Estimating cell type composition using isoform expression one gene at a time

Human tissue samples are often mixtures of heterogeneous cell types, which can confound the analyses of gene expression data derived from such tissues. The cell type composition of a tissue sample may itself be of interest and is needed for proper analysis of differential gene expression. A variety of computational methods have been developed to estimate cell type proportions using gene-level expression data. However, RNA isoforms can also be differentially expressed across cell types, and isoform-level expression could be equally or more informative for determining cell type origin than gene-level expression. We propose a new computational method, IsoDeconvMM, which estimates cell type fractions using isoform-level gene expression data. A novel and useful feature of IsoDeconvMM is that it can estimate cell type proportions using only a single gene, though in practice we recommend aggregating estimates of a few dozen genes to obtain more accurate results. We demonstrate the performance of IsoDeconvMM using a unique data set with cell type–specific RNA-seq data across more than 135 individuals. This data set allows us to evaluate different methods given the biological variation of cell type–specific gene expression data across individuals. We further complement this analysis with additional simulations.     

A simple and effective method for the isolation of inner cell mass samples from human blastocysts for gene expression analysis

The isolation of pure inner cell mass (ICM) and trophectoderm (TE) cells from a single human blastocyst is necessary to obtain accurate gene expression patterns of these cells, which will aid in the understanding of the primary steps of embryo differentiation. However, previously developed pure ICM isolation methods are either time-consuming or alter the normal gene expression patterns of these cells. Here, we demonstrate a simple and effective method of ICM samples isolation from human blastocysts. In total, 35 human blastocysts of all stages with expanded and good morphology were incubated in calcium/magnesium-free HEPES medium for 5 min before micromanipulation. With the aid of a laser, a biopsy pipette was inserted directly into the blastocoel for the suction-based removal of ICM samples. The ICM samples were obtained through simple mechanical pulling force or laser assistance, and each isolation process required 3–4 min. The isolated ICM and TE fractions were subjected to single-cell real-time quantitative RT-PCR to evaluate keratin 18 (KRT18) expression. Finally, 33 paired ICM and TE samples were verified using gene expression analysis. KRT18 was readily detectable in all TE cells but absent in 30 ICM counterparts, indicating a pure ICM isolation rate of 90.9% (30/33). The relative KRT18 expression of three TE samples compared with their three contaminated ICM counterparts was 19-fold (P?<?0.001), indicating that the contamination was very weak. These results demonstrate that our ICM isolation method is simple and effective.     

Fluorescently Activated Cell Sorting Followed by Microarray Profiling of Helper T Cell Subtypes from Human Peripheral Blood

Background

Peripheral blood samples have been subjected to comprehensive gene expression profiling to identify biomarkers for a wide range of diseases. However, blood samples include red blood cells, white blood cells, and platelets. White blood cells comprise polymorphonuclear leukocytes, monocytes, and various types of lymphocytes. Blood is not distinguishable, irrespective of whether the expression profiles reflect alterations in (a) gene expression patterns in each cell type or (b) the proportion of cell types in blood. CD4⁺ Th cells are classified into two functionally distinct subclasses, namely Th1 and Th2 cells, on the basis of the unique characteristics of their secreted cytokines and their roles in the immune system. Th1 and Th2 cells play an important role not only in the pathogenesis of human inflammatory, allergic, and autoimmune diseases, but also in diseases that are not considered to be immune or inflammatory disorders. However, analyses of minor cellular components such as CD4⁺ cell subpopulations have not been performed, partly because of the limited number of these cells in collected samples.

Methodology/Principal Findings

We describe fluorescently activated cell sorting followed by microarray (FACS–array) technology as a useful experimental strategy for characterizing the expression profiles of specific immune cells in the circulation. We performed reproducible gene expression profiling of Th1 and Th2, respectively. Our data suggest that this procedure provides reliable information on the gene expression profiles of certain small immune cell populations. Moreover, our data suggest that GZMK, GZMH, EOMES, IGFBP3, and STOM may be novel markers for distinguishing Th1 cells from Th2 cells, whereas IL17RB and CNTNAP1 can be Th2-specific markers.

Conclusions/Significance

Our approach may help in identifying aberrations and novel therapeutic or diagnostic targets for diseases that affect Th1 or Th2 responses and elucidating the involvement of a subpopulation of immune cells in some diseases.     

Validation of rat reference genes for improved quantitative gene expression analysis using low density arrays

Real-time PCR has become increasingly important in gene expression profiling research, and it is widely agreed that normalized data are required for accurate estimates of messenger RNA (mRNA) expression. With increased gene expression profiling in preclinical research and toxicogenomics, a need for reference genes in the rat has emerged, and the studies in this area have not yet been thoroughly evaluated. The purpose of our study was to evaluate a panel of rat reference genes for variation of gene expression in different tissue types. We selected 48 known target genes based on their putative invariability. The gene expression of all targets was examined in 11 types of rat tissues using TaqMan low density array (LDA) technology. The variability of each gene was assessed using a two-step statistical model. The analysis of mean expression using multiple reference genes was shown to provide accurate and reliable normalized expression data. The least five variable genes from each specific tissue were recommended for future tissue-specific studies. Finally, a subset of investigated rat reference genes showing the least variation is recommended for further evaluation using the LDA platform. Our work should considerably enhance a researcher's ability to simply and efficiently identify appropriate reference genes for given experiments.     

Use of Formalin-Fixed Paraffin-Embedded Samples for Gene Expression Studies in Breast Cancer Patients

To obtain gene expression profiles from samples collected in clinical trials, we conducted a pilot study to assess feasibility and estimate sample attrition rates when profiling formalin-fixed, paraffin-embedded specimens. Ten matched fresh-frozen and fixed breast cancer samples were profiled using the Illumina HT-12 and Ref-8 chips, respectively. The profiles obtained with Ref 8, were neither technically nor biologically reliable since they failed to yield the expected separation between estrogen receptor positive and negative samples. With the use of Affymetrix HG-U133 2.0 Plus chips on fixed samples and a quantitative polymerase chain reaction -based sample pre-assessment step, results were satisfactory in terms of biological reliability, despite the low number of present calls (M = 21%±5). Compared with the Illumina DASL WG platform, Affymetrix data showed a wider interquartile range (1.32 vs 0.57, P<2.2 E-16,) and larger fold changes. The Affymetrix chips were used to run a pilot study on 60 fixed breast cancers. By including in the workflow the sample pre-assessment steps, 96% of the samples predicted to give good results (44/46), were in fact rated as satisfactory from the point of view of technical and biological meaningfulness. Our gene expression profiles showed strong agreement with immunohistochemistry data, were able to reproduce breast cancer molecular subtypes, and allowed the validation of an estrogen receptor status classifier derived in frozen samples. The approach is therefore suitable to profile formalin-fixed paraffin-embedded samples collected in clinical trials, provided that quality controls are run both before (sample pre-assessment) and after hybridization on the array.     

Surrogate variable analysis using partial least squares (SVA-PLS) in gene expression studies

MOTIVATION: In a typical gene expression profiling study, our prime objective is to identify the genes that are differentially expressed between the samples from two different tissue types. Commonly, standard analysis of variance (ANOVA)/regression is implemented to identify the relative effects of these genes over the two types of samples from their respective arrays of expression levels. But, this technique becomes fundamentally flawed when there are unaccounted sources of variability in these arrays (latent variables attributable to different biological, environmental or other factors relevant in the context). These factors distort the true picture of differential gene expression between the two tissue types and introduce spurious signals of expression heterogeneity. As a result, many genes which are actually differentially expressed are not detected, whereas many others are falsely identified as positives. Moreover, these distortions can be different for different genes. Thus, it is also not possible to get rid of these variations by simple array normalizations. This both-way error can lead to a serious loss in sensitivity and specificity, thereby causing a severe inefficiency in the underlying multiple testing problem. In this work, we attempt to identify the hidden effects of the underlying latent factors in a gene expression profiling study by partial least squares (PLS) and apply ANCOVA technique with the PLS-identified signatures of these hidden effects as covariates, in order to identify the genes that are truly differentially expressed between the two concerned tissue types. RESULTS: We compare the performance of our method SVA-PLS with standard ANOVA and a relatively recent technique of surrogate variable analysis (SVA), on a wide variety of simulation settings (incorporating different effects of the hidden variable, under situations with varying signal intensities and gene groupings). In all settings, our method yields the highest sensitivity while maintaining relatively reasonable values for the specificity, false discovery rate and false non-discovery rate. Application of our method to gene expression profiling for acute megakaryoblastic leukemia shows that our method detects an additional six genes, that are missed by both the standard ANOVA method as well as SVA, but may be relevant to this disease, as can be seen from mining the existing literature.     

Establishment of pure neuronal and muscle precursor cell cultures fromDrosophila early gastrula stage embryos

Summary Primary cultures ofDrosophila gastrula stage embryonic cells will divide and terminally differentiate into morphologically recognizable neurons and muscles. The phenotypically mixed nature of this primary culture system has made it difficult to effectively analyze various parameters of cell growth and differentiation for individual cell types. We report here a simple and economic method to separate early embryonic precursors for different cell types, using a shallow linear reorienting Ficoll gradient at unit gravity. The separated cells were collected into fractions, cultured, and analyzed for their growth and differentiation patterns. The larger and denser cells of the first fractions differentiated to yield pure neuronal cultures, as judged by morphologic, immunologic, and biochemical criteria. Cells in the last fractions differentiated into a predominantly muscle-enriched cell population, which also contained a very small percentage of neurons morphologically distinct from those in the pure neuronal fractions. Approximately 35% of the early gastrula stage embryonic cells differentiate into neuronal cells, and 65% of the non-neuronal lineage cells later develop into predominantly muscle population. The method is highly reproducible, can process 3×10⁷ cells per procedure, and the recovery is >90% of the input cells. The separated cells are suitable for cell biological analyses as well as for biochemical and molecular studies of neuron and muscle precursors. Deceased.     

Gene Expression Signature Differentiates Histology But Not Progression Status of Early-Stage NSCLC

Advances in molecular analyses based on high-throughput technologies can contribute to a more accurate classification of non–small cell lung cancer (NSCLC), as well as a better prediction of both the disease course and the efficacy of targeted therapies. Here we set out to analyze whether global gene expression profiling performed in a group of early-stage NSCLC patients can contribute to classifying tumor subtypes and predicting the disease prognosis. Gene expression profiling was performed with the use of the microarray technology in a training set of 108 NSCLC samples. Subsequently, the recorded findings were validated further in an independent cohort of 44 samples. We demonstrated that the specific gene patterns differed significantly between lung adenocarcinoma (AC) and squamous cell lung carcinoma (SCC) samples. Furthermore, we developed and validated a novel 53-gene signature distinguishing SCC from AC with 93% accuracy. Evaluation of the classifier performance in the validation set showed that our predictor classified the AC patients with 100% sensitivity and 88% specificity. We revealed that gene expression patterns observed in the early stages of NSCLC may help elucidate the histological distinctions of tumors through identification of different gene-mediated biological processes involved in the pathogenesis of histologically distinct tumors. However, we showed here that the gene expression profiles did not provide additional value in predicting the progression status of the early-stage NSCLC. Nevertheless, the gene expression signature analysis enabled us to perform a reliable subclassification of NSCLC tumors, and it can therefore become a useful diagnostic tool for a more accurate selection of patients for targeted therapies.     

The transcriptome in blood: challenges and solutions for robust expression profiling

Peripheral blood may be the most feasible tissue source in clinical assessment of differences in gene expression between diseases and drug treatments due to accessibility. Yet, gene expression profiling from blood remains a challenge. Blood is a complicated biological system consisting of a variety of cell types at different stages of development. In addition, blood is also one of the most variable tissue types for gene expression analysis. The success of a blood microarray study depends on the choice of cell isolation method and preparation technique. In this review, we give a brief overview of the current status of using blood as a source for expression profiling and discuss potential applications of this method in the practices of clinical research.     

Tissue Compartment Analysis for Biomarker Discovery by Gene Expression Profiling

Background

Although high throughput technologies for gene profiling are reliable tools, sample/tissue heterogeneity limits their outcomes when applied to identify molecular markers. Indeed, inter-sample differences in cell composition contribute to scatter the data, preventing detection of small but relevant changes in gene expression level. To date, attempts to circumvent this difficulty were based on isolation of the different cell structures constituting biological samples. As an alternate approach, we developed a tissue compartment analysis (TCA) method to assess the cell composition of tissue samples, and applied it to standardize data and to identify biomarkers.

Methodology/Principal Findings

TCA is based on the comparison of mRNA expression levels of specific markers of the different constitutive structures in pure isolated structures, on the one hand, and in the whole sample on the other. TCA method was here developed with human kidney samples, as an example of highly heterogeneous organ. It was validated by comparison of the data with those obtained by histo-morphometry. TCA demonstrated the extreme variety of composition of kidney samples, with abundance of specific structures varying from 5 to 95% of the whole sample. TCA permitted to accurately standardize gene expression level amongst >100 kidney biopsies, and to identify otherwise imperceptible molecular disease markers.

Conclusions/Significance

Because TCA does not require specific preparation of sample, it can be applied to all existing tissue or cDNA libraries or to published data sets, inasmuch specific operational compartments markers are available. In human, where the small size of tissue samples collected in clinical practice accounts for high structural diversity, TCA is well suited for the identification of molecular markers of diseases, and the follow up of identified markers in single patients for diagnosis/prognosis and evaluation of therapy efficiency. In laboratory animals, TCA will interestingly be applied to central nervous system where tissue heterogeneity is a limiting factor.     

Gene signatures derived from a c-MET-driven liver cancer mouse model predict survival of patients with hepatocellular carcinoma

Biomarkers derived from gene expression profiling data may have a high false-positive rate and must be rigorously validated using independent clinical data sets, which are not always available. Although animal model systems could provide alternative data sets to formulate hypotheses and limit the number of signatures to be tested in clinical samples, the predictive power of such an approach is not yet proven. The present study aims to analyze the molecular signatures of liver cancer in a c-MET-transgenic mouse model and investigate its prognostic relevance to human hepatocellular carcinoma (HCC). Tissue samples were obtained from tumor (TU), adjacent non-tumor (AN) and distant normal (DN) liver in Tet-operator regulated (TRE) human c-MET transgenic mice (n = 21) as well as from a Chinese cohort of 272 HBV- and 9 HCV-associated HCC patients. Whole genome microarray expression profiling was conducted in Affymetrix gene expression chips, and prognostic significances of gene expression signatures were evaluated across the two species. Our data revealed parallels between mouse and human liver tumors, including down-regulation of metabolic pathways and up-regulation of cell cycle processes. The mouse tumors were most similar to a subset of patient samples characterized by activation of the Wnt pathway, but distinctive in the p53 pathway signals. Of potential clinical utility, we identified a set of genes that were down regulated in both mouse tumors and human HCC having significant predictive power on overall and disease-free survival, which were highly enriched for metabolic functions. In conclusions, this study provides evidence that a disease model can serve as a possible platform for generating hypotheses to be tested in human tissues and highlights an efficient method for generating biomarker signatures before extensive clinical trials have been initiated.