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This is kind of stunning. They appear to have discovered something like 30 novel (previously unknown) immune cell receptor interactions (Fig 1). Then they bui

This is kind of stunning. They appear to have discovered something like 30 novel (previously unknown) immune cell receptor interactions (Fig 1). Then they built a quantitive model, looked into where these interactions are localized in the body, and lots more beside. A truly ridiculous amount of information for a single paper. This seems to be the 'what's it all about' paragraph in the paper: > "The immune system is distinctive for being a distributed system. It is not fixed to a single localized organ in the body, but rather is made up of numerous specialized cell types that must adaptably organize their intercellular connections to respond to pathogens and other threats wherever they may appear. We provide a systematic and quantitative view of the cell-surface proteins that enable immune cells to dynamically wire their interactions. The receptor interactions that we report in our network each merit further individualized study to characterize their full roles in health and disease." For those interested in computational modeling (basically they simulate immune cells circulating freely within the body and interacting based on their receptor types), here's the supplementary description link (pdf), which is enough for a whole paper on its own. https://static-content.springer.com/esm/art%3A10.1038%2Fs41586-022-05028-x/MediaObjects/41586_2022_5028_MOESM1_ESM.pdf https://static-content.springer.com/esm/art%3A10.1038%2Fs415...

photochemsyn 4y ago
After looking at this a bit more, here's something interesting. First, it's a very physical model, treating cells as 3D entities with 2D interactions (cell surfaces), and they use their measured binding constants (essentially a measure of protein-protein stickiness across all their proteins) as a key parameter, along with measures of protein expression in various immune system cell types. They're classified into: CD4 T cells, Helper CD4 T cells, Natural killer cells, CD8 T cells, Dendritic cells, Monocytes, Regulatory T cells, B cells. This is all quite complex, here's a simplified 6 min overview of some of their roles, note the importance of receptor-receptor interactions at every stage: https://www.youtube.com/watch?v=6eMh0Vpyoik https://www.youtube.com/watch?v=6eMh0Vpyoik For a nice visualization of immune cells attacking cancer cells: https://www.youtube.com/watch?v=K09xzIQ8zsg https://www.youtube.com/watch?v=K09xzIQ8zsg So, this is a complex dynamic network, and a key feature of such networks is that a change at one point in the network can percolate across the entire network, which can be critical to their normal functioning, as well a play roles in failure /pathology. The ability to model such casading effects could be game-changing in terms of developing treatments such as targeting cancer cells, blocking autoimmune activation, etc. From supplement: > "Another direct test of the predictive power of this model would be to experimentally block specific cell-surface protein interactions (such as by adding recombinant ligands that competitively inhibit their receptors’ interactions) and gauge how cell-to-cell contact frequencies change. If these changes are predictable from our “ground-up” mass action kinetics model, that would demonstrate the utility of our systematic approach."
- dylan604 4y ago
  Thanks for those links. I'm not an oncologist, nor do I play one on TV, so I've only paid a cursory glance to where the current understanding of cancer cell behavior was. After watching the second video you linked, I'm now extremely curious about this field, and kind of wish I could go back in time to chose a different career path. The amount of details now known on how cells, proteins, etc work is totally fascinating to me.

photochemsyn 4y ago

After looking at this a bit more, here's something interesting. First, it's a very physical model, treating cells as 3D entities with 2D interactions (cell surfaces), and they use their measured binding constants (essentially a measure of protein-protein stickiness across all their proteins) as a key parameter, along with measures of protein expression in various immune system cell types. They're classified into: CD4 T cells, Helper CD4 T cells, Natural killer cells, CD8 T cells, Dendritic cells, Monocytes, Regulatory T cells, B cells. This is all quite complex, here's a simplified 6 min overview of some of their roles, note the importance of receptor-receptor interactions at every stage: https://www.youtube.com/watch?v=6eMh0Vpyoik https://www.youtube.com/watch?v=6eMh0Vpyoik For a nice visualization of immune cells attacking cancer cells: https://www.youtube.com/watch?v=K09xzIQ8zsg https://www.youtube.com/watch?v=K09xzIQ8zsg So, this is a complex dynamic network, and a key feature of such networks is that a change at one point in the network can percolate across the entire network, which can be critical to their normal functioning, as well a play roles in failure /pathology. The ability to model such casading effects could be game-changing in terms of developing treatments such as targeting cancer cells, blocking autoimmune activation, etc. From supplement: > "Another direct test of the predictive power of this model would be to experimentally block specific cell-surface protein interactions (such as by adding recombinant ligands that competitively inhibit their receptors’ interactions) and gauge how cell-to-cell contact frequencies change. If these changes are predictable from our “ground-up” mass action kinetics model, that would demonstrate the utility of our systematic approach."

dylan604 4y ago
Thanks for those links. I'm not an oncologist, nor do I play one on TV, so I've only paid a cursory glance to where the current understanding of cancer cell behavior was. After watching the second video you linked, I'm now extremely curious about this field, and kind of wish I could go back in time to chose a different career path. The amount of details now known on how cells, proteins, etc work is totally fascinating to me.