Massive Analysis of cDNA Ends = MACE = 3′ mRNA-Seq
High Resolution Gene Expression at only 10% of RNA-Seq costs
For the analysis of gene expression, we have developed a high-resolution and cost efficient RNA-Seq variant. MACE, an improved variant of “3′ single end mRNA Seq” is based on sequencing only one single moelecule per transcript. Therefore, each NGS read represents one single transcript. Each molecule is barcoded with a unique sequence (“TrueQuant“) for PCR bias elimination. MACE reveals gene expression, allele frequencies and alternative polyadenylation at only a fraction of the sequencing costs of regular RNA-Seq.
Please feel free to watch the video for a quick introduction, further information can be found below. MACE is also available as a kit for your own lab.
MACE vs. full length RNA-Seq
mRNA transcripts – many in low copies, few in many copies
The most interesting transcripts such as transcription factors or receptors are usually transcribed in very few copies, while a handful of transcript-species e.g. of structural proteins can make up 80% of a transcriptome. Rare transcripts are usually invisible on Microarrays and with RNA-seq, they can only be detected at very deep sequencing depth of 100 million sequences or more.
Another problem of RNA-Seq is that longer transcripts are overrepresented and shorter transcripts underrepresented. Therefore, a “normalisation” of the data is performed which is problematic, as different methods for the normalisation result in different sets of allegedly differentially expressed genes (e.g. Soneson & Delorenzi 2013).
Because in MACE (figure below) only one single read per transcript molecule is sequenced, short and rare transcripts are identified already at 10-20 times lower sequencing depth, when compared to full length RNA-Seq and no length-based normalisation is required.
MACE vs. other 3′ RNA-Seq methods
MACE was developed in 2007 when we invented the “TrueQuant” Method (Unique Molecule Identifiers, UMIs). The MACE technology therefore has included the TrueQuant barcodes, individually barcoding each molecule prior to the PCR steps. The individual barcodes allow identification of PCR copies and sequencing errors and are a pivotal element of our techniques. This is an important difference to other 3’End sequencing techniques without this error correction possibility. Also, MACE does NOT use random hexamers for the cDNA production to avoid the introduction of bias (see Hansen et al. 2010).
Comparison to Microarrays
More transcripts for your money!
The numbers represent the average transcript-frequency, the percentage describes the ratio of transcript species present at this frequency. 74 % of the transcript-species in this tissue (Mouse-spleen) are only expressed at levels below 20 copies per million. With MACE (left) these low-copy transcripts, among them key players of the cellular function like transcription factors or receptors, are securely identified while they remain obscure with microarrays (right). The costs for both techniques are in a similar range.
Why “number of reads” does not equal “sequencing depth”
in MACE, we use our TrueQuant molecular barcoding technique to eliminate PCR introduced bias. In a regular RNA-Seq experiment, the quality of the data is often thought to be related to the numbers of sequenced reads. However, during PCR steps of the sample preparation procedure, the original template molecules are amplified. While only the template molecules carry the information, the PCR products are simply copies of them and in theory only one copy would be sufficient, while all others do not contribute to the information content of the sample. It is pivotal to identify these “PCR copies” and eliminate them, as they can introduce a bias into the data. At the same time, only those “copies” can be kept that have the best read quality, which strongly improves the data quality in general. The best currency for RNA-Seq quality is therefore the number of unique reads in the sample, not the number of reads. With TrueQuant, we can eliminate PCR copies and therefore strongly improve the results.
Degraded Material ? FFPE derived ? No problem!
As only the 3′ ends of the mRNAs are required, MACE works very well also with differently degraded material. Down to a RIN (RNA Integrity Number) of 3, MACE is fully functional. Because only on e tag is sequenced per transcript, differentially degraded sampels can be readily compared, unlike with regular RNA-Seq. This is because for the latter the number or generated reads per molecule is depending on the degradation status.
TranSNiPtomics by MACE
Reduced complexity: concentration on the highly polymorphic 3′ Ends for gene-based marker and allele identification!
MACE reduces the sequencing to the SNP-rich 3‘ Ends of the cDNA. This ensures high coverage of this polymorphic region, even of low-level expressed transcripts. Therefore, SNPs and other variants can be reliably detected by MACE, simultaneously with the exact measurement of gene expression levels. Because of the much lower costs when compared to RNA-Seq, MACE is the ideal platform for simultaneous genotyping and gene expression analysis. All identified polymorphism (alleles) are highly valuable markers, because they are located within the gene, and not, as with most other GBS-techniques, randomly distributed throughout the genome. MACE- derived markers or alleles are therefore highly coupled to traits. Thanks to parallelization of our processes, we can analyze hundreds of samples simultaneously. MACE is therefore also increasingly used for genotyping purposes.
MACE, 20 M reads: Alleles are clearly distinguishable
Below: RNA-Seq, 20 M reads: The different alleles cannot be identified securely!
Allele Specific Expression (ASE)
Thanks to the high coverage of the sequenced region and the allele-specificity of the 3’ Ends, MACE is also ideally suited for the analyses of Allele Specific Expression (ASE).
Alternative Polyadenylation (APA)
Similarly, Alternative Poly-Adenylation (APA) can be monitored very accurately. Because APA can prevent the transcript to interact with miRNAs (see e.g. Mayr & Bartel, 2009), APA leads for example to increased stability of transcripts e.g. oncogenes. Hence, the analysis of APA is pivotal in the context of miRNA research. MACE was used to generate a comprehensive database for APA in different organisms (Müller et al. 2014; APADB) and MACE data can be conveniently compared to the database by using our online tool “APADB”.
MACE: As Service or Kit
We use highly standardized procedures for the MACE and can thanks to Liquid Handling Robotics process 96 samples in parallel.
If you own a sequencer, you can alternatively also purchase our brand-new MACE kit!
Because MACE data has some special characteristics, we include the bioinformatics analysis both for the service and the kit. Therefore, you do not need to set up your own bioinformatics and can profit from our constantly improved and widely used bioinformatics pipeline.
Besides the raw-data, you receive ready-to-use results tables containing NCBI or ENSEMBL IDs and descriptions, expression values, p-values for differential expression, Fold Changes, SNPs, etc., which can conveniently be analyzed e.g. with other bioinformatics analysis tools or Excel.
In addition, we provide Gene Ontology (GO) Enrichment Analyses and plots and graphs which can be easily accessed and browsed using our Web-Interface (http://tools.genxpro.net).
Data is provided via WEB-access or can be shipped on a hard-disk.
please feel free to download a MACE dataset from human huvec cells: MACE Example_human_huvec_cells
More information about the table content can be found here: MACE results table: ‘all_comparisons_merged’
Ship us the tissue or RNA, we make the rest !
Simply provide >100 ng of DNA-free RNA or enough raw material to obtain an equivalent amount. We will accopmlish the entire analysis and provide result tables, SNPs, GO enrichment analyses, provide the best candidates for markers and perform other analyses if requested.