Gene expression profiling of single epidermal, basal and trichome cells of Arabidopsis thaliana.

Samples of single epidermal, basal and trichome cells were collected by glass microcapillaries from 7-week-old Arabidopsis thaliana leaves. Transcript amplification of these single-cell samples was performed by RT PCR. For gene expression profiling, we hybridized the amplified transcriptome of each individual cell type to nylon membranes spotted with 16,000 Arabidopsis expressed sequence tags (ESTs). Initial analysis of the array filter data enabled us to functionally categorize transcripts that were present in each individual cell type. In order to confirm the filter array data, we used RT PCR. Results of this RT PCR approach confirmed the presence of 12 selected candidate genes in agreement with array filter hybridization data. Further, transcripts involved in detoxification and sulfur metabolism could be identified in epidermal cell extracts. Together, the results of our study provide the localization of approximately 1000 expressed genes to either pavement, basal or trichome cells. To cluster transcripts with similar expression levels, we developed a novel mathematical algorithm. Based on the mean and standard deviation, ratios of expression levels of a transcript were defined for pairs of the three cell types. This numerical analysis enabled subdivision into 67 categories of genes differentially expressed in epidermal, basal and trichome cells. Transcripts in each category displayed similar ratios of expression levels in the three cell types. Examples of these clusters are presented and discussed in Appendix A.

Robust detection and verification of linear relationships to generate metabolic networks using estimates of technical errors.

BACKGROUND: The size and magnitude of the metabolome, the ratio between individual metabolites and the response of metabolic networks is controlled by multiple cellular factors. A tight control over metabolite ratios will be reflected by a linear relationship of pairs of metabolite due to the flexibility of metabolic pathways. Hence, unbiased detection and validation of linear metabolic variance can be interpreted in terms of biological control. For robust analyses, criteria for rejecting or accepting linearities need to be developed despite technical measurement errors. The entirety of all pair wise linear metabolic relationships then yields insights into the network of cellular regulation. RESULTS: The Bayesian law was applied for detecting linearities that are validated by explaining the residues by the degree of technical measurement errors. Test statistics were developed and the algorithm was tested on simulated data using 3-150 samples and 0-100% technical error. Under the null hypothesis of the existence of a linear relationship, type I errors remained below 5% for data sets consisting of more than four samples, whereas the type II error rate quickly raised with increasing technical errors. Conversely, a filter was developed to balance the error rates in the opposite direction. A minimum of 20 biological replicates is recommended if technical errors remain below 20% relative standard deviation and if thresholds for false error rates are acceptable at less than 5%. The algorithm was proven to be robust against outliers, unlike Pearson's correlations. CONCLUSION: The algorithm facilitates finding linear relationships in complex datasets, which is radically different from estimating linearity parameters from given linear relationships. Without filter, it provides high sensitivity and fair specificity. If the filter is activated, high specificity but only fair sensitivity is yielded. Total error rates are more favorable with deactivated filters, and hence, metabolomic networks should be generated without the filter. In addition, Bayesian likelihoods facilitate the detection of multiple linear dependencies between two variables. This property of the algorithm enables its use as a discovery tool and to generate novel hypotheses of the existence of otherwise hidden biological factors.