Studying BDNF/TrkB Signaling: High-Throughput Microfluidic Gene Expression Analysis from Rare or Limited Samples of Adult and Aged Central Neurons
Neuromethods, ISSN: 1940-6045, Vol: 143, Page: 77-86
2019
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
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Book Chapter Description
High-throughput next generation sequencing technologies are an invaluable tool to gain insight into the transcriptional states of large cohorts of cells. Such data can help to shed light on the organization of tissues and pathways under normal and pathological conditions. In our case, we are using the above technology to decipher how the enkephalinergic medium spiny neurons (MSNs) of the striatum adapt to aging in the presence or absence of BDNF/TrkB signaling. However, sequencing data must be validated, ideally with an alternative method that interrogates the transcriptional state of cells, and is able to detect gene expression in rare single cells or bulk cells with high sensitivity. Thus, we have optimized a protocol for high-throughput microfluidic analysis [Fluidigm Dynamic Array Integrated Microfluidic Circuit (IFC)] to validate RNA sequencing data from a limited number of adult and aged sorted neurons. Here is a detailed description of this protocol.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85069442255&origin=inward; http://dx.doi.org/10.1007/7657_2017_4; http://link.springer.com/10.1007/7657_2017_4; http://link.springer.com/content/pdf/10.1007/7657_2017_4; https://dx.doi.org/10.1007/7657_2017_4; https://link.springer.com/protocol/10.1007%2F7657_2017_4
Springer Science and Business Media LLC
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