Experimental and Computational Methods for Allelic Imbalance Analysis from Single-Nucleus RNA-seq Data.

Single-cell RNA-seq (scRNA-seq) is emerging as a powerful tool for understanding gene function across diverse cells. Recently, this has included the use of allele-specific expression (ASE) analysis to better understand how variation in the human genome affects RNA expression at the single-cell level. We reasoned that because intronic reads are more prevalent in single-nucleus RNA-Seq (snRNA-Seq), and introns are under lower purifying selection and thus enriched for genetic variants, that snRNA-seq should facilitate single-cell analysis of ASE. Here we demonstrate how experimental and computational choices can improve the results of allelic imbalance analysis. We explore how experimental choices, such as RNA source, read length, sequencing depth, genotyping, etc., impact the power of ASE-based methods. We developed a new suite of computational tools to process and analyze scRNA-seq and snRNA-seq for ASE. As hypothesized, we extracted more ASE information from reads in intronic regions than those in exonic regions and show how read length can be set to increase power. Additionally, hybrid selection improved our power to detect allelic imbalance in genes of interest. We also explored methods to recover allele-specific isoform expression levels from both long- and short-read snRNA-seq. To further investigate ASE in the context of human disease, we applied our methods to a Parkinson's disease cohort of 94 individuals and show that ASE analysis had more power than eQTL analysis to identify significant SNP/gene pairs in our direct comparison of the two methods. Overall, we provide an end-to-end experimental and computational approach for future studies.

A Distinct Transcriptional Program in Human CAR T Cells Bearing the 4-1BB Signaling Domain Revealed by scRNA-Seq.

T cells engineered to express chimeric antigen receptors (CARs) targeting CD19 have produced impressive outcomes for the treatment of B cell malignancies, but different products vary in kinetics, persistence, and toxicity profiles based on the co-stimulatory domains included in the CAR. In this study, we performed transcriptional profiling of bulk CAR T cell populations and single cells to characterize the transcriptional states of human T cells transduced with CD3ζ, 4-1BB-CD3ζ (BBζ), or CD28-CD3ζ (28ζ) co-stimulatory domains at rest and after activation by triggering their CAR or their endogenous T cell receptor (TCR). We identified a transcriptional signature common across CARs with the CD3ζ signaling domain, as well as a distinct program associated with the 4-1BB co-stimulatory domain at rest and after activation. CAR T cells bearing BBζ had increased expression of human leukocyte antigen (HLA) class II genes, ENPP2, and interleukin (IL)-21 axis genes, and decreased PD1 compared to 28ζ CAR T cells. Similar to previous studies, we also found BBζ CAR CD8 T cells to be enriched in a central memory cell phenotype and fatty acid metabolism genes. Our data uncovered transcriptional signatures related to costimulatory domains and demonstrated that signaling domains included in CARs uniquely shape the transcriptional programs of T cells.

An Ultrahigh-Dimensional Mapping Model of High-order Epistatic Networks for Complex Traits.

BACKGROUND: Genetic interactions involving more than two loci have been thought to affect quantitatively inherited traits and diseases more pervasively than previously appreciated. However, the detection of such high-order interactions to chart a complete portrait of genetic architecture has not been well explored. METHODS: We present an ultrahigh-dimensional model to systematically characterize genetic main effects and interaction effects of various orders among all possible markers in a genetic mapping or association study. The model was built on the extension of a variable selection procedure, called iFORM, derived from forward selection. The model shows its unique power to estimate the magnitudes and signs of high-order epistatic effects, in addition to those of main effects and pairwise epistatic effects. RESULTS: The statistical properties of the model were tested and validated through simulation studies. By analyzing a real data for shoot growth in a mapping population of woody plant, mei (Prunus mume), we demonstrated the usefulness and utility of the model in practical genetic studies. The model has identified important high-order interactions that contribute to shoot growth for mei. CONCLUSION: The model provides a tool to precisely construct genotype-phenotype maps for quantitative traits by identifying any possible high-order epistasis which is often ignored in the current genetic literature.

HpQTL: a geometric morphometric platform to compute the genetic architecture of heterophylly.

Heterophylly, i.e. morphological changes in leaves along the axis of an individual plant, is regarded as a strategy used by plants to cope with environmental change. However, little is known of the extent to which heterophylly is controlled by genes and how each underlying gene exerts its effect on heterophyllous variation. We described a geometric morphometric model that can quantify heterophylly in plants and further constructed an R-based computing platform by integrating this model into a genetic mapping and association setting. The platform, named HpQTL, allows specific quantitative trait loci mediating heterophyllous variation to be mapped throughout the genome. The statistical properties of HpQTL were examined and validated via computer simulation. Its biological relevance was demonstrated by results from a real data analysis of heterophylly in a wood plant, mei (Prunus mume). HpQTL provides a powerful tool to analyze heterophylly and its underlying genetic architecture in a quantitative manner. It also contributes a new approach for genome-wide association studies aimed to dissect the programmed regulation of plant development and evolution.

The genetic architecture of shoot-root covariation during seedling emergence of a desert tree, Populus euphratica.

The coordination of shoots and roots is critical for plants to adapt to changing environments by fine-tuning energy production in leaves and the availability of water and nutrients from roots. To understand the genetic architecture of how these two organs covary during developmental ontogeny, we conducted a mapping experiment using Euphrates poplar (Populus euphratica), a so-called hero tree able to grow in the desert. We geminated intraspecific F1 seeds of Euphrates Poplar individually in a tube to obtain a total of 370 seedlings, whose shoot and taproot lengths were measured repeatedly during the early stage of growth. By fitting a growth equation, we estimated asymptotic growth, relative growth rate, the timing of inflection point and duration of linear growth for both shoot and taproot growth. Treating these heterochronic parameters as phenotypes, a univariate mapping model detected 19 heterochronic quantitative trait loci (hQTLs), of which 15 mediate the forms of shoot growth and four mediate taproot growth. A bivariate mapping model identified 11 pleiotropic hQTLs that determine the covariation of shoot and taproot growth. Most QTLs detected reside within the region of candidate genes with various functions, thus confirming their roles in the biochemical processes underlying plant growth.

iFORM/eQTL: an ultrahigh-dimensional platform for inferring the global genetic architecture of gene transcripts.

Knowledge about how changes in gene expression are encoded by expression quantitative trait loci (eQTLs) is a key to construct the genotype-phenotype map for complex traits or diseases. Traditional eQTL mapping is to associate one transcript with a single marker at a time, thereby limiting our inference about a complete picture of the genetic architecture of gene expression. Here, we implemented an ultrahigh-dimensional variable selection model to build a computing platform that can systematically scan main effects and interaction effects among all possible loci and identify a set of significant eQTLs modulating differentiation and function of gene expression. This platform, named iFORM/eQTL, was assembled by forward-selection-based procedures to tackle complex covariance structures of gene-gene interactions. iFORM/eQTL can particularly discern the role of cis-QTLs, trans-QTLs and their epistatic interactions in gene expression. Results from the reanalysis of a published genetic and genomic data set through iFORM/eQTL gain new discoveries on the genetic origin of gene expression differentiation in Caenorhabditis elegans, which could not be detected by a traditional one-locus/one-transcript analysis approach.

Epigenetic game theory: How to compute the epigenetic control of maternal-to-zygotic transition.

Epigenetic reprogramming is thought to play a critical role in maintaining the normal development of embryos. How the methylation state of paternal and maternal genomes regulates embryogenesis depends on the interaction and coordination of the gametes of two sexes. While there is abundant research in exploring the epigenetic interactions of sperms and oocytes, a knowledge gap exists in the mechanistic quantitation of these interactions and their impact on embryo development. This review aims at formulating a modeling framework to address this gap through the integration and synthesis of evolutionary game theory and the latest discoveries of the epigenetic control of embryo development by next-generation sequencing. This framework, named epigenetic game theory or epiGame, views embryogenesis as an ecological system in which two highly distinct and specialized gametes coordinate through either cooperation or competition, or both, to maximize the fitness of embryos under Darwinian selection. By implementing a system of ordinary differential equations, epiGame quantifies the pattern and relative magnitude of the methylation effects on embryogenesis by the mechanisms of cooperation and competition. epiGame may gain new insight into reproductive biology and can be potentially applied to design personalized medicines for genetic disorder intervention.

Nurses' Perceptions and Attitudes Toward Use of Oral Patient-Controlled Analgesia.

Patient-controlled analgesia (PCA) administered intravenously is a generally well-accepted therapy by nurses and patients. PCA devices are now available for oral medications, allowing patients to self-administer pain pills without requesting them from the nurse. Successful introduction of new pain medication delivery devices can depend on nurses' knowledge and attitudes. The aim of this institutional review board approved project was to evaluate nurses' perceptions and attitudes toward using an oral PCA device for patients' pain. A 4-week study was designed and conducted at an academic medical center on an orthopedic unit and a women's health unit. Nurse participants received education on using the oral PCA device and were invited to complete a pre- and poststudy knowledge and attitude survey regarding pain management. Nurses and patients also completed a questionnaire about perceptions related to using the oral PCA device. Findings showed that nurses' attitudes toward using the oral PCA device were less favorable than those of patients, suggesting that nurses may require additional education for acceptance of this device. Results from 37 nurses showed improvement in overall knowledge and attitudes, from 70.8% pretest to 74.2% post-test. Although improvement was not statistically significant (p = .1637), two items showed significant improvement. Knowledge about the effectiveness of NSAIDS was 27.5% pretest compared with 60.0% post-test (p = .0028); and understanding about use of opioids in patients with a history of substance abuse was 50% pretest compared with 70% post-test (p = .0531). Helping nurses overcome the perceived barriers to use of an oral PCA device has potential implications for better pain management as well as enhanced patient satisfaction.

A block mixture model to map eQTLs for gene clustering and networking.

To study how genes function in a cellular and physiological process, a general procedure is to classify gene expression profiles into categories based on their similarity and reconstruct a regulatory network for functional elements. However, this procedure has not been implemented with the genetic mechanisms that underlie the organization of gene clusters and networks, despite much effort made to map expression quantitative trait loci (eQTLs) that affect the expression of individual genes. Here we address this issue by developing a computational approach that integrates gene clustering and network reconstruction with genetic mapping into a unifying framework. The approach can not only identify specific eQTLs that control how genes are clustered and organized toward biological functions, but also enable the investigation of the biological mechanisms that individual eQTLs perturb in a signaling pathway. We applied the new approach to characterize the effects of eQTLs on the structure and organization of gene clusters in Caenorhabditis elegans. This study provides the first characterization, to our knowledge, of the effects of genetic variants on the regulatory network of gene expression. The approach developed can also facilitate the genetic dissection of other dynamic processes, including development, physiology and disease progression in any organisms.

Redesigning an inpatient pediatric service using Lean to improve throughput efficiency.

BACKGROUND: Data suggest that delays in discharges from inpatient units affect hospital throughput and contribute to emergency department crowding. Lean/Six Sigma (LSS) has been shown to improve inefficiencies in other industries. There are no published data on what impact LSS can have on advancing and sustaining earlier patient discharges. OBJECTIVE: Determine the impact LSS has on advancing times of placement of discharge order and patient discharge compared to control. Secondary outcomes were length of stay (LOS) and readmission rates. DESIGN: Prospective quality study with concurrent controls. SETTING: Academic medical center. PATIENTS: Hospitalized pediatric patients compared to remainder of the children's hospital services. INTERVENTIONS: Staff reallocation, creation of standard workflow, multidisciplinary predischarge planning, and creation of a discharge checklist. MEASUREMENTS: Median time of discharge order entry and median time of actual patient discharge, proportion of patients discharged before noon and 2 pm, and LOS and readmission rates. RESULTS: The median time of order entry was 10:45 compared to 14:05, and the median time of discharge was 14:15 compared to 15:48. The LOS and the readmission rates remained the same in both cohorts. The control group had faster baseline discharge order entry and patient discharge, but discharge performance did not improve, despite a significantly lower average daily census. CONCLUSIONS: We determined that Lean approaches can have an immediate and sustained impact on advancing patient discharges, with no negative affect on LOS or readmission rates. Our intervention generated consistent results independent of personnel during the busiest months of the year at a tertiary care children's hospital.