Biology subjects

Yuen, D.

Publications and source records attributed to Yuen, D..

6 recordsLinked to original sources

bioRxiv2026

The GA4GH Workflow Execution Service: A Standard API for Workflow Execution and Management in Genomics

Rapid growth in life science data generation has led to large and increasingly distributed collections of sensitive genomic data. To analyse this data, researchers increasingly rely on computational workflows to enable scalable, portable and reproducible analysis. However, these workflows are executed by specialized workflow engines across heterogeneous compute environments, each with its own requirements. As a result, users often need engine- and compute-environment-specific knowledge to submit, monitor and manage workflow execution, increasing the barriers to interoperability. These challenges are compounded in large-scale projects, where workflow runs must be dispatched to local clusters, cloud platforms or data hosting infrastructures with different underlying technologies. We describe the Global Alliance for Genomics and Health (GA4GH) Workflow Execution Service (WES) API, an approved GA4GH standard first released in 2018, together with its data model and the endpoints that define a unified interface for workflow execution. By standardizing workflow invocation, the WES API allows a single client to submit, monitor and cancel workflow runs across diverse workflow engines and compute backends, such as scheduling clusters or cloud platforms. We surveyed thirteen adoptions of the standard, covering standalone services and platforms in four programming languages. In a two-site demonstration, a WES implementation from Germany and one from Japan executed the same federated allele-frequency analysis with site-specific adjustments and only aggregated results leaving either site, demonstrating that it is possible to run the same analysis across independently built and operated WES services in different countries. From the gaps identified, we propose extensions for community discussion.

bioinformatics↗

bioRxiv2026

Long-read sequencing quantifies synthetic mRNA abundance, integrity and host response in vivo

Synthetic mRNA can be used to reprogram biological systems and is increasingly used in vaccines, gene therapies and other advanced therapeutics. However, measuring mRNA abundance, molecular integrity and biological effects in complex samples remains challenging. Existing assays typically quantify short transcript regions or infer delivery from lipid or protein readouts. Here we present a long-read nanopore sequencing method that directly quantifies synthetic mRNA in complex cell and tissue samples. The approach enables absolute quantification of full-length synthetic mRNA, maps degradation at nucleotide resolution and simultaneously profiles associated host transcriptional responses. Applied to lipid nanoparticle (LNP) delivered mRNA in mice, the method revealed tissue-specific delivery and degradation patterns and uncovered a critical disconnect between mRNA accumulation and protein expression across organs. This approach enables integrated measurement of mRNA fate, integrity and biological responses, and will enable mechanistic studies of RNA delivery, stability, translation and innate immune recognition.

pharmacology and toxicology↗

bioRxiv2024

A Versatile Antibody Capture System that Drives Precise In Vivo Delivery of mRNA loaded Lipid Nanoparticles and Enhances Gene Expression

Efficient and precise delivery of mRNA is critical to advance mRNA therapies beyond their current use as vaccines. Lipid nanoparticles (LNP) efficiently encapsulate and protect mRNA, but non-specific cellular uptake may lead to off-target delivery and minimal delivery to target cells. Functionalizing LNPs with antibodies enables targeted mRNA delivery, but traditional modification techniques require complex conjugation and purification, which often reduces antibody affinity. Here, we present a simple method for capturing antibodies in their optimal orientation on LNPs, without antibody modification or complex purification. This strategy uses an optimally oriented anti-Fc nanobody on the LNP surface to capture antibodies, resulting in protein expression levels >1000 times higher than non-targeted LNPs and >8 times higher than conventional antibody functionalization techniques. These precisely targeted LNPs showed highly efficient in vivo targeting to T cells, with minimal delivery to other immune cells. This approach enables the rapid development of targeted LNPs and has the potential to broaden the use of mRNA therapies.

bioengineering↗

bioRxiv2020

Unravelling cytosolic delivery of endosomal escape peptides with a quantitative endosomal escape assay (SLEEQ)

Endosomal escape is an essential requirement but a major obstacle to efficient delivery of therapeutic peptides, proteins and nucleic acids. Current understanding of endosomal escape mechanisms remains limited due to significant number of conflicting reports, which are compounded by low sensitivity and indirect assays. To resolve this, we developed a highly sensitive Split Luciferase Endosomal Escape Quantification (SLEEQ) assay to probe mechanisms of cytosolic delivery. We applied SLEEQ to evaluate the endosomal escape of a range of widely studied putative endosomal escape peptides (EEPs). We demonstrated that positively-charged EEPs enhanced cytosolic delivery as a result of increased non-specific cell membrane association, rather than increased endosomal escape efficiency. These findings transform our current understanding of how EEPs increase cytosolic delivery. SLEEQ is a powerful tool that addresses fundamental questions in intracellular drug delivery and will significantly improve the way materials are engineered to increase therapeutic delivery to the cytosol.

cell biology↗

bioRxiv2019

SNAPSwitch: A Molecular Sensor to Quantify the Localization of Proteins, DNA and Nanoparticles in Cells

Intracellular trafficking governs receptor signalling, pathogenesis, immune responses and the cellular fate of nanomedicines. These processes are typically tracked by confocal microscopy, where colocalization of fluorescent markers implies an interaction or co-compartmentalization. However, this type of analysis is inherently low-throughput, is limited by the resolution of microscopy, and can miss fleeting interactions. To address this, we have developed a localization sensor composed of a quenched and attachable SNAP-tag substrate (SNAPSwitch). SNAPSwitch enables quantitative detection of protein, nucleic acid and nanoparticle trafficking to locations of interest within live cells using flow cytometry. Using this approach, we followed the trafficking of DNA complexes travelling from endosomes into the cytosol and to the nucleus. We also show that antibody targeted to the transferrin (CD71) or hyaluronan (CD44) receptor is initially sorted into different compartments following endocytosis. These results demonstrate SNAPSwitch is a high-throughput and broadly applicable tool to quantitatively track the localization of materials in cells.

cell biology↗

bioRxiv2017

Large-Scale Uniform Analysis of Cancer Whole Genomes in Multiple Computing Environments

The International Cancer Genome Consortium (ICGC)s Pan-Cancer Analysis of Whole Genomes (PCAWG) project aimed to categorize somatic and germline variations in both coding and non-coding regions in over 2,800 cancer patients. To provide this dataset to the research working groups for downstream analysis, the PCAWG Technical Working Group marshalled ~800TB of sequencing data from distributed geographical locations; developed portable software for uniform alignment, variant calling, artifact filtering and variant merging; performed the analysis in a geographically and technologically disparate collection of compute environments; and disseminated high-quality validated consensus variants to the working groups. The PCAWG dataset has been mirrored to multiple repositories and can be located using the ICGC Data Portal. The PCAWG workflows are also available as Docker images through Dockstore enabling researchers to replicate our analysis on their own data.

genomics↗