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

Kenneth H. Hu, John P. Eichorst, Chris S. McGinnis, David M. Patterson, Eric D. Chow, Kelly Kersten, Stephen C. Jameson, Zev J. Gartner, Arjun A. Rao, Matthew F. Krummel

Research output: Contribution to journalArticlepeer-review

29 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)833-843
Number of pages11
JournalNature Methods
Issue number8
StatePublished - Aug 1 2020

Bibliographical note

Funding Information:
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.

Publisher Copyright:
© 2020, The Author(s), under exclusive licence to Springer Nature America, Inc.

PubMed: MeSH publication types

  • Journal Article
  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't


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