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Optimized single-nucleus transcriptional profiling by combinatorial indexing


Single-cell combinatorial indexing RNA sequencing (sci-RNA-seq) is a powerful method for recovering gene expression data from an exponentially scalable number of individual cells or nuclei. However, sci-RNA-seq is a complex protocol that has historically exhibited variable performance on different tissues, as well as lower sensitivity than alternative methods. Here, we report a simplified, optimized version of the sci-RNA-seq protocol with three rounds of split-pool indexing that is faster, more robust and more sensitive and has a higher yield than the original protocol, with reagent costs on the order of 1 cent per cell or less. The total hands-on time from nuclei isolation to final library preparation takes 2–3 d, depending on the number of samples sharing the experiment. The improvements also allow RNA profiling from tissues rich in RNases like older mouse embryos or adult tissues that were problematic for the original method. We showcase the optimized protocol via whole-organism analysis of an E16.5 mouse embryo, profiling ~380,000 nuclei in a single experiment. Finally, we introduce a ‘Tiny-Sci’ protocol for experiments in which input material is very limited.

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Fig. 1: Summary of the optimized sci-RNA-seq3 protocol.
Fig. 2: Detailed schematic of the sci-RNA-seq3 combinatorial indexing strategy.
Fig. 3: Yoyo-1 stained nuclei from an E16.5 mouse embryo visualized on a Countess Cell Counter.
Fig. 4: Smashing tissue in a foil packet on a slab of dry ice with a hammer.
Fig. 5: Nuclear pellet size.
Fig. 6: Visualizing protease digestion of the nuclei by Yoyo-1 staining.
Fig. 7: Evaluating the libraries after PCR.
Fig. 8: High-quality data of E16.5 mouse embryo generated by application of the optimized sci-RNA-seq3 protocol.

Data availability

Raw data from the E16.5 mouse embryo is available for download from the NCBI Gene Expression Omnibus repository with accession number GSE186824. Original photos and gels have been deposited at Figshare (


  1. Domcke, S. et al. A human cell atlas of fetal chromatin accessibility. Science 370, eaba7612 (2020).

    Article  CAS  Google Scholar 

  2. Cusanovich, D. A. et al. Multiplex single cell profiling of chromatin accessibility by combinatorial cellular indexing. Science 348, 910–914 (2015).

    Article  CAS  Google Scholar 

  3. Cao, J. et al. Comprehensive single-cell transcriptional profiling of a multicellular organism. Science 357, 661–667 (2017).

    Article  CAS  Google Scholar 

  4. Cao, J. et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature 566, 496–502 (2019).

    Article  CAS  Google Scholar 

  5. Rosenberg, A. B. et al. Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding. Science 360, 176–182 (2018).

    Article  CAS  Google Scholar 

  6. Datlinger, P. et al. Ultra-high throughput single-cell RNA sequencing by combinatorial fluidic indexing. Nat. Methods 18, 635–642 (2021).

    Article  CAS  Google Scholar 

  7. Lareau, C. A. et al. Droplet-based combinatorial indexing for massive-scale single-cell chromatin accessibility. Nat. Biotechnol. 37, 916–924 (2019).

    Article  CAS  Google Scholar 

  8. Vitak, S. A. et al. Sequencing thousands of single-cell genomes with combinatorial indexing. Nat. Methods 14, 302–308 (2017).

    Article  CAS  Google Scholar 

  9. Yin, Y. et al. High-throughput single-cell sequencing with linear amplification. Mol. Cell 76, 676–690.e10 (2019).

    Article  CAS  Google Scholar 

  10. Ramani, V. et al. Massively multiplex single-cell Hi-C. Nat. Methods 14, 263–266 (2017).

    Article  CAS  Google Scholar 

  11. Mulqueen, R. M. et al. Highly scalable generation of DNA methylation profiles in single cells. Nat. Biotechnol. 36, 428–431 (2018).

    Article  CAS  Google Scholar 

  12. Cao, J. et al. Joint profiling of chromatin accessibility and gene expression in thousands of single cells. Science 361, 1380–1385 (2018).

    Article  CAS  Google Scholar 

  13. 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  CAS  Google Scholar 

  14. Hwang, B. et al. SCITO-seq: single-cell combinatorial indexed cytometry sequencing. Nat. Methods 18, 903–911 (2021).

    Article  CAS  Google Scholar 

  15. Wang, Q. et al. CoBATCH for high-throughput single-cell epigenomic profiling. Mol. Cell 76, 206–216.e7 (2019).

    Article  CAS  Google Scholar 

  16. Srivatsan, S. R. et al. Massively multiplex chemical transcriptomics at single-cell resolution. Science 367, 45–51 (2020).

    Article  CAS  Google Scholar 

  17. Srivatsan, S. R. et al. Embryo-scale, single-cell spatial transcriptomics. Science 373, 111–117 (2021).

    Article  CAS  Google Scholar 

  18. Cao, J. et al. A human cell atlas of fetal gene expression. Science 370, eaba7721 (2020).

    Article  CAS  Google Scholar 

  19. Qiu, C. et al. Systematic reconstruction of cellular trajectories across mouse embryogenesis. Nat. Genet. 54, 328–341 (2022).

    Article  CAS  Google Scholar 

  20. Ehrenberg, L., Fedorcsak, I. & Solymosy, F. Diethyl pyrocarbonate in nucleic acid research. Prog. Nucleic Acid Res. Mol. Biol. 16, 189–262 (1976).

    Article  CAS  Google Scholar 

  21. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    Article  CAS  Google Scholar 

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We thank Bridget Kulesakara for the sucrose buffer advice, Jase Gehring for fixative expertise and Riza Daza for helpful feedback. We thank Diana O’Day, Mai Le, Roshella Gomes, Saskia Ilcisin and Dana Jackson for protocol testing, and the entire Shendure Lab for support and encouragement. This work was funded in part by NIH R01HG010632 to J.S. and C.T., UM1HG011586 to J.S. and B.J.B., R35GM137916 to B.J.B. and T32HG000035 to E.N. J.S. is an Investigator of the Howard Hughes Medical Institute.

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Authors and Affiliations



B.K.M. developed the improved protocol with input and testing from E.N., M.P., R.G.-G., S.S., R.B.-G. and J.C. C.Q. performed all data analysis. E.N. collected and staged mouse embryos. J.C. developed the original protocol. B.J.B., C.T. and J.S. supervised aspects of the work, with J.S. providing overall oversight. B.K.M., C.Q. and J.S. wrote the paper, with input from all authors.

Corresponding authors

Correspondence to Beth K. Martin or Jay Shendure.

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Competing interests

J.S. is a scientific advisory board member, consultant and/or cofounder of Cajal Neuroscience, Guardant Health, Maze Therapeutics, Camp4 Therapeutics, Phase Genomics, Adaptive Biotechnologies and Scale Biosciences. C.T. is a founder of Scale Biosciences. All other authors have no competing interests.

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Nature Protocols thanks Linas Mazutis, Samantha Morris and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.2

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Key references using this protocol

Qiu, C. et al. Nat. Genet. 54, 328–341 (2022):

Cao, J. et al. Science 357, 661–667 (2017):

Cao, J. et al. Nature 566, 496–502 (2019):

Supplementary information

Supplementary Information

Supplementary Figs. 1–4 and Supplementary Results

Supplementary Table 1

Indexed primer sets

Supplementary Video 1

Demonstration of nuclei isolation

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Martin, B.K., Qiu, C., Nichols, E. et al. Optimized single-nucleus transcriptional profiling by combinatorial indexing. Nat Protoc 18, 188–207 (2023).

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