LIONESS redefines brain tissue imaging
Large collaboration at ISTA yields an unprecedented 'live' view into the brain's complexity

Date:
July 10, 2023
Source:
Institute of Science and Technology Austria
Summary:
Scientists have come together to present a new way to observe the
brain's structure and dynamics -- in a high resolution and without
damaging the tissue.


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FULL STORY ========================================================================== Brain tissue is one of the most intricate specimens that scientists have arguably ever dealt with. Packed with currently immeasurable amount of information, the human brain is the most sophisticated computational
device with its network of around 86 billion neurons. Understanding
such complexity is a difficult task, and hence making progress requires technologies to unravel the tiny, complex interactions taking place in
the brain at microscopic scales.

Imaging is therefore an enabling tool in neuroscience.

The new imaging and virtual reconstruction technology developed by Johann Danzl's group at ISTA is a big leap in imaging brain activity and is
aptly named LIONESS -- Live Information Optimized Nanoscopy Enabling
Saturated Segmentation. LIONESS is a pipeline to image, reconstruct,
and analyze live brain tissue with a comprehensiveness and spatial
resolution not possible until now.

"With LIONESS, for the first time, it is possible to get a comprehensive,
dense reconstruction of living brain tissue. By imaging the tissue
multiple times, LIONESS allows us to observe and measure the dynamic
cellular biology in the brain take its course," says first author Philipp Velicky. "The output is a reconstructed image of the cellular arrangements
in three dimensions, with time making up the fourth dimension, as the
sample can be imaged over minutes, hours, or days," he adds.

With LIONESS neuroscientists can image living brain tissue and achieve
high- resolution 3D imagery without damaging the living sample.

Collaboration and AI the Key The strength of LIONESS lies in refined
optics and in the two levels of deep learning -- a method of Artificial Intelligence -- that make up its core: the first enhances the image
quality and the second identifies the different cellular structures in
the dense neuronal environment.

The pipeline is a result of a collaboration between the Danzl group,
Bickel group, Jonas group, Novarino group, and ISTA's Scientific Service
Units, as well as other international collaborators. "Our approach was
to assemble a dynamic group of scientists with unique combined expertise
across disciplinary boundaries, who work together to close a technology
gap in the analysis of brain tissue," Johann Danzl of ISTA says.

Surpassing hurdles Previously it was possible to get reconstructions of
brain tissue by using Electron Microscopy. This method images the sample
based on its interactions with electrons. Despite its ability to capture
images at a few nanometers -- a millionth of a millimeter -- resolution, Electron Microscopy requires a sample to be fixed in one biological state, which needs to be physically sectioned to obtain 3D information. Hence,
no dynamic information can be obtained.

Another previously known technique of Light Microscopy allows observation
of living systems and record intact tissue volumes by slicing them
"optically" rather than physically. However, Light Microscopy is severely hampered in its resolving power by the very properties of the light waves
it uses to generate an image. Its best-case resolution is a few hundred nanometers, much too coarse-grained to capture important cellular details
in brain tissue.

Using Super-resolution Light Microscopy scientists can break
this resolution barrier. Recent work in this field, dubbed SUSHI (Super-resolution Shadow Imaging), showed that applying dye molecules
to the spaces around cells and applying the Nobel Prize-winning super-resolution technique STED (Stimulated Emission Depletion) microscopy reveals super-resolved 'shadows' of all the cellular structures and thus visualizes them in the tissue. Nevertheless, it has been impossible to
image entire volumes of brain tissue with resolution enhancement that
matches the brain tissue's complex 3D architecture. This is because
increasing resolution also entails a high load of imaging light on the
sample, which may damage or 'fry' the subtle, living tissue.

Herein lies the prowess of LIONESS, having been developed for,
according to the authors, "fast and mild" imaging conditions, thus
keeping the sample alive. The technique does so while providing
isotropic super-resolution -- meaning that it is equally good in all
three spatial dimensions -- that allows visualization of the tissue's
cellular components in 3D nanoscale resolved detail.

LIONESS collects only as little information from the sample as needed
during the imaging step. This is followed by the first deep learning step
to fill in additional information on the brain tissue's structure in a
process called Image Restoration. In this innovative way, it achieves
a resolution of around 130 nanometers, while being gentle enough for
imaging of living brain tissue in real-time. Together, these steps
allow for a second step of deep learning, this time to make sense of the extremely complex imaging data and identify the neuronal structures in
an automated manner.

Homing In "The interdisciplinary approach allowed us to break the
intertwined limitations in resolving power and light exposure to the
living system, to make sense of the complex 3D data, and to couple
the tissue's cellular architecture with molecular and functional
measurements," says Danzl.

For virtual reconstruction, Danzl and Velicky teamed up with visual
computing experts: the Bickel group at ISTA and the group led by
Hanspeter Pfister at Harvard University, who contributed their expertise
in automated segmentation - - the process of automatically recognizing
the cellular structures in the tissue -- and visualization, with further support by ISTA's image analysis staff scientist Christoph Sommer. For sophisticated labeling strategies, neuroscientists and chemists from
Edinburgh, Berlin, and ISTA contributed.

Consequently, it was possible to bridge functional measurements, i.e. to
read out the cellular structures together with biological signaling
activity in the same living neuronal circuit. This was done by imaging
Calcium ion fluxes into cells and measuring the cellular electrical
activity in collaboration with the Jonas group at ISTA. The Novarino group contributed human cerebral organoids, often nicknamed mini-brains that
mimic human brain development. The authors underline that all of this
was facilitated through expert support by ISTA's top-notch scientific
service units.

Brain structure and activity are highly dynamic; its structures evolve
as the brain performs and learns new tasks. This aspect of the brain
is often referred to as "plasticity." Hence, observing the changes in
the brain's tissue architecture is essential to unlocking the secrets
behind its plasticity. The new tool developed at ISTA shows potential for understanding the functional architecture of brain tissue and potentially
other organs by revealing the subcellular structures and capturing how
these might change over time.

* RELATED_TOPICS
o Mind_&_Brain
# Brain-Computer_Interfaces # Brain_Injury # Intelligence
o Matter_&_Energy
# Medical_Technology # Optics # Biochemistry
o Computers_&_Math
# Neural_Interfaces # Computer_Graphics # Communications
* RELATED_TERMS
o Scanning_electron_microscope o Conflict_resolution o Amygdala
o Perfectionism_(psychology) o Homosexuality o Alpha_wave o
Brain_damage o Aggression

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========================================================================== Journal Reference:
1. Philipp Velicky, Eder Miguel, Julia M. Michalska, Julia Lyudchik,
Donglai
Wei, Zudi Lin, Jake F. Watson, Jakob Troidl, Johanna Beyer,
Yoav Ben- Simon, Christoph Sommer, Wiebke Jahr, Alban Cenameri,
Johannes Broichhagen, Seth G. N. Grant, Peter Jonas, Gaia Novarino,
Hanspeter Pfister, Bernd Bickel, Johann G. Danzl. Dense 4D nanoscale
reconstruction of living brain tissue. Nature Methods, 2023; DOI:
10.1038/s41592-023- 01936-6 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2023/07/230710113914.htm

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