Congratulations to Sumin on joining the Leading Edge Fellows program as a 2025 fellow. Read the full announcement.

Congratulations to Domenic - now Dr. Narducci - on successfully defending his PhD Thursday May 22nd, 2025. Well done Domenic!

Congratulations to Matteo, Domenic, and Simon on their new preprint on chromatin dynamics in close collaboration with Jessica Matthias from Abberior. By integrating a transformative new microscopy technique - MINFLUX - with traditional methods - single-molecule tracking & super-resolution live-cell imaging- -  we were able to track chromatin dynamics over 7 orders of magnitude in time (a range of 36,000,000), which allowed us to quantify chromatin dynamics to understand how it moves and provide insights into first passage time processes in the nucleus. You can read the full preprint on BioRxiv.

It was bittersweet to say farewell to Michele Gabriele - with food and karaoke. Michele was the first post-doc to join the lab on the very first day of the lab, Feb 1st 2020. Michele helped found the lab, co-led the first paper from the lab on CTCF/cohesin loop dynamics (Gabriele*, Brandao*, Grosse-Holz* et al. Science 2022), won several fellowships including an AICF fellowship and an NIH K99 Pathway to Independent Award, and Michele will now found his own lab starting in June 2025 at the San Raffaele Telethon Institute for Gene Therapy (SR-Tiget), Milan, Italy. The Gabriele lab is now online where you can see what Michele will be up to: https://www.gabrielelab.com/

Thanks to Michele for 5 great years and all the best for your new lab!

Congrats to Viraat - our collaborative work lead by Corri Sept and Martin Aryee is now published. You can read the full paper here.

Check out our new preprint with Jie Liu's lab on a general AI model that can predict diverse genomic outputs such as transcription and genome structure, which is now on BioRxiv.

Congratulations to Clarice and Varshini on their new preprint - in collaboration with co-first author Fan Feng and Jie Liu - describing Cleopatra, a new ML model that can predict 3D genome structure at very high resolution. You can read the full preprint on BioRxiv.