To identify regionally enriched gene expression within the brain of the adult mouse strain C57BL/6J, we used the precision of Laser Capture Microdissection (LCM; Figure 1) 16 to isolate component tissues and construct SAGE libraries from 17 brain regions as well as the whole adult mouse brain for comparison (Methods). As shown in Table 1, these libraries have been sampled to a depth of > 100,000 tags each, a level shown to be adequate for the discovery of medium-to-high level transcripts 8. Bioinformatics analysis of differential gene expression was performed as described in Methods. Since the majority of transcripts were detected in multiple libraries, we employed a heuristic approach to identify and rank expression patterns (outlined in Table 2). For each brain region, we ranked genes from 1–91 based on the level and pattern of expression in descending order. Expression specificity of a ranked list of 1999 SAGE-identified genes was then confirmed by examining related literature information and Allen Brain Atlas in situ hybridization data. Based on this collective information, region-specific or region-enriched genes were further considered.

Of the 237 genes identified as displaying regionally enriched expression in this study, 132 genes [see Additional file 1] displayed expression patterns listed in Table 2. Only 22 genes were found in a single library and five of these (A930006D11Rik, Chrna6, Gdf10, Hcrt, and Hes3) were determined to be tissue-specific at a statistically significant level (tag counts > 5, P < 0.05).

As an indication of complexity of the adult mouse brain transcriptome, within the 18 Pleiades libraries (including whole adult brain library) expression was observed for 11,836 genes of the total 17,098 genes detectable within the Mouse Atlas (total number of tags mapped to the Mouse Atlas libraries was approximately 8.8 million including singletons). In contrast, the Allen Brain Atlas (ABA) contains expression patterns of approximately 16,000 genes across the entire adult C57BL/6J mouse brain (Susan Sunkin, ABA, personal communication); of these genes, roughly 65.5% (10,479/16,000) were detectable in the 18 Pleiades libraries. Furthermore, the Pleiades libraries provided about 8% (1,357/17,357) additional genes to the total number of genes detectable by ABA.

We also analyzed SAGE data to measure transcriptome similarity between selected tissues. The premise was that tissues would cluster together or diverge based on the degree to which their genes are differentially expressed. Hierarchical clustering was done based on unweighted average distance between formed clusters (see description in Methods), the results of which are displayed in the form of a dendrogram (Figure 2). A pattern of divergent tissue clusters consistently emerges: a cluster of neuronal tissues and several discrete single tissue clusters including Ependymal Layers, Cerebellum White Matter and Cerebellum Purkinje Cell Layer. Among neuronal tissues, the Ventral and Medial Thalamus consistently clustered tightly together and had the lowest expression divergence between any two pairs of tissues. Additionally, Visual Cortex, Primary Motor Cortex, Amygdala (basolateral), Amygdala (central), and Dorsal Striatum also clustered together. Segregation of the Ependymal tissue into a separate single cluster makes sense given its non-neuronal nature 17, and the Cerebellar White Matter is composed of myelinated axonal processes. Clustering is usually sensitive to the specific expression divergence measure used. However, we tried several empirical measures, as well as different P values for selecting differentially expressed genes, and observed that the main pattern of clustering outlined above remains unchanged.

We included in the present analysis several additional brain regions and cell-types, for example, Blood-Brain Barrier, Barrington's Nucleus, Astroglia etc., for which SAGE libraries had not been constructed. Therefore, to expand our set of genes with regionally enriched expression for all brain regions, we then scrutinized literature from PubMed. We obtained a list of Medline records using Boolean logic with search term combinations indicated in Table 3. To facilitate retrieval of publications from a large literature database such as PubMed, we also developed a semi-automated literature mining strategy (see Methods and Figure 3) based on natural language processing. In this approach we looked for the appearance of a gene name or synonym and a brain region in a sentence. Of the 99.7 million sentences searched, 314,515 occurrences of a brain region term were found; 4,395 mouse genes names, or the names of their human orthologs, were found to appear within the same sentence as a brain region (not shown).

The candidature of literature-mined genes was verified by assessing available expression data (reporter gene expression, microarray expression profile, radioactive/non-radioactive in situ hybridization) in publications, and confirmed with in situ hybridization data from the Allen Brain Atlas (see below). In addition to promoter-reporter fusion data from the literature, reporter expression data for BAC (Bacterial Artificial Chromosome) transgenic mice, when available from the GENSAT database 18, was also considered as complementary evidence of expression [see Additional file 2].

The entire Allen Brain Atlas (ABA) data set can be searched via a web-based application 1314. We used this feature to examine expression patterns of genes identified as regionally enriched by SAGE and/or the literature. This verification was particularly apt for SAGE because ABA in situ hybridization patterns were also derived from the same mouse strain C57BL/6J. We also employed the ABA Anatomic Search tool to identify additional genes whose expression patterns cluster within brain regions of interest. While this approach short-listed genes for major regions (Thalamus, Cerebral Cortex etc.) of the mouse brain listed under Anatomic Search, we also searched within these regions to identify expression in sub-regions of interest, e.g. within Pons for genes expressed in Locus Coeruleus. Recent introduction of the alternative ABA search tool, NeuroBlast, also proved to be useful. We used NeuroBlast to retrieve genes co-expressed with a seeded (query) gene in a region of interest. Identification of regionally enriched co-expressed genes in this manner is indispensable in subsequent identification of shared regulatory elements for efficient mini-promoter design.

Thus, SAGE analysis of the adult mouse brain transcriptome combined with meta-analysis using data mining resources described above identified 237 genes as showing regionally enriched expression (Table 4). A summary of the meta-analysis that supports regionally enriched expression is presented [see Additional file 2]; where available, this file includes examples of supporting ABA images downloaded from the ABA website (please see Availability & requirements for more information)

The Gene Ontology (GO) resource 19 is a powerful tool to identify common functions shared by genes identified by high-throughput gene expression methods such as SAGE. We searched for over-representation of GO terms among our set of genes from each of three ontology classes: Biological Process, Molecular Function and Cellular Component (Methods). Of 237 genes in our selection, we found annotations for 216 genes in the whole mouse genome set of 18535 annotated genes (as of March 18, 2008). From this list, we determined the top 12 statistically over-represented GO terms [see Additional file 3]. Annotations for the test selection of genes were compared with GO annotations of the whole mouse genome. Significant biological processes involved nervous system development, transmission of nerve impulse, cell-cell signaling, neurogenesis, behavior etc. Significant molecular functions involved neuropeptide hormone activity, sequence-specific DNA binding, neurotransmitter receptor activity, steroid hormone receptor activity, neurotransmitter transporter activity etc. Products of some of these genes also tended to be localized in the extracellular region, plasma membrane, synapse, or within transcription factor complexes. Thus, it appears that many of the genes we identified have established neurological functions, which accounts for their regionally enriched expression. It is noteworthy that we found 28 transcription factor encoding genes representing 16 of 30 regions/cell-types of interest (Table 5). This information combined with identification of regulatory sequences within promoters of selected genes will aid the design of mini-promoters specific for each brain region. Because our selection of the 237 genes was biased towards those with known functions, we also carried out GO analysis on genes expressed in each of 18 SAGE libraries [see Additional file 4]. Specific neurological functions were less apparent among over-represented GO terms for these larger sets than for the 237 genes presented in this study.

Mice used in our experiments were all adult male C57BL/6J mice (12-week old post-natal). All procedures used in these experiments were in accordance with the Canada Council on Animal Care and approved by the University of British Columbia Animal Care Committee (A05-1748). All experiments were conducted in accordance with Canadian and International standards for animal care. All efforts were made to minimize the number and suffering of any animals used in these experiments.

Whole brains were manually dissected at room temperature from the intact bodies of mice. To minimize the effects of stress on gene expression, the mother, and the entire litter remained in the family cage until harvest. Mice were removed, one at a time and killed in a separate room, by cervical dislocation. Tissue was immediately flash frozen in liquid nitrogen and stored at -80°C until further processing. Frozen tissue was disrupted and homogenized for 30 seconds with a Polytron^® PT 1200CL hand-held homogenizer (Kinematica AG, through Brinkmann™ Instruments Inc, Mississauga, Canada) at a setting of 3 (~13,000 RPM), which had been equipped with a 7-mm easy-care generator (PT-DA 1207/2EC). Total RNA was extracted using RNeasy Lipid Tissue Mini Kit (Qiagen Inc., Missisauga, Canada), following the manufacturer's protocol with the modification of using 1.5-ml Phase Lock Gel™ Heavy Tube (Eppendorf Scientific, through Fisher Scientific, Ottawa, Canada) for more robust phase separation. Also, while on the column, samples underwent DNase I treatment during RNA extraction. Standard care was used to avoid RNA degradation: reagents were prepared with diethyl pyrocarbonate (DEPC)-treated water and all surfaces and equipment were treated with an RNase decontamination solution (RNaseZap^® and RNaseZap^® Wipes; Ambion Inc., Austin, Texas, USA). The quality and quantity of the RNA samples were tested on an Agilent 2100 Bioanalyzer with the RNA 6000 Nano LabChip^® Kit (Agilent Technologies Canada Inc., Mississauga, Canada).

Brains (1–3 per region; exception: 7 per Ependymal and Subependymal Layers), recovered as above, were immediately frozen on dry ice and mounted in OCT (Optimal Cutting Temperature) embedding medium. For the Visual Cortex (SM147), Cerebellar White Matter (SM182), Dorsal Striatum (SM195), and Cerebellar Purkinje cells (SM196) sagittal sections were processed, while coronal sections were used for the remaining tissues. Cryosections (20 μm) of fresh-frozen tissues were mounted onto RNase-free membrane slides (Molecular Machines & Industries AG (MMI), Glattbrugg, Switzerland) manufactured for LCM. To identify the desired regions for processing by LCM, each slide was individually stained with a modified Nissl-substance stain using cresyl violet (CV) dye (Polysciences, Inc., Warrington, PA) as follows: Slide-mounted sections were air-dried for 2–3 min and the surrounding OCT medium was rinsed off with 1× PBS (made with DEPC water). Tissue was fixed for 30 sec with 75% ethanol, stained for 1 min with 0.5% CV, then sequentially rinsed for 5–10 sec with 75%, 95%, and 100% ethanol. After air-drying for 2–3 min, sections were immediately dissected with the SL μCUT system (MMI AG; Glattbrugg, Switzerland) under the 10× objective of a Nikon Eclipse TE2000-S, at laser power < 70 mV, for no longer than 15 min. The cut regions were collected onto the adhesive cap of a 500-μl microfuge tube (MMI AG, Glattbrugg, Switzerland) designed for the SL μCUT system, digested with 30 μl lysis buffer RLT (RNeasy Micro Kit; Qiagen Inc., Missisauga, Canada), and transferred from the cap to the vial. The samples were vortexed, centrifuged for 5 sec, and then stored at -80°C until RNA extraction (as above). High-quality samples were pooled within groups for SAGE library generation.

The LongSAGE-Lite method was used to construct the libraries as previously described 5. In brief, first strand cDNA was synthesized with Powerscript Reverse Transcriptase (Clontech, BD Biosciences, Mississauga, Canada) and LITE1/LITE TS primer mix (Invitrogen, Carlsbad, CA) using 15–120 ng of DNase-treated total RNA, and amplified by a 20-cycle PCR according to the SAGE-Lite method 38. SAGE-Lite biochemistry for the generation of full-length cDNA libraries is based upon the SMART (Switching Mechanism At the 5' end of RNA Transcripts) cDNA synthesis strategy (Clontech, BD Biosciences, Mississauga, Canada). Following amplification, the cDNA were processed according to an adaptation of the standard LongSAGE protocol using the I-SAGE Long kit (Invitrogen, Carlsbad, CA). The SAGE protocol includes steps of anchoring by NlaIII, tagging by MmeI, and generating 131 bp ditags by T4 DNA ligase. The 131 bp ditags were amplified using the scale-up PCR varying from 23–27 cycles depending on the optimal scale up condition as described in the protocol, and were digested with NlaIII to remove adapter sequences. Purified 36-bp ditags were ligated to form concatemers that were cloned into SphI-digested pZErO-1 vector (Invitrogen, Carlsbad, CA), and transformations were done using One Shot DH10B T1 electrocompetent E. coli (Invitrogen, Carlsbad, CA).

After transformants had been screened by colony PCR, the fraction containing concatemers of sizes ranging from 900 bp-1300 bp was chosen for sequencing. Colonies were picked using a Q-Pix robot (Genetix, Beaverton, OR) and inoculated into 2xYT media with Zeocin (50 μg/ml) and glycerol (7.5%). After overnight culture, glycerol stocks were used to inoculate larger volume cultures for plasmid preparation, carried out using a standard alkaline-lysis procedure adapted for high-throughput processing with microtiter plates. DNA sequencing was performed with BigDye v3.1 dye terminator cycle sequencing reactions run on Tetrad thermal cyclers (MJ Research, Waltham, MA). Products from the sequencing reaction were purified by ethanol precipitation and then run on capillary DNA sequencers (Model 3730xl, Applied Biosystems, Foster City, CA).

Following inspection of data quality from a first 384-well sequencing plate, each library was sequenced to a depth of > 100,000 raw tags. The resulting sequence data were collected automatically and processed by both trimming the reads for sequence quality and removing sequences from non-recombinant clones, vector DNA and linker-derived tags. Processed data can be found on the Mouse Atlas website (please see Availability & requirements for more information)

To obtain high quality SAGE tags for this study, all raw SAGE tags underwent a three-step cluster modification process developed by Siddiqui et al. 8. In the first step, we calculated for each tag a P value based on the Phred quality score 39 to identify single nucleotide variants likely to originate from sequencing error. In the second step, we used tag sequence clustering to group such variants to combine tags likely to originate from a common transcript. Thus, some singletons were clustered and counted as a more abundant tag. The third step was to filter out low quality tags and compare each P value to a meta-library P value calculated from all SAGE libraries. Tag-to-gene-mapping was then carried out using DiscoverySpace 4.0 application 40. All cluster-modified tags were then mapped to transcripts in the NCBI Reference Sequence Collection 41. The remaining unmapped tags were mapped to transcripts in the Mammalian Gene Collection 42, followed by the Ensembl database 43. Only sense transcripts and unique mappings were considered, and tags that mapped to more than one transcript in any of the three transcript databases were discarded. The three mapping results were subsequently merged based on gene symbol.

For each gene, a P value was assigned to each target (TL; brain region of interest) and off-target (OTL; background region) library pair using the P value option in DiscoverySpace. The P value was computed based on Audic-Claverie algorithms 44 to assess confidence level of differential expression between two transcript libraries. A ranking system was implemented to facilitate selection of candidate genes with specific or enriched expression in each target library (Table 2). Region-specific transcripts were obtained by selecting transcripts detected with 5 tags or more only in one target library. To identify region-enriched transcripts, those detected in one target library and one off-target library (P_TL-OTL value < = 0.05) were selected. Transcripts detected in multiple libraries were ranked based on pre-defined P value limits of differential expression (P_TL-TL, P_TL-OTL), as well as additional criteria such as target and off-target library counts. Transcripts whose expression patterns did not fit these criteria were not ranked.

To analyze transcriptome similarity of tissues, a dendrogram was generated using MATLAB 7 (The MathWorks, Natick, MA) based on hierarchical clustering using the Unweighted Pair Group Method with Arithmetic Mean (UPGMA). The input data is a list of objects (tissue SAGE libraries) with their pair-wise distances (expression divergence ED; see below), and the output is a dendrogram. Initially, each object is in its own cluster; then, at each step of the hierarchical clustering the nearest two clusters are combined into a higher-level cluster. The distance between any two clusters A and B is taken to be the average of all distances between pairs of objects in A and B. Thus, we defined pair-wise distance or expression divergence (ED) between any two tissues as the fraction of differentially expressed genes in their corresponding SAGE libraries, using the formula:

ED_(p) = N_diff(p)/N

(N_diff(p) = number of differentially expressed genes for a given P value, N = number of shared genes between two corresponding libraries).

All synonyms for 28,000 mouse genes were obtained from Entrez (RefSeq release 14) combined with Ensembl (build 34) of the mouse genome. Synonyms for the human orthologs were obtained using Compara (Ensembl build 34) to identify similarities between human and mouse together with Homologene (version 47) for homolog detection. In each case, Ensembl and Entrez were used as cross-references for gene identifiers. From these search strings, all names found in the English dictionary were subtracted to remove obfuscating gene terms such as "Ice". Abstracts were parsed from Medline (extraction performed September 7, 2006) and the complete text of articles were parsed from PubMed Central 45, and converted into individual sentences using the medical sentence parser 46. Each sentence was searched for the co-occurrence of gene names with brain regions of interest. For each brain region, expanded search terms were applied referring to finer structures appropriate to the region as defined by the ontology available from the Allen Brain Atlas website 13. The number of sentences with gene names and brain regions obtained is greater than the number of sentences with only brain regions because of the plural nature of both search terms. We scrutinized retrieved publications for details indicating regionally enriched/specific expression in a brain region.

Gene Ontology 19 over-representation analysis was performed for the 237 genes using the BiNGO 47 plug-in for the Cytoscape 48 software package. Significance of over-representation of GO terms was calculated using the hypergeometric test, corrected for multiple testing with a Benjamini & Hochberg false discovery rate correction 49, and a cut-off of 0.05 was applied to the result. The test selection of 237 genes was compared to all GO annotated genes in the mouse genome (18535 genes, as of March 18, 2008).