Sign in
BioengineeringbioRxiv
Preprint

Bead-based RNA Detection with Cas13a: Multi-scale Design and Validation

Abdul Muyeed BhuiyaiD, Siddhansh AgarwaliD, Carlos F. NgiD, Mélanie OttiD, Sungmin SoniD, Daniel A. FletcheriD

Abstract

CRISPR-based assays offer sensitive and specific nucleic acid detection for a broad range of diagnostic and surveillance applications. While standard solution-based assays are simple, they typically lack the ability to detect multiple targets individually without the need for more complex instrumentation and multi-step workflows. Surface-based strategies offer greater potential for multiplexing since the identity of a target can be associated with a location on a slide or a specific bead, as in the case of DNA arrays or Luminex bead assays. However, the design principles for optimizing surface-based CRISPR assays have yet to be developed. Here we present a multi-scale analytical framework integrating nanoscale enzyme kinetics, microscale diffusion constraints, and macroscale assay parameters that we use to develop a bead-based Cas13a assay where the bead becomes fluorescent when the target RNA for that bead is detected. The assay, which we call SurfCas, uses surface-bound guide RNAs and reporter RNAs to detect one or more soluble target RNAs in a one-pot assay. We show that SurfCas has a sensitivity approaching bulk reactions, and we demonstrate multiplexed detection of target RNAs in a complex biological matrix. The design principles we establish for surface-based CRISPR assays offer guidance on how to further improve SurfCas sensitivity and scale multiplexed detection of target RNAs in a single reaction.

PDFDOI

02 /Specification & Classification

Click any tag to browse matching papers

Reproducibility ledger

Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.

Sign in to file a reproducibility report.

Similarity Map
SelectedSame fieldCross-field
THISQin '21Nguyen '20Chen '24Rananaware '23Huyke '22Aguilar-Gon '26
69% semantic matchBioengineering
P. Qin, D. Yu, Q. He+12 more·bioRxiv·Jul 17, 2021