Single-cell RNA sequencing in cardiovascular development, disease and medicine

Assessing parameter efficient methods for pre-trained language model in annotating scRNA-seq data

Annotating cell types of single-cell RNA sequencing (scRNA-seq) data is crucial for studying cellular heterogeneity in the tumor microenvironment. Recently, large-scale pre-trained language models (PLMs) have achieved significant progress in cell-type annotation of scRNA-seq data. This approach effectively addresses previous methods' shortcomings in performance and generalization. However, fine-tuning PLMs for different downstream tasks demands considerable computational resources, rendering it impractical. Hence, a new research branch introduces parameter-efficient fine-tuning (PEFT). This involves optimizing a few parameters while leaving the majority unchanged, leading to substantial reductions in computational expenses. Here, we utilize scBERT, a large-scale pre-trained model, to explore the capabilities of three PEFT methods in scRNA-seq cell type annotation. Extensive benchmark studies across several datasets demonstrate the superior applicability of PEFT methods. Furthermore, downstream analysis using models obtained through PEFT showcases their utility in novel cell type discovery and model interpretability for potential marker genes. Our findings underscore the considerable potential of PEFT in PLM-based cell type annotation, presenting novel perspectives for the analysis of scRNA-seq data.

scRNA-seq reveals the diversity of the developing cardiac cell lineage and molecular players in heart rhythm regulation

We utilized scRNA-seq to delineate the diversity of cell types in the zebrafish heart. Transcriptome profiling of over 50,000 cells at 48 and 72 hpf defined at least 18 discrete cell lineages of the developing heart. Utilizing well-established gene signatures, we identified a population of cells likely to be the primary pacemaker and characterized the transcriptome profile defining this critical cell type. Two previously uncharacterized genes, atp1b3b and colec10, were found to be enriched in the sinoatrial cardiomyocytes. CRISPR/Cas9-mediated knockout of these two genes significantly reduced heart rate, implicating their role in cardiac development and conduction. Additionally, we describe other cardiac cell lineages, including the endothelial and neural cells, providing their expression profiles as a resource. Our results established a detailed atlas of the developing heart, providing valuable insights into cellular and molecular mechanisms, and pinpointed potential new players in heart rhythm regulation.

Revisiting Cardiac Biology in the Era of Single Cell and Spatial Omics

Throughout our lifetime, each beat of the heart requires the coordinated action of multiple cardiac cell types. Understanding cardiac cell biology, its intricate microenvironments, and the mechanisms that govern their function in health and disease are crucial to designing novel therapeutical and behavioral interventions. Recent advances in single-cell and spatial omics technologies have significantly propelled this understanding, offering novel insights into the cellular diversity and function and the complex interactions of cardiac tissue. This review provides a comprehensive overview of the cellular landscape of the heart, bridging the gap between suspension-based and emerging in situ approaches, focusing on the experimental and computational challenges, comparative analyses of mouse and human cardiac systems, and the rising contextualization of cardiac cells within their niches. As we explore the heart at this unprecedented resolution, integrating insights from both mouse and human studies will pave the way for novel diagnostic tools and therapeutic interventions, ultimately improving outcomes for patients with cardiovascular diseases.

Single-Cell and Spatial Transcriptome Analysis of Dermal Fibroblast Development in Perinatal Mouse Skin: Dynamic Lineage Differentiation and Key Driver Genes

Several single-cell RNA studies of developing mouse skin have elucidated the molecular and cellular processes involved in skin development. However, they have primarily focused on either the fetal or early postnatal period, leaving a gap in our understanding of skin development. In this study, we conducted a comprehensive time-series analysis by combining single-cell RNA-sequencing datasets collected at different stages of development (embryonic days 13.5, 14.5, and 16.5 and postnatal days 0, 2, and 4) and validated our findings through multipanel in situ spatial transcriptomics. Our analysis indicated that embryonic fibroblasts exhibit heterogeneity from a very early stage and that the rapid determination of each lineage occurs within days after birth. The expression of putative key driver genes, including Hey1, Ebf1, Runx3, and Sox11 for the dermal papilla trajectory; Lrrc15 for the dermal sheath trajectory; Zfp536 and Nrn1 for the papillary fibroblast trajectory; and Lrrn4cl and Mfap5 for the fascia fibroblast trajectory, was detected in the corresponding, spatially identified cell types. Finally, cell-to-cell interaction analysis indicated that the dermal papilla lineage is the primary source of the noncanonical Wnt pathway during skin development. Together, our study provides a transcriptomic reference for future research in the field of skin development and regeneration.

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.

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.

The cGAS-STING pathway in cardiovascular diseases: from basic research to clinical perspectives

The cyclic guanosine monophosphate (GMP)-adenosine monophosphate (AMP) synthase-stimulator of interferon genes (cGAS-STING) signaling pathway, an important component of the innate immune system, is involved in the development of several diseases. Ectopic DNA-induced inflammatory responses are involved in several pathological processes. Repeated damage to tissues and metabolic organelles releases a large number of damage-associated molecular patterns (mitochondrial DNA, nuclear DNA, and exogenous DNA). The DNA fragments released into the cytoplasm are sensed by the sensor cGAS to initiate immune responses through the bridging protein STING. Many recent studies have revealed a regulatory role of the cGAS-STING signaling pathway in cardiovascular diseases (CVDs) such as myocardial infarction, heart failure, atherosclerosis, and aortic dissection/aneurysm. Furthermore, increasing evidence suggests that inhibiting the cGAS-STING signaling pathway can significantly inhibit myocardial hypertrophy and inflammatory cell infiltration. Therefore, this review is intended to identify risk factors for activating the cGAS-STING pathway to reduce risks and to simultaneously further elucidate the biological function of this pathway in the cardiovascular field, as well as its potential as a therapeutic target.

A review of single-cell transcriptomics and epigenomics studies in maternal and child health

Single-cell sequencing technologies enhance our understanding of cellular dynamics throughout pregnancy. We outlined the workflow of single-cell sequencing techniques and reviewed single-cell studies in maternal and child health. We conducted a literature review of single cell studies on maternal and child health using PubMed. We summarized the findings from 16 single-cell atlases of the human and mammalian placenta across gestational stages and 31 single-cell studies on maternal exposures and complications including infection, obesity, diet, gestational diabetes, pre-eclampsia, environmental exposure and preterm birth. Single-cell studies provides insights on novel cell types in placenta and cell type-specific marks associated with maternal exposures and complications.

GLP-1 receptor agonists and myocardial metabolism in atrial fibrillation

Atrial fibrillation (AF) is the most common cardiac arrhythmia. Many medical conditions, including hypertension, diabetes, obesity, sleep apnea, and heart failure (HF), increase the risk for AF. Cardiomyocytes have unique metabolic characteristics to maintain adenosine triphosphate production. Significant changes occur in myocardial metabolism in AF. Glucagon-like peptide-1 receptor agonists (GLP-1RAs) have been used to control blood glucose fluctuations and weight in the treatment of type 2 diabetes mellitus (T2DM) and obesity. GLP-1RAs have also been shown to reduce oxidative stress, inflammation, autonomic nervous system modulation, and mitochondrial function. This article reviews the changes in metabolic characteristics in cardiomyocytes in AF. Although the clinical trial outcomes are unsatisfactory, the findings demonstrate that GLP-1 RAs can improve myocardial metabolism in the presence of various risk factors, lowering the incidence of AF.

Systematic mapping of TF-mediated cell fate changes by a pooled induction coupled with scRNA-seq and multi-omics approaches

Transcriptional regulation controls cellular functions through interactions between transcription factors (TFs) and their chromosomal targets. However, understanding the fate conversion potential of multiple TFs in an inducible manner remains limited. Here, we introduce iTF-seq as a method for identifying individual TFs that can alter cell fate toward specific lineages at a single-cell level. iTF-seq enables time course monitoring of transcriptome changes, and with biotinylated individual TFs, it provides a multi-omics approach to understanding the mechanisms behind TF-mediated cell fate changes. Our iTF-seq study in mouse embryonic stem cells identified multiple TFs that trigger rapid transcriptome changes indicative of differentiation within a day of induction. Moreover, cells expressing these potent TFs often show a slower cell cycle and increased cell death. Further analysis using bioChIP-seq revealed that GCM1 and OTX2 act as pioneer factors and activators by increasing gene accessibility and activating the expression of lineage specification genes during cell fate conversion. iTF-seq has utility in both mapping cell fate conversion and understanding cell fate conversion mechanisms.

Neutrophil extracellular traps promoting fibroblast activation and aggravating limb ischemia through Wnt5a pathway

Although the formation of NETs contributes to cancer cell invasion and distant metastasis, its role in the pathological progression of limb ischemia remains unknown. This study investigated the functional significance of NETs in cell-cell crosstalk during limb ischemia. The changes of cell subsets in lower limb ischemia samples were detected by single-cell RNA sequencing. The expression of neutrophil extracellular traps (NETs) related markers in lower limb ischemia samples was detected by immunohistochemistry and Western blotting. The signaling pathway of NETs activation in fibroblasts was verified by immunofluorescence, PCR and Western blotting. Through single-cell RNA sequencing (scRNA-seq), we identified 9 distinct cell clusters, with significantly upregulated activation levels in fibroblasts and neutrophils and phenotypic transformation of smooth muscle cells (SMCs) into a proliferative state in ischemic tissue. At the same time, the interaction between fibroblasts and smooth muscle cells was significantly enhanced in ischemic tissue. NETs levels rise and fibroblast activation is induced in ischemic conditions. Mechanistically, activated fibroblasts promote smooth muscle cell proliferation through the Wnt5a pathway. In ischemic mice, inhibition of Wnt5a mitigated vascular remodeling and subsequent ischemia. These findings highlighting the role of cell-cell crosstalk in ischemia and vascular remodeling. We found that the NETs-initiated fibroblast-SMC interaction is a critical regulator of limb ischemia via Wnt5a pathway, a potential therapeutic target for the treatment.

A Meta-Analysis Approach to Gene Regulatory Network Inference Identifies Key Regulators of Cardiovascular Diseases

Cardiovascular diseases (CVDs) represent a major concern for global health, whose mechanistic understanding is complicated by a complex interplay between genetic predisposition and environmental factors. Specifically, heart failure (HF), encompassing dilated cardiomyopathy (DC), ischemic cardiomyopathy (ICM), and hypertrophic cardiomyopathy (HCM), is a topic of substantial interest in basic and clinical research. Here, we used a Partial Correlation Coefficient-based algorithm (PCC) within the context of a meta-analysis framework to construct a Gene Regulatory Network (GRN) that identifies key regulators whose activity is perturbed in Heart Failure. By integrating data from multiple independent studies, our approach unveiled crucial regulatory associations between transcription factors (TFs) and structural genes, emphasizing their pivotal roles in regulating metabolic pathways, such as fatty acid metabolism, oxidative stress response, epithelial-to-mesenchymal transition, and coagulation. In addition to known associations, our analysis also identified novel regulators, including the identification of TFs FPM315 and OVOL2, which are implicated in dilated cardiomyopathies, and TEAD1 and TEAD2 in both dilated and ischemic cardiomyopathies. Moreover, we uncovered alterations in adipogenesis and oxidative phosphorylation pathways in hypertrophic cardiomyopathy and discovered a role for IL2 STAT5 signaling in heart failure. Our findings underscore the importance of TF activity in the initiation and progression of cardiac disease, highlighting their potential as pharmacological targets.

Intestinal cell diversity and treatment responses in a parasitic nematode at single cell resolution

BACKGROUND

Parasitic nematodes, significant pathogens for humans, animals, and plants, depend on diverse organ systems for intra-host survival. Understanding the cellular diversity and molecular variations underlying these functions holds promise for developing novel therapeutics, with specific emphasis on the neuromuscular system's functional diversity. The nematode intestine, crucial for anthelmintic therapies, exhibits diverse cellular phenotypes, and unraveling this diversity at the single-cell level is essential for advancing knowledge in anthelmintic research across various organ systems.

RESULTS

Here, using novel single-cell transcriptomics datasets, we delineate cellular diversity within the intestine of adult female Ascaris suum, a parasitic nematode species that infects animals and people. Gene transcripts expressed in individual nuclei of untreated intestinal cells resolved three phenotypic clusters, while lower stringency resolved additional subclusters and more potential diversity. Clusters 1 and 3 phenotypes displayed variable congruence with scRNA phenotypes of C. elegans intestinal cells, whereas the A. suum cluster 2 phenotype was markedly unique. Distinct functional pathway enrichment characterized each A. suum intestinal cell cluster. Cluster 2 was distinctly enriched for Clade III-associated genes, suggesting it evolved within clade III nematodes. Clusters also demonstrated differential transcriptional responsiveness to nematode intestinal toxic treatments, with Cluster 2 displaying the least responses to short-term intra-pseudocoelomic nematode intestinal toxin treatments.

CONCLUSIONS

This investigation presents advances in knowledge related to biological differences among major cell populations of adult A. suum intestinal cells. For the first time, diverse nematode intestinal cell populations were characterized, and associated biological markers of these cells were identified to support tracking of constituent cells under experimental conditions. These advances will promote better understanding of this and other parasitic nematodes of global importance, and will help to guide future anthelmintic treatments.

A Multimodal Omics Framework to Empower Target Discovery for Cardiovascular Regeneration

Ischaemic heart disease is a global healthcare challenge with high morbidity and mortality. Early revascularisation in acute myocardial infarction has improved survival; however, limited regenerative capacity and microvascular dysfunction often lead to impaired function and the development of heart failure. New mechanistic insights are required to identify robust targets for the development of novel strategies to promote regeneration. Single-cell RNA sequencing (scRNA-seq) has enabled profiling and analysis of the transcriptomes of individual cells at high resolution. Applications of scRNA-seq have generated single-cell atlases for multiple species, revealed distinct cellular compositions for different regions of the heart, and defined multiple mechanisms involved in myocardial injury-induced regeneration. In this review, we summarise findings from studies of healthy and injured hearts in multiple species and spanning different developmental stages. Based on this transformative technology, we propose a multi-species, multi-omics, meta-analysis framework to drive the discovery of new targets to promote cardiovascular regeneration.

Understanding the intricacies of cellular senescence in atherosclerosis: Mechanisms and therapeutic implications

Cardiovascular disease is currently the largest cause of mortality and disability globally, surpassing communicable diseases, and atherosclerosis is the main contributor to this epidemic. Aging is intimately linked to atherosclerosis development and progression, however, the mechanism of aging in atherosclerosis is not well known. To emphasize the significant research on the involvement of senescent cells in atherosclerosis, we begin by outlining compelling evidence that indicates various types of senescent cells and SASP factors linked to atherosclerotic phenotypes. We subsequently provide a comprehensive summary of the existing knowledge, shedding light on the intricate mechanisms through which cellular senescence contributes to the pathogenesis of atherosclerosis. Further, we cover that senescence can be identified by both structural changes and several senescence-associated biomarkers. Finally, we discuss that preventing accelerated cellular senescence represents an important therapeutic potential, as permanent changes may occur in advanced atherosclerosis. Together, the review summarizes the relationship between cellular senescence and atherosclerosis, and inspects the molecular knowledge, and potential clinical significance of senescent cells in developing senescent-based therapy, thus providing crucial insights into their biology and potential therapeutic exploration.

Decoding the biology and clinical implication of neutrophils in intracranial aneurysm

OBJECTIVE

Abundant neutrophils have been identified in both ruptured and unruptured intracranial aneurysm (IA) domes, with their function and clinical implication being poorly characterized.

MATERIALS AND METHODS

We employed single-cell RNA sequencing (scRNA-Seq) datasets of both human and murine model, and external bulk mRNA sequencing datasets to thoroughly explore the features and functional heterogeneous of neutrophils infiltrating the IA dome.

RESULTS

We found that both unruptured and ruptured IA dome contain a substantial population of neutrophils, characterized by FCGR3B, G0S2, CSF3R, and CXCR2. These cells exhibited heterogeneity in terms of function and differentiation. Despite similar transcriptional activation, neutrophils in IA dome expressed a repertoire of gene programs that mimicked transcriptomic alterations observed from bone marrow to peripheral blood, showing self-similarity. In addition, the recruitment of neutrophils in unruptured IA was primarily mediated by monocytes/macrophages, and once ruptured, both neutrophils, and a specific subset of inflammatory smooth muscle cells (SMCs) were involved in the process. The receiver operator characteristic curve (ROC) analysis indicated that distinct neutrophil subclusters were associated with IA formation and rupture, respectively. By reviewing current studies, we found that neutrophils play a detrimental role to IA wall integrity through secreting specific ligands, ferroptosis driven by ALOX5AP and PTGS2, and the formation of neutrophil extracellular traps (NETs) mediated by PADI4.

INTERPRETATION

This study delineated the biology and potential clinical implications of neutrophils in IA dome and provided a reliable basis for future researches.

Targeting ERBB2 and PIK3R1 as a therapeutic strategy for dilated cardiomyopathy: A single-cell sequencing and mendelian randomization analysis

Background

Dilated cardiomyopathy (DCM) is widely recognized as a significant contributor to heart failure. Nevertheless, the absence of pharmaceutical interventions capable of reversing disease progression and improving prognosis underscores the imperative for additional research in this area.

Methods

First, we identified and evaluated three gene sets, namely "SC-DCM", "EP-DCM" and "Drug", using big data and multiple bioinformatics analysis methods. Accordingly, drug-treatable ("Hub") genes in DCM were identified. Following this, four microarray expression profile datasets were employed to authenticate the expression levels and discriminatory efficacy of "Hub" genes. Additionally, mendelian randomization analysis was conducted to ascertain the causal association between the "Hub genes" and heart failure. Finally, the "DGIdb" was applied to identify "Hub" genes-targeted drugs. The "ssGSEA" algorithm assessed the level of immune cell infiltration in DCM.

Results

Enrichment analysis showed that the "SC-DCM" and "EP-DCM" gene sets were closely associated with DCM. PIK3R1 and ERBB2 were identified as drug-treatable genes in DCM. Additional analysis using MR supported a causal relationship between ERBB2 and heart failure, but not PIK3R1. Moreover, PIK3R1 was positively correlated with immune activation, while ERBB2 was negatively correlated. We found that everolimus was a pharmacological inhibitor for both PIK3R1 and ERBB2. However, no pharmacological agonist was found for ERBB2.

Conclusion

PIK3R1 and ERBB2 are drug-treatable genes in DCM. ERBB2 has a causal effect on heart failure, and its normal expression may play a role in preventing the progression of DCM to heart failure. In addition, there is a cross-expression of PIK3R1 and ERBB2 genes in both DCM and tumors. The adaptive immune system and PIK3R1 may be involved in DCM disease progression, while ERBB2 exerts a protective effect against DCM.

Assessing Cardiac Contractility From Single Molecules to Whole Hearts

Fundamentally, the heart needs to generate sufficient force and power output to dynamically meet the needs of the body. Cardiomyocytes contain specialized structures referred to as sarcomeres that power and regulate contraction. Disruption of sarcomeric function or regulation impairs contractility and leads to cardiomyopathies and heart failure. Basic, translational, and clinical studies have adapted numerous methods to assess cardiac contraction in a variety of pathophysiological contexts. These tools measure aspects of cardiac contraction at different scales ranging from single molecules to whole organisms. Moreover, these studies have revealed new pathogenic mechanisms of heart disease leading to the development of novel therapies targeting contractility. In this review, the authors explore the breadth of tools available for studying cardiac contractile function across scales, discuss their strengths and limitations, highlight new insights into cardiac physiology and pathophysiology, and describe how these insights can be harnessed for therapeutic candidate development and translational.

Single-cell transcriptomic profiling of heart reveals ANGPTL4 linking fibroblasts and angiogenesis in heart failure with preserved ejection fraction

INTRODUCTION

Despite the high morbidity and mortality, the effective therapies for heart failure with preserved fraction (HFpEF) are limited as the poor understand of its pathophysiological basis.

OBJECTIVE

This study was aimed to characterize the cellular heterogeneity and potential mechanisms of HFpEF at single-cell resolution.

METHODS

An HFpEF mouse model was induced by a high-fat diet with N-nitro-L-arginine methyl ester. Cells from the hearts were subjected to single-cell sequencing. The key protein expression was measured with Immunohistochemistry and immunofluorescence staining.

RESULTS

In HFpEF hearts, myocardial fibroblasts exhibited higher levels of fibrosis. Furthermore, an increased number of fibroblasts differentiated into high-metabolism and high-fibrosis phenotypes. The expression levels of genes encoding certain pro-angiogenic secreted proteins were decreased in the HFpEF group, as confirmed by bulk RNA sequencing. Additionally, the proportion of the endothelial cell (EC) lineages in the HFpEF group was significantly downregulated, with low angiogenesis and high apoptosis phenotypes observed in these EC lineages. Interestingly, the fibroblasts in the HFpEF heart might cross-link with the EC lineages via over-secretion of ANGPTL4, thus displaying an anti-angiogenic function. Immunohistochemistry and immunofluorescence staining then revealed the downregulation of vascular density and upregulation of ANGPTL4 expression in HFpEF hearts. Finally, we predicted ANGPTL4as a potential druggable target using DrugnomeAI.

CONCLUSION

In conclusion, this study comprehensively characterized the angiogenesis impairment in HFpEF hearts at single-cell resolution and proposed that ANGPTL4 secretion by fibroblasts may be a potential mechanism underlying this angiogenic abnormality.

Regeneration of Thyroid Glands in the Spleen Restores Homeostasis in Thyroidectomy Mice

Surgical removal of the thyroid gland (TG) for treating thyroid disorders leaves the patients on lifelong hormone replacement that partially compensates the physiological needs, but regenerating TG is challenging. Here, an approach is reported to regenerate TG within the spleen for fully restoring the thyroid's functions in mice, by transplanting thyroid tissue blocks to the spleen. Within 48 h, the transplanted tissue efficiently revascularizes, forming thyroid follicles similar to the native gland after 4 weeks. Structurally, the ectopically generated thyroid integrates with the surrounding splenic tissue while maintaining its integrity, separate from the lymphatic tissue. Functionally, it fully restores the native functions of the TG in hormone regulation in response to physiological stimuli, outperforming the established method of oral levothyroxine therapy in maintaining systemic homeostasis. The study demonstrates the full restoration of thyroid functions post-thyroidectomy by intrasplenic TG regeneration, providing fresh insights for designing novel therapies for thyroid-related disorders.

Mapping Cell Atlases at the Single-Cell Level

Recent advancements in single-cell technologies have led to rapid developments in the construction of cell atlases. These atlases have the potential to provide detailed information about every cell type in different organisms, enabling the characterization of cellular diversity at the single-cell level. Global efforts in developing comprehensive cell atlases have profound implications for both basic research and clinical applications. This review provides a broad overview of the cellular diversity and dynamics across various biological systems. In addition, the incorporation of machine learning techniques into cell atlas analyses opens up exciting prospects for the field of integrative biology.

Single-cell RNA sequencing to understand host-virus interactions

Single-cell RNA sequencing (scRNA-seq) has allowed for the profiling of host and virus transcripts and host-virus interactions at single-cell resolution. This review summarizes the existing scRNA-seq technologies together with their strengths and weaknesses. The applications of scRNA-seq in various virological studies are discussed in depth, which broaden the understanding of the immune atlas, host-virus interactions, and immune repertoire. scRNA-seq can be widely used for virology in the near future to better understand the pathogenic mechanisms and discover more effective therapeutic strategies.

Parameter-Efficient Fine-Tuning Enhances Adaptation of Single Cell Large Language Model for Cell Type Identification

Single-cell sequencing transformed biology and medicine, providing an unprecedented high-resolution view at the cellular level. However, the vast variability inherent in single-cell sequencing data impedes its utility for in-depth downstream analysis. Inspired by the foundation models in natural language processing, recent advancements have led to the development of single-cell Large Language Models (scLLMs). These models are designed to discern universal patterns across diverse single-cell datasets, thereby enhancing the signal-to-noise ratio. Despite their potential, multiple studies indicate existing scLLMs do not perform well in zero-short settings, highlighting a pressing need for more effective adaptation techniques. This research proposes several adaptation techniques for scLLMs by preserving the original model parameters while selectively updating newly introduced tensors. This approach aims to overcome the limitations associated with traditional fine-tuning practices, such as catastrophic forgetting and computational inefficiencies. We introduce two Parameter-Efficient Fine-Tuning (PEFT) strategies specifically tailored to refine scLLMs for cell type identification. Our investigations utilizing scGPT demonstrate that PEFT can enhance performance, with the added benefit of up to a 90% reduction in parameter training compared to conventional fine-tuning methodologies. This work paves the way for a new direction in leveraging single-cell models with greater efficiency and efficacy in single-cell biology.

Single-cell RNA sequencing reveals the role of mitochondrial dysfunction in the cardiogenic toxicity of perfluorooctane sulfonate in human embryonic stem cells

Perfluorooctane sulfonate (PFOS), an endocrine-disrupting chemical pollutant, affects embryonic heart development; however, the mechanisms underlying its toxicity have not been fully elucidated. Here, Single-cell RNA sequencing (scRNA-seq) was used to investigate the overall effects of PFOS on myocardial differentiation from human embryonic stem cells (hESCs). Additionally, apoptosis, mitochondrial membrane potential, and ATP assays were performed. Downregulated cardiogenesis-related genes and inhibited cardiac differentiation were observed after PFOS exposure in vitro. The percentages of cardiomyocyte and cardiac progenitor cell clusters decreased significantly following exposure to PFOS, while the proportion of primitive endoderm cell was increased in PFOS group. Moreover, PFOS inhibited myocardial differentiation and blocked cellular development at the early- and middle-stage. A Gene Ontology analysis and pseudo-time trajectory illustrated that PFOS disturbed multiple processes related to cardiogenesis and oxidative phosphorylation in the mitochondria. Furthermore, PFOS decreased mitochondrial membrane potential and induced apoptosis. These results offer meaningful insights into the cardiogenic toxicity of PFOS exposure during heart formation as well as the adverse effects of PFOS on mitochondria.

Cardiac Development at a Single-Cell Resolution

Mammalian cardiac development is a complex, multistage process. Though traditional lineage tracing studies have characterized the broad trajectories of cardiac progenitors, the advent and rapid optimization of single-cell RNA sequencing methods have yielded an ever-expanding toolkit for characterizing heterogeneous cell populations in the developing heart. Importantly, they have allowed for a robust profiling of the spatiotemporal transcriptomic landscape of the human and mouse heart, revealing the diversity of cardiac cells-myocyte and non-myocyte-over the course of development. These studies have yielded insights into novel cardiac progenitor populations, chamber-specific developmental signatures, the gene regulatory networks governing cardiac development, and, thus, the etiologies of congenital heart diseases. Furthermore, single-cell RNA sequencing has allowed for the exquisite characterization of distinct cardiac populations such as the hard-to-capture cardiac conduction system and the intracardiac immune population. Therefore, single-cell profiling has also resulted in new insights into the regulation of cardiac regeneration and injury repair. Single-cell multiomics approaches combining transcriptomics, genomics, and epigenomics may uncover an even more comprehensive atlas of human cardiac biology. Single-cell analyses of the developing and adult mammalian heart offer an unprecedented look into the fundamental mechanisms of cardiac development and the complex diseases that may arise from it.

SINGLE-CELL TRANSCRIPTOME ANALYSIS IN HEALTH AND DISEASE

ABSTRACT

The analysis of the single-cell transcriptome has emerged as a powerful tool to gain insights on the basic mechanisms of health and disease. It is widely used to reveal the cellular diversity and complexity of tissues at cellular resolution by RNA sequencing of the whole transcriptome from a single cell. Equally, it is applied to discover an unknown, rare population of cells in the tissue. The prime advantage of single-cell transcriptome analysis is the detection of stochastic nature of gene expression of the cell in tissue. Moreover, the availability of multiple platforms for the single-cell transcriptome has broadened its approaches to using cells of different sizes and shapes, including the capture of short or full-length transcripts, which is helpful in the analysis of challenging biological samples. And with the development of numerous packages in R and Python, new directions in the computational analysis of single-cell transcriptomes can be taken to characterize healthy versus diseased tissues to obtain novel pathological insights. Downstream analysis such as differential gene expression analysis, gene ontology term analysis, Kyoto Encyclopedia of Genes and Genomes pathway analysis, cell-cell interaction analysis, and trajectory analysis has become standard practice in the workflow of single-cell transcriptome analysis to further examine the biology of different cell types. Here, we provide a broad overview of single-cell transcriptome analysis in health and disease conditions currently applied in various studies.

State transition and intercellular communication of synovial fibroblasts in response to chronic and acute shoulder injuries unveiled by single-cell transcriptomic analyses

PURPOSE

We aimed to investigate the heterogeneity of synovial fibroblasts and their potential to undergo cell state transitions at the resolution of single cells.

MATERIALS AND METHODS

We employed the single-cell RNA sequencing (scRNA-seq) approach to comprehensively map the cellular landscape of the shoulder synovium in individuals with chronic rotator cuff tears (RCTs) and acute proximal humerus fractures (PHFs). Utilizing unbiased clustering analysis, we successfully identified distinct subpopulations of fibroblasts within the synovial environment. We utilized Monocle 3 to delineate the trajectory of synovial fibroblast state transition. And we used CellPhone DB v2.1.0 to predict cell-cell communication patterns within the synovial microenvironment.

RESULTS

We identified eight main cell clusters in the shoulder synovium. Unbiased clustering analysis identified four synovial fibroblast subpopulations, with diverse biological functions associated with protein secretion, ECM remodeling, inflammation regulation and cell division. Lining, mesenchymal, pro-inflammatory and proliferative fibroblasts subsets were identified. Combining the results from StemID and characteristic gene features, mesenchymal fibroblasts exhibited characteristics of fibroblast progenitor cells. The trajectory of synovial fibroblast state transition showed a transition from mesenchymal to pro-inflammatory and lining phenotypes. In addition, the cross talk between fibroblast subclusters increased in degenerative shoulder diseases compared to acute trauma.

CONCLUSION

We successfully generated the scRNA-seq transcriptomic atlas of the shoulder synovium, which provides a comprehensive understanding of the heterogeneity of synovial fibroblasts, their potential to undergo state transitions, and their intercellular communication in the context of chronic degenerative and acute traumatic shoulder diseases.

Pathway centric analysis for single-cell RNA-seq and spatial transcriptomics data with GSDensity

Advances in single-cell technology have enabled molecular dissection of heterogeneous biospecimens at unprecedented scales and resolutions. Cluster-centric approaches are widely applied in analyzing single-cell data, however they have limited power in dissecting and interpreting highly heterogenous, dynamically evolving data. Here, we present GSDensity, a graph-modeling approach that allows users to obtain pathway-centric interpretation and dissection of single-cell and spatial transcriptomics (ST) data without performing clustering. Using pathway gene sets, we show that GSDensity can accurately detect biologically distinct cells and reveal novel cell-pathway associations ignored by existing methods. Moreover, GSDensity, combined with trajectory analysis can identify curated pathways that are active at various stages of mouse brain development. Finally, GSDensity can identify spatially relevant pathways in mouse brains and human tumors including those following high-order organizational patterns in the ST data. Particularly, we create a pan-cancer ST map revealing spatially relevant and recurrently active pathways across six different tumor types.

Microarray Analysis for Transcriptomic Profiling of Myocardium in Patients with Fatal Myocardial Infarction

Transcriptomic evidence from human myocardium in myocardial infarction (MI) is still not sufficient. Thus, there is a need for studies on human cardiac samples in relation to the clinical data of patients. The purpose of our pilot study was to investigate the transcriptomic profile of myocardium in the infarct zone, in comparison to the remote myocardium, in patients with fatal MI, via microarray analysis. This study included four patients with fatal MI type 1. We selected histologically verified samples from within the infarct area (n = 4) and remote myocardium (n = 4). The whole transcriptome was evaluated using microarray analysis. Differentially expressed genes (DEGs) clustered in the infarct area and in the remote myocardium allowed their differentiation. We identified a total of 1785 DEGs (8.32%) in the infarct area, including 1692 up-regulated (94.79%) and 93 down-regulated (5.21%) genes. The top 10 up-regulated genes were TRAIL, SUCLA2, NAE1, PDCL3, OSBPL5, FCGR2C, SELE, CEP63, ST3GAL3 and C4orf3. In the infarct area, we found up-regulation of seventeen apoptosis-related genes, eleven necroptosis-related, and six necrosis-related genes. Transcriptome profiling of the myocardium in patients with MI remains a relevant area of research for the formation of new scientific hypotheses and a potential way to increase the translational significance of studies into myocardial infarction.

Single-cell RNA sequencing in osteoarthritis

Osteoarthritis is a progressive and heterogeneous joint disease with complex pathogenesis. The various phenotypes associated with each patient suggest that better subgrouping of tissues associated with genotypes in different phases of osteoarthritis may provide new insights into the onset and progression of the disease. Recently, single-cell RNA sequencing was used to describe osteoarthritis pathogenesis on a high-resolution view surpassing traditional technologies. Herein, this review summarizes the microstructural changes in articular cartilage, meniscus, synovium and subchondral bone that are mainly due to crosstalk amongst chondrocytes, osteoblasts, fibroblasts and endothelial cells during osteoarthritis progression. Next, we focus on the promising targets discovered by single-cell RNA sequencing and its potential applications in target drugs and tissue engineering. Additionally, the limited amount of research on the evaluation of bone-related biomaterials is reviewed. Based on the pre-clinical findings, we elaborate on the potential clinical values of single-cell RNA sequencing for the therapeutic strategies of osteoarthritis. Finally, a perspective on the future development of patient-centred medicine for osteoarthritis therapy combining other single-cell multi-omics technologies is discussed. This review will provide new insights into osteoarthritis pathogenesis on a cellular level and the field of applications of single-cell RNA sequencing in personalized therapeutics for osteoarthritis in the future.

Transfer learning for clustering single-cell RNA-seq data crossing-species and batch, case on uterine fibroids

Due to the high dimensionality and sparsity of the gene expression matrix in single-cell RNA-sequencing (scRNA-seq) data, coupled with significant noise generated by shallow sequencing, it poses a great challenge for cell clustering methods. While numerous computational methods have been proposed, the majority of existing approaches center on processing the target dataset itself. This approach disregards the wealth of knowledge present within other species and batches of scRNA-seq data. In light of this, our paper proposes a novel method named graph-based deep embedding clustering (GDEC) that leverages transfer learning across species and batches. GDEC integrates graph convolutional networks, effectively overcoming the challenges posed by sparse gene expression matrices. Additionally, the incorporation of DEC in GDEC enables the partitioning of cell clusters within a lower-dimensional space, thereby mitigating the adverse effects of noise on clustering outcomes. GDEC constructs a model based on existing scRNA-seq datasets and then applying transfer learning techniques to fine-tune the model using a limited amount of prior knowledge gleaned from the target dataset. This empowers GDEC to adeptly cluster scRNA-seq data cross different species and batches. Through cross-species and cross-batch clustering experiments, we conducted a comparative analysis between GDEC and conventional packages. Furthermore, we implemented GDEC on the scRNA-seq data of uterine fibroids. Compared results obtained from the Seurat package, GDEC unveiled a novel cell type (epithelial cells) and identified a notable number of new pathways among various cell types, thus underscoring the enhanced analytical capabilities of GDEC. Availability and implementation: https://github.com/YuzhiSun/GDEC/tree/main.

A critical assessment of clustering algorithms to improve cell clustering and identification in single-cell transcriptome study

Cell clustering is typically the initial step in single-cell RNA sequencing (scRNA-seq) analyses. The performance of clustering considerably impacts the validity and reproducibility of cell identification. A variety of clustering algorithms have been developed for scRNA-seq data. These algorithms generate cell label sets that assign each cell to a cluster. However, different algorithms usually yield different label sets, which can introduce variations in cell-type identification based on the generated label sets. Currently, the performance of these algorithms has not been systematically evaluated in single-cell transcriptome studies. Herein, we performed a critical assessment of seven state-of-the-art clustering algorithms including four deep learning-based clustering algorithms and commonly used methods Seurat, Cosine-based Tanimoto similarity-refined graph for community detection using Leiden's algorithm (CosTaL) and Single-cell consensus clustering (SC3). We used diverse evaluation indices based on 10 different scRNA-seq benchmarks to systematically evaluate their clustering performance. Our results show that CosTaL, Seurat, Deep Embedding for Single-cell Clustering (DESC) and SC3 consistently outperformed Single-Cell Clustering Assessment Framework and scDeepCluster based on nine effectiveness scores. Notably, CosTaL and DESC demonstrated superior performance in clustering specific cell types. The performance of the single-cell Variational Inference tools varied across different datasets, suggesting its sensitivity to certain dataset characteristics. Notably, DESC exhibited promising results for cell subtype identification and capturing cellular heterogeneity. In addition, SC3 requires more memory and exhibits slower computation speed compared to other algorithms for the same dataset. In sum, this study provides useful guidance for selecting appropriate clustering methods in scRNA-seq data analysis.

Function and regulation of ubiquitin-like SUMO system in heart

The small ubiquitin-related modifier (SUMOylation) system is a conserved, reversible, post-translational protein modification pathway covalently attached to the lysine residues of proteins in eukaryotic cells, and SUMOylation is catalyzed by SUMO-specific activating enzyme (E1), binding enzyme (E2) and ligase (E3). Sentrin-specific proteases (SENPs) can cleave the isopeptide bond of a SUMO conjugate and catalyze the deSUMOylation reaction. SUMOylation can regulate the activity of proteins in many important cellular processes, including transcriptional regulation, cell cycle progression, signal transduction, DNA damage repair and protein stability. Biological experiments in vivo and in vitro have confirmed the key role of the SUMO conjugation/deconjugation system in energy metabolism, Ca2+ cycle homeostasis and protein quality control in cardiomyocytes. In this review, we summarized the research progress of the SUMO conjugation/deconjugation system and SUMOylation-mediated cardiac actions based on related studies published in recent years, and highlighted the further research areas to clarify the role of the SUMO system in the heart by using emerging technologies.

A statistical framework for differential pseudotime analysis with multiple single-cell RNA-seq samples

Pseudotime analysis with single-cell RNA-sequencing (scRNA-seq) data has been widely used to study dynamic gene regulatory programs along continuous biological processes. While many methods have been developed to infer the pseudotemporal trajectories of cells within a biological sample, it remains a challenge to compare pseudotemporal patterns with multiple samples (or replicates) across different experimental conditions. Here, we introduce Lamian, a comprehensive and statistically-rigorous computational framework for differential multi-sample pseudotime analysis. Lamian can be used to identify changes in a biological process associated with sample covariates, such as different biological conditions while adjusting for batch effects, and to detect changes in gene expression, cell density, and topology of a pseudotemporal trajectory. Unlike existing methods that ignore sample variability, Lamian draws statistical inference after accounting for cross-sample variability and hence substantially reduces sample-specific false discoveries that are not generalizable to new samples. Using both real scRNA-seq and simulation data, including an analysis of differential immune response programs between COVID-19 patients with different disease severity levels, we demonstrate the advantages of Lamian in decoding cellular gene expression programs in continuous biological processes.

Organotypic heterogeneity in microvascular endothelial cell responses in sepsis-a molecular treasure trove and pharmacological Gordian knot

In the last decades, it has become evident that endothelial cells (ECs) in the microvasculature play an important role in the pathophysiology of sepsis-associated multiple organ dysfunction syndrome (MODS). Studies on how ECs orchestrate leukocyte recruitment, control microvascular integrity and permeability, and regulate the haemostatic balance have provided a wealth of knowledge and potential molecular targets that could be considered for pharmacological intervention in sepsis. Yet, this information has not been translated into effective treatments. As MODS affects specific vascular beds, (organotypic) endothelial heterogeneity may be an important contributing factor to this lack of success. On the other hand, given the involvement of ECs in sepsis, this heterogeneity could also be leveraged for therapeutic gain to target specific sites of the vasculature given its full accessibility to drugs. In this review, we describe current knowledge that defines heterogeneity of organ-specific microvascular ECs at the molecular level and elaborate on studies that have reported EC responses across organ systems in sepsis patients and animal models of sepsis. We discuss hypothesis-driven, single-molecule studies that have formed the basis of our understanding of endothelial cell engagement in sepsis pathophysiology, and include recent studies employing high-throughput technologies. The latter deliver comprehensive data sets to describe molecular signatures for organotypic ECs that could lead to new hypotheses and form the foundation for rational pharmacological intervention and biomarker panel development. Particularly results from single cell RNA sequencing and spatial transcriptomics studies are eagerly awaited as they are expected to unveil the full spatiotemporal signature of EC responses to sepsis. With increasing awareness of the existence of distinct sepsis subphenotypes, and the need to develop new drug regimen and companion diagnostics, a better understanding of the molecular pathways exploited by ECs in sepsis pathophysiology will be a cornerstone to halt the detrimental processes that lead to MODS.

Biology and therapeutic targeting of vascular endothelial growth factor A

The formation of new blood vessels, called angiogenesis, is an essential pathophysiological process in which several families of regulators have been implicated. Among these, vascular endothelial growth factor A (VEGFA; also known as VEGF) and its two tyrosine kinase receptors, VEGFR1 and VEGFR2, represent a key signalling pathway mediating physiological angiogenesis and are also major therapeutic targets. VEGFA is a member of the gene family that includes VEGFB, VEGFC, VEGFD and placental growth factor (PLGF). Three decades after its initial isolation and cloning, VEGFA is arguably the most extensively investigated signalling system in angiogenesis. Although many mediators of angiogenesis have been identified, including members of the FGF family, angiopoietins, TGFβ and sphingosine 1-phosphate, all current FDA-approved anti-angiogenic drugs target the VEGF pathway. Anti-VEGF agents are widely used in oncology and, in combination with chemotherapy or immunotherapy, are now the standard of care in multiple malignancies. Anti-VEGF drugs have also revolutionized the treatment of neovascular eye disorders such as age-related macular degeneration and ischaemic retinal disorders. In this Review, we emphasize the molecular, structural and cellular basis of VEGFA action as well as recent findings illustrating unexpected interactions with other pathways and provocative reports on the role of VEGFA in regenerative medicine. We also discuss clinical and translational aspects of VEGFA. Given the crucial role that VEGFA plays in regulating angiogenesis in health and disease, this molecule is largely the focus of this Review.

Combined near infrared photoacoustic imaging and ultrasound detects vulnerable atherosclerotic plaque

Atherosclerosis is an inflammatory process resulting in the deposition of cholesterol and cellular debris, narrowing of the vessel lumen and clot formation. Characterization of the morphology and vulnerability of the lesion is essential for effective clinical management. Here, near-infrared auto-photoacoustic (NIRAPA) imaging is shown to detect plaque components and, when combined with ultrasound imaging, to differentiate stable and vulnerable plaque. In an ex vivo study of photoacoustic imaging of excised plaque from 25 patients, 88.2% sensitivity and 71.4% specificity were achieved using a clinically-relevant protocol. In order to determine the origin of the NIRAPA signal, immunohistochemistry, spatial transcriptomics and spatial proteomics were co-registered with imaging and applied to adjacent plaque sections. The highest NIRAPA signal was spatially correlated with bilirubin and associated blood-based residue and with the cytoplasmic contents of inflammatory macrophages bearing CD74, HLA-DR, CD14 and CD163 markers. In summary, we establish the potential to apply the NIRAPA-ultrasound imaging combination to detect vulnerable carotid plaque and a methodology for fusing molecular imaging with spatial transcriptomic and proteomic methods.

Role of Pericytes in Cardiomyopathy-Associated Myocardial Infarction Revealed by Multiple Single-Cell Sequencing Analysis

Acute myocardial infarction (AMI) is one of the leading causes of cardiovascular death worldwide. AMI with cardiomyopathy is accompanied by a poor long-term prognosis. However, limited studies have focused on the mechanism of cardiomyopathy associated with AMI. Pericytes are important to the microvascular function in the heart, yet little attention has been paid to their function in myocardial infarction until now. In this study, we integrated single-cell data from individuals with cardiomyopathy and myocardial infarction (MI) GWAS data to reveal the potential function of pericytes in cardiomyopathy-associated MI. We found that pericytes were concentrated in the left atrium and left ventricle tissues. DLC1/GUCY1A2/EGFLAM were the top three uniquely expressed genes in pericytes (p < 0.05). The marker genes of pericytes were enriched in renin secretion, vascular smooth muscle contraction, gap junction, purine metabolism, and diabetic cardiomyopathy pathways (p < 0.05). Among these pathways, the renin secretion and purine metabolism pathways were also found in the process of MI. In cardiomyopathy patients, the biosynthesis of collagen, modulating enzymes, and collagen formation were uniquely negatively regulated in pericytes compared to other cell types (p < 0.05). COL4A2/COL4A1/SMAD3 were the hub genes in pericyte function involved in cardiomyopathy and AMI. In conclusion, this study provides new evidence about the importance of pericytes in the pathogenesis of cardiomyopathy-associated MI. DLC1/GUCY1A2/EGFLAM were highly expressed in pericytes. The hub genes COL4A2/COL4A1/SMAD3 may be potential research targets for cardiomyopathy-associated MI.

Cardiac cellular diversity and functionality in cardiac repair by single-cell transcriptomics

Cardiac repair after myocardial infarction (MI) is orchestrated by multiple intrinsic mechanisms in the heart. Identifying cardiac cell heterogeneity and its effect on processes that mediate the ischemic myocardium repair may be key to developing novel therapeutics for preventing heart failure. With the rapid advancement of single-cell transcriptomics, recent studies have uncovered novel cardiac cell populations, dynamics of cell type composition, and molecular signatures of MI-associated cells at the single-cell level. In this review, we summarized the main findings during cardiac repair by applying single-cell transcriptomics, including endogenous myocardial regeneration, myocardial fibrosis, angiogenesis, and the immune microenvironment. Finally, we also discussed the integrative analysis of spatial multi-omics transcriptomics and single-cell transcriptomics. This review provided a basis for future studies to further advance the mechanism and development of therapeutic approaches for cardiac repair.

Single-cell analysis technologies for cancer research: from tumor-specific single cell discovery to cancer therapy

Single-cell sequencing (SCS) technology is changing our understanding of cellular components, functions, and interactions across organisms, because of its inherent advantage of avoiding noise resulting from genotypic and phenotypic heterogeneity across numerous samples. By directly and individually measuring multiple molecular characteristics of thousands to millions of single cells, SCS technology can characterize multiple cell types and uncover the mechanisms of gene regulatory networks, the dynamics of transcription, and the functional state of proteomic profiling. In this context, we conducted systematic research on SCS techniques, including the fundamental concepts, procedural steps, and applications of scDNA, scRNA, scATAC, scCITE, and scSNARE methods, focusing on the unique clinical advantages of SCS, particularly in cancer therapy. We have explored challenging but critical areas such as circulating tumor cells (CTCs), lineage tracing, tumor heterogeneity, drug resistance, and tumor immunotherapy. Despite challenges in managing and analyzing the large amounts of data that result from SCS, this technique is expected to reveal new horizons in cancer research. This review aims to emphasize the key role of SCS in cancer research and promote the application of single-cell technologies to cancer therapy.

Distinct mononuclear diploid cardiac subpopulation with minimal cell-cell communications persists in embryonic and adult mammalian heart

A small proportion of mononuclear diploid cardiomyocytes (MNDCMs), with regeneration potential, could persist in adult mammalian heart. However, the heterogeneity of MNDCMs and changes during development remains to be illuminated. To this end, 12 645 cardiac cells were generated from embryonic day 17.5 and postnatal days 2 and 8 mice by single-cell RNA sequencing. Three cardiac developmental paths were identified: two switching to cardiomyocytes (CM) maturation with close CM-fibroblast (FB) communications and one maintaining MNDCM status with least CM-FB communications. Proliferative MNDCMs having interactions with macrophages and non-proliferative MNDCMs (non-pMNDCMs) with minimal cell-cell communications were identified in the third path. The non-pMNDCMs possessed distinct properties: the lowest mitochondrial metabolisms, the highest glycolysis, and high expression of Myl4 and Tnni1. Single-nucleus RNA sequencing and immunohistochemical staining further proved that the Myl4+Tnni1+ MNDCMs persisted in embryonic and adult hearts. These MNDCMs were mapped to the heart by integrating the spatial and single-cell transcriptomic data. In conclusion, a novel non-pMNDCM subpopulation with minimal cell-cell communications was unveiled, highlighting the importance of microenvironment contribution to CM fate during maturation. These findings could improve the understanding of MNDCM heterogeneity and cardiac development, thus providing new clues for approaches to effective cardiac regeneration.

Essential procedures of single-cell RNA sequencing in multiple myeloma and its translational value

Multiple myeloma (MM) is a malignant neoplasm characterized by clonal proliferation of abnormal plasma cells. In many countries, it ranks as the second most prevalent malignant neoplasm of the hematopoietic system. Although treatment methods for MM have been continuously improved and the survival of patients has been dramatically prolonged, MM remains an incurable disease with a high probability of recurrence. As such, there are still many challenges to be addressed. One promising approach is single-cell RNA sequencing (scRNA-seq), which can elucidate the transcriptome heterogeneity of individual cells and reveal previously unknown cell types or states in complex tissues. In this review, we outlined the experimental workflow of scRNA-seq in MM, listed some commonly used scRNA-seq platforms and analytical tools. In addition, with the advent of scRNA-seq, many studies have made new progress in the key molecular mechanisms during MM clonal evolution, cell interactions and molecular regulation in the microenvironment, and drug resistance mechanisms in target therapy. We summarized the main findings and sequencing platforms for applying scRNA-seq to MM research and proposed broad directions for targeted therapies based on these findings.

Adult tissue-specific stem cell interaction: novel technologies and research advances

Adult tissue-specific stem cells play a dominant role in tissue homeostasis and regeneration. Various in vivo markers of adult tissue-specific stem cells have been increasingly reported by lineage tracing in genetic mouse models, indicating that marked cells differentiation is crucial during homeostasis and regeneration. How adult tissue-specific stem cells with indicated markers contact the adjacent lineage with indicated markers is of significance to be studied. Novel methods bring future findings. Recent advances in lineage tracing, synthetic receptor systems, proximity labeling, and transcriptomics have enabled easier and more accurate cell behavior visualization and qualitative and quantitative analysis of cell-cell interactions than ever before. These technological innovations have prompted researchers to re-evaluate previous experimental results, providing increasingly compelling experimental results for understanding the mechanisms of cell-cell interactions. This review aimed to describe the recent methodological advances of dual enzyme lineage tracing system, the synthetic receptor system, proximity labeling, single-cell RNA sequencing and spatial transcriptomics in the study of adult tissue-specific stem cells interactions. An enhanced understanding of the mechanisms of adult tissue-specific stem cells interaction is important for tissue regeneration and maintenance of homeostasis in organisms.

Single-cell sequencing: New insights for intervertebral disc degeneration

Over the past decade, single-cell RNA sequencing (scRNA-seq) has revolutionized research on biological mechanisms of diseases. Moreover, this technique has been utilized to identify and characterize unique cell types and subpopulations, thereby illuminating cellular heterogeneity. The true value of scRNA-seq lies in its ability to detect transcriptional alterations or perturbed pathways within specific cell types under pathological conditions. In the context of intervertebral disc degeneration (IVDD), the pathophysiological foundation is largely rooted in inflammation. The primary target cells of IVDD are nucleus pulposus cells, annulus fibrosus cells, cartilage endplate cells, and macrophages. The advancements in scRNA-seq technology have triggered remarkable progress in IVDD treatment, leading to breakthroughs in the identification of cell subsets, functional analysis, novel therapeutic targets, and the differentiation and development of various cell types. This review is the first of its kind to introduce the application of scRNA-seq techniques in IVDD, with a focus on the most recent scRNA-seq studies that have defined the populations of various cell types and specific cell-cell interactions in IVDD. Furthermore, we highlight several promising future research directions for scRNA-seq in IVDD.

Single-cell RNA sequencing in dissecting microenvironment of age-related macular degeneration: Challenges and perspectives

Age-related macular degeneration (AMD) is the leading cause of blindness in individuals over the age of 50 years, yet its etiology and pathogenesis largely remain uncovered. Single-cell RNA sequencing (scRNA-seq) technologies are recently developed and have a number of advantages over conventional bulk RNA sequencing techniques in uncovering the heterogeneity of complex microenvironments containing numerous cell types and cell communications during various biological processes. In this review, we summarize the latest discovered cellular components and regulatory mechanisms during AMD development revealed by scRNA-seq. In addition, we discuss the main challenges and future directions in exploring the pathophysiology of AMD equipped with single-cell technologies. Our review underscores the importance of multimodal single-cell platforms (such as single-cell spatiotemporal multi-omics and single-cell exosome omics) as new approaches for basic and clinical AMD research in identifying biomarker, characterizing cellular responses to drug treatment and environmental stimulation.

Single-Cell Transcriptional Landscape Reveals the Regulatory Network and Its Heterogeneity of Renal Mitochondrial Damages in Diabetic Kidney Disease

Diabetic kidney disease (DKD) is one of the common chronic microvascular complications of diabetes in which mitochondrial disorder plays an important role in its pathogenesis. The current study delved into the single-cell level transcriptome heterogeneity of mitochondrial homeostasis in db/db mice, an animal model for study of type 2 diabetes and DKD, with single-cell RNA sequencing (scRNA-Seq) and bulk RNA-seq analyses. From the comprehensive dataset comprising 13 meticulously captured and authenticated renal cell types, an unsupervised cluster analysis of mitochondria-related genes within the descending loop of Henle, collecting duct principal cell, endothelial, B cells and macrophage, showed that they had two types of cell subsets, i.e., health-dominant and DKD-dominant clusters. Pseudotime analysis, cell communication and transcription factors forecast resulted in identification of the hub differentially expressed genes between these two clusters and unveiled that the hierarchical regulatory network of receptor-TF-target genes was triggered by mitochondrial degeneration. Furthermore, the collecting duct principal cells were found to be regulated by the decline of Fzd7, which contributed to the impaired cellular proliferation and development, apoptosis and inactive cell cycle, as well as diminished capacity for material transport. Thereby, both scRNA-Seq and bulk RNA-Seq data from the current study elucidate the heterogeneity of mitochondrial disorders among distinct cell types, particularly in the collecting duct principal cells and B cells during the DKD progression and drug administration, which provide novel insights for better understanding the pathogenesis of DKD.

Identification of renal cyst cells of type I Nephronophthisis by single-nucleus RNA sequencing

Background: Nephronophthisis (NPH) is the most common genetic cause of end-stage renal disease (ESRD) in childhood, and NPHP1 is the major pathogenic gene. Cyst formation at the corticomedullary junction is a pathological feature of NPH, but the mechanism underlying cystogenesis is not well understood. The isolation and identification of cystic cell subpopulation could help to identify their origins and provide vital clues to the mechanisms underlying cystogenesis in NPH. Methods: Single-nucleus RNA sequencing (snRNA-seq) was performed to produce an atlas of NPHP1 renal cells. Kidney samples were collected from WT (Nphp1 +/+) mice and NPHP1 (Nphp1 del2-20/del2-20) model mice. Results: A comprehensive atlas of the renal cellular landscape in NPHP1 was generated, consisting of 14 basic renal cell types as well as a subpopulation of DCT cells that was overrepresented in NPHP1 kidneys compared to WT kidneys. GO analysis revealed significant downregulation of genes associated with tubular development and kidney morphogenesis in this subpopulation. Furthermore, the reconstruction of differentiation trajectories of individual cells within this subpopulation confirmed that a specific group of cells in NPHP1 mice become arrested at an early stage of differentiation and proliferate to form cysts. We demonstrate that Niban1 is a specific molecular marker of cystic cells in both mice and human NPHP1. Conclusion: In summary, we report a novel subpopulation of DCT cells, marked by Niban1, that are classified as cystic cells in the NPHP1 mice kidney. These results offer fresh insights into the cellular and molecular basis of cystogenesis in NPH.

New discoveries in the field of metabolism by applying single-cell and spatial omics

Single-cell multi-Omics (SCM-Omics) and spatial multi-Omics (SM-Omics) technologies provide state-of-the-art methods for exploring the composition and function of cell types in tissues/organs. Since its emergence in 2009, single-cell RNA sequencing (scRNA-seq) has yielded many groundbreaking new discoveries. The combination of this method with the emergence and development of SM-Omics techniques has been a pioneering strategy in neuroscience, developmental biology, and cancer research, especially for assessing tumor heterogeneity and T-cell infiltration. In recent years, the application of these methods in the study of metabolic diseases has also increased. The emerging SCM-Omics and SM-Omics approaches allow the molecular and spatial analysis of cells to explore regulatory states and determine cell fate, and thus provide promising tools for unraveling heterogeneous metabolic processes and making them amenable to intervention. Here, we review the evolution of SCM-Omics and SM-Omics technologies, and describe the progress in the application of SCM-Omics and SM-Omics in metabolism-related diseases, including obesity, diabetes, nonalcoholic fatty liver disease (NAFLD) and cardiovascular disease (CVD). We also conclude that the application of SCM-Omics and SM-Omics approaches can help resolve the molecular mechanisms underlying the pathogenesis of metabolic diseases in the body and facilitate therapeutic measures for metabolism-related diseases. This review concludes with an overview of the current status of this emerging field and the outlook for its future.

DCATS: differential composition analysis for flexible single-cell experimental designs

Differential composition analysis - the identification of cell types that have statistically significant changes in abundance between multiple experimental conditions - is one of the most common tasks in single cell omic data analysis. However, it remains challenging to perform differential composition analysis in the presence of flexible experimental designs and uncertainty in cell type assignment. Here, we introduce a statistical model and an open source R package, DCATS, for differential composition analysis based on a beta-binomial regression framework that addresses these challenges. Our empirical evaluation shows that DCATS consistently maintains high sensitivity and specificity compared to state-of-the-art methods.

Quantitative proteomic profiling identifies global protein network dynamics in murine embryonic heart development

Defining the mechanisms that govern heart development is essential for identifying the etiology of congenital heart disease. Here, quantitative proteomics was used to measure temporal changes in the proteome at critical stages of murine embryonic heart development. Global temporal profiles of the over 7,300 proteins uncovered signature cardiac protein interaction networks that linked protein dynamics with molecular pathways. Using this integrated dataset, we identified and demonstrated a functional role for the mevalonate pathway in regulating the cell cycle of embryonic cardiomyocytes. Overall, our proteomic datasets are a resource for studying events that regulate embryonic heart development and contribute to congenital heart disease.