The heart proteome project

[ypriverol]

Aim of the project

This project aims to have full single-cell transcriptomics and tissue proteome expression profiles across different species. These are the possible biological questions:

• How conserved are gene expression profiles across species?
• Can we identify conserved genes or pathways that show similar expression patterns across species, especially in heart tissues?
• What evolutionary pressures maintain these conserved expressions?
• What are the species-specific transcriptional and proteomic signatures?
• Which genes and proteins are uniquely expressed in specific species (e.g., human vs. mouse vs. zebrafish) and how do these relate to their physiological differences?
• Can these unique profiles help us understand species-specific expression profiles?
• How does single-cell RNA expression correlate with tissue-level proteomics?
• Can we map single-cell transcriptomics data to bulk proteome data to identify discrepancies or consistencies in expression?
• How do different heart compartments (e.g., LV, RV, LA, RA) differ in terms of single-cell gene expression and tissue proteomics across species?
• Are there compartment-specific transcriptional or proteomic markers that could explain functional differences between parts of the heart?
• Do the expression profiles of these compartments vary significantly across species?
• What is the role of cell type heterogeneity in heart function across species?
• How does cellular diversity in the heart, as seen in single-cell RNA-seq data, contribute to species-specific heart function?
• Do certain cell types or subpopulations show stronger evolutionary conservation in terms of their transcriptomic or proteomic profiles?
• Can cross-species data improve machine-learning models for predicting tissue-specific protein expression?
• Can we use single-cell transcriptomics data from multiple species to build models that predict protein expression in tissues, accounting for post-transcriptional regulation?
• Could this approach help in reducing the batch effect when integrating data from different species or experiments?
• How do gene regulatory networks differ across species in the heart?
• Are transcriptional regulatory networks conserved between species, or do they show significant rewiring?
• How do regulatory elements (e.g., enhancers, promoters) influence gene expression in different species, and how does this relate to protein abundance?
• How do evolutionary adaptations manifest at the molecular level in different species?
• Are there specific adaptations in the gene or protein expression profiles that are linked to species-specific cardiovascular function (e.g., heart size, metabolism)?
• Can these data reveal how species adapted to different environmental challenges (e.g., zebrafish in aquatic environments vs. humans in terrestrial ones)?

Species and hearts compartments for both omics

Species	Compartments	SC Datasets	Proteomics Datasets	RNA-Seq
Human	LV, RV, LA, RA	-	PXD006675, PXD008722, MSV000087095, Others here	GSE22253, E-GEOD-8331, E-GEOD-2035
Mouse	LV, RV, LA, RA	-	PXD012636,PXD009577
Rat	LV, RV, LA, RA	-	PXD012636
Pig	LV, RV, LA, RA	-	PXD012636, PXD009577
Sus scrofa	-	GSE245458
Gallus	-	-	PXD005288
Zebrafish	-	-	PXD012636
Frog	-	-	PXD009577

[enriquea]

@ypriverol How should I better to add more datasets? editing your table?
Here some SC interesting ones I found:

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dataset description	accession	database	parts	reference
scRNAseq Human Heart Development	phs002031.v1.p1	dbGAP	LV, RV, LA, RA	https://doi.org/10.1038/s41586-024-07171-z
Murine heart	GSE193346	GEO	LV, RV, LA, RA	https://doi.org/10.1038/s41467-022-35691-7
scRNAseq Human Heart Adult	E-MTAB-12916	ArrayExpress	LV, RV, LA, RA	https://doi.org/10.1038/s41586-023-06311-1