Rapid detection of viral infections is critical for controlling disease spread and guiding treatment decisions. In this work, researchers at the University of California, Berkeley have developed a new approach to detect multiple viruses and variants at once using RNA-based technology.

Traditional diagnostic methods like PCR are highly sensitive but require converting RNA into DNA and amplifying it, which adds time and complexity. Newer CRISPR-based systems, such as Cas13a, can directly detect RNA without these steps. However, these methods have struggled to detect multiple targets in a single test without splitting the sample.

The researchers discovered that Cas13a behaves differently depending on how it interacts with specific RNA targets. By carefully modifying the guide RNA, called crRNA, they were able to control how quickly Cas13a cuts RNA. This creates unique kinetic patterns, or signatures, for different viruses.

Using a droplet-based system, the team showed that these kinetic signatures can be used like barcodes to distinguish between different respiratory viruses and even different variants of SARS-CoV-2 in the same sample. This means multiple pathogens can be identified simultaneously without additional steps.

A droplet assay measures Cas13a single-molecule kinetics

a, Schematic showing the increased signal accumulation rate for a single Cas13a confined in decreasing volumes (red, target RNA). The table quantitatively shows the effect of reaction volume on the signal slope, assuming that only a single target is present. Each droplet contains hundreds of thousands copies of the Cas13a–crRNA ribonucleoprotein (RNP) complexes and millions of quenched RNA reporters. b, Bright-field (top left) and fluorescence images of droplet Cas13a reactions taken with a 20× objective lens. T = 0 min, 10 min and 20 min since the beginning of imaging. The images shown are representative of three independent experiments, each yielding similar results. Scale bar, 65 µm. c, Fluorescence signal over time in three positive droplets (green lines) and one negative droplet (grey line). The signal is calculated from the mean fluorescence intensity change within a droplet normalized by its initial value after background subtraction. An image is taken every 30 s and corrected for photobleaching. d, Single-Cas13a trans-cleavage rate measured from individual droplets containing crRNA 4, SARS-CoV-2 RNA and 400 nM 5U-fluorescent reporter (N = 478 droplets). e, The data in d are expressed in turnover frequency by normalizing the signal for droplet size. f, Schematic showing two possible outcomes of a Cas13 droplet reaction combining two crRNAs. The RNA targets for crRNA 2 and crRNA 4 are cr2tg and cr4tg, respectively. If the whole N gene is loaded into a droplet containing both crRNAs, the fluorescence signal will accumulate twice as fast as the droplet containing only one target sequence (complete loading). However, if the N gene is fragmented before encapsulation and parts of the N gene are loaded into two different droplets, the number of positive droplets will be doubled, and the signal of each droplet will be the same (fragmented loading). g, The results of Cas13a reaction with 2.5 × 10⁴ copies per µl of IVT N gene and without any target RNA in droplets containing crRNA 2, crRNA 4 or both crRNAs are quantified after 1 h of reaction incubation from three technical replicates. The distribution of signal per droplet is represented as a box and whisker plot showing the median, lower and upper quartiles, and minimum and maximum values ignoring outliers. Data from the triplicate runs were combined for each condition. h, Same experiment as described in f, but the number of positive droplets is shown. The mean of total positive droplets is 14.8, 11.2, 25.6, 0.08 and 0.04, for crRNA 2, crRNA 4 or both crRNAs with target RNAs and crRNA 2 and crRNA 4 without any target, respectively. The mean and standard deviation were derived from triplicates.

This approach connects closely with advances in RNA sequencing, as both rely on detecting and interpreting RNA signals to understand biological systems. By improving how RNA can be rapidly and specifically identified, this method could enhance diagnostic capabilities in clinical and public health settings.

Overall, the findings demonstrate a flexible and scalable way to expand RNA-based diagnostics. By simply adjusting the crRNA design, the system can be adapted to detect new targets, making it a powerful tool for future infectious disease monitoring.