Single-cell genomics to understand disease pathogenesis

Private information leakage from single-cell count matrices

The increase in publicly available human single-cell datasets, encompassing millions of cells from many donors, has significantly enhanced our understanding of complex biological processes. However, the accessibility of these datasets raises significant privacy concerns. Due to the inherent noise in single-cell measurements and the scarcity of population-scale single-cell datasets, recent private information quantification studies have focused on bulk gene expression data sharing. To address this gap, we demonstrate that individuals in single-cell gene expression datasets are vulnerable to linking attacks, where attackers can infer their sensitive phenotypic information using publicly available tissue or cell-type-specific expression quantitative trait loci (eQTLs) information. We further develop a method for genotype prediction and genotype-phenotype linking that remains effective without relying on eQTL information. We show that variants from one study can be exploited to uncover private information about individuals in another study.

Nanoplasmonic biosensors for environmental sustainability and human health

Monitoring the health conditions of the environment and humans is essential for ensuring human well-being, promoting global health, and achieving sustainability. Innovative biosensors are crucial in accurately monitoring health conditions, uncovering the hidden connections between the environment and human well-being, and understanding how environmental factors trigger autoimmune diseases, neurodegenerative diseases, and infectious diseases. This review evaluates the use of nanoplasmonic biosensors that can monitor environmental health and human diseases according to target analytes of different sizes and scales, providing valuable insights for preventive medicine. We begin by explaining the fundamental principles and mechanisms of nanoplasmonic biosensors. We investigate the potential of nanoplasmonic techniques for detecting various biological molecules, extracellular vesicles (EVs), pathogens, and cells. We also explore the possibility of wearable nanoplasmonic biosensors to monitor the physiological network and healthy connectivity of humans, animals, plants, and organisms. This review will guide the design of next-generation nanoplasmonic biosensors to advance sustainable global healthcare for humans, the environment, and the planet.

IGF1 and CXCR4 Respectively Related With Inhibited M1 Macrophage Polarization in Keloids

PURPOSE

The pathophysiology of keloid remains unclear. Exploring the immune heterogeneity and new biomarkers of keloids can help design new therapeutic targets for keloid treatments and prevention.

METHODS

The authors performed single-cell RNA sequencing analysis and bulk data differential gene expression analysis of public datasets(GSE92566 and GSE163973). They used Gene Ontology (GO), Gene Set Enrichment Analysis (GSEA), and immune infiltration analysis to identify the function of the differential expressed genes. Besides, the authors performed qt-PCR on keloid tissue and adjacent normal tissues from 3 patients for further verification.

RESULTS

M2 macrophage increased in keloid samples than M1 macrophage. The authors identified 2 potential novel biomarkers of keloid, IGF1 and CXCR4, which could inhibit M1 macrophage polarization. The potential mechanism could be inhibiting immune responses and anti-inflammatory activities through INF signaling and E2F targeting. The differential expression of the 2 genes was verified by clinical samples.

CONCLUSIONS

The authors identified 2 immune signaling molecules associated with keloid formation (IGF1 and CXCR4) and analyzed their potential pathogenic mechanisms.

Single-cell analysis reveals a unique microenvironment in peri-implantitis

AIM

This study aimed to reveal the unique microenvironment of peri-implantitis through single-cell analysis.

MATERIALS AND METHODS

Herein, we performed single-cell RNA sequencing (scRNA-seq) of biopsies from patients with peri-implantitis (PI) and compared the results with healthy individuals (H) and patients with periodontitis (PD).

RESULTS

Decreased numbers of stromal cells and increased immune cells were found in the PI group, which implies a severe inflammatory infiltration. The fibroblasts were found to be heterogeneous and the specific pro-inflammatory CXCL13+ sub-cluster was more represented in the PI group, in contrast to the PD and H groups. Furthermore, more neutrophil infiltration was detected in the PI group than in the PD group, and cell-cell communication and ligand-receptor pairs revealed most neutrophils were recruited by CXCL13+ fibroblasts through CXCL8/CXCL6-CXCR2/CXCR1. Notably, our study demonstrated that the unique microenvironment of the PI group promoted the differentiation of monocyte/macrophage lineage cells into osteoclasts, which might explain the faster and more severe bone resorption in the progression of PI than PD.

CONCLUSIONS

Collectively, this study suggests a unique immune microenvironment of PI, which may explain the differences between PI and PD in the clinic. These outcomes will aid in finding new specific and effective treatments for PI.

Stochastic modeling of a gene regulatory network driving B cell development in germinal centers

Germinal centers (GCs) are the key histological structures of the adaptive immune system, responsible for the development and selection of B cells producing high-affinity antibodies against antigens. Due to their level of complexity, unexpected malfunctioning may lead to a range of pathologies, including various malignant formations. One promising way to improve the understanding of malignant transformation is to study the underlying gene regulatory networks (GRNs) associated with cell development and differentiation. Evaluation and inference of the GRN structure from gene expression data is a challenging task in systems biology: recent achievements in single-cell (SC) transcriptomics allow the generation of SC gene expression data, which can be used to sharpen the knowledge on GRN structure. In order to understand whether a particular network of three key gene regulators (BCL6, IRF4, BLIMP1), influenced by two external stimuli signals (surface receptors BCR and CD40), is able to describe GC B cell differentiation, we used a stochastic model to fit SC transcriptomic data from a human lymphoid organ dataset. The model is defined mathematically as a piecewise-deterministic Markov process. We showed that after parameter tuning, the model qualitatively recapitulates mRNA distributions corresponding to GC and plasmablast stages of B cell differentiation. Thus, the model can assist in validating the GRN structure and, in the future, could lead to better understanding of the different types of dysfunction of the regulatory mechanisms.

Implementing Whole Genome Sequencing (WGS) in Clinical Practice: Advantages, Challenges, and Future Perspectives

The integration of whole genome sequencing (WGS) into all aspects of modern medicine represents the next step in the evolution of healthcare. Using this technology, scientists and physicians can observe the entire human genome comprehensively, generating a plethora of new sequencing data. Modern computational analysis entails advanced algorithms for variant detection, as well as complex models for classification. Data science and machine learning play a crucial role in the processing and interpretation of results, using enormous databases and statistics to discover new and support current genotype-phenotype correlations. In clinical practice, this technology has greatly enabled the development of personalized medicine, approaching each patient individually and in accordance with their genetic and biochemical profile. The most propulsive areas include rare disease genomics, oncogenomics, pharmacogenomics, neonatal screening, and infectious disease genomics. Another crucial application of WGS lies in the field of multi-omics, working towards the complete integration of human biomolecular data. Further technological development of sequencing technologies has led to the birth of third and fourth-generation sequencing, which include long-read sequencing, single-cell genomics, and nanopore sequencing. These technologies, alongside their continued implementation into medical research and practice, show great promise for the future of the field of medicine.

scX: A user-friendly tool for scRNA-seq exploration

Single-cell RNA sequencing (scRNA-seq) has transformed our ability to explore biological systems. Nevertheless, proficient expertise is essential for handling and interpreting the data. In this paper, we present scX, an R package built on the Shiny framework that streamlines the analysis, exploration, and visualization of single-cell experiments. With an interactive graphic interface, implemented as a web application, scX provides easy access to key scRNAseq analyses, including marker identification, gene expression profiling, and differential gene expression analysis. Additionally, scX seamlessly integrates with commonly used single-cell Seurat and Single-CellExperiment R objects, resulting in efficient processing and visualization of varied datasets. Overall, scX serves as a valuable and user-friendly tool for effortless exploration and sharing of single-cell data, simplifying some of the complexities inherent in scRNAseq analysis.

moSCminer: a cell subtype classification framework based on the attention neural network integrating the single-cell multi-omics dataset on the cloud

Single-cell omics sequencing has rapidly advanced, enabling the quantification of diverse omics profiles at a single-cell resolution. To facilitate comprehensive biological insights, such as cellular differentiation trajectories, precise annotation of cell subtypes is essential. Conventional methods involve clustering cells and manually assigning subtypes based on canonical markers, a labor-intensive and expert-dependent process. Hence, an automated computational prediction framework is crucial. While several classification frameworks for predicting cell subtypes from single-cell RNA sequencing datasets exist, these methods solely rely on single-omics data, offering insights at a single molecular level. They often miss inter-omic correlations and a holistic understanding of cellular processes. To address this, the integration of multi-omics datasets from individual cells is essential for accurate subtype annotation. This article introduces moSCminer, a novel framework for classifying cell subtypes that harnesses the power of single-cell multi-omics sequencing datasets through an attention-based neural network operating at the omics level. By integrating three distinct omics datasets-gene expression, DNA methylation, and DNA accessibility-while accounting for their biological relationships, moSCminer excels at learning the relative significance of each omics feature. It then transforms this knowledge into a novel representation for cell subtype classification. Comparative evaluations against standard machine learning-based classifiers demonstrate moSCminer's superior performance, consistently achieving the highest average performance on real datasets. The efficacy of multi-omics integration is further corroborated through an in-depth analysis of the omics-level attention module, which identifies potential markers for cell subtype annotation. To enhance accessibility and scalability, moSCminer is accessible as a user-friendly web-based platform seamlessly connected to a cloud system, publicly accessible at http://203.252.206.118:5568. Notably, this study marks the pioneering integration of three single-cell multi-omics datasets for cell subtype identification.

Analysis of intercellular communication in the osteosarcoma microenvironment based on single cell sequencing data

Osteosarcoma (OS) is the most common primary bone cancer in children and young adults, patient survival rates have not improved in recent decades. To further understand the interrelationship between different cell types in the tumor microenvironment of osteosarcoma, we comprehensively analyzed single-cell sequencing data from six patients with untreated osteosarcoma. Nine major cell types were identified from a total of 46,046 cells based on unbiased clustering of gene expression profiles and canonical markers. Osteosarcoma from different patients display heterogeneity in cellular composition. Myeloid cells were the most commonly represented cell type, followed by osteoblastic and TILs. Copy number variation (CNV) results identified amplifications and deletions in malignant osteoblastic cells and fibroblasts. Trajectory analysis based on RNA velocity showed that osteoclasts in the OS microenvironment could be differentiated from myeloid cells. Furthermore, we explored the intercellular communications in OS microenvironment and identified multiple ligand-receptor pairs between myeloid cells, osteoblastic cells and their cells, including 21 ligand-receptor pair genes that significantly associated with survival outcomes. Importantly, we found chemotherapy may have an effect on cellular communication in the OS microenvironment by analyzing single-cell sequencing data from seven primary osteosarcoma patients who received chemotherapy. We believe these observations will improve our understanding of potential mechanisms of microenvironment contributions to OS progression and help identify potential targets for new treatment development in the future.

SCING: Inference of robust, interpretable gene regulatory networks from single cell and spatial transcriptomics

Gene regulatory network (GRN) inference is an integral part of understanding physiology and disease. Single cell/nuclei RNA-seq (scRNA-seq/snRNA-seq) data has been used to elucidate cell-type GRNs; however, the accuracy and speed of current scRNAseq-based GRN approaches are suboptimal. Here, we present Single Cell INtegrative Gene regulatory network inference (SCING), a gradient boosting and mutual information-based approach for identifying robust GRNs from scRNA-seq, snRNA-seq, and spatial transcriptomics data. Performance evaluation using Perturb-seq datasets, held-out data, and the mouse cell atlas combined with the DisGeNET database demonstrates the improved accuracy and biological interpretability of SCING compared to existing methods. We applied SCING to the entire mouse single cell atlas, human Alzheimer's disease (AD), and mouse AD spatial transcriptomics. SCING GRNs reveal unique disease subnetwork modeling capabilities, have intrinsic capacity to correct for batch effects, retrieve disease relevant genes and pathways, and are informative on spatial specificity of disease pathogenesis.

Droplet microarray platforms for high-throughput drug screening

High-throughput screening platforms are fundamental for the rapid and efficient processing of large amounts of experimental data. Parallelization and miniaturization of experiments are important for improving their cost-effectiveness. The development of miniaturized high-throughput screening platforms is essential in the fields of biotechnology, medicine, and pharmacology. Currently, most laboratories use 96- or 384-well microtiter plates for screening; however, they have disadvantages, such as high reagent and cell consumption, low throughput, and inability to avoid cross-contamination, which need to be further optimized. Droplet microarrays, as novel screening platforms, can effectively avoid these shortcomings. Here, the preparation method of the droplet microarray, method of adding compounds in parallel, and means to read the results are briefly described. Next, the latest research on droplet microarray platforms in biomedicine is presented, including their application in high-throughput culture, cell screening, high-throughput nucleic acid screening, drug development, and individualized medicine. Finally, the challenges and future trends in droplet microarray technology are summarized.

Methanol fixation is the method of choice for droplet-based single-cell transcriptomics of neural cells

The main critical step in single-cell transcriptomics is sample preparation. Several methods have been developed to preserve cells after dissociation to uncouple sample handling from library preparation. Yet, the suitability of these methods depends on the cell types to be processed. In this project, we perform a systematic comparison of preservation methods for droplet-based single-cell RNA-seq on neural and glial cells derived from induced pluripotent stem cells. Our results show that while DMSO provides the highest cell quality in terms of RNA molecules and genes detected per cell, it strongly affects the cellular composition and induces the expression of stress and apoptosis genes. In contrast, methanol fixed samples display a cellular composition similar to fresh samples and provide a good cell quality and little expression biases. Taken together, our results show that methanol fixation is the method of choice for performing droplet-based single-cell transcriptomics experiments on neural cell populations.

deCS: A Tool for Systematic Cell Type Annotations of Single-cell RNA Sequencing Data among Human Tissues

Single-cell RNA sequencing (scRNA-seq) is revolutionizing the study of complex and dynamic cellular mechanisms. However, cell type annotation remains a main challenge as it largely relies on a priori knowledge and manual curation, which is cumbersome and subjective. The increasing number of scRNA-seq datasets, as well as numerous published genetic studies, has motivated us to build a comprehensive human cell type reference atlas.Here, we present decoding Cell type Specificity (deCS), an automatic cell type annotation method augmented by a comprehensive collection of human cell type expression profiles and marker genes. We used deCS to annotate scRNA-seq data from various tissue types and systematically evaluated the annotation accuracy under different conditions, including reference panels, sequencing depth, and feature selection strategies. Our results demonstrate that expanding the references is critical for improving annotation accuracy. Compared to many existing state-of-the-art annotation tools, deCS significantly reduced computation time and increased accuracy. deCS can be integrated into the standard scRNA-seq analytical pipeline to enhance cell type annotation. Finally, we demonstrated the broad utility of deCS to identify trait-cell type associations in 51 human complex traits, providing deep insights into the cellular mechanisms underlying disease pathogenesis. All documents for deCS, including source code, user manual, demo data, and tutorials, are freely available at https://github.com/bsml320/deCS.

Interpretable and context-free deconvolution of multi-scale whole transcriptomic data with UniCell deconvolve

We introduce UniCell: Deconvolve Base (UCDBase), a pre-trained, interpretable, deep learning model to deconvolve cell type fractions and predict cell identity across Spatial, bulk-RNA-Seq, and scRNA-Seq datasets without contextualized reference data. UCD is trained on 10 million pseudo-mixtures from a fully-integrated scRNA-Seq training database comprising over 28 million annotated single cells spanning 840 unique cell types from 898 studies. We show that our UCDBase and transfer-learning models achieve comparable or superior performance on in-silico mixture deconvolution to existing, reference-based, state-of-the-art methods. Feature attribute analysis uncovers gene signatures associated with cell-type specific inflammatory-fibrotic responses in ischemic kidney injury, discerns cancer subtypes, and accurately deconvolves tumor microenvironments. UCD identifies pathologic changes in cell fractions among bulk-RNA-Seq data for several disease states. Applied to lung cancer scRNA-Seq data, UCD annotates and distinguishes normal from cancerous cells. Overall, UCD enhances transcriptomic data analysis, aiding in assessment of cellular and spatial context.

Striatin family proteins: The neglected scaffolds

The Striatin family of proteins constitutes Striatin, SG2NA, and Zinedin. Members of this family of proteins act as a signaling scaffold due to the presence of multiple protein-protein interaction domains. At least two members of this family, namely Zinedin and SG2NA, have a proven role in cancer cell proliferation. SG2NA, the second member of this family, undergoes alternative splicing and gives rise to several isoforms which are differentially regulated in a tissue-dependent manner. SG2NA evolved earlier than the other two members of the family, and SG2NA undergoes not only alternative splicing but also other posttranscriptional gene regulation. Striatin also undergoes alternative splicing, and as a result, it gives rise to multiple isoforms. It has been shown that this family of proteins plays a significant role in estrogen signaling, neuroprotection, cancer as well as in cell cycle regulation. Members of the striatin family form a complex network of signaling hubs with different kinases and phosphatases, and other signaling proteins named STRIPAK. Here, in the present manuscript, we thoroughly reviewed the findings on striatin family members to elaborate on the overall structural and functional idea of this family of proteins. We also commented on the involvement of these proteins in STRIPAK complexes and their functional relevance.

Single-Cell Molecular Barcoding to Decode Multimodal Information Defining Cell States

Single-cell research has provided a breakthrough in biology to understand heterogeneous cell groups, such as tissues and organs, in development and disease. Molecular barcoding and subsequent sequencing technology insert a singlecell barcode into isolated single cells, allowing separation cell by cell. Given that multimodal information from a cell defines precise cellular states, recent technical advances in methods focus on simultaneously extracting multimodal data recorded in different biological materials (DNA, RNA, protein, etc.). This review summarizes recently developed singlecell multiomics approaches regarding genome, epigenome, and protein profiles with the transcriptome. In particular, we focus on how to anchor or tag molecules from a cell, improve throughputs with sample multiplexing, and record lineages, and we further discuss the future developments of the technology.

High-Resolution Bacterial Cytological Profiling Reveals Intrapopulation Morphological Variations upon Antibiotic Exposure

Phenotypic heterogeneity is crucial to bacterial survival and could provide insights into the mechanism of action (MOA) of antibiotics, especially those with polypharmacological actions. Although phenotypic changes among individual cells could be detected by existing profiling methods, due to the data complexity, only population average data were commonly used, thereby overlooking the heterogeneity. In this study, we developed a high-resolution bacterial cytological profiling method that can capture morphological variations of bacteria upon antibiotic treatment. With an unprecedented single-cell resolution, this method classifies morphological changes of individual cells into known MOAs with an overall accuracy above 90%. We next showed that combinations of two antibiotics induce altered cell morphologies that are either unique or similar to that of an antibiotic in the combinations. With these combinatorial profiles, this method successfully revealed multiple cytological changes caused by a natural product-derived compound that, by itself, is inactive against Acinetobacter baumannii but synergistically exerts its multiple antibacterial activities in the presence of colistin. The findings have paved the way for future single-cell profiling in bacteria and have highlighted previously underappreciated intrapopulation variations caused by antibiotic perturbation.

Associating pathways with diseases using single-cell expression profiles and making inferences about potential drugs

Finding direct dependencies between genetic pathways and diseases has been the target of multiple studies as it has many applications. However, due to cellular heterogeneity and limitations of the number of samples for bulk expression profiles, such studies have faced hurdles in the past. Here, we propose a method to perform single-cell expression-based inference of association between pathway, disease and cell-type (sci-PDC), which can help to understand their cause and effect and guide precision therapy. Our approach highlighted reliable relationships between a few diseases and pathways. Using the example of diabetes, we have demonstrated how sci-PDC helps in tracking variation of association between pathways and diseases with changes in age and species. The variation in pathways-disease associations in mice and humans revealed critical facts about the suitability of the mouse model for a few pathways in the context of diabetes. The coherence between results from our method and previous reports, including information about the drug target pathways, highlights its reliability for multidimensional utility.

Single-cell sequencing reveals the antifibrotic effects of YAP/TAZ in systemic sclerosis

Systemic sclerosis (SSc) is a heterogeneous disease with skin fibrosis. Yes-associated protein (YAP)/transcriptional coactivator with PDZ-binding motif (TAZ) is associated with fibrotic response. This work attempted to determine the precise mechanism of YAP/TAZ in SSc. Single-cell sequencing (scRNA-seq) was used to analyse the differential gene expression between SSc patients and healthy volunteers, showing that the YAP/TAZ signalling pathway was enriched in the fibroblasts of SSc patients. Subsequently, enzyme-linked immunosorbent assay and immunohistochemical analyses were conducted to examine the levels of YAP and TAZ in mild and severe SSc patients. YAP and TAZ were highly expressed in the serum and skin tissues of mild and severe SSc patients, especially severe SSc patients. Additionally, an SSc mouse model was induced by bleomycin, and the impacts of YAP/TAZ knockdown on the pathological changes in skin and lung tissues were detected by haematoxylin and eosin staining and Masson staining. Knockdown of YAP and TAZ inhibited α-SMA mRNA and protein expression in skin and lung tissues of SSc mice. Inhibition of YAP and TAZ reduced skin inflammation and thickness and repressed lung inflammation and fibrosis in SSc mice. Importantly, knockdown of YAP and TAZ synergistically inhibited inflammation and fibrosis in skin and lung tissues in SSc mice. In conclusion, this work demonstrated that knockdown of YAP and TAZ exerted a synergistic effect on alleviating SSc in mice. Thus, this work suggests that YAP/TAZ is a potential target for SSc treatment.

Biomedical data, computational methods and tools for evaluating disease-disease associations

In recent decades, exploring potential relationships between diseases has been an active research field. With the rapid accumulation of disease-related biomedical data, a lot of computational methods and tools/platforms have been developed to reveal intrinsic relationship between diseases, which can provide useful insights to the study of complex diseases, e.g. understanding molecular mechanisms of diseases and discovering new treatment of diseases. Human complex diseases involve both external phenotypic abnormalities and complex internal molecular mechanisms in organisms. Computational methods with different types of biomedical data from phenotype to genotype can evaluate disease-disease associations at different levels, providing a comprehensive perspective for understanding diseases. In this review, available biomedical data and databases for evaluating disease-disease associations are first summarized. Then, existing computational methods for disease-disease associations are reviewed and classified into five groups in terms of the usages of biomedical data, including disease semantic-based, phenotype-based, function-based, representation learning-based and text mining-based methods. Further, we summarize software tools/platforms for computation and analysis of disease-disease associations. Finally, we give a discussion and summary on the research of disease-disease associations. This review provides a systematic overview for current disease association research, which could promote the development and applications of computational methods and tools/platforms for disease-disease associations.

MRSD: A quantitative approach for assessing suitability of RNA-seq in the investigation of mis-splicing in Mendelian disease

Variable levels of gene expression between tissues complicates the use of RNA sequencing of patient biosamples to delineate the impact of genomic variants. Here, we describe a gene- and tissue-specific metric to inform the feasibility of RNA sequencing. This overcomes limitations of using expression values alone as a metric to predict RNA-sequencing utility. We have derived a metric, minimum required sequencing depth (MRSD), that estimates the depth of sequencing required from RNA sequencing to achieve user-specified sequencing coverage of a gene, transcript, or group of genes. We applied MRSD across four human biosamples: whole blood, lymphoblastoid cell lines (LCLs), skeletal muscle, and cultured fibroblasts. MRSD has high precision (90.1%-98.2%) and overcomes transcript region-specific sequencing biases. Applying MRSD scoring to established disease gene panels shows that fibroblasts, of these four biosamples, are the optimum source of RNA for 63.1% of gene panels. Using this approach, up to 67.8% of the variants of uncertain significance in ClinVar that are predicted to impact splicing could be assayed by RNA sequencing in at least one of the biosamples. We demonstrate the utility and benefits of MRSD as a metric to inform functional assessment of splicing aberrations, in particular in the context of Mendelian genetic disorders to improve diagnostic yield.

Tailoring Cardiac Synthetic Transcriptional Modulation Towards Precision Medicine

Molecular and genetic differences between individual cells within tissues underlie cellular heterogeneities defining organ physiology and function in homeostasis as well as in disease states. Transcriptional control of endogenous gene expression has been intensively studied for decades. Thanks to a fast-developing field of single cell genomics, we are facing an unprecedented leap in information available pertaining organ biology offering a comprehensive overview. The single-cell technologies that arose aided in resolving the precise cellular composition of many organ systems in the past years. Importantly, when applied to diseased tissues, the novel approaches have been immensely improving our understanding of the underlying pathophysiology of common human diseases. With this information, precise prediction of regulatory elements controlling gene expression upon perturbations in a given cell type or a specific context will be realistic. Simultaneously, the technological advances in CRISPR-mediated regulation of gene transcription as well as their application in the context of epigenome modulation, have opened up novel avenues for targeted therapy and personalized medicine. Here, we discuss the fast-paced advancements during the recent years and the applications thereof in the context of cardiac biology and common cardiac disease. The combination of single cell technologies and the deep knowledge of fundamental biology of the diseased heart together with the CRISPR-mediated modulation of gene regulatory networks will be instrumental in tailoring the right strategies for personalized and precision medicine in the near future. In this review, we provide a brief overview of how single cell transcriptomics has advanced our knowledge and paved the way for emerging CRISPR/Cas9-technologies in clinical applications in cardiac biomedicine.

MyoData: An expression knowledgebase at single cell/nucleus level for the discovery of coding-noncoding RNA functional interactions in skeletal muscle

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Regulation of gene expression through non-coding RNAs at single myofiber and nucleus resolution.

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Reinterpretation of KEGG pathways with microRNA and long non-coding RNA activities.

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miR-149, -214, and let-7e alter mitochondrial shape.

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The long non-coding RNA Pvt1 is a sponge for miR-27a.

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miR-208b regulates Sox6; miR-214 regulates both Sox6 and Slc16a3.

Non-coding RNAs represent the largest part of transcribed mammalian genomes and prevalently exert regulatory functions. Long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) can modulate the activity of each other. Skeletal muscle is the most abundant tissue in mammals. It is composed of different cell types with myofibers that represent the smallest complete contractile system. Considering that lncRNAs and miRNAs are more cell type-specific than coding RNAs, to understand their function it is imperative to evaluate their expression and action within single myofibers. In this database, we collected gene expression data for coding and non-coding genes in single myofibers and used them to produce interaction networks based on expression correlations. Since biological pathways are more informative than networks based on gene expression correlation, to understand how altered genes participate in the studied phenotype, we integrated KEGG pathways with miRNAs and lncRNAs. The database also integrates single nucleus gene expression data on skeletal muscle in different patho-physiological conditions. We demonstrated that these networks can serve as a framework from which to dissect new miRNA and lncRNA functions to experimentally validate. Some interactions included in the database have been previously experimentally validated using high throughput methods. These can be the basis for further functional studies. Using database information, we demonstrate the involvement of miR-149, -214 and let-7e in mitochondria shaping; the ability of the lncRNA Pvt1 to mitigate the action of miR-27a via sponging; and the regulatory activity of miR-214 on Sox6 and Slc16a3. The MyoData is available at https://myodata.bio.unipd.it.