Nanostring Technologies, United Kingdom
True digital (i.e., counting based) multiplexed gene expression can currently only be performed using RNA-seq or optically-barcoded single-nucleic-acid counting (nCounter technology). We demonstrate that a simple modification to the nCounter protocol enables digital quantification of 800 unique transcripts in a single cell, offers several potential advantages relative to single-cell microfluidic PCR, and much better counting statistics than single-cell (or normal input) RNA-seq (when compared to whole transcriptome). The nCounter Single Cell protocol incorporates reverse transcription and linear pre-amplification (10 to 18 cycles) with a highly multiplexed pool of up to 800 gene-specific primer pairs in a single tube, followed by hybridization with optically-barcoded nucleic-acid-labels. Microfluidic qPCR methods require the same pre-amplification step, but must be followed by splitting the amplified sample into 96 separate wells and performing an additional series of up to 40 PCR amplification cycles. nCounter technology requires no sample-splitting (a true multiplex) or additional amplification cycles and (compared to RNA-seq) doesn’t require library generation because single-molecules are counted directly. Gene-expression measurements of flow-sorted single-cells using nCounter, revealed the stochastic “on-off” behavior. The “summed” (aggregate) gene expression profile from multiple individual flow-sorted cells was (essentially) identical to pools of multiple flow-sorted cells (10 per tube and 100 per tube), proving digital linearity of 800-targets at the single-cell level for the first time. When comparing to RNA-seq (whole transcriptome), nCounter (on panels of 100’s-of-genes) resolved a constant ~ 2 million on-target reads per sample (~10,000X coverage), compared with < 100,000 on-target reads for RNA-seq (as expected for a non-targeted approach). Hundreds-to-thousands of single-cells, 800 targets-each, can be examined per-week on an nCounter system: enabling single-cell digital biology.
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