Detect significant changes in gene-expression:
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 of challenging samples. Therefore, you can rely on uniform transcript coverage, regardless of input amount.
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.