Search Results (139)

Inherited retinal dystrophies (IRDs) represent a diverse group of disorders caused by mutations in genes essential for retinal function and maintenance. Traditional bulk RNA sequencing techniques provide valuable information for deciphering disease pathogenesis but lack the resolution to capture variation among specific cell clusters during disease progression. In contrast, single-cell transcriptomics, including single-cell RNA sequencing (scRNA-seq), enables detailed examination of distinct retinal clusters in both healthy and diseased states, uncovering unique gene expression signatures and early molecular changes preceding photoreceptor cell death in IRDs. These insights not only deepen our understanding of the complex pathogenesis of IRDs but also highlight potential targets for novel therapeutic interventions. In this review, we examine the recent literature on the application of single-cell transcriptomics in IRDs to explore how these techniques enhance our understanding of disease mechanisms and contribute to the identification of new therapeutic targets. Full article

Transcriptomic profiling is a powerful tool for dissecting the cellular and molecular complexity of ocular tissues, providing insights into retinal development, corneal disease, macular degeneration, and glaucoma. With the expansion of microarray, bulk RNA sequencing (RNA-seq), and single-cell RNA-seq technologies, artificial intelligence (AI) has emerged as a key strategy for analyzing high-dimensional gene expression data. This review synthesizes AI-enabled transcriptomic studies in ophthalmology from 2019 to 2025, highlighting how supervised and unsupervised machine learning (ML) methods have advanced biomarker discovery, cell type classification, and eye development and ocular disease modeling. Here, we discuss unsupervised techniques, such as principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), uniform manifold approximation and projection (UMAP), and weighted gene co-expression network analysis (WGCNA), now the standard in single-cell workflows. Supervised approaches are also discussed, including the least absolute shrinkage and selection operator (LASSO), support vector machines (SVMs), and random forests (RFs), and their utility in identifying diagnostic and prognostic markers in age-related macular degeneration (AMD), diabetic retinopathy (DR), glaucoma, keratoconus, thyroid eye disease, and posterior capsule opacification (PCO), as well as deep learning frameworks, such as variational autoencoders and neural networks that support multi-omics integration. Despite challenges in interpretability and standardization, explainable AI and multimodal approaches offer promising avenues for advancing precision ophthalmology. Full article

Understanding the interactions between specific cell types within tissue environments is essential for elucidating key biological processes, such as immune responses, cancer progression, inflammation, and development, in both physiological and pathological studies. The predominant methods for analyzing cell–cell interactions (CCI) rely primarily on statistical inference using mapping or network-based techniques. However, these approaches often struggle to prioritize meaningful interactions owing to the high sparsity and heterogeneity inherent in single-cell RNA sequencing (scRNA-seq) data, where small but biologically important differences can be easily overlooked. To overcome these limitations, we developed PriorCCI, a deep-learning framework that leverages a convolutional neural network (CNN) alongside Grad-CAM++, an explainable artificial intelligence algorithm. This study aims to provide a scalable, interpretable, and biologically meaningful framework for systematically identifying and prioritizing key ligand–receptor interactions between defined cell-type pairs from single-cell RNA-seq data, particularly in complex environments such as tumors. PriorCCI effectively prioritizes interactions between cancer and other cell types within the tumor microenvironment and accurately identifies biologically significant interactions related to angiogenesis. By providing a visual interpretation of gene-pair contributions, our approach enables robust inference of gene–gene interactions across distinct cell types from scRNA-seq data. Full article

Precision medicine is transforming hematologic cancer care by tailoring treatments to individual patient profiles and moving beyond the traditional “one-size-fits-all” model. This review outlines foundational technologies, disease-specific advances, and emerging directions in precision hematology. The field is enabled by molecular profiling techniques, including next-generation sequencing (NGS), whole-exome sequencing (WES), and RNA sequencing (RNA-seq), as well as epigenomic and proteomic analyses. Complementary tools such as liquid biopsy and minimal residual disease (MRD) monitoring have improved diagnosis, risk stratification, and therapeutic decision making. We discuss major molecular targets and personalized strategies across hematologic malignancies: FLT3 and IDH1/2 in acute myeloid leukemia (AML); Philadelphia chromosome–positive and Ph-like subtypes in acute lymphoblastic leukemia (ALL); BCR-ABL1 in chronic myeloid leukemia (CML); TP53 and IGHV mutations in chronic lymphocytic leukemia (CLL); molecular subtypes and immune targets in diffuse large B-cell lymphoma (DLBCL) and other lymphomas; and B-cell maturation antigen (BCMA) in multiple myeloma. Despite significant progress, challenges remain, including high costs, disparities in access, a lack of standardization, and integration barriers in clinical practice. However, advances in single-cell sequencing, spatial transcriptomics, drug repurposing, immunotherapies, pan-cancer trials, precision prevention, and AI-guided algorithms offer promising avenues to refine treatment and improve outcomes. Overcoming these barriers will be critical for ensuring the equitable and widespread implementation of precision medicine in routine hematologic oncology care. Full article

The advent of personalized medicine and novel therapeutic strategies has transformed the treatment landscape of non-small cell lung cancer (NSCLC), significantly improving patient survival. However, only a minority of patients experience a durable benefit, as intrinsic or acquired resistance remains a major challenge. Understanding the complex mechanisms of resistance—linked to tumor biology, the tumor microenvironment (TME), and host factors—is crucial to overcoming these barriers. Recent innovations in diagnostics, including artificial intelligence and liquid biopsy, offer promising tools to refine therapeutic decisions. Machine Learning and Deep Learning provide predictive algorithms that enhance diagnostic accuracy and prognostic assessment. Techniques like single-cell RNA sequencing and pathomics offer deeper insights into the role of the TME. Liquid biopsy, as a minimally invasive method, enables real-time detection of circulating tumor components, facilitating the identification of predictive and prognostic biomarkers and illuminating tumor heterogeneity. These translational research advances are revolutionizing the understanding of cancer biology and are key to optimizing personalized treatment strategies. This review highlights emerging tools aimed at improving diagnostic and therapeutic precision in NSCLC, underscoring their role in decoding the interplay between tumor cells, the TME, and the host to ultimately improve patient outcomes. Full article

The global prevalence of hepatocellular carcinoma (HCC) is getting worse, leading to an urgent need for improved diagnostic and prognostic strategies. Liquid biopsy, which analyzes circulating tumor cells (CTCs), cell-free DNA (cfDNA), cell-free RNA (cfRNA), and extracellular vesicles (EVs), has emerged as a minimally invasive and promising alternative to traditional tissue biopsy. These biomarkers can be detected using sensitive molecular techniques such as digital PCR, quantitative PCR, methylation-specific assays, immunoaffinity-based CTC isolation, nanoparticle tracking analysis, ELISA, next-generation sequencing, whole-genome sequencing, and whole-exome sequencing. Despite several advantages, liquid biopsy still has challenges like sensitivity, cost-effectiveness, and clinical accessibility. Reports highlight the significance of multi-analyte liquid biopsy panels in enhancing diagnostic sensitivity and specificity. This approach offers a more comprehensive molecular profile of HCC, early detection, and tracking therapeutic treatment, particularly in those cases where single-analyte assays and imaging fail. The technological advancement in the isolation and analysis of CTC, cell-free nucleic acids, and EVs is increasing our understanding of extracting genetic information from HCC tumors and discovering mechanisms of therapeutic resistance. Furthermore, crucial information on tumor-specific transcriptomic and genomic changes can be obtained using cfRNA and cfDNA released into the peripheral blood by tumor cells. This review provides an overview of current liquid biopsy strategies in HCC and their use for early detection, prognosis, and monitoring the effectiveness of HCC therapy. Full article

Wood is an important raw material for industrial applications. Its fiber-specific genetic modification provides an effective strategy to alter wood characteristics in tree breeding. Here, we performed a cross-analysis of previously reported single-cell RNA sequencing and the AspWood database during wood formation to identify potential xylem fiber-dominant expressing genes in poplar. As a result, 32 candidate genes were obtained, and subsequently, we further examined the expression of these genes in fibers and/or vessels of stem secondary xylem using the laser capture microdissection technique and RT-qPCR. Analysis identified nine candidate genes, including PtrFLA12-2, PtrIRX12, PtrFLA12-6, PtrMYB52, PtrMYB103, PtrMAP70, PtrLRR-1, PtrKIFC2-3, and PtrNAC12. Next, we cloned the promoter regions of the nine candidate genes and created promoter::GUS transgenic poplars. Histochemical GUS staining was used to investigate the tissue expression activities of these gene promoters in transgenic poplars. In one month, transgenic plantlets grown in medium showed intensive GUS staining signals that were visible in the leaves and apical buds, suggesting substantial expression activities of these gene promoters in plantlets predominantly undergoing primary growth. In contrast, for three-month-old transgenic poplars in the greenhouse with predominantly developed secondary stem tissues, the promoters of seven of nine candidate genes, including PtrMYB103, PtrIRX12, and PtrMAP70, showed secondary xylem fiber-dominant GUS signals with considerable spatial specificity. Overall, this study presents xylem fiber-dominant promoters that are well-suited for specifically expressing genes of interest in wood fibers for forest tree breeding. Full article

Background: Cashmere is one of the important economic products of goats, and the KRTAP gene family, as an important family of regulatory genes in the growth process of cashmere fiber, largely affects the quality of cashmere. Methods: In this study, the KRTAP6-3 gene was identified and located on goat chromosome 1 using a goat genome homology search combined with a phylogenetic tree approach. The Longdong cashmere goat KRTAP6-3 gene variation and its effect on cashmere quality were explored by using the polymerase chain reaction single-stranded conformation polymorphism (PCR-SSCP) technique, in situ hybridization, and the allele presence/absence model. Results: The results identified a total of six SNPs in KRTAP6-3, three of which were located in the coding region and two of which were synonymous mutations, in addition to 45- bp deletion sequences detected in alleles C and F. Moreover, the KRTAP6-3 mRNA showed a strong expression signal in the cortical layer of the primary and secondary follicles in the inner root sheaths, as well as in the cells of the hair papillae and the matrices during the anagen phase, and signaling at the sites described above is attenuated during the telogen phase. The presence of allele C was associated with increased MFD (mean fiber diameter) (p < 0.01). The MFD of goats with allele C genotype (genotype AC) was significantly higher (p < 0.05) than that of goats without allele C genotype (genotypes AA and AB). Conclusions: This indicates that genetic variation in the KRTAP6-3 gene in goats is significantly associated with cashmere traits and can serve as a candidate gene for molecular markers of cashmere traits. Full article

Background: Circulating cell-free DNA (cfDNA) has been widely used as a prognostic marker for different cancers. Objective: In this study, we used 30 cfDNA samples from oral squamous cell carcinoma (OSCC), 199 public OSCC samples, and 192 normal samples to study various correlation factors that could improve the early-stage diagnostics and/or prognosis of OSCC. Methods: The statistical correlation between healthy and OSCC patients was done and deep sequencing analyses was performed to study various genomic alterations likes copy number variation (CNV), and single nucleotide variants (SNVs), gene fusion and genomic integration of viruses. Results: We found that the OSCC patient cfDNA concentration can serve as an indicator of tumor stage, malignancy, and survival prognosis. Deep genome sequencing of cfDNA revealed genomic alterations, such as CNVs, fusion genes, and viral integrations. The CNV analysis suggested a correlation with amplification and deletion in chromosomes at loci 1q, 2q, 3p, 3q, and chromosome 8 at loci q22. Moreover, at these loci, amplification of TP53, PIK3CA, and other genes related to keratinization in OSCC patients was observed. In addition, we identified a novel abundant fusion gene, TRMO-TRNT1 ‘chimera’, in seven high-grade tumor samples. The parental genes of this chimera, TRMO and TRNT1, are known to play roles in tRNA modification and DNA repair, respectively. We have identified SNVs in our OSCC cohort. Some of these SNVs, like KMT2C, MUC3A, and MUC6, have been identified as common cases in different cancer populations. Finally, we detected contigs integrations of human papillomavirus, simian virus, and enterovirus in the OSCC samples, which may point to the potential causes of OSCC. Conclusions: Our results indicate that the liquid biopsy technique may thus serve as a sensitive tool to study OSCC patient genomic alterations by exploring cfDNA circulating in the plasma, providing an easy-to-use blood test in the future. Full article

Inferring time-varying gene regulatory networks from time-series single-cell RNA sequencing (scRNA-seq) data remains a challenging task. The existing methods have notable limitations as most are either designed for reconstructing time-varying networks from bulk microarray data or constrained to inferring stationary networks from scRNA-seq data, failing to capture the dynamic regulatory changes at the single-cell level. Furthermore, scRNA-seq data present unique challenges, including sparsity, dropout events, and the need to account for heterogeneity across individual cells. These challenges complicate the accurate capture of gene regulatory network dynamics over time. In this work, we propose a novel f-divergence-based dynamic gene regulatory network inference method (f-DyGRN), which applies f-divergence to quantify the temporal variations in gene expression across individual single cells. Our approach integrates a first-order Granger causality model with various regularization techniques and partial correlation analysis to reconstruct gene regulatory networks from scRNA-seq data. To infer dynamic regulatory networks at different stages, we employ a moving window strategy, which allows for the capture of dynamic changes in gene interactions over time. We applied this method to analyze both simulated and real scRNA-seq data from THP-1 human myeloid monocytic leukemia cells, comparing its performance with the existing approaches. Our results demonstrate that f-DyGRN, when equipped with a suitable f-divergence measure, outperforms most of the existing methods in reconstructing dynamic regulatory networks from time-series scRNA-seq data. Full article

Advancements in immune monitoring and modulation technologies are driving transformative changes in cancer immunotherapy. These innovations are crucial for assessing patient-specific immune responses, enabling more accurate predictions of therapeutic efficacy and enhancing treatment outcomes. This review provides a comprehensive overview of current technologies used in immune monitoring, such as flow cytometry, single-cell RNA sequencing, and multiplex cytokine profiling. It also explores cutting-edge immune modulation methods, such as biomaterials that activate immune cells and genetically engineered cell-based therapies. We examine the strengths and limitations of these techniques and identify areas where further progress is needed. In particular, we explore how personalized therapies, real-time monitoring systems, and artificial intelligence shape the future of immune-based treatments. Through a comparative analysis of existing platforms and emerging solutions, this paper underscores the importance of integrating diverse scientific approaches—from immunology and bioengineering to data science—in advancing safer, more effective cancer treatments. This interdisciplinary approach promises to enhance the precision and accessibility of immune-based therapies, offering new hope for improved cancer care. Full article

Next-generation sequencing (NGS) has been well applied to assess genetic abnormalities in various biological samples to investigate disease mechanisms. With the advent of high-throughput and automatic testing platforms, NGS can identify radiation-sensitive and dose-responsive biomarkers, contributing to triage patients and determining risk groups for treatment in a nuclear emergency. While bulk NGS provides a snapshot of the average gene expression or genomic changes within a group of cells after the radiation, it cannot provide information on individual cells within the population. On the other hand, single-cell sequencing involves isolating individual cells and sequencing the genetic material from each cell separately. This approach allows for the identification of gene expression and genomic changes in individual cells, providing a high-resolution view of cellular diversity and heterogeneity within a sample. Single-cell sequencing is particularly useful to identify cell-specific features of dose-response and organ-response genes. While single-cell RNA sequencing (scRNA-seq) technology is still emerging in radiation research, it holds significant promise for identifying biomarkers related to radiation exposure and tailoring post-radiation medical care. This review aims to focus on current methods of radiation dosimetry and recently identified biomarkers associated with radiation exposure. Additionally, it addresses the development of NGS techniques in the context of radiation situations, such as cancer treatment and emergency events, with a particular emphasis on single-cell sequencing technology. Full article

In recent years, the role played by exosomes in lung diseases has been investigated. Exosomes have been shown to contribute to reductions in lung inflammation and pulmonary fibrosis. However, the role played by exosomes in pulmonary oxygen toxicity and the mechanism involved have not yet been reported. In the present work, we aimed to investigate the mechanism by which stem cell exosomes protect lung tissue and the potential molecular regulatory network involved. In this study, we employed single-cell RNA sequencing techniques to elucidate the unique cellular and molecular mechanisms underlying the progression of exosome therapy for pulmonary oxygen toxicity. We found changes in cell populations after exosome treatment, characterized by the expression of different molecular markers. We also integrated single-cell RNA sequencing (scRNA-seq) and bulk analysis to identify the protective effects of mesenchymal stem cell exosomes (MSC-Exos) in a mouse pulmonary oxygen toxicity (POT) model. scRNA-seq revealed dynamic shifts in the lung cellular composition after exosome treatment, including a reduction in inflammatory lymphoid cells (NK, B cells, CD8⁺ T, CD4⁺ T) and restored alveolar epithelial populations (AT1/AT2). A comprehensive gene expression analysis showed that inflammatory pathways associated with oxidative stress were significantly upregulated. In addition, our analysis of the intercellular interaction network revealed that there was a significant reduction in intercellular signal transduction in the POT group compared to the exosome-treated group. These results not only shed light on the unique cellular heterogeneity and potential pathogenesis following exosome therapy, but they also deepen our understanding of molecular pathophysiology and provide new avenues for targeted therapeutic strategies. Full article

Endothelial cells (ECs) are critical drivers of tumour progression, and their angiogenic process has been widely studied. However, the post-angiogenic transition of tip endothelial cells after sprouting remains insufficiently characterised. In this study, we utilised single-cell RNA sequencing analyses to identify a novel EC transition signature associated with endothelial permeability, migration, metabolism, and vascular maturation. Within the transition pathway, we discovered a critical EC subpopulation, termed tip-to-capillary ECs (TC-ECs), that was enriched in tumour tissues. Comparative analyses of TC-ECs with tip and capillary ECs revealed distinct differences in pathway activity, cellular communication, and transcription factor activity. The EC transition signature demonstrated substantial prognostic significance, validated across multiple cancer cohorts from TCGA data, particularly in bladder cancer. Subsequently, we constructed a robust prognostic model for bladder cancer by integrating the EC transition signature with multiple machine-learning techniques. Compared with 31 existing models across the TCGA-BLCA, GSE32894, GSE32548, and GSE70691 cohorts, our model exhibited superior predictive performance. Stratification analysis identified significant differences between different risk groups regarding pathway activity, cellular infiltration, and therapeutic sensitivity. In conclusion, our comprehensive investigation identified a novel EC transition signature and developed a prognostic model for patient stratification, offering new insights into endothelial heterogeneity, angiogenesis regulation, and precision medicine. Full article

The success of assisted reproductive technology is contingent upon the growth potential of embryos post-vitrification process. When compared to in vivo embryos, it has been found that the high intracellular lipid accumulation inside the in vitro-derived embryos results in poor survival during vitrification. Based on this finding, the present study assessed the impact of incorporating forskolin and linoleic acid (FL) entering in vitro culture (IVC) on the embryos’ cryo-survival, lipid content, and viability throughout vitrification. Lipid metabolomics and single-cell RNA sequencing (scRNA-seq) techniques were used to determine the underlying mechanism that the therapies were mimicking. It was observed that out of 726 identified lipids, 26 were expressed differentially between the control and FL groups, with 12 lipids upregulated and 14 lipids downregulated. These lipids were classified as Triacylglycerol (TG), Diacylglycerol (DG), Phosphatidylcholine (PC), and so on. A total of 1079 DEGs were detected between the FL and control groups, consisting of 644 upregulated genes and 435 downregulated genes. These DEGs were significantly enhanced in the arachidonic acid metabolism, lipolysis, fatty acid metabolism, cAMP signaling pathway, and other critical developmental pathways. Based on the observation, it was concluded that forskolin and linoleic acid decreased the droplet content of embryos by modulating lipid metabolism, thus enhancing the vitrified bovine embryos’ cryo-survival. Full article