Image2Reg: Linking chromatin images to gene regulation using genetic and chemical perturbation screens.

Daniel Paysan, Adityanarayanan Radhakrishnan, Xinyi Zhang, G V Shivashankar, Caroline Uhler

Abstract read

In one paragraph

Article in Cell systems, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

5 authors.

Daniel PaysanETH Zurich, Raemisstrasse 101, Zurich 8092, Switzerland; Paul Scherrer Institute, Forschungsstrasse 111, Villigen 5232, Switzerland.

Adityanarayanan RadhakrishnanMassachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA.

Xinyi ZhangMassachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA.

G V ShivashankarETH Zurich, Raemisstrasse 101, Zurich 8092, Switzerland; Paul Scherrer Institute, Forschungsstrasse 111, Villigen 5232, Switzerland. Electronic address: gshivasha@ethz.ch.

Caroline UhlerMassachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA. Electronic address: cuhler@mit.edu.

Funding

Causal Representation Learning for the Spatial Analysis of Transcriptomic and Imaging Data in Tissue ContextsDP2AT012345 · NCCIH · BROAD INSTITUTE, INC. · PI UHLER, CAROLINE · 2022 to 2025

$2.3M

NCCIH NIH HHS DP2 AT012345

6 · The paper itself

Abstract

Representation learning provides an opportunity to uncover the link between 3D genome organization and gene regulatory networks, thereby connecting the physical and the biochemical space of a cell. Our method, Image2Reg, uses chromatin images obtained in large-scale genetic and chemical perturbation screens. Through convolutional neural networks, Image2Reg generates gene embedding that represents the effect of gene perturbation on chromatin organization. In addition, combining protein-protein interaction data with cell-type-specific transcriptomic data through a graph convolutional network, we obtain a gene embedding that represents the regulatory effect of genes. Finally, Image2Reg learns a map between the resulting physical and biochemical representation of cells, allowing us to predict the perturbed gene modules based on chromatin images. Our results confirm the deep link between chromatin organization and gene regulation and demonstrate that it can be harnessed to identify drug targets and genes upstream of perturbed phenotypes from a simple and inexpensive chromatin staining.

Indexed as

ChromatinGene Expression RegulationImage Processing, Computer-AssistedGene Regulatory NetworksHumansNeural Networks, ComputerChromatinchromatindrug targetsgene regulationgenome organizationmachine learningmicroscopy imagingmultiomicsperturbationsrepresentation learning

Identifiers

PMID40359941

DOI10.1016/j.cels.2025.101293

PMCPMC12181048

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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.