Single-nuclear transcriptome profiling identifies persistent fibroblast activation in hypertrophic and failing human hearts of patients with longstanding disease

AIMS

Cardiac fibrosis drives the progression of heart failure in ischaemic and hypertrophic cardiomyopathy. Therefore, the development of specific anti-fibrotic treatment regimens to counteract cardiac fibrosis is of high clinical relevance. Hence, this study examined the presence of persistent fibroblast activation during longstanding human heart disease at a single-cell resolution to identify putative therapeutic targets to counteract pathological cardiac fibrosis in patients.

METHODS AND RESULTS

We used single-nuclei RNA sequencing with human tissues from two samples of one healthy donor, and five hypertrophic and two failing hearts. Unsupervised sub-clustering of 7110 nuclei led to the identification of 7 distinct fibroblast clusters. De-convolution of cardiac fibroblast heterogeneity revealed a distinct population of human cardiac fibroblasts with a molecular signature of persistent fibroblast activation and a transcriptional switch towards a pro-fibrotic extra-cellular matrix composition in patients with established cardiac hypertrophy and heart failure. This sub-cluster was characterized by high expression of POSTN, RUNX1, CILP, and a target gene adipocyte enhancer-binding protein 1 (AEBP1) (all P < 0.001). Strikingly, elevated circulating AEBP1 blood level were also detected in a validation cohort of patients with confirmed cardiac fibrosis and hypertrophic cardiomyopathy by cardiac magnetic resonance imaging (P < 0.01). Since endogenous AEBP1 expression was increased in patients with established cardiac hypertrophy and heart failure, we assessed the functional consequence of siRNA-mediated AEBP1 silencing in human cardiac fibroblasts. Indeed, AEBP1 silencing reduced proliferation, migration, and fibroblast contractile capacity and α-SMA gene expression, which is a hallmark of fibroblast activation (all P < 0.05). Mechanistically, the anti-fibrotic effects of AEBP1 silencing were linked to transforming growth factor-beta pathway modulation.

CONCLUSION

Together, this study identifies persistent fibroblast activation in patients with longstanding heart disease, which might be detected by circulating AEBP1 and therapeutically modulated by its targeted silencing in human cardiac fibroblasts.

Bulk brain tissue cell-type deconvolution with bias correction for single-nuclei RNA sequencing data using DeTREM

BACKGROUND

Quantifying cell-type abundance in bulk tissue RNA-sequencing enables researchers to better understand complex systems. Newer deconvolution methodologies, such as MuSiC, use cell-type signatures derived from single-cell RNA-sequencing (scRNA-seq) data to make these calculations. Single-nuclei RNA-sequencing (snRNA-seq) reference data can be used instead of scRNA-seq data for tissues such as human brain where single-cell data are difficult to obtain, but accuracy suffers due to sequencing differences between the technologies.

RESULTS

We propose a modification to MuSiC entitled 'DeTREM' which compensates for sequencing differences between the cell-type signature and bulk RNA-seq datasets in order to better predict cell-type fractions. We show DeTREM to be more accurate than MuSiC in simulated and real human brain bulk RNA-sequencing datasets with various cell-type abundance estimates. We also compare DeTREM to SCDC and CIBERSORTx, two recent deconvolution methods that use scRNA-seq cell-type signatures. We find that they perform well in simulated data but produce less accurate results than DeTREM when used to deconvolute human brain data.

CONCLUSION

DeTREM improves the deconvolution accuracy of MuSiC and outperforms other deconvolution methods when applied to snRNA-seq data. DeTREM enables accurate cell-type deconvolution in situations where scRNA-seq data are not available. This modification improves characterization cell-type specific effects in brain tissue and identification of cell-type abundance differences under various conditions.

Single-Nuclei RNA Sequencing of 5 Regions of the Human Prenatal Brain Implicates Developing Neuron Populations in Genetic Risk for Schizophrenia

BACKGROUND

While a variety of evidence supports a prenatal component in schizophrenia, there are few data regarding the cell populations involved. We sought to identify cells of the human prenatal brain mediating genetic risk for schizophrenia by integrating cell-specific gene expression measures generated through single-nuclei RNA sequencing with recent large-scale genome-wide association study (GWAS) and exome sequencing data for the condition.

METHODS

Single-nuclei RNA sequencing was performed on 5 brain regions (frontal cortex, ganglionic eminence, hippocampus, thalamus, and cerebellum) from 3 fetuses from the second trimester of gestation. Enrichment of schizophrenia common variant genetic liability and rare damaging coding variation was assessed in relation to gene expression specificity within each identified cell population.

RESULTS

Common risk variants were prominently enriched within genes with high expression specificity for developing neuron populations within the frontal cortex, ganglionic eminence, and hippocampus. Enrichments were largely independent of genes expressed in neuronal populations of the adult brain that have been implicated in schizophrenia through the same methods. Genes containing an excess of rare damaging variants in schizophrenia had higher expression specificity for developing glutamatergic neurons of the frontal cortex and hippocampus that were also enriched for common variant liability.

CONCLUSIONS

We found evidence for a distinct contribution of prenatal neuronal development to genetic risk for schizophrenia, involving specific populations of developing neurons within the second-trimester fetal brain. Our study significantly advances the understanding of the neurodevelopmental origins of schizophrenia and provides a resource with which to investigate the prenatal antecedents of other psychiatric and neurologic disorders.

Corrigendum: Uncovering the pharmacology of Ginkgo biloba folium in the cell-type-specific targets of Parkinson's disease with a deep learning algorithm

[This corrects the article DOI: 10.3389/fphar.2022.1007556.].