Biology subjects

Ho, D.

Publications and source records attributed to Ho, D..

At least 19 recordsLinked to original sources

bioRxiv2026

Cell Cycle Sensing Shapes Human T Cell Fate and Exhaustion Programs

Cell cycle (CC) dynamics are reflected in diverse T cell processes such as TCR activation, expansion, contraction, differentiation, senescence, anergy, and exhaustion; linking CC behaviors to functional and dysfunctional T cell states in development and disease. Progression through CC checkpoints is also tightly linked to cell fate decisions across development. Yet, much remains unknown about the connection between CC sensing and T cell differentiation programs. To disentangle the relationship across T cell state, time-since-activation, receptor signaling, division, and CC, we leverage high-throughput single-cell mass cytometry for parallel measurement of these diverse biological states. By modulating CC progression and receptor signaling with inhibitors as well as tonic signaling Chimeric Antigen Receptor (CAR) models of T cell exhaustion, we reveal that earlier G1/S CC programs crosstalk with receptor signaling to control T cell fate, and that exhaustion programs are downstream to aberrant, S-G2 phase CC arrest signatures in tonic CAR signaling in vitro, in situ, and in vivo across human cancers in association with CD8 T-lymphocyte dysfunction.

immunology↗

bioRxiv2025

A deep single cell mass cytometry approach to capture canonical and noncanonical cell cycle states

The cell cycle (CC) is involved in diverse cell processes like cell differentiation, immune cell expansion, and tumorigenesis but current single-cell (sc) strategies study CC as: (i) coarse phases, (ii) are based on transcriptomic data, (iii) leverage imaging modalities that are not extensible outside adherent cell culture, or (iv) do not enable high-throughput, single-cell multiplexing. To solve this, we developed an expanded, metal-tagged antibody approach for Mass Cytometry (MC) with 48 CC-related molecules that more deeply phenotypes the diversity of scCC states. Using cytometry by time of flight (CyTOF), we quantified CC states across various suspension and adherent cell lines and stimulated primary human T cells. Our approach captures the diversity of scCC states, including atypical molecular signatures that do not meet the canonical definitions of CC phases in the literature. Multiplexing with pharmacologically-induced CC arrest reveals that drug-induced CC perturbations can exacerbate reported noncanonical states and induce previously unobserved states. Notably, cells escaping CC inhibitor action in primary cells demonstrated more aberrant CC states compared to their untreated counterparts. Overall, our approach enables deeper phenotyping of CC biology that generalizes to diverse cell systems and can simultaneously multiplex experimental perturbation & disease systems, or integrate with other MC measurement platforms.

cell biology↗

bioRxiv2025

TARPON - a Telomere Analysis and Research Pipeline Optimized for Nanopore

BackgroundLong-read sequencing has transformed many areas of biology and holds significant promise for telomere research by enabling analysis of nucleotide-level resolution chromosome arm-specific telomere length in both model organisms and humans. However, the adoption of new technologies, particularly in clinical or diagnostic contexts, requires careful validation to recognize potential technical and computational limitations. ResultsWe present TARPON (Telomere Analysis and Research Pipeline Optimized for Nanopore), a best-practices Nextflow pipeline designed for the analysis of telomeres sequenced on the Oxford Nanopore Technologies (ONT) platform. TARPON can be executed via the command line or integrated into ONTs EPI2ME agent, providing a user-friendly graphical interface for those without computational training. Nextflows container-based architecture eliminates dependency conflicts, thereby streamlining deployment across platforms. TARPON isolates telomeric repeat-containing reads, assigns strand specificity, and identifies enrichment probes that can be used both for demultiplexing and for confirming capture-based library preparation. To ensure that the analysis is restricted to full-length telomeres, reads lacking a capture probe or non-telomeric sequence on the opposite end are excluded. A sliding-window approach defines the subtelomere-to-telomere boundary, followed by quality filtering to remove low-quality or subtelomeric reads that passed earlier steps. The pipeline generates customizable statistics, text-based summaries, and publication-ready visualizations (HTML, PNG, PDF). While default settings are optimized for diagnostic workflows, all parameters are easily adjustable via the GUI or command line to support diverse applications. These include telomere analyses in variant-rich samples (e.g., ALT-positive tumors) and organisms with non-canonical telomeric repeats such as some insects (GTTAG) and certain plants (GGTTTAG). ConclusionsTARPON is the first complete and experimentally validated pipeline for Nanopore-based telomere analysis requiring no data pre-processing or prior bioinformatics expertise, while offering flexibility for advanced users.

bioinformatics↗

bioRxiv2024

Determining sex differences in drug combinations targeting aortic valve myofibroblast activation using an artificial intelligence derived platform

Aortic valve stenosis (AVS) is a sexually dimorphic disease where aortic valve leaflets develop fibrosis and calcification, leading to heart failure if untreated. Sex differences in AVS progression depend on valvular interstitial cells (VICs) activating to myofibroblasts that drive aberrant extracellular matrix remodeling. To date, no treatment strategies have leveraged cellular sex differences to determine drug combinations that effectively target VIC myofibroblast activation. Here, we harnessed IDentif.AI, an artificial intelligence (AI)-derived drug optimization platform, to optimize sex-specific synergistic drug combinations that may prevent and reverse VIC myofibroblast activation on hydrogel biomaterials. The results reveal that anti-fibrotic drug efficacy and combinatorial interactions are dependent on cell sex. This study provides a framework for developing clinically relevant AVS treatment strategies through the integration of high-throughput hydrogel cell culture platforms and AI-driven drug optimization. The workflow towards designing targeted AVS drug combinations may help accelerate AVS drug development for male and female patients and address health disparities in AVS treatment outcomes.

bioengineering↗

bioRxiv2024

Early life microbial succession in the gut follows common patterns in humans across the globe

Characterizing the dynamics of microbial community succession in the infant gut microbiome is crucial for understanding child health and development, but no normative model currently exists. Here, we estimate child age using gut microbial taxonomic relative abundances from metagenomes, with high temporal resolution ({+/-}3 months) for the first 1.5 years of life. Using 3,154 samples from 1,827 infants across 12 countries, we trained a random forest model, achieving a root mean square error of 2.61 months. We identified key taxonomic predictors of age, including declines in Bifidobacterium spp. and increases in Faecalibacterium prausnitzii and Lachnospiraceae. Microbial succession patterns are conserved across infants from diverse human populations, suggesting universal developmental trajectories. Functional analysis confirmed trends in key microbial genes involved in feeding transitions and dietary exposures. This model provides a normative benchmark of "microbiome age" for assessing early gut maturation that can be used alongside other measures of child development.

microbiology↗

bioRxiv2023

Unraveling human hematopoietic progenitor cell diversity through association with intrinsic regulatory factors

Hematopoietic stem and progenitor cell (HSPC) transplantation is an essential therapy for hematological conditions, but finer definitions of human HSPC subsets with associated function could enable better tuning of grafts and more routine, lower-risk application. To deeply phenotype HSPCs, following a screen of 328 antigens, we quantified 41 surface proteins and functional regulators on millions of CD34+ and CD34- cells, spanning four primary human hematopoietic tissues: bone marrow, mobilized peripheral blood, cord blood, and fetal liver. We propose more granular definitions of HSPC subsets and provide new, detailed differentiation trajectories of erythroid and myeloid lineages. These aspects of our revised human hematopoietic model were validated with corresponding epigenetic analysis and in vitro clonal differentiation assays. Overall, we demonstrate the utility of using molecular regulators as surrogates for cellular identity and functional potential, providing a framework for description, prospective isolation, and cross-tissue comparison of HSPCs in humans.

cell biology↗

bioRxiv2023

Quantification of circulating tumor DNA using deep learning

Quantification of circulating tumor DNA (ctDNA) levels in blood enables non-invasive surveillance of cancer progression. Fragle is an ultra-fast deep learning-based method for ctDNA quantification directly from cell-free DNA fragment length profiles. We developed Fragle using low-pass whole genome sequence (lpWGS) data from multiple cancer types and healthy control cohorts, demonstrating high accuracy, and improved lower limit of detection in independent cohorts as compared to existing tumor-naive methods. Uniquely, Fragle is also compatible with targeted sequencing data, exhibiting high accuracy across both research and commercial targeted gene panels. We used this method to study longitudinal plasma samples from colorectal cancer patients, identifying strong concordance of ctDNA dynamics and treatment response. Furthermore, prediction of minimal residual disease in resected lung cancer patients demonstrated significant risk stratification beyond a tumor-naive gene panel. Overall, Fragle is a versatile, fast, and accurate method for ctDNA quantification with potential for broad clinical utility.

bioinformatics↗

bioRxiv2022

Functional screens of barcoded expression libraries uncover new gene functions in carbon utilization among gut Bactereroidales

A mechanistic understanding of host-microbe interactions in the gut microbiome is hindered by poorly annotated bacterial genomes. While functional genomics can generate large gene-to- phenotype datasets to accelerate functional discovery, their applications to study gut anaerobes have been limited. For instance, most gain-of-function screens of gut-derived genes have been performed in Escherichia coli and assayed in a small number of conditions. To address these challenges, we developed Barcoded Overexpression BActerial shotgun library sequencing (Boba-seq). We demonstrate the power of this approach by assaying genes from diverse gut Bacteroidales overexpressed in Bacteroides thetaiotaomicron. From hundreds of experiments, we identified new functions and novel phenotypes for 29 genes involved in carbohydrate metabolism or tolerance to antibiotics or bile salts. Highlights include the discovery of a D- glucosamine kinase, a raffinose transporter, and several routes that increase tolerance to bile salts through lipid biosynthesis. This approach can be readily applied to develop screens in other strains and additional phenotypic assay types.

microbiology↗

bioRxiv2022

SARS-CoV-2 Omicron BA.2.12.1, BA.4, and BA.5 subvariants evolved to extend antibody evasion

SARS-CoV-2 Omicron subvariants BA.2.12.1 and BA.4/5 have surged dramatically to become dominant in the United States and South Africa, respectively1,2. These novel subvariants carrying additional mutations in their spike proteins raise concerns that they may further evade neutralizing antibodies, thereby further compromising the efficacy of COVID-19 vaccines and therapeutic monoclonals. We now report findings from a systematic antigenic analysis of these surging Omicron subvariants. BA.2.12.1 is only modestly (1.8-fold) more resistant to sera from vaccinated and boosted individuals than BA.2. However, BA.4/5 is substantially (4.2-fold) more resistant and thus more likely to lead to vaccine breakthrough infections. Mutation at spike residue L452 found in both BA.2.12.1 and BA.4/5 facilitates escape from some antibodies directed to the so-called class 2 and 3 regions of the receptor-binding domain3. The F486V mutation found in BA.4/5 facilitates escape from certain class 1 and 2 antibodies but compromises the spike affinity for the viral receptor. The R493Q reversion mutation, however, restores receptor affinity and consequently the fitness of BA.4/5. Among therapeutic antibodies authorized for clinical use, only bebtelovimab retains full potency against both BA.2.12.1 and BA.4/5. The Omicron lineage of SARS-CoV-2 continues to evolve, successively yielding subvariants that are not only more transmissible but also more evasive to antibodies.

microbiology↗

bioRxiv2021

Natural variation in gene expression and Zika virus susceptibility revealed by villages of neural progenitor cells

Variation in the human genome contributes to abundant diversity in human traits and vulnerabilities, but the underlying molecular and cellular mechanisms are not yet known, and will need scalable approaches to accelerate their recognition. Here, we advanced and applied an experimental platform that analyzes genetic, molecular, and phenotypic heterogeneity across cells from very many human donors cultured in a single, shared in vitro environment, with algorithms (Dropulation and Census-seq) for assigning phenotypes to individual donors. We used natural genetic variation and synthetic (CRISPR-Cas9) genetic perturbations to analyze the vulnerability of neural progenitor cells to infection with Zika virus. These analyses identified a common variant in the antiviral IFITM3 gene that regulated IFITM3 expression and explained most inter-individual variation in NPCs susceptibility to Zika virus infectivity. These and other approaches could provide scalable ways to recognize the impact of genes and genetic variation on cellular phenotypes. HIGHLIGHTSO_LIMeasuring cellular phenotypes in iPSCs and hPSC-derived NPCs from many donors C_LIO_LIEffects of donor sex, cell source, genetic and other variables on hPSC RNA expression C_LIO_LINatural genetic variation and synthetic perturbation screens both identify IFITM3 in NPC susceptibility to Zika virus C_LIO_LIA common genetic variant in IFITM3 explains most inter-individual variation in NPC susceptibility to Zika virus C_LI

genomics↗

bioRxiv2020

LISA: Learned Indexes for DNA Sequence Analysis

Next Generation Sequencing (NGS) is transforming fields like genomics, transcriptomics, and epigenetics with rapidly increasing throughput at reduced cost. This also demands overcoming performance bottlenecks in the downstream analysis of the sequencing data. A key performance bottleneck is searching for exact matches of entire or substrings of short DNA/RNA sequence queries in a long reference sequence database. This task is typically performed by using an index of the reference - such as FM-index, suffix arrays, suffix trees, hash tables, or lookup tables. In this paper, we propose accelerating this sequence search by substituting or enhancing the indexes with machine learning based indexes - called learned indexes - and present LISA (Learned Indexes for Sequence Analysis). We evaluate LISA through a number of case studies - that cover widely used software tools; short and long reads; human, animal, and plant genome datasets; DNA and RNA sequences; various traditional indexing techniques (FM-indexes, hash tables and suffix arrays) - and demonstrate significant performance benefits in a majority of them. For example, our experiments on real datasets show that LISA achieves speedups of up to 2.2 fold and 4.7 fold over the state-of-the-art FM-index based implementations for exact sequence search modules in popular tools bowtie2 and BWA-MEM2, respectively. Code availabilityLISA-based FM-index: https://github.com/IntelLabs/Trans-Omics-Acceleration-Library/tree/master/src/LISA-FMI LISA-based hash-table: https://github.com/IntelLabs/Trans-Omics-Acceleration-Library/tree/master/src/LISA-hash LISA applied to BWA-MEM2: https://github.com/bwa-mem2/bwa-mem2/tree/bwa-mem2-lisa.

bioinformatics↗

bioRxiv2020

A benchmarking study of SARS-CoV-2 whole-genome sequencing protocols using COVID-19 patient samples

The COVID-19 pandemic is a once-in-a-lifetime event, exceeding mortality rates of the flu pandemics from the 1950s and 1960s. Whole-genome sequencing (WGS) of SARS-CoV-2 plays a critical role in understanding the disease. Performance variation exists across SARS-CoV-2 viral WGS technologies, but there is currently no benchmarking study comparing different WGS sequencing protocols. We compared seven different SARS-CoV-2 WGS library protocols using RNA from patient nasopharyngeal swab samples under two storage conditions. We constructed multiple WGS libraries encompassing three different viral inputs: 1,000,000, 250,000 and 1,000 copies. Libraries were sequenced using two distinct platforms with varying sequencing depths and read lengths. We found large differences in mappability and genome coverage, and variations in sensitivity, reproducibility and precision of single-nucleotide variant calling across different protocols. We ranked the performance of protocols based on six different metrics. Our results indicated that the most appropriate protocol depended on viral input amount and sequencing depth. Our findings offer guidance in choosing appropriate WGS protocols to characterize SARS-CoV-2 and its evolution.

genomics↗

bioRxiv2020

A transcription regulatory network within the ACE2 locus may promote a pro-viral environment for SARS-CoV-2 by modulating expression of host factors.

IntroductionA novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was recently identified as the pathogen responsible for the COVID-19 outbreak. SARS-CoV-2 triggers severe pneumonia, which leads to acute respiratory distress syndrome and death in severe cases. As reported, SARS-CoV-2 is 80% genetically identical to the 2003 SARS-CoV virus. Angiotensin-converting enzyme 2 (ACE2) has been identified as the main receptor for entry of both SARS-CoV and SARS-CoV-2 into human cells. ACE2 is normally expressed in cardiovascular and lung type II alveolar epithelial cells, where it positively modulates the RAS system that regulates blood flow, pressure, and fluid homeostasis. Thus, virus-induced reduction of ACE2 gene expression is considered to make a significant contribution to severe acute respiratory failure. Chromatin remodeling plays a significant role in the regulation of ACE2 gene expression and the activity of regulatory elements within the genome. MethodsHere, we integrated data on physical chromatin interactions within the genome organization (captured by Hi-C) with tissue-specific gene expression data to identify spatial expression quantitative trait loci (eQTLs) and thus regulatory elements located within the ACE2 gene. ResultsWe identified regulatory elements within ACE2 that control the expression of PIR, CA5B, and VPS13C in the lung. The gene products of these genes are involved in inflammatory responses, de novo pyrimidine and polyamine synthesis, and the endoplasmic reticulum, respectively. ConclusionOur study, although limited by the fact that the identification of the regulatory interactions is putative until proven by targeted experiments, supports the hypothesis that viral silencing of ACE2 alters the activity of gene regulatory regions and promotes an intra-cellular environment suitable for viral replication.

genomics↗

bioRxiv2019

Detection of genomic alterations in breast cancer with circulating tumour DNA sequencing

Analysis of circulating cell-free DNA (cfDNA) data has opened new opportunities for characterizing tumour mutational landscapes with many applications in genomic-driven oncology. We developed a customized targeted cfDNA sequencing approach for breast cancer (BC) using unique molecular identifiers (UMIs) for error correction. Our assay, spanning a 284.5 kb target region, is combined with freely-available bioinformatics pipelines that provide ultra-sensitive detection of single nucleotide variants (SNVs), and reliable identification of copy number variations (CNVs) directly from plasma DNA. In a cohort of 35 BC patients, our approach detected actionable driver and clonal SNVs at low (~0.5%) frequency levels in cfDNA that were concordant (83.3%) with sequencing of primary and/or metastatic solid tumour sites. We also detected ERRB2 gene CNVs used for HER2 subtype classification with 80% precision compared to immunohistochemistry. Further, we evaluated fragmentation profiles of cfDNA in BC and observed distinct differences compared to data from healthy individuals. Our results show that the developed assay addresses the majority of tumour associated aberrations directly from plasma DNA, and thus may be used to elucidate genomic alterations in liquid biopsy studies.

genomics↗

bioRxiv2018

Liquid biopsy in gastrointestinal stromal tumors -- Simultaneous detection of primary and secondary mutations with an NGS based circulating tumor DNA assay

Introduction Introduction Methods Results Discussion References Gastrointestinal Stromal tumors (GISTs) are the most common neoplasms of mesenchymal origin arising from the gastrointestinal tract. GISTs are mostly characterised by one predominant activating mutation in KIT (KIT proto-oncogene receptor tyrosine kinase) or PDGFRA (platelet derived growth factor receptor alpha) genes1-3. Under pharmacological pressure, one or more secondary mutations can develop in KIT, conferring resistance to tyrosine kinase inhibitor, imatinib. In imatinib-resistant GISTs, secondary mutations can occur in the ATP binding pocket, encoded by exons 13 and 14 or in the kinase activation loop, encoded predominantly by exon 174. In vitro studies ha ...

molecular biology↗

bioRxiv2018

Genome-wide screens in accelerated human stem cell-derived neural progenitor cells identify Zika virus host factors and drivers of proliferation

Neural progenitor cells (NPCs) are essential to brain development and their dysfunction is linked to several disorders, including autism, Zika Virus Congenital Syndrome, and cancer. Understanding of these conditions has been improved by advancements with stem cell-derived NPC models. However, current differentiation methods require many days or weeks to generate NPCs and show variability in efficacy among cell lines. Here, we describe human Stem cell-derived NGN2-accelerated Progenitor cells (SNaPs), which are produced in only 48 hours. SNaPs express canonical forebrain NPC protein markers, are proliferative, multipotent, and like other human NPCs, are susceptible to Zika-mediated death. We further demonstrate SNaPs are valuable for large-scale investigations of genetic and environmental influencers of neurodevelopment by deploying them for genome-wide CRISPR-Cas9 screens. Our studies expand knowledge of NPCs by identifying known and novel Zika host factors, as well as new regulators of NPC proliferation validated by re-identification of the autism spectrum gene PTEN.

neuroscience↗

bioRxiv2018

Genome-Scale CRISPR Screening Identifies Novel Human Pluripotent Gene Networks

Human pluripotent stem cells (hPSCs) generate a wide variety of disease-relevant cells that can be used to improve the translation of preclinical research. Despite the potential of hPSCs, their use for genetic screening has been limited because of technical challenges. We developed a renewable Cas9/sgRNA-hPSC library where loss-of-function mutations can be induced at will. Our inducible-mutant hPSC library can be used for an unlimited number of genome-wide screens. We screened for novel genes involved in 3 of the fundamental properties of hPSCs: Their ability to self-renew/survive, their capacity to differentiate into somatic cells, and their inability to survive as single-cell clones. We identified a plethora of novel genes with unidentified roles in hPSCs. These results are available as a resource for the community to increase the understanding of both human development and genetics. In the future, our stem cell library approach will be a powerful tool to identify disease-modifying genes.\n\nVISUAL ABSTRACT\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=176 SRC=\"FIGDIR/small/323436_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (48K):\norg.highwire.dtl.DTLVardef@5101e4org.highwire.dtl.DTLVardef@5321f2org.highwire.dtl.DTLVardef@1d9c94corg.highwire.dtl.DTLVardef@12ebd53_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2017

P53 toxicity is a hurdle to CRISPR/CAS9 screening and engineering in human pluripotent stem cells

CRISPR/Cas9 has revolutionized our ability to engineer genomes and to conduct genome-wide screens in human cells. While some cell types are easily modified with Cas9, human pluripotent stem cells (hPSCs) poorly tolerate Cas9 and are difficult to engineer. Using a stable Cas9 cell line or transient delivery of ribonucleoproteins (RNPs) we achieved an average insertion or deletion efficiency greater than 80%. This high efficiency made it apparent that double strand breaks (DSBs) induced by Cas9 are toxic and kill most treated hPSCs. Cas9 toxicity creates an obstacle to the high-throughput use CRISPR/Cas9 for genome-engineering and screening in hPSCs. We demonstrated the toxic response is tp53-dependent and the toxic effect of tp53 severely reduces the efficiency of precise genome-engineering in hPSCs. Our results highlight that CRISPR-based therapies derived from hPSCs should proceed with caution. Following engineering, it is critical to monitor for tp53 function, especially in hPSCs which spontaneously acquire tp53 mutations.

molecular biology↗