The cardiac conduction system: History, development, and disease.

The heart is the first organ to form during embryonic development, establishing the circulatory infrastructure necessary to sustain life and enable downstream organogenesis. Critical to the heart's function is its ability to initiate and propagate electrical impulses that allow for the coordinated contraction and relaxation of its chambers, and thus, the movement of blood and nutrients. Several specialized structures within the heart, collectively known as the cardiac conduction system (CCS), are responsible for this phenomenon. In this review, we discuss the discovery and scientific history of the mammalian cardiac conduction system as well as the key genes and transcription factors implicated in the formation of its major structures. We also describe known human diseases related to CCS development and explore existing challenges in the clinical context.

THE IMPACT OF ROUTINE CARDIAC TROPONIN I-BASED CARDIOTOXICITY SCREENING ON CLINICAL OUTCOMES IN PATIENTS ON CANCER IMMUNOTHERAPY.

Purpose: Immune checkpoint inhibitors (ICI) used as cancer therapy have been associated with a range of cardiac immune-related adverse events (irAEs), including fulminant myocarditis with a high case fatality rate. Early detection through cardiotoxicity screening by biomarker monitoring can lead to prompt intervention and improved patient outcomes. In this study, we investigate the association between cardiotoxicity screening with routine serial troponin I monitoring in asymptomatic patients receiving ICI, cardiovascular adverse event (CV AE) detection, and overall survival (OS). Methods: We instituted a standardized troponin I screening protocol at baseline and with each ICI dose (every 2-4 weeks) in all patients receiving ICI at our center starting Jan 2019. We subsequently collected data in 825 patients receiving ICI at our institution from January 2018 to October 2021. Of these patients, 428 underwent cardiotoxicity screening with serial troponin I monitoring during ICI administration (Jan 2019-Oct 2021) and 397 patients were unmonitored (Jan 2018-Dec 2018). We followed patients for nine months following their first dose of ICI and compared outcomes of CV AEs and OS between monitored and unmonitored patients. Additionally, we investigated rates of CV AEs, all-cause mortality, and oncologic time-to-treatment failure (TTF) between patients with an elevated troponin I value during the monitoring period versus patients without elevated troponin I. Results: We found a lower rate of severe (grades 4-5) CV AEs, resulting in critical illness or death, in patients who underwent troponin monitoring (0.5%) compared to patients who did not undergo monitoring (1.8%), (HR 0.17, 95% CI 0.02-0.79, p = 0.04). There was no difference in overall CV AEs (grades 3-5) or OS between monitored and unmonitored patients. In the entire cohort, patients with at least one elevated troponin I during the follow up period, during routine monitoring or unmonitored, had a higher risk of overall CV AEs (HR 10.96, 95% CI 4.65-25.85, p<0.001) as well as overall mortality (HR 2.67, 95% CI 1.69 - 4.10, p<0.001) compared to those without elevated troponin. Oncologic time-to-treatment failure (TTF) was not significantly different in a sub-cohort of monitored vs. unmonitored patients. Conclusions: Patients undergoing cardiotoxicity screening with troponin I monitoring during ICI therapy had a lower rate of severe (grade 4-5) CV AEs compared patients who were not screened. Troponin I elevation in screened and unscreened patients was significantly associated with increased CV AEs as well as increased mortality. Troponin I monitoring did not impact oncologic time-to-treatment-failure in a sub-cohort analysis of patients treated with ICI. These results provide preliminary evidence for clinical utility of cardiotoxicity screening with troponin I monitoring in patients receiving ICI therapy.

Combined lineage tracing and scRNA-seq reveals unexpected first heart field predominance of human iPSC differentiation.

During mammalian development, the left and right ventricles arise from early populations of cardiac progenitors known as the first and second heart fields, respectively. While these populations have been extensively studied in non-human model systems, their identification and study in vivo human tissues have been limited due to the ethical and technical limitations of accessing gastrulation-stage human embryos. Human-induced pluripotent stem cells (hiPSCs) present an exciting alternative for modeling early human embryogenesis due to their well-established ability to differentiate into all embryonic germ layers. Here, we describe the development of a TBX5/MYL2 lineage tracing reporter system that allows for the identification of FHF- progenitors and their descendants including left ventricular cardiomyocytes. Furthermore, using single-cell RNA sequencing (scRNA-seq) with oligonucleotide-based sample multiplexing, we extensively profiled differentiating hiPSCs across 12 timepoints in two independent iPSC lines. Surprisingly, our reporter system and scRNA-seq analysis revealed a predominance of FHF differentiation using the small molecule Wnt-based 2D differentiation protocol. We compared this data with existing murine and 3D cardiac organoid scRNA-seq data and confirmed the dominance of left ventricular cardiomyocytes (>90%) in our hiPSC-derived progeny. Together, our work provides the scientific community with a powerful new genetic lineage tracing approach as well as a single-cell transcriptomic atlas of hiPSCs undergoing cardiac differentiation.

devCellPy is a machine learning-enabled pipeline for automated annotation of complex multilayered single-cell transcriptomic data.

A major informatic challenge in single cell RNA-sequencing analysis is the precise annotation of datasets where cells exhibit complex multilayered identities or transitory states. Here, we present devCellPy a highly accurate and precise machine learning-enabled tool that enables automated prediction of cell types across complex annotation hierarchies. To demonstrate the power of devCellPy, we construct a murine cardiac developmental atlas from published datasets encompassing 104,199 cells from E6.5-E16.5 and train devCellPy to generate a cardiac prediction algorithm. Using this algorithm, we observe a high prediction accuracy (>90%) across multiple layers of annotation and across de novo murine developmental data. Furthermore, we conduct a cross-species prediction of cardiomyocyte subtypes from in vitro-derived human induced pluripotent stem cells and unexpectedly uncover a predominance of left ventricular (LV) identity that we confirmed by an LV-specific TBX5 lineage tracing system. Together, our results show devCellPy to be a useful tool for automated cell prediction across complex cellular hierarchies, species, and experimental systems.