A model-based hierarchical Bayesian approach to Sholl analysis.

MOTIVATION: Due to the link between microglial morphology and function, morphological changes in microglia are frequently used to identify pathological immune responses in the central nervous system. In the absence of pathology, microglia are responsible for maintaining homeostasis, and their morphology can be indicative of how the healthy brain behaves in the presence of external stimuli and genetic differences. Despite recent interest in high throughput methods for morphological analysis, Sholl analysis is still widely used for quantifying microglia morphology via imaging data. Often, the raw data are naturally hierarchical, minimally including many cells per image and many images per animal. However, existing methods for performing downstream inference on Sholl data rely on truncating this hierarchy so rudimentary statistical testing procedures can be used. RESULTS: To fill this longstanding gap, we introduce a parametric hierarchical Bayesian model-based approach for analyzing Sholl data, so that inference can be performed without aggressive reduction of otherwise very rich data. We apply our model to real data and perform simulation studies comparing the proposed method with a popular alternative. AVAILABILITY AND IMPLEMENTATION: Software to reproduce the results presented in this article is available at: https://github.com/vonkaenelerik/hierarchical_sholl. An R package implementing the proposed models is available at: https://github.com/vonkaenelerik/ShollBayes.

Patterns of Unwanted Biological and Technical Expression Variation Among 49 Human Tissues.

Tissue gene expression studies are impacted by biological and technical sources of variation, which can be broadly classified into wanted and unwanted variation. The latter, if not addressed, results in misleading biological conclusions. Methods have been proposed to reduce unwanted variation, such as normalization and batch correction. A more accurate understanding of all causes of variation could significantly improve the ability of these methods to remove unwanted variation while retaining variation corresponding to the biological question of interest. We used 17,282 samples from 49 human tissues in the Genotype-Tissue Expression data set (v8) to investigate patterns and causes of expression variation. Transcript expression was transformed to z-scores, and only the most variable 2% of transcripts were evaluated and clustered based on coexpression patterns. Clustered gene sets were assigned to different biological or technical causes based on histologic appearances and metadata elements. We identified 522 variable transcript clusters (median: 11 per tissue) among the samples. Of these, 63% were confidently explained, 16% were likely explained, 7% were low confidence explanations, and 14% had no clear cause. Histologic analysis annotated 46 clusters. Other common causes of variability included sex, sequencing contamination, immunoglobulin diversity, and compositional tissue differences. Less common biological causes included death interval (Hardy score), disease status, and age. Technical causes included blood draw timing and harvesting differences. Many of the causes of variation in bulk tissue expression were identifiable in the Tabula Sapiens data set of single-cell expression. This is among the largest explorations of the underlying sources of tissue expression variation. It uncovered expected and unexpected causes of variable gene expression and demonstrated the utility of matched histologic specimens. It further demonstrated the value of acquiring meaningful tissue harvesting metadata elements to use for improved normalization, batch correction, and analysis of both bulk and single-cell RNA-seq data.

The effect of tissue composition on gene co-expression.

Variable cellular composition of tissue samples represents a significant challenge for the interpretation of genomic profiling studies. Substantial effort has been devoted to modeling and adjusting for compositional differences when estimating differential expression between sample types. However, relatively little attention has been given to the effect of tissue composition on co-expression estimates. In this study, we illustrate the effect of variable cell-type composition on correlation-based network estimation and provide a mathematical decomposition of the tissue-level correlation. We show that a class of deconvolution methods developed to separate tumor and stromal signatures can be applied to two component cell-type mixtures. In simulated and real data, we identify conditions in which a deconvolution approach would be beneficial. Our results suggest that uncorrelated cell-type-specific markers are ideally suited to deconvolute both the expression and co-expression patterns of an individual cell type. We provide a Shiny application for users to interactively explore the effect of cell-type composition on correlation-based co-expression estimation for any cell types of interest.

Autoregressive modeling and diagnostics for qPCR amplification.

MOTIVATION: Current methods used to analyze real-time quantitative polymerase chain reaction (qPCR) data exhibit systematic deviations from the assumed model over the progression of the reaction. Slight variations in the amount of the initial target molecule or in early amplifications are likely responsible for these deviations. Commonly used 4- and 5-parameter sigmoidal models appear to be particularly susceptible to this issue, often displaying patterns of autocorrelation in the residuals. The presence of this phenomenon, even for technical replicates, suggests that these parametric models may be misspecified. Specifically, they do not account for the sequential dependent nature of the amplification process that underlies qPCR fluorescence measurements. RESULTS: We demonstrate that a Smooth Transition Autoregressive (STAR) model addresses this limitation by explicitly modeling the dependence between cycles and the gradual transition between amplification regimes. In summary, application of a STAR model to qPCR amplification data improves model fit and reduces autocorrelation in the residuals. AVAILABILITY AND IMPLEMENTATION: R scripts to reproduce all the analyses and results described in this manuscript can be found at: https://github.com/bhsu4/GAPDH.SO. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A systems genomics approach uncovers molecular associates of RSV severity.

Respiratory syncytial virus (RSV) infection results in millions of hospitalizations and thousands of deaths each year. Variations in the adaptive and innate immune response appear to be associated with RSV severity. To investigate the host response to RSV infection in infants, we performed a systems-level study of RSV pathophysiology, incorporating high-throughput measurements of the peripheral innate and adaptive immune systems and the airway epithelium and microbiota. We implemented a novel multi-omic data integration method based on multilayered principal component analysis, penalized regression, and feature weight back-propagation, which enabled us to identify cellular pathways associated with RSV severity. In both airway and immune cells, we found an association between RSV severity and activation of pathways controlling Th17 and acute phase response signaling, as well as inhibition of B cell receptor signaling. Dysregulation of both the humoral and mucosal response to RSV may play a critical role in determining illness severity.

Airway Gene Expression Correlates of Respiratory Syncytial Virus Disease Severity and Microbiome Composition in Infants.

BACKGROUND: Respiratory syncytial virus (RSV) is the leading cause of severe respiratory disease in infants. The causes and correlates of severe illness in the majority of infants are poorly defined. METHODS: We recruited a cohort of RSV-infected infants and simultaneously assayed the molecular status of their airways and the presence of airway microbiota. We used rigorous statistical approaches to identify gene expression patterns associated with disease severity and microbiota composition, separately and in combination. RESULTS: We measured comprehensive airway gene expression patterns in 106 infants with primary RSV infection. We identified an airway gene expression signature of severe illness dominated by excessive chemokine expression. We also found an association between Haemophilus influenzae, disease severity, and airway lymphocyte accumulation. Exploring the time of onset of clinical symptoms revealed acute activation of interferon signaling following RSV infection in infants with mild or moderate illness, which was absent in subjects with severe illness. CONCLUSIONS: Our data reveal that airway gene expression patterns distinguish mild/moderate from severe illness. Furthermore, our data identify biomarkers that may be therapeutic targets or useful for measuring efficacy of intervention responses.

Spatial Proteomic Approach to Characterize Skeletal Muscle Myofibers.

Skeletal muscle myofibers have differential protein expression resulting in functionally distinct slow- and fast-twitch types. While certain protein classes are well-characterized, the depth of all proteins involved in this process is unknown. We utilized the Human Protein Atlas (HPA) and the HPASubC tool to classify mosaic expression patterns of staining across 49,600 unique tissue microarray (TMA) images using a visual proteomic approach. We identified 2164 proteins with potential mosaic expression, of which 1605 were categorized as "likely" or "real." This list included both well-known fiber-type-specific and novel proteins. A comparison of the 1605 mosaic proteins with a mass spectrometry (MS)-derived proteomic dataset of single human muscle fibers led to the assignment of 111 proteins to fiber types. We additionally used a multiplexed immunohistochemistry approach, a multiplexed RNA-ISH approach, and STRING v11 to further assign or suggest fiber types of newly characterized mosaic proteins. This visual proteomic analysis of mature skeletal muscle myofibers greatly expands the known repertoire of twitch-type-specific proteins.

Identification of the novel Ido1 imprinted locus and its potential epigenetic role in pregnancy loss.

Previous studies show that aberrant tryptophan catabolism reduces maternal immune tolerance and adversely impacts pregnancy outcomes. Tryptophan depletion in pregnancy is facilitated by increased activity of tryptophan-depleting enzymes [i.e. the indolamine-2,3 dioxygenase (IDO)1 and IDO2) in the placenta. In mice, inhibition of IDO1 activity during pregnancy results in fetal loss; however, despite its important role, regulation of Ido1 gene transcription is unknown. The current study shows that the Ido1 and Ido2 genes are imprinted and maternally expressed in mouse placentas. DNA methylation analysis demonstrates that nine CpG sites at the Ido1 promoter constitute a differentially methylated region that is highly methylated in sperm but unmethylated in oocytes. Bisulfite cloning sequencing analysis shows that the paternal allele is hypermethylated while the maternal allele shows low levels of methylation in E9.5 placenta. Further study in E9.5 placentas from the CBA/J X DBA/2 spontaneous abortion mouse model reveals that aberrant methylation of Ido1 is linked to pregnancy loss. DNA methylation analysis in humans shows that IDO1 is hypermethylated in human sperm but partially methylated in placentas, suggesting similar methylation patterns to mouse. Importantly, analysis in euploid placentas from first trimester pregnancy loss reveals that IDO1 methylation significantly differs between the two placenta cohorts, with most CpG sites showing increased percent of methylation in miscarriage placentas. Our study suggests that DNA methylation is linked to regulation of Ido1/IDO1 expression and altered Ido1/IDO1 DNA methylation can adversely influence pregnancy outcomes.

Big Strides in Cellular MicroRNA Expression.

A lack of knowledge of the cellular origin of miRNAs has greatly confounded functional and biomarkers studies. Recently, three studies characterized miRNA expression patterns across >78 human cell types. These combined data expand our knowledge of miRNA expression localization and confirm that many miRNAs show cell type-specific expression patterns.

Multiple imputation and direct estimation for qPCR data with non-detects.

BACKGROUND: Quantitative real-time PCR (qPCR) is one of the most widely used methods to measure gene expression. An important aspect of qPCR data that has been largely ignored is the presence of non-detects: reactions failing to exceed the quantification threshold and therefore lacking a measurement of expression. While most current software replaces these non-detects with a value representing the limit of detection, this introduces substantial bias in the estimation of both absolute and differential expression. Single imputation procedures, while an improvement on previously used methods, underestimate residual variance, which can lead to anti-conservative inference. RESULTS: We propose to treat non-detects as non-random missing data, model the missing data mechanism, and use this model to impute missing values or obtain direct estimates of model parameters. To account for the uncertainty inherent in the imputation, we propose a multiple imputation procedure, which provides a set of plausible values for each non-detect. We assess the proposed methods via simulation studies and demonstrate the applicability of these methods to three experimental data sets. We compare our methods to mean imputation, single imputation, and a penalized EM algorithm incorporating non-random missingness (PEMM). The developed methods are implemented in the R/Bioconductor package nondetects. CONCLUSIONS: The statistical methods introduced here reduce discrepancies in gene expression values derived from qPCR experiments in the presence of non-detects, providing increased confidence in downstream analyses.

Toward the human cellular microRNAome.

MicroRNAs are short RNAs that serve as regulators of gene expression and are essential components of normal development as well as modulators of disease. MicroRNAs generally act cell-autonomously, and thus their localization to specific cell types is needed to guide our understanding of microRNA activity. Current tissue-level data have caused considerable confusion, and comprehensive cell-level data do not yet exist. Here, we establish the landscape of human cell-specific microRNA expression. This project evaluated 8 billion small RNA-seq reads from 46 primary cell types, 42 cancer or immortalized cell lines, and 26 tissues. It identified both specific and ubiquitous patterns of expression that strongly correlate with adjacent superenhancer activity. Analysis of unaligned RNA reads uncovered 207 unknown minor strand (passenger) microRNAs of known microRNA loci and 495 novel putative microRNA loci. Although cancer cell lines generally recapitulated the expression patterns of matched primary cells, their isomiR sequence families exhibited increased disorder, suggesting DROSHA- and DICER1-dependent microRNA processing variability. Cell-specific patterns of microRNA expression were used to de-convolute variable cellular composition of colon and adipose tissue samples, highlighting one use of these cell-specific microRNA expression data. Characterization of cellular microRNA expression across a wide variety of cell types provides a new understanding of this critical regulatory RNA species.

Systematic exploration of cell morphological phenotypes associated with a transcriptomic query.

Cell morphological phenotypes, including shape, size, intensity, and texture of cellular compartments have been shown to change in response to perturbation with small molecule compounds. Image-based cell profiling or cell morphological profiling has been used to associate changes of cell morphological features with alterations in cellular function and to infer molecular mechanisms of action. Recently, the Library of Integrated Network-based Cellular Signatures (LINCS) Project has measured gene expression and performed image-based cell profiling on cell lines treated with 9515 unique compounds. These data provide an opportunity to study the interdependence between transcription and cell morphology. Previous methods to investigate cell phenotypes have focused on targeting candidate genes as components of known pathways, RNAi morphological profiling, and cataloging morphological defects; however, these methods do not provide an explicit model to link transcriptomic changes with corresponding alterations in morphology. To address this, we propose a cell morphology enrichment analysis to assess the association between transcriptomic alterations and changes in cell morphology. Additionally, for a new transcriptomic query, our approach can be used to predict associated changes in cellular morphology. We demonstrate the utility of our method by applying it to cell morphological changes in a human bone osteosarcoma cell line.

Complex Sources of Variation in Tissue Expression Data: Analysis of the GTEx Lung Transcriptome.

The sources of gene expression variability in human tissues are thought to be a complex interplay of technical, compositional, and disease-related factors. To better understand these contributions, we investigated expression variability in a relatively homogeneous tissue expression dataset from the Genotype-Tissue Expression (GTEx) resource. In addition to identifying technical sources, such as sequencing date and post-mortem interval, we also identified several biological sources of variation. An in-depth analysis of the 175 genes with the greatest variation among 133 lung tissue samples identified five distinct clusters of highly correlated genes. One large cluster included surfactant genes (SFTPA1, SFTPA2, and SFTPC), which are expressed exclusively in type II pneumocytes, cells that proliferate in ventilator associated lung injury. High surfactant expression was strongly associated with death on a ventilator and type II pneumocyte hyperplasia. A second large cluster included dynein (DNAH9 and DNAH12) and mucin (MUC5B and MUC16) genes, which are exclusive to the respiratory epithelium and goblet cells of bronchial structures. This indicates heterogeneous bronchiole sampling due to the harvesting location in the lung. A small cluster included acute-phase reactant genes (SAA1, SAA2, and SAA2-SAA4). The final two small clusters were technical and gender related. To summarize, in a collection of normal lung samples, we found that tissue heterogeneity caused by harvesting location (medial or lateral lung) and late therapeutic intervention (mechanical ventilation) were major contributors to expression variation. These unexpected sources of variation were the result of altered cell ratios in the tissue samples, an underappreciated source of expression variation.

Cardioprotection by the mitochondrial unfolded protein response requires ATF5.

The mitochondrial unfolded protein response (UPRmt) is a cytoprotective signaling pathway triggered by mitochondrial dysfunction. UPRmt activation upregulates chaperones, proteases, antioxidants, and glycolysis at the gene level to restore proteostasis and cell energetics. Activating transcription factor 5 (ATF5) is a proposed mediator of the mammalian UPRmt. Herein, we hypothesized pharmacological UPRmt activation may protect against cardiac ischemia-reperfusion (I/R) injury in an ATF5-dependent manner. Accordingly, in vivo administration of the UPRmt inducers oligomycin or doxycycline 6 h before ex vivo I/R injury (perfused heart) was cardioprotective in wild-type but not global Atf5-/- mice. Acute ex vivo UPRmt activation was not cardioprotective, and loss of ATF5 did not impact baseline I/R injury without UPRmt induction. In vivo UPRmt induction significantly upregulated many known UPRmt-linked genes (cardiac quantitative PCR and Western blot analysis), and RNA-Seq revealed an UPRmt-induced ATF5-dependent gene set, which may contribute to cardioprotection. This is the first in vivo proof of a role for ATF5 in the mammalian UPRmt and the first demonstration that UPRmt is a cardioprotective drug target.NEW & NOTEWORTHY Cardioprotection can be induced by drugs that activate the mitochondrial unfolded protein response (UPRmt). UPRmt protection is dependent on activating transcription factor 5 (ATF5). This is the first in vivo evidence for a role of ATF5 in the mammalian UPRmt.

A benchmark for microRNA quantification algorithms using the OpenArray platform.

BACKGROUND: Several techniques have been tailored to the quantification of microRNA expression, including hybridization arrays, quantitative PCR (qPCR), and high-throughput sequencing. Each of these has certain strengths and limitations depending both on the technology itself and the algorithm used to convert raw data into expression estimates. Reliable quantification of microRNA expression is challenging in part due to the relatively low abundance and short length of the miRNAs. While substantial research has been devoted to the development of methods to quantify mRNA expression, relatively little effort has been spent on microRNA expression. RESULTS: In this work, we focus on the Life Technologies TaqMan OpenArray(Ⓡ) system, a qPCR-based platform to measure microRNA expression. Several algorithms currently exist to estimate expression from the raw amplification data produced by qPCR-based technologies. To assess and compare the performance of these methods, we performed a set of dilution/mixture experiments to create a benchmark data set. We also developed a suite of statistical assessments that evaluate many different aspects of performance: accuracy, precision, titration response, number of complete features, limit of detection, and data quality. The benchmark data and software are freely available via two R/Bioconductor packages, miRcomp and miRcompData. Finally, we demonstrate use of our software by comparing two widely used algorithms and providing assessments for four other algorithms. CONCLUSIONS: Benchmark data sets and software are crucial tools for the assessment and comparison of competing algorithms. We believe that the miRcomp and miRcompData packages will facilitate the development of new methodology for microRNA expression estimation.

Lessons from miR-143/145: the importance of cell-type localization of miRNAs.

miR-143 and miR-145 are co-expressed microRNAs (miRNAs) that have been extensively studied as potential tumor suppressors. These miRNAs are highly expressed in the colon and are consistently reported as being downregulated in colorectal and other cancers. Through regulation of multiple targets, they elicit potent effects on cancer cell growth and tumorigenesis. Importantly, a recent discovery demonstrates that miR-143 and miR-145 are not expressed in colonic epithelial cells; rather, these two miRNAs are highly expressed in mesenchymal cells such as fibroblasts and smooth muscle cells. The expression patterns of miR-143 and miR-145 and other miRNAs were initially determined from tissue level data without consideration that multiple different cell types, each with their own unique miRNA expression patterns, make up each tissue. Herein, we discuss the early reports on the identification of dysregulated miR-143 and miR-145 expression in colorectal cancer and how lack of consideration of cellular composition of normal tissue led to the misconception that these miRNAs are downregulated in cancer. We evaluate mechanistic data from miR-143/145 studies in context of their cell type-restricted expression pattern and the potential of these miRNAs to be considered tumor suppressors. Further, we examine other examples of miRNAs being investigated in inappropriate cell types modulating pathways in a non-biological fashion. Our review highlights the importance of determining the cellular expression pattern of each miRNA, so that downstream studies are conducted in the appropriate cell type.

The Gene Expression Barcode 3.0: improved data processing and mining tools.

The Gene Expression Barcode project, http://barcode.luhs.org, seeks to determine the genes expressed for every tissue and cell type in humans and mice. Understanding the absolute expression of genes across tissues and cell types has applications in basic cell biology, hypothesis generation for gene function and clinical predictions using gene expression signatures. In its current version, this project uses the abundant publicly available microarray data sets combined with a suite of single-array preprocessing, quality control and analysis methods. In this article, we present the improvements that have been made since the previous version of the Gene Expression Barcode in 2011. These include a variety of new data mining tools and summaries, estimated transcriptomes and curated annotations.

On non-detects in qPCR data.

MOTIVATION: Quantitative real-time PCR (qPCR) is one of the most widely used methods to measure gene expression. Despite extensive research in qPCR laboratory protocols, normalization and statistical analysis, little attention has been given to qPCR non-detects-those reactions failing to produce a minimum amount of signal. RESULTS: We show that the common methods of handling qPCR non-detects lead to biased inference. Furthermore, we show that non-detects do not represent data missing completely at random and likely represent missing data occurring not at random. We propose a model of the missing data mechanism and develop a method to directly model non-detects as missing data. Finally, we show that our approach results in a sizeable reduction in bias when estimating both absolute and differential gene expression. AVAILABILITY AND IMPLEMENTATION: The proposed algorithm is implemented in the R package, nondetects. This package also contains the raw data for the three example datasets used in this manuscript. The package is freely available at http://mnmccall.com/software and as part of the Bioconductor project.

Affymetrix GeneChip microarray preprocessing for multivariate analyses.

Affymetrix GeneChip microarrays are the most widely used high-throughput technology to measure gene expression, and a wide variety of preprocessing methods have been developed to transform probe intensities reported by a microarray scanner into gene expression estimates. There have been numerous comparisons of these preprocessing methods, focusing on the most common analyses-detection of differential expression and gene or sample clustering. Recently, more complex multivariate analyses, such as gene co-expression, differential co-expression, gene set analysis and network modeling, are becoming more common; however, the same preprocessing methods are typically applied. In this article, we examine the effect of preprocessing methods on some of these multivariate analyses and provide guidance to the user as to which methods are most appropriate.