PT - JOURNAL ARTICLE AU - Jonathan L. Schmid-Burgk AU - David Li AU - David Feldman AU - Mikołaj Słabicki AU - Jacob Borrajo AU - Jonathan Strecker AU - Brian Cleary AU - Aviv Regev AU - Feng Zhang TI - LAMP-Seq: Population-Scale COVID-19 Diagnostics Using a Compressed Barcode Space AID - 10.1101/2020.04.06.025635 DP - 2020 Jan 01 TA - bioRxiv PG - 2020.04.06.025635 4099 - http://biorxiv.org/content/early/2020/04/08/2020.04.06.025635.short 4100 - http://biorxiv.org/content/early/2020/04/08/2020.04.06.025635.full AB - The ongoing COVID-19 pandemic has already caused devastating losses. Early evidence shows that the exponential spread of COVID-19 can be slowed by restrictive isolation measures, but these place a tremendous burden on society. Moreover, once these restrictions are lifted, the exponential spread is likely to re-emerge. It has been suggested that population-scale testing can help break the cycle of isolation and spread, but current detection methods are not capable of such large-scale processing. Here we propose LAMP-Seq, a barcoded Reverse-Transcription Loop-mediated Isothermal Amplification (RT-LAMP) protocol that could dramatically reduce the cost and complexity of population-scale testing. In this approach, individual samples are processed in a single heat step, producing barcoded amplicons that can be shipped to a sequencing center, pooled, and analyzed en masse. Using unique barcode combinations per sample from a compressed barcode space enables extensive pooling, significantly reducing cost and organizational efforts. Given the low cost and scalability of next-generation sequencing, we believe that this method can be affordably scaled to analyze millions of samples per day using existing sequencing infrastructure.