Search Results (953)

Brain-Derived Neurotrophic Factor (BDNF), a key member of the neurotrophin family, is critically involved in neuronal survival, synaptic plasticity, learning, and memory. While its roles in mammals have been extensively documented, the molecular regulatory mechanisms governing BDNF expression and its causal contributions to complex cognitive behaviors remain poorly understood in non-mammalian vertebrates—particularly for the domestic pigeon (Columba livia domestica), a species distinguished by its remarkable spatial navigation and homing capabilities. This review synthesizes the current evidence on BDNF in the pigeon central nervous system across five thematic domains: molecular structure and isoform diversity, transcriptional and epigenetic regulatory networks, involvement in neural development, associations with cognitive and navigational behaviors, and potential translational applications. A particular emphasis is placed on the region-specific and activity-dependent expression patterns of BDNF in brain structures such as the hippocampal formation (HF), optic tectum, and striatum, and their functional relevance to visual processing, homing behavior, and stress adaptation. To date, most findings remain correlational; therefore, establishing a mechanistic understanding necessitates the integration of advanced methodologies—including single-cell omics, CRISPR-based gene editing, and high-resolution behavioral phenotyping—to causally link BDNF dynamics, neural circuit modulation, and spatial cognition. This synthesis aims to bridge gaps in comparative neurobiology, inform molecular approaches to avian cognitive enhancement, and support evidence-based strategies for racing pigeon breeding and welfare assessment. Full article

This article addresses a current point of contention in the field of single-molecule/single-particle tracking, as well as the relevant literature, and supplements it with some published cell-based experiments to illustrate our conclusions and known theorems. We attempt to explain the controversy surrounding the differing biophysical and cell biological results of studies on the individual molecule and those “at the single-molecule level” as well as at the level of many molecules in such a way that even readers who are unfamiliar with the subject can understand it without having to read all the mathematical, physical, and biophysical references. Given this abundance of studies in the literature, it is obvious that genuine single-molecule studies are urgently needed, i.e., single-molecule studies that focus on increasing the sensitivity of the temporal resolution of single-molecule measurements and not just on spatial resolution. Full article

Transcriptome profiling is a cornerstone of functional genomics, enabling the detailed characterization of gene expression in health and disease. Bulk RNA sequencing (bulk RNAseq) remains the most widely used approach in clinical and large-cohort studies due to its cost-effectiveness, robustness, and comprehensive transcriptome coverage. However, bulk RNAseq inherently averages gene expression signals across heterogeneous cell populations, thereby masking cellular diversity and obscuring rare cell types. In contrast, single-cell RNA sequencing (scRNAseq) enables a high-resolution analysis of cellular heterogeneity, allowing the identification of distinct cell types, transitional states, and developmental trajectories. Nevertheless, scRNAseq is associated with higher cost, limited scalability, increased technical noise, sparse expression matrices, and protocol-dependent biases introduced during tissue dissociation or nuclear isolation. In this review, we summarize the conceptual and methodological foundations of integrating bulk RNAseq and scRNAseq data, emphasizing their complementary strengths and limitations. We discuss how scRNAseq-derived cell-type atlases can serve as reference matrices for computational reconstruction (deconvolution) of bulk RNAseq profiles and examine key sources of technical and biological variability. Furthermore, we outline major integration strategies, including reference-based deconvolution, pseudobulk aggregation, and Bayesian joint modeling to provide an overview of widely used analytical tools and essential components of scRNAseq data processing workflows. Full article

Glioblastoma (GBM) remains the most aggressive primary malignant brain tumor in adults, with poor survival largely driven by diffuse cellular infiltration, profound heterogeneity, and near-universal recurrence following standard therapy. Although maximizing the extent of resection is a key determinant of patient outcome, current clinical imaging modalities lack the spatial resolution necessary to detect microscopic tumor invasion and therapy-resistant cell populations. Emerging in vivo imaging technologies capable of cellular and near-single-cell resolution have therefore become a major focus in preclinical neuro-oncology research, with growing relevance for surgical guidance, treatment adaptation, and translational discovery. This review evaluates multiple optical imaging modalities, including multi-photon microscopy, near-infrared II fluorescence imaging, bioluminescence imaging, photoacoustic imaging, optical coherence tomography, confocal laser endomicroscopy, Raman spectroscopy, autofluorescence microscopy, and fluorescence macroscopy with a focus on their ability to detect residual GBM cells. Despite significant advances, these approaches remain constrained by limitations in molecular target availability, probe delivery across the blood–brain barrier, and signal variability within heterogeneous tumor regions. The biological complexity of GBM further challenges detection, as residual tumor cells are spatially dispersed and phenotypically diverse, limiting the effectiveness of single-marker or single-modality strategies. Together, these findings highlight the need for integrated, biologically informed imaging approaches to improve detection of residual disease and guide surgical decision making. Full article

Prostate cancer is one of the most prevalent malignancies among men and remains a major clinical challenge due to the complex tumor microenvironment. Understanding gene expression dynamics at both cellular and spatial levels is essential for improving therapeutic strategies. In this study, we performed an integrated multi-omics analysis using single-cell RNA sequencing and spatial transcriptomics. scRNA-seq data from 15 prostate samples, including 8 normal and 7 tumor tissues, were analyzed to characterize distinct cellular populations. Spatial transcriptomic profiling was conducted on three FFPE prostate tissue sections, including adjacent normal tissue, acinar cell carcinoma, and invasive adenocarcinoma, using the standard 10x Genomics Visium FFPE platform (55 µm capture spots). Single-cell analysis revealed heterogeneity among epithelial, stromal, and immune cell populations, highlighting complex signaling networks in which myeloid cells may contribute to tumor progression through immune suppression and epithelial adaptability. Spatial transcriptomic analysis further identified region-specific expression patterns and spatially restricted tumor niches, including the regional establishment of TXNIP and BIRC3 as genes associated with metabolic stress and inflammatory survival pathways. The spatial colocalization of BIRC3 with tumor vasculature in invasive carcinoma tissue suggests a novel interaction. Our discoveries using an integrated single-cell and spatial transcriptomic approach reveal a high-resolution molecular map of prostate cancer with spatial features that may provide further therapeutic investigation. Full article

Background: Psoriasis frequently relapses after treatment withdrawal, consistent with persistent epigenetic programs in lesional immune cells. Lysine acetylation is a reversible regulatory layer linking chromatin accessibility, transcription factor activity, and immune-cell effector programs; yet, its cell-type-resolved landscape and clinical stratification value in psoriasis remain incompletely defined. Methods: We integrated four bulk transcriptome cohorts of psoriatic and healthy skin (746 psoriasis, 515 controls) with two public skin scRNA-seq datasets. A diagnostic acetylation-regulator signature was derived from 33 curated acetylation regulators, and acetylation endotypes were defined by unsupervised clustering. The cell-type-specific expression was mapped at the single-cell resolution. Key regulators were validated by quantitative real-time polymerase chain reaction (qRT-PCR) in an imiquimod-induced psoriasis-like mouse model, and further verified in an independent dataset (GSE136757). Motif enrichment and drug–target mining were used to prioritize transcriptional regulators and candidate epigenetic therapeutics. Results: Sixteen acetylation regulators were differentially expressed in bulk skin, with histone deacetylase (HDAC1) showing the strongest upregulation and lysine acetyltransferase (KAT2A) the strongest downregulation. A 13-gene acetylation signature discriminated psoriasis from controls (area under the curve, AUC 0.886) and separated lesional samples into two acetylation endotypes with divergent pathway states (hypoxia–glycolysis versus oxidative-stress-dominated programs). Single-cell mapping demonstrated immune-restricted acetylation modules, including CREB binding protein (CREBBP)-enriched neutrophils, histone deacetylase 1 (HDAC1)-high cluster of differentiation (CD)8⁺ T cells, and lysine acetyltransferase 6A (KAT6A)/lymphoid enhancer binding factor (LEF1)-enriched CD4⁺ and regulatory T cell (Treg) subsets, coincident with interleukin (IL)-17-related inflammatory programs. In mice, qRT-PCR confirmed the coordinated dysregulation of hub genes and highlighted Hnf1a and Kat6a as reproducible candidates. External validation using the GSE136757 dataset further supports their robust diagnostic performance. Motif analysis nominated interferon regulatory factor (IRF4), YY transcription factor (YY2), and zinc finger protein (ZNF404) as putative transcriptional mediators downstream of acetylation programs, and drug–target mining prioritized epigenetic compounds with subtype-relevant potential, including histone deacetylase (HDAC) inhibitors (e.g., entinostat) and the p300/CREB binding protein (CBP) inhibitor A485. Conclusions: This integrative atlas links acetylation regulators to specific immune compartments, defines acetylation endotypes associated with distinct inflammatory programs, and provides a rationale for stratified epigenetic target selection in psoriasis. Full article

Background/Objectives: Sex differences influence skin physiology, immune regulation, and disease susceptibility, but the cellular organization of sex-biased transcriptional programs in healthy human skin remains incompletely defined. We aimed to define sex-associated differences in cellular composition and gene expression in healthy adult truncal skin at single-cell resolution. Methods: We constructed a sex-resolved single-cell transcriptomic atlas of healthy human truncal skin by integrating scRNA-seq data from 12 donors (5 males, 7 females). After quality control, 107,967 cells were classified into 14 major cell types. Sex-associated differences were assessed using donor-level pseudo-bulk analyses at both whole-skin and cell-type-resolved levels. Results: The cellular composition was conserved between sexes, with significant differences in mast cells and regulatory T cells. Whole-skin pseudo-bulk analysis identified distinct male-biased and female-biased transcriptional programs. Male-biased signals were linked to extracellular matrix organization and immune responses, while female-biased signals involved ion transport and neuromodulation. Cell-type-resolved analysis revealed that most sex-biased genes were lineage-specific, with minimal cross-lineage sharing. Conclusions: Sexual dimorphism in healthy human truncal skin is encoded through lineage-structured transcriptional regulation rather than broad compositional changes, providing a framework for understanding sex-biased skin homeostasis and disease susceptibility. Full article

Background/Objectives: Cardiac embryonic development is a highly coordinated and dynamic process governed by precise spatiotemporal gene regulation. Increasing evidence indicates that cellular heterogeneity and lineage specification during heart development are tightly controlled by complex gene regulatory networks (GRNs) and epigenetic mechanisms. Recent advances in single-cell multi-omics technologies provide unprecedented resolution to dissect these regulatory processes. This review aims to summarise current applications of single-cell multi-omics approaches to elucidate gene regulatory mechanisms underlying cardiac embryogenesis and their implications for congenital heart disease (CHD). Methods: We systematically reviewed recent literature on single-cell RNA sequencing (scRNA-seq), single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq), spatial transcriptomics, and integrative multi-omics analyses applied to embryonic heart development. Studies were analysed to evaluate how these technologies contribute to cell-type identification, lineage trajectory reconstruction, GRN inference, and epigenetic landscape characterisation. Results: Single-cell multi-omics approaches have enabled the construction of high-resolution cardiac cell atlases, revealing previously unrecognised cellular heterogeneity and transitional states during heart development. Integrative analyses of transcriptomic and chromatin accessibility data have provided insights into lineage commitment, key transcription factors, enhancer–promoter interactions, and dynamic GRNs. These findings have advanced understanding of developmental genetics in cardiac morphogenesis and offered new perspectives on the molecular mechanisms underlying CHD. Conclusions: Single-cell multi-omics technologies provide a powerful framework for investigating gene regulatory mechanisms during cardiac embryogenesis. Continued methodological refinement and integrative analyses are expected to further clarify developmental processes and facilitate translational insights into CHD. Full article

Head and neck squamous cell carcinoma (HNSCC) continues to pose a major global health challenge, with over 600,000 new cases diagnosed annually and persistently poor survival outcomes despite advances in surgery, radiotherapy, and immunotherapy. Growing evidence implicates metabolic reprogramming, including enhanced glycolysis, glutaminolysis, lipid synthesis, and one-carbon/redox flux as a central driver of HNC initiation, progression, and therapy resistance. In contrast, metabolic crosstalk within the hypoxic, acidic tumor microenvironment (TME) further shapes immune evasion and stromal support. Recent innovations in mass spectrometry platforms (LC-MS, GC-MS, NMR) have attracted attention in clinical therapeutics, and spatial metabolomics imaging techniques now enable high-resolution in situ mapping of metabolites, revealing intratumoral heterogeneity and offering new insights into tumor-immune–stromal interactions and potential biomarkers for precision oncology. In this review, we integrate critical methodological considerations from sample collection and data-analysis workflows to analytical pitfalls with a balanced, pathway-focused analysis of HNSCC dysmetabolism, examine tumor immune stromal metabolic interactions, and highlight translational opportunities through emerging biomarkers, targeted inhibitors, and cutting-edge approaches such as single-cell and AI-driven metabolomics to chart a roadmap toward precision oncology for HNSCC. Full article

Super-resolution microscopy has transformed biological imaging by enabling nanoscale visualization of cellular structures beyond the diffraction limit. However, its effective application in highly dense molecular environments still poses challenges. This is the case for 3D PhotoActivated Localization Microscopy (PALM) achieved through astigmatism in bacterial cells. The limited volume of a single bacterium highly increases the probability of the intensity profiles emitted by single chromophores to overlap, thus strongly decreasing the number of localizations, leading to dramatic undersampling. Dual-color 3D super-resolution in Escherichia coli is achieved through a combination of PALM with Expansion Microscopy (Ex-PALM). PALM provides high specificity through photoactivable (PA) fusion proteins and high localization precision, while ExM physically expands the specimen and separate densely packed molecules. This hybrid approach enables dual-color 3D single-molecule localization with about 3 nm spatial resolution, thus allowing one to measure distances down to the molecular scale. This is achieved by optimizing ExM protocols in bacteria to achieve a 4-fold isotropic expansion, by minimizing both chromatic aberrations and signal crosstalk, and by improving single-molecule sensitivity through highly selective inclined illumination. The method is applied to measure the spatial distribution of HisF and HisH proteins, involved in E. coli histidine biosynthesis. By tagging each protein with a photoactivable fluorescent protein, Ex-PALM reveals that after being synthetized, they co-localize in the bacterial volume with an average 3D distance of 19 nm. By combining labeling specificity with Ex-PALM, an effective method is developed for studying molecular organization in prokaryotes and in high-density samples in general, such as cell organelles or molecular condensates, with broad applications in microbiology, synthetic biology, and cellular biophysics. Full article

Severe Fever with Thrombocytopenia Syndrome (SFTS) is caused by the SFTS virus (SFTSV) and is associated with a high mortality rate. Although previous studies have reported RNA modifications such as m6A on SFTSV RNA, an integrated analysis of native viral transcript architecture and multiple RNA modification types within infected cells remains lacking. Here, we used Oxford Nanopore direct RNA sequencing (DRS) to analyze native SFTSV RNA in infected cells, combining strand-specific alignment, isoform reconstruction through read endpoint clustering, isoform-level quantification, and signal-level modification identification using unmodified in vitro transcripts as a baseline. This approach allowed us to construct detailed maps of the L, M, and bidirectionally encoded S segments at single-molecule, isoform-level resolution. The results reveal a “length-layering” pattern in SFTSV transcription, anchored by recurrent 3′ termination hotspots: only a few full-length transcripts dominate expression, whereas multiple reproducible truncated isoforms were associated with discrete termination windows, a pattern less consistent with random degradation alone and suggestive of regulated transcript termination. At the single-nucleotide level, the modification landscape is predominantly Ψ (pseudouridine), followed by m⁵C (5-methylcytosine), with sparse m⁶A (N6-methyladenosine). Modification hotspots are co-located across isoforms at the same genomic coordinates, exhibiting segmental/strand asymmetry, with sharper peaks on (−) RNA. These patterns provide a testable framework and raise the possibility that transcript-boundary organization and site-constrained Ψ/m5C signals may be associated with variation in viral RNA output. More broadly, isoform proportions around termination hotspots and Ψ/m5C-enriched regions at conserved sites may serve as quantitative features for characterizing viral RNA organization and prioritizing targets for future functional investigation. Our single-molecule integrated map establishes a reproducible methodological framework for studying SFTSV RNA regulation and provides a resource for future work aimed at assessing how transcript boundaries and RNA modification patterns may relate to polymerase activity and virus–host interaction. Full article

Glioblastoma (GBM) remains one of the most lethal cancers, and despite advancements in understanding its underlying molecular signature, effective therapeutics are still lacking. The multifaceted challenges of designing treatments for GBM are compounded by the inability to identify a definitive cell of origin, the understanding of which is crucial for developing impactful therapies and ultimately improving patient outcomes. High-resolution technologies, including single-cell and single-nucleus RNA sequencing, spatial transcriptomics, multi-omics, next generation glioma models, bioinformatics, and artificial intelligence are creating an important opportunity to comprehensively map the cellular origin of GBM and its evolutionary dynamics. Accumulating evidence support neural stem cells (NSCs) and oligodendrocyte precursor cells (OPCs) as primary candidates, providing critical insights into the ontogeny of GBM. This comprehensive review synthesizes current knowledge on the cellular origins of GBM and evaluates advanced methodologies, deepening our understanding of its development. Full article

Background/Objectives: Milk somatic cells reflect the cellular composition and functional state of the lactating mammary gland and represent a valuable, non-invasive source for transcriptomic studies. Single-cell RNA sequencing (scRNA-seq) enables cell-type-resolved analysis of bovine milk; however, sequencing depth strongly influences the detection of lowly expressed genes and the resolution of transcriptional cell states. The aim of this study was to further characterise the single-cell transcriptome of bovine milk somatic cells, with particular emphasis on high-resolution gene expression profiling and cellular heterogeneity. Methods: Milk somatic cells were isolated from two healthy Holstein Friesian cows in mid-lactation and profiled using a droplet-based scRNA-seq platform. Newly generated high-depth datasets were integrated with two previously published bovine milk scRNA-seq datasets using an identical bioinformatics pipeline. Data integration, clustering and cell-type annotation were performed using the Seurat framework, and transcription factor expression was evaluated across datasets with different sequencing depths. Results: Single-cell transcriptomic analysis revealed a diverse cellular landscape in bovine milk, comprising epithelial, progenitor, and immune cell populations. Unsupervised clustering identified 21 transcriptionally distinct clusters, including multiple CD8⁺ T-cell subpopulations, monocytes, neutrophils, mast cells, and B cells, as well as luminal epithelial and luminal progenitor cells. While overall cell-type composition was comparable across datasets, deeply sequenced samples exhibited higher transcriptomic complexity and enabled refined resolution of immune and epithelial subpopulations. Deeper sequencing facilitated the detection of low-abundance transcription factors that were not observed in lower-depth datasets. Among these, NANOG was detected exclusively in deeply sequenced samples, suggesting the presence of rare transcriptional states associated with cellular plasticity. Conclusions: This study expands the single-cell transcriptomic landscape of bovine milk somatic cells and demonstrates the importance of sequencing depth for resolving functional cellular heterogeneity. The results highlight milk as a powerful, non-invasive source for investigating mammary gland biology and cellular plasticity during lactation. Full article

Flowers, the reproductive frontline of plants, are highly sensitive to environmental stresses. Yet, despite their ecological and agricultural importance, the molecular regulation of stress responses in flowers remains comparatively limited. In this context, this review emphasizes how integrative, flower-centered research combining molecular, physiological, and ecological perspectives is essential to safeguard fertility, crop yields, and biodiversity under increasingly variable climates. Advances in single-cell and spatial omics, high-resolution phenotyping, and genome and epigenome editing have the power to unprecedentedly reveal how flowers detect, decode, and respond to environmental stress. By reframing flowers as dynamic molecular decision points rather than passive stress factors, this review outlines a roadmap for flower-centered climate resilience research. Full article

Advances in sequencing technologies have transformed human microbiome research, yet most analyses still rely on species-level profiles. However, strains rather than species represent the true ecological and functional units of the microbiome. Individual strains can vary substantially in gene content, metabolic capacity, virulence factors, antimicrobial resistance, and host-interaction properties. These differences critically influence immune responses, epithelial barrier integrity, disease susceptibility, and therapeutic outcomes. Here, we synthesize recent human microbiome studies that provide robust strain-resolved evidence, focusing on three major themes: (i) the emergence and long-term persistence of personalized strain repertoires, (ii) strain-specific pathobiont traits that drive host pathology, and (iii) the implications of strain-level ecology for the development of next-generation microbiome therapeutics. We also highlight key methodological innovations including high-resolution amplicon profiling, advanced metagenomic and single-cell genomics, and culture-based functional approaches that collectively enable strain-level resolution and are reshaping the field. Full article