Biology subjects

Lee, B. C.

Publications and source records attributed to Lee, B. C..

7 recordsLinked to original sources

bioRxiv2025

Methionine sulfoxide reductase B3 antioxidant activity is indispensable for mouse inner ear cuticular plate structure and hair bundle integrity

Methionine sulfoxide reductases (MSR) are enzymes responsible for catalyzing the reduction of methionine-sulfoxides. We previously demonstrated that variants in human MSRB3, a member of the MSR family, are associated with profound autosomal recessive prelingual non-syndromic hearing loss DFNB74. To better understand the role of MSRB3 in the auditory pathway, we generated a complete MsrB3 gene knock-out mouse model of human DFNB74 deafness. The MsrB3 deficient mouse showed profound hearing loss by postnatal day 16 (P16), which was accompanied by hair cell morphological abnormalities including altered stereocilia bundle shape and cuticular plate degeneration followed by hair cell apoptotic death. Although the absence of MSRB3 primarily affected the actin cytoskeleton, rootlets were present, and the expression and localization of major F-actin stereocilia-core proteins were unaltered. Biochemical assays demonstrated that wild-type MSRB3, but not MSRB3 harboring p.Cys89Gly, the same variant reported for human deafness DFNB74, can repolymerize oxidized actin. Consistent with these in vitro data, we observed a decreased ratio of reduced/total actin in the inner ears of Msrb3 knock-out mice. These data suggest a protective role of MSRB3 in the maintenance and maturation of stereocilia and hair cells, a conserved mechanism aimed at maintaining actin redox dynamics in these sensory cells.

genetics↗

bioRxiv2024

High-resolution single particle imaging at 100-200 keV with the Gatan Alpine direct electron detector.

Developments in direct electron detector technology have played a pivotal role in enabling high-resolution structural studies by cryo-EM at 200 and 300 keV. Yet, theory and recent experiments indicate advantages to imaging at 100 keV, energies for which the current detectors have not been optimized. In this study, we evaluated the Gatan Alpine detector, designed for operation at 100 and 200 keV. Compared to the Gatan K3, Alpine demonstrated a significant DQE improvement at these voltages, specifically a ~4-fold improvement at Nyquist at 100 keV. In single-particle cryo-EM experiments, Alpine datasets yielded better than 2 [A] resolution reconstructions of apoferritin at 120 and 200 keV on a ThermoFisher Scientific (TFS) Glacios microscope. We also achieved a ~3.2 [A] resolution reconstruction for a 115 kDa asymmetric protein complex, proving its effectiveness with complex biological samples. In-depth analysis revealed that Alpine reconstructions are comparable to K3 reconstructions at 200 keV, and remarkably, reconstruction from Alpine at 120 keV on a TFS Glacios surpassed all but the 300 keV data from a TFS Titan Krios with GIF/K3. Additionally, we show Alpines capability for high-resolution data acquisition and screening on lower-end systems by obtaining ~3 [A] resolution reconstructions of apoferritin and aldolase at 100 keV and detailed 2D averages of a 55 kDa sample using a side-entry cryo holder. Overall, we show that Gatan Alpine performs well with the standard 200 keV imaging systems and may potentially capture the benefits of lower accelerating voltages, possibly bringing smaller sized particles within the scope of cryo-EM.

biophysics↗

bioRxiv2021

Change in protein, oil and fatty acid contents in soybeans (Glycine max (L.) Merr.) of different seed coat colors and seed weight

Soybean seeds are one of the best sources of plant-based high-quality proteins and oils. The contents of these metabolites are affected by both environmental and genetic factors. In this study, 49 soybean germplasms were cultivated in Korea, the contents of total protein, total oil and five fatty acids were determined, and the influences of seed coat color and seed weight on each were assessed. The total protein and total oil contents were evaluated using Kjeldahl and Soxhlet methods and were in the ranges of 36.28-44.19% and 13.45-19.20%, respectively. Moreover, the contents of individual fatty acids were determined as area percentage from acquired gas-chromatography peaks. The contents of palmitic, stearic, oleic, linoleic and linolenic acids were in the ranges of 9.90-12.55, 2.45-4.00, 14.97-38.74, 43.22-60.26, and 5.37-12.33%, respectively and each significantly varied between the soybean germplasms. Unlike total oil and fatty acid contents, total protein content was not significantly affected by both seed coat color and seed weight. Cluster analysis grouped the soybeans into two classes with notable content differences. Fatty acids were the main factors for the variabilities seen between the soybean germplasms as observed in the principal component analysis. Correlation analysis revealed a significant but negative association between total oil and total protein contents (r = -0.714, p < 0.0001). Besides, a trade-off relationship was observed between oleic acid and linoleic acid (r = -0.936, p < 0.0001) which was reflected with respect to both seed coat color and seed weight. Among all colored soybeans, pale-yellow soybeans had the highest and the lowest levels of oleic acid and linoleic acid, respectively each being significantly different from the rest of colored soybeans (p < 0.05). Likewise, oleic acid content increased with seed weight while that of linoleic acid decreased with seed weight (p < 0.05). In general, this study showed the significance of seed coat color and seed weight to discriminate soybean genotypes, mainly in terms of their fatty acid contents. Moreover, the soybean germplasms with distinct characters and fatty acid contents identified in this study could be important genetic resources for cultivar development.

plant biology↗

bioRxiv2020

Solving the where problem in neuroanatomy: a generative framework with learned mappings to register multimodal, incomplete data into a reference brain

A current focus of research in neuroscience is to enumerate, map and annotate neuronal cell types in whole vertebrate brains using different modalities of data acquisition. Mapping these molecular and anatomical datasets into a common reference space remains a key challenge. While several brain-to-atlas mapping workflows exist, they do not adequately address challenges of modern high throughput neuroimaging, including multimodal and multiscale signals, missing data or non reference signals, and geometric quantification of individual variation. Our solution is to implement a generative statistical model that describes the likelihood of imaging data given a sequence of transforms of an atlas image, and a framework for maximum a posteriori estimation of unknown parameters capturing the issues listed above. The key idea in our approach is to minimize the difference between synthetic image volumes and real data over these parameter. Rather than merely using mappings as a "normalization" step, we implement tools for using their local metric changes as an opportunity for geometric quantification of technical and biological sources of variation in an unprecedented manner. While the framework is used to compute pairwise mappings, our approach particularly allows for easy compositions across chains of multimodality datasets. We apply these methods across a broad range of datasets including various combinations of in-vivo and ex-vivo MRI, 3D STP and fMOST data sets, 2D serial histology sections, and brains processed for snRNAseq with tissue partially removed. We show biological utility by quantifying cell density and diffeomorphic characterization of brain shape fluctuations across biological covariates. We note that the magnitude of individual variation is often greater than differences between different sample preparation techniques. To facilitate community accessibility, we implement our algorithm as open source, include a web based framework, and implement input and output dataset standards. Our work establishes a quantitative, scalable and streamlined workflow for unifying a broad spectrum of multi-modal whole-brain light microscopic data volumes into a coordinate-based atlas framework. This work enables large scale integration of whole brain data sets that are essential in modern neuroscience.

neuroscience↗

bioRxiv2018

A High-throughput Neurohistological Pipeline for Brain-Wide Mesoscale Connectivity Mapping of the Common Marmoset

Understanding the connectivity architecture of entire vertebrate brains is a fundamental but difficult task. MRI based methods offer whole brain coverage, but remain indirect in the approach to connectivity mapping. Recent progress has been made in directly mapping whole-brain connectivity architecture in the mouse at the mesoscopic scale. The basic approach uses tracer injections systematically placed on a grid of locations spanning the brain and computational analysis of the resulting whole brain data sets. Scaling this approach to bigger primate brains poses nontrivial technical challenges. Here we present an integrated neurohistological pipeline as well as a grid-based tracer injection strategy for systematic mesoscale connectivity mapping in the common Marmoset (Callithrix jacchus). Individual brains are sectioned into [~]1700 20{micro}m sections using the tape transfer technique, permitting high quality 3D reconstruction of a series of histochemical stains (Nissl, myelin) interleaved with tracer labelled sections. Combining the resulting 3D volumes, containing informative cytoarchitectonic markers, with in-vivo and ex-vivo MRI, and using an integrated computational pipeline, we are able to overcome the significant individual variation exhibited by Marmosets to obtain routine and high quality maps to a common atlas framework. This will facilitate the systematic assembly of a mesoscale connectivity matrix together with unprecedented 3D reconstructions of brain-wide projection patterns in a primate brain. While component instruments or protocols may be available from previous work, we believe that this is the first detailed systems-level presentation of the methodology required for high-throughput neuroanatomy in a model primate.

neuroscience↗

bioRxiv2018

Continuity between koniocellular layers of dorsal lateral geniculate and inferior pulvinar nuclei in common marmosets

In primates, the koniocellular (K) layers of the dorsal lateral geniculate nucleus (LGN) and the calbindin-rich subdivisions of the inferior pulvinar (IPul) nucleus are considered part of a thalamic matrix system which projects diffusely to superficial cortical layers. Activity in the matrix system is proposed to coordinate oscillatory activity in thalamocortical loops. Further, since both K cells and IPul are involved in visual processing pathways, especially in alternative pathways to visual cortex after V1 lesion in early life (\"blindsight\"), their functional similarities have been strongly implicated. Here we tested the hypothesis that calbindin-positive K cells and IPul cells constitute a continuous group of cells. By combining immunohistochemistry and a high-throughput neuronal tracing method, we found that both K cells and IPul form reciprocal connections with striate and extrastriate cortices; whereas principal laminae of LGN do not receive inputs from extrastriate cortex and only project sparsely to these areas. Retrograde labelled cells in lateral division of IPul merged seamlessly into the retrograde labelled cells in K layers. These results supported the continuity between LGN K layers and IPul, providing the anatomical basis for functional congruity of this part of dorsal thalamic matrix.

neuroscience↗

bioRxiv2018

On variational solutions for whole brain serial-section histology using the computational anatomy random orbit model

This paper presents a variational framework for dense diffeomorphic atlas-mapping onto high-throughput histology stacks at the 20 m meso-scale. The observed sections are modelled as Gaussian random fields conditioned on a sequence of unknown section by section rigid motions and unknown diffeomorphic transformation of a three-dimensional atlas. To regularize over the high-dimensionality of our parameter space (which is a product space of the rigid motion dimensions and the diffeomorphism dimensions), the histology stacks are modelled as arising from a first order Sobolev space smoothness prior. We show that the joint maximum a-posteriori, penalized-likelihood estimator of our high dimensional parameter space emerges as a joint optimization interleaving rigid motion estimation for histology restacking and large deformation diffeomorphic metric mapping to atlas coordinates. We show that joint optimization in this parameter space solves the classical curvature non-identifiability of the histology stacking problem. The algorithms are demonstrated on a collection of whole-brain histological image stacks from the Mouse Brain Architecture Project.\n\nAuthor SummaryNew developments in neural tracing techniques have motivated the widespread use of histology as a modality for exploring the circuitry of the brain. Automated mapping of pre-labeled atlases onto modern large datasets of histological imagery is a critical step for elucidating the brains neural circuitry and shape. This task is challenging as histological sections are imaged independently and the reconstruction of the unsectioned volume is nontrivial. Typically, neuroanatomists use reference volumes of the same subject (e.g. MRI) to guide reconstruction. However, obtaining reference imagery is often non-standard, as in high-throughput animal models like mouse histology. Others have proposed using anatomical atlases as guides, but have not accounted for the intrinsic nonlinear shape difference from atlas to subject. Our method addresses these limitations by jointly optimizing reconstruction informed by an atlas simultaneously with the nonlinear change of coordinates that encapsulates anatomical variation. This accounts for intrinsic shape differences and enables rigorous, direct comparisons of atlas and subject coordinates. Using simulations, we demonstrate that our method recovers the reconstruction parameters more accurately than atlas-free models and innately produces accurate segmentations from simultaneous atlas mapping. We also demonstrate our method on the Mouse Brain Architecture dataset, successfully mapping and reconstructing over 500 brains.

neuroscience↗