Charles‐Antoine Dutertre","Laurence Gluch","Mun Ngah Hui","Jane McNeil Beith","Andrew Parker","Elizabeth Robbins"],"author_count":40,"year":2021,"published":"2021-09-01","venue":"Nature Genetics","cited_by":1775,"doi":"10.1038/s41588-021-00911-1","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1038/s41588-021-00911-1","archive_url":"https://space-kitty.com/papers/doi:10.1038/s41588-021-00911-1","updated":null,"pmid":"34493872","pmcid":null,"openalex_id":"W3198511950","pdf_url":null,"abstract":null,"abstract_license":null,"license":null,"is_open_access":true,"peer_reviewed":null,"categories":["Single-cell and spatial transcriptomics"],"details":null,"provenance":"openalex","attribution":"Metadata and citation counts: OpenAlex (CC0)."},{"id":"doi:10.1038/s41592-021-01264-7","kind":"article","title":"Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram","authors":["Biancalani T","Scalia G","Buffoni L","Avasthi R","Lu Z","Sanger A","Tokcan N","Vanderburg CR","Segerstolpe Å","Zhang M","Avraham-Davidi I","Vickovic S","Nitzan M","Ma S","Subramanian A","Lipinski M","Buenrostro J","Brown NB","Fanelli D","Zhuang X","Macosko EZ","Regev A"],"author_count":22,"year":2021,"published":"2021-10-28","venue":"Nature methods","cited_by":921,"doi":"10.1038/s41592-021-01264-7","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1038/s41592-021-01264-7","archive_url":"https://space-kitty.com/papers/doi:10.1038/s41592-021-01264-7","updated":null,"pmid":"34711971","pmcid":"PMC8566243","openalex_id":null,"pdf_url":"https://europepmc.org/articles/PMC8566243/pdf","abstract":"Charting an organs' biological atlas requires us to spatially resolve the entire single-cell transcriptome, and to relate such cellular features to the anatomical scale. Single-cell and single-nucleus RNA-seq (sc/snRNA-seq) can profile cells comprehensively, but lose spatial information. Spatial transcriptomics allows for spatial measurements, but at lower resolution and with limited sensitivity. Targeted in situ technologies solve both issues, but are limited in gene throughput. To overcome these limitations we present Tangram, a method that aligns sc/snRNA-seq data to various forms of spatial data collected from the same region, including MERFISH, STARmap, smFISH, Spatial Transcriptomics (Visium) and histological images. Tangram can map any type of sc/snRNA-seq data, including multimodal data such as those from SHARE-seq, which we used to reveal spatial patterns of chromatin accessibility. We demonstrate Tangram on healthy mouse brain tissue, by reconstructing a genome-wide anatomically integrated spatial map at single-cell resolution of the visual and somatomotor areas.","abstract_license":"CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)","license":"CC-BY","is_open_access":true,"peer_reviewed":null,"categories":[],"details":null,"provenance":"europepmc","attribution":"Abstract of \"Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram\" by Biancalani T, Scalia G, Buffoni L et al., https://doi.org/10.1038/s41592-021-01264-7, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1038/s41467-022-29366-6","kind":"article","title":"Single-cell and spatial analysis reveal interaction of FAP + fibroblasts and SPP1 + macrophages in colorectal cancer","authors":["Qi J","Sun H","Zhang Y","Wang Z","Xun Z","Li Z","Ding X","Bao R","Hong L","Jia W","Fang F","Liu H","Chen L","Zhong J","Zou D","Liu L","Han L","Ginhoux F","Liu Y","Ye Y","Su B"],"author_count":21,"year":2022,"published":"2022-04-01","venue":"Nature communications","cited_by":820,"doi":"10.1038/s41467-022-29366-6","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1038/s41467-022-29366-6","archive_url":"https://space-kitty.com/papers/doi:10.1038/s41467-022-29366-6","updated":null,"pmid":"35365629","pmcid":"PMC8976074","openalex_id":null,"pdf_url":"https://europepmc.org/articles/PMC8976074/pdf","abstract":"Colorectal cancer (CRC) is among the most common malignancies with limited treatments other than surgery. The tumor microenvironment (TME) profiling enables the discovery of potential therapeutic targets. Here, we profile 54,103 cells from tumor and adjacent tissues to characterize cellular composition and elucidate the potential origin and regulation of tumor-enriched cell types in CRC. We demonstrate that the tumor-specific FAP + fibroblasts and SPP1 + macrophages were positively correlated in 14 independent CRC cohorts containing 2550 samples and validate their close localization by immuno-fluorescent staining and spatial transcriptomics. This interaction might be regulated by chemerin, TGF-β, and interleukin-1, which would stimulate the formation of immune-excluded desmoplasic structure and limit the T cell infiltration. Furthermore, we find patients with high FAP or SPP1 expression achieved less therapeutic benefit from an anti-PD-L1 therapy cohort. Our results provide a potential therapeutic strategy by disrupting FAP + fibroblasts and SPP1 + macrophages interaction to improve immunotherapy.","abstract_license":"CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)","license":"CC-BY","is_open_access":true,"peer_reviewed":null,"categories":[],"details":null,"provenance":"europepmc","attribution":"Abstract of \"Single-cell and spatial analysis reveal interaction of FAP + fibroblasts and SPP1 + macrophages in colorectal cancer\" by Qi J, Sun H, Zhang Y et al., https://doi.org/10.1038/s41467-022-29366-6, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1038/nature13182","kind":"article","title":"A promoter-level mammalian expression atlas","authors":["Alistair R. R. Forrest","Hideya Kawaji","Michiel de Hoon","Timo Lassmann","Marina Lizio","Masayoshi Itoh","Nicolas Bertin","Erik Arner","Charles Plessy","Morana Vitezic","Jessica Severin","Yuri Ishizu","Takahiro Arakawa","Alessandro Bonetti","Alexander Maxwell Burroughs","Shiro Fukuda","Masaaki Furuno","Matthias Harbers","Jayson Harshbarger","Akira Hasegawa","Yuki Hasegawa","Takehiro Hashimoto","Fumi Hori","Bogumił Kaczkowski","Kaoru Kaida"],"author_count":100,"year":2014,"published":"2014-03-01","venue":"Nature","cited_by":1981,"doi":"10.1038/nature13182","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1038/nature13182","archive_url":"https://space-kitty.com/papers/doi:10.1038/nature13182","updated":null,"pmid":"24670764","pmcid":null,"openalex_id":"W1986223165","pdf_url":"https://www.nature.com/articles/nature13182.pdf","abstract":"Regulated transcription controls the diversity, developmental pathways and spatial organization of the hundreds of cell types that make up a mammal. Using single-molecule cDNA sequencing, we mapped transcription start sites (TSSs) and their usage in human and mouse primary cells, cell lines and tissues to produce a comprehensive overview of mammalian gene expression across the human body. We find that few genes are truly ‘housekeeping’, whereas many mammalian promoters are composite entities composed of several closely separated TSSs, with independent cell-type-specific expression profiles. TSSs specific to different cell types evolve at different rates, whereas promoters of broadly expressed genes are the most conserved. Promoter-based expression analysis reveals key transcription factors defining cell states and links them to binding-site motifs. The functions of identified novel transcripts can be predicted by coexpression and sample ontology enrichment analyses. The functional annotation of the mammalian genome 5 (FANTOM5) project provides comprehensive expression profiles and functional annotation of mammalian cell-type-specific transcriptomes with wide applications in biomedical research. A study from the FANTOM consortium using single-molecule cDNA sequencing of transcription start sites and their usage in human and mouse primary cells, cell lines and tissues reveals insights into the specificity and diversity of transcription patterns across different mammalian cell types. FANTOM5 (standing for functional annotation of the mammalian genome 5) is the fifth major stage of a major international collaboration that aims to dissect the transcriptional regulatory networks that define every human cell type. Two Articles in this issue of Nature present some of the project's latest results. The first paper uses the FANTOM5 panel of tissue and primary cell samples to define an atlas of active, in vivo bidirectionally transcribed enhancers across the human body. These authors show that bidirectional capped RNAs are a signature feature of active enhancers and identify more than 40,000 enhancer candidates from over 800 human cell and tissue samples. The enhancer atlas is used to compare regulatory programs between different cell types and identify disease-associated regulatory SNPs, and will be a resource for studies on cell-type-specific enhancers. In the second paper, single-molecule sequencing is used to map human and mouse transcription start sites and their usage in a panel of distinct human and mouse primary cells, cell lines and tissues to produce the most comprehensive mammalian gene expression atlas to date. The data provide a plethora of insights into open reading frames and promoters across different cell types in addition to valuable annotation of mammalian cell-type-specific transcriptomes.","abstract_license":"Public domain (publisher license, via OpenAlex)","license":"public-domain","is_open_access":true,"peer_reviewed":null,"categories":["Genomics and Chromatin Dynamics"],"details":null,"provenance":"openalex","attribution":"Metadata and citation counts: OpenAlex (CC0)."},{"id":"doi:10.1038/s41586-020-2922-4","kind":"article","title":"A molecular cell atlas of the human lung from single-cell RNA sequencing","authors":["Kyle J. Travaglini","Ahmad N. Nabhan","Lolita Penland","Rahul Sinha","Astrid Gillich","Rene V. Sit","Stephen Chang","Stephanie D. Conley","Yasuo Mori","Jun Seita","Gerald J. Berry","Joseph B. Shrager","Ross J. Metzger","Christin S. Kuo","Norma F Neff","Irving L. Weissman","Stephen R. Quake","Mark A. Krasnow"],"author_count":18,"year":2020,"published":"2020-11-18","venue":"Nature","cited_by":1709,"doi":"10.1038/s41586-020-2922-4","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1038/s41586-020-2922-4","archive_url":"https://space-kitty.com/papers/doi:10.1038/s41586-020-2922-4","updated":null,"pmid":"33208946","pmcid":null,"openalex_id":"W3101697142","pdf_url":"https://www.nature.com/articles/s41586-020-2922-4.pdf","abstract":null,"abstract_license":null,"license":null,"is_open_access":true,"peer_reviewed":null,"categories":["Single-cell and spatial transcriptomics"],"details":null,"provenance":"openalex","attribution":"Metadata and citation counts: OpenAlex (CC0)."},{"id":"doi:10.1016/j.cell.2022.05.013","kind":"article","title":"Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq","authors":["Replogle JM","Saunders RA","Pogson AN","Hussmann JA","Lenail A","Guna A","Mascibroda L","Wagner EJ","Adelman K","Lithwick-Yanai G","Iremadze N","Oberstrass F","Lipson D","Bonnar JL","Jost M","Norman TM","Weissman JS"],"author_count":17,"year":2022,"published":"2022-06-09","venue":"Cell","cited_by":769,"doi":"10.1016/j.cell.2022.05.013","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1016/j.cell.2022.05.013","archive_url":"https://space-kitty.com/papers/doi:10.1016/j.cell.2022.05.013","updated":null,"pmid":"35688146","pmcid":"PMC9380471","openalex_id":null,"pdf_url":"https://europepmc.org/articles/PMC9380471/pdf","abstract":"A central goal of genetics is to define the relationships between genotypes and phenotypes. High-content phenotypic screens such as Perturb-seq (CRISPR-based screens with single-cell RNA-sequencing readouts) enable massively parallel functional genomic mapping but, to date, have been used at limited scales. Here, we perform genome-scale Perturb-seq targeting all expressed genes with CRISPR interference (CRISPRi) across >2.5 million human cells. We use transcriptional phenotypes to predict the function of poorly characterized genes, uncovering new regulators of ribosome biogenesis (including CCDC86, ZNF236, and SPATA5L1), transcription (C7orf26), and mitochondrial respiration (TMEM242). In addition to assigning gene function, single-cell transcriptional phenotypes allow for in-depth dissection of complex cellular phenomena-from RNA processing to differentiation. We leverage this ability to systematically identify genetic drivers and consequences of aneuploidy and to discover an unanticipated layer of stress-specific regulation of the mitochondrial genome. Our information-rich genotype-phenotype map reveals a multidimensional portrait of gene and cellular function.","abstract_license":"CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)","license":"CC-BY","is_open_access":true,"peer_reviewed":null,"categories":[],"details":null,"provenance":"europepmc","attribution":"Abstract of \"Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq\" by Replogle JM, Saunders RA, Pogson AN et al., https://doi.org/10.1016/j.cell.2022.05.013, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1016/j.crmeth.2023.100498","kind":"article","title":"hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data","authors":["Morabito S","Reese F","Rahimzadeh N","Miyoshi E","Swarup V"],"author_count":5,"year":2023,"published":"2023-06-12","venue":"Cell reports methods","cited_by":724,"doi":"10.1016/j.crmeth.2023.100498","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1016/j.crmeth.2023.100498","archive_url":"https://space-kitty.com/papers/doi:10.1016/j.crmeth.2023.100498","updated":null,"pmid":"37426759","pmcid":"PMC10326379","openalex_id":null,"pdf_url":"https://europepmc.org/articles/PMC10326379/pdf","abstract":"Biological systems are immensely complex, organized into a multi-scale hierarchy of functional units based on tightly regulated interactions between distinct molecules, cells, organs, and organisms. While experimental methods enable transcriptome-wide measurements across millions of cells, popular bioinformatic tools do not support systems-level analysis. Here we present hdWGCNA, a comprehensive framework for analyzing co-expression networks in high-dimensional transcriptomics data such as single-cell and spatial RNA sequencing (RNA-seq). hdWGCNA provides functions for network inference, gene module identification, gene enrichment analysis, statistical tests, and data visualization. Beyond conventional single-cell RNA-seq, hdWGCNA is capable of performing isoform-level network analysis using long-read single-cell data. We showcase hdWGCNA using data from autism spectrum disorder and Alzheimer's disease brain samples, identifying disease-relevant co-expression network modules. hdWGCNA is directly compatible with Seurat, a widely used R package for single-cell and spatial transcriptomics analysis, and we demonstrate the scalability of hdWGCNA by analyzing a dataset containing nearly 1 million cells.","abstract_license":"CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)","license":"CC-BY","is_open_access":true,"peer_reviewed":null,"categories":[],"details":null,"provenance":"europepmc","attribution":"Abstract of \"hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data\" by Morabito S, Reese F, Rahimzadeh N et al., https://doi.org/10.1016/j.crmeth.2023.100498, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1016/j.cell.2020.05.039","kind":"article","title":"Multimodal Analysis of Composition and Spatial Architecture in Human Squamous Cell Carcinoma","authors":["Ji AL","Rubin AJ","Thrane K","Jiang S","Reynolds DL","Meyers RM","Guo MG","George BM","Mollbrink A","Bergenstråhle J","Larsson L","Bai Y","Zhu B","Bhaduri A","Meyers JM","Rovira-Clavé X","Hollmig ST","Aasi SZ","Nolan GP","Lundeberg J","Khavari PA"],"author_count":21,"year":2020,"published":"2020-06-23","venue":"Cell","cited_by":881,"doi":"10.1016/j.cell.2020.05.039","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1016/j.cell.2020.05.039","archive_url":"https://space-kitty.com/papers/doi:10.1016/j.cell.2020.05.039","updated":null,"pmid":"32579974","pmcid":"PMC7391009","openalex_id":null,"pdf_url":"https://europepmc.org/articles/PMC7391009/pdf","abstract":"To define the cellular composition and architecture of cutaneous squamous cell carcinoma (cSCC), we combined single-cell RNA sequencing with spatial transcriptomics and multiplexed ion beam imaging from a series of human cSCCs and matched normal skin. cSCC exhibited four tumor subpopulations, three recapitulating normal epidermal states, and a tumor-specific keratinocyte (TSK) population unique to cancer, which localized to a fibrovascular niche. Integration of single-cell and spatial data mapped ligand-receptor networks to specific cell types, revealing TSK cells as a hub for intercellular communication. Multiple features of potential immunosuppression were observed, including T regulatory cell (Treg) co-localization with CD8 T cells in compartmentalized tumor stroma. Finally, single-cell characterization of human tumor xenografts and in vivo CRISPR screens identified essential roles for specific tumor subpopulation-enriched gene networks in tumorigenesis. These data define cSCC tumor and stromal cell subpopulations, the spatial niches where they interact, and the communicating gene networks that they engage in cancer.","abstract_license":"CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)","license":"CC-BY","is_open_access":true,"peer_reviewed":null,"categories":[],"details":null,"provenance":"europepmc","attribution":"Abstract of \"Multimodal Analysis of Composition and Spatial Architecture in Human Squamous Cell Carcinoma\" by Ji AL, Rubin AJ, Thrane K et al., https://doi.org/10.1016/j.cell.2020.05.039, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1038/s41586-022-05435-0","kind":"article","title":"Effect of the intratumoral microbiota on spatial and cellular heterogeneity in cancer","authors":["Galeano Niño JL","Wu H","LaCourse KD","Kempchinsky AG","Baryiames A","Barber B","Futran N","Houlton J","Sather C","Sicinska E","Taylor A","Minot SS","Johnston CD","Bullman S"],"author_count":14,"year":2022,"published":"2022-11-16","venue":"Nature","cited_by":750,"doi":"10.1038/s41586-022-05435-0","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1038/s41586-022-05435-0","archive_url":"https://space-kitty.com/papers/doi:10.1038/s41586-022-05435-0","updated":null,"pmid":"36385528","pmcid":"PMC9684076","openalex_id":null,"pdf_url":"https://europepmc.org/articles/PMC9684076/pdf","abstract":"The tumour-associated microbiota is an intrinsic component of the tumour microenvironment across human cancer types 1,2 . Intratumoral host-microbiota studies have so far largely relied on bulk tissue analysis 1-3 , which obscures the spatial distribution and localized effect of the microbiota within tumours. Here, by applying in situ spatial-profiling technologies 4 and single-cell RNA sequencing 5 to oral squamous cell carcinoma and colorectal cancer, we reveal spatial, cellular and molecular host-microbe interactions. We adapted 10x Visium spatial transcriptomics to determine the identity and in situ location of intratumoral microbial communities within patient tissues. Using GeoMx digital spatial profiling 6 , we show that bacterial communities populate microniches that are less vascularized, highly immuno‑suppressive and associated with malignant cells with lower levels of Ki-67 as compared to bacteria-negative tumour regions. We developed a single-cell RNA-sequencing method that we name INVADEseq (invasion-adhesion-directed expression sequencing) and, by applying this to patient tumours, identify cell-associated bacteria and the host cells with which they interact, as well as uncovering alterations in transcriptional pathways that are involved in inflammation, metastasis, cell dormancy and DNA repair. Through functional studies, we show that cancer cells that are infected with bacteria invade their surrounding environment as single cells and recruit myeloid cells to bacterial regions. Collectively, our data reveal that the distribution of the microbiota within a tumour is not random; instead, it is highly organized in microniches with immune and epithelial cell functions that promote cancer progression.","abstract_license":"CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)","license":"CC-BY","is_open_access":true,"peer_reviewed":null,"categories":[],"details":null,"provenance":"europepmc","attribution":"Abstract of \"Effect of the intratumoral microbiota on spatial and cellular heterogeneity in cancer\" by Galeano Niño JL, Wu H, LaCourse KD et al., https://doi.org/10.1038/s41586-022-05435-0, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1038/s41586-023-05769-3","kind":"article","title":"An atlas of healthy and injured cell states and niches in the human kidney","authors":["Lake BB","Menon R","Winfree S","Hu Q","Melo Ferreira R","Kalhor K","Barwinska D","Otto EA","Ferkowicz M","Diep D","Plongthongkum N","Knoten A","Urata S","Mariani LH","Naik AS","Eddy S","Zhang B","Wu Y","Salamon D","Williams JC","Wang X","Balderrama KS","Hoover PJ","Murray E","Marshall JL"],"author_count":50,"year":2023,"published":"2023-07-19","venue":"Nature","cited_by":704,"doi":"10.1038/s41586-023-05769-3","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1038/s41586-023-05769-3","archive_url":"https://space-kitty.com/papers/doi:10.1038/s41586-023-05769-3","updated":null,"pmid":"37468583","pmcid":"PMC10356613","openalex_id":null,"pdf_url":"https://europepmc.org/articles/PMC10356613/pdf","abstract":"Understanding kidney disease relies on defining the complexity of cell types and states, their associated molecular profiles and interactions within tissue neighbourhoods 1 . Here we applied multiple single-cell and single-nucleus assays (>400,000 nuclei or cells) and spatial imaging technologies to a broad spectrum of healthy reference kidneys (45 donors) and diseased kidneys (48 patients). This has provided a high-resolution cellular atlas of 51 main cell types, which include rare and previously undescribed cell populations. The multi-omic approach provides detailed transcriptomic profiles, regulatory factors and spatial localizations spanning the entire kidney. We also define 28 cellular states across nephron segments and interstitium that were altered in kidney injury, encompassing cycling, adaptive (successful or maladaptive repair), transitioning and degenerative states. Molecular signatures permitted the localization of these states within injury neighbourhoods using spatial transcriptomics, while large-scale 3D imaging analysis (around 1.2 million neighbourhoods) provided corresponding linkages to active immune responses. These analyses defined biological pathways that are relevant to injury time-course and niches, including signatures underlying epithelial repair that predicted maladaptive states associated with a decline in kidney function. This integrated multimodal spatial cell atlas of healthy and diseased human kidneys represents a comprehensive benchmark of cellular states, neighbourhoods, outcome-associated signatures and publicly available interactive visualizations.","abstract_license":"CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)","license":"CC-BY","is_open_access":true,"peer_reviewed":null,"categories":[],"details":null,"provenance":"europepmc","attribution":"Abstract of \"An atlas of healthy and injured cell states and niches in the human kidney\" by Lake BB, Menon R, Winfree S et al., https://doi.org/10.1038/s41586-023-05769-3, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1016/j.cell.2018.02.001","kind":"article","title":"Mapping the Mouse Cell Atlas by Microwell-Seq","authors":["Xiaoping Han","Renying Wang","Yincong Zhou","Lijiang Fei","Huiyu Sun","Shujing Lai","Assieh Saadatpour","Ziming Zhou","Haide Chen","Fang Ye","Daosheng Huang","Yang Xu","Wentao Huang","Mengmeng Jiang","Xinyi Jiang","Jie Mao","Yao Chen","Chenyu Lu","Jin Han Xie","Qun Fang","Yibin Wang","Rui Yue","Tiefeng Li","He Huang","Stuart H. Orkin"],"author_count":28,"year":2018,"published":"2018-02-01","venue":"Cell","cited_by":1597,"doi":"10.1016/j.cell.2018.02.001","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1016/j.cell.2018.02.001","archive_url":"https://space-kitty.com/papers/doi:10.1016/j.cell.2018.02.001","updated":null,"pmid":"29474909","pmcid":null,"openalex_id":"W2788348358","pdf_url":"https://www.cell.com/article/S0092867418301168/pdf","abstract":null,"abstract_license":null,"license":null,"is_open_access":true,"peer_reviewed":null,"categories":["Single-cell and spatial transcriptomics"],"details":null,"provenance":"openalex","attribution":"Metadata and citation counts: OpenAlex (CC0)."},{"id":"doi:10.1186/s13073-022-01075-1","kind":"review","title":"An introduction to spatial transcriptomics for biomedical research","authors":["Williams CG","Lee HJ","Asatsuma T","Vento-Tormo R","Haque A"],"author_count":5,"year":2022,"published":"2022-06-27","venue":"Genome medicine","cited_by":727,"doi":"10.1186/s13073-022-01075-1","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1186/s13073-022-01075-1","archive_url":"https://space-kitty.com/papers/doi:10.1186/s13073-022-01075-1","updated":null,"pmid":"35761361","pmcid":"PMC9238181","openalex_id":null,"pdf_url":"https://europepmc.org/articles/PMC9238181/pdf","abstract":"Single-cell transcriptomics (scRNA-seq) has become essential for biomedical research over the past decade, particularly in developmental biology, cancer, immunology, and neuroscience. Most commercially available scRNA-seq protocols require cells to be recovered intact and viable from tissue. This has precluded many cell types from study and largely destroys the spatial context that could otherwise inform analyses of cell identity and function. An increasing number of commercially available platforms now facilitate spatially resolved, high-dimensional assessment of gene transcription, known as 'spatial transcriptomics'. Here, we introduce different classes of method, which either record the locations of hybridized mRNA molecules in tissue, image the positions of cells themselves prior to assessment, or employ spatial arrays of mRNA probes of pre-determined location. We review sizes of tissue area that can be assessed, their spatial resolution, and the number and types of genes that can be profiled. We discuss if tissue preservation influences choice of platform, and provide guidance on whether specific platforms may be better suited to discovery screens or hypothesis testing. Finally, we introduce bioinformatic methods for analysing spatial transcriptomic data, including pre-processing, integration with existing scRNA-seq data, and inference of cell-cell interactions. Spatial -omics methods are already improving our understanding of human tissues in research, diagnostic, and therapeutic settings. To build upon these recent advancements, we provide entry-level guidance for those seeking to employ spatial transcriptomics in their own biomedical research.","abstract_license":"CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)","license":"CC-BY","is_open_access":true,"peer_reviewed":null,"categories":[],"details":null,"provenance":"europepmc","attribution":"Abstract of \"An introduction to spatial transcriptomics for biomedical research\" by Williams CG, Lee HJ, Asatsuma T et al., https://doi.org/10.1186/s13073-022-01075-1, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1038/s41586-023-06812-z","kind":"article","title":"A high-resolution transcriptomic and spatial atlas of cell types in the whole mouse brain","authors":["Zizhen Yao","Cindy T. J. van Velthoven","Michael Kunst","Meng Zhang","Delissa McMillen","Changkyu Lee","Won Jung","Jeff Goldy","Aliya Abdelhak","Matthew Aitken","Katherine S. Baker","Pamela M. Baker","Eliza R. Barkan","Darren Bertagnolli","Ashwin A. Bhandiwad","Cameron Bielstein","Prajal Bishwakarma","Jazmin Campos","Daniel Carey","Tamara Casper","Anish Bhaswanth Chakka","Rushil Chakrabarty","Sakshi Chavan","Min Chen","Michael Clark"],"author_count":100,"year":2023,"published":"2023-12-13","venue":"Nature","cited_by":1325,"doi":"10.1038/s41586-023-06812-z","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1038/s41586-023-06812-z","archive_url":"https://space-kitty.com/papers/doi:10.1038/s41586-023-06812-z","updated":null,"pmid":"37034735","pmcid":"PMC10719114","openalex_id":"W4389670555","pdf_url":"https://europepmc.org/articles/PMC10719114/pdf","abstract":". Here we report a comprehensive and high-resolution transcriptomic and spatial cell-type atlas for the whole adult mouse brain. The cell-type atlas was created by combining a single-cell RNA-sequencing (scRNA-seq) dataset of around 7 million cells profiled (approximately 4.0 million cells passing quality control), and a spatial transcriptomic dataset of approximately 4.3 million cells using multiplexed error-robust fluorescence in situ hybridization (MERFISH). The atlas is hierarchically organized into 4 nested levels of classification: 34 classes, 338 subclasses, 1,201 supertypes and 5,322 clusters. We present an online platform, Allen Brain Cell Atlas, to visualize the mouse whole-brain cell-type atlas along with the single-cell RNA-sequencing and MERFISH datasets. We systematically analysed the neuronal and non-neuronal cell types across the brain and identified a high degree of correspondence between transcriptomic identity and spatial specificity for each cell type. The results reveal unique features of cell-type organization in different brain regions-in particular, a dichotomy between the dorsal and ventral parts of the brain. The dorsal part contains relatively fewer yet highly divergent neuronal types, whereas the ventral part contains more numerous neuronal types that are more closely related to each other. Our study also uncovered extraordinary diversity and heterogeneity in neurotransmitter and neuropeptide expression and co-expression patterns in different cell types. Finally, we found that transcription factors are major determinants of cell-type classification and identified a combinatorial transcription factor code that defines cell types across all parts of the brain. The whole mouse brain transcriptomic and spatial cell-type atlas establishes a benchmark reference atlas and a foundational resource for integrative investigations of cellular and circuit function, development and evolution of the mammalian brain.","abstract_license":"CC-BY (publisher license, via OpenAlex)","license":"cc-by","is_open_access":true,"peer_reviewed":null,"categories":["Single-cell and spatial transcriptomics"],"details":null,"provenance":"openalex+europepmc","attribution":"Abstract of \"A high-resolution transcriptomic and spatial atlas of cell types in the whole mouse brain\" by Zizhen Yao, Cindy T. J. van Velthoven, Michael Kunst et al., https://doi.org/10.1038/s41586-023-06812-z, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1016/j.cell.2018.07.028","kind":"article","title":"Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain","authors":["Arpiar B. Saunders","Evan Z. Macosko","Alec Wysoker","Melissa Goldman","Fenna Marie Krienen","Heather de Rivera","Elizabeth Bien","Matthew L. Baum","Laura Bortolin","Shuyu Wang","Aleksandrina Goeva","James C. Nemesh","Nolan Kamitaki","Sara A. Brumbaugh","David Kulp","Steven A. McCarroll"],"author_count":16,"year":2018,"published":"2018-08-01","venue":"Cell","cited_by":1587,"doi":"10.1016/j.cell.2018.07.028","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1016/j.cell.2018.07.028","archive_url":"https://space-kitty.com/papers/doi:10.1016/j.cell.2018.07.028","updated":null,"pmid":"30096299","pmcid":null,"openalex_id":"W2885927965","pdf_url":"http://www.cell.com/article/S0092867418309553/pdf","abstract":null,"abstract_license":null,"license":null,"is_open_access":true,"peer_reviewed":null,"categories":["Single-cell and spatial transcriptomics"],"details":null,"provenance":"openalex","attribution":"Metadata and citation counts: OpenAlex (CC0)."},{"id":"doi:10.1093/database/baz046","kind":"article","title":"PanglaoDB: a web server for exploration of mouse and human single-cell RNA sequencing data","authors":["Oscar Franzén","Li‐Ming Gan","Johan Björkegren"],"author_count":3,"year":2019,"published":"2019-01-01","venue":"Database","cited_by":1550,"doi":"10.1093/database/baz046","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1093/database/baz046","archive_url":"https://space-kitty.com/papers/doi:10.1093/database/baz046","updated":null,"pmid":"30951143","pmcid":null,"openalex_id":"W2931036699","pdf_url":"https://academic.oup.com/database/article-pdf/doi/10.1093/database/baz046/28277084/baz046.pdf","abstract":"Single-cell RNA sequencing is an increasingly used method to measure gene expression at the single cell level and build cell-type atlases of tissues. Hundreds of single-cell sequencing datasets have already been published. However, studies are frequently deposited as raw data, a format difficult to access for biological researchers due to the need for data processing using complex computational pipelines. We have implemented an online database, PanglaoDB, accessible through a user-friendly interface that can be used to explore published mouse and human single cell RNA sequencing studies. PanglaoDB contains pre-processed and pre-computed analyses from more than 1054 single-cell experiments covering most major single cell platforms and protocols, based on more than 4 million cells from a wide range of tissues and organs. The online interface allows users to query and explore cell types, genetic pathways and regulatory networks. In addition, we have established a community-curated cell-type marker compendium, containing more than 6000 gene-cell-type associations, as a resource for automatic annotation of cell types.","abstract_license":"CC-BY (publisher license, via OpenAlex)","license":"cc-by","is_open_access":true,"peer_reviewed":null,"categories":["Single-cell and spatial transcriptomics"],"details":null,"provenance":"openalex","attribution":"Abstract of \"PanglaoDB: a web server for exploration of mouse and human single-cell RNA sequencing data\" by Oscar Franzén, Li‐Ming Gan, Johan Björkegren, https://doi.org/10.1093/database/baz046, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1038/s41591-020-0944-y","kind":"article","title":"A single-cell atlas of the peripheral immune response in patients with severe COVID-19","authors":["Aaron J. Wilk","Arjun Rustagi","Nancy Q. Zhao","Jonasel Roque","Giovanny J. Martínez-Colón","Julia L. McKechnie","Geoffrey T. Ivison","Thanmayi Ranganath","Rosemary Vergara","Taylor Hollis","Laura J. Simpson","Philip Grant","Aruna K Subramanian","Angela J. Rogers","Catherine A. Blish"],"author_count":15,"year":2020,"published":"2020-06-08","venue":"Nature Medicine","cited_by":1493,"doi":"10.1038/s41591-020-0944-y","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1038/s41591-020-0944-y","archive_url":"https://space-kitty.com/papers/doi:10.1038/s41591-020-0944-y","updated":null,"pmid":"32511639","pmcid":null,"openalex_id":"W3034168389","pdf_url":"https://www.nature.com/articles/s41591-020-0944-y.pdf","abstract":null,"abstract_license":null,"license":null,"is_open_access":true,"peer_reviewed":null,"categories":["COVID-19 Clinical Research Studies"],"details":null,"provenance":"openalex","attribution":"Metadata and citation counts: OpenAlex (CC0)."},{"id":"doi:10.1126/science.abe6474","kind":"article","title":"Pan-cancer single-cell landscape of tumor-infiltrating T cells","authors":["Liangtao Zheng","Shishang Qin","Wen Si","Anqiang Wang","Baocai Xing","Ranran Gao","Xianwen Ren","Li Wang","Xiaojiang Wu","Ji Zhang","Nan Wu","Ning Zhang","Hong Zheng","Hanqiang Ouyang","Keyuan Chen","Zhaode Bu","Xueda Hu","Jiafu Ji","Zemin Zhang"],"author_count":19,"year":2021,"published":"2021-12-16","venue":"Science","cited_by":1401,"doi":"10.1126/science.abe6474","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1126/science.abe6474","archive_url":"https://space-kitty.com/papers/doi:10.1126/science.abe6474","updated":null,"pmid":"34914499","pmcid":null,"openalex_id":"W4200468913","pdf_url":null,"abstract":null,"abstract_license":null,"license":null,"is_open_access":false,"peer_reviewed":null,"categories":["Cancer Immunotherapy and Biomarkers"],"details":null,"provenance":"openalex","attribution":"Metadata and citation counts: OpenAlex (CC0)."}],"next":"https://space-kitty.com/api/v1/topics/single-cell-spatial/papers?sort=top&limit=25&offset=25"}