Evidence map›Paper›PMID 40839662›Full record

ArticlePLoS computational biology2025

CytoSpatio: Learning cell type spatial relationships using multirange, multitype point process models.

Haoran Chen, Yangyuan Zhang, Robert F Murphy

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. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Haoran ChenRay and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.
Yangyuan ZhangRay and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.
Robert F MurphyRay and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.ORCID 0000-0003-0358-901X

Funding

Integrated Interdisciplinary, inter-university PhD Program Computational Biology (T32)T32EB009403 · NIBIB · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI BAHAR, IVET, BAR-JOSEPH, ZIV · 2009 to 2023
$4.4M
Computational tools for uniform processing and integration of human reference atlas data [2 of 5]OT2OD033761 · OD · CARNEGIE-MELLON UNIVERSITY · PI PATEN, BENEDICT, RUFFALO, MATTHEW · 2022 to 2025
$4.2M
Comprehensive, Flexible and FAIR Tools for the HuBMAP HIVEOT2OD026682 · OD · CARNEGIE-MELLON UNIVERSITY · PI PATEN, BENEDICT, RUFFALO, MATTHEW · 2018 to 2021
$3.4M
NIBIB NIH HHS T32 EB009403NIH HHS OT2 OD026682NIH HHS OT2 OD033761
6 · The paper itself

Abstract

Recent advances in multiplexed fluorescence imaging have provided new opportunities for deciphering the complex spatial relationships among various cell types across diverse tissues. We introduce CytoSpatio, open-source software that constructs generative, multirange, and multitype point process models that capture interactions among multiple cell types at various distances simultaneously. On analyzing five cell types across five tissues, our software showed consistent spatial relationships within the same tissue type, with certain cell types like proliferating T cells consistently clustering across tissue types. It also revealed that the attraction-repulsion relationships between cell types like B cells and CD4-positive T cells vary with tissue type. Models for a published dataset demonstrated consistency with prior findings. CytoSpatio can also generate synthetic tissue patterns from learned models, a capability not provided by previous descriptive, motif-based approaches. This potentially allows spatially realistic simulations of how cell relationships affect tissue biochemistry.

Indexed as

Models, BiologicalSoftwareAlgorithmsAnimalsCD4-Positive T-LymphocytesComputational BiologyComputer SimulationHumansMice

Identifiers

PMID40839662
PMCPMC12396756

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Registered trials

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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.