Single-cell and spatial transcriptomics approaches of cardiovascular development and disease

Molecular and spatial signatures of human and rat corpus cavernosum physiopathological processes at single-cell resolution

The composition and cellular heterogeneity of the corpus cavernosum (CC) microenvironment have been characterized, but the spatial heterogeneity at the molecular level remains unexplored. In this study, we integrate single-cell RNA sequencing (scRNA-seq) and spatial transcriptome sequencing to comprehensively chart the spatial cellular landscape of the human and rat CC under normal and disease conditions. We observe differences in the proportions of cell subtypes and marker genes between humans and rats. Based on the analysis of the fibroblast (FB) niche, we also find that the enrichment scores of mechanical force signaling vary across different regions and correlate with the spatial distribution of FB subtypes. In vitro, the soft and hard extracellular matrix (ECM) induces the differentiation of FBs into apolipoprotein (APO)+ FBs and cartilage oligomeric matrix protein (COMP)+ FBs, respectively. In summary, our study provides a cross-species and physiopathological transcriptomic atlas of the CC, contributing to a further understanding of the molecular anatomy and regulation of penile erection.

Spot-Based Global Registration for Subpixel Stitching of Single-Molecule Resolution Images for Tissue-Scale Spatial Transcriptomics

Single-molecule imaging at the tissue scale has revolutionized our understanding of biology by providing unprecedented insight into the molecular expression of individual cells and their spatial organization within tissues. However, achieving precise image stitching at the single-molecule level remains a challenge, primarily due to heterogeneous background signals and dim labeling signals in single-molecule images. This paper introduces Spot-Based Global Registration (SBGR), a novel strategy that shifts the focus from raw images to identified molecular spots for high-resolution image alignment. The use of spot-based data enables straightforward and robust evaluation of the credibility of estimated translations and stitching performance. The method outperforms existing image-based stitching methods, achieving subpixel accuracy (83 ± 36 nm) with exceptional consistency. Furthermore, SBGR incorporates a mechanism to surgically remove duplicate spots in overlapping regions, maximizing information recovery from duplicate measurements. In conclusion, SBGR emerges as a robust and accurate solution for stitching single-molecule resolution images in tissue-scale spatial transcriptomics, offering versatility and potential for high-resolution spatial analysis.

SPADE: spatial deconvolution for domain specific cell-type estimation

Understanding gene expression in different cell types within their spatial context is a key goal in genomics research. SPADE (SPAtial DEconvolution), our proposed method, addresses this by integrating spatial patterns into the analysis of cell type composition. This approach uses a combination of single-cell RNA sequencing, spatial transcriptomics, and histological data to accurately estimate the proportions of cell types in various locations. Our analyses of synthetic data have demonstrated SPADE's capability to discern cell type-specific spatial patterns effectively. When applied to real-life datasets, SPADE provides insights into cellular dynamics and the composition of tumor tissues. This enhances our comprehension of complex biological systems and aids in exploring cellular diversity. SPADE represents a significant advancement in deciphering spatial gene expression patterns, offering a powerful tool for the detailed investigation of cell types in spatial transcriptomics.

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.

Deciphering Drug Targets and Actions with Single-Cell and Spatial Resolution

Recent advances in chemical, molecular, and genetic approaches have provided us with an unprecedented capacity to identify drug-target interactions across the whole proteome and genome. Meanwhile, rapid developments of single-cell and spatial omics technologies are revolutionizing our understanding of the molecular architecture of biological systems. However, a significant gap remains in how we align our understanding of drug actions, traditionally based on molecular affinities, with the in vivo cellular and spatial tissue heterogeneity revealed by these newer techniques. Here, we review state-of-the-art methods for profiling drug-target interactions and emerging multiomics tools to delineate the tissue heterogeneity at single-cell resolution. Highlighting the recent technical advances enabling high-resolution, multiplexable in situ small-molecule drug imaging (clearing-assisted tissue click chemistry, or CATCH), we foresee the integration of single-cell and spatial omics platforms, data, and concepts into the future framework of defining and understanding in vivo drug-target interactions and mechanisms of actions.

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.

Padlock Probe-Based Targeted In Situ Sequencing: Overview of Methods and Applications

Elucidating spatiotemporal changes in gene expression has been an essential goal in studies of health, development, and disease. In the emerging field of spatially resolved transcriptomics, gene expression profiles are acquired with the tissue architecture maintained, sometimes at cellular resolution. This has allowed for the development of spatial cell atlases, studies of cell-cell interactions, and in situ cell typing. In this review, we focus on padlock probe-based in situ sequencing, which is a targeted spatially resolved transcriptomic method. We summarize recent methodological and computational tool developments and discuss key applications. We also discuss compatibility with other methods and integration with multiomic platforms for future applications.

Integrated analysis of plasma proteome and cortex single-cell transcriptome reveals the novel biomarkers during cortical aging

Background

With the increase of age, multiple physiological functions of people begin gradually degenerating. Regardless of natural aging or pathological aging, the decline in cognitive function is one of the most obvious features in the process of brain aging. Brain aging is a key factor for several neuropsychiatric disorders and for most neurodegenerative diseases characterized by onset typically occurring late in life and with worsening of symptoms over time. Therefore, the early prevention and intervention of aging progression are particularly important. Since there is no unified conclusion about the plasma diagnostic biomarkers of brain aging, this paper innovatively employed the combined multi-omics analysis to delineate the plasma markers of brain aging.

Methods

In order to search for specific aging markers in plasma during cerebral cortex aging, we used multi-omics analysis to screen out differential genes/proteins by integrating two prefrontal cortex (PFC) single-nucleus transcriptome sequencing (snRNA-seq) datasets and one plasma proteome sequencing datasets. Then plasma samples were collected from 20 young people and 20 elder people to verify the selected differential genes/proteins with ELISA assay.

Results

We first integrated snRNA-seq data of the post-mortem human PFC and generated profiles of 65,064 nuclei from 14 subjects across adult (44-58 years), early-aging (69-79 years), and late-aging (85-94 years) stages. Seven major cell types were classified based on established markers, including oligodendrocyte, excitatory neurons, oligodendrocyte progenitor cells, astrocytes, microglia, inhibitory neurons, and endotheliocytes. A total of 93 cell-specific genes were identified to be significantly associated with age. Afterward, plasma proteomics data from 2,925 plasma proteins across 4,263 young adults to nonagenarians (18-95 years old) were combined with the outcomes from snRNA-seq data to obtain 12 differential genes/proteins (GPC5, CA10, DGKB, ST6GALNAC5, DSCAM, IL1RAPL2, TMEM132C, VCAN, APOE, PYH1R, CNTN2, SPOCK3). Finally, we verified the 12 differential genes by ELISA and found that the expression trends of five biomarkers (DSCAM, CNTN2, IL1RAPL2, CA10, GPC5) were correlated with brain aging.

Conclusion

Five differentially expressed proteins (DSCAM, CNTN2, IL1RAPL2, CA10, GPC5) can be considered as one of the screening indicators of brain aging, and provide a scientific basis for clinical diagnosis and intervention.

Spatial transcriptomics in human skin research

Spatial transcriptomics is a revolutionary technique that enables researchers to characterise tissue architecture and localisation of gene expression. A plethora of technologies that map gene expression are currently being developed, aiming to facilitate spatially resolved, high-dimensional assessment of gene transcription in the context of human skin research. Knowing which gene is expressed by which cell and in which location within skin, facilitates understanding of skin function and dysfunction in both health and disease. In this review, we summarise the available spatial transcriptomic methods and we describe their application to a broad spectrum of dermatological diseases.

Non-Coding RNAs Regulating Mitochondrial Functions and the Oxidative Stress Response as Putative Targets against Age-Related Macular Degeneration (AMD)

Age-related macular degeneration (AMD) is an ever-increasing, insidious disease which reduces the quality of life of millions of elderly people around the world. AMD is characterised by damage to the retinal pigment epithelium (RPE) in the macula region of the retina. The origins of this multi-factorial disease are complex and still not fully understood. Oxidative stress and mitochondrial imbalance in the RPE are believed to be important factors in the development of AMD. In this review, the regulation of the mitochondrial function and antioxidant stress response by non-coding RNAs (ncRNAs), newly emerged epigenetic factors, is discussed. These molecules include microRNAs, long non-coding RNAs, and circular non-coding RNAs. They act mainly as mRNA suppressors, controllers of other ncRNAs, or by interacting with proteins. We include here examples of these RNA molecules which affect various mitochondrial processes and antioxidant signaling of the cell. As a future prospect, the possibility to manipulate these ncRNAs to strengthen mitochondrial and antioxidant response functions is discussed. Non-coding RNAs could be used as potential diagnostic markers for AMD, and in the future, also as therapeutic targets, either by suppressing or increasing their expression. In addition to AMD, it is possible that non-coding RNAs could be regulators in other oxidative stress-related degenerative diseases.

Echidna: integrated simulations of single-cell immune receptor repertoires and transcriptomes

Motivation

Single-cell sequencing now enables the recovery of full-length immune receptor repertoires [B cell receptor (BCR) and T cell receptor (TCR) repertoires], in addition to gene expression information. The feature-rich datasets produced from such experiments require extensive and diverse computational analyses, each of which can significantly influence the downstream immunological interpretations, such as clonal selection and expansion. Simulations produce validated standard datasets, where the underlying generative model can be precisely defined and furthermore perturbed to investigate specific questions of interest. Currently, there is no tool that can be used to simulate single-cell datasets incorporating immune receptor repertoires and gene expression.

Results

We developed Echidna, an R package that simulates immune receptors and transcriptomes at single-cell resolution with user-tunable parameters controlling a wide range of features such as clonal expansion, germline gene usage, somatic hypermutation, transcriptional phenotypes and spatial location. Echidna can additionally simulate time-resolved B cell evolution, producing mutational networks with complex selection histories incorporating class-switching and B cell subtype information. We demonstrated the benchmarking potential of Echidna by simulating clonal lineages and comparing the known simulated networks with those inferred from only the BCR sequences as input. Finally, we simulated immune repertoire information onto existing spatial transcriptomic experiments, thereby generating novel datasets that could be used to develop and integrate methods to profile clonal selection in a spatially resolved manner. Together, Echidna provides a framework that can incorporate experimental data to simulate single-cell immune repertoires to aid software development and bioinformatic benchmarking of clonotyping, phylogenetics, transcriptomics and machine learning strategies.

Availability and implementation

The R package and code used in this manuscript can be found at github.com/alexyermanos/echidna and also in the R package Platypus (Yermanos et al., 2021). Installation instructions and the vignette for Echidna is described in the Platypus Computational Ecosystem (https://alexyermanos.github.io/Platypus/index.html). Publicly available data and corresponding sample accession numbers can be found in Supplementary Tables S2 and S3.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Microfluidic cell sorter sample preparation for genomic assays

Single-cell RNA-Sequencing has led to many novel discoveries such as the detection of rare cell populations, microbial populations, and cancer mutations. The quality of single-cell transcriptomics relies heavily on sample preparation and cell sorting techniques that best preserve RNA quality while removing dead cells or debris prior to cDNA generation and library preparation. Magnetic bead cell enrichment is a simple process of cleaning up a sample but can only separate on a single-criterion. Droplet-based cell sorters, on the other hand, allows for higher purity of sorted cells gated on several fluorescent and scatter properties. The downside of traditional droplet-based sorters is their operational complexity, accessibility, and potential stress on cells due to their high-pressure pumps. The WOLF® Cell Sorter, and WOLF G2®, developed by NanoCellect Biomedical, are novel microfluidic-based cell sorters that use gentle sorting technology compatible with several RNA-sequencing platforms. The experiments highlighted here demonstrate how microfluidic sorting can be successfully used to remove debris and unwanted cells prior to genomic sample preparation resulting in more data per cell and improved library complexity.

High-resolution Slide-seqV2 spatial transcriptomics enables discovery of disease-specific cell neighborhoods and pathways

High-resolution spatial transcriptomics enables mapping of RNA expression directly from intact tissue sections; however, its utility for the elucidation of disease processes and therapeutically actionable pathways remains unexplored. We applied Slide-seqV2 to mouse and human kidneys, in healthy and distinct disease paradigms. First, we established the feasibility of Slide-seqV2 in tissue from nine distinct human kidneys, which revealed a cell neighborhood centered around a population of LYVE1+ macrophages. Second, in a mouse model of diabetic kidney disease, we detected changes in the cellular organization of the spatially restricted kidney filter and blood-flow-regulating apparatus. Third, in a mouse model of a toxic proteinopathy, we identified previously unknown, disease-specific cell neighborhoods centered around macrophages. In a spatially restricted subpopulation of epithelial cells, we discovered perturbations in 77 genes associated with the unfolded protein response. Our studies illustrate and experimentally validate the utility of Slide-seqV2 for the discovery of disease-specific cell neighborhoods.

Treating Duchenne Muscular Dystrophy: The Promise of Stem Cells, Artificial Intelligence, and Multi-Omics

Muscular dystrophies are chronic and debilitating disorders caused by progressive muscle wasting. Duchenne muscular dystrophy (DMD) is the most common type. DMD is a well-characterized genetic disorder caused by the absence of dystrophin. Although some therapies exist to treat the symptoms and there are ongoing efforts to correct the underlying molecular defect, patients with muscular dystrophies would greatly benefit from new therapies that target the specific pathways contributing directly to the muscle disorders. Three new advances are poised to change the landscape of therapies for muscular dystrophies such as DMD. First, the advent of human induced pluripotent stem cells (iPSCs) allows researchers to design effective treatment strategies that make up for the gaps missed by conventional “one size fits all” strategies. By characterizing tissue alterations with single-cell resolution and having molecular profiles for therapeutic treatments for a variety of cell types, clinical researchers can design multi-pronged interventions to not just delay degenerative processes, but regenerate healthy tissues. Second, artificial intelligence (AI) will play a significant role in developing future therapies by allowing the aggregation and synthesis of large and disparate datasets to help reveal underlying molecular mechanisms. Third, disease models using a high volume of multi-omics data gathered from diverse sources carry valuable information about converging and diverging pathways. Using these new tools, the results of previous and emerging studies will catalyze precision medicine-based drug development that can tackle devastating disorders such as DMD.

Recent advances in spatially resolved transcriptomics: challenges and opportunities

Single-cell RNA sequencing (scRNA-seq) has greatly advanced our understanding of cellular heterogeneity by profiling individual cell transcriptomes. However, cell dissociation from the tissue structure causes a loss of spatial information, which hinders the identification of intercellular communication networks and global transcriptional patterns present in the tissue architecture. To overcome this limitation, novel transcriptomic platforms that preserve spatial information have been actively developed. Significant achievements in imaging technologies have enabled in situ targeted transcriptomic profiling in single cells at singlemolecule resolution. In addition, technologies based on mRNA capture followed by sequencing have made possible profiling of the genome-wide transcriptome at the 55-100 μm resolution. Unfortunately, neither imaging-based technology nor capturebased method elucidates a complete picture of the spatial transcriptome in a tissue. Therefore, addressing specific biological questions requires balancing experimental throughput and spatial resolution, mandating the efforts to develop computational algorithms that are pivotal to circumvent technology-specific limitations. In this review, we focus on the current state-of-the-art spatially resolved transcriptomic technologies, describe their applications in a variety of biological domains, and explore recent discoveries demonstrating their enormous potential in biomedical research. We further highlight novel integrative computational methodologies with other data modalities that provide a framework to derive biological insight into heterogeneous and complex tissue organization. [BMB Reports 2022; 55(3): 113-124].

Single-Cell RNA Sequencing (scRNA-seq) in Cardiac Tissue: Applications and Limitations

Cardiovascular diseases (CVDs) are a group of disorders of the blood vessels and heart, which are considered as the leading causes of death worldwide. The pathology of CVDs could be related to the functional abnormalities of multiple cell types in the heart. Single-cell RNA sequencing (scRNA-seq) technology is a powerful method for characterizing individual cells and elucidating the molecular mechanisms by providing a high resolution of transcriptomic changes at the single-cell level. Specifically, scRNA-seq has provided novel insights into CVDs by identifying rare cardiac cell types, inferring the trajectory tree, estimating RNA velocity, elucidating the cell-cell communication, and comparing healthy and pathological heart samples. In this review, we summarize the different scRNA-seq platforms and published single-cell datasets in the cardiovascular field, and describe the utilities and limitations of this technology. Lastly, we discuss the future perspective of the application of scRNA-seq technology into cardiovascular research.

The metabolic-epigenetic nexus in type 2 diabetes mellitus

The prevalence of type 2 diabetes mellitus (T2DM) continues to rise globally. Yet the aetiology and pathophysiology of this noncommunicable, polygenic disease, is poorly understood. Lifestyle factors, such as poor dietary intake, lack of exercise, and abnormal glycaemia, are purported to play a role in disease onset and progression, and these environmental factors may disrupt specific epigenetic mechanisms, leading to a reprogramming of gene transcription. The hyperglycaemic cell per se, alters epigenetics through chemical modifications to DNA and histones via metabolic intermediates such as succinate, α-ketoglutarate and O-GlcNAc. To illustrate, α-ketoglutarate is considered a salient co-factor in the activation of the ten-eleven translocation (TET) dioxygenases, which drives DNA demethylation. On the contrary, succinate and other mitochondrial tricarboxylic acid cycle intermediates, inhibit TET activity predisposing to a state of hypermethylation. Hyperglycaemia depletes intracellular ascorbic acid, and damages DNA by enhancing the production of reactive oxygen species (ROS); this compromised cell milieu exacerbates the oxidation of 5-methylcytosine alongside a destabilisation of TET. These metabolic connections may regulate DNA methylation, affecting gene transcription and pancreatic islet β-cell function in T2DM. This complex interrelationship between metabolism and epigenetic alterations may provide a conceptual foundation for understanding how pathologic stimuli modify and control the intricacies of T2DM. As such, this narrative review will comprehensively evaluate and detail the interplay between metabolism and epigenetic modifications in T2DM.