Biology subjects

Wildenberg, G.

Publications and source records attributed to Wildenberg, G..

3 recordsLinked to original sources

bioRxiv2023

Photoemission electron microscopy for connectomics

1Detailing the physical basis of neural circuits with large-volume serial electron microscopy (EM), connectomics, has emerged as an invaluable tool in the neuroscience armamentarium. However, imaging synaptic resolution connectomes is currently limited to either transmission electron microscopy (TEM) or scanning electron microscopy (SEM). Here, we describe a third way, using photoemission electron microscopy (PEEM) which illuminates ultra-thin brain slices collected on solid substrates with UV light and images the photoelectron emission pattern with a wide-field electron microscope. PEEM works with existing sample preparations for EM and routinely provides sufficient resolution and contrast to reveal myelinated axons, somata, dendrites, and sub-cellular organelles. Under optimized conditions, PEEM provides synaptic resolution; and simulation and experiments show that PEEM can be transformatively fast, at Gigahertz pixel rates. We conclude that PEEM imaging leverages attractive aspects of SEM and TEM, namely reliable sample collection on robust substrates combined with fast wide-field imaging, and could enable faster data acquisition for next-generation circuit mapping.

neuroscience↗

bioRxiv2022

Model-free analysis in the spectral domain of postmortem mouse brain EPSI reveals inconsistencies with model-based analyses of the free induction decay

PurposeDysmyelinating disorders lead to abnormalities in myelin structure that produce detectable effects in an echo-planar spectroscopic imaging (EPSI) signal. To estimate the voxel-wise proportion of myelin, data are typically fit to compartmental models in the time domain. This work characterizes limitations in these models by comparing high-resolution water spectra measured in postmortem fixed mouse brains to spectra predicted from time-domain models fit to the same data, specifically by comparing spectra from control and shiverer mice, a model for dysmyelination. MethodsPerfusion-fixed, resected control (n = 5) and shiverer (n = 4) mouse brains were imaged using 3D EPSI with 100 {micro}m isotropic resolution. The free induction decay (FID) was sampled every 2.74 ms over 192 echoes and Fourier transformed to produce water spectra with 1.9 Hz resolution. FIDs were also fit to two biophysical models and the resulting fits were converted to spectra with a Fourier transform. Spectral asymmetry was computed and compared before and after fitting the data to models. ResultsSpectra derived from both models did not show the magnitude of asymmetric broadening observed in the raw data. Correlations between data- and model-derived asymmetries and estimated frequency shifts are weak, leading to a reduction in spectral sensitivity to changes in white-matter structure after fitting the data to models. ConclusionThe results demonstrate spectral inconsistencies between biophysical model predictions and measured data, promoting the further incorporation of spectral analysis methods to develop and benchmark new model-based approaches.

biophysics↗

bioRxiv2021

Large-scale dendritic spine extraction and analysis through petascale computing

The synapse is a central player in the nervous system serving as the key structure that permits the relay of electrical and chemical signals from one neuron to another. The anatomy of the synapse contains important information about the signals and the strength of signal it transmits. Because of their small size, however, electron microscopy (EM) is the only method capable of directly visualizing synapse morphology and remains the gold standard for studying synapse morphology. Historically, EM has been limited to small fields of view and often only in 2D, but recent advances in automated serial EM ("connectomics") have enabled collecting large EM volumes that capture significant fractions of neurons and the different classes of synapses they receive (i.e. shaft, spine, soma, axon). However, even with recent advances in automatic segmentation methods, extracting neuronal and synaptic profiles from these connectomics datasets are difficult to scale over large EM volumes. Without methods that speed up automatic segmentation over large volumes, the full potential of utilizing these new EM methods to advance studies related to synapse morphologies will never be fully realized. To solve this problem, we describe our work to leverage Argonne leadership-scale supercomputers for segmentation of a 0.6 terabyte dataset using state of the art machine learning-based segmentation methods on a significant fraction of the 11.69 petaFLOPs supercomputer Theta at Argonne National Laboratory. We describe an iterative pipeline that couples human and machine feedback to produce accurate segmentation results in time frames that will make connectomics a more routine method for exploring how synapse biology changes across a number of biological conditions. Finally, we demonstrate how dendritic spines can be algorithmically extracted from the segmentation dataset for analysis of spine morphologies. Advancing this effort at large compute scale is expected to yield benefits in turnaround time for segmentation of individual datasets, accelerating the path to biology results and providing population-level insight into how thousands of synapses originate from different neurons; we expect to also reap benefits in terms of greater accuracy from the more compute-intensive algorithms these systems enable.

neuroscience↗