Transcriptomics technologies are 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. Here, mRNA serves as a transient intermediary molecule in the information network, whilst non-coding RNAs perform additional diverse functions. A transcriptome captures a snapshot in time of the total transcripts present in a cell. Transcriptomics technologies provide a broad account of which cellular processes are active and which are dormant. A major challenge in molecular biology is to understand how a single genome gives rise to a variety of cells. Another is how gene expression is regulated.
The first attempts to study whole transcriptomes began in the early 1990s. Subsequent technological advances since the late 1990s have repeatedly transformed the field and made transcriptomics a widespread discipline in biological sciences. There are two key contemporary techniques in the field: microarrays, which quantify a set of predetermined sequences, and RNA-Seq, which uses high-throughput sequencing to record all transcripts. As the technology improved, the volume of data produced by each transcriptome experiment increased. As a result, data analysis methods have steadily been adapted to more accurately and efficiently analyse increasingly large volumes of data. Transcriptome databases have consequently been growing bigger and more useful as transcriptomes continue to be collected and shared by researchers. It would be almost impossible to interpret the information contained in a transcriptome without the knowledge of previous experiments.
Measuring the expression of an organism's genes in different tissues or conditions, or at different times, gives information on how genes are regulated and reveals details of an organism's biology. It can also be used to infer the functions of previously unannotated genes. Transcriptome analysis has enabled greater insight into gene expression changes in different organisms and has been instrumental in the understanding of human disease. An analysis of gene expression in its entirety allows detection of broad coordinated trends which cannot be discerned by more targeted assays.
History
Transcriptomics has been characterised by the development of new techniques which have redefined what is possible every decade or so and rendered previous technologies obsolete. The first attempt at capturing a partial human transcriptome was published in 1991 and reported 609 mRNA sequences from the human brain. In 2008, two human transcriptomes, composed of millions of transcript-derived sequences covering 16,000 genes, were published, and by 2015 transcriptomes had been published for hundreds of individuals. Transcriptomes of different disease states, tissues, or even single cells are now routinely generated. This explosion in transcriptomics has been driven by the rapid development of new technologies with improved sensitivity and economy.
Before transcriptomics Studies of individual transcripts were being performed several decades before any transcriptomics approaches were available. Libraries of silkmoth mRNA transcripts were collected and converted to complementary DNA (cDNA) for storage using reverse transcriptase in the late 1970s. In the 1980s, low-throughput sequencing using the Sanger method was used to sequence random transcripts, producing expressed sequence tags (ESTs). The Sanger method of sequencing was predominant until the advent of high-throughput methods such as sequencing by synthesis (Solexa/Illumina). ESTs came to prominence during the 1990s as an efficient method to determine the gene content of an organism without sequencing the entire genome. Amounts of individual transcripts were quantified using Northern blotting, nylon membrane arrays, and later reverse transcriptase quantitative PCR (RT-qPCR) methods, but these methods are laborious and can only capture a tiny subsection of a transcriptome. Consequently, the manner in which a transcriptome as a whole is expressed and regulated remained unknown until higher-throughput techniques were developed.
Early attempts The word "transcriptome" was first used in the 1990s. In 1995, one of the earliest sequencing-based transcriptomic methods was developed, serial analysis of gene expression (SAGE), which worked by Sanger sequencing of concatenated random transcript fragments. Transcripts were quantified by matching the fragments to known genes. A variant of SAGE using high-throughput sequencing techniques, called digital gene expression analysis, was also briefly used. However, these methods were largely overtaken by high throughput sequencing of entire transcripts, which provided additional information on transcript structure such as splice variants.
Development of contemporary techniques + Comparison of contemporary methodsRNA-SeqMicroarrayThroughput1 day to 1 week per experiment1–2 days per experimentInput RNA amountLow ~ 1 ng total RNAHigh ~ 1 μg mRNALabour intensityHigh (sample preparation and data analysis)LowPrior knowledgeNone required, although a reference genome/transcriptome sequence is usefulReference genome/transcriptome is required for design of probesQuantitation accuracy~90% (limited by sequence coverage)>90% (limited by fluorescence detection accuracy)Sequence resolutionRNA-Seq can detect SNPs and splice variants (limited by sequencing accuracy of ~99%)Specialised arrays can detect mRNA splice variants (limited by probe design and cross-hybridisation)Sensitivity1 transcript per million (approximate, limited by sequence coverage)1 transcript per thousand (approximate, limited by fluorescence detection)Dynamic range100,000:1 (limited by sequence coverage)1,000:1 (limited by fluorescence saturation)Technical reproducibility>99%>99%
The dominant contemporary techniques, microarrays and RNA-Seq, were developed in the mid-1990s and 2000s. Microarrays that measure the abundances of a defined set of transcripts via their hybridisation to an array of complementary probes were first published in 1995. Microarray technology allowed the assay of thousands of transcripts simultaneously and at a greatly reduced cost per gene and labour saving. Both spotted oligonucleotide arrays and Affymetrix high-density arrays were the method of choice for transcriptional profiling until the late 2000s. Over this period, a range of microarrays were produced to cover known genes in model or economically important organisms. Advances in design and manufacture of arrays improved the specificity of probes and allowed more genes to be tested on a single array. Advances in fluorescence detection increased the sensitivity and measurement accuracy for low abundance transcripts.
RNA-Seq is accomplished by reverse transcribing RNA in vitro and sequencing the resulting cDNAs. Transcript abundance is derived from the number of counts from each transcript. The technique has therefore been heavily influenced by the development of high-throughput sequencing technologies. Massively parallel signature sequencing (MPSS) was an early example based on generating 16–20 bp sequences via a complex series of hybridisations,In molecular biology, hybridisation is a phenomenon in which single-stranded deoxyribonucleic acid (DNA) or ribonucleic acid (RNA) molecules anneal to complementary DNA or RNA. and was used in 2004 to validate the expression of ten thousand genes in Arabidopsis thaliana. The earliest RNA-Seq work was published in 2006 with one hundred thousand transcripts sequenced using 454 technology. This was sufficient coverage to quantify relative transcript abundance. RNA-Seq began to increase in popularity after 2008 when new Solexa/Illumina technologies allowed one billion transcript sequences to be recorded. This yield now allows for the quantification and comparison of human transcriptomes.
Data gathering Generating data on RNA transcripts can be achieved via either of two main principles: sequencing of individual transcripts (ESTs, or RNA-Seq) or hybridisation of transcripts to an ordered array of nucleotide probes (microarrays).
Isolation of RNA All transcriptomic methods require RNA to first be isolated from the experimental organism before transcripts can be recorded. Although biological systems are incredibly diverse, RNA extraction techniques are broadly similar and involve mechanical disruption of cells or tissues, disruption of RNase with chaotropic salts, disruption of macromolecules and nucleotide complexes, separation of RNA from undesired biomolecules including DNA, and concentration of the RNA via precipitation from solution or elution from a solid matrix. Isolated RNA may additionally be treated with DNase to digest any traces of DNA. It is necessary to enrich messenger RNA as total RNA extracts are typically 98% ribosomal RNA. Enrichment for transcripts can be performed by poly-A affinity methods or by depletion of ribosomal RNA using sequence-specific probes. Degraded RNA may affect downstream results; for example, mRNA enrichment from degraded samples will result in the depletion of 5' mRNA ends and an uneven signal across the length of a transcript. Snap-freezing of tissue prior to RNA isolation is typical, and care is taken to reduce exposure to RNase enzymes once isolation is complete.
Expressed sequence tags An expressed sequence tag (EST) is a short nucleotide sequence generated from a single RNA transcript. RNA is first copied as complementary DNA (cDNA) by a reverse transcriptase enzyme before the resultant cDNA is sequenced. Because ESTs can be collected without prior knowledge of the organism from which they come, they can be made from mixtures of organisms or environmental samples.Some examples of environmental samples include: sea water, soil, or air. Although higher-throughput methods are now used, EST libraries commonly provided sequence information for early microarray designs; for example, a barley microarray was designed from 350,000 previously sequenced ESTs.
Serial and cap analysis of gene expression (SAGE/CAGE)
Serial analysis of gene expression (SAGE) was a development of EST methodology to increase the throughput of the tags generated and allow some quantitation of transcript abundance. cDNA is generated from the RNA but is then digested into 11 bp "tag" fragments using restriction enzymes that cut DNA at a specific sequence, and 11 base pairs along from that sequence. These cDNA tags are then joined head-to-tail into long strands (>500 bp) and sequenced using low-throughput, but long read-length methods such as Sanger sequencing. The sequences are then divided back into their original 11 bp tags using computer software in a process called deconvolution. If a high-quality reference genome is available, these tags may be matched to their corresponding gene in the genome. If a reference genome is unavailable, the tags can be directly used as diagnostic markers if found to be differentially expressed in a disease state.
The cap analysis gene expression (CAGE) method is a variant of SAGE that sequences tags from the 5' end of an mRNA transcript only. Therefore, the transcriptional start site of genes can be identified when the tags are aligned to a reference genome. Identifying gene start sites is of use for promoter analysis and for the cloning of full-length cDNAs.
SAGE and CAGE methods produce information on more genes than was possible when sequencing single ESTs, but sample preparation and data analysis are typically more labour-intensive.
Microarrays
Principles and advances