Ka Yee Yeung

Background: Vitamin K2 (K2) research has demonstrated that with sufficient K2 intake, vitamin K-dependent proteins are activated to regulate bone diseases and inflammation-related diseases, offering an innovative strategy to address ongoing musculoskeletal injury (MSKi) and high adiposity challenges that threaten health promotion, performance, and readiness in service members (SMs). Objective: This study aimed to characterize K2 status in a military sample and explore the relationship between dietary K2 intake and bone health, metabolic health, and inflammation status in the context of SM health and operational readiness or deployability. Methods: At baseline and 12 months, Army and Air Force units provided data from demographic and food frequency questionnaires, body composition, bone density, cardiometabolic, and biomarker measurements. Activity trackers monitored physical activity and sleep duration continuously. Results: Between August 2023 and July 2024, we enrolled 47 active duty SMs. The sample was predominantly male, racially and ethnically diverse, averaged 27 years of age, and had an average of 6.5 years of service. At baseline, 45% met the criteria for stage I hypertension and 45% exceeded military body fat standards. Participants reported insufficient baseline dietary intake of vitamin K1+K2 and vitamin D. Over the 12 months, fermented food consumption slightly increased, activity behaviors remained unchanged, and participants self-reported 10 noncombat MSKis. Participants reported no significant changes in body composition, body fat specifically, or dietary intake of vitamin D or vitamin K2. Exploratory proteomics analysis identified links between K2 and inflammation, bone architecture, glucose homeostasis, and insulin receptors. Discussion: MSKi, high body fat, and meeting optimal nutrition goals remain readiness challenges for SMs. Exploring K2 identified meaningful metrics and actionable strategies to address these challenges. Highlighting data-informed interventions adds to nursing’s nutritional and cardiometabolic knowledge base and clinical interventions, as the nursing workforce is positioned to influence every step of patient care: conducting research, generating data to inform clinical practice and interventions, and ensuring evidence-based patient education and counseling to optimize SM health and readiness.

We present an interactive Data Integrator to facilitate uniform data processing and integration with datasets generated by the NIH funded MorPhiC (Molecular Phenotypes of Null Alleles in Cells) program. In the dataset identification step, users can select published bulk RNA sequencing (RNA-seq) datasets generated by the MorPhiC program, specify external datasets from the Gene Expression Omnibus (GEO) repository, or upload raw RNA-seq data. Subsequently, users can select a subset of samples and indicate experimental design by assigning groups to samples. External data will then be processed using the MorPhiC approved bulk RNA-seq analytical pipeline, and differential expression will be inferred. Users also have the option to download the uniformly processed counts table.

Single-cell RNA sequencing (scRNA-seq) has become a routine method for measuring cell activities. We present a novel and generalizable methodology using serverless cloud computing to accelerate computationally intensive workflows. We create an on-demand “supercomputer” using rapidly deployable cloud serverless functions as automatically provisioned computation units. We tested our methodology of optimizing an scRNA-seq workflow by leveraging serverless functions on the cloud using two publicly available peripheral blood mononuclear cell (PBMC) datasets. In addition, we demonstrate our approach using a 450 GB human scRNA-seq knockout dataset, comprising 13 samples from different developmental time points, designed to study the temporal impact of perturbations on pancreatic differentiation. We compared the execution time of the scRNA-seq serverless workflow with an optimized workflow without serverless functions running on identical hardware, and demonstrate speedups for all tested datasets, reaching 7.0-fold for the largest dataset. Our software is open source and distributed under the MIT license. Code and documentation are publicly available at https://github.com/BioDepot/scRNA-serverless.

Spatial proteomics provides a spatially resolved view of protein expression and localization within cells and tissues by mapping the location and abundance of proteins. There is a need for fully-integrated end-to-end imaging workflows for spatial proteomic analysis that are flexible, reproducible, and support graphical and interactive visualizations. We present a modular and interactive spatial proteomic image analysis workflow with individual containerized steps that empowers biomedical researchers to reproducibly execute and customize complex analyses. Our workflow consists of cell segmentation, unsupervised clustering with optional batch correction, validation of clusters on the image, and cell type clustering results visualization. A form-based graphical interface can be utilized to execute and customize multi-step workflows with a single click or interactively adjust image processing steps within the workflow, apply workflows to various datasets, and modify input parameters as needed. We illustrated the functionality of our workflow using human normal tonsil and colorectal cancer tissues stained by high-plex immunohistochemistry.

The exponential growth of omics data requires novel strategies for storage, transfer, and processing of said data. We present a scheduler based on the Temporal.io workflow framework which enables two key optimizations of bioinformatics workflows. Firstly, we enable users to transparently map workflow steps to diverse execution environments, including high-performance computing (HPC) resources managed by the SLURM resource manager through an easy-to-use graphical user interface. Secondly, we enable asynchronous execution of workflows, a feature which guarantees that workflows will achieve reasonable resource utilization even when the scheduler cannot make use of a system’s full RAM and CPU resources. Thirdly, we propose a universal, platform agnostic JSON representation of workflows that allows platform-specific execution details to be abstracted away from the core scientific logic. Our work includes a custom executor plugin that supports translation of workflows from an external language, such as Nextflow, to our universal JSON format. Finally, we develop a graphical user interface to make our scheduler easy-to-use for non-technical users. When benchmarked on a bulk RNA sequencing workflow, these features reduced the cost and time requirements. We illustrated the merits of our cross-platform method using credit allocations from federally funded supercomputers.

Background: Post-acute sequelae of SARS-CoV-2 infection (PASC) affects millions globally, yet the molecular mechanisms underlying acute COVID-19 and its chronic sequelae remain poorly understood. Methods: We performed an integrative transcriptomic analysis of three independent RNA-seq datasets, capturing the complete COVID-19 pathophysiology from health through acute severe infection to post-acute sequelae and mortality (n=142 total samples). We implemented a containerized analytical pipeline from data download, quantification, differential gene expression to uniformly process these three RNA-seq datasets. Results: Our analysis reveals striking molecular dichotomies contrasting disease phases with profound clinical implications. Acute severe/critical COVID-19 reveals predominant enrichment of TNF-α signaling via NF-κB pathways (normalized enrichment score >2.5, FDR 2.2, FDR <0.005), indicating important shifts toward cellular adaptation. Pathway signature analysis identifies core differentially expressed genes that reliably distinguish disease phases, thereby offering objective biomarkers for precision diagnosis and monitoring. Conclusions: These findings establish a comprehensive molecular framework distinguishing acute inflammatory from chronic metabolic COVID-19 phases, with potential clinical applicability. TNF-α/NF-κB pathway signatures identify patients at risk for severe disease progression, while Myc/OXPHOS signatures allow objective PASC diagnosis, addressing current reliance on subjective and eliminative diagnosis. This integrative analytical framework has utility beyond COVID-19, offering an applicable approach for precision medicine implementation across other diseases processes. Clinical Significance: This study transforms COVID-19 from a symptom-based to a molecularly-defined disease spectrum, enabling precision diagnosis, prognostic monitoring, classification, and targeted therapeutic possibilities based on pathway-specific biomarkers rather than subjective clinical assessments.

Spatial proteomics provides a spatially resolved view of protein expression and localization within cells and tissues by mapping the location and abundance of proteins. There is a need for fully-integrated end-to-end imaging workflows for spatial proteomic analysis that are flexible, high-throughput, and support graphical and interactive visualizations. We present a modular and interactive spatial proteomic image analysis workflow with individual steps containerized that empowers biomedical researchers to reproducibly execute and customize complex analyses. Our workflow consists of cell segmentation, unsupervised clustering, validation of clusters on the image, and cell type clustering results visualization. Users can utilize a form-based graphical interface to execute and customize multi-step workflows with a single click or interactively adjust image processing steps within the workflow, apply workflows to various datasets, and modify input parameters as needed. We illustrated the functionality of our workflow using a cancer imaging dataset consisting of a tissue microarray (TMA) stained by high-plex immunohistochemistry. This TMA contained a variety of cancer and tissue cell types to assess the broad applicability of this workflow to different biopsy and tissue types.

Singe cell RNA sequencing (scRNA-seq) has become a routine method for measuring cell activities. Processing large scRNA-seq datasets requires high-performance computing resources. The emergence of cloud computing allows us to leverage its on-demand capabilities without major investment in infrastructure. Serverless computing provides cost efficiency by allowing users to pay only for actual resource usage, eliminating the necessity for pre-allocated server capacities. Additionally, there is no requirement to set up servers in advance. We present a novel and generalizable methodology using serverless cloud computing to accelerate computationally intensive workflows. We create an on-demand “supercomputer” using rapidly deployable cloud serverless functions as automatically provisioned computation units. We tested our methodology of optimizing a scRNA-seq workflow by leveraging serverless functions on the cloud using two publicly available peripheral blood mononuclear cell (PBMC) datasets. In addition, we demonstrate our approach using data generated by the NIH MorPhiC program, where we process a 450 GB human scRNA-seq dataset across 86 cell lines designed to study the temporal impact of perturbations on pancreatic differentiation. We compared the total execution time of the scRNA-seq serverless workflow with the traditional workflow without using serverless functions, and demonstrate major speedup for large scRNA-seq datasets.

Cancer data is widely available in repositories such as the National Cancer Institute (NCI) Genomic Data Commons (GDC). These datasets could serve as controls or comparisons in compendium analyses with user data, avoiding the expense and time of generating additional datasets. However, the user must be able to process their new data in the same manner for these comparisons to be useful. This can be non-trivial. Although the executables themselves are usually available in repositories, the GDC pipelines that describe that entire analysis workflow are currently published as text-based standard operating procedures (SOPs). It is difficult to document a computational workflow to the level of detail and accuracy required to reproduce the results. Discrepancies between versions and exclusions of details accumulate as the documentation inevitably lags behind code revisions. Our goal is to enhance the utility of the GDC by converting the SOPs into an accessible and executable format. Specifically, we converted the GDC DNA sequencing (DNA-Seq) and the GDC mRNA sequencing (mRNA-Seq) SOPs into reproducible, self-installing, containerized, and interactive graphical workflows. These can be applied to reproducibly process user data and to harmonize datasets across repositories. Using our publicly available graphical workflows, we harmonize raw RNA-Seq datasets from the GDC and the Genotype-Tissue Expression (GTEx) project that were originally processed using different methodologies to illustrate the importance of uniform processing of control and treatment data for accurate inference of differentially expressed genes. By disseminating the analytical methodology in a reproducible and executable form, we greatly increase the utility of the GDC by enabling researchers to uniformly process custom data and datasets across multiple repositories to enhance data interpretation. Our approach and open-source executable workflows of making the analytical process as readily available as the data can be applied to other data repositories to increase their impact on scientific research.

Recent advances in functional genomics and human cellular models have substantially enhanced our understanding of the structure and regulation of the human genome. However, our grasp of the molecular functions of human genes remains incomplete and biased towards specific gene classes. The Molecular Phenotypes of Null Alleles in Cells (MorPhiC) Consortium aims to address this gap by creating a comprehensive catalogue of the molecular and cellular phenotypes associated with null alleles of all human genes using in vitro multicellular systems. In this Perspective, we present the strategic vision of the MorPhiC Consortium and discuss various strategies for generating null alleles, as well as the challenges involved. We describe the cellular models and scalable phenotypic readouts that will be used in the consortium’s initial phase, focusing on 1,000 protein-coding genes. The resulting molecular and cellular data will be compiled into a catalogue of null-allele phenotypes. The methodologies developed in this phase will establish best practices for extending these approaches to all human protein-coding genes. The resources generated—including engineered cell lines, plasmids, phenotypic data, genomic information and computational tools—will be made available to the broader research community to facilitate deeper insights into human gene functions.