SUMMARYA comprehensive cellular anatomy of normal human prostate is essential for solving the cellular origins of benign prostatic hyperplasia and prostate cancer. The tools used to analyze the contribution of individual cell types are not robust. We provide a cellular atlas of the young adult human prostate and prostatic urethra using an iterative process of single-cell RNA sequencing (scRNA-seq) and flow cytometry on ~98,000 cells taken from different anatomical regions. Immunohistochemistry with newly derived cell type-specific markers revealed the distribution of each epithelial and stromal cell type on whole mounts, revising our understanding of zonal anatomy. Based on discovered cell surface markers, flow cytometry antibody panels were designed to improve the purification of each cell type, with each gate confirmed by scRNA-seq. The molecular classification, anatomical distribution, and purification tools for each cell type in the human prostate create a powerful resource for experimental design in human prostate disease.

A cellular anatomy of normal human organs is essential for solving the cellular origins of disease. We report the first comprehensive cellular atlas of the young adult human prostate and prostatic urethra using an iterative process of single cell RNA sequencing and flow cytometry on ~98,000 cells taken from different anatomical regions. Two previously unrecognized epithelial cell types were identified by KRT13 and SCGB1A1 expression and found to be highly similar to hillock and club cells of the proximal lung. It was demonstrated by immunohistochemistry that prostate club and hillock cells are similarly concentrated in the proximal prostate. We also optimized a new flow cytometry antibody panel to improve cell type-specific purification based on newly established cellular markers. The molecular classification, anatomical distribution, and purification methods for each cell type in the human prostate create a powerful new resource for experimental design in human prostate disease.

ObjectiveARID1A is commonly mutated in pancreatic ductal adenocarcinoma (PDAC), but the functional effects of ARID1A mutations in the pancreas are unclear. Understanding the molecular mechanisms that drive PDAC formation may lead to novel therapies.DesignConcurrent conditional Arid1a deletion and Kras activation mutations were modelled in mice. Small-interfering RNA (siRNA) and CRISPR/Cas9 were used to abrogate ARID1A in human pancreatic ductal epithelial cells.ResultsWe found that pancreas-specific Arid1a loss in mice was sufficient to induce inflammation, pancreatic intraepithelial neoplasia (PanIN) and mucinous cysts. Concurrent Kras activation accelerated the development of cysts that resembled intraductal papillary mucinous neoplasm. Lineage-specific Arid1a deletion confirmed compartment-specific tumour-suppressive effects. Duct-specific Arid1a loss promoted dilated ducts with occasional cyst and PDAC formation. Heterozygous acinar-specific Arid1a loss resulted in accelerated PanIN and PDAC formation with worse survival. RNA-seq showed that Arid1a loss induced gene networks associated with Myc activity and protein translation. ARID1A knockdown in human pancreatic ductal epithelial cells induced increased MYC expression and protein synthesis that was abrogated with MYC knockdown. ChIP-seq against H3K27ac demonstrated an increase in activated enhancers/promoters.ConclusionsArid1a suppresses pancreatic neoplasia in a compartment-specific manner. In duct cells, this process appears to be associated with MYC-facilitated protein synthesis.

Aim of the Study. The three conventional pulse-diagnostic palpation locations (PLs) on both wrists are Cun, Guan, and Chi, and each location reveals different clinical information. To identify anatomical or hemodynamic specificity, we used ultrasonographic imaging to determine the arterial diameter, radial artery depth, and arterial blood flow velocity at the three PLs and at nearby non-PL segments. Methods. We applied an ultrasound scanner to 44 subjects and studied the changes in the arterial diameter and depth as well as in the average/maximum blood flow velocities along the radial artery at three PLs and three non-PLs located more proximally than Chi. Results. All of the measurements at all of the PLs were significantly different (P < 0.01). Artery depth was significantly different among the non-PLs; however, this difference became insignificant after normalization to the arm circumference. Conclusions. Substantial changes in the hemodynamic and anatomical properties of the radial artery around the three PLs were insignificant at the nearby non-PLs segments. This finding may provide a partial explanation for the diagnostic use of “Cun, Guan, and Chi.”

Sasang constitution diagnosis has traditionally been conducted by a Sasang constitutional medicine (SCM) doctor who examines the external appearance, temperament and various symptoms of an individual and then collectively analyzes this information to determine their own constitutions. However, because this process is subjective and not quantitative, many researchers have been attempting to develop objective and reasonable methods of determining constitutions. In Korea, even though a wide range of research regarding SCM has been conducted, most of the work has not been revealed internationally. So in this review, the authors have searched the Journal of Sasang Constitutional Medicine, as well as other Korean domestic journal databases and Pubmed for research regarding modernized constitution diagnosis methods so to provide the understanding of current research state and outlook for future research.

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Quantifying ear deformity using linear measurements and mathematical modeling is difficult due to the ear’s complex shape. Machine learning techniques, such as convolutional neural networks (CNNs), are well-suited for this role. CNNs are deep learning methods capable of finding complex patterns from medical images, automatically building solution models capable of machine diagnosis. In this study, we applied CNN to automatically identify ear deformity from 2D photographs. Institutional review board (IRB) approval was obtained for this retrospective study to train and test the CNNs. Photographs of patients with and without ear deformity were obtained as standard of care in our photography studio. Profile photographs were obtained for one or both ears. A total of 671 profile pictures were used in this study including: 457 photographs of patients with ear deformity and 214 photographs of patients with normal ears. Photographs were cropped to the ear boundary and randomly divided into training (60%), validation (20%), and testing (20%) datasets. We modified the softmax classifier in the last layer in GoogLeNet, a deep CNN, to generate an ear deformity detection model in Matlab. All images were deemed of high quality and usable for training and testing. It took about 2 hours to train the system and the training accuracy reached almost 100%. The test accuracy was about 94.1%. We demonstrate that deep learning has a great potential in identifying ear deformity. These machine learning techniques hold the promise in being used in the future to evaluate treatment outcomes.

Clathrin-mediated endocytosis (CME) begins with the nucleation of clathrin assembly on the plasma membrane, followed by stabilization and growth/maturation of clathrin-coated pits (CCPs) that eventually pinch off and internalize as clathrin-coated vesicles. This highly regulated process involves a myriad of endocytic accessory proteins (EAPs), many of which are multidomain proteins that encode a wide range of biochemical activities. Although domain-specific activities of EAPs have been extensively studied, their precise stage-specific functions have been identified in only a few cases. Using single-guide RNA (sgRNA)/dCas9 and small interfering RNA (siRNA)-mediated protein knockdown, combined with an image-based analysis pipeline, we have determined the phenotypic signature of 67 EAPs throughout the maturation process of CCPs. Based on these data, we show that EAPs can be partitioned into phenotypic clusters, which differentially affect CCP maturation and dynamics. Importantly, these clusters do not correlate with functional modules based on biochemical activities. Furthermore, we discover a critical role for SNARE proteins and their adaptors during early stages of CCP nucleation and stabilization and highlight the importance of GAK throughout CCP maturation that is consistent with GAK’s multifunctional domain architecture. Together, these findings provide systematic, mechanistic insights into the plasticity and robustness of CME.