Biology subjects

Fung, J.

Publications and source records attributed to Fung, J..

10 recordsLinked to original sources

bioRxiv2026

Dynamo: An open-source Python application for quantifying neuronal structural plasticity over time

Neurons exhibit tremendous structural plasticity during the growth of dendritic and axonal arbors in early brain circuit formation, followed by experience-driven structural plasticity throughout life. Advances in labeling and in vivo time-lapse imaging allow capture of dynamic structural changes within intact and awake animals, and fluorescent biosensors of neural activity offer opportunities to link structural and functional plasticity. However, the resulting large multi-dimensional data sets are challenging to quantify due to time-consuming tracking of minute structural changes throughout complex neuronal morphologies across time. Here, we present Dynamo: an open-source Python application that enables dynamic morphometrics, the quantitative analysis of morphological changes over time, by streamlining arbor reconstruction, registration of structures across time, and quantitative analyses of growth behavior. Dynamo yields rich characterization of neural structural changes necessary for determining how rapid growth events culminate into long-term patterning, linking structural and functional plasticity, and for identifying underlying molecular mechanisms.

neuroscience↗

bioRxiv2026

Spatial pharmaco-multiomics reveals drug distribution, metabolic niches, and spatially constrained resistance in medulloblastoma

Single-cell and spatial transcriptomic studies have provided insights into the developmental origins and intratumoural heterogeneity of SHH medulloblastoma (SHH-MB) and suggested how targeted drugs such as CDK4/6 inhibitors remodel tumour ecosystems, yet the interplay between local drug exposure, metabolism, cell state, and drug resistance remains poorly understood. Here we developed a same-section spatial pharmaco-multiomics framework that integrates MALDI-MSI-based spatial metabolomics with Visium whole-transcriptome profiling and high-resolution Xenium imaging to map palbociclib distribution, metabolite landscapes, and transcriptional programs within the same histological contexts of an SHH-MB PDOX model and primary human tumours. Palbociclib-rich tumour bulk exhibited broad suppression of E2F-driven proliferation and a shift toward neuronal differentiation, corroborating and extending prior findings. In contrast, drug-poor tumour-brain interfaces and perivascular regions retained E2F-high proliferative states and were enriched for mesenchymal-like stromal cells and ECM-remodelling genes, indicating anatomically constrained reservoirs of tolerance. Spatial metabolomics linked these interface niches to ganglioside (GM2) and sphingomyelin enrichment, while differentiated, drug-exposed regions displayed phosphatidylcholine, phosphatidic-acid signatures consistent with neuronal maturation. Integrated pathway analysis further revealed a "mitochondrial tuning" program, with upregulation of histidine, folate/one-carbon, CoA, and lipoate metabolism with redox and oxidative-phosphorylation support. These signatures were specific to therapy-exposed border cells. Rare palbociclib-positive, E2F-high resistant spots additionally exhibited mitotic checkpoint and DNA-repair signatures, implying a drug-induced resistance axis independent of scarcity. Together, our study provides a generalisable same-section spatial pharmaco-multiomics pipeline and a spatially resolved model of CDK4/6 response, nominating interface-focused metabolic and cell-intrinsic vulnerabilities for combination therapy.

cancer biology↗

bioRxiv2024

Analyzing high-throughput assay data to advance the rapid screening of environmental chemicals for human reproductive toxicity

While high-throughput (HTP) assays have been proposed as platforms to rapidly assess reproductive toxicity, there is currently a lack of established assays that specifically address germline development/function and fertility. We assessed the applicability domains of yeast (S. cerevisiae) and nematode (C. elegans) HTP assays in toxicity screening of 124 environmental chemicals, determining their agreement in identifying toxicants and their concordance with reproductive toxicity in vivo. We integrated data generated in the two models and compared results using a streamlined, semi-automated benchmark dose (BMD) modeling approach. We then extracted and modeled relevant mammalian in vivo data available for the matching chemicals included in the Toxicological Reference Database (ToxRefDB). We ranked potencies of common compounds using the BMD and evaluated correlation between the datasets using Pearson and Spearman correlation coefficients. We found moderate to good correlation across the three data sets, with r = 0.48 (95% CI: 0.28-1.00, p<0.001) and rs = 0.40 (p=0.002) for the parametric and rank order correlations between the HTP BMDs; r = 0.95 (95% CI: 0.76-1.00, p=0.0005) and rs = 0.89 (p=0.006) between the yeast assay and ToxRefDB BMDs; and r = 0.81 (95% CI: 0.28-1.00, p=0.014) and rs = 0.75 (p=0.033) between the worm assay and ToxRefDB BMDs. Our findings underscore the potential of these HTP assays to identify environmental chemicals that exhibit reproductive toxicity. Integrating these HTP datasets into mammalian in vivo prediction models using machine learning methods could further enhance the predictive value of these assays in future rapid screening efforts.

developmental biology↗

bioRxiv2024

Screening and characterization of 133 physiologically-relevant environmental chemicals for reproductive toxicity

Reproduction is a functional outcome that relies on complex cellular, tissue, and organ interactions that span the developmental period to adulthood. Thus, the assessment of its disruption by environmental chemicals is remarkably painstaking in conventional toxicological animal models and does not scale up to the number of chemicals present in our environment and requiring testing. We adapted a previously described low-throughput in vivo chromosome segregation assay using C. elegans predictive of reproductive toxicity and leveraged available public data sources (ToxCast, ICE) to screen and characterize 133 physiologically-relevant chemicals in a high-throughput manner. The screening outcome was further validated in a second, independent in vivo assay assessing embryonic viability. In total, 13 chemicals were classified as reproductive toxicants with the two most active chemicals belonging to the large family of Quaternary Ammonium Compounds (QACs) commonly used as disinfectants but with limited available reproductive toxicity data. We compared the results from the C. elegans assay with ToxCast in vitro data compiled from 700+ cell response assays and 300+ signaling pathways-based assays. We did not observe a difference in the bioactivity or in average potency (AC50) between the top and bottom chemicals. However, the intended target categories were significantly different between the classified chemicals with, in particular, an over-representation of steroid hormone targets for the high Z-score chemicals. Taken together, these results point to the value of in vivo models that scale to high-throughput level for reproductive toxicity assessment and to the need to prioritize the assessment of QACs impacts on reproduction.

pharmacology and toxicology↗

bioRxiv2022

Phenotype to genotype mapping using supervised and unsupervised learning

The relationship between the genotype, the genetic instructions encoded into a genome, and phenotype, the macroscopic realization of such instructions, remains mostly uncharted. In addition, tools able to uncover the connection between the phenotype with a specific set of responsible genes are still under definition. In this work, we focus on yeast organelles called vacuoles, which are cell membrane compartments that vary size and shape in response to various stimuli, and we develop a framework relating changes of cellular morphology to genetic modification. The method is a combination of convolutional neural network (CNN) and an unsupervised learning pipeline, which employs a deep-learning based segmentation, classification, and anomaly detection algorithm. From the live 3D fluorescence vacuole images, we observe that different genetic mutations generate distinct vacuole phenotypes and that the same mutation might correspond to more than one vacuole morphology. We trained a Unet architecture to segment our cellular images and obtain precise, quantitative information in 2D depth-encoded images. We then used an unsupervised learning approach to cluster the vacuole types and to establish a correlation between genotype and vacuole morphology. Using this procedure, we obtained 4 phenotypic groups. We extracted a set of 131 morphological features from the segmented vacuoles images, reduced to 50 after a tree-based feature selection. We obtained a purity of 85% adopting a Fuzzy K-Means based algorithm on a random subset of 880 images, containing all the detected phenotypic groups. Finally, we trained a CNN on the labels assigned during clustering. The CNN has been used for prediction of a large dataset (6942 images) with high accuracy (80%). Our approach can be applied extensively for live fluorescence image analysis and most importantly can unveil the basic principles relating genotype to vacuole phenotype in yeast cell, which can be thought as a first step for inferring cell designing principles to generate organelles with a specific, desired morphology.

bioinformatics↗

bioRxiv2022

A high-throughput approach to identify reproductive toxicants among environmental chemicals using an in vivo evaluation of gametogenesis in budding yeast Saccharomyces cerevisiae

BackgroundEnvironmental chemical exposures are likely making important contributions to current levels of infertility and its increasing incidence. Yet the US produces high volumes of industrial chemicals for which there is limited data on their potential human reproduction toxicity. Current assays typically used in policy and regulatory settings involve costly and timeconsuming whole-animal rodent tests which limit the rapidity with which one can assess the thousands of chemicals yet to be tested. ObjectiveOur aim was to develop a fast and reliable strategy to evaluate a large number of chemicals for reproductive toxicity by developing a high-throughput toxicity assessment using the yeast S. cerevisiae. MethodsYeast are chronically exposed to each environmental chemical at two doses, 30 M and 100 M, in a 96-well plate-based format throughout gametogenesis. Non-gametes are removed and chemicals are washed away before gamete viability is measured using absorbance at 600 nm to produce growth curves. The difference in time at half-maximal saturation with and without exposure is used to determine the extent of reproductive toxicity. ResultsWe validated our assay using bisphenol A (BPA), a well-established mammalian reproductive toxicant. We find that BPA in yeast has similar detrimental effects in meiosis as shown in worms and mammals. Competition assays with BPA analogs reveal that two of out of 19 BPA analogs examined (bisphenol E and 17{beta}-estradiol) show synergistic effects with BPA at doses tested and none show antagonistic effects. Out of 179 additional environmental chemicals, we designated 57 chemicals as reproductively toxic. Finally, by comparing chemicals in our cohort that have been evaluated for reproductive toxicity in mammalian studies, we find a statistically significant association between toxic chemicals in yeast and mammals. ConclusionWe show that a high-throughput assay using yeast may be a useful approach for rapidly and reliably identifying chemicals that pose a reproductive risk.

pharmacology and toxicology↗

bioRxiv2021

A Role for Synaptonemal Complex in Meiotic Mismatch Repair

During meiosis a large subset of interhomolog recombination repair intermediates form within the physical context of the synaptonemal complex (SC), a protein-rich structure assembled at the interface of aligned homologous chromosomes. However, the functional relationship between SC structure and homologous recombination remains poorly defined. In prior work we determined that tripartite SC is dispensable for recombination in S. cerevisiae; SC central element proteins Ecm11 and Gmc2 instead limit the number of recombination events. Here we report that while dispensable for recombination per se, SC central element proteins influence the processing of interhomolog recombination intermediates in a manner that minimizes errors in mismatch correction. Failure to correct mis-paired bases within heteroduplex at meiotic recombination sites leads to genotypically sectored colonies (post meiotic segregation events) arising from mitotic proliferation of mismatch-containing spores. We discovered an increase in post-meiotic segregation at the THR1 locus in cells lacking Ecm11 or Gmc2, or in the SC-deficient but crossover recombination-proficient zip1[{Delta}21-163] mutant. High-throughput sequencing of octad meiotic products revealed a genome-wide increase in recombination events with uncorrected mismatches in ecm11 mutants relative to wild type. Meiotic cells missing Ecm11 also display longer gene conversion tracts, but tract length alone does not account for the higher frequency of uncorrected mismatches. Interestingly, the per-nucleotide mismatch frequency is elevated in ecm11 mutants when analyzing all gene conversion tracts, but is similar between wild type and ecm11 if one considers only those events with uncorrected mismatches. Our data suggest that a subset of recombination events is similarly susceptible to mismatch repair errors in both wild type and ecm11 strains, but in ecm11 mutants many more recombination events fall into this inefficient repair category. Finally, we observe elevated post-meiotic segregation at THR1 in mutants with a dual deficiency in MutS{gamma}-mediated crossover recombination and SC assembly, but not in the mlh3 mutant, which lacks MutS{gamma} crossovers but has abundant SC. We propose that SC structure promotes efficient mismatch repair of joint molecule recombination intermediates resolved via both MutS{gamma}-associated and MutS{gamma}-independent pathways, and is the molecular basis for elevated post-meiotic segregation in both MutS{gamma} crossover-proficient (ecm11, gmc2) and MutS{gamma} crossover-deficient (msh4, zip3) strains.

genetics↗

bioRxiv2021

Nanotraps for the containment and clearance of SARS-CoV-2

SARS-CoV-2 enters host cells through its viral spike protein binding to angiotensin-converting enzyme 2 (ACE2) receptors on the host cells. Here we show functionalized nanoparticles, termed "Nanotraps", completely inhibited SARS-CoV-2 infection by blocking the interaction between the spike protein of SARS-CoV-2 and the ACE2 of host cells. The liposomal-based Nanotrap surfaces were functionalized with either recombinant ACE2 proteins or anti-SARS-CoV-2 neutralizing antibodies and phagocytosis-specific phosphatidylserines. The Nanotraps effectively captured SARS-CoV-2 and completely blocked SARS-CoV-2 infection to ACE2-expressing human cell lines and primary lung cells; the phosphatidylserine triggered subsequent phagocytosis of the virus-bound, biodegradable Nanotraps by macrophages, leading to the clearance of pseudotyped and authentic virus in vitro. Furthermore, the Nanotraps demonstrated excellent biosafety profile in vitro and in vivo. Finally, the Nanotraps inhibited pseudotyped SARS-CoV-2 infection in live human lungs in an ex vivo lung perfusion system. In summary, Nanotraps represent a new nanomedicine for the inhibition of SARS-CoV-2 infection. HighlightsO_LINanotraps block interaction between SARS-CoV-2 spike protein and host ACE2 receptors C_LIO_LINanotraps trigger macrophages to engulf and clear virus without becoming infected C_LIO_LINanotraps showed excellent biosafety profiles in vitro and in vivo C_LIO_LINanotraps blocked infection to living human lungs in ex vivo lung perfusion system C_LI Progress and PotentialTo address the global challenge of creating treatments for SARS-CoV-2 infection, we devised a nanomedicine termed "Nanotraps" that can completely capture and eliminate the SARS-CoV-2 virus. The Nanotraps integrate protein engineering, immunology, and nanotechnology and are effective, biocompatible, safe, stable, feasible for mass production. The Nanotraps have the potential to be formulated into a nasal spray or inhaler for easy administration and direct delivery to the respiratory system, or as an oral or ocular liquid, or subcutaneous, intramuscular or intravenous injection to target different sites of SARS-CoV-2 exposure, thus offering flexibility in administration and treatment. More broadly, the highly versatile Nanotrap platform could be further developed into new vaccines and therapeutics against a broad range of diseases in infection, autoimmunity and cancer, by incorporating with different small molecule drugs, RNA, DNA, peptides, recombinant proteins, and antibodies.

bioengineering↗

bioRxiv2019

Stereo In-Line Holographic Digital Microscope

Biologists use optical microscopes to study plankton in the lab, but their size, complexity and cost makes widespread deployment of microscopes in lakes and oceans challenging. Monitoring the morphology, behavior and distribution of plankton in situ is essential as they are excellent indicators of marine environment health and provide a majority of Earths oxygen and carbon sequestration. Direct in-line holographic microscopy (DIHM) eliminates many of these obstacles, but image reconstruction is computationally intensive and produces monochromatic images. By using one laser and one white LED, it is possible to obtain the 3D location plankton by triangulation, limiting holographic reconstruction to only the voxels occupied by the plankton, reducing computation by several orders of magnitude. The color information from the white LED assists in the classification of plankton, as phytoplankton contains green-colored chlorophyll. The reconstructed plankton images are rendered in a 3D interactive environment, viewable from a browser, providing the user the experience of observing plankton from inside a drop of water.

cell biology↗

bioRxiv2018

Replication of MERS and SARS coronaviruses in bat cells offers insights to their ancestral origins

Previous findings of Middle East Respiratory Syndrome coronavirus (MERS-CoV)-related viruses in bats, and the ability of Tylonycteris-BatCoV HKU4 spike protein to utilize MERS-CoV receptor, human dipeptidyl peptidase 4 hDPP4, suggest a bat ancestral origin of MERS-CoV. We developed 12 primary bat cell lines from seven bat species, including Tylonycteris pachypus, Pipistrellus abramus and Rhinolophus sinicus (hosts of Tylonycteris-BatCoV HKU4, Pipistrellus-BatCoV HKU5 and SARS-related-CoV respectively), and tested their susceptibilities to MERS-CoVs, SARS-CoV and human coronavirus 229E (HCoV-229E). Five cell lines, including P. abramus and R. sinicus but not T. pachypus cells, were susceptible to human MERS-CoV EMC/2012. However, three tested camel MERS-CoV strains showed different infectivities, with only two strains capable of infecting three and one cell lines respectively. SARS-CoV can only replicate in R. sinicus cells, while HCoV-229E cannot replicate in any bat cells. Bat dipeptidyl peptidase 4 (DPP4) sequences were closely related to those of human and non-human primates but distinct from dromedary DPP4 sequence. Critical residues for binding to MERS-CoV spike protein were mostly conserved in bat DPP4. DPP4 was expressed in the five bat cells susceptible to MERS-CoV, with significantly higher mRNA expression levels than those in non-susceptible cells (P=0.0174), supporting that DPP4 expression is critical for MERS-CoV infection in bats. However, overexpression of T. pachypus DPP4 failed to confer MERS-CoV susceptibility in T. pachypus cells, suggesting other cellular factors in determining viral replication. The broad cellular tropism of MERS-CoV should prompt further exploration of host diversity of related viruses to identify its ancestral origin.

microbiology↗