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Lars Velten

@larsplus.bsky.social
510 followers 193 following 85 posts

Group leader @crg.eu | blood, single cell, synthetic genomics | Dad | 🇩🇪🇪🇸🇹🇿 | 🚵🧗🏃

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Lars Velten @larsplus.bsky.social · 04/09/2026
We therefore started decomposing RNA-seq datasets into program activity (wrote a small algorithm for that). When we looked at the AML atlas data, we lived a big surprise: One of our Perturb-seq programs predicts long-term survival in AML very well! Independent of genotype. (3/n)
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Lars Velten @larsplus.bsky.social · 04/09/2026
In perturb-seq, Joe identified "gene regulatory programs". These are groups of genes that share common regulators - not co-expression programs. This nicely grouped genes into pathways. So we asked: are these genes sets any useful? (2/n)
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Lars Velten @larsplus.bsky.social · 04/03/2026
Now, synthetic enhancers with single cells - great work by @juruehle.bsky.social : www.biorxiv.org/content/10.6...
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Lars Velten @larsplus.bsky.social · 05/12/2025
ISCO (Innovations in Single-Cell OMICS) will be back in beautiful Barcelona! 🗓️ 28th/29th of May 2026 📍Barcelona Biomedical Research Park @prbb.org (beachfront!) Keynotes: @bartdeplancke.bsky.social and @bocklab.bsky.social Submit your abstract and present your research! www.isco-conference.eu
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Lars Velten @larsplus.bsky.social · 20/11/2025
We are looking for a postdoc to join our team! If you're interested in translating a cutting edge genomics technology (www.nature.com/articles/s41...) to real-life applications in hematology, this is for you. We offer a unique working environment ON THE BEACH: recruitment.crg.eu/content/jobs...
Yes, this is where you would work (the round building, not the boat)
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Lars Velten @larsplus.bsky.social · 21/05/2025
@martinabraun.bsky.social showed that in human a) clonal expansions are ubiqutious by age 50 and b) CH mutation linked clones are just a small subset of clonal expansions, with similar functional biases as “driverless” clones (9/n)
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Lars Velten @larsplus.bsky.social · 21/05/2025
We then built an algorithm, EPI-Clone, that extracts clonal information from scTAM-seq data. It recapitulates ground truth clonal labels with high accuracy. This was more or less our preprint, online since April 2024. Since then a lot has happened: (7/n)
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Lars Velten @larsplus.bsky.social · 21/05/2025
This made us wonder if these two layers can be separated. Again to our surprise, we discovered that the CpGs that change with differentiation (“dynamic” CpGs) are different from the ones that are clone-specific (“static CpGs”) (5/n)
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Lars Velten @larsplus.bsky.social · 21/05/2025
We performed targeted single-cell profiling of 453 CpGs on cells that had been barcoded with a lentivirus, so we knew clonal identity. To our big surprise, these data clustered both by differentiation state, and by clone. (4/n)
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Lars Velten @larsplus.bsky.social · 08/05/2025
Across all 42 TFs, we systematically quantified antagonsims and synergies: Non-additive and neutralizing interactions are much more common in primary cells than in a cancer cell lime, where most combinations activate.
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Lars Velten @larsplus.bsky.social · 08/05/2025
Using these data, we constructed an AI that can create new enhancers that work as predicted (10/n)
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Lars Velten @larsplus.bsky.social · 08/05/2025
These negative synergies create a sensor of TF ratios. If one TF’s expression dominates, you get activation. If both are there, repression. A perfect design to ensure that target genes are expressed specifically at the right place. (9/n)
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Lars Velten @larsplus.bsky.social · 08/05/2025
We found (functional and alphafold) evidence that direct TF-TF interactions may be involved (8/n)
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Lars Velten @larsplus.bsky.social · 08/05/2025
Combinatorial enhancers do something even more crazy: In several cases, pairs of strong, universal activators turn into cell state specific repressors. Here are the two most extreme cases: (7/n)
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Lars Velten @larsplus.bsky.social · 08/05/2025
To dissect this behavior, we classified TFs into activators, repressors, or dual factors. To our surprise, some TFs are activators at low occupancy, and Repressors at high occupancy. For in-depth biophysical modelling by @rmartinezcorral.bsky.social , see our paper! (6/n)
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Lars Velten @larsplus.bsky.social · 08/05/2025
When we looked at the activity of our constructs, we found that enhancers composed of binding site pairs are often highly cell state specific, whereas enhancers composed of single sites are not. (5/n)
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Lars Velten @larsplus.bsky.social · 08/05/2025
We used synthetic DNA to reduce complexity. Specifically, we placed sites for 38 TFs in a random DNA background, at different combinations and arrangements. We measured the activity of these constructs in 7 cell states of hematopoietic stem cell differentiation. (4/n)
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Lars Velten @larsplus.bsky.social · 08/05/2025
During hematopoiesis, TFs are expressed in continuous and overlapping gradients. Nonetheless, their targets are highly cell state specific. How can that be? (2/n)
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