How do you image the large and the small at the same time? We developed new š¬ technology to image centimeter-scale specimens - including whole mouse brains š§ - with diffraction-limited resolution and without sectioning. #mesoscale#imaging
ExA-SPIM is a technology based on innovations in (1) tissue processing and (2) large-scale microscopy š¬. Our team developed new methods for expansion for microscopy (ExM) of large tissue volumes. #teamscience#expansionmicroscopy
By combining ExM with a new SPIM system based on technologies from electronics metrology, ExA-SPIM pushes past the volumetric imaging barrier š§ that constrains previous imaging approaches. (inspired by recent plot from @Daetwyler_St, @RetoPaul) #lightsheet#microscopy
š§µ (3/n)
The lens š at the heart of ExA-SPIM provides NA = 0.305 (1 Āµm resolution) over an incredible 16.8 mm FOV. This is paired with a 151 megapixel CMOS camera š·, far exceeding the capabilities of traditional technologies. They are big! #bigscience
We modified the optical system to achieve an aberration free working distance of 35 mm (!) into liquid š§ mounting media. #deepimaging#highresolution
š§µ (5/n)
ExA-SPIM can image an incredible 200 x 52 x 35 mm3 volume, with 1 Āµm near-isotropic resolution over a 10.6 x 8.0 mm field of view. - at up to a 1 gigavoxels/sec šØ! #largevolume
š§µ (6/n)
ExA-SPIM can therefore image an entire 3X expanded š mouse brain - with 300 nm effective resolution - in only 15 tiles - and in less than one day. This produces ~100 TB of raw data! #highspeed
š§µ (7/n)
With no sectioning and few tiles, these volumes are easily stitched and merged for downstream analysis, including tracing āļø and reconstruction of individual neurons across the mouse brain. #neuralcircuits#hortacloud
ExA-SPIM is also a charm for larger brains š§ . We imaged a 1x1x1.5 cm block of macaque brain. ExA-SPIM reveals brightly ā” labeled cortico-spinal tract neurons, their dendritic arbors, and incredibly dense dendritic spines as well as descending axons. #connectomics
š§µ (9/n)
ExA-SPIM required new š acquisition software for controlling the microscope. Data is streamed using ACQUIRE, a new multi-camera šø streaming software, led by @ChanZuckerberg in collaboration with our team. #acquisitionsoftware#napari
Using a combination of high-speed networking, fast on-premises storage, and real-time compression, our current imaging pipeline enables an imaging throughput to cloud āļø storage of >100 TB per day, using commodity hardware. #cloudcomputing
Thoughts on fastest ways to down-scale ND matrices by factor of 2 in Python? [0::2] is blazing fast but throws away information. skimage.transform.downscale_local_mean() is great - but quite a bit slower.
[::2] aka striding is by far the fastest, but is blind and throws away information. The downsample_with_averaging speed of ~400 MB/sec uint16 matches what I was seeing when I created the post (it is not able to outpace a sCMOS camera during acquisition).