Ultra-low input transcriptomics

Robust total transcriptome analysis from single cell level

This is why you should use ultra-low input transcriptomics:

Gene-expression analysis becomes more informative when small populations are screened. Samples that contain multiple cell types show much variation, mostly reflecting the sample-composition. Selecting cell clusters that differ solely in those aspects that you want to study, are easier to interpret. It allows you to detect significant changes in gene-expression for low abundant transcripts. Discovery of biomarkers and transcription-factors has never been so easy.

The low-input transcriptomics method is specifically designed for input amounts down to 2 pg, generally the amount present in a single cell. This method is applicable for good quality RNA and successfully validated for FFPE material and other challenging samples. Therefore, you can rely on uniform transcript coverage, regardless of input amount or sample type.

Unique Molecular Identifiers

GenomeScan’s low-input service minimizes the PCR duplication rate. Multiple measurements of the same molecule present the original sample are reduced, and PCR duplicates that do occur are filtered out.

How do we know that all reads arise from different transcripts? This method includes Unique Molecular Identifiers (UMI’s) which are ligated to the mRNA molecules early in the procedure to provide a unique tag. Duplicate reads can easily be filtered out during data-analysis, leading to clean and consistent data-sets.

See how we work with you on your project:

  • Starting from 2 pg RNA
  • Ideally deliver as much RNA as possible, to allow for QC and normalization
  • Minimally validated RNA input: 2 pg
  • For RNA quality of RIN / RQN ≥7.0
  • Sequencing of Total RNA (generally ~40% of total RNA is mRNA)
  • Quality score Q30 of ≥80% for PE 150 reads
  • Turn-around time: 4-6 weeks
  • Sequencing on NovaSeq 6000 PE150
  • Data required for Ultra-low input RNA-seq: generally 20 reads

The specifications of the data-set that you can expect are listed in your personal quotation.

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Generally, the sequencing quality score (Q score; ≥30 represents high quality) must be ≥80% for PE 150 reads (Illumina’s official guarantee ≥75%). Our average score of 2018 was
Q30 ≥ 90%.

We perform all RNA-seq experiments under ISO/IEC 17025 and ISO 15189.

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Option A              Count table: contains the expression levels (FPKM values) per gene
Option B              Differential gene-expression analysis
Option C              Extended data-analysis including PCA plot, heatmap, pathway analysis

Discuss your project with Raymond

We have years of experience in RNA sequencing. Talk with us about best practices and how we can help your research.

Raymond Egging

Director Marketing & Sales Diagnostics/R&D

+31 71 568 1050