scINRB: single-cell gene expression imputation with network regularization and bulk RNA-seq data.

Single-cell RNA sequencing (scRNA-seq) facilitates the study of cell type heterogeneity and the construction of cell atlas. However, due to its limitations, many genes may be detected to have zero expressions, i.e. dropout events, leading to bias in downstream analyses and hindering the identification and characterization of cell types and cell functions. Although many imputation methods have been developed, their performances are generally lower than expected across different kinds and dimensions of data and application scenarios. Therefore, developing an accurate and robust single-cell gene expression data imputation method is still essential. Considering to maintain the original cell-cell and gene-gene correlations and leverage bulk RNA sequencing (bulk RNA-seq) data information, we propose scINRB, a single-cell gene expression imputation method with network regularization and bulk RNA-seq data. scINRB adopts network-regularized non-negative matrix factorization to ensure that the imputed data maintains the cell-cell and gene-gene similarities and also approaches the gene average expression calculated from bulk RNA-seq data. To evaluate the performance, we test scINRB on simulated and experimental datasets and compare it with other commonly used imputation methods. The results show that scINRB recovers gene expression accurately even in the case of high dropout rates and dimensions, preserves cell-cell and gene-gene similarities and improves various downstream analyses including visualization, clustering and trajectory inference.

PANoptosis, an indicator of COVID-19 severity and outcomes.

Coronavirus disease 2019 (COVID-19) has been wreaking havoc for 3 years. PANoptosis, a distinct and physiologically relevant inflammatory programmed cell death, perpetuates cytokine storm and multi-organ injuries in COVID-19. Although PANoptosis performs indispensable roles in host defense, further investigation is needed to elucidate the exact processes through which PANoptosis modulates immunological responses and prognosis in COVID-19. This study conducted a bioinformatics analysis of online single-cell RNA sequence (scRNA-seq) and bulk RNA-seq datasets to explore the potential of PANoptosis as an indicator of COVID-19 severity. The degree of PANoptosis in bronchoalveolar lavage fluid (BALF) and peripheral blood mononuclear cells (PBMC) indicated the severity of COVID-19. Single-cell transcriptomics identified pro-inflammatory monocytes as one of the primary sites of PANoptosis in COVID-19. The study subsequently demonstrated the immune and metabolic characteristics of this group of pro-inflammatory monocytes. In addition, the analysis illustrated that dexamethasone was likely to alleviate inflammation in COVID-19 by mitigating PANoptosis. Finally, the study showed that the PANoptosis-related genes could predict the intensive care unit admission (ICU) and outcomes of COVID-19 patients who are hospitalized.

Error modelled gene expression analysis (EMOGEA) provides a superior overview of time course RNA-seq measurements and low count gene expression.

Temporal RNA-sequencing (RNA-seq) studies of bulk samples provide an opportunity for improved understanding of gene regulation during dynamic phenomena such as development, tumor progression or response to an incremental dose of a pharmacotherapeutic. Moreover, single-cell RNA-seq (scRNA-seq) data implicitly exhibit temporal characteristics because gene expression values recapitulate dynamic processes such as cellular transitions. Unfortunately, temporal RNA-seq data continue to be analyzed by methods that ignore this ordinal structure and yield results that are often difficult to interpret. Here, we present Error Modelled Gene Expression Analysis (EMOGEA), a framework for analyzing RNA-seq data that incorporates measurement uncertainty, while introducing a special formulation for those acquired to monitor dynamic phenomena. This method is specifically suited for RNA-seq studies in which low-count transcripts with small-fold changes lead to significant biological effects. Such transcripts include genes involved in signaling and non-coding RNAs that inherently exhibit low levels of expression. Using simulation studies, we show that this framework down-weights samples that exhibit extreme responses such as batch effects allowing them to be modeled with the rest of the samples and maintain the degrees of freedom originally envisioned for a study. Using temporal experimental data, we demonstrate the framework by extracting a cascade of gene expression waves from a well-designed RNA-seq study of zebrafish embryogenesis and an scRNA-seq study of mouse pre-implantation and provide unique biological insights into the regulation of genes in each wave. For non-ordinal measurements, we show that EMOGEA has a much higher rate of true positive calls and a vanishingly small rate of false negative discoveries compared to common approaches. Finally, we provide two packages in Python and R that are self-contained and easy to use, including test data.

Agile Genetics: Single gene resolution without the fuss.

Gene discovery reveals new biology, expands the utility of marker-assisted selection, and enables targeted mutagenesis. Still, such discoveries can take over a decade. We present a general strategy, "Agile Genetics," that uses nested, structured populations to overcome common limits on gene resolution. Extensive simulation work on realistic genetic architectures shows that, at population sizes of >5000 samples, single gene-resolution can be achieved using bulk segregant pools. At this scale, read depth and technical replication become major drivers of resolution. Emerging enrichment methods to address coverage are on the horizon; we describe one possibility - iterative depth sequencing (ID-seq). In addition, graph-based pangenomics in experimental populations will continue to maximize accuracy and improve interpretation. Based on this merger of agronomic scale with molecular and bioinformatic innovation, we predict a new age of rapid gene discovery.

Distinguishing homogeneous advanced oxidation processes in bulk water from heterogeneous surface reactions in organic oxidation.

Clarifying the reaction pathways at the solid-water interface and in bulk water solution is of great significance for the design of heterogeneous catalysts for selective oxidation of organic pollutants. However, achieving this goal is daunting because of the intricate interfacial reactions at the catalyst surface. Herein, we unravel the origin of the organic oxidation reactions with metal oxide catalysts, revealing that the radical-based advanced oxidation processes (AOPs) prevail in bulk water but not on the solid catalyst surfaces. We show that such differing reaction pathways widely exist in various chemical oxidation (e.g., high-valent Mn3+ and MnOX) and Fenton and Fenton-like catalytic oxidation (e.g., Fe2+ and FeOCl catalyzing H2O2, Co2+ and Co3O4 catalyzing persulfate) systems. Compared with the radical-based degradation and polymerization pathways of one-electron indirect AOP in homogeneous reactions, the heterogeneous catalysts provide unique surface properties to trigger surface-dependent coupling and polymerization pathways of a two-electron direct oxidative transfer process. These findings provide a fundamental understanding of catalytic organic oxidation processes at the solid-water interface, which could guide the design of heterogeneous nanocatalysts.

Bubble: a fast single-cell RNA-seq imputation using an autoencoder constrained by bulk RNA-seq data.

Single-cell RNA-sequencing technology (scRNA-seq) brings research to single-cell resolution. However, a major drawback of scRNA-seq is large sparsity, i.e. expressed genes with no reads due to technical noise or limited sequence depth during the scRNA-seq protocol. This phenomenon is also called 'dropout' events, which likely affect downstream analyses such as differential expression analysis, the clustering and visualization of cell subpopulations, cellular trajectory inference, etc. Therefore, there is a need to develop a method to identify and impute these dropout events. We propose Bubble, which first identifies dropout events from all zeros based on expression rate and coefficient of variation of genes within cell subpopulation, and then leverages an autoencoder constrained by bulk RNA-seq data to only impute those values. Unlike other deep learning-based imputation methods, Bubble fuses the matched bulk RNA-seq data as a constraint to reduce the introduction of false positive signals. Using simulated and several real scRNA-seq datasets, we demonstrate that Bubble enhances the recovery of missing values, gene-to-gene and cell-to-cell correlations, and reduces the introduction of false positive signals. Regarding some crucial downstream analyses of scRNA-seq data, Bubble facilitates the identification of differentially expressed genes, improves the performance of clustering and visualization, and aids the construction of cellular trajectory. More importantly, Bubble provides fast and scalable imputation with minimal memory usage.

bulkAnalyseR: an accessible, interactive pipeline for analysing and sharing bulk multi-modal sequencing data.

Bulk sequencing experiments (single- and multi-omics) are essential for exploring wide-ranging biological questions. To facilitate interactive, exploratory tasks, coupled with the sharing of easily accessible information, we present bulkAnalyseR, a package integrating state-of-the-art approaches using an expression matrix as the starting point (pre-processing functions are available as part of the package). Static summary images are replaced with interactive panels illustrating quality-checking, differential expression analysis (with noise detection) and biological interpretation (enrichment analyses, identification of expression patterns, followed by inference and comparison of regulatory interactions). bulkAnalyseR can handle different modalities, facilitating robust integration and comparison of cis-, trans- and customised regulatory networks.

Distinct transcriptional alterations distinguish Lewy body disease from Alzheimer's disease.

Lewy body dementia and Alzheimer's disease (AD) are leading causes of cognitive impairment, characterized by distinct but overlapping neuropathological hallmarks. Lewy body disease (LBD) is characterized by alpha-synuclein aggregates in the form of Lewy bodies as well as the deposition of extracellular amyloid plaques, with many cases also exhibiting neurofibrillary tangle (NFT) pathology. In contrast, Alzheimer's disease is characterized by amyloid plaques and neurofibrillary tangles. Both conditions often co-occur with additional neuropathological changes, such as vascular disease and TDP-43 pathology. To elucidate shared and distinct molecular signatures underlying these mixed neuropathologies, we extensively analyzed transcriptional changes in the anterior cingulate cortex, a brain region critically involved in cognitive processes. We performed bulk tissue RNAseq from the anterior cingulate cortex and determined differentially expressed genes (q-value < 0.05) in control (n = 81), Lewy body disease (n = 436), Alzheimer's disease (n = 53), and pathological amyloid cases consisting of amyloid pathology with minimal or no tau pathology (n = 39). We used gene set enrichment and weighted gene correlation network analysis (WGCNA) to understand the pathways associated with each neuropathologically defined group. Lewy body disease cases had strong up-regulation of inflammatory pathways and down-regulation of metabolic pathways. The Lewy body disease cases were further subdivided into either high Thal amyloid, Braak NFT, or low pathological burden cohorts. Compared to the control cases, the Lewy body disease cohorts consistently showed up-regulation for genes involved in protein folding and cytokine immune response, as well as down-regulation of fatty acid metabolism. Surprisingly, concomitant tau pathology within the Lewy body disease cases resulted in no additional changes. Some core inflammatory pathways were shared between Alzheimer's disease and Lewy body disease but with numerous disease-specific changes. Direct comparison of Lewy body disease cohorts versus Alzheimer's disease cases revealed strong enrichment of synaptic signaling, behavior, and neuronal system pathways. Females had a stronger response overall in both Lewy body and Alzheimer's disease, with several sex-specific changes. Overall, the results identify genes commonly and uniquely dysregulated in neuropathologically defined Lewy body disease and Alzheimer's disease cases, shedding light on shared and distinct molecular pathways. Additionally, the study underscores the importance of considering sex-specific changes in understanding the complex transcriptional landscape of these neurodegenerative diseases.

Cellular depletion of major cathepsin proteases reveals their concerted activities for lysosomal proteolysis.

Proteins delivered by endocytosis or autophagy to lysosomes are degraded by exo- and endoproteases. In humans 15 lysosomal cathepsins (CTS) act as important physiological regulators. The cysteine proteases CTSB and CTSL and the aspartic protease CTSD are the most abundant and functional important lysosomal proteinases. Whereas their general functions in proteolysis in the lysosome, their individual substrate, cleavage specificity, and their possible sequential action on substrate proteins have been previously studied, their functional redundancy is still poorly understood. To address a possible common role of highly expressed and functional important CTS proteases, we generated CTSB-, CTSD-, CTSL-, and CTSBDL-triple deficient (KO) human neuroblastoma-derived SH-SY5Y cells and CTSB-, CTSD-, CTSL-, CTSZ and CTSBDLZ-quadruple deficient (KO) HeLa cells. These cells with a combined cathepsin deficiency exhibited enlarged lysosomes and accumulated lipofuscin-like storage material. The lack of the three (SH-SY5Y) or four (HeLa) major CTSs caused an impaired autophagic flux and reduced degradation of endocytosed albumin. Proteome analyses of parental and CTS-depleted cells revealed an enrichment of cleaved peptides, lysosome/autophagy-associated proteins, and potentially endocytosed membrane proteins like the amyloid precursor protein (APP), which can be subject to endocytic degradation. Amino- and carboxyterminal APP fragments accumulated in the multiple CTS-deficient cells, suggesting that multiple CTS-mediated cleavage events regularly process APP. In summary, our analyses support the idea that different lysosomal cathepsins act in concert, have at least partially and functionally redundant substrates, regulate protein degradation in autophagy, and control cellular proteostasis, as exemplified by their involvement in the degradation of APP fragments.

Photovoltaic effect by soft phonon excitation.

SignificanceThe quantum-mechanical geometric phase of electrons provides various phenomena such as the dissipationless photocurrent generation through the shift current mechanism. So far, the photocurrent generations are limited to above or near the band-gap photon energy, which contradicts the increasing demand of the low-energy photonic functionality. We demonstrate the photocurrent through the optical phonon excitations in ferroelectric BaTiO3 by using the terahertz light with photon energy far below the band gap. This photocurrent without electron-hole pair generation is never explained by the semiclassical treatment of electrons and only arises from the quantum-mechanical geometric phase. The observed photon-to-current conversion efficiency is as large as that for electronic excitation, which can be well accounted for by newly developed theoretical formulation of shift current.

Synaptotagmin-1 is a bidirectional Ca2+ sensor for neuronal endocytosis.

Exocytosis and endocytosis are tightly coupled. In addition to initiating exocytosis, Ca2+ plays critical roles in exocytosis­endocytosis coupling in neurons and nonneuronal cells. Both positive and negative roles of Ca2+ in endocytosis have been reported; however, Ca2+ inhibition in endocytosis remains debatable with unknown mechanisms. Here, we show that synaptotagmin-1 (Syt1), the primary Ca2+ sensor initiating exocytosis, plays bidirectional and opposite roles in exocytosis­endocytosis coupling by promoting slow, small-sized clathrin-mediated endocytosis but inhibiting fast, large-sized bulk endocytosis. Ca2+-binding ability is required for Syt1 to regulate both types of endocytic pathways, the disruption of which leads to inefficient vesicle recycling under mild stimulation and excessive membrane retrieval following intense stimulation. Ca2+-dependent membrane tubulation may explain the opposite endocytic roles of Syt1 and provides a general membrane-remodeling working model for endocytosis determination. Thus, Syt1 is a primary bidirectional Ca2+ sensor facilitating clathrin-mediated endocytosis but clamping bulk endocytosis, probably by manipulating membrane curvature to ensure both efficient and precise coupling of endocytosis to exocytosis.

A human stomach cell type transcriptome atlas.

BACKGROUND: The identification of cell type-specific genes and their modification under different conditions is central to our understanding of human health and disease. The stomach, a hollow organ in the upper gastrointestinal tract, provides an acidic environment that contributes to microbial defence and facilitates the activity of secreted digestive enzymes to process food and nutrients into chyme. In contrast to other sections of the gastrointestinal tract, detailed descriptions of cell type gene enrichment profiles in the stomach are absent from the major single-cell sequencing-based atlases. RESULTS: Here, we use an integrative correlation analysis method to predict human stomach cell type transcriptome signatures using unfractionated stomach RNAseq data from 359 individuals. We profile parietal, chief, gastric mucous, gastric enteroendocrine, mitotic, endothelial, fibroblast, macrophage, neutrophil, T-cell, and plasma cells, identifying over 1600 cell type-enriched genes. CONCLUSIONS: We uncover the cell type expression profile of several non-coding genes strongly associated with the progression of gastric cancer and, using a sex-based subset analysis, uncover a panel of male-only chief cell-enriched genes. This study provides a roadmap to further understand human stomach biology.

Disease heterogeneity and molecular classification of inflammatory palmoplantar diseases.

BACKGROUND: Palmoplantar pustulosis (PPP) is an inflammatory disease characterized by relapsing eruptions of neutrophil-filled, sterile pustules on the palms and soles that can be clinically difficult to differentiate from non-pustular palmoplantar psoriasis (palmPP) and dyshidrotic palmoplantar eczema (DPE). OBJECTIVE: We sought to identify overlapping and unique PPP, palmPP, and DPE drivers to provide molecular insight into their pathogenesis. METHODS: We performed bulk RNA sequencing of lesional PPP (n = 33), palmPP (n = 5), and DPE (n = 28) samples, as well as 5 healthy nonacral and 10 healthy acral skin samples. RESULTS: Acral skin showed a unique immune environment, likely contributing to a unique niche for palmoplantar inflammatory diseases. Compared to healthy acral skin, PPP, palmPP, and DPE displayed a broad overlapping transcriptomic signature characterized by shared upregulation of proinflammatory cytokines (TNF, IL-36), chemokines, and T-cell-associated genes, along with unique disease features of each disease state, including enriched neutrophil processes in PPP and to a lesser extent in palmPP, and lipid antigen processing in DPE. Strikingly, unsupervised clustering and trajectory analyses demonstrated divergent inflammatory profiles within the 3 disease states. These identified putative key upstream immunologic switches, including eicosanoids, interferon responses, and neutrophil degranulation, contributing to disease heterogeneity. CONCLUSION: A molecular overlap exists between different inflammatory palmoplantar diseases that supersedes clinical and histologic assessment. This highlights the heterogeneity within each condition, suggesting limitations of current disease classification and the need to move toward a molecular classification of inflammatory acral diseases.

Directly Converting Bulk Wood into Branch Micro-Nano Fibers to Synergistically Enhance the Strength and Toughness via Interface Engineering.

Hierarchical biobased micro/nanomaterials offer great potential as the next-generation building blocks for robust films or macroscopic fibers with high strength, while their capability in suppressing crack propagation when subject to damage is hindered by their limited length. Herein, we employed an approach to directly convert bulk wood into fibers with a high aspect ratio and nanosized branching structures. Particularly, the length of microfibers surpassed 1 mm with that of the nanosized branches reaching up to 300 µm. The presence of both interwoven micro- and nanofibers endowed the product with substantially improved tensile strength (393.99 MPa) and toughness (19.07 MJ m-3). The unique mechanical properties arose from mutual filling and the hierarchical deformation facilitated by branched nanofibers, which collectively contributed to effective energy dissipation. Hence, the nanotransformation strategy opens the door toward a facial, scalable method for building high-strength film or macroscopic fibers available in various advanced applications.

Flexoelectric Engineering of Bulk Photovoltaic Photodetector.

The bulk photovoltaic effect (BPVE) offers an interesting approach to generate a steady photocurrent in a single-phase material under homogeneous illumination, and it has been extensively investigated in ferroelectrics exhibiting spontaneous polarization that breaks inversion symmetry. Flexoelectricity breaks inversion symmetry via a strain gradient in the otherwise nonpolar materials, enabling manipulation of ferroelectric order without an electric field. Combining these two effects, we demonstrate active mechanical control of BPVE in suspended 2-dimensional CuInP2S6 (CIPS) that is ferroelectric yet sensitive to electric field, which enables practical photodetection with an order of magnitude enhancement in performance. The suspended CIPS exhibits a 20-fold increase in photocurrent, which can be continuously modulated by either mechanical force or light polarization. The flexoelectrically engineered photodetection device, activated by air pressure and without any optimization, possesses a responsivity of 2.45 × 10-2 A/W and a detectivity of 1.73 × 1011 jones, which are superior to those of ferroelectric-based photodetection and comparable to those of the commercial Si photodiode.

Transcriptomics and Spatial Proteomics for Discovery and Validation of Missing Proteins in the Human Ovary.

Efforts to understand the complexities of human biology encompass multidimensional aspects, with proteins emerging as crucial components. However, studying the human ovary introduces unique challenges due to its complex dynamics and changes over a lifetime, varied cellular composition, and limited sample access. Here, four new RNA-seq samples of ovarian cortex spanning ages of 7 to 32 were sequenced and added to the existing data in the Human Protein Atlas (HPA) database www.proteinatlas.org, opening the doors to unique possibilities for exploration of oocyte-specific proteins. Based on transcriptomics analysis of the four new tissue samples representing both prepubertal girls and women of fertile age, we selected 20 protein candidates that lacked previous evidence at the protein level, so-called "missing proteins" (MPs). The proteins were validated using high-resolution antibody-based profiling and single-cell transcriptomics. Fourteen proteins exhibited consistent single-cell expression patterns in oocytes and granulosa cells, confirming their presence in the ovary and suggesting that these proteins play important roles in ovarian function, thus proposing that these 14 proteins should no longer be classified as MPs. This research significantly advances the understanding of MPs, unearthing fresh avenues for prospective exploration. By integrating innovative methodologies and leveraging the wealth of data in the HPA database, these insights contribute to refining our understanding of protein roles within the human ovary and opening the doors for further investigations into missing proteins and human reproduction.

Integration of single-cell and RNA-seq data to explore the role of focal adhesion-related genes in osteoporosis.

Integrin-based focal adhesion is one of the major mechanosensory in osteocytes. The aim of this study was to mine the hub genes associated with focal adhesion and investigate their roles in osteoporosis based on the data of single-cell RNA sequencing and RNA-sequencing. Two hub genes (FAM129A and RNF24) with the same expression trend and AUC values greater than 0.7 in both GSE56815 and GSE56116 cohorts were uncovered. The nomogram was created to predict the risk of OP based on two hub genes. Subsequently, the competing endogenous RNA network was established based on two hub genes, 14 microRNAs and five long noncoding RNAs. Meanwhile, transcription factors-hub gene network was established based on two hub genes and 14 TFs. Finally, 73 drugs were predicted, of which there were 13 drugs targeting FAM129A and 66 drugs targeting RNF24. In both mouse and human blood samples, FAM129A expression was decreased in granulocytes and RNF24 expression was increased in monocytes. In the mouse experiment, FAM129A and anti-RNF24 were found to partially alleviate the progression of osteoporosis. In conclusion, two hub genes related to focal adhesion were identified by combined scRNA-seq and RNA-seq analyses, which might supply a new insight for the treatment and evaluation of OP.

Multi-omics analysis reveals the association between specific solute carrier proteins gene expression patterns and the immune suppressive microenvironment in glioma.

Glioma is the most prevalent malignant brain tumour. Currently, reshaping its tumour microenvironment has emerged as an appealing strategy to enhance therapeutic efficacy. As the largest group of transmembrane transport proteins, solute carrier proteins (SLCs) are responsible for the transmembrane transport of various metabolites and ions. They play a crucial role in regulating the metabolism and functions of malignant cells and immune cells within the tumour microenvironment, making them a promising target in cancer therapy. Through multidimensional data analysis and experimental validation, we investigated the genetic landscape of SLCs in glioma. We established a classification system comprising 7-SLCs to predict the prognosis of glioma patients and their potential responses to immunotherapy and chemotherapy. Our findings unveiled specific SLC expression patterns and their correlation with the immune-suppressive microenvironment and metabolic status. The 7-SLC classification system was validated in distinguishing subgroups within the microenvironment, specifically identifying subsets involving malignant cells and tumour-associated macrophages. Furthermore, the orphan protein SLC43A3, a core member of the 7-SLC classification system, was identified as a key facilitator of tumour cell proliferation and migration, suggesting its potential as a novel target for cancer therapy.

Identification and validation of RNA-binding protein SLC3A2 regulates melanocyte ferroptosis in vitiligo by integrated analysis of single-cell and bulk RNA-sequencing.

BACKGROUND: The pathogenesis of vitiligo remains unclear. The genes encoding vitiligo-related RNA-binding proteins (RBPs) and their underlying pathogenic mechanism have not been determined. RESULTS: Single-cell transcriptome sequencing (scRNA-seq) data from the CNCB database was obtained to identify distinct cell types and subpopulations and the relative proportion changes in vitiligo and healthy samples. We identified 14 different cell types and 28 cell subpopulations. The proportion of each cell subpopulation significantly differed between the patients with vitiligo and healthy groups. Using RBP genes for unsupervised clustering, we obtained the specific RBP genes of different cell types in vitiligo and healthy groups. The RBP gene expression was highly heterogeneous; there were significant differences in some cell types, such as keratinocytes, Langerhans, and melanocytes, while there were no significant differences in other cells, such as T cells and fibroblasts, in the two groups. The melanocyte-specific RBP genes were enriched in the apoptosis and immune-related pathways in the patients with vitiligo. Combined with the bulk RNA-seq data of melanocytes, key RBP genes related to melanocytes were identified, including eight upregulated RBP genes (CDKN2A, HLA-A, RPL12, RPL29, RPL31, RPS19, RPS21, and RPS28) and one downregulated RBP gene (SLC3A2). Cell experiments were conducted to explore the role of the key RBP gene SLC3A2 in vitiligo. Cell experiments confirmed that melanocyte proliferation decreased, whereas apoptosis increased, after SLC3A2 knockdown. SLC3A2 knockdown in melanocytes also decreased the SOD activity and melanin content; increased the Fe2+, ROS, and MDA content; significantly increased the expression levels of TYR and COX2; and decreased the expression levels of glutathione and GPX4. CONCLUSION: We identified the RBP genes of different cell subsets in patients with vitiligo and confirmed that downregulating SLC3A2 can promote ferroptosis in melanocytes. These findings provide new insights into the pathogenesis of vitiligo.

Pain Sensitivity Genes as Therapeutic Targets in Knee Osteoarthritis: A Comprehensive Analysis.

Pain sensitivity is a significant factor in knee osteoarthritis (KOA), influencing patient outcomes and complicating treatment. Genetic differences, particularly in pain-sensing genes (PSRGs), are known to contribute to the variability in pain experiences among KOA patients. This study aims to systematically analyze PSRGs in KOA to better understand their role and potential as therapeutic targets. We utilized bulk RNA-seq data from the GSE114007 and GSE169077 datasets to identify differentially expressed genes, with 20 genes found to be significantly altered. Key PSRGs, including PENK, NGF, HOXD1, and TRPA1, were identified using LASSO, SVM, and random forest algorithms. Further, KEGG and GO enrichment analyses revealed pathways such as "Neuroactive ligand-receptor interaction" and "ECM-receptor interaction," which were validated through external datasets. Single-cell RNA-seq analysis from GSE152805, GSE133449, and GSE104782 datasets demonstrated the heterogeneity and dynamic expression of PSRGs across different cell subpopulations in synovium, meniscus, and cartilage samples. UMAP and pseudotime analyses were used to visualize spatial distribution and developmental trajectories of these genes. The findings emphasize the critical roles of PSRGs in KOA, highlighting their potential as therapeutic targets and suggesting that integrating genetic information into clinical practice could significantly improve pain management and treatment strategies for KOA.