Retinal ganglion cell differentiation in cultured mouse retinal explants

The availability of genetically engineered mice harboring specific mutations in genes affecting one or more retinal cell types affords new opportunities for investigating the genetic regulatory mechanisms of vertebrate retina formation. When identifying critical regulatory genes involved in retina development it is often advantageous to complement in vivo analysis with in vitro characterization. In particular, by combining classical techniques of retinal explant culturing with gene transfer procedures relying on herpes simple virus (HSV) amplicon vectors, gain-of-function analysis with genes of interest can be performed quickly and efficiently. Here, details are provided for isolating and culturing explants containing retinal progenitor cells and for infecting the explants with HSV expression vectors that perturb or rescue retinal ganglion cells, the first cell type to differentiate in the retina. In addition, the availability of sensitive techniques to monitor gene expression, including detection of reporter gene expression using antibodies and detection of endogenous marker gene expression using quantitative RT-PCR, provides an effective means for comparing wild-type and mutant retinas from genetically engineered mice.     

Classification of cDNA array genes that have a highly significant discriminative power due to their unique distribution in four brain regions

Novel statistical methods were used to distinguish functionally distinct brain regions using their cDNA array gene expression profiles, and it was found that one of four specific factors is often associated with the most regionally discriminative genes. The gene expression profiles for the substantia nigra (SN), striatum (STR), parietal cortex (PC), and posterolateral cortical amygdaloid nucleus (PLCo) brain regions were determined from each brain region. An F-test identified 339 genes of the 1185 array genes as having a P < or = 0.01 and applied a gene ranking and selection method based on Soft Independent Modeling of Class Analogy (SIMCA) to obtain 59 of the most discriminative genes. Their discriminative power was validated in three steps. The most convincing step showed their ability to correctly predict the brain regional classifications for 18 "test" gene expression sets obtained from the four regions. A two-way Hierarchical Cluster Analysis organized the 59 genes in six clusters according to their expression differences in the brain regions. Expression patterns in the SN and STR regions greatly differed from each other and the PC and PLCo. The closer similarity in the gene expression patterns of the PC and PLCo was probably due to their functional similarity. The important factors in determining differences in the regional gene expression profiles in six clusters were (1) regional myelin/oligodendrocyte levels, (2) resident neuron types, (3) neurotransmitter innervation profiles, and (4) Ca++-dependent signaling and second messenger systems.     

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.     

Proliferation of dental follicle-derived cell populations in heat-stress conditions

Objectives: Isolation and purification of adult stem cells (ASC) are a great challenge. Our objectives were to determine whether ASC are more heat‐tolerant than non‐stem cells, and to explore if ASC could be enriched by heat‐stress treatments. Materials and methods: Rat dental follicle cells were cultured in a variety of media to obtain either a heterogeneous cell population (H‐DFC) consisting of stem cells and non‐stem cells, or a homogenous cell population (DFC) containing non‐stem cells only. Real‐time RT‐PCR was conducted to compare expression of heat‐shock proteins (HSPs) between the two populations. To study heat tolerance, H‐DFC and DFC were incubated under heat‐stress conditions and cell proliferation was evaluated by alamar blue reduction assay. Furthermore, cells resulting from heat‐stress treatments were evaluated for differentiation capability and expression of stem cell markers. Results: H‐DFC expressed higher levels of HSP110, HSP70s and HSP27s than did DFC. H‐DFC increased levels of proliferation at 40 °C compared to controls grown at 37 °C; no significant reduction in proliferation occurred at temperatures below 40.5 °C. In contrast, DFC showed significant reduction in proliferation under all heat‐stress treatments. Heat‐stressed H‐DFC had increased differentiation capability and increased expression of stem cell markers. Conclusion: Stem cells appear to be more tolerant to heat stress than non‐stem cells. Incubation of a heterogeneous cell population in heat‐stress conditions resulted in increased stem cell numbers.     

Golgi enzymes that synthesize plant cell wall polysaccharides: finding and evaluating candidates in the genomic era

Although the synthesis of cell wall polysaccharides is a critical process during plant cell growth and differentiation, many of the wall biosynthetic genes have not yet been identified. This review focuses on the synthesis of non-cellulosic matrix polysaccharides formed in the Golgi apparatus. Our consideration is limited to two types of plant cell wall biosynthetic enzymes: glycan synthases and glycosyltransferases. Classical means of identifying these enzymes and the genes that encode them rely on biochemical purification of enzyme activity to obtain amino acid sequence data that is then used to identify the corresponding gene. This type of approach is difficult, especially when acceptor substrates for activity assays are unavailable, as is the case for many enzymes. However, bioinformatics and functional genomics provide powerful alternative means of identifying and evaluating candidate genes. Database searches using various strategies and expression profiling can identify candidate genes. The involvement of these genes in wall biosynthesis can be evaluated using genetic, reverse genetic, biochemical, and heterologous expression methods. Recent advances using these methods are considered in this review.     

Global analysis of hematopoietic and vascular endothelial gene expression by tissue specific microarray profiling in zebrafish

In this study, we utilize fluorescent activated cell sorting (FACS) of cells from transgenic zebrafish coupled with microarray analysis to globally analyze expression of cell type specific genes. We find that it is possible to isolate cell populations from Tg(fli1:egfp)(y1) zebrafish embryos that are enriched in vascular, hematopoietic and pharyngeal arch cell types. Microarray analysis of GFP+ versus GFP- cells isolated from Tg(fli1:egfp)(y1) embryos identifies genes expressed in hematopoietic, vascular and pharyngeal arch tissue, consistent with the expression of the fli1:egfp transgene in these cell types. Comparison of expression profiles from GFP+ cells isolated from embryos at two different time points reveals that genes expressed in different fli1+ cell types display distinct temporal expression profiles. We also demonstrate the utility of this approach for gene discovery by identifying numerous previously uncharacterized genes that we find are expressed in fli1:egfp-positive cells, including new markers of blood, endothelial and pharyngeal arch cell types. In parallel, we have developed a database to allow easy access to both our microarray and in situ results. Our results demonstrate that this is a robust approach for identification of cell type specific genes as well as for global analysis of cell type specific gene expression in zebrafish embryos.     

Multiple gene detection by in situ RT-PCR in isolated plant cells and tissues

With the number of functional genomic approaches in plant biology increasing daily, the demand for rapid and reliable RNA localization techniques for gene characterization is being felt. We present herein a novel, liquid phase in situ RT-PCR (IS-RT-PCR) protocol using a combination of gene-specific fluorescent primers and spectral confocal microscopy to localize target RNA in epicotyl sections and xylogenic suspension cultures of Zinnia elegans. Potential sources of artefacts from fixation to gene detection were systematically eliminated using both fluorescent primers and nucleotides for 18S rRNA gene detection, resulting in a set of optimal parameters for IS-RT-PCR that may be readily adapted to any target gene. By judiciously choosing fluorescent primers with non-overlapping fluorochromes, we have shown that our technique is readily adapted to multiplex IS-RT-PCR, enabling the simultaneous localization of more than one gene within a complex tissue or heterogeneous cell population. A 6-carboxy-2',4,4',5',7,7'-hexachlorofluorescein (6-HEX)-labelled primer and a tetrachloro-6-carboxy-fluorescein (TET)-labelled primer were designed for two marker genes associated with programmed cell death in tracheary elements (TEs): an endonuclease (Zen1) and a cysteine protease (ZcP4), respectively. An additional Cyan5 (Cy5)-labelled primer was used to monitor 18SrRNA expression. As expected, the 18S signal was constitutively expressed throughout epicotyls sections and living cells in xylogenic in vitro cultures, whereas Zen1 and ZcP4 were co-localized in forming TEs both in planta and in vitro. Analogous to clustering analysis of gene expression using microarrays to elucidate common metabolic pathways and developmental processes, this novel technique is perfectly adapted to gaining a better understanding of gene function via the coordinated expression of genes in specific cell types of complex tissues and cell populations.     

A Network Biology Approach Identifies Molecular Cross-Talk between Normal Prostate Epithelial and Prostate Carcinoma Cells

The advent of functional genomics has enabled the genome-wide characterization of the molecular state of cells and tissues, virtually at every level of biological organization. The difficulty in organizing and mining this unprecedented amount of information has stimulated the development of computational methods designed to infer the underlying structure of regulatory networks from observational data. These important developments had a profound impact in biological sciences since they triggered the development of a novel data-driven investigative approach. In cancer research, this strategy has been particularly successful. It has contributed to the identification of novel biomarkers, to a better characterization of disease heterogeneity and to a more in depth understanding of cancer pathophysiology. However, so far these approaches have not explicitly addressed the challenge of identifying networks representing the interaction of different cell types in a complex tissue. Since these interactions represent an essential part of the biology of both diseased and healthy tissues, it is of paramount importance that this challenge is addressed. Here we report the definition of a network reverse engineering strategy designed to infer directional signals linking adjacent cell types within a complex tissue. The application of this inference strategy to prostate cancer genome-wide expression profiling data validated the approach and revealed that normal epithelial cells exert an anti-tumour activity on prostate carcinoma cells. Moreover, by using a Bayesian hierarchical model integrating genetics and gene expression data and combining this with survival analysis, we show that the expression of putative cell communication genes related to focal adhesion and secretion is affected by epistatic gene copy number variation and it is predictive of patient survival. Ultimately, this study represents a generalizable approach to the challenge of deciphering cell communication networks in a wide spectrum of biological systems.     

Knowledge-based gene expression classification via matrix factorization

MOTIVATION: Modern machine learning methods based on matrix decomposition techniques, like independent component analysis (ICA) or non-negative matrix factorization (NMF), provide new and efficient analysis tools which are currently explored to analyze gene expression profiles. These exploratory feature extraction techniques yield expression modes (ICA) or metagenes (NMF). These extracted features are considered indicative of underlying regulatory processes. They can as well be applied to the classification of gene expression datasets by grouping samples into different categories for diagnostic purposes or group genes into functional categories for further investigation of related metabolic pathways and regulatory networks. RESULTS: In this study we focus on unsupervised matrix factorization techniques and apply ICA and sparse NMF to microarray datasets. The latter monitor the gene expression levels of human peripheral blood cells during differentiation from monocytes to macrophages. We show that these tools are able to identify relevant signatures in the deduced component matrices and extract informative sets of marker genes from these gene expression profiles. The methods rely on the joint discriminative power of a set of marker genes rather than on single marker genes. With these sets of marker genes, corroborated by leave-one-out or random forest cross-validation, the datasets could easily be classified into related diagnostic categories. The latter correspond to either monocytes versus macrophages or healthy vs Niemann Pick C disease patients.     

Identification of a five-gene signature with prognostic value in colorectal cancer

Colorectal cancer (CRC) ranks as one of the most commonly diagnosed malignancies worldwide. Although mortality rates have been decreasing, the prognosis of CRC patients is still highly dependent on the individual. Therefore, identifying and understanding novel biomarkers for CRC prognosis remains crucial. The gene expression profiles of five-gene expression omnibus (GEO) data sets of CRC were first downloaded. A total of 352 consistent differentially expressed genes (DEGs) were identified for CRC and paired with normal tissues. Functional analysis including gene ontology and Kyoto encyclopedia of genes and genomes pathway enrichment revealed that these DEGs were related to metabolic pathways, tight junctions, and the cell cycle. Ten hub DEGs were identified based on the search tool for the retrieval of interacting genes database and protein–protein interaction networks. By using univariate Cox proportional hazard regression analysis, we found 11 survival-related genes among these DEGs. We finally established a five-gene signature (kinesin family member 15, N-acetyltransferase 2, glutathione peroxidase 3, secretogranin II, and chloride channel accessory 1) with prognostic value in CRC by step multivariate Cox regression analysis. Based on this risk scoring system, patients in the high-risk group had significantly poorer survival results compared with those in the low-risk group (log-rank test, p < 0.0001). Finally, we validated our gene signature scoring system in two independent GEO cohorts (GSE17536 and GSE33113). We found all five of the signature genes to be DEGs in The Cancer Genome Atlas database. In conclusion, our findings suggest that our five DEG-based signature can provide a novel biomarker with useful applications in CRC prognosis.     

Meta-analysis of differentiating mouse embryonic stem cell gene expression kinetics reveals early change of a small gene set

Stem cell differentiation involves critical changes in gene expression. Identification of these should provide endpoints useful for optimizing stem cell propagation as well as potential clues about mechanisms governing stem cell maintenance. Here we describe the results of a new meta-analysis methodology applied to multiple gene expression datasets from three mouse embryonic stem cell (ESC) lines obtained at specific time points during the course of their differentiation into various lineages. We developed methods to identify genes with expression changes that correlated with the altered frequency of functionally defined, undifferentiated ESC in culture. In each dataset, we computed a novel statistical confidence measure for every gene which captured the certainty that a particular gene exhibited an expression pattern of interest within that dataset. This permitted a joint analysis of the datasets, despite the different experimental designs. Using a ranking scheme that favored genes exhibiting patterns of interest, we focused on the top 88 genes whose expression was consistently changed when ESC were induced to differentiate. Seven of these (103728_at, 8430410A17Rik, Klf2, Nr0b1, Sox2, Tcl1, and Zfp42) showed a rapid decrease in expression concurrent with a decrease in frequency of undifferentiated cells and remained predictive when evaluated in additional maintenance and differentiating protocols. Through a novel meta-analysis, this study identifies a small set of genes whose expression is useful for identifying changes in stem cell frequencies in cultures of mouse ESC. The methods and findings have broader applicability to understanding the regulation of self-renewal of other stem cell types.     

Molecular screens for inner ear genes

Identification of the genes that encode proteins that are important for proper function of specific inner ear cell types is central to our understanding of the molecular basis of hearing and balance. Whereas the combination of electrophysiology and biophysics has resulted in an exquisite understanding of inner ear function, little is known about the proteins that confer these properties at the cellular level. Furthermore, the genes that control inner ear development, susceptibility to wear and tear, regeneration from damage, and age-related degeneration, are largely unknown. This review discusses tools that have been developed during the past few years to address this imbalance between a thorough physiologic characterization of inner ear function and a detailed understanding at a molecular level of the proteins involved in these functions. Creation of inner ear cDNA libraries has laid the foundation for the discovery of genes that are specifically expressed by cell types of the inner ear and that encode proteins that are important for molecular processes in these cells. In conjunction with expressed sequence tag database analysis, cDNA subtraction, and DNA arrays, functionally important genes, whose specific expression patterns are usually verified by gene expression analysis, can be identified. Discussion of these techniques takes into account the specific characteristics of the inner ear in relation to its study using molecular biological approaches.     

Genome-wide expression analysis of plant cell cycle modulated genes

Genome-wide expression analysis is rapidly becoming an essential tool for identifying and analysing genes involved in, or controlling, various biological processes ranging from development to responses to environmental cues. The control of cell division involves the temporal expression of different sets of genes, allowing the dividing cell to progress through the different phases of the cell cycle. A landmark study using DNA microarrays to follow the patterns of gene expression in synchronously dividing yeast cells has allowed the identification of several hundreds of genes that are involved in the cell cycle. Although DNA microarrays provide a convenient tool for genome-wide expression analysis, their use is limited to organisms for which the complete genome sequence or a large cDNA collection is available. For other organisms, including most plant species, DNA fragment analysis based methods, such as cDNA-AFLP, provide a more appropriate tool for genome-wide expression analysis. Furthermore, cDNA-AFLP exhibits properties that complement DNA microarrays and, hence, constitutes a useful tool for gene discovery.     

Identification of differentially expressed gene categories in microarray studies using nonparametric multivariate analysis

MOTIVATION: The field of microarray data analysis is shifting emphasis from methods for identifying differentially expressed genes to methods for identifying differentially expressed gene categories. The latter approaches utilize a priori information about genes to group genes into categories and enhance the interpretation of experiments aimed at identifying expression differences across treatments. While almost all of the existing approaches for identifying differentially expressed gene categories are practically useful, they suffer from a variety of drawbacks. Perhaps most notably, many popular tools are based exclusively on gene-specific statistics that cannot detect many types of multivariate expression change. RESULTS: We have developed a nonparametric multivariate method for identifying gene categories whose multivariate expression distribution differs across two or more conditions. We illustrate our approach and compare its performance to several existing procedures via the analysis of a real data set and a unique data-based simulation study designed to capture the challenges and complexities of practical data analysis. We show that our method has good power for differentiating between differentially expressed and non-differentially expressed gene categories, and we utilize a resampling based strategy for controlling the false discovery rate when testing multiple categories. AVAILABILITY: R code (www.r-project.org) for implementing our approach is available from the first author by request.