Big thanks to all labs that made their work freely available on Addgene @janeliagenie.bsky.social, @hoddana.bsky.social, @loogerl.bsky.social, @massecklab.bsky.social, Dorus Gadella, and many others. Their openness is a real model for how science ought to work.
Another thing we learned. Blue light resistance is easy to break. We found a few variants along the way that made blue light resistance worse. A clear takeaway is that this is something that should be screened for up front, rather than just optimizing for response sizes.
Weโre now testing ScaRCaMP-2.0 in vivo and are working hard to continue to optimize this to create a better tool for all-optical experiments. This is still a work in progress, but we hope this provides a new path for blue-light resistant biosensors! Reach out to us if you want to try this out!
We simulated possible structures of our sensor and @dkoveal.bsky.social noticed a possible mechanism that looks distinct from other GECIs. We used this to further optimize our sensor to create ScaRCaMP-2.0, which has double the response size of ScaRCaMP-1.0 and retains its blue-light resistance.
We screened candidate sensors for both response sizes and blue light resistance. After pushing the boulder up the hill for nearly two years, @xshirleyz.bsky.social made our first sensor, ScaRCaMP-1.0. Its response size isnโt huge, but it is impressively resistant to blue light (up to 200 mW/mm^2).
Almost all red FPs used in GECIs respond to blue light. This makes multiplexing complicated and sometimes impossible. We found that nearly all state-of-the-art red GECIs have some blue light sensitivity. So, we set out to make a new GECI using mScarlet-I3 that is highly resistant to blue light.
For a variety of reasons itโs preferrable to use red GECIs with blue-light-actuated optogenetic tools. Current GECIs are high SNR and express well in vivo, but...
GECIs are foundational tools in systems neuroscience. There has been recent interest in using them in โall-opticalโ experiments where we can read/write neural activity using different parts of the visible spectrum.
We made a highly blue-light resistant red-fluorescent genetically encoded calcium sensor GECI) www.biorxiv.org/content/10.6...
BluePrintโข ๐บ๏ธ below:
Second preprint from the lab. Collab with @dkoveal.bsky.social, with many more to come! Effort led by @xshirleyz.bsky.social with help from Brittany Addison, @ezeyulu00.bsky.social, Claire Deng (on the grad school market, better act fast, Claireโs amazing!), @ajemanuel.bsky.social, and many others!
Monitoring gene expression in the brain with a simple blood test?
In collaboration with @jerzyszablowski.bsky.social & his lab we figured out how to do it nonhuman primates using noninvasive neuroengineering of synthetic serum markers.
nam11.safelinks.protection.outlook.com?url=https%3A...
๐ฆ๐ฝ๐ผ๐ป๐๐ฎ๐ป๐ฒ๐ผ๐๐ ๐ฏ๐ฒ๐ต๐ฎ๐๐ถ๐ผ๐ฟ ๐ถ๐ ๐ฎ ๐๐๐ฐ๐ฐ๐ฒ๐๐๐ถ๐ผ๐ป ๐ผ๐ณ ๐๐ฒ๐น๐ณ-๐ฑ๐ถ๐ฟ๐ฒ๐ฐ๐๐ฒ๐ฑ ๐๐ฎ๐๐ธ๐
More hidden markov models for cool behaviors and brain data
#neuroskyence
doi.org/10.1016/j.ne...
Thanks Ariel, excited to get this out into the world!
This work was driven by the singular vision and dedication of Zeynep Ulutas, @ezeyulu00.bsky.social . We could not have finished this without @amartyapradhan.bsky.social . @dkoveal.bsky.social provided key guidance on chemistry.
Weโre pumped about the future of this technique and are excited to share it with the community. Thereโs still a lot to optimize, but weโd like to optimize it with you! Seriously, reach out to us, we're nice!
We built a dataset with 100,000(ish) automatically labeled frames. Since the labels are localized through fluorescence, no labeling ambiguity! Turns out: with this many frames you can train techniques like SLEAP and DLC to label body parts with close to sub-mm precision.
We figured out another cool trick. Markerless trackers require labeling of hundreds of frames (click, click, rinse & repeat 500 times). We worked out a machine learning pipeline to take data where we fluorescently tagged 10 body parts and automatically labeled all frames without human intervention.
We successfully targeted the fatty tissue beneath the skin and even into the knee joint! Both of these were readily resolvable with off-the-shelf cameras. If youโre handy, you can pluck out the IR filter in your cameras and try it yourself.
We hit our first hurdle. Standard particle mixtures diffused away within 48 hours. So we formulated alternatives, and now we can watch the insides of mice glow for months as they freely move! We call the end-result Quantum Dot-based Pose estimation in vivo orโฆQD-Pi (pun intended).
With the first person in the lab, @ezeyulu00.bsky.social , we came up with the idea of injecting near-infrared-fluorescent nanoparticles to make little orbs of light inside the body that we could see from the outside.
Markerless trackers like DLC, SLEAP, DANNCE, Lightning Pose, and others have totally revolutionized tracking movement, since they donโt require the mice to wear anything. BUT, their precision is generally not as good as marker-based methods.
Unlike Mr. Serkis, mice donโt like the suit, and they donโt like anything being attached to their skin. And even if you did manage to get something onto/into the skin, they have lots of fatty tissue, which makes their gelatinous bodies hard to track from the โoutside-inโ.
If we want to understand movement, we have to track it. In people, we can look like Andy Serkis and wear a Styrofoam ball bedazzled swimsuit.
Our first preprint has been accepted for publication www.science.org/doi/10.1126/... !!
tldr: @ezeyulu00.bsky.social , @amartyapradhan.bsky.social , @dkoveal.bsky.social and I developed a method using injectable nanoparticles to turn mice intoโฆconstellations in motion. ๐งตโคต๏ธ
another new reagent from the lab! pubs.acs.org/doi/10.1021/...
Impressive study in @nature from my colleague Marcus Stephenson-Jones at UCL in the @sainsburywellcome.bsky.social
www.nature.com/articles/s41...
Very honored to be awarded tenure officially this week.
SO grateful to my amazing lab, and to mentors, sponsors, and friends who played an absolutely essential part in this.
Our latest! We measure dopamine signals as rats disambiguate cues that predict reward or threat. We find that dopamine flexibly tracks the changing salience and value of cues, but according to region-specific scales, rapid within-trial dopamine fluctuations prioritize different stimulus features.
And that's a wrap on #InterfaceNeuroGT!
Thank you to the organizers and attendees for a great meeting. See you next year at @riceneuro.bsky.social!
@crozell.bsky.social @vulcnethologist.bsky.social @gtresearchnews.bsky.social
The final #InterfaceNeuroGT session covered joint modeling of brain and behavior. Speakers highlighted how they're using innovative computational and experimental approaches to understand how neural activity influences behavior and intention.
Chaired by @vulcnethologist.bsky.social.
