Space Kitty Archive

Single-Cell & Spatial Omics

Single-cell atlases, spatial transcriptomics and proteomics, perturbation screens and foundation models of cells.

354 papersnewest Nov 1, 2026

Most influential

  • Integrating single-cell transcriptomic data across different conditions, technologies, and species
    Andrew Butler, Paul Hoffman, Peter Smibert et al. · 2018 · Nature Biotechnology
    Article13,167 citationsdoi:10.1038/nbt.4096
  • Inference and analysis of cell-cell communication using CellChat
    Suoqin Jin, Christian Fernando Guerrero‐Juarez, Lihua Zhang et al. · 2021 · Nature Communications

    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 t…

    Abstract excerpt by Suoqin Jin et al., CC-BY, https://doi.org/10.1038/s41467-021-21246-9; truncated.

    Article8,376 citationsdoi:10.1038/s41467-021-21246-9
  • Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets
    Evan Z. Macosko, Anindita Basu, Rahul Satija et al. · 2015 · Cell
    Article7,108 citationsdoi:10.1016/j.cell.2015.05.002
  • The single-cell transcriptional landscape of mammalian organogenesis
    Junyue Cao, Malte Spielmann, Xiaojie Qiu et al. · 2019 · Nature
    Article4,481 citationsdoi:10.1038/s41586-019-0969-x
  • An Integrative Model of Cellular States, Plasticity, and Genetics for Glioblastoma
    Cyril Neftel, Julie Laffy, Mariella G. Filbin et al. · 2019 · Cell
    Article2,844 citationsdoi:10.1016/j.cell.2019.06.024
  • Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays
    Ao Chen, Sha Liao, Mengnan Cheng et al. · 2022 · Cell

    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. …

    Abstract excerpt by Ao Chen et al., CC-BY, https://doi.org/10.1016/j.cell.2022.04.003; truncated.

    Article1,782 citationsdoi:10.1016/j.cell.2022.04.003
  • An atlas of active enhancers across human cell types and tissues
    Robin Andersson, Claudia Gebhard, Irene Miguel-Escalada et al. · 2014 · Nature
    Article2,549 citationsdoi:10.1038/nature12787
  • Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches
    Guilliams M, Bonnardel J, Haest B et al. · 2022 · Cell

    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 str…

    Abstract excerpt by Guilliams M et al., CC-BY, https://doi.org/10.1016/j.cell.2021.12.018; truncated.

    Article886 citationsdoi:10.1016/j.cell.2021.12.018
  • A single-cell and spatially resolved atlas of human breast cancers
    Sunny Z. Wu, Ghamdan Al‐Eryani, Daniel Lee Roden et al. · 2021 · Nature Genetics
    Article1,775 citationsdoi:10.1038/s41588-021-00911-1
  • Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram
    Biancalani T, Scalia G, Buffoni L et al. · 2021 · Nature methods

    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 r…

    Abstract excerpt by Biancalani T et al., CC-BY, https://doi.org/10.1038/s41592-021-01264-7; truncated.

    Article921 citationsdoi:10.1038/s41592-021-01264-7
  • Single-cell and spatial analysis reveal interaction of FAP + fibroblasts and SPP1 + macrophages in colorectal cancer
    Qi J, Sun H, Zhang Y et al. · 2022 · Nature communications

    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 tum…

    Abstract excerpt by Qi J et al., CC-BY, https://doi.org/10.1038/s41467-022-29366-6; truncated.

    Article820 citationsdoi:10.1038/s41467-022-29366-6
  • A promoter-level mammalian expression atlas
    Alistair R. R. Forrest, Hideya Kawaji, Michiel de Hoon et al. · 2014 · Nature

    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 ex…

    Article1,981 citationsdoi:10.1038/nature13182
  • A molecular cell atlas of the human lung from single-cell RNA sequencing
    Kyle J. Travaglini, Ahmad N. Nabhan, Lolita Penland et al. · 2020 · Nature
    Article1,709 citationsdoi:10.1038/s41586-020-2922-4
  • Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq
    Replogle JM, Saunders RA, Pogson AN et al. · 2022 · Cell

    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…

    Abstract excerpt by Replogle JM et al., CC-BY, https://doi.org/10.1016/j.cell.2022.05.013; truncated.

    Article769 citationsdoi:10.1016/j.cell.2022.05.013
  • hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data
    Morabito S, Reese F, Rahimzadeh N et al. · 2023 · Cell reports methods

    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. He…

    Abstract excerpt by Morabito S et al., CC-BY, https://doi.org/10.1016/j.crmeth.2023.100498; truncated.

    Article724 citationsdoi:10.1016/j.crmeth.2023.100498

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