Biology subjects

Jang, W.-D.

Publications and source records attributed to Jang, W.-D..

4 recordsLinked to original sources

bioRxiv2026

Chemical activation of mitophagy via the N-degron pathway alleviates mitochondrial neuropathies

Pharmacological activation of mitophagy offers a promising strategy to eliminate dysfunctional mitochondria. We previously identified the autophagy receptor p62/SQSTM1 as an N-recognin whose activity is enhanced by Arg/N-degrons. Here, we show that Arg/N-degrons generated by ATE1-encoded R-transferase regulate p62-mediated mitophagy by promoting its recruitment to damaged mitochondria. Structural modification of Arg/N-degrons yielded ATB1071, a 443.5-Da orally bioavailable compound that activates p62 and induces stress-selective mitophagy through both Parkin-independent pathways involving NIPSNAP1 and NIPSNAP2, and a Parkin-dependent pathway involving the substrate EBP1/PA2G4. In Ndufs4-/- mice, a Leigh syndrome (LS) model, ATB1071 induced mitophagy in the brain and exerted therapeutic benefits by reducing neuroinflammation, improving muscle strength and neuromuscular coordination, and extending lifespan. In cerebral ischemia-reperfusion (IR) model mice, ATB1071 reduced infarct volume and neuronal death, and ameliorated multiple behavioral deficits through EBP1-dependent mitophagy. Pharmacokinetic (PK) and toxicological analyses support ATB1071 as a preclinical candidate for mitochondria-associated neurological injury.

cell biology↗

bioRxiv2021

UnMICST: Deep learning with real augmentation for robust segmentation of highly multiplexed images of human tissues

Newly developed technologies have made it feasible to routinely collect highly multiplexed (20-60 channel) images at subcellular resolution from human tissues for research and diagnostic purposes. Extracting single cell data from such images requires efficient and accurate image segmentation, a challenging problem that has recently benefited from the use of deep learning. In this paper, we demonstrate two approaches to improving tissue segmentation that are applicable to multiple deep learning frameworks. The first uses "real augmentations" that comprise defocused and saturated image data collected on the same instruments as the actual data; using real augmentation improves model accuracy to a significantly greater degree than computational augmentation (Gaussian blurring). The second involves imaging the nuclear envelope to better identify nuclear outlines. The two approaches cumulatively and substantially improve segmentation on a wide range of tissue types and provide a set of improved segmentation models. We speculate that the use of real augmentations may have applications in image processing outside of microscopy.

bioinformatics↗

bioRxiv2020

Channel Embedding for Informative Protein Identification from Highly Multiplexed Images

Interest is growing rapidly in using deep learning to classify biomedical images, and interpreting these deep-learned models is necessary for life-critical decisions and scientific discovery. Effective interpretation techniques accelerate biomarker discovery and provide new insights into the etiology, diagnosis, and treatment of disease. Most interpretation techniques aim to discover spatially-salient regions within images, but few techniques consider imagery with multiple channels of information. For instance, highly multiplexed tumor and tissue images have 30-100 channels and require interpretation methods that work across many channels to provide deep molecular insights. We propose a novel channel embedding method that extracts features from each channel. We then use these features to train a classifier for prediction. Using this channel embedding, we apply an interpretation method to rank the most discriminative channels. To validate our approach, we conduct an ablation study on a synthetic dataset. Moreover, we demonstrate that our method aligns with biological findings on highly multiplexed images of breast cancer cells while outperforming baseline pipelines.

pathology↗

bioRxiv2019

Facetto: Combining Unsupervised and Supervised Learning for Hierarchical Phenotype Analysis in Multi-Channel Image Data

Facetto is a scalable visual analytics application that is used to discover single-cell phenotypes in high-dimensional multi-channel microscopy images of human tumors and tissues. Such images represent the cutting edge of digital histology and promise to revolutionize how diseases such as cancer are studied, diagnosed, and treated. Highly multiplexed tissue images are complex, comprising 109 or more pixels, 60-plus channels, and millions of individual cells. This makes manual analysis challenging and error-prone. Existing automated approaches are also inadequate, in large part, because they are unable to effectively exploit the deep knowledge of human tissue biology available to anatomic pathologists. To overcome these challenges, Facetto enables a semi-automated analysis of cell types and states. It integrates unsupervised and supervised learning into the image and feature exploration process and offers tools for analytical provenance. Experts can cluster the data to discover new types of cancer and immune cells and use clustering results to train a convolutional neural network that classifies new cells accordingly. Likewise, the output of classifiers can be clustered to discover aggregate patterns and phenotype subsets. We also introduce a new hierarchical approach to keep track of analysis steps and data subsets created by users; this assists in the identification of cell types. Users can build phenotype trees and interact with the resulting hierarchical structures of both high-dimensional feature and image spaces. We report on use-cases in which domain scientists explore various large-scale fluorescence imaging datasets. We demonstrate how Facetto assists users in steering the clustering and classification process, inspecting analysis results, and gaining new scientific insights into cancer biology.

systems biology↗