Single-cell RNA sequencing: Technical advancements and biological applications

Single-cell RNA sequencing reveals heterogeneity and differential expression of the maternal-fetal interface during ICP and normal pregnancy

OBJECTIVE

Intrahepatic cholestasis of pregnancy (ICP) can cause adverse perinatal outcomes. Previous studies have demonstrated that the placenta of an ICP pregnancy differs in morphology and gene expression from the placenta of a normal pregnancy. To date, however, the genetic mechanism by which ICP affects the placenta is poorly understood. Therefore, the aim of this study was to investigate the differences in main cell types, gene signatures, cell ratio, and functional changes in the placenta between ICP and normal pregnancy.

METHODS

Single-cell RNA sequencing (scRNA-seq) technology was used to detect the gene expression of all cells at the placental maternal-fetal interface. Two individuals were analyzed - one with ICP and one without ICP. The classification of cell types was determined by a graph-based clustering algorithm. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using the R software phyper () function and DAVID website. The differentially expressed genes (DEGs) encoding transcription factors (TFs) were identified using getorf and DIAMOND software.

RESULTS

We identified 14 cell types and 22 distinct cell subtypes that showed unique functional properties. Additionally, we found differences in the proportions of fibroblasts 1, helper T (Th) cells, extravillous trophoblasts, and villous cytotrophoblasts, and we observed heterogeneity of gene expression between ICP and control placentas. Furthermore, we identified 263 DEGs that belonged to TF families, including zf-C2H2, HMGI/HMGY, and Homeobox. In addition, 28 imprinted genes were preferentially expressed in specific cell types, such as PEG3 and PEG10 in trophoblasts as well as DLK1 and DIO3 in fibroblasts.

CONCLUSIONS

Our results revealed the differences in cell-type ratios, gene expression, and functional changes between ICP and normal placentas, and heterogeneity was found among cell subgroups. Hence, the imbalance of various cell types affects placental activity to varying degrees, indicating the complexity of the cell networks that form the placental tissue system, and this alteration of placental function is associated with adverse events in the perinatal period.

Single-cell atlas of healthy vocal folds and cellular function in the endothelial-to-mesenchymal transition

The vocal fold is an architecturally complex organ comprising a heterogeneous mixture of various layers of individual epithelial and mesenchymal cell lineages. Here we performed single-cell RNA sequencing profiling of 5836 cells from the vocal folds of adult Sprague-Dawley rats. Combined with immunostaining, we generated a spatial and transcriptional map of the vocal fold cells and characterized the subpopulations of epithelial cells, mesenchymal cells, endothelial cells, and immune cells. We also identified a novel epithelial-to-mesenchymal transition-associated epithelial cell subset that was mainly found in the basal epithelial layers. We further confirmed that this subset acts as intermediate cells with similar genetic features to epithelial-to-mesenchymal transition in head and neck squamous cell carcinoma. Finally, we present the complex intracellular communication network involved homeostasis using CellChat analysis. These studies define the cellular and molecular framework of the biology and pathology of the VF mucosa and reveal the functional importance of developmental pathways in pathological states in cancer.

Single-Cell RNA Sequencing Deconstructs the Distribution of Immune Cells Within Abdominal Aortic Aneurysms in Mice

BACKGROUND

Inflammation is a key component in the development of abdominal aortic aneurysm (AAA), yet insights into the roles of immune cells and their interactions in this process are limited.

METHODS

Using single-cell RNA transcriptomic analysis, we deconstructed the CD45+ cell population in elastase-induced murine AAA at the single-cell level. We isolated each group of immune cells from murine AAA tissue at different time points and divided them into several subtypes, listed the remarkable differentially expressed genes, explored the developmental trajectories of immune cells, and demonstrated the interactions among them.

RESULTS

Our findings reveal significant differences in several immune cell subsets, including macrophages, dendritic cells, and T cells, within the AAA microenvironment compared with the normal aorta. Especially, conventional dendritic cell type 1 exclusively existed in the AAA tissue rather than the normal aortas. Via CellChat analysis, we identified several intercellular communication pathways like visfatin, which targets monocyte differentiation and neutrophil extracellular trap-mediated interaction between neutrophils and dendritic cells, which might contribute to AAA development. Some of these pathways were validated in human AAA.

CONCLUSIONS

Despite the absence of external pathogenic stimuli, AAA tissues develop a complex inflammatory microenvironment involving numerous immune cells. In-depth studies of the inflammatory network shall provide new strategies for patients with AAA.

Single-cell sequencing reveals glial cell involvement in development of neuropathic pain via myelin sheath lesion formation in the spinal cord

BACKGROUND

Neuropathic pain (NP), which results from injury or lesion of the somatosensory nervous system, is intimately associated with glial cells. The roles of microglia and astrocytes in NP have been broadly described, while studies on oligodendrocytes have largely focused on axonal myelination. The mechanisms of oligodendrocytes and their interactions with other glial cells in NP development remain uncertain.

METHODS

To explore the function of the interaction of the three glial cells and their interactions on myelin development in NP, we evaluated changes in NP and myelin morphology after a chronic constriction injury (CCI) model in mice, and used single-cell sequencing to reveal the subpopulations characteristics of oligodendrocytes, microglia, and astrocytes in the spinal cord tissues, as well as their relationship with myelin lesions; the proliferation and differentiation trajectories of oligodendrocyte subpopulations were also revealed using pseudotime cell trajectory and RNA velocity analysis. In addition, we identified chemokine ligand-receptor pairs between glial cells by cellular communication and verified them using immunofluorescence.

RESULTS

Our study showed that NP peaked on day 7 after CCI in mice, a time at which myelin lesions were present in both the spinal cord and sciatic nerve. Oligodendrocytes, microglia, and astrocytes subpopulations in spinal cord tissue were heterogeneous after CCI and all were involved in suppressing the process of immune defense and myelin production. In addition, the differentiation trajectory of oligodendrocytes involved a unidirectional lattice process of OPC-1-Oligo-9, which was arrested at the Oligo-2 stage under the influence of microglia and astrocytes. And the CADM1-CADM1, NRP1-VEGFA interactions between glial cells are enhanced after CCI and they had a key role in myelin lesions and demyelination.

CONCLUSIONS

Our study reveals the close relationship between the differentiation block of oligodendrocytes after CCI and their interaction with microglia and astrocytes-mediated myelin lesions and NP. CADM1/CADM1 and NRP-1/VEGFA may serve as potential therapeutic targets for use in the treatment of NP.

Single-nucleus multiomics unravels the genetic mechanisms underlying musk secretion in Chinese forest musk deer (Moschus berezovskii)

Musk secreted by the musk glands in male forest musk deer (FMD; Moschus berezovskii) is highly valued for its pharmaceutical and perfumery applications. However, the regulatory mechanisms underlying musk secretion are not well understood. This study aimed to investigate the genes and transcription factors involved in musk secretion across different periods and ages. We analyzed the musk glands of adult male FMD during the non-secretory and secretory periods, as well as juvenile and adult male FMD during the secretory period, using single-cell multiome ATAC+gene expression technique. Our analysis identified 13 cell types, including acinar cells of Types 1 and 2. Chromatin accessibility analysis and gene expression data confirmed that the genes Map3k2, Hsd17b12, and Jun are critical for musk secretion. Additionally, EHF, NR4A2, and FOXO1 proteins play crucial regulatory roles. Weighted gene co-expression network analysis (WGCNA) highlighted the importance of GnRH signaling pathway in musk secretion. Gene set enrichment analysis (GSEA) showed that the steroid hormone biosynthesis pathway is notably enriched in acinar cells. Furthermore, intercellular communication appears to influence both the initiation and maintenance of musk secretion. These findings provide valuable insights into the molecular pathways of musk secretion in FMD, offering potential avenues for increasing musk production and developing treatment for inflammation and tumors.

Machine learning algorithms integrate bulk and single-cell RNA data to unveil oxidative stress following intracerebral hemorrhage

BACKGROUND

Increased oxidative stress (OS) activity following intracerebral hemorrhage (ICH) had significantly impacting patient prognosis. Identifying optimal genes associated with OS could enhance the understanding of OS after ICH.

METHODS

We employed single-cell RNA sequencing (scRNA-seq) to investigate the heterogeneity of OS across various cellular tiers following ICH, aiming to acquire biological insights into ICH. We utilized AUCell, Ucell, singscore, ssgsea, and AddModuleScore algorithms, along with correlation analysis, to identify hub genes influencing high OS post-ICH. Furthermore, we employed four machine learning algorithms, eXtreme Gradient Boosting, Boruta, Random Forest, and Least Absolute Shrinkage and Selection Operator, to identify the optimal feature genes. To validate the accuracy of our analysis, we conducted validation in ICH animal experiments.

RESULTS

After analyzing the scRNA-seq dataset using various algorithms, we found that OS activity exhibited heterogeneity across different cellular layers following ICH, with particularly heightened activity observed in monocytes. Further integration of bulk data and machine learning algorithms revealed that ANXA2 and COTL1 were closely associated with high OS after ICH. Our animal experiments demonstrated an increase in OS expression post-ICH. Additionally, the protein expression of ANXA2 and COTL1 was significantly elevated and co-localized with microglia. Pearson correlation coefficient analysis revealed a significant correlation between ANXA2 and OS, indicating strong consistency (r = 0.84, p < 0.05). Similar results were observed for COTL1 and OS (r = 0.69, p < 0.05).

CONCLUSIONS

Following ICH, ANXA2 and COTL1 might penetrate the brain via monocytes, localize within microglia, and enhance OS activity. This might help us better understand OS after ICH.

Single-Cell RNA Sequencing Reveals a Unique Fibroblastic Subset and Immune Disorder in Lichen Sclerosus Urethral Stricture

Purpose

Lichen sclerosus urethral stricture disease (LS USD) is a refractory and progressive disease primarily affecting the anterior urethra in males. Various potential etiological factors, such as genetics, autoimmunity, infection, and exposure to infectious urine, have been suggested. However, the accurate etiology of LS in the male urethra remains unclear.

Patients and Methods

In this study, we conducted single-cell RNA sequencing to identify the transcriptional profiles of three patients with LS USD and three patients with non-LS USD. Immunofluorescence was used to confirm the single-cell sequence results.

Results

Our study revealed distinct subsets of vein endothelial cells (ECs), smooth muscle cells (SMCs), and fibroblasts (FBs) with high proportions in LS USD, contributing to the tissue microenvironment primarily involved in proinflammatory and immune responses. In particular, FBs displayed a unique subset, Fib7, which is exclusively present in LS USD, and exhibited high expression levels of SAA1 and SAA2. The accumulation of macrophages, along with the dysregulated ratios of M1/M2-like phenotype macrophages, may be engaged in the pathogenesis of LS USD. Through cell-cell communication analysis, we identified significant interactions involving CXCL8/ACKR1 and CCR7/CCL19 in LS USD. Remarkably, Fib7 exhibited exclusive communication with IL-1B macrophages through the SAA1/FPR2 receptor-ligand pair.

Conclusion

Our study provides a profound understanding of the tissue microenvironment in LS USD, which may be valuable for understanding the pathogenesis of LS USD.

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.

Prognostic model of fibroblasts in idiopathic pulmonary fibrosis by combined bulk and single-cell RNA-sequencing

Background

Fibroblasts play an important role in the development of idiopathic pulmonary fibrosis (IPF).

Methods

We employed single-cell RNA-sequencing data obtained from the Gene Expression Omnibus database to perform cell clustering and annotation analyses. We then performed secondary clustering of fibroblasts and conducted functional enrichment and cell trajectory analyses of the two newly defined fibroblast subtypes. Bulk RNA-sequencing data were used to perform consensus clustering and weighted gene co-expression network analysis. We constructed a fibroblast-related prognostic model using least absolute shrinkage, selection operator regression, and Cox regression analysis. The prognostic model was validated using a validation dataset. Immune infiltration and functional enrichment analyses were conducted for patients in the high- and low-risk IPF groups.

Results

We characterized two fibroblast subtypes that are active in IPF (F3+ and ROBO2+). Using fibroblast-related genes, we identified five genes (CXCL14, TM4SF1, CYTL1, SOD3, and MMP10) for the prognostic model. The area under the curve values of our prognostic model were 0.852, 0.859, and 0.844 at one, two, and three years in the training set, and 0.837, 0.758, and 0.821 at one, two, and three years in the validation set, respectively.

Conclusion

This study annotates and characterizes different subtypes of fibroblasts in IPF.

scRNA sequencing technology for PitNET studies

Pituitary neuroendocrine tumors (PitNETs) are common, most likely benign tumors with complex clinical characteristics related to hormone hypersecretion and/or growing sellar tumor mass. PitNET types are classified according to their expression of specific transcriptional factors (TFs) and hormone secretion levels. Some types show aggressive, invasive, and reoccurrence behavior. Current research is being conducted to understand the molecular mechanisms regulating these high-heterogeneous neoplasms originating from adenohypophysis, and single-cell RNA sequencing (scRNA-seq) technology is now playing an essential role in these studies due to its remarkable resolution at the single-cell level. This review describes recent studies on human PitNETs performed with scRNA-seq technology, highlighting the potential of this approach in revealing these tumor pathologies, behavior, and regulatory mechanisms.

Single-cell omics: experimental workflow, data analyses and applications

Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.

Single-cell technologies in psoriasis

Psoriasis is a chronic and recurrent inflammatory skin disorder. The primary manifestation of psoriasis arises from disturbances in the cutaneous immune microenvironment, but the specific functions of the cellular components within this microenvironment remain unknown. Recent advancements in single-cell technologies have enabled the detection of multi-omics at the level of individual cells, including single-cell transcriptome, proteome, and metabolome, which have been successfully applied in studying autoimmune diseases, and other pathologies. These techniques allow the identification of heterogeneous cell clusters and their varying contributions to disease development. Considering the immunological traits of psoriasis, an in-depth exploration of immune cells and their interactions with cutaneous parenchymal cells can markedly advance our comprehension of the mechanisms underlying the onset and recurrence of psoriasis. In this comprehensive review, we present an overview of recent applications of single-cell technologies in psoriasis, aiming to improve our understanding of the underlying mechanisms of this disorder.

Advance and Application of Single-cell Transcriptomics in Auditory Research

Hearing loss and deafness, as a worldwide disability disease, have been troubling human beings. However, the auditory organ of the inner ear is highly heterogeneous and has a very limited number of cells, which are largely uncharacterized in depth. Recently, with the development and utilization of single-cell RNA sequencing (scRNA-seq), researchers have been able to unveil the complex and sophisticated biological mechanisms of various types of cells in the auditory organ at the single-cell level and address the challenges of cellular heterogeneity that are not resolved through by conventional bulk RNA sequencing (bulk RNA-seq). Herein, we reviewed the application of scRNA-seq technology in auditory research, with the aim of providing a reference for the development of auditory organs, the pathogenesis of hearing loss, and regenerative therapy. Prospects about spatial transcriptomic scRNA-seq, single-cell based genome, and Live-seq technology will also be discussed.

Persistent transcriptional changes in cardiac adaptive immune cells following myocardial infarction: New evidence from the re-analysis of publicly available single cell and nuclei RNA-sequencing data sets

INTRODUCTION

Chronic immunopathology contributes to the development of heart failure after a myocardial infarction. Both T and B cells of the adaptive immune system are present in the myocardium and have been suggested to be involved in post-MI immunopathology.

METHODS

We analyzed the B and T cell populations isolated from previously published single cell RNA-sequencing data sets (PMID: 32130914, PMID: 35948637, PMID: 32971526 and PMID: 35926050), of the mouse and human heart, using differential expression analysis, functional enrichment analysis, gene regulatory inferences, and integration with autoimmune and cardiovascular GWAS.

RESULTS

Already at baseline, mature effector B and T cells are present in the human and mouse heart, having increased activity in transcription factors maintaining tolerance (e.g. DEAF1, JDP2, SPI-B). Following MI, T cells upregulate pro-inflammatory transcript levels (e.g. Cd11, Gzmk, Prf1), while B cells upregulate activation markers (e.g. Il6, Il1rn, Ccl6) and collagen (e.g. Col5a2, Col4a1, Col1a2). Importantly, pro-inflammatory and fibrotic transcription factors (e.g. NFKB1, CREM, REL) remain active in T cells, while B cells maintain elevated activity in transcription factors related to immunoglobulin production (e.g. ERG, REL) in both mouse and human post-MI hearts. Notably, genes differentially expressed in post-MI T and B cells are associated with cardiovascular and autoimmune disease.

CONCLUSION

These findings highlight the varied and time-dependent dynamic roles of post-MI T and B cells. They appear ready-to-go and are activated immediately after MI, thus participate in the acute wound healing response. However, they subsequently remain in a state of pro-inflammatory activation contributing to persistent immunopathology.

A bibliometric study of the intellectual base and global research hotspots for single-cell sequencing [2009-2022] in breast cancer

Background

Breast cancer is the most widespread malignant tumor worldwide. Single-cell sequencing technology offers novel insights and methods to understand the onset, progression, and treatment of tumors. Nevertheless, there is currently an absence of a thorough and unbiased report on the comprehensive research status of single-cell sequencing in breast cancer. This study seeks to summarize and quantify the dynamics and trends of research on breast cancer single-cell sequencing by bibliometric analysis.

Methods

Research articles and reviews related to breast cancer single-cell sequencing were selected from the WoSCC database. Visualization of data regarding countries, institutions, authors, references, and keywords was performed by CiteSpace and VOSviewer software.

Results

583 articles and reviews were analyzed in this study. The quantity of publications related to breast cancer single-cell sequencing has been increasing annually. These studies originate from 302 institutions in 46 countries, with YMAX S WICHA producing the most publications and WANG Y being the most cited author. Nature Communications is the most researched journal, while Nature holds the highest number of citations. These journals predominantly cover topics in the molecular/biological/immunological fields. Moreover, an analysis of reference and keyword bursts revealed that current research trends in this area are primarily centered on "clonal evolution," "tumor microenvironment," and "immunotherapy."

Conclusion

Breast cancer single-cell sequencing is a rapidly growing area of scientific interest. Future research requires more frequent and in-depth collaborations among countries, institutions, and authors. Furthermore, "clonal evolution," "tumor microenvironment," and "immunotherapy" are likely to become major focal points in upcoming research on breast cancer single-cell sequencing.

Optimal sequencing budget allocation for trajectory reconstruction of single cells

BACKGROUND

Charting cellular trajectories over gene expression is key to understanding dynamic cellular processes and their underlying mechanisms. While advances in single-cell RNA-sequencing technologies and computational methods have pushed forward the recovery of such trajectories, trajectory inference remains a challenge due to the noisy, sparse, and high-dimensional nature of single-cell data. This challenge can be alleviated by increasing either the number of cells sampled along the trajectory (breadth) or the sequencing depth, i.e. the number of reads captured per cell (depth). Generally, these two factors are coupled due to an inherent breadth-depth tradeoff that arises when the sequencing budget is constrained due to financial or technical limitations.

RESULTS

Here we study the optimal allocation of a fixed sequencing budget to optimize the recovery of trajectory attributes. Empirical results reveal that reconstruction accuracy of internal cell structure in expression space scales with the logarithm of either the breadth or depth of sequencing. We additionally observe a power law relationship between the optimal number of sampled cells and the corresponding sequencing budget. For linear trajectories, non-monotonicity in trajectory reconstruction across the breadth-depth tradeoff can impact downstream inference, such as expression pattern analysis along the trajectory. We demonstrate these results for five single-cell RNA-sequencing datasets encompassing differentiation of embryonic stem cells, pancreatic beta cells, hepatoblast and multipotent hematopoietic cells, as well as induced reprogramming of embryonic fibroblasts into neurons. By addressing the challenges of single-cell data, our study offers insights into maximizing the efficiency of cellular trajectory analysis through strategic allocation of sequencing resources.

Advances in integrating single-cell sequencing data to unravel the mechanism of ferroptosis in cancer

Ferroptosis, a commonly observed type of programmed cell death caused by abnormal metabolic and biochemical mechanisms, is frequently triggered by cellular stress. The occurrence of ferroptosis is predominantly linked to pathophysiological conditions due to the substantial impact of various metabolic pathways, including fatty acid metabolism and iron regulation, on cellular reactions to lipid peroxidation and ferroptosis. This mode of cell death serves as a fundamental factor in the development of numerous diseases, thereby presenting a range of therapeutic targets. Single-cell sequencing technology provides insights into the cellular and molecular characteristics of individual cells, as opposed to bulk sequencing, which provides data in a more generalized manner. Single-cell sequencing has found extensive application in the field of cancer research. This paper reviews the progress made in ferroptosis-associated cancer research using single-cell sequencing, including ferroptosis-associated pathways, immune checkpoints, biomarkers, and the identification of cell clusters associated with ferroptosis in tumors. In general, the utilization of single-cell sequencing technology has the potential to contribute significantly to the investigation of the mechanistic regulatory pathways linked to ferroptosis. Moreover, it can shed light on the intricate connection between ferroptosis and cancer. This technology holds great promise in advancing tumor-wide diagnosis, targeted therapy, and prognosis prediction.

Macrophage migration inhibitory factor mediates skin aging via CD74: Insights from single-cell and bulk RNA sequencing data

Cell-cell communication is crucial for regulating signaling and cellular function. However, the precise cellular and molecular changes remain poorly understood in skin aging. Based on single-cell and bulk RNA data, we explored the role of cell-cell ligand-receptor interaction in skin aging. We found that the macrophage migration inhibitory factor (MIF)/CD74 ligand-receptor complex was significantly upregulatedin aged skin, showing the predominant paracrine effect of keratinocytes on fibroblasts. Enrichment analysis and in vitro experiment revealed a close association of the activation of the MIF/CD74 with inflammatory pathways and immune response. Mechanistically, MIF/CD74 could significantly inhibit PPARγ protein, which thus significantly increased the degree of fibroblast senescence, and significantly up-regulated the expression of senescence-associated secretory phenotype (SASP) factors and FOS gene. Therefore, our study reveals that MIF/CD74 inhibits the activation of the PPAR signaling pathway, subsequently inducing the production of SASP factors and the upregulation of FOS expression, ultimately accelerating fibroblast senescence.

Integrated Analysis Construct a Tumor-Associated Macrophage Novel Signature with Promising Implications in Predicting the Prognosis and Immunotherapeutic Response of Gastric Cancer Patients

BACKGROUND

Gastric cancer (GC) remains one of the most prevalent malignant tumors worldwide. At present, tumor-associated macrophages (TAMs) are essential in the progression, metastasis, and drug resistance of tumors. Therefore, TAMs can be a crucial target for tumor treatment.

AIMS

We intended to investigate the TAM characteristics in GC and develop a risk signature based on TAM to predict the prognosis of GC patients.

METHODS

The single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data were acquired from a publicly available database. We utilized the Seurat pipeline to process the scRNA-seq data and determine TAM cell types using marker genes. Univariate Cox regression analysis was utilized to examine TAM-related prognostic genes, and then we employed Lasso-Cox regression analysis, and Multivariate Cox regression analysis established a novel risk profile to forecast the clinical value of the model with a new nomogram combining risk profiles and clinicopathological characteristics.

RESULTS

The current study employed scRNA-seq data to identify five TAM clusters in GC, among which four were significantly associated with GC prognosis. Accordingly, we further developed a TAM-related risk signature utilizing nine genes. After evaluation, our model accurately predicted the prognosis of gastric cancer. Generally, GC patients with low TAMS scores exhibited a more favorable prognosis, greater benefits from immunotherapy, and higher levels of immune cell infiltration.

CONCLUSIONS

The prognosis of GC can be effectively predicted by TAM-based risk signatures, and the signature may provide a new perspective for comprehensively guiding clinical diagnosis, prediction, and immunotherapy for gastric cancer.

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

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

SCSMD: Single Cell Consistent Clustering based on Spectral Matrix Decomposition

Cluster analysis, a pivotal step in single-cell sequencing data analysis, presents substantial opportunities to effectively unveil the molecular mechanisms underlying cellular heterogeneity and intercellular phenotypic variations. However, the inherent imperfections arise as different clustering algorithms yield diverse estimates of cluster numbers and cluster assignments. This study introduces Single Cell Consistent Clustering based on Spectral Matrix Decomposition (SCSMD), a comprehensive clustering approach that integrates the strengths of multiple methods to determine the optimal clustering scheme. Testing the performance of SCSMD across different distances and employing the bespoke evaluation metric, the methodological selection undergoes validation to ensure the optimal efficacy of the SCSMD. A consistent clustering test is conducted on 15 authentic scRNA-seq datasets. The application of SCSMD to human embryonic stem cell scRNA-seq data successfully identifies known cell types and delineates their developmental trajectories. Similarly, when applied to glioblastoma cells, SCSMD accurately detects pre-existing cell types and provides finer sub-division within one of the original clusters. The results affirm the robust performance of our SCSMD method in terms of both the number of clusters and cluster assignments. Moreover, we have broadened the application scope of SCSMD to encompass larger datasets, thereby furnishing additional evidence of its superiority. The findings suggest that SCSMD is poised for application to additional scRNA-seq datasets and for further downstream analyses.

Advanced sequencing-based high-throughput and long-read single-cell transcriptome analysis

Cells are the fundamental building blocks of living systems, exhibiting significant heterogeneity. The transcriptome connects the cellular genotype and phenotype, and profiling single-cell transcriptomes is critical for uncovering distinct cell types, states, and the interplay between cells in development, health, and disease. Nevertheless, single-cell transcriptome analysis faces daunting challenges due to the low abundance and diverse nature of RNAs in individual cells, as well as their heterogeneous expression. The advent and continuous advancements of next-generation sequencing (NGS) and third-generation sequencing (TGS) technologies have solved these problems and facilitated the high-throughput, sensitive, full-length, and rapid profiling of single-cell RNAs. In this review, we provide a broad introduction to current methodologies for single-cell transcriptome sequencing. First, state-of-the-art advancements in high-throughput and full-length single-cell RNA sequencing (scRNA-seq) platforms using NGS are reviewed. Next, TGS-based long-read scRNA-seq methods are summarized. Finally, a brief conclusion and perspectives for comprehensive single-cell transcriptome analysis are discussed.

Advances in sequencing and omics studies in prostate cancer: unveiling molecular pathogenesis and clinical applications

Background

Prostate cancer (PCa) is one of the most threatening health problems for the elderly males. However, our understanding of the disease has been limited by the research technology for a long time. Recently, the maturity of sequencing technology and omics studies has been accelerating the studies of PCa, establishing themselves as an essential impetus in this field.

Methods

We assessed Web of Science (WoS) database for publications of sequencing and omics studies in PCa on July 3rd, 2023. Bibliometrix was used to conduct ulterior bibliometric analysis of countries/affiliations, authors, sources, publications, and keywords. Subsequently, purposeful large amounts of literature reading were proceeded to analyze research hotspots in this field.

Results

3325 publications were included in the study. Research associated with sequencing and omics studies in PCa had shown an obvious increase recently. The USA and China were the most productive countries, and harbored close collaboration. CHINNAIYAN AM was identified as the most influential author, and CANCER RESEARCH exhibited huge impact in this field. Highly cited publications and their co-citation relationships were used to filtrate literatures for subsequent literature reading. Based on keyword analysis and large amounts of literature reading, 'the molecular pathogenesis of PCa' and 'the clinical application of sequencing and omics studies in PCa' were summarized as two research hotspots in the field.

Conclusion

Sequencing technology had a deep impact on the studies of PCa. Sequencing and omics studies in PCa helped researchers reveal the molecular pathogenesis, and provided new possibilities for the clinical practice of PCa.

scTPC: a novel semisupervised deep clustering model for scRNA-seq data

MOTIVATION

Continuous advancements in single-cell RNA sequencing (scRNA-seq) technology have enabled researchers to further explore the study of cell heterogeneity, trajectory inference, identification of rare cell types, and neurology. Accurate scRNA-seq data clustering is crucial in single-cell sequencing data analysis. However, the high dimensionality, sparsity, and presence of "false" zero values in the data can pose challenges to clustering. Furthermore, current unsupervised clustering algorithms have not effectively leveraged prior biological knowledge, making cell clustering even more challenging.

RESULTS

This study investigates a semisupervised clustering model called scTPC, which integrates the triplet constraint, pairwise constraint, and cross-entropy constraint based on deep learning. Specifically, the model begins by pretraining a denoising autoencoder based on a zero-inflated negative binomial distribution. Deep clustering is then performed in the learned latent feature space using triplet constraints and pairwise constraints generated from partial labeled cells. Finally, to address imbalanced cell-type datasets, a weighted cross-entropy loss is introduced to optimize the model. A series of experimental results on 10 real scRNA-seq datasets and five simulated datasets demonstrate that scTPC achieves accurate clustering with a well-designed framework.

AVAILABILITY AND IMPLEMENTATION

scTPC is a Python-based algorithm, and the code is available from https://github.com/LF-Yang/Code or https://zenodo.org/records/10951780.

Single-cell transcriptomics: background, technologies, applications, and challenges

Single-cell sequencing was developed as a high-throughput tool to elucidate unusual and transient cell states that are barely visible in the bulk. This technology reveals the evolutionary status of cells and differences between populations, helps to identify unique cell subtypes and states, reveals regulatory relationships between genes, targets and molecular mechanisms in disease processes, tumor heterogeneity, the state of the immune environment, etc. However, the high cost and technical limitations of single-cell sequencing initially prevented its widespread application, but with advances in research, numerous new single-cell sequencing techniques have been discovered, lowering the cost barrier. Many single-cell sequencing platforms and bioinformatics methods have recently become commercially available, allowing researchers to make fascinating observations. They are now increasingly being used in various industries. Several protocols have been discovered in this context and each technique has unique characteristics, capabilities and challenges. This review presents the latest advancements in single-cell transcriptomics technologies. This includes single-cell transcriptomics approaches, workflows and statistical approaches to data processing, as well as the potential advances, applications, opportunities and challenges of single-cell transcriptomics technology. You will also get an overview of the entry points for spatial transcriptomics and multi-omics.

Comparative analysis of 10X Chromium vs. BD Rhapsody whole transcriptome single-cell sequencing technologies in complex human tissues

The development of single-cell omics tools has enabled scientists to study the tumor microenvironment (TME) in unprecedented detail. However, each of the different techniques may have its unique strengths and limitations. Here we directly compared two commercially available high-throughput single-cell RNA sequencing (scRNA-seq) technologies - droplet-based 10X Chromium vs. microwell-based BD Rhapsody - using paired samples from patients with localized prostate cancer (PCa) undergoing a radical prostatectomy. Although high technical consistency was observed in unraveling the whole transcriptome, the relative abundance of cell populations differed. Cells with low mRNA content such as T cells were underrepresented in the droplet-based system, at least partly due to lower RNA capture rates. In contrast, microwell-based scRNA-seq recovered less cells of epithelial origin. Moreover, we discovered platform-dependent variabilities in mRNA quantification and cell-type marker annotation. Overall, our study provides important information for selection of the appropriate scRNA-seq platform and for the interpretation of published results.

Split Pool Ligation-based Single-cell Transcriptome sequencing (SPLiT-seq) data processing pipeline comparison

BACKGROUND

Single-cell sequencing techniques are revolutionizing every field of biology by providing the ability to measure the abundance of biological molecules at a single-cell resolution. Although single-cell sequencing approaches have been developed for several molecular modalities, single-cell transcriptome sequencing is the most prevalent and widely applied technique. SPLiT-seq (split-pool ligation-based transcriptome sequencing) is one of these single-cell transcriptome techniques that applies a unique combinatorial-barcoding approach by splitting and pooling cells into multi-well plates containing barcodes. This unique approach required the development of dedicated computational tools to preprocess the data and extract the count matrices. Here we compare eight bioinformatic pipelines (alevin-fry splitp, LR-splitpipe, SCSit, splitpipe, splitpipeline, SPLiTseq-demultiplex, STARsolo and zUMI) that have been developed to process SPLiT-seq data. We provide an overview of the tools, their computational performance, functionality and impact on downstream processing of the single-cell data, which vary greatly depending on the tool used.

RESULTS

We show that STARsolo, splitpipe and alevin-fry splitp can all handle large amount of data within reasonable time. In contrast, the other five pipelines are slow when handling large datasets. When using smaller dataset, cell barcode results are similar with the exception of SPLiTseq-demultiplex and splitpipeline. LR-splitpipe that is originally designed for processing long-read sequencing data is the slowest of all pipelines. Alevin-fry produced different down-stream results that are difficult to interpret. STARsolo functions nearly identical to splitpipe and produce results that are highly similar to each other. However, STARsolo lacks the function to collapse random hexamer reads for which some additional coding is required.

CONCLUSION

Our comprehensive comparative analysis aids users in selecting the most suitable analysis tool for efficient SPLiT-seq data processing, while also detailing the specific prerequisites for each of these pipelines. From the available pipelines, we recommend splitpipe or STARSolo for SPLiT-seq data analysis.

The Impact of FBN1-α5β1 Axis in Fibro/Adipogenic Progenitor Cells (FAPCD9-) on Intramuscular Fat Content in Pigs

Intramuscular fat (IMF) plays a crucial role in enhancing meat quality, enriching meat flavor, and overall improving palatability. In this study, Single-cell RNA sequencing was employed to analyze the longissimus dorsi (LD) obtained from Guangdong small-ear spotted pigs (GDSS, with high IMF) and Yorkshire pigs (YK, with low IMF). GDSS had significantly more Fibro/Adipogenic Progenitor (FAPs), in which the CD9 negative FAPs (FAPCD9-) having adipogenic potential, as demonstrated by in vitro assays using cells originated from mouse muscle. On the other hand, Yorkshire had more fibro-inflammatory progenitors (FIPs, marked with FAPCD9+), presenting higher expression of the FBN1-Integrin α5β1. FBN1-Integrin α5β1 could inhibit insulin signaling in FAPCD9-, suppressing adipogenic differentiation. Our results demonstrated that fat-type pigs possess a greater number of FAPCD9-, which are the exclusive cells in muscle capable of differentiating into adipocytes. Moreover, lean-type pigs exhibit higher expression of FBN1-Integrin α5β1 axis, which inhibits adipocyte differentiation. These results appropriately explain the observed higher IMF content in fat-type pigs.

A novel multiscale framework for delineating cancer evolution from subclonal compositions

OBJECTIVE

Owing to the heterogeneity in the evolution of cancer, distinguishing between diverse growth patterns and predicting long-term outcomes based on short-term measurements poses a great challenge.

METHODS

A novel multiscale framework is proposed to unravel the connections between the population dynamics of cancer growth (i.e., aggressive, bounded, and indolent) and the cellular-subclonal dynamics of cancer evolution. This framework employs the non-negative lasso (NN-LASSO) algorithm to forge a link between an ordinary differential equation (ODE)-based population model and a cellular evolution model.

RESULTS

The findings of our current work not only affirm the impact of subclonal composition on growth dynamics but also identify two significant subclones within heterogeneous growth patterns. Moreover, the subclonal compositions at the initial time are able to accurately discriminate diverse growth patterns through a machine learning algorithm.

CONCLUSION

The proposed multiscale framework successfully delineates the intricate landscape of cancer evolution, bridging the gap between long-term growth dynamics and short-term measurements, both in simulated and real-world data. This methodology provides a novel avenue for thorough exploration into the realm of cancer evolution.

Regenerative inflammation: When immune cells help to re-build tissues

Inflammation is an essential immune response critical for responding to infection, injury and maintenance of tissue homeostasis. Upon injury, regenerative inflammation promotes tissue repair by a timed and coordinated infiltration of diverse cell types and the secretion of growth factors, cytokines and lipids mediators. Remarkably, throughout evolution as well as mammalian development, this type of physiological inflammation is highly associated with immunosuppression. For instance, regenerative inflammation is the consequence of an in situ macrophage polarization resulting in a transition from pro-inflammatory to anti-inflammatory/pro-regenerative response. Immune cells are the first responders upon injury, infiltrating the damaged tissue and initiating a pro-inflammatory response depleting cell debris and necrotic cells. After phagocytosis, macrophages undergo multiple coordinated metabolic and transcriptional changes allowing the transition and dictating the initiation of the regenerative phase. Differences between a highly efficient, complete ad integrum tissue repair, such as, acute skeletal muscle injury, and insufficient regenerative inflammation, as the one developing in Duchenne Muscular Dystrophy (DMD), highlight the importance of a coordinated response orchestrated by immune cells. During regenerative inflammation, these cells interact with others and alter the niche, affecting the character of inflammation itself and, therefore, the progression of tissue repair. Comparing acute muscle injury and chronic inflammation in DMD, we review how the same cells and molecules in different numbers, concentration and timing contribute to very different outcomes. Thus, it is important to understand and identify the distinct functions and secreted molecules of macrophages, and potentially other immune cells, during tissue repair, and the contributors to the macrophage switch leveraging this knowledge in treating diseases.

Multimodal Nature of the Single-cell Primate Brain Atlas: Morphology, Transcriptome, Electrophysiology, and Connectivity

Primates exhibit complex brain structures that augment cognitive function. The neocortex fulfills high-cognitive functions through billions of connected neurons. These neurons have distinct transcriptomic, morphological, and electrophysiological properties, and their connectivity principles vary. These features endow the primate brain atlas with a multimodal nature. The recent integration of next-generation sequencing with modified patch-clamp techniques is revolutionizing the way to census the primate neocortex, enabling a multimodal neuronal atlas to be established in great detail: (1) single-cell/single-nucleus RNA-seq technology establishes high-throughput transcriptomic references, covering all major transcriptomic cell types; (2) patch-seq links the morphological and electrophysiological features to the transcriptomic reference; (3) multicell patch-clamp delineates the principles of local connectivity. Here, we review the applications of these technologies in the primate neocortex and discuss the current advances and tentative gaps for a comprehensive understanding of the primate neocortex.

Aortic Cellular Heterogeneity in Health and Disease: Novel Insights Into Aortic Diseases From Single-Cell RNA Transcriptomic Data Sets

Aortic diseases such as atherosclerosis, aortic aneurysms, and aortic stiffening are significant complications that can have significant impact on end-stage cardiovascular disease. With limited pharmacological therapeutic strategies that target the structural changes in the aorta, surgical intervention remains the only option for some patients with these diseases. Although there have been significant contributions to our understanding of the cellular architecture of the diseased aorta, particularly in the context of atherosclerosis, furthering our insight into the cellular drivers of disease is required. The major cell types of the aorta are well defined; however, the advent of single-cell RNA sequencing provides unrivaled insights into the cellular heterogeneity of each aortic cell type and the inferred biological processes associated with each cell in health and disease. This review discusses previous concepts that have now been enhanced with recent advances made by single-cell RNA sequencing with a focus on aortic cellular heterogeneity.

Integrating single-cell and bulk sequencing data to identify glycosylation-based genes in non-alcoholic fatty liver disease-associated hepatocellular carcinoma

Background

The incidence of non-alcoholic fatty liver disease (NAFLD) associated hepatocellular carcinoma (HCC) has been increasing. However, the role of glycosylation, an important modification that alters cellular differentiation and immune regulation, in the progression of NAFLD to HCC is rare.

Methods

We used the NAFLD-HCC single-cell dataset to identify variation in the expression of glycosylation patterns between different cells and used the HCC bulk dataset to establish a link between these variations and the prognosis of HCC patients. Then, machine learning algorithms were used to identify those glycosylation-related signatures with prognostic significance and to construct a model for predicting the prognosis of HCC patients. Moreover, it was validated in high-fat diet-induced mice and clinical cohorts.

Results

The NAFLD-HCC Glycogene Risk Model (NHGRM) signature included the following genes: SPP1, SOCS2, SAPCD2, S100A9, RAMP3, and CSAD. The higher NHGRM scores were associated with a poorer prognosis, stronger immune-related features, immune cell infiltration and immunity scores. Animal experiments, external and clinical cohorts confirmed the expression of these genes.

Conclusion

The genetic signature we identified may serve as a potential indicator of survival in patients with NAFLD-HCC and provide new perspectives for elucidating the role of glycosylation-related signatures in this pathologic process.

Current insights and future perspectives of ultraviolet radiation (UV) exposure: Friends and foes to the skin and beyond the skin

Ultraviolet (UV) radiation is ubiquitous in the environment, which has been classified as an established human carcinogen. As the largest and outermost organ of the body, direct exposure of skin to sunlight or UV radiation can result in sunburn, inflammation, photo-immunosuppression, photoaging and even skin cancers. To date, there are tactics to protect the skin by preventing UV radiation and reducing the amount of UV radiation to the skin. Nevertheless, deciphering the essential regulatory mechanisms may pave the way for therapeutic interventions against UV-induced skin disorders. Additionally, UV light is considered beneficial for specific skin-related conditions in medical UV therapy. Recent evidence indicates that the biological effects of UV exposure extend beyond the skin and include the treatment of inflammatory diseases, solid tumors and certain abnormal behaviors. This review mainly focuses on the effects of UV on the skin. Moreover, novel findings of the biological effects of UV in other organs and systems are also summarized. Nevertheless, the mechanisms through which UV affects the human organism remain to be fully elucidated to achieve a more comprehensive understanding of its biological effects.

scRNA-seq of colorectal cancer shows regional immune atlas with the function of CD20+ B cells

Colorectal cancer (CRC) from different regions exhibits different histological, genetic characteristics, and molecular subtypes, even in response to conventional chemotherapies and immunotherapies. To characterize the immune landscape in different regions of CRC and search for potential therapeutic targets, we analyzed 39,484 single-cell transcription data from 19 samples of CRC and paired normal tissues from four regions to identify the immune characteristics of CRC among anatomic locations, especially in B cells. We discovered that immune cell infiltration in tumors significantly varied among different regions of CRC. B cells from right- and left-sided CRC had different development trajectories, but both had extensive interactions with myeloid cells and T cells. Survival analysis suggested that CD20+ B cells correlated with good prognosis in CRC patients, especially on the right side. Furthermore, the depletion of CD20+ B cells demonstrated that anti-CD20 promoted tumor growth progression and reversed the tumor-killing activity of anti-PD-1 treatment in vivo and in vitro. Our results highlight the characterization of the immune landscape of CRC in different regions. CD20+ B-cell infiltration has been associated with CRC patient prognosis and may promote the tumor-killing role of PD-1 antibodies.

Unlocking the potential of T-cell metabolism reprogramming: Advancing single-cell approaches for precision immunotherapy in tumour immunity

As single-cell RNA sequencing enables the detailed clustering of T-cell subpopulations and facilitates the analysis of T-cell metabolic states and metabolite dynamics, it has gained prominence as the preferred tool for understanding heterogeneous cellular metabolism. Furthermore, the synergistic or inhibitory effects of various metabolic pathways within T cells in the tumour microenvironment are coordinated, and increased activity of specific metabolic pathways generally corresponds to increased functional activity, leading to diverse T-cell behaviours related to the effects of tumour immune cells, which shows the potential of tumour-specific T cells to induce persistent immune responses. A holistic understanding of how metabolic heterogeneity governs the immune function of specific T-cell subsets is key to obtaining field-level insights into immunometabolism. Therefore, exploring the mechanisms underlying the interplay between T-cell metabolism and immune functions will pave the way for precise immunotherapy approaches in the future, which will empower us to explore new methods for combating tumours with enhanced efficacy.

Adipose tissue macrophage heterogeneity in the single-cell genomics era

It is now well-accepted that obesity-induced inflammation plays an important role in the development of insulin resistance and type 2 diabetes. A key source of the inflammation is the murine epididymal and human visceral adipose tissue. The current paradigm is that obesity activates multiple proinflammatory immune cell types in adipose tissue, including adipose-tissue macrophages (ATMs), T Helper 1 (Th1) T cells, and natural killer (NK) cells, while concomitantly suppressing anti-inflammatory immune cells such as T Helper 2 (Th2) T cells and regulatory T cells (Tregs). A key feature of the current paradigm is that obesity induces the anti-inflammatory M2 ATMs in lean adipose tissue to polarize into proinflammatory M1 ATMs. However, recent single-cell transcriptomics studies suggest that the story is much more complex. Here we describe the single-cell genomics technologies that have been developed recently and the emerging results from studies using these technologies. While further studies are needed, it is clear that ATMs are highly heterogeneous. Moreover, while a variety of ATM clusters with quite distinct features have been found to be expanded by obesity, none truly resemble classical M1 ATMs. It is likely that single-cell transcriptomics technology will further revolutionize the field, thereby promoting our understanding of ATMs, adipose-tissue inflammation, and insulin resistance and accelerating the development of therapies for type 2 diabetes.

Predicting cell-type specific disease genes of diabetes with the biological network

Type 2 diabetes (T2D) is a chronic condition that can lead to significant harm, such as heart disease, kidney disease, nerve damage, and blindness. Although T2D-related genes have been identified through Genome-wide association studies (GWAS) and various computational methods, the biological mechanism of T2D at the cell type level remains unclear. Exploring cell type-specific genes related to T2D is essential to understand the cellular mechanisms underlying the disease. To address this issue, we introduce DiGCellNet (predicting Disease Genes with Cell type specificity based on biological Networks), a model that integrates graph convolutional network (GCN) and multi-task learning (MTL) to predict T2D-associated cell type-specific genes based on the biological network. Our work represents the first attempt to predict cell type-specific disease genes using GCN and MTL. We evaluate our approach by predicting genes specific to four cell types and demonstrate that the proposed DiGCellNet outperforms other models that combine node embeddings with traditional machine learning algorithms. Moreover, DiGCellNet successfully identifies CALM1 as a gene specific to beta cell type in T2D cases, and this association is confirmed using an independent dataset. The code is available at https://github.com/23AIBox/23AIBox-DiGCellNet.

Integrated analysis reveals crosstalk between pyroptosis and immune regulation in renal fibrosis

Purpose

The pathogenesis of renal fibrosis (RF) involves intricate interactions between profibrotic processes and immune responses. This study aimed to explore the potential involvement of the pyroptosis signaling pathway in immune microenvironment regulation within the context of RF. Through comprehensive bioinformatics analysis and experimental validation, we investigated the influence of pyroptosis on the immune landscape in RF.

Methods

We obtained RNA-seq datasets from Gene Expression Omnibus (GEO) databases and identified Pyroptosis-Associated Regulators (PARs) through literature reviews. Systematic evaluation of alterations in 27 PARs was performed in RF and normal kidney samples, followed by relevant functional analyses. Unsupervised cluster analysis revealed distinct pyroptosis modification patterns. Using single-sample gene set enrichment analysis (ssGSEA), we examined the correlation between pyroptosis and immune infiltration. Hub regulators were identified via weighted gene coexpression network analysis (WGCNA) and further validated in a single-cell RNA-seq dataset. We also established a unilateral ureteral obstruction-induced RF mouse model to verify the expression of key regulators at the mRNA and protein levels.

Results

Our comprehensive analysis revealed altered expression of 19 PARs in RF samples compared to normal samples. Five hub regulators, namely PYCARD, CASP1, AIM2, NOD2, and CASP9, exhibited potential as biomarkers for RF. Based on these regulators, a classifier capable of distinguishing normal samples from RF samples was developed. Furthermore, we identified correlations between immune features and PARs expression, with PYCARD positively associated with regulatory T cells abundance in fibrotic tissues. Unsupervised clustering of RF samples yielded two distinct subtypes (Subtype A and Subtype B), with Subtype B characterized by active immune responses against RF. Subsequent WGCNA analysis identified PYCARD, CASP1, and NOD2 as hub PARs in the pyroptosis modification patterns. Single-cell level validation confirmed PYCARD expression in myofibroblasts, implicating its significance in the stress response of myofibroblasts to injury. In vivo experimental validation further demonstrated elevated PYCARD expression in RF, accompanied by infiltration of Foxp3+ regulatory T cells.

Conclusions

Our findings suggest that pyroptosis plays a pivotal role in orchestrating the immune microenvironment of RF. This study provides valuable insights into the pathogenesis of RF and highlights potential targets for future therapeutic interventions.

Single-cell transcriptomics of peripheral blood mononuclear cells indicates impaired immune and inflammatory responses in alcohol-associated hepatitis

Alcohol-associated hepatitis (AH) is often diagnosed at advanced stages, and severe AH usually carries poor prognosis and high short-term mortality. In addition, it is challenging to understand the molecular mechanisms of immune dysregulation and inflammation in AH due to the cellular complexity and heterogeneity. Using single-cell RNA sequencing, previous studies found that AH causes dysfunctional innate immune response in monocytes, involving activation of pattern recognition receptors (PRRs) and cytokine signaling pathways. To better understand the coordinated systemic immune response in AH patients, we performed combined single-cell transcriptome, cell-surface protein, and lymphocyte antigen receptor analysis of peripheral blood mononuclear cell (PBMC) samples. Our results showed inflammatory cytokines and chemokines were highly expressed in AH, including IL-2, IL-32, CXC3R1 and CXCL16 in monocytes and NK cells, whereas HLA-DR genes were reduced in monocytes. In addition, we also found altered differentiation of T-helper cells (TH1 and TH17), which could further lead to neutrophil recruitment and macrophage activation. Lastly, our results also suggest impaired NK-cell activation and differentiation in AH with reduced gene expression of KLRC2 and increased gene expression of KLRG1. Our findings indicate different mechanisms may be involved in impaired immune and inflammatory responses for the cellular subtypes of the PBMCs in AH.

Hemocyte Nuclei Isolation from Adult Drosophila melanogaster for snRNA-seq

In adult Drosophila, most of the hemocytes are macrophage-like cells (so called plasmatocytes), which serve various functions in organ homeostasis and immune defense. Ontogeny and functions are largely conserved between vertebrate and invertebrate macrophages. Hence, Drosophila offers a powerful genetic toolbox to study macrophage function and genetically modulate these cells. Technological advances in high-throughput sequencing approaches allowed to give an in-depth characterization of vertebrate macrophage populations and their heterogenous composition within different organs as well as changes in disease. Embryonic and larval hemocytes in Drosophila have been recently analyzed in single-cell RNA-sequencing (scRNA-seq) approaches during infection and steady state. These analyses revealed anatomical and functional Drosophila hemocyte subtypes dedicated to specific tasks. Only recently, the Fly Cell Atlas provided a whole transcriptomic single-cell atlas via single-nuclei RNA-sequencing (snRNA-seq) of adult Drosophila including many different tissues and cell types where hemocytes were also included. Yet, a specific protocol to isolate nuclei from adult hemocytes for snRNA-seq and study these cells in different experimental conditions was not available. In this chapter, we give a detailed protocol to purify hemocyte nuclei from adult Drosophila, which can be used in subsequent analyses such as snRNA-seq.

Identification of central genes for endometriosis through integration of single-cell RNA sequencing and bulk RNA sequencing analysis

This study aimed to identify the key genes involved in the development of endometriosis and construct an accurate predictive model to provide new directions for the diagnosis and treatment of endometriosis. Using bioinformatics analysis, we employed the single-cell cell communication method to identify the key cell subtypes. By combining chip data and integrating differential analysis, WGCNA analysis, and the least absolute shrinkage and selection operator (LASSO) model, key genes were identified for immune infiltration and functional enrichment analyses. Cell communication analysis identified tissue stem cells as the key subtype. Differential analysis revealed 1879 differentially expressed genes, whereas WGCNA identified 357 module genes. The LASSO model further selects 4 key genes: Adipocyte Enhancer Binding Protein 1(AEBP1), MBNL1, GREM1, and DES. All 4 key genes showed significant correlations with immune cell content. Moreover, these genes were significantly expressed in single cells. The predictive model demonstrated good diagnostic performance. Through scRNA-seq, WGCNA, and LASSO methodologies, DES, GREM1, MBNL1, and AEBP1 emerged as crucial core genes linked to tissue stem cell markers in endometriosis. These genes have promising applications as diagnostic markers and therapeutic targets for endometriosis.

Single-cell landscape of immune cells during the progression from HBV infection to HBV cirrhosis and HBV-associated hepatocellular carcinoma

Introduction

Immune cells play crucial roles in the development of chronic hepatitis B virus (HBV) infection, leading to cirrhosis and hepatocellular carcinoma (HCC). However, their functions at different disease stages are not fully understood.

Methods

In this study, we used single-cell RNA sequencing (scRNA-seq) to characterize the human liver immune microenvironment at different disease stages. We analyzed scRNA-seq data from 118,455 immune cells obtained from livers of six healthy individuals, four patients with HBV infection, five patients with HBV cirrhosis, and three patients with HBV-associated HCC.

Results

Our results showed an accumulation of scar-associated macrophages during disease progression, and we identified two relevant immune subsets, Macrophage-CD9/IL18 and macrophage-CD9/IFI6. Macrophage-CD9/IL18 expanded from HBV infection to cirrhosis, while macrophage-CD9/IFI6 expanded from cirrhosis to HCC. We verified the existence of Macrophage-CD9/IFI6 using multiplex immunofluorescence staining. We also found an increase in cytotoxic NK Cell-GNLY during progression from cirrhosis to HCC. Additionally, the proportion of CD4 T cell-TNFAIP3, CD8 T cell-TNF (effector CD8 T cells), and CD8 T cell-CD53 increased, while the proportion of Treg cells decreased from HBV infection to cirrhosis. The proportion of Treg and CD8 T cell-LAG3 (Exhausted CD8 T cell) enhanced, while the proportion of CD8 T cell-TNF (effector CD8 T cells) decreased from cirrhosis to HCC. Furthermore, GSEA enrichment analyses revealed that MAPK, ERBB, and P53 signaling pathways in myeloid cells were gradually inhibited from HBV infection to cirrhosis and HCC.

Discussion

Our study provides important insights into changes in the hepatic immune environment during the progression of HBV-related liver disease, which may help improve the management of HBV-infected liver diseases.

Single-Cell RNA-Seq Analysis Identifies Angiotensinogen and Galanin as Unique Molecular Markers of Acinar Cells in Murine Salivary Glands

The submandibular gland (SMG) and sublingual gland (SLG) are two of three major salivary glands in mammals and comprise serous and mucous acinar cells. The two glands share some functional properties, which are largely dependent on the types of acinar cells. In recent years, while ScRNA-seq (single-cell sequencing) with a 10 × platform has been used to explore molecular markers in salivary glands, few studies have examined the acinar heterogeneity and unique molecular markers between SMG and SLG. This study aimed to identify the molecular markers of acinar cells in the SLG and SMG. We performed ScRNA-seq analyses in 4-week-old mice and verified the screened molecular markers using reverse transcription-quantitative real-time PCR, immunohistochemistry, and immunofluorescence. Our results showed prominently heterogeneous acinar cells, although there was great similarity in the cluster composition between the two glands at 4 weeks. Furthermore, we demonstrated that Agt is a specific marker of SMG serous acinar cells, whereas Gal is a specific marker of SLG mucous acinar cells. Trajectory inference revealed that Agt and Gal represent two types of differential acinar cell clusters during late development in adults. Thus, we reveal previously unknown specific markers for salivary acinar cell diversity, which has extensive implications for their further functional research.

Detection of isoforms and genomic alterations by high-throughput full-length single-cell RNA sequencing in ovarian cancer

Understanding the complex background of cancer requires genotype-phenotype information in single-cell resolution. Here, we perform long-read single-cell RNA sequencing (scRNA-seq) on clinical samples from three ovarian cancer patients presenting with omental metastasis and increase the PacBio sequencing depth to 12,000 reads per cell. Our approach captures 152,000 isoforms, of which over 52,000 were not previously reported. Isoform-level analysis accounting for non-coding isoforms reveals 20% overestimation of protein-coding gene expression on average. We also detect cell type-specific isoform and poly-adenylation site usage in tumor and mesothelial cells, and find that mesothelial cells transition into cancer-associated fibroblasts in the metastasis, partly through the TGF-β/miR-29/Collagen axis. Furthermore, we identify gene fusions, including an experimentally validated IGF2BP2::TESPA1 fusion, which is misclassified as high TESPA1 expression in matched short-read data, and call mutations confirmed by targeted NGS cancer gene panel results. With these findings, we envision long-read scRNA-seq to become increasingly relevant in oncology and personalized medicine.

Integrated bulk and single-cell transcriptomes reveal pyroptotic signature in prognosis and therapeutic options of hepatocellular carcinoma by combining deep learning

Although some pyroptosis-related (PR) prognostic models for cancers have been reported, pyroptosis-based features have not been fully discovered at the single-cell level in hepatocellular carcinoma (HCC). In this study, by deeply integrating single-cell and bulk transcriptome data, we systematically investigated significance of the shared pyroptotic signature at both single-cell and bulk levels in HCC prognosis. Based on the pyroptotic signature, a robust PR risk system was constructed to quantify the prognostic risk of individual patient. To further verify capacity of the pyroptotic signature on predicting patients' prognosis, an attention mechanism-based deep neural network classification model was constructed. The mechanisms of prognostic difference in the patients with distinct PR risk were dissected on tumor stemness, cancer pathways, transcriptional regulation, immune infiltration and cell communications. A nomogram model combining PR risk with clinicopathologic data was constructed to evaluate the prognosis of individual patients in clinic. The PR risk could also evaluate therapeutic response to neoadjuvant therapies in HCC patients. In conclusion, the constructed PR risk system enables a comprehensive assessment of tumor microenvironment characteristics, accurate prognosis prediction and rational therapeutic options in HCC.

A novel f-divergence based generative adversarial imputation method for scRNA-seq data analysis

Comprehensive analysis of single-cell RNA sequencing (scRNA-seq) data can enhance our understanding of cellular diversity and aid in the development of personalized therapies for individuals. The abundance of missing values, known as dropouts, makes the analysis of scRNA-seq data a challenging task. Most traditional methods made assumptions about specific distributions for missing values, which limit their capability to capture the intricacy of high-dimensional scRNA-seq data. Moreover, the imputation performance of traditional methods decreases with higher missing rates. We propose a novel f-divergence based generative adversarial imputation method, called sc-fGAIN, for the scRNA-seq data imputation. Our studies identify four f-divergence functions, namely cross-entropy, Kullback-Leibler (KL), reverse KL, and Jensen-Shannon, that can be effectively integrated with the generative adversarial imputation network to generate imputed values without any assumptions, and mathematically prove that the distribution of imputed data using sc-fGAIN algorithm is same as the distribution of original data. Real scRNA-seq data analysis has shown that, compared to many traditional methods, the imputed values generated by sc-fGAIN algorithm have a smaller root-mean-square error, and it is robust to varying missing rates, moreover, it can reduce imputation variability. The flexibility offered by the f-divergence allows the sc-fGAIN method to accommodate various types of data, making it a more universal approach for imputing missing values of scRNA-seq data.

Sanguisorba officinalis ethyl acetate extract attenuates ulcerative colitis through inhibiting PI3K-AKT/NF-κB/ STAT3 pathway uncovered by single-cell RNA sequencing

BACKGROUND

Ulcerative colitis (UC) accounts for the untreatable illness nowadays. Bloody stools are the primary symptom of UC, and the first-line drugs used to treat UC are associated with several drawbacks and negative side effects. S. officinalis has long been used as a medicine to treat intestinal infections and bloody stools. However, what the precise molecular mechanism, the exact etiology, and the material basis of the disease remain unclear.

PURPOSE

This work aimed to comprehensively explore pharmacological effects as well as molecular mechanisms underlying the active fraction of S. officinalis, and to produce a comprehensive and brand-new guideline map of its chemical base and mechanism of action.

METHODS

First, different polarity S. officinalis extracts were orally administered to the DSS-induced UC model mice for the sake of investigating its active constituents. Using the UPLC-orbitrap high-resolution mass spectrometry (UPLC-Q-Orbitrap-HRMS) technique, the most active S. officinalis (S. officinalis ethyl acetate fraction, SOEA) extract was characterized. Subsequently, the effectiveness of its active fraction on UC was evaluated through phenotypic observation (such as weight loss, colon length, and stool characteristics), and histological examination of pathological injuries, mRNA and protein expression. Cell profile, cell-cell interactions and molecular mechanisms of SOEA in different cell types of the colon tissue from UC mice were described using single-cell RNA sequencing (scRNA-seq). As a final step, the molecular mechanisms were validated by appropriate molecular biological methods.

RESULTS

For the first time, this study revealed the significant efficacy of SOEA in the treatment of UC. SOEA reduced DAI and body weight loss, recovered the colon length, and mitigated colonic pathological injuries along with mucosal barrier by promoting goblet cell proliferation. Following treatment with SOEA, inflammatory factors showed decreased mRNA and protein expression. SOEA restored the dynamic equilibrium of cell profile and cell-cell interactions in colon tissue. All of these results were attributed to the ability of SOEA to inhibit the PI3K-AKT/NF-κB/STATAT pathway.

CONCLUSIONS

By integrating the chemical information of SOEA derived from UPLC-Q-Orbitrap-HRMS with single-cell transcriptomic data extracted from scRNA-seq, this study demonstrates that SOEA exerts the therapeutic effect through suppressing PI3K-AKT/NF-B/STAT3 pathway to improve clinical symptoms, inflammatory response, mucosal barrier, and intercellular interactions in UC, and effectively eliminates the interference of cellular heterogeneity.

Unveiling the novel immune and molecular signatures of ovarian cancer: insights and innovations from single-cell sequencing

Ovarian cancer is a highly heterogeneous and lethal malignancy with limited treatment options. Over the past decade, single-cell sequencing has emerged as an advanced biological technology capable of decoding the landscape of ovarian cancer at the single-cell resolution. It operates at the level of genes, transcriptomes, proteins, epigenomes, and metabolisms, providing detailed information that is distinct from bulk sequencing methods, which only offer average data for specific lesions. Single-cell sequencing technology provides detailed insights into the immune and molecular mechanisms underlying tumor occurrence, development, drug resistance, and immune escape. These insights can guide the development of innovative diagnostic markers, therapeutic strategies, and prognostic indicators. Overall, this review provides a comprehensive summary of the diverse applications of single-cell sequencing in ovarian cancer. It encompasses the identification and characterization of novel cell subpopulations, the elucidation of tumor heterogeneity, the investigation of the tumor microenvironment, the analysis of mechanisms underlying metastasis, and the integration of innovative approaches such as organoid models and multi-omics analysis.

Single-cell landscape of immune cells in human livers affected by HBV-related cirrhosis

Background & Aims

HBV infection is one of the leading causes of liver cirrhosis. However, the immune microenvironment in patients with HBV cirrhosis remains elusive.

Methods

Single-cell RNA sequencing was used to analyse the transcriptomes of 76,210 immune cells in the livers of six healthy individuals and in five patients with HBV cirrhosis.

Results

Patients with HBV cirrhosis have a unique immune ecosystem characterised by an accumulation of macrophage-CD9/IL18, macrophage-C1QA, NK Cell-JUNB, CD4+ T cell-IL7R, and a loss of B cell-IGLC1 clusters. Furthermore, our analysis predicted enhanced cell communication between myeloid cells and all immune cells in patients with HBV-related cirrhosis. Pseudo-time analysis of myeloid cells, natural killer (NK) cells, and B cells demonstrated a significant accumulation of mature cells and a depletion of naive cells in HBV cirrhosis. In addition, we observed an increase in antigen processing and presentation capacities in myeloid cells in patients with HBV cirrhosis, whereas NK cell-mediated cytotoxicity was substantially reduced.

Conclusions

Our results provide valuable insight into the immune landscape of HBV cirrhosis, suggesting that HBV cirrhosis is associated with the accumulation of activated myeloid cells and impaired cytotoxicity in NK cells.

Impact and implications

The absence of single-cell transcriptome profiling of immune cells in HBV cirrhosis hinders our understanding of the underlying mechanisms driving disease progression. To address this knowledge gap, our study unveils a distinctive immune ecosystem in HBV cirrhosis and represents a crucial advancement in elucidating the impact of the immune milieu on the development of this condition. These findings constitute significant strides towards the identification of more effective therapeutic approaches for HBV cirrhosis and are relevant for healthcare professionals, researchers, and pharmaceutical developers dedicated to combating this disease.