ProtocolPublished: 30 September 2025Jingyi Ren1,2 na1,Hu Zeng1,2 na1,Jiahao Huang1,2 na1,Jiakun Tian ORCID: orcid.org/0000-0002-1238-780X1,2,Morgan Wu ORCID: orcid.org/0009-0007-1300-73152,Hailing Shi ORCID: orcid.org/0000-0001-7355-378X1,2,Xin Sui ORCID: orcid.org/0000-0002-7286-94481,2,Connie Kangni Wang ORCID: orcid.org/0000-0002-9616-51541,2,Haowen Zhou1,2,Zefang Tang ORCID: orcid.org/0000-0002-5264-95601,2,Shuchen Luo ORCID: orcid.org/0000-0001-8754-14131,2 &…Xiao Wang ORCID: orcid.org/0000-0002-3090-98941,2 Nature Protocols (2025)Cite this articleSubjectsGene expression profilingRNASingle-cell imagingTranscriptomicsAbstractControlled gene expression programs have a crucial role in shaping cellular functions and activities. At the core of this process lies the RNA life cycle, ensuring protein products are synthesized in the right place at the right time. Here we detail an integrated protocol for imaging-based highly multiplexed in situ profiling of spatial transcriptome using antibody-based protein comapping (STARmap PLUS), spatial translatome mapping (RIBOmap) and spatiotemporal transcriptome mapping (TEMPOmap). These methods selectively convert targeted RNAs, ribosome-bound mRNAs or metabolically labeled RNAs to DNA amplicons with gene-unique barcodes, which are read out through in situ sequencing under a confocal microscope. Compared with other methods, they provide the analytical capacity to track the spatial and temporal dynamics of thousands of RNA species in intact cells and tissues. Our protocol can be readily performed in laboratories experienced in working with RNA and equipped with confocal microscopy instruments. The wet lab experiments in preparing the amplicon library take 2–3 d, followed by variable sequencing times depending on the sample size and target gene number. The spatially resolved single-cell profiles enable downstream analysis, including cell type classification, cell cycle identification and determination of RNA life cycle kinetic parameters through computational analysis guided by the established tutorials. This spatial omics toolkit will help users to better understand spatial and temporal RNA dynamics in heterogeneous cells and tissues.Key pointsThis protocol for highly multiplexed in situ profiling of spatial transcriptome uses STARmap PLUS, RIBOmap or TEMPOmap to selectively convert targeted RNAs, ribosome-bound mRNAs or metabolically labeled RNAs to DNA amplicons with gene-unique barcodes, which are read out through imaging-based in situ sequencing.These new methodologies provide the analytical capacity to track the spatial and temporal dynamics of thousands of RNA species and their translational status in intact cells and tissues.This is a preview of subscription content, access via your institutionAccess optionsAccess Nature and 54 other Nature Portfolio journalsGet Nature+, our best-value online-access subscription27,99 € / 30 dayscancel any timeLearn moreSubscribe to this journalReceive 12 print issues and online access269,00 € per yearonly 22,42 € per issueLearn moreBuy this articlePurchase on SpringerLinkInstant access to full article PDFBuy nowPrices may be subject to local taxes which are calculated during checkoutFig. 1: Overview of spatial omics technologies that track the RNA life cycle.Fig. 2: Probe design.Fig. 3: Schematic diagrams of STARmap PLUS, RIBOmap and TEMPOmap.Fig. 4: Schematic overview of the SEDAL color-coding and decoding principle.Fig. 5: Complete overview of experimental and computational procedures of STARmap PLUS, RIBOmap and TEMPOmap.Fig. 6: Data processing pipeline and representative images showing the anticipated results of STARmap PLUS, RIBOmap and TEMPOmap.Data availabilityThe datasets mentioned and discussed in this protocol are available in the supporting primary research articles15,16,17. All of the processed sequencing data are available in the Single Cell Portal (STARmap PLUS: https://singlecell.broadinstitute.org/single_cell/study/SCP1375, https://singlecell.broadinstitute.org/single_cell/study/SCP1830; RIBOmap: https://singlecell.broadinstitute.org/single_cell/study/SCP1835; TEMPOmap: https://singlecell.broadinstitute.org/single_cell/study/SCP1792) and Zenodo (STARmap PLUS: https://doi.org/10.5281/zenodo.7332091, https://doi.org/10.5281/zenodo.8327576; RIBOmap: https://doi.org/10.5281/zenodo.8041114; TEMPOmap: https://doi.org/10.5281/zenodo.7803716). The demo dataset for tutorial purposes is available via Single Cell Portal at https://singlecell.broadinstitute.org/single_cell/study/SCP2637 and via Zenodo at https://doi.org/10.5281/zenodo.11176779 (ref. 54). Additional information is available at the Wang Lab website (https://www.wangxiaolab.org). Additional raw images or data are available for research purposes upon request from the corresponding author.Code availabilityAll codes and analyses are available via GitHub (STARmap PLUS: https://github.com/wanglab-broad/mAD-analysis, https://github.com/wanglab-broad/mCNS-atlas; RIBOmap: https://github.com/wanglab-broad/RIBOmap-analysis; TEMPOmap: https://github.com/wanglab-broad/TEMPOmap) and Zenodo (STARmap PLUS: https://doi.org/10.5281/zenodo.7332091; RIBOmap: https://doi.org/10.5281/zenodo.8041114; TEMPOmap: https://doi.org/10.5281/zenodo.7803716). Probe design is available via GitHub at https://github.com/wanglab-broad/probe-design. Starfinder analysis tool will be maintained and updated at https://github.com/wanglab-broad/starfinder. ClusterMap segmentation method is available at https://github.com/wanglab-broad/ClusterMap. Additional requests can be made by contacting the corresponding author.ReferencesTabula Sapiens Consortium*. et al. The Tabula Sapiens: a multiple-organ, single-cell transcriptomic atlas of humans. Science 376, eabl4896 (2022).Article Google Scholar Elmentaite, R., Domínguez Conde, C., Yang, L. & Teichmann, S. A. Single-cell atlases: shared and tissue-specific cell types across human organs. Nat. Rev. Genet. 23, 395–410 (2022).Article PubMed Google Scholar 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–aaa6090 (2015).Article Google Scholar Eng, C.-H. L. et al. Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH. Nature 568, 235–239 (2019).Article PubMed PubMed Central Google Scholar Borm, L. E. et al. Scalable in situ single-cell profiling by electrophoretic capture of mRNA using EEL FISH. Nat. Biotechnol. 41, 222–231 (2023).PubMed Google Scholar 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).Article PubMed PubMed Central Google Scholar Sun, Y.-C. et al. Integrating barcoded neuroanatomy with spatial transcriptional profiling enables identification of gene correlates of projections. Nat. Neurosci. 24, 873–885 (2021).Article PubMed PubMed Central Google Scholar Lee, J. H. et al. Highly multiplexed subcellular RNA sequencing in situ. Science 343, 1360–1363 (2014).Article PubMed PubMed Central Google Scholar Alon, S. et al. Expansion sequencing: spatially precise in situ transcriptomics in intact biological systems. Science 371, eaax2656 (2021).Article PubMed PubMed Central Google Scholar Wang, X. et al. Three-dimensional intact-tissue sequencing of single-cell transcriptional states. Science 361, eaat5691 (2018).Article PubMed PubMed Central Google Scholar Schwanhüusser, B. et al. Global quantification of mammalian gene expression control. Nature 473, 337–342 (2011).Article Google Scholar Battich, N. et al. Sequencing metabolically labeled transcripts in single cells reveals mRNA turnover strategies. Science 367, 1151 (2020).Article PubMed Google Scholar VanInsberghe, M., van den Berg, J., Andersson-Rolf, A., Clevers, H. & van Oudenaarden, A. Single-cell Ribo-seq reveals cell cycle-dependent translational pausing. Nature 597, 561–565 (2021).Article PubMed Google Scholar Ozadam, H. et al. Single-cell quantification of ribosome occupancy in early mouse development. Nature 618, 1057–1064 (2023).Article PubMed PubMed Central Google Scholar Zeng, H. et al. Integrative in situ mapping of single-cell transcriptional states and tissue histopathology in a mouse model of Alzheimer’s disease. Nat. Neurosci. 26, 430–446 (2023).PubMed PubMed Central Google Scholar Zeng, H. et al. Spatially resolved single-cell translatomics at molecular resolution. Science 380, eadd3067 (2023).Article PubMed PubMed Central Google Scholar Ren, J. et al. Spatiotemporally resolved transcriptomics reveals the subcellular RNA kinetic landscape. Nat. Methods 20, 695–705 (2023).Article PubMed PubMed Central Google Scholar Ren, J., Luo, S., Shi, H. & Wang, X. Spatial omics advances for in situ RNA biology. Mol. Cell 84, 3737–3757 (2024).Article PubMed Google Scholar Liu, Y. et al. High-plex protein and whole transcriptome co-mapping at cellular resolution with spatial CITE-seq. Nat. Biotechnol. 41, 1405–1409 (2023).Article PubMed PubMed Central Google Scholar Ben-Chetrit, N. et al. Integration of whole transcriptome spatial profiling with protein markers. Nat. Biotechnol. 41, 788–793 (2023).Article PubMed PubMed Central Google Scholar Vickovic, S. et al. SM-Omics is an automated platform for high-throughput spatial multi-omics. Nat. Commun 13, 795 (2022).Article PubMed PubMed Central Google Scholar Frei, A. P. et al. Highly multiplexed simultaneous detection of RNAs and proteins in single cells. Nat. Methods 13, 269–275 (2016).Article PubMed PubMed Central Google Scholar Burke, K. S., Antilla, K. A. & Tirrell, D. A. A fluorescence in situ hybridization method to quantify mRNA translation by visualizing ribosome–mRNA interactions in single cells. ACS Cent. Sci. 3, 425–433 (2017).Article PubMed PubMed Central Google Scholar tom Dieck, S. et al. Direct visualization of newly synthesized target proteins in situ. Nat. Methods 12, 411–414 (2015).Article PubMed Google Scholar Erhard, F. et al. scSLAM-seq reveals core features of transcription dynamics in single cells. Nature 571, 419–423 (2019).Article PubMed Google Scholar Cao, J., Zhou, W., Steemers, F., Trapnell, C. & Shendure, J. Sci-fate characterizes the dynamics of gene expression in single cells. Nat. Biotechnol. 38, 980–988 (2020).Article PubMed PubMed Central Google Scholar Hendriks, G.-J. et al. NASC-seq monitors RNA synthesis in single cells. Nat. Commun. 10, 3138 (2019).Article PubMed PubMed Central Google Scholar Qiu, Q. et al. Massively parallel and time-resolved RNA sequencing in single cells with scNT-seq. Nat. Methods 17, 991–1001 (2020).Article PubMed PubMed Central Google Scholar Shi, H. et al. Spatial atlas of the mouse central nervous system at molecular resolution. Nature 625, 552–561 (2023).Article Google Scholar Maher, K. et al. Mitigating autocorrelation during spatially resolved transcriptomics data analysis. Preprint at bioRxiv https://doi.org/10.1101/2023.06.30.547258 (2023).Chen, H. et al. Branched chemically modified poly(A) tails enhance the translation capacity of mRNA. Nat. Biotechnol 43, 194–203 (2025).Article PubMed Google Scholar Chen, H. et al. Chemical and topological design of multicapped mRNA and capped circular RNA to augment translation. Nat. Biotechnol 43, 1128–1143 (2025).Article PubMed Google Scholar He, Y. et al. ClusterMap for multi-scale clustering analysis of spatial gene expression. Nat. Commun. 12, 5909 (2021).Article PubMed PubMed Central Google Scholar Li, Q. et al. Multimodal charting of molecular and functional cell states via in situ electro-sequencing. Cell 186, 2002–2017.e21 (2023).Article PubMed PubMed Central Google Scholar Sui, X. et al. Scalable spatial single-cell transcriptomics and translatomics in 3D thick tissue blocks. Preprint at bioRxiv https://doi.org/10.1101/2024.08.05.606553 (2024).Jao, C. Y. & Salic, A. Exploring RNA transcription and turnover in vivo by using click chemistry. Proc. Natl Acad. Sci. USA 105, 15779–15784 (2008).Article PubMed PubMed Central Google Scholar Rothschild, D. et al. Diversity of ribosomes at the level of rRNA variation associated with human health and disease. Cell Genom 4, 100629 (2024).Article PubMed PubMed Central Google Scholar Tang, Z. et al. Search and match across spatial omics samples at single-cell resolution. Nat. Methods 21, 1818–1829 (2024).Article PubMed PubMed Central Google Scholar Goltsev, Y. & Nolan, G. CODEX multiplexed tissue imaging. Nat. Rev. Immunol. 23, 613 (2023).Article PubMed Google Scholar Fazal, F. M. et al. Atlas of subcellular RNA localization revealed by APEX-seq. Cell 178, 473–490.e26 (2019).Article PubMed PubMed Central Google Scholar Tirosh, I. et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352, 189–196 (2016).Article PubMed PubMed Central Google Scholar Zhang, Y. et al. Gene panel selection for targeted spatial transcriptomics. Genome Biol. 25, 35 (2024).Article PubMed PubMed Central Google Scholar Chou, H.-H. Shared probe design and existing microarray reanalysis using PICKY. BMC Bioinformatics 11, 196 (2010).Article PubMed PubMed Central Google Scholar Bushnell, B. BBMap: a fast, accurate, splice-aware aligner. US Department of Energy Office of Scientific and Technical Information https://www.osti.gov/biblio/1241166 (2014).Zadeh, J. N. et al. NUPACK: analysis and design of nucleic acid systems. J. Comput. Chem. 32, 170–173 (2011).Article PubMed Google Scholar Altschul, S. F. et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25, 3389–3402 (1997).Article PubMed PubMed Central Google Scholar Weigert, M. et al. Star-convex polyhedra for 3D object detection and segmentation in microscopy. in Proc. 2020 IEEE Winter Conference on Applications of Computer Vision 3655–3662 (IEEE, 2020).Stirling, D. R. et al. CellProfiler 4: improvements in speed, utility and usability. BMC Bioinformatics 22, 433 (2021).Article PubMed PubMed Central Google Scholar Hao, Y. et al. Dictionary learning for integrative, multimodal and scalable single-cell analysis. Nat. Biotechnol. 42, 293–304 (2024).Article PubMed Google Scholar Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 1–5 (2018).Article Google Scholar Palla, G. et al. Squidpy: a scalable framework for spatial omics analysis. Nat. Methods 19, 171–178 (2022).Article PubMed PubMed Central Google Scholar Lee, J. H. et al. Fluorescent in situ sequencing (FISSEQ) of RNA for gene expression profiling in intact cells and tissues. Nat. Protoc. 10, 442–458 (2015).Article PubMed PubMed Central Google Scholar Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e21 (2019).Article PubMed PubMed Central Google Scholar Ren, J. et al. Example dataset and expected outcomes of Spatially resolved in situ profiling of mRNA life cycle at transcriptome scale in intact cells and tissues. Zenodo https://doi.org/10.5281/zenodo.11176779 (2024).Download referencesAcknowledgementsH.S. is supported by Helen Hay Whitney Foundation postdoctoral fellowship. X.W. gratefully acknowledges support from the Searle Scholars Program, Thomas D. and Virginia W. Cabot Professorship, Edward Scolnick Professorship, Ono Pharma Breakthrough Science Initiative Award, Packard Fellowship for Science and Engineering, Merkin Institute Fellowship, NIH DP2 New Innovator Award, and Stanley Center gift from the Broad Institute. We thank Y. Zhou, W. X. Wang, M. Wu, Q. Zhang and Y. He for their helpful suggestions to the manuscript.Author informationAuthor notesThese authors contributed equally: Jingyi Ren, Hu Zeng, Jiahao Huang.Authors and AffiliationsDepartment of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USAJingyi Ren, Hu Zeng, Jiahao Huang, Jiakun Tian, Hailing Shi, Xin Sui, Connie Kangni Wang, Haowen Zhou, Zefang Tang, Shuchen Luo & Xiao WangBroad Institute of MIT and Harvard, Cambridge, MA, USAJingyi Ren, Hu Zeng, Jiahao Huang, Jiakun Tian, Morgan Wu, Hailing Shi, Xin Sui, Connie Kangni Wang, Haowen Zhou, Zefang Tang, Shuchen Luo & Xiao WangAuthorsJingyi RenView author publicationsSearch author on:PubMed Google ScholarHu ZengView author publicationsSearch author on:PubMed Google ScholarJiahao HuangView author publicationsSearch author on:PubMed Google ScholarJiakun TianView author publicationsSearch author on:PubMed Google ScholarMorgan WuView author publicationsSearch author on:PubMed Google ScholarHailing ShiView author publicationsSearch author on:PubMed Google ScholarXin SuiView author publicationsSearch author on:PubMed Google ScholarConnie Kangni WangView author publicationsSearch author on:PubMed Google ScholarHaowen ZhouView author publicationsSearch author on:PubMed Google ScholarZefang TangView author publicationsSearch author on:PubMed Google ScholarShuchen LuoView author publicationsSearch author on:PubMed Google ScholarXiao WangView author publicationsSearch author on:PubMed Google ScholarContributionsJ.R. and H. Zeng. designed the protocols and performed the experiments. J.H., X.S., C.K.W., H. Zhou, M.W., Z.T. and S.L. performed the computational analysis. J.H. and M.W. cleaned up the starfinder package. J.R., H. Zeng and J.H. wrote the manuscript. H.S., J.T. and X.S. provided critical comments for the manuscript. X.W. supervised the study. All authors critically reviewed and revised the manuscript.Corresponding authorCorrespondence to Xiao Wang.Ethics declarationsCompeting interestsX.W. is a scientific cofounder of Stellaromics. X.W., J.R. and H. Zeng are inventors on patent applications (International Application No. PCT/US2022/031275, No. PCT/US2022/035271 and No. PCT/US2022/028012) related to STARmap PLUS, RIBOmap and TEMPOmap. The other authors declare no competing interests.Peer reviewPeer review informationNature Protocols thanks Junyue Cao and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.Additional informationPublisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Key referencesZeng, H. et al. Nat. Neurosci. 26, 430–446 (2023): https://doi.org/10.1038/s41593-022-01251-xZeng, H. et al. Science 380, eadd3067 (2023): https://doi.org/10.1126/science.add3067Ren, J. et al. Nat. Methods 20, 695–705 (2023): https://doi.org/10.1038/s41592-023-01829-8Shi, H. et al. Nature 622, 552–561 (2023): https://doi.org/10.1038/s41586-023-06569-5Chen, H. et al. Nat. Biotechnol. 43, 194–203 (2025): https://doi.org/10.1038/s41587-024-02174-7Supplementary informationReporting SummarySupplementary Table 1Complete list of primer and padlock probe hybridization regions for human transcriptome.Supplementary Table 2Complete list of primer and padlock probe hybridization regions for mouse transcriptome.Rights and permissionsSpringer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.Reprints and permissionsAbout this article