Biology subjects

Merotto, L.

Publications and source records attributed to Merotto, L..

3 recordsLinked to original sources

bioRxiv2024

Benchmarking second-generation methods for cell-type deconvolution of transcriptomic data

BackgroundIn silico cell-type deconvolution from bulk transcriptomics data is a powerful technique to gain insights into the cellular composition of complex tissues. While first-generation methods used precomputed expression signatures covering limited cell types and tissues, second-generation tools use single-cell RNA sequencing data to build custom signatures for deconvoluting arbitrary cell types, tissues, and organisms. This flexibility poses significant challenges in assessing their deconvolution performance. ResultsHere, we comprehensively benchmark second-generation tools, disentangling different sources of variation and bias using a diverse panel of real and simulated data. Our results reveal substantial differences in accuracy, scalability, and robustness across methods, depending on factors such as cell-type similarity, reference composition, and dataset origin. Conclusions.Our study highlights the strengths, limitations, and complementarity of state-of-the-art tools, shedding light on how different data characteristics and confounders impact deconvolution performance. We provide the scientific community with an ecosystem of tools and resources, omnideconv, simplifying the application, benchmarking, and optimization of deconvolution methods.

bioinformatics↗

bioRxiv2024

Toll-like receptor 3 orchestrates a conserved mechanism of heart regeneration

The humans heart responds to tissue damage with persistent fibrotic scarring. Unlike humans, zebrafish can repair cardiac injury and re-grow heart tissue throughout life. Recently, Toll-like receptor 3 (Tlr3) was identified as an important mediator of cardiac regeneration in neonatal mice. However, no functional analysis of tlr3 knock-out mutant zebrafish in respect to cardiac regeneration has yet been performed. We hypothesize that TLR3 signalling plays a central, conserved role in driving cardiac regeneration upon injury. Therefore, we focused on tlr3 mediated cardiac regeneration in zebrafish, ultimately discovering an evolutionary conserved mechanism of heart repair. Using histological, behavioural, and RNA-Sequencing analysis, we uncovered a conserved mechanism of tlr3 mediated cardiac repair after myocardial injury. Upon myocardial cryoinjury subjection, survival is decreased in tlr3-/- fish as compared to wildtype controls. Tlr3-/- zebrafish fail to recruit immune cells to the injured ventricle, resulting in impaired DNA repair and transcriptional reprogramming of cardiomyocytes. Mechanistically, we uncover an evolutionary conserved mechanism of tlr3 activation in fibroblasts promoting monocyte migration towards an injured ventricular area. Our data reveal tlr3 as a novel therapeutic target to promote cardiac regeneration. Every experiment including human participants has been approved by the ethics committee of the Medical University of Innsbruck (Ref. Nr.: 1262/2023). All experiments including the use of laboratory animals have been approved by the federal ministry of education, science, and research of Austria (Ref. Nr.: 2020-0.345.504).

molecular biology↗

bioRxiv2022

SimBu: Bias-aware simulation of bulk RNA-seq data with variable cell type composition

MotivationAs complex tissues are typically composed of various cell types, deconvolution tools have been developed to computationally infer their cellular composition from bulk RNA sequencing (RNA-seq) data. To comprehensively assess deconvolution performance, gold-standard datasets are indispensable. Gold-standard, experimental techniques like flow cytometry or immunohistochemistry are resource-intensive and cannot be systematically applied to the numerous cell types and tissues profiled with high-throughput transcriptomics. The simulation of pseudo-bulk data, generated by aggregating single-cell RNA-seq (scRNA-seq) expression profiles in pre-defined proportions, offers a scalable and cost-effective alternative. This makes it feasible to create in silico gold standards that allow fine-grained control of cell-type fractions not conceivable in an experimental setup. However, at present, no simulation software for generating pseudo-bulk RNA-seq data exists. ResultsWe developed SimBu, an R package capable of simulating pseudo-bulk samples based on various simulation scenarios, designed to test specific features of deconvolution methods. A unique feature of SimBu is the modelling of cell-type-specific mRNA bias using experimentally-derived or data-driven scaling factors. Here, we show that SimBu can generate realistic pseudo-bulk data, recapitulating the biological and statistical features of real RNA-seq data. Finally, we illustrate the impact of mRNA bias on the evaluation of deconvolution tools and provide recommendations for the selection of suitable methods for estimating mRNA content. ConclusionSimBu is a user-friendly and flexible tool for simulating realistic pseudo-bulk RNA-seq datasets serving as in silico gold-standard for assessing cell-type deconvolution methods. AvailabilitySimBu is freely available at https://github.com/omnideconv/SimBu as an R package under the GPL-3 license. Contactalex.dietrich@tum.de and markus.list@tum.de Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗