Deep dynamical models of single-cell multiomic velocities predict loss-of-function and rescue perturbations in B cells Article Swipe
Alireza Karbalayghareh
,
Darko Barišić
,
Christopher R. Chin
,
Ari Melnick
,
Christina S. Leslie
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.04.24.650458
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.04.24.650458
We present DynaVelo, a generative neural ordinary differential equation (ODE) model that learns the joint dynamics of gene expression and transcription factor (TF) motif activities in evolving cell systems using single-cell multiome data. DynaVelo leverages partial RNA velocity information together with single-cell TF motif accessibility data to improve the modeling of cell state dynamics and identification of TF drivers. We show that DynaVelo recovers the complex and bifurcating in vivo dynamics of wildtype murine germinal center (GC) B cells and reveals how these cell dynamics change under loss-of-function mutations in epigenetic regulators. DynaVelo resolves how TF motif activities evolve along latent time trajectories using analysis of training cells or through generated trajectories from the model. In silico perturbation analysis further enables DynaVelo to infer dynamic and cell-state-specific gene regulatory networks (GRNs), recovering many known TF-to-gene edges in the wildtype GC GRN and predicting those that are disrupted in mutants. Finally, in silico gene and TF perturbations allow both the prediction of cell dynamics under loss-of-function genetic mutations and the identification of TF perturbations to rescue loss-of-function dynamic and immunological phenotypes. DynaVelo therefore provides a powerful new deep learning framework for modeling and perturbing dynamic cell systems by harnessing single-cell multiome data sets.
Related Topics
Concepts
In silico
Ode
Computational biology
Biology
Transcription factor
Gene regulatory network
Gene
Genetics
Computer science
Gene expression
Mathematics
Applied mathematics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2025.04.24.650458
- https://www.biorxiv.org/content/biorxiv/early/2025/04/26/2025.04.24.650458.full.pdf
- OA Status
- green
- Cited By
- 1
- References
- 36
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4409847126
All OpenAlex metadata
Raw OpenAlex JSON
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- DOI
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https://doi.org/10.1101/2025.04.24.650458Digital Object Identifier
- Title
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Deep dynamical models of single-cell multiomic velocities predict loss-of-function and rescue perturbations in B cellsWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
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2025Year of publication
- Publication date
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2025-04-26Full publication date if available
- Authors
-
Alireza Karbalayghareh, Darko Barišić, Christopher R. Chin, Ari Melnick, Christina S. LeslieList of authors in order
- Landing page
-
https://doi.org/10.1101/2025.04.24.650458Publisher landing page
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https://www.biorxiv.org/content/biorxiv/early/2025/04/26/2025.04.24.650458.full.pdfDirect link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://www.biorxiv.org/content/biorxiv/early/2025/04/26/2025.04.24.650458.full.pdfDirect OA link when available
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In silico, Ode, Computational biology, Biology, Transcription factor, Gene regulatory network, Gene, Genetics, Computer science, Gene expression, Mathematics, Applied mathematicsTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2024: 1Per-year citation counts (last 5 years)
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36Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.powerful | 185 |
| abstract_inverted_index.provides | 183 |
| abstract_inverted_index.recovers | 64 |
| abstract_inverted_index.resolves | 94 |
| abstract_inverted_index.together | 40 |
| abstract_inverted_index.training | 107 |
| abstract_inverted_index.velocity | 38 |
| abstract_inverted_index.wildtype | 73, 139 |
| abstract_inverted_index.DynaVelo, | 3 |
| abstract_inverted_index.disrupted | 147 |
| abstract_inverted_index.framework | 189 |
| abstract_inverted_index.generated | 111 |
| abstract_inverted_index.leverages | 35 |
| abstract_inverted_index.mutations | 89, 167 |
| abstract_inverted_index.therefore | 182 |
| abstract_inverted_index.TF-to-gene | 135 |
| abstract_inverted_index.activities | 25, 98 |
| abstract_inverted_index.epigenetic | 91 |
| abstract_inverted_index.expression | 19 |
| abstract_inverted_index.generative | 5 |
| abstract_inverted_index.harnessing | 198 |
| abstract_inverted_index.perturbing | 193 |
| abstract_inverted_index.predicting | 143 |
| abstract_inverted_index.prediction | 160 |
| abstract_inverted_index.recovering | 132 |
| abstract_inverted_index.regulatory | 129 |
| abstract_inverted_index.bifurcating | 68 |
| abstract_inverted_index.information | 39 |
| abstract_inverted_index.phenotypes. | 180 |
| abstract_inverted_index.regulators. | 92 |
| abstract_inverted_index.single-cell | 31, 42, 199 |
| abstract_inverted_index.differential | 8 |
| abstract_inverted_index.perturbation | 118 |
| abstract_inverted_index.trajectories | 103, 112 |
| abstract_inverted_index.accessibility | 45 |
| abstract_inverted_index.immunological | 179 |
| abstract_inverted_index.perturbations | 156, 173 |
| abstract_inverted_index.transcription | 21 |
| abstract_inverted_index.identification | 56, 170 |
| abstract_inverted_index.loss-of-function | 88, 165, 176 |
| abstract_inverted_index.cell-state-specific | 127 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.13739045 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |