Biology subjects

Xie, J.

Publications and source records attributed to Xie, J..

At least 91 records · Page 5Linked to original sources

bioRxiv2025

A Spatial Multi-Omic Framework Identifies Gliomas Permissive to TIL Expansion

Tumor-infiltrating lymphocyte (TIL) therapy is effective in several tumor types; however, its feasibility in immune subversive tumors like glioblastoma is unclear. We expanded TILs from glioblastoma specimens and observed marked variability in yield, composition, function, and TCR clonality. By interrogating TILs expanded ex vivo alongside their sourced glioma tissue, we sought to identify determinants of successful (TIL+) vs unsuccessful TIL expansion (TIL-). Expanded TILs were predominantly effector memory CD4 cells and exhibited oligoclonal TCR enrichment. Despite similar T cell abundance in TIL vs TIL- tumors, TIL tumors exhibited distinct spatial organization and cellular interactions, including increased endothelial-immune interactions/ proximity and enrichment of vascular-associated niches. CD4 T cells localized near CD68 macrophages in TIL tumors, while they were positioned near CD163 CD206 macrophages in TIL- tumors. Thus, TIL expansion and functionality are linked to spatial organization and myeloid context; these features may enable biomarker-driven stratification for future TIL therapy in gliomas.

cancer biology↗

bioRxiv2025

Identification and Implications for Tumor Heterogeneity of a DNA Methylation-Based Signature Classifying Pancreatic Ductal Adenocarcinoma Based on their Cellular Origin

BackgroundPancreatic ductal adenocarcinoma (PDAC) arises from distinct cellular origins, yet the extent to which DNA methylation patterns from normal pancreatic cells are preserved in tumor cells remains unclear. Identifying cell-of-origin signatures may enhance PDAC classification and therapeutic stratification. ObjectiveTo determine whether DNA methylation signatures in normal acinar and ductal pancreatic cells are retained in PDAC cell lines and to develop a robust classifier for distinguishing tumor origins. DesignWe performed DNA methylation profiling using the Illumina Infinium Mouse MethylationEPIC array on normal acinar and ductal cells and their PDAC derivatives in genetically engineered mouse models (GEMMs). Differential methylation analysis, and hierarchical clustering were used to identify and validate a conserved cell-of-origin DNA methylation signature. A logistic regression model was developed for classification. ResultsWe identified 178 CpG sites that remain preserved during tumorigenesis and effectively distinguished acinar- and ductal-derived PDAC cell lines. This signature was validated across independent sample sets, primary tumors, and orthotopic allografts. It successfully classified cell lines of unknown origin, including PDAC samples from KPC mice, and revealed the impact of oncogenic mutations on tumor fate. A logistic regression model supported these findings, confirming the robustness of the classification approach. Furthermore, the cell of origin influenced key PDAC characteristics, including treatment response, highlighting its potential role in molecular subtyping and patient stratification. ConclusionA preserved DNA methylation signature during pancreatic carcinogenesis distinguishes PDAC origins and influences tumor behavior. These results highlight the potential of DNA methylation profiling for tumor classification and personalized treatment strategies. They also raise important questions about the relevance of KPC mice as a preclinical model and the mechanisms driving PDAC heterogeneity. What is already known on this topicO_LIHuman PDAC exhibits significant heterogeneity, with molecular subtyping (classical vs basal-like) providing some insights into tumor behavior and clinical outcomes. C_LIO_LIMouse acinar and ductal cells can give rise to PDAC, influencing tumor characteristics and survival outcomes. C_LIO_LIDNA methylation is a powerful tool for tracing cellular identity and distinguishing cancer subtypes based on epigenetic profiles. C_LI What this study addsO_LIA cell-of-origin methylation signature is preserved during mouse carcinogenesis and across diverse experimental settings, providing a reliable tool for tumor classification. C_LIO_LIA DNA methylation-based classification system reliably distinguishes between acinar- and ductal-origin PDAC, filling a critical gap in methods to trace tumor lineage. C_LIO_LIAcinar- and ductal-derived PDACs exhibit distinct methylation patterns that correlate with differences in tumor behavior, such as chemoresistance, highlighting the biological relevance of cellular origin in PDAC. C_LIO_LIThe study provides new insights into how cell-of-origin influences PDAC heterogeneity and could lead to more precise therapeutic strategies tailored to the tumors lineage. C_LI How this study might affect research, practice or policyO_LIThis study provides a new, reliable method for classifying PDAC based on its cellular origin, which could significantly improve tumor classification in both preclinical and clinical settings, aiding in more accurate diagnoses and prognostic predictions. C_LIO_LIThe identification of distinct methylation patterns linked to tumor behavior offers valuable insights for developing personalized treatment strategies, as therapies could be tailored based on the tumors cellular origin and associated molecular characteristics. C_LIO_LIThe preservation of cell-of-origin methylation signature suggests the potential for developing a universal biomarker for PDAC classification, which could guide future clinical trials, therapeutic targeting, and patient stratification in PDAC care. C_LI

cancer biology↗

bioRxiv2025

Inwardly rectifying potassium channels regulate membrane potential polarization and direction sensing during neutrophil chemotaxis

Potassium channels regulate membrane potential and diverse physiological processes, including cell migration. However, the specific function of the inwardly rectifying potassium channels in immune cell chemotaxis is unknown. Here, we identified that the inwardly rectifying potassium channel Kir7.1 (KCNJ13) maintains the resting membrane potential and is required for directional sensing during neutrophil chemotaxis. Pharmacological or genetic inhibition of Kir7.1 in neutrophils impaired direction sensing toward various chemoattractants without affecting cell polarization in multiple neutrophil models. Using genetically encoded voltage indicators, we observed oscillating depolarization of the membrane potential in protrusions in zebrafish neutrophils, and Kir7.1 is required for polarized depolarization towards the chemokine source. Focal depolarization with optogenetic tools biases pseudopod selection and induces de novo protrusions. Global hyperpolarizing neutrophils stalled cell migration. Furthermore, Kir7.1 regulates GPCR signaling activation. This work adds membrane potential to the intricate feedforward mechanism, coupling the adaptive and excitable network required to steer immune cells in complex tissue environments.

cell biology↗

bioRxiv2025

The response of ETV6-NTRK3 condensates to TRK inhibitors elucidates their precise phase separationmechanism

NTRK fusion oncoproteins are considered to mediate oncogenesis in multiple rare cancers. These fusion oncoproteins form cytoplasmic kinase condensates to hyper-activate downstream signaling pathways. However, their responses to TRK inhibitors have not been investigated. Here we show that TRK inhibitors induce a quantity increasement and liquid-to-solid transition of ETV6-NTRK3 condensates. During this process, the ETV6-NTRK3 condensates undergo rapid dissolution and subsequent reemergence, with entirely altered components. We proposed a novel phase separation model for ETV6-NTRK3 that both ETV6 and NTRK3 fragments contribute to the condensates formation. These findings refresh the understanding of the phase separation mechanism of NTRK fusion proteins and provide basis for phenotype-based TRK inhibitor screening strategy.

cell biology↗

bioRxiv2025

A spontaneous mutation in a key C4 pathway gene significantly alters leaf δ13C uncoupling its relationship with WUE and photosynthetic performance in Zea mays

Increases in global temperature and drought are negatively impacting the yields of major crops. Therefore, targeted improvements to intrinsic water use efficiency (WUEi) are needed to reduce the water required for agricultural production. While it is very time-consuming to directly measure WUEi, stable carbon isotope ratios ({delta}13C) are a reliable high throughput proxy trait for quantifying WUEi in C3 species. While genetic studies have improved our understanding of the relationship between WUEi and {delta}13C in C4 species, the knowledge needed to implement {delta}13C in breeding schemes is incomplete. Using a Zea mays line with an extremely negative {delta}13C value, a quantitative genetics approach was used to identify a large deletion in carbonic anhydrase1 (cah1). Carbonic anhydrase is the first enzymatic step of the C4 photosynthetic pathway and is known to affect {delta}13C. Surprisingly, the line with the mutant allele has significantly higher carbonic anhydrase activity with a concurrent reduction in {delta}13C, opposite of what would be expected based on C4 carbon isotope fractionation theory. These observed decouple {delta}13C and WUEi, which calls for further investigation into carbon isotope discrimination in C4 species.

plant biology↗

bioRxiv2025

Population-scale gene expression analysis reveals the contribution of expression diversity to the modern wheat improvement

Changes in gene expression are crucial for crop breeding, yet population genomics has primarily focused on sequence polymorphisms rather than gene expression diversity. The strategy of using single genome reference for RNA-seq analysis could not handle introgression bias, especially for hexaploidy wheat. Here, we conducted RNA-seq for 328 wheat lines, including representative diverse landraces and elite cultivars from China and the United States, to investigate the role of gene expression variation in shaping agronomic traits. Using pan-genome resources, we identified 20,615 more transcripts than using the Chinese Spring reference genome alone. We constructed a pan-gene atlas regulatory map through eQTL analysis, demonstrating that genes introgressed from wild relatives were under tight genetic control. Genes responding to environmental stress show higher activity after introgressed into the wheat genome, demonstrating how long-term breeding selection impacted the gene expression regulation of targeted introgression. Multi-omics modeling identified 231 high-confidence candidate genes for 34 field agronomic traits and the seedling resistance phenotypes of 8 powdery mildew isolates. More than one fifth of those candidates have no homolog in Chinese Spring reference genome. By utilizing the indexed KN9204 EMS library, 80% candidates showed significant trait difference between wild type and mutant lines. Furthermore, directional shifts in genes of which expression were changed by breeding improvement demonstrated distinct adaptations to local environments. Our study constructed a pan-gene atlas to correct the reference bias of reads mapping in RNA-seq studies and revealed the expression patterns of introgressed genes within the wheat genome and their regulatory mechanisms, which highlighted the impact of breeding selection on gene expression of the worlds most important crop.

plant biology↗

bioRxiv2025

Deep-learning-based interpolation of longitudinal microbiome data powers biologically informative discovery

The human microbiome is a foundational and dynamic foundation for several health-related functions and disease processes. Advances in microbiome sequencing have enabled the characterization of microbial communities in several niches. Longitudinal microbiome studies further strive to discover clinically informative microbial community trajectories. However, these data are fraught with dropout events, high noise, and irregular sampling that limit and prevent the use of many available longitudinal analysis tools. To address these challenges, we introduce Bidirectional GRU-ODE-Bayes (BGOB), a deep learning framework developed for longitudinal microbiome interpolation. BGOB combines bidirectional information flow and ODE-based continuous modeling to jointly interpolate and smoothen trends across individual participants, providing uniform, denoised time intervals across patients. BGOB enables vastly improved performance in differential abundance testing and time-to-event analysis, and makes possible longitudinal analyses requiring uniformity, such as lead-lag detection and temporal clustering. After interpolation, previously low-powered datasets are able to broadly recapitulate known microbiology and elucidate interacting microbial communities. We highlight several associations between microbial taxa and disease, including novel species associated with Early Childhood Caries and disruption of key healthy gut microbiota in Inflammatory Bowel Disease. The BGOB package is publicly available at https://github.com/Rachel-Lyu/BGOB_n_test.

microbiology↗

bioRxiv2025

SUGP1 loss is the sole driver of SF3B1 hotspot mutant missplicing in cancer

SF3B1 is the most frequently mutated splicing factor in cancer. Mechanistically, such mutations cause missplicing by promoting aberrant 3 splice site usage; however, how this occurs remains controversial. To address this issue, we employed a computational screen of 600 splicing-related proteins to identify those whose reduced expression recapitulated mutant SF3B1 splicing dysregulation. Strikingly, our analysis revealed only two proteins whose loss reproduced this effect. Extending our previous findings, loss of the G-patch protein SUGP1 recapitulated almost all splicing defects induced by SF3B1 hotspot mutations. Unexpectedly, loss of the RNA helicase Aquarius (AQR) reproduced [~]40% of these defects. However, we found that AQR knockdown caused significant SUGP1 missplicing and reduced protein levels, suggesting that AQR loss reproduced mutant SF3B1 splicing defects only indirectly. This study advances our understanding of missplicing caused by oncogenic SF3B1 mutations, and highlights the fundamental role of SUGP1 in this process.

molecular biology↗

bioRxiv2025

Proteomics-based models of gene expression and cellular control of cotton fiber development

The shapes and material properties of cotton seed coat trichoblasts are the basis of a multibillion-dollar natural fiber industry. As such, these highly specialized cells are low-hanging fruit for intentional trait engineering. However, broad successes will require more mechanistic knowledge about their systems-level cellular controls. This time-series study integrates daily measurements of purified fiber transcriptomes and proteomes with multiscale fiber phenotyping datasets that span the same developmental interval. Abundance profiles of the subcellular proteomes are the foundation of the analyses. This resource article provides direct information concerning which homoeologs operate and informative depictions of how compartmentalized cellular systems change during developmental transitions. Prediction accuracy was partially validated by analysis of the protein expression group 11, which contained multiple known secondary cell wall cellulose synthases and dozens of unknown proteins and an averaged profile that was strongly correlated with a sharp state transition in cellulose microfibril alignment and increased cellulose content. The dataset as a whole can serve as a hypothesis-generating machine to guide future experiments that relate to cell shape and growth rate control, reversible tissue formation, and cell wall remodeling. Integration of mRNA and protein abundance revealed widespread evidence for post-transcriptional control. In addition, there were hundreds of transcriptionally controlled genes with differing timepoints of transition. This latter gene set can be used to more reliably analyze transcriptional control networks and to generate collections of gene expression drivers for cotton fiber research. The protein and transcript data are organized into user-friendly tables and a web interface that can be searched using any plant ortholog of interest based on developmental time, abundance, annotations, or phenotypic association.

plant biology↗

bioRxiv2025

Epidermal stem cell-derived extracellular vesicles induce fibroblasts mesenchymal-epidermal transition to alleviate hypertrophic scar via the miR-200s/ZEBs axis

Hypertrophic scar (HS) is a prevalent yet unresolved wound healing complication characterized by persistent hyperactive and proliferative fibroblasts, leading to excessive extracellular matrix (ECM) synthesis and collagen contraction. Our previous studies have identified epidermal stem cells (ESCs) as critical for wound healing and HS remodeling, with its extracellular vesicles (EVs) playing a vital role. However, the specific mechanisms remain unclear. In this study, we first discovered that ESC-EVs could effectively induce the mesenchymal-epidermal transition (MET) of HS fibroblasts (HSFs) and inhibit their biological activity. Furthermore, by next-generation sequencing and multiplexed CRISPR/Cas9 system, we elucidated that this therapeutic effect is mediated by the miR-200 family (miR-200s) encapsulated in ESC-EVs, which targeted and inhibited ZEB1 and ZEB2 in HSFs. This vital role and mechanism have been thoroughly validated in both in vitro cell experiments and in vivo rat tail HS (RHS) models. These findings not only shed light on a previously unidentified mechanism of ESC-EVs for HS, but also provide potential novel targets and strategies for its precise treatment.

cell biology↗

bioRxiv2025

Single-Cell Genomics Elucidates Molecular Variations and Regulatory Mechanisms in Circulating Immune Cells

The human peripheral blood displays diverse molecular characteristics across populations, understanding the drivers and underlying mechanisms of which remains challenging. Here, we introduce the Chinese Immune Multi-Omics Atlas (CIMA), elucidating sex-, age-, and genetic-related molecular variations by analyzing multi-omics data from 428 adults with over 10 million immune cells. CIMA generated an enhancer-driven gene regulatory network, identifying 237 high-quality regulons and revealing cell type-specific regulatory mechanisms. Additionally, 11,521 lead cis-expression quantitative trait loci (eQTLs) and 46,339 chromatin accessibility QTLs (caQTLs) were identified at cell type level. CIMA also uncovered pleiotropic associations among immune-related disease risk loci, eQTLs, and caQTLs in a cell type-specific manner. Lastly, a novel cell language model, CIMA-CLM, was developed to predict chromatin accessibility and noncoding variant effects using chromatin sequences and gene expressions. This work represents a population-scale multi-omics resource of human immune cells, providing a valuable reference for future investigation of immune-related diseases.

immunology↗

bioRxiv2025

Super-omics and PCAS reveal rheumatoid arthritis as a tumor-like disease

Rheumatoid arthritis (RA) is a debilitating systemic autoimmune disorder that significantly impairs quality of life. To elucidate the molecular alterations in RA progression, we performed comprehensive super-omics analyses on synovial tissues from patients with joint trauma, arthritis, and RA. These analyses included transcriptomics, proteomics, metabolomics, microbiomics, and fourteen PTMs (phosphorylation, acetylation, lactylation, O-GlcNAc glycosylation, arginine monomethylation, lysine monomethylation, lysine dimethylation, lysine trimethylation, succinylation, malonylation, glutarylation, tyrosine nitration, N-glycosylation, and O-glycosylation). Additionally, we developed a protein-centered association study (PCAS) method to integrate these complex datasets. Using this approach, we identified key proteins, such as STK17B, and its interactors, which may be crucial in RA pathogenesis. Tumor-like features, including aberrant angiogenesis and epithelial-mesenchymal transition, were observed in RA, alongside the potential involvement of oncogenes and tumor suppressor genes. Finally, we constructed a molecular interaction blueprint of RA, providing a comprehensive framework for advancing RA pathogenesis, diagnosis, and therapy.

immunology↗

bioRxiv2025

APMAT analysis reveals the association between CD8 T cell receptors, cognate antigen, and T cell phenotype and persistence

Elucidating the relationships between a class I peptide antigen, a CD8 T cell receptor (TCR) specific to that antigen, and the T cell phenotype that emerges following antigen stimulation, remains a mostly unsolved problem, largely due to the lack of large data sets that can be mined to resolve such relationships. Here, we describe Antigen-TCR Pairing and Multiomic Analysis of T-cells (APMAT), an integrated experimental-computational framework designed for the high-throughput capture and analysis of CD8 T cells, with paired antigen, TCR sequence, and single-cell transcriptome. Starting with 951 putative antigens representing a comprehensive survey of the SARS-CoV-2 viral proteome, we utilize APMAT for the capture and single cell analysis of CD8 T cells from 62 HLA A*02:01 COVID-19 participants. We leverage this unique, comprehensive dataset to integrate with peptide antigen properties, TCR CDR3 sequences, and T cell phenotypes to show that distinct physicochemical features of the antigen-TCR pairs strongly associate with both T cell phenotype and T cell persistence. This analysis suggests that CD8+ T cell phenotype following antigen stimulation is at least partially deterministic, rather than the result of stochastic biological properties.

immunology↗

bioRxiv2025

Highly divergent mesnidoviruses found in TSA database, especially in spiders

The mesnidoviruses are positive single-stranded viruses belonging to the order Nidovirales. They were mostly found in mosquitoes, and represent a link from small to large nidoviruses. In this study, we report 18 newly discovered nidoviruses in TSA database, most of them are from spiders (Order Arachnida). After reassembly, we got 9 nearly complete genomes with sizes ranged from [~]19,500 to 33,500 nt. Further phylogenetic analyses assigned these nidoviruses to Mesnidovirineae. Among them, the spider viruses represent a new clade. Pairwise evolutionary distance (PED) analysis showed these mesnidoviruses are highly divergent, with PED values from 2.2 to 4.9 with existing ones. Even among themselves the PED values are notable: with most exceeding the threshold of 0.32 that defines a new virus genus, suggesting these viruses are new species or belong to new higher-ranking taxa. In summary, we reported 18 mesnidoviruses in spiders, insects, plant and Anthozoa, the highly divergence suggested that spiders may harbor diverse mesnidoviruses. As the first nidovirus reported in Arachnida and Anthozoa, this discovery will contribute to the evolution study of Nidovirales.

microbiology↗

bioRxiv2024

Targeting endothelial ERG to mitigate vascular regression and neuronal ischemia in retinopathies

Retinopathy of prematurity (ROP) and diabetic retinopathy (DR) are ocular disorders in which a loss of retinal vasculature leads to ischemia followed by a compensatory neovascularization response. In mice, this is modeled using oxygen-induced retinopathy (OIR), whereby neonatal animals are transiently housed under hyperoxic conditions that result in central retina vessel regression and subsequent neovascularization. Using endothelial cell (EC)-specific gene deletion, we found that loss of two ETS-family transcription factors, ERG and FLI1, led to regression of OIR-induced neovascular vessels but failed to improve visual function, suggesting that relevant retinal damage occurs prior to and independently of neovascularization. Turning our attention to the initial stage of OIR, we found that hyperoxia repressed ERG expression in retinal ECs of wild type mice, raising the possibility that oxygen-induced ERG downregulation promotes vessel regression during the initiation of OIR-induced pathology. We therefore developed a murine model of EC-specific ERG overexpression and found it sufficient to prevent hyperoxia-induced vascular regression, neuronal cell death, and neovascularization in the OIR model. Importantly, ERG overexpression also improved visual function in OIR-challenged mice. Moreover, we show that both ERG and FLI1 are downregulated in the retinal vessels of human patients with early stages of DR, suggesting that neovascular disorders of the eye may share common mechanisms underlying pathological retinal capillary regression. Collectively, these data suggest that the regulation of vascular regression by EC-expressed ETS transcription factors may be adapted towards novel therapeutic approaches for the prevention and/or alleviation of ocular neovascular disorders.

pathology↗

bioRxiv2024

Encephalomyocarditis virus non-structural protein 2C degrades NDP52 autophagy protein to promote its own survival

Encephalomyocarditis virus (EMCV) is an important zoonotic pathogen that causes encephalitis and myocarditis, primarily in pigs and in some mammals. Nuclear dot protein (NDP) 52 is an important autophagy adaptor protein that is known to target microbial pathogens, including viruses into autophagosomes to facilitate the selective autophagy process. Here, we wanted to investigate how an important zoonotic virus such as EMCV interacts with NDP52. We found that NDP52 negatively regulates the entry and replication phases of EMCV and interacts with EMCV VP1/VP2 proteins to mediate its autophagic degradation. EMCV counteracts this by exerting autophagy induction through its encoded 2C protein. The autophagy machinery then hijacks NDP52 and transports it to lysosomes for subsequent degradation via late endosomal molecules Rab7 and Rab9. Our study describes a novel mechanism by which EMCV escapes the hosts antiviral response to promote its survival by hijacking the autophagy pathway using the non-structural protein 2C of EMCV. ImportanceEMCV is an important zoonotic pathogen that can induce autophagy, but the regulatory role of autophagy receptors in EMCV infection has been rarely reported. Herein, we show that NDP52 is a novel host limiting factor for EMCV, which not only inhibits early EMCV endocytosis, but also restricts EMCV replication in vitro by controlling autophagic degradation of EMCV structural proteins VP1 and VP2. In order to counteract this, EMCV uses the N-terminal region of its non-structural protein 2C to interact with NDP52, and hijacks the autophagy machine to transport NDP52 to lysosomes for degradation, which weakens NDP52-mediated degradation of VP1 and VP2. Importantly, we reveal a novel mechanism by which EMCV evades the host antiviral response, contributing to a deeper understanding of the infectious mechanism of EMCV and providing new ideas for the design of antiviral drug targets.

microbiology↗

bioRxiv2024

Engineered paediatric tumours retain patient tumour genotype and phenotype for precision medicine.

Precision medicine for paediatric and adult cancers that includes drug sensitivity profiling, can identify effective therapies for individual patients. However, obtaining adequate biopsy samples for high-throughput (HTP) screening remains challenging, with tumours needing to be expanded in culture or patient-derived xenografts - this is time-consuming and often unsuccessful. Herein, we have developed paediatric patient-derived tumour models using an engineered extracellular matrix (ECM) tissue mimic hydrogel system and HTP 3D bioprinting. Gene expression analysis from neuroblastoma and sarcoma patients identified key components of the ECM in these tumour types. Engineered hydrogels with ECM-mimic peptides were used to create patient-specific tumour organoids, modelling tumour growth conditions. Expanded tumour organoids recapitulated the genetic and phenotypic characteristics of the original tumours and retained tumourgenicity. Screening of these models identified individualised drug sensitivities. Our approach offers a timely and clinically relevant technology platform for precision medicine in paediatric cancers, potentially transforming preclinical testing across cancer types.

cancer biology↗

bioRxiv2024

Marker gene fishing for single-cell data with complex heterogeneity

In single-cell studies, cells can be characterized with multiple sources of heterogeneity such as cell type, developmental stage, cell cycle phase, activation state, and so on. In some studies, many nuisance sources of heterogeneity (SOH) are of no interest, but may confound the identification of the SOH of interest, and thus affect the accurate annotate the corresponding cell subpopulations. In this paper, we develop B-Lightning, a novel and robust method designed to identify marker genes and cell subpopulations correponding to a SOH (e.g., cell activation status), isolating it from other sources of heterogeneity (e.g., cell type, cell cycle phase). B-Lightning uses an iterative approach to enrich a small set of trustworthy marker genes to more reliable marker genes and boost the signals of the SOH of interest. Multiple numerical and experimental studies showed that B-Lightning outperforms existing methods in terms of sensitivity and robustness in identifying marker genes. Moreover, it increases the power to differentiate cell subpopulations of interest from other heterogeneous cohorts. B-Lightning successfully identified new senescence markers in ciliated cells from human idiopathic pulmonary fibrosis (IPF) lung tissues, new T cell memory and effector markers in the context of SARS-COV-2 infections, and their synchronized patterns which were previously neglected. This paper highlights B-Lightnings potential as a powerful tool for single-cell data analysis, particularly in complex data sets where sources of heterogeneity of interest are entangled with numerous nuisance factors.

bioinformatics↗