Evidence map›Paper›PMID 42039614›Full record

ArticlebioRxiv : the preprint server for biology2026

SpaTRACE: Spatiotemporal recurrent auto-encoder for reconstructing signaling and regulatory networks from spatiotemporal transcriptomics data.

Hongliang Zhou, Hao Chen, Zoe Rudnick, Sarah I Baalbaki, Yuchen Shao, Young Je Lee, Jose Lugo-Martinez

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2026. 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

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

7 authors.

Hongliang ZhouRay and Stephanie Lane Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, 15213, USA.ORCID 0009-0006-5919-8885
Hao ChenDepartment of Computer Science, University of Illinois Chicago, Chicago, IL, 60607, USA.
Zoe RudnickRay and Stephanie Lane Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, 15213, USA.
Sarah I BaalbakiRay and Stephanie Lane Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, 15213, USA.
Yuchen ShaoRay and Stephanie Lane Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, 15213, USA.
Young Je LeeRay and Stephanie Lane Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, 15213, USA.
Jose Lugo-MartinezRay and Stephanie Lane Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, 15213, USA.ORCID 0000-0002-6312-1577

Funding

TriState SenNET (Lung and Heart) Tissue Map and Atlas consortiumU54AG075931 · NIA · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI TOREN FINKEL, Melanie Koenigshoff · 2021 to 2026
$14.0M
NIA NIH HHS U54 AG075931
6 · The paper itself

Abstract

Cell-cell communication and gene regulatory programs jointly coordinate cellular behaviors during development, regeneration, and disease. Recent advances in spatial transcriptomics enable measurement of gene expression with spatial context across developmental trajectories, providing new opportunities to study dynamic signaling and regulatory processes. However, most existing methods analyze either ligand-receptor (LR) signaling or gene regulatory networks (GRNs) separately, rely on curated interaction databases, and often assume steady-state gene expression, limiting their ability to capture temporal regulatory dynamics and discover novel interactions. We present SpaTRACE, a spatiotemporal recurrent autoencoder framework for joint inference of intercellular signaling and gene regulatory networks from spatial transcriptomics data. SpaTRACE models time-lagged dependencies along pseudotime-sampled cellular trajectories using an attention-based encoder-decoder architecture that predicts future target gene expression from upstream intra- and intercellular signals. The learned attention structure enables simultaneous reconstruction of transcription factor-target gene (TF-TG) regulatory interactions, ligand-receptor-target gene (LR-TG) signaling pathways, and ligand-receptor binding relationships without requiring predefined LR databases. Across synthetic benchmarks, SpaTRACE accurately recovers both GRN and signaling interactions and outperforms existing GRN and cell-cell communication inference methods. Applications to mouse midbrain development reveal transcriptional regulators and signaling programs associated with neuronal differentiation, while analysis of axolotl brain regeneration identifies stage-specific signaling dynamics and candidate interactions involved in tissue repair. Availability: Source code and documentation are available at https://github.com/VariaanZhou/SpaTRACE.

Indexed as

Cell-Cell Communication InferenceComputational GenomicsGene Regulatory Network Inference

Identifiers

PMID42039614
PMCPMC13105044

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.