Unraveling the Dynamic Transcriptome

The advent of large-scale transcriptional profiling techniques signalled a new age in biology. Instead of understanding the expression and action of single genes, the field of transcriptomics allows for the examination of whole transcriptome changes across a variety of biological conditions. These techniques have resulted in a massive accumulation of gene expression data and the need for refinement in the formulation of biological questions when using such data. Instead of collecting data about changes in expression profile at the level of the organism or of particular organs, we can now focus on both the default and responsive transcriptional states of tissues and individual cells and generate novel hypotheses as to how these states collectively form a functioning organism. Transcriptional profiling has indeed become a well-used component of a biologist's toolbox. This review will describe some of the unique biological insights into plant functions that have been revealed from this type of analysis. For the readers' convenience, references mentioned throughout this review have been summarized in Table 1 Selected Plant Expression Profiling Studies This table summarizes the main references mentioned in this review and other references of interest. FACS, fluorescence-activated cell sorting. Selected Plant Expression Profiling Studies This table summarizes the main references mentioned in this review and other references of interest. FACS, fluorescence-activated cell sorting. The more commonly used large-scale techniques employ one of two strategies. In the first strategy, sequence tags from a given RNA sample are generated. In the second, mRNA populations of interest are hybridized with a large number of probes immobilized on a suitable substrate (e.g., various types of microarrays and BeadArrays). These strategies are complementary to each other. Generation of tags is independent of knowledge of gene annotation but does require extensive sequencing and a reference genome to determine gene identity. Low-abundance transcripts tend to be underrepresented. Tags used can include ESTs of ∼200 to 900 nucleotides, the 15-nucleotide tags used in serial analysis of gene expression (SAGE), and the 17- to 20-nucleotide tags used in massively parallel signature sequencing (MPSS). ESTs are created by sequencing the 5′ or 3′ ends of randomly isolated transcript cDNA. These tags are longer than those of SAGE and MPSS techniques but nonetheless represent only partial gene sequences. In SAGE, a restriction enzyme that is a frequent cutter (usually NlaIII) is used to digest and release cDNA molecules attached to oligo(dT)-coated beads. A linker is then attached to the cDNA fragment that contains a site for a Type IIs restriction enzyme (usually BsmF1), and this enzyme is then used to release a short 15-bp tag. These tags are then ligated together and the concatemer clones are sequenced. The abundance of sequence signatures reflects gene expression in the tissue being sampled (Velculescu et al., 2000). The generation of MPSS tags differs only slightly from the generation of SAGE tags. Instead of cutting with NlaIII, the cDNA molecules attached to beads are cut with DpnII, and each transcript receives a tag that contains a MmeI site, thus generating a 20-bp tag. A major difference between the two methods is that MPSS uses a nonconventional sequencing method that uses beads and allows sequencing of at least 5 × 105 tags at one time (www.lynxgen.com). The SAGE and MPSS techniques give quantitative measures of transcript abundance. These techniques have been used to generate expression data across tissue types or developmental stages in higher plants (Ewing et al., 1999; Ogihara et al., 2003; Fizames et al., 2004). Alternative methods of gene expression profiling include microarrays (Schena et al., 1995; Stears et al., 2003) and the more recently developed BeadArrays (Kuhn et al., 2004). Solid substrates can include rectangular slides (chips) in the case of microarrays or beads arrayed in fiber optic bundles in the case of BeadArrays. Variability in chip arrays, both inter- and intra-array, is often high due to a number of factors, including probe (cDNA or oligomer) spotting and hybridization parameters. However, chip arrays allow for the screening of thousands of probes, often representing the whole genome in parallel. Conversely, BeadArray technology has low variability. Each bead has several hundred thousand copies of an oligomer, and individual bead types representing different oligomers are quantitatively pooled and then densely packed into etched fiber optic bundles that are arranged in a 96-array matrix (Kuhn et al., 2004). Each array is therefore unique because arrays generally have greater than or equal to five beads of each type such that all sequences are represented multiple times. The randomness of the array minimizes the effect of spatially localized artifacts, and redundancy increases measurement precision and robustness. As of yet, this technique has only been applied to a study involving 587 human genes (Kuhn et al., 2004). Nevertheless, the potential to develop this system in the analysis of whole genomes is quite promising. One limitation of these techniques is that the genes assayed are dependent on which probes are immobilized to the substrate. However, if probes corresponding to low abundance transcripts are present, this can circumvent one of the main issues with microarrays. A primary caveat of the array methods is that the measurement of gene expression profiles is based on a hybridization ratio and therefore determines relative transcript abundance. RNA gel blot analysis (Van Zhong and Burns, 2003; Parisi et al., 2004), quantitative RT-PCR (Leonhardt et al., 2004; Jiao et al., 2005), and even promoter–reporter experiments generally appear to confirm differential gene expression indicated by microarray analysis. In addition, ribonuclease protection assays and in situ hybridization can also validate microarray results (Chuaqui et al., 2002). However, in a detailed study of 48 human genes, a substantial percentage of genes (13 to 16%) showed poor expression correlation between quantitative PCR and normalized microarray data (Dallas et al., 2005). Therefore, caution is required when interpreting microarray results, and data should ideally be verified with multiple methods. Until recently, mRNA populations used in microarray analysis have been derived from whole organs, such as leaves and roots. However, plant organs are extremely heterogeneous, and growth, development, and responses to environmental stimuli and developmental signals differ among cells within an organ (Brandt, 2005). Use of mRNA extracted from bulk materials leads to a loss of detailed information about tissue and cell-specific genomic responses. Therefore, it is becoming more common to extract RNA from specific tissues and cells. Tissue and cell-specific RNA populations have been derived from enriched populations of cells or tissues, such as dissected tissues and organs, in vitro tissue cultures, and mutants in which certain cells or tissues are enriched or absent (Demura et al., 2002; Brandt, 2005; Grimanelli et al., 2005). Due to the effects of tissue preparation on gene expression, questionable biological significance of in vitro cultures, and pleiotropic mutational effects, researchers are developing more biologically relevant techniques in which RNA is extracted from very small quantities of living or rapidly extracted tissue (for in depth reviews, see Brandt, 2005; Lee et al., 2005). Laser capture microdissection (LCM), where tissue is fixed and cut out by laser dissection (Nakazono et al., 2003; Woll et al., 2005), protoplasting, and fluorescence-activated cell sorting of green fluorescent protein–marked cells (Birnbaum et al., 2003, 2005) are examples of such techniques. Micropipetting can also be used to extract cell contents from targeted surface cells (Brandt, 2005). If enough RNA is extracted, it can be used directly for microarray analysis. Generally, insufficient quantities of RNA are obtained with these methods. In these cases, cDNA is generated from the RNA and then amplified exponentially with PCR or, to avoid differential amplification, linearly with T7 RNA polymerase. With these rapidly developing techniques, we are beginning to understand the tissue and cell specificity of plant transcriptional regulation, thus fine-tuning our understanding of how plant genome expression changes at the cellular level. Expression maps of organisms using expression profiling are now constructed at an increasingly high resolution as both expression profile platforms and microdissection techniques evolve. Expression maps exist for plant organs such as leaves, roots, flowers, and seeds in many plant species, including Arabidopsis, maize, and soybean (Birnbaum et al., 2003; Honys and Twell, 2003; Nakazono et al., 2003; Vodkin et al., 2004; Grimanelli et al., 2005; Poroyko et al., 2005; Schmid et al., 2005; Tung et al., 2005). The maps detail the default developmental state of these organs. A default developmental state is considered to be growth under optimal (often laboratory) conditions. Superimposed upon these organ-specific maps are developmental time series that catalogue changes in expression over developmental time in addition to spatial information using tissue or cell-type expression profiles. As an example, the Arabidopsis AtGenExpress data set profiles Arabidopsis development in 79 diverse samples representing different organ types and developmental stages (Schmid et al., 2005). The Arabidopsis root digital in situ describes three developmental stages along the longitudinal axis and five radial cell layers using dissection and fluorescence-activated cell sorting of green fluorescent protein–marked lines (Birnbaum et al., 2003). A comparison of transcripts expressed in the whole root in the AtGenExpress data set to the transcripts present in the root digital in situ revealed that 392 genes were missing in the AtGenExpress data set, thus highlighting the heterogeneity of plant organs and the loss of signal when averaged across an organ. Many organs have distinct transcriptional signatures (Honys and Twell, 2003; Schmid et al., 2005). Principle component analysis of the AtGenExpress data set showed that overall morphological similarity according to organ type is well reflected in eigenvalue distances, whereas developmental stage or environmental conditions are only minor components (Schmid et al., 2005). As a corollary to this, functional gene assignment using gene ontology categories showed that organs that are morphologically similar contain similar functional gene categories. In soybean and Arabidopsis, genes whose products are involved in photosynthesis and carbon dioxide fixation are overrepresented in leaves and underexpressed in tissues, such as the root and pollen (Vodkin et al., 2004; Schmid et al., 2005). Pollen-enriched transcripts in Arabidopsis and rice contain an abundance of genes involved in signaling, vesicle trafficking, the cytoskeleton, and membrane transport (Pina et al., 2005). Higher-resolution expression profiles of specific cell types can reveal further information about an organ. LCM and expression analysis of maize coleoptile tissues revealed that genes involved in the shikimate and phenylpropanoid pathways, which are required for secondary metabolites, such as flavonoids, lignins, pigments, and UV light protectants, are epidermis specific, whereas and genes involved in carbon dioxide fixation are tissue specific (Nakazono et al., 2003). of Arabidopsis cell and cell tissue revealed that a gene involved in and an are expressed in cells (Leonhardt et al., 2004). Studies such as these can of specific cell types within the organ. 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