mehmetcanucar.bsky.social
new PI @University of Sheffield -using mathematical models to understand living matter
https://www.sheffield.ac.uk/mps/people/all-academic-staff/mehmet-can-ucar
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ahahah oh no only my mom was supposed to see this footage 🙈 now I officially have to redefine my research 😅
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ahahah I figured my time has finally come after >10 years of not stepping near anything more sophisticated than a vacuum..
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congrats Adrien this looks super cool! 😍
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AND: you can easily play around with the system using VisualPDE developed by @blindmath.bsky.social!! 😍
👉 shorturl.at/kfH4I
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Thanks Andrew! Wow it's so cool to play around with it using VisualPDE!! 😍
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Thank you for these refernces! It looks indeed quite similar to leader-follower type behavior! :)
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This was a really cool work with @ehannezo.bsky.social & Michael Sixt, with Zane Alsberga leading experiments and building on our recent work with Jonna Alanko!
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Overall, we find that self-generated chemotaxis provides an elegant strategy for robust migration of heterogeneous cell populations -which could be important for understanding immune function & beyond!
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Turns out, mechanical nonreciprocity only affects migration dynamics when consumer cells are weakly chemotactic: For sufficiently chemotactic consumers, biochemical non-reciprocity dominates over mechanical forces!
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Finally, we explored whether mechanical interactions between different cell types could also drive efficient co-migration, like in nonreciprocal interactions where population A attracts B, while B repels A 💔
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Importantly, after fitting our model with experimental data, we found that DC-T cell migration sits close to the optimal regime (🌟), where both co-migration and colocalization are maximized:
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Moreover, the spatial profiles of the cell populations matched the theoretical predictions:
T cells (sensors) showed a concentration peak ahead of DCs (consumers), despite being unable to migrate alone -purely driven by DC-generated gradients!
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Strikingly, as predicted by the theory:
🔹DCs (consumers) and T cells (sensors) migrated as coupled travelling waves 🌊
🔹T cells stayed at a conserved distance ahead of DCs!
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To test these predictions, we teamed up with the Sixt Lab (ISTA) to design a minimal in vitro experiment:
Dendritic cells (DCs), which shape the gradient, and T cells, acting as sensors, migrated in 1D channels, with cells continuously entering from a reservoir:
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Interestingly, if sensor cells are too chemotactic, they move too far ahead of the consumers, preventing colocalization -a problem for immune cells that need physical contact to communicate.
This suggests an optimal parameter regime balancing co-migration & colocalization! ⚖
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🟢When sensor cells become more chemotactic than consumers, they can migrate ahead as a spatially localized “pulse”..
..but the sensor-consumer separation is bounded, as sensors will eventually reach the flat region of the attractant profile!
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We find that the chemotactic ability of the sensors controls a sharp transition:
🔴If sensors aren’t chemotactic enough, they can’t follow the gradients generated by consumers and fall behind, leading to an uncoupled migration dynamics.
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Inspired by our recent finding that some immune cells can’t self-generate chemical gradients (science.org/doi/10.1126/...), but can "surf" on gradients generated by other cell types, we model:
(i) Consumers: sensing & degrading chemoattractants,
(ii) Sensors: following gradients.
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hello! :) can i be added as well?
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(ii) Collective morphodynamics in nervous system patterning (details: www.findaphd.com/phds/project... )
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Some possible projects are:
(i) Collective cell migration via self-organized signals (for details: www.findaphd.com/phds/project... )
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We're based in the School of Mathematical & Physical Sciences @sheffielduni.bsky.social -right next to the amazing peak district! 😍