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| Status |
Public on Jul 06, 2023 |
| Title |
Deep dynamical modelling of developmental trajectories with temporal transcriptomics [EXPERIMENT_DATA] |
| Organism |
Mus musculus |
| Experiment type |
Expression profiling by high throughput sequencing
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| Summary |
Developmental cell fate decisions are dynamic processes driven by the complex behaviour of gene regulatory networks. A challenge in studying these processes using single-cell genomics is that the data provides only a static snapshot with no detail of dynamics. Metabolic labelling and splicing can provide time-resolved information, but current methods have limitations. Here, we present experimental and computational methods that overcome these limitations to allow dynamical modelling of gene expression from single-cell data. We developed sci-FATE2, an optimised metabolic labelling method that substantially increases data quality, and profiled approximately 45,000 embryonic stem cells differentiating into multiple neural tube identities. To recover dynamics, we developed velvet, a deep learning framework that extends beyond instantaneous velocity estimation by modelling gene expression dynamics through a neural stochastic differential equation system within a variational autoencoder. Velvet outperforms current velocity tools across quantitative benchmarks, and predicts trajectory distributions that accurately recapitulate underlying dataset distributions while conserving known biology. Velvet trajectory distributions capture dynamical aspects such as decision boundaries between alternative fates and correlative gene regulatory structure. Using velvet to provide a dynamical description of in vitro neural patterning, we highlight a process of sequential decision making and fate-specific patterns of developmental signalling. Together, these experimental and computational methods recast single-cell analyses from descriptions of observed data distributions to models of the dynamics that generated them, providing a new framework for investigating developmental gene regulation and cell fate decisions.
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| Overall design |
Four replicates from in vitro neural differentiation: replicates 1-3 collected on days 3,4,5,6,7,8. Replicate 4 collected between day 3 and 4 on 5 hour intervals (day 3 + 5, 10, 15, 20hr). All differentiations RA + 500nM SAG, ventral neural tube differentiation
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| Contributor(s) |
Maizels RJ, Snell D, Briscoe J |
| Citation missing |
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| Submission date |
Jul 05, 2023 |
| Last update date |
Jul 06, 2023 |
| Contact name |
Rory James Maizels |
| E-mail(s) |
rory.maizels@crick.ac.uk
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| Organization name |
The Francis Crick Institute
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| Lab |
Briscoe Lab
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| Street address |
1 Midland Road
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| City |
London |
| ZIP/Postal code |
NW11AT |
| Country |
United Kingdom |
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| Platforms (1) |
| GPL24247 |
Illumina NovaSeq 6000 (Mus musculus) |
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| Samples (4)
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| GSM7548957 |
replicate 1, sample collected days 3,4,5,6,7,8 |
| GSM7548958 |
replicate 2, sample collected days 3,4,5,6,7,8 |
| GSM7548959 |
replicate 3, sample collected days 3,4,5,6,7,8 |
| GSM7548960 |
replicate 4, sample collected days 3.2, 3.4, 3.6, 3.8 |
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| This SubSeries is part of SuperSeries: |
| GSE236520 |
Deep dynamical modelling of developmental trajectories with temporal transcriptomics |
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| Relations |
| BioProject |
PRJNA991513 |
| Supplementary file |
Size |
Download |
File type/resource |
| GSE236512_processed_data_counting.h5ad.gz |
2.3 Gb |
(ftp)(http) |
H5AD |
| GSE236512_processed_data_estimate.h5ad.gz |
2.4 Gb |
(ftp)(http) |
H5AD |
| GSE236512_processed_data_splicing.h5ad.gz |
1.2 Gb |
(ftp)(http) |
H5AD |
SRA Run Selector |
| Raw data are available in SRA |
| Processed data are available on Series record |
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