mRNA-Seq whole-transcriptome analysis of a single cell

Unraveling novel mRNA transcripts of the human DNA N-glycosylase 1 (NTHL1) gene with the implementation of an innovative targeted DNA-seq assay

The human NTHL1 gene encodes a DNA glycosylase that plays a key role in the base excision repair (BER) pathway, repairing oxidative DNA damage and maintaining genome integrity. The physiological activity of NTHL1 is crucial in preventing genetic alterations that can lead to cancer. In this study, we employed an innovative targeted DNA sequencing (DNA-seq) methodology to explore the transcriptional landscape of the NTHL1 gene, revealing previously uncharacterized alternative splicing events and novel exons. Our designed approach provided significantly improved sequencing depth and coverage, enabling the identification of novel NTHL1 mRNA transcripts. Bioinformatics analysis confirmed all annotated splice junctions of the main NTHL1 transcripts (v.1 - v.3) and revealed novel mRNA transcripts (NTHL1 v.4 - v.9) derived from splicing events between annotated exons as well as mRNAs containing previously uncharacterized exons (NTHL1 v.10 - v.14). Quantitative PCR analysis highlighted a diverse expression pattern of these novel transcripts across different human cell lines, suggesting cell-specific roles and regulatory mechanisms. Notably, NTHL1 v.5 was overexpressed in luminal A breast cancer cells (MCF-7), while v.13 was prominent in triple negative (BT-20), HER2 + breast cancer (SK-BR-3), prostate, colorectal cancer cells and HEK-293 cells. Our findings suggest that specific novel NTHL1 transcripts may encode protein isoforms with distinct structural features, as indicated by ribosome profiling datasets, while others containing premature termination codons could function as long non-coding RNAs. These insights enhance our understanding of NTHL1 regulatory role and its potential as a biomarker and therapeutic target in human malignancies. This study underscores the importance of exploring the transcriptional diversity of NTHL1 to fully elucidate its role in cancer pathobiology.

NTCdb: Single-cell transcriptome database of human inflammatory-associated diseases

With both the advancement of technology and the decline in costs, single-cell transcriptomics sequencing has become widespread in the biomedical area in recent years. It can facilitate the pathogenic characteristics at the single-cell level, which will assist clinical researchers in exploring the mechanism of diseases. As a result, single-cell transcriptome data based on clinical samples grew exponentially. However, there is still a lack of a comprehensive database about immunocytes in inflammatory-associated diseases. To address this deficiency, we propose a human inflammatory-associated disease-based single-cell transcriptome database, NTCdb (www.ntcdb.org.cn). NTCdb integrates the open-source data of 1,023,166 cells derived from 11 tissues of 17 inflammatory-associated diseases in a uniform pipeline. It provides a set of analyzing results, including cell communication analysis, enrichment analysis, and Pseudo-Time analysis, to obtain various characteristics of immune cells in inflammatory-associated disease. Taking COVID-19 as a case study, NTCdb displays important information including potentially significant functions of certain cells, genes, and signaling pathways, as well as the commonalities of specific immunocytes between different inflammatory-associated disease.

Single-cell RNA sequencing in diabetic kidney disease: a literature review

Diabetic kidney disease (DKD) is the leading cause of end-stage renal disease (ESRD), and its pathogenesis has not been clarified. Current research suggests that DKD involves multiple cell types and extra-renal factors, and it is particularly important to clarify the pathogenesis and identify new therapeutic targets. Single-cell RNA sequencing (scRNA-seq) technology is high-throughput sequencing of the transcriptomes of individual cells at the single-cell level, which is an effective technology for exploring the development of diseases by comparing genetic information, reflecting the differences in genetic information between cells, and identifying different cell subpopulations. Accumulating evidence supports the role of scRNA-seq in revealing the pathogenesis of diabetes and strengthening our understanding of the molecular mechanisms of DKD. We reviewed the scRNA-seq data this time. Then, we analyzed and discussed the applications of scRNA-seq technology in DKD research, including annotation of cell types, identification of novel cell types (or subtypes), identification of intercellular communication, analysis of cell differentiation trajectories, gene expression detection, and analysis of gene regulatory networks, and lastly, we explored the future perspectives of scRNA-seq technology in DKD research.

A comprehensive review of the applications of RNA sequencing in celiac disease research

RNA sequencing (RNA-seq) has undergone substantial advancements in recent decades and has emerged as a vital technique for profiling the transcriptome. The transition from bulk sequencing to single-cell and spatial approaches has facilitated the achievement of higher precision at cell resolution. It provides valuable biological knowledge about individual immune cells and aids in the discovery of the molecular mechanisms that contribute to the development of autoimmune diseases. Celiac disease (CeD) is an autoimmune disorder characterized by a strong immune response to gluten consumption. RNA-seq has led to significantly advanced research in multiple fields, particularly in CeD research. It has been instrumental in studies involving comparative transcriptomics, nutritional genomics and wheat research, cancer research in the context of CeD, genetic and noncoding RNA-mediated epigenetic insights, disease monitoring and biomarker discovery, regulation of mitochondrial functions, therapeutic target identification and drug mechanism of action, dietary factors, immune cell profiling and the immune landscape. This review offers a comprehensive examination of recent RNA-seq technology research in the field of CeD, highlighting future challenges and opportunities for its application.

Glycyrrhizic acid alleviates concanavalin A-induced acute liver injury by regulating monocyte-derived macrophages

Autoimmune hepatitis (AIH) is characterized by persistent liver inflammation induced by aberrant immune responses. Glycyrrhizic acid (GA), a prominent bioactive ingredient of licorice, has shown potential as a safe and effective treatment for AIH. However, the immune regulatory mechanism by which GA exerts its therapeutic effect on AIH remains elusive. In this study, we found that GA intervention significantly alleviated ConA-induced acute liver injury in mice. Cytometry by time-of-flight (CyTOF) analysis revealed that GA increased the abundance of anti-inflammatory F4/80loCD11bhiMHCIIhi MoMF-1 and decreased the abundance of pro-inflammatory F4/80loCD11bhiiNOShi MoMF-3. Multiplex immunofluorescence demonstrated the infiltration of MoMFs in liver tissues. Single-cell RNA sequencing (scRNA-seq) analysis indicated that GA facilitated the immune activation in MoMFs, regulated gene expression of diverse cytokines secreted by MoMFs, and played a role in shaping the immune microenvironment. By integrating the results of CyTOF with scRNA-seq, our study comprehensively elucidates the immune landscape of ConA-induced liver injury following GA intervention, advancing the understanding of GA's mechanism of action. However, it is important to note that some single-cell data in this study remain raw and require further processing and annotation. Our findings suggest that GA alleviates ConA-induced acute liver injury by regulating the function of MoMFs, opening potential avenues for AIH treatment and management, and providing a theoretical basis for the design of novel MoMFs-centered immunotherapies.

Cell dynamics revealed by microscopy advances

Cell biology emerges from spatiotemporally coordinated molecular processes. Recent advances in live-cell microscopy, fueled by a surge in optical, molecular, and computational technologies, have enabled dynamic observations from single molecules to whole organisms. Despite technological leaps, there is still an untapped opportunity to fully leverage their capabilities toward biological insight. We highlight how single-molecule imaging has transformed our understanding of biological processes, with a focus on chromatin organization and transcription in the nucleus. We describe how this was enabled by the close integration of new imaging techniques with analysis tools and discuss the challenges to make a comparable impact at larger scales from organelles to organisms. By highlighting recent successful examples, we describe an outlook of ever-increasing data and the need for seamless integration between dataset visualization and quantification to realize the full potential warranted by advances in new imaging technologies.

Skin in the game: a review of single-cell and spatial transcriptomics in dermatological research

Single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) are two emerging research technologies that uniquely characterize gene expression microenvironments on a cellular or subcellular level. The skin, a clinically accessible tissue composed of diverse, essential cell populations, serves as an ideal target for these high-resolution investigative approaches. Using these tools, researchers are assembling a compendium of data and discoveries in healthy skin as well as a range of dermatologic pathophysiologies, including atopic dermatitis, psoriasis, and cutaneous malignancies. The ongoing advancement of single-cell approaches, coupled with anticipated decreases in cost with increased adoption, will reshape dermatologic research, profoundly influencing disease characterization, prognosis, and ultimately clinical practice.

Single-cell sequencing analysis of multiple myeloma heterogeneity and identification of new theranostic targets

Multiple myeloma (MM) is a heterogeneous and incurable tumor characterized by the malignant proliferation of plasma cells. It is necessary to clarify the heterogeneity of MM and identify new theranostic targets. We constructed a single-cell transcriptome profile of 48,293 bone marrow cells from MM patients and health donors (HDs) annotated with 7 continuous B lymphocyte lineages. Through CellChat, we discovered that the communication among B lymphocyte lineages between MM and HDs was disrupted, and unique signaling molecules were observed. Through pseudotime analysis, it was found that the differences between MM and HDs were mainly reflected in plasma cells. These differences are primarily related to various biological processes involving mitochondria. Then, we identified the key subpopulation associated with the malignant proliferation of plasma cells. This group of cells exhibited strong proliferation ability, high CNV scores, high expression of frequently mutated genes, and strong glucose metabolic activity. Furthermore, we demonstrated the therapeutic potential of WNK1 as a target. Our study provides new insights into the development of B cells and the heterogeneity of plasma cells in MM and suggests that WNK1 is a potential therapeutic target for MM.

Single-cell RNA sequencing: an emerging tool revealing dysregulated innate and adaptive immune response at single cell level in Kawasaki disease

INTRODUCTION

Kawasaki disease [KD] is a systemic disorder characterized by acute febrile illness due to widespread medium-vessel vasculitis, mainly affecting children. Despite the ongoing advanced research into the disease pathophysiology and molecular mechanisms, the exact etiopathogenesis of KD is still an enigma. Recently, single-cell RNA sequencing [scRNA-seq], has been utilized to elucidate the pathophysiology of KD at a resolution higher than that of previous methods.

AREA COVERED

In the present article, we re-emphasize the pivotal role of this high-resolution technique, scRNA-seq, in the characterization of immune cell transcriptomic profile and signaling/response pathways in KD and explore the diagnostic, prognostic, and therapeutic potential of this new technique in KD. Using combinations of the search phrases 'KD, scRNA-seq, CAA, childhood vasculitis' a literature search was carried out on Scopus, Google Scholar, and PubMed until the beginning of 2024.

EXPERT OPINION

scRNA-seq presents a transformative tool for dissecting KD at the cellular level. By revealing rare cell populations, gene expression alterations, and disease-specific pathways, scRNA-seq aids in understanding the intricacies of KD pathogenesis. This review will provide new insights into pathogenesis of KD and the field of applications of scRNA-seq in personalized therapeutics for KD in the future.

Droplet-based single-cell sequencing: Strategies and applications

Notable advancements in single-cell omics technologies have not only addressed longstanding challenges but also enabled unprecedented studies of cellular heterogeneity with unprecedented resolution and scale. These strides have led to groundbreaking insights into complex biological systems, paving the way for a more profound comprehension of human biology and diseases. The droplet microfluidic technology has become a crucial component in many single-cell sequencing workflows in terms of throughput, cost-effectiveness, and automation. Utilizing a microfluidic chip to encapsulate and profile individual cells within droplets has significantly improved single-cell research. Therefore, this review aims to comprehensively elaborate the droplet microfluidics-assisted omics methods from a single-cell perspective. The strategies for using droplet microfluidics in the realms of genomics, epigenomics, transcriptomics, and proteomics analyses are first introduced. On this basis, the focus then turns to the latest applications of this technology in different sequencing patterns, including mono- and multi-omics. Finally, the challenges and further perspectives of droplet-based single-cell sequencing in both foundational research and commercial applications are discussed.

Single and few cell analysis for correlative light microscopy, metabolomics, and targeted proteomics

The interactions of proteins, membranes, nucleic acid, and metabolites shape a cell's phenotype. These interactions are stochastic, and each cell develops differently, making it difficult to synchronize cell populations. Consequently, studying biological processes at the single- or few-cell level is often necessary to avoid signal dilution below the detection limit or averaging over many cells. We have developed a method to study metabolites and proteins from a small number of or even a single adherent eukaryotic cell. Initially, cells are lysed by short electroporation and aspirated with a microcapillary under a fluorescent microscope. The lysate is placed on a carrier slide for further analysis using liquid-chromatography mass spectrometry (LC-MS) and/or reverse-phase protein (RPPA) approach. This method allows for a correlative measurement of (i) cellular structures and metabolites and (ii) cellular structures and proteins on the single-cell level. The correlative measurement of cellular structure by light-microscopy, metabolites by LC-MS, and targeted protein detection by RPPA was possible on the few-cell level. We discuss the method, potential applications, limitations, and future improvements.

Single-cell analysis via manifold fitting: A framework for RNA clustering and beyond

Single-cell RNA sequencing (scRNA-seq) data, susceptible to noise arising from biological variability and technical errors, can distort gene expression analysis and impact cell similarity assessments, particularly in heterogeneous populations. Current methods, including deep learning approaches, often struggle to accurately characterize cell relationships due to this inherent noise. To address these challenges, we introduce scAMF (Single-cell Analysis via Manifold Fitting), a framework designed to enhance clustering accuracy and data visualization in scRNA-seq studies. At the heart of scAMF lies the manifold fitting module, which effectively denoises scRNA-seq data by unfolding their distribution in the ambient space. This unfolding aligns the gene expression vector of each cell more closely with its underlying structure, bringing it spatially closer to other cells of the same cell type. To comprehensively assess the impact of scAMF, we compile a collection of 25 publicly available scRNA-seq datasets spanning various sequencing platforms, species, and organ types, forming an extensive RNA data bank. In our comparative studies, benchmarking scAMF against existing scRNA-seq analysis algorithms in this data bank, we consistently observe that scAMF outperforms in terms of clustering efficiency and data visualization clarity. Further experimental analysis reveals that this enhanced performance stems from scAMF's ability to improve the spatial distribution of the data and capture class-consistent neighborhoods. These findings underscore the promising application potential of manifold fitting as a tool in scRNA-seq analysis, signaling a significant enhancement in the precision and reliability of data interpretation in this critical field of study.

A single-nucleus transcriptomic atlas characterizes cell types and their molecular features in the ovary of adult Nile tilapia

In fish species, there is limited analysis of signature transcriptome profiles at the single-cell level in gonadal cells. Here, the molecular signatures of distinct ovarian cell categories in adult Nile tilapia (Oreochromis niloticus) were analysed using single-nucleus RNA sequencing (snRNA-seq). We identified four cell types (oogonia, oocytes, granulosa cell, and thecal cell) based on their specifically expressed genes and biological functions. Similarly, we found some key pathways involved in ovarian development that may affect germline-somatic interactions. A cell-to-cell communication network between the distinct cell types was constructed. We found that the bidirectional communication is mandatory for the development of germ cells and somatic cells in fish ovaries, and the granulosa cells and thecal cells play a central regulating role in the cell network in fish ovary. Additionally, we identified some novel candidate marker genes for various types of ovarian cells and also validated them using in situ hybridization. Our work reveals an ovarian atlas at the cellular and molecular levels and contributes to providing insights into oogenesis and gonad development in fish.

Conjugation of primary amine groups in targeted proteomics

Primary amines, in the form of unmodified N-terminus of peptide/protein and unmodified lysine residue, are perhaps the most important functional groups that can serve as the starting points in proteomic analysis, especially via mass spectrometry-based approaches. A variety of multifunctional probes that conjugate primary amine groups through covalent bonds have been developed and employed to facilitate protein/protein complex characterization, including identification, quantification, structure and localization elucidation, protein-protein interaction investigation, and so forth. As an integral part of more accurate peptide quantification in targeted proteomics, isobaric stable isotope-coded primary amine labeling approaches eventually facilitated protein/peptide characterization at the single-cell level, paving the way for single-cell proteomics. The development and advances in the field can be reviewed in terms of key components of a multifunctional probe: functional groups and chemistry for primary amine conjugation; hetero-bifunctional moiety for separation/enrichment of conjugated protein/protein complex; and functionalized linker/spacer. Perspectives are primarily focused on optimizing primary amine conjugation under physiological conditions to improve characterization of native proteins, especially those associated with the surface of living cells/microorganisms.

Progress in toxicogenomics to protect human health

Toxicogenomics measures molecular features, such as transcripts, proteins, metabolites and epigenomic modifications, to understand and predict the toxicological effects of environmental and pharmaceutical exposures. Transcriptomics has become an integral tool in contemporary toxicology research owing to innovations in gene expression profiling that can provide mechanistic and quantitative information at scale. These data can be used to predict toxicological hazards through the use of transcriptomic biomarkers, network inference analyses, pattern-matching approaches and artificial intelligence. Furthermore, emerging approaches, such as high-throughput dose-response modelling, can leverage toxicogenomic data for human health protection even in the absence of predicting specific hazards. Finally, single-cell transcriptomics and multi-omics provide detailed insights into toxicological mechanisms. Here, we review the progress since the inception of toxicogenomics in applying transcriptomics towards toxicology testing and highlight advances that are transforming risk assessment.

Multi-omics integration reveals potential stage-specific druggable targets in T-cell acute lymphoblastic leukemia

T-cell acute lymphoblastic leukemia (T-ALL), a heterogeneous hematological malignancy, is caused by the developmental arrest of normal T-cell progenitors. The development of targeted therapeutic regimens is impeded by poor knowledge of the stage-specific aberrances in this disease. In this study, we performed multi-omics integration analysis, which included mRNA expression, chromatin accessibility, and gene-dependency database analyses, to identify potential stage-specific druggable targets and repositioned drugs for this disease. This multi-omics integration helped identify 29 potential pathological genes for T-ALL. These genes exhibited tissue-specific expression profiles and were enriched in the cell cycle, hematopoietic stem cell differentiation, and the AMPK signaling pathway. Of these, four known druggable targets (CDK6, TUBA1A, TUBB, and TYMS) showed dysregulated and stage-specific expression in malignant T cells and may serve as stage-specific targets in T-ALL. The TUBA1A expression level was higher in the early T cell precursor (ETP)-ALL cells, while TUBB and TYMS were mainly highly expressed in malignant T cells arrested at the CD4 and CD8 double-positive or single-positive stage. CDK6 exhibited a U-shaped expression pattern in malignant T cells along the naïve to maturation stages. Furthermore, mebendazole and gemcitabine, which target TUBA1A and TYMS, respectively, exerted stage-specific inhibitory effects on T-ALL cell lines, indicating their potential stage-specific antileukemic role in T-ALL. Collectively, our findings might aid in identifying potential stage-specific druggable targets and are promising for achieving more precise therapeutic strategies for T-ALL.

Targeting of human cancer stem cells predicts efficacy and toxicity of FDA-approved oncology drugs

Cancer remains the leading cause of death worldwide with approved oncology drugs continuing to have heterogenous patient responses and accompanied adverse effects (AEs) that limits effectiveness. Here, we examined >100 FDA-approved oncology drugs in the context of stemness using a surrogate model of transformed human pluripotent cancer stem cells (CSCs) vs. healthy stem cells (hSCs) capable of distinguishing abnormal self-renewal and differentiation. Although a proportion of these drugs had no effects (inactive), a larger portion affected CSCs (active), and a unique subset preferentially affected CSCs over hSCs (selective). Single cell gene expression and protein profiling of each drug's FDA recognized target provided a molecular correlation of responses in CSCs vs. hSCs. Uniquely, drugs selective for CSCs demonstrated clinical efficacy, measured by overall survival, and reduced AEs. Our findings reveal that while unintentional, half of anticancer drugs are active against CSCs and associated with improved clinical outcomes. Based on these findings, we suggest ability to target CSC targeting should be included as a property of early onco-therapeutic development.

Spatial landscapes of cancers: insights and opportunities

Solid tumours comprise many different cell types organized in spatially structured arrangements, with substantial intratumour and intertumour heterogeneity. Advances in spatial profiling technologies over the past decade hold promise to capture the complexity of these cellular architectures to build a holistic view of the intricate molecular mechanisms that shape the tumour ecosystem. Some of these mechanisms act at the cellular scale and are controlled by cell-autonomous programmes or communication between nearby cells, whereas other mechanisms result from coordinated efforts between large networks of cells and extracellular molecules organized into tissues and organs. In this Review we provide insights into the application of single-cell and spatial profiling tools, with a focus on spatially resolved transcriptomic tools developed to understand the cellular architecture of the tumour microenvironment and identify opportunities to use them to improve clinical management of cancers.

The heterogeneity of erythroid cells: insight at the single-cell transcriptome level

Erythroid cells, the most prevalent cell type in blood, are one of the earliest products and permeate through the entire process of hematopoietic development in the human body, the oxygen-transporting function of which is crucial for maintaining overall health and life support. Previous investigations into erythrocyte differentiation and development have primarily focused on population-level analyses, lacking the single-cell perspective essential for comprehending the intricate pathways of erythroid maturation, differentiation, and the encompassing cellular heterogeneity. The continuous optimization of single-cell transcriptome sequencing technology, or single-cell RNA sequencing (scRNA-seq), provides a powerful tool for life sciences research, which has a particular superiority in the identification of unprecedented cell subgroups, the analyzing of cellular heterogeneity, and the transcriptomic characteristics of individual cells. Over the past decade, remarkable strides have been taken in the realm of single-cell RNA sequencing technology, profoundly enhancing our understanding of erythroid cells. In this review, we systematically summarize the recent developments in single-cell transcriptome sequencing technology and emphasize their substantial impact on the study of erythroid cells, highlighting their contributions, including the exploration of functional heterogeneity within erythroid populations, the identification of novel erythrocyte subgroups, the tracking of different erythroid lineages, and the unveiling of mechanisms governing erythroid fate decisions. These findings not only invigorate erythroid cell research but also offer new perspectives on the management of diseases related to erythroid cells.

Single-cell analysis identifies critical regulators of spermatogonial development and differentiation in cattle-yak bulls

Spermatogenesis is a continuous process in which functional sperm are produced through a series of mitotic and meiotic divisions and morphological changes in germ cells. The aberrant development and fate transitions of spermatogenic cells cause hybrid sterility in mammals. Cattle-yak, a hybrid animal between taurine cattle (Bos taurus) and yak (Bos grunniens), exhibits male-specific sterility due to spermatogenic failure. In the present study, we performed single-cell RNA sequencing analysis to identify differences in testicular cell composition and the developmental trajectory of spermatogenic cells between yak and cattle-yak. The composition and molecular signatures of spermatogonial subtypes were dramatically different between these 2 animals, and the expression of genes associated with stem cell maintenance, cell differentiation and meiotic entry was altered in cattle-yak, indicating the impairment of undifferentiated spermatogonial fate decisions. Cell communication analysis revealed that signaling within different spermatogenic cell subpopulations was weakened, and progenitor spermatogonia were unable to or delayed receiving and sending signals for transformation to the next stage in cattle-yak. Simultaneously, the communication between niche cells and germ cells was also abnormal. Collectively, we obtained the expression profiles of transcriptome signatures of different germ cells and testicular somatic cell populations at the single-cell level and identified critical regulators of spermatogonial differentiation and meiosis in yak and sterile cattle-yak. The findings of this study shed light on the genetic mechanisms that lead to hybrid sterility and speciation in bovid species.

Unraveling flavivirus pathogenesis: from bulk to single-cell RNA-sequencing strategies

The global spread of flaviviruses has triggered major outbreaks worldwide, significantly impacting public health, society, and economies. This has intensified research efforts to understand how flaviviruses interact with their hosts and manipulate the immune system, underscoring the need for advanced research tools. RNA-sequencing (RNA-seq) technologies have revolutionized our understanding of flavivirus infections by offering transcriptome analysis to dissect the intricate dynamics of virus-host interactions. Bulk RNA-seq provides a macroscopic overview of gene expression changes in virus-infected cells, offering insights into infection mechanisms and host responses at the molecular level. Single-cell RNA sequencing (scRNAseq) provides unprecedented resolution by analyzing individual infected cells, revealing remarkable cellular heterogeneity within the host response. A particularly innovative advancement, virus-inclusive single-cell RNA sequencing (viscRNA-seq), addresses the challenges posed by non-polyadenylated flavivirus genomes, unveiling intricate details of virus-host interactions. In this review, we discuss the contributions of bulk RNA-seq, scRNA-seq, and viscRNA-seq to the field, exploring their implications in cell line experiments and studies on patients infected with various flavivirus species. Comprehensive transcriptome analyses from RNA-seq technologies are pivotal in accelerating the development of effective diagnostics and therapeutics, paving the way for innovative treatments and enhancing our preparedness for future outbreaks.

Single-Cell RNA-Sequencing: Opening New Horizons for Breast Cancer Research

Breast cancer is the most prevalent malignant tumor among women with high heterogeneity. Traditional techniques frequently struggle to comprehensively capture the intricacy and variety of cellular states and interactions within breast cancer. As global precision medicine rapidly advances, single-cell RNA sequencing (scRNA-seq) has become a highly effective technique, revolutionizing breast cancer research by offering unprecedented insights into the cellular heterogeneity and complexity of breast cancer. This cutting-edge technology facilitates the analysis of gene expression profiles at the single-cell level, uncovering diverse cell types and states within the tumor microenvironment. By dissecting the cellular composition and transcriptional signatures of breast cancer cells, scRNA-seq provides new perspectives for understanding the mechanisms behind tumor therapy, drug resistance and metastasis in breast cancer. In this review, we summarized the working principle and workflow of scRNA-seq and emphasized the major applications and discoveries of scRNA-seq in breast cancer research, highlighting its impact on our comprehension of breast cancer biology and its potential for guiding personalized treatment strategies.

DrugFormer: Graph-Enhanced Language Model to Predict Drug Sensitivity

Drug resistance poses a crucial challenge in healthcare, with response rates to chemotherapy and targeted therapy remaining low. Individual patient's resistance is exacerbated by the intricate heterogeneity of tumor cells, presenting significant obstacles to effective treatment. To address this challenge, DrugFormer, a novel graph-augmented large language model designed to predict drug resistance at single-cell level is proposed. DrugFormer integrates both serialized gene tokens and gene-based knowledge graphs for the accurate predictions of drug response. After training on comprehensive single-cell data with drug response information, DrugFormer model presents outperformance, with higher F1, precision, and recall in predicting drug response. Based on the scRNA-seq data from refractory multiple myeloma (MM) and acute myeloid leukemia (AML) patients, DrugFormer demonstrates high efficacy in identifying resistant cells and uncovering underlying molecular mechanisms. Through pseudotime trajectory analysisunique drug-resistant cellular states associated with poor patient outcomes are revealed. Furthermore, DrugFormer identifies potential therapeutic targets, such as COX8A, for overcoming drug resistance across different cancer types. In conclusion, DrugFormer represents a significant advancement in the field of drug resistance prediction, offering a powerful tool for unraveling the heterogeneity of cellular response to drugs and guiding personalized treatment strategies.

Application and research progress of single cell sequencing technology in leukemia

Leukemia is a malignant tumor with high heterogeneity and a complex evolutionary process. It is difficult to resolve the heterogeneity and clonal evolution of leukemia cells by applying traditional bulk sequencing techniques, thus preventing a deep understanding of the mechanisms of leukemia development and the identification of potential therapeutic targets. However, with the development and application of single-cell sequencing technology, it is now possible to investigate the gene expression profile, mutations, and epigenetic features of leukemia at the single-cell level, thus providing a new perspective for leukemia research. In this article, we review the recent applications and advances of single-cell sequencing technology in leukemia research, discuss its potential for enhancing our understanding of the mechanisms of leukemia development, discovering therapeutic targets and personalized treatment, and provide reference guidelines for the significance of this technology in clinical research.

Community assessment of methods to deconvolve cellular composition from bulk gene expression

We evaluate deconvolution methods, which infer levels of immune infiltration from bulk expression of tumor samples, through a community-wide DREAM Challenge. We assess six published and 22 community-contributed methods using in vitro and in silico transcriptional profiles of admixed cancer and healthy immune cells. Several published methods predict most cell types well, though they either were not trained to evaluate all functional CD8+ T cell states or do so with low accuracy. Several community-contributed methods address this gap, including a deep learning-based approach, whose strong performance establishes the applicability of this paradigm to deconvolution. Despite being developed largely using immune cells from healthy tissues, deconvolution methods predict levels of tumor-derived immune cells well. Our admixed and purified transcriptional profiles will be a valuable resource for developing deconvolution methods, including in response to common challenges we observe across methods, such as sensitive identification of functional CD4+ T cell states.

Application of Pan-Omics Technologies in Research on Important Economic Traits for Ruminants

The economic significance of ruminants in agriculture underscores the need for advanced research methodologies to enhance their traits. This review aims to elucidate the transformative role of pan-omics technologies in ruminant research, focusing on their application in uncovering the genetic mechanisms underlying complex traits such as growth, reproduction, production performance, and rumen function. Pan-omics analysis not only helps in identifying key genes and their regulatory networks associated with important economic traits but also reveals the impact of environmental factors on trait expression. By integrating genomics, epigenomics, transcriptomics, metabolomics, and microbiomics, pan-omics enables a comprehensive analysis of the interplay between genetics and environmental factors, offering a holistic understanding of trait expression. We explore specific examples of economic traits where these technologies have been pivotal, highlighting key genes and regulatory networks identified through pan-omics approaches. Additionally, we trace the historical evolution of each omics field, detailing their progression from foundational discoveries to high-throughput platforms. This review provides a critical synthesis of recent advancements, offering new insights and practical recommendations for the application of pan-omics in the ruminant industry. The broader implications for modern animal husbandry are discussed, emphasizing the potential for these technologies to drive sustainable improvements in ruminant production systems.

scSwinFormer: A Transformer-Based Cell-Type Annotation Method for scRNA-Seq Data Using Smooth Gene Embedding and Global Features

Single-cell omics techniques have made it possible to analyze individual cells in biological samples, providing us with a more detailed understanding of cellular heterogeneity and biological systems. Accurate identification of cell types is critical for single-cell RNA sequencing (scRNA-seq) analysis. However, scRNA-seq data are usually high dimensional and sparse, posing a great challenge to analyze scRNA-seq data. Existing cell-type annotation methods are either constrained in modeling scRNA-seq data or lack consideration of long-term dependencies of characterized genes. In this work, we developed a Transformer-based deep learning method, scSwinFormer, for the cell-type annotation of large-scale scRNA-seq data. Sequence modeling of scRNA-seq data is performed using the smooth gene embedding module, and then, the potential dependencies of genes are captured by the self-attention module. Subsequently, the global information inherent in scRNA-seq data is synthesized using the Cell Token, thereby facilitating accurate cell-type annotation. We evaluated the performance of our model against current state-of-the-art scRNA-seq cell-type annotation methods on multiple real data sets. ScSwinFormer outperforms the current state-of-the-art scRNA-seq cell-type annotation methods in both external and benchmark data set experiments.

Single-Cell Sequencing: High-Resolution Analysis of Cellular Heterogeneity in Autoimmune Diseases

Autoimmune diseases (AIDs) are complex in etiology and diverse in classification but clinically show similar symptoms such as joint pain and skin problems. As a result, the diagnosis is challenging, and usually, only broad treatments can be available. Consequently, the clinical responses in patients with different types of AIDs are unsatisfactory. Therefore, it is necessary to conduct more research to figure out the pathogenesis and therapeutic targets of AIDs. This requires research technologies with strong extraction and prediction capabilities. Single-cell sequencing technology analyses the genomic, epigenomic, or transcriptomic information at the single-cell level. It can define different cell types and states in greater detail, further revealing the molecular mechanisms that drive disease progression. These advantages enable cell biology research to achieve an unprecedented resolution and scale, bringing a whole new vision to life science research. In recent years, single-cell technology especially single-cell RNA sequencing (scRNA-seq) has been widely used in various disease research. In this paper, we present the innovations and applications of single-cell sequencing in the medical field and focus on the application contributing to the differential diagnosis and precise treatment of AIDs. Despite some limitations, single-cell sequencing has a wide range of applications in AIDs. We finally present a prospect for the development of single-cell sequencing. These ideas may provide some inspiration for subsequent research.

Exploring the heterogeneous targets of metabolic aging at single-cell resolution

Our limited understanding of metabolic aging poses major challenges to comprehending the diverse cellular alterations that contribute to age-related decline, and to devising targeted interventions. This review provides insights into the heterogeneous nature of cellular metabolism during aging and its response to interventions, with a specific focus on cellular heterogeneity and its implications. By synthesizing recent findings using single-cell approaches, we explored the vulnerabilities of distinct cell types and key metabolic pathways. Delving into the cell type-specific alterations underlying the efficacy of systemic interventions, we also discuss the complexity of integrating single-cell data and advocate for leveraging computational tools and artificial intelligence to harness the full potential of these data, develop effective strategies against metabolic aging, and promote healthy aging.

Transcriptional Dynamics and Key Regulators of Adipogenesis in Mouse Embryonic Stem Cells: Insights from Robust Rank Aggregation Analysis

Embryonic stem cells are crucial for studying developmental biology due to their self-renewal and pluripotency capabilities. This research investigates the differentiation of mouse ESCs into adipocytes, offering insights into obesity and metabolic disorders. Using a monolayer differentiation approach over 30 days, lipid accumulation and adipogenic markers, such as Cebpb, Pparg, and Fabp4, confirmed successful differentiation. RNA sequencing revealed extensive transcriptional changes, with over 15,000 differentially expressed genes linked to transcription regulation, cell cycle, and DNA repair. This study utilized Robust Rank Aggregation to identify critical regulatory genes like PPARG, CEBPA, and EP300. Network analysis further highlighted Atf5, Ccnd1, and Nr4a1 as potential key players in adipogenesis and its mature state, validated through RT-PCR. While key adipogenic factors showed plateaued expression levels, suggesting early differentiation events, this study underscores the value of ESCs in modeling adipogenesis. These findings contribute to our understanding of adipocyte differentiation and have significant implications for therapeutic strategies targeting metabolic diseases.

The promise of molecular science in brain health. What breakthroughs are anticipated in the next 20 years?

Brain health means optimal physiological brain function across the normal life-course. It encompasses not only healthy brain aging but also brain diseases, their diagnosis and treatment. In all these areas, molecular science has advanced our understanding. This multi-disciplinary review combines viewpoints from laboratory science, clinical medicine and the bioscience industry. First, we review the advances that molecular science has brought to brain health in the past twenty years. These include therapeutic antibodies for CNS diseases (multiple sclerosis, Alzheimer disease) and the dramatic introduction of RNA-targeted therapeutics. Second, we highlight areas where greater molecular understanding is needed. Salient examples are the relation of brain structure to cognitive symptoms, and molecular biomarkers for diagnosis, target discovery and testing of interventions. Finally, we speculate on aspects of molecular science that are likely to advance brain health in the next twenty years. These include: cell senescence and chronobiology; gene editing (notably, CRISPR) and RNA targeting (RNA interference, miRNA manipulation); brain-immune interactions; novel drug targets (AQP4, HIF1, Toll-like receptors); and novel chemistry to make new drugs (molecular machines, quantum molecular modelling and "click" chemistry). Early testing of the relationships between molecular pathways and clinical manifestations will drive much-needed breakthroughs in neurology and psychiatry.

Beyond the Heartbeat: Single-Cell Omics Redefining Cardiovascular Research

PURPOSE OF REVIEW

This review aims to explore recent advances in single-cell omics techniques as applied to various regions of the human heart, illuminating cellular diversity, regulatory networks, and disease mechanisms. We examine the contributions of single-cell transcriptomics, genomics, proteomics, epigenomics, and spatial transcriptomics in unraveling the complexity of cardiac tissues.

RECENT FINDINGS

Recent strides in single-cell omics technologies have revolutionized our understanding of the heart's cellular composition, cell type heterogeneity, and molecular dynamics. These advancements have elucidated pathological conditions as well as the cellular landscape in heart development. We highlight emerging applications of integrated single-cell omics, particularly for cardiac regeneration, disease modeling, and precision medicine, and emphasize the transformative potential of these technologies to advance cardiovascular research and clinical practice.

From Cell to Gene: Deciphering the Mechanism of Heart Failure With Single-Cell Sequencing

Heart failure (HF) is a prevalent cardiovascular disease with significant morbidity and mortality rates worldwide. Due to the intricate structure of the heart, diverse cell types, and the complex pathogenesis of HF, further in-depth investigation into the underlying mechanisms  is required. The elucidation of the heterogeneity of cardiomyocytes and the intercellular communication network is particularly important. Traditional high-throughput sequencing methods provide an average measure of gene expression, failing to capture the "heterogeneity" between cells and impacting the accuracy of gene function knowledge. In contrast, single-cell sequencing techniques allow for the amplification of the entire genome or transcriptome at the individual cell level, facilitating the examination of gene structure and expression with unparalleled precision. This approach offers valuable insights into disease mechanisms, enabling the identification of changes in cellular components and gene expressions during hypertrophy associated with HF. Moreover, it reveals distinct cell populations and their unique roles in the HF microenvironment, providing a comprehensive understanding of the cellular landscape that underpins HF pathogenesis. This review focuses on the insights provided by single-cell sequencing techniques into the mechanisms underlying HF and discusses the challenges encountered in current cardiovascular research.

Translation efficiency covariation across cell types is a conserved organizing principle of mammalian transcriptomes

Characterization of shared patterns of RNA expression between genes across conditions has led to the discovery of regulatory networks and novel biological functions. However, it is unclear if such coordination extends to translation, a critical step in gene expression. Here, we uniformly analyzed 3,819 ribosome profiling datasets from 117 human and 94 mouse tissues and cell lines. We introduce the concept of Translation Efficiency Covariation (TEC), identifying coordinated translation patterns across cell types. We nominate potential mechanisms driving shared patterns of translation regulation. TEC is conserved across human and mouse cells and helps uncover gene functions. Moreover, our observations indicate that proteins that physically interact are highly enriched for positive covariation at both translational and transcriptional levels. Our findings establish translational covariation as a conserved organizing principle of mammalian transcriptomes.

Unraveling immune cell heterogeneity in autoimmune arthritis: insights from single-cell RNA sequencing

Single-cell RNA sequencing (scRNA-seq) has transformed our understanding of immune-mediated arthritis, which comprises rheumatoid arthritis and spondyloarthritis. This review outlines the key findings and advancements in scRNA-seq studies focused on the pathogenesis of autoimmune arthritis and its clinical application. In rheumatoid arthritis, scRNA-seq has elucidated the heterogeneity among synovial fibroblasts and immune cell subsets in inflammatory sites, offering insights into disease mechanisms and the differences in treatment responses. Various studies have identified distinct synovial fibroblast subpopulations, such as THY1+ inflammatory and THY1- destructive fibroblasts. Furthermore, scRNA-seq has revealed diverse T cell profiles in the synovium, including peripheral helper T cells and clonally expanded CD8+ T cells, shedding light on potential therapeutic targets and predictive markers of treatment response. Similarly, in spondyloarthritis, particularly psoriatic arthritis and ankylosing spondylitis, scRNA-seq studies have identified distinct cellular profiles associated with disease pathology. Challenges such as cost and sample size limitations persist, but collaborative efforts and utilization of public databases hold promise for overcoming these obstacles. Overall, scRNA-seq emerges as a powerful tool for dissecting cellular heterogeneity and driving precision medicine in immune-mediated arthritis.

COMSE: analysis of single-cell RNA-seq data using community detection-based feature selection

BACKGROUND

Single-cell RNA sequencing enables studying cells individually, yet high gene dimensions and low cell numbers challenge analysis. And only a subset of the genes detected are involved in the biological processes underlying cell-type specific functions.

RESULT

In this study, we present COMSE, an unsupervised feature selection framework using community detection to capture informative genes from scRNA-seq data. COMSE identified homogenous cell substates with high resolution, as demonstrated by distinguishing different cell cycle stages. Evaluations based on real and simulated scRNA-seq datasets showed COMSE outperformed methods even with high dropout rates in cell clustering assignment. We also demonstrate that by identifying communities of genes associated with batch effects, COMSE parses signals reflecting biological difference from noise arising due to differences in sequencing protocols, thereby enabling integrated analysis of scRNA-seq datasets of different sources.

CONCLUSIONS

COMSE provides an efficient unsupervised framework that selects highly informative genes in scRNA-seq data improving cell sub-states identification and cell clustering. It identifies gene subsets that reveal biological and technical heterogeneity, supporting applications like batch effect correction and pathway analysis. It also provides robust results for bulk RNA-seq data analysis.

Cell-cell communication: new insights and clinical implications

Multicellular organisms are composed of diverse cell types that must coordinate their behaviors through communication. Cell-cell communication (CCC) is essential for growth, development, differentiation, tissue and organ formation, maintenance, and physiological regulation. Cells communicate through direct contact or at a distance using ligand-receptor interactions. So cellular communication encompasses two essential processes: cell signal conduction for generation and intercellular transmission of signals, and cell signal transduction for reception and procession of signals. Deciphering intercellular communication networks is critical for understanding cell differentiation, development, and metabolism. First, we comprehensively review the historical milestones in CCC studies, followed by a detailed description of the mechanisms of signal molecule transmission and the importance of the main signaling pathways they mediate in maintaining biological functions. Then we systematically introduce a series of human diseases caused by abnormalities in cell communication and their progress in clinical applications. Finally, we summarize various methods for monitoring cell interactions, including cell imaging, proximity-based chemical labeling, mechanical force analysis, downstream analysis strategies, and single-cell technologies. These methods aim to illustrate how biological functions depend on these interactions and the complexity of their regulatory signaling pathways to regulate crucial physiological processes, including tissue homeostasis, cell development, and immune responses in diseases. In addition, this review enhances our understanding of the biological processes that occur after cell-cell binding, highlighting its application in discovering new therapeutic targets and biomarkers related to precision medicine. This collective understanding provides a foundation for developing new targeted drugs and personalized treatments.

A Comparison of Tools That Identify Tumor Cells by Inferring Copy Number Variations from Single-Cell Experiments in Pancreatic Ductal Adenocarcinoma

Single-cell RNA sequencing (scRNA-seq) technique has enabled detailed analysis of gene expression at the single cell level, enhancing the understanding of subtle mechanisms that underly pathologies and drug resistance. To derive such biological meaning from sequencing data in oncology, some critical processing must be performed, including identification of the tumor cells by markers and algorithms that infer copy number variations (CNVs). We compared the performance of sciCNV, InferCNV, CopyKAT and SCEVAN tools that identify tumor cells by inferring CNVs from scRNA-seq data. Sequencing data from Pancreatic Ductal Adenocarcinoma (PDAC) patients, adjacent and healthy tissues were analyzed, and the predicted tumor cells were compared to those identified by well-assessed PDAC markers. Results from InferCNV, CopyKAT and SCEVAN overlapped by less than 30% with InferCNV showing the highest sensitivity (0.72) and SCEVAN the highest specificity (0.75). We show that the predictions are highly dependent on the sample and the software used, and that they return so many false positives hence are of little use in verifying or filtering predictions made via tumor biomarkers. We highlight how critical this processing can be, warn against the blind use of these software and point out the great need for more reliable algorithms.

Innate Immune Cells in Melanoma: Implications for Immunotherapy

The innate immune system, composed of neutrophils, basophils, eosinophils, myeloid-derived suppressor cells (MDSCs), macrophages, dendritic cells (DCs), mast cells (MCs), and innate lymphoid cells (ILCs), is the first line of defense. Growing evidence demonstrates the crucial role of innate immunity in tumor initiation and progression. Several studies support the idea that innate immunity, through the release of pro- and/or anti-inflammatory cytokines and tumor growth factors, plays a significant role in the pathogenesis, progression, and prognosis of cutaneous malignant melanoma (MM). Cutaneous melanoma is the most common skin cancer, with an incidence that rapidly increased in recent decades. Melanoma is a highly immunogenic tumor, due to its high mutational burden. The metastatic form retains a high mortality. The advent of immunotherapy revolutionized the therapeutic approach to this tumor and significantly ameliorated the patients' clinical outcome. In this review, we will recapitulate the multiple roles of innate immune cells in melanoma and the related implications for immunotherapy.

Advances in single-cell transcriptomics in animal research

Understanding biological mechanisms is fundamental for improving animal production and health to meet the growing demand for high-quality protein. As an emerging biotechnology, single-cell transcriptomics has been gradually applied in diverse aspects of animal research, offering an effective method to study the gene expression of high-throughput single cells of different tissues/organs in animals. In an unprecedented manner, researchers have identified cell types/subtypes and their marker genes, inferred cellular fate trajectories, and revealed cell‒cell interactions in animals using single-cell transcriptomics. In this paper, we introduce the development of single-cell technology and review the processes, advancements, and applications of single-cell transcriptomics in animal research. We summarize recent efforts using single-cell transcriptomics to obtain a more profound understanding of animal nutrition and health, reproductive performance, genetics, and disease models in different livestock species. Moreover, the practical experience accumulated based on a large number of cases is highlighted to provide a reference for determining key factors (e.g., sample size, cell clustering, and cell type annotation) in single-cell transcriptomics analysis. We also discuss the limitations and outlook of single-cell transcriptomics in the current stage. This paper describes the comprehensive progress of single-cell transcriptomics in animal research, offering novel insights and sustainable advancements in agricultural productivity and animal health.

GSK3 regulation Wnt/β-catenin signaling affects adipogenesis in bovine skeletal muscle fibro/adipogenic progenitors

Clarifying the cellular origin and regulatory mechanisms of intramuscular fat (IMF) deposition is crucial for improving beef quality. Here, we used single-nucleus RNA sequencing to analyze the structure and heterogeneity of skeletal muscle cell populations in different developmental stages of Yanbian cattle and identified eight cell types in two developmental stages of calves and adults. Among them, fibro/adipogenic progenitors (FAPs) expressing CD29 (ITGA7)pos and CD56 (NCAM1)neg surface markers were committed to IMF deposition in beef cattle and expressed major Wnt ligands and receptors. LY2090314/XAV-939 was used to activate/inhibit Wnt/β-catenin signal. The results showed that the blockade of Glycogen Synthase Kinase 3 (GSK3) by LY2090314 promoted the stabilization of β-catenin and reduced the expression of genes related adipogenic differentiation (e.g., PPARγ and C/EBPα) in bovine FAPs, confirming the anti-adipogenic effect of GSK3. XAV-939 inhibition of the Wnt/β-catenin pathway promoted the lipid accumulation capacity of FAPs. Furthermore, we found that blocking GSK3 enhanced the paracrine effects of FAPs-MuSCs and increased myotube formation in muscle satellite cells (MuSCs). Overall, our results outline a single-cell atlas of skeletal muscle development in Yanbian cattle, revealed the role of Wnt/GSK3/β-catenin signaling in FAPs adipogenesis, and provide a theoretical basis for further regulation of bovine IMF deposition.

Concepts and new developments in droplet-based single cell multi-omics

Single cell sequencing technologies have become a fixture in the molecular profiling of cells due to their ease, flexibility, and commercial availability. In particular, partitioning individual cells inside oil droplets via microfluidic reactions enables transcriptomic or multi-omic measurements for thousands of cells in parallel. Complementing the multitude of biological discoveries from genomics analyses, the past decade has brought new capabilities from assay baselines to enable a deeper understanding of the complex data from single cell multi-omics. Here, we highlight four innovations that have improved the reliability and understanding of droplet microfluidic assays. We emphasize new developments that further orient principles of technology development and guidelines for the design, benchmarking, and implementation of new droplet-based methodologies.

Aging- and alcohol-associated spatial transcriptomic signature in mouse acute pancreatitis reveals heterogeneity of inflammation and potential pathogenic factors

The rapidly aging population is consuming more alcohol, leading to increased alcohol-associated acute pancreatitis (AAP) with high mortality. However, the mechanisms remain undefined, and currently there are no effective therapies available. This study aims to elucidate aging- and alcohol-associated spatial transcriptomic signature by establishing an aging AAP mouse model and applying Visium spatial transcriptomics for understanding of the mechanisms in the context of the pancreatic tissue. Upon alcohol diet feeding and caerulein treatment, aging mice (18 months) developed significantly more severe AAP with 5.0-fold increase of injury score and 2.4-fold increase of amylase compared to young mice (3 months). Via Visium spatial transcriptomics, eight distinct tissue clusters were revealed from aggregated transcriptomes of aging and young AAP mice: five acinar, two stromal, and one islet, which were then merged into three clusters: acinar, stromal, and islet for the comparative analysis. Compared to young AAP mice, > 1300 differentially expressed genes (DEGs) and approximately 3000 differentially regulated pathways were identified in aging AAP mice. The top five DEGs upregulated in aging AAP mice include Mmp8, Ppbp, Serpina3m, Cxcl13, and Hamp with heterogeneous distributions among the clusters. Taken together, this study demonstrates spatial heterogeneity of inflammatory processes in aging AAP mice, offering novel insights into the mechanisms and potential drivers for AAP development. KEY MESSAGES: Mechanisms regarding high mortality of AAP in aging remain undefined. An aging AAP mouse model was developed recapturing clinical exhibition in humans. Spatial transcriptomics identified contrasted DEGs in aging vs. young AAP mice. Top five DEGs were Mmp8, Ppbp, Serpina3m, Cxcl13, and Hamp in aging vs. young AAP mice. Our findings shed insights for identification of molecular drivers in aging AAP.

Single-cell RNA sequencing technology in human spermatogenesis: Progresses and perspectives

Spermatogenesis, a key part of the spermiation process, is regulated by a combination of key cells, such as primordial germ cells, spermatogonial stem cells, and somatic cells, such as Sertoli cells. Abnormal spermatogenesis can lead to azoospermia, testicular tumors, and other diseases related to male infertility. The application of single-cell RNA sequencing (scRNA-seq) technology in male reproduction is gradually increasing with its unique insight into deep mining and analysis. The data cover different periods of neonatal, prepubertal, pubertal, and adult stages. Different types of male infertility diseases including obstructive and non-obstructive azoospermia (NOA), Klinefelter Syndrome (KS), Sertoli Cell Only Syndrome (SCOS), and testicular tumors are also covered. We briefly review the principles and application of scRNA-seq and summarize the research results and application directions in spermatogenesis in different periods and pathological states. Moreover, we discuss the challenges of applying this technology in male reproduction and the prospects of combining it with other technologies.

Genetic variation across and within individuals

Germline variation and somatic mutation are intricately connected and together shape human traits and disease risks. Germline variants are present from conception, but they vary between individuals and accumulate over generations. By contrast, somatic mutations accumulate throughout life in a mosaic manner within an individual due to intrinsic and extrinsic sources of mutations and selection pressures acting on cells. Recent advancements, such as improved detection methods and increased resources for association studies, have drastically expanded our ability to investigate germline and somatic genetic variation and compare underlying mutational processes. A better understanding of the similarities and differences in the types, rates and patterns of germline and somatic variants, as well as their interplay, will help elucidate the mechanisms underlying their distinct yet interlinked roles in human health and biology.

Population Diversity at the Single-Cell Level

Population-scale single-cell genomics is a transformative approach for unraveling the intricate links between genetic and cellular variation. This approach is facilitated by cutting-edge experimental methodologies, including the development of high-throughput single-cell multiomics and advances in multiplexed environmental and genetic perturbations. Examining the effects of natural or synthetic genetic variants across cellular contexts provides insights into the mutual influence of genetics and the environment in shaping cellular heterogeneity. The development of computational methodologies further enables detailed quantitative analysis of molecular variation, offering an opportunity to examine the respective roles of stochastic, intercellular, and interindividual variation. Future opportunities lie in leveraging long-read sequencing, refining disease-relevant cellular models, and embracing predictive and generative machine learning models. These advancements hold the potential for a deeper understanding of the genetic architecture of human molecular traits, which in turn has important implications for understanding the genetic causes of human disease.

RNA Sequencing in Disease Diagnosis

RNA sequencing (RNA-seq) enables the accurate measurement of multiple transcriptomic phenotypes for modeling the impacts of disease variants. Advances in technologies, experimental protocols, and analysis strategies are rapidly expanding the application of RNA-seq to identify disease biomarkers, tissue- and cell-type-specific impacts, and the spatial localization of disease-associated mechanisms. Ongoing international efforts to construct biobank-scale transcriptomic repositories with matched genomic data across diverse population groups are further increasing the utility of RNA-seq approaches by providing large-scale normative reference resources. The availability of these resources, combined with improved computational analysis pipelines, has enabled the detection of aberrant transcriptomic phenotypes underlying rare diseases. Further expansion of these resources, across both somatic and developmental tissues, is expected to soon provide unprecedented insights to resolve disease origin, mechanism of action, and causal gene contributions, suggesting the continued high utility of RNA-seq in disease diagnosis.

Graph-Regularized Non-Negative Matrix Factorization for Single-Cell Clustering in scRNA-Seq Data

The advent of single-cell RNA sequencing (scRNA-seq) has brought forth fresh perspectives on intricate biological processes, revealing the nuances and divergences present among distinct cells. Accurate single-cell analysis is a crucial prerequisite for in-depth investigation into the underlying mechanisms of heterogeneity. Due to various technical noises, like the impact of dropout values, scRNA-seq data remains challenging to interpret. In this work, we propose an unsupervised learning framework for scRNA-seq data analysis (aka Sc-GNNMF). Based on the non-negativity and sparsity of scRNA-seq data, we propose employing graph-regularized non-negative matrix factorization (GNNMF) algorithm for the analysis of scRNA-seq data, which involves estimating cell-cell sparse similarity and gene-gene sparse similarity through Laplacian kernels and p-nearest neighbor graphs ( p-NNG). By assuming intrinsic geometric local invariance, we use a weighted p-nearest known neighbors ( p-NKN) to optimize the scRNA-seq data. The optimized scRNA-seq data then participates in the matrix decomposition process, promoting the closeness of cells with similar types in cell-gene data space and determining a more suitable embedding space for clustering. Sc-GNNMF demonstrates superior performance compared to other methods and maintains satisfactory compatibility and robustness, as evidenced by experiments on 11 real scRNA-seq datasets. Furthermore, Sc-GNNMF yields excellent results in clustering tasks, extracting useful gene markers, and pseudo-temporal analysis.