Most thrilling is that we can identify active T cells based on relative cell size and morphology, and watch T cells activate (differentially based on condition) after interacting with cancer cells.
With a Markov model, we deconvolved when, in frame t-1, there is one cancer cell and one T cell in a window, and in frame t there is one cancer cell and two T cells. We were able to quantify how often this doubling of T cells attacking a cancer cell was due to proliferation or due to recruitment.
In summary, we found that, compared to the SH KO control condition, TCR T cells with the RASA2 KO have a longer dwell time and cripple cancer cells more effectively this way, whereas TCR T cells with the CUL5 KO proliferated more frequently upon activation, adding more T cells to the fight.
With five new collaborations in the works, and a paper characterizing the differences using explainable AI already accepted as an oral presentation at #PSB2025 (lead by high school senior Marcus Blennemann), look for future work in this space! https://www.biorxiv.org/content/10.1101/2024.10.01.616134v1
Feedback welcome! And please play with these data! There is a lot more signal there.
Thank you to @bioimagearchive.bsky.social for hosting these Incucyte image data -- this is a new thing for them, and they have been so kind in working through the details of submission (link coming soon!)! 🎉
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https://www.biorxiv.org/content/10.1101/2024.10.01.616134v1
https://github.com/vanvalenlab/Caliban-2024_Schwartz_et_al
https://github.com/bee-hive/occident
Thank you to @bioimagearchive.bsky.social for hosting these Incucyte image data -- this is a new thing for them, and they have been so kind in working through the details of submission (link coming soon!)! 🎉