Machine learning-based integration of CD8 T cell-related gene signatures for comprehensive prognostic assessment in lung adenocarcinoma.

Background: Lung adenocarcinoma (LUAD) stands as the most prevalent histological subtype of lung cancer, exhibiting heterogeneity in outcomes and diverse responses to therapy. CD8 T cells are consistently present throughout all stages of tumor development and play a pivotal role within the tumor microenvironment (TME). Our objective was to investigate the expression profiles of CD8 T cell marker genes, establish a prognostic risk model based on these genes in LUAD, and explore its relationship with immunotherapy response. Methods: By leveraging the expression data and clinical records from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) cohorts, we identified 23 consensus prognostic genes. Employing ten machine-learning algorithms, we generated 101 combinations, ultimately selecting the optimal algorithm to construct an artificial intelligence-derived prognostic signature named riskScore. This selection was based on the average concordance index (C-index) across three testing cohorts. Results: RiskScore emerged as an independent risk factor for overall survival (OS), progression-free interval (PFI), disease-free interval (DFI), and disease-specific survival (DSS) in LUAD. Notably, riskScore exhibited notably superior predictive accuracy compared to traditional clinical variables. Furthermore, we observed a positive correlation between the high-risk riskScore group and tumor-promoting biological functions, lower tumor mutational burden (TMB), lower neoantigen (NEO) load, and lower microsatellite instability (MSI) scores, as well as reduced immune cell infiltration and an increased probability of immune evasion within the TME. Of significance, the immunophenoscore (IPS) score displayed significant differences among risk subgroups, and riskScore effectively stratified patients in the IMvigor210 and GSE135222 immunotherapy cohort based on their survival outcomes. Additionally, we identified potential drugs that could target specific risk subgroups. Conclusions: In summary, riskScore demonstrates its potential as a robust and promising tool for guiding clinical management and tailoring individualized treatments for LUAD patients.

Single-cell transcriptomics reveals tumor landscape in ovarian carcinosarcoma. / 单细胞转录组学揭示卵巢癌肉瘤的肿瘤特征.

OBJECTIVES: The present study used single-cell RNA sequencing (scRNA-seq) to characterize the cellular composition of ovarian carcinosarcoma (OCS) and identify its molecular characteristics. METHODS: scRNA-seq was performed in resected primary OCS for an in-depth analysis of tumor cells and the tumor microenvironment. Immunohistochemistry staining was used for validation. The scRNA-seq data of OCS were compared with those of high-grade serous ovarian carcinoma (HGSOC) tumors and other OCS tumors. RESULTS: Both malignant epithelial and malignant mesenchymal cells were observed in the OCS patient of this study. We identified four epithelial cell subclusters with different biological roles. Among them, epithelial subcluster 4 presented high levels of breast cancer type 1 susceptibility protein homolog (BRCA1) and DNA topoisomerase 2-α (TOP2A) expression and was related to drug resistance and cell cycle. We analyzed the interaction between epithelial and mesenchymal cells and found that fibroblast growth factor (FGF) and pleiotrophin (PTN) signalings were the main pathways contributing to communication between these cells. Moreover, we compared the malignant epithelial and mesenchymal cells of this OCS tumor with our previous published HGSOC scRNA-seq data and OCS data. All the epithelial subclusters in the OCS tumor could be found in the HGSOC samples. Notably, the mesenchymal subcluster C14 exhibited specific expression patterns in the OCS tumor, characterized by elevated expression of cytochrome P450 family 24 subfamily A member 1 (CYP24A1), collagen type XXIII α1 chain (COL23A1), cholecystokinin (CCK), bone morphogenetic protein 7 (BMP7), PTN, Wnt inhibitory factor 1 (WIF1), and insulin-like growth factor 2 (IGF2). Moreover, this subcluster showed distinct characteristics when compared with both another previously published OCS tumor and normal ovarian tissue. CONCLUSIONS: This study provides the single-cell transcriptomics signature of human OCS, which constitutes a new resource for elucidating OCS diversity.

Exploring the Influence of T Cell Marker Gene Expression on the Pathobiology and Clinical Prognostic Outcomes in Intestinal-Type Gastric Carcinoma.

BACKGROUND: Gastric cancer (GC) poses a significant global health challenge. This study is aimed at elucidating the role of the immune system, particularly T cells and their subtypes, in the pathogenesis and progression of intestinal-type gastric carcinoma (GC), and at evaluating the predictive utility of a T cell marker gene-based risk score for overall survival. METHODS: We performed an extensive analysis using single-cell RNA sequencing data to map the diversity of immune cells and identify specific T cell marker genes within GC. Pseudotime trajectory analysis was employed to observe the expression patterns of tumor-related pathways and transcription factors (TFs) at various disease stages. We developed a risk score using data from The Cancer Genome Atlas (TCGA) as a training set and validated it with the GSE15459 dataset. RESULTS: Our analysis revealed distinct patterns of T cell marker gene expression associated with different stages of GC. The risk score, based on these markers, successfully stratified patients into high-risk and low-risk groups with significantly different overall survival prospects. High-risk patients exhibited poorer survival outcomes compared to low-risk patients (p < 0.05). Additionally, the risk score was capable of identifying patients across a spectrum from chronic atrophic gastritis to early GC. CONCLUSION: The findings enhance the understanding of the tumor immune microenvironment in GC and propose new immunotherapeutic targets. The T cell marker gene-based risk score offers a potential tool for gastroenterologists to tailor treatment plans more precisely according to the cancer's severity.

Establishment and transcriptome analysis of single blastomere-derived cell lines from zebrafish.

Maintaining chromosome euploidy in zebrafish embryonic cells is challenging because of the degradation of genomic integrity during cell passaging. In this study, we report the derivation of zebrafish cell lines from single blastomeres. These cell lines have a stable chromosome status attributed to BMP4 and exhibit continuous proliferation in vitro. Twenty zebrafish cell lines are successfully established from single blastomeres. Single-cell transcriptome sequencing analysis confirms the fidelity of gene expression profiles throughout long-term culturing of at least 45 passages. The long-term cultured cells are specialized into epithelial cells, exhibiting similar expression patterns validated by integrative transcriptomic analysis. Overall, this work provides a protocol for establishing zebrafish cell lines from single blastomeres, which can serve as valuable tools for in vitro investigations of epithelial cell dynamics in terms of life-death balance and cell fate determination during normal homeostasis.

Quantitative comparison of single-cell RNA sequencing versus single-molecule RNA imaging for quantifying transcriptional noise.

Stochastic fluctuations (noise) in transcription generate substantial cell-to-cell variability. However, how best to quantify genome-wide noise, remains unclear. Here we utilize a small-molecule perturbation (IdU) to amplify noise and assess noise quantification from numerous scRNA-seq algorithms on human and mouse datasets, and then compare to noise quantification from single-molecule RNA FISH (smFISH) for a panel of representative genes. We find that various scRNA-seq analyses report amplified noise, without altered mean-expression levels, for ~90% of genes and that smFISH analysis verifies noise amplification for the vast majority of genes tested. Collectively, the analyses suggest that most scRNA-seq algorithms are appropriate for quantifying noise including a simple normalization approach, although all of these systematically underestimate noise compared to smFISH. From a practical standpoint, this analysis argues that IdU is a globally penetrant noise-enhancer molecule-amplifying noise without altering mean-expression levels-which could enable investigations of the physiological impacts of transcriptional noise.

Characterizing Rare Dormant and Cycling Lineages Using the Watermelon System.

Barcode-based lineage tracing approaches enable molecular characterization of clonal cell families. Barcodes that are expressed as mRNA can be used to deconvolve lineage identity from single-cell RNA sequencing transcriptional data. Here we describe the Watermelon system, which facilitates the simultaneous tracing of lineage, transcriptional, and proliferative state at a single cell level.

Thyroid hormone T4 mitigates traumatic brain injury in mice by dynamically remodeling cell type specific genes, pathways, and networks in hippocampus and frontal cortex.

The complex pathology of mild traumatic brain injury (mTBI) is a main contributor to the difficulties in achieving a successful therapeutic regimen. Thyroxine (T4) administration has been shown to prevent the cognitive impairments induced by mTBI in mice but the mechanism is poorly understood. To understand the underlying mechanism, we carried out a single cell transcriptomic study to investigate the spatiotemporal effects of T4 on individual cell types in the hippocampus and frontal cortex at three post-injury stages in a mouse model of mTBI. We found that T4 treatment altered the proportions and transcriptomes of numerous cell types across tissues and timepoints, particularly oligodendrocytes, astrocytes, and microglia, which are crucial for injury repair. T4 also reversed the expression of mTBI-affected genes such as Ttr, mt-Rnr2, Ggn12, Malat1, Gnaq, and Myo3a, as well as numerous pathways such as cell/energy/iron metabolism, immune response, nervous system, and cytoskeleton-related pathways. Cell-type specific network modeling revealed that T4 mitigated select mTBI-perturbed dynamic shifts in subnetworks related to cell cycle, stress response, and RNA processing in oligodendrocytes. Cross cell-type ligand-receptor networks revealed the roles of App, Hmgb1, Fn1, and Tnf in mTBI, with the latter two ligands having been previously identified as TBI network hubs. mTBI and/or T4 signature genes were enriched for human genome-wide association study (GWAS) candidate genes for cognitive, psychiatric and neurodegenerative disorders related to mTBI. Our systems-level single cell analysis elucidated the temporal and spatial dynamic reprogramming of cell-type specific genes, pathways, and networks, as well as cell-cell communications as the mechanisms through which T4 mitigates cognitive dysfunction induced by mTBI.

Characterizing mitochondrial features in osteoarthritis through integrative multi-omics and machine learning analysis.

Purpose: Osteoarthritis (OA) stands as the most prevalent joint disorder. Mitochondrial dysfunction has been linked to the pathogenesis of OA. The main goal of this study is to uncover the pivotal role of mitochondria in the mechanisms driving OA development. Materials and methods: We acquired seven bulk RNA-seq datasets from the Gene Expression Omnibus (GEO) database and examined the expression levels of differentially expressed genes related to mitochondria in OA. We utilized single-sample gene set enrichment analysis (ssGSEA), gene set enrichment analysis (GSEA), and weighted gene co-expression network analysis (WGCNA) analyses to explore the functional mechanisms associated with these genes. Seven machine learning algorithms were utilized to identify hub mitochondria-related genes and develop a predictive model. Further analyses included pathway enrichment, immune infiltration, gene-disease relationships, and mRNA-miRNA network construction based on these hub mitochondria-related genes. genome-wide association studies (GWAS) analysis was performed using the Gene Atlas database. GSEA, gene set variation analysis (GSVA), protein pathway analysis, and WGCNA were employed to investigate relevant pathways in subtypes. The Harmonizome database was employed to analyze the expression of hub mitochondria-related genes across various human tissues. Single-cell data analysis was conducted to examine patterns of gene expression distribution and pseudo-temporal changes. Additionally, The real-time polymerase chain reaction (RT-PCR) was used to validate the expression of these hub mitochondria-related genes. Results: In OA, the mitochondria-related pathway was significantly activated. Nine hub mitochondria-related genes (SIRT4, DNAJC15, NFS1, FKBP8, SLC25A37, CARS2, MTHFD2, ETFDH, and PDK4) were identified. They constructed predictive models with good ability to predict OA. These genes are primarily associated with macrophages. Unsupervised consensus clustering identified two mitochondria-associated isoforms that are primarily associated with metabolism. Single-cell analysis showed that they were all expressed in single cells and varied with cell differentiation. RT-PCR showed that they were all significantly expressed in OA. Conclusion: SIRT4, DNAJC15, NFS1, FKBP8, SLC25A37, CARS2, MTHFD2, ETFDH, and PDK4 are potential mitochondrial target genes for studying OA. The classification of mitochondria-associated isoforms could help to personalize treatment for OA patients.

Understanding testicular single cell transcriptional atlas: from developmental complications to male infertility.

Spermatogenesis is a multi-step biological process where mitotically active diploid (2n) spermatogonia differentiate into haploid (n) spermatozoa via regulated meiotic programming. The alarming rise in male infertility has become a global concern during the past decade thereby demanding an extensive profiling of testicular gene expression. Advancements in Next-Generation Sequencing (NGS) technologies have revolutionized our empathy towards complex biological events including spermatogenesis. However, despite multiple attempts made in the past to reveal the testicular transcriptional signature(s) either with bulk tissues or at the single-cell, level, comprehensive reviews on testicular transcriptomics and associated disorders are limited. Notably, technologies explicating the genome-wide gene expression patterns during various stages of spermatogenic progression provide the dynamic molecular landscape of testicular transcription. Our review discusses the advantages of single-cell RNA-sequencing (Sc-RNA-seq) over bulk RNA-seq concerning testicular tissues. Additionally, we highlight the cellular heterogeneity, spatial transcriptomics, dynamic gene expression and cell-to-cell interactions with distinct cell populations within the testes including germ cells (Gc), Sertoli cells (Sc), Peritubular cells (PTc), Leydig cells (Lc), etc. Furthermore, we provide a summary of key finding of single-cell transcriptomic studies that have shed light on developmental mechanisms implicated in testicular disorders and male infertility. These insights emphasize the pivotal roles of Sc-RNA-seq in advancing our knowledge regarding testicular transcriptional landscape and may serve as a potential resource to formulate future clinical interventions for male reproductive health.

Beyond benchmarking and towards predictive models of dataset-specific single-cell RNA-seq pipeline performance.

BACKGROUND: The advent of single-cell RNA-sequencing (scRNA-seq) has driven significant computational methods development for all steps in the scRNA-seq data analysis pipeline, including filtering, normalization, and clustering. The large number of methods and their resulting parameter combinations has created a combinatorial set of possible pipelines to analyze scRNA-seq data, which leads to the obvious question: which is best? Several benchmarking studies compare methods but frequently find variable performance depending on dataset and pipeline characteristics. Alternatively, the large number of scRNA-seq datasets along with advances in supervised machine learning raise a tantalizing possibility: could the optimal pipeline be predicted for a given dataset? RESULTS: Here, we begin to answer this question by applying 288 scRNA-seq analysis pipelines to 86 datasets and quantifying pipeline success via a range of measures evaluating cluster purity and biological plausibility. We build supervised machine learning models to predict pipeline success given a range of dataset and pipeline characteristics. We find that prediction performance is significantly better than random and that in many cases pipelines predicted to perform well provide clustering outputs similar to expert-annotated cell type labels. We identify characteristics of datasets that correlate with strong prediction performance that could guide when such prediction models may be useful. CONCLUSIONS: Supervised machine learning models have utility for recommending analysis pipelines and therefore the potential to alleviate the burden of choosing from the near-infinite number of possibilities. Different aspects of datasets influence the predictive performance of such models which will further guide users.

Single-cell transcriptomics of blood identified IFIT1+ neutrophil subcluster expansion in NTM-PD patients.

OBJECTIVE: Non-tuberculous mycobacterial pulmonary disease (NTM-PD) is caused by an imbalance between pathogens and impaired host immune responses. Mycobacterium avium complex (MAC) and Mycobacterium abscessus (MAB) are the two major pathogens that cause NTM-PD. In this study, we sought to dissect the transcriptomes of peripheral blood immune cells at the single-cell resolution in NTM-PD patients and explore potential clinical markers for NTM-PD diagnosis and treatment. METHODS: Peripheral blood samples were collected from six NTM-PD patients, including three MAB-PD patients, three MAC-PD patients, and two healthy controls. We employed single-cell RNA sequencing (scRNA-seq) to define the transcriptomic landscape at a single-cell resolution. A comprehensive scRNA-seq analysis was performed, and flow cytometry was conducted to validate the results of scRNA-seq. RESULTS: A total of 27,898 cells were analyzed. Nine T-cells, six mononuclear phagocytes (MPs), and four neutrophil subclusters were defined. During NTM infection, naïve T-cells were reduced, and effector T-cells increased. High cytotoxic activities were shown in T-cells of NTM-PD patients. The proportion of inflammatory and activated MPs subclusters was enriched in NTM-PD patients. Among neutrophil subclusters, an IFIT1+ neutrophil subcluster was expanded in NTM-PD compared to healthy controls. This suggests that IFIT1+ neutrophil subcluster might play an important role in host defense against NTM. Functional enrichment analysis of this subcluster suggested that it is related to interferon response. Cell-cell interaction analysis revealed enhanced CXCL8-CXCR1/2 interactions between the IFIT1+ neutrophil subcluster and NK cells, NKT cells, classical mononuclear phagocytes subcluster 1 (classical Mo1), classical mononuclear phagocytes subcluster 2 (classical Mo2) in NTM-PD patients compared to healthy controls. CONCLUSIONS: Our data revealed disease-specific immune cell subclusters and provided potential new targets of NTM-PD. Specific expansion of IFIT1+ neutrophil subclusters and the CXCL8-CXCR1/2 axis may be involved in the pathogenesis of NTM-PD. These insights may have implications for the diagnosis and treatment of NTM-PD.

TNBC response to paclitaxel phenocopies interferon response which reveals cell cycle-associated resistance mechanisms.

Paclitaxel is a standard of care neoadjuvant therapy for patients with triple negative breast cancer (TNBC); however, it shows limited benefit for locally advanced or metastatic disease. Here we used a coordinated experimental-computational approach to explore the influence of paclitaxel on the cellular and molecular responses of TNBC cells. We found that escalating doses of paclitaxel resulted in multinucleation, promotion of senescence, and initiation of DNA damage induced apoptosis. Single-cell RNA sequencing (scRNA-seq) of TNBC cells after paclitaxel treatment revealed upregulation of innate immune programs canonically associated with interferon response and downregulation of cell cycle progression programs. Systematic exploration of transcriptional responses to paclitaxel and cancer-associated microenvironmental factors revealed common gene programs induced by paclitaxel, IFNB, and IFNG. Transcription factor (TF) enrichment analysis identified 13 TFs that were both enriched based on activity of downstream targets and also significantly upregulated after paclitaxel treatment. Functional assessment with siRNA knockdown confirmed that the TFs FOSL1, NFE2L2 and ELF3 mediate cellular proliferation and also regulate nuclear structure. We further explored the influence of these TFs on paclitaxel-induced cell cycle behavior via live cell imaging, which revealed altered progression rates through G1, S/G2 and M phases. We found that ELF3 knockdown synergized with paclitaxel treatment to lock cells in a G1 state and prevent cell cycle progression. Analysis of publicly available breast cancer patient data showed that high ELF3 expression was associated with poor prognosis and enrichment programs associated with cell cycle progression. Together these analyses disentangle the diverse aspects of paclitaxel response and identify ELF3 upregulation as a putative biomarker of paclitaxel resistance in TNBC.

Single-cell analysis defines LGALS1 + fibroblasts that promote proliferation and migration of intrahepatic cholangiocarcinoma.

The incidence rate of intrahepatic cholangiocarcinoma (ICC), which has a poor prognosis, is rapidly increasing. To investigate the intratumor heterogeneity of ICC, we analyzed single-cell RNA sequencing data from the primary tumor and adjacent normal tissues of 14 treatment-naïve patients. We identified ten major cell types, along with 45 subclusters of cells. Notably, we identified a fibroblast cluster, Fibroblast_LUM+, which was preferably enriched in tumor tissues and actively interacted with cholangiocytes. LGALS1 was verified as a marker gene of Fibroblast_LUM+, contributing to the malignant phenotype of ICC. The higher amount of LGALS1 + fibroblasts were associated with poorer overall survival in ICC patients. LGALS1 + fibroblasts activated the proliferation and migration of tumor cells by upregulating the expression levels of CCR2, ADAM15, and ß-integrin. Silencing LGALS1 in cancer-associated fibroblasts (CAFs) suppressed CAF-augmented tumor cell migration and invasion in vitro as well as tumor formation in vivo, suggesting that blockade of LGALS1 serves as a potential therapeutic approach for ICC. Taken together, our single-cell analysis provides insight into the interaction between malignant cells and specific subtypes of fibroblasts. Our work will further the understanding of the intratumor heterogeneity of ICC and provide novel strategies for the treatment of ICC by targeting fibroblasts in the tumor microenvironment.

Single cell RNA-sequencing of feline peripheral immune cells with V(D)J repertoire and cross species analysis of T lymphocytes.

Introduction: The domestic cat (Felis catus) is a valued companion animal and a model for virally induced cancers and immunodeficiencies. However, species-specific limitations such as a scarcity of immune cell markers constrain our ability to resolve immune cell subsets at sufficient detail. The goal of this study was to characterize circulating feline T cells and other leukocytes based on their transcriptomic landscape and T-cell receptor repertoire using single cell RNA-sequencing. Methods: Peripheral blood from 4 healthy cats was enriched for T cells by flow cytometry cell sorting using a mouse anti-feline CD5 monoclonal antibody. Libraries for whole transcriptome, alpha/beta T cell receptor transcripts and gamma/delta T cell receptor transcripts were constructed using the 10x Genomics Chromium Next GEM Single Cell 5' reagent kit and the Chromium Single Cell V(D)J Enrichment Kit with custom reverse primers for the feline orthologs. Results: Unsupervised clustering of whole transcriptome data revealed 7 major cell populations - T cells, neutrophils, monocytic cells, B cells, plasmacytoid dendritic cells, mast cells and platelets. Sub cluster analysis of T cells resolved naive (CD4+ and CD8+), CD4+ effector T cells, CD8+ cytotoxic T cells and gamma/delta T cells. Cross species analysis revealed a high conservation of T cell subsets along an effector gradient with equitable representation of veterinary species (horse, dog, pig) and humans with the cat. Our V(D)J repertoire analysis demonstrated a skewed T-cell receptor alpha gene usage and a restricted T-cell receptor gamma junctional length in CD8+ cytotoxic T cells compared to other alpha/beta T cell subsets. Among myeloid cells, we resolved three clusters of classical monocytes with polarization into pro- and anti-inflammatory phenotypes in addition to a cluster of conventional dendritic cells. Lastly, our neutrophil sub clustering revealed a larger mature neutrophil cluster and a smaller exhausted/activated cluster. Discussion: Our study is the first to characterize subsets of circulating T cells utilizing an integrative approach of single cell RNA-sequencing, V(D)J repertoire analysis and cross species analysis. In addition, we characterize the transcriptome of several myeloid cell subsets and demonstrate immune cell relatedness across different species.

Status of single-cell RNA sequencing for reproductive toxicology in zebrafish and the transcriptomic trade-off.

The utilization of transcriptomic studies identifying profiles of gene expression, especially in toxicogenomics, has catapulted next-generation sequencing to the forefront of reproductive toxicology. An innovative yet underutilized RNA sequencing technique emerging into this field is single-cell RNA sequencing (scRNA-seq), which provides sequencing at the individual cellular level of gonad tissue. ScRNA-seq provides a novel and unique perspective for identifying distinct cellular profiles, including identification of rare cell subtypes. The specificity of scRNA-seq is a powerful tool for reproductive toxicity research, especially for translational animal models including zebrafish. Studies to date not only have focused on 'tissue atlassing' or characterizing what cell types make up different tissues but have also begun to include toxicant exposure as a factor that this review aims to explore. Future scRNA-seq studies will contribute to understanding exposure-induced outcomes; however, the trade-offs with traditional methods need to be considered.

Advancing skeletal health and disease research with single-cell RNA sequencing.

Orthopedic conditions have emerged as global health concerns, impacting approximately 1.7 billion individuals worldwide. However, the limited understanding of the underlying pathological processes at the cellular and molecular level has hindered the development of comprehensive treatment options for these disorders. The advent of single-cell RNA sequencing (scRNA-seq) technology has revolutionized biomedical research by enabling detailed examination of cellular and molecular diversity. Nevertheless, investigating mechanisms at the single-cell level in highly mineralized skeletal tissue poses technical challenges. In this comprehensive review, we present a streamlined approach to obtaining high-quality single cells from skeletal tissue and provide an overview of existing scRNA-seq technologies employed in skeletal studies along with practical bioinformatic analysis pipelines. By utilizing these methodologies, crucial insights into the developmental dynamics, maintenance of homeostasis, and pathological processes involved in spine, joint, bone, muscle, and tendon disorders have been uncovered. Specifically focusing on the joint diseases of degenerative disc disease, osteoarthritis, and rheumatoid arthritis using scRNA-seq has provided novel insights and a more nuanced comprehension. These findings have paved the way for discovering novel therapeutic targets that offer potential benefits to patients suffering from diverse skeletal disorders.

Autoimmune CD8+ T cells in type 1 diabetes: from single-cell RNA sequencing to T-cell receptor redirection.

Type 1 diabetes (T1D) is an organ-specific autoimmune disease caused by pancreatic ß cell destruction and mediated primarily by autoreactive CD8+ T cells. It has been shown that only a small number of stem cell-like ß cell-specific CD8+ T cells are needed to convert normal mice into T1D mice; thus, it is likely that T1D can be cured or significantly improved by modulating or altering self-reactive CD8+ T cells. However, stem cell-type, effector and exhausted CD8+ T cells play intricate and important roles in T1D. The highly diverse T-cell receptors (TCRs) also make precise and stable targeted therapy more difficult. Therefore, this review will investigate the mechanisms of autoimmune CD8+ T cells and TCRs in T1D, as well as the related single-cell RNA sequencing (ScRNA-Seq), CRISPR/Cas9, chimeric antigen receptor T-cell (CAR-T) and T-cell receptor-gene engineered T cells (TCR-T), for a detailed and clear overview. This review highlights that targeting CD8+ T cells and their TCRs may be a potential strategy for predicting or treating T1D.

Applications of single-cell RNA sequencing in spermatogenesis and molecular evolution.

Spermatogenic cell heterogeneity is determined by the complex process of spermatogenesis differentiation. However, effectively revealing the regulatory mechanisms underlying mammalian spermatogenic cell development and differentiation via traditional methods is difficult. Advances in technology have led to the emergence of many single-cell transcriptome sequencing protocols, which have partially addressed these challenges. In this review, we detail the principles of 10x Genomics technology and summarize the methods for downstream analysis of single-cell transcriptome sequencing data. Furthermore, we explore the role of single-cell transcriptome sequencing in revealing the heterogeneity of testicular ecological niche cells, delineating the establishment and disruption of testicular immune homeostasis during human spermatogenesis, investigating abnormal spermatogenesis in humans, and, ultimately, elucidating the molecular evolution of mammalian spermatogenesis.

Unveiling novel insights in acute myeloid leukemia through single-cell RNA sequencing.

Acute myeloid leukemia (AML) is a complex and heterogeneous group of aggressive hematopoietic stem cell disease. The presence of diverse and functionally distinct populations of leukemia cells within the same patient's bone marrow or blood poses a significant challenge in diagnosing and treating AML. A substantial proportion of AML patients demonstrate resistance to induction chemotherapy and a grim prognosis upon relapse. The rapid advance in next generation sequencing technologies, such as single-cell RNA-sequencing (scRNA-seq), has revolutionized our understanding of AML pathogenesis by enabling high-resolution interrogation of the cellular heterogeneity in the AML ecosystem, and their transcriptional signatures at a single-cell level. New studies have successfully characterized the inextricably intertwined interactions among AML cells, immune cells and bone marrow microenvironment and their contributions to the AML development, therapeutic resistance and relapse. These findings have deepened and broadened our understanding the complexity and heterogeneity of AML, which are difficult to detect with bulk RNA-seq. This review encapsulates the burgeoning body of knowledge generated through scRNA-seq, providing the novel insights and discoveries it has unveiled in AML biology. Furthermore, we discuss the potential implications of scRNA-seq in therapeutic opportunities, focusing on immunotherapy. Finally, we highlight the current limitations and future direction of scRNA-seq in the field.