Evidence map›Paper›PMID 41932341›Full record

ArticleCell systems2026

Single-cell morphodynamical trajectories enable prediction of gene expression accompanying cell state change.

Jeremy Copperman, Ian C Mclean, Sean M Gross, Jalim Singh, Vaibhav Murthy, Young Hwan Chang, Alexander E Davies, Daniel M Zuckerman, Laura M Heiser

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cell systems, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Jeremy CoppermanCancer Early Detection Advanced Research Center, Oregon Health and Science University, Portland, OR 97239, USA. Electronic address: copperma@ohsu.edu.
Ian C McleanDepartment of Biomedical Engineering, Oregon Health and Science University, Portland, OR 97239, USA.
Sean M GrossDepartment of Biomedical Engineering, Oregon Health and Science University, Portland, OR 97239, USA.
Jalim SinghDepartment of Biomedical Engineering, Oregon Health and Science University, Portland, OR 97239, USA.
Vaibhav MurthyCancer Early Detection Advanced Research Center, Oregon Health and Science University, Portland, OR 97239, USA; Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR 97239, USA.
Young Hwan ChangDepartment of Biomedical Engineering, Oregon Health and Science University, Portland, OR 97239, USA; Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97239, USA.
Alexander E DaviesCancer Early Detection Advanced Research Center, Oregon Health and Science University, Portland, OR 97239, USA; Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR 97239, USA; Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97239, USA; Division of Oncological Sciences, Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97239, USA.
Daniel M ZuckermanDepartment of Biomedical Engineering, Oregon Health and Science University, Portland, OR 97239, USA; Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97239, USA. Electronic address: zuckermd@ohsu.edu.
Laura M HeiserDepartment of Biomedical Engineering, Oregon Health and Science University, Portland, OR 97239, USA; Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97239, USA. Electronic address: heiserl@ohsu.edu.

Funding

OutreachU54HG008100 · NHGRI · OREGON HEALTH & SCIENCE UNIVERSITY · PI GRAY, JOE W., HEISER, LAURA MADELINE · 2014 to 2019
$10.5M
Understanding the Impact of Microscale and Nanoscale Heterogeneity and ResistanceU54CA209988 · NCI · OREGON HEALTH & SCIENCE UNIVERSITY · PI DEMIR, EMEK, HEISER, LAURA MADELINE · 2017 to 2021
$10.3M
Omic and Multidimensional Spatial Atlas of Metastatic Breast CancerU2CCA233280 · NCI · OREGON HEALTH & SCIENCE UNIVERSITY · PI GOECKS, JEREMY · 2018 to 2023
$9.7M
Targeting ERK-AKT-mediated single-cell drug response heterogeneity in metastatic osteosarcomaK01OD031811 · OD · OREGON HEALTH & SCIENCE UNIVERSITY · PI DAVIES, ALEXANDER E · 2021 to 2025
$599k
NCI NIH HHS U2C CA233280NCI NIH HHS U54 CA209988NHGRI NIH HHS U54 HG008100NIH HHS K01 OD031811
6 · The paper itself

Abstract

Extracellular signals induce changes to molecular programs that modulate cellular phenotypes, but the connection between dynamically adapting phenotypic states and the molecular programs that define them is not well understood. Here, we develop data-driven models of single-cell phenotypic responses by linking gene transcription levels to "morphodynamics"-changes in cell morphology and motility observable in single-cell trajectories extracted from time-lapse image data. The single-cell trajectories enable a computational approach to map live-cell dynamics to snapshot gene transcript levels, which we term MMIST, molecular and morphodynamics-integrated single-cell trajectories. MMIST identifies a cell state landscape bound by epithelial and mesenchymal endpoints, with distinct sequences of intermediates. This analysis predicts expression of thousands of RNA transcripts through extracellular signal-induced epithelial-mesenchymal transition (EMT) and mesenchymal-epithelial transition (MET) with near-continuous time resolution. The MMIST framework leverages true single-cell dynamical behavior to generate molecular-level omic inferences and is broadly applicable across biological domains, imaging approaches, and molecular snapshot data.

Indexed as

Single-Cell AnalysisAnimalsCell MovementEpithelial-Mesenchymal TransitionGene ExpressionHumansSingle-Cell Gene Expression AnalysisTime-Lapse Imagingcell stateEMTligand responseMarkov modelmorphodynamicssingle-cell dynamics

Identifiers

PMID41932341
PMCPMC13134452

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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.