# Single-Cell & Spatial Omics Domain: Biotechnology & Life Sciences · 356 papers indexed · newest 2026-10-29 Single-cell atlases, spatial transcriptomics and proteomics, perturbation screens and foundation models of cells. Key concepts: single-cell, spatial transcriptomics, cell atlas, single-cell foundation model, perturb-seq ## Most influential - **[Integrating single-cell transcriptomic data across different conditions, technologies, and species](https://space-kitty.com/papers/doi:10.1038/nbt.4096)**. Andrew Butler, Paul Hoffman, Peter Smibert et al. · 2018 · Nature Biotechnology (doi:10.1038/nbt.4096 · 13,167 citations). Source: - **[Inference and analysis of cell-cell communication using CellChat](https://space-kitty.com/papers/doi:10.1038/s41467-021-21246-9)**. Suoqin Jin, Christian Fernando Guerrero‐Juarez, Lihua Zhang et al. · 2021 · Nature Communications (doi:10.1038/s41467-021-21246-9 · 8,376 citations). Source: > 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 function… > _Abstract excerpt by Suoqin Jin et al., CC-BY, https://doi.org/10.1038/s41467-021-21246-9; truncated._ - **[Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets](https://space-kitty.com/papers/doi:10.1016/j.cell.2015.05.002)**. Evan Z. Macosko, Anindita Basu, Rahul Satija et al. · 2015 · Cell (doi:10.1016/j.cell.2015.05.002 · 7,108 citations). Source: - **[The single-cell transcriptional landscape of mammalian organogenesis](https://space-kitty.com/papers/doi:10.1038/s41586-019-0969-x)**. Junyue Cao, Malte Spielmann, Xiaojie Qiu et al. · 2019 · Nature (doi:10.1038/s41586-019-0969-x · 4,481 citations). Source: - **[An Integrative Model of Cellular States, Plasticity, and Genetics for Glioblastoma](https://space-kitty.com/papers/doi:10.1016/j.cell.2019.06.024)**. Cyril Neftel, Julie Laffy, Mariella G. Filbin et al. · 2019 · Cell (doi:10.1016/j.cell.2019.06.024 · 2,844 citations). Source: - **[Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays](https://space-kitty.com/papers/doi:10.1016/j.cell.2022.04.003)**. Ao Chen, Sha Liao, Mengnan Cheng et al. · 2022 · Cell (doi:10.1016/j.cell.2022.04.003 · 1,782 citations). Source: > 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… > _Abstract excerpt by Ao Chen et al., CC-BY, https://doi.org/10.1016/j.cell.2022.04.003; truncated._ - **[An atlas of active enhancers across human cell types and tissues](https://space-kitty.com/papers/doi:10.1038/nature12787)**. Robin Andersson, Claudia Gebhard, Irene Miguel-Escalada et al. · 2014 · Nature (doi:10.1038/nature12787 · 2,549 citations). Source: - **[Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches](https://space-kitty.com/papers/doi:10.1016/j.cell.2021.12.018)**. Guilliams M, Bonnardel J, Haest B et al. · 2022 · Cell (doi:10.1016/j.cell.2021.12.018 · 886 citations). Source: > 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 Ku… > _Abstract excerpt by Guilliams M et al., CC-BY, https://doi.org/10.1016/j.cell.2021.12.018; truncated._ - **[A single-cell and spatially resolved atlas of human breast cancers](https://space-kitty.com/papers/doi:10.1038/s41588-021-00911-1)**. Sunny Z. Wu, Ghamdan Al‐Eryani, Daniel Lee Roden et al. · 2021 · Nature Genetics (doi:10.1038/s41588-021-00911-1 · 1,775 citations). Source: - **[Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram](https://space-kitty.com/papers/doi:10.1038/s41592-021-01264-7)**. Biancalani T, Scalia G, Buffoni L et al. · 2021 · Nature methods (doi:10.1038/s41592-021-01264-7 · 921 citations). Source: > 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 spatia… > _Abstract excerpt by Biancalani T et al., CC-BY, https://doi.org/10.1038/s41592-021-01264-7; truncated._ - **[Single-cell and spatial analysis reveal interaction of FAP + fibroblasts and SPP1 + macrophages in colorectal cancer](https://space-kitty.com/papers/doi:10.1038/s41467-022-29366-6)**. Qi J, Sun H, Zhang Y et al. · 2022 · Nature communications (doi:10.1038/s41467-022-29366-6 · 820 citations). Source: > 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-… > _Abstract excerpt by Qi J et al., CC-BY, https://doi.org/10.1038/s41467-022-29366-6; truncated._ - **[A promoter-level mammalian expression atlas](https://space-kitty.com/papers/doi:10.1038/nature13182)**. Alistair R. R. Forrest, Hideya Kawaji, Michiel de Hoon et al. · 2014 · Nature (doi:10.1038/nature13182 · 1,981 citations). Source: > 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. TS… - **[A molecular cell atlas of the human lung from single-cell RNA sequencing](https://space-kitty.com/papers/doi:10.1038/s41586-020-2922-4)**. Kyle J. Travaglini, Ahmad N. Nabhan, Lolita Penland et al. · 2020 · Nature (doi:10.1038/s41586-020-2922-4 · 1,709 citations). Source: - **[Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq](https://space-kitty.com/papers/doi:10.1016/j.cell.2022.05.013)**. Replogle JM, Saunders RA, Pogson AN et al. · 2022 · Cell (doi:10.1016/j.cell.2022.05.013 · 769 citations). Source: > 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 CC… > _Abstract excerpt by Replogle JM et al., CC-BY, https://doi.org/10.1016/j.cell.2022.05.013; truncated._ - **[hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data](https://space-kitty.com/papers/doi:10.1016/j.crmeth.2023.100498)**. Morabito S, Reese F, Rahimzadeh N et al. · 2023 · Cell reports methods (doi:10.1016/j.crmeth.2023.100498 · 724 citations). Source: > 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 … > _Abstract excerpt by Morabito S et al., CC-BY, https://doi.org/10.1016/j.crmeth.2023.100498; truncated._ ## Newest - **[Learning developmental dynamics from lineage context](https://space-kitty.com/papers/arxiv:2610.40447)**. Gokul Gowri, Nic Fishman, William N. Colgan et al. · 2026 (arXiv:2610.40447). Source: - **[BASILISC: Bayesian Alignment of Siamese-Integrated Latents for Inferring Single-cell Correspondence](https://space-kitty.com/papers/arxiv:2610.39050)**. Sepideh Saran, Mahsa Ghanbari, Uwe Ohler · 2026 (arXiv:2610.39050). Source: - **[Single-Cell Dissection of Microglial Dysfunction in Diabetes-Associated Neurodegenerative Vulnerability](https://space-kitty.com/papers/doi:10.1002/advs.202600023)**. Zhang J, Yau TK, Tse J et al. · 2026 · Advanced science (Weinheim, Baden-Wurttemberg, Germany) (doi:10.1002/advs.202600023). Source: - **[Cell3D: a comprehensive database for interactive 3D visualization and crosstalk analysis of single-cell and spatial omics](https://space-kitty.com/papers/doi:10.1093/nar/gkag1055)**. Liu Q, He Y, Sun R et al. · 2026 · Nucleic acids research (doi:10.1093/nar/gkag1055). Source: - **[Biology-Guided Context-Aware Residual Propagation for Spatial Gene Expression Prediction from Histology](https://space-kitty.com/papers/arxiv:2610.35167)**. Vu Van An, Long D. Nguyen, Ho Viet Duc Luong et al. · 2026 (arXiv:2610.35167). Source: - **[Escaping cells dilute CRISPR screen responses unequally across cell types](https://space-kitty.com/papers/arxiv:2610.35508)**. Xuanrui Huang · 2026 (arXiv:2610.35508). Source: - **[Active Learning of Conditional Generative Models via the Transport Neural Tangent Kernel](https://space-kitty.com/papers/arxiv:2610.35652)**. Jayoung Ryu, Kyunghyun Cho, Romain Lopez · 2026 (arXiv:2610.35652). Source: - **[SpatialRegNet: spatially resolved mapping of active regulatory network of human and mouse](https://space-kitty.com/papers/doi:10.1093/nar/gkag1037)**. Li F, Fan W, Kang A et al. · 2026 · Nucleic acids research (doi:10.1093/nar/gkag1037). Source: - **[Mid-zone hepatocytes trade proliferation for survival via Atf4-Chop axis in early acute liver injury](https://space-kitty.com/papers/doi:10.7554/elife.108646)**. Zhu Y, Deng C, Chen B et al. · 2026 · eLife (doi:10.7554/elife.108646). Source: - **[Multiscale Topology-Regulated Nonlinear Dynamics for Protein B-Factor Prediction and Single-Cell Type Classification](https://space-kitty.com/papers/arxiv:2610.33727)**. Zihan Wang, Mushal Zia, Xiaoqi Wei et al. · 2026 (arXiv:2610.33727). Source: --- Space Kitty Archive · https://space-kitty.com · API: https://space-kitty.com/api/v1 · MCP: https://space-kitty.com/mcp · Premium: $0.20/request via x402 (Base Sepolia (testnet)). Metadata: arXiv (CC0), OpenAlex (CC0), Europe PMC (CC BY/CC0 works), ClinicalTrials.gov.