Biology subjects

Chang, H. H.

Publications and source records attributed to Chang, H. H..

6 recordsLinked to original sources

bioRxiv2026

Apollo-IRE1: A Genetically Encoded Sensor for Live Cell and Multiplexed Imaging of ER Stress

Pancreatic beta cells face exceptional protein folding demands from high insulin production requirements, placing extraordinary stress on the ER and contributing to dysfunction in diabetes pathogenesis. Monitoring ER stress dynamics in living cells remains challenging due to the destructive nature of traditional biochemical methods and the limitations of existing fluorescent sensors. Here, we present Apollo-IRE1, a genetically encoded sensor that reports on stress-induced IRE1 oligomerization and associated change in homoFRET via changes in fluorescence anisotropy. Apollo-IRE1 provides a ratiometric, intensity-independent readout, resulting in low day-to-day variability and a minimal spectral bandwidth, enabling multiplexed imaging alongside other cellular parameters. Photobleaching and enhancement curve analysis show that Apollo-IRE1 exists in apparent monomeric, dimeric, and oligomeric states corresponding to baseline, moderate, and terminal ER stress conditions. The sensor also responds rapidly to chemical and physiological ER stressors in both immortalized beta-cell lines and primary mouse islet cells. These data establish Apollo-IRE1 as a practical tool for investigating ER stress dynamics in beta cells and other contexts where longitudinal single-cell measurements are essential.

bioengineering↗

bioRxiv2026

Massive-scale single-nucleus multi-omics identifies novel rare noncoding drivers of Parkinson's disease

Most genetic variants contributing to complex diseases reside in the noncoding genome. While common variants uncovered by genome-wide association studies often fail to explain much of the observed heritability of these diseases, rare variants often have higher effect sizes and cumulatively explain a larger portion of heritability. However, rare variants, particularly rare noncoding variants, have remained under-characterized largely due to the difficulties of accurately predicting variant functionality at scale, given that each individual carries an average of [~]10,000 rare variants. Here, we generated multi-omic data from >3.3 million nuclei sampled from five brain regions across a cohort of 80 individuals with Parkinsons disease (PD) and 21 neurologically normal control individuals with matched 30x whole-genome sequencing. We use this data to identify cell type-specific features of PD, map cell type-specific chromatin accessibility and expression quantitative trait loci, and train machine learning models to predict the effect of variants on gene regulation. We identify rare noncoding variants statistically associated with sporadic PD and extend our approaches to predict drivers of familial PD of unknown genetic origin. Our results underscore the significance of rare noncoding variants in complex diseases and provide a roadmap for applying similar approaches in other disease systems.

genetics↗

bioRxiv2022

Targeting Apollo-NADP+ to image NADPH generation in pancreatic beta-cell organelles

NADPH/NADP+ redox state supports numerous reactions related to cell growth and survival; yet the full impact is difficult to appreciate due to organelle compartmentalization of NADPH and NADP+. To study glucose-stimulated NADPH production in pancreatic beta-cell organelles, we targeted the Apollo-NADP+ sensor by first selecting the most pH stable version of the single colour sensor. We subsequently targeted mTurquoise2-Apollo-NADP+ to various organelles and confirmed activity in the cytoplasm, mitochondrial matrix, nucleus, and peroxisome. Finally, we measured the glucose- and glutamine-stimulated NADPH responses by single and dual colour imaging of the targeted sensors. Overall, we developed multiple organelle-targeted Apollo-NADP+ sensors to reveal the prominent role of beta-cell mitochondria in determining NADPH production in the cytoplasm, nucleus, and peroxisome.

cell biology↗

bioRxiv2020

Nicotinamide promotes differentiation of pancreatic endocrine progenitors from human pluripotent stem cells through poly (ADP-ribose) polymerase inhibition

The generation of pancreatic endocrine progenitor cells is an important step in the differentiation of beta cells from human pluripotent stem cells (hPSC). This stage is marked by the expression of Nkx6.1, a transcription factor with well understood downstream targets but with unclear upstream regulators. In hPSC differentiation, Nkx6.1 is strongly induced by nicotinamide, a derivative of vitamin B3, which has three known functions within a cell. Nicotinamide inhibits two classes of enzymes known as poly-ADP-ribose polymerases (PARPs) and sirtuins. It also contributes to the cellular pool of nicotinamide adenosine deoxynucleotide (NAD+) after conversion in the nicotinamide salvage pathway. Induction of Nkx6.1 expression in pancreatic endocrine progenitors by nicotinamide was mimicked by 3 PARP inhibitors (PJ34, olaparib, NU1025). Small molecule inhibition of the nicotinamide salvage pathway reduced Nkx6.1 expression but not Pdx1 expression and caused alteration in NAD+/NADH ratio. Nkx6.1 expression was not affected by sirtuin inhibition. Metabolic profiling of differentiating pancreatic and endocrine progenitors showed that oxygen consumption increases as differentiation progresses, and that nicotinamide reduces oxygen consumption rate. Expression of Nkx6.1 and other beta cell related genes, including Ins2 and Pdx1 increased in mouse islets after exposure to nicotinamide. In summary, nicotinamide induced Nkx6.1 expression in differentiating human pancreatic endocrine progenitors through inhibition of the PARP family of enzymes. Nicotinamide administration was also associated with increased NAD+/NADH ratio, without affecting Nkx6.1 expression. Similarly, the association between nicotinamide and Nkx6.1 expression was also seen in isolated mouse islets. These observations show a link between the regulation of beta cell identity and the effectors of NAD+ metabolism, suggesting possible therapeutic targets in the field of diabetes.

developmental biology↗

bioRxiv2020

Learning from local to global - an efficient distributed algorithm for modeling time-to-event data

ObjectivesWe developed and evaluated a privacy-preserving One-shot Distributed Algorithm to fit a multi-center Cox proportional hazard model (ODAC) without sharing patient-level information across sites. MethodsUsing patient-level data from a single site combined with only aggregated information from other sites, we constructed a surrogate likelihood function, approximating the Cox partial likelihood function obtained using patient-level data from all sites. By maximizing the surrogate likelihood function, each site obtained a local estimate of the model parameter, and the ODAC estimator was constructed as a weighted average of all the local estimates. We evaluated the performance of ODAC with (1) a simulation study, and (2) a real-world use case study using four datasets from the Observational Health Data Sciences and Informatics (OHDSI) network. ResultsOur simulation study showed that ODAC provided estimates nearly the same as the estimator obtained by analyzing, in a single dataset, the combined patient-level data from all sites (i.e., the pooled estimator). The relative bias was less than 0.1% across all scenarios. The accuracy of ODAC remained high across different sample sizes and event rates. On the other hand, the metaanalysis estimator, which was obtained by the inverse variance weighted average of the sitespecific estimates, had substantial bias when the event rate is less than 5%, with the relative bias reaching 20% when the event rate is 1%. In the OHDSI network application, the ODAC estimates have a relative bias less than 5% for 15 out of 16 log hazard ratios; while the meta-analysis estimates had substantially higher bias than ODAC. ConclusionsODAC is a privacy-preserving and non-iterative method for implementing time-to-event analyses across multiple sites. It provides estimates on par with the pooled estimator and substantially outperforms the meta-analysis estimator when the event is uncommon, making it extremely suitable for studying rare events and diseases in a distributed manner.

bioinformatics↗

bioRxiv2020

Cell segmentation using deep learning: comparing label and label-free approaches using hyper-labeled image stacks

Deep learning provides an opportunity to automatically segment and extract cellular features from high-throughput microscopy images. Many labeling strategies have been developed for this purpose, ranging from the use of fluorescent markers to label-free approaches. However, differences in the channels available to each respective training dataset make it difficult to directly compare the effectiveness of these strategies across studies. Here we explore training models using subimage stacks composed of channels sampled from larger, hyper-labeled, image stacks. This allows us to directly compare a variety of labeling strategies and training approaches on identical cells. This approach revealed that fluorescence-based strategies generally provide higher segmentation accuracies but were less accurate than label-free models when labeling was inconsistent. The relative strengths of label and label-free techniques could be combined through the use of merging fluorescence channels and out-of-focus brightfield images. Beyond comparing labeling strategies, using subimage stacks for training was also found to provide a method of simulating a wide range of labeling conditions, increasing the ability of the final model to accommodate a greater range of experimental setups.

bioengineering↗