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Alzheimer’s disease (AD) is the most common form of dementia, characterized by progressive cognitive impairment and neurodegeneration. Extensive clinical and genomic studies have revealed biomarkers, risk factors, pathways, and targets of AD in the past decade. However, the exact molecular basis of AD development and progression remains elusive. The emerging single-cell sequencing technology can potentially provide cell-level insights into the disease. Here we systematically review the state-of-the-art bioinformatics approaches to analyze single-cell sequencing data and their applications to AD in 14 major directions, including 1) quality control and normalization, 2) dimension reduction and feature extraction, 3) cell clustering analysis, 4) cell type inference and annotation, 5) differential expression, 6) trajectory inference, 7) copy number variation analysis, 8) integration of single-cell multi-omics, 9) epigenomic analysis, 10) gene network inference, 11) prioritization of cell subpopulations, 12) integrative analysis of human and mouse sc-RNA-seq data, 13) spatial transcriptomics, and 14) comparison of single cell AD mouse model studies and single cell human AD studies. We also address challenges in using human postmortem and mouse tissues and outline future developments in single cell sequencing data analysis. Importantly, we have implemented our recommended workflow for each major analytic direction and applied them to a large single nucleus RNA-sequencing (snRNA-seq) dataset in AD. Key analytic results are reported while the scripts and the data are shared with the research community through  GitHub. In summary, this comprehensive review provides insights into various approaches to analyze single cell sequencing data and offers specific guidelines for study design and a variety of analytic directions. The review and the accompanied software tools will serve as a valuable resource for studying cellular and molecular mechanisms of AD, other diseases, or biological systems at the single cell level.

Despite marked development in cancer therapies during recent decades, the prognosis for advanced cancer remains poor. The conventional tumor–cell‐centric view of cancer can only explain part of cancer progression, and thus a thorough understanding of the tumor microenvironment (TME) is crucial. Among cells within the TME, cancer‐associated fibroblasts (CAFs) are attracting attention as a target for cancer therapy. However, CAFs present a heterogeneous population of cells and more detailed classification of CAFs and investigation of functions of each subset is needed to develop novel CAF‐targeted therapies. In this context, application of newly developed approaches to single‐cell analysis has already made an impact on our understanding of the heterogeneity of CAFs. Here, we review the recent literature on CAF heterogeneity and function, and discuss the possibility of novel therapies targeting CAF subsets. The conventional tumor–cell‐centric view of cancer can only explain a part of cancer progression, thus understanding of the tumor microenvironment (TME) is crucial. Among cells within the TME, cancer‐associated fibroblasts (CAFs) are attracting attention as a target for cancer therapy. Here, we review the recent literature that has improved our understanding of heterogeneity in CAFs and function of each subset, and discuss the possibility of novel therapies targeting CAF subsets.

Hepatocellular carcinoma (HCC), the sixth most prevalent cancer globally, is characterized by high recurrence rates and poor prognosis. Investigating the heterogeneity of relapsed HCC and identifying key therapeutic targets may facilitate the design of effective anticancer therapies. In this study, integrative analysis of single‐cell RNA sequencing data of primary and early‐relapsed HCC revealed increased proportions of infiltrating CD8+ T cells along with malignant cells and a decrease in CD4+ T cells in relapsed HCC. Cellular interaction and immunohistochemical analysis proposed MIF‐(CD74 + CXCR4) signaling pathway as a key mechanism by which malignant cells influence immune cells within the tumor microenvironment. Notably, primary malignant cells showed greater differentiation and proliferation potential, whereas relapsed cells exhibited enhanced epithelial–mesenchymal transition and inflammation, along with upregulated glycogen synthesis and metabolism‐related gene expression. Using machine learning techniques on bulk RNA‐seq data, we developed a relapsed tumor cell‐related risk score (RTRS) that independently predicts overall and recurrence‐free survival time with higher accuracy compared with conventional clinical variables. Prognostic biomarkers and potential therapeutic targets were validated via RT‐qPCR using mouse implantation models. This comprehensive investigation elucidates the heterogeneity of relapsed HCC and constructs a novel postoperative recurrence prognostic model, paving the way for targeted therapies and improved patient outcomes. This study leveraged public datasets and integrative bioinformatic analysis to dissect malignant cell heterogeneity between relapsed and primary HCC, focusing on intercellular communication, differentiation status, metabolic activity, and transcriptomic profiles. We developed a relapsed tumor cell‐related risk score (RTRS) model with prognostic relevance. Our findings highlight the utility of computational approaches in advancing precision oncology strategies for recurrent HCC.

A comprehensive analysis of the immune response triggered by intraoperative radiation therapy (IORT) remains incomplete. In this study, single‐cell RNA sequencing and single‐cell T cell receptor sequencing are conducted on peripheral blood mononuclear cells (PBMCs) from patient with early‐stage breast cancer before and after IORT. Following IORT combined with surgery (defined as IORT+Surgery), PBMC counts remained stable, with increased proportions of T cells, mononuclear phagocytes, and plasma cells, and a reduction in neutrophil proportions. The cytotoxic score of CD8Teff_GZMK cells increased significantly post‐IORT. Communication between CD8Teff_GZMK cells and other immune cells via MIF_CD74 and MIF_TNFRSF14 is decreased after IORT. cDCs showed an upregulation of the MCH II signaling pathway, while memory B cells exhibited enhanced activation of the B cell pathway. T cell clones expanded significantly after treatment. IORT+Surgery demonstrated the ability to partially suppress the anti‐tumor effects of neutrophils. Flow cytometry analysis and co‐culture experiments are utilized to delve deeper into the functional alterations in T cells. IORT+Surgery significantly enhanced T cell cytotoxic activity. Blockade of PD‐1 of post‐IORT PBMCs shows higher T‐cell activity than that of pre‐IORT PBMCs. This research highlights IORT's impact on immune cells, offering insights for targeting immune responses in breast cancer. This study investigates the immune response to intraoperative radiation therapy (IORT) in early‐stage breast cancer using single‐cell RNA and T cell receptor sequencing. IORT combined with surgery increases T cell cytotoxicity, alters immune cell communication, and modulates T cell clonal expansion, offering insights into immune targeting strategies for breast cancer.

Single‐cell RNA sequencing (scRNA‐seq) is a novel technology that allows transcriptomic analyses of individual cells. During the past decade, scRNA‐seq sensitivity, accuracy, and efficiency have improved due to innovations including more sensitive, automated, and cost‐effective single‐cell isolation methods with higher throughput as well as ongoing technological development of scRNA‐seq protocols. Among the variety of current approaches with distinct features, researchers can choose the most suitable method to carry out their research. By profiling single cells in a complex population mix, scRNA‐seq presents great advantages over traditional sequencing methods in dissecting heterogeneity in cell populations hidden in bulk analysis and exploring rare cell types associated with tumorigenesis and metastasis. scRNA‐seq studies in recent years in the field of breast cancer research have clustered breast cancer cell populations with different molecular subtypes to identify distinct populations that may correlate with poor prognosis and drug resistance. The technology has also been used to explain tumor microenvironment heterogeneity by identifying distinct immune cell subsets that may be associated with immunosurveillance and are potential immunotherapy targets. Moreover, scRNA‐seq has diverse applications in breast cancer research besides exploring heterogeneity, including the analysis of cell‐cell communications, regulatory single‐cell states, immune cell distributions, and more. scRNA‐seq is also a promising tool that can facilitate individualized therapy due to its ability to define cell subsets with potential treatment targets. Although scRNA‐seq studies of therapeutic selection in breast cancer are currently limited, the application of this technology in this field is prospective. Joint efforts and original ideas are needed to better implement scRNA‐seq technologies in breast cancer research to pave the way for individualized treatment management. This review provides a brief introduction on the currently available scRNA‐seq approaches along with their corresponding strengths and weaknesses and may act as a reference for the selection of suitable methods for research. We also discuss the current applications of scRNA‐seq in breast cancer research for tumor heterogeneity analysis, individualized therapy, and the other research directions mentioned above by reviewing corresponding published studies. Finally, we discuss the limitations of current scRNA‐seq technologies and technical problems that remain to be overcome.

Background Recently, pioneering expression quantitative trait loci (eQTL) studies on single cell RNA sequencing (scRNA-seq) data have revealed new and cell-specific regulatory single nucleotide variants (SNVs). Here, we present an alternative QTL-related approach applicable to transcribed SNV loci from scRNA-seq data: scReQTL. ScReQTL uses Variant Allele Fraction (VAF RNA ) at expressed biallelic loci, and corelates it to gene expression from the corresponding cell. Results Our approach employs the advantage that, when estimated from multiple cells, VAF RNA can be used to assess effects of SNVs in a single sample or individual. In this setting scReQTL operates in the context of identical genotypes, where it is likely to capture RNA-mediated genetic interactions with cell-specific and transient effects. Applying scReQTL on scRNA-seq data generated on the 10 × Genomics Chromium platform using 26,640 mesenchymal cells derived from adipose tissue obtained from three healthy female donors, we identified 1272 unique scReQTLs. ScReQTLs common between individuals or cell types were consistent in terms of the directionality of the relationship and the effect size. Comparative assessment with eQTLs from bulk sequencing data showed that scReQTL analysis identifies a distinct set of SNV-gene correlations, that are substantially enriched in known gene-gene interactions and significant genome-wide association studies (GWAS) loci. Conclusion ScReQTL is relevant to the rapidly growing source of scRNA-seq data and can be applied to outline SNVs potentially contributing to cell type-specific and/or dynamic genetic interactions from an individual scRNA-seq dataset. Availability: https://github.com/HorvathLab/NGS/tree/master/scReQTL

High‐grade serous tubo‐ovarian cancer (HGSTOC) is an aggressive gynecological malignancy including homologous recombination deficient (HRD) and homologous recombination proficient (HRP) groups. Despite the therapeutic potential of poly (ADP‐ribose) polymerase inhibitors (PARPis) and anti‐PDCD1 antibodies, acquired resistance in HRD and suboptimal response in HRP patients necessitate more precise treatment. Herein, single‐cell RNA and single‐cell T‐cell receptor sequencing on 5 HRD and 3 HRP tumors are performed to decipher the heterogeneous tumor immune microenvironment (TIME), along with multiplex immunohistochemistry staining and animal experiments for validation. HRD tumors are enriched with immunogenic epithelial cells, FGFR1+PDGFRβ+ myCAFs, M1 macrophages, tumor reactive CD8+/CD4+ Tregs, whereas HRP tumors are enriched with HDAC1‐expressing epithelial cells, indolent CAFs, M2 macrophages, and bystander CD4+/CD8+ T cells. Significantly, customized therapies are proposed. For HRD patients, targeting FGFR1+PDGFRβ+ myCAFs via tyrosine kinase inhibitors, targeting Tregs via anti‐CCR8 antibodies/TNFRSF4 stimulation, and targeting CXCL13+ exhausted T cells by blocking PDCD1/CTLA‐4/LAG‐3/TIGIT are proposed. For HRP patients, targeting indolent CAFs, targeting M2 macrophages via CSF‐1/CSF‐1R inhibitors, targeting bystander T cells via tumor vaccines, and targeting epithelial cells via HDAC inhibitors. The study provides comprehensive insights into HRD and HRP TIME and tailored therapeutic approaches, addressing the challenges of PARPi‐resistant HRD and refractory HRP tumors. A comprehensive single‐cell atlas of homologous recombination deficient (HRD) and homologous recombination proficient (HRP) ovarian cancer is depicted. Through verification of mIHC and in‐vivo xenograft validation, the distinct tumor immune microenvironment is unveiled, the promising targets specifically for HRD and HRP tumors are proposed, providing novel insights for developing effective clinical therapies customized for HRD and HRP patients.

Cleft lip and/or primary palate (CL/P) represent a prevalent congenital malformation, the aetiology of which is highly intricate. Although it is generally accepted that the condition arises from failed fusion between the upper lip and primary palate, the precise mechanism underlying this fusion process remains enigmatic. In this study, we utilized transposase‐accessible chromatin sequencing (scATAC‐seq) and single‐cell RNA sequencing (scRNA‐seq) to interrogate lambdoidal junction tissue derived from C57BL/6J mouse embryos at critical stages of embryogenesis (10.5, 11.5 and 12.5 embryonic days). We successfully identified distinct subgroups of mesenchymal and ectodermal cells involved in the fusion process and characterized their unique transcriptional profiles. Furthermore, we conducted cell differentiation trajectory analysis, revealing a dynamic repertoire of genes that are sequentially activated or repressed during pseudotime, facilitating the transition of relevant cell types. Additionally, we employed scATAC data to identify key genes associated with the fusion process and demonstrated differential chromatin accessibility across major cell types. Finally, we constructed a dynamic intercellular communication network and predicted upstream transcriptional regulators of critical genes involved in important signalling pathways. Our findings provide a valuable resource for future studies on upper lip and primary palate development, as well as congenital defects.

Background This study aimed to investigate the potential mechanism of paeonol in the treatment of neuropathic pain. Methods Relevant mechanisms were explored through microglial pseudotime analysis and the use of specific inhibitors in cell experiments. In animal experiments, 32 SD rats were randomly divided into the sham operation group, the chronic constrictive injury (CCI) group, the ibuprofen group, and the paeonol group. We performed behavioral testing, ELISA, PCR, Western blotting, immunohistochemistry, and immunofluorescence analysis. Results The pseudotime analysis of microglia found that RhoA, Rock1, and p38MAPK were highly expressed in activated microglia, and the expression patterns of these genes were consistent with the expression trends of the M1 markers CD32 and CD86. Paeonol decreased the levels of M1 markers (IL1β, iNOS, CD32, IL6) and increased the levels of M2 markers (IL10, CD206, ARG‐1) in LPS‐induced microglia. The expression of iNOS, IL1β, RhoA, and Rock1 was significantly increased in LPS‐treated microglia, while paeonol decreased the expression of these proteins. Thermal hyperalgesia occurred after CCI surgery, and paeonol provided relief. In addition, paeonol decreased the levels of IL1β and IL8 and increased the levels of IL4 and TGF‐β in the serum of CCI rats. Paeonol decreased expression levels of M1 markers and increased expression levels of M2 markers in the spinal cord. Paeonol decreased IBA‐1, IL1β, RhoA, RhoA‐GTP, COX2, Rock1, and p‐p38MAPK levels in the spinal dorsal horn. Conclusion Paeonol relieves neuropathic pain by modulating microglial M1 and M2 phenotypes through the RhoA/p38 MAPK pathway. Experiment flow chart.

Single‐cell RNA sequencing (scRNA‐seq) enables characterizing the cellular heterogeneity in human tissues. Recent technological advances have enabled the first population‐scale scRNA‐seq studies in hundreds of individuals, allowing to assay genetic effects with single‐cell resolution. However, existing strategies to analyze these data remain based on principles established for the genetic analysis of bulk RNA‐seq. In particular, current methods depend on a priori definitions of discrete cell types, and hence cannot assess allelic effects across subtle cell types and cell states. To address this, we propose the Cell Regulatory Map (CellRegMap), a statistical framework to test for and quantify genetic effects on gene expression in individual cells. CellRegMap provides a principled approach to identify and characterize genotype–context interactions of known eQTL variants using scRNA‐seq data. This model‐based approach resolves allelic effects across cellular contexts of different granularity, including genetic effects specific to cell subtypes and continuous cell transitions. We validate CellRegMap using simulated data and apply it to previously identified eQTL from two recent studies of differentiating iPSCs, where we uncover hundreds of eQTL displaying heterogeneity of genetic effects across cellular contexts. Finally, we identify fine‐grained genetic regulation in neuronal subtypes for eQTL that are colocalized with human disease variants. Synopsis CellRegMap is a statistical framework to identify and characterise genetic effects on gene expression in single cells. The model has enabled the identification of hundreds of context‐specific eQTL, including variants that are colocalized with human disease variants. CellRegMap is a statistical framework to map eQTL using single‐cell RNA‐seq, mitigating the need to define discrete cell groups. CellRegMap can detect fine‐grained context‐specific genetic regulation and regulatory modules that comprise eQTL with shared patterns of activity in distinct cellular contexts. Cell‐context interactions identified using CellRegMap can help characterise colocalization events with human disease variants identified from genome‐wide association studies. Graphical Abstract CellRegMap is a statistical framework to identify and characterise genetic effects on gene expression in single cells. The model has enabled the identification of hundreds of context‐specific eQTL, including variants that are colocalized with human disease variants.