- Created by: ellenrobinson
- Created on: 05-05-19 10:53
The technologies that are focused on studying the genome of an organism including genes, exons, introns, promoters, transcription factors, and many other genome-related functions.
Technologies used: Next-generation sequencing, Sanger sequencing, de novo assembling, bioinformatics.
This particular area of study aims to utilize the comprehensive datasets and use this information to understand as well as describe gene and cell functions.
This deals with the study of the complete set of epigenetic modifications on the genetic material of a cell, known as the epigenome. Epigenetic modifications are changes to the DNA structure or histones of cells
The technologies that are focused on studying the proteins of an organism or the proteome.
Technologies used: Enzyme-linked immunosorbent assay (ELISA), mass spectrometric immunoassay (MSIA)
The scope of Metabolomics is the identification of any small molecule produced as intermediate or end product in any chemical process taking place within the organism.
Technologies used: Gas chromatography fused with mass spectrometry (GC-MS), liquid chromatography fused with mass spectrometry (LC-MS).
The technologies that are focused on studying the cellular lipids of an organism or a biological system.
Technologies used: Electrospray ionization (ESI) and matrix-assisted laser desorption/ionization (MALDI).
Biological systems multi-omics
Biological systems multi-omics from the genome, epigenome, transcriptome, proteome and metabolome to the phenome. Heterogeneous genomic data exist within and between levels, for example:
- Single-nucleotide polymorphism (SNP)
- Copy number variation (CNV)
- Loss of heterozygosity (LOH)
Genomic rearrangement, such as translocation, at the genome level;
- DNA methylation
- histone modification
- chromatin accessibility
- transcription factor (TF) binding
Micro RNA (miRNA) at the epigenome level; gene expression and alternative splicing at the transcriptome level; protein expression and post-translational modification at the proteome level; and metabolite profiling at the metabolome level.
The transcriptome is defined as the complete set of transcripts in a cell, and their quantity, for a specific developmental stage or physiological condition. The full range of RNA molecules expressed by an organism.
The transcriptome actively changes
The transcriptome is composed of messenger or coding RNAs and a variety of non-coding RNAs (ncRNAs) which warrant specialized library preparation methods as well as suitable bioinformatics procedures for data processing and quantify their abundance for functional analysis. The majority of RNA molecules in the eukaryotic cells are tRNAs and rRNAs. mRNA accounts for only 1–5% of the total cellular RNA
Transcriptomics is the study of the transcriptome, the complete set of RNA transcripts that are produced by the genome, under specific circumstances or in a specific cell using high-throughput methods.
A transcriptomics dataset, produced by either next-generation sequencing (NGS) or microarray platforms.
Random individual transcripts from cDNA libraries generated by reverse transcriptase. RT-PCR/Q-PCR.
1. RNA isolation
2. Separation of RNA on denaturing gel by size
3. Transfer RNA separated by size on a membrane
4. Hybridize with labelled DNA-probe
Nucleic acid hybridization
Base pairing permits the detection of a sequence that is complementary to the probe.
1. Slowly heated, DNA denatures
2. Complementary base sequences are added to denatured DNA
3. Binds to DNA strands
4. Forms double-stranded hybrid molecule.
Quantitative reverse transcription PCR (RT-qPCR) is used when the starting material is RNA. In this method, RNA is first transcribed into complementary DNA (cDNA) by reverse transcriptase from total RNA or messenger RNA (mRNA). The cDNA is then used as the template for the qPCR reaction.
In quantitative reverse transcription PCR (RT-qPCR)
- RNA is first transcribed into complementary DNA (cDNA) by reverse transcriptase from total RNA or messenger RNA (mRNA).
- The cDNA is then used as the template for the qPCR reaction. The more specific mRNA template (representing one gene for example) was in the cells where the RNA was isolated from, the more cDNA can be made. The amount of the starting cDNA will correlate with the amount of PCR product in the Q-PCR reaction.
Primers for Reverse Transcription
Three different approaches can be used for priming cDNA reactions in two-step assays:
- oligo(dT) primers
- random primers
- sequence specific primers.
Often, a mixture of oligo(dT)s and random primers is used. These primers anneal to the template mRNA strand and provide reverse transcriptase enzymes a starting point for synthesis.
Choosing total RNA vs. mRNA
mRNA may provide slightly more sensitivity, but total RNA is often used because it has important advantages over mRNA as a starting material.
RT-qPCR is used in a variety of applications including gene expression analysis.
The amplification plot shows two phases, an exponential phase followed by a non-exponential plateau phase. During the exponential phase, the amount of PCR product doubles in each cycle.
As the reaction proceeds, however, reaction components are consumed, and ultimately one or more of the components becomes limiting. At this point, the reaction slows and enters the plateau phase.
The cycle number at which this occurs is called the quantification cycle, or Cq. Because the Cq value is measured in the exponential phase when reagents are not limited, real-time qPCR can be used to reliably and accurately calculate the initial amount of template present in the reaction based on the known exponential function describing the reaction progress.
The Cq of a reaction is determined mainly by the amount of template present at the start of the amplification reaction.
The techniques used to study an organism’s transcriptome, the sum of all of its RNA transcripts. The information content of an organism is recorded in the DNA of its genome and expressed through transcription.
There are two key contemporary techniques in the field: microarrays, which quantify a set of predetermined sequences, and RNA sequencing (RNA-Seq), which uses high-throughput sequencing to capture all sequences.
Transcriptomic analysis has enabled the study of how gene expression changes in different organisms and has been instrumental in the understanding of human disease.
Microarrays are simply small glass or silicon slides on the surface of which are arrayed thousands of DNA sequences.
Gene expression arrays are used to detect the levels of all the expressed genes in an experimental sample
mRNAs are isolated from control and experimental cells or tissues and reverse transcribed resulting in labeled cDNAs with different fluorophores
Competitive hybridization cDNAs to the microarray is proportional to the relative abundance of each mRNA in the two samples.
The relative levels of red and green fluorescence are measured by automated optical microscopic scanning and are displayed as a single color.
Red or orange indicates increased expression in the red (experimental) sample, green or yellow-green indicates lower expression, and yellow indicates equal levels of expression in the control and experiment.
Probes: single strand DNA oligonucleotides with sequences of all genes from the species (unique 20‐60 nucelotide long) that specifically bind the mRNA
Uses of Microarrays:
• Determine what genes are active in a cell and at what levels
• Compare the gene expression profiles of a control vs treated sample - Determine what genes have increased or decreased in during an experimental condition
• Determine which genes have biological significance in a system
• Discovery of new genes and pathways
Types of Microarrays
· Expression Arrays: Genome-wide transcription analysis is performed using labeled cDNA from experimental samples hybridized to a microarray containing sequences from all ORFs of the organism being used
· Protein microarrays (Proteomics): Detect antibodies or enzymes in a biological system
· CGH arrays (Comparative genomic hybridization): Provides DNA and chromosomal information; allows the detection of copy number changes in any DNA sequence compared between two samples
· SNP Arrays: Permit genome-wide genotyping of single-nucleotide polymorphisms
· Antibody Arrays: Assay hundreds of native proteins simultaneously and compare protein abundances in a variety of biological samples
· Exon arrays: Detect alternative splice variant detection
DNA microarrays in medicine
- Discovery of target gene
- The discovery of drugs, pharmacogenomics
- Personalized treatment
Limitations of microarrays in transcriptomics
- Prior knowledge of gene sequences
- Cross-hybridization artefact: similar sequences hybridize to the same probe
- Limited ability to quantify the degree of gene expression
- RNA-Seq is an approach to transcriptome profiling that uses deep-sequencing technologies
- RNA-Seq also provides a far more precise measurement of levels of transcripts and their isoforms than other methods
The resulting sequence reads are aligned with the reference genome or transcriptome, and classified as three types: exonic reads, junction reads and poly(A) end-reads.
- Throughput improving rapidly
- Highly expressed genes dominate mRNA, generally >75% of the mRNA comes from <5% of the genes.
High-Throughput DNA Sequencing High-throughput sequencing is faster and cheaper than traditional methods. It involves
- Chemical amplification of DNA fragments
- Synthesis of complementary strands using color-labeled nucleotides.
Splicing quantitative trait locus (sQTL) analysis
- Quantifying alternative splicing by using RNA-Seq data
- Quantitative profiles of alternative splicing are treated as traits and tested for association with genotypes
- Discovering genetic variants associated with alternative splicing
Single-cell transcriptome analyses
Single-cell transcriptome analyses of tissues and cell types, analysed independently with single-cell RNA-seq and clustered based on their gene expression profiles.
Gene expression and regulation within or between cell types and between tissues.
Analysis of the pattern of proteins or messenger RNAs (mRNAs) in histological tissue sections is a cornerstone in biomedical research and diagnostics.
Spatially resolved transcriptomics strategies have the potential to act as a high-throughput molecular microscope.
Arrayed reverse transcription primers with unique positional barcodes, they demonstrated high-quality RNA-sequencing data. Spatial transcriptomics provides quantitative gene expression data and visualization of the distribution of mRNAs within tissue sections and enables novel types of bioinformatics analyses, valuable in research and diagnostics.
Technique to measure the expression dynamics of each gene in a single cell.
· Level and rate of change of expression to be estimated simultaneously for each gene in a single cell
· Temporal resolution
· No information about the spatial organization of cells
· Increase in gene expression - more unspliced mRNA
· Decrease in gene expression – more spliced mRNA
The time derivative of the gene expression state—can be directly estimated by distinguishing between unspliced and spliced mRNAs in common single-cell RNA sequencing protocols.
RNA seq in personalized medicine
Paths to clinical translation for RNA-based assays. For the development of biomarkers, RNA-based assays can be based on either tissue or liquid biopsies.
RNA profiling of tissue biopsies can involve either the primary or metastatic sites.
- Liquid biopsies are most often blood-based but can include other fluids in select cancers
- RNA-based biomarkers are being developed for all aspects of cancer medicine, from initial diagnosis and staging