Congrats Rita, and thanks for everything you did at Nature Methods! Hopefully our paths cross again at some point 😀

Welcome to our blue space! SMLMS 2025 is actively in the making! Soon we will be able to reveal our amazing speaker line-up here in Bonn! Stay tuned! While waiting, maybe already bookmark smlms.org! 👈
See you in August 2025 in Bonn!

What a great way to end the year! ✨

Today in @cellpress.bsky.social we report the structure and function of the Shedu anti-phage defense system.

tinyurl.com/4crj6dnx

A long 🧵...

Razendsnel meerdere eiwitten tegelijk volgen in een cel: met deze nieuwe methode lukt het Wetenschappers hebben een methode ontwikkeld waarmee voor het eerst razendsnel de bewegingen van meerdere eiwitdeeltjes in een bacteriële cel tegelijk gevol...

This week, I was interviewed by the Dutch newsradio
@bnrnieuwsradio.bsky.social about my latest work: TARDIS, a computationally novel way to track single proteins inside living cells. Listen to my radio-debut here (Dutch): bnr.nl/podcast/wete... or read all about the research here: rdcu.be/dv1sr

Openly accessible here: rdcu.be/dv1sr !

Finally, huge kudo's to all my labs in all three countries (WUR, NL, CarnegieMellon, USA, and UniBonn GER), and to funding from the #Humboldt Postdoc Fellowship @humboldt-foundation.de , Bonn Argelander program, VLAG@WUR, NSF, CarnegieMellon, and UniBonn! (8/8)

This method wouldn't be here without @uendesfelder.bsky.social , @hohlbeinlab.bsky.social and Dr. Turkowyd, and I love the improvements during the review process led by @ritastrack.bsky.social - it increased the fundamental, mathematical underpinning of TARDIS ánd allows more flexibility in fitting!

GitHub - kjamartens/TARDIS-public: Public releases of TARDIS Public releases of TARDIS. Contribute to kjamartens/TARDIS-public development by creating an account on GitHub.

TARDIS promises to open up the possibility of performing spt data analysis in/with wildly novel conditions/probes. It will also directly benefit from all future endeavors in (mobile) particle localization at high density. Try TARDIS yourself here: github.com/kjamartens/T...

We used TARDIS to look at the in vivo movement of RNA polymerase, and show that we can easily lower the measurement time by a factor of ~5. This was limited by the required high-density localization, NOT by TARDIS performance. (5/8)

We went to crazy lengths to try to overwhelm TARDIS, but only managed to get a 'suboptimal' error of ~5% once we (far) surpassed the limits of mobile single-molecule localization AND had only 1.5 loc/traj on average AND > 50% of the dataset was pure noise (!) (4/8)

This concept accurately obtains either an analytically fitted distribution (anything is possible here), or a jump-distance histogram. It is far more robust than any tracking algorithm (at high complexity), and constantly surprised us with its robustness. (3/8)

TARDIS is a conceptually new method to perform single-particle tracking analysis: all localizations are compared to themselves with a time-shift. The intraparticle population is separated from the interparticle distribution by observing time delays longer than track lengths: (2/8)

TARDIS (Temporal Analysis of Relative Distances) is now out in
Nature Methods! nature.com/articles/s41... It redefines single-particle tracking: spt is no longer limited by tracking algorithms - TARDIS offers at least 10x higher throughput, exceptional noise robustness: a 🧵 (1/8)