Cardiac Development at a Single-Cell Resolution.

Mammalian cardiac development is a complex, multistage process. Though traditional lineage tracing studies have characterized the broad trajectories of cardiac progenitors, the advent and rapid optimization of single-cell RNA sequencing methods have yielded an ever-expanding toolkit for characterizing heterogeneous cell populations in the developing heart. Importantly, they have allowed for a robust profiling of the spatiotemporal transcriptomic landscape of the human and mouse heart, revealing the diversity of cardiac cells-myocyte and non-myocyte-over the course of development. These studies have yielded insights into novel cardiac progenitor populations, chamber-specific developmental signatures, the gene regulatory networks governing cardiac development, and, thus, the etiologies of congenital heart diseases. Furthermore, single-cell RNA sequencing has allowed for the exquisite characterization of distinct cardiac populations such as the hard-to-capture cardiac conduction system and the intracardiac immune population. Therefore, single-cell profiling has also resulted in new insights into the regulation of cardiac regeneration and injury repair. Single-cell multiomics approaches combining transcriptomics, genomics, and epigenomics may uncover an even more comprehensive atlas of human cardiac biology. Single-cell analyses of the developing and adult mammalian heart offer an unprecedented look into the fundamental mechanisms of cardiac development and the complex diseases that may arise from it.

Therapist Feedback and Implications on Adoption of an Artificial Intelligence-Based Co-Facilitator for Online Cancer Support Groups: Mixed Methods Single-Arm Usability Study.

BACKGROUND: The recent onset of the COVID-19 pandemic and the social distancing requirement have created an increased demand for virtual support programs. Advances in artificial intelligence (AI) may offer novel solutions to management challenges such as the lack of emotional connections within virtual group interventions. Using typed text from online support groups, AI can help identify the potential risk of mental health concerns, alert group facilitator(s), and automatically recommend tailored resources while monitoring patient outcomes. OBJECTIVE: The aim of this mixed methods, single-arm study was to evaluate the feasibility, acceptability, validity, and reliability of an AI-based co-facilitator (AICF) among CancerChatCanada therapists and participants to monitor online support group participants' distress through a real-time analysis of texts posted during the support group sessions. Specifically, AICF (1) generated participant profiles with discussion topic summaries and emotion trajectories for each session, (2) identified participant(s) at risk for increased emotional distress and alerted the therapist for follow-up, and (3) automatically suggested tailored recommendations based on participant needs. Online support group participants consisted of patients with various types of cancer, and the therapists were clinically trained social workers. METHODS: Our study reports on the mixed methods evaluation of AICF, including therapists' opinions as well as quantitative measures. AICF's ability to detect distress was evaluated by the patient's real-time emoji check-in, the Linguistic Inquiry and Word Count software, and the Impact of Event Scale-Revised. RESULTS: Although quantitative results showed only some validity of AICF's ability in detecting distress, the qualitative results showed that AICF was able to detect real-time issues that are amenable to treatment, thus allowing therapists to be more proactive in supporting every group member on an individual basis. However, therapists are concerned about the ethical liability of AICF's distress detection function. CONCLUSIONS: Future works will look into wearable sensors and facial cues by using videoconferencing to overcome the barriers associated with text-based online support groups. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/21453.

Nuclear RNA binding regulates TDP-43 nuclear localization and passive nuclear export.

Nuclear clearance of the RNA-binding protein TDP-43 is a hallmark of neurodegeneration and an important therapeutic target. Our current understanding of TDP-43 nucleocytoplasmic transport does not fully explain its predominantly nuclear localization or mislocalization in disease. Here, we show that TDP-43 exits nuclei by passive diffusion, independent of facilitated mRNA export. RNA polymerase II blockade and RNase treatment induce TDP-43 nuclear efflux, suggesting that nuclear RNAs sequester TDP-43 in nuclei and limit its availability for passive export. Induction of TDP-43 nuclear efflux by short, GU-rich oligomers (presumably by outcompeting TDP-43 binding to endogenous nuclear RNAs), and nuclear retention conferred by splicing inhibition, demonstrate that nuclear TDP-43 localization depends on binding to GU-rich nuclear RNAs. Indeed, RNA-binding domain mutations markedly reduce TDP-43 nuclear localization and abolish transcription blockade-induced nuclear efflux. Thus, the nuclear abundance of GU-RNAs, dictated by the balance of transcription, pre-mRNA processing, and RNA export, regulates TDP-43 nuclear localization.

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.

In vivo visualization and molecular targeting of the cardiac conduction system.

Accidental injury to the cardiac conduction system (CCS), a network of specialized cells embedded within the heart and indistinguishable from the surrounding heart muscle tissue, is a major complication in cardiac surgeries. Here, we addressed this unmet need by engineering targeted antibody-dye conjugates directed against the CCS, allowing for the visualization of the CCS in vivo following a single intravenous injection in mice. These optical imaging tools showed high sensitivity, specificity, and resolution, with no adverse effects on CCS function. Further, with the goal of creating a viable prototype for human use, we generated a fully human monoclonal Fab that similarly targets the CCS with high specificity. We demonstrate that, when conjugated to an alternative cargo, this Fab can also be used to modulate CCS biology in vivo, providing a proof of principle for targeted cardiac therapeutics. Finally, in performing differential gene expression analyses of the entire murine CCS at single-cell resolution, we uncovered and validated a suite of additional cell surface markers that can be used to molecularly target the distinct subcomponents of the CCS, each prone to distinct life-threatening arrhythmias. These findings lay the foundation for translational approaches targeting the CCS for visualization and therapy in cardiothoracic surgery, cardiac imaging, and arrhythmia management.

Nuclear Transport Assays in Permeabilized Mouse Cortical Neurons.

Disruption of nucleocytoplasmic transport is increasingly implicated in the pathogenesis of neurodegenerative diseases. Moreover, there is a growing recognition of cell-specific differences in nuclear pore complex structure, prompting a need to adapt nuclear transport methods for use in neurons. Permeabilized cell assays, in which the plasma membrane is selectively perforated by digitonin, are widely used to study passive and active nuclear transport in immortalized cell lines but have not been applied to neuronal cultures. In our initial attempts, we observed the rapid loss of nuclear membrane integrity in primary mouse cortical neurons exposed to even low concentrations of digitonin. We hypothesized that neuronal nuclear membranes may be uniquely vulnerable to the loss of cytoplasmic support. After testing multiple approaches to improve nuclear stability, we observed optimal nuclear integrity following hypotonic lysis in the presence of a concentrated bovine serum albumin cushion. Neuronal nuclei prepared by this approach reliably import recombinant fluorescent cargo in an energy-dependent manner, facilitating analysis of nuclear import by high content microscopy with automated analysis. We anticipate that this method will be broadly applicable to studies of passive and active nuclear transport in primary neurons.

C9orf72 arginine-rich dipeptide repeat proteins disrupt karyopherin-mediated nuclear import.

Disruption of nucleocytoplasmic transport is increasingly implicated in the pathogenesis of neurodegenerative diseases, including ALS caused by a C9orf72 hexanucleotide repeat expansion. However, the mechanism(s) remain unclear. Karyopherins, including importin ß and its cargo adaptors, have been shown to co-precipitate with the C9orf72 arginine-containing dipeptide repeat proteins (R-DPRs), poly-glycine arginine (GR) and poly-proline arginine (PR), and are protective in genetic modifier screens. Here, we show that R-DPRs interact with importin ß, disrupt its cargo loading, and inhibit nuclear import of importin ß, importin α/ß, and transportin cargoes in permeabilized mouse neurons and HeLa cells, in a manner that can be rescued by RNA. Although R-DPRs induce widespread protein aggregation in this in vitro system, transport disruption is not due to nucleocytoplasmic transport protein sequestration, nor blockade of the phenylalanine-glycine (FG)-rich nuclear pore complex. Our results support a model in which R-DPRs interfere with cargo loading on karyopherins.

Dynamic Transcriptional Responses to Injury of Regenerative and Non-regenerative Cardiomyocytes Revealed by Single-Nucleus RNA Sequencing.

The adult mammalian heart is incapable of regeneration following injury. In contrast, the neonatal mouse heart can efficiently regenerate during the first week of life. The molecular mechanisms that mediate the regenerative response and its blockade in later life are not understood. Here, by single-nucleus RNA sequencing, we map the dynamic transcriptional landscape of five distinct cardiomyocyte populations in healthy, injured, and regenerating mouse hearts. We identify immature cardiomyocytes that enter the cell cycle following injury and disappear as the heart loses the ability to regenerate. These proliferative neonatal cardiomyocytes display a unique transcriptional program dependent on nuclear transcription factor Y subunit alpha (NFYa) and nuclear factor erythroid 2-like 1 (NFE2L1) transcription factors, which exert proliferative and protective functions, respectively. Cardiac overexpression of these two factors conferred protection against ischemic injury in mature mouse hearts that were otherwise non-regenerative. These findings advance our understanding of the cellular basis of neonatal heart regeneration and reveal a transcriptional landscape for heart repair following injury.