May 30, 2023

Exploring tissue architecture using spatial transcriptomics – Nature

  • 1.

    Barresi, M. J. F. & Gilbert, S. F. Developmental Biology (Sinauer Associates, 2019).

  • 2.

    Damjanov, I. & McCue, P. A. Histopathology: A Color Atlas and Textbook (Lippincott Williams & Wilkins, 1996).

  • 3.

    Safai, B. & Good, R. A. Immunodermatology (Springer Science & Business Media, 2013).

  • 4.

    Lehmann, R. & Tautz, D. in Methods in Cell Biology Vol. 44 (eds Lawrence, S. B. & Fyrberg, E. A.) 575–598 (Academic Press, 1994).

  • 5.

    Swanson, P. E. Foundations of immunohistochemistry. A practical review. Am. J. Clin. Pathol. 90, 333–339 (1988).

    CAS 

    Google Scholar
     

  • 6.

    Mincarelli, L., Lister, A., Lipscombe, J. & Macaulay, I. C. Defining cell identity with single-cell omics. Proteomics 18, e1700312 (2018).


    Google Scholar
     

  • 7.

    Macaulay, I. C., Ponting, C. P. & Voet, T. Single-cell multiomics: multiple measurements from single cells. Trends Genet. 33, 155–168 (2017).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 8.

    Tanay, A & Regev A. Scaling single-cell genomics from phenomenology to mechanism. Nature 541, 331–338 (2017).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 9.

    Xia, B. & Yanai, I. A periodic table of cell types. Development 146, dev169854 (2019).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 10.

    Stubbington, M. J. T., Rozenblatt-Rosen, O., Regev, A. & Teichmann, S. Single-cell transcriptomics to explore the immune system in health and disease. Science  358, 58–63 (2017).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 11.

    Lawson, D. A., Kessenbrock, K., Davis, R. T., Pervolarakis, N. & Werb, Z. Tumour heterogeneity and metastasis at single-cell resolution. Nat. Cell Biol. 20, 1349–1360 (2018).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 12.

    Papalexi, E. & Satija, R. Single-cell RNA sequencing to explore immune cell heterogeneity. Nat. Rev. Immunol. 18, 35–45 (2018).

    CAS 

    Google Scholar
     

  • 13.

    Combs, P. A. & Eisen, M. B. Sequencing mRNA from cryo-sliced Drosophila embryos to determine genome-wide spatial patterns of gene expression. PLoS ONE 8, e71820 (2013).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 14.

    Junker, J. P. et al. Genome-wide RNA tomography in the zebrafish embryo. Cell 159, 662–675 (2014).

    CAS 

    Google Scholar
     

  • 15.

    Lacraz, G. P. A. et al. Tomo-seq identifies SOX9 as a key regulator of cardiac fibrosis during ischemic injury. Circulation 136, 1396–1409 (2017).

    CAS 

    Google Scholar
     

  • 16.

    van den Brink, S. C. et al. Single-cell and spatial transcriptomics reveal somitogenesis in gastruloids. Nature 582, 405–409 (2020).

    ADS 

    Google Scholar
     

  • 17.

    Nichterwitz, S. et al. Laser capture microscopy coupled with Smart-seq2 for precise spatial transcriptomic profiling. Nat. Commun. 7, 12139 (2016).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 18.

    Nichterwitz, S., Benitez, J. A., Hoogstraaten, R., Deng, Q. & Hedlund, E. LCM-seq: a method for spatial transcriptomic profiling using laser capture microdissection coupled with PolyA-based RNA sequencing. Methods Mol. Biol. 1649, 95–110 (2018).

    CAS 

    Google Scholar
     

  • 19.

    Aguila, J. et al. Spatial transcriptomics and in silico random pooling identify novel markers of vulnerable and resistant midbrain dopamine neurons. Preprint at https://doi.org/10.1101/334417 (2021).

  • 20.

    Moor, A. E. et al. Spatial reconstruction of single enterocytes uncovers broad zonation along the intestinal villus axis. Cell 175, 1156–1167.e15 (2018).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 21.

    Chen, J. et al. Spatial transcriptomic analysis of cryosectioned tissue samples with Geo-seq. Nat. Protoc. 12, 566–580 (2017).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 22.

    Tirosh, I. et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352, 189–196 (2016).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 23.

    Peng, G. et al. Molecular architecture of lineage allocation and tissue organization in early mouse embryo. Nature 572, 528–532 (2019).

    ADS 
    CAS 

    Google Scholar
     

  • 24.

    Geiss, G. K. et al. Direct multiplexed measurement of gene expression with color-coded probe pairs. Nat. Biotechnol. 26, 317–325 (2008).

    CAS 

    Google Scholar
     

  • 25.

    Halpern, K. B. et al. Paired-cell sequencing enables spatial gene expression mapping of liver endothelial cells. Nat. Biotechnol. 36, 962–970 (2018).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 26.

    Boisset, J.-C. et al. Mapping the physical network of cellular interactions. Nat. Methods 15, 547–553 (2018).

    CAS 

    Google Scholar
     

  • 27.

    Giladi, A. et al. Dissecting cellular crosstalk by sequencing physically interacting cells. Nat. Biotechnol. 38, 629–637 (2020).

    CAS 

    Google Scholar
     

  • 28.

    Manco, R. et al. Clump sequencing exposes the spatial expression programs of intestinal secretory cells. Nat. Commun. 12, 3074 (2021).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 29.

    Satija, R., Farrell, J. A., Gennert, D., Schier, A. F. & Regev, A. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33, 495–502 (2015).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 30.

    Achim, K. et al. High-throughput spatial mapping of single-cell RNA-seq data to tissue of origin. Nat. Biotechnol. 33, 503–509 (2015).

    CAS 

    Google Scholar
     

  • 31.

    Pettit, J.-B. et al. Identifying cell types from spatially referenced single-cell expression datasets. PLOS Comput. Biol. 10, e1003824 (2014).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 32.

    Karaiskos, N. et al. The Drosophila embryo at single-cell transcriptome resolution. Science 358, 194–199 (2017).

    ADS 
    CAS 

    Google Scholar
     

  • 33.

    Cang, Z. & Nie, Q. Inferring spatial and signaling relationships between cells from single cell transcriptomic data. Nat. Commun. 11, 2084 (2020).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 34.

    Zhuang, X. Spatially resolved single-cell genomics and transcriptomics by imaging. Nat. Methods 18, 18–22 (2021).

    CAS 

    Google Scholar
     

  • 35.

    Larsson, L., Frisén, J. & Lundeberg, J. Spatially resolved transcriptomics adds a new dimension to genomics. Nat. Methods 18, 15–18 (2021).

    CAS 

    Google Scholar
     

  • 36.

    Crosetto, N., Bienko, M. & van Oudenaarden, A. Spatially resolved transcriptomics and beyond. Nat. Rev. Genet. 16, 57–66 (2015).

    CAS 

    Google Scholar
     

  • 37.

    Moor, A. E. & Itzkovitz, S. Spatial transcriptomics: paving the way for tissue-level systems biology. Curr. Opin. Biotechnol. 46, 126–133 (2017).

    CAS 

    Google Scholar
     

  • 38.

    Asp, M., Bergenstråhle, J. & Lundeberg, J. Spatially resolved transcriptomes-next generation tools for tissue exploration. BioEssays 42, e1900221 (2020).


    Google Scholar
     

  • 39.

    Waylen, L. N., Nim, H. T., Martelotto, L. G. & Ramialison, M. From whole-mount to single-cell spatial assessment of gene expression in 3D. Commun. Biol. 3, 602 (2020).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 40.

    Teves, J. M. & Won, K. J. Mapping cellular coordinates through advances in spatial transcriptomics technology. Mol. Cells 43, 591–599 (2020).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 41.

    Ståhl, P. L. et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353, 78–82 (2016). This paper was the first to perform array-based spatial transcriptomics, using positional barcodes at a resolution of 200 μm, and demonstrated the approach on the mouse olfactory bulb.

    ADS 

    Google Scholar
     

  • 42.

    Jemt, A. et al. An automated approach to prepare tissue-derived spatially barcoded RNA-sequencing libraries. Sci. Rep. 6, 37137 (2016).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 43.

    Salmén, F. et al. Barcoded solid-phase RNA capture for spatial transcriptomics profiling in mammalian tissue sections. Nat. Protoc. 13, 2501–2534 (2018).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 44.

    Thrane, K., Eriksson, H., Maaskola, J., Hansson, J. & Lundeberg, J. Spatially resolved transcriptomics enables dissection of genetic heterogeneity in stage III cutaneous malignant melanoma. Cancer Res. 78, 5970–5979 (2018).

    CAS 

    Google Scholar
     

  • 45.

    Baron, M. et al. The stress-like cancer cell state is a consistent component of tumorigenesis. Cell Syst. 11, 536–546.e7 (2020).

    CAS 

    Google Scholar
     

  • 46.

    Moncada, R. et al. Integrating microarray-based spatial transcriptomics and single-cell RNA-seq reveals tissue architecture in pancreatic ductal adenocarcinomas. Nat. Biotechnol. 38, 333–342 (2020).

    CAS 

    Google Scholar
     

  • 47.

    Ji, A. L. et al. Multimodal analysis of composition and spatial architecture in human squamous cell carcinoma. Cell 182, 497–514.e22 (2020).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 48.

    Hildebrandt, F. et al. Spatial transcriptomics to define transcriptional patterns of zonation and structural components in the liver. Preprint at https://doi.org/10.1101/2021.01.11.426100 (2021).

  • 49.

    Spatial Transcriptomics. 10x Genomics https://www.10xgenomics.com/spatial-transcriptomics/ (2021).

  • 50.

    Ortiz, C. et al. Molecular atlas of the adult mouse brain. Sci. Adv. 6, eabb3446 (2020).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 51.

    Grauel, A. L. et al. TGFβ-blockade uncovers stromal plasticity in tumors by revealing the existence of a subset of interferon-licensed fibroblasts. Nat. Commun. 11, 6315 (2020).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 52.

    Asp, M. et al. A spatiotemporal organ-wide gene expression and cell atlas of the developing human heart. Cell 179, 1647–1660.e19 (2019).

    CAS 

    Google Scholar
     

  • 53.

    Rodriques, S. G. et al. Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution. Science 363, 1463–1467 (2019). This paper describes Slide-seq, an array-based method with 10μm resolution, performed in the cerebellum and hippocampus.

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 54.

    Stickels, R. R. et al. Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2. Nat. Biotechnol. 39, 313–319 (2021).

    CAS 

    Google Scholar
     

  • 55.

    Vickovic, S. et al. High-definition spatial transcriptomics for in situ tissue profiling. Nat. Methods 16, 987–990 (2019).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 56.

    Liu, Y. et al. High-spatial-resolution multi-omics sequencing via deterministic barcoding in tissue. Cell 183, 1665–1681.e18 (2020).

    CAS 

    Google Scholar
     

  • 57.

    Chen, A. et al. Large field of view-spatially resolved transcriptomics at nanoscale resolution. Preprint at https://doi.org/10.1101/2021.01.17.427004 (2021).

  • 58.

    Cho, C.-S. et al. Microscopic examination of spatial transcriptome using Seq-scope. Cell 184, 3559–3572.e22 (2021).

    CAS 

    Google Scholar
     

  • 59.

    Fu, X. et al. Continuous polony gels for tissue mapping with high resolution and RNA capture efficiency. Preprint at https://doi.org/10.1101/2021.03.17.435795 (2021).

  • 60.

    Ke, R. et al. In situ sequencing for RNA analysis in preserved tissue and cells. Nat. Methods 10, 857–860 (2013). This was the first report of ISS, which was used to map the expression of 31 transcripts using four-base reads in breast cancer.

    CAS 

    Google Scholar
     

  • 61.

    Darmanis, S. et al. Single-cell RNA-seq analysis of infiltrating neoplastic cells at the migrating front of human glioblastoma. Cell Rep. 21, 1399–1410 (2017).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 62.

    Carow, B. et al. Spatial and temporal localization of immune transcripts defines hallmarks and diversity in the tuberculosis granuloma. Nat. Commun. 10, 1823 (2019).

    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 63.

    Tiklová, K. et al. Single-cell RNA sequencing reveals midbrain dopamine neuron diversity emerging during mouse brain development. Nat. Commun. 10, 581 (2019).

    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 64.

    Wang, X. et al. Three-dimensional intact-tissue sequencing of single-cell transcriptional states. Science 361, eaat5691 (2018).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 65.

    Chen, X., Sun, Y.-C., Church, G. M., Lee, J. H. & Zador, A. M. Efficient in situ barcode sequencing using padlock probe-based BaristaSeq. Nucleic Acids Res. 46, e22 (2018).

    CAS 

    Google Scholar
     

  • 66.

    Chen, X. et al. High-throughput mapping of long-range neuronal projection using in situ sequencing. Cell 179, 772–786.e19 (2019).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 67.

    Gyllborg, D. et al. Hybridization-based in situ sequencing (HybISS) for spatially resolved transcriptomics in human and mouse brain tissue. Nucleic Acids Res. 48, e112 (2020).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 68.

    Fürth, D., Hatini, V. & Lee, J. H. In situ transcriptome accessibility sequencing (INSTA-seq). Preprint at https://doi.org/10.1101/722819 (2019).

  • 69.

    Alon, S. et al. Expansion sequencing: spatially precise in situ transcriptomics in intact biological systems. Science 371, eaax2656 (2021).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 70.

    Lee, J. H. et al. Highly multiplexed subcellular RNA sequencing in situ. Science 343, 1360–1363 (2014). This paper introduced an untargeted ISS method, FISSEQ, that generated 30 base reads from 8,102 genes in human primary fibroblasts.

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 71.

    Codeluppi, S. et al. Spatial organization of the somatosensory cortex revealed by osmFISH. Nat. Methods 15, 932–935 (2018).

    CAS 

    Google Scholar
     

  • 72.

    Chen, K. H., Boettiger, A. N., Moffitt, J. R., Wang, S. & Zhuang, X. Spatially resolved, highly multiplexed RNA profiling in single cells. Science 348, aaa6090 (2015).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 73.

    Moffitt, J. R. et al. High-throughput single-cell gene-expression profiling with multiplexed error-robust fluorescence in situ hybridization. Proc. Natl Acad. Sci. USA 113, 11046–11051 (2016).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 74.

    Wang, G., Moffitt, J. R. & Zhuang, X. Multiplexed imaging of high-density libraries of RNAs with MERFISH and expansion microscopy. Sci. Rep. 8, 4847 (2018).

    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 75.

    Xia, C., Babcock, H. P., Moffitt, J. R. & Zhuang, X. Multiplexed detection of RNA using MERFISH and branched DNA amplification. Sci. Rep. 9, 7721 (2019).

    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 76.

    Moffitt, J. R. et al. Molecular, spatial, and functional single-cell profiling of the hypothalamic preoptic region. Science 362, eaau5324 (2018).

    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 77.

    Lubeck, E., Coskun, A. F., Zhiyentayev, T., Ahmad, M. & Cai, L. Single-cell in situ RNA profiling by sequential hybridization. Nat. Methods 11, 360–361 (2014).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 78.

    Shah, S., Lubeck, E., Zhou, W. & Cai, L. In situ transcription profiling of single cells reveals spatial organization of cells in the mouse hippocampus. Neuron 92, 342–357 (2016).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 79.

    Shah, S. et al. Dynamics and spatial genomics of the nascent transcriptome by intron seqFISH. Cell 174, 363–376.e16 (2018).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 80.

    Eng, C. L. et al. Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH. Nature 568, 235–239 (2019).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 81.

    Najman, L. & Schmitt, M. Watershed of a continuous function. Signal Processing 38, 99–112 (1994).


    Google Scholar
     

  • 82.

    Park, J. et al. Segmentation-free inference of cell types from in situ transcriptomics data. Preprint at https://doi.org/10.1101/800748 (2020).

  • 83.

    Littman, R. et al. JSTA: joint cell segmentation and cell type annotation for spatial transcriptomics. Preprint at https://doi.org/10.1101/2020.09.18.304147 (2020).

  • 84.

    Qian, X. et al. Probabilistic cell typing enables fine mapping of closely related cell types in situ. Nat. Methods 17, 101–106 (2020).

    CAS 

    Google Scholar
     

  • 85.

    BRAIN Initiative Cell Census Network (BICCN). A multimodal cell census and atlas of the mammalian primary motor cortex. Preprint at https://doi.org/10.1101/2020.10.19.343129.

  • 86.

    Kebschull, J. M. et al. Cerebellar nuclei evolved by repeatedly duplicating a conserved cell-type set. Science 370, eabd5059 (2020).

    CAS 

    Google Scholar
     

  • 87.

    Maynard, K. R. et al. Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex. Nat. Neurosci. 24, 425–1436 (2021).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 88.

    Chen, H. et al. Dissecting mammalian spermatogenesis using spatial transcriptomics. Preprint at https://doi.org/10.1101/2020.10.17.343335 (2020).

  • 89.

    Fawkner-Corbett, D. et al. Spatiotemporal analysis of human intestinal development at single-cell resolution. Cell 184, 810–826.e23 (2021).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 90.

    Garcia-Alonso, L. et al. Mapping the temporal and spatial dynamics of the human endometrium in vivo and in vitro. Preprint at https://doi.org/10.1101/2021.01.02.425073 (2021).

  • 91.

    Rozenblatt-Rosen, O. et al. The Human Tumor Atlas Network: charting tumor transitions across space and time at single-cell resolution. Cell 181, 236–249 (2020).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 92.

    Lein, E. S. et al. Genome-wide atlas of gene expression in the adult mouse brain. Nature 445, 168–176 (2007).

    ADS 
    CAS 

    Google Scholar
     

  • 93.

    Berglund, E. et al. Spatial maps of prostate cancer transcriptomes reveal an unexplored landscape of heterogeneity. Nat. Commun. 9, 2419 (2018).

    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 94.

    Wang, Y., Ma, S. & Ruzzo, W. L. Spatial modeling of prostate cancer metabolic gene expression reveals extensive heterogeneity and selective vulnerabilities. Sci. Rep. 10, 3490 (2020).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 95.

    Hwang, W. L. et al. Single-nucleus and spatial transcriptomics of archival pancreatic cancer reveals multi-compartment reprogramming after neoadjuvant treatment. https://doi.org/10.1101/2020.08.25.267336 (2020).

  • 96.

    Smith, E. A. & Hodges, H. C. The spatial and genomic hierarchy of tumor ecosystems revealed by single-cell technologies. Trends Cancer 5, 411–425 (2019).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 97.

    Navarro, J. F. et al. Spatial transcriptomics reveals genes associated with dysregulated mitochondrial functions and stress signaling in Alzheimer disease. iScience 23, 101556 (2020).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 98.

    Chen, W.-T. et al. Spatial transcriptomics and in situ sequencing to study Alzheimer’s disease. Cell 182, 976–991.e19 (2020).

    CAS 

    Google Scholar
     

  • 99.

    Maniatis, S. et al. Spatiotemporal dynamics of molecular pathology in amyotrophic lateral sclerosis. Science 364, 89–93 (2019).

    ADS 
    CAS 

    Google Scholar
     

  • 100.

    Ma, F. et al. Single cell and spatial transcriptomics defines the cellular architecture of the antimicrobial response network in human leprosy granulomas. https://doi.org/10.1101/2020.12.01.406819 (2020).

  • 101.

    Boyd, D. F. et al. Exuberant fibroblast activity compromises lung function via ADAMTS4. Nature 587, 466–471 (2020).

    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 102.

    Janosevic, D. et al. The orchestrated cellular and molecular responses of the kidney to endotoxin define a precise sepsis timeline. eLife 10, e62270 (2021).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 103.

    Carlberg, K. et al. Exploring inflammatory signatures in arthritic joint biopsies with Spatial Transcriptomics. Sci. Rep. 9, 18975 (2019).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 104.

    Vickovic, S. et al. Three-dimensional spatial transcriptomics uncovers cell type dynamics in the rheumatoid arthritis synovium. Preprint at https://doi.org/10.1101/2020.12.10.420463 (2020).

  • 105.

    Tukey, J. W. Exploratory Data Analysis (1970). John Tukey established the field of exploratory data analysis as an approach to discover trends prior to testing for any particular model.

  • 106.

    Yanai, I. & Lercher, M. What is the question? Genome Biol. 20, 289 (2019).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 107.

    Yanai, I. & Lercher, M. The data-hypothesis conversation. Genome Biol. 22, 58 (2021).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 108.

    Dries, R. et al. Giotto: a toolbox for integrative analysis and visualization of spatial expression data. Genome Biol. 22, 78 (2021).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 109.

    Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e21 (2019).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 110.

    Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 111.

    Bergenstråhle, J., Larsson, L. & Lundeberg, J. Seamless integration of image and molecular analysis for spatial transcriptomics workflows. BMC Genomics 21, 482 (2020).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 112.

    Pham, D. et al. stLearn: integrating spatial location, tissue morphology and gene expression to find cell types, cell-cell interactions and spatial trajectories within undissociated tissues. Preprint at https://doi.org/10.1101/2020.05.31.125658 (2020).

  • 113.

    Hafemeister, C. & Satija, R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol. 20, 296 (2019).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 114.

    Zhou, B. & Jin, W. Visualization of single cell RNA-seq data using t-SNE in R. Methods Mol. Biol. 2117, 159–167 (2020).

    ADS 
    CAS 

    Google Scholar
     

  • 115.

    Becht, E. et al. Dimensionality reduction for visualizing single-cell data using UMAP. Nat. Biotechnol. 37, 38–44 (2018).


    Google Scholar
     

  • 116.

    Kiselev, V. Y., Andrews, T. S. & Hemberg, M. Challenges in unsupervised clustering of single-cell RNA-seq data. Nat. Rev. Genet. 20, 273–282 (2019).

    CAS 

    Google Scholar
     

  • 117.

    Lundmark, A. et al. Gene expression profiling of periodontitis-affected gingival tissue by spatial transcriptomics. Sci. Rep. 8, 9370 (2018).

    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 118.

    Giacomello, S. et al. Spatially resolved transcriptome profiling in model plant species. Nat. Plants 3, 17061 (2017).

    CAS 

    Google Scholar
     

  • 119.

    Lee, D. D. & Seung, H. S. Learning the parts of objects by non-negative matrix factorization. Nature 401, 788–791 (1999).

    ADS 
    CAS 
    MATH 

    Google Scholar
     

  • 120.

    Gao, Y. & Church, G. Improving molecular cancer class discovery through sparse non-negative matrix factorization. Bioinformatics 21, 3970–3975 (2005).

    CAS 

    Google Scholar
     

  • 121.

    Zhao, E. et al. Spatial transcriptomics at subspot resolution with BayesSpace. Nat. Biotechnol. https://doi.org/10.1038/s41587-021-00935-2 (2021).

  • 122.

    Hunter, M. V., Moncada, R., Weiss, J. M., Yanai, I. & White, R. M. Spatial transcriptomics reveals the architecture of the tumor/microenvironment interface. Preprint at https://doi.org/10.1101/2020.11.05.368753 (2021).

  • 123.

    Moran, P. A. P. Notes on continuous stochastic phenomena. Biometrika 37, 17–23 (1950).

    MathSciNet 
    CAS 
    PubMed 
    MATH 

    Google Scholar
     

  • 124.

    Vandenbon, A. & Diez, D. A clustering-independent method for finding differentially expressed genes in single-cell transcriptome data. Nat. Commun. 11, 4318 (2020).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 125.

    Edsgärd, D., Johnsson, P. & Sandberg, R. Identification of spatial expression trends in single-cell gene expression data. Nat. Methods 15, 339–342 (2018).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 126.

    Illian, J., Penttinen, A., Stoyan, H. & Stoyan, D. Statistical Analysis and Modelling of Spatial Point Patterns (John Wiley & Sons, 2008).

  • 127.

    Svensson, V., Teichmann, S. A. & Stegle, O. SpatialDE: identification of spatially variable genes. Nat. Methods 15, 343–346 (2018).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 128.

    Sun, S., Zhu, J. & Zhou, X. Statistical analysis of spatial expression patterns for spatially resolved transcriptomic studies. Nat. Methods 17, 193–200 (2020).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 129.

    Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 130.

    The Gene Ontology Consortium. Gene ontology: tool for the unification of biology. Nat. Genet. 25, 25–29 (2000).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 131.

    Kanehisa, M. & Goto, S. KEGG: Kyoto Encyclopedia of Genes And Genomes. Nucleic Acids Res. 28, 27–30 (2000).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 132.

    Elosua, M., Nieto, P., Mereu, E., Gut, I. & Heyn, H. SPOTlight: Seeded NMF regression to Deconvolute Spatial Transcriptomics Spots with Single-Cell Transcriptomes. https://doi.org/10.1101/2020.06.03.131334.

  • 133.

    Andersson, A. et al. Single-cell and spatial transcriptomics enables probabilistic inference of cell type topography. Commun. Biol. 3, 565 (2020).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 134.

    Kleshchevnikov, V. et al. Comprehensive mapping of tissue cell architecture via integrated single cell and spatial transcriptomics. Preprint at https://doi.org/10.1101/2020.11.15.378125 (2020).

  • 135.

    Cable, D. M. et al. Robust decomposition of cell type mixtures in spatial transcriptomics. Nat. Biotechnol. https://doi.org/10.1038/s41587-021-00830-w (2021).

  • 136.

    Lopez, R. et al. Multi-resolution deconvolution of spatial transcriptomics data reveals continuous patterns of inflammation. Preprint at https://doi.org/10.1101/2021.05.10.443517 (2021).

  • 137.

    Zeira, R., Land, M. & Raphael, B. J. Alignment and integration of spatial transcriptomics data. Preprint at https://doi.org/10.1101/2021.03.16.435604 (2021).

  • 138.

    Su, J. & Song, Q. DSTG: Deconvoluting Spatial Transcriptomics Data through Graph-based artificial intelligence. Brief. Bioinform. https://doi.org/10.1093/bib/bbaa414 (2021).

  • 139.

    Biancalani, T. et al. Deep learning and alignment of spatially-resolved whole transcriptomes of single cells in the mouse brain with Tangram. Preprint at https://doi.org/10.1101/2020.08.29.272831 (2020).

  • 140.

    Nelson, M. E., Riva, S. G. & Cvejic, A. SMaSH: A scalable, general marker gene identification framework for single-cell RNA sequencing and spatial transcriptomics. Preprint at https://doi.org/10.1101/2021.04.08.438978 (2021).

  • 141.

    Dong, R. & Yuan, G.-C. SpatialDWLS: accurate deconvolution of spatial transcriptomic data. Preprint at https://doi.org/10.1101/2021.02.02.429429 (2021).

  • 142.

    Teng, H., Yuan, Y. & Bar-Joseph, Z. Cell type assignments for spatial transcriptomics data. Preprint at https://doi.org/10.1101/2021.02.25.432887 (2021).

  • 143.

    La Manno, G. et al. RNA velocity of single cells. Nature 560, 494–498 (2018).

    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 144.

    Patel, A. P. et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344, 1396–1401 (2014).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 145.

    Elyanow, R., Zeira, R., Land, M. & Raphael, B. STARCH: Copy number and clone inference from spatial transcriptomics data. Phys. Biol. 18, 035001 (2021).

    CAS 

    Google Scholar
     

  • 146.

    Armingol, E., Officer, A., Harismendy, O. & Lewis, N. E. Deciphering cell–cell interactions and communication from gene expression. Nat. Rev. Genet. 22, 71–88 (2021).

    CAS 

    Google Scholar
     

  • 147.

    Efremova, M., Vento-Tormo, M., Teichmann, S. A. & Vento-Tormo, R. CellPhoneDB: inferring cell-cell communication from combined expression of multi-subunit ligand-receptor complexes. Nat. Protoc. 15, 1484–1506 (2020).

    CAS 
    PubMed 

    Google Scholar
     

  • 148.

    Browaeys, R., Saelens, W. & Saeys, Y. NicheNet: modeling intercellular communication by linking ligands to target genes. Nat. Methods 17, 159–162 (2020).

    CAS 

    Google Scholar
     

  • 149.

    Bergenstråhle, L. et al. Super-resolved spatial transcriptomics by deep data fusion. Preprint at https://doi.org/10.1101/2020.02.28.963413 (2020).

  • 150.

    Tan, X., Su, A., Tran, M. & Nguyen, Q. SpaCell: integrating tissue morphology and spatial gene expression to predict disease cells. Bioinformatics 36, 2293–2294 (2020).

    CAS 

    Google Scholar
     

  • 151.

    Monjo, T., Koido, M., Nagasawa, S., Suzuki, Y. & Kamatani, Y. Efficient prediction of a spatial transcriptomics profile better characterizes breast cancer tissue sections without costly experimentation. Preprint at https://doi.org/10.1101/2021.04.22.440763 (2021).

  • 152.

    Bao, F. et al. Characterizing tissue composition through combined analysis of single-cell morphologies and transcriptional states. Preprint at https://doi.org/10.1101/2020.09.05.284539 (2021).

  • 153.

    He, B. et al. Integrating spatial gene expression and breast tumour morphology via deep learning. Nat. Biomed. Eng. 4, 827–834 (2020).

    CAS 

    Google Scholar
     

  • 154.

    Levy-Jurgenson, A., Tekpli, X., Kristensen, V. N. & Yakhini, Z. Spatial transcriptomics inferred from pathology whole-slide images links tumor heterogeneity to survival in breast and lung cancer. Sci. Rep. 10, 18802 (2020).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 155.

    Takei, Y. et al. Integrated spatial genomics reveals global architecture of single nuclei. Nature 590, 344–350 (2021).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 156.

    Deng, Y. et al. Spatial epigenome sequencing at tissue scale and cellular level. Preprint at https://doi.org/10.1101/2021.03.11.434985 (2021).

  • 157.

    Nguyen, H. Q. et al. 3D mapping and accelerated super-resolution imaging of the human genome using in situ sequencing. Nat. Methods 17, 822–832 (2020).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 158.

    Su, J.-H., Zheng, P., Kinrot, S. S., Bintu, B. & Zhuang, X. Genome-scale imaging of the 3D organization and transcriptional activity of chromatin. Cell 182, 1641–1659.e26 (2020).

    CAS 

    Google Scholar
     

  • 159.

    Payne, A. C. et al. In situ genome sequencing resolves DNA sequence and structure in intact biological samples. Science 371, eaay3446 (2021).

    CAS 

    Google Scholar
     

  • 160.

    Schulz, D. et al. Simultaneous multiplexed imaging of mRNA and proteins with subcellular resolution in breast cancer tissue samples by mass cytometry. Cell Syst. 6, 531 (2018).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 161.

    Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14, 865–868 (2017).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 162.

    Angelo, M. et al. Multiplexed ion beam imaging of human breast tumors. Nat. Med. 20, 436–442 (2014).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 163.

    Keren, L. et al. A structured tumor-immune microenvironment in triple negative breast cancer revealed by multiplexed ion beam imaging. Cell 174, 1373–1387.e19 (2018).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 164.

    Piehowski, P. D. et al. Automated mass spectrometry imaging of over 2000 proteins from tissue sections at 100-μm spatial resolution. Nat. Commun. 11, 8 (2020).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 165.

    Lin, J.-R. et al. Highly multiplexed immunofluorescence imaging of human tissues and tumors using t-CyCIF and conventional optical microscopes. eLife 7, e31657 (2018).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 166.

    Goltsev, Y. et al. Deep profiling of mouse splenic architecture with CODEX multiplexed imaging. Cell 174, 968–981.e15 (2018).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 167.

    Kohman, R. E. & Church, G. M. Fluorescent in situ sequencing of DNA barcoded antibodies. Preprint at https://doi.org/10.1101/2020.04.27.060624 (2020).

  • 168.

    Liu, Y., Enninful, A., Deng, Y. & Fan, R. Spatial transcriptome sequencing of FFPE tissues at cellular level. Preprint at https://doi.org/10.1101/2020.10.13.338475 (2020).

  • 169.

    Nagarajan, M. B., Tentori, A. M., Zhang, W. C., Slack, F. J. & Doyle, P. S. Spatially resolved and multiplexed MicroRNA quantification from tissue using nanoliter well arrays. Microsyst. Nanoeng. 6, 51 (2020).

    ADS 
    CAS 

    Google Scholar
     

  • 170.

    Lander, E. S. et al. Initial sequencing and analysis of the human genome. Nature 409, 860–921 (2001).

    ADS 
    CAS 

    Google Scholar
     

  • 171.

    Venter, J. C. et al. The sequence of the human genome. Science 291, 1304–1351 (2001).

    ADS 
    CAS 

    Google Scholar
     

  • 172.

    Nitzan, M., Karaiskos, N., Friedman, N. & Rajewsky, N. Gene expression cartography. Nature 576, 132–137 (2019).

    ADS 
    CAS 

    Google Scholar
     

  • 173.

    Xia, C., Fan, J., Emanuel, G., Hao, J. & Zhuang, X. Spatial transcriptome profiling by MERFISH reveals subcellular RNA compartmentalization and cell cycle-dependent gene expression. Proc. Natl Acad. Sci. USA 116, 19490–19499 (2019).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 174.

    Friedrich, S. & Sonnhammer, E. L. L. Fusion transcript detection using spatial transcriptomics. BMC Med. Genomics 13, 110 (2020).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 175.

    Joglekar, A. et al. A spatially resolved brain region- and cell type-specific isoform atlas of the postnatal mouse brain. Nat. Commun. 12, 463 (2021).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 176.

    Frieda, K. L. et al. Synthetic recording and in situ readout of lineage information in single cells. Nature 541, 107–111 (2017).

    ADS 
    CAS 

    Google Scholar
     

  • 177.

    Rodriques, S. G. et al. Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution. Science 363, 1463–1467 (2019).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

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