Biology subjects

Liu, T.

Publications and source records attributed to Liu, T..

At least 163 records · Page 9Linked to original sources

bioRxiv2025

Ultraviolet B Induces Cutaneous Lupus Erythematosus by Triggering a Ribotoxic-Stress-Response-Dependent MIF-p38 Feedback Loop in Keratinocytes

Ultraviolet B (UVB) is one of the most common triggers of cutaneous lupus erythematosus (CLE), but the mechanism is still unclear. Here, we discovered that the expression of macrophage migration inhibitory factor (MIF), a multipotent cytokine, was significantly increased in keratinocytes of lupus patients in scRNA-seq data and clinical samples. We further discovered that UVB exposure induced MIF releasing from lupus keratinocytes and contributed to the tissue remodeling and inflammation by regulating the expression of matrix metalloproteinases (MMPs) and pro-inflammatory cytokines in keratinocytes and fibroblasts. Mechanistically, UVB exposure triggered a ribotoxic stress response (RSR) in lupus keratinocytes, which was featured by the increased level of phospho-ZAK. The RSR induced MIF releasing through the activation of p38 and GSDMD cleavage. The released MIF could further promote the activation of p38 through its receptor CD74, forming a positive feedback loop triggered by UVB exposure in lupus keratinocytes. Intradermal injection of a Mif-shRNA AAV significantly improved the skin lesions in lupus mice. To further investigate the potentials of MIF as a therapeutic target, we developed a microneedle patch capable of intradermal administration of a MIF inhibitor, which effectively protected lupus mice from skin lesions induced by UVB exposure. In conclusion, our findings suggest a novel mechanism by which UVB exposure contributes to the development of CLE by triggering a positive feedback loop of MIF-p38 in lupus keratinocytes. Our results also demonstrate the translational potentials of microneedle patch targeting MIF in the treatment of CLE.

immunology↗

bioRxiv2024

A Palette of Bridged Bicycle-Strengthened Fluorophores

Organic fluorophores are the keystone of advanced biological imaging. The vast chemical space of fluorophores has been extensively explored in seek of molecules with ideal properties. However, within the current molecular constraints, there appears to be a trade-off between high brightness, robust photostability, and tunable biochemical properties. Herein we report a general strategy to systematically boost the performance of donor-acceptor-type fluorophores by leveraging SO2 and O-substituted azabicyclo[3.2.1] octane auxochromes. These bicyclic heterocycles give rise to a collection of Bridged dyes (BD) spanning the UV and visible range with top-notch quantum efficiencies, enhanced water solubility, and tunable cell-permeability. Notably, these azabicyclic fluorophores showed remarkable photostability than its tetramethyl or azatidine analogue, at the same time completely resistant to oxidative photobluing rendered by the Bredts rule. Functionalized BD dyes are tailored for applications in single-molecule imaging, super-resolution imaging (STED and SIM) in fixed or live mammalian cells and plant cells, and live zebrafish imaging or chemigenetic voltage imaging. Synergizing with advanced imaging methods, the bridge bicycle dyes represent a versatile palette for biological researches.

biophysics↗

bioRxiv2024

ITGB4 promotes the progression of colorectal cancer by activating Wnt/β-catenin signaling pathway via targeting EZR

ObjectivesColorectal cancer (CRC) is a major cause of cancer-related mortality worldwide. Integrin beta 4 (ITGB4) has been previously identified as being overexpressed in CRC; however, its precise oncogenic mechanism remains unclear. The present study aimed to elucidate the functional role of ITGB4 in CRC progression and identify its downstream molecular effectors to provide new insights for targeted therapy. MethodsThe biological functions of ITGB4 were investigated in CRC cell lines (SW480 and HCT116) using a series of in vitro assays, including CCK-8, colony formation, Transwell migration and invasion, and flow cytometry for apoptosis following ITGB4 knockdown. An in vivo xenograft mouse model was used to evaluate the effect of ITGB4 on tumor growth. Downstream targets were screened using RNA sequencing (RNA-seq) and validated by co-immunoprecipitation and co-immunofluorescence. The underlying signaling pathway was investigated by Western blotting and functional rescue experiments. ResultsKnockdown of ITGB4 significantly suppressed CRC cell proliferation, migration, and invasion, while promoting apoptosis in vitro. Similarly, silencing ITGB4 markedly inhibited tumor growth in the in vivo xenograft model. RNA-seq analysis identified Ezrin (EZR) as a key downstream target of ITGB4, and a direct protein-protein interaction was confirmed between them. Mechanistically, ITGB4 knockdown decreased the expression of EZR at both the mRNA and protein levels. ITGB4 was demonstrated to exert its pro-tumorigenic effects through the regulation of EZR, which subsequently activated the Wnt/{beta}-catenin signaling pathway. Interestingly, EZR overexpression partially restored ITGB4 levels, suggesting a potential positive feedback loop via Wnt/{beta}-catenin signaling that further amplifies this oncogenic axis. Notably, the malignant phenotypes suppressed by ITGB4 silencing were significantly rescued by the overexpression of EZR. ConclusionThe present study identified a novel ITGB4/EZR/Wnt/{beta}-catenin signaling axis in colorectal cancer. ITGB4 promotes CRC progression by modulating EZR expression and subsequently activating the Wnt/{beta}-catenin pathway. These findings highlight ITGB4 as a potential prognostic biomarker and a promising therapeutic target for CRC.

molecular biology↗

bioRxiv2024

Assessment of DNA methylation from a single genomic region of ELOV2 is sufficient to predict chronological age

Estimation of chronological age is particularly informative in forensic contexts. Assessment of DNA methylation status allows for the prediction of age, though the accuracy and ease of manipulation may vary across different models. In this study, we started with a carefully designed discovery cohort recruiting more elderly subjects than other age categories, to diminish the effect of epigenetic drifting. We analyzed DNA methylation from a single genomic region of ELOV2, which was sufficient to construct an age-prediction model comprising 15 CpG sites. This model is further validated by an independent cohort as well as a multi-center test using trace dried bloodstains. The nature of our analytical pipeline, when combined the assessment of a single genomic locus with high-throughput sequencing, can easily be scaled up with low cost. Taken together, we propose a new age-prediction model featuring accuracy, ease of manipulation, high-throughput, and low cost. This model can be readily applied in both classic and newly emergent forensic contexts that require age estimation.

genetics↗

bioRxiv2024

PathOmCLIP: Connecting tumor histology with spatial gene expression via locally enhanced contrastive learning of Pathology and Single-cell foundation model

Tumor morphological features from histology images are a cornerstone of clinical pathology, diagnostic biomarkers, and basic cancer biology research. Spatial transcriptomics, which provides spatially resolved gene expression profiles overlaid on histology images, offers a unique opportunity to integrate morphological and expression features, thereby deepening our understanding of tumor biology. However, spatial transcriptomics experiments with patient samples in either clinical trials or clinical care are costly and challenging, whereas histology images are generated routinely and available for many legacy prospective cohorts of disease progression and outcomes in well-annotated cohorts. Inferring spatial transcriptomics profiles computationally from these histology images would significantly expand our understanding of tumor biology, but paired data for training multi-modal spatial-histology models remains limited. Here, we tackle this challenge by incorporating performant foundation models pre-trained on massive datasets of pathology images and single-cell RNA-Seq, respectively, which provide useful embeddings to underpin multi-modal models. To this end, we developed PathOmCLIP, a model trained with contrastive loss to create a joint-embedding space between a histopathology foundation model and a single-cell RNA-seq foundation model. We incorporate a set transformer to gather localized neighborhood tumor architecture following contrastive training, which further enhances performance and is necessary to obtain robust results. We validate PathOmCLIP across five tumor types and achieve significant performance improvements in gene expression prediction tasks over other methods. PathOmCLIP can be applied to many archived histology images, unlocking valuable clinical information and facilitating new biomarker discoveries.

bioinformatics↗

bioRxiv2024

Modeling and predicting single-cell multi-gene perturbation responses with scLAMBDA

Understanding cellular responses to genetic perturbations is essential for understanding gene regulation and phenotype formation. While high-throughput single-cell RNA-sequencing has facilitated detailed profiling of heterogeneous transcriptional responses to perturbations at the single-cell level, there remains a pressing need for computational models that can decode the mechanisms driving these responses and accurately predict outcomes to prioritize target genes for experimental design. Here, we present scLAMBDA, a deep generative learning framework designed to model and predict single-cell transcriptional responses to genetic perturbations, including single-gene and combinatorial multi-gene perturbations. By leveraging gene embeddings derived from large language models, scLAMBDA effectively integrates prior biological knowledge and disentangles basal cell states from perturbation-specific salient representations. Through comprehensive evaluations on multiple single-cell CRISPR Perturb-seq datasets, scLAMBDA consistently outperformed state-of-the-art methods in predicting perturbation outcomes, achieving higher prediction accuracy. Notably, scLAMBDA demonstrated robust generalization to unseen target genes and perturbations, and its predictions captured both average expression changes and the heterogeneity of single-cell responses. Furthermore, its predictions enable diverse downstream analyses, including the identification of differentially expressed genes and the exploration of genetic interactions, demonstrating its utility and versatility.

bioinformatics↗

bioRxiv2024

A TROP2/Claudin Program Mediates Immune Exclusion to Impede Checkpoint Blockade in Breast Cancer

BackgroundImmune exclusion inhibits anti-tumor immunity and response to immunotherapy, but its mechanisms remain poorly defined. In triple-negative breast cancer (TNBC), an aggressive and generally immune-rich subtype, an immune-cold microenvironment predicts poor prognosis due to a limited response to chemotherapy and immune checkpoint inhibitors. This study aimed to identify mechanisms regulating immune infiltration in TNBC. MethodsWe performed spatial transcriptomic analysis comparing immune-enriched versus immune-cold treatment-naive TNBCs. Functional analyses, including loss-of-function and reconstitution experiments, were conducted to investigate the role of Trophoblast Cell-Surface Antigen 2 (TROP2), a key target of anti-cancer Antibody Drug Conjugates (ADCs), in promoting TNBC progression. A humanized TROP2 syngeneic TNBC model was used to assess the effects of TROP2-targeting in combination with anti-PD1 therapy. Additionally, data from patients treated with immune checkpoint blockade were used to test hypotheses from the preclinical findings. ResultsWe reveal that TROP2 controls barrier-mediated immune exclusion in TNBC through Claudin 7 association and tight junction regulation. TROP2 expression is inversely correlated with T cell infiltration and predicts poor outcomes in TNBC. We demonstrate that TROP2 is sufficient to drive tumor progression in vivo in a CD8 T cell-dependent manner, while its loss deregulates expression and localization of multiple tight junction proteins, enabling T cell infiltration. We show that TROP2 targeting via hRS7, the antibody component of the ADC Sacituzumab govitecan (SG), enhances the anti-PD1 response and improves T cell accessibility and effector function. Correspondingly, TROP2 expression is highly associated with lack of response to anti-PD1 therapy in human breast cancer. ConclusionsThis study defines a new mechanism of barrier-mediated immune exclusion in cancer controlled by TROP2-dependent tight junctions. This mechanism drives tumor progression but can be targeted via TROP2-directed therapy to activate anti-tumor immunity and enhance immunotherapy response. What is already known on this topicBarrier-mediated immune exclusion is emerging as a mechanism of immune evasion in cancer such as triple-negative breast cancer (TNBC), but its molecular underpinnings remain poorly understood. TROP2 is a surface glycoprotein targeted by antibody-drug conjugates such as Sacituzumab govitecan, yet its functional role beyond drug delivery has been unclear. What this study addsThis study identifies TROP2 as a key regulator of tight junction-mediated immune exclusion in TNBC, independent of its intracellular signaling function. TROP2 promotes an immune-cold tumor microenvironment by enforcing mechanical barriers that limit T cell infiltration. How this study might affect research, practice or policyThese findings establish TROP2 as a functional driver of immune evasion and provide a mechanistic rationale for combining TROP2-targeting therapies with immune checkpoint inhibitors to overcome resistance in immune-excluded tumors.

cancer biology↗

bioRxiv2024

Beyond Integration: SuperGLUE Facilitates Explainable Training Framework for Multi-modal Data Analysis

Single-cell Multi-modal Data Integration has been an area of active research in recent years. However, it is difficult to unify the integration process of different omics in a pipeline, and evaluate the contributions of data integration. In this manuscript, we revisit the definition and contributions of multi-modal data integration, and propose a novel and scalable method based on probabilistic deep learning with an explainable framework powered by statistical modeling to extract meaningful information after data integration. Our proposed method is capable of integrating different types of omic and sensing data. It offers an approach to discovering important relationships among biological features or cell states. We demonstrate that our method outperforms other baseline models in preserving both local and global structures and perform a comprehensive analysis for mining structural relationships in complex biological systems, including inference of gene regulatory networks, extraction of significant biological linkages, and analysis of differentially regulatory relationships.

bioinformatics↗

bioRxiv2024

RobustCell: A Model Attack-Defense Framework for Robust Transcriptomic Data Analysis

Computational methods should be accurate and robust for tasks in biology and medicine, especially when facing different types of attacks, defined as perturbations of benign data that can cause a significant drop in method performance. Therefore, there is a need for robust models that can defend attacks. In this manuscript, we propose a novel framework named RobustCell to analyze attack-defense methods in single-cell and spatial transcriptomic data analysis. In this biological context, we consider three types of attacks as well as two types of defenses in our framework and systemically evaluate the performances of the existing methods on their performance of both clustering and annotating single cells and spatial transcriptomic data. Our evaluations show that successful attacks can impair the performances of various methods, including single-cell foundation models. A good defense policy can protect the models from performance drops. Finally, we analyze the contributions of specific genes toward the cell-type annotation task by running the single-gene and group-genes attack methods. Overall, RobustCell is a user-friendly and extension-flexible framework for analyzing the risks and safety of analyzing transcriptomic data under different attacks.

bioinformatics↗

bioRxiv2024

Beta burst characteristics and coupling within the sensorimotor cortical-subthalamic nucleus circuit in Parkinson's disease

BackgroundBursts of exaggerated subthalamic nucleus (STN) beta activity contribute to clinical impairments in Parkinsons disease (PD). Few studies have explored the characteristics and coupling of bursts across the sensorimotor cortical-STN circuit. ObjectiveWe sought to (1) establish the characteristics of sensorimotor cortical and STN bursts during naturalistic behaviours, and (2) determine the predictability of STN bursts from motor cortical recordings. MethodsWe analysed 1,478 hours of wirelessly streamed bilateral sensorimotor cortical and STN recordings from 5 PD patients. ResultsSTN bursts were longer than cortical bursts and had shorter inter-burst intervals. Long bursts (>200ms) in both structures displayed temporal overlap (>30%), with an estimated cortico-STN conduction delay of 8ms. Furthermore, approximately 27% of all STN bursts were preceded by a cortical burst. ConclusionCortical beta bursts tend to precede STN beta bursts, with short delays. However, subcortical mechanisms are also likely to contribute to STN burst initiation and propagation.

neuroscience↗

bioRxiv2024

Learning multi-cellular representations of single-cell transcriptomics data enables characterization of patient-level disease states

Single-cell RNA-seq (scRNA-seq) has become a prominent tool for studying human biology and disease. The availability of massive scRNA-seq datasets and advanced machine learning techniques has recently driven the development of single-cell foundation models that provide informative and versatile cell representations based on expression profiles. However, to understand disease states, we need to consider entire tissue ecosystems, simultaneously considering many different interacting cells. Here, we tackle this challenge by generating patient-level representations derived from multi-cellular expression context measured with scRNA-seq of tissues. We develop PaSCient, a novel model that employs a multi-level representation learning paradigm and provides importance scores at the individual cell and gene levels for fine-grained analysis across multiple cell types and gene programs characteristic of a given disease. We apply PaSCient to learn a disease model across a large-scale scRNA-seq atlas of 24.3 million cells from over 5,000 patients. Comprehensive and rigorous benchmarking demonstrates the superiority of PaSCient in disease classification and its multiple downstream applications, including dimensionality reduction, gene/cell type prioritization, and patient subgroup discovery.

bioinformatics↗

bioRxiv2024

Inhibition of Cardiac p38 Highlights the Role of the Phosphoproteome in Heart Failure Progression

Heart failure (HF) is a complex condition characterized by the inability of the heart to pump sufficient oxygen to the organs to meet their metabolic needs. Among the altered signal transduction pathways associated with HF pathogenesis, the p38 mitogen-activated protein kinase (p38 MAPK) pathway--activated in response to stress-- has attracted considerable attention for its potential role in HF progression and cardiac hypertrophy. However, the exact mechanisms by which p38 MAPK influences HF remain unclear. Addressing knowledge gaps may provide insight on why p38 inhibition has yielded inconsistent outcomes in clinical trials. Here we investigate the effects of p38 MAPK inhibition via SB203580 on cardiac remodeling in a guinea pig model of HF and sudden cardiac death. Using a well-established HF model with ascending aortic constriction and daily isoproterenol (ACi) administration, we assessed proteomic changes across three groups: sham-operated controls, untreated ACi, and ACi treated with SB203580 (ACiSB). Cardiac function was evaluated by M-mode echocardiography, while proteome and phosphoproteome profiles were analyzed using multiplexed tandem mass tag labeling and LC-MS/MS. Our findings demonstrate that chronic SB203580 treatment offers protection against progressive decline in cardiac function in HF. The proteomic data indicate that SB203580-treatment exerts broad protection of the cardiac phosphoproteome, beyond inhibiting maladaptive p38-dependent phosphorylation, extending to PKA and AMPK networks among others, ultimately protecting the phosphorylation status of critical myofibrillar and Ca2+-handling proteins. Though SB203580 had a more restricted impact on widespread protein changes in HF, its biosignature was consistent with preserved mitochondrial energetics as well as reduced oxidative and inflammatory stress.

molecular biology↗

bioRxiv2024

Inhibition of GEF-H1-RhoA signaling in inflammation with a stapled peptide mimicry of the RhoA67-78 helix

Guanine exchange factors (GEFs) are considered hard to drug with conventional small molecules, they lack conventional deep binding pockets and binding ligands are seldom reported. Here we report the design of a stapled peptide stP5 targeting the interaction between cytoskeletal regulator RhoA GTPase and its activator guanine exchange factor H1 (GEF-H1). StP5 is a modified RhoA mimic based on a previously identified bioactive -helical epitope to GEF-H1. StP5 effectively inhibits GEF-H1-induced morphological and transcriptional changes in cellular models for inflammation and does not affect the related GEF p114RhoGEF (ARHGEF18). StP5 peptide is approximately 100 fold more active in cellular assays than the unstapled P5 peptide. We provide a bioinformatic analysis of the stP5 bindings site in different GEFs, providing a basis for this selectivity. The GEF-H1 inhibitor stP5 represents a step towards fully drugging GEF-H1. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=102 SRC="FIGDIR/small/624118v1_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@d783b3org.highwire.dtl.DTLVardef@10798b2org.highwire.dtl.DTLVardef@1ba0817org.highwire.dtl.DTLVardef@693367_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2024

PRC1 and CTCF-Mediated Transition from Poised to Active Chromatin Loops Drives Bivalent Gene Activation

Polycomb Repressive Complex 1 (PRC1) and CCCTC-binding factor (CTCF) are critical regulators of 3D chromatin architecture that influence cellular transcriptional programs. Spatial chromatin structures comprise conserved compartments, topologically associating domains (TADs), and dynamic, cell-type-specific chromatin loops. Although the role of CTCF in chromatin organization is well-known, the involvement of PRC1 is less understood. In this study, we identified an unexpected, essential role for the canonical Pcgf2-containing PRC1 complex (cPRC1.2), a known transcriptional repressor, in activating bivalent genes during differentiation. Our Hi-C analysis revealed that cPRC1.2 forms chromatin loops at bivalent promoters, rendering them silent yet poised for activation. Using mouse embryonic stem cells (ESCs) with CRISPR/Cas9-mediated gene editing, we found that the loss of Pcgf2, though not affecting the global level of H2AK119ub1, disrupts these cPRC1.2 loops in ESCs and impairs the transcriptional induction of crucial target genes necessary for neuronal differentiation. Furthermore, we identified CTCF enrichment at cPRC1.2 loop anchors and at Polycomb group (PcG) bodies, nuclear foci with concentrated PRC1 and its tethered chromatin domains, suggesting that PRC1 and CTCF cooperatively shape chromatin loop structures. Through virtual 4C and other genomic analyses, we discovered that establishing neuronal progenitor cell (NPC) identity involves a switch from cPRC1.2-mediated chromatin loops to CTCF-mediated active loops, enabling the expression of critical lineage-specific factors. This study uncovers a novel mechanism by which pre-formed PRC1 and CTCF loops at lineage-specific genes maintain a poised state for subsequent gene activation, advancing our understanding of the role of chromatin architecture in controlling cell fate transitions.

genomics↗

bioRxiv2024

Tachykinin1-expressing neurons in the parasubthalamic nucleus control active avoidance learning

Active avoidance is a type of instrumental behavior that requires an organism actively to engage in specific actions to avoid or escape from a potentially aversive stimulus and is crucial for the survival and well-being of organisms. It requires a widely distributed, hard-wired neural circuits spanning multiple brain regions, including the amygdala and thalamus. However, less is known about whether and how the hypothalamus encodes and controls active avoidance learning. Here we identify a previously unknown role for the parasubthalamic nucleus (PSTN), located in the lateral subdivision of the posterior hypothalamus, in the encoding and control of active avoidance learning. Fiber photometry calcium imaging shows that the activity of tachykinin1-expressing PSTN (PSTNTac1) neurons progressively increases during this learning. Cell-type specific ablation and optogenetic inhibition of PSTNTac1 neurons attenuates active avoidance learning, whereas optogenetic activation of these cells promotes this learning via a negative motivational drive. Moreover, the PSTN mediates this learning differentially through its downstream targets. Together, this study identifies the PSTN as a new member of the neural networks involved in active avoidance learning and offers us potential implications for therapeutic interventions targeting anxiety disorders and other conditions involving maladaptive avoidance learning.

neuroscience↗

bioRxiv2024

Identifying simultaneous inhibition of riboflavin metabolism and MEK as a novel therapeutic strategy for pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) remains a formidable challenge with a survival rate of approximately 10%, largely due to the lack of diagnostic and therapeutic options. To identify novel therapeutic targets for PDAC, we previously developed a Drosophila melanogaster model, the 4-hit fly, which mimics the alterations in KRAS, TP53, CDKN2A, and SMAD4 genes observed in PDAC. Through whole-body genetic screening using the 4-hit model, we identified riboflavin (RF) metabolism as a novel potential therapeutic target for PDAC. Additionally, transcript levels of four out of five genes that are involved in RF metabolism were elevated in human PDAC compared to non-tumor tissues. Roseoflavin (RoF), an antimetabolite of RF, resulted in a variety range of metabolic changes, decreased mitochondrial respiratory chain activity, and suppressed proliferation in cultured PDAC cells. Furthermore, the combination of RoF with the MEK inhibitor drug trametinib significantly suppressed the growth of human PDAC xenografts compared to either treatment alone. These findings underscore that dual targeting of RF metabolism and MEK represents a novel therapeutic strategy for PDAC.

cancer biology↗

bioRxiv2024

Temporal High-Resolution Atlas of Human Blood Leukocyte Composition in Response to Respiratory Virus Inoculation

BackgroundRespiratory viral infections produce highly variable clinical outcomes, even after controlled exposure. Human viral challenge studies are well suited to study this variation because they capture immune profiles before inoculation and across defined post-challenge intervals. However, how circulating immune-cell populations change over time, and which responses are shared or virus-specific across respiratory viruses, remains incompletely defined. MethodsWe leveraged HR-VILAGE-3K3M, a curated human respiratory viral challenge transcriptomic resource, to analyze longitudinal whole-blood profiles from H1N1, H3N2, RSV, and HRV challenge studies. Using statistical deconvolution and xCell-based digital sorting, we estimated changes in major leukocyte populations and 28 immune-cell subtypes in symptomatic infected (Sx/Inf) and asymptomatic uninfected (Asx/Uninf) participants. Across viruses, symptomatic infection was marked by a broad shared pattern of innate immune activation together with reduced circulating lymphocyte enrichment. Activated dendritic cells, monocytes, and neutrophils increased following challenge, whereas strong pDC activation was observed primarily in influenza studies. Most T-cell and B-cell populations decreased after challenge. Plasma-cell enrichment increased progressively over time. Despite this shared architecture, response timing differed by virus: H3N2 showed the earliest changes, followed by H1N1, whereas RSV responses were delayed and HRV showed more distinct immune-cell trajectories. Migration-associated transcriptional programs were detected across both innate and adaptive immune-cell populations, supporting coordinated immune-cell trafficking during infection. Baseline immune composition also differed between outcome groups, suggesting that pre-existing immune-state differences may influence infection outcomes. ConclusionsThese findings define a cellular atlas of human respiratory viral challenge responses and show that respiratory viruses induce a broadly conserved immune-response program with distinct virus-specific kinetics. This framework may help clarify immune features associated with natural resistance, symptomatic infection, and vaccine-relevant antiviral immunity.

genomics↗

bioRxiv2024

scMODAL: A general deep learning framework for comprehensive single-cell multi-omics data alignment with feature links

Recent advancements in single-cell technologies have enabled comprehensive characterization of cellular states through transcriptomic, epigenomic, and proteomic profiling at single-cell resolution. These technologies have significantly deepened our understanding of cell functions and disease mechanisms from various omics perspectives. As these technologies evolve rapidly and data resources expand, there is a growing need for computational methods that can integrate information from different modalities to facilitate joint analysis of single-cell multi-omics data. However, integrating single-cell omics datasets presents unique challenges due to varied feature correlations and technology-specific limitations. To address these challenges, we introduce scMODAL, a deep learning framework tailored for single-cell multi-omics data alignment using feature links. scMODAL integrates datasets with limited known positively correlated features, leveraging neural networks and generative adversarial networks to align cell embeddings and preserve feature topology. Our experiments demonstrate scMODALs effectiveness in removing unwanted variation, preserving biological information, and accurately identifying cell subpopulations across diverse datasets. scMODAL not only advances integration tasks but also supports downstream analyses such as feature imputation and feature relationship inference, offering a robust solution for advancing single-cell multi-omics research.

bioinformatics↗