A critical review of microfluidic systems for CRISPR assays

Reviewed are nucleic acid detection assays that incorporate clustered regularly interspaced short palindromic repeats (CRISPR)-based diagnostics and microfluidic devices and techniques. The review serves as a reference for researchers who wish to use CRISPR-Cas systems for diagnostics in microfluidic devices. The review is organized in sections reflecting a basic five-step workflow common to most CRISPR-based assays. These steps are analyte extraction, pre-amplification, target recognition, transduction, and detection. The systems described include custom microfluidic chips and custom (benchtop) chip control devices for automated assays steps. Also included are partition formats for digital assays and lateral flow biosensors as a readout modality. CRISPR-based, microfluidics-driven assays offer highly specific detection and are compatible with parallel, combinatorial implementation. They are highly reconfigurable, and assays are compatible with isothermal and even room temperature operation. A major drawback of these assays is the fact that reports of kinetic rates of these enzymes have been highly inconsistent (many demonstrably erroneous), and the low kinetic rate activity of these enzymes limits achievable sensitivity without pre-amplification. Further, the current state-of-the-art of CRISPR assays is such that nearly all systems rely on off-chip assays steps, particularly off-chip sample preparation.

Detection and Discrimination of Single Nucleotide Polymorphisms by Quantification of CRISPR-Cas Catalytic Efficiency

The specificity of CRISPR-Cas12 assays is attractive for the detection of single nucleotide polymorphisms (SNPs) implicated in, e.g., cancer and SARS-CoV-2 variants. Such assays often employ endpoint measurements of SNP or wild type (WT) activated Cas12 trans-cleavage activity; however, the fundamental kinetic effects of SNP versus WT activation remain unknown. We here show that endpoint-based assays are limited by arbitrary experimental choices (like used reporter concentration and assay duration) and work best for known target concentrations. More importantly, we show that SNP (versus WT) activation results in measurable kinetic shifts in the Cas12 trans-cleavage substrate affinity (K_M) and apparent catalytic efficiency (k_cat^*/K_M). To address endpoint-based assay limitations, we then develop an assay based on the quantification of Michaelis-Menten parameters and apply this assay to a 20 base pair WT target of the SARS-CoV-2 E gene. We find that the k_cat^*/K_M measured for WT is 130-fold greater than the lowest k_cat^*/K_M among all 60 measured SNPs (compared to a 4.8-fold for endpoint fluorescence of the same SNP). K_M also offers a strong ability to distinguish SNPs, varies 27-fold over all the cases, and, importantly, is insensitive to the target concentration. Last, we point out trends among kinetic rates and SNP base and location within the CRISPR-Cas12 targeted region.

Web-Based Open-Source Tool for Isotachophoresis

We present the development of a client-side web-based simulator for complex electrophoresis phenomena, including isotachophoresis. The simulation tool is called Client-based Application for Fast Electrophoresis Simulation (CAFES). CAFES uses the broad cross-browser compatibility of JavaScript to provide a rapid and easy-to-use tool for coupled unsteady electromigration, diffusion, and equilibrium electrolyte reactions among multiple weak electrolytes. The code uses a stationary grid (for simplicity) and an adaptive time step to provide reliable estimates of ion concentration dynamics (including pH profile evolution), requiring no prior installation nor compilation. CAFES also offers a large database of commonly used species and their relevant physicochemical properties. We present a validation of predictions from CAFES by comparing them to experimental data of peak- and plateau-mode isotachophoresis experiments. The code yields accurate estimates of interface velocity, plateau length and relative intensity, and pH variations while significantly reducing the computation time compared to existing codes. The tool is open-source and available for free at https://microfluidics.stanford.edu/cafes.