Biology subjects

Wu, Y.-L.

Publications and source records attributed to Wu, Y.-L..

13 recordsLinked to original sources

bioRxiv2026

UELer: a Jupyter-based framework for interactive exploration of multiplexed imaging datasets

Summary Multiplexed imaging and spatial proteomics generate complex datasets that require both computational analysis and visual inspection. However, these tasks mostly occur in separate environments because interactive viewers generally require a local display or an additional data server beyond the remote Jupyter sessions itself where large datasets are computationally analyzed. We here present UELer, an interactive viewer that links multi-channel image views with quantitative analysis results directly within Jupyter notebooks, requiring no dedicated infrastructure beyond the notebook session. Cells selected through computational analysis and summary plots can be inspected directly in their tissue context, and selections made in the image can be made available to any downstream analysis. Together, these capabilities support interactive data exploration, iterative cell annotation, and reproducible retrieval of selected regions. Availability and Implementation UELer is a Python package built on ipywidgets and runs in Jupyter environments supporting ipywidgets 8.1 or later, tested in JupyterLab and Visual Studio Code on Linux, macOS, and Windows. It is freely available under GPL-3.0 license and can be installed via pip. Source code and documentation are available at https://github.com/HartmannLab/UELer and https://hartmannlab.github.io/UELer/. An online, no-install version runs remotely via BinderHub (https://mybinder.org/v2/gh/HartmannLab/UELer/main), accessible through the script/run_ueler_binder.ipynb notebook.

bioinformatics↗

bioRxiv2026

Cell-of-Origin, not Oncogenic Effect, Determines esmoplastic Immune Exclusion in KRAS-Driven Liver Cancer

Intrahepatic cholangiocarcinoma (iCCA) and hepatocellular carcinoma (HCC) are the two most common primary liver cancers and share common risk factors. Yet they exhibit distinct oncogenic driver landscapes and fundamentally different tumor microenvironments (TME), with iCCA characterised by dense desmoplastic stroma that limits therapeutic efficacy. Whether these differences reflect oncogenic context or the developmental lineage of the cancer cell has remained unresolved. Here, using syngeneic orthotopic murine models derived from CRISPR-engineered cholangiocyte and hepatocyte organoids each carrying Trp53 deletion and KrasG12D mutation, we show that cell-of-origin, not oncogenic pathway activation, is the dominant determinant of TME architecture. Spatial proteomics of [~]390,000 cells reveals that cholangiocyte-derived tumors develop a stromal barrier of peripherally enriched SMA+ cancer-associated fibroblasts (CAFs) that physically excludes immune cells and elevates PD-1/PD-L1 engagement, whereas hepatocyte derived tumors permit broader immune infiltration. Transcriptional variance partitioning confirms lineage as the primary source of gene expression divergence. Integrating murine and human transcriptomic and secretomic datasets, we identify LAMC2 and uPA as cholangiocyte lineage-specific secreted factors that trigger CAF activation. Genetic deletion of either factor markedly impairs iCCA formation in vivo. These findings establish that lineage-encoded secretory programmes create a desmoplastic and immune-excluded stroma and identify LAMC2 and uPA as functionally relevant modulators of TME in KRAS-driven iCCA.

cancer biology↗

bioRxiv2025

Multi-step implementation of meiotic crossover patterning

Crossover formation during meiosis is a tightly controlled process in which genetic information is exchanged between homologous chromosomes to increase the diversity of the progeny. In this process, an excess of double-strand breaks is introduced, but only a limited subset is ultimately processed into crossovers. Imbalances in the distribution of crossovers can lead to errors in chromosome segregation, with devastating consequences on the health of the progeny. However, the selection of which breaks are designated to become crossovers is still poorly understood, as both its timing and the ultimate molecular mechanisms are under debate. Here, we used 3D dual-color single-molecule localization microscopy and real-time confocal imaging, combined with advanced image analysis, to investigate the timing and mechanism of crossover designation in C. elegans. We show that meiotic crossover patterning is not established by a single decision point but depends on a dynamic, multi-layered regulation process. An initial, early selection process restricts potential crossovers to a small subset of double-strand break sites that already exhibit basic patterning features, including assurance and interference. A second, later step fine-tunes this pattern to ultimately ensure genome integrity and promote accurate chromosome segregation. Real-time imaging reveals that although the full process takes more than seven hours, key molecular events occur within minutes, high-lighting how rapid local dynamic changes can give rise to an overall slow but extremely robust crossover regulation program.

cell biology↗

bioRxiv2024

Novel naphthyridones targeting Pannexin 1 for colitis management

Pannexin 1 (PANX1) forms cell-surface channels capable of releasing signaling metabolites for diverse patho-physiological processes. While inhibiting dysregulated PANX1 is proposed as a therapeutic strategy for many pathological conditions, including inflammatory bowel disease (IBD), low efficacy or poor specificity of classical PANX1 inhibitors introduces uncertainty for their applications in basic and translational research. Here, we performed hit-to-lead optimization and identified a naphthyridone, compound 12, as a new PANX1 inhibitor with an IC50 of 0.73 M that does not affect pannexin-homologous LRRC8/SWELL1 channels. Using structure-activity relationship analysis, mutagenesis, cell thermal shift assays, and molecular docking, we revealed that compound 12 directly engages PANX1 Trp74 residue. Using a dextran sodium sulfate mouse model of IBD, we found that compound 12 markedly reduced colitis severity, highlighting new PANX1 inhibitors as a proof-of-concept treatment for IBD. These data describe the mechanism of action for a new PANX1 inhibitor, identify the binding site for future drug design, and present a targeted strategy for treating IBD. TeaserA specific PANX1 inhibitor presents a proof-of-concept treatment for inflammatory bowel disease.

molecular biology↗

bioRxiv2024

StomaVision: stomatal trait analysis through deep learning

StomaVision is an automated tool designed for high-throughput detection and measurement of stomatal traits, such as stomatal number, pore size, and closure rate. It provides insights into plant responses to environmental cues, streamlining the analysis of micrographs from field-grown plants across various species, including monocots and dicots. Enhanced by a novel collection method that utilizes video recording, StomaVision increases the number of captured images for robust statistical analysis. Accessible via an intuitive web interface at and available for local use in a containerized environment at , this tool ensures long-term usability by minimizing the impact of software updates and maintaining functionality with minimal setup requirements. The application of StomaVision has provided significant physiological insights, such as variations in stomatal density, opening rates, and total pore area under heat stress. These traits correlate with critical physiological processes, including gas exchange, carbon assimilation, and water use efficiency, demonstrating the tools utility in advancing our understanding of plant physiology. The ability of StomaVision to identify differences in responses to varying durations of heat treatment highlights its value in plant science research. Plain language summaryStomaVision is a tool that automatically counts and measures tiny openings on plant leaves, helping us learn how plants deal with their surroundings. It is easy to use and works well with various plant species. This tool helps scientists see how plants change under stress, making plant research easier and more accurate.

plant biology↗

bioRxiv2023

A Weakly Supervised U-Net Model for Precise Whole Brain Immunolabeled Cell Detection

Cell segmentations low precision due to the intensity differences hinders widespread use of whole brain microscopy imaging. Previous studies used ResNet or CNN to account for this problem, but are unapplicable to immunolabeled signals across samples. Here we present a semiauto ground truth generation and weakly-supervised U-Net-based Deep-learning precise segmentation pipeline for whole brain immunopositive c-FOS signals, which reveals the distinct neural activity maps with different social motivations.

neuroscience↗

bioRxiv2021

Nanoscale structural organization and stoichiometry of the budding yeast kinetochore

Proper chromosome segregation is crucial for cell division. In eukaryotes, this is achieved by the kinetochore, an evolutionarily conserved multi-protein complex that physically links the DNA to spindle microtubules and takes an active role in monitoring and correcting erroneous spindle-chromosome attachments. Our mechanistic understanding of these functions and how they ensure an error-free outcome of mitosis is still limited, partly because we lack a comprehensive understanding of the kinetochore structure in the cell. In this study, we use single-molecule localization microscopy to visualize individual kinetochore complexes in situ in budding yeast. For major kinetochore proteins, we measured their abundance and position within the metaphase kinetochore. Based on this comprehensive dataset, we propose a quantitative model of the budding yeast kinetochore. While confirming many aspects of previous reports based on bulk imaging, our results present a unifying nanoscale model of the kinetochore in budding yeast.

cell biology↗

bioRxiv2021

DNA-free CRISPR-Cas9 gene editing of tetraploid tomatoes using protoplast regeneration

Wild tomatoes are important genomic resources for tomato research and breeding. Development of a foreign DNA-free CRISPR-Cas delivery system has potential to mitigate public concern about genetically modified organisms. Here, we established a DNA-free protoplast regeneration and CRISPR-Cas9 genome editing system for Solanum peruvianum, an important resource for tomato introgression breeding. We generated mutants for genes involved in small interfering RNAs (siRNA) biogenesis, RNA-DEPENDENT RNA POLYMERASE 6 (SpRDR6) and SUPPRESSOR OF GENE SILENCING 3 (SpSGS3); pathogen-related peptide precursors, PATHOGENESIS-RELATED PROTEIN-1 (SpPR-1) and PROSYSTEMIN (SpProsys); and fungal resistance (MILDEW RESISTANT LOCUS O, SpMlo1) using diploid or tetraploid protoplasts derived from in vitro-grown shoots. The ploidy level of these regenerants was not affected by PEG-calcium-mediated transfection, CRISPR reagents, or the target genes. By karyotyping and whole genome sequencing analysis, we confirmed that CRISPR-Cas9 editing did not introduce chromosomal changes or unintended genome editing sites. All mutated genes in both diploid and tetraploid regenerants were heritable in the next generation. spsgs3 null T0 regenerants and sprdr6 null T1 progeny had wiry, sterile phenotypes in both diploid and tetraploid lines. The sterility of the spsgs3 null mutant was partially rescued, and fruits were obtained by grafting to wild-type stock and pollination with wild-type pollen. The resulting seeds contained the mutated alleles. Tomato yellow leaf curl virus proliferated at higher levels in spsgs3 and sprdr6 mutants than in the wild type. Therefore, this protoplast regeneration technique should greatly facilitate tomato polyploidization and enable the use of CRISPR-Cas for S. peruvianum domestication and tomato breeding. One-sentence summaryDNA-free CRISPR-Cas9 genome editing in wild tomatoes creates stable and inheritable diploid and tetraploid regenerants.

plant biology↗

bioRxiv2021

Superresolution microscopy reveals partial preassembly and subsequent bending of the clathrin coat during endocytosis

Eukaryotic cells use clathrin-mediated endocytosis to take up a large range of extracellular cargos. During endocytosis, a clathrin coat forms on the plasma membrane, but it remains controversial when and how it is remodeled into a spherical vesicle. Here, we use 3D superresolution microscopy to determine the precise geometry of the clathrin coat at large numbers of endocytic sites. Through pseudo-temporal sorting, we determine the average trajectory of clathrin remodeling during endocytosis. We find that clathrin coats assemble first on flat membranes to 50% of the coat area, before they become rapidly and continuously bent, and confirm this mechanism in three cell lines. We introduce the cooperative curvature model, which is based on positive feedback for curvature generation. It accurately describes the measured shapes and dynamics of the clathrin coat and could represent a general mechanism for clathrin coat remodeling on the plasma membrane.

cell biology↗

bioRxiv2021

Maximum-likelihood model fitting for quantitative analysis of SMLM data

Quantitative analysis is an important part of any single-molecule localization microscopy (SMLM) data analysis workflow to extract biological insights from the coordinates of the single fluorophores, but current approaches are restricted to simple geometries or do not work on heterogenous structures. Here, we present LocMoFit (Localization Model Fit), an open-source framework to fit an arbitrary model directly to the localization coordinates in SMLM data. Using maximum likelihood estimation, this tool extracts the most likely parameters for a given model that best describe the data, and can select the most likely model from alternative models. We demonstrate the versatility of LocMoFit by measuring precise dimensions of the nuclear pore complex and microtubules. We also use LocMoFit to assemble static and dynamic multi-color protein density maps from thousands of snapshots. In case an underlying geometry cannot be postulated, LocMoFit can perform single-particle averaging of super-resolution structures without any assumption about geometry or symmetry. We provide extensive simulation and visualization routines to validate the robustness of LocMoFit and tutorials based on example data to enable any user to increase the information content they can extract from their SMLM data.

biophysics↗

bioRxiv2019

Long-Term Outcomes of Pediatric Graves Disease Patients Treated with Anti-Thyroid Drugs: Experience from a Taiwan Medical Center

Graves disease (GD) is the most common cause of thyrotoxicosis in children and adolescents, accounting for 15% of all thyroid diseases during childhood. Anti-thyroid drugs (ATD) are recommended as the first-line treatment in children and adolescents. However, the remission rate is lower in children than in adults, and the optimal treatment duration and favorite factors associated with remission remain unknown. We aimed to investigate long-term outcomes of pediatric GD patients receiving ATD. We retrospectively reviewed medical charts of 300 pediatric GD subjects, who were initially treated with ATD and followed up for more than one year, from 1985 to 2017 at MacKay Childrens Hospital. The 300 patients comprised 257 (85.7%) females and 43 (14.3%) males, median age at diagnosis was 11.6 (range 2.7-17.8) years, and median follow-up period was 4.7 (range 1.1-23.9) years. Overall, 122 patients achieved the criteria for discontinuing ATD treatment, seventy-nine (39.9%) patients achieved remission, with a median follow-up period of 5.3 (range 1.5-20.1) years. Patients in the remission group were more likely to be aged < 5 years (remission vs. relapse vs. ongoing ATD; 11.4 vs. 0 vs. 2.6%, P=0.02), less likely to have a family history of thyroid disease (24.1 vs. 42.1 vs. 52.6 %, P=0.001), and had lower TRAb levels (42.8 vs. 53.6 vs. 65.1 %, P=0.02).\n\nConclusionLong-term ATD remains an effective treatment option for GD in children and adolescents. Pediatric GD patients aged < 5 years, having no family history of thyroid disease and having lower TRAb levels were more likely to achieve remission.

physiology↗

bioRxiv2019

Nuclear pores as versatile reference standards for quantitative superresolution microscopy

Quantitative fluorescence and superresolution microscopy are often limited by insufficient data quality or artifacts. In this context, it is essential to have biologically relevant control samples to benchmark and optimize the quality of microscopes, labels and imaging conditions.\n\nHere we exploit the stereotypic arrangement of proteins in the nuclear pore complex as in situ reference structures to characterize the performance of a variety of microscopy modalities. We created four genome edited cell lines in which we endogenously labeled the nucleoporin Nup96 with mEGFP, SNAP-tag or HaloTag or the photoconvertible fluorescent protein mMaple. We demonstrate their use a) as 3D resolution standards for calibration and quality control, b) to quantify absolute labeling efficiencies and c) as precise reference standards for molecular counting.\n\nThese cell lines will enable the broad community to assess the quality of their microscopes and labels, and to perform quantitative, absolute measurements.

biophysics↗

bioRxiv2019

Depth-dependent PSF calibration and aberration correction for 3D single-molecule localization

3D Single molecule localization microscopy relies on fitting of the individual molecules with a point spread function (PSF) model. The reconstructed images often show local squeezing or expansion in z. A common cause are depth-induced aberrations in conjunction with an imperfect PSF model calibrated from beads on a coverslip, resulting in a mismatch between measured PSF and real PSF. Here, we developed a strategy for accurate z-localization in which we use the imperfect PSF model for fitting, determine the fitting errors and correct for them in a post-processing step. We present an open-source software tool and a simple experimental calibration procedure that allow retrieving accurate z-positions in any PSF engineering approach or fitting modality, even at large imaging depths.

biophysics↗