Michael Lin, MD PhD
@michaelzlin.bsky.social
Harvard → UCLA → HMS → UCSD → Associate Prof. of Neurobiology & Bioengineering at Stanford → Molecules, medicines, & SARSCoV2. Bad manners blocked.
Michael Z. Lin is a Taiwanese-American biochemist and bioengineer. He is a professor of neurobiology and bioengineering at Stanford University. He is best known for his work on engineering optically and chemically controllable proteins. .. more
Neuroscience
42%
Biology
41%
Somehow managed to survive crossing these streets until I was 5 years old.
Look closely and you'll see the Buddha of Baguashan. Would walk up to it nearly every day, along a stream where housewives did their laundry.
Look closely and you'll see the Buddha of Baguashan. Would walk up to it nearly every day, along a stream where housewives did their laundry.
Back in my birthplace, Taiwan. Within 2 generations it went from agrarian and poor to creating the 2-nm technology that drives the world economy. My grandparents grew up as farmers, my parents still save every last grain of rice. Step out of Taipei and you realize the old Taiwan is still there.
Reposted by Michael Z. Lin
Why scientists are rethinking the immune effects of SARS-CoV-2
“Immunity debt,” a theory to explain the global surge in non-covid infections since pandemic restrictions were lifted, is increasingly being challenged by emerging evidence. Nick Tsergas reports
Myco...
www.bmj.com
Immunity debt's "...explanatory power has faded as the number of non-covid infections has kept rising each year...
A growing number of scientists believe that the SARS-CoV-2 virus may instead be subtly altering our immune systems." 🛟😷medsky
A growing number of scientists believe that the SARS-CoV-2 virus may instead be subtly altering our immune systems." 🛟😷medsky
Thanks too to @spencerlaveresmith.bsky.social, @michaelgoard.bsky.social, @andyalexander.bsky.social, Sung-Soo Kim, Mathieu Louis, and MCDB students for thought-provoking discussions. It's inspiring to see so many people advancing the frontiers of neuroscience with sharp ideas and smart execution.
Had a really nice visit to UCSB, learning about the awesome neuroscience research there and presenting our work on voltage imaging in the molecular cellular and developmental biology seminar series.
Thanks to my host Ikuko Smith, who might have the best office view in all of UC!
Thanks to my host Ikuko Smith, who might have the best office view in all of UC!
That's exactly right, but if you want ms temporal resolution, well then you have to image at near kHz speeds
Which then means the quality of the optics and camera are more important now.
Which then means the quality of the optics and camera are more important now.
Oh not your review, which was really great (and thanks for citing us).
True at slower speeds GECIs have higher SNRs. The problem becomes when people assume SNR differences in the cell are proportional to molecular performance. It may be okay within a design (eg GCaMP3 vs 6) but not between designs.
True at slower speeds GECIs have higher SNRs. The problem becomes when people assume SNR differences in the cell are proportional to molecular performance. It may be okay within a design (eg GCaMP3 vs 6) but not between designs.
Two-Photon Lifetime Imaging of Voltage Indicating Proteins as a Probe of Absolute Membrane Voltage
Genetically encoded voltage indicators (GEVIs) can report cellular electrophysiology with high resolution in space and time. Two-photon (2P) fluoresce…
www.sciencedirect.com
It depends on how much you are willing to overexpress the GCaMP. Brinks and Cohen estimated the abundance difference as 100x. We find we can overexpress GCaMP to get 100x more per cell body, but then get into the realm of lower dF/F.
www.sciencedirect.com/science/arti...
www.sciencedirect.com/science/arti...
www.nature.com
In fact, I should point out that we recently worked with Promega to improve bioluminescent imaging in the brain further. We identified cephalofurimazine-9 (CFz9), a modification of the original CFz, as a substrate with higher sustainable brightness in the brain.
www.nature.com/articles/s41...
www.nature.com/articles/s41...
Our hope is to speed up biological and therapeutic discovery for all researchers by visualizing a wide variety of cells, pathogens, pathways etc, all noninvasively and using simple inexpensive equipment. So far the results are looking good!
The visit (at Promega that is) was especially illuminating and motivating. It was great to learn that the in vivo NanoLuc substrates we identified together with the Promega chemists are really catching on in the biomedical research community, especially for tumor and immune cell detection.
Had the pleasure of visiting @promegacorporation.bsky.social's chemistry group in beautiful San Luis Obispo. Thanks to Wenhui Zhou and the other amazing chemists for hosting me!
Didn't get a photo at Promega itself but here's one from our vineyard visit afterwards. Just a typical SLO evening.
Didn't get a photo at Promega itself but here's one from our vineyard visit afterwards. Just a typical SLO evening.
It's like saying fewer people have worked on engineering airplanes than cars, so there are fewer pilots than drivers — each part might be true, but the former didn't cause the latter. Rather it's an inherently harder problem where both engineering and usage require specialized and expensive methods.
In sum, the belief that GEVIs haven't been engineered as much as GECIs and thus voltage imaging is less commonly performed than calcium imaging links two true statements as cause-effect when they are more related as effect-effect: both are effects of the difficulty of working with voltage.
Eventually this will get cleared up, maybe after we get a chance to present our comparisons between GECIs and GEVIs at different rates. For now though just a reminder that simple explanations are not always 100% correct.
So it's no longer the case that GEVIs are lagging behind in molecular performance because of insufficient engineering. Rather GEVIs have a higher performance bar to clear and will always need better equipment. Still, GEVIs are already better for AP tracking if you can image fast.
The slow imaging rates that GECIs allow are a good fit for the ~20 fps rates of laser-scanning microscopes and CMOS cameras. So GECIs achieving higher usage over GEVIs is due to Ca presenting dual luxuries of abundance and time, allowing big slow signals with common equipment.
And then GEVIs track electrical events which need ~25x faster rates to image, making for 25x shorter exposures. SNR is related to the square root of photon counts per frame, so the per-cell SNR is lower for voltage imaging at those faster rates, than for calcium imaging at its typical ~20fps rate.
So why didn't everyone use ASAP3 like they used GCaMP6? Because GEVIs are about (by some estimates) ~25x less per cell abundant as GECIs, which wipes out that 25x advantage i photons/molecule once we integrate over the cell.
But the ASAP3 GEVI in 2019 already superceded the GCaMPs at the time (GCaMP6f), if we score by photon flux change per molecule per AP. How do we know? Because ASAP3 gives 20% change per AP, as does GCaMP6f, but each molecule of ASAP3 starts at 50% the brightness of GFP while GCaMP6f starts at 2%
Granted, GECIs are indeed easier to screen than GEVIs (just add calcium) and more groups have worked on them. And even GCaMP3, with speed and responsivity only 1/10th of what we have now with GCaMP8f, was good enough to see some activity in vivo, so got used early.
Read yet another review today that ascribes GECIs' larger SNR vs GEVIs to their being evolved earlier (suggesting the GECIs are better optimized). This assumption is understandable but incorrect. GEVIs' photonic response per molecule per AP have been as good as GECIs since ASAP3.
But if we were to just consistently put 25% of funds into addressing obvious technology bottlenecks and allow researchers the stability and infrastructure support necessary to do a good job e.g. private-sector-like pay and systems, we'd get our answers faster and for less money than happening now.
As to why, I'd guess that funding entities both public and private want to answer human-relevant questions, e.g. how to treat cancer or mental illness, and want those answers ASAP, so don't mind big bets on those claiming they know how to use existing data and methods to give useful answers now.
We have billions of dollars going into modeling biology using AI but just a fraction of that going into measuring currently unseen phenomena in vivo, or improving current in vivo assays to be cheaper and faster. It's spending more on fancier streetlights but in the same locations.
I always find it interesting how much money and energy society will spend in building and debating uncertain models of the natural world using the same limited knowledge, relative to how much support is given to developing methods to improve the amount and resolution of actual data.