Our brain is a complex organ. Billions of nerve cells are wired in an intricate network, constantly processing signals, enabling us to recall memories or to move our bodies. Making sense of this complicated network requires a precise look into how these nerve cells are arranged and connected. A new microscopy method, light-microscopy-based connectomics (LICONN), has been developed by scientists at the Institute of Science and Technology Austria (ISTA) and Google Research, and could now help piece together this puzzle.
LICONN combines conventional light microscopy technology with the properties of hydrogel and experimental techniques, artificial intelligence, and analytical methods. The researchers claim LICONN is the first technology beyond electron microscopy (EM) that is capable of reconstructing brain tissue with all the synaptic connections between neurons. It also opens up the possibility of visualizing complex molecular machinery alongside the structure of neurons, all while utilizing standard light microscopes for measurements.
LICONN has been developed by Johann Danzl, MD, PhD, Mojtaba R. Tavakoli, PhD, Julia Lyudchik, and colleagues at the High-Resolution Optical Imaging for Biology research group at ISTA, together with collaborators at the lab of Gaia Novarino, PhD, at ISTA and Michal Januszewski and Viren Jain at Google Research. The team reported on the method in Nature, in a paper titled “Light-microscopy-based connectomic reconstruction of mammalian brain tissue,” in which they stated, “Here we present a technology that can be used to densely reconstruct brain circuitry with light microscopy at synaptic resolution … We engineered a high-fidelity iterative hydrogel expansion scheme paired with protein-density staining and high-speed diffraction-limited readout that enables manual neuronal tracing and deep-learning-based cellular segmentation.”
The brain is made up of an incredibly dense, complex, and fine-grained arrangement of neurons with support cells, which together constitute a functional network that enables brain function, the authors wrote. “The information-processing capability of the brain’s cellular network depends on the physical wiring pattern between neurons and their molecular and functional characteristics.”
Determining how neurons are connected and reconstructing the circuitry that underlies information processing requires the ability to accurately trace cellular circuit components, including axons and dendritic spines, as well as resolving synaptic connections and assigning them to specific neurons, the team continued.
Light microscopes have been evolving for centuries. Scientists use light microscopy to illuminate the most intricate biological structures. However, unraveling the complex details and architecture of the brain remains a seemingly impossible challenge, considering its billions of densely packed neurons, each linked to other cells via thousands of synapses. So while “light microscopy is uniquely positioned to visualize specific molecules,” the investigators noted, “… dense, synapse-level circuit reconstruction by light microscopy has been out of reach, owing to limitations in resolution, contrast, and volumetric imaging capability.”
The researchers suggest that LICONN, the Danzl group’s newest microscopy technique, offers a breakthrough. The technology acts like a meticulous puzzle solver, assembling the intricate brain networks by piecing together the finest neuronal processes and correctly linking each synaptic connection to its respective neuron. “Up to now, no light microscopy technique could do that,” said Danzl. “It was a longstanding goal of our group to build such a pipeline for reconstructing brain tissue. And LICONN can do this while placing specific molecules into the context of the structural reconstruction.” What stands out is that the image acquisition is done on a standard off-the-shelf microscope, which is very fast and offers multicolor capability. The technique can be reproduced anywhere in the world, as scientists do not require high-end, expensive equipment that would be needed for current approaches for brain tissue reconstruction.
To obtain this level of detail, the resolution has to be extraordinarily high, around a few tens of nanometers, 10,000 times smaller than the width of a human hair. To accomplish that with LICONN, the team made use of the chemical and physical properties of hydrogel, a three-dimensional polymer network that can take up water and swell, but in a highly controlled manner. “Our strategy for dense light-microscopy-based connectomics achieves an increase in resolution through hydrogel expansion rather than optical super-resolution,” they further explained.
The brain tissue of interest is embedded in this hydrogel. “Cellular components are linked to the hydrogel, meaning the cells’ fine ultrastructure is imprinted onto the gel and preserved for microscopy,” explained Danzl. Before imaging, the structures are expanded by adding water to the material. As a result, the gel elongates in size in every direction but maintains the relative spatial arrangements of the tissue’s structures with extremely high fidelity.
Traditional light microscopes are classically limited in their resolving power to around 250–300 nm. While this is adequate to visualize larger cellular structures, it is insufficient to reconstruct the densely packed brain tissue. “The hydrogel expansion pulls features of the brain tissue so far apart that we can resolve them with a standard light microscope,” Tavakoli commented. “This method enhances the effective resolution by 16 times, achieving a resolution better than 20 nm.” And as the authors further pointed out, “… although LICONN sample preparation introduces new strategies to achieve high-fidelity tissue expansion, the protocol is not fundamentally more complex than previous expansion techniques that have been widely adopted.”
Capturing microscopic images results in the collection of numerous data points, with the intricacy of the datasets reflecting the brain’s complexity. This means that manually interpreting and reconstructing all the neuronal structures on a sizable scale would be far too laborious. Instead, Google Research’s deep-learning techniques were trained to segment the individual cells in the tissue. “Specifically, we trained flood-filling networks (FFNs), which have achieved state-of-the-art segmentation accuracy on diverse connectomic datasets,” they stated. Jain explained further, “Automating the identification of neurons and their elaborate structures on a wider scale using artificial intelligence made the daunting task of reconstructing all the cellular components practically tractable. The ability to concomitantly visualize specific molecules adds a new quality of information.”
![Illuminating the dark. Tavakoli, Lyudchik, and Danzl discuss a close-up image of the hippocampus—a brain region responsible for memory formation and spatial navigation—in the microscopy room at the Institute of Science and Technology Austria (ISTA). [© ISTA]](https://www.genengnews.com/wp-content/uploads/2025/05/low-res-1-300x200.jpeg "Illuminating the dark. Tavakoli, Lyudchik, and Danzl discuss a close-up image of the hippocampus—a brain region responsible for memory formation and spatial navigation—in the microscopy room at the Institute of Science and Technology Austria (ISTA). [© ISTA]")
Illuminating the dark. Tavakoli, Lyudchik, and Danzl discuss a close-up image of the hippocampus—a brain region responsible for memory formation and spatial navigation—in the microscopy room at the Institute of Science and Technology Austria (ISTA). [© ISTA]
Lyudchik, a PhD student and computer scientist in the Danzl group, played an instrumental role in interpreting the complex datasets. “Thanks to the exceptionally high resolution of the data, it was possible to automatically detect the synaptic connections between neurons and to transform raw brain imaging data into detailed connectivity maps. This is a complex image processing challenge,” Lyudchik explained. “In addition, the methods had to be both efficient and scalable, given that even a small piece of brain tissue can contain tens of thousands of synaptic connections.”
LICONN makes it possible to map the location of specific molecules onto the neuronal reconstructions, such as those involved in the transmission of signals between neurons at synapses. Lyudchik’s artistic vein helped her create stunning 3D renderings of the brain network, as visualizations are powerful tools to make complex scientific data more accessible and interpretable.
By following this comprehensive pipeline, scientists can meticulously reconstruct brain tissue and visualize neuronal connections and networks. The interplay between experimentation and analysis across disciplines—from imaging and experimentation at ISTA to Google Research’s application of advanced deep learning technologies and the computational analysis at ISTA—results in 3D visualizations of the brain’s architecture at a new level of complexity. “LICONN brings us a step closer to assembling the puzzle pieces of the mammalian brain and better understanding its functioning both in health and disease,” Danzl concluded.
In their paper the team commented, “… LICONN forms a technological basis for the routine adoption of connectomic studies in non-specialized neuroscience labs, as well as enabling high-resolution studies in organs other than the brain … LICONN was developed to reconstruct arguably the most complex tissue structure, specifically identifying and tracing the finest neuronal processes, such as axons and spines, in the brain; we expect the technology to be broadly useful in other organs and systems in which high-resolution tissue analysis is desirable.”
In an accompanying News and Views, Joergen Kornfeld, PhD, at the MRC Laboratory of Molecular Biology, suggested that LICONN represents an important addition to the connectomics toolbox. “It finally enables light microscopy to enter the contested arena of brain-imaging methods for connectomics,” which has, so far, been dominated by competing electron microscopy approaches. “Its ability to combine structural and molecular information in an accessible way should enable many laboratories to pursue connectomic studies,” Kornfeld stated.
The post Light Microscopy-Based Connectomics Reconstructs Brain Tissue Including Synaptic Connections appeared first on GEN - Genetic Engineering and Biotechnology News.
LICONN combines conventional light microscopy technology with the properties of hydrogel and experimental techniques, artificial intelligence, and analytical methods. The researchers claim LICONN is the first technology beyond electron microscopy (EM) that is capable of reconstructing brain tissue with all the synaptic connections between neurons. It also opens up the possibility of visualizing complex molecular machinery alongside the structure of neurons, all while utilizing standard light microscopes for measurements.
LICONN has been developed by Johann Danzl, MD, PhD, Mojtaba R. Tavakoli, PhD, Julia Lyudchik, and colleagues at the High-Resolution Optical Imaging for Biology research group at ISTA, together with collaborators at the lab of Gaia Novarino, PhD, at ISTA and Michal Januszewski and Viren Jain at Google Research. The team reported on the method in Nature, in a paper titled “Light-microscopy-based connectomic reconstruction of mammalian brain tissue,” in which they stated, “Here we present a technology that can be used to densely reconstruct brain circuitry with light microscopy at synaptic resolution … We engineered a high-fidelity iterative hydrogel expansion scheme paired with protein-density staining and high-speed diffraction-limited readout that enables manual neuronal tracing and deep-learning-based cellular segmentation.”
The brain is made up of an incredibly dense, complex, and fine-grained arrangement of neurons with support cells, which together constitute a functional network that enables brain function, the authors wrote. “The information-processing capability of the brain’s cellular network depends on the physical wiring pattern between neurons and their molecular and functional characteristics.”
Determining how neurons are connected and reconstructing the circuitry that underlies information processing requires the ability to accurately trace cellular circuit components, including axons and dendritic spines, as well as resolving synaptic connections and assigning them to specific neurons, the team continued.
Light microscopes have been evolving for centuries. Scientists use light microscopy to illuminate the most intricate biological structures. However, unraveling the complex details and architecture of the brain remains a seemingly impossible challenge, considering its billions of densely packed neurons, each linked to other cells via thousands of synapses. So while “light microscopy is uniquely positioned to visualize specific molecules,” the investigators noted, “… dense, synapse-level circuit reconstruction by light microscopy has been out of reach, owing to limitations in resolution, contrast, and volumetric imaging capability.”
The researchers suggest that LICONN, the Danzl group’s newest microscopy technique, offers a breakthrough. The technology acts like a meticulous puzzle solver, assembling the intricate brain networks by piecing together the finest neuronal processes and correctly linking each synaptic connection to its respective neuron. “Up to now, no light microscopy technique could do that,” said Danzl. “It was a longstanding goal of our group to build such a pipeline for reconstructing brain tissue. And LICONN can do this while placing specific molecules into the context of the structural reconstruction.” What stands out is that the image acquisition is done on a standard off-the-shelf microscope, which is very fast and offers multicolor capability. The technique can be reproduced anywhere in the world, as scientists do not require high-end, expensive equipment that would be needed for current approaches for brain tissue reconstruction.
To obtain this level of detail, the resolution has to be extraordinarily high, around a few tens of nanometers, 10,000 times smaller than the width of a human hair. To accomplish that with LICONN, the team made use of the chemical and physical properties of hydrogel, a three-dimensional polymer network that can take up water and swell, but in a highly controlled manner. “Our strategy for dense light-microscopy-based connectomics achieves an increase in resolution through hydrogel expansion rather than optical super-resolution,” they further explained.
The brain tissue of interest is embedded in this hydrogel. “Cellular components are linked to the hydrogel, meaning the cells’ fine ultrastructure is imprinted onto the gel and preserved for microscopy,” explained Danzl. Before imaging, the structures are expanded by adding water to the material. As a result, the gel elongates in size in every direction but maintains the relative spatial arrangements of the tissue’s structures with extremely high fidelity.
Traditional light microscopes are classically limited in their resolving power to around 250–300 nm. While this is adequate to visualize larger cellular structures, it is insufficient to reconstruct the densely packed brain tissue. “The hydrogel expansion pulls features of the brain tissue so far apart that we can resolve them with a standard light microscope,” Tavakoli commented. “This method enhances the effective resolution by 16 times, achieving a resolution better than 20 nm.” And as the authors further pointed out, “… although LICONN sample preparation introduces new strategies to achieve high-fidelity tissue expansion, the protocol is not fundamentally more complex than previous expansion techniques that have been widely adopted.”
Capturing microscopic images results in the collection of numerous data points, with the intricacy of the datasets reflecting the brain’s complexity. This means that manually interpreting and reconstructing all the neuronal structures on a sizable scale would be far too laborious. Instead, Google Research’s deep-learning techniques were trained to segment the individual cells in the tissue. “Specifically, we trained flood-filling networks (FFNs), which have achieved state-of-the-art segmentation accuracy on diverse connectomic datasets,” they stated. Jain explained further, “Automating the identification of neurons and their elaborate structures on a wider scale using artificial intelligence made the daunting task of reconstructing all the cellular components practically tractable. The ability to concomitantly visualize specific molecules adds a new quality of information.”
Illuminating the dark. Tavakoli, Lyudchik, and Danzl discuss a close-up image of the hippocampus—a brain region responsible for memory formation and spatial navigation—in the microscopy room at the Institute of Science and Technology Austria (ISTA). [© ISTA]
Lyudchik, a PhD student and computer scientist in the Danzl group, played an instrumental role in interpreting the complex datasets. “Thanks to the exceptionally high resolution of the data, it was possible to automatically detect the synaptic connections between neurons and to transform raw brain imaging data into detailed connectivity maps. This is a complex image processing challenge,” Lyudchik explained. “In addition, the methods had to be both efficient and scalable, given that even a small piece of brain tissue can contain tens of thousands of synaptic connections.”
LICONN makes it possible to map the location of specific molecules onto the neuronal reconstructions, such as those involved in the transmission of signals between neurons at synapses. Lyudchik’s artistic vein helped her create stunning 3D renderings of the brain network, as visualizations are powerful tools to make complex scientific data more accessible and interpretable.
By following this comprehensive pipeline, scientists can meticulously reconstruct brain tissue and visualize neuronal connections and networks. The interplay between experimentation and analysis across disciplines—from imaging and experimentation at ISTA to Google Research’s application of advanced deep learning technologies and the computational analysis at ISTA—results in 3D visualizations of the brain’s architecture at a new level of complexity. “LICONN brings us a step closer to assembling the puzzle pieces of the mammalian brain and better understanding its functioning both in health and disease,” Danzl concluded.
In their paper the team commented, “… LICONN forms a technological basis for the routine adoption of connectomic studies in non-specialized neuroscience labs, as well as enabling high-resolution studies in organs other than the brain … LICONN was developed to reconstruct arguably the most complex tissue structure, specifically identifying and tracing the finest neuronal processes, such as axons and spines, in the brain; we expect the technology to be broadly useful in other organs and systems in which high-resolution tissue analysis is desirable.”
In an accompanying News and Views, Joergen Kornfeld, PhD, at the MRC Laboratory of Molecular Biology, suggested that LICONN represents an important addition to the connectomics toolbox. “It finally enables light microscopy to enter the contested arena of brain-imaging methods for connectomics,” which has, so far, been dominated by competing electron microscopy approaches. “Its ability to combine structural and molecular information in an accessible way should enable many laboratories to pursue connectomic studies,” Kornfeld stated.
The post Light Microscopy-Based Connectomics Reconstructs Brain Tissue Including Synaptic Connections appeared first on GEN - Genetic Engineering and Biotechnology News.