Ureter Smooth Muscle Cell Orientation in Rat Is Predominantly Longitudinal

<div><p>In ureter peristalsis, the orientation of the contracting smooth muscle cells is essential, yet current descriptions of orientation and composition of the smooth muscle layer in human as well as in rat ureter are inconsistent. The present study aims to improve quantification of smooth muscle orientation in rat ureters as a basis for mechanistic understanding of peristalsis. A crucial step in our approach is to use two-photon laser scanning microscopy and image analysis providing objective, quantitative data on smooth muscle cell orientation in intact ureters, avoiding the usual sectioning artifacts. In 36 rat ureter segments, originating from a proximal, middle or distal site and from a left or right ureter, we found close to the adventitia a well-defined longitudinal smooth muscle orientation. Towards the lamina propria, the orientation gradually became slightly more disperse, yet the main orientation remained longitudinal. We conclude that smooth muscle cell orientation in rat ureter is predominantly longitudinal, though the orientation gradually becomes more disperse towards the proprial side. These findings do <i>not</i> support identification of separate layers. The observed longitudinal orientation suggests that smooth muscle contraction would rather cause local shortening of the ureter, than cause luminal constriction. However, the net-like connective tissue of the ureter wall may translate local longitudinal shortening into co-local luminal constriction, facilitating peristalsis. Our quantitative, minimally invasive approach is a crucial step towards more mechanistic insight into ureter peristalsis, and may also be used to study smooth muscle cell orientation in other tube-like structures like gut and blood vessels.</p></div

Image acquisition and processing workflow.

<p>(<b>A</b>) Image stacks of the muscle layer (of the mounted, submerged ureter) were acquired at increasing depth (<i>z</i>) from the adventitial to the proprial side, at proximal, middle and distal locations (i.e., three segments per ureter). (<b>B</b>) Given the curvature of the vessel, the region of interest (ROI) for quantitative analysis was adjusted to ensure reliable cell density estimation and to limit cross-talk of cells resident at other depths within the wall. (<b>C</b>) A stack of raw images, showing smooth muscle cell (SMC) nuclei stained with SYTO 13. (<b>D</b>) Raw images (panel <b>C</b>) were filtered using cellness filtering (step 1) to identify SMC nuclei. Subsequently, a ROI was applied and individual SMC angles (α) were determined. α was defined with reference to the longitudinal axis of the vessel (<i>x</i>-direction). (<b>E</b>) SMC angles were plotted as a function of depth to evaluate transmural changes in orientation, taking circularity of the data into account. (<b>F</b>) A kernel density estimation (KDE) plot was used to estimate the orientation distribution. On this KDE, octile lines were plotted to clarify changes in orientation dispersion with depth.</p

Control sample: By orientation distinguishable smooth muscle layers in the small intestine.

<p>In order to verify our image processing method, we applied the exact same preparation and staining method to a rat small intestine, an organ with clearly delineated longitudinal and circumferential smooth muscle layers <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0086207#pone.0086207-Maier1" target="_blank">[8]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0086207#pone.0086207-Disselhorst1" target="_blank">[10]</a>. Panels <b>A–C</b> show orientation distributions analogous to the panels in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0086207#pone-0086207-g004" target="_blank"><b>Figure 4</b></a>, at three different sites in the intestine. Panel <b>D</b> shows the average orientation distribution, calculated from panels <b>A–C</b>. All panels clearly show a transition from a superficial, longitudinal smooth muscle orientation to a circumferential orientation at the deep end. The patterns shown are all slightly shifted to the right by ∼10°, which is caused by the fact that the two pipettes used to mount the intestine were not perfectly aligned (i.e., microscopy images were rotated by ∼10°).</p

Smooth muscle cell (SMC) orientation in intact rat ureter is predominantly longitudinal.

<p>Panel <b>A</b> shows the orientation distribution (3D plot) and the cell density (2D graph) as function of depth, averaged over 36 rat ureter segments. Normalized depth 0 corresponds to the adventitial side of the muscle layer and 1 to the proprial side. At the adventitial side (normalized depth 0 to 0.5) there is a high probability that the angle of the SMCs with respect to the longitudinal axis of the ureter is about 0°. Towards the proprial side the SMC orientation gradually disperses but remains centered around 0°, as further illustrated in panel <b>B</b> by the distributions at four distinct normalized depths (<i>z</i>_n) as indicated. At these depths, standard deviations (σ) are given. In the distribution plot (<b>A</b>) and its top view (<b>C</b>) the curves delimit the octiles of the orientation distribution.</p

Example orientation patterns of two ureter segments.

<p>Panels <b>A</b> and <b>B</b> (view as defined in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0086207#pone-0086207-g003" target="_blank"><b>Figure 3C</b></a>) show two examples of orientation patterns acquired in different ureter segments. The pattern in <b>A</b> remains longitudal from adventitial to proprial side, whereas the pattern in <b>B</b> disperses towards the proprial side.</p

Variability in dispersion among different stacks.

<p>To assess variability in dispersion between stacks, we calculated for each stack the orientation standard deviation (SD) at normalized depths 0.2, 0.4, 0.6 and 0.8, as we already did for the entire distribution in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0086207#pone-0086207-g003" target="_blank"><b>Figure 3B</b></a>. The 25th, 50th and 75th percentile of the SD values at each of these normalized depths are shown. Towards the proprial side (normalized depth 0.8), the SD showed a doubled variation among stacks (inter-quartile range (IQR) of 41°-25° = 16°) than at the adventitial side (normalized depth 0.2, IQR of 29°-21° = 8°). Overall, observations between samples consistently show a longitudinal orientation.</p

Implications of Genetic Testing in Dilated Cardiomyopathy

Background: Genetic analysis is a first-tier test in dilated cardiomyopathy (DCM). Electrical phenotypes are common in genetic DCM, but their exact contribution to the clinical course and outcome is unknown. We determined the prevalence of pathogenic gene variants in a large unselected DCM population and determined the role of electrical phenotypes in association with outcome. Methods: This study included 689 patients with DCM from the Maastricht Cardiomyopathy Registry, undergoing genetic evaluation using a 48 cardiomyopathy-associated gene-panel, echocardiography, endomyocardial biopsies, and Holter monitoring. Upon detection of a pathogenic variant in a patient with DCM, familial segregation was performed. Outcome was defined as cardiovascular death, heart transplantation, heart failure hospitalization, and/or occurrence of life-threatening arrhythmias. Results: A (likely) pathogenic gene variant was found in 19% of patients, varying from 36% in familial to 13% in nonfamilial DCM. Family segregation analysis showed familial disease in 46% of patients with DCM who were initially deemed nonfamilial by history. Overall, 18% of patients with a nongenetic risk factor had a pathogenic gene variant. Almost all pathogenic gene variants occurred in just 12 genes previously shown to have robust disease association with DCM. Genetic DCM was independently associated with electrical phenotypes such as atrial fibrillation, nonsustained ventricular tachycardia, and atrioventricular block and inversely correlated with the presence of a left bundle branch block (P Conclusions: One in 5 patients with an established nongenetic risk factor or a nonfamilial disease still carries a pathogenic gene variant. Genetic DCM is characterized by a profile of electrical phenotypes (atrial fibrillation, nonsustained ventricular tachycardia, and atrioventricular block), which carries increased risk for adverse outcomes. Based on these findings, we envisage a broader role for genetic testing in DCM