Retrieve all datasets
get_datasets(
query = NA_character_,
filter = NA_character_,
taxa = NA_character_,
uris = NA_character_,
offset = 0L,
limit = 20L,
sort = "+id",
raw = getOption("gemma.raw", FALSE),
memoised = getOption("gemma.memoised", FALSE),
file = getOption("gemma.file", NA_character_),
overwrite = getOption("gemma.overwrite", FALSE)
)The search query. Queries can include plain text or ontology terms They also support conjunctions ("alpha AND beta"), disjunctions ("alpha OR beta") grouping ("(alpha OR beta) AND gamma"), prefixing ("alpha*"), wildcard characters ("BRCA?") and fuzzy matches ("alpha~").
Filter results by matching expression. Use filter_properties
function to get a list of all available parameters. These properties can be
combined using "and" "or" clauses and may contain common operators such as "=", "<" or "in".
(e.g. "taxon.commonName = human", "taxon.commonName in (human,mouse), "id < 1000")
A vector of taxon common names (e.g. human, mouse, rat). Providing multiple
species will return results for all species. These are appended
to the filter and equivalent to filtering for taxon.commonName property
A vector of ontology term URIs. Providing multiple terms will
return results containing any of the terms and their children. These are
appended to the filter and equivalent to filtering for allCharacteristics.valueUri
The offset of the first retrieved result.
Defaults to 20. Limits the result to specified amount
of objects. Has a maximum value of 100. Use together with offset and
the totalElements attribute in the output to
compile all data if needed.
Order results by the given property and direction. The '+' sign indicate ascending order whereas the '-' indicate descending.
TRUE to receive results as-is from Gemma, or FALSE to enable
parsing. Raw results usually contain additional fields and flags that are
omitted in the parsed results.
Whether or not to save to cache for future calls with the
same inputs and use the result saved in cache if a result is already saved.
Doing options(gemma.memoised = TRUE) will ensure that the cache is always
used. Use forget_gemma_memoised to clear the cache.
The name of a file to save the results to, or NULL to not write
results to a file. If raw == TRUE, the output will be the raw endpoint from the
API, likely a JSON or a gzip file. Otherwise, it will be a RDS file.
Whether or not to overwrite if a file exists at the specified filename.
A data table with information about the queried dataset(s). A list if
raw = TRUE. Returns an empty list if no datasets matched.
The fields of the output data.table are:
experiment.shortName: Shortname given to the dataset within Gemma. Often corresponds to accession ID
experiment.name: Full title of the dataset
experiment.ID: Internal ID of the dataset.
experiment.description: Description of the dataset
experiment.troubled: Did an automatic process within gemma or a curator mark the dataset as "troubled"
experiment.accession: Accession ID of the dataset in the external database it was taken from
experiment.database: The name of the database where the dataset was taken from
experiment.URI: URI of the original database
experiment.sampleCount: Number of samples in the dataset
experiment.batchEffectText: A text field describing whether the dataset has batch effects
experiment.batchCorrected: Whether batch correction has been performed on the dataset.
experiment.batchConfound: 0 if batch info isn't available, -1 if batch counfoud is detected, 1 if batch information is available and no batch confound found
experiment.batchEffect: -1 if batch p value < 0.0001, 1 if batch p value > 0.1, 0 if otherwise and when there is no batch information is available or when the data is confounded with batches.
experiment.rawData: -1 if no raw data available, 1 if raw data was available. When available, Gemma reprocesses raw data to get expression values and batches
geeq.qScore: Data quality score given to the dataset by Gemma.
geeq.sScore: Suitability score given to the dataset by Gemma. Refers to factors like batches, platforms and other aspects of experimental design
taxon.name: Name of the species
taxon.scientific: Scientific name for the taxon
taxon.ID: Internal identifier given to the species by Gemma
taxon.NCBI: NCBI ID of the taxon
taxon.database.name: Underlying database used in Gemma for the taxon
taxon.database.ID: ID of the underyling database used in Gemma for the taxon
get_datasets()
#> experiment.shortName
#> <char>
#> 1: GSE2018
#> 2: GSE2872
#> 3: GSE4523
#> 4: GSE4036
#> 5: GSE4034
#> 6: GSE2866
#> 7: GSE3253
#> 8: GSE2005
#> 9: GSE2426
#> 10: GSE2161
#> 11: GSE2871
#> 12: GSE2869
#> 13: GSE2868
#> 14: GSE2867
#> 15: GSE3489
#> 16: GSE1997
#> 17: GSE1837
#> 18: GSE2178
#> 19: GSE2031
#> 20: GSE1611
#> experiment.shortName
#> experiment.name
#> <char>
#> 1: Human Lung Transplant - BAL
#> 2: d'mel-affy-rat-168311
#> 3: Expression Studies of Melanotransferrin in Mouse Brain
#> 4: perro-affy-human-186940
#> 5: palme-affy-mouse-198967
#> 6: Donarum-3R01NS040270-03S1
#> 7: Exaggerated neuroinflammation and sickness behavior in aged mice after activation of the peripheral innate immune system
#> 8: Hippocampus of HuD overexpressor mice. Perrone-Bizzozero-5R01NS030255-12
#> 9: Pre-Neoplastic Stage of Medulloblastoma
#> 10: Identification of genes that are dysregulated in the telencephalon of Dlx1/2 mutants. Rubenstein-2R01MH049428-11
#> 11: giza-affy-rat-84719
#> 12: Rosenfeld-5R01NS034934-14
#> 13: Tosini-1R01NS043459-01A1
#> 14: Zoghbi-5R01NS027699-14
#> 15: Patterns of gene dysregulation in the frontal cortex of patients with HIV encephalitis
#> 16: Hippocampal development in fetal alcohol exposed rats. Perrone-Bizzozero-5R01NS030255-12-3
#> 17: Comparison of SNc and VTA dopaminergic neurons. Greene-5P50NS038399-050001
#> 18: Mouse Expression DB 2001 CHMC
#> 19: Identification of genes and quantitative trait loci (QTL) that control hematopoietic stem cell functioning
#> 20: Transcriptome of Ts1Cje and euploids cerebellum
#> experiment.name
#> experiment.ID
#> <int>
#> 1: 1
#> 2: 2
#> 3: 3
#> 4: 4
#> 5: 5
#> 6: 6
#> 7: 9
#> 8: 10
#> 9: 11
#> 10: 12
#> 11: 13
#> 12: 15
#> 13: 16
#> 14: 17
#> 15: 18
#> 16: 19
#> 17: 21
#> 18: 24
#> 19: 25
#> 20: 26
#> experiment.ID
#> experiment.description
#> <char>
#> 1: Bronchoalveolar lavage samples collected from lung transplant recipients. Numeric portion of sample name is an arbitrary patient ID and AxBx number indicates the perivascular (A) and bronchiolar (B) scores from biopsies collected on the same day as the BAL fluid was collected. Several patients have more than one sample in this series and can be determined by patient number followed by a lower case letter. Acute rejection state is determined by the combined A and B score - specifically, a combined AB score of 2 or greater is considered an acute rejection.
#> 2: Neurological diseases disrupt the quality of the lives of patients and often lead to their death prematurely. A common feature of most neurological diseases is the degeneration of neurons, which results from an inappropriate activation of apoptosis. Drugs that inhibit neuronal apoptosis could thus be candidates for therapeutic intervention in neurodegenerative disorders. We have identified (and recently reported) a chemical called GW5074 that inhibits apoptosis in a variety of cell culture paradigms of neuronal apoptosis. Additionally, we have found that GW5074 reduces neurodegeneration and improves behavioral outcome in a mouse model of Huntington's disease. Although GW5074 is a c-Raf inhibitor, we know very little about the molecular mechanisms underlying its neuroprotective action. Identifying genes that are regulated by GW5074 in neurons will shed insight into this issue. We believe that neuroprotection by GW5074 involves the regulation of several genes. Some of these genes are likely to be induced whereas the expression of other genes might be inhibited. The specific aim is to identify genes that are differentially expressed in neurons treated with GW5074. We believe that neuroprotection by GW5074 involves the regulation of several genes. Some of these genes are likely to be induced whereas the expression of other genes might be inhibited. Cultures of cerebellar granule neurons undergo apoptosis when switched from medium containing levated levels of potassium (high K+ or HK) to medium containing low potassium (LK). Although cell death begins at about 18 h, we have found that the by 6 h after LK treatment these neurons are irreversibly committed to cell death. We will treat cerebellar granule neuron cultures with LK medium (which induces them to undergo apoptosis) or with GW5074 (1 uM). We will extract RNA at two time-points after treatment: 3 h and 6 h. Analysis at the two different time-points will show us whether changes in expression of specific genes is transient or sustained or whether the changes occurs early or relatively late in the process. Neuronal cultures will be prepared from 1 week old Wistar rats. The cultures will be maintained in culture for 1 week before treatment. Following treatment the cells will be lysed and total RNA isolated. The RNA will be stored at -80oC and shipped to the microarray facility for analysis. The experiment will be done in triplicate. Thus, for each time-point (3 or 6h treatment) we will be hope to provide 3 sets of samples (each set coming from a different culture preparation and containing lysates from cells treated with LK or GW5074). Having samples from 3 independent cultures will mitigate any expression differences resulting from subtle variations in culture quality or in the preparation or quality of RNA.\nDate GSE2872 Last Updated: Jul 06 2005\nContributors: S R D'Mello\nIncludes GDS1837.\n Update date: Jun 09 2006.\n Dataset description GDS1837: Analysis of cultured cerebellar granule neurons in low potassium medium 3 and 6 hours following treatment with the c-Raf inhibitor GW5074. GW5074 blocks low potassium induced cell death. Results provide insight into the neuroprotective action of GW5074.
#> 3: Melanotransferrin (MTf) or melanoma tumor antigen p97 is an iron (Fe)-binding transferrin homolog expressed highly on melanomas and at lower levels on normal tissues. It has been suggested that MTf is involved in a variety of processes such as Fe metabolism and cellular differentiation. Considering the crucial role of Fe in many metabolic pathways e.g., DNA synthesis, it is important to understand the function of MTf. To define the roles of MTf, a MTf knockout (MTf -/-) mouse model was developed. Examination of the MTf -/- mice demonstrated no phenotypic differences compared to wild-type littermates. However, microarray analysis showed differential expression of molecules involved in proliferation such as Mef2a, Tcf4, Gls and Apod in MTf -/- mice compared to MTf +/+ littermates, suggesting a role for MTf in proliferation and tumorigenesis.\nDate GSE4523 Last Updated: Jun 13 2006\nContributors: Louise L Dunn Yohan Suryo Rahmanto Eric O Sekyere Des R Richardson\nIncludes GDS1964.\n Update date: Jun 14 2006.\n Dataset description GDS1964: Analysis of brain from melanotransferrin (MTf) knockout mutants. MTf or melanoma tumor antigen p97 is a membrane bound iron binding transferrin homolog highly expressed in melanomas and at lower levels in normal tissues. Results provide insight into the function of MTf.
#> 4: Our laboratory has developed the first mouse model overexpressing a RNA-binding protein, the ELAV-like protein HuD, in the CNS under the control of the CaMKinII alpha promoter. Initial behavioral characterization of the mice revealed that they had significant learning deficits together with abnormalities in prepulse inhibition (PPI). At the molecular level, we found that the expression of the growth-associated protein GAP-43, one of the targets of HuD, was increased in the hippocampus of HuD transgenic mice. To characterize these mice further and to evaluate the utility of these animals in understanding human diseases, we propose to use DNA microarray methods. To test our hypothesis we propose 3 specific aims: 1) To characterize the pattern of gene expression in the hippocampus of HuD overexpressor mice 2) To compare the pattern of gene expression in our mouse model with that in the hippocampus of rats prenatally exposed to alcohol (FAS model) and 3) To compare the pattern of gene expression in our mouse model with that shown in post-mortem tissues of patients with schizophrenia. In our previous protocols we examined the pattern of gene expression in our HuD transgenic mice and in rats prenaltally exposed to alcohol. A report by another group (Hakak et al, 2001) showed that three of the HuD targets were upregulated in the prefrontal cortex of patients with schizophrenia. To evaluate whether other target of HuD may be affected in this illness, in the current protocol, we want to compare the pattern of expression in our transgenic mice with in tissue from patients with schizophrenia Based on the behavioral and molecular properties of our HuD transgenic mice we hypothesize that these animals may be good models for the studying the basis of learning disabilities and of diseases that show deficits in PPI such as fetal alcohol syndrome and schizophrenia. All 28 samples are derived from cerebellar tissues for patients with schizophrenia and matched controls. The specimens were obtained from the Maryland Brain Collection according to NIH guidelines for confidentially and privacy. The protocol used in these studies was reviewed by our HRRC which found that our studies do not fall within the category of protocols monitored by the IRB (see attached letter form the HRRC). Specimens from 14 patients with a diagnosis of schizophrenia performed according to DSM-IV criteria and 14 sex-, age- and PMI-matched controls was included in this study. No differences were found between patients and control subjects in the average age (45±12 versus 43±10 years, p=0.86) or PMI (12±5 versus 16±6 hours, p=0.11). We will provide 28 samples containing 5 ug of RNA each in DEPC water (see validation of the quality of the RNA below). In addition, we include in our study animals treated with haloperidol as control for medication. These samples will be submitted in a separate protocol.\nDate GSE4036 Last Updated: Jan 13 2006\nContributors: Nora I Perrone-Bizzozero\nIncludes GDS1917.\n Update date: Jun 14 2006.\n Dataset description GDS1917: Analysis of cortical samples corresponding to the crus I/VIIa area of the cerebellum from schizophrenia patients. A study indicates that targets of the RNA-binding ELAV-like protein HuD are overexpressed in the prefrontal cortex of patients with schizophrenia.
#> 5: Fear conditioning (FC) is a behavioral paradigm that measures an animal's ability to learn fear related information. FC is measured by pairing a mild foot-shock with the surroundings in which the shock was recieved. Upon being placed back in the context, mice exhibit freezing behavior, which is a species-specific response to fear. We have previously used selective breeding to produce lines of mice with high or low levels of freezing behavior. This experiment is a replication of a previous experiment that produced lines of mice with high or low levels of freezing behavior. These lines derive from different progenitor mouse strains. We are able to identify alleles that govern the genetic variability for FC by using chromosomal markers in these selected lines. Using microarrays, we will identify differences in gene expression in two key brain regions: amygdala and hippocampus. Gene expression differences and data regarding chromosomal regions involved in the behavior will be compared to identify particular genes that are both differentially expressed and whose expression is governed by alleles that fall into critical chromosomal regions. We will compare gene expresion in the amygdala and hippocampus (brain regions known to be relevant to fear behavior) from the these two lines of mice and to the those in the previous experiment. Bayesian statistics will be used in an effort to identify gene expression that affects fear behavior. We hypothesize that selection has acted in part by changing the frequency of alleles that cause differential expression of key genes in the amygdala and hippocampus of our selected lines. Slective breeding changes the frequency of trait relevand (FC) alleles. A relevant allele is expected to increas in one selected line and decrease in the oppositely selected line. Some trait relevant alleles are expected to cause changes in the level of expression at particular genes. Amygdala and hippocampus will be rapidly dissected out of experimentally naïve mice from each line. Naïve mice will be used for expression studies since the behavior of the mice in the FC test can be reliably anticipated due to their lineage. We have practiced these procedures, and can accurately and reproducibly remove these regions in less than 5 minutes. Different mice will be used to collect each brain region, since the dissection of hippocampus disrupts the removal of amygdala. We will collect enough samples from each region to accommodate a total of 6 microarrays per brain region, per line, thus we will use a total of 24 microarrays. We anticipate that a single brain region will be sufficient to for a microarray. However, we propose to utilize three samples per microarray, because this will reduce variability due to environmental factors and due to slight variability in our dissection procedures. Once this tissue is removed, we will isolate RNA for shipment to the Microarray consortium. We will also collect spleens from each subject as a source of genomic DNA, in order to permit direct comparison of genotype and expression phenotypes. Once we have the results of the microarray analysis, we use WebQTL.org to identify the chromosomal locations of alleles that are know to influence the expression of genes for which we have found differential expression. We will then superimpose this information on trait relevant chromosomal regions identified from our selected lines. This will allow us to rapidly identify genes which may account for genetic variability in FC due to differential expression. Such genes will then be subjected to further study.\r\nDate GSE4034 Last Updated: Jan 13 2006\r\n\r\nContributors: Abraham A Palmer\r\n\r\nIncludes GDS1901.\r\n Update date: Jun 09 2006.\r\n Dataset description GDS1901: Analysis of the amygdalae and hippocampi of strains exhibiting low or high freezing behavior in response to fear. Previous work identified alleles that contribute to the variability in freezing behavior, and the chromosomal location of these alleles.\r
#> 6: Succinate semialdehyde dehydrogenase (SSADH) deficiency is a rare autosomal recessive disorder effecting approximately 350 people around the world. Patients suffering from SSADH deficiency experience language acquisition failure, memory deficiencies, autism, increased aggressive behaviors, and seizures. There is a chemical buildup of both gamma-aminobutyric acid (GABA) and gamma-hydroxybutyric acid (GHB) in the neurological system of these patients. The Aldh5a1-/- knock out mouse model of SSADH deficiency shows the same chemical imbalances as the human disease, with additional fatal tonic-clonic seizures at three weeks of age. The elucidation of seizure causing pathways will facilitate treatment of seizure phenotypes in diseases with related epilepsy. Gene expression patterns within the hippocampus, cerebellum, and cortex of SSADH deficient mice (Aldh5a1-/- mice) will be compared to wild type mice at a time point immediately prior to fatal seizures. We hypothesis that the SSADH deficient mice experience a dysfunction of glutamate/GABA/ glutamine neurotransmitter cycle linked to astroglial metabolism and/or uptake of neuronally-released glutamate. The increased levels of GHB and GABA lead to down regulation of GABA-B-Receptor leading to seizures. The SSADH deficient phenotype may also be caused by ongoing oxidative damage and the pathological role of succinic semialdehyde. SSADH deficient mice (Aldh5a1-/- knock out) exhibit fatal seizures around three weeks of age. Mutant and wild type mice will be sacrificed between 17 and 19 days of life, and brain sections will be extracted and frozen (using a standard protocol). Hippocampus, cerebellum, and cortex from three mutant mice and three wild type mice will individually be expression profiled on the Affymetrix platform, giving a total of eighteen arrays. Comparative analysis will then be carried out, evaluating the transcript differences between mutant and wild type mice in each brain region.\nDate GSE2866 Last Updated: Jun 08 2006\nContributors: E A Donarum\nIncludes GDS1745.\n Update date: Jun 08 2006.\n Dataset description GDS1745: Analysis of brain hippocampi, cerebella, and cortices of succinate semialdehyde dehydrogenase (SSADH)-deficient mutants at 3 weeks of age, when fatal seizures occur. Results indicate that SSADH deficiency results in the dysregulation of genes involved in myelin structure and compaction.
#> 7: Acute cognitive impairment (i.e., delirium) is common in elderly emergency department patients and frequently results from infections that are unrelated to the central nervous system. Since activation of the peripheral innate immune system induces brain microglia to produce inflammatory cytokines that are responsible for behavioral deficits, we investigated if aging exacerbated neuroinflammation and sickness behavior after peripheral injection of lipopolysaccharide (LPS). Microarray analysis revealed a transcriptional profile indicating the presence of primed or activated microglia and increased inflammation in the aged brain. Furthermore, aged mice had a unique gene expression profile in the brain after an intraperitoneal injection of LPS, and the LPS-induced elevation in the brain inflammatory cytokines and oxidative stress was both exaggerated and prolonged compared with adults. Aged mice were anorectic longer and lost more weight than adults after peripheral LPS administration. Moreover, reductions in both locomotor and social behavior remained 24 h later in aged mice, when adults had fully recovered, and the exaggerated neuroinflammatory response in aged mice was not reliably paralleled by increased circulating cytokines in the periphery. Taken together, these data establish that activation of the peripheral innate immune system leads to exacerbated neuroinflammation in the aged as compared with adult mice. This dysregulated link between the peripheral and central innate immune system is likely to be involved in the severe behavioral deficits that frequently occur in older adults with systemic infections.\nDate GSE3253 Last Updated: Oct 28 2005\nContributors: Johnathan P Godbout Jing Chen Amy F Richwine Brian M Berg Keith K Kelley Rodney W Johnson Jayne Abraham\nIncludes GDS1311.\n Update date: Nov 04 2005.\n Dataset description GDS1311: Analysis of aged brain after activation of the peripheral innate immune system by intraperitoneal injection of E. coli lipopolysaccharide (LPS). Results provide insight into molecular events underlying severe behavioral deficits (delirium) common in older adults with systemic infections.
#> 8: Post-transcriptional mechanisms play an important role in the control of gene expression. RNA-binding proteins are key players in the post-transcriptional control of many neural genes and they participate in multiple processes, from RNA splicing and mRNA transport to mRNA stability and translation. Our laboratory has developed the first mouse model overexpressing a RNA-binding protein, the ELAV-like protein HuD, in the CNS under the control of the CaMKinII alpha promoter. Initial behavioral characterization of the mice revealed that they had significant learning deficits together with abnormalities in prepulse inhibition (PPI). At the molecular level, we found that the expression of the growth-associated protein GAP-43, one of the targets of HuD, was increased in the hippocampus of HuD transgenic mice. To characterize these mice further and to evaluate the utility of these animals in understanding human diseases, we propose to use DNA microarray methods. To test our hypothesis we propose 2 specific aims: 1)To characterize the pattern of gene expression in the hippocampus of HuD overexpressor mice 2)To compare the pattern of gene expression in our mouse model with that in the hippocampus of rats prenatally exposed to alcohol (FAS model) and in post-mortem tissues of patients with schizophrenia Based on the behavioral and molecular properties of our HuD transgenic mice we hypothesize that these animals may be good models for the studying the basis of learning disabilities and of diseases that show deficits in PPI such as fetal alcohol syndrome and schizophrenia. All mice are in C57BL/6 background and are male approximately 60 days old. Initial studies were performed in animals that were not subjected to any experimental manipulation. Animals were bred and sacrificed according to our approved animal protocol. The brain was rapidly dissected on ice and we isolated the hippocampus, which has the highest expression of the transgene. After dissection both hippocampi were frozen in liquid nitrogen, pooled and stored at -80C until analysis. RNA samples were isolated using RNAeasy Qiagen columns. For our first experiment, we want to examine the pattern of gene expression in the hippocampus of 3 transgenic mice and 3 non-transgenic littermates. RNAs from the 6 hippocampi were of high quality as revealed by the integrity of the 28S and 18S rRNA. We will provide 6 samples containing 10 ug of RNA each in DEPC water at a concentration of about 0.5 ug/ul. Three of the samples (#1, #2 and # 3) are from transgenic mice and three from their non-transgenic littermates (#4, #5 and #6).\nDate GSE2005 Last Updated: May 29 2005\nContributors: Nora I Perrone-Bizzozero\nIncludes GDS1111.\n Update date: May 09 2005.\n Dataset description GDS1111: Analysis of hippocampus of 60 day old C57BL/6 males overexpressing the RNA binding protein HuD. HuD overexpressor mice exhibit learning deficits and abnormalities in prepulse inhibition.
#> 9: SUMMARY Medulloblastoma is the most common malignant brain tumor in children. It is thought to result from transformation of granule cell precursors (GCPs) in the developing cerebellum, but little is known about the early stages of the disease. Here we identify a pre-neoplastic stage of medulloblastoma in patched heterozygous mice, a model of the human disease. We show that pre-neoplastic cells are present in the majority of patched mutants, although only 16% of these mice develop tumors. Pre-neoplastic cells, like tumor cells, exhibit activation of the Sonic hedgehog pathway and constitutive proliferation. Importantly, they also lack expression of the wild-type patched allele, suggesting that loss of patched is an early event in tumorigenesis. While pre-neoplastic cells resemble GCPs and tumor cells in many respects, they have a distinct molecular signature. Genes that mark the pre-neoplastic stage include regulators of migration, apoptosis and differentiation, processes critical for normal development but previously unrecognized for their role in medulloblastoma. The identification and molecular characterization of pre-neoplastic cells provides insight into the early steps in medulloblastoma formation, and may yield important markers for early detection and therapy of this disease. ANIMALS Patched heterozygous mice (Goodrich et al., 1997) were obtained from Matthew Scott?s lab at Stanford, and maintained by breeding with 129X1/SvJ mice from Jackson Laboratories. Math1-GFP transgenic mice (Lumpkin et al., 2003) were provided by Jane Johnson at UT Southwestern Medical Center. Math1-GFP/patched+/- mice were generated by crossing patched heterozygotes with Math1-GFP mice, and then backcrossing to Math1-GFP mice three times before further analysis. All mice were maintained in the Cancer Center Isolation Facility at Duke University Medical Center. ISOLATION OF CELLS Granule cell precursors (GCPs) were isolated from 7-day-old (P7) patched+/- mice; preneoplastic cells were obtained from 6 week-old patched mutants; and tumor cells were obtained from 10-25-week old patched mutants displaying physical and behavioral signs of medulloblastoma. Cells were isolated from each source using a protocol described in (Wechsler- Reya and Scott, 1999). Briefly, cerebella were digested in solution containing 10 U/ml papain Oliver et al. Pre-Neoplastic Stage of Medulloblastoma - 8 - (Worthington, Lakewood, NJ) and 250 U/ml DNase (Sigma) and triturated to obtain a cell suspension. This suspension was centrifuged through a step gradient of 35% and 65% Percoll (Amersham Biosciences, Piscataway, NJ), and cells were harvested from the 35%-65% interface. Cells were resuspended in serum-free culture medium consisting of Neurobasal containing B27 supplement, sodium pyruvate, L-glutamine, and penicillin/streptomycin (all from Invitrogen, Carlsbad, California) and counted on a hemacytometer. Cells used for RNA isolation were centrifuged and flash frozen in liquid nitrogen. For proliferation assays or immunostaining, cells were plated on poly-D-lysine (PDL)-coated tissue culture vessels and incubated in serum-free culture medium. MICROARRAY HYBRIDIZATION AND ANALYSIS RNA from GCPs, pre-neoplastic cells and tumor cells (isolated as described above, but not FACS-sorted) and from normal adult cerebellum (not dissociated) was converted to cDNA using the Superscript Choice cDNA kit (Invitrogen) and a T7-dT(24) primer (Genset/Proligo, Boulder, CO). cRNA was generated using a T7-transcription/labeling kit from Enzo Life Sciences and hybridized to Affymetrix U74Av2 chips (Affymetrix, Santa Clara, CA). Chips were scanned, and hybridization data were acquired using Affymetrix Suite 5.0 software. Affymetrix CEL files were normalized and quantified using Bioconductor software with the gcRMA model to quantify gene expression levels (Gentleman and Carey, 2002). Unsupervised principal components analysis (PCA) was used to identify the relationships among normal adult cerebellum, GCPs, pre-neoplastic cells and tumor cells based on expression profiles. To identify genes that were differentially expressed among GCPs, pre-neoplastic cells and tumor cells, supervised analysis was carried out. A gene-by-gene ANOVA model with three groups (GCP, pre-neoplastic, tumor) was used to fit the log2-transformed intensities. To correct for multiple comparisons, the nominal p-value was adjusted using the false discovery rate (Benjamini and Hochberg, 1995). Genes were considered to be differentially expressed if they satisfied all of the following criteria: a difference in expression greater than 1.9-fold between any two groups, a maximum absolute intensity difference larger than 32 units, and an adjusted pvalue < 0.01. There were 118 genes that met these criteria. The identities of differentially expressed genes were verified by integrating data from the Affymetrix and Unigene databases. Gene functions were determined using information from Gene Ontology, Unigene, LocusLink and PubMed databases. Clustering was performed with Cluster and Treeview (Eisen et al., 1998). All statistical analysis was performed using R-1.7 software (Dalgaard, 2002). Results were visualized with Spotfire 6.0 (Somerville, MA). CORRESPONDING AUTHOR Robert J. Wechsler-Reya* Dept. of Pharmacology & Cancer Biology, Duke University Medical Center Box 3813, Durham, NC 27710 Phone: 919-613-8754. Fax: 919-668-3556. Email: rw.reya@duke.edu.\nDate GSE2426 Last Updated: Oct 28 2005\nContributors: R J Wechsler-Reya Trang T Huynh Simon M Lin Jonathan F Wells Tracy A Read Anriada Mehmeti Jessica D Kessler Robert J Wechsler-Reya Trudy G Oliver\nIncludes GDS1110.\n Update date: May 09 2005.\n Dataset description GDS1110: Analysis of granule cell precursors, pre-neoplastic cells, and tumor cells obtained from 7 day old, 6 week old, and 10 to 25 week old patched heterozygous animals, respectively. Results provide insight into the mechanisms of pathogenesis of medulloblastoma at the early stage.
#> 10: The Dlx homeodomain transcription factors are implicated in regulating the function of inhibitory neurons; therefore understanding their functions will provide insights into disorders such as epilepsy, mental retardation, autism and cerebral palsy. Identify genes that are dysregulated in the telencephalon of Dlx1/2 mutants. I am sending separate samples of the Basal Ganglia (BG) and cortex (Ctx). Comparing gene expression in the embryonic basal ganglia and cortex in wild type and dlx1/2 mutant mice will provide information regarding the types of genes that are downstream of Dlx1/2 function. Perform gene array analyses in duplicate or triplicate; compare expression profile of total RNA from wild type and dlx1/2 telencephalons. I am sending separate samples of the Basal Ganglia (BG) and cortex. I want to perform gene expression comparison between: 1) wild type and dlx1/2 basal ganglia (BG) 2) wild typee and dlx1/2 cortex (ctx) Age of all mice has been indicated as 14 days to satisfy system requirements, however all mice are embryonic day 14.5.\nDate GSE2161 Last Updated: May 29 2005\nContributors: John L Rubenstein\nIncludes GDS1084.\n Update date: May 08 2005.\n Dataset description GDS1084: Expression profiling of cortex and basal ganglia from embryonic telencephalon of homozygous and heterozygous Dlx1/2 mutants. Mutants examined at embryonic day 14.5. Dlx homeobox transcription factors are implicated in regulating the function of inhibitory neurons.
#> 11: Traumatic brain injury (TBI) induces a complex cascade of molecular and physiological effects. This study proposes to investigate the gene expression profile in cortex and hippocampus over early time points, following two different injury severities. These results will complement prior knowledge of both metabolic and neuroplastic changes after TBI, as well as serve as a starting point to investigate additional gene families whose expression is altered after TBI. To characterize the profile of gene expression following a diffuse traumatic brain injury of varying severity in adult rats. Distinct patterns of gene expression following traumatic brain injury will occur in a time- and injury-dependent fashion. In particular, changes in expression of enzymes involved in energy metabolism and neuroplasticity will be detected. Adult rats will be subjected to mild and severe lateral fluid percussion injury OR sham surgery without injury. At various post-injury timepoints (0.5, 4 and 24 hours), animals will be sacrificed, brain regions (parietal cortex and hippocampus, ipsilateral and contralateral to injury) will be dissected and RNA isolated. RNA will be used to synthesize cRNA probes for microarray hybridization. RNA from 2 matched animals will be pooled onto a single chip (U34A rat, Affymetrix). Comparisons will be made between sham and injured animals, with brain region, injury severity, and post-injury time point as the experimental variables.\nDate GSE2871 Last Updated: Jul 06 2005\nContributors: C C Giza\nIncludes GDS1795.\n Update date: Jun 09 2006.\n Dataset description GDS1795: Analysis of cortices and hippocampi of animals 4 and 24 hours after mild or severe traumatic brain injury (TBI). TBI induced by lateral fluid percussion. Gene expression in tissues ipsi- and contralateral to the injury examined.
#> 12: Using targeted gene deletion, we have firmly established that the Class IV POU domain transcription factor Brn-3.2 controls a developmental program regulating axon pathfinding in the mouse visual system. We have isolated and identified downstream gene targets of Brn-3.2, using Representational Difference Analysis (RDA), on cDNA populations derived from wildtype and Brn-3.2-/- retina at the appropriate embryonic stage. One of these candidate genes, the Homeodomain Interacting Protein Kinase 2 (HIPK2), is postulated to be a transcriptional coregulator, based on its in vitro interactions with repressor homeodomain proteins of the NK class, as well as other components of repressor complexes. HIPK2 has also been shown to be involved in post-translational modification of two major transcriptional regulators, p53 and CtBP. Expression of a dominant-negative form of HIPK2 in sensory neurons affects the innervation patterns of their target tissues, suggesting an axon pathfinding defect. The aim of this project is to identify targets for the Homeodomain Interacting Protein Kinase 2 (HIPK2), that we isolated as a downstream gene target of the class IV POU domain transcription factor Brn-3.2, and to investigate their function in the Brn-3.2 dependent pathway regulating axon pathfinding. We hypothesize that the genes regulated by HIPK2 will play a critical role in axon pathfinding and that the results of this study will provide novel insights into the molecular mechanisms of axon pathfinding, and possibly neural plasticity and regeneration, and therefore, be of great interest to the field of neurobiology in general. In order to uncover potential biological role(s) of HIPK2 in neural development, and particularly axon pathfinding, we generated transgenic mouse models for in vivo studies. Our preliminary observations in transgenic mice, indicate that a form of the molecule expected to act as a dominant-negative and designed to be expressed in sensory neurons, affects the innervation patterns of their target tissues, suggesting an axon pathfinding defect. We plan to take advantage of this model to identify genes regulated by HIPK2, and which are likely to be involved in axon pathfinding. To this end, we will compare gene expression profiles in sensory neurons isolated wild-type and transgenic mice. In our experimental paradigm, the trigeminal ganglion represents the ideal sensory structure in which to perform such an analysis: 1) High levels of transgene can be expressed in the trigeminal ganglion, as assessed by the expression of LacZ from the bicistronic construct which contains IRES-LacZ downstream of a dominant negative form of HIPK2. 2) The trigeminal ganglion can be dissected at embryonic day 13.5, when the phenotype is apparent, with ease and free of contamination from surrounding tissues. 3) The amounts of RNA that can be isolated from a single ganglion are in the range of 200-300 ng, which should be sufficient for microarray analysis following linear amplification of RNA. One series will be carried out with transgenic embryos and a control series will be carried out with wildtype littermates. Animals will be prepared and sacrificed by a standard protocol. Tissue will be rapidly dissected from E13.5 trigeminal ganglion, frozen in liquid nitrogen, and stored at -80C until RNA is extracted. RNA will be prepared using RNeasy Micro Kit. We will be providing 4 tissue samples for each of the wildtype and transgenic animals to mitigate any expression differences resulting from mouse to mouse variation.\nDate GSE2869 Last Updated: Jul 06 2005\nContributors: M Rosenfeld\nIncludes GDS1793.\n Update date: Jun 13 2006.\n Dataset description GDS1793: Analysis of the trigeminal ganglia of transgenics expressing a dominant-negative form of homeodomain interacting protein kinase 2 (HIPK2). HIPK2 is a downstream target of Brn-3.2, a class IV POU domain transcription factor that controls a developmental program regulating axon pathfinding.
#> 13: It is well known that many genes are expressed in the retina where they show a clear daily pattern of expression. Although several studies have investigated gene expression in the retina, no study ? so far - has investigated the daily and the circadian pattern of gene expression using microarray. In the present study we propose to investigate the daily and circadian pattern of expression in the retina. To determine gene expression in the retina in Light:Dark cycles and in constant condition (constant dim light) in the rat. We hypothesize that (a) different genes are expressed in different retinal layers and (b) the pattern of expression of the same genes can differ among the different layers. Wistar rats (3 for each time point) will be killed in the middle of the day (ZT6) and in the middle of the night (ZT18). Eyeballs will be collected and the retina will be removed and immediately frozen and then stored at -80 oC. The same experiment will be repeated on animals that have been maintained in constant dim light for 3. Retinas (3 for each time point) will be obtained from animal killed at CT6 (middle subjective day) and CT 18 (middle subjectice night) and stored at ? 80 oC. The samples will be then shipped to NINDS-NIMH array facility for analysis.\nDate GSE2868 Last Updated: Oct 28 2005\nContributors: G Tosini\nIncludes GDS1759.\n Update date: Jun 16 2006.\n Dataset description GDS1759: Analysis of retinas under a light-dark cycle at Zeitgeber times (ZT) 6 and 18 or in constant dim light at circadian times (CT) 6 and 18. Gene expression in retinas compared to that in pineal glands.
#> 14: A number of human neurodegenerative diseases result from the expansion of a glutamine repeat within the disease-causing protein. Spinocerebellar ataxia type1 is one such disease, caused by expansion of a polyglutamine tract in the novel protein ataxin-1. To faithfully model SCA1 in the mouse, we generated knock-in mice carrying 154 CAG repeats in the mouse Sca1 locus. These mice reproduced many aspects of the human disease. Despite ubiquitous expression of the mutant protein, they developed slowly progressive selective neurodegeneration , which is most distinct in Purkinje cells and spinal cord. Alterations in gene expression have been proposed to be involved in the pathogenesis of polyglutamine disease including SCA1, but the changes of gene expression in an authentic disease model have not been characterized. We believe that knowledge of these genes will give insight into the pathophysiology of SCA1 and may ultimately be relevant to the treatment of SCA1. We will determine gene expression patterns in the cerebellum and forebrain at three different time points. We propose that the genes whose expression is affected by mutant ataxin-1 expression are effectors for neuronal dysfunction and neuronal degeneration in the knock-in mice. We also hypothesize the difference in the expression levels of these genes accounts for selective vulnerability of neurons. We will examine three time points: 4 weeks, 11 weeks, and 20 weeks of age. We have shown that the mice developed motor incoordination as revealed by rotating rod test as early as 5 weeks of age but it was not until 10 weeks that they start showing clear neurological phenotype such as clasping. At each point, we will compare the pattern between the mutant and wild-type littermate. Animals will be prepared and sacrificed by a standard procedure. Cerebellum and spinal cord are the most affected areas whereas forebrain shows less neurodegeneration. Tissue will be rapidly dissected from cerebellum and forebrain, frozen in liquid nitrogen, and stored at ?80 C until analysis. Tissues will be sent to the centers (as opposed to RNA). We will be providing 9 tissue samples from three litters for each time point to mitigate any expression differences resulting from subtle differences of procedure or environment where the mice grow.\nDate GSE2867 Last Updated: Jul 06 2005\nContributors: H Zoghbi\nIncludes GDS1756.\n Update date: May 02 2006.\n Dataset description GDS1756: Analysis of cerebellum and forebrain tissue of knock-in mice carrying 154 CAG repeats in the spinocerebellar ataxia type 1 (SCA1) locus at 4 and 12 weeks of age. Results provide insight into the pathophysiology of the neurodegenerative disease SCA1.
#> 15: The neurodegenerative process in HIV encephalitis (HIVE) is associated with extensive damage to the dendritic and synaptic structure that often leads to cognitive impairment. Several mechanisms might be at play, including release of neurotoxins, oxidative stress and decreased activity of neurotrophic factors. Furthermore, HIV-mediated dysregulation of genes involved in neuronal maintenance might play an important role. For this purpose, cRNA was prepared from the brains of 17 AIDS patients for analysis with the Affymetrix Human U95Av2 GeneChip and analyzed with the GeneSpring Expression Analysis Software. Out of 12,625 genes analyzed, 74 were downregulated and 59 were upregulated compared to controls. Initial alternative analysis of RNA was performed by ribonuclease protection assay (RPA). In cases with HIVE, downregulated genes included neuronal molecules involved in synaptic plasticity and transmission (ion channels, synaptogyrin, synapsin II), cell cycle (p35, p39, CDC-L2, CDC42, PAK1) and signaling molecules (PI3K, Ras-Raf-MEK1), transcription factors and cytoskeletal components (MAP-1B, MAP-2, tubulin, adducin-2). Upregulated genes included those involved in neuroimmune (IgG, MHC, ?2microglobulin) and anti-viral responses (interferon-inducible molecules), transcription (STAT1, OLIG2, Pax-6) and signaling modulation (MEK3, EphB1) of the cytoskeleton (myosin, aduccin-3, radixin, dystrobrevin). Taken together, this study suggests that HIV proteins released from infected macrophages might not only induce a neuroinflammatory response, but also may promote neurodegeneration by interfering with neuronal transcription of genes involved in regulating signaling and cytoskeletal molecules important in maintaining synapto-dendritic functioning and integrity.\nDate GSE3489 Last Updated: Mar 07 2006\nContributors: Eleanor S Roberts Dianne Langford Anthony Adame Edward Rockenstein Leslie Crews Howard S Fox Eliezer Masiliah Ian Everall\nIncludes GDS1726.\n Update date: May 30 2006.\n Dataset description GDS1726: Analysis of brain frontal cortex of HIV-seropositive patients with HIV encephalitis (HIVE). HIVE affects >40% of AIDS patients, promoting neurodegeneration and cognitive impairment. Results suggest HIV-mediated dysregulation of genes involved in neuronal maintenance might play an important role.
#> 16: Study the effect of fetal alcohol exposure (FAE) on hippocampal development Compare the pattern of gene expression in the hippocampus of FAE and control rats fed either an isocaloric diet or a normal diet, at post-natal day 5 of development. FAE will delay the maturation of the hippocampus Rats were fed one of three diet, a liquid diet with 5% ethanol (FAE group), an isocaloric liquid diet (Isocalorc group) or nomal lab chao (normal group).\nDate GSE1997 Last Updated: Feb 02 2006\nContributors: Nora I Perrone-Bizzozero\nIncludes GDS1660.\n Update date: Jun 08 2006.\n Dataset description GDS1660: Analysis of hippocampi of 5-day-old animals subjected to fetal alcohol exposure (FAE) with 5% ETOH. FAE affects the development and function of the hippocampus.
#> 17: The cardinal clinical features of Parkinson's disease (PD) (rigidity, rest tremor, bradykinesia, and postural instability) result from selective loss of midbrain dopaminergic neurons. More specifically, dopaminergic neurons in the substantia nigra pars compacta (SNc) are much more susceptible to damage than the adjacent dopaminergic neurons in the ventral tegmental area (VTA). This dichotomy is not only seen in human Parkinsons disease, but also in many animal models of PD, including administration of the mitochondrial toxin rotenone to rats, which replicates many of the behavioral and neuropathological features of PD. The factors underlying this selective vulnerability are unknown, but could be related to differences in neuronal circuitry, differences in glial support, or intrinsic differences between the neuronal populations of the two regions. Elucidation of these factors may lead to a greater understanding of the pathogenesis and treatment of Parkinson's disease. We will determine gene expression profiles of untreated rat SNc and VTA dopaminergic neurons using laser capture microscopy to obtain region-specific neuronal mRNA. There are intrinsic differences in gene expression between dopaminergic neurons in the rat SNc and VTA that result in greater susceptibility of SNc neurons to degeneration in experimental parkinsonism. These differences may be related to dopamine metabolism, oxidative metabolism and stress, protein aggregation, or other unforseen pathways. We will compare gene expression profiles between SNc and VTA dopaminergic neurons in normal rats. No treatment or time points will be studied in this experiment. Animals will be anesthetized, sacrificed by decapitation, and brains frozen on dry ice. Frozen sections will be collected onto glass microscope slides and rapidly immunostained for tyrosine hydroxylase to identify dopaminergic neurons. SNc and VTA neurons (approx. 200 per sample) will be isolated using laser capture microscopy. Total RNA will be extracted and poly-A RNA will be amplified using a modified Eberwine protocol. aRNA will be sent to the centers for labeling and hybridization to Affymetrix rat U34A arrays. We have confirmed with the center that our aRNA protocol is compatible with the centers amplification protocols; in fact, it is essentially identical. We will be providing a two-round amplification product to the center for labeling and hybridization. We recognize that using RNA after three rounds of amplification may decrease sensitivity for low copy number transcripts, but favor this approach versus pooling our samples (which are inherently paired) at this point. We have discussed this point in detail with the center. SNc and VTA samples from eight animals (16 samples total) will be provided to mitigate differences specific to individual animals. With the assisatnce of the center, paired t-tests will be used to determine differential expression between the two regions. Permutational t-test analysis and/or Benjamini and Hochberg analysis of expression ratios will be used to protect against multiple comparisons. Selected differentially expressed genes will be validated on separate tissue samples using quantitative RT-PCR or in situ hybridization.\nDate GSE1837 Last Updated: Feb 02 2006\nContributors: James G Greene\nIncludes GDS1641.\n Update date: Jun 08 2006.\n Dataset description GDS1641: Analysis of dopaminergic neurons of the substantia nigra pars compacta (SNc) and the ventral tegmental area (VTA) from normal animals. SNc dopaminergic neurons are more susceptible to degeneration in Parkinson's disease than VTA dopaminergic neurons.
#> 18: Expression profiles of different mouse tissue samples profiles.\nDate GSE2178 Last Updated: May 29 2005\nContributors: Michael Mucenski Bruce J Aronow Belinda Peach Mitchell Cohen Amy Moseley Susan E Waltz John Maggio Sandra Degen Steve Potter Thomas Doetschman Chris Erwin Joanna A Groden James Lessard Linda Parysek R Hirsch MaryBeth Genter Jianhua Zhang Kathleen Anderson Jonathan D Katz John Dedman Michael Lehman Jorge A Bezerra Michael D Bates Ming Xu Dan Wiginton Jeff A Molkentin\nIncludes GDS1322.\n Update date: Nov 09 2005.\n Dataset description GDS1322: Gene expression profiles across a wide variety of tissue samples. The tissues examined include nervous system, skeletal muscle, male and female reproductive organs, gastrointestinal tract, as well as organs in a developmental context including lung, liver, kidney, and heart.
#> 19: We have combined large-scale mRNA expression and gene mapping methods to identify genes and loci that control hematopoietic stem cell (HSC) functioning. mRNA expression levels were measured in purified HSC isolated from a panel of densely genotyped recombinant inbred mouse strains. Quantitative trait loci (QTLs) associated with variation in expression of thousands of transcripts were mapped. Comparison of the physical transcript position with the location of the controlling QTL identified polymorphic cis-acting stem cell genes. In addition, multiple trans-acting control loci were highlighted that modify expression of large numbers of genes. These groups of co-regulated transcripts identify pathways that specify variation in stem cells. We illustrate this concept with the identification of strong candidate genes involved with HSC turnover. We compared expression QTLs in HSC and brain from the same animals, and document both shared and tissue-specific QTLs. Our data are accessible through WebQTL, a web-based interface that allows custom genetic linkage analysis and identification of co-regulated transcripts.\nDate GSE2031 Last Updated: Jun 29 2005\nContributors: Bert Dontje Kenneth F Manly Sue Sutton Michael Cooke Rudi Alberts Gerald de Haan Jintao Wang Edo Vellenga Ellen Weersing Elissa Chesler Leonid Bystrykh Andrew I Su Tim Wiltshire Mathew T Pletcher Robert W Williams Ritsert C Jansen Lu Lu\nIncludes GDS1077.\n Update date: Mar 16 2005.\n Dataset description GDS1077: Expression profiling of Lin- Sca-1+ c-kit+ hematopoietic stem cells (HSC) from 22 different BXD recombinant inbred (RI) strains. Each RI strain is homozygous for alleles at about 98% of loci. Results combined with QTL mapping to identify candidate genes for the control of HSC function.
#> 20: Transcriptome analysis of Ts1Cje (mouse model of Down syndrome) and euploids murine cerebellum during postnatal development.\nDate GSE1611 Last Updated: Mar 27 2006\nContributors: Randal Moldrich Isabelle Rivals Pierre-Marie Sinet Stylianos E Antonarakis Robert Lyle Geoffroy Golfier Luce Dauphinot Laurence Ettwiller Kiyoko Toyama Charles J Epstein Léon Personnaz Maire-Claude Potier Minh Tran Dang Jean Rossier\nIncludes GDS994.\n Update date: Jan 11 2005.\n Dataset description GDS994: Expression profiling of brain cerebella from Ts1Cje males at postnatal days 0, 15, and 30. RNA pooled from 3 males at each developmental stage. Results provide insight into the molecular changes contributing to the pathogenesis of Down syndrome.
#> experiment.description
#> experiment.troubled experiment.accession experiment.database
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#> 2: FALSE GSE2872 GEO
#> 3: FALSE GSE4523 GEO
#> 4: FALSE GSE4036 GEO
#> 5: FALSE GSE4034 GEO
#> 6: FALSE GSE2866 GEO
#> 7: FALSE GSE3253 GEO
#> 8: FALSE GSE2005 GEO
#> 9: FALSE GSE2426 GEO
#> 10: FALSE GSE2161 GEO
#> 11: FALSE GSE2871 GEO
#> 12: FALSE GSE2869 GEO
#> 13: FALSE GSE2868 GEO
#> 14: FALSE GSE2867 GEO
#> 15: FALSE GSE3489 GEO
#> 16: FALSE GSE1997 GEO
#> 17: FALSE GSE1837 GEO
#> 18: FALSE GSE2178 GEO
#> 19: FALSE GSE2031 GEO
#> 20: FALSE GSE1611 GEO
#> experiment.troubled experiment.accession experiment.database
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#> 1: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE2018
#> 2: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE2872
#> 3: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE4523
#> 4: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE4036
#> 5: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE4034
#> 6: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE2866
#> 7: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE3253
#> 8: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE2005
#> 9: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE2426
#> 10: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE2161
#> 11: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE2871
#> 12: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE2869
#> 13: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE2868
#> 14: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE2867
#> 15: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE3489
#> 16: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE1997
#> 17: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE1837
#> 18: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE2178
#> 19: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE2031
#> 20: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE1611
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#> 11: BATCH_EFFECT_FAILURE FALSE
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#> geeq.qScore geeq.sScore taxon.name taxon.scientific taxon.ID taxon.NCBI
#> <num> <num> <char> <char> <int> <int>
#> 1: 0.9976341 0.8750 human Homo sapiens 1 9606
#> 2: 0.8552303 0.5000 rat Rattus norvegicus 3 10116
#> 3: 0.2739433 0.5875 mouse Mus musculus 2 10090
#> 4: 0.9971246 0.7500 human Homo sapiens 1 9606
#> 5: 0.5703423 1.0000 mouse Mus musculus 2 10090
#> 6: 0.4273933 0.6875 mouse Mus musculus 2 10090
#> 7: 0.2661131 0.3750 mouse Mus musculus 2 10090
#> 8: 0.8565510 0.6500 mouse Mus musculus 2 10090
#> 9: 0.8558619 0.6875 mouse Mus musculus 2 10090
#> 10: 0.8557322 0.8375 mouse Mus musculus 2 10090
#> 11: 0.5688754 0.6875 rat Rattus norvegicus 3 10116
#> 12: 0.8558936 0.5875 mouse Mus musculus 2 10090
#> 13: 0.7119963 0.5625 rat Rattus norvegicus 3 10116
#> 14: 0.8469065 0.6250 mouse Mus musculus 2 10090
#> 15: 0.4098798 0.5000 human Homo sapiens 1 9606
#> 16: 0.7101798 0.1875 rat Rattus norvegicus 3 10116
#> 17: 0.9979230 0.3125 rat Rattus norvegicus 3 10116
#> 18: -0.2259327 0.0625 mouse Mus musculus 2 10090
#> 19: 0.1386041 0.8125 mouse Mus musculus 2 10090
#> 20: 0.8565754 0.6875 mouse Mus musculus 2 10090
#> geeq.qScore geeq.sScore taxon.name taxon.scientific taxon.ID taxon.NCBI
#> taxon.database.name taxon.database.ID
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#> 1: hg38 87
#> 2: rn6 86
#> 3: mm10 81
#> 4: hg38 87
#> 5: mm10 81
#> 6: mm10 81
#> 7: mm10 81
#> 8: mm10 81
#> 9: mm10 81
#> 10: mm10 81
#> 11: rn6 86
#> 12: mm10 81
#> 13: rn6 86
#> 14: mm10 81
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#> 17: rn6 86
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#> taxon.database.name taxon.database.ID
get_datasets(taxa = c("mouse", "human"), uris = "http://purl.obolibrary.org/obo/UBERON_0002048")
#> experiment.shortName
#> <char>
#> 1: GSE2018
#> 2: GSE2178
#> 3: GSE476
#> 4: GSE478
#> 5: GSE484
#> 6: GSE485
#> 7: GSE1037
#> 8: GSE1133.2
#> 9: GSE479
#> 10: GSE97
#> 11: GSE446
#> 12: bhattacharjee-lung
#> 13: ramaswamy-cancer
#> 14: staunton-nci60
#> 15: GSE2361
#> 16: GSE740
#> 17: GSE96
#> 18: GSE4345
#> 19: GSE6858
#> 20: GSE4772
#> experiment.shortName
#> experiment.name
#> <char>
#> 1: Human Lung Transplant - BAL
#> 2: Mouse Expression DB 2001 CHMC
#> 3: Ozone effect on airways hyperpermability
#> 4: Alveoli loss during caloric restriction time course
#> 5: Alveoli septation inhibition and protection
#> 6: Genetic basis of sensitivity to pulmonary fibrosis
#> 7: Lung cancer
#> 8: tissue-specific pattern of mRNA expression - Mus musculus
#> 9: Alveolar septation
#> 10: Large-scale analysis of the mouse transcriptome
#> 11: Lungs from ovalbumen sensitized and challenged C3H mice
#> 12: bhattacharjee-lung
#> 13: ramaswamy-cancer
#> 14: staunton-nci60
#> 15: Expression Proflies of Human Normal tissues
#> 16: Rosetta_Merck_Splicing_Experiment
#> 17: Large-scale analysis of the human transcriptome
#> 18: In Vivo Regulation of Human Skeletal Muscle Gene Expression by Thyroid Hormone
#> 19: Expression data from experimental murine asthma
#> 20: Expression data from pre natal and post natal CCAM samples and relative controls
#> experiment.name
#> experiment.ID
#> <int>
#> 1: 1
#> 2: 24
#> 3: 55
#> 4: 56
#> 5: 57
#> 6: 58
#> 7: 141
#> 8: 166
#> 9: 171
#> 10: 181
#> 11: 197
#> 12: 204
#> 13: 219
#> 14: 225
#> 15: 305
#> 16: 306
#> 17: 398
#> 18: 453
#> 19: 502
#> 20: 532
#> experiment.ID
#> experiment.description
#> <char>
#> 1: Bronchoalveolar lavage samples collected from lung transplant recipients. Numeric portion of sample name is an arbitrary patient ID and AxBx number indicates the perivascular (A) and bronchiolar (B) scores from biopsies collected on the same day as the BAL fluid was collected. Several patients have more than one sample in this series and can be determined by patient number followed by a lower case letter. Acute rejection state is determined by the combined A and B score - specifically, a combined AB score of 2 or greater is considered an acute rejection.
#> 2: Expression profiles of different mouse tissue samples profiles.\nDate GSE2178 Last Updated: May 29 2005\nContributors: Michael Mucenski Bruce J Aronow Belinda Peach Mitchell Cohen Amy Moseley Susan E Waltz John Maggio Sandra Degen Steve Potter Thomas Doetschman Chris Erwin Joanna A Groden James Lessard Linda Parysek R Hirsch MaryBeth Genter Jianhua Zhang Kathleen Anderson Jonathan D Katz John Dedman Michael Lehman Jorge A Bezerra Michael D Bates Ming Xu Dan Wiginton Jeff A Molkentin\nIncludes GDS1322.\n Update date: Nov 09 2005.\n Dataset description GDS1322: Gene expression profiles across a wide variety of tissue samples. The tissues examined include nervous system, skeletal muscle, male and female reproductive organs, gastrointestinal tract, as well as organs in a developmental context including lung, liver, kidney, and heart.
#> 3: Ozone is a very common environmental pollutant and has been associated with the exacerbation of cardiopulmonary diseases like asthma. In this study the molecular mechanisms underlying the effect of ozone on airways hyperpermability were investigated. Two strains of mice, HeJ (Tlr4 mutant) and OuJ (wildtype) were exposed continuously to air or 0.3ppm of ozone. Lungs were removed and RNA was collected to generate expression profiles.\nDate GSE476 Last Updated: Jun 15 2005\nContributors: Stephan Kleeberger\nIncludes GDS239.\n Update date: Mar 22 2004.\n Dataset description GDS239: Ozone is an environmental pollutant associated with exacerbation of cardiopulmonary diseases like asthma. HeJ (Tlr4 mutant) and OuJ (wild type) exposed continuously to air or 0.3 ppm of ozone, and lungs examined.
#> 4: Pulmonary alveoli are complex architectural units thought to undergo endogenous or pharmacologically induced programs of regeneration and degeneration. To study the molecular mechanism of alveoli loss mice were calorie restricted at different timepoints. Lungs were harvested and processed for RNA extraction.\nDate GSE478 Last Updated: Jul 14 2006\nContributors: Donald Massaro Gloria D Massaro\nIncludes GDS241.\n Update date: Jun 27 2003.\n Dataset description GDS241: Temporal study of lung alveoli destruction induced by caloric restriction. Alveoli number and surface area are linked to oxygen consumption, and calorie restriction lowers oxygen consumption.
#> 5: It has been shown that dexamethasone (Dex) impairs the normal lung septation that occurs in the early postnatal period. Treatment with retinoic acid (ATRA) abrogates the effects of Dex. To understand the molecular basis for the Dex indiced inhibition of the formation of the alveoli and the ability of ATRA to prevent the inhibition of septation, gene expression was analyzed in 4-day old mice treated with diluent (control), Dex-treated and ATRA+Dex-treated.\nDate GSE484 Last Updated: Aug 24 2005\nContributors: Linda Clerch\nIncludes GDS250.\n Update date: Jun 27 2003.\n Dataset description GDS250: Analysis of how dexamethasone (DEX) inhibits normal lung septation and alveoli formation, and mechanism by which retinoic acid (ATRA) protects against DEX. Three day old mice treated with ATRA or control. On day 4, half of each group administered DEX.
#> 6: The murine pulmonary response is strain specific. Susceptible strains such as C57BL6/J experience an inflammatory response followed by progressive lung disease. Resistant strains such as Balb/c do not experience significant degrees of inflammation or fibrosis. This study aims to determine the genetic basis of sensitivity differences between strains.\nDate GSE485 Last Updated: Jul 27 2006\nContributors: David Moller Y W Chen\nIncludes GDS251.\n Update date: Jun 27 2003.\n Dataset description GDS251: Determination of genetic basis of sensitivity to pulmonary fibrosis induced by bleomycin by comparing susceptible (C57BL6/J) and resistant (Balb/c) strains. C57BL6/J experience inflammatory response then progressive lung disease, Balb/c do not.
#> 7: Two prognostically significant subtypes of high-grade lung neuroendocrine tumors independent of small-cell and large-cell neuroendocrine carcinomas identified by gene expression profiles. BACKGROUND: Classification of high-grade neuroendocrine tumors (HGNT) of the lung currently recognises large-cell neuroendocrine carcinoma (LCNEC) and small-cell lung carcinoma (SCLC) as distinct groups. However, a similarity in histology for these two carcinomas and uncertain clinical course have led to suggestions that a single HGNT classification would be more appropriate. Gene expression profiling, which can reproduce histopathological classification, and often defines new subclasses with prognostic significance, can be used to resolve HGNT classification. METHODS: We used cDNA microarrays with 40386 elements to analyze the gene expression profiles of 38 surgically resected samples of lung neuroendocrine tumors and 11 SCLC cell lines. Samples of large-cell carcinoma, adenocarcinoma, and normal lung were also included to give a total of 105 samples analyzed. The data were subjected to filtering to yield informative genes before unsupervised hierarchical clustering that identified relatedness of tumor samples. FINDINGS: Distinct groups for carcinoids, large-cell carcinoma, adenocarcinoma, and normal lung were readily identified. However, we were unable to distinguish LCNEC from SCLC by gene expression profiling. Three independent rounds of unsupervised hierarchical clustering consistently divided SCLC samples into two main groups with LCNEC samples largely integrated with these groups. Furthermore, patients in one of the groups identified by clustering had a significantly better clinical outcome than the other (83% vs 12% survived for 5 years; p=0.0094. None of the highly proliferative SCLC cell lines subsequently analyzed clustered with this good-prognosis group. INTERPRETATION: Our findings show that HGNT of the lung can be classified into two groups independent of SCLC and LCNEC. To this end, we have identified many genes, some of which encode well-characterized markers of cancer that distinguish the HGNT groups. These results have implications for the diagnosis, classification, and treatment of lung neuroendocrine tumors, and provide important insights into their underlying biology.\nDate GSE1037 Last Updated: May 29 2005\nContributors: Virtanen Carl Ishikawa Yuichi Jones Michael Yuichi Ishikawa Daisuke Ishikawa Carl Virtanen Michael H Jones\nIncludes GDS619.\n Update date: Jul 12 2004.\n Dataset description GDS619: Molecular classification of lung high-grade neuroendocrine tumor (HGNT) groups. Carcinoids, large-cell carcinoma, adenocarcinoma, small-cell lung carcinoma cell lines, and normal lung examined.
#> 8: The tissue-specific pattern of mRNA expression can indicate important clues about gene function. High-density oligonucleotide arrays offer the opportunity to examine patterns of gene expression on a genome scale. Toward this end, we have designed custom arrays that interrogate the expression of the vast majority of protein-encoding human and mouse genes and have used them to profile a panel of 79 human and 61 mouse tissues. The resulting data set provides the expression patterns for thousands of predicted genes, as well as known and poorly characterized genes, from mice and humans. We have explored this data set for global trends in gene expression, evaluated commonly used lines of evidence in gene prediction methodologies, and investigated patterns indicative of chromosomal organization of transcription. We describe hundreds of regions of correlated transcription and show that some are subject to both tissue and parental allele-specific expression, suggesting a link between spatial expression and imprinting.\nDate GSE1133.2 Last Updated: \nContributors: John B Hogenesch John R Walker Gabriel Kreiman Tim Wiltshire David Block Mimi Hayakawa Hilmar Lapp Richard Soden Michael P Cooke Serge Batalov Keith A Ching Andrew I Su Jie Zhang\nFrom GSE1133\nIncludes GDS592.\n Update date: May 13 2004.\n Dataset description GDS592: Gene atlas of mouse protein-encoding transcriptome. Gene expression profiles from 61 physiologically normal tissues. Samples generated from adult (10-12 week old) C57BL/6 mice (4 male, 3 female) by dissection.
#> 9: The Mg-delta strain of fibrillin-1 deficient mice display an early defect in alveolar septation. This study attempts to identify expression patterns in the mutant mouse lung compared to their wild-type littermates that suggest pathways that are critical for normal septation. This experiment focuses on postnatal days 1 and 5. Other time points will be added at a later date.\nDate GSE479 Last Updated: Jul 27 2006\nContributors: Enid R Neptune\nIncludes GDS243.\n Update date: Jul 27 2006.\n Dataset description GDS243: Study of alveolar septation using the Mg-delta strain of fibrillin-1 deficient mice which display an early defect in alveolar septation, and serve as a model for Marfan syndrome and emphysema.\nIncludes GDS244.\n Update date: Jul 27 2006.\n Dataset description GDS244: Study of alveolar septation using the Mg-delta strain of fibrillin-1 deficient mice which display an early defect in alveolar septation, and serve as a model for Marfan syndrome and emphysema.\nIncludes GDS242.\n Update date: Jul 27 2006.\n Dataset description GDS242: Study of alveolar septation using the Mg-delta strain of fibrillin-1 deficient mice which display an early defect in alveolar septation, and serve as a model for Marfan syndrome and emphysema. [Switched to use MG-U74A/B/C by Gemma]
#> 10: High-throughput gene expression profiling has become an important tool for investigating transcriptional activity in a variety of biological samples. To date, the vast majority of these experiments have focused on specific biological processes and perturbations. Here, we have generated and analyzed gene expression from a set of samples spanning a broad range of biological conditions. Specifically, we profiled gene expression from 91 human and mouse samples across a diverse array of tissues, organs, and cell lines. Because these samples predominantly come from the normal physiological state in the human and mouse, this dataset represents a preliminary, but substantial, description of the normal mammalian transcriptome. We have used this dataset to illustrate methods of mining these data, and to reveal insights into molecular and physiological gene function, mechanisms of transcriptional regulation, disease etiology, and comparative genomics. Finally, to allow the scientific community to use this resource, we have built a free and publicly accessible website (http://expression.gnf.org) that integrates data visualization and curation of current gene annotations.\nDate GSE97 Last Updated: May 29 2005\nContributors: M P Cooke J R Walker R G Vega K A Ching L M Sapinoso A I Su T Wiltshire A Patapoutian P G Schultz A P Orth A Moqrich Y Hakak J B Hogenesch G M Hampton\nIncludes GDS182.\n Update date: May 13 2004.\n Dataset description GDS182: Gene expression profiles from a diverse array of tissues, organs, and cell lines, from the normal physiological state. Represents a preliminary description of the normal mammalian transcriptome.
#> 11: Profile of gene expression in lungs of ovalbumen sensitized and challenged C3H mice.\nDate GSE446 Last Updated: May 29 2005\nIncludes GDS347.\n Update date: Mar 22 2004.\n Dataset description GDS347: Lungs of ovalbumin sensitized/challenged C3H mice compared with controls. C3H are ovalbumin resistant. Attempt to identify genes involved in antigen-induced airway hyperresponsiveness.
#> 12: Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses.
#> 13: Multiclass cancer diagnosis using tumor gene expression signatures.
#> 14: Chemosensitivity prediction by transcriptional profiling.
#> 15: We performed expression profiling of 36 types of normal human tissues and identified 2,503 tissue-specific genes. We then systematically studied the expression of these genes in cancers by re-analyzing a large collection of published DNA microarray datasets. Our study shows that integration of each gene's breadth of expression (BOE) in normal tissues is important for biological interpretation of the expression profiles of cancers in terms of tumor differentiation, cell lineage and metastasis. Twenty five total RNA specimens were purchased from Clontech (Palo Alto, CA), Ambion (Austin, TX) and Strategene (La Jolla, CA). We tried to cover as many tissue types as possible by using pooled RNA samples. In order to define breadth-of-expression (BOE) accurately at a reasonable cost, we tried to cover as many tissue types as possible by using pooled RNA samples. Each specimen represents a human organ. We used RNA samples pooled from 2 to 84 donors to avoid differences at the individual level. Detailed sample information and Affymetrix .CEL files are available at http://www.genome.rcast.u-tokyo.ac.jp/normal/ Publication:Ge X et al., Interpreting expression profiles of cancers by genome-wide survey of breadth of expression in normal tissues. Genomics. 2005 Aug;86(2):127-141. PMID: 15950434\nLast Updated (by provider): May 11 2006\nContributors: Xijin Ge SanMing Wang Siego Ihara Hiroyuki Aburatani Yutaka Midorikawa Shogo Yamamoto Shuichi Tsutsumi\nIncludes GDS1096.\n Update date: Apr 08 2005.\n Dataset description GDS1096: Expression profiling of 36 types of normal tissue. Each RNA tissue sample pooled from several donors. Results identify tissue specific genes and provide baselines for interpreting gene expression in cancer.
#> 16: This series represents 52 tissues hybridized across 5 different chip patterns. Probes were placed at every exon-exon junction in each transcript.\nLast Updated (by provider): May 29 2005\nContributors: Christopher D Armour Eric E Schadt Daniel D Shoemaker Patrick M Loerch Philip Garrett-Engele Jason M Johnson Roland Stoughton Ralph Santos Zhengyan Kan John Castle\nIncludes GDS830.\n Update date: Nov 10 2004.\n Dataset description GDS830: Monitoring of mRNA splice variants for more than 10,000 multi-exon genes using arrays with oligonucleotide probes positioned at exon-exon junctions. mRNA from 52 tissues and cell lines examined. Results provide tissue distribution of splice variants and identify novel splice variants.\nIncludes GDS829.\n Update date: Nov 10 2004.\n Dataset description GDS829: Monitoring of mRNA splice variants for more than 10,000 multi-exon genes using arrays with oligonucleotide probes positioned at exon-exon junctions. mRNA from 52 tissues and cell lines examined. Results provide tissue distribution of splice variants and identify novel splice variants.\nIncludes GDS833.\n Update date: Nov 10 2004.\n Dataset description GDS833: Monitoring of mRNA splice variants for more than 10,000 multi-exon genes using arrays with oligonucleotide probes positioned at exon-exon junctions. mRNA from 52 tissues and cell lines examined. Results provide tissue distribution of splice variants and identify novel splice variants.\nIncludes GDS831.\n Update date: Nov 10 2004.\n Dataset description GDS831: Monitoring of mRNA splice variants for more than 10,000 multi-exon genes using arrays with oligonucleotide probes positioned at exon-exon junctions. mRNA from 52 tissues and cell lines examined. Results provide tissue distribution of splice variants and identify novel splice variants.\nIncludes GDS832.\n Update date: Nov 10 2004.\n Dataset description GDS832: Monitoring of mRNA splice variants for more than 10,000 multi-exon genes using arrays with oligonucleotide probes positioned at exon-exon junctions. mRNA from 52 tissues and cell lines examined. Results provide tissue distribution of splice variants and identify novel splice variants. [Switched to use Rosetta_Merged by Gemma]
#> 17: High-throughput gene expression profiling has become an important tool for investigating transcriptional activity in a variety of biological samples. To date, the vast majority of these experiments have focused on specific biological processes and perturbations. Here, we have generated and analyzed gene expression from a set of samples spanning a broad range of biological conditions. Specifically, we profiled gene expression from 91 human and mouse samples across a diverse array of tissues, organs, and cell lines. Because these samples predominantly come from the normal physiological state in the human and mouse, this dataset represents a preliminary, but substantial, description of the normal mammalian transcriptome. We have used this dataset to illustrate methods of mining these data, and to reveal insights into molecular and physiological gene function, mechanisms of transcriptional regulation, disease etiology, and comparative genomics. Finally, to allow the scientific community to use this resource, we have built a free and publicly accessible website (http://expression.gnf.org) that integrates data visualization and curation of current gene annotations.\nLast Updated (by provider): May 29 2005\nContributors: A Moqrich R G Vega P G Schultz M P Cooke A I Su T Wiltshire A P Orth K A Ching L M Sapinoso J B Hogenesch J R Walker Y Hakak A Patapoutian G M Hampton\nIncludes GDS181.\n Update date: May 13 2004.\n Dataset description GDS181: Gene expression profiles from a diverse array of tissues, organs, and cell lines, from the normal physiological state. Represents a preliminary description of the normal mammalian transcriptome.
#> 18: This SuperSeries is composed of the following subset Series: GSE3461: Gene expression in miscellaneous human tissues and cell lines GSE3462: Triiodothyronine Treatment: Effects on vastus lateralis skeletal muscle Abstract: Thyroid hormones are key regulators of metabolism that modulate transcription via nuclear receptors. Hyperthyroidism is associated with increased metabolic rate, protein breakdown, and weight loss. Although the molecular actions of thyroid hormones have been studied thoroughly, their pleiotropic effects are mediated by complex changes in expression of an unknown number of target genes. Here, we measured patterns of skeletal muscle gene expression in five healthy men treated for 14 days with 75 µg of triiodothyronine, using 24,000 cDNA element microarrays. To analyze the data, we used a new statistical method that identifies significant changes in expression and estimates the false discovery rate. The 381 up-regulated genes were involved in a wide range of cellular functions including transcriptional control, mRNA maturation, protein turnover, signal transduction, cellular trafficking, and energy metabolism. Only two genes were down-regulated. Most of the genes are novel targets of thyroid hormone. Cluster analysis of triiodothyronine-regulated gene expression among 19 different human tissues or cell lines revealed sets of coregulated genes that serve similar biologic functions. These results define molecular signatures that help to understand the physiology and pathophysiology of thyroid hormone action.\nLast Updated (by provider): Dec 12 2006\n [Switched to use SMHu24k by Gemma]
#> 19: Experimental asthma was induced in BALB/c mice by sensitization and challenge with the allergen ovalbumin. Control groups received PBS. To investigate the innate immune component of experimental asthma, we also analyzed recombinase activating gene (RAG) deficient mice following exposure to ovalbumin and control PBS\nLast Updated (by provider): Apr 23 2007\nContributors: Xin Lu Patricia W Finn Vipul V Jain David L Perkins
#> 20: This study aims at giving an insight on gene expression in CCAM.\nLast Updated (by provider): May 05 2006\nContributors: Agnes Paquet Sam Hawgood Amy Wagner
#> experiment.description
#> experiment.troubled experiment.accession experiment.database
#> <lgcl> <char> <char>
#> 1: FALSE GSE2018 GEO
#> 2: FALSE GSE2178 GEO
#> 3: FALSE GSE476 GEO
#> 4: FALSE GSE478 GEO
#> 5: FALSE GSE484 GEO
#> 6: FALSE GSE485 GEO
#> 7: FALSE GSE1037 GEO
#> 8: FALSE GSE1133 GEO
#> 9: FALSE GSE479 GEO
#> 10: FALSE GSE97 GEO
#> 11: FALSE GSE446 GEO
#> 12: FALSE <NA> <NA>
#> 13: FALSE <NA> <NA>
#> 14: FALSE <NA> <NA>
#> 15: FALSE GSE2361 GEO
#> 16: FALSE GSE740 GEO
#> 17: FALSE GSE96 GEO
#> 18: FALSE GSE4345 GEO
#> 19: FALSE GSE6858 GEO
#> 20: FALSE GSE4772 GEO
#> experiment.troubled experiment.accession experiment.database
#> experiment.URI
#> <char>
#> 1: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE2018
#> 2: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE2178
#> 3: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE476
#> 4: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE478
#> 5: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE484
#> 6: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE485
#> 7: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE1037
#> 8: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE1133
#> 9: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE479
#> 10: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE97
#> 11: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE446
#> 12: <NA>
#> 13: <NA>
#> 14: <NA>
#> 15: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE2361
#> 16: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE740
#> 17: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE96
#> 18: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE4345
#> 19: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6858
#> 20: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE4772
#> experiment.URI
#> experiment.sampleCount experiment.lastUpdated
#> <int> <POSc>
#> 1: 34 2024-02-10 08:31:41
#> 2: 161 2023-12-18 06:10:33
#> 3: 8 2023-12-19 23:41:59
#> 4: 24 2023-12-19 23:54:57
#> 5: 8 2023-12-20 01:12:41
#> 6: 24 2023-12-20 01:32:02
#> 7: 91 2024-01-25 08:33:32
#> 8: 122 2023-12-19 19:15:53
#> 9: 8 2023-12-20 00:06:15
#> 10: 90 2024-03-14 07:35:01
#> 11: 5 2023-12-19 19:22:01
#> 12: 203 2023-09-07 21:43:44
#> 13: 280 2023-09-07 21:47:00
#> 14: 60 2023-09-07 21:49:19
#> 15: 36 2023-12-18 11:53:39
#> 16: 54 2023-12-21 00:01:48
#> 17: 85 2023-12-21 12:41:55
#> 18: 24 2024-07-04 16:51:22
#> 19: 16 2023-12-20 20:39:44
#> 20: 14 2023-12-19 23:48:33
#> experiment.sampleCount experiment.lastUpdated
#> experiment.batchEffectText experiment.batchCorrected
#> <char> <lgcl>
#> 1: NO_BATCH_EFFECT_SUCCESS FALSE
#> 2: NO_BATCH_INFO FALSE
#> 3: SINGLE_BATCH_SUCCESS FALSE
#> 4: BATCH_EFFECT_FAILURE FALSE
#> 5: SINGLE_BATCH_SUCCESS FALSE
#> 6: BATCH_EFFECT_FAILURE FALSE
#> 7: PROBLEMATIC_BATCH_INFO_FAILURE FALSE
#> 8: BATCH_EFFECT_FAILURE FALSE
#> 9: NO_BATCH_EFFECT_SUCCESS FALSE
#> 10: BATCH_EFFECT_FAILURE FALSE
#> 11: NO_BATCH_INFO FALSE
#> 12: NO_BATCH_INFO FALSE
#> 13: NO_BATCH_INFO FALSE
#> 14: NO_BATCH_INFO FALSE
#> 15: BATCH_EFFECT_FAILURE FALSE
#> 16: NO_BATCH_INFO FALSE
#> 17: BATCH_EFFECT_FAILURE FALSE
#> 18: NO_BATCH_INFO FALSE
#> 19: BATCH_CORRECTED_SUCCESS TRUE
#> 20: NO_BATCH_INFO FALSE
#> experiment.batchEffectText experiment.batchCorrected
#> experiment.batchConfound experiment.batchEffect experiment.rawData
#> <num> <num> <num>
#> 1: 1 1 1
#> 2: 0 0 -1
#> 3: 1 1 1
#> 4: -1 0 1
#> 5: 1 1 1
#> 6: -1 0 1
#> 7: 0 0 -1
#> 8: 1 1 -1
#> 9: 1 0 1
#> 10: 1 1 -1
#> 11: 0 0 -1
#> 12: 0 0 -1
#> 13: 0 0 -1
#> 14: 0 0 -1
#> 15: 1 0 1
#> 16: 0 0 -1
#> 17: -1 0 1
#> 18: 0 0 -1
#> 19: 1 1 1
#> 20: 0 0 -1
#> experiment.batchConfound experiment.batchEffect experiment.rawData
#> geeq.qScore geeq.sScore taxon.name taxon.scientific taxon.ID taxon.NCBI
#> <num> <num> <char> <char> <int> <int>
#> 1: 0.99763407 0.8750 human Homo sapiens 1 9606
#> 2: -0.22593272 0.0625 mouse Mus musculus 2 10090
#> 3: 0.85574622 0.6500 mouse Mus musculus 2 10090
#> 4: 0.42626507 0.8125 mouse Mus musculus 2 10090
#> 5: 0.85510163 0.6500 mouse Mus musculus 2 10090
#> 6: 0.42565509 0.5625 mouse Mus musculus 2 10090
#> 7: -0.08397851 0.1875 human Homo sapiens 1 9606
#> 8: 0.84599960 0.2500 mouse Mus musculus 2 10090
#> 9: 0.42857143 0.1875 mouse Mus musculus 2 10090
#> 10: 0.55722466 0.3125 mouse Mus musculus 2 10090
#> 11: -0.03621544 -0.3750 mouse Mus musculus 2 10090
#> 12: 0.39301864 0.2500 human Homo sapiens 1 9606
#> 13: 0.23585518 0.0000 human Homo sapiens 1 9606
#> 14: 0.24928653 0.1250 human Homo sapiens 1 9606
#> 15: 0.54414795 0.8750 human Homo sapiens 1 9606
#> 16: -0.14285714 -0.2500 human Homo sapiens 1 9606
#> 17: 0.28369987 0.7500 human Homo sapiens 1 9606
#> 18: -0.13451104 0.1250 human Homo sapiens 1 9606
#> 19: 0.85445543 0.8750 mouse Mus musculus 2 10090
#> 20: 0.28025816 0.3750 human Homo sapiens 1 9606
#> geeq.qScore geeq.sScore taxon.name taxon.scientific taxon.ID taxon.NCBI
#> taxon.database.name taxon.database.ID
#> <char> <int>
#> 1: hg38 87
#> 2: mm10 81
#> 3: mm10 81
#> 4: mm10 81
#> 5: mm10 81
#> 6: mm10 81
#> 7: hg38 87
#> 8: mm10 81
#> 9: mm10 81
#> 10: mm10 81
#> 11: mm10 81
#> 12: hg38 87
#> 13: hg38 87
#> 14: hg38 87
#> 15: hg38 87
#> 16: hg38 87
#> 17: hg38 87
#> 18: hg38 87
#> 19: mm10 81
#> 20: hg38 87
#> taxon.database.name taxon.database.ID
# filter below is equivalent to the call above
get_datasets(filter = "taxon.commonName in (mouse,human) and allCharacteristics.valueUri = http://purl.obolibrary.org/obo/UBERON_0002048")
#> experiment.shortName
#> <char>
#> 1: GSE2018
#> 2: GSE2178
#> 3: GSE476
#> 4: GSE478
#> 5: GSE484
#> 6: GSE485
#> 7: GSE1037
#> 8: GSE1133.2
#> 9: GSE479
#> 10: GSE97
#> 11: GSE446
#> 12: bhattacharjee-lung
#> 13: ramaswamy-cancer
#> 14: staunton-nci60
#> 15: GSE2361
#> 16: GSE740
#> 17: GSE96
#> 18: GSE4345
#> 19: GSE6858
#> 20: GSE4772
#> experiment.shortName
#> experiment.name
#> <char>
#> 1: Human Lung Transplant - BAL
#> 2: Mouse Expression DB 2001 CHMC
#> 3: Ozone effect on airways hyperpermability
#> 4: Alveoli loss during caloric restriction time course
#> 5: Alveoli septation inhibition and protection
#> 6: Genetic basis of sensitivity to pulmonary fibrosis
#> 7: Lung cancer
#> 8: tissue-specific pattern of mRNA expression - Mus musculus
#> 9: Alveolar septation
#> 10: Large-scale analysis of the mouse transcriptome
#> 11: Lungs from ovalbumen sensitized and challenged C3H mice
#> 12: bhattacharjee-lung
#> 13: ramaswamy-cancer
#> 14: staunton-nci60
#> 15: Expression Proflies of Human Normal tissues
#> 16: Rosetta_Merck_Splicing_Experiment
#> 17: Large-scale analysis of the human transcriptome
#> 18: In Vivo Regulation of Human Skeletal Muscle Gene Expression by Thyroid Hormone
#> 19: Expression data from experimental murine asthma
#> 20: Expression data from pre natal and post natal CCAM samples and relative controls
#> experiment.name
#> experiment.ID
#> <int>
#> 1: 1
#> 2: 24
#> 3: 55
#> 4: 56
#> 5: 57
#> 6: 58
#> 7: 141
#> 8: 166
#> 9: 171
#> 10: 181
#> 11: 197
#> 12: 204
#> 13: 219
#> 14: 225
#> 15: 305
#> 16: 306
#> 17: 398
#> 18: 453
#> 19: 502
#> 20: 532
#> experiment.ID
#> experiment.description
#> <char>
#> 1: Bronchoalveolar lavage samples collected from lung transplant recipients. Numeric portion of sample name is an arbitrary patient ID and AxBx number indicates the perivascular (A) and bronchiolar (B) scores from biopsies collected on the same day as the BAL fluid was collected. Several patients have more than one sample in this series and can be determined by patient number followed by a lower case letter. Acute rejection state is determined by the combined A and B score - specifically, a combined AB score of 2 or greater is considered an acute rejection.
#> 2: Expression profiles of different mouse tissue samples profiles.\nDate GSE2178 Last Updated: May 29 2005\nContributors: Michael Mucenski Bruce J Aronow Belinda Peach Mitchell Cohen Amy Moseley Susan E Waltz John Maggio Sandra Degen Steve Potter Thomas Doetschman Chris Erwin Joanna A Groden James Lessard Linda Parysek R Hirsch MaryBeth Genter Jianhua Zhang Kathleen Anderson Jonathan D Katz John Dedman Michael Lehman Jorge A Bezerra Michael D Bates Ming Xu Dan Wiginton Jeff A Molkentin\nIncludes GDS1322.\n Update date: Nov 09 2005.\n Dataset description GDS1322: Gene expression profiles across a wide variety of tissue samples. The tissues examined include nervous system, skeletal muscle, male and female reproductive organs, gastrointestinal tract, as well as organs in a developmental context including lung, liver, kidney, and heart.
#> 3: Ozone is a very common environmental pollutant and has been associated with the exacerbation of cardiopulmonary diseases like asthma. In this study the molecular mechanisms underlying the effect of ozone on airways hyperpermability were investigated. Two strains of mice, HeJ (Tlr4 mutant) and OuJ (wildtype) were exposed continuously to air or 0.3ppm of ozone. Lungs were removed and RNA was collected to generate expression profiles.\nDate GSE476 Last Updated: Jun 15 2005\nContributors: Stephan Kleeberger\nIncludes GDS239.\n Update date: Mar 22 2004.\n Dataset description GDS239: Ozone is an environmental pollutant associated with exacerbation of cardiopulmonary diseases like asthma. HeJ (Tlr4 mutant) and OuJ (wild type) exposed continuously to air or 0.3 ppm of ozone, and lungs examined.
#> 4: Pulmonary alveoli are complex architectural units thought to undergo endogenous or pharmacologically induced programs of regeneration and degeneration. To study the molecular mechanism of alveoli loss mice were calorie restricted at different timepoints. Lungs were harvested and processed for RNA extraction.\nDate GSE478 Last Updated: Jul 14 2006\nContributors: Donald Massaro Gloria D Massaro\nIncludes GDS241.\n Update date: Jun 27 2003.\n Dataset description GDS241: Temporal study of lung alveoli destruction induced by caloric restriction. Alveoli number and surface area are linked to oxygen consumption, and calorie restriction lowers oxygen consumption.
#> 5: It has been shown that dexamethasone (Dex) impairs the normal lung septation that occurs in the early postnatal period. Treatment with retinoic acid (ATRA) abrogates the effects of Dex. To understand the molecular basis for the Dex indiced inhibition of the formation of the alveoli and the ability of ATRA to prevent the inhibition of septation, gene expression was analyzed in 4-day old mice treated with diluent (control), Dex-treated and ATRA+Dex-treated.\nDate GSE484 Last Updated: Aug 24 2005\nContributors: Linda Clerch\nIncludes GDS250.\n Update date: Jun 27 2003.\n Dataset description GDS250: Analysis of how dexamethasone (DEX) inhibits normal lung septation and alveoli formation, and mechanism by which retinoic acid (ATRA) protects against DEX. Three day old mice treated with ATRA or control. On day 4, half of each group administered DEX.
#> 6: The murine pulmonary response is strain specific. Susceptible strains such as C57BL6/J experience an inflammatory response followed by progressive lung disease. Resistant strains such as Balb/c do not experience significant degrees of inflammation or fibrosis. This study aims to determine the genetic basis of sensitivity differences between strains.\nDate GSE485 Last Updated: Jul 27 2006\nContributors: David Moller Y W Chen\nIncludes GDS251.\n Update date: Jun 27 2003.\n Dataset description GDS251: Determination of genetic basis of sensitivity to pulmonary fibrosis induced by bleomycin by comparing susceptible (C57BL6/J) and resistant (Balb/c) strains. C57BL6/J experience inflammatory response then progressive lung disease, Balb/c do not.
#> 7: Two prognostically significant subtypes of high-grade lung neuroendocrine tumors independent of small-cell and large-cell neuroendocrine carcinomas identified by gene expression profiles. BACKGROUND: Classification of high-grade neuroendocrine tumors (HGNT) of the lung currently recognises large-cell neuroendocrine carcinoma (LCNEC) and small-cell lung carcinoma (SCLC) as distinct groups. However, a similarity in histology for these two carcinomas and uncertain clinical course have led to suggestions that a single HGNT classification would be more appropriate. Gene expression profiling, which can reproduce histopathological classification, and often defines new subclasses with prognostic significance, can be used to resolve HGNT classification. METHODS: We used cDNA microarrays with 40386 elements to analyze the gene expression profiles of 38 surgically resected samples of lung neuroendocrine tumors and 11 SCLC cell lines. Samples of large-cell carcinoma, adenocarcinoma, and normal lung were also included to give a total of 105 samples analyzed. The data were subjected to filtering to yield informative genes before unsupervised hierarchical clustering that identified relatedness of tumor samples. FINDINGS: Distinct groups for carcinoids, large-cell carcinoma, adenocarcinoma, and normal lung were readily identified. However, we were unable to distinguish LCNEC from SCLC by gene expression profiling. Three independent rounds of unsupervised hierarchical clustering consistently divided SCLC samples into two main groups with LCNEC samples largely integrated with these groups. Furthermore, patients in one of the groups identified by clustering had a significantly better clinical outcome than the other (83% vs 12% survived for 5 years; p=0.0094. None of the highly proliferative SCLC cell lines subsequently analyzed clustered with this good-prognosis group. INTERPRETATION: Our findings show that HGNT of the lung can be classified into two groups independent of SCLC and LCNEC. To this end, we have identified many genes, some of which encode well-characterized markers of cancer that distinguish the HGNT groups. These results have implications for the diagnosis, classification, and treatment of lung neuroendocrine tumors, and provide important insights into their underlying biology.\nDate GSE1037 Last Updated: May 29 2005\nContributors: Virtanen Carl Ishikawa Yuichi Jones Michael Yuichi Ishikawa Daisuke Ishikawa Carl Virtanen Michael H Jones\nIncludes GDS619.\n Update date: Jul 12 2004.\n Dataset description GDS619: Molecular classification of lung high-grade neuroendocrine tumor (HGNT) groups. Carcinoids, large-cell carcinoma, adenocarcinoma, small-cell lung carcinoma cell lines, and normal lung examined.
#> 8: The tissue-specific pattern of mRNA expression can indicate important clues about gene function. High-density oligonucleotide arrays offer the opportunity to examine patterns of gene expression on a genome scale. Toward this end, we have designed custom arrays that interrogate the expression of the vast majority of protein-encoding human and mouse genes and have used them to profile a panel of 79 human and 61 mouse tissues. The resulting data set provides the expression patterns for thousands of predicted genes, as well as known and poorly characterized genes, from mice and humans. We have explored this data set for global trends in gene expression, evaluated commonly used lines of evidence in gene prediction methodologies, and investigated patterns indicative of chromosomal organization of transcription. We describe hundreds of regions of correlated transcription and show that some are subject to both tissue and parental allele-specific expression, suggesting a link between spatial expression and imprinting.\nDate GSE1133.2 Last Updated: \nContributors: John B Hogenesch John R Walker Gabriel Kreiman Tim Wiltshire David Block Mimi Hayakawa Hilmar Lapp Richard Soden Michael P Cooke Serge Batalov Keith A Ching Andrew I Su Jie Zhang\nFrom GSE1133\nIncludes GDS592.\n Update date: May 13 2004.\n Dataset description GDS592: Gene atlas of mouse protein-encoding transcriptome. Gene expression profiles from 61 physiologically normal tissues. Samples generated from adult (10-12 week old) C57BL/6 mice (4 male, 3 female) by dissection.
#> 9: The Mg-delta strain of fibrillin-1 deficient mice display an early defect in alveolar septation. This study attempts to identify expression patterns in the mutant mouse lung compared to their wild-type littermates that suggest pathways that are critical for normal septation. This experiment focuses on postnatal days 1 and 5. Other time points will be added at a later date.\nDate GSE479 Last Updated: Jul 27 2006\nContributors: Enid R Neptune\nIncludes GDS243.\n Update date: Jul 27 2006.\n Dataset description GDS243: Study of alveolar septation using the Mg-delta strain of fibrillin-1 deficient mice which display an early defect in alveolar septation, and serve as a model for Marfan syndrome and emphysema.\nIncludes GDS244.\n Update date: Jul 27 2006.\n Dataset description GDS244: Study of alveolar septation using the Mg-delta strain of fibrillin-1 deficient mice which display an early defect in alveolar septation, and serve as a model for Marfan syndrome and emphysema.\nIncludes GDS242.\n Update date: Jul 27 2006.\n Dataset description GDS242: Study of alveolar septation using the Mg-delta strain of fibrillin-1 deficient mice which display an early defect in alveolar septation, and serve as a model for Marfan syndrome and emphysema. [Switched to use MG-U74A/B/C by Gemma]
#> 10: High-throughput gene expression profiling has become an important tool for investigating transcriptional activity in a variety of biological samples. To date, the vast majority of these experiments have focused on specific biological processes and perturbations. Here, we have generated and analyzed gene expression from a set of samples spanning a broad range of biological conditions. Specifically, we profiled gene expression from 91 human and mouse samples across a diverse array of tissues, organs, and cell lines. Because these samples predominantly come from the normal physiological state in the human and mouse, this dataset represents a preliminary, but substantial, description of the normal mammalian transcriptome. We have used this dataset to illustrate methods of mining these data, and to reveal insights into molecular and physiological gene function, mechanisms of transcriptional regulation, disease etiology, and comparative genomics. Finally, to allow the scientific community to use this resource, we have built a free and publicly accessible website (http://expression.gnf.org) that integrates data visualization and curation of current gene annotations.\nDate GSE97 Last Updated: May 29 2005\nContributors: M P Cooke J R Walker R G Vega K A Ching L M Sapinoso A I Su T Wiltshire A Patapoutian P G Schultz A P Orth A Moqrich Y Hakak J B Hogenesch G M Hampton\nIncludes GDS182.\n Update date: May 13 2004.\n Dataset description GDS182: Gene expression profiles from a diverse array of tissues, organs, and cell lines, from the normal physiological state. Represents a preliminary description of the normal mammalian transcriptome.
#> 11: Profile of gene expression in lungs of ovalbumen sensitized and challenged C3H mice.\nDate GSE446 Last Updated: May 29 2005\nIncludes GDS347.\n Update date: Mar 22 2004.\n Dataset description GDS347: Lungs of ovalbumin sensitized/challenged C3H mice compared with controls. C3H are ovalbumin resistant. Attempt to identify genes involved in antigen-induced airway hyperresponsiveness.
#> 12: Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses.
#> 13: Multiclass cancer diagnosis using tumor gene expression signatures.
#> 14: Chemosensitivity prediction by transcriptional profiling.
#> 15: We performed expression profiling of 36 types of normal human tissues and identified 2,503 tissue-specific genes. We then systematically studied the expression of these genes in cancers by re-analyzing a large collection of published DNA microarray datasets. Our study shows that integration of each gene's breadth of expression (BOE) in normal tissues is important for biological interpretation of the expression profiles of cancers in terms of tumor differentiation, cell lineage and metastasis. Twenty five total RNA specimens were purchased from Clontech (Palo Alto, CA), Ambion (Austin, TX) and Strategene (La Jolla, CA). We tried to cover as many tissue types as possible by using pooled RNA samples. In order to define breadth-of-expression (BOE) accurately at a reasonable cost, we tried to cover as many tissue types as possible by using pooled RNA samples. Each specimen represents a human organ. We used RNA samples pooled from 2 to 84 donors to avoid differences at the individual level. Detailed sample information and Affymetrix .CEL files are available at http://www.genome.rcast.u-tokyo.ac.jp/normal/ Publication:Ge X et al., Interpreting expression profiles of cancers by genome-wide survey of breadth of expression in normal tissues. Genomics. 2005 Aug;86(2):127-141. PMID: 15950434\nLast Updated (by provider): May 11 2006\nContributors: Xijin Ge SanMing Wang Siego Ihara Hiroyuki Aburatani Yutaka Midorikawa Shogo Yamamoto Shuichi Tsutsumi\nIncludes GDS1096.\n Update date: Apr 08 2005.\n Dataset description GDS1096: Expression profiling of 36 types of normal tissue. Each RNA tissue sample pooled from several donors. Results identify tissue specific genes and provide baselines for interpreting gene expression in cancer.
#> 16: This series represents 52 tissues hybridized across 5 different chip patterns. Probes were placed at every exon-exon junction in each transcript.\nLast Updated (by provider): May 29 2005\nContributors: Christopher D Armour Eric E Schadt Daniel D Shoemaker Patrick M Loerch Philip Garrett-Engele Jason M Johnson Roland Stoughton Ralph Santos Zhengyan Kan John Castle\nIncludes GDS830.\n Update date: Nov 10 2004.\n Dataset description GDS830: Monitoring of mRNA splice variants for more than 10,000 multi-exon genes using arrays with oligonucleotide probes positioned at exon-exon junctions. mRNA from 52 tissues and cell lines examined. Results provide tissue distribution of splice variants and identify novel splice variants.\nIncludes GDS829.\n Update date: Nov 10 2004.\n Dataset description GDS829: Monitoring of mRNA splice variants for more than 10,000 multi-exon genes using arrays with oligonucleotide probes positioned at exon-exon junctions. mRNA from 52 tissues and cell lines examined. Results provide tissue distribution of splice variants and identify novel splice variants.\nIncludes GDS833.\n Update date: Nov 10 2004.\n Dataset description GDS833: Monitoring of mRNA splice variants for more than 10,000 multi-exon genes using arrays with oligonucleotide probes positioned at exon-exon junctions. mRNA from 52 tissues and cell lines examined. Results provide tissue distribution of splice variants and identify novel splice variants.\nIncludes GDS831.\n Update date: Nov 10 2004.\n Dataset description GDS831: Monitoring of mRNA splice variants for more than 10,000 multi-exon genes using arrays with oligonucleotide probes positioned at exon-exon junctions. mRNA from 52 tissues and cell lines examined. Results provide tissue distribution of splice variants and identify novel splice variants.\nIncludes GDS832.\n Update date: Nov 10 2004.\n Dataset description GDS832: Monitoring of mRNA splice variants for more than 10,000 multi-exon genes using arrays with oligonucleotide probes positioned at exon-exon junctions. mRNA from 52 tissues and cell lines examined. Results provide tissue distribution of splice variants and identify novel splice variants. [Switched to use Rosetta_Merged by Gemma]
#> 17: High-throughput gene expression profiling has become an important tool for investigating transcriptional activity in a variety of biological samples. To date, the vast majority of these experiments have focused on specific biological processes and perturbations. Here, we have generated and analyzed gene expression from a set of samples spanning a broad range of biological conditions. Specifically, we profiled gene expression from 91 human and mouse samples across a diverse array of tissues, organs, and cell lines. Because these samples predominantly come from the normal physiological state in the human and mouse, this dataset represents a preliminary, but substantial, description of the normal mammalian transcriptome. We have used this dataset to illustrate methods of mining these data, and to reveal insights into molecular and physiological gene function, mechanisms of transcriptional regulation, disease etiology, and comparative genomics. Finally, to allow the scientific community to use this resource, we have built a free and publicly accessible website (http://expression.gnf.org) that integrates data visualization and curation of current gene annotations.\nLast Updated (by provider): May 29 2005\nContributors: A Moqrich R G Vega P G Schultz M P Cooke A I Su T Wiltshire A P Orth K A Ching L M Sapinoso J B Hogenesch J R Walker Y Hakak A Patapoutian G M Hampton\nIncludes GDS181.\n Update date: May 13 2004.\n Dataset description GDS181: Gene expression profiles from a diverse array of tissues, organs, and cell lines, from the normal physiological state. Represents a preliminary description of the normal mammalian transcriptome.
#> 18: This SuperSeries is composed of the following subset Series: GSE3461: Gene expression in miscellaneous human tissues and cell lines GSE3462: Triiodothyronine Treatment: Effects on vastus lateralis skeletal muscle Abstract: Thyroid hormones are key regulators of metabolism that modulate transcription via nuclear receptors. Hyperthyroidism is associated with increased metabolic rate, protein breakdown, and weight loss. Although the molecular actions of thyroid hormones have been studied thoroughly, their pleiotropic effects are mediated by complex changes in expression of an unknown number of target genes. Here, we measured patterns of skeletal muscle gene expression in five healthy men treated for 14 days with 75 µg of triiodothyronine, using 24,000 cDNA element microarrays. To analyze the data, we used a new statistical method that identifies significant changes in expression and estimates the false discovery rate. The 381 up-regulated genes were involved in a wide range of cellular functions including transcriptional control, mRNA maturation, protein turnover, signal transduction, cellular trafficking, and energy metabolism. Only two genes were down-regulated. Most of the genes are novel targets of thyroid hormone. Cluster analysis of triiodothyronine-regulated gene expression among 19 different human tissues or cell lines revealed sets of coregulated genes that serve similar biologic functions. These results define molecular signatures that help to understand the physiology and pathophysiology of thyroid hormone action.\nLast Updated (by provider): Dec 12 2006\n [Switched to use SMHu24k by Gemma]
#> 19: Experimental asthma was induced in BALB/c mice by sensitization and challenge with the allergen ovalbumin. Control groups received PBS. To investigate the innate immune component of experimental asthma, we also analyzed recombinase activating gene (RAG) deficient mice following exposure to ovalbumin and control PBS\nLast Updated (by provider): Apr 23 2007\nContributors: Xin Lu Patricia W Finn Vipul V Jain David L Perkins
#> 20: This study aims at giving an insight on gene expression in CCAM.\nLast Updated (by provider): May 05 2006\nContributors: Agnes Paquet Sam Hawgood Amy Wagner
#> experiment.description
#> experiment.troubled experiment.accession experiment.database
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#> 4: FALSE GSE478 GEO
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#> 6: FALSE GSE485 GEO
#> 7: FALSE GSE1037 GEO
#> 8: FALSE GSE1133 GEO
#> 9: FALSE GSE479 GEO
#> 10: FALSE GSE97 GEO
#> 11: FALSE GSE446 GEO
#> 12: FALSE <NA> <NA>
#> 13: FALSE <NA> <NA>
#> 14: FALSE <NA> <NA>
#> 15: FALSE GSE2361 GEO
#> 16: FALSE GSE740 GEO
#> 17: FALSE GSE96 GEO
#> 18: FALSE GSE4345 GEO
#> 19: FALSE GSE6858 GEO
#> 20: FALSE GSE4772 GEO
#> experiment.troubled experiment.accession experiment.database
#> experiment.URI
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#> 1: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE2018
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#> 3: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE476
#> 4: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE478
#> 5: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE484
#> 6: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE485
#> 7: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE1037
#> 8: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE1133
#> 9: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE479
#> 10: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE97
#> 11: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE446
#> 12: <NA>
#> 13: <NA>
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#> 15: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE2361
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#> 17: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE96
#> 18: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE4345
#> 19: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6858
#> 20: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE4772
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#> geeq.qScore geeq.sScore taxon.name taxon.scientific taxon.ID taxon.NCBI
#> <num> <num> <char> <char> <int> <int>
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#> 2: -0.22593272 0.0625 mouse Mus musculus 2 10090
#> 3: 0.85574622 0.6500 mouse Mus musculus 2 10090
#> 4: 0.42626507 0.8125 mouse Mus musculus 2 10090
#> 5: 0.85510163 0.6500 mouse Mus musculus 2 10090
#> 6: 0.42565509 0.5625 mouse Mus musculus 2 10090
#> 7: -0.08397851 0.1875 human Homo sapiens 1 9606
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#> 9: 0.42857143 0.1875 mouse Mus musculus 2 10090
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#> 11: -0.03621544 -0.3750 mouse Mus musculus 2 10090
#> 12: 0.39301864 0.2500 human Homo sapiens 1 9606
#> 13: 0.23585518 0.0000 human Homo sapiens 1 9606
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#> taxon.database.name taxon.database.ID
get_datasets(query = "lung")
#> experiment.shortName
#> <char>
#> 1: GSE2018
#> 2: GSE2178
#> 3: GSE580
#> 4: GSE476
#> 5: GSE478
#> 6: GSE484
#> 7: GSE485
#> 8: GSE994
#> 9: GSE1037
#> 10: GSE470
#> 11: GSE1133.2
#> 12: GSE479
#> 13: GSE90
#> 14: GSE97
#> 15: GSE446
#> 16: bhattacharjee-lung
#> 17: ramaswamy-cancer
#> 18: staunton-nci60
#> 19: GSE1481
#> 20: GSE2361
#> experiment.shortName
#> experiment.name
#> <char>
#> 1: Human Lung Transplant - BAL
#> 2: Mouse Expression DB 2001 CHMC
#> 3: CX43 KO vs WT cortical astrocytes
#> 4: Ozone effect on airways hyperpermability
#> 5: Alveoli loss during caloric restriction time course
#> 6: Alveoli septation inhibition and protection
#> 7: Genetic basis of sensitivity to pulmonary fibrosis
#> 8: Effects of cigarette smoke on the human airway epithelial cell transcriptome
#> 9: Lung cancer
#> 10: Asthma exacerbatory factors
#> 11: tissue-specific pattern of mRNA expression - Mus musculus
#> 12: Alveolar septation
#> 13: Expression profiling by p53 status
#> 14: Large-scale analysis of the mouse transcriptome
#> 15: Lungs from ovalbumen sensitized and challenged C3H mice
#> 16: bhattacharjee-lung
#> 17: ramaswamy-cancer
#> 18: staunton-nci60
#> 19: LCCS vs control comparison
#> 20: Expression Proflies of Human Normal tissues
#> experiment.name
#> experiment.ID
#> <int>
#> 1: 1
#> 2: 24
#> 3: 43
#> 4: 55
#> 5: 56
#> 6: 57
#> 7: 58
#> 8: 128
#> 9: 141
#> 10: 155
#> 11: 166
#> 12: 171
#> 13: 179
#> 14: 181
#> 15: 197
#> 16: 204
#> 17: 219
#> 18: 225
#> 19: 247
#> 20: 305
#> experiment.ID
#> experiment.description
#> <char>
#> 1: Bronchoalveolar lavage samples collected from lung transplant recipients. Numeric portion of sample name is an arbitrary patient ID and AxBx number indicates the perivascular (A) and bronchiolar (B) scores from biopsies collected on the same day as the BAL fluid was collected. Several patients have more than one sample in this series and can be determined by patient number followed by a lower case letter. Acute rejection state is determined by the combined A and B score - specifically, a combined AB score of 2 or greater is considered an acute rejection.
#> 2: Expression profiles of different mouse tissue samples profiles.\nDate GSE2178 Last Updated: May 29 2005\nContributors: Michael Mucenski Bruce J Aronow Belinda Peach Mitchell Cohen Amy Moseley Susan E Waltz John Maggio Sandra Degen Steve Potter Thomas Doetschman Chris Erwin Joanna A Groden James Lessard Linda Parysek R Hirsch MaryBeth Genter Jianhua Zhang Kathleen Anderson Jonathan D Katz John Dedman Michael Lehman Jorge A Bezerra Michael D Bates Ming Xu Dan Wiginton Jeff A Molkentin\nIncludes GDS1322.\n Update date: Nov 09 2005.\n Dataset description GDS1322: Gene expression profiles across a wide variety of tissue samples. The tissues examined include nervous system, skeletal muscle, male and female reproductive organs, gastrointestinal tract, as well as organs in a developmental context including lung, liver, kidney, and heart.
#> 3: RNA samples extracted from astrocytes cultured from wild type and Cx43 null neonatal mice were dye labeled and individually co-hybridized with a reference of labeled cDNAs pooled from a variety of tissues on eight gene arrays containing 8975 mouse DNA sequences.\nDate GSE580 Last Updated: May 29 2005\nContributors: Dumitru A Iacobas Sanda Iacobas David C Spray Eliana Scemes Marcia Urban-Maldonado\nIncludes GDS455.\n Update date: Nov 19 2003.\n Dataset description GDS455: Analysis of cultured cortical astrocytes isolated from neonatal wild type and connexin-43 (Cx43) knockout mice. Results imply that gap junction gene expression alters numerous processes in addition to intercellular communication.
#> 4: Ozone is a very common environmental pollutant and has been associated with the exacerbation of cardiopulmonary diseases like asthma. In this study the molecular mechanisms underlying the effect of ozone on airways hyperpermability were investigated. Two strains of mice, HeJ (Tlr4 mutant) and OuJ (wildtype) were exposed continuously to air or 0.3ppm of ozone. Lungs were removed and RNA was collected to generate expression profiles.\nDate GSE476 Last Updated: Jun 15 2005\nContributors: Stephan Kleeberger\nIncludes GDS239.\n Update date: Mar 22 2004.\n Dataset description GDS239: Ozone is an environmental pollutant associated with exacerbation of cardiopulmonary diseases like asthma. HeJ (Tlr4 mutant) and OuJ (wild type) exposed continuously to air or 0.3 ppm of ozone, and lungs examined.
#> 5: Pulmonary alveoli are complex architectural units thought to undergo endogenous or pharmacologically induced programs of regeneration and degeneration. To study the molecular mechanism of alveoli loss mice were calorie restricted at different timepoints. Lungs were harvested and processed for RNA extraction.\nDate GSE478 Last Updated: Jul 14 2006\nContributors: Donald Massaro Gloria D Massaro\nIncludes GDS241.\n Update date: Jun 27 2003.\n Dataset description GDS241: Temporal study of lung alveoli destruction induced by caloric restriction. Alveoli number and surface area are linked to oxygen consumption, and calorie restriction lowers oxygen consumption.
#> 6: It has been shown that dexamethasone (Dex) impairs the normal lung septation that occurs in the early postnatal period. Treatment with retinoic acid (ATRA) abrogates the effects of Dex. To understand the molecular basis for the Dex indiced inhibition of the formation of the alveoli and the ability of ATRA to prevent the inhibition of septation, gene expression was analyzed in 4-day old mice treated with diluent (control), Dex-treated and ATRA+Dex-treated.\nDate GSE484 Last Updated: Aug 24 2005\nContributors: Linda Clerch\nIncludes GDS250.\n Update date: Jun 27 2003.\n Dataset description GDS250: Analysis of how dexamethasone (DEX) inhibits normal lung septation and alveoli formation, and mechanism by which retinoic acid (ATRA) protects against DEX. Three day old mice treated with ATRA or control. On day 4, half of each group administered DEX.
#> 7: The murine pulmonary response is strain specific. Susceptible strains such as C57BL6/J experience an inflammatory response followed by progressive lung disease. Resistant strains such as Balb/c do not experience significant degrees of inflammation or fibrosis. This study aims to determine the genetic basis of sensitivity differences between strains.\nDate GSE485 Last Updated: Jul 27 2006\nContributors: David Moller Y W Chen\nIncludes GDS251.\n Update date: Jun 27 2003.\n Dataset description GDS251: Determination of genetic basis of sensitivity to pulmonary fibrosis induced by bleomycin by comparing susceptible (C57BL6/J) and resistant (Balb/c) strains. C57BL6/J experience inflammatory response then progressive lung disease, Balb/c do not.
#> 8: A number of studies have shown that cigarette smoking produces a field defect, such that genetic mutations induced by smoking occur throughout the lung and its intra and extra-pulmonary airways. Based on this concept, we have begun this study, which has as its goal the definition of the normal airway transcriptome, an analysis of how that transcriptome is affected by cigarette smoke, and to explore the reversibility of altered gene expression when smoking has been discontinued. We have obtained brushings from intra-pulmonary airways (the right upper lobe carina) and scrapings from the buccal mucosa, from normal smoking and non-smoking volunteers (including 34 Current Smokers, 23 Never Smokers and 18 Former Smokers). RNA was isolated from these samples and gene expression profiles from intra-pulmonary airway epithelial cells were analyzed using Affymetrix U133A human gene expression arrays. All microarray data from the experiments described above have been stored, preprocessed and analyzed in a relational MySQL database that is accessible through our website at http://pulm.bumc.bu.edu/aged.\nDate GSE994 Last Updated: May 29 2005\nContributors: Jennifer Beane Frank Schembri John Palma Gang Liu Jerome Brody Avrum Spira Xuemei Yang Vishal Shah\nIncludes GDS534.\n Update date: May 02 2004.\n Dataset description GDS534: Analysis of cigarette smoking-induced changes in bronchial epithelia, and reversibility of effects when smoking is discontinued. May provide insight to molecular events leading to chronic obstructive pulmonary disease (COPD) and lung cancer.
#> 9: Two prognostically significant subtypes of high-grade lung neuroendocrine tumors independent of small-cell and large-cell neuroendocrine carcinomas identified by gene expression profiles. BACKGROUND: Classification of high-grade neuroendocrine tumors (HGNT) of the lung currently recognises large-cell neuroendocrine carcinoma (LCNEC) and small-cell lung carcinoma (SCLC) as distinct groups. However, a similarity in histology for these two carcinomas and uncertain clinical course have led to suggestions that a single HGNT classification would be more appropriate. Gene expression profiling, which can reproduce histopathological classification, and often defines new subclasses with prognostic significance, can be used to resolve HGNT classification. METHODS: We used cDNA microarrays with 40386 elements to analyze the gene expression profiles of 38 surgically resected samples of lung neuroendocrine tumors and 11 SCLC cell lines. Samples of large-cell carcinoma, adenocarcinoma, and normal lung were also included to give a total of 105 samples analyzed. The data were subjected to filtering to yield informative genes before unsupervised hierarchical clustering that identified relatedness of tumor samples. FINDINGS: Distinct groups for carcinoids, large-cell carcinoma, adenocarcinoma, and normal lung were readily identified. However, we were unable to distinguish LCNEC from SCLC by gene expression profiling. Three independent rounds of unsupervised hierarchical clustering consistently divided SCLC samples into two main groups with LCNEC samples largely integrated with these groups. Furthermore, patients in one of the groups identified by clustering had a significantly better clinical outcome than the other (83% vs 12% survived for 5 years; p=0.0094. None of the highly proliferative SCLC cell lines subsequently analyzed clustered with this good-prognosis group. INTERPRETATION: Our findings show that HGNT of the lung can be classified into two groups independent of SCLC and LCNEC. To this end, we have identified many genes, some of which encode well-characterized markers of cancer that distinguish the HGNT groups. These results have implications for the diagnosis, classification, and treatment of lung neuroendocrine tumors, and provide important insights into their underlying biology.\nDate GSE1037 Last Updated: May 29 2005\nContributors: Virtanen Carl Ishikawa Yuichi Jones Michael Yuichi Ishikawa Daisuke Ishikawa Carl Virtanen Michael H Jones\nIncludes GDS619.\n Update date: Jul 12 2004.\n Dataset description GDS619: Molecular classification of lung high-grade neuroendocrine tumor (HGNT) groups. Carcinoids, large-cell carcinoma, adenocarcinoma, small-cell lung carcinoma cell lines, and normal lung examined.
#> 10: The exacerbation of disease in asthmatics has been linked to both exposure to environmental agents as well as to the presence of virus in airways, particularly rhinovirus. The hypothesis tested in these experiments is that differences in gene expression profiles in epithelial cells derived from asthmatic and normal airways can be linked to enhanced responsiveness of the epithelium in its pro-inflammatory, immulogic or other activities that may lead to the exacerbation of disease.\nDate GSE470 Last Updated: Aug 24 2005\nContributors: W Spannhake\nIncludes GDS261.\n Update date: Nov 18 2003.\n Dataset description GDS261: Comparison of epithelial cells derived from asthmatic and normal bronchial airways, and examination of factors that enhance inflammatory and immunologic responses which exacerbate asthma. Effects of ozone and rhinovirus examined.
#> 11: The tissue-specific pattern of mRNA expression can indicate important clues about gene function. High-density oligonucleotide arrays offer the opportunity to examine patterns of gene expression on a genome scale. Toward this end, we have designed custom arrays that interrogate the expression of the vast majority of protein-encoding human and mouse genes and have used them to profile a panel of 79 human and 61 mouse tissues. The resulting data set provides the expression patterns for thousands of predicted genes, as well as known and poorly characterized genes, from mice and humans. We have explored this data set for global trends in gene expression, evaluated commonly used lines of evidence in gene prediction methodologies, and investigated patterns indicative of chromosomal organization of transcription. We describe hundreds of regions of correlated transcription and show that some are subject to both tissue and parental allele-specific expression, suggesting a link between spatial expression and imprinting.\nDate GSE1133.2 Last Updated: \nContributors: John B Hogenesch John R Walker Gabriel Kreiman Tim Wiltshire David Block Mimi Hayakawa Hilmar Lapp Richard Soden Michael P Cooke Serge Batalov Keith A Ching Andrew I Su Jie Zhang\nFrom GSE1133\nIncludes GDS592.\n Update date: May 13 2004.\n Dataset description GDS592: Gene atlas of mouse protein-encoding transcriptome. Gene expression profiles from 61 physiologically normal tissues. Samples generated from adult (10-12 week old) C57BL/6 mice (4 male, 3 female) by dissection.
#> 12: The Mg-delta strain of fibrillin-1 deficient mice display an early defect in alveolar septation. This study attempts to identify expression patterns in the mutant mouse lung compared to their wild-type littermates that suggest pathways that are critical for normal septation. This experiment focuses on postnatal days 1 and 5. Other time points will be added at a later date.\nDate GSE479 Last Updated: Jul 27 2006\nContributors: Enid R Neptune\nIncludes GDS243.\n Update date: Jul 27 2006.\n Dataset description GDS243: Study of alveolar septation using the Mg-delta strain of fibrillin-1 deficient mice which display an early defect in alveolar septation, and serve as a model for Marfan syndrome and emphysema.\nIncludes GDS244.\n Update date: Jul 27 2006.\n Dataset description GDS244: Study of alveolar septation using the Mg-delta strain of fibrillin-1 deficient mice which display an early defect in alveolar septation, and serve as a model for Marfan syndrome and emphysema.\nIncludes GDS242.\n Update date: Jul 27 2006.\n Dataset description GDS242: Study of alveolar septation using the Mg-delta strain of fibrillin-1 deficient mice which display an early defect in alveolar septation, and serve as a model for Marfan syndrome and emphysema. [Switched to use MG-U74A/B/C by Gemma]
#> 13: Human colorectal carcinoma-derived cell lines, p53 -/-, +/-, +/+ at 0 and 12 hrs after growth arrest.\nDate GSE90 Last Updated: May 29 2005\nContributors: Sandya Liyanarachchi Heejei Yoon Janet C Lockman Ramana Davuluri Natalia S Pellegata Fred A Wright Albert de la Chapelle\nIncludes GDS170.\n Update date: Apr 17 2003.\n Dataset description GDS170: Analysis of tumor suppressor protein p53 gene dosage effects in HCT116 colorectal carcinoma-derived cell lines p53 -/-, +/- and +/+ at 0 and 12 hours. Cells with different numbers of functional TP53 alleles can be distinguished by gene expression profile.
#> 14: High-throughput gene expression profiling has become an important tool for investigating transcriptional activity in a variety of biological samples. To date, the vast majority of these experiments have focused on specific biological processes and perturbations. Here, we have generated and analyzed gene expression from a set of samples spanning a broad range of biological conditions. Specifically, we profiled gene expression from 91 human and mouse samples across a diverse array of tissues, organs, and cell lines. Because these samples predominantly come from the normal physiological state in the human and mouse, this dataset represents a preliminary, but substantial, description of the normal mammalian transcriptome. We have used this dataset to illustrate methods of mining these data, and to reveal insights into molecular and physiological gene function, mechanisms of transcriptional regulation, disease etiology, and comparative genomics. Finally, to allow the scientific community to use this resource, we have built a free and publicly accessible website (http://expression.gnf.org) that integrates data visualization and curation of current gene annotations.\nDate GSE97 Last Updated: May 29 2005\nContributors: M P Cooke J R Walker R G Vega K A Ching L M Sapinoso A I Su T Wiltshire A Patapoutian P G Schultz A P Orth A Moqrich Y Hakak J B Hogenesch G M Hampton\nIncludes GDS182.\n Update date: May 13 2004.\n Dataset description GDS182: Gene expression profiles from a diverse array of tissues, organs, and cell lines, from the normal physiological state. Represents a preliminary description of the normal mammalian transcriptome.
#> 15: Profile of gene expression in lungs of ovalbumen sensitized and challenged C3H mice.\nDate GSE446 Last Updated: May 29 2005\nIncludes GDS347.\n Update date: Mar 22 2004.\n Dataset description GDS347: Lungs of ovalbumin sensitized/challenged C3H mice compared with controls. C3H are ovalbumin resistant. Attempt to identify genes involved in antigen-induced airway hyperresponsiveness.
#> 16: Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses.
#> 17: Multiclass cancer diagnosis using tumor gene expression signatures.
#> 18: Chemosensitivity prediction by transcriptional profiling.
#> 19: Collection of data from ´mRNA obtained from the spinal cord of hree human fetuses suffering from Lethal Congenital Contracture Syndrome and two age-matched human fetuses aborted to unrelated causes.\nLast Updated (by provider): Nov 10 2005\nContributors: Niklas Pakkasjärvi\nIncludes GDS1295.\n Update date: Nov 09 2005.\n Dataset description GDS1295: Analysis of spinal cord from fetuses with lethal congenital contracture syndrome (LCCS). LCCS is autosomal recessive, causes death in 32nd gestational week, and involves degeneration of anterior horn motor neurons in spinal cord. Results provide insight into pathways active in motoneuron disease.
#> 20: We performed expression profiling of 36 types of normal human tissues and identified 2,503 tissue-specific genes. We then systematically studied the expression of these genes in cancers by re-analyzing a large collection of published DNA microarray datasets. Our study shows that integration of each gene's breadth of expression (BOE) in normal tissues is important for biological interpretation of the expression profiles of cancers in terms of tumor differentiation, cell lineage and metastasis. Twenty five total RNA specimens were purchased from Clontech (Palo Alto, CA), Ambion (Austin, TX) and Strategene (La Jolla, CA). We tried to cover as many tissue types as possible by using pooled RNA samples. In order to define breadth-of-expression (BOE) accurately at a reasonable cost, we tried to cover as many tissue types as possible by using pooled RNA samples. Each specimen represents a human organ. We used RNA samples pooled from 2 to 84 donors to avoid differences at the individual level. Detailed sample information and Affymetrix .CEL files are available at http://www.genome.rcast.u-tokyo.ac.jp/normal/ Publication:Ge X et al., Interpreting expression profiles of cancers by genome-wide survey of breadth of expression in normal tissues. Genomics. 2005 Aug;86(2):127-141. PMID: 15950434\nLast Updated (by provider): May 11 2006\nContributors: Xijin Ge SanMing Wang Siego Ihara Hiroyuki Aburatani Yutaka Midorikawa Shogo Yamamoto Shuichi Tsutsumi\nIncludes GDS1096.\n Update date: Apr 08 2005.\n Dataset description GDS1096: Expression profiling of 36 types of normal tissue. Each RNA tissue sample pooled from several donors. Results identify tissue specific genes and provide baselines for interpreting gene expression in cancer.
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