Omer Ali Bayraktar

NEW: Technical specialist with @bayraktarlab.bsky.social

Generate large-scale spatial transcriptomic datasets to create comparative cell atlases of ALS, Alzheimer’s disease and Parkinson’s disease

#scienceJobs #neuroskyence

sanger.wd103.myworkdayjobs.com/en-US/Wellco...

NEW: Postdoc in neurodegenerative disease spatial transcriptomics and ML with @bayraktarlab.bsky.social/Mo Lotfollahi

Join a new project to create comparative cell atlases of ALS, Alzheimer’s disease and Parkinson’s disease

#postdocJobs #neuroskyence

sanger.wd103.myworkdayjobs.com/en-US/Wellco...

📢 @bayraktarlab.bsky.social & colabs at @sangerinstitute.bsky.social are assembling the @opentargets.org funded Neuroimmune Team 🧠🧬-Leverage AI to uncover neurodegeneration

Tech Spec: sanger.wd103.myworkdayjobs.com/en-US/Wellco...
Postdoc AI: sanger.wd103.myworkdayjobs.com/en-US/Wellco...

Very excited about our new chapter with @mhaniffa.bsky.social !

Thanks Muzz!

Thanks Adrien!

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...

Two massive glioblastoma papers, datasets, trajectories, insights, and.. a very cool new method for GRN inference - scDORI -from @steglelab.bsky.social @oliverstegle.bsky.social @bayraktarlab.bsky.social & Moritz Mall www.biorxiv.org/content/10.1... www.biorxiv.org/content/10.1...

Thanks Chris!

Grateful to >60 authors for their amazing work + @wellcomeleap.bsky.social (special thx to Jason Swedlow for leading DeltaT!) & @cziscience.bsky.social for their support. @ebi.embl.org @crick.ac.uk @cambridgeuni.bsky.social @dkfz.bsky.social @sangerinstitute.bsky.social

GBM-Space: Multi-modal Atlas of Glioblastoma Explore GBM-Space, a comprehensive cell and tissue Atlas of Glioblastoma combining spatial transcriptomics, single-cell data, and more.

Finally, you can access & navigate almost all of our GB single cell and spatial data in our portal @ www.gbmspace.org built with webatlas2 (go.nature.com/4dEr9mN)

There are many implications & LOTS of detail in the preprints (e.g. mesenchymal cancer states is really a gliosis/hypoxia response & GB tumour subtypes are likely confounded) bit.ly/4mkrWgs bit.ly/3FbI6Ic

Taken together, these 2 studies start to unveil what i call the “rules” of glioblastoma. Despite their incredible complexity, these tumours are governed by specific cellular & tissue mechanisms. We believe/hope this understanding will be a foundation for new therapies

In GB, as opposed to unrestrained plasticity of cancer cells, we predict transition highways and barriers aligned with paper 1. And we mechanistically validate a transcriptional repressor safeguarding neuronal like cancer states!

scDORI enables us to examine GRNs in a continuous manner across cellular trajectories. Hence, you don’t just map GRNs / TF regulators, you can actually infer cell plasticity and predict the future state of a given cell

Here we developed a new deep learning based GRN inference framework: scDORI. See the thread from Manu for more details but few highlights here bsky.app/profile/manu...

In paper 2, we leverage our massive snRNA-ATACseq data to predict cancer cell state transitions & trajectories from gene regulatory networks. Led in @oliverstegle.bsky.social and Moritz Mall's labs by @manusaraswat.bsky.social @lauraruedag.bsky.social Elisa + Tannia & Fani

Futile wound healing drives mesenchymal-like cell phenotypes in human glioblastoma Glioblastoma is the deadliest brain cancer, characterized by large cellular diversity whose complexity and organizing principles are only starting to be uncovered. Both neurodevelopment-like and mesen...

This picture is very consistent with a concurrent study from @sten.linnarsson.org - check their paper out: www.biorxiv.org/content/10.1...

Hence, we define a stereotyped trajectory of cancer cells at the heart of GB heterogeneity - unifying previously defined cell states & tumour subtypes

Finally, cancer cells are not riding alone. We found that this cancer cell trajectory is intimately linked to myeloid heterogeneity and unfolds across regionalised myeloid signalling environments.

This led to a wonderful side quest: we mapped tumour subclones in space! We developed SpaceTree to deconvolve tumour subclones in Visium data and found that they are finely spatially intermixed (within Visium spots) in the tissue! github.com/PMBio/spaceT...

These results suggest that this trajectory cuts across the genetic hierarchy of GB i.e. shared across tumours/subclones. We used spatial DNA-seq (LCM-WGS) to validate this +found the trajectory is shared, yet genetically malleable.

Unbiased transcriptomic trajectory analysis (i.e. our cell2fate RNA velocity go.nature.com/4km62aI) finds that this is trajectory shared across tumours + genetic subclones. And we define a molecular progression conserved across tumours/clones

When you look into the tumour areas/states dominated by gliosis/hypoxia, these transitions become clear. You can see cancer cells travelling from developmental astrocyte like states gradually into gliosis + hypoxia response (more on this below)

..to resolving this trajectory. Integrating single nuclei + spatial data revealed stepwise spatial transitions of cancer cell states from the tumour invasive edge towards the necrotic core. This looked like spatially organised cancer cell transitions as GB tumours rapidly expand.

It started with single nuclei RNA-seq. Profiling >1 million cells, we saw within each tumour that cancer cells vary from developmental progenitor-like states (e.g.NPC/OPC) to those marked by gliosis (i.e. glial injury response) & hypoxia Yet, the spatial/tissue context was the key…

Integrating these modalities, we interpreted human GB biology from scratch. We mapped GB heterogeneity to a spatially-patterned trajectory of cancer cells in tissue, where they transition from developmental-like towards glial injury response & hypoxia states.

In paper 1, we tackled this big question. We created the GBM-space, the most comprehensive atlas of human GB. It uniquely pairs single cell + spatial modalities to bridge the cell, tissue and genetic basis of GB. Led by the amazing team of Grant, Tannia, Fani & Olga

BUT despite elegant work, we still don’t know how/why cancer cells diversify & transition across states. Do cancer cells follow a defined trajectory across tumours? Or is it a chaos of unrestrained plasticity? Treatments will continue to fail if we lack this basic understanding

Glioblastoma (GB) is a rapidly fatal brain tumour. Within each GB tumour, cancer cells exist across diverse cellular states + can switch their states (i.e. plasticity) + are thought to make tumour subtypes….

Over the past 3 years, we led a fantastic @wellcomeleap.bsky.social collaboration w. @oliverstegle.bsky.social, Behjati, Mall, Mair, @jamesbriscoe.bsky.social & @juliosaezrod.bsky.social labs. We created a massive multiomic atlas & new comp models to discover human GB biology www.gbmspace.org