Methods for isolation and transcriptional profiling of individual cells from the human heart

Machine learning and multi-omics integration: advancing cardiovascular translational research and clinical practice

The global burden of cardiovascular diseases continues to rise, making their prevention, diagnosis and treatment increasingly critical. With advancements and breakthroughs in omics technologies such as high-throughput sequencing, multi-omics approaches can offer a closer reflection of the complex physiological and pathological changes in the body from a molecular perspective, providing new microscopic insights into cardiovascular diseases research. However, due to the vast volume and complexity of data, accurately describing, utilising, and translating these biomedical data demands substantial effort. Researchers and clinicians are actively developing artificial intelligence (AI) methods for data-driven knowledge discovery and causal inference using various omics data. These AI approaches, integrated with multi-omics research, have shown promising outcomes in cardiovascular studies. In this review, we outline the methods for integrating machine learning, one of the most successful applications of AI, with omics data and summarise representative AI models developed that leverage various omics data to facilitate the exploration of cardiovascular diseases from underlying mechanisms to clinical practice. Particular emphasis is placed on the effectiveness of using AI to extract potential molecular information to address current knowledge gaps. We discuss the challenges and opportunities of integrating omics with AI into routine diagnostic and therapeutic practices and anticipate the future development of novel AI models for wider application in the field of cardiovascular diseases.

Single-cell RNA sequencing in stroke and traumatic brain injury: Current achievements, challenges, and future perspectives on transcriptomic profiling

Single-cell RNA sequencing (scRNA-seq) is a high-throughput transcriptomic approach with the power to identify rare cells, discover new cellular subclusters, and describe novel genes. scRNA-seq can simultaneously reveal dynamic shifts in cellular phenotypes and heterogeneities in cellular subtypes. Since the publication of the first protocol on scRNA-seq in 2009, this evolving technology has continued to improve, through the use of cell-specific barcodes, adoption of droplet-based systems, and development of advanced computational methods. Despite induction of the cellular stress response during the tissue dissociation process, scRNA-seq remains a popular technology, and commercially available scRNA-seq methods have been applied to the brain. Recent advances in spatial transcriptomics now allow the researcher to capture the positional context of transcriptional activity, strengthening our knowledge of cellular organization and cell-cell interactions in spatially intact tissues. A combination of spatial transcriptomic data with proteomic, metabolomic, or chromatin accessibility data is a promising direction for future research. Herein, we provide an overview of the workflow, data analyses methods, and pros and cons of scRNA-seq technology. We also summarize the latest achievements of scRNA-seq in stroke and acute traumatic brain injury, and describe future applications of scRNA-seq and spatial transcriptomics.

Optimized protocol for the multiomics processing of cryopreserved human kidney tissue

Biobanking of tissue from clinically obtained kidney biopsies for later analysis with multiomic approaches, such as single-cell technologies, proteomics, metabolomics, and the different types of imaging, is an inevitable step to overcome the need of disease model systems and toward translational medicine. Hence, collection protocols that ensure integration into daily clinical routines by the usage of preservation media that do not require liquid nitrogen but instantly preserve kidney tissue for both clinical and scientific analyses are necessary. Thus, we modified a robust single-nucleus dissociation protocol for kidney tissue stored snap-frozen or in the preservation media RNAlater and CellCover. Using at first porcine kidney tissue as a surrogate for human kidney tissue, we conducted single-nucleus RNA sequencing with the widely recognized Chromium 10X Genomics platform. The resulting datasets from each storage condition were analyzed to identify any potential variations in transcriptomic profiles. Furthermore, we assessed the suitability of the preservation media for additional analysis techniques such as proteomics, metabolomics, and the preservation of tissue architecture for histopathological examination including immunofluorescence staining. In this study, we show that in daily clinical routines, the preservation medium RNAlater facilitates the collection of highly preserved human kidney biopsies and enables further analysis with cutting-edge techniques like single-nucleus RNA sequencing, proteomics, and histopathological evaluation. Only metabolome analysis is currently restricted to snap-frozen tissue. This work will contribute to build tissue biobanks with well-defined cohorts of the respective kidney disease that can be deeply molecularly characterized, opening up new horizons for the identification of unique cells, pathways and biomarkers for the prevention, early identification, and targeted therapy of kidney diseases.NEW & NOTEWORTHY In this study, we addressed challenges in integrating clinically obtained kidney biopsies into everyday clinical routines. Using porcine kidneys, we evaluated preservation media (RNAlater and CellCover) versus snap freezing for multi-omics processing. Our analyses highlighted RNAlater's suitability for single-nucleus RNA sequencing, proteome analysis and histopathological evaluation. Only metabolomics are currently restricted to snap-frozen biopsies. Our research established a cryopreservation protocol that facilitates tissue biobanking for advancing precision medicine in nephrology.

Multi-scalar data integration decoding risk genes for chronic kidney disease

BACKGROUND

Chronic Kidney Disease (CKD) impacts over 10% of the global population, and recent advancements in high-throughput analytical technologies are uncovering the complex physiology underlying this condition. By integrating Genome-Wide Association Studies (GWAS), RNA sequencing (RNA-seq/RNA array), and single-cell RNA sequencing (scRNA-seq) data, our study aimed to explore the genes and cell types relevant to CKD traits.

METHODS

GWAS summary data for end-stage renal failure (ESRD) and decreased eGFR (CKD) with or without diabetes and (micro)proteinuria were obtained from the GWAS Catalog and the UK Biobank (UKB) database. Two gene Expression Omnibus (GEO) transcriptome datasets were used to establish glomerular and tubular gene expression differences between CKD patients and healthy individuals. Two scRNA-seq datasets were utilized to obtain the expression of key genes at the single-cell level. The expression profile, differentially expressed genes (DEGs), gene-gene interaction, and pathway enrichment were analysed for these CKD risk genes.

RESULTS

A total of 779 distinct SNPs were identified from GWAS across different CKD traits, involving 681 genes. While many of these risk genes are specific to the CKD traits of renal failure, decreased eGFR, and (micro)proteinuria, they share common pathways, including extracellular matrix (ECM). ECM modeling was enriched in upregulated glomerular and tubular DEGs from CKD kidneys compared to healthy controls, with the expression of relevant collagen genes, such as COL1A2, prevalent in fibroblasts/myofibroblasts. Additionally, immune responses, including T cell differentiation, were dysregulated in CKD kidneys. The late podocyte signature gene THSD7A was enriched in podocytes but downregulated in CKD. We also highlighted that the regulated risk genes of CKD are mainly expressed in tubular cells and immune cells in the kidney.

CONCLUSIONS

Our integrated analysis highlight the genes, pathways, and relevant cell types associational with the pathogenesis of kidney traits, as a basis for further mechanistic studies to understand the pathogenesis of CKD.

METTL3-mediated m6A modification of OTUD1 aggravates press overload induced myocardial hypertrophy by deubiquitinating PGAM5

Background: Pathological cardiac hypertrophy, a condition that contributes to heart failure, is characterized by its intricate pathogenesis. The meticulous regulation of protein function, localization, and degradation is a crucial role played by deubiquitinating enzymes in cardiac pathophysiology. This study clarifies the participation and molecular mechanism of OTUD1 (OTU Deubiquitinase 1) in pathological cardiac hypertrophy. Methods: We generated a cardiac-specific Otud1 knockout mouse line (Otud1-CKO) and adeno-associated virus serotype 9-Otud1 mice to determine the role of Otud1 in cardiac hypertrophy. Its impact on cardiomyocytes enlargement was investigated using the adenovirus. RNA immunoprecipitation was used to validate the specific m6a methyltransferase interacted with OTUD1 transcript. RNA sequencing in conjunction with immunoprecipitation-mass spectrometry analysis was employed to identify the direct targets of OTUD1. A series of depletion mutant plasmids were constructed to detect the interaction domain of OTUD1 and its targets. Results: Ang II-stimulated neonatal rat cardiac myocytes and mice hearts subjected to transverse aortic constriction (TAC) showed increased protein levels of Otud1. Cardiac hypertrophy and dysfunction were less frequent in Otud1-CKO mice during TAC treatment, while Otud1 overexpression worsened cardiac hypertrophy and remodeling. METTL3 mediated m6A modification of OTUD1 transcript promoted mRNA stability and elevated protein expression. In terms of pathogenesis, Otud1 plays a crucial role in cardiac hypertrophy by targeting Pgam5, leading to the robust activation of the Ask1-p38/JNK signal pathway to accelerate cardiac hypertrophy. Significantly, the pro-hypertrophy effects of Otud1 overexpression were largely eliminated when Ask1 knockdown. Conclusion: Our findings confirm that targeting the OTUD1-PGAM5 axis holds significant potential as a therapeutic approach for heart failure associated with pathological hypertrophy.

Multimodal Analytical Tools to Enhance Mechanistic Understanding of Aortic Valve Calcification

This review focuses on technologies at the core of calcific aortic valve disease (CAVD) and drug target research advancement, including transcriptomics, proteomics, and molecular imaging. We examine how bulk RNA sequencing and single-cell RNA sequencing have engendered organismal genomes and transcriptomes, promoting the analysis of tissue gene expression profiles and cell subpopulations, respectively. We bring into focus how the field is also largely influenced by increasingly accessible proteome profiling techniques. In unison, global transcriptional and protein expression analyses allow for increased understanding of cellular behavior and pathogenic pathways under pathologic stimuli including stress, inflammation, low-density lipoprotein accumulation, increased calcium and phosphate levels, and vascular injury. We also look at how direct investigation of protein signatures paves the way for identification of targetable pathways for pharmacologic intervention. Here, we note that imaging techniques, once a clinical diagnostic tool for late-stage CAVD, have since been refined to address a clinical need to identify microcalcifications using positron emission tomography/computed tomography and even detect in vivo cellular events indicative of early stage CAVD and map the expression of identified proteins in animal models. Together, these techniques generate a holistic approach to CAVD investigation, with the potential to identify additional novel regulatory pathways.

Analysis of genetic variant associated with heart failure mortality implicates thymic stromal lymphopoietin as mediator of strain-induced myocardial fibroblast-mast cell crosstalk and fibrosis

Heart failure (HF) is a leading cause of death and disability globally. Heritable factors and the extent and pattern of myocardial fibrosis are important determinants of outcomes in patients with HF. In a genome-wide association study of mortality in HF, we recently identified a genetic polymorphism on chromosome 5q22 associated with HF mortality. Here, we sought to study the mechanisms by which this variant may influence myocardial disease processes. We find that the risk allele is located in an enhancer motif upstream of the TSLP gene (encoding thymic stromal lymphopoietin), conferring increased binding of the transcription factor nescient helix-loop helix 1 (NHLH1) and increased TSLP expression in human heart. Further, we find that increased strain of primary human myocardial fibroblasts results in increased TSLP expression and that the TSLP receptor is expressed in myocardial mast cells in human single nuclei RNA sequence data. Finally, we show that TSLP overexpression induces increased transforming growth factor β expression in myocardial mast cells and tissue fibrosis. Collectively, our findings based on follow-up of a human genetic finding implicate a novel pathway in myocardial tissue homeostasis and remodeling.

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 RNA Sequencing and Its Applications in the Study of Psychiatric Disorders

Neuroscience is currently one of the most challenging research fields owing to the enormous complexity of the mammalian nervous system. We are yet to understand precise transcriptional programs that govern cell fate during neurodevelopment, resolve the connectome of the mammalian brain, and determine the etiology of various neurodegenerative and psychiatric disorders. Technological advances in the past decade, notably single-cell RNA sequencing, have enabled huge progress in our understanding of such features. Our current knowledge of the transcriptome is largely derived from bulk RNA sequencing, which reveals only the average gene expression of millions of cells, potentially missing out on minor transcriptome differences between cells detectable only at single-cell resolution. Since 2009, several single-cell RNA sequencing techniques have emerged that enable the accurate classification of neuronal and glial cell subtypes beyond classical molecular markers and electrophysiological features and allow the identification of previously unknown cell types. Furthermore, it enables the interrogation of molecular and disease-relevant mechanisms and offers further possibilities for the discovery of new drug targets and disease biomarkers. This review intends to familiarize the reader with the main single-cell RNA sequencing techniques developed throughout the past decade and discusses their application in the fields of brain cell taxonomy, neurodevelopment, and psychiatric disorders.

Single-cell transcriptomes in the heart: when every epigenome counts

The response of an organ to stimuli emerges from the actions of individual cells. Recent cardiac single-cell RNA-sequencing studies of development, injury, and reprogramming have uncovered heterogeneous populations even among previously well-defined cell types, raising questions about what level of experimental resolution corresponds to disease-relevant, tissue-level phenotypes. In this review, we explore the biological meaning behind this cellular heterogeneity by undertaking an exhaustive analysis of single-cell transcriptomics in the heart (including a comprehensive, annotated compendium of studies published to date) and evaluating new models for the cardiac function that have emerged from these studies (including discussion and schematics that depict new hypotheses in the field). We evaluate the evidence to support the biological actions of newly identified cell populations and debate questions related to the role of cell-to-cell variability in development and disease. Finally, we present emerging epigenomic approaches that, when combined with single-cell RNA-sequencing, can resolve basic mechanisms of gene regulation and variability in cell phenotype.

Strategy of Patient-Specific Therapeutics in Cardiovascular Disease Through Single-Cell RNA Sequencing

Recently, single cell RNA sequencing (scRNA-seq) technology has enabled the discovery of novel or rare subtypes of cells and their characteristics. This technique has advanced unprecedented biomedical research by enabling the profiling and analysis of the transcriptomes of single cells at high resolution and throughput. Thus, scRNA-seq has contributed to recent advances in cardiovascular research by the generation of cell atlases of heart and blood vessels and the elucidation of mechanisms involved in cardiovascular development and diseases. This review summarizes the overall workflow of the scRNA-seq technique itself and key findings in the cardiovascular development and diseases based on the previous studies. In particular, we focused on how the single-cell sequencing technology can be utilized in clinical field and precision medicine to treat specific diseases.

Challenges and considerations for single-cell and spatially resolved transcriptomics sample collection during spaceflight

Single-cell RNA sequencing (scRNA-seq) and spatially resolved transcriptomics (SRT) have experienced rapid development in recent years. The findings of spaceflight-based scRNA-seq and SRT investigations are likely to improve our understanding of life in space and our comprehension of gene expression in various cell systems and tissue dynamics. However, compared to their Earth-based counterparts, gene expression experiments conducted in spaceflight have not experienced the same pace of development. Out of the hundreds of spaceflight gene expression datasets available, only a few used scRNA-seq and SRT. In this perspective piece, we explore the growing importance of scRNA-seq and SRT in space biology and discuss the challenges and considerations relevant to robust experimental design to enable growth of these methods in the field.

A human adipose tissue cell-type transcriptome atlas

The importance of defining cell-type-specific genes is well acknowledged. Technological advances facilitate high-resolution sequencing of single cells, but practical challenges remain. Adipose tissue is composed primarily of adipocytes, large buoyant cells requiring extensive, artefact-generating processing for separation and analysis. Thus, adipocyte data are frequently absent from single-cell RNA sequencing (scRNA-seq) datasets, despite being the primary functional cell type. Here, we decipher cell-type-enriched transcriptomes from unfractionated human adipose tissue RNA-seq data. We profile all major constituent cell types, using 527 visceral adipose tissue (VAT) or 646 subcutaneous adipose tissue (SAT) samples, identifying over 2,300 cell-type-enriched transcripts. Sex-subset analysis uncovers a panel of male-only cell-type-enriched genes. By resolving expression profiles of genes differentially expressed between SAT and VAT, we identify mesothelial cells as the primary driver of this variation. This study provides an accessible method to profile cell-type-enriched transcriptomes using bulk RNA-seq, generating a roadmap for adipose tissue biology.

Single cell RNA sequencing approaches to cardiac development and congenital heart disease

The development of single cell RNA sequencing technologies has accelerated the ability of scientists to understand healthy and disease states of the cardiovascular system. Congenital heart defects occur in approximately 40,000 births each year and 1 out of 4 children are born with critical congenital heart disease requiring surgical interventions and a lifetime of monitoring. An understanding of how the normal heart develops and how each cell contributes to normal and pathological anatomy is an important goal in pediatric cardiovascular research. Single cell sequencing has provided the tools to increase the ability to discover rare cell types and novel genes involved in normal cardiac development. Knowledge of gene expression of single cells within cardiac tissue has contributed to the understanding of how each cell type contributes to the anatomic structures of the heart. In this review, we summarize how single cell RNA sequencing has been utilized to understand cardiac developmental processes and congenital heart disease. We discuss the advantages and disadvantages of whole cell versus single nuclei RNA sequencing and describe the approaches to analyze the interactomes, transcriptomes, and differentiation trajectory from single cell data. We summarize the currently available single cell RNA sequencing technologies and technical aspects of performing single cell analysis and how to overcome common obstacles. We also review data from the recently published human and mouse fetal heart atlases and advancements that have occurred within the field due to the application of these single cell tools. Finally we highlight the potential for single cell technologies to uncover novel mechanisms of disease pathogenesis by leveraging findings from genome wide association studies.