Biology subjects

Berning, M.

Publications and source records attributed to Berning, M..

4 recordsLinked to original sources

bioRxiv2025

Structure-Function Mapping of Olfactory Bulb Circuits with Synchrotron X-ray Nanotomography

Information is routed between brain areas via parallel streams. Neurons may share common inputs yet convey distinct information to different downstream targets. Here, we leverage the anatomical organisation of the mouse olfactory bulb (OB), where dozens of projection neurons (mitral and tufted cells, M/TCs) affiliate with a single input unit, a glomerulus1-3. To link functional properties of M/TCs to their anatomical glomerular association at scale, we combine in vivo two-photon (2P) imaging with synchrotron {micro}CT4-6 anatomical analysis and targeted X- ray nano-holotomography (XNH)7,8. Improving XNH resolution for mm3 volumes enables us to reliably identify subcellular features, automatically segment >80,000 cell nuclei in individual experiments, and delineate several hundred functionally imaged projection neurons and their detailed morphology, including up to 20 M/TCs per individual glomerulus ("sister" cells). In over 2400 sister cell pairs, we consistently find that odour response amplitudes to a panel of 47 monomolecular odours are conserved between sister cells, with, however, distinct responses to individual odours. Responses correlated with anatomical features such as cell body position and lateral dendritic arborisation. Thus, sister cells neither simply relay glomerular inputs nor are they dominated by network activity. Instead, they show a "balanced diversity" in their responses, enabling efficient encoding of odour stimuli whilst retaining the overall structure of odour space. Thus, synchrotron X-ray tomography can reliably link subcellular anatomy to function in a non-destructive way across the mm3 scale. With recent advances in X-ray optics9 and the emergence of 4th generation synchrotrons10,11, it becomes conceivable to extend this highly accessible approach to entire brain regions with increasing resolution.

neuroscience↗

bioRxiv2021

Functional and multiscale 3D structural investigation of brain tissue through correlative in vivo physiology, synchrotron micro-tomography and volume electron microscopy

Attributing in vivo neurophysiology to the brains ultrastructure requires a large field of view containing contextual anatomy. Electron microscopy (EM) is the gold standard technique to identify ultrastructure, yet acquiring volumes containing full mammalian neural circuits is challenging and time consuming using EM. Here, we show that synchrotron X-ray computed tomography (SXRT) provides rapid imaging of EM-prepared tissue volumes of several cubic millimetres. Resolution was sufficient for distinguishing cell bodies as well as for tracing apical dendrites in olfactory bulb and hippocampus, for up to 350 m. Correlating EM with SXRT allowed us to associate dendritic spines on pyramidal cell apical dendrites in the stratum radiatum to their corresponding soma locations. Superficial pyramidal neurons had larger spine apparatus density compared to deeper ones, implying differential synaptic plasticity for superficial and deeper cells. Finally, we show that X-ray tomography and volume EM can be reliably correlated to prior in vivo imaging. Thus, combining functional measurements with multiscale X-ray microscopy and volume EM establishes a correlative workflow that enables functional and structural investigation of subcellular features in the context of cellular morphologies, tissues and ultimately whole organs.

neuroscience↗

bioRxiv2018

Dense connectomic reconstruction in layer 4 of the somatosensory cortex

The dense circuit structure of the mammalian cerebral cortex is still unknown. With developments in 3-dimensional (3D) electron microscopy, the imaging of sizeable volumes of neuropil has become possible, but dense reconstruction of connectomes from such image data is the limiting step. Here, we report the dense reconstruction of a volume of about 500,000 m3 from layer 4 of mouse barrel cortex, about 300 times larger than previous dense reconstructions from the mammalian cerebral cortex. Using a novel reconstruction technique, FocusEM, we were able to reconstruct a total of 0.9 meters of dendrites and about 1.8 meters of axons investing only about 4,000 human work hours, about 10-25 times more efficient than previous dense circuit reconstructions. We find that connectomic data alone allows the definition of inhibitory axon types that show established principles of synaptic specificity for subcellular postsynaptic compartments. We find that also a fraction of excitatory axons exhibit such subcellular target specificity. Only about 35% of inhibitory and 55% of excitatory synaptic subcellular innervation can be predicted from the geometrical availability of membrane surface, revoking coarser models of random wiring for synaptic connections in cortical layer 4. We furthermore find evidence for enhanced variability of synaptic input composition between neurons at the level of primary dendrites in cortical layer 4. Finally, we obtain evidence for Hebbian synaptic weight adaptation in at least 24% of connections; at least 35% of connections show no sign of such previous plasticity. Together, these results establish an approach to connectomic phenotyping of local dense neuronal circuitry in the mammalian cortex.

neuroscience↗

bioRxiv2017

SynEM: Automated synapse detection for connectomics

Nerve tissue contains a high density of chemical synapses, about 1 per {micro}m3 in the mammalian cerebral cortex. Thus, even for small blocks of nerve tissue, dense connectomic mapping requires the identification of millions to billions of synapses, of which about 80-90% are excitatory synapses. While the focus of connectomic data analysis has been on neurite reconstruction, synapse detection becomes limiting when datasets grow in size and dense mapping is required. Here, we report SynEM, a method for automated detection of excitatory synapses from conventionally en-bloc stained 3D electron microscopy image stacks. The approach is based on a segmentation of the image data and focuses on classifying borders between neuronal processes as synaptic or non-synaptic. SynEM yields 98% precision and recall in binary excitatory cortical connectomes with no user interaction. It scales to large volumes of cortical neuropil, plausibly even whole-brain datasets. SynEM removes the burden of manual synapse annotation for large densely mapped connectomes.

neuroscience↗