3D-cardiomics: A spatial transcriptional atlas of the mammalian heart.

Understanding the spatial gene expression and regulation in the heart is key to uncovering its developmental and physiological processes, during homeostasis and disease. Numerous techniques exist to gain gene expression and regulation information in organs such as the heart, but few utilize intuitive true-to-life three-dimensional representations to analyze and visualise results. Here we combined transcriptomics with 3D-modelling to interrogate spatial gene expression in the mammalian heart. For this, we microdissected and sequenced transcriptome-wide 18 anatomical sections of the adult mouse heart. Our study has unveiled known and novel genes that display complex spatial expression in the heart sub-compartments. We have also created 3D-cardiomics, an interface for spatial transcriptome analysis and visualization that allows the easy exploration of these data in a 3D model of the heart. 3D-cardiomics is accessible from http://3d-cardiomics.erc.monash.edu/.

MonaGO: a novel gene ontology enrichment analysis visualisation system.

BACKGROUND: Gene ontology (GO) enrichment analysis is frequently undertaken during exploration of various -omics data sets. Despite the wide array of tools available to biologists to perform this analysis, meaningful visualisation of the overrepresented GO in a manner which is easy to interpret is still lacking. RESULTS: Monash Gene Ontology (MonaGO) is a novel web-based visualisation system that provides an intuitive, interactive and responsive interface for performing GO enrichment analysis and visualising the results. MonaGO supports gene lists as well as GO terms as inputs. Visualisation results can be exported as high-resolution images or restored in new sessions, allowing reproducibility of the analysis. An extensive comparison between MonaGO and 11 state-of-the-art GO enrichment visualisation tools based on 9 features revealed that MonaGO is a unique platform that simultaneously allows interactive visualisation within one single output page, directly accessible through a web browser with customisable display options. CONCLUSION: MonaGO combines dynamic clustering and interactive visualisation as well as customisation options to assist biologists in obtaining meaningful representation of overrepresented GO terms, producing simplified outputs in an unbiased manner. MonaGO will facilitate the interpretation of GO analysis and will assist the biologists into the representation of the results.

Disparities in spatially variable gene calling highlight the need for benchmarking spatial transcriptomics methods.

Identifying spatially variable genes (SVGs) is a key step in the analysis of spatially resolved transcriptomics data. SVGs provide biological insights by defining transcriptomic differences within tissues, which was previously unachievable using RNA-sequencing technologies. However, the increasing number of published tools designed to define SVG sets currently lack benchmarking methods to accurately assess performance. This study compares results of 6 purpose-built packages for SVG identification across 9 public and 5 simulated datasets and highlights discrepancies between results. Additional tools for generation of simulated data and development of benchmarking methods are required to improve methods for identifying SVGs.

Parallel use of human stem cell lung and heart models provide insights for SARS-CoV-2 treatment.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) primarily infects the respiratory tract, but pulmonary and cardiac complications occur in severe coronavirus disease 2019 (COVID-19). To elucidate molecular mechanisms in the lung and heart, we conducted paired experiments in human stem cell-derived lung alveolar type II (AT2) epithelial cell and cardiac cultures infected with SARS-CoV-2. With CRISPR-Cas9-mediated knockout of ACE2, we demonstrated that angiotensin-converting enzyme 2 (ACE2) was essential for SARS-CoV-2 infection of both cell types but that further processing in lung cells required TMPRSS2, while cardiac cells required the endosomal pathway. Host responses were significantly different; transcriptome profiling and phosphoproteomics responses depended strongly on the cell type. We identified several antiviral compounds with distinct antiviral and toxicity profiles in lung AT2 and cardiac cells, highlighting the importance of using several relevant cell types for evaluation of antiviral drugs. Our data provide new insights into rational drug combinations for effective treatment of a virus that affects multiple organ systems.

Parallel use of pluripotent human stem cell lung and heart models provide new insights for treatment of SARS-CoV-2.

SARS-CoV-2 primarily infects the respiratory tract, but pulmonary and cardiac complications occur in severe COVID-19. To elucidate molecular mechanisms in the lung and heart, we conducted paired experiments in human stem cell-derived lung alveolar type II (AT2) epithelial cell and cardiac cultures infected with SARS-CoV-2. With CRISPR- Cas9 mediated knock-out of ACE2, we demonstrated that angiotensin converting enzyme 2 (ACE2) was essential for SARS-CoV-2 infection of both cell types but further processing in lung cells required TMPRSS2 while cardiac cells required the endosomal pathway. Host responses were significantly different; transcriptome profiling and phosphoproteomics responses depended strongly on the cell type. We identified several antiviral compounds with distinct antiviral and toxicity profiles in lung AT2 and cardiac cells, highlighting the importance of using several relevant cell types for evaluation of antiviral drugs. Our data provide new insights into rational drug combinations for effective treatment of a virus that affects multiple organ systems. One-sentence summary: Rational treatment strategies for SARS-CoV-2 derived from human PSC models.

A cis-regulatory-directed pipeline for the identification of genes involved in cardiac development and disease.

BACKGROUND: Congenital heart diseases are the major cause of death in newborns, but the genetic etiology of this developmental disorder is not fully known. The conventional approach to identify the disease-causing genes focuses on screening genes that display heart-specific expression during development. However, this approach would have discounted genes that are expressed widely in other tissues but may play critical roles in heart development. RESULTS: We report an efficient pipeline of genome-wide gene discovery based on the identification of a cardiac-specific cis-regulatory element signature that points to candidate genes involved in heart development and congenital heart disease. With this pipeline, we retrieve 76% of the known cardiac developmental genes and predict 35 novel genes that previously had no known connectivity to heart development. Functional validation of these novel cardiac genes by RNAi-mediated knockdown of the conserved orthologs in Drosophila cardiac tissue reveals that disrupting the activity of 71% of these genes leads to adult mortality. Among these genes, RpL14, RpS24, and Rpn8 are associated with heart phenotypes. CONCLUSIONS: Our pipeline has enabled the discovery of novel genes with roles in heart development. This workflow, which relies on screening for non-coding cis-regulatory signatures, is amenable for identifying developmental and disease genes for an organ without constraining to genes that are expressed exclusively in the organ of interest.

NUMT Confounding Biases Mitochondrial Heteroplasmy Calls in Favor of the Reference Allele.

Homology between mitochondrial DNA (mtDNA) and nuclear DNA of mitochondrial origin (nuMTs) causes confounding when aligning short sequence reads to the reference human genome, as the true sequence origin cannot be determined. Using a systematic in silico approach, we here report the impact of all potential mitochondrial variants on alignment accuracy and variant calling. A total of 49,707 possible mutations were introduced across the 16,569 bp reference mitochondrial genome (16,569 × 3 alternative alleles), one variant at-at-time. The resulting in silico fragmentation and alignment to the entire reference genome (GRCh38) revealed preferential mapping of mutated mitochondrial fragments to nuclear loci, as variants increased loci similarity to nuMTs, for a total of 807, 362, and 41 variants at 333, 144, and 27 positions when using 100, 150, and 300 bp single-end fragments. We subsequently modeled these affected variants at 50% heteroplasmy and carried out variant calling, observing bias in the reported allele frequencies in favor of the reference allele. Four variants (chrM:6023A, chrM:4456T, chrM:5147A, and chrM:7521A) including a possible hypertension factor, chrM:4456T, caused 100% loss of coverage at the mutated position (with all 100 bp single-end fragments aligning to homologous, nuclear positions instead of chrM), rendering these variants undetectable when aligning to the entire reference genome. Furthermore, four mitochondrial variants reported to be pathogenic were found to cause significant loss of coverage and select haplogroup-defining SNPs were shown to exacerbate the loss of coverage caused by surrounding variants. Increased fragment length and use of paired-end reads both improved alignment accuracy.