Scalable nonparametric clustering with unified marker gene selection for single-cell RNA-seq data.

Clustering is commonly used in single-cell RNA-sequencing (scRNA-seq) pipelines to characterize cellular heterogeneity. However, current methods face two main limitations. First, they require user-specified heuristics which add time and complexity to bioinformatic workflows; second, they rely on post-selective differential expression analyses to identify marker genes driving cluster differences, which has been shown to be subject to inflated false discovery rates. We address these challenges by introducing nonparametric clustering of single-cell populations (NCLUSION): an infinite mixture model that leverages Bayesian sparse priors to identify marker genes while simultaneously performing clustering on single-cell expression data. NCLUSION uses a scalable variational inference algorithm to perform these analyses on datasets with up to millions of cells. By analyzing publicly available scRNA-seq studies, we demonstrate that NCLUSION (i) matches the performance of other state-of-the-art clustering techniques with significantly reduced runtime and (ii) provides statistically robust and biologically relevant transcriptomic signatures for each of the clusters it identifies. Overall, NCLUSION represents a reliable hypothesis-generating tool for understanding patterns of expression variation present in single-cell populations.

Operative Versus Nonoperative Management of Displaced Midshaft Clavicle Fractures in Pediatric and Adolescent Patients: A Systematic Review and Meta-Analysis.

OBJECTIVES: The purpose of this study was to systematically review and quantitatively analyze outcomes in operative versus nonoperative management of displaced midshaft clavicle fractures in pediatric and adolescent patients. DATA SOURCES: Using the Preferred Reporting items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, systematic searches of PubMed and EMBASE were conducted to identify English-language studies reporting outcomes in displaced pediatric midshaft clavicle fractures from 1997 to 2018. STUDY SELECTION: Studies that reported on outcomes of operative and/or nonoperative treatment of displaced midshaft clavicle fractures in patients younger than 19 years were included. DATA EXTRACTION: Patient and treatment characteristics, union rates, time to union, time to return to activity, patient-reported outcome measures, and complications were extracted. DATA SYNTHESIS: All extracted data were recorded and qualitatively compared. QuickDASH (Quick Disabilities of the Arm, Shoulder, and Hand) scores and Constant scores were pooled using random-effects modeling and compared among studies, which adequately reported data for hypothesis testing. CONCLUSIONS: Three thousand eight hundred ten articles were identified, and 12 met inclusion criteria. These studies encompassed 497 patients with an average age of 14.1 years (8-18 years, range). Both operative and nonoperative management of displaced midshaft clavicle fractures in this population provide excellent rates of union and patient-reported outcome measures. Compared with nonoperative management, operative management yielded faster return to activity, superior Constant scores, and equal QuickDASH scores. Operative management had higher complication rates and complications that required secondary operative treatment (mostly related to implant prominence). LEVEL OF EVIDENCE: Therapeutic Level III. See Instructions for Authors for a complete description of levels of evidence.

Heuristic bias in stem cell biology.

When studying purified hematopoietic stem cells, the urge for mechanisms and reductionist approaches appears to be overwhelming. The prime focus of the field has recently been on the study of highly purified hematopoietic stem cells using various lineage and stem cell-specific markers, all of which adequately and conveniently fit the established hierarchical stem cell model. This methodology is tainted with bias and has led to incomplete conclusions. Much of our own work has shown that the purified hematopoietic stem cell, which has been so heavily studied, is not representative of the total population of hematopoietic stem cells and that rather than functioning within a hierarchical model of expansion the true hematopoietic stem cell is one that is actively cycling through various differentiation potentials within a dynamic continuum. Additional work with increased emphasis on studying whole populations and direct mechanistic studies to these populations is needed. Furthermore, the most productive studies may well be mechanistic at the cellular or tissue levels. Lastly, the application of robust machine learning algorithms may provide insight into the dynamic variability and flux of stem cell fate and differentiation potential.

Stem cells and extracellular vesicles: biological regulators of physiology and disease.

Many different subpopulations of subcellular extracellular vesicles (EVs) have been described. EVs are released from all cell types and have been shown to regulate normal physiological homeostasis, as well as pathological states by influencing cell proliferation, differentiation, organ homing, injury and recovery, as well as disease progression. In this review, we focus on the bidirectional actions of vesicles from normal and diseased cells on normal or leukemic target cells; and on the leukemic microenvironment as a whole. EVs from human bone marrow mesenchymal stem cells (MSC) can have a healing effect, reversing the malignant phenotype in prostate and colorectal cancer, as well as mitigating radiation damage to marrow. The role of EVs in leukemia and their bimodal cross talk with the encompassing microenvironment remains to be fully characterized. This may provide insight for clinical advances via the application of EVs as potential therapy and the employment of statistical and machine learning models to capture the pleiotropic effects EVs endow to a dynamic microenvironment, possibly allowing for precise therapeutic intervention.