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
Robust total transcriptome analysis from single cell level
Why use ultra-low input transcriptomics?
Discuss your project with me
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.
Service workflow
1. Initial meeting
When you consider using one of our services, our scientific team will help choosing the optimal project design. Each service can be customized by adding options. You will receive an overview of project budget and deliverables, so you can make an informed choice. Once you return your Purchase Order form, your project manager will contact you to discuss the next steps.
2. Sample delivery
Sample specifications for ultra-low input transcriptomics are:
- Optimal total RNA amount: ≥ 30 ng / sample
- Minimal RNA quantity: 0.25 ng
- Minimal volume of 25 μl/sample
- Minimal Quality: RQN: 3 ; RIN: 3
- Optimal Quality: RQN: ≥6 ; RIN: ≥7
3. Sample entry QC
After sample arrival, Entry-QC is performed to determinate the sample quality and quantity.
A report with QC results will be provided and results and progression of the project can be discussed.
4. Library prep/QC and sequencing
The library resulting from sample preparation goes into a library QC step to ensure sufficient material per sample and the right library size.
- Sequencing PE 150 on the NovaSeq
- Standard read depth 30M /sample
- Unique Molecular Identifier tags
5. Data QC
The raw data that is generated by the sequencer must be divided to obtain one data file per sample. This is called demultiplexing.
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.
6. Reporting
You receive your Project Report together with your data. It contains an overview of the experimental methods used, sample specifications, and a summary of all technical information for publication. Deliverables:
- 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.
Service specifications
We have summarized key information about our ultra-low input transcriptomics service into a service specification sheet.
Let's get the conversation started for your next NGS project
Please either fill in this form or email us directly at info@genomescan.nl
and we will get in touch with you to discuss your requirements.
