Scientists reveal an unprecedented ‘live’ view of the brain’s complexity

ISTA’s collaborative efforts yield an unprecedented ‘live’ view of brain complexity.

The human brain, with its intricate network of some 86 billion neurons, is arguably among the most complex specimens scientists have ever encountered. It contains an enormous, yet currently immeasurable, wealth of information, making it the pinnacle of computational hardware.

Understanding this level of complexity is challenging, which makes it imperative for us to use advanced techniques that can decode the subtle and complex interactions that take place within the brain at microscopic levels. Thus, imaging is emerging as a pivotal tool in the world of neuroscience.

A new virtual imaging and reconstruction technique developed by Johan Danzel’s group at ISTA represents a major leap in imaging brain activity and has been dubbed LIONESS – Direct Information Enhanced Nanoscopy Enables Saturated Segmentation. LIONESS is a pipeline for imaging, reconstructing, and analyzing live brain tissue with a comprehensiveness and spatial resolution not yet possible.

LIONESS defines the complexity of dense brain tissue. A: A complex neural environment B: LIONESS can image and reconstruct the sample in a way that illustrates many of the dynamic structures and functions in living brain tissue. Credit: Johan Danzel

“With LIONESS, for the first time, it is possible to obtain a comprehensive and dense reconstruction of living brain tissue. “By imaging the tissue multiple times, LIONESS allows us to observe and measure the dynamic cellular biology of the brain as it runs,” says first author Philippe Velecki. “The result is a reconstructed image of cellular arrangements in three dimensions, with time making up the fourth dimension, where the sample can be imaged over the course of minutes, hours or days.”

Collaboration and AI is key

LIONESS’ strength lies in the improved optics and in the two levels of deep learning – one of the methods of artificial intelligence – that make up its core: the first enhances image quality and the second identifies different cellular structures in the dense neural environment.

The pipeline is the result of collaboration between the Danzl Group, the Bickel Group, the Jonas Group, the Novarino Group, and ISTA’s science service units, as well as other international collaborators. “Our approach was to bring together a dynamic group of scientists with unique combined expertise across discipline boundaries, who work together to bridge the technology gap in brain tissue analysis,” says Johan Danzel of ISTA.

A pipeline to reconstruct live brain tissue. Acquisition of microscopy with optimal laser focus – Image processing (DL) – Segmentation (DL) – 3D optical analysis. Credit: Johan Danzel

Overcoming obstacles

Previously it was possible to reconstruct brain tissue using electron microscopy. This method visualizes the sample based on its interactions with the electrons. Despite its ability to capture images with a resolution of a few nanometers — a millionth of a millimeter — electron microscopy requires fixing a sample into a single biological state, which needs to be physically divided to obtain three-dimensional information. Hence, dynamic information cannot be obtained.

Another previously known technique is light microscopy that allows the observation of living systems and the recording of volumes of healthy tissue by slicing them “optically” rather than physically. However, the ability of the light microscope is severely hampered by the properties of the light waves it uses to create the image. Their best-case resolution is a few hundred nanometers, and they are too coarse-grained to capture important cellular details in brain tissue.

Using super-resolution light microscopy, scientists are able to break this resolution barrier. Recent work in this area, dubbed SUSHI (super-resolution shadow imaging), showed that applying dye molecules to the pericellular spaces and applying the Nobel Prize-winning super-resolution microscopy technique STED (stimulated emission depletion) reveal ultra-fine ‘shadows’ . of all cellular structures and thus their visualization in tissues.

LIONESS can image the sample and reconstruct it in a way that illustrates many of the dynamic structures and functions in living brain tissue. Photography: ISTA Julia Lyudchik

However, it was impossible to image whole volumes of brain tissue with the resolution improvement that corresponds to the complex 3D architecture of brain tissue. This is because increased resolution also entails a higher loading of the imaging light on the sample, which can damage or “fry” the microorganisms.

Therein lies the ingenuity of LIONESS, which was developed, according to the authors, for “rapid and mild” imaging conditions, thus keeping the specimen alive. This technology does this while providing superior isotropic resolution – meaning it is equally good in all three spatial dimensions – allowing the cellular components of tissues to be visualized in 3D. nanoscale Details resolved.

LIONESS collects as little information from the sample as needed during the imaging step. This is followed by the first step of deep learning to fill in additional information about the structure of brain tissue in a process called image restoration. In this innovative way, it achieves a resolution of around 130 nanometers, while being gentle enough to image live brain tissue in real time. Together, these steps allow for a second step of deep learning, this time to make sense of very complex imaging data and to identify neural structures in an automated way.

ISTA scientist Johann Danzel in his laboratory at the Institute of Science and Technology in Austria. Credit: Nadine Boncione | ISTA

homing in

“The multidisciplinary approach has allowed us to break down the interlocking constraints of solving a living system’s exposure to energy and light, to understand complex 3D data, and to correlate the cellular architecture of tissues with molecular and functional measurements,” says Danzel.

For the virtual reconstruction, Danzel and Velicky collaborated with visual computing experts: Bickel’s 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 cellular structures in tissues—and visualization, with further support from By Christoph Sommer, image analysis team at ISTA. For sophisticated labeling strategies, neuroscientists and chemists from Edinburgh, Berlin and ISTA contributed.

Thus, it was possible to correlate functional measurements, i.e. reading of cellular structures, with the activity of biological signals in the same living neural circuit. This was done by imaging the influx of calcium ions into cells and measuring cellular electrical activity in collaboration with Jonas’ group at ISTA. Novarino’s group has contributed human brain organs, often called minibrains, that mimic the development of the human brain. The authors contend that all of this was facilitated by expert support by ISTA’s high-level scientific service units.

The structure and activity of the brain is very dynamic. Its structures evolve as the brain performs and learns new tasks. This side of the brain is often referred to as “plasticity”. Hence, observing changes in the structure of brain tissue is essential to unlocking the secrets behind its plasticity. The new tool developed at ISTA shows that it is possible to understand the functional structure of brain tissue and possibly other organs by revealing subcellular structures and capturing how they change over time.

Reference: “Dense Four-Dimensional Nanoscale Reconstructions of Living Brain Tissue” by Philipp Velicy, Eder Miguel, Julia M. Mikalska, Julia Lyudczyk, Dongli Wei, Zodi Lin, Jake F. Watson, Jacob Troedel, Johanna Baer, ​​Yoav Ben Simon, Christoph Sommer, Webeki Jaher, Alban Sinamiri, Johannes Bruichhagen, Seth GN Grant, Peter Jonas, Jaya Novarino, Hanspeter Pfister, Bernd Bickel and Johan G. Danzel July 10, 2023, nature ways.
doi: 10.1038/s41592-023-01936-6

The study was funded by the Austrian Science Fund, Gesellschaft für Forschungsförderung NÖ (NFB), H2020 Marie Skłodowska-Curie Actions, European Research Council H2020, Frontier Humanities Programme, Simons Foundation, Wellcome Fund and the National Science Foundation. .