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.
Ultra-low Input Transcriptomics
Why use ultra-low input transcriptomics?
Detect significant changes in gene expression
Gene expression analysis becomes more informative when small populations are screened. 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 of challenging samples. Therefore, you can rely on uniform transcript coverage, regardless of input amount.
1. Initial meeting
2. Sample delivery
- Starting from 10 pg RNA
- Ideally deliver as much RNA as possible, to allow for QC and normalization
- Minimally validated RNA input: 2pg
- For RNA quality of RIN / RQN ≥7.0
3. Sample entry QC
4. Library prep/QC and sequencing
- Sequencing PE 150 on the NovaSeq 6000
- Data required for Ultra-low input RNA-seq: generally 20-30 million reads
5. Data QC
Your project manager checks the data quality by analyzing the quality metrics of the run. Final inspection of the data takes place and the dataset is transferred onto our portal. It is also possible to receive your data on hard disk.
- TAT: 3 weeks
- Sequencing of Total RNA (generally ~40% of total RNA is mRNA) (raw data)
- Quality score Q30 of ≥80% for PE 150 reads
- Count table: contains the expression levels (FPKM values) per gene
- Differential gene-expression analysis
- Extended data-analysis including PCA plot, heatmap, pathway analysis
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.
We have summarized key information about our ultra-low input transcriptomics service into a service specification sheet.
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