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Volume 50 Issue 9
Sep.  2023
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Article Navigation >  Journal of Genetics and Genomics  > 2023  >  50(9): 625-640

Spatially resolved transcriptomics: a comprehensive review of their technological advances, applications, and challenges

doi: 10.1016/j.jgg.2023.03.011 cstr: 32370.14.j.jgg.2023.03.011
  •  

    a. BGI-Hangzhou, Hangzhou, Zhejiang 310012, China;

  •  

    b. BGI-Shenzhen, Shenzhen, Guangdong 518103, China;

  •  

    c. College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China;

  •  

    d. State Key Laboratory of Agricultural Genomics, BGI-Shenzhen, Shenzhen, Guangdong 518083, China;

  •  

    e. Guangdong Provincial Key Laboratory of Genome Read and Write, Shenzhen, Guangdong 518120, China

Funds:

Longqi Liu was supported by the National Natural Science Foundation of China (31900466).

This work was supported by the Shenzhen Key Laboratory of Single-Cell Omics (ZDSYS20190902093613831) and the Guangdong Provincial Key Laboratory of Genome Read and Write (2017B030301011)

  • Received Date: 2022-10-05
  • Accepted Date: 2023-03-16
  • Rev Recd Date: 2023-03-11
  • Publish Date: 2023-03-27
    Abstract
  • The ability to explore life kingdoms is largely driven by innovations and breakthroughs in technology, from the invention of the microscope 350 years ago to the recent emergence of single-cell sequencing, by which the scientific community has been able to visualize life at an unprecedented resolution. Most recently, the Spatially Resolved Transcriptomics (SRT) technologies have filled the gap in probing the spatial or even three-dimensional organization of the molecular foundation behind the molecular mysteries of life, including the origin of different cellular populations developed from totipotent cells and human diseases. In this review, we introduce recent progresses and challenges on SRT from the perspectives of technologies and bioinformatic tools, as well as the representative SRT applications. With the currently fast-moving progress of the SRT technologies and promising results from early adopted research projects, we can foresee the bright future of such new tools in understanding life at the most profound analytical level.
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  • [1]
    Abdelaal, T., Mourragui, S., Mahfouz, A.,Reinders, M.J.T., 2020. SpaGE: Spatial Gene Enhancement using scRNA-seq. Nucleic Acids Res. 48, e107.
    [2]
    Alon, S., Goodwin, D.R., Sinha, A., Wassie, A.T., Chen, F., Daugharthy, E.R., Bando, Y., Kajita, A., Xue, A.G., Marrett, K., et al., 2021. Expansion sequencing: spatially precise in situ transcriptomics in intact biological systems. Science 371.
    [3]
    Arnol, D., Schapiro, D., Bodenmiller, B., Saez-Rodriguez, J.,Stegle, O., 2019. Modeling cell-cell interactions from spatial molecular data with spatial variance component analysis. Cell Rep. 29, 202-211.e206.
    [4]
    Asp, M., Giacomello, S., Larsson, L., Wu, C., Furth, D., Qian, X., Wardell, E., Custodio, J., Reimegard, J., Salmen, F., et al., 2019. A spatiotemporal organ-wide gene expression and cell atlas of the developing human heart. Cell 179, 1647-1660.e1619.
    [5]
    Battich, N., Stoeger, T.,Pelkmans, L., 2013. Image-based transcriptomics in thousands of single human cells at single-molecule resolution. Nat. Methods 10, 1127-1133.
    [6]
    Ben-Chetrit, N., Niu, X., Swett, A.D., Sotelo, J., Jiao, M.S., Roelli, P., Stoeckius, M., Landau, D.A., 2022. Integrated protein and transcriptome high-throughput spatial profiling bioRxiv, 2022.2003.2015.484516.
    [7]
    Berglund, E., Saarenpaa, S., Jemt, A., Gruselius, J., Larsson, L., Bergenstrahle, L., Lundeberg, J.,Giacomello S., 2020. Automation of spatial transcriptomics library preparation to enable rapid and robust insights into spatial organization of tissues. BMC Genomics 21, 1-10.
    [8]
    Biancalani, T., Scalia, G., Buffoni, L., Avasthi, R., Lu, Z., Sanger, A., Tokcan, N., Vanderburg, C.R., Segerstolpe, A.,Zhang, M., 2021. Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram. Nat. Methods 18, 1352-1362.
    [9]
    Biermann, J., Melms, J.C., Amin, A.D., Wang, Y., Caprio, L.A., Karz, A., Tagore, S., Barrera, I., Ibarra-Arellano, M.A., Andreatta, M., et al., 2022. Dissecting the treatment-naive ecosystem of human melanoma brain metastasis. Cell 185, 2591–2608.
    [10]
    Booeshaghi, A.S., Yao, Z., van Velthoven, C., Smith, K., Tasic, B., Zeng, H., Pachter, L., 2021. Isoform cell-type specificity in the mouse primary motor cortex. Nature 598, 195–199.
    [11]
    Borm, L.E., Mossi Albiach, A., Mannens, C.C.A., Janusauskas, J., Ozgun, C., Fernandez-Garcia, D., Hodge, R., Castillo, F., Hedin, C.R.H., Villablanca, E.J., et al., 2022. Scalable in situ single-cell profiling by electrophoretic capture of mRNA using EEL FISH. Nat. Biotechnol.
    [12]
    Boyd, D.F., Allen, E.K., Randolph, A.G., Guo, X.J., Weng, Y., Sanders, C.J., Bajracharya, R., Lee, N.K., Guy, C.S., Vogel, P., et al., 2020. Exuberant fibroblast activity compromises lung function via ADAMTS4. Nature 587, 466–471.
    [13]
    Burgess, D.J., 2019. Spatial transcriptomics coming of age. Nat. Rev. Genet. 20, 317.
    [14]
    Cable, D.M., Murray, E., Zou, L.S., Goeva, A., Macosko, E.Z., Chen, F.,Irizarry, R.A., 2022. Robust decomposition of cell type mixtures in spatial transcriptomics. Nat. Biotechnol. 40, 517-526.
    [15]
    Calvanese, V., Capellera-Garcia, S., Ma, F., Fares, I., Liebscher, S., Ng, E.S., Ekstrand, S., Aguadé-Gorgorió, J., Vavilina, A., Lefaudeux, D., et al., 2022. Mapping human haematopoietic stem cells from haemogenic endothelium to birth. Nature 604, 534–540.
    [16]
    Cang, Z., Zhao, Y., Almet, A.A., Stabell, A., Ramos, R., Plikus, M., Atwood, S.X.,Nie, Q., 2022. Screening cell-cell communication in spatial transcriptomics via collective optimal transport. bioRxiv, 2022.2008.2024.505185.
    [17]
    Cassella, L.,Ephrussi, A., 2022. Subcellular spatial transcriptomics identifies three mechanistically different classes of localizing RNAs. Nat. Commun. 13, 6355.
    [18]
    Chen, X., Sun, Y.C., Zhan, H., Kebschull, J.M., Fischer, H., Matho, K., Huang, Z., Gillis, J., Zador, A.M., 2019. High-throughput mapping of long-range neuronal projection using in situ sequencing. Cell 179, 772–786.e19.
    [19]
    Chen, J., Suo, S., Tam, P.P., Han, J.-D.J., Peng, G.,Jing, N., 2017. Spatial transcriptomic analysis of cryosectioned tissue samples with Geo-seq. Nat. Protoc. 12, 566-580.
    [20]
    Chen, F., Tillberg, P.W.,Boyden, E.S., 2015a. Optical imaging. Expansion microscopy. Science 347, 543-548.
    [21]
    Chang, T., Han, W., Jiang, M., Li, J., Shen, J., Chen, Z., Fei, P., Ren, X., Pang, Y., Wang, G., et al., 2022. Rapid and Signal Crowdedness-Robust In-Situ Sequencing through Hybrid Block Coding bioRxiv,, 2022.2011.2016.516714.
    [22]
    Chen, K.H., Boettiger, A.N., Moffitt, J.R., Wang, S.,Zhuang, X., 2015b. RNA imaging. Spatially resolved, highly multiplexed RNA profiling in single cells. Science 348, aaa6090.
    [23]
    Chen, W.T., Lu, A., Craessaerts, K., Pavie, B., Sala Frigerio, C., Corthout, N., Qian, X., Laláková, J., Kühnemund, M., Voytyuk, I., et al., 2020. Spatial Transcriptomics and In Situ Sequencing to Study Alzheimer’s Disease. Cell 182, 976–991.
    [24]
    Chen, X., Sun, Y.C., Church, G.M., Lee, J.H.,Zador, A.M., 2018. Efficient in situ barcode sequencing using padlock probe-based BaristaSeq. Nucleic Acids Res. 46, e22.
    [25]
    Chen, A., Liao, S., Cheng, M., Ma, K., Wu, L., Lai, Y., Qiu, X., Yang, J., Xu, J., Hao, S., et al., 2022a. Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays. Cell 185, 1777-1792.e1721.
    [26]
    Chen, A., Sun, Y., Lei, Y., Li, C., Liao, S., Liang, Z., Lin, F., Yuan, N., Li, M., Wang, K., et al., 2022b. Global spatial transcriptome of macaque brain at single-cell resolution. bioRxiv, 2022.2003.2023.485448.
    [27]
    Cheng, J., Liao, J., Shao, X., Lu, X.,Fan, X., 2021. Multiplexing methods for simultaneous large-scale transcriptomic profiling of samples at single-cell resolution. Adv. Sci. 8, 2101229.
    [28]
    Cheng, M., Wu, L., Han, L., Huang, X., Lai, Y., Xu, J., Wang, S., Li, M., Zheng, H., Feng, W., et al., 2022. A cellular resolution spatial transcriptomic landscape of the medial structures in postnatal mouse brain. Front. Cell Dev. Biol. 10, 878346.
    [29]
    Cho, C.S., Xi, J., Si, Y., Park, S.R., Hsu, J.E., Kim, M., Jun, G., Kang, H.M.,Lee, J.H., 2021. Microscopic examination of spatial transcriptome using Seq-Scope. Cell 184, 3559-3572.e3522.
    [30]
    Codeluppi, S., Borm, L.E., Zeisel, A., La Manno, G., van Lunteren, J.A., Svensson, C.I., Linnarsson, S., 2018. Spatial organization of the somatosensory cortex revealed by osmFISH. Nat. Methods 15, 932–935.
    [31]
    Crosetto, N., Bienko, M.,van Oudenaarden, A., 2015. Spatially resolved transcriptomics and beyond. Nat. Rev. Genet. 16, 57-66.
    [32]
    Cross, A.R., de Andrea, C.E., Villalba-Esparza, M., Landecho, M.F., Cerundolo, L., Weeratunga, P., Etherington, R.E., Denney, L., Ogg, G., Ho, L.P., et al., 2022. Spatial transcriptomic characterization of COVID-19 pneumonitis identifies immune circuits related to tissue injury. JCI Insight.
    [33]
    Cui, G., Chen, J., Chen, S., Qian, Y., Peng, G.,Jing, N., 2019. Spatio-temporal transcriptome construction of early mouse embryo with Geo-seq and Auto-seq. Protoc. Exch.
    [34]
    Currenti, J., Qiao, L., Pai, R., Gupta, S., STOmics-GenX, 2022. CRISPR based approach to improve cell identity specific gene detection from spatially resolved transcriptomics. bioRXiv, https://doi.org/10.1101/2022.12.08.519589.
    [35]
    Dar, D., Dar, N., Cai, L., Newman, D.K., 2021. Spatial transcriptomics of planktonic and sessile bacterial populations at single-cell resolution. Science 373.
    [36]
    Deng, Y., Bartosovic, M., Kukanja, P., Zhang, D., Liu, Y., Su, G., Enninful, A., Bai, Z., Castelo-Branco, G.,Fan, R., 2022a. Spatial-CUT&Tag: spatially resolved chromatin modification profiling at the cellular level. Science 375, 681-686.
    [37]
    Deng, Y., Bartosovic, M., Ma, S., Zhang, D., Kukanja, P., Xiao, Y., Su, G., Liu, Y., Qin, X., Rosoklija, G.B., et al., 2022b. Spatial profiling of chromatin accessibility in mouse and human tissues. Nature 609, 375-383.
    [38]
    Dhainaut, M., Rose, S.A., Akturk, G., Wroblewska, A., Nielsen, S.R., Park, E.S., Buckup, M., Roudko, V., Pia, L., Sweeney, R., et al., 2022. Spatial CRISPR genomics identifies regulators of the tumor microenvironment. Cell 185, 1223-1239.e1220.
    [39]
    Di Bella, D.J., Habibi, E., Stickels, R.R., Scalia, G., Brown, J., Yadollaahpour, P., Yang, S.M., Abbate, C., Biancalani, A., Macosko, E.Z., Chen, F., Regev, A., Arlotta, P., 2021. Molecular logic of cellular diversification in the mouse cerebral cortex. Nature 595, 554–559.
    [40]
    Ding, J., Adiconis, X., Simmons, S.K., Kowalczyk, M.S., Hession, C.C., Marjanovic, N.D., Hughes, T.K., Wadsworth, M.H., Burks, T., Nguyen, L.T., et al., 2020. Systematic comparison of single-cell and single-nucleus RNA-sequencing methods. Nat. Biotechnol. 38, 737-746.
    [41]
    Dong, R.,Yuan, G.C., 2021. SpatialDWLS: accurate deconvolution of spatial transcriptomic data. Genome Biol. 22, 145.
    [42]
    Dong, K.,Zhang, S., 2022. Deciphering spatial domains from spatially resolved transcriptomics with an adaptive graph attention auto-encoder. Nat. Commun. 13, 1739.
    [43]
    Editorial, 2021. Method of the Year 2020: spatially resolved transcriptomics. Nat. Methods 18, 1.
    [44]
    Elosua-Bayes, M., Nieto, P., Mereu, E., Gut, I.,Heyn, H., 2021. SPOTlight: seeded NMF regression to deconvolute spatial transcriptomics spots with single-cell transcriptomes. Nucleic Acids Res. 49, e50.
    [45]
    Eng, C.L., Lawson M., Zhu, Q., Dries, R., Koulena, N., Takei, Y., Yun, J., Cronin, C., Karp, C., Yuan, G.C., et al., 2019. Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH. Nature 568, 235-239.
    [46]
    Eng, C.L., Shah, S., Thomassie, J., Cai, L., 2017. Profiling the transcriptome with RNA SPOTs. Nat. Methods 14, 1153–1155.
    [47]
    Engblom, C., Thrane, K., Lin, Q., Andersson, A., Toosi, H., Chen, X., Steiner, E., Mantovani, G., Hagemann-Jensen, M., Saarenpää, S., et al., 2022. Spatial transcriptomics of T and B cell receptors uncovers lymphocyte clonal dynamics in human tissue bioRxiv, 2022.2011.2022.516865.
    [48]
    Erickson, A., He, M., Berglund, E., Marklund, M., Mirzazadeh, R., Schultz, N., Kvastad, L., Andersson, A., Bergenstråhle, L., Bergenstråhle, J., et al., 2022. Spatially resolved clonal copy number alterations in benign and malignant tissue. Nature 608, 360–367.
    [49]
    Fan, Y., Andrusivova, Z., Wu, Y., Chai, C., Larsson, L., He, M., Luo, L., Lundeberg, J.,Wang, B., 2022. Expansion spatial transcriptomics. bioRxiv, 2022.2010.2025.513696.
    [50]
    Fang, S., Chen, B., Zhang, Y., Sun, H., Liu, L., Liu, S., Li, Y.,Xu, X., 2022. Computational approaches and challenges in spatial transcriptomics. Genomics Proteomics Bioinformatics.
    [51]
    Fang, R., Xia, C., Close, J.L., Zhang, M., He, J., Huang, Z., Halpern, A.R., Long, B., Miller, J.A., Lein, E.S., et al., 2022. Conservation and divergence of cortical cell organization in human and mouse revealed by MERFISH. Science 377, 56–62.
    [52]
    Fawkner-Corbett, D., Antanaviciute, A., Parikh, K., Jagielowicz, M., Gerós, A.S., Gupta, T., Ashley, N., Khamis, D., Fowler, D., Morrissey, E., et al., 2021. Spatiotemporal analysis of human intestinal development at single-cell resolution. Cell 184, 810–826.e823.
    [53]
    Femino, A.M., Fay, F.S., Fogarty, K.,Singer, R.H., 1998. Visualization of single RNA transcripts in situ. Science 280, 585-590.
    [54]
    Fu, X., Sun, L., Dong, R., Chen, J.Y., Silakit, R., Condon, L.F., Lin, Y., Lin, S., Palmiter, R.D., Gu, L., 2022. Polony gels enable amplifiable DNA stamping and spatial transcriptomics of chronic pain. Cell 185, 4621–4633.
    [55]
    Galeano Niño, J.L., Wu, H., LaCourse, K.D., Kempchinsky, A.G., Baryiames, A., Barber, B., Futran, N., Houlton, J., Sather, C., Sicinska, E., et al., 2022. Effect of the intratumoral microbiota on spatial and cellular heterogeneity in cancer. Nature 611, 810–817.
    [56]
    Gall, J.G.,Pardue, M.L., 1969. Formation and detection of RNA-DNA hybrid molecules in cytological preparations. Proc. Natl. Acad. Sci. U. S. A. 63, 378-383.
    [57]
    Gao, S., Shi, Q., Zhang, Y., Liang, G., Kang, Z., Huang, B., Ma, D., Wang, L., Jiao, J., Fang, X., et al., 2022. Identification of HSC/MPP expansion units in fetal liver by single-cell spatiotemporal transcriptomics. Cell Res. 32, 38–53.
    [58]
    Garcia-Alonso, L., Lorenzi, V., Mazzeo, C.I., Alves-Lopes, J.P., Roberts, K., Sancho-Serra, C., Engelbert, J., Marečková, M., Gruhn, W.H., Botting, R.A., et al., 2022. Single-cell roadmap of human gonadal development. Nature 607, 540–547.
    [59]
    Giacomello, S., Salmen, F., Terebieniec, B.K., Vickovic, S., Navarro, J.F., Alexeyenko, A., Reimegard, J., McKee, L.S., Mannapperuma, C., Bulone, V., et al., 2017. Spatially resolved transcriptome profiling in model plant species. Nat. Plants 3, 17061.
    [60]
    Giolai, M., Verweij, W., Lister, A., Heavens, D., Macaulay, I., Clark, M.D., 2019. Spatially resolved transcriptomics reveals plant host responses to pathogens. Plant Methods 15, 114.
    [61]
    Gracia Villacampa, E., Larsson, L., Mirzazadeh, R., Kvastad, L., Andersson, A., Mollbrink, A., Kokaraki, G., Monteil, V., Schultz, N., Appelberg, K.S., et al., 2021. Genome-wide spatial expression profiling in formalin-fixed tissues. Cell Genomics 1, 100065.
    [62]
    Grisanti Canozo, F.J., Zuo, Z., Martin, J.F.,Samee, M.A.H., 2022. Cell-type modeling in spatial transcriptomics data elucidates spatially variable colocalization and communication between cell-types in mouse brain. Cell Syst. 13, 58-70.e55.
    [63]
    Grün, D., Lyubimova, A., Kester, L., Wiebrands, K., Basak, O., Sasaki, N., Clevers, H., van Oudenaarden, A., n.d. 2015，Single-cell messenger RNA sequencing reveals rare intestinal cell types. Nature 525, 251–255.
    [64]
    Gyllborg, D., Langseth, C.M., Qian, X., Choi, E., Salas, S.M., Hilscher, M.M., Lein, E.S., Nilsson, M., 2020. Hybridization-based in situ sequencing HybISS for spatially resolved transcriptomics in human and mouse brain tissue. Nucleic Acids Res 48, e112.
    [65]
    Haase, C., Gustafsson, K., Mei, S., Yeh, S.C., Richter, D., Milosevic, J., Turcotte, R., Kharchenko, P.V., Sykes, D.B., Scadden, D.T., et al., 2022. Image-seq: spatially resolved single-cell sequencing guided by in situ and in vivo imaging. Nat Methods 19, 1622–1633.
    [66]
    Halpern, K.B., Shenhav, R., Matcovitch-Natan, O., Toth, B., Lemze, D., Golan, M., Massasa, E.E., Baydatch, S., Landen, S., Moor, A.E., et al., 2017. Single-cell spatial reconstruction reveals global division of labour in the mammalian liver. Nature 542, 352–356.
    [67]
    Harrison, P.R., Conkie, D., Paul, J.,Jones, K., 1973. Localisation of cellular globin messenger RNA by in situ hybridisation to complementary DNA. FEBS Lett. 32, 109-112.
    [68]
    He, P., Lim, K., Sun, D., Pett, J.P., Jeng, Q., Polanski, K., Dong, Z., Bolt, L., Richardson, L., Mamanova, L., et al., 2022. A human fetal lung cell atlas uncovers proximal-distal gradients of differentiation and key regulators of epithelial fates. Cell 185, 4841–4860.
    [69]
    He, Z., Maynard, A., Jain, A., Gerber, T., Petri, R., Lin, H.C., Santel, M., Ly, K., Dupre, J.S., Sidow, L., et al., 2022. Lineage recording in human cerebral organoids. Nat. Methods 19, 90-99.
    [70]
    Herman, J.S., Sagar, Grün, D., 2018. FateID infers cell fate bias in multipotent progenitors from single-cell RNA-seq data. Nat. Methods 15, 379–386.
    [71]
    Holgersen, E.M., Gandhi, S., Zhou, Y., Kim, J., Vaz, B., Bogojeski, J., Bugno, M., Shalev, Z., Cheung-Ong, K., Goncalves, J., et al., 2021. Transcriptome-wide off-target effects of steric-blocking oligonucleotides. Nucleic Acid Ther. 31, 392-403.
    [72]
    Honda, M., Oki, S., Kimura, R., Harada, A., Maehara, K., Tanaka, K., Meno, C., Ohkawa, Y., 2021. High-depth spatial transcriptome analysis by photo-isolation chemistry. Nat. Commun. 12, 4416.
    [73]
    Hu, K.H., Eichorst, J.P., McGinnis, C.S., Patterson, D.M., Chow, E.D., Kersten, K., Jameson, S.C., Gartner, Z.J., Rao, A.A., Krummel, M.F., 2020. ZipSeq: barcoding for real-time mapping of single cell transcriptomes. Nat Methods 17, 833–843.
    [74]
    Hu, J., Li, X., Coleman, K., Schroeder, A., Ma, N., Irwin, D.J., Lee, E.B., Shinohara, R.T.,Li, M., 2021. SpaGCN: integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network. Nat. Methods 18, 1342-1351.
    [75]
    Jerby-Arnon, L.,Regev, A., 2022. DIALOGUE maps multicellular programs in tissue from single-cell or spatial transcriptomics data. Nat. Biotechnol.
    [76]
    Ji, A.L., Rubin, A.J., Thrane, K., Jiang, S., Reynolds, D.L., Meyers, R.M., Guo, M.G., George, B.M., Mollbrink, A., Bergenstråhle, J., et al., 2020. Multimodal Analysis of Composition and Spatial Architecture in Human Squamous Cell Carcinoma. Cell 182, 497–514.
    [77]
    Jiang, F., Zhou, X., Qian, Y., Zhu, M., Wang, L., Li, Z., Shen, Q., Qu, F., Cui, G., Chen, K., et al., 2023. Simultaneous profiling of spatial gene expression and chromatin accessibility for mouse brain development bioRxiv, 2022.2003.2022.485333.
    [78]
    Jin, S., Guerrero-Juarez, C.F., Zhang, L., Chang, I., Ramos, R., Kuan, C.-H., Myung, P., Plikus, M.V.,Nie, Q., 2021. Inference and analysis of cell-cell communication using CellChat. Nat. Commun. 12, 1-20.
    [79]
    Jin, Z., Yu, N., Bai, J., Liu, Z., Li, H., Zhang, J., Liang, C., 2022. Cross-amplified Barcodes on Slides for Spatial Transcriptomics Sequencing bioRxiv,, 2022.2008.2025.504658.
    [80]
    Joglekar, A., Prjibelski, A., Mahfouz, A., Collier, P., Lin, S., Schlusche, A.K., Marrocco, J., Williams, S.R., Haase, B., Hayes, A., et al., 2021. A spatially resolved brain region- and cell type-specific isoform atlas of the postnatal mouse brain. Nat. Commun. 12, 463.
    [81]
    Junker, J.P., Noel, E.S., Guryev, V., Peterson, K.A., Shah, G., Huisken, J., McMahon, A.P., Berezikov, E., Bakkers, J.,van Oudenaarden, A., 2014. Genome-wide RNA Tomography in the zebrafish embryo. Cell 159, 662-675.
    [82]
    Kadur Lakshminarasimha Murthy, P., Sontake, V., Tata, A., Kobayashi, Y., Macadlo, L., Okuda, K., Conchola, A.S., Nakano, S., Gregory, S., Miller, L.A., et al., 2022. Human distal lung maps and lineage hierarchies reveal a bipotent progenitor. Nature 604, 111–119.
    [83]
    Karras, P., Bordeu, I., Pozniak, J., Nowosad, A., Pazzi, C., Van Raemdonck, N., Landeloos, E., Van Herck, Y., Pedri, D., Bervoets, G., et al., 2022. A cellular hierarchy in melanoma uncouples growth and metastasis. Nature 610, 190–198.
    [84]
    Kathe, C., Skinnider, M.A., Hutson, T.H., Regazzi, N., Gautier, M., Demesmaeker, R., Komi, S., Ceto, S., James, N.D., Cho, N., et al., 2022. The neurons that restore walking after paralysis. Nature 611, 540–547.
    [85]
    Ke, R., Mignardi, M., Pacureanu, A., Svedlund, J., Botling, J., Wahlby, C.,Nilsson, M., 2013. In situ sequencing for RNA analysis in preserved tissue and cells. Nat. Methods 10, 857-860.
    [86]
    Kebschull, J.M., Richman, E.B., Ringach, N., Friedmann, D., Albarran, E., Kolluru, S.S., Jones, R.C., Allen, W.E., Wang, Y., Cho, S.W., et al., 2020. Cerebellar nuclei evolved by repeatedly duplicating a conserved cell-type set. Science 370.
    [87]
    Kim, H., Lovkvist, C., Martin, P., Kim, J.,Won, K.J., 2022. Detecting cell contact-dependent gene expression from spatial transcriptomics data. bioRxiv, 2022.2002.2016.480673.
    [88]
    Kim, D.W., Yao, Z., Graybuck, L.T., Kim, T.K., Nguyen, T.N., Smith, K.A., Fong, O., Yi, L., Koulena, N., Pierson, N., et al., 2019. Multimodal Analysis of Cell Types in a Hypothalamic Node Controlling Social Behavior. Cell 179, 713–728.
    [89]
    Konieczny, P., Xing, Y., Sidhu, I., Subudhi, I., Mansfield, K.P., Hsieh, B., Biancur, D.E., Larsen, S.B., Cammer, M., Li, D., et al., 2022. Interleukin-17 governs hypoxic adaptation of injured epithelium. Science 377, eabg9302.
    [90]
    Kriebel, A.R.,Welch, J.D., 2022. UINMF performs mosaic integration of single-cell multi-omic datasets using nonnegative matrix factorization. Nat. Commun. 13, 1-17.
    [91]
    Kumar, V., Ramnarayanan, K., Sundar, R., Padmanabhan, N., Srivastava, S., Koiwa, M., Yasuda, T., Koh, V., Huang, K.K., Tay, S.T., et al., 2022. Single-Cell Atlas of Lineage States, Tumor Microenvironment, and Subtype-Specific Expression Programs in Gastric Cancer. Cancer Discov 12, 670–691.
    [92]
    Kuppe, C., Ramirez Flores, R.O., Li, Z., Hayat, S., Levinson, R.T., Liao, X., Hannani, M.T., Tanevski, J., Wünnemann, F., Nagai, J.S., et al., 2022. Spatial multi-omic map of human myocardial infarction. Nature 608, 766–777.
    [93]
    La Manno, G., Siletti, K., Furlan, A., Gyllborg, D., Vinsland, E., Mossi Albiach, A., Mattsson Langseth, C., Khven, I., Lederer, A.R., Dratva, L.M., et al., 2021. Molecular architecture of the developing mouse brain. Nature 596, 92–96.
    [94]
    Langer-Safer, P.R., Levine, M.,Ward, D.C., 1982. Immunological method for mapping genes on Drosophila polytene chromosomes. Proc. Natl. Acad. Sci. U. S. A. 79, 4381-4385.
    [95]
    Larsson, C., Grundberg, I., Soderberg, O.,Nilsson, M., 2010. In situ detection and genotyping of individual mRNA molecules. Nat. Methods 7, 395-397.
    [96]
    Lee, Y., Bogdanoff, D., Wang, Y., Hartoularos, G.C., Woo, J.M., Mowery, C.T., Nisonoff, H.M., Lee, D.S., Sun, Y., Lee, J., et al., 2021. XYZeq: Spatially resolved single-cell RNA sequencing reveals expression heterogeneity in the tumor microenvironment. Sci. Adv. 7.
    [97]
    Lee, J.H., Daugharthy, E.R., Scheiman, J., Kalhor, R., Yang, J.L., Ferrante, T.C., Terry, R., Jeanty, S.S., Li, C., Amamoto, R., et al., 2014. Highly multiplexed subcellular RNA sequencing in situ. Science 343, 1360-1363.
    [98]
    Lei, Y., Cheng, M., Li, Z., Zhuang, Z., Wu, L., Sun, Y., Han, L., Huang, Z., Wang, Y., Wang, Z., et al., 2022. Spatially resolved gene regulatory and disease vulnerability map of the adult Macaque cortex. bioRxiv, 2020.2005.2014.087601.
    [99]
    Li, B., Zhang, W., Guo, C., Xu, H., Li, L., Fang, M., Hu, Y., Zhang, X., Yao, X., Tang, M., et al., 2022a. Benchmarking spatial and single-cell transcriptomics integration methods for transcript distribution prediction and cell type deconvolution. Nat. Methods 19, 662-670.
    [100]
    Li, H., Ma, T., Hao, M., Wei, L.,Zhang, X., 2022b. Decoding functional cell-cell communication events by multi-view graph learning on spatial transcriptomics. bioRxiv, 2022.2006.2022.496105.
    [101]
    Littman, R., Hemminger, Z., Foreman, R., Arneson, D., Zhang, G., Gomez-Pinilla, F., Yang, X.,Wollman, R., 2021. Joint cell segmentation and cell type annotation for spatial transcriptomics. Mol. Syst. Biol. 17, e10108.
    [102]
    Liu, C., Wu, T., Fan, F., Liu, Y., Wu, L., Junkin, M., Wang, Z., Yu, Y., Wang, W., Wei, W., et al., 2019. A portable and cost-effective microfluidic system for massively parallel single-cell transcriptome profiling. bioRxiv, 818450.
    [103]
    Liu, J., Gao, C., Sodicoff, J., Kozareva, V., Macosko, E.Z.,Welch, J.D., 2020a. Jointly defining cell types from multiple single-cell datasets using LIGER. Nat. Protoc. 15, 3632-3662.
    [104]
    Liu, Y., DiStasio, M., Su, G., Asashima, H., Enninful, A., Qin, X., Deng, Y., Bordignon, P., Cassano, M., Tomayko, M., et al., 2022d. Spatial-CITE-seq: spatially resolved high-plex protein and whole transcriptome co-mapping. bioRxiv 2022.2004.2001.486788.
    [105]
    Liu, Y., Enninful, A., Deng, Y.,Fan, R., 2020b. Spatial transcriptome sequencing of FFPE tissues at cellular level. bioRxiv, 2020.2010.2013.338475.
    [106]
    Liu, Y., Yang, M., Deng, Y., Su, G., Enninful, A., Guo, C.C., Tebaldi, T., Zhang, D., Kim, D., Bai, Z., et al., 2020c. High-spatial-resolution multi-omics sequencing via deterministic barcoding in tissue. Cell 183, 1665-1681.e1618.
    [107]
    Liu, C., Li, R., Li, Y., Lin, X., Zhao, K., Liu, Q., Wang, S., Yang, X., Shi, X., Ma, Y., et al., 2022a. Spatiotemporal mapping of gene expression landscapes and developmental trajectories during zebrafish embryogenesis. Dev. Cell 57, 1284-1298.e1285.
    [108]
    Liu, S., Iorgulescu, J.B., Li, S., Borji, M., Barrera-Lopez, I.A., Shanmugam, V., Lyu, H., Morriss, J.W., Garcia, Z.N., Murray, E., et al., 2022b. Spatial maps of T cell receptors and transcriptomes reveal distinct immune niches and interactions in the adaptive immune response. Immunity 55, 1940-1952.e1945.
    [109]
    Liu, X., Mao, D., Song, Y., Zhu, L., Isak, A.N., Lu, C., Deng, G., Chen, F., Sun, F., Yang, Y., et al., 2022c. Computer-aided design of reversible hybridization chain reaction CAD-HCR enables multiplexed single-cell spatial proteomics imaging. Sci. Adv. 8, eabk0133.
    [110]
    Liu, S., Punthambaker, S., Iyer, E.P.R., Ferrante, T., Goodwin, D., Fürth, D., Pawlowski, A.C., Jindal, K., Tam, J.M., Mifflin, L., et al., 2021. Barcoded oligonucleotides ligated on RNA amplified for multiplexed and parallel in situ analyses. Nucleic Acids Res 49, e58.
    [111]
    Liu, Z., Sun, D.,Wang, C., 2022c. Evaluation of cell-cell interaction methods by integrating single-cell RNA sequencing data with spatial information. Genome Biol. 23, 218.
    [112]
    Lohoff, T., Ghazanfar, S., Missarova, A., Koulena, N., Pierson, N., Griffiths, J.A., Bardot, E.S., Eng, C.L., Tyser, R.C.V., Argelaguet, R., et al., 2022. Integration of spatial and single-cell transcriptomic data elucidates mouse organogenesis. Nat. Biotechnol. 40, 74-85.
    [113]
    Lopez, R., Nazaret, A., Langevin, M., Samaran, J., Regier, J., Jordan, M.I.,Yosef, N., 2019. A joint model of unpaired data from scRNA-seq and spatial transcriptomics for imputing missing gene expression measurements. arXiv preprint arXiv:1905.02269.
    [114]
    Lubeck, E.,Cai, L., 2012. Single-cell systems biology by super-resolution imaging and combinatorial labeling. Nat. Methods 9, 743-748.
    [115]
    Lubeck, E., Coskun, A.F., Zhiyentayev, T., Ahmad, M.,Cai, L., 2014. Single-cell in situ RNA profiling by sequential hybridization. Nat. Methods 11, 360-361.
    [116]
    Single-cell analyses of axolotl telencephalon organization, neurogenesis, and regeneration
    [117]
    Maniatis, S., Äijö, T., Vickovic, S., Braine, C., Kang, K., Mollbrink, A., Fagegaltier, D., Andrusivová, Ž., Saarenpää, S., Saiz-Castro, G., et al., 2019. Spatiotemporal dynamics of molecular pathology in amyotrophic lateral sclerosis. Science 364, 89–93.
    [118]
    Matsunaga, H., Arikawa, K., Yamazaki, M., Wagatsuma, R., Ide, K., Zachariah, S.A., Takamochi, K., Suzuki, K., Hayashi, T., Hosokawa, M., et al., 2022. Reproducible and sensitive micro-tissue RNA-sequencing from formalin-fixed paraffin-embedded tissue for spatial gene expression analysis. bioRxiv 2022.2003.2029.486169.
    [119]
    Maynard, K.R., Collado-Torres, L., Weber, L.M., Uytingco, C., Barry, B.K., Williams, S.R., Catallini, J.L., 2nd, Tran, M.N., Besich, Z., Tippani, M., et al., 2021. Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex. Nat. Neurosci 24, 425–436.
    [120]
    McKellar, D.W., Mantri, M., Hinchman, M., Parker, J.S.L., Sethupathy, P., Cosgrove, B.D.,De Vlaminck, I., 2022. In situ polyadenylation enables spatial mapping of the total transcriptome. bioRxiv, 2022.2004.2020.488964.
    [121]
    Merritt, C.R., Ong, G.T., Church, S.E., Barker, K., Danaher, P., Geiss, G., Hoang, M., Jung, J., Liang, Y., McKay-Fleisch, J., et al., 2020. Multiplex digital spatial profiling of proteins and RNA in fixed tissue. Nat. Biotechnol. 38, 586–599.
    [122]
    Moffitt, J.R., Bambah-Mukku, D., Eichhorn, S.W., Vaughn, E., Shekhar, K., Perez, J.D., Rubinstein, N.D., Hao, J., Regev, A., Dulac, C., et al., 2018. Molecular, spatial, and functional single-cell profiling of the hypothalamic preoptic region. Science 362.
    [123]
    Moffitt, J.R., Hao, J., Wang, G., Chen, K.H., Babcock, H.P., Zhuang, X., 2016. High-throughput single-cell gene-expression profiling with multiplexed error-robust fluorescence in situ hybridization. Proc. Natl. Acad. Sci. U. S. A. 113, 11046–11051.
    [124]
    Moffitt, J.R., Lundberg, E.,Heyn, H., 2022. The emerging landscape of spatial profiling technologies. Nat. Rev. Genet. 23, 741-759.
    [125]
    Mohamed, S.K., Nounu, A.,Novacek, V., 2021. Biological applications of knowledge graph embedding models. Brief. Bioinformatics 22, 1679-1693.
    [126]
    Mohenska, M., Tan, N.M., Tokolyi, A., Furtado, M.B., Costa, M.W., Perry, A.J., Hatwell-Humble, J., van Duijvenboden, K., Nim, H.T.,Ji, Y.M., 2022. 3D-cardiomics: a spatial transcriptional atlas of the mammalian heart. J. Mol. Cell. Cardiol. 163, 20-32.
    [127]
    Moris, N., Anlas, K., van den Brink, S.C., Alemany, A., Schröder, J., Ghimire, S., Balayo, T., van Oudenaarden, A., Martinez Arias, A., 2020. An in vitro model of early anteroposterior organization during human development. Nature 582, 410–415.
    [128]
    Navarro, J.F., Sjostrand, J., Salmen, F., Lundeberg, J.,Stahl, P.L., 2017. ST Pipeline: an automated pipeline for spatial mapping of unique transcripts. Bioinformatics 33, 2591-2593.
    [129]
    Nichterwitz, S., Chen, G., Aguila Benitez, J., Yilmaz, M., Storvall, H., Cao, M., Sandberg, R., Deng, Q.,Hedlund, E., 2016. Laser capture microscopy coupled with Smart-seq2 for precise spatial transcriptomic profiling. Nat. Commun. 7, 12139.
    [130]
    Ortiz, C., Navarro, J.F., Jurek, A., Martin, A., Lundeberg, J.,Meletis, K., 2020. Molecular atlas of the adult mouse brain. Sci. Adv. 6, eabb3446.
    [131]
    Osterhout, J.A., Kapoor, V., Eichhorn, S.W., Vaughn, E., Moore, J.D., Liu, D., Lee, D., DeNardo, L.A., Luo, L., Zhuang, X., et al., 2022. A preoptic neuronal population controls fever and appetite during sickness. Nature 606, 937–944.
    [132]
    Ou, Z., Lin, S., Qiu, J., Ding, W., Ren, P., Chen, D., Wang, J., Tong, Y., Wu, D., Chen, A., et al., 2022. Single-Nucleus RNA Sequencing and Spatial Transcriptomics Reveal the Immunological Microenvironment of Cervical Squamous Cell Carcinoma. Adv. Sci. Weinh. 9, e2203040.
    [133]
    Parigi, S.M., Larsson, L., Das, S., Ramirez Flores, R.O., Frede, A., Tripathi, K.P., Diaz, O.E., Selin, K., Morales, R.A., Luo, X., et al., 2022. The spatial transcriptomic landscape of the healing mouse intestine following damage. Nat. Commun. 13, 828.
    [134]
    Pelka, K., Hofree, M., Chen, J.H., Sarkizova, S., Pirl, J.D., Jorgji, V., Bejnood, A., Dionne, D., Ge, W.H., Xu, K.H., et al., 2021. Spatially organized multicellular immune hubs in human colorectal cancer. Cell 184, 4734–4752.
    [135]
    Peng, G., Suo, S., Cui, G., Yu, F., Wang, R., Chen, J., Chen, S., Liu, Z., Chen, G., Qian, Y., et al., 2019. Molecular architecture of lineage allocation and tissue organization in early mouse embryo. Nature 572, 528-532.
    [136]
    Petukhov, V., Xu, R.J., Soldatov, R.A., Cadinu, P., Khodosevich, K., Moffitt, J.R.,Kharchenko, P.V., 2022. Cell segmentation in imaging-based spatial transcriptomics. Nat. Biotechnol. 40, 345-354.
    [137]
    Raredon, M.S.B., Yang, J., Kothapalli, N., Lewis, W., Kaminski, N., Niklason, L.E.,Kluger, Y., 2022. Comprehensive visualization of cell-cell interactions in single-cell and spatial transcriptomics with NICHES. bioRxiv, 2022.2001.2023.477401.
    [138]
    Ratz, M., Berlin, L.v., Larsson, L., Martin, M., Westholm, J.O., Manno, G.L., Lundeberg, J., Frisén, 2021. Cell types and clonal relations in the mouse brain revealed by single-cell and spatial transcriptomics. bioRxiv 2021.2008.2031.458418.
    [139]
    Ravi, V.M., Neidert, N., Will, P., Joseph, K., Maier, J.P., Kückelhaus, J., Vollmer, L., Goeldner, J.M., Behringer, S.P., Scherer, F., et al., 2022. T-cell dysfunction in the glioblastoma microenvironment is mediated by myeloid cells releasing interleukin-10. Nat. Commun. 13, 925.
    [140]
    Ren, J., Zhou, H., Zeng, H., Wang, C.K., Huang, J., Qiu, X., Maher, K., Lin, Z., He, Y., Tang, X., et al., 2022. Spatiotemporally resolved transcriptomics reveals subcellular RNA kinetic landscape. bioRxiv, 2022.2009.2027.509606.
    [141]
    Rodriques, S.G., Stickels, R.R., Goeva, A., Martin, C.A., Murray, E., Vanderburg, C.R., Welch, J., Chen, L.M., Chen, F.,Macosko, E.Z., 2019. Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution. Science 363, 1463-1467.
    [142]
    Rouhanifard, S.H., Mellis, I.A., Dunagin, M., Bayatpour, S., Jiang, C.L., Dardani, I., Symmons, O., Emert, B., Torre, E., Cote, A., et al., 2018. ClampFISH detects individual nucleic acid molecules using click chemistry-based amplification. Nat. Biotechnol.
    [143]
    Saarenpää, S., Shalev, O., Ashkenazy, H., Oliveira-Carlos, V.d., Lundberg, D.S., Weigel, D., Giacomello, S., 2022. Spatially resolved host-bacteria-fungi interactomes via spatial metatranscriptomics bioRxiv, 2022.2007.2018.496977.
    [144]
    Satija, R., Farrell, J.A., Gennert, D., Schier, A.F.,Regev, A., 2015. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33, 495-502.
    [145]
    Shao, X., Li, C., Yang, H., Lu, X., Liao, J., Qian, J., Wang, K., Cheng, J., Yang, P.,Chen, H., 2022. Knowledge-graph-based cell-cell communication inference for spatially resolved transcriptomic data with SpaTalk. bioRxiv.
    [146]
    Sharma, A., Seow, J.J.W., Dutertre, C.A., Pai, R., Blériot, C., Mishra, A., Wong, R.M.M., Singh, G.S.N., Sudhagar, S., Khalilnezhad, S., et al., 2020. Onco-fetal Reprogramming of Endothelial Cells Drives Immunosuppressive Macrophages in Hepatocellular Carcinoma. Cell 183, 377–394.e321.
    [147]
    Shi, H., He, Y., Zhou, Y., Huang, J., Wang, B., Tang, Z., Tan, P., Wu, M., Lin, Z., Ren, J. et al., 2022. Spatial atlas of the mouse central nervous system at molecular resolution. bioRxiv. https://doi.org/10.1101/2022.06.20.496914.
    [148]
    Singer, R.H.,Ward, D.C., 1982. Actin gene expression visualized in chicken muscle tissue culture by using in situ hybridization with a biotinated nucleotide analog. Proc. Natl. Acad. Sci. U. S. A. 79, 7331-7335.
    [149]
    Embryo-scale, single-cell spatial transcriptomics
    [150]
    Stahl, P.L., Salmen, F., Vickovic, S., Lundmark, A., Navarro, J.F., Magnusson, J., Giacomello, S., Asp, M., Westholm, J.O., Huss, M., et al., 2016. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353, 78-82.
    [151]
    Stickels, R.R., Murray, E., Kumar, P., Li, J., Marshall, J.L., Di Bella, D.J., Arlotta, P., Macosko, E.Z.,Chen, F., 2021. Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2. Nat. Biotechnol. 39, 313-319.
    [152]
    Stogsdill, J.A., Kim, K., Binan, L., Farhi, S.L., Levin, J.Z., Arlotta, P., 2022. Pyramidal neuron subtype diversity governs microglia states in the neocortex. Nature 608, 750–756.
    [153]
    Sun, Y.C., Chen, X., Fischer, S., Lu, S., Zhan, H., Gillis, J., Zador, A.M., 2021. Integrating barcoded neuroanatomy with spatial transcriptional profiling enables identification of gene correlates of projections. Nat. Neurosci 24, 873–885.
    [154]
    Suo, C., Dann, E., Goh, I., Jardine, L., Kleshchevnikov, V., Park, J.E., Botting, R.A., Stephenson, E., Engelbert, J., Tuong, Z.K., et al., 2022. Mapping the developing human immune system across organs. Science 376, eabo0510.
    [155]
    Tang, X., Chen, J., Zhang, X., Liu, X., Xie, Z., Wei, K., Qiu, J., Ma, W., Lin, C., Ke, R., 2022. Improved in situ sequencing for high-resolution targeted spatial transcriptomic analysis in tissue sections. bioRxiv 2022.2010.2016.512401.
    [156]
    Tautz, D.,Pfeifle, C., 1989. A non-radioactive in situ hybridization method for the localization of specific RNAs in Drosophila embryos reveals translational control of the segmentation gene hunchback. Chromosoma 98, 81-85.
    [157]
    Tian, L., Chen, F.,Macosko, E.Z., 2022. The expanding vistas of spatial transcriptomics. Nat. Biotechnol.
    [158]
    Uzquiano, A., Kedaigle, A.J., Pigoni, M., Paulsen, B., Adiconis, X., Kim, K., Faits, T., Nagaraja, S., Antón-Bolaños, N., Gerhardinger, C., et al., 2022. Proper acquisition of cell class identity in organoids allows definition of fate specification programs of the human cerebral cortex. Cell 185, 3770–3788.
    [159]
    van den Brink, S.C., Alemany, A., van Batenburg, V., Moris, N., Blotenburg, M., Vivié, J., Baillie-Johnson, P., Nichols, J., Sonnen, K.F., Martinez Arias, A., et al., 2020. Single-cell and spatial transcriptomics reveal somitogenesis in gastruloids. Nature 582, 405–409.
    [160]
    Vickovic, S., Eraslan, G., Salmen, F., Klughammer, J., Stenbeck L., Schapiro, D., Aijo, T., Bonneau, R., Bergenstrahle, L., Navarro, J.F., et al., 2019. High-definition spatial transcriptomics for in situ tissue profiling. Nat. Methods 16, 987-990.
    [161]
    Vickovic, S., Lotstedt, B., Klughammer, J., Mages, S., Segerstolpe, A., Rozenblatt-Rosen, O.,Regev, A., 2022. SM-Omics is an automated platform for high-throughput spatial multi-omics. Nat. Commun. 13, 795.
    [162]
    Vickovic, S., Stahl, P.L., Salmen, F., 2016. Massive and parallel expression profiling using microarrayed single-cell sequencing. Nat Commun. 7, 13182.
    [163]
    Vu, T., Vallmitjana, A., Gu, J., La, K., Xu, Q., Flores, J., Zimak, J., Shiu, J., Hosohama, L., Wu, J., et al., 2022. Spatial transcriptomics using combinatorial fluorescence spectral and lifetime encoding, imaging and analysis. Nat. Commun. 13, 169.
    [164]
    Wang, X., Allen, W.E., Wright, M.A., Sylwestrak, E.L., Samusik, N., Vesuna, S., Evans, K., Liu, C., Ramakrishnan, C., Liu, J., et al., 2018. Three-dimensional intact-tissue sequencing of single-cell transcriptional states. Science 361.
    [165]
    Wang, Y., Eddison, M., Fleishman, G., Weigert, M., Xu, S., Wang, T., Rokicki, K., Goina, C., Henry, F.E., Lemire, A.L., et al., 2021. EASI-FISH for thick tissue defines lateral hypothalamus spatio-molecular organization. Cell 184, 6361-6377.e6324.
    [166]
    Wang, F., Flanagan, J., Su, N., Wang, L.C., Bui, S., Nielson, A., Wu, X., Vo, H.T., Ma, X.J., Luo, Y., 2012. RNAscope: a novel in situ RNA analysis platform for formalin-fixed, paraffin-embedded tissues. J. Mol. Diagn. 14, 22–29.
    [167]
    Wang, M., Hu, Q., Lv, T., Wang, Y., Lan, Q., Xiang, R., Tu, Z., Wei, Y., Han, K., Shi, C., et al., 2022. High-resolution 3D spatiotemporal transcriptomic maps of developing Drosophila embryos and larvae. Dev. Cell 57, 1271-1283.e1274.
    [168]
    Wang, G., Moffitt, J.R., Zhuang, X., 2018a. Multiplexed imaging of high-density libraries of RNAs with MERFISH and expansion microscopy. Sci. Rep. 8, 4847.
    [169]
    Wassie, A.T., Zhao, Y.,Boyden, E.S., 2019. Expansion microscopy: principles and uses in biological research. Nat. Methods 16, 33-41.
    [170]
    Wei, R., He, S., Bai, S., Sei, E., Hu, M., Thompson, A., Chen, K., Krishnamurthy, S.,Navin, N.E., 2022a. Spatial charting of single-cell transcriptomes in tissues. Nat. Biotechnol.
    [171]
    Wei, X., Fu, S., Li, H., Liu, Y., Wang, S., Feng, W., Yang, Y., Liu, X., Zeng, Y.Y., Cheng, M., et al., 2022b. Single-cell Stereo-seq reveals induced progenitor cells involved in axolotl brain regeneration. Science 377, eabp9444.
    [172]
    Weinstein, J.A., Regev, A., Zhang, F., 2019. DNA Microscopy: Optics-free Spatio-genetic Imaging by a Stand-Alone Chemical Reaction. Cell 178, 229–241.
    [173]
    Wirth, J., Compera, N., Yin, K., Brood, S., Chang, S., Martinez-Jimenez, C.P., Meier, M., 2022. Spatial Transcriptomics Using Multiplexed Deterministic Barcoding in. Tissue. bioRxiv 2022.2008.2030.505834.
    [174]
    Wong, K., Navarro, J.F., Bergenstrahle, L., Stahl, P.L.,Lundeberg, J., 2018. ST Spot Detector: a web-based application for automatic spot and tissue detection for spatial Transcriptomics image datasets. Bioinformatics 34, 1966-1968.
    [175]
    Wu, R., et al., 2021a. Comprehensive analysis of spatial architecture in primary liver cancer. Sci Adv 7 (51), eabg3750.
    [176]
    Wu, L., Yan, J., Bai, Y., Chen, F., Xu, J., Zou, X., Huang, A., Hou, L., Zhong, Y., Jing, Z., et al., 2021. Spatially-resolved transcriptomics analyses of invasive fronts in solid tumors. bioRxiv, 2021.2010.2021.465135.
    [177]
    Xia, C., Fan, J., Emanuel, G., Hao, J.,Zhuang, X., 2019. Spatial transcriptome profiling by MERFISH reveals subcellular RNA compartmentalization and cell cycle-dependent gene expression. Proc. Natl. Acad. Sci. U. S. A. 116, 19490-19499.
    [178]
    Xia, K., Sun, H.X., Li, J., Li, J., Zhao, Y., Chen, L., Qin, C., Chen, R., Chen, Z., Liu, G., et al., 2022. The single-cell stereo-seq reveals region-specific cell subtypes and transcriptome profiling in Arabidopsis leaves. Dev. Cell 57, 1299-1310.e1294.
    [179]
    Yuan, Y.,Bar-Joseph, Z., 2020. GCNG: graph convolutional networks for inferring gene interaction from spatial transcriptomics data. Genome Biol. 21, 300.
    [180]
    Zeira, R., Land, M., Strzalkowski, A.,Raphael, B.J., 2022. Alignment and integration of spatial transcriptomics data. Nat. Methods 19, 567-575.
    [181]
    Zeng, H., Huang, J., Ren, J., Wang, C.K., Tang, Z., Zhou, Y., Aditham, A., Shi, H., Sui, X.,Wang, X., 2022. Spatially resolved single-cell translatomics at molecular resolution. bioRxiv, 2022.2009.2027.509605.
    [182]
    Zhang, X., Hu, C., Huang, C., Wei, Y., Li, X., Hu, M., Li, H., Wu, J., Czajkowsky, D.M., Guo, Y., et al., 2022. Robust Acquisition of Spatial Transcriptional Programs in Tissues With Immunofluorescence-Guided Laser Capture Microdissection. Front Cell Dev. Biol. 10, 853188.
    [183]
    Zhang, Y., Liu, T., Hu, X., Wang, M., Wang, J., Zou, B., Tan, P., Cui, T., Dou, Y.,Ning, L., 2021. CellCall: integrating paired ligand-receptor and transcription factor activities for cell-cell communication. Nucleic Acids Res. 49, 8520-8534.
    [184]
    Zeng, H., Huang, J., Zhou, H., Meilandt, W.J., Dejanovic, B., Zhou, Y., Bohlen, C.J., Lee, S.H., Ren, J., Liu, A., et al., 2023. Integrative in situ mapping of single-cell transcriptional states and tissue histopathology in a mouse model of Alzheimer's disease. Nat. Neurosci.
    [185]
    Zhang, M., Eichhorn, S.W., Zingg, B., Yao, Z., Cotter, K., Zeng, H., Dong, H., Zhuang, X., 2021. Spatially resolved cell atlas of the mouse primary motor cortex by MERFISH. Nature 598, 137–143.
    [186]
    Zhang, R., Feng, Y., Ma, W., Guo, Y., Luo, M., Li, Y., Zang, Y., Dong, X., Lu, S., Guo, Q., et al., 2022. Spatial transcriptome unveils a discontinuous inflammatory pattern in proficient mismatch repair colorectal adenocarcinoma. Fundam. Res.
    [187]
    Zhao, T., Chiang, Z.D., Morriss, J.W., LaFave, L.M., Murray, E.M., Del Priore, I., Meli, K., Lareau, C.A., Nadaf, N.M., Li, J., et al., 2022. Spatial genomics enables multi-modal study of clonal heterogeneity in tissues. Nature 601, 85-91.
    [188]
    Zhao, H., Tian, G., Hu, A., 2022. Matrix-seq: An adjustable-resolution spatial transcriptomics via microfluidic matrix-based barcoding bioRxiv,, 2022.2008.2005.502952.
    [189]
    Zimmerman, S.M., Fropf, R., Kulasekara, B.R., Griswold, M., Appelbe, O., Bahrami, A., Boykin, R., Buhr, D.L., Fuhrman, K., Hoang, M.L., et al., 2022. Spatially resolved whole transcriptome profiling in human and mouse tissue using Digital Spatial Profiling. bioRxiv 2021.2009.2029.462442.
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