Recurrent neural networks for classification of human embryonic stem cell-derived cardiomyocytes

Classification of human embryonic stem cell-derived cardiomyocytes (hESC-CMs) into phenotypes such as atrial-like or ventricular-like is important for applications in cardiac regenerative medicine and drug screening. However, a key challenge is the lack of ground truth labels for the phenotype of hESC-CMs: Whereas adult phenotypes are well-characterized in terms of the shape of their action potentials (APs), the understanding of how the shape of the AP of immature CMs relates to that of adult CMs remains limited. Recently, a new metamorphosis distance has been proposed to determine if a query immature AP is closer to a particular adult AP phenotype. However, the metamorphosis distance is difficult to compute making it unsuitable for classifying a large number of CMs. This thesis proposes two recurrent neural networks (RNNs) with long short-term memory (LSTM) units for classifying hESC-CMs. The first network is trained using a semi-supervised approach, in which the parameters of the network are learned by minimizing a loss function consisting of two terms: a supervised term that uses labeled data obtained from computational models of adult CMs, and an unsupervised term that uses a contrastive loss to encourage the labels of similar APs (as measured by the metamorphosis distance) to be the same. The second network is trained using a domain adaptation approach that captures the domain shift between immature and adult cells by adding a term to the loss function that penalizes their maximum mean discrepancy (MMD) in feature space. Experiments confirm the benefit of integrating information from both adult and stem cell-derived domains in the learning scheme and show that the proposed semi-supervised method generates results similar to the state of the art (94.73%) with clear computational advantages when applied to new samples. Experimental results on the domain adapted learning approach confirm that it not only is more computational efficient but also outperforms the state of the art in terms of clustering quality. In summary, the main contributions of this thesis are to formulate the classification of hESC-CM APs in the framework of artificial neural networks and to show that this new formulation improves with respect to the state of the art for this task in terms of both performance and computational efficiency

Revealing New Mouse Epicardial Cell Markers through Transcriptomics

The epicardium has key functions during myocardial development, by contributing to the formation of coronary endothelial and smooth muscle cells, cardiac fibroblasts, and potentially cardiomyocytes. The epicardium plays a morphogenetic role by emitting signals to promote and maintain cardiomyocyte proliferation. In a regenerative context, the adult epicardium might comprise a progenitor cell population that can be induced to contribute to cardiac repair. Although some genes involved in epicardial function have been identified, a detailed molecular profile of epicardial gene expression has not been available.Using laser capture microscopy, we isolated the epicardial layer from the adult murine heart before or after cardiac infarction in wildtype mice and mice expressing a transgenic IGF-1 propeptide (mIGF-1) that enhances cardiac repair, and analyzed the transcription profile using DNA microarrays.Expression of epithelial genes such as basonuclin, dermokine, and glycoprotein M6A are highly enriched in the epicardial layer, which maintains expression of selected embryonic genes involved in epicardial development in mIGF-1 transgenic hearts. After myocardial infarct, a subset of differentially expressed genes are down-regulated in the epicardium representing an epicardium-specific signature that responds to injury.This study presents the description of the murine epicardial transcriptome obtained from snap frozen tissues, providing essential information for further analysis of this important cardiac cell layer

Revealing New Mouse Epicardial Cell Markers through Transcriptomics

The epicardium has key functions during myocardial development, by contributing to the formation of coronary endothelial and smooth muscle cells, cardiac fibroblasts, and potentially cardiomyocytes. The epicardium plays a morphogenetic role by emitting signals to promote and maintain cardiomyocyte proliferation. In a regenerative context, the adult epicardium might comprise a progenitor cell population that can be induced to contribute to cardiac repair. Although some genes involved in epicardial function have been identified, a detailed molecular profile of epicardial gene expression has not been available.Using laser capture microscopy, we isolated the epicardial layer from the adult murine heart before or after cardiac infarction in wildtype mice and mice expressing a transgenic IGF-1 propeptide (mIGF-1) that enhances cardiac repair, and analyzed the transcription profile using DNA microarrays.Expression of epithelial genes such as basonuclin, dermokine, and glycoprotein M6A are highly enriched in the epicardial layer, which maintains expression of selected embryonic genes involved in epicardial development in mIGF-1 transgenic hearts. After myocardial infarct, a subset of differentially expressed genes are down-regulated in the epicardium representing an epicardium-specific signature that responds to injury.This study presents the description of the murine epicardial transcriptome obtained from snap frozen tissues, providing essential information for further analysis of this important cardiac cell layer

Actin-microtubule cytoskeletal interplay mediated by MRTF-A/SRF signaling promotes dilated cardiomyopathy caused by LMNA mutations

Publisher Copyright: © 2022, The Author(s).Mutations in the lamin A/C gene (LMNA) cause dilated cardiomyopathy associated with increased activity of ERK1/2 in the heart. We recently showed that ERK1/2 phosphorylates cofilin-1 on threonine 25 (phospho(T25)-cofilin-1) that in turn disassembles the actin cytoskeleton. Here, we show that in muscle cells carrying a cardiomyopathy-causing LMNA mutation, phospho(T25)-cofilin-1 binds to myocardin-related transcription factor A (MRTF-A) in the cytoplasm, thus preventing the stimulation of serum response factor (SRF) in the nucleus. Inhibiting the MRTF-A/SRF axis leads to decreased α-tubulin acetylation by reducing the expression of ATAT1 gene encoding α-tubulin acetyltransferase 1. Hence, tubulin acetylation is decreased in cardiomyocytes derived from male patients with LMNA mutations and in heart and isolated cardiomyocytes from Lmnap.H222P/H222P male mice. In Atat1 knockout mice, deficient for acetylated α-tubulin, we observe left ventricular dilation and mislocalization of Connexin 43 (Cx43) in heart. Increasing α-tubulin acetylation levels in Lmnap.H222P/H222P mice with tubastatin A treatment restores the proper localization of Cx43 and improves cardiac function. In summary, we show for the first time an actin-microtubule cytoskeletal interplay mediated by cofilin-1 and MRTF-A/SRF, promoting the dilated cardiomyopathy caused by LMNA mutations. Our findings suggest that modulating α-tubulin acetylation levels is a feasible strategy for improving cardiac function.Peer reviewe

Responsiveness of genes to long-range transcriptional regulation

Developmental genes are highly regulated at the level of transcription and exhibit complex spatial and temporal expression patterns. Key developmental loci are frequently spanned by clusters of conserved non-coding elements (CNEs), referred to as genomic regulatory blocks (GRBs), that have been subject to extreme levels of purifying selection during metazoan evolution. CNEs have been shown to function as long-range enhancers, activating transcription of their developmental target genes over vast genomic distances and bypassing more proximally located unresponsive genes (bystanders). Despite their role in the establishment of cell identity during development, many of these long-range regulatory landscapes remain poorly characterised. In this thesis, I develop a computational method for the genome-wide identification of regulatory enhancer-promoter associations in human and mouse, based on co-variation of enhancer and promoter transcriptional activity across a comprehensive set of tissues and cell types, in combination with chromatin contact data. Using this method, I demonstrate that previously predicted GRB target genes are amongst the genes with the highest level of enhancer responsiveness in the genome, and are frequently associated with extremely long-range enhancers. Remarkably, the activity of some previously predicted bystanders is also weakly but significantly associated with enhancer activity, challenging the notion that the promoters of bystanders are unresponsive to enhancers. Next, I systematically annotate human genes with elevated enhancer responsiveness and identify more than 600 putative target genes, associated with the regulation of a wide range of developmental processes, from pattern specification to axonogenesis, as well as with disease. The analysis performed in this thesis has facilitated the identification of hundreds of previously uncharacterised enhancer-responsive genes and their long-range regulatory landscapes, allowing the study of their unique properties.Open Acces

eSPRESSO: topological clustering of single-cell transcriptomics data to reveal informative genes for spatio–temporal architectures of cells

[Background] Bioinformatics capability to analyze spatio–temporal dynamics of gene expression is essential in understanding animal development. Animal cells are spatially organized as functional tissues where cellular gene expression data contain information that governs morphogenesis during the developmental process. Although several computational tissue reconstruction methods using transcriptomics data have been proposed, those methods have been ineffective in arranging cells in their correct positions in tissues or organs unless spatial information is explicitly provided. [Results] This study demonstrates stochastic self-organizing map clustering with Markov chain Monte Carlo calculations for optimizing informative genes effectively reconstruct any spatio–temporal topology of cells from their transcriptome profiles with only a coarse topological guideline. The method, eSPRESSO (enhanced SPatial REconstruction by Stochastic Self-Organizing Map), provides a powerful in silico spatio–temporal tissue reconstruction capability, as confirmed by using human embryonic heart and mouse embryo, brain, embryonic heart, and liver lobule with generally high reproducibility (average max. accuracy = 92.0%), while revealing topologically informative genes, or spatial discriminator genes. Furthermore, eSPRESSO was used for temporal analysis of human pancreatic organoids to infer rational developmental trajectories with several candidate ‘temporal’ discriminator genes responsible for various cell type differentiations. [Conclusions] eSPRESSO provides a novel strategy for analyzing mechanisms underlying the spatio–temporal formation of cellular organizations

Evolution and Impact of High Content Imaging

Abstract/outline: The field of high content imaging has steadily evolved and expanded substantially across many industry and academic research institutions since it was first described in the early 1990′s. High content imaging refers to the automated acquisition and analysis of microscopic images from a variety of biological sample types. Integration of high content imaging microscopes with multiwell plate handling robotics enables high content imaging to be performed at scale and support medium- to high-throughput screening of pharmacological, genetic and diverse environmental perturbations upon complex biological systems ranging from 2D cell cultures to 3D tissue organoids to small model organisms. In this perspective article the authors provide a collective view on the following key discussion points relevant to the evolution of high content imaging:• Evolution and impact of high content imaging: An academic perspective• Evolution and impact of high content imaging: An industry perspective• Evolution of high content image analysis• Evolution of high content data analysis pipelines towards multiparametric and phenotypic profiling applications• The role of data integration and multiomics• The role and evolution of image data repositories and sharing standards• Future perspective of high content imaging hardware and softwar