Single-cell RNA sequencing technologies and applications: A brief overview

Mitigating ambient RNA and doublets effects on single cell transcriptomics analysis in cancer research

In cancer biology, where understanding the tumor microenvironment at high resolution is vital, ambient RNA contamination becomes a considerable problem. This hinders accurate delineation of intratumoral heterogeneity, complicates the identification of potential biomarkers, and decelerates advancements in precision oncology. To solve this problem, several computational approaches are created to determine the ambient RNA contribution from scRNA-seq datasets. Techniques like SoupX and DecontX assist in assessing and eliminating ambient RNA contamination from primary gene expression profiles. Practical solutions like CellBender employ deep learning techniques to concurrently address ambient RNA contamination and background noise, offering a contemporary end-to-end strategy for data preparation. This high-quality, reliable data enables clinicians and researchers to make effective decisions that will help ensure interventions are rooted in reproducible evidence, giving hope for developing more effective targeted therapies.

Generation of an Inducible Destabilized-Domain Cre Mouse Line to Target Disease Associated Microglia

The function of microglia during progression of Alzheimer's disease (AD) can be investigated using mouse models that enable genetic manipulation of microglial subpopulations in a temporal manner. We developed mouse lines that express either Cre recombinase (Cre) for constitutive targeting, or destabilized-domain Cre recombinase (DD-Cre) for inducible targeting from the Cst7 locus (Cst7 DD-Cre) to specifically manipulate disease associated microglia (DAM) and crossed with Ai14 tdTomato cre-reporter line mice. Cst7Cre was found to target all brain resident myeloid cells, due to transient developmental expression of Cst7, but no expression was found in the inducible Cst7 DD-Cre mice. Further crossing of this line with 5xFAD mice combined with dietary administration of trimethoprim to induce DD-Cre activity produces long-term labeling in DAM without evidence of leakiness, with tdTomato-expression restricted to cells surrounding plaques. Using this model, we found that DAMs are a subset of plaque-associated microglia (PAMs) and their transition to DAM increases with age and disease stage. Spatial transcriptomic analysis revealed that tdTomato+ cells show higher expression of disease and inflammatory genes compared to other microglial populations, including non-labeled PAMs. These models allow either complete cre-loxP targeting of all brain myeloid cells (Cst7Cre), or inducible targeting of DAMs, without leakiness (Cst7 DD-Cre).

Harnessing multi‑omics to revolutionize understanding and management of osteosarcoma: A pathway to precision medicine (Review)

Osteosarcoma, the most prevalent primary bone malignancy in children and adolescents, poses significant challenges due to its aggressive nature and propensity for metastasis. Despite advances in treatment, survival rates for high‑risk patients remain unsatisfactory, underscoring the urgent need for innovative approaches. This review explores the vital role of multi‑omics‑integrating genomics, transcriptomics, proteomics and metabolomics‑in unraveling the complex biological landscapes of osteosarcoma. By providing comprehensive insights into tumor heterogeneity, signaling pathways and metabolic reprogramming, multi‑omics facilitates the identification of novel biomarkers and therapeutic targets. The objective of the present study was to highlight the transformative potential of multi‑omics in enhancing the understanding and management of osteosarcoma, ultimately paving the way for personalized treatment strategies and improved patient outcomes. Through this synthesis, the study calls for a concerted effort to integrate multi‑omics into clinical practice, fostering a more precise approach to osteosarcoma care.

Metabolic alterations and immune heterogeneity in gastric cancer metastasis

Cellular metabolic reprogramming supports tumor proliferation, invasion, and metastasis by enhancing resistance to stress and immune clearance. Understanding these metabolic changes within the tumor microenvironment is vital to developing effective therapies. We conducted single-cell RNA sequencing on 11 gastric cancer (GC) samples and eight metastatic lesions, analyzing 92,842 cells across eight cell types, including cancer cells, stromal cells, and immune cells. Our findings highlight that the mitochondrial ATP synthase subunit ATP5MC2 uniquely alters during early GC metastasis. Experiments and clinical data confirmed that ATP5MC2 upregulation facilitates cancer cell proliferation, invasion, and metastasis. Constructing a single-cell atlas revealed significant immune cell heterogeneity associated with GC metastasis and its molecular subtypes. This study underscores the role of ATP5MC2-driven metabolic changes and diverse immune landscapes in promoting GC metastasis, offering new avenues for anti-metastatic treatment development.

Using Single-Cell RNA sequencing with Drosophila, Zebrafish, and mouse models for studying Alzheimer's and Parkinson's disease

Alzheimer's and Parkinson's disease are the most common neurodegenerative diseases, significantly affecting the elderly with no current cure available. With the rapidly aging global population, advancing research on these diseases becomes increasingly critical. Both disorders are often studied using model organisms, which enable researchers to investigate disease phenotypes and their underlying molecular mechanisms. In this review, we critically discuss the strengths and limitations of using Drosophila, zebrafish, and mice as models for Alzheimer's and Parkinson's research. A focus is the application of single-cell RNA sequencing, which has revolutionized the field by providing novel insights into the cellular and transcriptomic landscapes characterizing these diseases. We assess how combining animal disease modeling with high-throughput sequencing and computational approaches has advanced the field of Alzheimer's and Parkinson's disease research. Thereby, we highlight the importance of integrative multidisciplinary approaches to further our understanding of disease mechanisms and thus accelerating the development of successful therapeutic interventions.

Sequencing technologies to measure translation in single cells

Translation is one of the most energy-intensive processes in a cell and, accordingly, is tightly regulated. Genome-wide methods to measure translation and the translatome and to study the complex regulation of protein synthesis have enabled unprecedented characterization of this crucial step of gene expression. However, technological limitations have hampered our understanding of translation control in multicellular tissues, rare cell types and dynamic cellular processes. Recent optimizations, adaptations and new techniques have enabled these measurements to be made at single-cell resolution. In this Progress, we discuss single-cell sequencing technologies to measure translation, including ribosome profiling, ribosome affinity purification and spatial translatome methods.

Mapping immune cell dynamics and macrophage plasticity in breast cancer tumor microenvironment through single-cell analysis

Breast cancer (BRCA) is a complex disease influenced by the tumor microenvironment, where interactions between immune cells and cancer cells play a crucial role in tumor progression and response to therapy. Understanding the intricacies of these interactions requires detailed analysis at the single-cell level, enabling the identification of specific immune cell subpopulations and their functional roles within the tumor milieu. This study comprehensively analyzed immune cell subpopulations and macrophage subtypes in BRCA using single-cell RNA sequencing technology and various computational tools. Initially, Sc-Type software accurately identified and annotated immune cell subpopulations, followed by CNV analysis using infercnv software, revealing significant CNV variations in epithelial cells. Subsequently, macrophages were re-clustered into 5 clusters, and their biological significance and functional features were assessed. CellChat analysis elucidated potential interactions between macrophage subtypes and BRCA cells, primarily through SPP1-CD44 and LGALS9-CD44 signaling networks. Additionally, CytoTRACE and Monocle were employed to analyze cellular plasticity and differentiation trajectories of macrophage subtypes. Furthermore, efferocytosis-related gene set scoring, transcription factor analysis, and risk score development were conducted, followed by immune infiltration and tumor mutation burden analysis, revealing increased immune infiltration and higher TMB levels in the high-risk group. These findings offer crucial insights into the interaction mechanisms of immune cells and macrophage subtypes within the BRCA tumor microenvironment, aiding in the understanding of tumor progression and therapeutic interventions.

Glial changes and gene expression in Alzheimer's disease from snRNA-Seq and spatial transcriptomics

BackgroundAlzheimer's disease (AD) is characterized by cortical atrophy, glutamatergic neuron loss, and cognitive decline. However, large-scale quantitative assessments of cellular changes during AD pathology remain scarce.ObjectiveThis study aims to integrate single-nuclei sequencing data from the Seattle Alzheimer's Disease Cortical Atlas (SEA-AD) with spatial transcriptomics to quantify cellular changes in the prefrontal cortex and temporal gyrus, regions vulnerable to AD neuropathological changes (ADNC).MethodsWe mapped differentially expressed genes (DEGs) and analyzed their interactions with pathological factors such as APOE expression and Lewy bodies. Cellular proportions were assessed, focusing on neurons, glial cells, and immune cells.ResultsRORB-expressing L4-like neurons, though vulnerable to ADNC, exhibited stable cell numbers throughout disease progression. In contrast, astrocytes displayed increased reactivity, with upregulated cytokine signaling and oxidative stress responses, suggesting a role in neuroinflammation. A reduction in synaptic maintenance pathways indicated a decline in astrocytic support functions. Microglia showed heightened immune surveillance and phagocytic activity, indicating their role in maintaining cortical homeostasis.ConclusionsThe study underscores the critical roles of glial cells, particularly astrocytes and microglia, in AD progression. These findings contribute to a better understanding of cellular dynamics and may inform therapeutic strategies targeting glial cell function in AD.

Tumor heterogeneity in retinoblastoma: a literature review

Tumor heterogeneity, characterized by the presence of diverse cell populations within a tumor, is a key feature of the complex nature of cancer. This diversity arises from the emergence of cells with varying genomic, epigenetic, transcriptomic, and phenotypic profiles over the course of the disease. Host factors and the tumor microenvironment play crucial roles in driving both inter-patient and intra-patient heterogeneity. These diverse cell populations can exhibit different behaviors, such as varying rates of proliferation, responses to treatment, and potential for metastasis. Both inter-patient heterogeneity and intra-patient heterogeneity pose significant challenges to cancer therapeutics and management. In retinoblastoma, while heterogeneity at the clinical presentation level has been recognized for some time, recent attention has shifted towards understanding the underlying cellular heterogeneity. This review primarily focuses on retinoblastoma heterogeneity and its implications for therapeutic strategies and disease management, emphasizing the need for further research and exploration in this complex and challenging area.

Selenide-modified hyaluronic acid hydrogel promotes scleral remodeling during the recovery phase of form-deprivation myopia by inhibiting HIF-1α-mediated inflammation

This study investigates the molecular mechanisms by which selenide-modified hyaluronic acid hydrogel (Se-HA gel) promotes scleral remodeling during the recovery phase of form-deprivation myopia (FDM). The Se-HA gel was synthesized and characterized, exhibiting an average hydrodynamic diameter of 191.72 nm, a polydispersity index (PDI) of 0.19, and a zeta potential of -7.96 mV, indicating a monodisperse state in PBS. Both in vitro experiments and the FDM mouse model confirmed its therapeutic efficacy. At a concentration of 250 μg/mL, Se-HA gel significantly promoted fibroblast proliferation, inhibited apoptosis, and prevented transdifferentiation. A 200 mg/kg subtenon injection improved key ocular biometric parameters in FDM mice. Single-cell and transcriptomic sequencing analyses revealed that Se-HA gel facilitated scleral remodeling by downregulating Hypoxia-Inducible Factor 1 Alpha (HIF-1α) expression and regulating inflammation-related gene expression. Notably, HIF-1α overexpression reversed the beneficial effects of Se-HA gel, reinforcing its pivotal role in mediating these therapeutic outcomes. This study introduces a novel biomaterial-based strategy and identifies new molecular targets for myopia treatment. Furthermore, it addresses a critical gap in understanding how Se-HA gel promotes scleral remodeling through HIF-1α-mediated signaling pathways, with important scientific and translational potential in the field of myopia management.

Combining RNA-seq, molecular docking and experimental verification to explore the mechanism of BAM15 as a potential drug for atherosclerosis

BAM15 is a novel mitochondrial uncoupling agent derived from a synthetic source, that has been wildly explored for its ability to enhance mitochondrial respiration and metabolic flexibility. In this study, we investigated the underlying mechanisms of BAM15 on atherosclerosis (AS) through experimental validation, RNA-seq and molecular docking. The results showed that oral administration of BAM15 suppressed atherosclerosis in western diet (WD)-fed ApoE(-/-) mice and significantly improved the hyperlipidemia. And the increased serum ALT, AST and liver TC, TG, ALT, AST in ApoE(-/-) mice were reduced by BAM15 treatment. In in vitro experiments BAM15 inhibited RAW264.7 macrophages invasive ability and reduced palmitic acid-induced lipid accumulation. RNA-seq results confirmed the differential genes after BAM15 treatment and 140 common targets were identified by intersecting with AS-related targets. A protein-protein interaction (PPI) network analysis high-lighted IL1A, SRC and CSF3 as key targets of BAM15 against AS, which is further verified by molecular docking and western blot. Molecular dynamics analysis results confirmed that BAM15 exhibits strong affinity with the IL-1α, SRC and CSF3 proteins. This study indicates that BAM15 inhibits atherosclerosis through a multi-molecular mechanism, and we propose it as a novel anti-atherosclerotic drug.

SpaGRN: Investigating spatially informed regulatory paths for spatially resolved transcriptomics data

Cells spatially organize into distinct cell types or functional domains through localized gene regulatory networks. However, current spatially resolved transcriptomics analyses fail to integrate spatial constraints and proximal cell influences, limiting the mechanistic understanding of tissue organization. Here, we introduce SpaGRN, a statistical framework that reconstructs cell-type- or functional-domain-specific, dynamic, and spatial regulons by coupling intracellular spatial regulatory causality with extracellular signaling path information. Benchmarking across synthetic and real datasets demonstrates SpaGRN's superior precision over state-of-the-art tools in identifying context-dependent regulons. Applied to diverse spatially resolved transcriptomics platforms (Stereo-seq, STARmap, MERFISH, CosMx, Slide-seq, and 10x Visium), complex cancerous samples, and 3D datasets of developing Drosophila embryos and larvae, SpaGRN not only provides a versatile toolkit for decoding receptor-mediated spatial regulons but also reveals spatiotemporal regulatory mechanisms underlying organogenesis and inflammation.

Unraveling the future of genomics: CRISPR, single-cell omics, and the applications in cancer and immunology

The CRISPR system has transformed many research areas, including cancer and immunology, by providing a simple yet effective genome editing system. Its simplicity has facilitated large-scale experiments to assess gene functionality across diverse biological contexts, generating extensive datasets that boosted the development of computational methods and machine learning/artificial intelligence applications. Integrating CRISPR with single-cell technologies has further advanced our understanding of genome function and its role in many biological processes, providing unprecedented insights into human biology and disease mechanisms. This powerful combination has accelerated AI-driven analyses, enhancing disease diagnostics, risk prediction, and therapeutic innovations. This review provides a comprehensive overview of CRISPR-based genome editing systems, highlighting their advancements, current progress, challenges, and future opportunities, especially in cancer and immunology.

Single cell transcriptome sequencing indicates the cellular heterogeneity of small intestine tissue in celiac disease

Celiac disease (CeD) is an autoimmune small intestinal disease caused by gluten protein ingestion by genetically susceptible individuals. Genome-wide association studies and transcriptomic data have limited capacity to capture intercellular genetic variations. We aimed to construct a single cell transcriptome spectrum, analyze the immune microenvironment and cellular heterogeneity, discover disease-related specific genes and markers, and explore the pathogenesis of CeD. This study performed single cell RNA sequencing (scRNA-seq) on three small intestine biopsies from patients with CeD and three matched healthy Chinese controls. Immunohistochemistry (IHC) and quantitative polymerase chain reaction (qPCR) were used to validate potential diagnostic biomarkers of disease-differential genes. A total of 10 cell subpopulations were annotated, including three types of epithelial and stromal cells and seven types of immune cells. IHC revealed a pronounced overexpression of T cell disease-differential genes, TRAT1, BCL11B, and ETS1 in intraepithelial lymphocytes in the CeD group. Further clinical validation using qPCR confirmed that ETS1 (P = 0.010), TRAT1 (P < 0.001), and BCL11B (P = 0.036) were enriched in the CeD small intestinal tissue. The CD28/CTLA-4 pathway regulates the homeostasis of Treg cells. The IFITs family genes may serve as marker genes for antiviral specific CD4+ T cell subsets. CeD-derived subsets of CD8+ T cells frequently express genes associated with cytotoxicity, including IFNG, GZMK, GZMH, GZMB, SH2D1A, PRF1, and NKG7, as well as genes related to T cell exhaustion, such as PDCD10, CTLA4, TIGIT, PDCD1, and DUSP4. Inflammation and infection pathways were enriched in different cell populations. A single cell expression profile of CeD small intestinal tissue was successfully constructed using scRNA-seq in this study. New biomarkers for CeD-specific histopathology and potential therapeutic targets were discovered, and the biomarkers observed between inflammation and infection pathways were closely related to the onset of CeD.

OmicsCam Enables Trimodal Profiling of Mitochondrial Genome Editing

Mitochondrial DNA (mtDNA) editing can generate cellular and animal models of mitochondrial genetic disorders and holds promise for future ex vivo and in vivo therapeutic applications. However, due to the quantitative nature of mitochondrion genetics, as more base-editing tools evolve, it is crucial to evaluate not only their efficiency and specificity on the sequence level but also the resulting molecular phenotypes. Here, we devised a novel Omics Carrier microcapsule, abbreviated as OmicsCam, that achieves homogeneous reactions within a heterogeneous carrier membrane, enabling highly efficient multistep biochemistry workflows. Incorporating magnetic beads into the carrier enables high-throughput automation. We demonstrated simultaneous trimodal assessment of mtDNA editing efficiency, postediting cellular transcriptome, and chromatin accessibility in minute cell samples containing as few as 25,000 cells. Applying OmicsCam to two TALE-DdCBE-edited human cell lines revealed that ND4 gene knockdown led to the downregulation of the mitochondrial oxidative phosphorylation pathway and changes in NF-Y transcription factor-associated histone modification pathways in the cell nucleus. Our study provides the most comprehensive analysis of mitochondrial gene editing efficiency and molecular phenotypes to date, which not only facilitates the establishment of mitochondrial genotype-molecular phenotype relationships but also helps assess the global safety of mitochondrial genome nucleases prior to clinical use.

Genomics and multiomics in the age of precision medicine

Precision medicine is a transformative healthcare model that utilizes an understanding of a person's genome, environment, lifestyle, and interplay to deliver customized healthcare. Precision medicine has the potential to improve the health and productivity of the population, enhance patient trust and satisfaction in healthcare, and accrue health cost-benefits both at an individual and population level. Through faster and cost-effective genomics data, next-generation sequencing has provided us the impetus to understand the nuances of complex interactions between genes, diet, and lifestyle that are heterogeneous across the population. The emergence of multiomics technologies, including transcriptomics, proteomics, epigenomics, metabolomics, and microbiomics, has enhanced the knowledge necessary for maximizing the applicability of genomics data for better health outcomes. Integrative multiomics, the combination of multiple 'omics' data layered over each other, including the interconnections and interactions between them, helps us understand human health and disease better than any of them separately. Integration of these multiomics data is possible today with the phenomenal advancements in bioinformatics, data sciences, and artificial intelligence. Our review presents a broad perspective on the utility and feasibility of a genomics-first approach layered with other omics data, offering a practical model for adopting an integrated multiomics approach in pediatric health care and research. IMPACT: Precision medicine provides a paradigm shift from a conventional, reactive disease control approach to proactive disease prevention and health preservation. Phenomenal advancements in bioinformatics, data sciences, and artificial intelligence have made integrative multiomics feasible and help us understand human health and disease better than any of them separately. The genotype-first approach or reverse phenotyping has the potential to overcome the limitations of the phenotype-first approach by identifying new genotype-phenotype associations, enhancing the subclassification of diseases by widening the phenotypic spectrum of genetic variants, and understanding functional mechanisms of genetic variations.

Chromosomal Instability and Clonal Heterogeneity in Breast Cancer: From Mechanisms to Clinical Applications

BACKGROUND

Chromosomal instability (CIN) and clonal heterogeneity (CH) are fundamental hallmarks of breast cancer that drive tumor evolution, disease progression, and therapeutic resistance. Understanding the mechanisms underlying these phenomena is essential for improving cancer diagnosis, prognosis, and treatment strategies.

METHODS

In this review, we provide a comprehensive overview of the biological processes contributing to CIN and CH, highlighting their molecular determinants and clinical relevance.

RESULTS

We discuss the latest advances in detection methods, including single-cell sequencing and other high-resolution techniques, which have enhanced our ability to characterize intratumoral heterogeneity. Additionally, we explore how CIN and CH influence treatment responses, their potential as therapeutic targets, and their role in shaping the tumor immune microenvironment, which has implications for immunotherapy effectiveness.

CONCLUSIONS

By integrating recent findings, this review underscores the impact of CIN and CH on breast cancer progression and their translational implications for precision medicine.

Decoding B Cells in Autoimmune Diseases Through ScRNA + BCR-Seq: Current Knowledge and Future Directions

The combined application of single-cell RNA sequencing (scRNA-seq) and single-cell B-cell receptor sequencing (scBCR-seq) offers a multidimensional perspective for dissecting the immunopathological mechanisms of B cells in autoimmune diseases. This review systematically summarizes the principles of these techniques, the analytical framework, and their key applications in diseases such as systemic lupus erythematosus et. al. It reveals the dynamic correlations between the transcriptome of B-cell subsets and B-cell receptor (BCR) clones. Furthermore, we focus on the potential roles of dual BCR B cells and B/T biphenotypic cells in autoimmunity, emphasizing their exacerbation of disease progression through abnormal clonal expansion and autoantibody secretion. By sorting through cutting-edge advancements and bottleneck issues, this article aims to propel the innovation of multi-omics research and precision treatment paradigms for autoimmune diseases.

Mapping Cell Identity from scRNA-seq: A primer on computational methods

Single cell (sc) technologies mark a conceptual and methodological breakthrough in our way to study cells, the base units of life. Thanks to these technological developments, large-scale initiatives are currently ongoing aimed at mapping of all the cell types in the human body, with the ambitious aim to gain a cell-level resolution of physiological development and disease. Since its broad applicability and ease of interpretation scRNA-seq is probably the most common sc-based application. This assay uses high throughput RNA sequencing to capture gene expression profiles at the sc-level. Subsequently, under the assumption that differences in transcriptional programs correspond to distinct cellular identities, ad-hoc computational methods are used to infer cell types from gene expression patterns. A wide array of computational methods were developed for this task. However, depending on the underlying algorithmic approach and associated computational requirements, each method might have a specific range of application, with implications that are not always clear to the end user. Here we will provide a concise overview on state-of-the-art computational methods for cell identity annotation in scRNA-seq, tailored for new users and non-computational scientists. To this end, we classify existing tools in five main categories, and discuss their key strengths, limitations and range of application.

Cancer-associated fibroblasts in breast cancer in the single-cell era: Opportunities and challenges

Breast cancer is a leading cause of morbidity and mortality in women, and its progression is closely linked to the tumor microenvironment (TME). Cancer-associated fibroblasts (CAFs), key components of the TME, play a crucial role in promoting tumor growth by driving cancer cell proliferation, invasion, extracellular matrix (ECM) remodeling, inflammation, chemoresistance, and immunosuppression. CAFs exhibit considerable heterogeneity and are classified into subgroups based on different combinations of biomarkers. Single-cell RNA sequencing (scRNA-seq) enables high-throughput and high-resolution analysis of individual cells. Relying on this technology, it is possible to cluster complex CAFs according to different biomarkers to analyze the specific phenotypes and functions of different subpopulations. This review explores CAF clusters in breast cancer and their associated biomarkers, highlighting their roles in disease progression and potential for targeted therapies.

Application of bioinformatic tools in cell type classification for single-cell RNA-seq data

The advancements in single-cell RNA sequencing (scRNAseq) technology have significantly transformed genomics research, enabling the handling of thousands of cells in each experiment. As of now, 32,068 research studies have been cataloged in the Pubmed database. The primary aim of scRNAseq investigations is to identify cell types, understand the antitumor immune response, and identify new and uncommon cell types. Traditional techniques for identifying cell types include microscopy, histology, and pathological characteristics. However, the complexity of instruments and the need for precise experimental design make it difficult to fully capture the overall heterogeneity. Unsupervised clustering and supervised classification methods have been used to solve this task. Supervised cell type classification methods have gained popularity as large-scale, high-quality, well-annotated and more robust results compared to clustering methods. A recent study showed that support vector machine (SVM) gives a high-quality classification performance in different scenarios. In this article, we compare and evaluate the performance of four different kernels (sigmoid, linear, radial, polynomial) of SVM. The results of the experiments on three standard scRNA-seq datasets indicate that SVM with linear and SVM with sigmoid kernel classify the cells more accurately (approx. 99 %) where SVM linear kernel method has remarkably fast computation time and we also evaluate the results using some single cell specific evaluation matrices F-1 score, MCC, AUC value. Additionally, it sheds light on the potential use of kernels of SVM to give underlying information of single-cell RNA-Seq data more effectively.

Placenta at single-cell resolution in early and late preeclampsia: insights and clinical implications

Preeclampsia, one of the great obstetrical syndromes, manifests through diverse maternal and fetal complications and remains a leading contributor to adverse perinatal outcomes. In this review, we describe our work on single-cell and single-nuclei RNA sequencing to elucidate the molecular mechanisms that underlie early- and late-onset preeclampsia. Analysis of 46 cell types, encompassing approximately 90,000 cells from placental tissues collected after delivery, demonstrated cellular dysregulation in early-onset preeclampsia, whereas late-onset preeclampsia showed comparatively subtle changes. These findings were observed in all cell lines, including all types of trophoblast, lymphoid, myeloid, stromal, and endothelial cells. Key findings in early-onset preeclampsia included disrupted syncytiotrophoblast and extravillous trophoblast angiogenic signaling, characterized by an up-regulation of FLT1 and down-regulation of PGF, consistent with an angiogenic imbalance. The stromal and vascular compartments exhibited stress-induced transcriptomic shifts. Both endothelial cells and pericytes showed evidence of stress, including up-regulation of heat shock proteins and markers of apoptosis. In addition, the inflammation- and stress-responsive states were more abundant in early-onset preeclampsia than in matched controls. Inflammatory pathways were markedly up-regulated in both the maternal and fetal immune cells; for example, we observed a marked increase in pro-inflammatory cytokines, including secreted phosphoprotein 1 and C-X-C motif chemokine ligand 2 and 3. Conversely, late-onset preeclampsia retained adaptive placental features with localized dysregulation of extracellular matrix remodeling and angiogenic markers, underscoring its possible maternal cardiovascular etiology. Single-cell and single-nuclei RNA sequencing investigations of placental tissues support the proposed classification of preeclampsia into a placental dysfunction type, primarily presenting early in pregnancy, and a maternal cardiovascular maladaptation type, primarily presenting later in pregnancy, each with distinct biomarkers, risk factors, and therapeutic targets. The early-onset preeclampsia findings advocate for interventions that target angiogenic pathways, such as RNA-based therapies that target specific cells of the placenta, to modulate soluble fms-like tyrosine kinase-1 levels. In contrast, late-onset preeclampsia management may benefit from maternal cardiovascular optimization, including individualized antihypertensive and metabolic treatments. These results underscore the heterogeneity of preeclampsia, emphasizing the need for individualized diagnostic and therapeutic strategies. This molecular atlas of preeclampsia advances our understanding of the complex interplay among elements of the maternal-placental-fetal array, thereby bridging clinical phenotypes and cellular mechanisms. Future research should focus on integrating these insights into longitudinal studies to develop precision medicine approaches for preeclampsia to enhance outcomes for mothers and neonates.

Engineering Gizmos for Short Cancer Genetic Fragments Discrimination

Currently, mankind is fiercely struggling with cancer. Recently, we have been winning the battle against cancer through precision medicine and accompanying diagnostic methods, and we are raising many hopes with blockbuster drugs. It would be even better if we could read the cancer nucleotide sequence, identify them in advance, and suggest treatments simultaneously. However, this may be an impossible dream because it takes a lot of time and effort to diagnose and ensure all the long gene sequences of cancer at once. Thus, victory will be even closer if a rapid and accurate diagnosis of the cancer-specific gene biomarkers that will soon be imprinted can be made. With the advent of nanotechnology, a new short cancer diagnostic toolkit has been proposed to achieve the goal. This review presents a small diagnostic device that detects certain cancers' genetic fragments (simply 'Gizmo'). The development of numerous diagnostic methods has focused on (1) directly detecting pre-selectively targeted genes using novel diagnostic systems, and (2) indirectly detecting substantial improvements in diagnostic sensitivity only through detection signal amplification without existing gene amplification steps. Our fight against cancer is not a dream, but the result of success, and it is assumed that victory will accelerate as soon as possible.

Enhanced single-cell RNA-seq embedding through gene expression and data-driven gene-gene interaction integration

Single-cell RNA sequencing (scRNA-seq) provides unprecedented insights into cellular heterogeneity, enabling detailed analysis of complex biological systems at single-cell resolution. However, the high dimensionality and technical noise inherent in scRNA-seq data pose significant analytical challenges. While current embedding methods focus primarily on gene expression levels, they often overlook crucial gene-gene interactions that govern cellular identity and function. To address this limitation, we present a novel embedding approach that integrates both gene expression profiles and data-driven gene-gene interactions. Our method first constructs a Cell-Leaf Graph (CLG) using random forest models to capture regulatory relationships between genes, while simultaneously building a K-Nearest Neighbor Graph (KNNG) to represent expression similarities between cells. These graphs are then combined into an Enriched Cell-Leaf Graph (ECLG), which serves as input for a graph neural network to compute cell embeddings. By incorporating both expression levels and gene-gene interactions, our approach provides a more comprehensive representation of cellular states. Extensive evaluation across multiple datasets demonstrates that our method enhances the detection of rare cell populations and improves downstream analyses such as visualization, clustering, and trajectory inference. This integrated approach represents a significant advance in single-cell data analysis, offering a more complete framework for understanding cellular diversity and dynamics.

Hypothalamus and brainstem circuits in the regulation of glucose homeostasis

The central nervous system (CNS) senses and integrates blood glucose status, regulating its levels through communication with peripheral organs. Since traditional wisdom holds that the hypothalamus primarily controls glucose homeostasis, the brainstem, although less studied, has been emerging as a key player in blood glucose metabolism. Although the brainstem is reciprocally wired with the hypothalamus, their interactions are crucial for glucose control. Here, we focus on classic discoveries and recent advancements of hypothalamic and brainstem nodes that regulate glucose homeostasis. Based on the current progress and development for central regulation of blood sugar, we propose that the circuitry and cellular mechanisms for how hypothalamus and brainstem coordinate in blood sugar regulation are crucial; hence, a deeper understanding of both nuclei could shed light on a future cure for diabetes.

Integration of single-cell and bulk RNA sequencing data using machine learning identifies oxidative stress-related genes LUM and PCOLCE2 as potential biomarkers for heart failure

Oxidative stress (OS) is a pivotal mechanism driving the progression of cardiovascular diseases, particularly heart failure (HF). However, the comprehensive characterisation of OS-related genes in HF remains largely unexplored. In the present study, we analysed single-cell RNA sequencing datasets from the Gene Expression Omnibus and OS gene sets from GeneCards. We identified 167 OS-related genes potentially linked to HF by applying algorithms, such as AUCell, UCell, singscore, ssgsea, and AddModuleScore, combined with correlation analysis. Subsequently, we used feature selection algorithms, including least absolute shrinkage and selection operator, XGBoost, Boruta, random forest, gradient boosting machines, decision trees, and support vector machine recursive feature elimination, to identify lumican (LUM) and procollagen C-endopeptidase enhancer 2 (PCOLCE2) as key biomarker candidates with significant diagnostic potential. Bulk RNA-sequencing confirmed their elevated expression in patients with HF, highlighting their predictive utility. Single-cell analysis further revealed their upregulation primarily in fibroblasts, emphasising their cell-specific role in HF. To validate these findings, we developed a transverse aortic constriction-induced HF mouse model that showed enhanced cardiac OS activity and significant PCOLCE2 upregulation in the HF group. These results provide strong evidence of the involvement of OS-related mechanisms in HF. Herein, we propose a diagnostic strategy that provides novel insights into the molecular mechanisms underlying HF. However, further studies are required to validate its clinical utility and ensure its application in the diagnosis of HF.

Deciphering chondrocyte diversity in diabetic osteoarthritis through single-cell transcriptomics

The pathophysiological distinctions between osteoarthritis (OA) and diabetic osteoarthritis (DOA) are critical yet not well delineated. In this study, we employed single-cell RNA sequencing to clarify the unique cellular and molecular mechanisms underpinning the progression of both conditions. We identified a novel subpopulation of chondrocytes in DOA, termed 'Heat Shock' chondrocytes, marked by the expression of distinct molecular markers including HSPA1A, HSPA1B, HSPB1, and HSPA8. Our comprehensive gene expression analysis revealed a pronounced upregulation of inflammatory pathways associated with oxidative stress-namely the MAPK, NF-κB, and PI3K signaling pathways-in the effector and proliferating chondrocyte subpopulations, with a predominance in DOA. Further, our investigation into cell-cell communication demonstrated a significant diminution of intercellular signaling in DOA compared to OA. These insights not only elucidate distinct cellular heterogeneities and potential pathogenic mechanisms differentiating OA from DOA but also enhance our understanding of their molecular pathophysiology, offering novel avenues for targeted therapeutic strategies.

A systematic review of single-cell RNA sequencing applications and innovations

Bulk RNA sequencing is one type of RNA sequencing technique, as well as targeted RNA sequencing and whole transcriptome sequencing. It provides valuable insights into gene expression in specific cell populations or regions. However, these methods often miss the diversity of cells within complex tissues. This restriction is overcome by single-cell RNA sequencing, which records gene expression at the single-cell level. It offers a detailed picture of the diversity of cells. It is essential to study glucose homeostasis. It offers thorough explanations of cellular variation. Networks and Governance Dynamics The use of scRNA-seq in islet cells is reviewed in this study, along with sample preparation, sequencing, and computational analysis. It highlights advances in understanding cell types. Gene activity and cell interactions. Along with the challenges and limitations of scRNA-seq, this review highlights the importance of scRNA-seq in understanding complex biological processes and diseases. It is an essential resource for future research and method development in this field, which will help to build personalized treatment.

A review of pharmacological mechanisms, challenges and prospects of macromolecular glycopeptides

Macromolecular glycopeptides are natural products derived from various sources, distinguished by their structural diversity, multifaceted biological activities, and low toxicity. These compounds exhibit a wide range of biological functions, such as immunomodulation, antitumor effects, anti-inflammatory properties, antioxidant activity, and more. However, limited understanding of natural glycopeptides has hindered their development and practical application. To promote their advancement and utilization, it is crucial to thoroughly investigate the pharmacological mechanisms of glycopeptides and address the challenges in natural glycopeptide research. This review uniquely focuses on the primary biological activities and potential molecular mechanisms of glycopeptides as reported in recent literature. Moreover, we emphasize the current challenges in glycopeptide research, including extraction and isolation difficulties, purification challenges, structural analysis complexities, elucidation of structure-activity relationships, characterization of biosynthetic pathways, and ensuring bioavailability and stability. The future prospects for glycopeptide research are also explored. We argue that ongoing research into glycopeptides will significantly contribute to drug development and provide more effective therapeutic options and disease treatment alternatives for human health.

Potential role of liver-resident CD3+ macrophages in HBV clearance in a mouse hepatitis B model

Background & Aims

Chronic HBV infection usually causes cirrhosis and hepatocellular carcinoma. Comparative investigations of acute and chronic HBV cases would help determine the immune responses crucial for viral clearance.

Methods

A fast-cleared HBV mouse model was established in Alb-Cre mice via hydrodynamic injection of HBV plasmid, while persistent HBV model mice were generated via recombinant covalently closed circular DNA-adeno-associated virus 8 infection. The single-cell transcriptomes of CD45+ intrahepatic non-parenchymal cells from these mice were conducted. Multiplexed immunohistochemistry and flow cytometry were used to confirm the findings from single-cell transcriptomes. Transwell, coculture, and adoptive transfer experiments were performed to study the generation and functions of macrophages.

Results

Twenty-four clusters of immune cells were identified. Myeloid cells, including granulocytes, monocytes, and dendritic cells, are activated early in HBV fast-cleared mice. Significantly, a cluster of CD3+ macrophages was found in the viral clearance phase, which was confirmed in liver tissue from five acute patients with HBV. These cells highly expressed CXCL1, tumor necrosis factor alpha, and HBsAg-specific T cell receptors. The transwell assay revealed that CD3+ macrophages originate from macrophages (n = 6). T cells and anti-HBsAg antibodies are indispensable for their differentiation, which was further confirmed in T- and/or B-cell-deficient mice. Interestingly, these CD3+ macrophages capable of killing peptide-loaded hepatocytes and engulfing IgG-coated beads were persistently detectable in the mouse liver for 10 weeks after HBV clearance. The expression levels of CD5L and Bcl2, two classical antiapoptotic proteins, increased (p <0.001), suggesting that the CD3+ macrophages are long-term resident populations. Finally, adoptive transfer of CD3+ macrophages accelerated HBV clearance in mice (n = 5, p <0.01).

Conclusions

We identified long-term polyfunctional CD3+ macrophages residing in HBV fast-cleared livers that could help elucidate the immune responses involved in eliminating HBV.

Impact and implications

The liver is a special organ with unique immune characteristics and tolerance to foodborne antigens. Chronic infections can develop in newborns after exposure to HBV; however, acute infections usually occur in adults, indicating that immune cells in the liver tissue microenvironment can also effectively fight against the virus. Nevertheless, the mechanisms involved in acute HBV infection have rarely been studied. In this study, we identified a macrophage population with both T cell and macrophage characteristics in the livers of acute HBV model mice and revealed that these macrophages play important roles in HBV clearance. Moreover, we confirmed that this population is derived from macrophages in the presence of virus-specific T cells and antibodies. This finding highlights the complexity of antiviral immune responses in liver microenvironments.

Psychedelic Drugs in Mental Disorders: Current Clinical Scope and Deep Learning-Based Advanced Perspectives

Mental disorders are a representative type of brain disorder, including anxiety, major depressive depression (MDD), and autism spectrum disorder (ASD), that are caused by multiple etiologies, including genetic heterogeneity, epigenetic dysregulation, and aberrant morphological and biochemical conditions. Psychedelic drugs such as psilocybin and lysergic acid diethylamide (LSD) have been renewed as fascinating treatment options and have gradually demonstrated potential therapeutic effects in mental disorders. However, the multifaceted conditions of psychiatric disorders resulting from individuality, complex genetic interplay, and intricate neural circuits impact the systemic pharmacology of psychedelics, which disturbs the integration of mechanisms that may result in dissimilar medicinal efficiency. The precise prescription of psychedelic drugs remains unclear, and advanced approaches are needed to optimize drug development. Here, recent studies demonstrating the diverse pharmacological effects of psychedelics in mental disorders are reviewed, and emerging perspectives on structural function, the microbiota-gut-brain axis, and the transcriptome are discussed. Moreover, the applicability of deep learning is highlighted for the development of drugs on the basis of big data. These approaches may provide insight into pharmacological mechanisms and interindividual factors to enhance drug discovery and development for advanced precision medicine.

Recursive Clustering of Cellular Diversity in scRNA-Seq Data

In scRNA-seq analysis, cell clusters are typically defined by a single round of feature extraction and clustering. This approach may miss phenotypic differences in cell types that are characterized by genes not sufficiently represented in the feature set derived using all cells, such as rare cell types. This work explores an alternative approach, where cell clusters are identified by recursively performing feature extraction and clustering on previously identified clusters, such that each subclustering step uses features that are more specific to distinguishing the higher-resolution subclusters. We benchmark this recursive approach against the conventional, nonrecursive clustering approach and demonstrate that the recursive method results in robust improvement in cell type detection on four scRNA-seq datasets across a wide range of clustering resolution parameters. We apply the recursive approach to cluster scRNA-seq data obtained from patients with Crohn's disease belonging to three clinical phenotypes and observe that recursive clustering captures phenotypic differences only visible at specific levels of granularity within an interpretable hierarchical framework while defining cell clusters within a gene expression feature space more specific to each cluster.

Stem Cell Markers LGR5, LGR4 and Their Immediate Signalling Partners are Dysregulated in Preeclampsia

Leucine-rich repeat-containing G protein-coupled receptors 5/4 (LGR5/LGR4) are critical stem cell markers in epithelial tissues including intestine. They agonise wingless-related integration site (WNT) signalling. Until now, LGR5/LGR4 were uncharacterised in placenta, where analogous functions may exist. We characterised LGR5/LGR4, their ligands/targets in human placenta, with further assessments on dysregulation in preeclampsia/fetal growth restriction (FGR). LGR5 mRNA was unaltered in first trimester (n = 11), preterm (n = 9) and term (n = 11) placental lysate. LGR5 was enriched in human trophoblast stem cells (hTSCs) and downregulated with differentiation to extravillous trophoblasts (p < 0.0215) and syncytiotrophoblasts (p < 0.0350). In situ hybridisation localised LGR5 to unique, proliferative MKI67 + mononuclear trophoblasts underlying syncytium which concurred with proposed progenitor identities in single-cell transcriptomics. LGR5 expression was significantly reduced in placentas from early-onset preeclampsia (p < 0.0001, n = 81 versus n = 19 controls), late-onset preeclampsia (p = 0.0046, n = 20 versus n = 33 controls) and FGR (p = 0.0031, n = 34 versus n = 17 controls). LGR4 was elevated in first trimester versus preterm and term placentas (p = 0.0412), in placentas with early-onset preeclampsia (p = 0.0148) and in FGR (p = 0.0417). Transcriptomic analysis and in vitro hTSC differentiation to both trophoblast lineages suggested LGR4 increases with differentiation. Single-nucleus RNA sequencing of placental villous samples supported LGR5 and LGR4 localisation findings. Hypoxia/proinflammatory cytokine treatment modelling elements experienced by the placenta in placental insufficiency pathogenesis did not significantly alter LGR5/LGR4. Ligands R-spondins 1/3/4, and neutralising targets ring finger protein 43 (RNF43) and zinc and ring finger 3 (ZNRF3) were also reduced in placentas from preeclamptic pregnancies. This study is the first to describe LGR5/LGR4 and their signalling partner expression in human placenta. Their dysregulations in the preeclamptic placenta allude to disruptions to integral trophoblast stem cell function/differentiation that may occur during placental development related to WNT signalling.

The hidden connection between gut microbiota and periprosthetic joint infections: a scoping review

Background: Periprosthetic joint infections (PJIs) pose a significant challenge in orthopedic surgery, and emerging evidence suggests that the gut microbiome may play a crucial role in their development and management. Despite the rarity of these infections, the continuous increase in prosthetic joint arthroplasties has made understanding how to prevent them more pressing. A stronger comprehension of the disruption of the gut microbiome and how this can lead to more of these infections and other pre-surgical risks may be crucial in preventing them. Objective: This article aims to provide a stronger understanding of the topic through the analysis of different pieces of already existing literature to help draw new conclusions and raise potential questions that need answering. Methods: A comprehensive search strategy without filters was employed, and multiple papers were thoroughly analyzed, understood, and compiled into this paper. Conclusions: Despite the limitations of some of the analyzed studies and finite evidence, this paper suggests that there could be a connection between periprosthetic joint infections and a compromised gut microbiome. However, further research is required to draw a definitive conclusion.

Cancer associated fibroblasts in cancer development and therapy

Cancer-associated fibroblasts (CAFs) are key players in cancer development and therapy, and they exhibit multifaceted roles in the tumor microenvironment (TME). From their diverse cellular origins, CAFs undergo phenotypic and functional transformation upon interacting with tumor cells and their presence can adversely influence treatment outcomes and the severity of the cancer. Emerging evidence from single-cell RNA sequencing (scRNA-seq) studies have highlighted the heterogeneity and plasticity of CAFs, with subtypes identifiable through distinct gene expression profiles and functional properties. CAFs influence cancer development through multiple mechanisms, including regulation of extracellular matrix (ECM) remodeling, direct promotion of tumor growth through provision of metabolic support, promoting epithelial-mesenchymal transition (EMT) to enhance cancer invasiveness and growth, as well as stimulating cancer stem cell properties within the tumor. Moreover, CAFs can induce an immunosuppressive TME and contribute to therapeutic resistance. In this review, we summarize the fundamental knowledge and recent advances regarding CAFs, focusing on their sophisticated roles in cancer development and potential as therapeutic targets. We discuss various strategies to target CAFs, including ECM modulation, direct elimination, interruption of CAF-TME crosstalk, and CAF normalization, as approaches to developing more effective treatments. An improved understanding of the complex interplay between CAFs and TME is crucial for developing new and effective targeted therapies for cancer.

Resolving spatiotemporal dynamics in bacterial multicellular populations: approaches and challenges

SUMMARYThe development of multicellularity represents a key evolutionary transition that is crucial for the emergence of complex life forms. Although multicellularity has traditionally been studied in eukaryotes, it originates in prokaryotes. Coordinated aggregation of individual cells within the confines of a colony results in emerging, higher-level functions that benefit the population as a whole. During colony differentiation, an almost infinite number of ecological and physiological population-forming forces are at work, creating complex, intricate colony structures with divergent functions. Understanding the assembly and dynamics of such populations requires resolving individual cells or cell groups within such macroscopic structures. Addressing how each cell contributes to the collective action requires pushing the resolution boundaries of key technologies that will be presented in this review. In particular, single-cell techniques provide powerful tools for studying bacterial multicellularity with unprecedented spatial and temporal resolution. These advancements include novel microscopic techniques, mass spectrometry imaging, flow cytometry, spatial transcriptomics, single-bacteria RNA sequencing, and the integration of spatiotemporal transcriptomics with microscopy, alongside advanced microfluidic cultivation systems. This review encourages exploring the synergistic potential of the new technologies in the study of bacterial multicellularity, with a particular focus on individuals in differentiated bacterial biofilms (colonies). It highlights how resolving population structures at the single-cell level and understanding their respective functions can elucidate the overarching functions of bacterial multicellular populations.

Exploring the mechanism of action of succinic acid in ovarian cancer via single-cell sequencing of the tumor immune microenvironment

Background

The main treatments for ovarian cancer are surgery, chemotherapy, radiotherapy, and targeted therapy. Targeted therapy is a new treatment method that has emerged in recent years and relies on specific molecular targets to treat cancer. Succinic acid is a key intermediate product in the tricarboxylic acid cycle. Research has shown that succinic acid has antioxidant properties and can alleviate oxidative stress in cells and tissues. These findings indicate the potential application of succinic acid in antioxidant therapy and the prevention of oxidative damage. This study explored the potential targets and therapeutic mechanisms of succinic acid in ovarian cancer.

Methods

Using bioinformatics and single-cell sequencing technology, the hub genes related to succinic acid and ovarian cancer and the frequency and gene expression patterns of different cell types in ovarian cancer patients and normal individuals were analyzed.

Results

The frequency of immune cells, including B cells, CD4+ cells, CD8+ cells, macrophages, and plasma cells, was significantly increased in ovarian cancer patients, and the frequency of other cell types, such as endothelial cells, NK cells, and pericytes/SMCs, was decreased. Further research revealed three key hub genes: SPP1, SLPI, and CD9. The expression patterns of these genes in ovarian cancer were closely related to different cell types. SPP1 was expressed mainly in macrophages, SLPI was expressed in epithelial cells, and CD9 was expressed in pericytes/SMCs and epithelial cells. SPP1, SLPI, and CD9 and their mechanisms of action may be potential targets for the treatment of ovarian cancer with succinic acid.

Conclusions

This study investigated the potential therapeutic targets and mechanisms of succinic acid in ovarian cancer and the differences in immune cell infiltration and gene expression patterns, providing important insights for future tumor immunotherapy research.

Integrated single-cell transcriptome and comparative genome analysis reveals the origin of intermuscular bones in zebrafish

The evolutionary process of intermuscular bones (IBs) is complex, the molecular regulatory mechanisms of their development are not clear, and even the genes involved in the evolution and development of IBs are poorly understood. In this study, comparative genomic analysis of four fish species with IBs and eleven fish species without IBs identified 106 genes that are more conservatively evolved in fish species with IBs, but highly variable in fish species without IBs. These genes are mainly involved in swimming behavior and BMP signaling pathways. We performed single-cell transcriptome sequencing of IBs origin tissues in zebrafish before and after IBs formation and found that osteoblasts and mesenchymal stem cells (MSCs) increased significantly after IBs formation. RNA velocity analysis showed that osteoblasts in IBs differentiate from MSCs, and the differentiation trajectory of MSCs into osteoblasts was successfully constructed by pseudo-time analysis. Combined with the results of multi-omics analysis, seven candidate genes associated with IBs development were screened and knocked out in zebrafish. It was found that foxn3 mutation resulted in a delay in IB development, whereas bmp6 mutation resulted in a total loss of IB. By comparing the transcriptome of IBs tissues between bmp6+/+ zebrafish and bmp6-/- zebrafish, we found that bmp6 deletion may inhibit the differentiation of MSCs into osteoblasts while promoting the formation of osteoclasts and ultimately inhibiting the formation of IBs. This study provides new insights into the molecular regulatory mechanisms and evolutionary processes of IB development.

Mapping molecular landscapes in triple-negative breast cancer: insights from spatial transcriptomics

The tumor microenvironment (TME) of triple-negative breast cancer (TNBC) is a highly heterogeneous and very aggressive form of the disease that has few suitable treatment options; however, spatial transcriptomics (ST) is a powerful tool for elucidation of the TME in TNBC. Because of its spatial context preservation, ST has a unique capability to map tumor-stroma interactions, immune infiltration, and therapy resistance mechanisms (which are key to understanding TNBC progression), compared with conventional transcriptomics. This review shows the use of ST in TNBC, its utilization in spatial biomarker identification, intratumoral heterogeneity definition, molecular subtyping refinement, and prediction of immunotherapy responses. Recent insight from ST-driven insights has explained the key spatial patterns on immune evasion, chemotherapy resistance, racial disparities of TNBC, and aspects for patient stratification and therapeutic decision. With the integration of ST with the subjects of proteomics and imaging mass cytometry, this approach has been enlarged and is now applied in precision medicine and biomarker discovery. Recently, advancements in AI-based spatial analysis for tumor classification, identification of biomarkers, and creation of therapy prediction models have occurred. However, continued developments in ST technologies, computational tools, and partnerships amongst multiple centers to facilitate the integration of ST into clinical routine practice are needed to unlock novel therapeutic targets.

Elucidating neuroepigenetic mechanisms to inform targeted therapeutics for brain disorders

The evolving field of neuroepigenetics provides important insights into the molecular foundations of brain function. Novel sequencing technologies have identified patient-specific mutations and gene expression profiles involved in shaping the epigenetic landscape during neurodevelopment and in disease. Traditional methods to investigate the consequences of chromatin-related mutations provide valuable phenotypic insights but often lack information on the biochemical mechanisms underlying these processes. Recent studies, however, are beginning to elucidate how structural and/or functional aspects of histone, DNA, and RNA post-translational modifications affect transcriptional landscapes and neurological phenotypes. Here, we review the identification of epigenetic regulators from genomic studies of brain disease, as well as mechanistic findings that reveal the intricacies of neuronal chromatin regulation. We then discuss how these mechanistic studies serve as a guideline for future neuroepigenetics investigations. We end by proposing a roadmap to future therapies that exploit these findings by coupling them to recent advances in targeted therapeutics.

Comprehensive guide for epigenetics and transcriptomics data quality control

Host response to environmental exposures such as pathogens and chemicals can include modifications to the epigenome and transcriptome. Improved signature discovery, including the identification of the agent and timing of exposure, has been enabled by advancements in assaying techniques to detect RNA expression, DNA base modifications, histone modifications, and chromatin accessibility. The interrogation of the epigenome and transcriptome cascade requires analyzing disparate datasets from multiple assay types, often at single-cell resolution, derived from the same biospecimen. However, there remains a paucity of rigorous quality control standards of those datasets that reflect quality assurance of the underlying assay. This guide outlines a comprehensive suite of metrics that can be used to ensure quality from 11 different epigenetics and transcriptomics assays. Recommended mitigative actions to address failed metrics are provided. The workflow presented aims to improve benchwork protocols and dataset quality to enable accurate discovery of exposure signatures.

Distinct Immunological Features Compared to Lichen Planus and Oral Lichen Planus

Purpose

Lichen planus (LP) and oral lichen planus (OLP) share clinical and histological similarities, yet their distinct immunopathological mechanisms make differentiation and management challenging. Clarifying these differences is essential for accurate diagnosis and treatment. This study aimed to investigate the systemic immune profile of OLP using single-cell transcriptomics, identifying distinct immune cell subsets and signaling pathways contributing to its chronic inflammatory state. Additionally, it sought to compare the inflammatory lesion microenvironments of OLP and LP by analyzing key immune pathways and cellular interactions.

Methods

Peripheral blood mononuclear cells (PBMCs) were obtained from 16 OLP patients and 5 healthy controls. Single-cell transcriptomic data from PBMCs and lesion tissues of OLP and LP were analyzed to profile immune and inflammatory signatures. Key molecular findings were validated using independent datasets and enzyme-linked immunosorbent assays (ELISA).

Results

Prostaglandin D2 synthase (PTGDS), a pivotal enzyme in prostaglandin metabolism, emerged as a diagnostic marker with elevated expression in NK cells from OLP patients. Additionally, a novel CXCR4 high-TSC22D3 high CD4 cytotoxic T cell subset with enhanced cytotoxicity was identified, potentially contributing to OLP pathogenesis. OLP blood samples also demonstrated significant upregulation of TNF and TLR signaling in NK cells, indicating a heightened chronic inflammatory state. Comparative tissue analysis revealed intensified TNF-driven inflammation and a disrupted HIF1A- vascular endothelial growth factor (VEGF) interactions in OLP, contrasting with LP's robust VEGF-mediated angiogenesis.

Discussion

These findings highlight distinct immunopathogenic mechanisms between OLP and LP. The upregulation of PTGDS in NK cells and CXCR4 high-TSC22D3 high CD4 cytotoxic T cells in PBMCs indicates systemic immune dysregulation in OLP, while tissue-level differences suggest impaired vascular remodeling and chronic inflammation. These insights underscore the need for targeted immunomodulatory therapies.

Conclusion

This study identifies distinct immune signatures that differentiate OLP from LP, highlighting potential therapeutic targets that require further validation for personalized treatment strategies.

Current Applications of Single-Cell RNA Sequencing in Glioblastoma: A Scoping Review

Background and Objective: The discovery of novel molecular biomarkers via next-generation sequencing technologies has revolutionized how glioblastomas (GBMs) are classified nowadays. This has resulted in more precise diagnostic, prognostic, and therapeutic approaches to address this malignancy. The present work examines the applications of single-cell RNA sequencing (scRNA-seq) in GBM, focusing on its potential to address tumor complexity and therapeutic resistance and improve patient outcomes. Methods: A scoping review of original studies published between 2009 and 2024 was conducted using the PUBMED and EMBASE databases. Studies in English or Spanish related to single-cell analysis and GBM were included. Key Findings: The database search yielded 453 publications. Themes related to scRNA-seq applied for the diagnosis, prognosis, treatment, and understanding of the cancer biology of GBM were used as criteria for article selection. Of the 24 studies that were included in the review, 11 focused on the tumor microenvironment and cell subpopulations in GBM samples, 5 investigated the use of sequencing to elucidate the GBM cancer biology, 3 examined disease prognosis using sequencing models, 3 applied translational research through scRNA-seq, and 2 addressed treatment-related problems in GBM elucidated by scRNA-seq. Conclusions: This scoping review explored the various clinical applications of scRNA-seq technologies in approaching GBM. The findings highlight the utility of this technology in unraveling the complex cellular and immune landscapes of GBM, paving the way for improved diagnosis and personalized treatments. This cutting-edge approach might strengthen treatment strategies against tumor progression and recurrence, setting the stage for multi-targeted interventions that could significantly improve outcomes for patients with aggressive, treatment-resistant GBMs.

Inonotus obliquus (chaga) ameliorates folic acid-induced renal fibrosis in mice: the crosstalk analysis among PT cells, macrophages and T cells based on single-cell sequencing

Background

Renal fibrosis, characterized by the abnormal accumulation of extracellular matrix in renal tissue and progressive loss of kidney function, is posing a significant challenge in clinical treatment. While several therapeutic options exist, effective treatments remain limited. Inonotus obliquus (Chaga), a traditional medicinal mushroom, has shown promising effects in chronic kidney disease (CKD), yet its cellular and molecular mechanisms remain largely unexplored.

Methods

We analysed the chemical composition of Chaga using UPLC-MS and predicted its biological targets using PubChem and Swiss Target Prediction. We used single-cell RNA sequencing to study cellular responses in a mouse model of folic acid-induced renal fibrosis, complemented by spatial transcriptomics to map cellular location patterns. Histological assessment was performed using H&E and Masson trichrome staining.

Results

For the first time, we employed single-cell RNA sequencing technology to investigate Chaga treatment in renal fibrosis. Histological analysis revealed that Chaga treatment significantly reduced renal tubular damage scores [from 5.00 (5.00, 5.00) to 2.00 (2.00, 2.00), p < 0.05] and decreased collagen deposition area (from 11.40% ± 3.01% to 4.06% ± 0.45%, p < 0.05) at day 14. Through analysis of 82,496 kidney cells, we identified 30 distinct cell clusters classified into eight cell types. Key findings include the downregulation of pro-inflammatory M1 macrophages and upregulation of anti-inflammatory M2 macrophages, alongside decreased T cell responses. Single-cell sequencing revealed differential gene expression in proximal tubular subpopulations associated with reduced fibrosis. Pathway and network pharmacology analyses of 60 identified compounds in Chaga and their 675 predicted targets suggested potential effects on immune and fibrotic pathways, particularly affecting Tregs and NKT cells. Cell-to-cell communication analyses revealed potential interactions between proximal tubular cells, macrophages, and T Cells, providing insights into possible mechanisms by which Chaga may ameliorate renal fibrosis.

Conclusion

Our study provided new insights into the potential therapeutic effects of Chaga in renal fibrosis through single-cell sequencing analysis. Our findings suggest that Chaga may represent a promising candidate for renal fibrosis treatment, though further experimental validation is needed to establish its clinical application.

A dataset of single-cell transcriptomic atlas of Bama pig and potential marker genes across seven tissues

The use of single-cell sequencing technology for single-cell transcriptomics studies in pigs is expanding progressively. However, the comprehensive classification of cell types across different anatomical tissues and organs of pig in multiple datasets remains relatively limited. This study employs single-cell and single-nucleus sequencing technologies in Bama pig to identify unique marker genes and their corresponding transcriptomic profiles across diverse cell types in various anatomical tissues and organs, including subcutaneous fat, visceral fat, psoas major muscle, liver, spleen, lung, and kidney. Through detailed data analyses, we observed widespread cellular diversity across various anatomical tissues and organs of Bama pig. This work contributes a comprehensive dataset that supports physiological studies and aids in the identification and prediction of potential marker genes through single-cell transcriptomics of these tissues. The methodologies and data employed in this study are designed to improve the accuracy of cell type identification and ensure consistent cell type allocation.

K-Volume Clustering Algorithms for scRNA-Seq Data Analysis

Clustering high-dimensional and structural data remains a key challenge in computational biology, especially for complex single-cell and multi-omics datasets. In this study, we present K-volume clustering, a novel algorithm that uses the total convex volume defined by points within a cluster as a biologically relevant and geometrically interpretable criterion. This method simultaneously optimizes both the hierarchical structure and the number of clusters at each level through nonlinear optimization. Validation on real datasets shows that K-volume clustering outperforms traditional methods across a range of biological applications. With its theoretical foundation and broad applicability, K-volume clustering holds great promise as a core tool for diverse data analysis tasks.

Exploring Single-Probe Single-Cell Mass Spectrometry: Current Trends and Future Directions

The Single-probe single-cell mass spectrometry (SCMS) is an innovative analytical technique designed for metabolomic profiling, offering a miniaturized, multifunctional device capable of direct coupling to mass spectrometers. It is an ambient technique leveraging microscale sampling and nanoelectrospray ionization (nanoESI), enabling the analysis of cells in their native environments without the need for extensive sample preparation. Due to its miniaturized design and versatility, this device allows for applications in diverse research areas, including single-cell metabolomics, quantification of target molecules in single cell, MS imaging (MSI) of tissue sections, and investigation of extracellular molecules in live single spheroids. This review explores recent advancements in Single-probe-based techniques and their applications, emphasizing their potential utility in advancing MS methodologies in microscale bioanalysis.

CSsingle: A Unified Tool for Robust Decomposition of Bulk and Spatial Transcriptomic Data Across Diverse Single-Cell References

We introduce CSsingle, a novel method that enhances the decomposition of bulk and spatial transcriptomic (ST) data by addressing key challenges in cellular heterogeneity. CSsingle applies cell size correction using ERCC spike-in controls, enabling it to account for variations in RNA content between cell types and achieve accurate bulk data deconvolution. In addition, it enables fine-scale analysis for ST data, advancing our understanding of tissue architecture and cellular interactions, particularly in complex microenvironments. We provide a unified tool for integrating bulk and ST with scRNA-seq data, advancing the study of complex biological systems and disease processes. The benchmark results demonstrate that CSsingle outperforms existing methods in accuracy and robustness. Validation using more than 700 normal and diseased samples from gastroesophageal tissue reveals the predominant presence of mosaic columnar cells (MCCs), which exhibit a gastric and intestinal mosaic phenotype in Barrett's esophagus and esophageal adenocarcinoma (EAC), in contrast to their very low detectable levels in esophageal squamous cell carcinoma and normal gastroesophageal tissue. We revealed a dynamic relationship between MCCs and squamous cells during immune checkpoint inhibitors (ICI)-based treatment in EAC patients, suggesting MCC expression signatures as predictive and prognostic markers of immunochemotherapy outcomes. Our findings reveal the critical role of MCC in the treatment of EAC and its potential as a biomarker to predict outcomes of immunochemotherapy, providing insight into tumor epithelial plasticity to guide personalized immunotherapeutic strategies.

Screening and validating genes associated with cuproptosis in systemic lupus erythematosus by expression profiling combined with machine learning

Cell death has long been a focal point in life sciences research, and recently, scientists have discovered a novel form of cell death induced by copper, termed cuproptosis. This paper aimed to identify genes associated with cuproptosis in systemic lupus erythematosus (SLE) through machine learning, combined with single-cell RNA sequencing (scRNA-seq), to screen and validate related genes. The analytical results were then experimentally verified. Two published microarray gene expression datasets (GSE65391 and GSE61635) from SLE and control peripheral blood samples were downloaded from the GEO database. The GSE65391 dataset was used as the training group, while the GSE61635 dataset served as the validation group. Differentially expressed genes from GSE65391 identified 12 differential genes. Nine diagnostic genes, considered potential biomarkers, were selected using the least absolute shrinkage and selection operator and support vector machine recursive feature elimination analysis. The receiver operating characteristic (ROC) curves for both the training and validation groups were used to calculate the area under the curve to assess discriminatory properties. CIBERSORT was used to assess the relationship between these diagnostic genes and a reference set of infiltrating immune cells. scRNA-seq data (GSE162577) from SLE patients were also obtained from the GEO database and analyzed. Experimental validation of the most important SLE biomarkers was performed. Twelve significantly different cuproptosis-related genes were identified in the GSE65391 training set. Immune cell analysis revealed 12 immune cell types and identified nine signature genes, including PDHB, glutaminase (GLS), DLAT, LIAS, MTF1, DLST, DLD, LIPT1, and FDX1. In the GSE61635 validation set, seven genes were weakly expressed, and two genes were strongly expressed in the treatment group. According to the ROC curves, PDHB and GLS demonstrated significant diagnostic value. Additionally, correlation analysis was conducted on the nine characteristic genes in relation to immune infiltration. The distribution of key genes in immune cells was determined using scRNA-seq data. Finally, the mRNA expression of the nine diagnostic genes was validated using qPCR.