Single-cell Sequencing Research Articles

A single amplified genome catalog reveals the dynamics of mobilome and resistome in the human microbiome

BackgroundThe increase in metagenome-assembled genomes (MAGs) has advanced our understanding of the functional characterization and taxonomic assignment within the human microbiome. However, MAGs, as population consensus genomes, often aggregate heterogeneity among species and strains, thereby obfuscating the precise relationships between microbial hosts and mobile genetic elements (MGEs). In contrast, single amplified genomes (SAGs) derived via single-cell genome sequencing can capture individual genomic content, including MGEs.ResultsWe introduce the first substantial SAG dataset (bbsag20) from the human oral and gut microbiome, comprising 17,202 SAGs above medium-quality without co-assembly. This collection unveils a diversity of bacterial lineages across 312 oral and 647 gut species, demonstrating different taxonomic compositions from MAGs. Moreover, the SAGs showed cellular-level evidence of the translocation of oral bacteria to the gut. We also identified broad-host-range MGEs harboring antibiotic resistance genes (ARGs), which were not detected in the MAGs.ConclusionsThe difference in taxonomic composition between SAGs and MAGs indicates that combining both methods would be effective in expanding the genome catalog. By connecting mobilomes and resistomes in individual samples, SAGs could meticulously chart a dynamic network of ARGs on MGEs, pinpointing potential ARG reservoirs and their spreading patterns in the microbial community.DX-3H-wDoVG35Spx9tFU_UVideo

Advances in the application of single-cell sequencing technology in the study of neonatal necrotizing enterocolitis

Advances in the application of single-cell sequencing technology in the study of neonatal necrotizing enterocolitis

The critical involvement of monocytes/macrophages in the pathogenesis of primary Sjögren's syndrome: New evidence from Mendelian randomization and single-cell sequencing

BackgroundPrimary Sjögren's syndrome (pSS) stands as a chronic autoimmune disease characterized by an elusive pathogenesis. The synergy of single-cell RNA sequencing and Mendelian randomization (MR) analysis provides an opportunity to comprehensively unravel the contributory role of monocytes/macrophages in the intricate pathogenesis of pSS. MethodsDifferentially expressed genes (DEGs) of various types of immune cells were analyzed after annotating single-cell RNA sequencing (scRNA-seq) data. MR analysis of expression quantitative trait loci (eQTL) and protein quantitative trait loci (pQTL) was conducted to search for key pathogenic genes and proteins. Cellular localization of pathogenic genes was performed based on scRNA-seq data. Variations in signaling pathways between immune cells were further analyzed. ResultsA total of 1434 significant DEGs were identified. Among these, 60 genes exhibited strong relevance to the occurrence of pSS, of which 32 genes differentially expressed in monocytes/macrophages. CTSS was found to be a significant risk protein with a p-value of 0.001 and an odds ratio of 1.384 (1.147–1.669), showing pronounced expression in monocytes/macrophages. Furthermore, monocytes/macrophages displayed heightened expression levels of MXD1, AMPD2, TNFSF10, FTL, UBXN11, CSF3R, and LILRA5. The analysis of intercellular signaling revealed increased signal intensity in both incoming and outgoing signals in monocytes/macrophages. The signaling interactions between monocytes/macrophages, B cells, and T cells exhibited varying degrees of deviation. ConclusionsThis study highlights the significant involvement of monocytes/macrophages in the pathogenesis of pSS, as evidenced by MR analysis and scRNA-seq analysis. This suggests monocytes/macrophages as a focal point for pathogenesis research and potential therapeutic targeting in pSS.

Single-cell sequencing decodes the secrets of the RAP phenomenon of corticotomy.

Corticotomy-assisted tooth movement is commonly performed in clinics, however, its time-limited efficacy and the fear of surgery among patients significantly limit its clinical application. Hence, researchers have investigated non-invasive methods to accelerate tooth movement. However, the molecular mechanisms underlying corticotomy-assisted tooth movement are not fully understood. Micro-CT and TRAP stain were used to tooth movement and bone resorption. Single-cell RNA sequencing was used to study the transcriptome heterogeneity of macrophages after corticotomy. Transmission electron microscopy and iron ion detection was used to evaluate ferroptosis and iron metabolism. In addition, we carried out immunohistochemistry, quantitative real-time and flow cytometry verify the effect of iron on macrophage polarization. Single-cell RNA sequencing of digested alveolar bone identified a significant increase in iron metabolism-related genes post-corticotomy. Macrophages play a central role in this field. Following the dimensionality reduction of macrophages, we revealed a new developmental state via pseudotime analysis post-corticotomy. SCENIC analysis revealed that Atf3 is a key transcription factor influencing this new state. We found that Atf3+ macrophages were closely associated with osteoclasts. Moreover, cell chat revealed an increase in cellular communication between Atf3+ macrophages and other cell types after corticotomy. These findings suggested that Atf3+ macrophages might play a key role in corticotomy-accelerated tooth movement, thus providing potential targets for drug development.

Processing pipelines and analytical methods for single-cell DNA methylation sequencing data.

Single-cell DNA methylation sequencing technology has seen rapid advancements in recent years, playing a crucial role in uncovering cellular heterogeneity and the mechanisms of epigenetic regulation. As sequencing technologies have progressed, the quality and quantity of single-cell methylation data have also increased, making standardized preprocessing workflows and appropriate analysis methods essential for ensuring data comparability and result reliability. However, a comprehensive data analysis pipeline to guide researchers in mining existing data has yet to be established. This review systematically summarizes the preprocessing steps and analysis methods for single-cell methylation data, introduces relevant algorithms and tools, and explores the application prospects of single-cell methylation technology in neuroscience, hematopoietic differentiation, and cancer research. The aim is to provide guidance for researchers in data analysis and to promote the development and application of single-cell methylation sequencing technology.

Osteoarthritis year in review 2024: Biology

Osteoarthritis (OA) research is a fast-growing and extremely wide field, in which a substantial increase in knowledge has been achieved over the last year. It covers many different topics, however, a PubMed search using the terms 'osteoarthritis' and 'biology' resulted in only a limited number of studies that were published between April 2023 and April 2024. In order to identify OA-relevant studies that focus on mechanistic studies of biological processes at the tissue, cellular, and molecular level, the following keywords were included as search terms: tissue interactions, single cell sequencing, transcriptomics, extracellular matrix, signaling, ion channels, and pain. The final selection of publications presented in this 'year in review' was influenced by the personal preferences of the authors, and eventually three larger key themes emerged: 1) Joint tissue interactions covering meniscus, subchondral bone, fat tissue, synovium, and synovial fluid. 2) Degeneration of the cartilage extracellular matrix and generation of bioactive fragments. 3) Receptors, ion channels, signaling pathways, and cellular metabolism. Many of the studies summarized here identified novel potential targets for OA treatment, and promising results were already obtained addressing these targets in different animal models. It will be exciting to see which findings can be translated into future clinical studies and eventually lead to novel treatment approaches for human OA.

Inferring replication timing and proliferation dynamics from single-cell DNA sequencing data

Dysregulated DNA replication is a cause and a consequence of aneuploidy in cancer, yet the interplay between copy number alterations (CNAs), replication timing (RT) and cell cycle dynamics remain understudied in aneuploid tumors. We developed a probabilistic method, PERT, for simultaneous inference of cell-specific replication and copy number states from single-cell whole genome sequencing (scWGS) data. We used PERT to investigate clone-specific RT and proliferation dynamics in >50,000 cells obtained from aneuploid and clonally heterogeneous cell lines, xenografts and primary cancers. We observed bidirectional relationships between RT and CNAs, with CNAs affecting X-inactivation producing the largest RT shifts. Additionally, we found that clone-specific S-phase enrichment positively correlated with ground-truth proliferation rates in genomically stable but not unstable cells. Together, these results demonstrate robust computational identification of S-phase cells from scWGS data, and highlight the importance of RT and cell cycle properties in studying the genomic evolution of aneuploid tumors.

Spatial Transcriptomics of the Respiratory System.

Over the last decade, single-cell genomics has revealed remarkable heterogeneity and plasticity of cell types in the lungs and airways. The challenge now is to understand how these cell types interact in three-dimensional space to perform lung functions, facilitating airflow and gas exchange while simultaneously providing barrier function to avoid infection. An explosion in novel spatially resolved gene expression technologies, coupled with computational tools that harness machine learning and deep learning, now promise to address this challenge. Here, we review the most commonly used spatial analysis workflows, highlighting their advantages and limitations, and outline recent developments in machine learning and artificial intelligence that will augment how we interpret spatial data. Together these technologies have the potential to transform our understanding of the respiratory system in health and disease, and we showcase studies in lung development, COVID-19, lung cancer, and fibrosis where spatially resolved transcriptomics is already providing novel insights.

Energy Score Analysis in Immune Cells Linked to Atypical Hemolytic Uremic Syndrome Using Single-Cell Sequencing

Energy Score Analysis in Immune Cells Linked to Atypical Hemolytic Uremic Syndrome Using Single-Cell Sequencing

Research progress on single-cell expression quantitative trait loci.

Expression quantitative trait loci (eQTL) represent genetic variants that regulate gene expression levels. eQTL analysis has become a crucial method for identifying the functional roles of disease-associated genetic variants in the post-genome-wide association study (GWAS) era, yielding numerous significant discoveries. Traditional eQTL analysis relies on whole-genome sequencing combined with bulk RNA-seq, which obscures gene expression differences between cells and thus fails to identify cell type- or state-dependent eQTL. This limitation makes it challenging to elucidate the roles of disease-associated genetic variants under specific conditions. In recent years, with the development and widespread application of single-cell RNA sequencing (scRNA-seq) technology, scRNA-seq-based eQTL (sc-eQTL) research has emerged as a focal point. The advantage of this approach lies in its ability to leverage the resolution and granularity of single-cell sequencing to uncover eQTL that are dependent on cell type, cell state, and cellular dynamics. This significantly enhances our ability to analyze genetic variants associated with gene expression. Consequently, it holds substantial significance for advancing our understanding of the formation of complex organs and the mechanisms underlying disease onset, progression, intervention, and treatment. This review comprehensively examines the recent advancements in sc-eQTL studies, focusing on their development, experimental design strategies, modeling approaches, and current challenges. The aim is to offer researchers novel perspectives for identifying disease-associated loci and elucidating gene regulatory mechanisms.

Heterogeneity in extracellular matrix and immune microenvironment of anterior vaginal wall revealed by single-cell sequencing in women with stress urinary incontinence

Stress urinary incontinence (SUI), characterized by involuntary urine leakage during increased abdominal pressure, remains poorly understood regarding its pathophysiology and treatment. In this study, we utilized single-cell sequencing to analyze the transcriptomic profiles of different cell types in anterior vaginal wall of SUI patients, aiming to explore the heterogeneity of the extracellular matrix (ECM) and immune microenvironment in SUI pathogenesis. Our results identified eleven cell types, including connective tissue cells, immune cells, and glial cells. Specifically, fibroblasts, smooth muscle cells, epithelial cells and T cells displayed transcriptional characteristics highly relevant to SUI pathogenesis. We observed that most cell types participate in ECM metabolism and immune-inflammatory responses, indicating a synergistic role of multiple vaginal cell types in SUI. Furthermore, altered intercellular communication, particularly between fibroblasts and T cells, was noted in SUI. This study provides novel single-cell insights into SUI and identifies potential biomarkers and therapeutic targets for future research.

Dissecting the Single-Cell Diversity and Heterogeneity Underlying Cervical Precancerous Lesions and Cancer Tissues.

The underlying cellular diversity and heterogeneity from cervix precancerous lesions to cervical squamous cell carcinoma (CSCC) is investigated. Four single-cell datasets including normal tissues, normal adjacent tissues, precancerous lesions, and cervical tumors were integrated to perform disease stage analysis. Single-cell compositional data analysis (scCODA) was utilized to reveal the compositional changes of each cell type. Differentially expressed genes (DEGs) among cell types were annotated using BioCarta. An assay for transposase-accessible chromatin sequencing (ATAC-seq) analysis was performed to correlate epigenetic alterations with gene expression profiles. Lastly, a logistic regression model was used to assess the similarity between the original and new cohort data (HRA001742). After global annotation, seven distinct cell types were categorized. Eight consensus-upregulated DEGs were identified in B cells among different disease statuses, which could be utilized to predict the overall survival of CSCC patients. Inferred copy number variation (CNV) analysis of epithelial cells guided disease progression classification. Trajectory and ATAC-seq integration analysis identified 95 key transcription factors (TF) and one immunohistochemistry (IHC) testified key-node TF (YY1) involved in epithelial cells from CSCC initiation to progression. The consistency of epithelial cell subpopulation markers was revealed with single-cell sequencing, bulk sequencing, and RT-qPCR detection. KRT8 and KRT15, markers of Epi6, showed progressively higher expression with disease progression as revealed by IHC detection. The logistic regression model testified the robustness of the resemblance of clusters among the various datasets utilized in this study. Valuable insights into CSCC cellular diversity and heterogeneity provide a foundation for future targeted therapy.

Mechanisms of radiation-induced tissue damage and response.

Radiation-induced tissue injury (RITI) is the most common complication in clinical tumor radiotherapy. Due to the heterogeneity in the response of different tissues to radiation (IR), radiotherapy will cause different types and degrees of RITI, which greatly limits the clinical application of radiotherapy. Efforts are continuously ongoing to elucidate the molecular mechanism of RITI and develop corresponding prevention and treatment drugs for RITI. Single-cell sequencing (Sc-seq) has emerged as a powerful tool in uncovering the molecular mechanisms of RITI and for identifying potential prevention targets by enhancing our understanding of the complex intercellular relationships, facilitating the identification of novel cell phenotypes, and allowing for the assessment of cell heterogeneity and spatiotemporal developmental trajectories. Based on a comprehensive review of the molecular mechanisms of RITI, we analyzed the molecular mechanisms and regulatory networks of different types of RITI in combination with Sc-seq and summarized the targeted intervention pathways and therapeutic drugs for RITI. Deciphering the diverse mechanisms underlying RITI can shed light on its pathogenesis and unveil new therapeutic avenues to potentially facilitate the repair or regeneration of currently irreversible RITI. Furthermore, we discuss how personalized therapeutic strategies based on Sc-seq offer clinical promise in mitigating RITI.

Generation, expansion, gene delivery, and single-cell profiling in rhesus macaque plasma B cells.

A key step in developing engineered B cells for therapeutic purposes is evaluation in immunocompetent, large-animal models. Therefore, we developed methods to purify, expand, and differentiate non-human primate (NHP; rhesus macaque) B cells. After 7days in culture, B cells expanded 10-fold, differentiated into a plasma cell phenotype (CD38, CD138), and secreted immunoglobulin G. Using single-cell sequencing and flow cytometry, we verified the presence of plasma cell genes in differentiated NHP B cells and unearthed less-recognized markers, such as CD59 and CD79A. In contrast with human cells, we found that the immune checkpoint molecule CD274 (PD-L1) and major histocompatibility complex (MHC) class I molecules were upregulated in NHP plasma cells in the transcriptional data. Lastly, we established the conditions for efficient transduction of NHP B cells with adeno-associated virus (AAV) vectors, achieving a delivery rate of approximately 60%. We envision that this work will accelerate proof-of-concept studies using engineered B cells in NHPs.

Deciphering the Sertoli Cell Signaling Pathway with Protein-Protein Interaction, Single-Cell Sequencing, and Gene Ontology.

Spermatogenesis constitutes a complex and intricate cascade of differentiation, indispensable for the male reproductive competence. The intercellular communication conduits of Sertoli cells (SCs) are pivotal in orchestrating this cascade ensuring sustenance and development of germ cells. Single cells and bioinformatics recently demonstrated articles are used for the regulatory modalities through which SCs modulate spermatogenesis, specifically via androgen receptors (ARs), the transforming growth factor-beta/Smad axis, mitogen-activated protein kinases, cAMP/protein kinase A (PKA), phosphatidylinositol 4,5-bisphosphate 3-kinase (PI3k)/AKT serine threonine kinase (Akt), AMP-activated protein kinase, and AR pathways. Within this framework, homeostasis of gap junction dynamics, cryptic sites and the activities at tight junctions and adherens junctions, with the integrity of the testicular barrier, glucose assimilation, lactate distribution, being governed also along with SC maturation. Disruptions in activities or abnormal concentration in derangements in AR, cAMP/PKA, and PI3k/Akt pathways, and as well as the molecules that comprise them, would present male infertility.

ScDRMAE: integrating masked autoencoder with residual attention networks to leverage omics feature dependencies for accurate cell clustering.

Cell clustering is foundational for analyzing the heterogeneity of biological tissues using single-cell sequencing data. With the maturation of single-cell multi-omics sequencing technologies, we can integrate multiple omics data to perform cell clustering, thereby overcoming the limitations of insufficient information from single omics data. Existing methods for cell clustering often only consider the differences in data patterns during the analysis of multi-omics data, but the dependencies between omics features of different cell types also significantly influence cell clustering. Moreover, the high dropout rates in scRNA-seq and scATAC-seq data can impact the performance of cell clustering. We propose a cell clustering model based on a masked autoencoder, scDRMAE. Utilizing a masking mechanism, scDRMAE effectively learns the relationships between different features and imputes false zeros caused by dropout events. To differentiate the importance of various omics data in cell clustering, we dynamically adjust the weights of different omics data through an attention mechanism. Finally, we use the K-means algorithm for cluster analysis of the fused multi-omics data. On commonly used sets of 15 multi-omics datasets, our method demonstrates superior cell clustering performance on multiple metrics compared to other computational methods. In addition, when datasets exhibit varying degrees of dropout noise, our method shows better performance and stronger stability on multiple metrics compared to other methods. Moreover, by analyzing the cell clusters classified by scDRMAE, we identified several biologically significant biomarkers that have been validated, further confirming the effectiveness of scDRMAE in cell clustering from a biological perspective.

CopyMix: Mixture model based single-cell clustering and copy number profiling using variational inference

Investigating tumor heterogeneity using single-cell sequencing technologies is imperative to understand how tumors evolve since each cell subpopulation harbors a unique set of genomic features that yields a unique phenotype, which is bound to have clinical relevance. Clustering of cells based on copy number data obtained from single-cell DNA sequencing provides an opportunity to identify different tumor cell subpopulations. Accordingly, computational methods have emerged for single-cell copy number profiling and clustering; however, these two tasks have been handled sequentially by applying various ad-hoc pre- and post-processing steps; hence, a procedure vulnerable to introducing clustering artifacts. We avoid the clustering artifact issues in our method, CopyMix, a Variational Inference for a novel mixture model, by jointly inferring cell clusters and their underlying copy number profile. Our probabilistic graphical model is an improved version of the mixture of hidden Markov models, which is designed uniquely to infer single-cell copy number profiling and clustering. For the evaluation, we used likelihood-ratio test, CH index, Silhouette, V-measure, total variation scores. CopyMix performs well on both biological and simulated data. Our favorable results indicate a considerable potential to obtain clinical impact by using CopyMix in studies of cancer tumor heterogeneity.

Scifer: an R/Bioconductor package for large-scale integration of Sanger sequencing and flow cytometry data of index-sorted single cells

Sanger sequencing remains widely used in various experimental contexts, often in combination with flow cytometry for indexing specific cell populations. However, existing software lacks the capability to automate quality control (QC) of raw Sanger sequencing data and integrate it with flow cytometry information on a large scale. Here, we introduce scifer, an R package now available in the latest release of Bioconductor (3.18) showcasing its effectiveness in seamlessly integrating these types of data as demonstrated by analyses of B cell and T cell receptor sequences. Scifer preprocesses raw data from index sorts and immune receptor Sanger sequencing. It identifies high-quality sequences based on selected parameters, such as length, Phred scores, and heavy-chain complementarity-determining region 3 (HCDR3) quality. As a result, the quality of germline assignments is significantly increased and spurious variable gene mutations are reduced. Scifer is automated and can process thousands of sequences in less than an hour. Its output provides quality control reports, FASTA files, summarized tables, and electropherograms for manual inspection. In summary, scifer is a user-friendly software that speeds up the analysis of immune receptor repertoire sequences, offering wide applicability.

Single-Cell Transcriptome Sequencing Identifies New Molecular Markers for Pathogenicity of IgA Deposition in the Mesangial Region

Single-Cell Transcriptome Sequencing Identifies New Molecular Markers for Pathogenicity of IgA Deposition in the Mesangial Region

A-265 Single cell sequencing delineates T-cell clonality and pathogenesis of the parapsoriasis disease group

A-265 Single cell sequencing delineates T-cell clonality and pathogenesis of the parapsoriasis disease group