April 11, 2025
In RNA sequencing (RNA-Seq), the accuracy and reproducibility of your results still hinge on one fundamental factor: proper RNA input. Among the most common, and often underestimated, challenges in RNA-Seq experiments is determining the optimal RNA quantity.
Too little RNA risks low signal-to-noise ratios and incomplete transcriptome coverage. Too much can lead to amplification bias, over-saturation of the library prep, and wasted sequencing resources. Even with ongoing improvements in RNA-Seq technologies, precise RNA quantification remains critical for generating high-quality, reproducible data.
This blog explores the key considerations for determining the ideal RNA input for RNA-Seq, addressing factors such as transcript complexity, sample quality, and sequencing depth to ensure robust and dependable outcomes.
The amount of RNA used in RNA-seq directly impacts the accuracy and consistency of gene expression measurements. RNA-seq works by converting RNA into complementary DNA (cDNA) for sequencing.
If your RNA sample is too small, some transcripts may go undetected, leading to gaps in your data and inaccurate results. Excess RNA can lead to high duplicate reads and diminishing returns on new transcript discovery.
Optimal RNA amounts ensure efficient cDNA synthesis, balanced library preparation, and reliable sequencing data.
Simply put, starting with the correct RNA quantity guarantees that the biological insights from your RNA-seq experiment are both accurate and scientifically meaningful.
Choosing the right protocol and understanding the RNA requirements are crucial steps in planning your RNA-seq experiment. The RNA input needed depends on the protocol you're using and whether you're focusing on specific RNA types or sequencing the entire RNA population.
Table representing types of RNA-seq approaches and the amount of total RNA recommended by each protocol
If you're looking at total RNA—including mRNA, non-coding RNA, and others—or using ribosomal RNA depletion, you generally start with 100 ng to 1 µg of total RNA. The exact amount depends on your RNA quality.
High-quality RNA samples (RIN ≥8) can reliably yield good results even at lower input amounts. Standard RNA-seq methods without PolyA selection often require less total RNA input, typically around 100 ng, since they include all RNA types.
Standard RNA-seq protocols using PolyA selection typically require 500 ng to 1 µg of high-quality total RNA. It requires more RNA because it focuses solely on mRNA, which makes up only 1–5% of total RNA.
Higher input ensures efficient enrichment of messenger RNA (mRNA) via binding of the polyadenylated (PolyA) tails for a diverse, high-quality library, maximizing transcript detection sensitivity and ensuring reliable quantification.
While traditional RNA-Seq offers a comprehensive approach to studying the transcriptome, these specialized protocols are designed to capture specific subsets of RNA or focus on particular aspects of gene expression, providing deeper insights into complex biological processes.
Short-read RNA-seq works by fragmenting RNA into smaller pieces (200-500 base pairs) for sequencing and reassembly. The required RNA quantity depends on the library preparation kit used.
Selecting the right kit based on your available RNA amount can greatly improve the quality and reliability of your RNA-seq experiment.
Long-read RNA-seq methods, such as the SMRTbell® prep kit 3.0 from PacBio Iso-Seq and the Nanopore Direct RNA Sequencing Kit from Oxford Nanopore, analyze full-length RNA transcripts, allowing for the detection of splice variants and isoforms. These methods require more RNA, typically between 300 ng and 500 ng, respectively, to ensure enough complete transcripts are captured.
Higher RNA input helps achieve accurate isoform detection and more comprehensive data. Using lower RNA input may limit transcript sensitivity and library complexity.
Low-input RNA-seq protocols are designed for scenarios where only small amounts of RNA are available, such as clinical samples, single-cell experiments, or archival tissues. The QIAseq UPXome RNA Library Kits from QIAGEN require 500 pg to 100 ng of total RNA, while the SMART-Seq® mRNA LP from Takara Bio works with as little as 10 pg of RNA.
They use amplification techniques that generate enough material from limited starting RNA. Because low input amounts can introduce amplification bias, you must handle and quantify samples carefully.
Unlike short and long-read approaches, bulk 3’ mRNA-seq technologies attach a sample barcode to the 3’ end of mRNA transcripts. This simplifies library preparation and reduces the RNA input required compared to traditional RNA-seq protocols.
QuantSeq-Pool:
QuantSeq-Pool allows you to process multiple RNA samples together in one reaction, significantly reducing RNA input per sample. You typically need just 10–120 ng of total RNA per sample. This low requirement makes QuantSeq-Pool useful for projects with limited RNA or numerous samples.
MERCURIUS BRB-seq:
In contrast, MERCURIUS BRB-seq employs barcoding methods to label various samples, combining them prior to sequencing. With this approach, RNA input per sample is extremely low, usually ranging between 10 ng–1 µg of total RNA. The reduced RNA requirement makes it ideal for experiments where RNA amounts are limited, such as clinical or small-scale studies.
Selecting these methods lets you perform cost-effective, high-throughput RNA-seq without needing large RNA quantities, ensuring robust and reliable gene expression data.
Understanding these distinctions and selecting the right protocol for your RNA quantity and sample type will significantly improve your RNA-seq experiment’s success.
The number of cells needed for sufficient RNA yield depends on the cell type and RNA quality. To obtain 1 µg of RNA, you'll typically need around 1 million eukaryotic cells.
For single-cell RNA-seq, you'll need fewer cells—usually around 10,000 to 100,000. But keep in mind, the RNA yield per cell is lower, and you might face challenges with RNA degradation or low yield from some cells.
If you're working with small-scale or low-input RNA-seq, you can get usable RNA from as few as 1,000 to 5,000 cells, but you’ll likely need to amplify the RNA carefully to avoid bias or loss of data.
Also, the RNA yield can vary by cell type—some cells, like neural cells or fat cells, produce less RNA than others, like liver or cancer cells. Proper handling and storage of the cells are crucial to keep the RNA intact for sequencing.
At Biostate AI, we specialize in extracting and sequencing RNA from even the most challenging or limited samples, all at an affordable cost.
Our advanced protocols allow us to process samples from different species and biological sources, providing consistent sequencing results, regardless of sample size or quality.
It's essential to use the right RNA quantity based on the protocol you're using to ensure successful RNA-seq experiments. While standard methods like PolyA selection require 500 ng to 1 µg of RNA, specialized protocols like low-input RNA-seq can work with as little as 10 pg. Selecting the correct RNA amount ensures reliable, high-quality results and helps optimize the sequencing process across various sample types and research scenarios.
Ready to revolutionize your RNA sequencing research? Connect with Biostate AI to discover how easily you can obtain comprehensive and affordable RNA-seq data, even from minimal RNA and diverse sample sources.
Get a quote now and start unlocking new insights with minimal sample requirements and maximum results.
RNA sequencing (RNA-seq) is a high-throughput sequencing technique used to analyze the quantity and sequences of RNA in a sample, providing insights into gene expression and transcriptome dynamics.
Unlike microarrays, which rely on pre-existing knowledge of gene sequences, RNA-seq does not require prior annotation, allowing for the discovery of novel transcripts and alternative splicing events.
The main steps include RNA extraction, library preparation (which may involve RNA selection or depletion), cDNA synthesis, amplification, sequencing, and data analysis.
Single-end sequencing reads one end of the cDNA fragment, while paired-end reads both ends, providing more information for transcript assembly and detection of isoforms.
Challenges include RNA degradation, biases introduced during library preparation, and the need for high sequencing depth to detect low-abundance transcripts.
