Biology subjects

Liu, L. Y.

Publications and source records attributed to Liu, L. Y..

16 recordsLinked to original sources

bioRxiv2025

A Physically-Realistic Simulator and Cosine-Based Decoder for MERFISH Spatial Transcriptomics

Imaging-based spatial transcriptomics technologies have opened new avenues for studying cellular organization and gene expression within intact tissues. However, the accuracy of downstream analyses depends critically on the decoding step that reconstructs barcodes from fluorescence patterns and maps them to gene identities. Despite a growing number of decoding methods, systematic benchmarking has been limited. Here, we introduce Serval, a modular framework for developing and benchmarking decoding methods across diverse spatial transcriptomics platforms. Serval separates key decoding stages into independently configurable modules, enabling flexible integration of alternative algorithms. Using this framework, we develop the Cosine decoder, a novel method that improves transcript recovery by optimizing cosine similarity to known barcodes. We evaluate Cosine and baseline methods on synthetic and real MERFISH datasets, showing that Cosine achieves higher transcript recovery and superior correlation with expression references compared to existing methods. Furthermore, we demonstrate that the Serval framework generalizes beyond MERFISH. By extending to the DART-FISH platform, we show that Cosine improves transcript recovery, clustering stability and supports more direct annotation of complex biological structures such as the human primary motor cortex. These results establish that modular decoding frameworks facilitate robust, platformagnostic benchmarking, ultimately supporting more accurate spatial transcriptomics analysis across diverse biological samples.

bioinformatics↗

bioRxiv2025

A Single-Cell Woodchuck Liver Atlas Identifies Healthy and Disease-Related Cellular Programs Conserved in Human

BackgroundModel organisms allowing for longitudinal examinations of liver disease pathogenesis are pivotal for the development of new therapeutic modalities. The eastern North American woodchuck develops chronic hepatitis and liver cancer after woodchuck hepatitis virus (WHV) infection, mirroring aspects of the natural history of the human hepatitis B virus (HBV). However, the cellular landscape of the woodchuck liver and the cell-level relevance of WHV infection to HBV infection is currently uncharacterized. MethodsWe employed single-cell RNA sequencing (RNA-seq) to generate an atlas of healthy woodchuck liver (63,389 cells, n=8) and peripheral blood mononuclear cells (PBMCs) (26,972 cells, n=7). Cell-specific and hepatic zonation gene signatures were validated using spatial transcriptomics (n=1). We employed our atlas to examine immune activation in stimulated precision cut-liver slices (PCLS) and disease-related pathway activation in chronic WHV infection (11,797 cells, n=3). We further employed our atlas to examine shared disease pathways between WHV infection and human HBV infection. ResultsOur atlas revealed woodchuck hepatic cellular diversity comparable to human and murine livers. Applying single-nucleus RNA-seq to PMA/ionomycin-stimulated precision cut liver slices revealed inflammation-associated activation signatures in T cell, myeloid and endothelial cell compartment. Finally, we describe intrahepatic T cells in chronic WHV hepatitis with both exhaustion and activation-associated signatures that resemble intrahepatic T cell genes signatures described in human chronic HBV. ConclusionsWe present a multi-omic atlas of healthy, diseased and ex vivo stimulated woodchuck liver. By identifying shared pathological processes between WHV and HBV infections, our findings reinforce the value of this preclinical model in translational research. This resource aims to advance studies on HBV pathogenesis and oncogenesis to speed the development of novel therapeutic strategies. Impact and Implications/Lay summaryThe liver plays important roles in metabolism, detoxification, and immune processes; liver transplantation is often the only treatment option for severe chronic liver diseases. Therefore, developing animal models that reflect human liver disease and can be studied throughout the disease course is crucial for the discovery of new treatment options. The woodchuck is an animal that develops chronic hepatitis and liver cancer after infection with woodchuck hepatitis virus, which models the human hepatitis type B virus infection (HBV) and associated hepatic carcinoma. However, our understanding of the cells that compose the woodchuck liver is limited, making it challenging to design and test cell-based therapeutics. In this study, we atlased the healthy and chronically infected woodchuck liver, found that liver cell types in woodchuck resemble those in humans, and employed the atlas to show similarities between WHV and HBV at the cell level, reinforcing the potential of WHV-infected woodchuck as a model for human HBV disease. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=196 SRC="FIGDIR/small/641192v1_ufig1.gif" ALT="Figure 1"> View larger version (47K): org.highwire.dtl.DTLVardef@18fdd40org.highwire.dtl.DTLVardef@6ccf2org.highwire.dtl.DTLVardef@7923e1org.highwire.dtl.DTLVardef@122a8d2_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2024

Pan-cancer N-glycoproteomic atlas of patient derived xenografts uncovers FAT2 as a therapeutic target for head and neck cancers

Cell surface proteins offer significant cancer therapeutic potential attributable to their accessible membrane localization and central role in cellular signaling. Despite this, their promise remains largely untapped due to the technical challenges inherent to profiling cell surface proteins. Here, we employed N-glycoproteomics to analyze 85 patient-derived xenografts (PDX), constructing Glyco PDXplorer - an in vivo pan-cancer atlas of cancer-derived cell surface proteins. We developed a target discovery pipeline to prioritize proteins with favorable expression profiles for immunotherapeutic targeting and validated FAT2 as a head and neck squamous cancer (HNSC) enriched surface protein with limited expression in normal tissue. Functional studies revealed that FAT2 is essential for HNSC growth and adhesion through regulation of surface architecture and integrin-PI3K signaling. Chimeric antigen receptor (CAR) T cells targeting FAT2 demonstrated potent anti-tumor activity in HNSC models. This work lays the foundation for developing FAT2-targeted therapies and represents a pivotal resource to inform therapeutic target discovery for multiple cancers. HIGHLIGHTSO_LIPan-cancer landscape of cancer-derived cell surface proteins detected in vivo C_LIO_LIDevelopment of a multi-omic discovery pipeline to prioritize proteins with optimal expression profiles as immunotherapy targets C_LIO_LIIdentification and validation of FAT2 as a head and neck squamous cancer enriched surface protein with minimal expression in normal tissues C_LIO_LIFAT2 coordinates cell surface organization, adhesion, growth and survival through the integrin-PI3K-AKT pathway C_LIO_LIFAT2 CAR T cells demonstrate anti-tumour activity in pre-clinical models C_LI

cancer biology↗

bioRxiv2024

Metapipeline-DNA: A Comprehensive Germline & Somatic Genomics Nextflow Pipeline

SummaryThe price, quality and throughout of DNA sequencing continue to improve. Algorithmic innovations have allowed inference of a growing range of features from DNA sequencing data, quantifying nuclear, mitochondrial and evolutionary aspects of both germline and somatic genomes. To automate analyses of the full range of genomic characteristics, we created an extensible Nextflow meta-pipeline called metapipeline-DNA. Metapipeline-DNA analyzes targeted and whole-genome sequencing data from raw reads through pre-processing, feature detection by multiple algorithms, quality-control and data- visualization. Each step can be run independently and is supported robust software engineering including automated failure-recovery, robust testing and consistent verifications of inputs, outputs and parameters. Metapipeline-DNA is cloud-compatible and highly configurable, with options to subset and optimize each analysis. Metapipeline-DNA facilitates high-scale, comprehensive analysis of DNA sequencing data. AvailabilityMetapipeline-DNA is an open-source Nextflow pipeline under the GPLv2 license and is available at https://github.com/uclahs-cds/metapipeline-DNA.

bioinformatics↗

bioRxiv2024

moPepGen: Rapid and Comprehensive Proteoform Identification

Proteogenomics is limited by challenges of modeling the complexities of gene expression. We create moPepGen, a graph-based algorithm that comprehensively generates non-canonical peptides in linear time. moPepGen works with multiple technologies, in multiple species and on all types of genetic and transcriptomic data. In human cancer proteomes, it enumerates previously unobservable noncanonical peptides arising from germline and somatic genomic variants, noncoding open reading frames, RNA fusions and RNA circularization.

bioinformatics↗

bioRxiv2024

Quantifying the seed sensitivity of cancer subclonal reconstruction algorithms

BackgroundIntra-tumoural heterogeneity complicates cancer prognosis and impairs treatment success. One of the ways subclonal reconstruction (SRC) quantifies intra-tumoural heterogeneity is by estimating the number of subclones present in bulk DNA sequencing data. SRC algorithms are probabilistic and need to be initialized by a random seed. However, the seeds used in bioinformatics algorithms are rarely reported in the literature. Thus, the impact of the initializing seed on SRC solutions has not been studied. To address this gap, we generated a set of ten random seeds to systematically benchmark the seed sensitivity of three probabilistic SRC algorithms: PyClone-VI, DPClust, and PhyloWGS. ResultsWe characterized the seed sensitivity of three algorithms across fourteen whole-genome sequences of head and neck squamous cell carcinoma and nine SRC pipelines, each composed of a single nucleotide variant caller, a copy number aberration caller and an SRC algorithm. This led to a total of 1470 subclonal reconstructions, including 1260 single-region and 210 multi-region reconstructions. The number of subclones estimated per patient vary across SRC pipelines, but all three SRC algorithms show substantial seed sensitivity: subclone estimates vary across different seeds for the same set of input using the same SRC algorithm. No seed consistently estimated the mode number of subclones across all patients for any SRC algorithm. ConclusionsThese findings highlight the variability in quantifying intra-tumoural heterogeneity introduced by the seed sensitivity of probabilistic SRC algorithms. We recommend that authors, reviewers and editors adopt guidelines to both report and randomize seed choices. It may also be valuable to consider seed-sensitivity in the benchmarking of newly developed SRC algorithms. These findings may be of interest in other areas of bioinformatics where seeded probabilistic algorithms are used and suggest consideration of formal seed reporting standards to enhance reproducibility.

bioinformatics↗

bioRxiv2023

Quercetin alleviates intestinal oxidative stress in Jilin White geese

Oxidative stress can occur during all stages of geese breeding, frequently damaging the intestinal tract and various tissues and organs. Quercetin is a flavonoid that exerts potential therapeutic effects against oxidative stress-induced cell damage and mitochondrial dysfunction. Herein, we investigated the alleviating effects of quercetin against lipopolysaccharide (LPS)-induced intestinal inflammation and oxidative stress in Jilin White geese. We selected 120 healthy male 7-d-old geese of similar BW and randomly divided them into six treatment groups, with five replicates each and four geese in each replicate. The groups were as follows: C (fed basal diet, i.p. injection of 0.5 mg/kg normal saline); L (fed basal diet, i.p. injection of 0.5 mg/kg LPS); Q1, Q2, Q3, and Q4 (diet + 150, 300, 450, or 600 mg/kg quercetin, respectively, i.p. injection of 0.5 mg/kg LPS). Samples were collected after experimental d 21. Our results showed that adding 300 mg/kg quercetin to the diet significantly improved growth performance after oxidative stress and reduced serum low-density lipoprotein and glutathione aminotransferase activities. Adding 450 mg/kg quercetin significantly increased the intestinal expression of downstream genes of the nuclear factor E2-related factor 2 pathway, heme oxygenase 1 (HO-1), reduced coenzyme/quinone oxidoreductase (NQO1), and glutathione oxidase (GSTP) in Jilin White geese. Considering production, adding 300 mg/kg quercetin to the diet could alleviate LPS-induced oxidative stress and reduce costs. These findings provide a reference hypothesis for the oxidative stress damage during geese production, which may help reduce the economic loss owing to geese mortality.

zoology↗

bioRxiv2023

Prostate Cancer Reshapes the Secreted and Extracellular Vesicle Urinary Proteomes

Urine is a complex biofluid that reflects both overall physiologic state and the state of the genitourinary tissues through which it passes. It contains both secreted proteins and proteins encapsulated in tissue-derived extracellular vesicles (EVs). To understand the population variability and clinical utility of urine, we quantified the secreted and EV proteomes from 190 men, including a subset with prostate cancer. We demonstrate that a simple protocol enriches prostatic proteins in urine. Secreted and EV proteins arise from different subcellular compartments. Urinary EVs are faithful surrogates of tissue proteomes, but secreted proteins in urine or cell line EVs are not. The urinary proteome is longitudinally stable over several years. It can accurately and non-invasively distinguish malignant from benign prostatic lesions, and can risk-stratify prostate tumors. This resource quantifies the complexity of the urinary proteome, and reveals the synergistic value of secreted and EV proteomes for translational and biomarker studies.

systems biology↗

bioRxiv2022

General stain deconvolution of histopathology images with physics-guided deep learning

Advances have been made in the use of deep learning to extract quantitative and predictive information from digital pathology slides, yet many barriers remain before clinical translation and deployment. In particular, models need to be generalizable despite the wide variations in image characteristics due to inter-scanner variability and differences in slide preparation protocols. This has led to an interest in stain deconvolution methods that could correct for the variability in image appearances. However, most existing stain deconvolution methods were developed and validated on specific datasets and perform poorly on unseen data. We developed Physics-Guided Deep Image Prior network for Stain deconvolution (PGDIPS), a method that combines a novel optical physics model and a self-supervised deep neural network to perform deconvolution for various classes and any number of stains, without the need of training data. PGDIPS outperformed state-of-the-art approaches for the deconvolution of conventional stain combinations, enabled analysis of previously unsupported special stains, and provided superior interpretability by explicitly encoding representations for stain properties and the light transmittance/absorbance process. PGDIPS is publicly available as an end-to-end off-the-shelf tool that does not require data curation, domain knowledge or high computation power.

bioinformatics↗

bioRxiv2022

New insight into the Germline Genes and CDR3 Repertoire of the TCRβ chain in Chiroptera

Bats are known to be natural reservoirs of many viruses, yet their unique immune system enables them to coexist with viruses without frequently exhibiting disease symptoms. The current understanding of the bat adaptive immune system is limited, as there is no database or tool capable of processing T-cell receptor (TCR) sequences for bats, and the diverse nature of the Chiroptera. We established a reference library of TCR-{beta} germline genes by annotating three Chiroptera: The Greater Horseshoe Bat (Rhinolophus ferrumequinum, R. ferrumequinum), The Pale spear-nosed Bat (Phyllostomus discolor, P. discolor), and the Common Pipistrelle (Pipistrellus pipistrellus, P. pipistrellus). The distinct variations in the distribution of TRBV genes among the three types of bats could have a direct impact on the diversity of the TCR repertoire, as evidenced by the presence of conserved amino acids that indicate the T-cell recognition of antigens in bats is MHC-restricted. Furthermore, we conducted an analysis of the TCR-{beta} repertoire in the Intermediate Horseshoe Bat (Rhinolophus affinis, R. affinis) using high-throughput sequencing (HTS). The bats TCR-{beta} repertoire is formed through rearrangement of the V-D-J-C genes, with D-J/V-D deletions and insertion resulting a high diversity.

immunology↗

bioRxiv2022

Glycoproteomics Identifies Plexin-B3 as Targetable Cell Surface Protein Required for Growth and Invasion of Triple Negative Breast Cancer Cells

Driven by the lack of targeted therapies, triple negative breast cancers (TNBC) have the worst overall survival of all breast cancer subtypes. Considering cell surface proteins are favorable drug targets and are predominantly glycosylated, glycoproteome profiling has significant potential to facilitate the identification of much-needed drug targets for TNBC. Here, we performed N-glycoproteomics on six TNBC and five normal control (NC) cell lines using hydrazide-based enrichment. Quantitative proteomics and integrative data mining led to the discovery of Plexin-B3 (PLXNB3), a previously undescribed TNBC-enriched cell surface protein. Furthermore, siRNA knock-down and CRISPR-Cas9 editing of in vitro and in vivo models show that PLXNB3 is required for TNBC cell line growth, invasion, and migration. Altogether, we provide insight into N-glycoproteome remodeling associated with TNBC and functional evaluation of an extracted target, which indicate the surface protein PLXNB3 as a potential therapeutic target for TNBC. HighlightsO_LIIn-depth N-glycoproteomic profiles of six TNBC and five NC cell line models C_LIO_LIIdentification of PLXNB3 as a novel TNBC-enriched cell surface protein C_LIO_LIPLXNB3 affects growth, invasion, and migration in TNBC models C_LIO_LIPLXNB3 inhibition represents a targeted treatment option for TNBC C_LI

cancer biology↗

bioRxiv2021

Single-cell profiling of healthy human kidney reveals features of sex-based transcriptional programs and tissue-specific immunity

Maintaining organ homeostasis requires complex functional synergy between distinct cell types, a snapshot of which is glimpsed through the simultaneously broad and granular analysis provided by single-cell atlases. Knowledge of the transcriptional programs underpinning the complex and specialized functions of human kidney cell populations at homeostasis is limited by difficulty accessing healthy, fresh tissue. Here, we present a single-cell perspective of healthy human kidney from 19 living donors, with equal contribution from males and females, profiling the transcriptome of 27677 high-quality cells to map healthy kidney at high resolution. Our sex-balanced dataset revealed sex-based differences in gene expression within proximal tubular cells, specifically, increased anti-oxidant metallothionein genes in females and the predominance of aerobic metabolism-related genes in males. Functional differences in metabolism were confirmed between male and female proximal tubular cells, with male cells exhibiting higher oxidative phosphorylation and higher levels of energy precursor metabolites. Within the immune niche, we identified kidney-specific lymphocyte populations with unique transcriptional profiles indicative of kidney-adapted functions and validated findings by flow cytometry. We observed significant heterogeneity in resident myeloid populations and identified an MRC1+ LYVE1+ FOLR2+ C1QC+ population as the predominant myeloid population in healthy kidney. This study provides a detailed cellular map of healthy human kidney, revealing novel insights into the complexity of renal parenchymal cells and kidney-resident immune populations.

immunology↗

bioRxiv2021

The TR locus annotation and characteristics of Rhinolophus ferrumequinum

T cell antigen receptors (TCRs) in vertebrate could be divided into {beta} or {gamma}{delta}, which are encoded by TRA/D, TRG and TRB loci respectively. TCRs play a central role in mammal cellular immunity, which are functionally produced by rearrangement of V gene, D gene, J gene and C gene in the loci. Bat is the only mammal with flying ability, and is considered as the main host of zoonotic virus, which occupies an important position in the field of public health. At present, little is known about the composition of bat TRs gene. According to the whole genome sequencing results of the Rhinolophus ferrumequinum, and referring to the TR/IG annotation rules formulated by IMGT. We make a complete annotation on the TRA/D, TRG and TRB loci of the Rhinolophus ferrumequinum. A total of 128 V segments, 3 D segments, 85 J segments and 6 C segments were annotated, in addition to compared with the known mammalian, the characteristics of the TRs locus and germline genes of the Rhinolophus ferrumequinum were analyzed.

molecular biology↗

bioRxiv2020

High sensitivity detection of SARS-CoV-2 using multiplex PCR and a multiplex-PCR-based metagenomic method

Many detection methods have been used or reported for the diagnosis and/or surveillance of COVID-19. Among them, reverse transcription polymerase chain reaction (RT-PCR) is the most commonly used because of its high sensitivity, typically claiming detection of about 5 copies of viruses. However, it has been reported that only 47-59% of the positive cases were identified by some RT-PCR methods, probably due to low viral load, timing of sampling, degradation of virus RNA in the sampling process, or possible mutations spanning the primer binding sites. Therefore, alternative and highly sensitive methods are imperative. With the goal of improving sensitivity and accommodating various application settings, we developed a multiplex-PCR-based method comprised of 343 pairs of specific primers, and demonstrated its efficiency to detect SARS-CoV-2 at low copy numbers. The assay produced clean characteristic target peaks of defined sizes, which allowed for direct identification of positives by electrophoresis. We further amplified the entire SARS-CoV-2 genome from 8 to half a million viral copies purified from 13 COVID-19 positive specimens, and detected mutations through next generation sequencing. Finally, we developed a multiplex-PCR-based metagenomic method in parallel, that required modest sequencing depth for uncovering SARS-CoV-2 mutational diversity and potentially novel or emerging isolates.

genomics↗

bioRxiv2018

The Inter and Intra-Tumoural Heterogeneity of Subclonal Reconstruction

Whole-genome sequencing can be used to estimate subclonal populations in tumours and this intra-tumoural heterogeneity is linked to clinical outcomes. Many algorithms have been developed for subclonal reconstruction, but their variabilities and consistencies are largely unknown. We evaluated sixteen pipelines for reconstructing the evolutionary histories of 293 localized prostate cancers from single samples, and eighteen pipelines for the reconstruction of 10 tumours with multi-region sampling. We show that predictions of subclonal architecture and timing of somatic mutations vary extensively across pipelines. Pipelines show consistent types of biases, with those incorporating SomaticSniper and Battenberg preferentially predicting homogenous cancer cell populations and those using MuTect tending to predict multiple populations of cancer cells. Subclonal reconstructions using multi-region sampling confirm that single-sample reconstructions systematically underestimate intra-tumoural heterogeneity, predicting on average fewer than half of the cancer cell populations identified by multi-region sequencing. Overall, these biases suggest caution in interpreting specific architectures and subclonal variants.

cancer biology↗

bioRxiv2018

Creating Standards for Evaluating Tumour Subclonal Reconstruction

Tumours evolve through time and space. Computational techniques have been developed to infer their evolutionary dynamics from DNA sequencing data. A growing number of studies have used these approaches to link molecular cancer evolution to clinical progression and response to therapy. There has not yet been a systematic evaluation of methods for reconstructing tumour subclonality, in part due to the underlying mathematical and biological complexity and to difficulties in creating gold-standards. To fill this gap, we systematically elucidated the key algorithmic problems in subclonal reconstruction and developed mathematically valid quantitative metrics for evaluating them. We then created approaches to simulate realistic tumour genomes, harbouring all known mutation types and processes both clonally and subclonally. We then simulated 580 tumour genomes for reconstruction, varying tumour read-depth and benchmarking somatic variant detection and subclonal reconstruction strategies. The inference of tumour phylogenies is rapidly becoming standard practice in cancer genome analysis; this study creates a baseline for its evaluation.

bioinformatics↗