See details in the bioRxiv and wish us luck with submission!

Huge thanks to everyone involved:

Valentin Borshchevskiy, Vadim Cherezov, @hofkenslab.bsky.social, @profjelle.bsky.social, Michael Börsch, Thomas Gensch!

doi.org/10.1101/2025.07.22.666123

Finally, how to make sense of (1) sub-ms agonist-induced dynamics seen in confocal, and (2) long-lived states (>100ms) in ABEL trap. How do they not average out?

In our updated model, sub-ms dynamics occurs within long-lived states:

Maybe, these long-lived states can interconvert on much longer timescales - in fact, Wei et al. showed long-lived states with dwell times of seconds before with TIRF:

doi.org/10.1016/j.st...

We conclude that A2A receptors must have long-lived states with different FRET efficiencies with dwell times of at least hundreds of ms. This way, we extend our previous estimation, based on confocal burst data, ~100x fold!

it seems very much like "static heterogeneity":

within a "Gaussian" or FRET values, receptors on the left shoulder of the distribution stay there for 100s of milliseconds, as if they had "memory"

only metaphorically, of course ...

Instead of sharp PR peaks and step-like transitions, we see broad distributions and continuous FRET changes🤔 Is conformational space a spectrum?🙃

Our dynamics analysis (inspired by BVA, FRET-FCS, and RASP) showed that dynamics on 1-100 ms scale is significant but subtle, so...

With ABEL-trap, we can see individual receptors for up to 2 seconds instead of milliseconds! in solution and without tethers

Consistent with our previous data, agonist NECA increases FRET in double-labeled A2A

This is the final piece of my PhD studies on #smFRET with A2A adenosine receptors #GPCR!

In our previous study, we observed sub-ms agonist-induced conformational dynamics of the A2A, but longer transitions (if any) were invisible in burst-wise data

doi.org/10.1038/s420...

👏Loud applause to @linnikdmitrii.bsky.social, who came up with idea and combined so many methods to characterize the system from every possible angle! And thanks to everyone who contributed to the study! 🪅

@membraneenzymology.bsky.social 🔬🧫
@janstevens.bsky.social @cg-martini.bsky.social ‬🤖🧑‍💻