Transcriptome analysis in primary neural stem cells using a tag cDNA amplification method
© Sievertzon et al; licensee BioMed Central Ltd. 2005
Received: 12 January 2005
Accepted: 15 April 2005
Published: 15 April 2005
Neural stem cells (NSCs) can be isolated from the adult mammalian brain and expanded in culture, in the form of cellular aggregates called neurospheres. Neurospheres provide an in vitro model for studying NSC behaviour and give information on the factors and mechanisms that govern their proliferation and differentiation. They are also a promising source for cell replacement therapies of the central nervous system. Neurospheres are complex structures consisting of several cell types of varying degrees of differentiation. One way of characterising neurospheres is to analyse their gene expression profiles. The value of such studies is however uncertain since they are heterogeneous structures and different populations of neurospheres may vary significantly in their gene expression.
To address this issue, we have used cDNA microarrays and a recently reported tag cDNA amplification method to analyse the gene expression profiles of neurospheres originating from separate isolations of the lateral ventricle wall of adult mice and passaged to varying degrees. Separate isolations as well as consecutive passages yield a high variability in gene expression while parallel cultures yield the lowest variability.
We demonstrate a low technical amplification variability using the employed amplification strategy and conclude that neurospheres from the same isolation and passage are sufficiently similar to be used for comparative gene expression analysis.
The most frequently used method to analyse scarce RNA samples is to employ RNA amplification technology [1, 2], enabling analysis of the full length transcripts. We have recently reported on an alternative transcriptome amplification method that minimises differences in transcript length in the amplification step [3, 4]. This method is based on fragmentation of the mRNA (cDNA) population followed by isolation of a unique, short and representative 3'end tag of each transcript prior to amplification by PCR. Here we have evaluated and applied the methodology on neural stem cells (NSCs).
NSCs can be isolated from the fetal or adult mammalian brain and grown in vitro in the presence of growth factors to form floating aggregates of cells denoted neurospheres [5–7]. A neurosphere is derived from one clonally expanded NSC or progenitor cell . As the original NSC or progenitor cell proliferates the new cells adhere to each other, eventually forming a neurosphere. Every neural stem cell in a neurosphere has the potential to differentiate towards a neuronal or a glial lineage depending on the internal neurosphere milieu and external signals. Neurospheres are thus complex structures consisting of many cell types that can have varying degrees of differentiation commitment, but that are all derived from the same clonally expanded cell. Neurospheres have extensive cell-cell contacts and a dense extracellular matrix. When plated onto solid support in combination with growth factor withdrawal the cells start to differentiate into all neural cell types (neurons, astrocytes and oligodendrocytes). In vitro expanded neural stem cells may therefore serve as an in vitro model of neurogenesis. The similarities between the in vivo and in vitro processes of neurogenesis are not well established although some characteristics are expected to be conserved  and therefore challenging a cell in vitro will unveil some of its developmental properties and potentials. By subjecting neurospheres to different microenvironments (e.g. through the addition or withdrawal of drugs or factors) it is possible to uncover factors and mechanisms important for proliferation or differentiation into certain cell lineages, for example neurons of a particular type [11, 12]. Furthermore, NSCs expanded as neurospheres also hold the promise of becoming an important source of cells for cell replacement therapies of different neurological diseases [13, 14].
Due to the great scientific interest in NSCs and the promise of their clinical use we decided to investigate NSCs from a gene expression perspective. An important aspect was to investigate if neurosphere heterogeneity  is reflected in their transcriptome. Neurosphere populations from different levels of technical and biological replication were analysed by taking advantage of microarrays with 5159 spotted mouse cDNA clones, in combination with a highly sensitive amplification method. We compared neurospheres cultured under identical conditions but in separate culture flasks, as well as from different passages and from parallel isolations. The results are discussed from the perspective of differences in the number and extent of differentially expressed genes.
Differential expression was determined in a series of microarray experiments, as outlined in Figure 1B. Neurosphere cultures were initiated from cells dissociated from three pools of adult lateral ventricle wall tissue dissected from either 3 or 10 mice, as three identical but separate isolations. Primary neurospheres were passaged one or two times and harvested three to four days after passage. Average neurosphere size was deemed a more critical factor than the length of the incubation time, hence some neurosphere cultures were incubated for one day longer than others to obtain uniformity in neurosphere size between cultures at the key times of passaging and harvesting. When passaged twice the neurospheres were split into two or three equivalent cultures. This allowed us to measure the variability in gene expression levels between different isolations, as well as between passages and between parallel cultures. In order to estimate the technical noise, self-to-self hybridisations were performed using RNA from one of the cultures. To confirm that we were able to detect differential gene expression cells in one of the parallel cultures were induced to differentiate into neurons, astrocytes and oligodendrocytes by withdrawing the growth factors from the culture medium, plating on solid support and adding serum (in this work referred to as differentiated cells). The nomenclature of the samples is given in Figure 1B. Seven different comparisons were made; A1-A2 (technical replicate), CI-CII and CII-CIII (culture replicates), B-CI (different passages), A2-CIII and CII-FI (different isolations) and F-G (neurospheres vs. differentiated cells). The use of short-term passaged neurospheres limits the number of cells that can be generated. Consequently the amount of RNA that can be isolated is below that normally used in labelling reactions for microarray hybridisations (approximately 10 μg total RNA without amplification). After mRNA isolation and cDNA synthesis we therefore chose to amplify the obtained material using the method described above. Two replicate and two dye-swap hybridisations were performed for each comparison, adding up to four hybridisations for each comparison in total.
Distribution of differentially expressed genes over fold change.
Total p < 0.001 (fdr)
Genes differentially expressed in the neurosphere vs. differentiated cells comparison (F-G).
GTL2, imprinted maternally expressed untranslated mRNA
Guanine nucleotide binding protein, alpha o
GTL2, imprinted maternally expressed untranslated mRNA
Myelin-associated oligodendrocytic basic protein
Sorbin and SH3 domain containing 1
RIKEN cDNA B230104P22 gene
Distal-less homeobox 1
ADP-ribosylation factor-like 10C
Myelin basic protein
Secreted acidic cysteine rich glycoprotein
Tissue inhibitor of metalloproteinase 2
Serine carboxypeptidase 1
Similar to Myl9 protein
GTL2, imprinted maternally expressed untranslated mRNA
Chromodomain helicase DNA binding protein 3
RIKEN cDNA 1110020A09 gene
Kelch-like 9 (Drosophila)
Myelin-associated oligodendrocytic basic protein
Secreted acidic cysteine rich glycoprotein
Regulator of G-protein signaling 14
Myelin basic protein
Kv channel-interacting protein 2
Heat shock 27 kDa protein 8
gamma-aminobutyric acid (GABA-A) receptor, subunit beta 1
RIKEN cDNA 2700055K07 gene
Calcium/calmodulin-dependent protein kinase II, beta
Sortilin-related receptor, LDLR class A repeats-containing
Early growth response 1
RIKEN cDNA 3732412D22 gene
Early growth response 1
Dishevelled associated activator of morphogenesis 2
Spectrin beta 3
The most highly represented gene ontology themes in the neurosphere vs. differentiated cells comparison (F-G).
no of genes on array
no of DE genes in F-G
enriched in NS
enriched in DC
mitotic cell cycle
neuromuscular physiol. process
transmission of nerve impulse
organismal physiological process
This study has taken advantage of a recent template amplification method to study neurospheres at the level of transcription. RNA from different isolations, cultures and passages was isolated, amplified and analysed by microarrays. The comparison was performed by analysis of the number of differentially expressed genes for the different conditions. The results show excellent performance of the amplification protocol. No differentially expressed genes were found in the technical replicates indicating that methodological noise in all comparisons should be considered minor.
Fluctuations of transcript levels in different populations of neurospheres
The array results for the different neurosphere conditions were much more divergent than the technical replications and we observe a varying degree of heterogeneity among the different neurosphere populations, obtained from different isolations of adult mouse lateral ventricle wall tissue, from different passages and from parallel cultures. The results show that there is a large variation in gene expression between neurospheres from different isolations as well as between neurospheres from the same isolation but from different passages. Neurospheres have previously been shown to gain altered properties through extensive, long-term passaging (more than 10 passages) . Short-term passaged neurospheres have been considered rather stable, with unaltered multipotency and capacity for self-renewal . Here we have shown that already between passages one and two neurospheres show altered gene expression with up to 383 DE genes (p < 0.001). Whether this is due to different properties of the parental, clonally expanded cell(s) giving rise to the neurospheres in each passage or some other reason needs to be further investigated.
Parallel culturing of neurospheres from the same isolation and the same number of passages, grown in identical conditions, show fewer DE genes (up to 82 genes, p < 0.001) than neurospheres compared between passages (383 genes, p < 0.001). Furthermore, when neurospheres are induced to differentiate and compared to undifferentiated neurospheres cultured in parallel, the number of genes DE as well as the magnitude of the M-values are clearly higher (748 genes, p < 0.001). These data indicate that an extended 3–4 day culturing, per se, is sufficient to induce changes in gene expression, but with careful experimental design and an appropriate number of biological replicates neurospheres cultured in parallel, from the same isolation and passage, may be used to study for example the effect of exposure to different microenvironments on gene expression.
The gene expression heterogeneity of neurospheres may be related to a number of different factors such as the age of the animal from which they were isolated, neurosphere size and the identity of the first clonally expanded cell . Our results are also confirmed by observations by Suslov and co-workers that examined the expression of 16 transcripts from single neurospheres of different sizes . The obtained information was used to cluster the individual neurospheres according to similar gene expression pattern. It revealed an inter-clonal heterogeneity that might reflect the maturity and developmental commitment of the parental clonogenic cell, as well as the size of the neurosphere and its time in culture. In another study it was shown that populations of neurospheres from different regions of the brain as well as from different species differ in properties such as growth rate, neuronal production and cell morphology .
Genes expressed in neurospheres
The different neurosphere populations show heterogeneity in their expression profiles, yet many of the genes expressed are representative of a neurosphere transcript signature. As described earlier, neurospheres consist of several cell types of varying degrees of differentiation, a dense extracellular matrix and extensive cell-cell contacts. Electron-microscopy studies of rat fetal striatum EGF-expanded neurospheres , have shown that they consist of two types of cells, electron-dense and electron-lucent cells, both of which could be either healthy, apoptotic or necrotic. These neurosphere cells also demonstrated an expression of the cell adhesion molecules E- and N-cadherin (Cdh1 and Cdh2), α- and β-catenin (Catna1, LOC297357 and RGD:70487) and growth factor receptors for epidermal growth factor (Egfr) and fibroblast growth factor (Fgfr1), as well as fibroblast growth factor 2 (Fgf2). Also neurospheres from adult human brain have been characterised, revealing the same type of heterogeneous, complex structure [28, 7]. These express a variety of different markers, such as nestin (NES; neural stem/progenitor and immature glial marker), vimentin (VIM; immature glia), glial fibrillary acidic protein (GFAP; astrocytes), β-III-tubulin (TUBB3; neuronal marker) and cell adhesion molecule L1 (L1CAM; neuronal marker), proteolipid protein 1 (PLP1; oligodendrocytes), B-cell CLL/lymphoma 2 (BCL2; anti-apoptotic), paired box gene 6 (PAX6; a developmentally regulated gene) and tenascin C (TNC; extracellular matrix protein). In our study the genes related to these phenotypes and markers are expressed at similar levels in all neurosphere replicates (CI-CII, CII-CIII, B-CI, A2-CIII and CII-F). For example we observe many genes involved in apoptosis; Bcl2-associated X protein (Bax), Bcl2-associated athanogene 1 (Bag1), cytochrome c-1 (Cyc1), death associated protein 3 (Dap3), programmed cell death 6 interacting protein (Pdcd6ip) and phosphoprotein enriched in astrocytes 15 (Pea15). Expressed are also α-E-catenin (Catna1), β-catenin (Catnb) and fibroblast growth factor 3 (Fgfr3), and other neurosphere markers such as nestin (Nes), glial fibrillary acidic protein (Gfap), β-III-tubulin (Tubb3) and proteolipid protein 1 (Plp1) (The complete data set is available in ArrayExpress using experiment accession number E-MEXP-297).
The list of DE genes in the neurosphere vs. differentiated cells comparison (Table 2 and additional data file 1: Differentially expressed genes in neurosphere vs. differentiated cells comparison) as well as an overview of the corresponding gene ontology classification (Table 3) also demonstrates the anticipated differences between neurospheres and differentiated neurospheres.
The genes observed to be differentially expressed in identical but parallel cultures appear to be random, shown by the low overlap in DE genes between the two parallel culture comparisons (Figure 5). The number of erroneously identified DE genes, due to biological fluctuations, could hence be lowered by increasing the number of biological replicates. Hereby random differences will be removed and true DE genes can be selected by statistical means. It should be noted that the random differences mainly correspond to small fold changes as compared to the larger changes in the neurospheres vs. differentiated cells. Reliable differences in gene expression could therefore be obtained and studied without increasing the number of replicates if a higher cut-off for DE genes, such as fold change > 2, was chosen.
We have shown that the tag cDNA amplification method is well suited for the analysis of neurospheres, demonstrating low technical variability. Furthermore we have demonstrated large differences between passages of neurospheres, but less variability between parallel cultures. The described variability appears to be random and the underlying cause(s) needs further investigations. The neurosphere variability can be addressed by increasing the number of biological replicates and careful experimental design, which will facilitate future use of neurospheres as a tool to study gene expression changes involved in neurogenesis.
Adult mouse neural stem cell culture
Three adult mouse neural stem cell cultures were initiated, the first originating from tissue isolated from ten mice (Culture 1) while the second (Culture 2) and third (Culture 3) cultures originated from three mice each. For each culture, identical dissection, dissociation and culture protocols were used. Briefly, the lateral wall of the lateral ventricle of 5–6-week-old mice was enzymatically dissociated in 0.8 mg/ml hyaluronidase and 0.5 mg/ml trypsin in Dulbecco's modified Eagle medium (DMEM) containing 4.5 mg/ml glucose and 80 U/ml DNase at 37°C for 20 min. The cells were gently triturated and mixed with three volumes of neurosphere medium (DMEM/F12, B27 supplement, 12.5 mM HEPES pH7.4) containing 20 ng/ml EGF, 100 U/ml penicillin and 100 μg/ml streptomycin. After passing through a 70-μm strainer, the cells were pelleted at 160 × g for 5 min. The supernatant was subsequently removed and the cells resuspended in neurosphere medium supplemented as above, plated in uncoated culture dishes and incubated at 37°C. Neurospheres were ready to be split 7–8 days after plating.
To split neurosphere cultures, neurospheres were collected by centrifugation at 160 × g for 5 min. The neurospheres were resuspended in 0.5 ml Trypsin/EDTA in HBSS (1x), incubated at 37°C for 2 min and triturated gently to aid dissociation. Following a further three-min incubation at 37°C and trituration, 3 volumes of ice-cold neurosphere medium containing EGF were added. The cells were pelleted at 220 × g for 4 min, resuspended in fresh neurosphere medium supplemented with 20 ng/ml EGF.
From Cultures 1, 2 & 3, dissociated cells were plated and grown in neurosphere medium supplemented with EGF for a further 3–4 days by which time secondary neurospheres had developed. The secondary neuropheres originating from Culture 1 were harvested for mRNA isolation (Sample A). Approximately a quarter of the secondary neurospheres originating from Culture 2 were also taken for mRNA isolation (Sample B), while the remainder were dissociated and replated in three equal fractions (100,000 cells / well (6 well plate)), cultured in neurosphere medium supplemented with EGF for 3 days, and harvested for mRNA isolation (Samples CI, CII, CIII). Secondary neurospheres originating from Culture 3 were dissociated and divided into two fractions. The first fraction was replated (100,000 cells / well (6 well plate)) and cultured identically to the cells generating Samples CI, CII & CIII. After 3 days, the cells were harvested for mRNA isolation (Sample F). The second fraction was replated in neurosphere medium supplemented with 1% fetal calf serum (FCS) onto poly-D-lysine plates to which the cells adhered. After incubating, overnight FCS concentration was reduced to 0.5%, and the cells cultured a further 2 days before centrifugation and subsequent mRNA isolation (Sample G, differentiated cells). All experiments were approved by the Karolinska Institute Ethical Committee.
Messenger RNA was isolated using Dynabeads® mRNA DIRECT™ Kit from Dynal (Dynal A.S., Norway), according to the manufacturer's instructions. First- and RNaseH dependent second-strand cDNA synthesis (SuperScript Choice System for cDNA Synthesis) was performed according to the manufacturer's instructions (Invitrogen, CA, USA) using 45 pmol biotinylated NotI-oligo(dT) primer (5'-biotin-GAGGTGCCAACCGCGGCCGC (T)15-3'). The cDNA was phenol-chloroform extracted and ethanol precipitated and the pellet was dissolved in 40 μl of 1 × TE (10 mM Tris-HCl, 1 mM EDTA). Excess NotI-oligo(dT) primer was removed by Chromaspinn TE-100 column (Clontech, CA, USA).
Amplification of 3'-end signature tags
The cDNA was fragmented and amplified according to a protocol previously described [3, 4]. Shortly, fragmentation of the cDNA was performed in 40 μl 1 × TE using an inverted sonication probe, using 16 × 10 sec pulses at 90% effect (Sonifier® B-12, Branson Sonic Power Company, CT, USA). Biotinylated 3'-end signature tags from the fragmented cDNA population were isolated onto 20 μl of paramagnetic streptavidin-coated beads (10 mg/ml) (Dynal A.S.) in 40 μl sample plus 40 μl Binding/Washing buffer (2 M NaCl, 0.1% Tween 20 in 1 × TE, pH 7.7) at 37°C for one hour with rotation. The immobilised signature tags were end repaired using 1.5 U T4 DNA polymerase (New England BioLabs, MA, USA) in a 30-μl reaction volume at 12°C for 20 minutes according to the supplier's recommendations. Blunt-end adapters (Sima18: 5'-GGATCCGCGGTG-3'; Sima19: 5'-TCTCCAGCCTCTCACCGCGGATCC-3') were pre-annealed and ligated onto the immobilised repaired 3'-end signature tags using a solution comprising 1.1 nmol adapter, ligase buffer (66 mM Tris-HCl, pH 7.6, 5 mM MgCl2, 5 mM DTT, 50 μg/ml BSA), 0.2 mM ATP, 1200 U T4 DNA ligase (New England BioLabs) in a final volume of 60 μl. Ligation was performed overnight at room temperature with constant rotation to keep beads in suspension. The signature tags were released from the magnetic beads by restriction with NotI (New England BioLabs) for 2 hours in a volume of 60 μl while keeping the beads in suspension. Five micro litres of the eluate containing the 3'-end signature tags was used as template in a subsequent PCR. The PCR was performed in 100 μl containing 200 μM of each dNTP, 0.75 μM Sima19, 0.75 μM NotI-oligo(dT) primer, 65 mM Tris-HCl pH 8.8, 4 mM MgCl2, 16 mM (NH4)2SO4, 0.5 μM BSA and 3 U AmpliTaq DNA polymerase (Perkin Elmer, MA, USA). Cycling was performed according to the following procedure, initial incubation at 72°C for 3 min, followed by addition of Taq DNA polymerase and subsequent cycling: 72°C for 20 min, 95°C for 1 min, 45°C for 5 min, 72°C for 15 min, followed by four cycles (95°C for 1 min, 50°C for 1 min, 72°C for 15 min), and 13 cycles (as previously optimised) (95°C for 1 min, 50°C for 1 min, 72°C for 2 min).
Target labelling and microarray hybridisation
The 3'-end signature tags were purified using QIAquick® PCR purification kit (Qiagen, Germany). Direct labelling was performed using Cy3-dCTP or Cy5-dCTP (Perkin Elmer, MA, USA) in a linear, asymmetric PCR. The reaction was performed in a 50-μl labelling mix containing 100–200 ng purified 3'-end signature tags, 80 μM dATP, dGTP and dTTP, 20 μM dCTP, 5 μM Sima19 primer, 2 mM MgCl2, 1 × PCR Buffer II (Applied Biosystems, Ca, USA), 3 U AmpliTaq Gold® (Applied Biosystems) and 60 pM Cy3-dCTP or Cy5-dCTP. The labelling mix was cycled as follows: 95°C for 12 min, then 20 cycles (95°C for 30 sec, 50°C for 30 sec, 72°C for 10 min). Excess primer and nucleotides were removed using QIAquick® PCR purification kit (Qiagen). The eluted labelling products were speed vacuumed until dry, then dissolved in 55 μl hybridisation buffer (24% formamide, 5 × SSC and 0.1% SDS) (20 × SSC contains 3 M NaCl and 0.3 M Na3citrate × 2H2O). Cy3 and Cy5 labellings were blended and mixed with 25 μg human Cot-1 DNA (Invitrogen) and 50 μg polyA DNA (Operon Biotechnologies GmbH, Germany). The arrays (ArrayExpress accession number E-MEXP-297, submission in progress)  contained 5169 probes originating from a lateral ventricle wall cDNA library (clone library "Mus Musculus Lateral Ventricle Wall C57BL/6 adult") and a set of control features all printed in duplicate. Details regaring the array manufacturing are available through ArrayExpress. Briefly, probes were generated through PCR amplification and subsequently purified using Multiscreen-384 filter plates (Millipore). Purified products in 50% DMSO were printed onto GAPS-II slides (Corning Inc) using the QArray arrayer (Genetix) and attached using 250 mJ UV-light (Stratalinker). The arrays were first prehybridised for 30 min in a 42°C prehybridisation solution (1% BSA, 5 × SSC, 0.1% SDS), then washed in water and isopropanol and dried through centrifugation. The sample was denatured in 95°C for 3 min, then applied to the array and incubated in a hybridisation chamber in 42°C for 18 hours. After hybridisation the arrays were washed in three successive wash buffers with increasing stringency: (1) 1 × SSC and 0.2% SDS, 42°C, (2) 0.1 × SSC and 0.2% SDS, room temperature, (3) 0.1 × SSC, room temperature. All wash steps were made on a shaking table for 4 min. After the last step the array was immediately centrifuged in a slide centrifuge and kept in the dark until scanning. Scanning was performed using the GMS 418 Array Scanner from Genetic MicroSystems (Affymetrix Inc, CA, USA).
Image and data analysis
All image and data analysis steps were conducted in GenePix Pro 188.8.131.52 (Axon Instruments Inc, CA, USA) or R . The analysis in R was carried out using Bioconductor , LIMMA , aroma  and the kth-package . The analysis was conducted according to the following workflow. (1) Image tiff-files were created by scanning the microarrays with the GMS 418 Array Scanner. (2) Feature identity and foreground/background intensities were extracted from the tiff files using GenePix Pro 184.108.40.206. (3) GenePix result files were imported into R and gene expression measurements were obtained for each feature by subtracting the median of the local background from the median of the foreground signal. (4) A filter was used to identify and correct for features that had one channel (Cy3 or Cy5) below the background or at zero and one channel stronger than the background. The signal in the weaker channel was for these spots set to one plus the intensity of the local background. Features with both channels below the background or at zero were removed from the data set. (5) A second filter was used to remove features that were saturated in both channels. (6) A third filter was used to remove features with abnormal size (below 110 and above 230 μm in diameter). (7) A fourth filter was used to remove features where both signals had more than 70% of the pixels in the feature below the local background signal plus two standard deviations. (8) The last filter was used to remove features that were flagged as not found by GenePix. (9) Filtered data was normalised separately for each individual block on the slide using a robust local regression, print-tip lowess normalisation . (10) An empirical Bayes moderated t-test [15–17] was used to rank the genes according to evidence of differential expression. The obtained p-values were adjusted for multiple testing using the false discovery rate adjustment  implemented in R. A p-value of less than 0.001 was considered significant and the associated gene termed differentially expressed (DE). The experimental design included reciprocal dye label assignments. These were swapped prior to the moderated t-test so that in each comparison the genes in the sample with an abbreviation that comes earlier in alphabetical order (e.g. B in B vs. CI) have positive M-values if they have a higher expression level.
List of abbreviations
neural stem cell
false discovery rate
We thank Anna Westring, Peter Nilsson and Cecilia Williams for valuable assistance and comments. This work was supported by grants from the Knut and Alice Wallenberg Foundation, the Wallenberg Consortium North, the Swedish Cancer Foundation and the Swedish Scientific Research Council.
- Van Gelder RN, von Zastrow ME, Yool A, Dement WC, Barchas JD, Eberwine JH: Amplified RNA synthesized from limited quantities of heterogeneous cDNA. Proc Natl Acad Sci U S A. 1990, 87: 1663-1667.PubMed CentralView ArticlePubMedGoogle Scholar
- Eberwine J, Yeh H, Miyashiro K, Cao Y, Nair S, Finnell R, Zettel M, Coleman P: Analysis of gene expression in single live neurons. Proc Natl Acad Sci U S A. 1992, 89: 3010-3014.PubMed CentralView ArticlePubMedGoogle Scholar
- Hertzberg M, Sievertzon M, Aspeborg H, Nilsson P, Sandberg G, Lundeberg J: cDNA microarray analysis of small plant tissue samples using a cDNA tag target amplification protocol. Plant J. 2001, 25: 585-591. 10.1046/j.1365-313x.2001.00972.x.View ArticlePubMedGoogle Scholar
- Sievertzon M, Agaton C, Nilsson P, Lundeberg J: Amplification of mRNA populations by a cDNA tag strategy. Biotechniques. 2004, 36: 253-259.PubMedGoogle Scholar
- Reynolds BA, Weiss S: Generation of neurons and astrocytes from isolated cells of the adult mammalian central nervous system. Science. 1992, 255: 1707-1710.View ArticlePubMedGoogle Scholar
- Gottlieb DI: Large-scale sources of neural stem cells. Annu Rev Neurosci. 2002, 25: 381-407. 10.1146/annurev.neuro.25.112701.142904.View ArticlePubMedGoogle Scholar
- Johansson CB, Svensson M, Wallstedt L, Janson AM, Frisen J: Neural stem cells in the adult human brain. Exp Cell Res. 1999, 253: 733-736. 10.1006/excr.1999.4678.View ArticlePubMedGoogle Scholar
- Suslov ON, Kukekov VG, Ignatova TN, Steindler DA: Neural stem cell heterogeneity demonstrated by molecular phenotyping of clonal neurospheres. Proc Natl Acad Sci U S A. 2002, 99: 14506-14511. 10.1073/pnas.212525299.PubMed CentralView ArticlePubMedGoogle Scholar
- Johansson CB, Momma S, Clarke DL, Risling M, Lendahl U, Frisen J: Identification of a neural stem cell in the adult mammalian central nervous system. Cell. 1999, 96: 25-34. 10.1016/S0092-8674(00)80956-3.View ArticlePubMedGoogle Scholar
- Qian X, Shen Q, Goderie SK, He W, Capela A, Davis AA, Temple S: Timing of CNS cell generation: a programmed sequence of neuron and glial cell production from isolated murine cortical stem cells. Neuron. 2000, 28: 69-80. 10.1016/S0896-6273(00)00086-6.View ArticlePubMedGoogle Scholar
- Galli R, Pagano SF, Gritti A, Vescovi AL: Regulation of neuronal differentiation in human CNS stem cell progeny by leukemia inhibitory factor. Dev Neurosci. 2000, 22: 86-95. 10.1159/000017430.View ArticlePubMedGoogle Scholar
- Caldwell MA, He X, Wilkie N, Pollack S, Marshall G, Wafford KA, Svendsen CN: Growth factors regulate the survival and fate of cells derived from human neurospheres. Nat Biotechnol. 2001, 19: 475-479. 10.1038/88158.View ArticlePubMedGoogle Scholar
- Lindvall O, Kokaia Z, Martinez-Serrano A: Stem cell therapy for human neurodegenerative disorders-how to make it work. Nat Med. 2004, 10 Suppl: S42-50. 10.1038/nm1064.View ArticlePubMedGoogle Scholar
- Uchida N, Buck DW, He D, Reitsma MJ, Masek M, Phan TV, Tsukamoto AS, Gage FH, Weissman IL: Direct isolation of human central nervous system stem cells. Proc Natl Acad Sci U S A. 2000, 97: 14720-14725. 10.1073/pnas.97.26.14720.PubMed CentralView ArticlePubMedGoogle Scholar
- Lönnstedt I, Speed T: Replicated microarray data. Statistica Sinica. 2002, 12: 31-46.Google Scholar
- Smyth G: Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Statistical Applications in Genetics and Molecular Biology. 2004, 3: Article 3.View ArticleGoogle Scholar
- Smyth GK, Michaud J, Scott H: The use of within-array duplicate spots for assessing differential expression in microarray experiments. Submitted. 2003, [http://www.statsci.org/smyth/pubs/dupcor.pdf]Google Scholar
- Jiang M, Spicher K, Boulay G, Wang Y, Birnbaumer L: Most central nervous system D2 dopamine receptors are coupled to their effectors by Go. Proc Natl Acad Sci U S A. 2001, 98: 3577-3582. 10.1073/pnas.051632598.PubMed CentralView ArticlePubMedGoogle Scholar
- Andrews GL, Yun K, Rubenstein JL, Mastick GS: Dlx transcription factors regulate differentiation of dopaminergic neurons of the ventral thalamus. Mol Cell Neurosci. 2003, 23: 107-120. 10.1016/S1044-7431(03)00016-2.View ArticlePubMedGoogle Scholar
- Saino-Saito S, Berlin R, Baker H: Dlx-1 and Dlx-2 expression in the adult mouse brain: relationship to dopaminergic phenotypic regulation. J Comp Neurol. 2003, 461: 18-30. 10.1002/cne.10611.View ArticlePubMedGoogle Scholar
- Yun K, Fischman S, Johnson J, Hrabe de Angelis M, Weinmaster G, Rubenstein JL: Modulation of the notch signaling by Mash1 and Dlx1/2 regulates sequential specification and differentiation of progenitor cell types in the subcortical telencephalon. Development. 2002, 129: 5029-5040.PubMedGoogle Scholar
- Hosack DA, Dennis GJ, Sherman BT, Lane HC, Lempicki RA: Identifying biological themes within lists of genes with EASE. Genome Biol. 2003, 4: R70-10.1186/gb-2003-4-10-r70.PubMed CentralView ArticlePubMedGoogle Scholar
- Morshead CM, Benveniste P, Iscove NN, van der Kooy D: Hematopoietic competence is a rare property of neural stem cells that may depend on genetic and epigenetic alterations. Nat Med. 2002, 8: 268-273. 10.1038/nm0302-268.View ArticlePubMedGoogle Scholar
- Reynolds BA, Weiss S: Clonal and population analyses demonstrate that an EGF-responsive mammalian embryonic CNS precursor is a stem cell. Dev Biol. 1996, 175: 1-13. 10.1006/dbio.1996.0090.View ArticlePubMedGoogle Scholar
- Bez A, Corsini E, Curti D, Biggiogera M, Colombo A, Nicosia RF, Pagano SF, Parati EA: Neurosphere and neurosphere-forming cells: morphological and ultrastructural characterization. Brain Res. 2003, 993: 18-29. 10.1016/j.brainres.2003.08.061.View ArticlePubMedGoogle Scholar
- Ostenfeld T, Joly E, Tai YT, Peters A, Caldwell M, Jauniaux E, Svendsen CN: Regional specification of rodent and human neurospheres. Brain Res Dev Brain Res. 2002, 134: 43-55. 10.1016/S0165-3806(01)00291-7.View ArticlePubMedGoogle Scholar
- Lobo MV, Alonso FJ, Redondo C, Lopez-Toledano MA, Caso E, Herranz AS, Paino CL, Reimers D, Bazan E: Cellular characterization of epidermal growth factor-expanded free-floating neurospheres. J Histochem Cytochem. 2003, 51: 89-103.View ArticlePubMedGoogle Scholar
- Kukekov VG, Laywell ED, Suslov O, Davies K, Scheffler B, Thomas LB, O'Brien TF, Kusakabe M, Steindler DA: Multipotent stem/progenitor cells with similar properties arise from two neurogenic regions of adult human brain. Exp Neurol. 1999, 156: 333-344. 10.1006/exnr.1999.7028.View ArticlePubMedGoogle Scholar
- ArrayExpress—a public repository for microarray gene expression data at the EBI. [http://www.ebi.ac.uk/arrayexpress]
- R Development Core Team: R: A language and environment for statistical computing. [http://www.R-project.org]
- Gentleman R, Carey V, Bates D, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J, Hornik K, Hothorn T, Huber W, Iacus S, Irizarry R, Leisch F, Li C, Maechler M, Rossini A, Sawitzki G, Smith C, Smyth G, Tierney L, Yang J, Zhang J: Bioconductor: open software development for computational biology and bioinformatics. Genome Biology. 2004, 5: R80-10.1186/gb-2004-5-10-r80.PubMed CentralView ArticlePubMedGoogle Scholar
- Bengtsson H: aroma - An R Object-oriented Microarray Analysis environment. [http://www.maths.lth.se/publications]
- Wirta V, Lindberg J, Gry Björklund M: The kth-package for microarray data analysis. 2004, [http://www.biotech.kth.se/molbio/microarray]Google Scholar
- Yang YH, Dudoit S, Luu P, Lin DM, Peng V, Ngai J, Speed TP: Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. Nucleic Acids Res. 2002, 30: e15-10.1093/nar/30.4.e15.PubMed CentralView ArticlePubMedGoogle Scholar
- Benjamini Y, Hochberg Y: Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series B. 1995, 57: 289-300.Google Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.