Single-cell–level digital twins for preterm birth prevention strategies
11 affiliations
- 1Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA.
- 2Department of Critical Care and Anesthesia, National Center for Child Health and Development, Tokyo, Japan.
- 3Department of Surgery, Stanford University School of Medicine, Stanford, CA, USA.
- 4APHP, Hopital Saint Antoine & Tenon, DMU DREAM, Department of Anesthesiology and Critical Care, Paris, France.
- 5Department of Pathology, University of California San Diego, La Jolla, CA, USA.
- 6Sorbonne Université, INSERM, Centre de Recherche Saint-Antoine, CRSA, F-75012 Paris, France.
- 7Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Stanford, CA, USA.
- 8Department of Obstetrics and Gynecology, Stanford University School of Medicine, Stanford, CA, USA.
- 9Division of Clinical Informatics and Digital Transformation, University of California, San Francisco, San Francisco, CA, USA.
- 10Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA, USA.
- 11Center for Reproductive Sciences, Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California San Francisco School of Medicine, San Francisco, CA, USA.
Abstract
Digital twin models can accelerate therapeutic development by enabling low-risk testing of candidate interventions. In preterm labor (PTL), a major pregnancy complication where clinical trials face ethical and financial barriers, digital twins are especially valuable for evaluating new therapies targeting immune dysfunctions driving PTL. Yet, current models lack single-cell resolution, limiting detection of cell type-specific mechanisms, off-target effects, and the design of personalized interventions. We present Simulated Immunome Modeling of Clinical Outcomes (SIMCO), a single-cell-level digital twin framework that models immunomodulatory treatment effects on the timing of labor using immunome-wide, single-cell simulations. SIMCO's digital twins are trained and validated on a newly generated mass cytometry atlas of the pregnant immunome exposed to nine candidate drugs preselected for PTL prevention. Applying SIMCO to an independent cohort of pregnant individuals, we simulate treatment effects on gestational length, screening for candidate drugs that delay labor timing and providing system-level mechanistic insight for each drug candidate. Tetrahydrofolate and the combination of aspirin and lansoprazole emerged as top candidates for PTL prevention, delaying labor onset primarily through enhanced mTOR signaling in innate immune cells and attenuated JAK/STAT signaling in naïve CD4⁺ T cells.
Related
- Evolution of treatment strategies and risk-stratified management of patent ductus arteriosus in preterm infants
- Early prophylactic low-dose hydrocortisone for bronchopulmonary dysplasia prevention in extremely preterm infants: a systematic review and meta-analysis
- Development and internal validation of an exploratory clinical prediction model for MRI-defined white matter injury in moderate-to-late preterm infants: a single-center retrospective study
- Suicide prevention and social media: stepping fully into the digital age?
- Understanding digital diffusion and online decision-making in public health promotion: a sequential mixed-methods study of the violence prevention charter in Canada
BibTeX
@article{einhaus2026singlecelllevel,
title = {{Single-cell–level digital twins for preterm birth prevention strategies}},
author = {Jakob Einhaus and Peter Neidlinger and Olivier Fondeur and Masaki Sato and Sasha Anronikov and Kotaro Miyazaki and Antoine Maechler and Jonas N. Amar and Kazuo Ando and Valentin Badea and Dyani K. Gaudilliere and Maximilian Sabayev and Dorien Feyaerts and Maïgane Diop and Amy S. Tsai and Amelie Cambriel and Edward A. Ganio and Romain Lagarde and Emmeline O’Kelly and Ina A. Stelzer and Julien Hedou and Ronald J. Wong and Yair J. Blumenfeld and Deirdre J. Lyell and Gary M. Shaw and Tomiko T. Oskotsky and Marina Sirota and Linda C. Giudice and David K. Stevenson and Nima Aghaeepour and Brice Gaudilliere},
journal = {Science Advances},
year = {2026},
month = oct,
doi = {10.1126/sciadv.aed2741},
url = {https://www.science.org/doi/10.1126/sciadv.aed2741},
note = {CC BY},
}
RIS
TY - JOUR
TI - Single-cell–level digital twins for preterm birth prevention strategies
AU - Jakob Einhaus
AU - Peter Neidlinger
AU - Olivier Fondeur
AU - Masaki Sato
AU - Sasha Anronikov
AU - Kotaro Miyazaki
AU - Antoine Maechler
AU - Jonas N. Amar
AU - Kazuo Ando
AU - Valentin Badea
AU - Dyani K. Gaudilliere
AU - Maximilian Sabayev
AU - Dorien Feyaerts
AU - Maïgane Diop
AU - Amy S. Tsai
AU - Amelie Cambriel
AU - Edward A. Ganio
AU - Romain Lagarde
AU - Emmeline O’Kelly
AU - Ina A. Stelzer
AU - Julien Hedou
AU - Ronald J. Wong
AU - Yair J. Blumenfeld
AU - Deirdre J. Lyell
AU - Gary M. Shaw
AU - Tomiko T. Oskotsky
AU - Marina Sirota
AU - Linda C. Giudice
AU - David K. Stevenson
AU - Nima Aghaeepour
AU - Brice Gaudilliere
PY - 2026
DA - 2026/10/21
JO - Science Advances
DO - 10.1126/sciadv.aed2741
UR - https://www.science.org/doi/10.1126/sciadv.aed2741
AB - Digital twin models can accelerate therapeutic development by enabling low-risk testing of candidate interventions. In preterm labor (PTL), a major pregnancy complication where clinical trials face ethical and financial barriers, digital twins are especially valuable for evaluating new therapies targeting immune dysfunctions driving PTL. Yet, current models lack single-cell resolution, limiting detection of cell type-specific mechanisms, off-target effects, and the design of personalized interventions. We present Simulated Immunome Modeling of Clinical Outcomes (SIMCO), a single-cell-level digital twin framework that models immunomodulatory treatment effects on the timing of labor using immunome-wide, single-cell simulations. SIMCO's digital twins are trained and validated on a newly generated mass cytometry atlas of the pregnant immunome exposed to nine candidate drugs preselected for PTL prevention. Applying SIMCO to an independent cohort of pregnant individuals, we simulate treatment effects on gestational length, screening for candidate drugs that delay labor timing and providing system-level mechanistic insight for each drug candidate. Tetrahydrofolate and the combination of aspirin and lansoprazole emerged as top candidates for PTL prevention, delaying labor onset primarily through enhanced mTOR signaling in innate immune cells and attenuated JAK/STAT signaling in naïve CD4⁺ T cells.
SN - 2375-2548
ER -