---
title: "SpaTemGRN: Joint modeling of multi-timepoint spatial transcriptomics for time‑resolved GRN inferen"
id: "biorxiv-19-joint-modeling-of-multi-timepoint-spatial-observations-for-time-resolved"
canonical_url: "https://medichelpline.com/clinical-feed/biorxiv-19-joint-modeling-of-multi-timepoint-spatial-observations-for-time-resolved"
content_type: "clinical_feed_article"
specialty: "General"
source_name: "bioRxiv (Biomedical Preprints)"
source_url: "https://www.biorxiv.org/content/10.64898/2026.07.28.738702v1?rss=1"
published_at: "2026-08-01T12:00:00.000Z"
evidence_level: "Verified Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# SpaTemGRN: Joint modeling of multi-timepoint spatial transcriptomics for time‑resolved GRN inferen
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/biorxiv-19-joint-modeling-of-multi-timepoint-spatial-observations-for-time-resolved
- **Specialty:** [General](https://medichelpline.com/clinical-feed/general.md)
- **Primary Source:** bioRxiv (Biomedical Preprints)
- **Source URL:** [Original Journal Publication](https://www.biorxiv.org/content/10.64898/2026.07.28.738702v1?rss=1)
- **Published At:** 2026-08-01T12:00:00.000Z
- **Evidence Rating:** Verified Feed
## Executive GIST (TL;DR)
- The authors present **SpaTemGRN**, a statistical framework that jointly models multi‑timepoint spatial transcriptomics slides to infer time‑resolved, spatial‑unit‑specific putative **gene regulatory networks** (GRNs). - SpaTemGRN enables later-stage spatial units to borrow information from spatially proximate and temporally preceding units, improving inference compared with single‑snapshot approaches. - In the App NL‑G‑F mouse model of Alzheimer’s disease, SpaTemGRN detected region‑ and age‑dependent strengthening of **complement–glia** regulatory coupling, with an early, distributed complement signature preceding later, spatially focal coupling to astrocytic and microglial responses. - In the developing mouse embryonic brain, the method identified progressively sharpening and spatially segregated regulatory programs as the neural tube regionalizes. - Across simulated datasets, SpaTemGRN recovered regulatory edges more robustly than four existing methods and showed the most stable performance across developmental or disease stages. - SpaTemGRN is offered as a flexible hypothesis‑generating tool for studying how spatially localized gene–gene dependencies are remodeled across biological stages. - All source code for SpaTemGRN is publicly available at the project GitHub repository: https://github.com/yibingjiang/SpaTemGRN. - The study is a preprint posted to bioRxiv and has not been peer reviewed; the authors declared no competing interests.
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Yibing Jiang 1 Department of Biostatistics, Harvard T.H. Chan School of Public Health; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Yibing%2BJiang%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Jiang%20Y&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AYibing%2BJiang%2B) * [ORCID record for Yibing Jiang](http://orcid.org/0009-0004-1391-6330 "Open in new tab") Yurui Li 2 School of Information Science, University of Illinois at Urbana-Champaign; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Yurui%2BLi%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Li%20Y&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AYurui%2BLi%2B) * [ORCID record for Yurui Li](http://orcid.org/0009-0000-8016-3958 "Open in new tab") Qiqi Xie 2 School of Information Science, University of Illinois at Urbana-Champaign; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Qiqi%2BXie%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Xie%20Q&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AQiqi%2BXie%2B) * [ORCID record for Qiqi Xie](http://orcid.org/0000-0003-4099-5287 "Open in new tab") Yang Li 3 Department of Ecology and Evolutionary Biology, University of Michigan * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Yang%2BLi%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Li%20Y&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AYang%2BLi%2B) * [ORCID record for Yang Li](http://orcid.org/0000-0003-2530-7167 "Open in new tab") Haohan Wang 2 School of Information Science, University of Illinois at Urbana-Champaign; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Haohan%2BWang%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Wang%20H&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AHaohan%2BWang%2B) * [ORCID record for Haohan Wang](http://orcid.org/0000-0002-1826-4069 "Open in new tab") * For correspondence: haohanw@illinois.edu * [Abstract](https://www.biorxiv.org/content/10.64898/2026.07.28.738702v1)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_art/node:5673628/1) * [Info/History](https://www.biorxiv.org/content/10.64898/2026.07.28.738702v1.article-info)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_info/node:5673628/1) * [Metrics](https://www.biorxiv.org/content/10.64898/2026.07.28.738702v1.article-metrics)[](https://www.biorxiv.org/panels_ajax_tab/article_tab_metrics/node:5673628/1) * [Supplementary material](https://www.biorxiv.org/content/10.64898/2026.07.28.738702v1.supplementary-material)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_data/node:5673628/1) * [ Preview PDF](https://www.biorxiv.org/content/10.64898/2026.07.28.738702v1.full.pdf+html)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_pdf/node:5673628/1) ![Loading](https://www.biorxiv.org/sites/all/modules/contrib/panels_ajax_tab/images/loading.gif) ## Abstract **Background:** How gene regulatory programs reorganize across space and time is central to development and disease, but current methods infer regulatory structure from single snapshots. The emergence of spatiotemporal transcriptomics calls for methods that resolve regulation along both axes. **Results:** We introduce SpaTemGRN, which jointly models observations from multi-timepoint slides in spatiotemporal transcriptomics to infer time-resolved spatial-unit-specific putative GRNs. Compared with existing methods, SpaTemGRN allows later-stage units to borrow statistical strength from spatially proximate and temporally preceding units. In the _App_ NL-G-F mouse model of Alzheimer's disease, SpaTemGRN revealed region- and age-dependent strengthening of complement–glia regulatory coupling. An early, broadly distributed complement signature precedes later, spatially focal coupling with astrocytic and microglial responses. In the developing mouse embryonic brain, SpaTemGRN identified progressively sharpening and spatially segregated regulatory programs as the early neural tube regionalizes. Across simulated datasets, SpaTemGRN recovered regulatory edges more robustly than four existing methods and maintained the most stable performance across stages. **Conclusions:** SpaTemGRN provides a flexible hypothesis-generating framework for investigating how spatially localized gene–gene dependencies are remodeled across biological stages. All source code for SpaTemGRN is available at https://github.com/yibingjiang/SpaTemGRN. ### Competing Interest Statement The authors have declared no competing interest. Copyright The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a [CC-BY-NC-ND 4.0 International license](http://creativecommons.org/licenses/by-nc-nd/4.0/). bioRxiv and medRxiv thank the following for their generous financial support: > The Chan Zuckerberg Initiative, Cold Spring Harbor Laboratory, the Sergey Brin Family Foundation, California Institute of Technology, Centre National de la Recherche Scientifique, Fred Hutchinson Cancer Center, Imperial College London, Massachusetts Institute of Technology, Stanford University, The University of Edinburgh, University of Washington, and Vrije Universiteit Amsterdam. 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