IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v13y2022i1d10.1038_s41467-022-29667-w.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-022-29667-w
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-022-29667-w?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kazunari Iwamoto & Yuki Shindo & Koichi Takahashi, 2016. "Modeling Cellular Noise Underlying Heterogeneous Cell Responses in the Epidermal Growth Factor Signaling Pathway," PLOS Computational Biology, Public Library of Science, vol. 12(11), pages 1-18, November.
    2. UnJin Lee & John J Skinner & John Reinitz & Marsha Rich Rosner & Eun-Jin Kim, 2015. "Noise-Driven Phenotypic Heterogeneity with Finite Correlation Time in Clonal Populations," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-17, July.
    3. David A Sivak & Matt Thomson, 2014. "Environmental Statistics and Optimal Regulation," PLOS Computational Biology, Public Library of Science, vol. 10(9), pages 1-12, September.
    4. Ming Ni & Antoine L Decrulle & Fanette Fontaine & Alice Demarez & Francois Taddei & Ariel B Lindner, 2012. "Pre-Disposition and Epigenetics Govern Variation in Bacterial Survival upon Stress," PLOS Genetics, Public Library of Science, vol. 8(12), pages 1-11, December.
    5. Liang Qiao & Robert B Nachbar & Ioannis G Kevrekidis & Stanislav Y Shvartsman, 2007. "Bistability and Oscillations in the Huang-Ferrell Model of MAPK Signaling," PLOS Computational Biology, Public Library of Science, vol. 3(9), pages 1-8, September.
    6. Yelyzaveta Shlyakhtina & Bianca Bloechl & Maximiliano M. Portal, 2023. "BdLT-Seq as a barcode decay-based method to unravel lineage-linked transcriptome plasticity," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    7. Anissa Guillemin & Ronan Duchesne & Fabien Crauste & Sandrine Gonin-Giraud & Olivier Gandrillon, 2019. "Drugs modulating stochastic gene expression affect the erythroid differentiation process," PLOS ONE, Public Library of Science, vol. 14(11), pages 1-19, November.
    8. Steven A Frank, 2013. "Evolution of Robustness and Cellular Stochasticity of Gene Expression," PLOS Biology, Public Library of Science, vol. 11(6), pages 1-3, June.
    9. Alok Maity & Roy Wollman, 2020. "Information transmission from NFkB signaling dynamics to gene expression," PLOS Computational Biology, Public Library of Science, vol. 16(8), pages 1-16, August.
    10. Suzanne Gaudet & Sabrina L Spencer & William W Chen & Peter K Sorger, 2012. "Exploring the Contextual Sensitivity of Factors that Determine Cell-to-Cell Variability in Receptor-Mediated Apoptosis," PLOS Computational Biology, Public Library of Science, vol. 8(4), pages 1-15, April.
    11. Hui Zhang & Yueling Chen & Yong Chen, 2012. "Noise Propagation in Gene Regulation Networks Involving Interlinked Positive and Negative Feedback Loops," PLOS ONE, Public Library of Science, vol. 7(12), pages 1-8, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-29667-w. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.