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Droplet-based microfluidic devices have become widely used to perform single-cell RNA sequencing (scRNA-seq). However, ambient RNA present in the cell suspension can be aberrantly counted along with a cell’s native mRNA and result in cross-contamination of transcripts between different cell populations. DecontX is a novel Bayesian method to estimate and remove contamination in individual cells. DecontX accurately predicts contamination levels in a mouse-human mixture dataset and removes aberrant expression of marker genes in PBMC datasets. We also compare the contamination levels between four different scRNA-seq protocols. Overall, DecontX can be incorporated into scRNA-seq workflows to improve downstream analyses.

Single-cell RNA sequencing (scRNA-seq) can be used to gain insights into cellular heterogeneity within complex tissues. However, various technical artifacts can be present in scRNA-seq data and should be assessed before performing downstream analyses. While several tools have been developed to perform individual quality control (QC) tasks, they are scattered in different packages across several programming environments. Here, to streamline the process of generating and visualizing QC metrics for scRNA-seq data, we built the SCTK-QC pipeline within the singleCellTK R package. The SCTK-QC workflow can import data from several single-cell platforms and preprocessing tools and includes steps for empty droplet detection, generation of standard QC metrics, prediction of doublets, and estimation of ambient RNA. It can run on the command line, within the R console, on the cloud platform or with an interactive graphical user interface. Overall, the SCTK-QC pipeline streamlines and standardizes the process of performing QC for scRNA-seq data. Quality control (QC) is a crucial step in single-cell RNA-seq data analysis. Here, the authors present the SCTK-QC pipeline which generates and visualizes a comprehensive set of QC metrics to streamline the process of detecting and removing poor quality cells and other artifacts.

The inhibition of fibrosis in filtering blebs is indispensable for lowering intraocular pressure (IOP) after PRESERFLO MicroShunt surgery (PMS). We previously reported the anti-fibrotic effect of OBP-801 (OBP) on a filtering bleb in a rabbit glaucoma filtration surgery model using a 22-gauge cannula. In this study, we investigated the effect of OBP after PMS in rabbit eyes for the development of a new practically applicable anti-fibrotic treatment. This study involved 19 Japanese white rabbits that underwent PMS in the right eye, with those eyes divided into 3 groups: (1) intraoperative subconjunctival injection of 0.02% mitomycin-C (MMC) ( n  = 6), (2) postoperative instillation of 100 nM OBP eye drops ( n  = 7), or (3) a balanced salt solution (BSS) control ( n  = 6). Bleb morphology and IOP were monitored for 12 weeks postoperative, with the bleb tissues then undergoing evaluation of fibrosis and Western blot analysis. A lower postoperative IOP was maintained in the OBP-group eyes, and at 12 weeks postoperative, the IOP was significantly lower in that group than in the BSS and MMC groups ( p  < 0.01). OBP-treated eyes showed no adverse effects and reduced levels of alpha-smooth muscle actin and collagen deposition, thus suggesting that OBP is a promising candidate for improving surgical outcomes post PMS.

Infections are important causes of postoperative morbidity after gastric surgery; currently, no factors have been identified that can predict postoperative infection. Adiponectin (ADN) mediates energy metabolism and functions as an immunomodulator. Perioperative ADN levels and perioperative immune functioning could be mutually related. Here we evaluated a potential biological marker to reliably predict the incidence of postoperative infections to prevent such comorbidities. We analyzed 150 consecutive patients who underwent elective gastric cancer surgery at the Shiga University of Medical Science Hospital (Shiga, Japan) from 1997 to 2009; of these, most surgeries (n = 100) were performed 2008 onwards. The patient characteristics and surgery-related factors between two groups (with and without infection) were compared by the paired t-test and χ(2) test, including preoperative ADN levels, postoperative day 1 ADN levels, and ADN ratio (postoperative ADN levels/preoperative ADN levels) as baseline factors. Logistic regression analysis was performed to access the independent association between ADN ratio and postoperative infection. Finally, receiver operating curves (ROCs) were constructed to examine its clinical utility. Sixty patients (40%) experienced postoperative infections. The baseline values of age, American Society of Anesthesiologists physical status, total operating time, blood loss, surgical procedure, C-reactive protein (CRP) levels, preoperative ADN levels, and ADN ratio were significantly different between groups. Logistic regression analysis using these factors indicated that type 2 diabetes mellitus (T2DM) and ADN ratio were significantly independent variables (*p<0.05, ** p<0.01, respectively). ROC analysis revealed that the useful cutoff values (sensitivity/specificity) for preoperative ADN levels, ADN ratio, blood loss, operating time, and CRP levels were 8.81(0.567/0.568), 0.76 (0.767/0.761), 405 g (0.717/0.693), 342 min (0.617/0.614), and 8.94 mg/dl (0.583/0.591), respectively. T2DM and ADN ratio were independent predictors of postoperative infection and ADN ratio was the most useful predictor for postoperative infection.

ObjectiveThe immune response to invasive carcinoma has been the focus of published work, but little is known about the adaptive immune response to bronchial premalignant lesions (PMLs), precursors of lung squamous cell carcinoma. This study was designed to characterize the T cell receptor (TCR) repertoire in PMLs and its association with clinical, pathological, and molecular features.MethodsEndobronchial biopsies (n=295) and brushings (n=137) from high-risk subjects (n=50), undergoing lung cancer screening at approximately 1-year intervals via autofluorescence bronchoscopy and CT, were profiled by RNA-seq. We applied the TCR Repertoire Utilities for Solid Tissue/Tumor tool to the RNA-seq data to identify TCR CDR3 sequences across all samples. In the biopsies, we measured the correlation of TCR diversity with previously derived immune-associated PML transcriptional signatures and PML outcome. We also quantified the spatial and temporal distribution of shared and clonally expanded TCRs. Using the biopsies and brushes, the ratio of private (ie, found in one patient only) and public (ie, found in two or more patients) TCRs was quantified, and the CDR3 sequences were compared with those found in curated databases with known antigen specificities.ResultsWe detected 39,303 unique TCR sequences across all samples. In PML biopsies, TCR diversity was negatively associated with a transcriptional signature of T cell mediated immune activation (p=4e-4) associated with PML outcome. Additionally, in lesions of the proliferative molecular subtype, TCR diversity was decreased in regressive versus progressive/persistent PMLs (p=0.045). Within each patient, TCRs were more likely to be shared between biopsies sampled at the same timepoint than biopsies sampled at the same anatomic location at different times. Clonally expanded TCRs, within a biopsied lesion, were more likely to be expanded at future time points than non-expanded clones. The majority of TCR sequences were found in a single sample, with only 3396 (8.6%) found in more than one sample and 1057 (2.7%) found in two or more patients (ie, public); however, when compared with a public database of CDR3 sequences, 4543 (11.6%) of TCRs were identified as public. TCRs with known antigen specificities were enriched among public TCRs (p<0.001).ConclusionsDecreased TCR diversity may reflect nascent immune responses that contribute to PML elimination. Further studies are needed to explore the potential for immunoprevention of PMLs.

Whole-genome doubling (WGD) is common in human cancers, occurring early in tumorigenesis and generating genetically unstable tetraploid cells that fuel tumour development 1 , 2 . Cells that undergo WGD (WGD + cells) must adapt to accommodate their abnormal tetraploid state; however, the nature of these adaptations, and whether they confer vulnerabilities that can be exploited therapeutically, is unclear. Here, using sequencing data from roughly 10,000 primary human cancer samples and essentiality data from approximately 600 cancer cell lines, we show that WGD gives rise to common genetic traits that are accompanied by unique vulnerabilities. We reveal that WGD + cells are more dependent than WGD − cells on signalling from the spindle-assembly checkpoint, DNA-replication factors and proteasome function. We also identify KIF18A , which encodes a mitotic kinesin protein, as being specifically required for the viability of WGD + cells. Although KIF18A is largely dispensable for accurate chromosome segregation during mitosis in WGD – cells, its loss induces notable mitotic errors in WGD + cells, ultimately impairing cell viability. Collectively, our results suggest new strategies for specifically targeting WGD + cancer cells while sparing the normal, non-transformed WGD − cells that comprise human tissue. Cancer cells that have undergone whole-genome doubling are more reliant than their near-diploid counterparts on DNA-replication factors, the spindle-assembly checkpoint and a mitotic kinesin protein, KIF18A.

Abstract Single-cell RNA-seq (scRNA-seq) has emerged as a powerful technique to quantify gene expression in individual cells and to elucidate the molecular and cellular building blocks of complex tissues. We developed a novel Bayesian hierarchical model called Cellular Latent Dirichlet Allocation (Celda) to perform co-clustering of genes into transcriptional modules and cells into subpopulations. Celda can quantify the probabilistic contribution of each gene to each module, each module to each cell population and each cell population to each sample. In a peripheral blood mononuclear cell dataset, Celda identified a subpopulation of proliferating T cells and a plasma cell which were missed by two other common single-cell workflows. Celda also identified transcriptional modules that could be used to characterize unique and shared biological programs across cell types. Finally, Celda outperformed other approaches for clustering genes into modules on simulated data. Celda presents a novel method for characterizing transcriptional programs and cellular heterogeneity in scRNA-seq data.

Background and Purpose Infections are important causes of postoperative morbidity after gastric surgery; currently, no factors have been identified that can predict postoperative infection. Adiponectin (ADN) mediates energy metabolism and functions as an immunomodulator. Perioperative ADN levels and perioperative immune functioning could be mutually related. Here we evaluated a potential biological marker to reliably predict the incidence of postoperative infections to prevent such comorbidities. Methods We analyzed 150 consecutive patients who underwent elective gastric cancer surgery at the Shiga University of Medical Science Hospital (Shiga, Japan) from 1997 to 2009; of these, most surgeries (n = 100) were performed 2008 onwards. The patient characteristics and surgery-related factors between two groups (with and without infection) were compared by the paired t-test and chi 2 test, including preoperative ADN levels, postoperative day 1 ADN levels, and ADN ratio (postoperative ADN levels/preoperative ADN levels) as baseline factors. Logistic regression analysis was performed to access the independent association between ADN ratio and postoperative infection. Finally, receiver operating curves (ROCs) were constructed to examine its clinical utility. Results Sixty patients (40%) experienced postoperative infections. The baseline values of age, American Society of Anesthesiologists physical status, total operating time, blood loss, surgical procedure, C-reactive protein (CRP) levels, preoperative ADN levels, and ADN ratio were significantly different between groups. Logistic regression analysis using these factors indicated that type 2 diabetes mellitus (T2DM) and ADN ratio were significantly independent variables (*p<0.05, ** p<0.01, respectively). ROC analysis revealed that the useful cutoff values (sensitivity/specificity) for preoperative ADN levels, ADN ratio, blood loss, operating time, and CRP levels were 8.81(0.567/0.568), 0.76 (0.767/0.761), 405 g (0.717/0.693), 342 min (0.617/0.614), and 8.94 mg/dl (0.583/0.591), respectively. Conclusion T2DM and ADN ratio were independent predictors of postoperative infection and ADN ratio was the most useful predictor for postoperative infection.