Also check out the cell fate engineering applications:
bsky.app/profile/josc...

@chatgtp.bsky.social, Manuel, A. Regev, B. Treutlein, @graycamplab.bsky.social, @fabiantheis.bsky.social

CellFlow was a highly collaborative team effort. Thanks to the great co-lead @josch1.bsky.social , and the fantastic team Daniil, Lea, Soeren, Alessandro, @le-and-er.bsky.social, Alejandro, @guillaumehu.bsky.social, @hsiuchuanlin.bsky.social, @nazbukina.bsky.social, Fatima, Theo,

Check out the paper for more applications, including cell fate engineering and organoid protocol optimisation!

Head to cellflow.readthedocs.io for tutorials, and get in touch—Plenty of exciting directions to explore!

We found CellFlow to consistently perform competitively across various benchmarks, including drug and genetic perturbation screens - while being able to address complex experimental setups other methods are not able to address.

We let CellFlow learn the perturbed development of entire embryos, allowing to model the continuous trajectories of single cells under different genetic perturbations.

We predicted donor-specific cytokine responses on 10 million cells! We found CellFlow to exhibit scaling laws in the number of seen conditions and gained interpretable insights into model training.

CellFlow builds on flow matching, optimal transport & attention mechanisms to learn an embedding of complex experimental conditions. This guides the flow from control to perturbed cells, generating realistic states while minimizing displacement costs.

@AimeeBastidas, @pacotael, @MartaTarquis, @ShreyParikh07, Ilan Gold, @heikolickert.bsky.social , @mostafabakhti.bsky.social @marcocuturi.bsky.social , @fabiantheis.bsky.social

This was a highly collaborative project, thanks to everyone involved, in particular to the co-leads @giopll.bsky.social , @mariuslange.bsky.social , Michal Klein, Zoe Piran, as well as Manuel Gander, @Laetitia_Ppx, Michael Sterr, @lamasa LamaSaber95, @Diana61204366,

moscot integrates straightforwardly with the @scverse.bsky.social ecosystem. Check out our tutorials and examples at moscot-tools.org to analyze your single-cell data, we encourage the community to contribute to moscot with novel OT applications!

moscot’s ability to study gene regulation allowed us to hypothesise Neurod2 to be an activator of epsilon cell formation. We indeed observed a reduction in ghrelin (hormone produced by eps. cells) in NEUROD2 knockout iPSCs

We generated a new dataset of the developing mouse pancreas across three time points with paired measurements of gene expression and ATAC data. Enrichment of Ngn3 high cells allows us to disentangle the poorly understood formation of delta and epsilon cells.

We develop a new method for the analysis of spatiotemporal datasets. We show how incorporating the spatial information improves the recovery of cell trajectories and demonstrate its use in a spatially resolved mouse embryogenesis dataset.

Moscot aligns large-scale spatial transcriptomics slides from different individuals to obtain a statistically more profound representation of the mouse brain.

We map CITE-seq data of the mouse liver to a spatial reference slide incorporating information from gene expression, protein, and spatial measurements, facilitating liver zonation.

Incorporating recent advances in OT, we make moscot scalable to atlas-scale datasets (see ott-jax.readthedocs.io/en/latest/, @marcocuturi.bsky.social). This allows to to recover trajectories in an atlas of mouse embryogenesis comprising 1.7 million cells.