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Yan Hu

@yanhu97.bsky.social

92 followers 94 following 18 posts

Postdoctoral Researcher in the Srivastava Lab at the Gladstone Institutes. Buenrostro Lab Alumni. Interested in gene regulation, computational biology, aging, and human diseases.

Posts Replies Media

Reposted by Yan Hu

Nature Methods @natmethods.nature.com · 12/09/2025

TDAC-seq is a method for targeted chromatin accessibility profiling that uses cytidine deaminases and long-read sequencing to resolve the effects of CRISPR edits on single chromatin fibers. www.nature.com/articles/s41...

Coupling CRISPR scanning with targeted chromatin accessibility profiling using a double-stranded DNA deaminase - Nature Methods

This paper presents TDAC-seq, a targeted chromatin-accessibility-profiling method using cytidine deaminases and long-read sequencing, to resolve the effects of CRISPR edits on single chromatin fibers.

Yan Hu @yanhu97.bsky.social · 27/01/2025

Thank you! I would love that 🥰🥰🥰 Excited for future collaboration opportunities too ❤️❤️❤️

Reposted by Yan Hu

Vijay Ramani @vram142.bsky.social · 24/01/2025

Amid concerning times, sharing a bit of positivity: our 1st preprint of 2025 (funded VIA NIH COMMON FUND), heroically led by Marty Yang (@martyyang.bsky.social) w/ huge assist from @genophoria.bsky.social lab. Lots to cover so let’s get this tweetorial started (1/n)! www.biorxiv.org/content/10.1...

Pervasive and programmed nucleosome distortion patterns on single mammalian chromatin fibers

We present a genome-scale method to map the single-molecule co-occupancy of structurally distinct nucleosomes, subnucleosomes, and other protein-DNA interactions via long-read high-resolution adenine ...

Yan Hu @yanhu97.bsky.social · 27/01/2025

This paper is so cool! I’ll try to read it a few more times to fully digest the new ideas here. Very inspiring work.

Yan Hu @yanhu97.bsky.social · 23/01/2025

We’d also like to give a shout out to all the amazing work that our study built upon, including but not limited to ChromBPNet, scBasset, TOBIAS, DNase footprinting, and many others! We hope that together we can use our understanding of gene regulation to advance human health.

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Yan Hu @yanhu97.bsky.social · 23/01/2025

This was an amazing team effort led by @yanhu97.bsky.social, @maxhorlbeck.bsky.social, and @ruochiz.bsky.social, joined by colleagues in our lab, the Wagers Lab, and GRO@Broad, supported by HSCRB, HSCI, @broadinstitute.org, @igvfconsortium.bsky.social, @bostonchildrens.bsky.social, EWSC, and GRO.

Yan Hu @yanhu97.bsky.social · 23/01/2025

Because PRINT/seq2PRINT are applicable to any standard bulk or single-cell ATAC-seq dataset, we hope many people will try it out on their existing or future data! Code, tutorials, and examples are available at github.com/buenrostrola...

GitHub - buenrostrolab/scPrinter

Contribute to buenrostrolab/scPrinter development by creating an account on GitHub.

Yan Hu @yanhu97.bsky.social · 23/01/2025

We believe PRINT will be a powerful tool to study gene regulation/dysregulation in complex biological systems, rare cell types, as well as diseases, opening up new opportunities to answer biological questions.

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Yan Hu @yanhu97.bsky.social · 23/01/2025

Interestingly, seq2PRINT captured de novo sequence motifs resembling composite motifs involving Runx, Ets, and Gata, many of which were supported by structural data from PDB or AlphaFold3 predictions, suggesting physical cooperations between TFs.

Yan Hu @yanhu97.bsky.social · 23/01/2025

Finally, in collaboration with the Wagers Lab at HSCRB, we examined the CRE alterations during murine hematopoietic stem cell aging. We observed global gain of Gata/AP-1/Runx/Ets/NF-I binding, loss of Ctcf/Nrf1/Yy1 binding, as well as weakened nucleosome footprints.

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Yan Hu @yanhu97.bsky.social · 23/01/2025

If we rank pseudobulks along the same differentiation lineage by their pseudo-time, we can reconstruct a “movie” of how TFs and nucleosomes reorganize during differentiation. We observed stepwise establishment of hemoglobin CREs during erythroid differentiation.

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Yan Hu @yanhu97.bsky.social · 23/01/2025

We found that instead of having only two states, open vs closed, each CRE can be bound by several distinct TF combinations across cell states/types. Individual CREs thus occupy complex regulatory states undetectable by simply quantifying overall accessibility.

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Yan Hu @yanhu97.bsky.social · 23/01/2025

The really exciting part is the combination of seq2PRINT with single cell data. By pseudobulking cells and using low-rank adaptation to tune seq2PRINT to the differences among cell states, we tracked the changes in TF binding across cell types in human hematopoiesis.

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Yan Hu @yanhu97.bsky.social · 23/01/2025

Unexpectedly, the deep learning model, which we named seq2PRINT, also captured the binding of “invisible” TFs that do not leave a visible footprint in scATAC-seq. We took this opportunity to build a highly accurate TF binding predictor using seq2PRINT.

Yan Hu @yanhu97.bsky.social · 23/01/2025

Building upon foundational work on DNA sequence models by the @anshulkundaje.bsky.social & others, we trained a deep learning model that predicts footprints from DNA sequence. We examined sequences that drive footprint predictions and saw that the model relies on the organization of TF motifs.

Yan Hu @yanhu97.bsky.social · 23/01/2025

Here is an example region showing multi-scale footprints of CTCF and flanking nucleosomes:

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Yan Hu @yanhu97.bsky.social · 23/01/2025

Here we present PRINT. By improving the statistical approach and varying the footprinting kernel size, we detect proteins and complexes across diverse sizes (TFs and nucleosomes).

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Yan Hu @yanhu97.bsky.social · 23/01/2025

Footprinting is a powerful method that detects protein binding through protection of bound DNA from enzymes or chemicals. However, it’s typically done on bulk samples, limiting insight into gene regulation in complex systems, and many TFs are “invisible” to footprinting.

Yan Hu @yanhu97.bsky.social · 23/01/2025

cis-regulatory elements (CREs) regulate gene expression by binding to regulatory proteins such as transcription factors (TFs) and histones. A tool that can track the binding dynamics of regulatory proteins with ultra-high resolution across cell states is long sought after.

Yan Hu @yanhu97.bsky.social · 23/01/2025

Super excited to share our new study from the @jbuenrostro.bsky.social Lab in @nature.com! We developed a computational method for tracking transcription factor and nucleosome binding using single-cell ATAC-seq and deep learning. Paper: www.nature.com/articles/s41...

Multiscale footprints reveal the organization of cis-regulatory elements - Nature

We developed PRINT, a computational method that identifies footprints of DNA–protein interactions from bulk and single-cell chromatin accessibility data across multiple scales of protein size.

Reposted by Yan Hu

Anshul Kundaje @anshulkundaje.bsky.social · 25/12/2024

Our ChromBPNet preprint out! www.biorxiv.org/content/10.1... Huge congrats to Anusri! This was quite a slog (for both of us) but we r very proud of this one! It is a long read but worth it IMHO. Methods r in the supp. materials. Bluetorial coming soon below 1/

ChromBPNet: bias factorized, base-resolution deep learning models of chromatin accessibility reveal cis-regulatory sequence syntax, transcription factor footprints and regulatory variants

Despite extensive mapping of cis-regulatory elements (cREs) across cellular contexts with chromatin accessibility assays, the sequence syntax and genetic variants that regulate transcription factor (T...