Explore the vast range of titles available.

Single‐cell RNA sequencing and spatial transcriptomics enable unprecedented insight into cellular and molecular pathways implicated in human skin aging and regeneration. Senescent cells are individual cells that are irreversibly cell cycle arrested and can accumulate across the human lifespan due to cell‐intrinsic and ‐extrinsic stressors. With an atlas of single‐cell RNA‐sequencing and spatial transcriptomics, epidermal and dermal senescence and its effects were investigated, with a focus on melanocytes and fibroblasts. Photoaging due to ultraviolet light exposure was associated with higher burdens of senescent cells, a sign of biological aging, compared to chronological aging. A skin‐specific cellular senescence gene set, termed SenSkin™, was curated and confirmed to be elevated in the context of photoaging, chronological aging, and non‐replicating CDKN1A+ (p21) cells. In the epidermis, senescent melanocytes were associated with elevated melanin synthesis, suggesting haphazard pigmentation, while in the dermis, senescent reticular dermal fibroblasts were associated with decreased collagen and elastic fiber synthesis. Spatial analysis revealed the tendency for senescent cells to cluster, particularly in photoaged skin. This work proposes a strategy for characterizing age‐related skin dysfunction through the lens of cellular senescence and suggests a role for senescent epidermal cells (i.e., melanocytes) and senescent dermal cells (i.e., reticular dermal fibroblasts) in age‐related skin sequelae. Bioinformatic analysis of scRNA‐seq and spatial transcriptomics of human skin aging revealed increased senescent cells, identified as CDKN1A+ non‐replicating cells, with sun exposure and chronological age. Senescent melanocytes in the epidermis expressed increased melanin biosynthesis, while senescent fibroblasts in the reticular dermis expressed decreased collagen and elastic fiber genes. Senescent cells showed a tendency to cluster, and their phenotypes were inferred to change with time. The graphical figure was created with BioRender.com.

Background Recent development of bioinformatics tools for Next Generation Sequencing data has facilitated complex analyses and prompted large scale experimental designs for comparative genomics. When combined with the advances in network inference tools, this can lead to powerful methodologies for mining genomics data, allowing development of pipelines that stretch from sequence reads mapping to network inference. However, integrating various methods and tools available over different platforms requires a programmatic framework to fully exploit their analytic capabilities. Integrating multiple genomic analysis tools faces challenges from standardization of input and output formats, normalization of results for performing comparative analyses, to developing intuitive and easy to control scripts and interfaces for the genomic analysis pipeline. Results We describe here NetSeekR, a network analysis R package that includes the capacity to analyze time series of RNA-Seq data, to perform correlation and regulatory network inferences and to use network analysis methods to summarize the results of a comparative genomics study. The software pipeline includes alignment of reads, differential gene expression analysis, correlation network analysis, regulatory network analysis, gene ontology enrichment analysis and network visualization of differentially expressed genes. The implementation provides support for multiple RNA-Seq read mapping methods and allows comparative analysis of the results obtained by different bioinformatics methods. Conclusion Our methodology increases the level of integration of genomics data analysis tools to network inference, facilitating hypothesis building, functional analysis and genomics discovery from large scale NGS data. When combined with network analysis and simulation tools, the pipeline allows for developing systems biology methods using large scale genomics data.

We develop an open-source package called AnnDictionary to facilitate the parallel, independent analysis of multiple anndata. AnnDictionary is built on top of LangChain and AnnData and supports all common large language model (LLM) providers. AnnDictionary only requires 1 line of code to configure or switch the LLM backend and it contains numerous multithreading optimizations to support the analysis of many anndata and large anndata. We use AnnDictionary to perform the first benchmarking study of all major LLMs at de novo cell-type annotation. LLMs vary greatly in absolute agreement with manual annotation based on model size. Inter-LLM agreement also varies with model size. We find that LLM annotation of most major cell types to be more than 80-90% accurate, and will maintain a leaderboard of LLM cell type annotation. Furthermore, we benchmark these LLMs at functional annotation of gene sets, and find that Claude 3.5 Sonnet recovers close matches of functional gene set annotations in over 80% of test sets. Cell type labelling in single-cell datasets remains a major bottleneck. Here, the authors present AnnDictionary, an open-source toolkit that enables atlas-scale analysis and provides the first benchmark of LLMs for de novo cell type annotation from marker genes, showing high accuracy at low cost.

Aging induces significant alterations in the immune system, with immunosenescence contributing to age‐related diseases. Peripheral blood mononuclear cells (PBMCs) offer a convenient and comprehensive snapshot of the body's immune status. In this study, we performed an integrated analysis of PBMCs using both bulk‐cell and single‐cell RNA‐seq data, spanning from children to frail elderlies, to investigate age‐related changes. We observed dynamic changes in the PBMC transcriptome during healthy aging, including dramatic shifts in inflammation, myeloid cells, and lymphocyte features during early life, followed by relative stability in later stages. Conversely, frail elderly individuals exhibited notable disruptions in peripheral immune cells, including an increased senescent phenotype in monocytes with elevated inflammatory cytokine expression, heightened effector activation in regulatory T cells, and functional impairment of cytotoxic lymphocytes. Overall, this study provides valuable insights into the complex dynamics of immunosenescence, elucidating the mechanisms driving abnormal inflammation and immunosuppression in frailty. Integrated analysis of bulk‐cell and single‐cell RNA‐seq data from childhood to frailty revealed nonlinear transcriptomic changes in peripheral blood mononuclear cells, marked by dramatic early‐life shifts in inflammation, myeloid cells, and lymphocyte features, followed by stability in healthy elderly individuals. In contrast, frail elderly individuals showed disrupted immune profiles, characterized by heightened monocyte‐driven inflammation, regulatory T‐cell activation, and impaired cytotoxic lymphocyte function.

The geroscience hypothesis suggests that understanding mechanisms underlying aging will enable us to delay and lessen age‐related disability and diseases. The role of mechanical factors has been increasingly appreciated in many aspects of the aging process. Here, we use mouse models to investigate changes in the biomechanics of the proximal pulmonary artery, lung function, and right ventricle function in aging. We found an age‐related decreased capacity to store energy and increased circumferential stiffness of the proximal pulmonary artery with age that is associated with a reorientation of collagen toward the circumferential direction, decreased exercise ability, and decreased function of the lung and right ventricle. The observed compromised mechanics in the proximal pulmonary artery are consistent across multiple mouse models of accelerated aging. Furthermore, transcriptional changes in the proximal pulmonary artery indicate that aging is associated with senescence of perivascular macrophages, adventitial fibroblasts, and medial smooth muscle cells. Older pulmonary arteries increase expression of genes associated with ECM turnover (including genes in the TGFβ pathway) and increased intercellular signaling amongst perivascular macrophages, fibroblasts, and smooth muscle cells. Our results provide promising biomarkers of aging for diagnosis and potential pathways and molecular targets for antiaging therapies. Mouse models revealed age‐associated increased circumferential stiffness of the proximal pulmonary artery that was associated with reorientation of collagen and decreased function of the lung and right ventricle. Age‐related transcriptional changes were indicative of senescence, ECM turnover, TGFβ signaling, and altered intercellular signaling among perivascular macrophages, fibroblasts, and smooth muscle cells.

Background Transcriptomic analysis is crucial for understanding the functional elements of the genome, with the classic method consisting of screening transcriptomics datasets for differentially expressed genes (DEGs). Additionally, since 2005, weighted gene co-expression network analysis (WGCNA) has emerged as a powerful method to explore relationships between genes. However, an approach combining both methods, i.e., filtering the transcriptome dataset by DEGs or other criteria, followed by WGCNA (DEGs + WGCNA), has become common. This is of concern because such approach can affect the resulting underlying architecture of the network under analysis and lead to wrong conclusions. Here, we explore a plot twist to transcriptome data analysis: applying WGCNA to exploit entire datasets without affecting the topology of the network, followed with the strength and relative simplicity of DEG analysis (WGCNA + DEGs). We tested WGCNA + DEGs against DEGs + WGCNA to publicly available transcriptomics data in one of the most transcriptomically complex tissues and delicate processes: vertebrate gonads undergoing sex differentiation. We further validate the general applicability of our approach through analysis of datasets from three distinct model systems: European sea bass, mouse, and human. Results In all cases, WGCNA + DEGs clearly outperformed DEGs + WGCNA. First, the network model fit and node connectivity measures and other network statistics improved. The gene lists filtered by each method were different, the number of modules associated with the trait of interest and key genes retained increased, and GO terms of biological processes provided a more nuanced representation of the biological question under consideration. Lastly, WGCNA + DEGs facilitated biomarker discovery. Conclusions We propose that building a co-expression network from an entire dataset, and only thereafter filtering by DEGs, should be the method to use in transcriptomic studies, regardless of biological system, species, or question being considered.

Background Despite therapeutic advances, metastatic melanoma, and particularly brain metastasis (MBM), remains a lethal burden for patients. Existing single-cell studies offer a more detailed view of melanoma and its microenvironment, which is crucial to improve diagnosis and treatment. Results We here present a computational reanalysis of single-nucleus data comparing 15 MBM and 10 extracranial melanoma metastases (ECM), considering recent best practice recommendations. We used cell type-specific pseudobulking and omit imputation during patient integration to gain complementary insights. Interestingly, our analysis revealed high homogeneity in tumor cell expression profiles within and between MBM and ECM. However, MBM displayed even higher homogeneity but a more flexible energy metabolism, suggesting a specific metastatic adaptation to the putatively more restricted brain microenvironment. While tumor cells were homogeneous, the metastasis microenvironment, especially lymphocytes and related immune-tumor interaction pathways, exhibited greater divergence between MBM and ECM. Overall, this suggests that major differences between MBM and ECM are potentially driven by variations in their microenvironment. Finally, a comparison of single-cell data to previous bulk studies, including their deconvoluted putative cell types, showed significant differences, potentially causing divergent conclusions. Conclusion Our study contributed to refine the understanding of differences between MBM and ECM, suggesting these are potentially more influenced by their local microenvironments. Future research and therapies could possibly focus on the metabolic flexibility of melanoma brain metastases and patient-specific immune pathway alterations.

Cancer stem‐like cells (CSCs) induce drug resistance and recurrence of tumors when they experience DNA replication stress. However, the mechanisms underlying DNA replication stress in CSCs and its compensation remain unclear. Here, we demonstrate that upregulated c‐Myc expression induces stronger DNA replication stress in patient‐derived breast CSCs than in differentiated cancer cells. Our results suggest critical roles for mini‐chromosome maintenance protein 10 (MCM10), a firing (activating) factor of DNA replication origins, to compensate for DNA replication stress in CSCs. MCM10 expression is upregulated in CSCs and is maintained by c‐Myc. c‐Myc‐dependent collisions between RNA transcription and DNA replication machinery may occur in nuclei, thereby causing DNA replication stress. MCM10 may activate dormant replication origins close to these collisions to ensure the progression of replication. Moreover, patient‐derived breast CSCs were found to be dependent on MCM10 for their maintenance, even after enrichment for CSCs that were resistant to paclitaxel, the standard chemotherapeutic agent. Further, MCM10 depletion decreased the growth of cancer cells, but not of normal cells. Therefore, MCM10 may robustly compensate for DNA replication stress and facilitate genome duplication in cancer cells in the S‐phase, which is more pronounced in CSCs. Overall, we provide a preclinical rationale to target the c‐Myc‐MCM10 axis for preventing drug resistance and recurrence of tumors. We provide evidence that upregulated c‐Myc expression induces stronger DNA replication stress in patient‐derived breast cancer stem‐like cells than in differentiated cancer cells. Our results suggest critical roles for mini‐chromosome maintenance protein 10 (MCM10), which is a firing (activating) factor of the DNA replication origins, to compensate for the DNA replication stress.

Given the abundance of stroke patients and deaths from stroke worldwide, many studies concerning the aftermath of stroke are being carried out. To reveal the precise effect of ischemic infarction, we conducted a comprehensive gene expression analysis. Alongside a middle cerebral artery occlusion (MCAO) Sprague–Dawley rat model, we used a group undergoing sham surgery for comparison, which was the same as MCAO surgery but without blood vessel occlusion. Subsequently, infarction of the brains of MCAO-treated rats occurred, but did not occur in the sham-treated rats. Using whole blood, we carried out DNA microarray analysis, revealing the gene expression alterations caused by stroke. Downregulation of immune pathways and cluster of differentiation (CD) molecules indicated immunodepression. By conducting miRNA microarray analysis, we extracted seven miRNAs as significantly regulated: miR-107-5p, miR-383-5p, miR-24-1-5p, mir-191b, miR-196b-5p, and miR-3552 were upregulated, and mir-194-1 was downregulated. Among these seven miRNAs, three had one target mRNA each that was extracted as differentially expressed, and the expression levels of all pairs were inversely correlated. This indicates the occurrence of miRNA–mRNA regulatory systems in blood: between miR-107-5p and H2A histone family member Z (H2afz), miR-196b-5p and protein tyrosine phosphatase receptor type C (Ptprc), and miR-3552 and serine/arginine-rich splicing factor 2 (Srsf2). Moreover, six miRNAs had matching human miRNAs with similar sequences, which are potential human stroke biomarkers.

This work proposes a novel cell annotation training framework, scSemiPLC, which significantly enhances the efficiency and accuracy of annotation by fully leveraging unlabeled data. In the semi-supervised learning component, the framework innovatively generates pseudo-labels through clustering. Subsequently, it evaluates the reliability of these pseudo-labels and assigns corresponding weights, thereby balancing both their quantity and quality. This approach provides new insights into the direction of automatic cell annotation within the realm of semi-supervised learning.