Generative inverse design steers human hematopoietic cell fate in vitro
- 1Department of Computational Health, Institute of Computational Biology, Helmholtz Zentrum Muenchen, Munich, Germany
Abstract
Inverse design has advanced the discovery of molecules and materials. Yet it remains largely unexplored for steering cell fate, where inputs are continuous, combinatorial culture conditions and outputs are distributions of cell states. Human hematopoiesis, in which combinations and doses of growth factors steer stem cells toward each blood lineage, is a demanding test case. While forward models can evaluate discrete conditions, the vast combinatorial space renders searching for a specific, target-producing design an intractable inverse problem. Here, we introduce LabCompass, an inverse-modeling framework that optimizes experimental designs toward candidates expected to produce specified target cell states, using a flow-matching generative model guided by cellular phenotypes predicted by a forward surrogate. We applied LabCompass to primary human CD34+ in vitro cultures, where continuous cytokine and small-molecule parameters govern hematopoietic cell fate. In a lab-in-the-loop setting, we experimentally validated model-driven designs and used these measurements to update the model across four successive loops. LabCompass recovered established lineage-inducing designs, improved predictions with new data, and enriched target populations across lineages. Incorporating new parameters expanded the design space and enriched rare populations beyond initial reach. Across loops, we generated a 78-million-cell spectral flow cytometry dataset and identified distinct culture conditions converging on the same target cell states. Our results establish generative inverse modeling as a compelling paradigm for navigating high-dimensional, continuous experimental spaces, and demonstrate that virtual cell models should be judged not only by how accurately they predict cellular responses, but also by how effectively they guide experiments toward desired biological outcomes.
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BibTeX
@misc{paulascondori2026generative,
title = {{Generative inverse design steers human hematopoietic cell fate in vitro}},
author = {L. Paulas-Condori and A. Palma and L. Consoli and J. A. Rubio-Lara and O. Hidajat and W. Zheng and D. Klein and D. G. Martinuzzi and Z. Wu and A. Dittadi and N. K. Wilson and B. Gottgens and F. J. Theis},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.07.756550},
url = {https://www.biorxiv.org/content/10.64898/2026.10.07.756550v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Generative inverse design steers human hematopoietic cell fate in vitro
AU - L. Paulas-Condori
AU - A. Palma
AU - L. Consoli
AU - J. A. Rubio-Lara
AU - O. Hidajat
AU - W. Zheng
AU - D. Klein
AU - D. G. Martinuzzi
AU - Z. Wu
AU - A. Dittadi
AU - N. K. Wilson
AU - B. Gottgens
AU - F. J. Theis
PY - 2026
DA - 2026/10/07
PB - bioRxiv
DO - 10.64898/2026.10.07.756550
UR - https://www.biorxiv.org/content/10.64898/2026.10.07.756550v1
AB - Inverse design has advanced the discovery of molecules and materials. Yet it remains largely unexplored for steering cell fate, where inputs are continuous, combinatorial culture conditions and outputs are distributions of cell states. Human hematopoiesis, in which combinations and doses of growth factors steer stem cells toward each blood lineage, is a demanding test case. While forward models can evaluate discrete conditions, the vast combinatorial space renders searching for a specific, target-producing design an intractable inverse problem. Here, we introduce LabCompass, an inverse-modeling framework that optimizes experimental designs toward candidates expected to produce specified target cell states, using a flow-matching generative model guided by cellular phenotypes predicted by a forward surrogate. We applied LabCompass to primary human CD34+ in vitro cultures, where continuous cytokine and small-molecule parameters govern hematopoietic cell fate. In a lab-in-the-loop setting, we experimentally validated model-driven designs and used these measurements to update the model across four successive loops. LabCompass recovered established lineage-inducing designs, improved predictions with new data, and enriched target populations across lineages. Incorporating new parameters expanded the design space and enriched rare populations beyond initial reach. Across loops, we generated a 78-million-cell spectral flow cytometry dataset and identified distinct culture conditions converging on the same target cell states. Our results establish generative inverse modeling as a compelling paradigm for navigating high-dimensional, continuous experimental spaces, and demonstrate that virtual cell models should be judged not only by how accurately they predict cellular responses, but also by how effectively they guide experiments toward desired biological outcomes.
ER -