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Quantitative Characterization of Myocardial Spatial Heterogeneities in Health and Disease

Soltisz, Andrew Michael
http://orcid.org/0000-0210-0-
http://rave.ohiolink.edu/etdc/view?acc_num=osu1689251630904641

Abstract Details

2023, Doctor of Philosophy, Ohio State University, Biomedical Engineering.
Quantitative colocalization analysis is a standard method in the life sciences used for evaluating the global spatial proximity of labeled biomolecules captured by fluorescence microscopy images. It is typically performed by characterizing the pixel-wise signal overlap or intensity correlation between spectral channels. However, this approach is critically flawed due to its focus on individual pixels which limits assessment to a single spatial scale constrained by the pixel’s size, thus making the analysis dependent on the achieved optical resolution and ignorant of the spatial information presented by non-overlapping signals. In this dissertation, I present an improved method for quantifying biomolecule spatial proximity using a novel application of point process analysis adapted for discrete image data, and subsequently utilize it to address two novel cardiac conundrums. The tool, called Spatial Pattern Analysis using Closest Events (SPACE), leverages the distances between signal-positive pixels to statistically characterize the spatial relationship between labeled biomolecules from fluorescence microscopy images. In chapter two, SPACE’s underlying theory and its adaption for discrete image-based data is described. Additionally, I characterize its sensitivity to segmentation parameters, image resolution, and signal sample size, and demonstrate its advantages over standard colocalization methods. With this tool, I hope to provide microscopists an improved method to robustly characterize spatial relationships independent of imaging modality or achieved resolution. In chapter three, SPACE is used to elucidate a novel, microtubule-based system for the distributed synthesis of membrane proteins in cardiomyocytes. Canonically, these cells are thought to produce membrane proteins in the peri-nuclear rough endoplasmic reticulum, then leverage the secretory-protein-trafficking pathway to transport nascent proteins to their sites of membrane insertion. By labeling cardiomyocyte mRNAs encoding for key electrogenic membrane proteins and characterizing their spatial distribution with SPACE, we observe and quantitatively characterize a deviation from this synthesis paradigm to a distributed one, with mRNAs and their sites of active translation distributed throughout the cell rather than in close proximity with nuclei. Additionally, we demonstrate that this novel protein synthesis pathway is, at least in part, driven my microtubule-based transport which aligns with recent studies of similarly large and highly differentiated cell types such as neurons and skeletal muscle. Finally, in chapter four, SPACE is used to evaluate the role of neuronal voltage gated sodium ion channels in cardiomyopathy and arrhythmogenesis within the context of late-stage type-2 diabetes. It is well understood that diabetic patients exhibit severe cardiac calcium ion mishandling, though the upstream causes for this are not well understood. Previous research in other cardiac diseases, such as long QT syndrome and catecholaminergic polymorphic ventricular tachycardia (CPVT), demonstrate ectopic diastolic calcium release induced by a persistent sodium ion current mediated by neuronal sodium channels. Preliminary work in diabetic mice suggest the presence of a similar electrophysiological phenotype. In this last chapter, I present evidence showing these mice indeed feature a persistent sodium current which is mediated by neuronal sodium channels and leverage SPACE to investigate the placement of these channels in T-tubules, a nanodomain of the cardiomyocyte where they can readily disrupt calcium handling to induce cardiomyopathy or arrhythmias. In a surprising result, SPACE revealed no significant abnormalities in neuronal sodium channel localization along t-tubules. This result, along with additional experimental data, point to reentrant arrhythmias resulting from maladaptive remodeling of the machinery of cardiac excitation, rather than triggered arrhythmias resulting from abnormal sodium/calcium cycling, may underlie the arrhythmia phenotype in diabetic cardiomyopathy.
Rengasayee Veeraraghavan (Advisor)
Przemysław Radwański (Committee Member)
Peter Craigmile (Committee Member)
Seth Weinberg (Committee Member)
217 p.
Biology; Biomedical Engineering; Biomedical Research; Biophysics; Biostatistics; Cellular Biology; Engineering; Scientific Imaging; Statistics
Image analysis; Spatial analysis; Fluorescence microscopy; Colocalization; Spatial statistics; Point pattern analysis; Nearest neighbor distribution; Empty space distribution; Heart; Translation; Protein Synthesis; Membrane Proteins; Type-II diabetes; Neuronal Na+ channels; NaV1.6; Diastolic Ca2+ release; arrhythmia; Heart failure; Cardiac

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Citations

  • Soltisz, A. M. (2023). Quantitative Characterization of Myocardial Spatial Heterogeneities in Health and Disease [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1689251630904641

    APA Style (7th edition)

  • Soltisz, Andrew. Quantitative Characterization of Myocardial Spatial Heterogeneities in Health and Disease. 2023. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1689251630904641.

    MLA Style (8th edition)

  • Soltisz, Andrew. "Quantitative Characterization of Myocardial Spatial Heterogeneities in Health and Disease." Doctoral dissertation, Ohio State University, 2023. http://rave.ohiolink.edu/etdc/view?acc_num=osu1689251630904641

    Chicago Manual of Style (17th edition)

osu1689251630904641
105
© 2023, some rights reserved.Creative Commons License Quantitative Characterization of Myocardial Spatial Heterogeneities in Health and Disease by Andrew Michael Soltisz is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by The Ohio State University and OhioLINK.