Mus musculus
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA865732 | ENA |
REPOSITORIES: ENA
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Project description:A transcriptome study in mouse hematopoietic stem cells was performed using a sensitive SAGE method, in an attempt to detect medium and low abundant transcripts expressed in these cells. Among a total of 31,380 unique transcript, 17,326 (55%) known genes were detected, 14,054 (45%) low-copy transcripts that have no matches to currently known genes. 3,899 (23%) were alternatively spliced transcripts of the known genes and 3,754 (22%) represent anti-sense transcripts from known genes. Mouse hematopoietic stem cells were purified from bone marrow cells using negative and positive selection with a Magnetic-Activated Cell Sorter (MACS). total RNA and mRNA were purified from the purified cells using Trizol reagent and magnetic oligo dT beads. Double strand cDNAs were synthesized using a cDNA synthesis kit and anchored oligo dT primers. After NlaIII digestion, 3’ cDNAs were isolated and amplified through 16-cycle PCR. SAGE tags were released from the 3’ cDNA after linker ligation. Ditags were formed, concatemerized and cloned into a pZERO vector. Sequencing reactions were performed with the ET sequencing terminator kit. Sequences were collected using a Megabase 1000 sequencer. SAGE tag sequences were extracted using SAGE 2000 software.
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2014-12-13 | E-GEOD-64140 | biostudies-arrayexpress
Project description:A transcriptome study in mouse hematopoietic stem cells was performed using a sensitive SAGE method, in an attempt to detect medium and low abundant transcripts expressed in these cells. Among a total of 31,380 unique transcript, 17,326 (55%) known genes were detected, 14,054 (45%) low-copy transcripts that have no matches to currently known genes. 3,899 (23%) were alternatively spliced transcripts of the known genes and 3,754 (22%) represent anti-sense transcripts from known genes.
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Project description:We performed a study of gene expression changes that occur during mouse aging in the striatum and cortex in specific neuronal populations. Using translating ribosome afifnity purificaiton, we captured cell type-specific mRNAs from Drd1a-expressing cortical neurons, Drd1a-expressing striatal neurons, Drd2-expressing cortical neurons, and Drd2-expressing striatal neurons.
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Project description:Recent large genome-wide association studies (GWAS) have identified multiple confident risk loci linked to addiction-associated behavioral traits. Genetic variants linked to addiction-associated traits lie largely in non-coding regions of the genome, likely disrupting cis-regulatory element (CRE) function. CREs tend to be highly cell type-specific and may contribute to the functional development of the neural circuits underlying addiction. Yet, a systematic approach for predicting the impact of risk variants on the CREs of specific cell populations is lacking. To dissect the cell types and brain regions underlying addiction-associated traits, we applied LD score regression to compare GWAS to genomic regions collected from human and mouse assays for open chromatin, which is associated with CRE activity. We found enrichment of addiction-associated variants in putative regulatory elements marked by open chromatin in neuronal (NeuN+) nuclei collected from multiple prefrontal cortical areas and striatal regions known to play major roles in reward and addiction. To further dissect the cell type-specific basis of addiction-associated traits, we also identified enrichments in human orthologs of open chromatin regions of mouse neuron subtypes: cortical excitatory, PV, D1, and D2. Lastly, we developed machine learning models from mouse cell type-specific regions of open chromatin to further dissect human NeuN+ open chromatin regions into cortical excitatory or striatal D1 and D2 neurons and predict the functional impact of addiction-associated genetic variants. Our results suggest that different neuron subtypes within the reward system play distinct roles in the variety of traits that contribute to addiction.
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