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ZipSeq: barcoding for real-time mapping of single cell transcriptomes

Nature Methods volume 17, pages 833–843 (2020)Cite this article

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Abstract

Spatial transcriptomics seeks to integrate single cell transcriptomic data within the three-dimensional space of multicellular biology. Current methods to correlate a cell’s position with its transcriptome in living tissues have various limitations. We developed an approach, called ‘ZipSeq’, that uses patterned illumination and photocaged oligonucleotides to serially print barcodes (‘zipcodes’) onto live cells in intact tissues, in real time and with an on-the-fly selection of patterns. Using ZipSeq, we mapped gene expression in three settings: in vitro wound healing, live lymph node sections and a live tumor microenvironment. In all cases, we discovered new gene expression patterns associated with histological structures. In the tumor microenvironment, this demonstrated a trajectory of myeloid and T cell differentiation from the periphery inward. A combinatorial variation of ZipSeq efficiently scales in the number of regions defined, providing a pathway for complete mapping of live tissues, subsequent to real-time imaging or perturbation.

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Fig. 1: Design of ZipSeq oligonucleotides, imaging setup and workflow.
Fig. 2: ZipSeq mapping of a live cell monolayer following wounding.
Fig. 3: ZipSeq mapping of single immune cell transcriptomes within a live lymph node section.
Fig. 4: ZipSeq mapping of immune cell transcriptional states in the tumor microenvironment.
Fig. 5: Increased mapping resolution reveals spatial patterns of gene expression in cell subpopulations.

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Data availability

For all scRNA-seq studies described here, transcript counts as determined by CellRanger count function as well as raw zipcode fastqs/counts as well as the modified genome for transgene alignment during CellRanger count can be found in the Gene Expression Omnibus under accession number GSE145502.

Lists of gene hits from differential expression analysis can be found in the Extended Data.

Raw image files from which figures are derived from can be found on Dryad at https://doi.org/10.7272/Q6H993DV.

Code availability

Visual Basics code for custom Metamorph User Program for delineation of multi-FOV spanning ROI for Mosaic illumination can be found on Github: https://github.com/BIDCatUCSF/VB-Plugin-for-Patterned-Illumination.

Python script used to generate zipcode counts from fastq available from CITE-seq10 (https://hoohm.github.io/CITE-seq-Count/).

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Acknowledgements

We thank the Biological Imaging Development Center at the University of California San Francisco (UCSF) for help with microscopy data collection and instrumentation. We also thank the Parnassus Flow Cytometry Core for flow cytometry instrumentation, supported by grant no. P30DK063720 and the Institute for Human Genomics at UCSF for sequencing and bioinformatics support. In addition, we thank the Computational Biology and Informatics core at the UCSF Helen Diller Family Comprehensive Cancer Center for computing resources. This work was supported from NIH/NCI grant nos. P30DK063720 (M.F.K.), 1R01CA197363 (M.F.K.) and R01GM135462 (Z.J.G.), and by the Parker Institute for Cancer Immunotherapy Opportunity grant (PICI). K.H.H. was supported by a NIH T32 training grant (no. 5T32CA177555-02) and is a PICI Scholar. M.F.K. is a PICI member researcher.

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Authors and Affiliations

  1. Department of Pathology, University of California San Francisco, San Francisco, CA, USA

    Kenneth H. Hu, John P. Eichorst, Kelly Kersten, Arjun A. Rao & Matthew F. Krummel

  2. ImmunoX Initiative, University of California San Francisco, San Francisco, CA, USA

    John P. Eichorst, Arjun A. Rao & Matthew F. Krummel

  3. Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA, USA

    Chris S. McGinnis, David M. Patterson & Zev J. Gartner

  4. Department of Biochemistry and Biophysics, Center for Advanced Technology, University of California San Francisco, San Francisco, CA, USA

    Eric D. Chow

  5. Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA

    Stephen C. Jameson

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Contributions

K.H.H. conceived and performed the experiments, analyzed data and wrote the manuscript. M.F.K. conceived the experiments, provided administrative and financial support, and wrote the manuscript. K.K. generated the PyMT-chOVA cell line. C.S.M., D.M.P., E.D.C. and Z.J.G. generated the lipid anchored oligonucleotide. J.P.E. developed custom interface for controlling illumination patterns during imaging. S.C.J. generated the KLF2-GFP reporter mouse and associated IF data. A.A.R. assisted with analysis of scRNA-seq data.

Corresponding author

Correspondence to Matthew F. Krummel.

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Competing interests

K.H.H. and M.F.K. are listed on a patent application regarding the ZipSeq approach.

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Peer review information Rita Strack was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–8.

Reporting Summary

Supplementary Data

All gene gits from differential expression analysis shown in volcano plots (Figs. 2f, 3e,f and 4l and Supplementary Fig. 5).

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Hu, K.H., Eichorst, J.P., McGinnis, C.S. et al. ZipSeq: barcoding for real-time mapping of single cell transcriptomes. Nat Methods 17, 833–843 (2020). https://doi.org/10.1038/s41592-020-0880-2

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