Rapid and accurate species identification for ecological studies and monitoring using CRISPR-based SHERLOCK
Cite this dataset
Baerwald, Melinda et al. (2020). Rapid and accurate species identification for ecological studies and monitoring using CRISPR-based SHERLOCK [Dataset]. Dryad. https://doi.org/10.5061/dryad.hdr7sqvd3
One of the most foundational aspects of ecological studies and monitoring is accurate species identification, but cryptic speciation and observer error can confound phenotype-based identification. The CRISPR-Cas toolkit has facilitated remarkable advances in many scientific disciplines, but the fields of ecology and conservation biology have yet to fully embrace this powerful technology. The recently developed CRISPR-Cas13a platform SHERLOCK (Specific High-sensitivity Enzymatic Reporter unLOCKing) enables highly accurate taxonomic identification and has all the characteristics needed to transition to ecological and environmental disciplines. Here we conducted a series of proof of principle experiments to characterize SHERLOCK’s ability to accurately, sensitively, and rapidly distinguished three fish species (two with protected status and one non-native) co-occurring in the San Francisco Estuary which are easily misidentified in the field. We improved SHERLOCK’s ease of field deployment by combining its rapid isothermal amplification and CRISPR genetic identification with a minimally invasive and extraction-free DNA collection protocol as well as the option of instrument-free lateral flow detection. This approach opens the door for redefining how, where and by whom genetic identifications occur in the future.
These data were generated using the SHERLOCK (Specific High-sensitivity Enzymatic Reporter unLOCKing). The data are background-normalized fluorescent intensity values, captured using a Bio-Rad CFX96 Touch Real-Time PCR Detection System. The only processing of the data is background subtraction (that is for each sample, subtracting the average fluorescence for the negative controls in that experiment).
To use this dataset, it would be necessary to understand the SHERLOCK method as described in:
Gootenberg, J. S. et al. Nucleic acid detection with CRISPR-Cas13a/C2c2. Science 356, 438–442 (2017).
There are no missing values.