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Manu Saraswat

@manusaraswat.bsky.social

230 followers 200 following 39 posts

PhD candidate in ML for genomics in Heidelberg, Germany with Oli Stegle Previously at Genentech, UBC and BITS Pilani https://scholar.google.com/citations?user=4yUtALcAAAAJ&hl=en&oi=ao

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Manu Saraswat @manusaraswat.bsky.social · May 16

🧠 Excited to share my main PhD project! We mapped the regulatory rules governing Glioblastoma plasticity using single-cell multi-omics and deep learning. This work is part of a two-paper series with @bayraktarlab.bsky.social @oliverstegle.bsky.social and @moritzmall.bsky.social, Preprint at end🧵👇

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Reposted by Manu Saraswat

gokcen.bsky.social @gokcen.bsky.social · 19d

My team has a postdoc position available now, join us!

careers.gene.com/us/en/job/20...

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Manu Saraswat @manusaraswat.bsky.social · Sep 8

Excited to be at @aithyra.bsky.social #AIinLifeScience symposium in Vienna. What a gorgeous venue!
Will be presenting posters on my latest work on personalized gene expression prediction and gene regulatory network inference

Manu Saraswat @manusaraswat.bsky.social · Aug 1

Dived into past, present & future of human genetics with brilliant students & mentors.Grateful for the chance to present my work on personalized sequence→expression prediction and discussions with @sashagusevposts.bsky.social @bpasaniuc.bsky.social @mashaals.bsky.social @tuuliel.bsky.social & others

Andrea Ganna @andganna.bsky.social · Jul 30

Leena Peltonen School of Human Genetics in full-swing!

@gosiatrynka.bsky.social
@dgmacarthur.bsky.social
@bpasaniuc.bsky.social
@tuuliel.bsky.social
@hilarycmartin.bsky.social
@sashagusevposts.bsky.social
@zkutalik.bsky.social
@mashaals.bsky.social
@alemedinarivera.bsky.social

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Reposted by Manu Saraswat

Peter Koo @pkoo562.bsky.social · Jul 16

Our work on "Evaluating the representational power of pre-trained DNA language models for regulatory genomics" led by @AmberZqt with help from @NiraliSomia & @stevenyuyy is finally published in Genome Biology! Check it out!

genomebiology.biomedcentral.com/articles/10....

Evaluating the representational power of pre-trained DNA language models for regulatory genomics - Genome Biology

Background The emergence of genomic language models (gLMs) offers an unsupervised approach to learning a wide diversity of cis-regulatory patterns in the non-coding genome without requiring labels of ...

genomebiology.biomedcentral.com

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Manu Saraswat @manusaraswat.bsky.social · Jun 23

All the best @paubadiam.bsky.social 🎉🎉

Reposted by Manu Saraswat

Gherman Novakovsky @gnovakovsky.bsky.social · May 29

Excited to share my first contribution here at Illumina! We developed PromoterAI, a deep neural network that accurately identifies non-coding promoter variants that disrupt gene expression.🧵 (1/)

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Manu Saraswat @manusaraswat.bsky.social · May 22

Thanks for your thoughts

Manu Saraswat @manusaraswat.bsky.social · May 22

I am genuinely wondering and curious what you think. It’s increasingly hard to see how academia can attract or retain top talent with offers like this, especially when industry offers 2–3x more. Something structural has to change if we’re serious about advancing AI in science.

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Manu Saraswat @manusaraswat.bsky.social · May 22

Hi Andrea,
Thanks for sharing this, it’s an exciting and meaningful opportunity. But to be honest, the listed salary (€42–49k for postdocs) is deeply misaligned with the qualifications you're asking for- post-PhD experience, publications in top AI and biology journals, teaching, and vision.

Reposted by Manu Saraswat

Omer Ali Bayraktar @bayraktarlab.bsky.social · May 16

How does tumour heterogeneity arise? How can we predict cancer cell plasticity? In 2 new studies, we trace #glioblastoma heterogeneity to a spatial cancer cell trajectory w. multimodal cell atlassing bit.ly/4mkrWgs & predict plasticity w. snRNA/ATAC+deep learning bit.ly/3FbI6Ic 🧵

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Manu Saraswat @manusaraswat.bsky.social · May 16

Thanks a lot for the shoutout Stein. scDORI builds upon the insights from the foundational works in GRN inference from your lab - Cistopic, SCENIC, SCENIC+
Thanks a lot for your contributions. Very exciting time to be in the field 🚀

Manu Saraswat @manusaraswat.bsky.social · May 16

Thanks to all our collaborators, specially @lauraruedag.bsky.social who co-led the computational analysis and was the best partner in crime one could ask for. What a delight it was!
Thanks to Elisa, Tannia and Fani for leading the experimental aspects.

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Manu Saraswat @manusaraswat.bsky.social · May 16

14/ We believe this approach can be applied to other cancers to uncover—and exploit—plasticity brakes. Get in touch if interested! #GBM #MultiOmics #CancerResearch #deeplearning #cancerneuroscience #GRNs #Cancer #singlecell

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Manu Saraswat @manusaraswat.bsky.social · May 16

13/ Want to learn more? Read our regulatory paper www.biorxiv.org/content/10.1... and companion spatial multi-omics study www.biorxiv.org/content/10.1...
Watch out for thread from @bayraktarlab.bsky.social on mapping conserved spatiotemporal trajectories of GB

Decoding Plasticity Regulators and Transition Trajectories in Glioblastoma with Single-cell Multiomics

Glioblastoma (GB) is one of the most lethal human cancers, marked by profound intratumoral heterogeneity and near-universal treatment resistance. Cellular plasticity, the capacity of cancer cells to t...

www.biorxiv.org

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Manu Saraswat @manusaraswat.bsky.social · May 16

13/ Our framework unifies regulatory and spatial logic of GB heterogeneity. While our companion paper showed subclones are intermixed across conserved tissue niches, our regulatory model explains WHY—they follow the same trajectory because they're constrained by the same regulatory rules!

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Manu Saraswat @manusaraswat.bsky.social · May 16

12/ Beyond chromatin changes, MYT1L transformed GB cells into less aggressive neuronal-like cells with: • Enhanced neurite-like morphology • Reduced tumor microtube connectivity • Decreased proliferation • In vivo: slower growth, less invasion, longer survival!

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Manu Saraswat @manusaraswat.bsky.social · May 16

11/ Our experimental validation was striking: • MYT1L overexpression closed >80% of differential chromatin regions • MYT1L knockout reopened access to plastic fates • MYT1L directly bound and repressed regulators of other GB states• 85% of scDORI's predicted TF targets confirmed!

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Manu Saraswat @manusaraswat.bsky.social · May 16

10/ 🔥 Can we use these GRNs to manipulate tumor identity and push GB cells into less plastic states? YES! We predicted MYT1L as the key regulatory bottleneck—a master repressor that locks cells into neuronal-like states by directly binding and suppressing the regulators of plastic states.

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Manu Saraswat @manusaraswat.bsky.social · May 16

9/ Remarkably, our regulatory roadmap explains tumor architecture! States with easy transitions exist in close spatial proximity, while states separated by regulatory barriers are spatially distant. The regulatory rules we've uncovered directly shape how tumors are organized!

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Manu Saraswat @manusaraswat.bsky.social · May 16

8/ This roadmap revealed striking asymmetry in GB plasticity: • OPC/NPC-like and AC-like states can easily activate multiple alternate fates • Neuronal-like states are "locked" by strong repression barriers • Transitions follow preferred directions (OPC→Neuronal easier than Neuronal→OPC)

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Manu Saraswat @manusaraswat.bsky.social · May 16

7/ 🔑 The big question: Which tumor states can easily transition to others? We developed metrics to quantify both activation potential (what enables transitions) and repression barriers (what prevents them), creating the first regulatory roadmap of GB plasticity.

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Manu Saraswat @manusaraswat.bsky.social · May 16

6/ The power of multi-omics: We can distinguish what a cell IS versus what it COULD BECOME. While only 16% of Topic Regulators are expressed across different tumor states, over 54% are epigenetically accessible—revealing "primed drivers" ready for activation during transitions!

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Manu Saraswat @manusaraswat.bsky.social · May 16

5/ Applied to GB, scDORI uncovered Topics that redefine tumor heterogeneity through regulatory logic. Each Topic links specific TFs, enhancers, and target genes that work together across tumor states. We identified key "Topic Regulators" (TRs)—master TFs for each Topic.

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Manu Saraswat @manusaraswat.bsky.social · May 16

4/ scDORI: • Scales to millions of cells • Models continuous cell state GRNs • Incorporates both activation AND repression signatures • Each cell is modeled as a mixture of regulatory Topics • Can be applied to ANY multi-omic dataset (happy to hear your feedback, separate 🧵 on soon!)

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Manu Saraswat @manusaraswat.bsky.social · May 16

3/ To decode GB's regulatory logic, we developed scDORI—an autoencoder that decomposes multi-omic profiles into "regulatory Topics." Each Topic represents specific TF-target gene relationships, modeling cells without requiring predefined cell-types. Code: github.com/bioFAM/scDoRI

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