Single-cell and spatial transcriptomics: Advances in heart development and disease applications

Cell-cell interactions in the heart: advanced cardiac models and omics technologies

A healthy heart comprises various cell types, including cardiomyocytes, endothelial cells, fibroblasts, immune cells, and among others, which work together to maintain optimal cardiac function. These cells engage in complex communication networks, known as cell-cell interactions (CCIs), which are essential for homeostasis, cardiac structure, and efficient function. However, in the context of cardiac diseases, the heart undergoes damage, leading to alterations in the cellular composition. Such pathological conditions trigger significant changes in CCIs, causing cell rearrangement and the transition between cell types. Studying these interactions can provide valuable insights into cardiac biology and disease mechanisms, enabling the development of new therapeutic strategies. While the development of cardiac organoids and advanced 3D co-culture technologies has revolutionized in vitro studies of CCIs, recent advancements in single-cell and spatial multi-omics technologies provide researchers with powerful and convenient tools to investigate CCIs at unprecedented resolution. This article provides a concise overview of CCIs observed in both normal and injured heart, with an emphasis on the cutting-edge methods used to study these interactions. It highlights recent advancements such as 3D co-culture systems, single-cell and spatial omics technologies, that have enhanced the understanding of CCIs. Additionally, it summarizes the practical applications of CCI research in advancing cardiovascular therapies, offering potential solutions for treating heart disease by targeting intercellular communication.

Melatonin Attenuates Diabetic Retinopathy by Regulating EndMT of Retinal Vascular Endothelial Cells via Inhibiting the HDAC7/FOXO1/ZEB1 Axis

Diabetic retinopathy (DR) is characterized as a microvascular disease. Nonproliferative diabetic retinopathy (NPDR) presents with alterations in retinal blood flow and vascular permeability, thickening of the basement membrane, loss of pericytes, and formation of acellular capillaries. Endothelial-mesenchymal transition (EndMT) of retinal microvessels may play a critical role in advancing NPDR. Melatonin, a hormone primarily secreted by the pineal gland, is a promising therapeutic for DR. This study explored the EndMT in retinal microvessels of NPDR and its related mechanisms. The effect of melatonin on the retina of diabetic rats was evaluated by electroretinogram (ERG) and histopathologic slide staining. Furthermore, the effect of melatonin on human retinal microvascular endothelial cells (HRMECs) was detected by EdU incorporation assay, scratch assay, transwell assay, and tube formation test. Techniques such as RNA-sequencing, overexpression or knockdown of target genes, extraction of cytoplasmic and nuclear protein, co-immunoprecipitation (co-IP), and multiplex immunofluorescence facilitated the exploration of the mechanisms involved. Our findings reveal, for the first time, that melatonin attenuates diabetic retinopathy by regulating EndMT of retinal vascular endothelial cells via inhibiting the HDAC7/FOXO1/ZEB1 axis. Collectively, these results suggest that melatonin holds potential as a therapeutic strategy to reduce retinal vascular damage and protect vision in NPDR.

Non-Coding RNA-Mediated Gene Regulation in Cardiovascular Disorders: Current Insights and Future Directions

Cardiovascular diseases (CVDs) pose a significant burden on global health. Developing effective diagnostic, therapeutic, and prognostic indicators for CVDs is critical. This narrative review explores the role of select non-coding RNAs (ncRNAs) and provides an in-depth exploration of the roles of miRNAs, lncRNAs, and circRNAs in different aspects of CVDs, offering insights into their mechanisms and potential clinical implications. The review also sheds light on the diverse functions of ncRNAs, including their modulation of gene expression, epigenetic modifications, and signaling pathways. It comprehensively analyzes the interplay between ncRNAs and cardiovascular health, paving the way for potential novel interventions. Finally, the review provides insights into the methodologies used to investigate ncRNA-mediated gene regulation in CVDs, as well as the implications and challenges associated with translating ncRNA research into clinical applications. Considering the broader implications, this research opens avenues for interdisciplinary collaborations, enhancing our understanding of CVDs across scientific disciplines.

Spatial Transcriptomics: A Powerful Tool in Disease Understanding and Drug Discovery

Recent advancements in modern science have provided robust tools for drug discovery. The rapid development of transcriptome sequencing technologies has given rise to single-cell transcriptomics and single-nucleus transcriptomics, increasing the accuracy of sequencing and accelerating the drug discovery process. With the evolution of single-cell transcriptomics, spatial transcriptomics (ST) technology has emerged as a derivative approach. Spatial transcriptomics has emerged as a hot topic in the field of omics research in recent years; it not only provides information on gene expression levels but also offers spatial information on gene expression. This technology has shown tremendous potential in research on disease understanding and drug discovery. In this article, we introduce the analytical strategies of spatial transcriptomics and review its applications in novel target discovery and drug mechanism unravelling. Moreover, we discuss the current challenges and issues in this research field that need to be addressed. In conclusion, spatial transcriptomics offers a new perspective for drug discovery.

Integrative multi-omics profiling in human decedents receiving pig heart xenografts

In a previous study, heart xenografts from 10-gene-edited pigs transplanted into two human decedents did not show evidence of acute-onset cellular- or antibody-mediated rejection. Here, to better understand the detailed molecular landscape following xenotransplantation, we carried out bulk and single-cell transcriptomics, lipidomics, proteomics and metabolomics on blood samples obtained from the transplanted decedents every 6 h, as well as histological and transcriptomic tissue profiling. We observed substantial early immune responses in peripheral blood mononuclear cells and xenograft tissue obtained from decedent 1 (male), associated with downstream T cell and natural killer cell activity. Longitudinal analyses indicated the presence of ischemia reperfusion injury, exacerbated by inadequate immunosuppression of T cells, consistent with previous findings of perioperative cardiac xenograft dysfunction in pig-to-nonhuman primate studies. Moreover, at 42 h after transplantation, substantial alterations in cellular metabolism and liver-damage pathways occurred, correlating with profound organ-wide physiological dysfunction. By contrast, relatively minor changes in RNA, protein, lipid and metabolism profiles were observed in decedent 2 (female) as compared to decedent 1. Overall, these multi-omics analyses delineate distinct responses to cardiac xenotransplantation in the two human decedents and reveal new insights into early molecular and immune responses after xenotransplantation. These findings may aid in the development of targeted therapeutic approaches to limit ischemia reperfusion injury-related phenotypes and improve outcomes.

Single-cell RNA sequencing reveals the transcriptional heterogeneity of Tbx18-positive cardiac cells during heart development

The T-box family transcription factor 18 (Tbx18) has been found to play a critical role in regulating the development of the mammalian heart during the primary stages of embryonic development while the cellular heterogeneity and landscape of Tbx18-positive (Tbx18+) cardiac cells remain incompletely characterized. Here, we analyzed prior published single-cell RNA sequencing (scRNA-seq) mouse heart data to explore the heterogeneity of Tbx18+ cardiac cell subpopulations and provide a comprehensive transcriptional landscape of Tbx18+ cardiac cells during their development. Bioinformatic analysis methods were utilized to identify the heterogeneity between cell groups. Based on the gene expression characteristics, Tbx18+ cardiac cells can be classified into a minimum of two distinct cell populations, namely fibroblast-like cells and cardiomyocytes. In terms of temporal heterogeneity, these cells exhibit three developmental stages, namely the MEM stage, ML_P0 stage, and P stage Tbx18+ cardiac cells. Furthermore, Tbx18+ cardiac cells encompass several cell types, including cardiac progenitor-like cells, cardiomyocytes, and epicardial/stromal cells, as determined by specific transcriptional regulatory networks. The scRNA-seq results revealed the involvement of extracellular matrix (ECM) signals and epicardial epithelial-to-mesenchymal transition (EMT) in the development of Tbx18+ cardiac cells. The utilization of a lineage-tracing model served to validate the crucial function of Tbx18 in the differentiation of cardiac cells. Consequently, these findings offer a comprehensive depiction of the cellular heterogeneity within Tbx18+ cardiac cells.

BiGATAE: a bipartite graph attention auto-encoder enhancing spatial domain identification from single-slice to multi-slices

Recent advancements in spatial transcriptomics technology have revolutionized our ability to comprehensively characterize gene expression patterns within the tissue microenvironment, enabling us to grasp their functional significance in a spatial context. One key field of research in spatial transcriptomics is the identification of spatial domains, which refers to distinct regions within the tissue where specific gene expression patterns are observed. Diverse methodologies have been proposed, each with its unique characteristics. As the availability of spatial transcriptomics data continues to expand, there is a growing need for methods that can integrate information from multiple slices to discover spatial domains. To extend the applicability of existing single-slice analysis methods to multi-slice clustering, we introduce BiGATAE (Bipartite Graph Attention Auto Encoder) that leverages gene expression information from adjacent tissue slices to enhance spatial transcriptomics data. BiGATAE comprises two steps: aligning slices to generate an adjacency matrix for different spots in consecutive slices and constructing a bipartite graph. Subsequently, it utilizes a graph attention network to integrate information across different slices. Then it can seamlessly integrate with pre-existing techniques. To evaluate the performance of BiGATAE, we conducted benchmarking analyses on three different datasets. The experimental results demonstrate that for existing single-slice clustering methods, the integration of BiGATAE significantly enhances their performance. Moreover, single-slice clustering methods integrated with BiGATAE outperform methods specifically designed for multi-slice integration. These results underscore the proficiency of BiGATAE in facilitating information transfer across multiple slices and its capacity to broaden the applicability and sustainability of pre-existing methods.