Happy to share the Biodiversity Cell Atlas white paper, out today in @nature.com. We look at the possibilities, challenges, and potential impacts of molecularly mapping cells across the tree of life.
www.nature.com/articles/s41...

Ending my two weeks conference marathon: last week Single Cell Spatial Omics #SCSO 600 year #KULeuven edition & now #SCG2025 - learned so much & am now already searching for the next one! Any recommendations for biodiversity, non-model organisms & genetics in 2026? 🪰🦋🐛🐌

The FlyBase situation has made the national news www.nbcnews.com/science/scie...

1/ First preprint from @jdemeul.bsky.social lab 🥳! We present our new multi-modal single-cell long-read method SPLONGGET (Single-cell Profiling of LONG-read Genome, Epigenome, and Transcriptome)! www.biorxiv.org/content/10.1...

Very proud of two new preprints from the lab:
1) CREsted: to train sequence-to-function deep learning models on scATAC-seq atlases, and use them to decipher enhancer logic and design synthetic enhancers. This has been a wonderful lab-wide collaborative effort. www.biorxiv.org/content/10.1...

Make sure to also check out the preprint on the new CREsted package: many more sequence-to-function models across different species! … and it works with HyDrop v2 data as well.
Congrats to @niklaskemp.bsky.social & @seppedewinter.bsky.social & all the others working on this super cool project!

The data is out at GEO, resources.aertslab.org/papers/hydro..., and the models at crested.readthedocs.io/en/stable/mo... - we are looking forward to your feedback!

Thanks a lot to the whole lab for the great teamwork and especially to @s-poovathingal.bsky.social and @steinaerts.bsky.social for the support, Marta for the bead optimization, and @lukasmahieu.bsky.social for the help with all Deep Learning questions!

With HyDrop v2, we offer an accessible, scalable, and low-cost method to generate scATAC-seq datasets suitable for training ready-to-use deep learning tools. HyDrop v2 data reliably captures biologically relevant regulatory features and achieves predictive performance comparable to commercial tools.

Just as in fly, the mouse model trained on HyDrop v2 mouse cortex data also correctly identifies and matches predictions of in vivo validated enhancers.

We also compared our multi-class embryo model to the embryo models by Alexander Stark's lab @impvienna.bsky.social (sciATAC). With both 10x and HyDrop data-based sequence-to-function models, we were able to replicate and refine their motif predictions without the need for fine-tuning our models.

In the Drosophila embryo, we scored the in vivo validated enhancers from the VDRC lines and were able to track down the most predictive 500 bp region within the 2kb enhancers using our sequence-to-function models with 10-fold cross-validation.

We trained sequence-to-function models with the new CREsted package on both species and used the Seq2PRINT for footprinting in HyDrop and 10x data. Both are comparable as well in terms of enhancer predictions, sequence explainability, and transcription factor footprinting.

Differentially accessible regions & motif enrichment are equivalent between HyDrop v2 & 10x. Due to the bead [email protected], HyDrop v2 experiments are very robust, and the data integrates seamlessly with commercially available chromatin accessibility methods (10x Genomics).

The costs of generating the library of, e.g., the mouse cortex across 35 experimental “runs” accumulate to merely 1,080.60 euros compared to an equivalent of 10x v2-based library preparation for 15,330.70 euros, excluding the sequencing cost. 35 experiments can be performed in just 1.5 days!

To demonstrate the cost-effective generation of large scATAC-seq atlases with HyDrop with increased sensitivity and scale, we evaluated the data quality in the mouse cortex (110k cells) and our new Drosophila embryo atlas and compared them to atlases generated on commercial platforms.

Our new preprint is out! We optimized our open-source platform, HyDrop (v2), for scATAC sequencing and generated new atlases for the mouse cortex and Drosophila embryo with 607k cells. Now, we can train sequence-to-function models on data generated with HyDrop v2!
www.biorxiv.org/content/10.1...