ArticleProceedings of the National Academy of Sciences of the United States of America2018
Fundamental limits on dynamic inference from single-cell snapshots.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 197 papers.
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197 citing papers in PubMed.
- Single-cell Transcriptomics in Autoimmune Diseases: Mechanistic Insights, Cross-disease Convergence, and Translational Challenges in Rheumatoid Arthritis, Systemic Lupus Erythematosus, Type 1 Diabetes, and Multiple Sclerosis.Clinical reviews in allergy & immunology · 2026Review
- Latent causal diffusions for single-cell perturbation modeling.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Article
- FlowMap: Geometry-Dynamics Consistent Embedding of RNA Velocity for Interpretable Cellular Trajectories.bioRxiv : the preprint server for biology · 2026Article
- Learning stochastic dynamics and cell-fate landscapes from single-cell snapshots via optimal transport.Science advances · 2026Article
- Integrated Single Cell Analysis Reveals the Transcriptional Heterogeneity of Mouse Double Negative T Cells.International journal of molecular sciences · 2026Article
- Genome-wide chromatin recording resolves dynamic cell state changes.Cell systems · 2026Article
- Single-cell and single-nucleus transcriptomics of the dorsal root ganglion in neuropathic pain: cell-state remodeling and translational prospects.Journal of translational medicine · 2026Review
- mmVelo: a deep generative model for estimating cell state-dependent dynamics across multiple modalities.Bioinformatics (Oxford, England) · 2026Article
- Genomic repeats for single-cell molecular recording.bioRxiv : the preprint server for biology · 2026Article
- Global StationaryOT: trajectory inference for aging time courses of single-cell snapshots.Bioinformatics (Oxford, England) · 2026Article
- Multiscale learning of gene network-driven phenotypic dynamics of single cells.Molecular systems biology · 2026Article
- Comprehensive benchmarking of RNA velocity methods across single-cell datasets.Genome biology · 2026Article
- Genome-wide chromatin recording resolves dynamic cell state changes.bioRxiv : the preprint server for biology · 2026Article
- Simultaneous measurements of translation rate and transcriptome uncovers linked regulation within an active bacterial cell population.Science advances · 2026Article
- SupeRJump: Determining normal and leukemic differentiation fate through semi-supervised jump diffusion modeling.bioRxiv : the preprint server for biology · 2026Article
- Single-cell morphodynamics predict cell fate decisions during mucociliary epithelial differentiation.Molecular systems biology · 2026Article
- The molecular asynchrony of single cells.bioRxiv : the preprint server for biology · 2026Article
- A modeller's guide for biomedical discovery.Disease models & mechanisms · 2026Article
- The differentiation of myeloid progenitors is effected by cascading waves of coordinated gene expression that remodel cellular physiology in a characteristic sequence.PLoS computational biology · 2026Article
137 more citing papers are in PubMed but not listed here.
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5 authors.
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Abstract
Single-cell expression profiling reveals the molecular states of individual cells with unprecedented detail. Because these methods destroy cells in the process of analysis, they cannot measure how gene expression changes over time. However, some information on dynamics is present in the data: the continuum of molecular states in the population can reflect the trajectory of a typical cell. Many methods for extracting single-cell dynamics from population data have been proposed. However, all such attempts face a common limitation: for any measured distribution of cell states, there are multiple dynamics that could give rise to it, and by extension, multiple possibilities for underlying mechanisms of gene regulation. Here, we describe the aspects of gene expression dynamics that cannot be inferred from a static snapshot alone and identify assumptions necessary to constrain a unique solution for cell dynamics from static snapshots. We translate these constraints into a practical algorithmic approach, population balance analysis (PBA), which makes use of a method from spectral graph theory to solve a class of high-dimensional differential equations. We use simulations to show the strengths and limitations of PBA, and then apply it to single-cell profiles of hematopoietic progenitor cells (HPCs). Cell state predictions from this analysis agree with HPC fate assays reported in several papers over the past two decades. By highlighting the fundamental limits on dynamic inference faced by any method, our framework provides a rigorous basis for dynamic interpretation of a gene expression continuum and clarifies best experimental designs for trajectory reconstruction from static snapshot measurements.
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