Have you ever wondered how scientists can pinpoint which gene is active inside a cell? In the vast world of gene expression studies, technologies like NanoString nCounter and RNA-Seq contribute hugely by helping scientists determine which gene is active inside a cell or tissue.
This article will explain these technologies in depth. Both methods are widely used due to their precision and ability to provide valuable insights into complex biological processes. NanoString nCounter and RNA-Seq are two distinct technologies that allow scientists to analyze RNA, a key player in gene expression.
While both aim to measure gene expression levels, their approach and the types of data they provide differ. NanoString nCounter is a direct digital counting system that provides highly accurate, multiplexed gene expression data. Meanwhile, RNA-Seq relies on high-throughput sequencing to capture the entire transcriptome, offering a broader view of gene activity.
Below, you’ll explore these two technologies in brief, as well as their key differences, limitations, and more.
NanoString RNA Sequencing Technology
Source: Wikipedia Common
What is NanoString RNA Sequencing? NanoString RNA sequencing technology is a hybridization-based approach for gene expression analysis that differs from traditional RNA sequencing (RNA-Seq). Instead of using next-generation sequencing (NGS), NanoString’s transcriptome analysis technology relies on digital color-coded molecular barcodes to directly quantify RNA transcripts without the need for reverse transcription or amplification.
Once you understand what NanoString RNA sequencing is below, you will delve into the topic in depth and understand digital molecular barcoding for mRNA quantification, the stages of non-string nCounter, and its advantages and limitations.
1. Digital molecular barcoding for mRNA quantification
Digital molecular barcoding is a key feature of NanoString RNA sequencing that ensures precise and direct quantification of mRNA without the need for cDNA synthesis or PCR amplification. This method addresses several challenges in RNA quantification by offering a digital approach, which means it counts individual molecules instead of relying on traditional methods that may involve sequencing. Incorporating NanoString transcriptome analysis into such workflows allows researchers to integrate digital quantification into high-throughput applications, ensuring consistent and reliable results across diverse research areas.
2. Stages: Hybridization, purification, and digital detection
Source: NIH
Nanostring RNA sequencing has three major steps that you need to understand to better understand the concept; let’s explore them all below.
1. Hybridization
The first step in NanoString RNA sequencing is hybridization. In this step, mRNA or miRNA samples are mixed with two types of probes: reporter probes (fluorescently labeled) and capture probes (biotin-labeled). Hybridization allows for direct digital detection without sequencing, a key advantage of NanoString RNA analysis over traditional sequencing methods.
2. Purification and Immobilization
Once hybridization is complete, the sample is transferred to the Prep Station, a robotic system that removes excess probes and immobilizes the RNA-probe complexes onto a nCounter cartridge. The capture probes bind to a Streptavidin-coated surface, ensuring the sample remains fixed for analysis. This step is crucial in NanoString transcriptome analysis to maintain accuracy and reproducibility.
3. Digital Detection
The cartridge is then placed in the Digital Analyzer, where a high-resolution Charge-Coupled Device (CCD) camera detects the fluorescent barcoded reporter probes. Each barcode corresponds to a unique RNA molecule, enabling precise quantification of mRNA and miRNA. The system scans multiple lanes, divides them into fields of view (FOVs), and counts the optical signals. The final output, a Reporter Code Count (RCC) file, provides absolute molecular counts, making NanoString RNA analysis a highly reliable method for gene expression studies.
3. Advantages of NanoString RNA sequencing: Precision, ease of use, minimal sample requirement
There are many advantages of NanoString RNA sequencing technology, such as its offering precise and high-quality analysis, making it a suitable way to analyze a low-quality sample such as those derived from formalin-fixed paraffin-embedded (FFPE) tissue. It also has a high multiplexing capacity that allows up to 800 mRNA targets to be measured in a single assay.
A major advantage of the nCounter platform is its technical reproducibility and robustness. It is also highly resistant to variations in RNA quantity and quality, reagent performance over time, and protocol adjustments, reinforcing its reliability in diverse experimental conditions. Another key feature is that NanoString does not require amplification, unlike qPCR and RNA sequencing, which depend on cDNA synthesis and amplification. This eliminates amplification bias and allows for more accurate quantification of gene expression levels.
NanoString RNA analysis is also easy to use and requires minimal hands-on time, which further makes it an appealing option for researchers. Its automated workflow capability simplifies the gene expression analysis process, resulting in less manual intervention. Additionally, the platform requires less sample processing and preparation compared to other high-throughput techniques, making it a more convenient and efficient choice for researchers.
4. Limitations of NanoString RNA analysis: Limited multiplexing, proprietary platform
With advantages, there are several limitations to the NanoString RNA analysis or NanoString transcriptome analysis, such as low-expressed genes may fall below the limit of detection (LOD) when sample quality, quantity, or processing is compromised, though this is generally minimal. The platform also has throughput constraints; batch effects in CodeSets require careful calibration. Its probe design is also a challenge, as sequences must be carefully selected to avoid untranslated regions (UTRs) and pseudogenes, which may not be expressed in certain diseases like cancer. Lastly, while NanoString excels at mRNA expression analysis, further validation is needed for gene fusions, miRNA, CNV, and protein analysis, limiting its current application scope.
As you explored above in depth about NanoString nCounter technology, below, you’ll explore other technology that is widely used by researchers to know the gene activity inside a cell. Let’s explore it below.
RNA Sequencing (RNA-Seq)
RNA sequencing process—source: Biorender
What Is RNA-Sequencing? RNA sequencing is a next-generation sequencing technique that analyzes the transcriptome to provide detailed information on RNA transcripts produced by the genome under specific conditions in a sample. This method has contributed hugely to the understanding of eukaryotic transcriptomes by revealing their complexity and variability. Compared to other techniques, RNA-Seq offers higher precision in measuring transcript levels and identifying isoforms.
There are four key techniques of RNA sequencing: bulk RNA sequencing, single-cell RNA sequencing, direct sequencing, and quantification of RNA. Other than these four techniques below, you’ll learn about the different stages of RNA sequencing, its advantages, and its limitations. Let’s explore them below!
1. Different techniques of RNA-sequencing
Source: NIH
Four different techniques of RNA sequencing exist, among which bulk RNA sequencing and single-cell RNA sequencing are widely used.
- Bulk RNA Sequencing: Analyzes RNA from a whole tissue or cell population, offering an overall view of gene expression across the sample.
- Single-Cell RNA Sequencing (scRNA-seq): Sequences RNA from individual cells, providing higher resolution insights into gene expression within diverse or heterogeneous cell populations
- Direct RNA sequencing (dRNA-Seq) uses nanopore technology to sequence native RNA without the need for conversion to cDNA. This method preserves RNA modifications, providing a more accurate representation of the transcriptome.
- Quantification of mRNA: Used to measure the abundance of mRNA transcripts in a sample. This is done by counting the sequencing reads mapped to specific genes or transcripts. Methods like qRT-PCR are commonly used to assess gene expression levels.
2. Stages: RNA isolation, library preparation, sequencing and data analysis
RNA sequencing (RNA-Seq) has these four main stages:
- RNA Isolation: Extracting high-quality RNA from biological samples is crucial, as degraded RNA can lead to inaccurate sequencing results.
- Library Preparation: After RNA isolation, the next step in transcriptome sequencing is to create an RNA-Seq library. This involves selecting the RNA species of interest and using a reverse transcription process to convert RNA into complementary DNA (cDNA).
- Sequencing: The prepared cDNA libraries are then sequenced using high-throughput next-generation sequencing (NGS) platforms. The choice of platform can affect read length, throughput, and accuracy, which in turn impacts the quality of the transcriptome data obtained.
- Data Analysis: Post-sequencing, the data undergoes computational analysis to align reads to a reference genome, quantify gene expression levels, and identify novel transcripts or alternative splicing events.
3. Advantages of RNA-sequencing: Novel discovery, high sensitivity, biomarker discovery
RNA sequencing (RNA-seq) offers several advantages, such as a comprehensive view of gene expression, allowing for the detection of both coding and non-coding RNA species. This is crucial in understanding the complexity of cancer, including its heterogeneity and evolution.
RNA-seq also helps in understanding drug resistance mechanisms, cancer immune microenvironments, and neoantigen identification. The technology’s ability to capture spatial transcriptomics, linking gene expression with tissue architecture, is hugely contributing to healthcare, especially in cancer research, by providing insights into how tumors interact with their surroundings.
RNA-seq, especially single-cell RNA sequencing (scRNA-seq), enables high-resolution analysis at the individual cell level, revealing molecular variations within the tumor microenvironment. This precision helps identify cancer-specific biomarkers, which can aid in early diagnosis and personalized treatment plans.
4. Limitations: Requires high-quality RNA, costly data analysis
This technique requires high-quality RNA to do dRNA-Seq. Degraded RNA can lead to poor sequencing outcomes and lack of accuracy, making it a challenge in RNA-based studies. It is also highly costly. While dRNA-Seq provides significant advantages in terms of resolution and modification detection, the data analysis is often more complex and expensive compared to traditional RNA-Seq. However, platforms like Biostate.ai have overcome these limitations by providing an end-to-end RNA sequencing solution, ensuring precision and efficiency at every step at an affordable rate.
As you have understood, both NanoString RNA sequencing and RNA sequencing. Below, you’ll explore the key differences between both of these approaches.
Key Differences Between NanoString nCounter and RNA-Seq
After exploring both approaches and understanding their limitations, benefits, and procedures, you’ll compare the key differences between them below to determine which method best suits your research needs.
While NanoString nCounter provides targeted, amplification-free quantification with minimal data processing, RNA-Seq offers a comprehensive, high-resolution analysis of the entire transcriptome. Let’s explore them below to help you make an informed decision.
| Feature | NanoString nCounter | RNA-Seq (Bulk & scRNA-Seq) |
| Detection Method | Direct digital detection, no amplification or sequencing required | Requires cDNA synthesis, amplification, and sequencing. It also supports de novo transcript discovery or alternative isoform analysis |
| Sensitivity & Specificity | High sensitivity for targeted genes, no amplification bias | High sensitivity for the whole transcriptome but affected by amplification bias |
| Multiplexing & Coverage | Measures up to 800 predefined genes per assay | Captures full transcriptome, including novel transcripts and isoforms |
| Sample Requirements | Works well with FFPE and low-quality samples | Requires high-quality RNA or live cells for scRNA-seq |
| Data Complexity & Analysis | Simple, predefined panels with easy-to-interpret results | Requires advanced bioinformatics for data processing and analysis |
| Cost & Throughput | Lower cost, suitable for large-scale targeted studies | High cost, especially for whole transcriptome analysis and single-cell profiling |
| Application Focus | Targeted gene expression in clinical and translational research | Broad discovery applications, including novel transcript detection and cell-state changes |
Overall, RNA-seq and scRNA-seq provide greater transcriptome-wide flexibility and deeper cellular resolution. NanoString nCounter technology offers a targeted, cost-effective, and reproducible alternative for gene expression analysis, particularly in clinical and translational research.
As mentioned above, you explored the comparison of both technologies and understood both in-depth separately; this has given you an understanding of which technology will better help you achieve your experiment’s aim. You have now reached the end of the concept and evaluation; below, you’ll find the summary of the article.
Conclusion
Both NanoString nCounter (NanoString transcriptome analysis) and RNA sequencing (RNA-seq) offer unique perks that make them a valuable tool for understanding the gene activity in a sample. NanoString nCounter is a highly targeted, cost-effective, and reproducible technology that excels in clinical and translational research, particularly when working with FFPE samples and degraded RNA. On the other hand, RNA-seq offers comprehensive transcriptomic profiling, allowing for de novo transcript discovery, isoform analysis, and single-cell resolution. However, it requires higher-quality RNA, extensive bioinformatics processing, and greater financial investment.
Choosing between the two depends on the research objectives. NanoString is ideal for focused gene expression studies with predefined targets, while RNA-seq is better suited for broad discovery applications where whole-transcriptome insights are needed. As a researcher, if you are looking to get RNA sequencing done at an affordable rate, then choose Biostate.ai because it provides a complete solution for RNA sequencing, handling every step from sample collection to final insights with precision and accuracy. Book Your Consultation Today!
Disclaimer: This article provides general information about comparative analysis of NanoString nCounter and RNA-Seq technologies. It is not intended as medical advice. For any medical concerns, always consult with a licensed healthcare professional.
FAQ
- What are the key differences between NanoString nCounter and RNA sequencing in terms of detection methods and data complexity?
The key difference between NanoString nCounter (NanoString transcriptome analysis) and RNA sequencing (RNA-seq) is in detection methods and data complexity. NanoString nCounter uses direct digital detection with color-coded molecular barcodes; this eliminates the need for amplification or sequencing. On the other hand, RNA-Seq relies on next-generation sequencing (NGS), requiring cDNA synthesis, amplification, and sequencing, allowing for a comprehensive transcriptomic analysis, including novel transcript discovery.
- How does digital molecular barcoding in NanoString RNA sequencing improve the accuracy of gene expression analysis compared to traditional RNA-Seq?
Digital molecular barcoding in NanoString RNA sequencing removes the need for cDNA synthesis and PCR amplification. This results in less amplification bias and offers precise, direct quantification of RNA molecules. Unlike RNA-Seq, which relies on sequencing depth and normalization, NanoString counts individual RNA transcripts, providing highly reproducible and accurate gene expression data, even from degraded or low-quality samples.
- What are the advantages and limitations of using NanoString nCounter for gene expression studies, particularly when working with FFPE samples?
NanoString nCounter is ideal for FFPE samples because it does not need RNA integrity or high-quality RNA, making it effective for the analysis of even degraded RNA. It offers high sensitivity, reproducibility, and minimal hands-on time, making it easy to use. However, its limitations include a fixed panel of genes, limited multiplexing (up to 800 targets per assay), and an inability to detect novel transcripts or isoforms, restricting its use for broad transcriptomic exploration.
- Why is RNA sequencing considered a superior method for discovering novel transcripts and isoforms compared to NanoString RNA analysis?
RNA-Seq provides whole-transcriptome coverage, allowing researchers to detect novel genes, alternative splicing events, and isoforms that NanoString cannot capture due to its predefined gene panels. RNA-Seq also enables de novo transcript discovery, making it superior for exploring gene expression beyond known targets.
- In what scenarios should researchers opt for NanoString nCounter over RNA-Seq, and how does cost factor into this decision?
Researchers should choose NanoString nCounter in case of targeted gene expression studies, clinical research, or when working with FFPE and low-quality RNA samples. It is a cost-effective way, as it does not require sequencing or complex bioinformatics, making it a good choice for high-throughput, routine expression analysis. RNA-Seq, while more expensive, is better suited for exploratory studies, biomarker discovery, and single-cell transcriptomics, where a broader view of gene expression is required.