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Spatial transcriptomics using combinatorial fluorescence spectral and lifetime encoding, imaging and analysis

Author

Listed:
  • Tam Vu

    (University of California, Irvine
    University of California, Irvine)

  • Alexander Vallmitjana

    (University of California, Irvine
    University of California, Irvine)

  • Joshua Gu

    (University of California, Irvine
    University of California, Irvine)

  • Kieu La

    (University of California, Irvine)

  • Qi Xu

    (University of California, Irvine)

  • Jesus Flores

    (University of California, Irvine
    CIRM Stem Cell Research Biotechnology Training Program at California State University, Long Beach)

  • Jan Zimak

    (University of California, Irvine)

  • Jessica Shiu

    (University of California, Irvine)

  • Linzi Hosohama

    (University of California, Irvine)

  • Jie Wu

    (University of California, Irvine
    University of California, Irvine)

  • Christopher Douglas

    (University of California, Irvine)

  • Marian L. Waterman

    (University of California, Irvine
    University of California, Irvine)

  • Anand Ganesan

    (University of California, Irvine
    University of California, Irvine
    University of California, Irvine)

  • Per Niklas Hedde

    (University of California, Irvine
    University of California, Irvine
    University of California, Irvine)

  • Enrico Gratton

    (University of California, Irvine
    University of California, Irvine
    University of California, Irvine)

  • Weian Zhao

    (University of California, Irvine
    University of California, Irvine
    University of California, Irvine
    University of California, Irvine)

Abstract

Multiplexed mRNA profiling in the spatial context provides new information enabling basic research and clinical applications. Unfortunately, existing spatial transcriptomics methods are limited due to either low multiplexing or complexity. Here, we introduce a spatialomics technology, termed Multi Omic Single-scan Assay with Integrated Combinatorial Analysis (MOSAICA), that integrates in situ labeling of mRNA and protein markers in cells or tissues with combinatorial fluorescence spectral and lifetime encoded probes, spectral and time-resolved fluorescence imaging, and machine learning-based decoding. We demonstrate MOSAICA’s multiplexing scalability in detecting 10-plex targets in fixed colorectal cancer cells using combinatorial labeling of five fluorophores with facile error-detection and removal of autofluorescence. MOSAICA’s analysis is strongly correlated with sequencing data (Pearson’s r = 0.96) and was further benchmarked using RNAscopeTM and LGC StellarisTM. We further apply MOSAICA for multiplexed analysis of clinical melanoma Formalin-Fixed Paraffin-Embedded (FFPE) tissues. We finally demonstrate simultaneous co-detection of protein and mRNA in cancer cells.

Suggested Citation

  • Tam Vu & Alexander Vallmitjana & Joshua Gu & Kieu La & Qi Xu & Jesus Flores & Jan Zimak & Jessica Shiu & Linzi Hosohama & Jie Wu & Christopher Douglas & Marian L. Waterman & Anand Ganesan & Per Niklas, 2022. "Spatial transcriptomics using combinatorial fluorescence spectral and lifetime encoding, imaging and analysis," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-021-27798-0
    DOI: 10.1038/s41467-021-27798-0
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