Special thanks to @moffittlab.bsky.social for a lot of help and advice along the way! More to come soon on speeding up the experimental side of large scale 3D spatial omics.

We tried your FEP chamber setup and the results are great with an Olympus 60X/NA 1.0 water dipping and U-ExM ciliate samples. Thanks for putting it out there!

Accompanied by a nice perspective piece from our colleague Lei Tian, whose own work on high-information content computational imaging was a strong inspiration for our own.
www.science.org/doi/10.1126/...

Tensorstore is much faster for read/write than vanilla zarr. Tools are here: github.com/QI2lab/opm-p...

Currently we only support our custom pymmcore-plus OPM format and ASI’s OPM format. Happy to work with you to support CZI LLSM files if you open an issue and provide some example data.

We have a bunch of fast skewed data handling tools (flatfield, decon, deskew, stitch) in Python, but they are not meant for real-time lazy viewing - we do that in our own custom acq. software. We use Tensorstore to write with compression at reasonably fast speeds.

We use a number of FLIR and Basler cameras with the high end Sony CMOS chips. For short exposures, they can work well. Another thing to look out for is that the hot pixels fluctuate on top of the true dead pixels. We correct a static map and then do a second correction that looks for outliers.

Our experience is the dark current (no cooling) and dead pixels (quality control) are higher than your standard sCMOS. We've mapped the pixel by pixel gain for a few different industrial CMOS chips and often find some non-linearities.

Thanks! We use a Nikon 100x/1.35 silicone oil for O1. The objectives in the setup are still the same as our previous Snouty-OPM papers.

As usual, all the code is developed in the open at our github. Control: github.com/QI2lab/opm-v2 and processing: github.com/QI2lab/opm-p....

The control code is fully hardware triggered and pretty performant already. We are actively working on speeding parts of the processing (fusion to ome-zarr).

Thank you!

Super cool! Would be fun to play with at some point.

This is yet another reason (in addition to MANY others) to call your representatives and senators. Likely more is going to be required, but it's a starting point.

CHCs served roughly 31 million people in 2023, 90% of which were at or below 200% the federal poverty level. That includes roughly 9.1 million pediatric patients.

Despite the court orders and the executive branch's claim that Medicaid is not effected, their Medicaid funding portals remain frozen.

Yeah, lead spheres and torsion bar suspended by 20 micron diameter tungsten wire. It's a really nice setup, but tricky for them to get right because of how long it takes to settle down before you can take data.