Adam Glaser Profile picture
Jun 12 ā€¢ 12 tweets ā€¢ 12 min read Twitter logo Read on Twitter
How do you image the large and the small at the same time? We developed new šŸ”¬ technology to image centimeter-scale specimens - including whole mouse brains šŸ§  - with diffraction-limited resolution and without sectioning. #mesoscale #imaging

biorxiv.org/content/10.110ā€¦

šŸ§µ (1/n)
ExA-SPIM is a technology based on innovations in (1) tissue processing and (2) large-scale microscopy šŸ”¬. Our team developed new methods for expansion for microscopy (ExM) of large tissue volumes. #teamscience #expansionmicroscopy

protocols.io/view/whole-mouā€¦

šŸ§µ (2/n)
By combining ExM with a new SPIM system based on technologies from electronics metrology, ExA-SPIM pushes past the volumetric imaging barrier šŸš§ that constrains previous imaging approaches. (inspired by recent plot from @Daetwyler_St, @RetoPaul) #lightsheet #microscopy

šŸ§µ (3/n)
The lens šŸ”Ž 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 šŸ“·, far exceeding the capabilities of traditional technologies. They are big! #bigscience

vision.vieworks.com/en/lens/veo_jm

šŸ§µ (4/n)
We modified the optical system to achieve an aberration free working distance of 35 mm (!) into liquid šŸ’§ mounting media. #deepimaging #highresolution

šŸ§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 šŸ’Ø!
#largevolume

šŸ§µ (6/n)
ExA-SPIM can therefore image an entire 3X expanded šŸ 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

šŸ§µ (7/n)
With no sectioning and few tiles, these volumes are easily stitched and merged for downstream analysis, including tracing āœļø and reconstruction of individual neurons across the mouse brain. #neuralcircuits #hortacloud

hortacloud.janelia.org

šŸ§µ (8/n)
ExA-SPIM is also a charm for larger brains šŸ§ . We imaged a 1x1x1.5 cm block of macaque brain. ExA-SPIM reveals brightly āš” labeled cortico-spinal tract neurons, their dendritic arbors, and incredibly dense dendritic spines as well as descending axons. #connectomics

šŸ§µ (9/n)
ExA-SPIM required new šŸ acquisition software for controlling the microscope. Data is streamed using ACQUIRE, a new multi-camera šŸ“ø streaming software, led by @ChanZuckerberg in collaboration with our team.
#acquisitionsoftware #napari

github.com/acquire-project

šŸ§µ (10/n)
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 ā˜ļø storage of >100 TB per day, using commodity hardware.
#cloudcomputing

šŸ§µ (11/n)
This collaboration was only possible at @AllenInstitute for Neural Dynamics, and represents the hard work of many team members! Including @vcj029, @svoboda314, @dyfbrain, @rclayreid, @sejdevries, @Poofjunior, @nclack, @kjcao, @KalmbachsCortex, and many others.
#teamscience Image

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More from @adam_k_glaser

Jul 28, 2021
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.
Great follow up article from what @DataNerdery shared here: towardsdatascience.com/countless-3d-vā€¦. Has some great comparisons on throughput of different approaches (including those already mentioned). Image
[::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).
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