Genome-Wide RNAseq and Array-Based NanoString Transcriptomic Technologies: Which To Use and When?

Compare the pros and cons of two widely used transcriptomic technologies - genome-wide RNAseq and array-based NanoString.

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The Rise of Transcriptomics in Oncology

The rapid expansion of precision oncology approaches in recent years has been fueled in part by technological innovations and an increased interest in personalized therapies. Precision medicine approaches not only rely on biomarkers to define optimal treatment strategies but also play important roles in the diagnosis of disease, treatment efficacy assessment, and/or disease progression.

Gene expression studies are a useful tool to identify biomarkers that are used to diagnose cancer and predict treatment efficacy and disease prognosis. The recent explosion of high-throughput technologies that generate large-scale molecular measurements has accelerated gene expression biomarker development. In particular, transcriptomics is a powerful approach for biomarker discovery because it profiles the entire set of RNA transcripts produced by a genome.

This post helps you choose the right transcriptomic technology for your mRNA profiling studies, focusing on the main steps, advantages, and disadvantages of RNAseq and the NanoString platform, with a short review of other available technologies.

RNA Sequencing (RNAseq)

RNA sequencing, or RNAseq as it is more commonly known, directly sequences and quantifies the number of mRNA molecules in the entire transcriptome. This technique is relevant to a wide range of applications, including identifying changes in gene expression associated with a specific disease state. For instance, profiling cancers with RNAseq provides key insights regarding tumor classification and progression by identifying variants expressed in different samples.

The Main Stages of RNAseq

There are four main steps in RNAseq which cover:

1. mRNA transcript fragmentation, followed by random primer binding to mRNA segments
2. Generation of double-stranded DNA from first- and second-strand cDNA synthesis via reverse transcription of the mRNA fragment
3. Tagging of the ends of the fragments with a phosphate group and poly(A) tail
4. Ligation of adaptors onto the cDNA fragments, which enables downstream PCR amplification and sequencing.

It’s key to understand the main advantages and disadvantages of RNAseq, to determine if it’s the optimal assay for your specific research needs.

Advantages of RNAseq

• Unlike traditional hybridization-based technologies (e.g. arrays), RNAseq does not require specific probes (i.e. species- or transcript-specific probes). This makes it a powerful tool for detecting novel transcripts, gene fusions, single nucleotide variants, indels (small insertions and deletions), and other changes that arrays are not able to detect.
  
  
• Genome-wide profiling technique that can assess the approximately 26,000 genes in the human genome, rendering it a powerful tool for biomarker discovery.
  
  
• RNAseq is highly sensitive and can be used to detect rare and low-abundance transcripts (e.g. weakly expressed genes, single transcripts in a cell) by simply increasing the depth of sequencing coverage.
  
  
• It enables discovery of novel complex genomic features, such as previously unknown transcriptional features, including alternative splicing, allele-specific expression, and newly transcribed regions.

Disadvantages of RNAseq

• High-quality RNA is needed, with a suggested RNA integrity number over 8.
  
  
• RNAseq also requires a minimum of 1µg total RNA, but ≥2µg is preferred, which are larger samples compared with NanoString.
  
  
• Complete removal of genomic DNA is key. Therefore, DNase treatment is necessary because amplified RNA cannot be differentiated from genomic DNA.
  
  
• Downstream analyses require experienced scientists to leverage the information to its full capacity. Processing raw data to obtain normalized gene expression can also be costly.

NanoString

NanoString Technologies, Inc. developed a technology known as “nCounter,” a variation on DNA microarrays, that is robust, sensitive, reproducible, easy to use, and amplification free. The NanoString platform counts individual mRNA transcripts. It’s useful for many potential applications, such as studying gene regulatory pathways, diagnostics, validating gene expression experiments in disease studies, including cancer, and ultimately generating highly translatable data for clinical use.

The Main Steps using the NanoString Platform

NanoString uses two hybridization probes (each ~35–50 bases in length) per mRNA molecule. Probe pairs are generated for each unique gene. The reporter probe carries the signal. The other probe “captures” the complex, immobilizing it so that a digital analyzer can detect the hybridized reporter probes (there are four different-colored fluorophores). The probes are mixed with the RNA in solution. The digital analyzer identifies, scans, and counts the molecular barcode per sample. Notably, since the probes anneal directly to the RNA, a reverse transcription step is not required to generate cDNA.

Advantages of NanoString

• High-quality RNA is not required. It is therefore ideal for scenarios where only poor-quality RNA is available, including formalin-fixed paraffin-embedded material.
  
  
• Smaller amounts of starting material are needed compared to RNAseq: 100ng of total RNA or lysate from 10,000 cells.
  
  
• There’s minimal background signal.
  
  
• It is amplification free.
  
  
• Reduced possibility for introducing bias.
  
  
• The NanoString platform has improved detection of low-expression RNA.
  
  
• Data analysis software is freely provided by NanoString Technologies, with readouts that are user friendly and suitable to nonspecialists.

Disadvantages of NanoString

• It is not suitable for biomarker discovery, since it employs approximately 800 preselected genes that are publication based.
  
  
• It is a proprietary platform that offers limited flexibility.

Comparison of Assay Properties for Illumina MiSeq RNAseq, NanoString nCounter, and DNA Microarray

Adapted from Narrandes and Xu. Gene Expression Detection Assay for Cancer Clinical Use. J Cancer. 2018;9(13):2249-2265.

Other Available mRNA Profiling Platforms

Several other sequencing approaches can be used for mRNA profiling, which are briefly summarized here including a list of some of the available commercial products.

3’RNA Sequencing:

Here, mRNAs are not fragmented before reverse transcription, and cDNAs are reverse transcribed only from the 3′ end of the mRNAs. Therefore, only one copy of cDNA is generated for each transcript. This means that when the cDNAs are sequenced, the number of reads is a direct reflection of the number of transcripts of a specific gene.

Commercial Products: Lexogen QuantSeq, QioSeq UPX 3’ Transcriptome

Specific Targeted Sequencing:

This approach targets RNA sequences by hybridization to DNA oligos followed by removing the unhybridized oligos and amplifying the remaining product.

Commercial Products: BioSpyder TempO-Seq, Ion Ampliseq Transcriptome

Traditional Microarrays:

These are used to quantify a defined set of transcripts by hybridizing the RNA to an array of complementary probes (which typically correspond to the 3′ end of the gene). This technology can be used to evaluate thousands of transcripts simultaneously. Advances in fluorescence detection have increased the ability to accurately detect low-abundance transcripts.

Commercial Products: Affymetrix U133A, Illlumina BeadChip HT-12 v3/v4

Newer Microarrays:

These arrays focus on 20,000+ well-annotated genes and provide a gene-level view of the transcriptome. These newer arrays consist of defined sets of probes that target characteristic exons rather than the 3’ end of a gene.

Commercial Product: Affymetrix Clariom S

Conclusion

Transcriptomic data can be reliably generated from different measurement platforms, with the relative merits and limitations of the RNAseq and NanoString gene expression profiling platforms summarized here. Ultimately, the choice of platform used will depend upon the study endpoints, required resolution and coverage, throughput, sample quality, availability, and budget.