Single-cell Sequencing Research Articles

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.

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.

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.

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

Transient chromatin decompaction at the start of D. melanogaster male embryonic germline development.

Embryonic germ cells develop rapidly to establish the foundation for future developmental trajectories, and in this process, they make critical lineage choices including the configuration of their unique identity and a decision on sex. Here, we use single-cell genomics patterns for the entire embryonic germline in Drosophila melanogaster along with the somatic gonadal precursors after embryonic gonad coalescence to investigate molecular mechanisms involved in the setting up and regulation of the germline program. Profiling of the early germline chromatin landscape revealed sex- and stage-specific features. In the male germline immediately after zygotic activation, the chromatin structure underwent a brief remodeling phase during which nucleosome density was lower and deconcentrated from promoter regions. These findings echoed enrichment analysis results of our genomics data in which top candidates were factors with the ability to mediate large-scale chromatin reorganization. Together, they point to the importance of chromatin regulation in the early germline and raise the possibility of a conserved epigenetic reprogramming-like process required for proper initiation of germline development.

SNRPB2 in the pan-cancer landscape: A bioinformatics exploration and validation in hepatocellular carcinoma

Aberrant splicing is a significant contributor to gene expression abnormalities in cancer. SNRPB2, a component of U2 small nuclear ribonucleoprotein particles (snRNPs), contributes to the assembly of the spliceosome, the molecular machinery responsible for splicing. To date, few studies have investigated the role of SNRPB2 in tumorigenesis. We examined data sourced from various public databases, such as The Cancer Genome Atlas(TCGA), the Clinical Proteomic Tumor Analysis Consortium(CPTAC), and Gene Expression Omnibus(GEO). Our investigation included gene expression, genomic and epigenomic scrutiny, gene set enrichment assessment(GSEA), and immune cell infiltration evaluation. Furthermore, we performed empirical validation to ascertain the impact of SNRPB2 suppression on the proliferation and migration of liver cancer cells. Analysis of gene expression revealed widespread upregulation of SNRPB2 across a spectrum of cancer types, with heightened levels of SNRPB2 expression in numerous tumors linked to unfavorable prognosis. Genomic and epigenomic assessments revealed connections between SNRPB2 expression and variations in SNRPB2 copy number, DNA methylation patterns, and RNA modifications. Through gene set enrichment analysis, the involvement of SNRPB2 in vital biological processes and pathways related to cancer was identified. Furthermore, scrutiny of immune cell infiltration suggested a potential relationship between SNRPB2 and the tumor microenvironment, which was reinforced by multiple single-cell sequencing profiles. Subsequent experimental validation revealed that silencing SNRPB2 effectively impeded the proliferation and migration of liver cancer cells. Taken together, these findings underscore the prospective utility of SNRPB2 as a prognostic biomarker and a promising candidate for immunotherapy in cancer. It is necessary to engage in additional exploration into its underlying mechanisms and clinical treatment potential.

Unveiling the Enigmatic Role of SLC35F3 in Lung Adenocarcinoma.

The role of solute carrier family 35 member F3 (SLC35F3) in lung adenocarcinoma (LUAD) remains unclear. To address this gap, we conducted a study employing bioinformatics analysis and experimental validation. This study aimed to examine the expression patterns of SLC35F3 in various cancer types, particularly focusing on LUAD, by analyzing data from the Cancer Genome Atlas (TCGA) database to evaluate its clinical relevance. The research also explored potential regulatory mechanisms of SLC35F3, including its interactions with immune infiltration, tumor mutational burden (TMB), and drug sensitivity in LUAD. The investigation included analyzing SLC35F3 expression in single-cell sequencing of LUAD cells, examining genetic variations of SLC35F3 in LUAD, and assessing SLC35F3 expression in cell lines using quantitative real-time PCR (qRT-PCR). The aberrant expression of SLC35F3 was observed in both pan-cancer and LUAD. In LUAD patients, a statistically significant increase in SLC35F3 expression was correlated with gender (p < 0.001) and was associated with poorer overall survival (OS) (p = 0.020). The expression of SLC35F3 was identified as an independent prognostic determinant in patients with LUAD (p = 0.032). SLC35F3 exhibited associations with various pathways, including cell cycle and more. SLC35F3 expression demonstrated correlations with immune infiltration, TMB, and some drugs in LUAD. Results indicated significant upregulation of SLC35F3 in both LUAD tissues and cell lines. SLC35F3 may serve as a prognostic biomarker and immunotherapeutic target for patients with LUAD. Not applicable.

Novel Single-Cell Sequencing Methods for Detection of Mosaic Chromosomal Alterations in the Kidneys

Novel Single-Cell Sequencing Methods for Detection of Mosaic Chromosomal Alterations in the Kidneys

Regionalized cell and gene signatures govern esophageal epithelial homeostasis.

Regionalized disease prevalence is a common feature of the gastrointestinal tract. Herein, we employed regionally resolved Smart-seq3 single-cell sequencing, generating a comprehensive cell atlas of the adult mouse esophagus. Characterizing the esophageal axis, we identify non-uniform distribution of epithelial basal cells, fibroblasts, and immune cells. In addition, we demonstrate a position-dependent, but cell subpopulation-independent, transcriptional signature, collectively generating a regionalized esophageal landscape. Combining invivo models with organoid co-cultures, we demonstrate that proximal and distal basal progenitor cell states are functionally distinct. We find that proximal fibroblasts are more permissive for organoid growth compared with distal fibroblasts and that the immune cell profile is regionalized in two dimensions, where proximal-distal and epithelial-stromal gradients impact epithelial maintenance. Finally, we predict and verify how WNT, BMP, insulin growth factor (IGF), and neuregulin (NRG) signaling are differentially engaged along the esophageal axis. We establish a cellular and transcriptional framework for understanding esophageal regionalization, providing a functional basis for epithelial disease susceptibility.

Cancer-Associated Fibroblasts Expressing Sulfatase 1 Facilitate VEGFA-Dependent Microenvironmental Remodeling to Support Colorectal Cancer.

Tumor stroma plays a critical role in fostering tumor progression and metastasis. Cancer-associated fibroblasts (CAF) are a major component of the tumor stroma. Identifying the key molecular determinants for the protumor properties of CAFs could enable the development of more effective treatment strategies. In this study, through analyses of single-cell sequencing data, we identified a population of CAFs expressing high levels of sulfatase 1 (SULF1), which was associated with poor prognosis in patients with colorectal cancer. Colorectal cancer models using mice with conditional SULF1 knockout in fibroblasts revealed the tumor-supportive function of SULF1+ CAFs. Mechanistically, SULF1+ CAFs enhanced the release of VEGFA from heparan sulfate proteoglycan. The increased bioavailability of VEGFA initiated the deposition of extracellular matrix and enhanced angiogenesis. In addition, intestinal microbiota-produced butyrate suppressed SULF1 expression in CAFs through its histone deacetylase (HDAC) inhibitory activity. The insufficient butyrate production in patients with colorectal cancer increased the abundance of SULF1+ CAFs, thereby promoting tumor progression. Importantly, tumor growth inhibition by HDAC was dependent on SULF1 expression in CAFs, and patients with colorectal cancer with more SULF1+ CAFs were more responsive to treatment with the HDAC inhibitor chidamide. Collectively, these findings unveil the critical role of SULF1+ CAFs in colorectal cancer and provide a strategy to stratify patients with colorectal cancer for HDAC inhibitor treatment. Significance: SULF1+ cancer-associated fibroblasts play a tumor-promoting role in colorectal cancer by stimulating extracellular matrix deposition and angiogenesis and can serve as a biomarker for the therapeutic response to HDAC inhibitors in patients.

Single-Cell Sequencing Detects Enrichment of ESKD Biomarkers in Injured Proximal Tubule

Single-Cell Sequencing Detects Enrichment of ESKD Biomarkers in Injured Proximal Tubule