NCBI Logo
GEO Logo
   NCBI > GEO > Accession DisplayHelp Not logged in | LoginHelp
GEO help: Mouse over screen elements for information.
          Go
Series GSE236517 Query DataSets for GSE236517
Status Public on Jul 06, 2023
Title Deep dynamical modelling of developmental trajectories with temporal transcriptomics [PROTOCOL_PILOT_DATA]
Organism Mus musculus
Experiment type Expression profiling by high throughput sequencing
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.
 
Overall design Different protocols (different chemical conversion protocols; different sci-RNA-seq protocols) are multiplexed within one sample and sequenced here using sci-FATE
 
Contributor(s) Maizels RJ, Snell D, Briscoe J
Citation missing Has this study been published? Please login to update or notify GEO.
Submission date Jul 05, 2023
Last update date Jul 06, 2023
Contact name Rory James Maizels
E-mail(s) rory.maizels@crick.ac.uk
Organization name The Francis Crick Institute
Lab Briscoe Lab
Street address 1 Midland Road
City London
ZIP/Postal code NW11AT
Country United Kingdom
 
Platforms (1)
GPL24247 Illumina NovaSeq 6000 (Mus musculus)
Samples (1)
GSM7549391 multiplexed different protocols
This SubSeries is part of SuperSeries:
GSE236520 Deep dynamical modelling of developmental trajectories with temporal transcriptomics
Relations
BioProject PRJNA991522

Download family Format
SOFT formatted family file(s) SOFTHelp
MINiML formatted family file(s) MINiMLHelp
Series Matrix File(s) TXTHelp

Supplementary file Size Download File type/resource
GSE236517_RAW.tar 711.8 Mb (http)(custom) TAR (of H5AD)
SRA Run SelectorHelp
Raw data are available in SRA
Processed data provided as supplementary file

| NLM | NIH | GEO Help | Disclaimer | Accessibility |
NCBI Home NCBI Search NCBI SiteMap