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Advances in mixed cell deconvolution enable quantification of cell types in spatial transcriptomic data

Author

Listed:
  • Patrick Danaher

    (NanoString Technologies)

  • Youngmi Kim

    (NanoString Technologies)

  • Brenn Nelson

    (NanoString Technologies)

  • Maddy Griswold

    (NanoString Technologies)

  • Zhi Yang

    (NanoString Technologies)

  • Erin Piazza

    (NanoString Technologies)

  • Joseph M. Beechem

    (NanoString Technologies)

Abstract

Mapping cell types across a tissue is a central concern of spatial biology, but cell type abundance is difficult to extract from spatial gene expression data. We introduce SpatialDecon, an algorithm for quantifying cell populations defined by single cell sequencing within the regions of spatial gene expression studies. SpatialDecon incorporates several advancements in gene expression deconvolution. We propose an algorithm harnessing log-normal regression and modelling background, outperforming classical least-squares methods. We compile cell profile matrices for 75 tissue types. We identify genes whose minimal expression by cancer cells makes them suitable for immune deconvolution in tumors. Using lung tumors, we create a dataset for benchmarking deconvolution methods against marker proteins. SpatialDecon is a simple and flexible tool for mapping cell types in spatial gene expression studies. It obtains cell abundance estimates that are spatially resolved, granular, and paired with highly multiplexed gene expression data.

Suggested Citation

  • Patrick Danaher & Youngmi Kim & Brenn Nelson & Maddy Griswold & Zhi Yang & Erin Piazza & Joseph M. Beechem, 2022. "Advances in mixed cell deconvolution enable quantification of cell types in spatial transcriptomic data," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-28020-5
    DOI: 10.1038/s41467-022-28020-5
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    Cited by:

    1. Qi Zhang & Rober Abdo & Cristiana Iosef & Tomonori Kaneko & Matthew Cecchini & Victor K. Han & Shawn Shun-Cheng Li, 2022. "The spatial transcriptomic landscape of non-small cell lung cancer brain metastasis," Nature Communications, Nature, vol. 13(1), pages 1-19, December.
    2. Chunman Zuo & Yijian Zhang & Chen Cao & Jinwang Feng & Mingqi Jiao & Luonan Chen, 2022. "Elucidating tumor heterogeneity from spatially resolved transcriptomics data by multi-view graph collaborative learning," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    3. Thomas M. Goralski & Lindsay Meyerdirk & Libby Breton & Laura Brasseur & Kevin Kurgat & Daniella DeWeerd & Lisa Turner & Katelyn Becker & Marie Adams & Daniel J. Newhouse & Michael X. Henderson, 2024. "Spatial transcriptomics reveals molecular dysfunction associated with cortical Lewy pathology," Nature Communications, Nature, vol. 15(1), pages 1-20, December.
    4. Xuejiao Liu & Simin Zhao & Keke Wang & Liting Zhou & Ming Jiang & Yunfeng Gao & Ran Yang & Shiwen Yan & Wen Zhang & Bingbing Lu & Feifei Liu & Ran Zhao & Wenting Liu & Zihan Zhang & Kangdong Liu & Xia, 2023. "Spatial transcriptomics analysis of esophageal squamous precancerous lesions and their progression to esophageal cancer," Nature Communications, Nature, vol. 14(1), pages 1-20, December.
    5. Wen Juan Tu & Michelle Melino & Jenny Dunn & Robert D. McCuaig & Helle Bielefeldt-Ohmann & Sofiya Tsimbalyuk & Jade K. Forwood & Taniya Ahuja & John Vandermeide & Xiao Tan & Minh Tran & Quan Nguyen & , 2023. "In vivo inhibition of nuclear ACE2 translocation protects against SARS-CoV-2 replication and lung damage through epigenetic imprinting," Nature Communications, Nature, vol. 14(1), pages 1-21, December.
    6. Haoyang Li & Juexiao Zhou & Zhongxiao Li & Siyuan Chen & Xingyu Liao & Bin Zhang & Ruochi Zhang & Yu Wang & Shiwei Sun & Xin Gao, 2023. "A comprehensive benchmarking with practical guidelines for cellular deconvolution of spatial transcriptomics," Nature Communications, Nature, vol. 14(1), pages 1-10, December.

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