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Science. Author manuscript; available in PMC 2018 October 27.
Published in final edited form as:
Science. 2018 April 27; 360(6387): 444–448. doi:10.1126/science.aas8836.
Field-deployable viral diagnostics using CRISPR-Cas13
Cameron Myhrvold#1,2,‡, Catherine A. Freije#1,2,3,‡, Jonathan S. Gootenberg#1,4,5,6,7, Omar 
O. Abudayyeh#1,5,6,7,8, Hayden C. Metsky1,9, Ann F. Durbin3,10, Max J. Kellner1, Amanda L. 
Tan11, Lauren M. Paul11, Leda A. Parham12, Kimberly F. Garcia12, Kayla G. Barnes1,2,13, 
Bridget Chak1,2, Adriano Mondini14, Mauricio L. Nogueira15, Sharon Isern11, Scott F. 
Michael11, Ivette Lorenzana12, Nathan L. Yozwiak1,2, Bronwyn L. MacInnis1,13, Irene 
Bosch10,16, Lee Gehrke3,10,17, Feng Zhang1,5,6,7, and Pardis C. Sabeti‡,1,2,3,13,18
1Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
2Center for Systems Biology, Department of Organismal and Evolutionary Biology, Harvard 
University, Cambridge, MA 02138, USA.
3Ph.D. Program in Virology, Division of Medical Sciences, Harvard Medical School, Boston, MA 
02115, USA.
4Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA.
5McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 
02139, USA.
6Department of Brain and Cognitive Science, Massachusetts Institute of Technology, Cambridge, 
MA 02139, USA.
7Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 
02139, USA.
8Department of Health Sciences and Technology, Massachusetts Institute of Technology, 
Cambridge, MA 02139, USA.
9Department of Electrical Engineering and Computer Science, Massachusetts Institute of 
Technology, Cambridge, MA 02139, USA.
10Institute for Medical Engineering and Science, Massachusetts Institute of Technology, 
Cambridge, MA 02139, USA.
‡Correspondence should be addressed to P.C.S. (pardis@broadinstitute.org), C.M. (cmyhrvol@broadinstitute.org), C.A.F. 
(cfreije@broadinstitute.org).
Author contributions: C.M. and C.A.F. conceived the study, performed experiments and data analysis (supervised by P.C.S.), and 
wrote the paper (with P.C.S and B.L.M.). J.S.G. and O.O.A. designed some experimental protocols, provided reagents, and gave 
technical advice (supervised by F.Z.). H.C.M. assisted with crRNA and RPA primer design and analysis of ZIKV genome coverage. 
M.J.K. purified Cas13 proteins used in this study. A.D.D. cultured and titered the ZIKV used in this study (supervised by I.B. and 
L.G.). A.L.T., L.M.P, K.G.B., B.C., B.L.M., N.L.Y., A.M., M.L.N., S.I., S.F.M., L.A.P., K.F.G., L.G., I.B., and I.L. provided critical 
insights or clinical samples used in this study. All authors reviewed the manuscript.
Competing interests: C.M., C.A.F., P.C.S., J.S.G., O.O.A., and F.Z are co-inventors on patent applications filed by the Broad Institute 
relating to work in this manuscript.
Data and materials availability: All data are available in the manuscript or the supplementary material. Details on MTAs related to 
the sharing of clinical samples are described in the Methods section.
Author Manuscript Author Manuscript Author Manuscript Author Manuscript
Myhrvold et al. Page 2
11Department of Biological Sciences, College of Arts and Sciences, Florida Gulf Coast University, 
Fort Myers, FL 33965, USA.
12Centro de Investigaciones Genética, Instituto de Investigacion en Microbiologia, Universidad 
Nacional Autónoma de Honduras, Tegucigalpa, Honduras.
13Department of Immunology and Infectious Disease, Harvard School of Public Health, Boston, 
MA 02115, USA.
14Araraquara Laboratory of Public Health, School of Pharmaceutical Sciences, São Paulo State 
University, São Paulo, Brazil.
15Laboratorio de Pesquisas em Virologia, Faculdade de Medicina de Sao Jose do Rio Preto, São 
Paulo, Brazil.
16Department of Medicine, Mount Sinai School of Medicine, New York, NY 10029, USA.
17Department of Microbiology and Immunobiology, Harvard Medical School, Boston, MA 02115, 
USA.
18Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA.
# These authors contributed equally to this work.
Abstract
Mitigating global infectious disease requires diagnostic tools that are sensitive, specific, and 
rapidly field-deployable. Here, we demonstrate that the Cas13-based SHERLOCK (Specific High 
Sensitivity Enzymatic Reporter UnLOCKing) platform can detect Zika virus (ZIKV) and dengue 
virus (DENV) in patient samples at concentrations down to 1 copy/μl. We develop HUDSON 
(Heating Unextracted Diagnostic Samples to Obliterate Nucleases), a protocol that pairs with 
SHERLOCK for viral detection directly from bodily fluids, enabling instrument-free DENV 
detection directly from patient samples in < 2 hours. We further demonstrate that SHERLOCK can 
distinguish the 4 DENV serotypes as well as region-specific strains of ZIKV from the 2015–2016 
pandemic. Finally, we report the rapid design and testing (<1 week) of instrument-free assays to 
detect clinically relevant viral single nucleotide polymorphisms.
Recent viral outbreaks have highlighted the challenges of diagnosing viral infections, 
particularly in areas far from clinical laboratories. Viral diagnosis was especially difficult 
during the 2015–2016 Zika virus (ZIKV) pandemic; low viral titers and transient 
infection(1, 2), combined with limitations of existing diagnostic technologies, contributed to 
ZIKV circulating for months before the first cases were confirmed clinically(3–5). An 
additional challenge for viral diagnostics is differentiating between related viruses that cause 
infections with similar symptoms, like ZIKV and dengue virus (DENV) (1). More generally, 
existing nucleic acid detection methods are very sensitive and rapidly adaptable, but most 
require extensive sample manipulation and expensive machinery(1, 6–8). In contrast, 
antigen-based rapid diagnostic tests require minimal equipment, but have lower sensitivity 
and specificity, and assay development can take months(9–11). An ideal diagnostic would 
combine the sensitivity, specificity, and flexibility of nucleic acid diagnostics with the speed 
and ease of use of antigen-based tests. Such a diagnostic could be rapidly developed and 
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deployed in the face of emerging viral outbreaks, and would be suitable for disease 
surveillance or routine clinical use in any context.
The Cas13-based nucleic acid detection platform SHERLOCK has the potential to address 
the key challenges associated with viral diagnostics. SHERLOCK combines isothermal 
amplification using Recombinase Polymerase Amplification (RPA)(12) with highly specific 
Cas13-based detection (Fig. 1A) (13). Cas13, an RNA-guided ribonuclease, provides 
specificity through CRISPR RNA (crRNA):target pairing, and additional sensitivity due to 
signal amplification by Cas13’s collateral cleavage activity(14, 15).
For SHERLOCK to excel at viral detection in any context, it should be paired with methods 
enabling direct detection from patient samples with a visual readout. Here, we test the 
performance of SHERLOCK for ZIKV and DENV detection on patient samples and develop 
HUDSON (Heating Unextracted Diagnostic Samples to Obliterate Nucleases), a method to 
enable rapid, sensitive detection of ZIKV and DENV directly from bodily fluids with a 
colorimetric readout, recently shown as part of SHERLOCKv2(16). Additionally, we design 
SHERLOCK assays to distinguish multiple viral species and strains and identify clinically 
relevant mutations.
Detection of ZIKV and DENV in patient samples provides a stringent test of the sensitivity 
of SHERLOCK and its tolerance of viral diversity. Our ZIKV SHERLOCK assay had 
single-copy (1 cp/μl) sensitivity when tested on seedstock cDNA (Fig. S1). We evaluated its 
performance on 40 cDNAs derived from samples collected during the 2015–2016 ZIKV 
pandemic, 37 from patient samples with suspected ZIKV infections and 3 from mosquito 
pools (Figs. 1B, S2, Table S1). For 16 samples from these patients, we benchmarked 
SHERLOCK by comparing its sensitivity and specificity to other nucleic acid amplification 
tests including the commercially available Altona Realstar Zika Virus RT-PCR assay (Figs. 
1C, S3–5, Table S2, see Supplementary Text). Of the 10 samples tested positive by the 
Altona assay, all 10 were detected by SHERLOCK (100% sensitivity); the other 6 samples 
were negative by both assays (100% specificity, 100% concordance). Our ZIKV assay had 
no false positives when tested on healthy urine and water (Fig. 1B). We then validated the 
ability of SHERLOCK to detect DENV, a related but more diverse flavivirus with similar 
symptoms to ZIKV infection. All 24 RT-PCR-positive DENV RNA samples were confirmed 
DENV positive after 1 hour of detection (Figs. 1D, S6–7, Table S3). SHERLOCK 
sensitively and specifically detects viral nucleic acids extracted from ZIKV and DENV 
patient samples.
Although SHERLOCK excels at detecting extracted nucleic acids, a field-deployable, rapid 
diagnostic test should not require an extraction step to detect viral nucleic acid in bodily 
fluids. Many viruses are shed in urine or saliva, including ZIKV and DENV, and sampling is 
not invasive(2, 7). To detect viral nucleic acid directly from bodily fluids via SHERLOCK, 
we developed HUDSON, a method to lyse viral particles and inactivate the high levels of 
RNases found in bodily fluids using heat and chemical reduction (Figs. 2A, S8)(17). 
HUDSON-treated urine or saliva could be directly added to RPA reactions without dilution 
or purification (blood products were diluted 1:3 to avoid solidification during heating), 
without inhibiting subsequent amplification or detection. HUDSON and SHERLOCK 
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enabled sensitive detection of free ZIKV nucleic acid spiked into urine, whole blood, 
plasma, serum, or saliva (Figs. S9–12). To mimic clinical infection, where viral nucleic acid 
is encapsulated in infections particles, we spiked infectious ZIKV particles into bodily 
fluids. HUDSON combined with SHERLOCK (Figs. S13–14) permitted sensitive detection 
of ZIKV RNA from infectious particles at 90 aM (45 cp/μl) in whole blood (Fig, S15) or 
serum (Fig. 2B), 0.9 aM (~1 cp/μl) in saliva (Fig. 2C), and 20 aM (10 cp/μl) in urine. Total 
turnaround time was < 2 h with fluorescent and colorimetric readout (Fig. 2D, Fig. S16). 
The sensitivity of HUDSON and SHERLOCK is comparable to ZIKV RNA concentrations 
observed in patient samples, which range from 1–1,000 cp/μl(1, 2). HUDSON, paired with 
the pan-DENV SHERLOCK assay, detected DENV in whole blood, serum, and saliva (Figs. 
S17–18). DENV was detected directly from 8 of 8 patient serum samples (Fig. 2E) and 3 of 
3 patient saliva samples tested (Fig 2F), with a total turnaround time < 1 hour from saliva 
despite lower viral titers than in serum (7). We directly detected DENV with a colorimetric 
readout using lateral flow strips (Fig. 2G), showcasing a HUDSON to SHERLOCK pipeline 
that can detect ZIKV or DENV directly from bodily fluids with minimal equipment.
Because many genetically and antigenically similar flaviviruses co-circulate and have 
similar symptoms, we developed diagnostic panels to distinguish related viral species and 
serotypes. We identified conserved regions within ZIKV, DENV, West Nile virus (WNV), 
and yellow fever virus (YFV) genomes and designed a flavivirus panel with universal-
flavivirus RPA primers that can amplify any of the 4 viruses, and species-specific crRNAs 
(Fig. 3A). This panel detected synthetic ZIKV, DENV, WNV, and YFV DNA targets with < 
0.22% off-target fluorescence (Fig. 3B, Figs. S19–20, see Methods), and identified the 
presence of all pairwise combinations of these 4 viruses, demonstrating the ability to detect 
mock co-infections (Fig. 3C, Figs. S21–22). We also designed a panel using DENV-specific 
RPA primers and serotype-specific crRNAs (Fig. 3D) that could distinguish between DENV 
serotypes 1–4 with < 3.2% off-target fluorescence (Fig. 3E, Figs. S23–24). This low level of 
off-target fluorescence allows for 100% specificity in differentiating between serotypes, 
providing an alternative to current serotype identification approaches (8). The DENV panel 
confirmed the serotypes of 12 RT-PCR-serotyped patient samples or clinical isolates (Fig 3F, 
Fig. S25), and identified 2 clinical isolates with mixed infection, a commonly observed 
phenomenon(18). SHERLOCK can therefore be extended to differentiate between related 
viruses or serotypes using a single amplification reaction.
SHERLOCK is uniquely poised for field-deployable variant identification, which would 
allow real-time tracking of microbial threats. Genotyping of single nucleotide 
polymorphisms (SNPs) typically involves PCR and either fluorescent- or mass spectrometry-
based detection, requiring extensive sample processing, expensive equipment, and limiting 
field-deployability (19). SHERLOCK can identify SNPs by placing a synthetic mismatch in 
the crRNA near the SNP, testing each target with an ancestral-specific and derived-specific 
crRNA (Fig. 4A)(13). We designed diagnostics for 3 region-specific SNPs from the 2015–
2016 ZIKV pandemic (Fig. 4B) and identified these SNPs in synthetic targets, a viral 
seedstock, and cDNA samples from Honduras, the Dominican Republic, and the United 
States (Fig. 4C–E). These results demonstrate that SHERLOCK can identify SNPs in 
samples from the ZIKV pandemic and highlight the single-nucleotide specificity of 
SHERLOCK.
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Rapid identification of emerging drug resistance and other clinically relevant mutations for 
viruses such as ZIKV and HIV would have great utility. A ZIKV point mutation in the PrM 
region (S139N) recently associated with fetal microcephaly was used as a test case for the 
rapid development of assays for variant identification (20). Within a week of the report’s 
publication (Fig. 4F, Fig. S26), we developed multiple SHERLOCK assays for the S139N 
mutation (Fig. 4G, Fig. S27), and could identify the mutation in patient samples from the 
2015–2016 ZIKV pandemic with a visual readout (Fig. 4H). To further illustrate the ease of 
developing SHERLOCK diagnostics for many clinically relevant mutations, we designed 
and tested assays for the six most commonly observed drug-resistance mutations in HIV 
reverse transcriptase(21) in 1 week (Fig. S28). These examples underscore that SHERLOCK 
could be used for monitoring clinically relevant variants in near real time.
Combining HUDSON and SHERLOCK, we have created a field-deployable viral diagnostic 
platform with high performance and minimal equipment or sample processing requirements. 
This platform is as sensitive and specific as amplification-based nucleic acid diagnostics (12, 
22–26), with similar speed and equipment requirements to rapid antigen tests(9–11). 
Furthermore, this approach can be easily adapted to detect virtually any virus present in 
bodily fluids, scaled to enable multiplexed detection(16), and the reagents can be lyophilized 
for cold-chain independence(13). Cas13-based detection is a promising next-generation 
diagnostic strategy with the potential to be implemented almost anywhere in the world to 
enable effective, rapid diagnosis of viral infections.
Supplementary Material
Refer to Web version on PubMed Central for supplementary material.
Acknowledgements:
We thank S. Schaffner, A. Lin, and other Sabeti lab members for useful feedback, J. Strecker for providing SUMO 
protease, and the Florida Department of Health, Miami-Dade County Mosquito Control, and Boca Biolistics for 
support with patient and mosquito samples.
Funding: We acknowledge funding from the Howard Hughes Medical Institute (HHMI), the Broad Institute CBTS 
Shark Tank, NIH U19AI110818, and DARPA D18AC00006. The views, opinions, and/or findings expressed should 
not be interpreted as representing the official views or policies of the DOD or U.S. Government. Approved for 
public release; distribution is unlimited. C.M. is supported by HHMI, O.O.A. by a Paul and Daisy Soros Fellowship 
and NIH F30 NRSA 1F30-CA210382, S.I. and S.F.M. by NIH NIAID R01AI099210, I.B. and L.G. by NIH AI 
100190, F.Z. by NIH grants (1R01-HG009761, 1R01-MH110049, 1DP1-HL141201); HHMI; the New York Stem 
Cell, Allen, and Vallee Foundations; the Tan-Yang Center at MIT; and J. and P. Poitras, and R. Metcalfe. F.Z. is a 
New York Stem Cell Foundation–Robertson Investigator. M.L.N. is supported by FAPESP (Grant#13/21719–3) and 
a CNPq Research Fellow.
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Fig. 1. ZIKV and DENV detection from patient samples and clinical isolates.
(A) Schematic of SHERLOCK. Nucleic acid is extracted from clinical samples, and the 
target is amplified by RPA using either RNA or DNA as input (RT-RPA or RPA, 
respectively). RPA products are detected in a reaction containing T7 RNA polymerase, 
Cas13, a target-specific crRNA, and an RNA reporter that fluoresces when cleaved. We 
tested SHERLOCK on (B) cDNA derived from 37 patient samples collected during the 
2015–2016 ZIKV pandemic and (C) cDNA from 16 patient samples that were compared 
head-to-head with the Altona RealStar Zika Virus RT-PCR assay. +: ZIKV seed stock cDNA 
(3×102 cp/μl) and ×: no input. (D) SHERLOCK on RNA extracted from 24 DENV-positive 
patient samples and clinical isolates. Dashed blue line: threshold for presence or absence of 
ZIKV or DENV (see Methods).
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Fig. 2. Direct detection of ZIKV and DENV in bodily fluids with HUDSON and SHERLOCK.
(A) Schematic of direct viral detection using HUDSON and SHERLOCK. (B-C) Detection 
of ZIKV RNA in particles diluted in healthy human serum (pink, B) or healthy human saliva 
(blue, C). Same PBS control used in A and B as experiments were performed together. (D) 
Detection of ZIKV RNA in particles diluted in healthy human urine (yellow). Black: non-
diluted viral stock. Grey: dilutions in PBS. Error bars indicate 1 S.D. based on 3 technical 
replicates. (E-F) Detection of DENV RNA directly from patient serum (E) and saliva 
samples (F). (G) Lateral flow detection of DENV from samples shown in (E-F). All samples 
were treated with TCEP/EDTA prior to heating.
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Fig. 3. Multivirus panels can differentiate viral species and serotypes.
(A-C) Panel of 4 related flaviviruses (A) used to detect individual viral targets (B) or paired 
viral targets (C) using species-specific crRNAs with 3 hours of detection. (D-E) 
Identification of DENV serotypes 1–4 using serotype-specific crRNAs (D), tested using 
synthetic targets with 3 hours of detection (E). (F) Identification of DENV serotypes in 
extracted RNA from patient samples. Each row is a sample, each column is a crRNA, target-
specific fluorescent values are normalized by row. Purple: DENV serotype identified. 
Synthetic targets were used at 104 cp/μl. Error bars indicate 1 S.D. based on 3 technical 
replicates. We expect off-target crRNAs to have close to zero target-specific fluorescence 
(see Methods). Primer, crRNA, and target sequences are in Tables S5–7.
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Fig. 4. Identification of adaptive and functional ZIKV mutations.
(A) SHERLOCK assays for SNP identification. Dark colors: derived crRNAs, light colors: 
ancestral crRNAs. (B-C) Three region-specific SNPs from the 2015–2016 ZIKV pandemic, 
including genomic locations of the SNPs and a simplified phylogenetic tree, tested using 
synthetic targets (104 cp/μl) and viral seedstock cDNA (3×102 cp/μl). (D-E) Identification of 
region-specific SNPs in ZIKV cDNA samples from the Dominican Republic (DOM), United 
States (USA), and Honduras (HND). The fluorescence ratio (derived crRNA fluorescence 
divided by ancestral crRNA fluorescence) for each SNP in each sample is shown in a bar 
plot (log2-transformed data in a heatmap). (F) Timeline for developing a SHERLOCK assay 
for a new SNP (more detail in Fig. S26). (G-H) Identification of a microcephaly-associated 
ZIKV mutation (PrM S139N) by fluorescent and colorimetric detection. In all panels, error 
bars indicate 1 S.D. based on 3 technical replicates.
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