Lecture 14 - Gene Expression Flashcards
how does gene expression differ
- by region in multicellular organisms
- in development
- in dynamic response to environmental signals
- in disease states
- by gene activity
what is the target of gene expression studies
mRNA
what type of RNA is most abundant in cells
rRNA, >70%
what type of RNA is least abundant in cells
mRNA, 3-4%
how is gene regulation mediated through RNA
- transcription
- RNA processing
- RNA export
- RNA surveillance
- siRNA
what does Northern Blotting detect
specific RNAs
how does Northern Blotting deal with RNA
it isolates RNA from cells, separates them using electrophoresis, and probes them with labeled cDNA from a specific gene
how is RNA quantified in Northern Blotting
signal intensity
what are the steps in creating cDNA libraries
- isolate RNA
- convert RNA to complementary DNA through reverse transcriptase
- subclone the RNA into a vector
- transform and select for it in E. coli
- sequence the cDNA inserts
what are microarrays
solid supports such as a membrane or glass microscope slide on which DNA of known sequence is deposited in a grid-like array
how are microarrays used to measure gene expression
- RNA is isolated from matched samples of interest
- RNA is converted to cDNA
- it is labeled with fluorescence or radioactivity
- it hybridizes to microarrays to measure the expression levels of thousands of genes
how do two colour microarrays work
- you have a test and reference sample
- each one is assigned a colour
- the colours on the microarray indicate which cells express those genes
e.g. red -> present in pathological cell, green -> present in normal cells, yellow -> present in both cells
what is the brightness proportional to in a microarray
amount of cDNA bound to the spot on a chip
what are the steps of RNA sequencing
- RNA is isolated from the target cells/organisms
- RNA is converted into cDNA
- high-throughput sequencing is used to sequence reads (e.g. using Illumina)
- reads are mapped to genes/genomes and reads are counted
what does data normalization do
separates true variation from variation due to experiment variability
what is an example of data normalization
using relative changes rather than absolute changes; fluorescence can be calculated as a ratio between experimental/control groups