Biology subjects

Ching, T.

Publications and source records attributed to Ching, T..

8 recordsLinked to original sources

bioRxiv2026

PROPEL: a high-throughput shear-stress platform reveals organotypic thresholds in endothelial mechano-adaptation

Endothelial cells exhibit organotypic specialization, yet the tools to decode whether hemodynamic shear shapes this diversity across vascular beds have remained limited. Here we present PROPEL, a tubing-free, magnetic stirrer-driven platform that delivers programmable laminar shear stress (2 to 60 dyn/cm{superscript 2}) to multiple cell types in parallel within the confines of a standard Petri dish, with the modular flexibility to easily introduce different substrate geometries including 3D vessel formats. Using this platform, we profiled six human endothelial subtypes across static, low, intermediate, and high shear. Phenotypic profiling revealed subtype-specific thresholds for alignment, elongation, and Golgi-nuclear polarization, including a dissociation between elongation and polarization in dermal microvascular and saphenous venous endothelial cells at low shear. Bulk RNA sequencing showed conserved transcriptional programs that shift progressively with shear magnitude, including induction of mechanotransduction pathways alongside suppression of proliferative programs. Phenotype-informed transcriptional comparisons further linked alignment transitions to engagement of cytoskeletal and metabolic signatures. In addition to conserved responses, organotypic-specific responses were also observed. Together, these findings establish PROPEL as a scalable platform for multi-endothelial, multi-shear transcriptomic and phenotypic profiling, and reveal that endothelial cells engage shear adaptation along two organotypic axes--a threshold axis governing when a subtype responds and a signature axis governing which the shear stress response--with implications for vascular bed-specific disease susceptibility.

cell biology↗

bioRxiv2023

Organ-on-a-Chip Fabrication using Dynamic Photomask

Organ-on-a-chip (OoC) technology is a powerful tool for creating physiologically relevant microscale models applicable to biomedical studies. Despite the advances in OoC technology, its fabrication method still primarily relies on soft lithography, which (1) lacks the adaptability to accommodate dynamic cell culture (e.g., spheroids and organoid culture) and (2) has a long design-to-prototype cycle that lowers its manufacturability. To overcome these challenges, we developed a system to fabricate OoC (consisting of microchannels and multiple cell types in a well-defined spatial arrangement) dynamically using a digital photomask aligned with a microchamber. Our approach used a pre-defined microfluidic chamber customized by xurography and cell-laden microfluidic channels photopatterned by a digital photomask; the entire design-to-prototype cycle was achieved within two hours. The versatility of our approach offered previously unattainable crucial features in the fabrication of OoC, including a gradual change in the height of the microchannels, and real-time modification of channel designs to trap live tissues (e.g., spheroids). In summary, this work highlights a versatile system to fabricate OoC that can accommodate various design requirements of microenvironments of specific organ tissues. We envision the effectiveness of our system for the rapid fabrication of OoC to contribute to the wide adoption of the technology for therapeutic screening and elucidation of disease mechanisms in both academic and industrial settings.

bioengineering↗

bioRxiv2023

3D Bioprinting Using Poly(ethylene-glycol)-dimethacrylate (PEGDMA) Composite

Recent progress in additive manufacturing has enabled rapid printing of bioinspired structures such as vasculature and alveoli using stereolithography (SLA) bioprinting. Bioinks for SLA often require synthetic polymers as additives to ensure the structural integrity of the printed cell-laden constructs. To this end, high molecular weight (MW) poly(ethylene-glycol)- diacrylate (PEGDA) (MW = 3400) is commonly used to enhance the mechanical property of crosslinked hydrogels, which requires in-house polymer synthesis or the acquisition of costly reagents. This research investigated the use of poly(ethylene-glycol)-dimethacrylate (PEGDMA) (MW = 1000) as a component of a composite bioink to enhance the mechanical properties of the SLA-printed constructs. We successfully demonstrated the fabrication of three-dimensional (3D) constructs with overhang and complex architecture, while human colorectal cancer cells (Caco-2) embedded in the crosslinked bioink exhibited the capability to proliferate on Day 6 of in vitro cell culture. Our study suggested PEGDMA as a viable alternative to high MW PEGDA used in SLA bioprinting. The accessibility to PEGDMA will facilitate the advance in 3D bioprinting to fabricate complex bioinspired structures and tissue surrogates for biomedical applications. Article HighlightsO_LIPoly(ethylene-glycol)-dimethacrylate (PEGDMA) can be used in cell-laden bioprinting to enhance the mechanical property of bioinks. C_LIO_LIPEGDMA-based bioink was non-cytotoxic and conducive to cell proliferation. C_LIO_LIThe facile preparation of PEGDMA composite ink will help to accelerate the research in tissue engineering via bioprinting. C_LI

bioengineering↗

bioRxiv2021

Biomimetic Vasculatures by 3D-Printed Porous Molds

Anatomically and biologically relevant vascular models are critical to progress our understanding of cardiovascular diseases (CVDs) that can lead to effective therapies. Despite advances in 3D bioprinting, recapitulating complex architectures (i.e., freestanding, branching, multilayered, perfusable) of a cell-laden vascular construct remains technically challenging, and the development of new techniques that can recapitulate both anatomical and biological features of blood vessels is of paramount importance. In this work, we introduce a unique, microfluidics-enabled molding technique that allows us to fabricate anatomically-relevant, cell-laden hydrogel vascular models. Our approach employed 3D-printed porous molds of poly(ethylene glycol) diacrylate (PEGDA) as templates to cast alginate-containing bioinks. Due to the porous and aqueous nature of the PEGDA mold, the calcium ion (Ca2+) was diffusively released to crosslink the bioinks to create hollow structures. Applying this technique, multiscale, multilayered vascular constructs that were freestanding and perfusable were readily fabricated using cell-compatible bioinks (i.e., alginate and gelatin methacryloyl (GelMA)). The bioinks were also readily customizable to either improve the compatibility with specific vascular cells or tune the mechanical modulus to mimic native blood vessels. Importantly, we successfully integrated smooth muscle cells and endothelial cells in a biomimetic organization within our vessel constructs and demonstrated a significant increase in monocyte adhesion upon stimulation with an inflammatory cytokine, tumor necrosis factor-alpha (TNF-). We also demonstrated that the fabricated vessels were amenable for testing percutaneous coronary interventions (i.e., drug-eluting balloons and stents) under physiologically-relevant mechanical states, such as vessel stretching and bending. Overall, we introduce a versatile fabrication technique with multi-faceted possibilities of generating biomimetic vascular models that can benefit future research in mechanistic understanding of CVD progression and the development of therapeutic interventions.

bioengineering↗

bioRxiv2017

Phenotypic associations of consensus driver genes in hepatocellular carcinoma

Although driver genes in hepatocellular carcinoma (HCC) have been investigated in various previous genetic studies, prevalence of key driver genes among heterogeneous populations is unknown. Moreover, the phenotypic associations of these driver genes are poorly understood. This report aims to reveal the phenotypic impacts of a group of consensus driver genes in HCC. We used MutSigCV and OncodriveFM modules implemented in the IntOGen pipeline to identify consensus driver genes across six HCC cohorts comprising 1,494 samples in total. To access their global impacts, we used TCGA mutations and copy number variations to predict the transcriptomics data, under generalized linear models. We further investigated the associations of the consensus driver genes to patient survival, age, gender, race and risk factors. We identify 10 consensus driver genes across six HCC cohorts in total. Integrative analysis of driver mutations, copy number variations and transcriptomic data reveals that these consensus driver mutations and their copy number variations are associated with majority (62.5%) of the mRNA transcriptome, but only a small fraction (8.9%) of miRNAs. Genes associated with TP53, CTNNB1, and ARID1A mutations contribute to the tripod of most densely connected pathway clusters. These driver genes are significantly associated with patients overall survival. Some driver genes are significantly linked to HCC gender (CTNNB1, ALB, TP53 and AXIN1), race (TP53 and CDKN2A), and age (RB1) disparities. This study prioritizes a group of consensus drivers in HCC, which collectively show vast impacts on the phenotypes. These driver genes may warrant as valuable therapeutic targets of HCC.

bioinformatics↗

bioRxiv2017

Opportunities And Obstacles For Deep Learning In Biology And Medicine

Deep learning, which describes a class of machine learning algorithms, has recently showed impressive results across a variety of domains. Biology and medicine are data rich, but the data are complex and often ill-understood. Problems of this nature may be particularly well-suited to deep learning techniques. We examine applications of deep learning to a variety of biomedical problems--patient classification, fundamental biological processes, and treatment of patients--and discuss whether deep learning will transform these tasks or if the biomedical sphere poses unique challenges. We find that deep learning has yet to revolutionize or definitively resolve any of these problems, but promising advances have been made on the prior state of the art. Even when improvement over a previous baseline has been modest, we have seen signs that deep learning methods may speed or aid human investigation. More work is needed to address concerns related to interpretability and how to best model each problem. Furthermore, the limited amount of labeled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning powering changes at both bench and bedside with the potential to transform several areas of biology and medicine.

bioinformatics↗

bioRxiv2016

Using Single Nucleotide Variations in Cancer Single-Cell RNA-Seq Data for Subpopulation Identification and Genotype-phenotype Linkage Analysis

Despite its popularity, characterization of subpopulations with transcript abundance is subject to a significant amount of noise. We propose to use effective and expressed nucleotide variations (eeSNVs) from scRNA-seq as alternative features for tumor subpopulation identification. We developed a linear modeling framework, SSrGE, to link eeSNVs associated with gene expression. In all the datasets tested, eeSNVs achieve better accuracies than gene expression for identifying subpopulations. Previously validated cancer-relevant genes are also highly ranked, confirming the significance of the method. Moreover, SSrGE is capable of analyzing coupled DNA-seq and RNA-seq data from the same single cells, demonstrating its value in integrating multi-omics single cell techniques. In summary, SNV features from scRNA-seq data have merits for both subpopulation identification and linkage of genotype-phenotype relationship. The method SSrGE is available at https://github.com/lanagarmire/SSrGE.

bioinformatics↗

bioRxiv2016

Cox-nnet: an artificial neural network Cox regression for prognosis prediction

Artificial neural networks (ANN) are computing architectures with massively parallel interconnections of simple neurons and has been applied to biomedical fields such as imaging analysis and diagnosis. We have developed a new ANN framework called Cox-nnet to predict patient prognosis from high throughput transcriptomics data. In over 10 TCGA RNA-Seq data sets, Cox-nnet achieves a statistically significant increase in predictive accuracy, compared to the other three methods including Cox-proportional hazards (Cox-PH), Random Forests Survival and CoxBoost. Cox-nnet also reveals richer biological information, from both pathway and gene levels. The outputs from the hidden layer node can provide a new approach for survival-sensitive dimension reduction. In summary, we have developed a new method for more accurate and efficient prognosis prediction on high throughput data, with functional biological insights. The source code is freely available at github.com/lanagarmire/cox-nnet.

bioinformatics↗