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Reconstructing developmental trajectories using latent dynamical systems and time-resolved transcriptomics

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journal contribution
posted on 2024-05-17, 10:48 authored by Rory J Maizels, Daniel M Snell, James Briscoe
The snapshot nature of single-cell transcriptomics presents a challenge for studying the dynamics of cell fate decisions. Metabolic labeling and splicing can provide temporal information at single-cell level, but current methods have limitations. Here, we present a framework that overcomes these limitations: experimentally, we developed sci-FATE2, an optimized method for metabolic labeling with increased data quality, which we used to profile 45,000 embryonic stem (ES) cells differentiating into neural tube identities. Computationally, we developed a two-stage framework for dynamical modeling: VelvetVAE, a variational autoencoder (VAE) for velocity inference that outperforms all other tools tested, and VelvetSDE, a neural stochastic differential equation (nSDE) framework for simulating trajectory distributions. These recapitulate underlying dataset distributions and capture features such as decision boundaries between alternative fates and fate-specific gene expression. These methods recast single-cell analyses from descriptions of observed data to models of the dynamics that generated them, providing a framework for investigating developmental fate decisions.


Crick (Grant ID: CC2032, Grant title: Briscoe CC2032) Crick (Grant ID: CC1064, Grant title: STP Advanced Sequencing) European Research Council (Grant ID: 742138 - LogNeuroDev, Grant title: ERC 742138 - LogNeuroDev)


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