RT-nPCR Test | 12 Jun 2020

Why in News

Researchers at the Centre for Cellular and Molecular Biology (CCMB) have developed a new test to detect novel coronavirus (SARS-CoV-2) named ‘Reverse Transcription nested Polymerase Chain Reaction (RT-nPCR) test.

Key Points

  • RT-nPCR Test:
    • It does not depend on RT-qPCR (testing approved by the ICMR) but uses standard RT-PCR as part of an endpoint assay (i.e. to measure biochemical activity of a sample).
    • It has been developed on a low-cost and low-tech model.
    • This new test is awaiting approval from the Indian Council of Medical Research (ICMR).
  • RT-qPCR Test:
    • The ICMR has recommended only Reverse Transcription Polymerase Chain Reaction (RT-qPCR) test for novel coronavirus testing.
      • PCR is an enzymatic reaction used to amplify DeoxyRibonucleic Acid (DNA).
      • Unlike conventional PCR, which relies on end point analysis, qPCR enables the analyst to monitor DNA amplification in real time, as the reaction proceeds.This allows quantification of DNA.
      • However, coronavirus is made up of Ribonucleic Acid (RNA). Therefore to detect coronavirus, RNA is converted into DNA using a technique called Reverse Transcription (RT).
  • RT-qPCR vs RT-nPCR:
    • In RT-qPCR, the viral RNA is quantified, whereas in RT-nPCR, the viral RNA that nests is studied.
    • RT-qPCR is costly, takes longer, needs special apparatus and can be performed only in a lab with sophisticated equipment. It requires a real time thermal cycler, which is an expensive instrument.
    • RT-nPCR is economical, can be tested on a large scale, does not require special apparatus and can be done in a lab with basic equipment. It needs a simple PCR machine.
    • Further, the CCMB scientists found a high percentage of false negative cases while comparing RT-qPCR with the new test.
      • The RT-nPCR test was able to identify 90% of the detected samples as positive by RT-qPCR. It also detected 13% samples as positive among samples that were negative by the standard RT-qPCR test (likely false negatives).

Source: PIB