Evidence map›Paper›PMID 40763304›Full record

ArticlePLoS computational biology2025

COEXIST: Coordinated single-cell integration of serial multiplexed tissue images.

Robert T Heussner, Cameron F Watson, Christopher Z Eddy, Kunlun Wang, Eric M Cramer, Allison L Creason, Gordon B Mills, Young Hwan Chang

Abstract read
In one paragraph

Article in PLoS computational biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Robert T HeussnerDepartment of Biomedical Engineering and Computational Biology Program, Oregon Health & Science University (OHSU), Portland, Oregon, United States of America.
Cameron F WatsonDepartment of Biomedical Engineering and Computational Biology Program, Oregon Health & Science University (OHSU), Portland, Oregon, United States of America.
Christopher Z EddyCancer Early Detection Advanced Research (CEDAR), Knight Cancer Institute, OHSU, Portland, Oregon, United States of America.
Kunlun WangDepartment of Biomedical Engineering and Computational Biology Program, Oregon Health & Science University (OHSU), Portland, Oregon, United States of America.
Eric M CramerDepartment of Biomedical Engineering and Computational Biology Program, Oregon Health & Science University (OHSU), Portland, Oregon, United States of America.
Allison L CreasonDepartment of Biomedical Engineering and Computational Biology Program, Oregon Health & Science University (OHSU), Portland, Oregon, United States of America.
Gordon B MillsKnight Cancer Institute, Oregon Health & Science University, Portland, Oregon, United States of America.
Young Hwan ChangDepartment of Biomedical Engineering and Computational Biology Program, Oregon Health & Science University (OHSU), Portland, Oregon, United States of America.ORCID 0000-0001-8764-1959

Funding

Omic and Multidimensional Spatial Atlas of Metastatic Breast CancerU2CCA233280 · NCI · OREGON HEALTH & SCIENCE UNIVERSITY · PI GOECKS, JEREMY · 2018 to 2023
$9.7M
Integrative single cell analyses of inflammation-derived circulating hybrid cells to identify aggressive diseaseR01CA253860 · NCI · OREGON HEALTH & SCIENCE UNIVERSITY · PI CHANG, YOUNG HWAN, WONG, MELISSA H. · 2021 to 2025
$3.0M
NCI NIH HHS R01 CA253860NCI NIH HHS U2C CA233280
6 · The paper itself

Abstract

Multiplexed tissue imaging (MTI) and other spatial profiling technologies commonly utilize serial tissue sectioning to comprehensively profile samples by imaging each section with unique biomarker panels or assays. The dependence on serial sections is attributed to technological limitations of MTI panel size or incompatible multi-assay protocols. Although image registration can align serially sectioned MTIs, integration at the single-cell level poses a challenge due to inherent biological heterogeneity. Existing computational methods overlook both cell population heterogeneity across modalities and spatial information, which are critical for effectively completing this task. To address this problem, we first use Monte-Carlo simulations to estimate the overlap between serial 5μm-thick sections. We then introduce COEXIST, a novel algorithm that synergistically combines shared molecular profiles with spatial information to seamlessly integrate serial sections at the single-cell level. We demonstrate COEXIST necessity and performance across several applications. These include combining MTI panels for improved spatial single-cell profiling, rectification of miscalled cell phenotypes using a single MTI panel, and the comparison of MTI platforms at single-cell resolution. COEXIST not only elevates MTI platform validation but also overcomes the constraints of MTI's panel size and the limitation of full nuclei on a single slide, capturing more intact nuclei in consecutive sections and thus enabling deeper profiling of cell lineages and functional states.

Indexed as

Image Processing, Computer-AssistedSingle-Cell AnalysisAlgorithmsAnimalsComputational BiologyHumansMonte Carlo Method

Identifiers

PMID40763304
PMCPMC12338771

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.