Biology subjects

Usdin, T. B.

Publications and source records attributed to Usdin, T. B..

6 recordsLinked to original sources

bioRxiv2025

Mesoscopic analysis of GABAergic marker expression in acetylcholine neurons in the whole mouse brain

In the central nervous system, acetylcholine (ACh) neurons coordinate neural network activity required for higher brain functions, such as attention, learning, and memory, as well as locomotion. Disturbances in cholinergic signaling have been described in many diseases of the developing and mature brain. Interestingly, ACh neurons can co-transmit GABA to support essential roles in brain function. However, the contributions of ACh/GABA co-transmission to brain function remain unclear. This underscores the need to better understand the heterogeneity of ACh neurons, particularly the sub-population of ACh neurons co-expressing GABAergic markers. We used various combinations of transgenic mouse lines to systematically label ACh neuron populations positive for different GABAergic markers in the brain. We developed a workflow combining tissue clearing, light-sheet fluorescence microscopy, and machine learning to image entire mouse brain hemispheres followed by quantification of ACh neurons throughout the brain. With this approach, we assessed the role of GABA co-transmission in ACh neuron function and quantified ACh and ACh/GABA neuron sub-populations in the brain. Our results suggest that GABA co-transmission from ACh neurons is not required to maintain the regular ACh neuron count in the brain. Furthermore, we report that a large subset of ACh neurons can potentially synthesize GABA by co-expressing the marker Gad2. However, most of these ACh neurons do not express vGAT, which would enable these neurons to release GABA. Taken together, we conclude that GABA co-transmission likely occurs only from a small population of ACh neurons restricted to few brain nuclei. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=177 SRC="FIGDIR/small/675394v1_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@1dc5915org.highwire.dtl.DTLVardef@15c872borg.highwire.dtl.DTLVardef@1ae3993org.highwire.dtl.DTLVardef@145f32b_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2025

Targeted gene transfer into developmentally defined cell populations of the primate brain

The primate brain possesses unique physiological and developmental features whose systematic investigation is hampered by a paucity of transgenic germline models and tools. Here, we present a minimally invasive method to introduce transgenes widely across the primate cerebral cortex using ultrasound-guided fetal intracerebroventricular viral injections (FIVI). This technique enables rapid-onset and long-lasting transgene expression following the delivery of recombinant adeno-associated viruses (rAAVs). By adjusting the gestational timing of injections, viral serotypes, and transcriptional regulatory elements, rAAV FIVI allows for systematic targeting of specific cell populations. We demonstrate the versatility of this method through restricted laminar expression in the cortex, Cre-dependent targeting of neurons, CRISPR-based gene editing, and labeling of peripheral somatosensory and retinal pathways. By mimicking key desirable features of germline transgenic models, this efficient and targeted method for gene transfer into the fetal primate brain opens new avenues for experimental and translational neuroscience across the lifespan.

neuroscience↗

bioRxiv2024

A conserved cell-type gradient across the human mediodorsal and paraventricular thalamus

The mediodorsal thalamus (MD) and adjacent midline nuclei are important for cognition and mental illness, but their cellular composition is not well defined. Using single-nucleus and spatial transcriptomics, we identified a conserved excitatory neuron gradient, with distinct spatial mapping of individual clusters. One end of the gradient was expanded in human MD compared to mice, which may be related to the expansion of granular prefrontal cortex in hominids. Moreover, neurons preferentially mapping onto the parvocellular division MD were associated with genetic risk for schizophrenia and bipolar disorder. Midbrain-derived inhibitory interneurons were enriched in human MD and implicated in genetic risk for major depressive disorder.

neuroscience↗

bioRxiv2023

Dissociable control of motivation and reinforcement by distinct ventral striatal dopamine receptors

Dopamine release in striatal circuits, including the nucleus accumbens (NAc), tracks separable features of reward such as motivation and reinforcement. However, the cellular and circuit mechanisms by which dopamine receptors transform dopamine release into distinct constructs of reward remain unclear. Here, we show that dopamine D3 receptor (D3R) signaling in the NAc drives motivated behavior by regulating local NAc microcircuits. Furthermore, D3Rs co-express with dopamine D1 receptors (D1Rs), which regulate reinforcement, but not motivation. Paralleling dissociable roles in reward function, we report non-overlapping physiological actions of D3R and D1R signaling in NAc neurons. Our results establish a novel cellular framework wherein dopamine signaling within the same NAc cell type is physiologically compartmentalized via actions on distinct dopamine receptors. This structural and functional organization provides neurons in a limbic circuit with the unique ability to orchestrate dissociable aspects of reward-related behaviors that are relevant to the etiology of neuropsychiatric disorders.

neuroscience↗

bioRxiv2022

Incorporating the image formation process into deep learning improves network performance in deconvolution applications

We present Richardson-Lucy Network (RLN), a fast and lightweight deep learning method for 3D fluorescence microscopy deconvolution. RLN combines the traditional Richardson-Lucy iteration with a fully convolutional network structure, improving network interpretability and robustness. Containing only [~]16 thousand parameters, RLN enables 4- to 50-fold faster processing than purely data-driven networks with many more parameters. By visual and quantitative analysis, we show that RLN provides better deconvolution, better generalizability, and fewer artifacts than other networks, especially along the axial dimension. RLN outperforms Richardson-Lucy deconvolution on volumes contaminated with severe out of focus fluorescence or noise and provides 4- to 6-fold faster reconstructions of large, cleared tissue datasets than classic multi-view pipelines. We demonstrate RLNs performance on cells, tissues, and embryos imaged with widefield-, light sheet-, and structured illumination microscopy.

biophysics↗

bioRxiv2019

Accelerating iterative deconvolution and multiview fusion by orders of magnitude

We describe theoretical and practical advances in algorithm and software design, resulting in ten to several thousand-fold faster deconvolution and multiview fusion than previous methods. First, we adapt methods from medical imaging, showing that an unmatched back projector accelerates Richardson-Lucy deconvolution by at least 10-fold, in most cases requiring only a single iteration. Second, we show that improvements in 3D image-based registration with GPU processing result in speedups of 10-100-fold over CPU processing. Third, we show that deep learning can provide further accelerations, particularly for deconvolution with a spatially varying point spread function. We illustrate the power of our methods from the subcellular to millimeter spatial scale, on diverse samples including single cells, nematode and zebrafish embryos, and cleared mouse tissue. Finally, we show that our methods facilitate the use of new microscopes that improve spatial resolution, including dual-view cleared tissue light-sheet microscopy and reflective lattice light-sheet microscopy.

bioengineering↗