Single-cell analysis of gene expression in the nervous system

The characteristic functions of tissues and organs results from the integrated activity of individual cells. Nowhere is this more evident than in the nervous system, where the activities of single neurons communicating via electrical and chemical signals mediate complex functions, such as learning and memory. The past decade has seen an explosion in the identification of genes encoding proteins, such as voltage-gated channels and neurotransmitter receptors, responsible for neuronal excitability. These studies have highlighted the fact that even within a neuroanatomically defined region, the coexistence of multiple cell types makes it difficult, if not impossible, to correlate patterns of gene expression with function The recent development of techniques sensitive enough to, study gene expression at the single-cell level promises to break this bottleneck to our further understanding. Using examples taken from our own laboratories and the work of others, we review these techniques, their application, and discuss some of the difficulties associated with the interpretation of the data.     

Inferring gene expression dynamics via functional regression analysis

Background

The recent emergence of high-throughput automated image acquisition technologies has forever changed how cell biologists collect and analyze data. Historically, the interpretation of cellular phenotypes in different experimental conditions has been dependent upon the expert opinions of well-trained biologists. Such qualitative analysis is particularly effective in detecting subtle, but important, deviations in phenotypes. However, while the rapid and continuing development of automated microscope-based technologies now facilitates the acquisition of trillions of cells in thousands of diverse experimental conditions, such as in the context of RNA interference (RNAi) or small-molecule screens, the massive size of these datasets precludes human analysis. Thus, the development of automated methods which aim to identify novel and biological relevant phenotypes online is one of the major challenges in high-throughput image-based screening. Ideally, phenotype discovery methods should be designed to utilize prior/existing information and tackle three challenging tasks, i.e. restoring pre-defined biological meaningful phenotypes, differentiating novel phenotypes from known ones and clarifying novel phenotypes from each other. Arbitrarily extracted information causes biased analysis, while combining the complete existing datasets with each new image is intractable in high-throughput screens.

Results

Here we present the design and implementation of a novel and robust online phenotype discovery method with broad applicability that can be used in diverse experimental contexts, especially high-throughput RNAi screens. This method features phenotype modelling and iterative cluster merging using improved gap statistics. A Gaussian Mixture Model (GMM) is employed to estimate the distribution of each existing phenotype, and then used as reference distribution in gap statistics. This method is broadly applicable to a number of different types of image-based datasets derived from a wide spectrum of experimental conditions and is suitable to adaptively process new images which are continuously added to existing datasets. Validations were carried out on different dataset, including published RNAi screening using Drosophila embryos [Additional files 1, 2], dataset for cell cycle phase identification using HeLa cells [Additional files 1, 3, 4] and synthetic dataset using polygons, our methods tackled three aforementioned tasks effectively with an accuracy range of 85%–90%. When our method is implemented in the context of a Drosophila genome-scale RNAi image-based screening of cultured cells aimed to identifying the contribution of individual genes towards the regulation of cell-shape, it efficiently discovers meaningful new phenotypes and provides novel biological insight. We also propose a two-step procedure to modify the novelty detection method based on one-class SVM, so that it can be used to online phenotype discovery. In different conditions, we compared the SVM based method with our method using various datasets and our methods consistently outperformed SVM based method in at least two of three tasks by 2% to 5%. These results demonstrate that our methods can be used to better identify novel phenotypes in image-based datasets from a wide range of conditions and organisms.

Conclusion

We demonstrate that our method can detect various novel phenotypes effectively in complex datasets. Experiment results also validate that our method performs consistently under different order of image input, variation of starting conditions including the number and composition of existing phenotypes, and dataset from different screens. In our findings, the proposed method is suitable for online phenotype discovery in diverse high-throughput image-based genetic and chemical screens.     

Altered gene expression pattern in the fatty liver dystrophy mouse reveals impaired insulin-mediated cytoskeleton dynamics.

The mouse fatty liver dystrophy (fld) mutation is characterized by transient hypertriglyceridemia and fatty liver during the neonatal period, followed by development of a peripheral neuropathy. To uncover the metabolic pathway that is disrupted by the fld mutation, we analyzed the altered pattern of gene expression in the fatty liver of fld neonates by representational difference analysis of cDNA. Differentially expressed genes detected include a novel member of the Ras superfamily of small GTP-binding proteins, a novel Ser/Thr kinase, and several actin cytoskeleton-associated proteins including actin, profilin, alpha-actinin, and myosin light chain. Because these proteins have a potential functional link in the propagation of hormone signals, we investigated cytoskeleton dynamics in fld cells in response to hormone treatment. These studies revealed that preadipocytes from fld mice exhibit impaired formation of actin membrane ruffles in response to insulin treatment. These findings suggest that the altered mRNA expression levels detected in fld tissue represent a compensatory response for the nonfunctional fld gene and that the fld gene product may be required for development of normal insulin response.     

Pathway analysis reveals functional convergence of gene expression profiles in breast cancer

Background

Angiotensin I-converting enzyme (ACE) plays an important role in cardiovascular homeostasis. There is evidence from different ethnic groups that circulating ACE levels are influenced by a quantitative trait locus (QTL) at the ACE gene on chromosome 17. The finding of significant residual familial correlations in different ethnic groups, after accounting for this QTL, and the finding of support for linkage to a locus on chromosome 4 in Mexican-American families strongly suggest that there may well be QTLs for ACE unlinked to the ACE gene.

Methods

A genome-wide panel of microsatellite markers, and a panel of biallelic polymorphisms in the ACE gene were typed in Nigerian families. Single locus models with fixed parameters were used to test for linkage to circulating ACE with and without adjustment for the effects of the ACE gene polymorphisms.

Results

Strong evidence was found for D17S2193 (Z_max = 3.5); other nearby markers on chromosome 17 also showed modest support. After adjustment for the effects of the ACE gene locus, evidence of "suggestive linkage" to circulating ACE was found for D4S1629 (Z_max = 2.2); this marker is very close to a locus previously shown to be linked to circulating ACE levels in Mexican-American families.

Conclusion

In this report we have provided further support for the notion that there are QTLs for ACE unlinked to the ACE gene; our findings for chromosome 4, which appear to replicate the findings of a previous independent study, should be considered strong grounds for a more detailed examination of this region in the search for genes/variants which influence ACE levels. The poor yields, thus far, in defining the genetic determinants of hypertension risk suggest a need to look beyond simple relationships between genotypes and the ultimate phenotype. In addition to incorporating information on important environmental exposures, a better understanding of the factors which influence the building blocks of the blood pressure homeostatic network is also required. Detailed studies of the genetic determinants of ACE, an important component of the renin-angiotensin system, have the potential to contribute to this strategic objective.     

Timing and dynamics of single cell gene expression in the arabinose utilization system

The arabinose utilization system of Escherichia coli displays a stochastic all-or-nothing response at intermediate levels of arabinose, where the population divides into a fraction catabolizing the sugar at a high rate (on-state) and a fraction not utilizing arabinose (off-state). Here we study this decision process in individual cells, focusing on the dynamics of the transition from the off- to the on-state. Using quantitative time-lapse microscopy, we determine the time delay between inducer addition and fluorescence onset of a GFP reporter. Through independent characterization of the GFP maturation process, we can separate the lag time caused by the reporter from the intrinsic activation time of the arabinose system. The resulting distribution of intrinsic time delays scales inversely with the external arabinose concentration, and is compatible with a simple stochastic model for arabinose uptake. Our findings support the idea that the heterogeneous timing of gene induction is causally related to a broad distribution of uptake proteins at the time of sugar addition.     

Homoeolog gene expression analysis reveals novel expression biases in upland hybrid cotton under intraspecific hybridization

Functional & Integrative Genomics - Hybridization is useful to enhance the yield potential of agronomic crops in the world. Cotton has genome doubling due to the allotetraploid process and...     

Molecular analysis of gene expression in the developing pontocerebellar projection system

As an approach toward understanding the molecular mechanisms of neuronal differentiation, we utilized DNA microarrays to elucidate global patterns of gene expression during pontocerebellar development. Through this analysis, we identified groups of genes specific to neuronal precursor cells, associated with axon outgrowth, and regulated in response to contact with synaptic target cells. In the cerebellum, we identified a phase of granule cell differentiation that is independent of interactions with other cerebellar cell types. Analysis of pontine gene expression revealed that distinct programs of gene expression, correlated with axon outgrowth and synapse formation, can be decoupled and are likely influenced by different cells in the cerebellar target environment. Our approach provides insight into the genetic programs underlying the differentiation of specific cell types in the pontocerebellar projection system.