For the motif enrichment analysis we already made use of cell type-specific ATAC peaks. So in that sense it already makes use of the data. But good suggestion to consider additional evaluations. Thank you!

Thank you :). In this example we trained a new model only on PBMC scATAC-seq data using the CREsted framework.

Check out the preprint: doi.org/10.64898/202... and the TF-MINDI package: github.com/aertslab/TF-MINDI. With @lukasmahieu.bsky.social ’s help this has become an amazing and user-friendly package, please give it a try and provide feedback.

I would like to thank my co-authors; @camielmannens.bsky.social for the embryo data and analysis together with @sten.linnarsson.org & Lijuan Hu. Valerie and Roel for all the experimental work. @steinaerts.bsky.social for supporting me through this long journey.

Finally, using topic-modelling we model TFBS co-occurrences. We validate these white-box enhancer models by performing cross-species integration of our TF-MINDI results, with a zebrafish developmental S2F model, and show that the topic models can recover zebrafish floor plate-specific regions.

Implanting either two high affinity coordinator instances or a single coordinator instance together with 5 additional TF-MINDI TFBS is sufficient to generate new facial mesenchyme enhancers.

To test the sufficiency of the TF-MINDI extracted enhancer code rules we turn to synthetic enhancer design in facial mesenchyme cells. A homeobox-ebox dimer motif (Coordinator) has been shown to be instrumental for this cell type. TF-MINDI identified Coordinator instances at varying affinities.

We validate the TF-MINDI instances using ChIP-seq data in PBMC. Showing that TF-MINDI is more accurate compared to traditional motif enrichment analysis tools.

Counting the number of TFBS instances across cell type-specific peaks reveals cell type-specific regulators in the form of a code-table. Similar codes are obtained for the organoid and embryo and we link instances to TFs based on cell type-specific TF-expression.

To obtain high dimensional embeddings of S2F identified motifs, annotate TFBS across cell-type specific peaks and model TFBS co-occurrences we developed a new python package named TF-MINDI. Resulting in > 400k annotated TFBS instances across the genome (each dot in the tSNE below is one instance).

We tackle this challenge in human neural development. We generate two multiome atlases: on neural tube organoids and on a 4 p.c.w. human embryo. The organoids recapitulate human embryonic development strikingly well. DeepNeuralTube S2F models recover most known cell type-specific TF binding motifs.

Great! Thank you so much!

Hi, looks very interesting!
Any chance that you can share the manuscript, for example using www.springernature.com/gp/researche...?

For those looking to read the article, it's available via this link rdcu.be/egQdA