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Membrane marker selection for segmenting single cell spatial proteomics data

Author

Listed:
  • Monica T. Dayao

    (Joint Carnegie Mellon University-University of Pittsburgh Ph.D. Program in Computational Biology
    Computational Biology Department, School of Computer Science, Carnegie Mellon University)

  • Maigan Brusko

    (Immunology and Laboratory Medicine, University of Florida)

  • Clive Wasserfall

    (Immunology and Laboratory Medicine, University of Florida)

  • Ziv Bar-Joseph

    (Computational Biology Department, School of Computer Science, Carnegie Mellon University
    Machine Learning Department, School of Computer Science, Carnegie Mellon University)

Abstract

The ability to profile spatial proteomics at the single cell level enables the study of cell types, their spatial distribution, and interactions in several tissues and conditions. Current methods for cell segmentation in such studies rely on known membrane or cell boundary markers. However, for many tissues, an optimal set of markers is not known, and even within a tissue, different cell types may express different markers. Here we present RAMCES, a method that uses a convolutional neural network to learn the optimal markers for a new sample and outputs a weighted combination of the selected markers for segmentation. Testing RAMCES on several existing datasets indicates that it correctly identifies cell boundary markers, improving on methods that rely on a single marker or those that extend nuclei segmentations. Application to new spatial proteomics data demonstrates its usefulness for accurately assigning cell types based on the proteins expressed in segmented cells.

Suggested Citation

  • Monica T. Dayao & Maigan Brusko & Clive Wasserfall & Ziv Bar-Joseph, 2022. "Membrane marker selection for segmenting single cell spatial proteomics data," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-29667-w
    DOI: 10.1038/s41467-022-29667-w
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    References listed on IDEAS

    as
    1. Amir Alavi & Matthew Ruffalo & Aiyappa Parvangada & Zhilin Huang & Ziv Bar-Joseph, 2018. "A web server for comparative analysis of single-cell RNA-seq data," Nature Communications, Nature, vol. 9(1), pages 1-11, December.
    2. Alex Sigal & Ron Milo & Ariel Cohen & Naama Geva-Zatorsky & Yael Klein & Yuvalal Liron & Nitzan Rosenfeld & Tamar Danon & Natalie Perzov & Uri Alon, 2006. "Variability and memory of protein levels in human cells," Nature, Nature, vol. 444(7119), pages 643-646, November.
    Full references (including those not matched with items on IDEAS)

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