SHARK enables accurate and sensitive RNA detection in a single reaction. In synthetic biosensors, the plasmid is transcribed into mRNA to initiate the protein expression, and the degradation of mRNA will obviously influence the output signals. Here, in SHARK platform, CRISPR/Cas13a can precisely recognize the RNA targets and activate its collateral cleavage activity, which is then used to regulate the mRNA levels to generate different concentrations of enzymes, finally outputting different modules of signals (Fig. 1a). To testify our hypothesis, the activation of CRISPR/Cas13a was investigated by incubating the target RNA with unspecific mRNA molecules produced by a HiScribe T7 High Yield RNA Synthesis Kit. With increasing target concentrations, most transcribed mRNA strands were digested into fragments, indicating that the target RNA recognized the crRNA and activated the catalytic cleavage (Fig. 1b). Then, we coupled the CFE system with CRISPR/Cas13a to build a series of SHARK platforms, which generated different kinds of enzymes that could be detected by many commercial kits, including sucrose invertase (sInv), glucose-6-phosphate dehydrogenase (G6PD) and luciferase (Luc) (Supplementary Fig. 1a). The gel results showed that compared with nonsense control RNA, the target RNA selectively activated the SHARK, and significantly repressed the enzyme expression (Fig. 1c). The kinetics of SHARK was also determined across a range of different concentrations of target RNA. For example, the activation of the SHARK sensor generating G6PD was easily visualized by naked eyes, and it exhibited much faster kinetics compared with that expressing enhanced green fluorescent protein (eGFP) as reporters (Fig. 1d), suggesting that the cascade amplification of signals via mRNA translation and enzymatic reaction greatly benefited the detection. The experimental (Supplementary Fig. 2a) and simulation results (Supplementary Fig. 2b,c) confirmed that as low as 10 fM RNA targets could trigger significant signal distinction with high reliability (CV = 5.51%). Taken together, SHARK could sensitively detect trace RNA at femtomolar range, behaving a better performance compared with other strategies, such as Cas13a-based detection17 and toehold switch synthetic gene networks14 (Fig. 1e).
SHARK output different modules of signals. The great advantage of CFE system is the versatile capability in rational design and integration of different reporter proteins. However, so far, only limited enzymes have been explored as reporters in the synthetic bio-sensors, such as eGFP and β-galactosidase14, 21. Here, to facilitate the signal readout, we explored to introduce several enzymes (i.e., sInv, G6PD and Luc) that can be easily detected using commercial kits into SHARK as reporters (Supplementary Note 1). First, we used sInv as reporters to build a biosensor (sInv-SHARK). The addition of target RNA activated SHARK, degraded the mRNA, and reduced the sInv expression, which catalyzed the conversion of sucrose to glucose (Fig. 2a). After loading the samples, we conveniently and sensitively detected trace RNA through measuring the glucose levels using a personal glucose meter (PGM, Accu-Chek Performa) with a limit of detection (LOD) at 11.72 fM (Fig. 2b). In contrast, we also developed a G6PD-SHARK sensor that converted the RNA signal to G6PD, and finally to the production of formazan, outputting colorimetric signals that can be visually measured without need for any instruments (Fig. 2d) with LOD at 9.50 fM (Fig. 2e). Differently, in a sensor of Luc-SHARK, the target RNA reduced the expression of luciferase, which catalyzed the bioluminescence reaction (Fig. 2g) with high activity, low background noise, and fast response time, benefiting the RNA detection with a large dynamic range (10− 4-102 nM) and LOD at 6.55 fM (Fig. 2h).
Then we investigated the specificity of SHARK. Most isothermal exponential amplification methods show poor performance in distinguishing gene strands with high sequence homology10, 22, 23. As designed, CRISPR/Cas13a system has the capability to discriminate single nucleotide mutations since that it requires strict crRNA-target paring to activate the collateral cleavage18, 24 (Supplementary Fig. 3a). To testify our hypothesis, we synthesized three RNAs analogs with single-base mutation. As expected, in all three kinds of sensors of SHARK, the RNA targets strictly recognized the cognate crRNA and yielded reduced signals, whereas those with one-base mismatch barely activated CRISPR/Cas13a system, and generated strong signals similar to those from scrambled RNA (Fig. 2c,f,i), indicating that SHARK possessed high specificity in discriminating single nucleotide alterations. Further, the interference of human total RNA in samples was also investigated, and the biosensors outputted reliable signals even with a high molar ratio of target RNA to total RNA at 1:1000 (Supplementary Fig. 3b-d). Thus, our SHARK has provided customizable signals with high sensitivity, specificity, and reliability, inspiring us to apply it in point-of-care testing of various RNA species at different scenarios.
Paper-loaded sInv-SHARK sensor facilitates rapid and reliable detection of SARS-CoV-2 using PGM. Infectious diseases caused by different viruses exert great threats to human health, and to date, the COVID-19 coronavirus pandemic has caused more than two million deaths25. Similar to other deadly respiratory syndromes (e.g., Severe acute respiratory syndrome coronavirus (SARS), Middle East Respiratory Syndrome Coronavirus (MERS)), rapid and accurate detection of SARS-CoV-2 will greatly benefit the contact tracing, treatment options and isolation requirements, meaning the urgent need to develop point-of-care technologies available at a variety of scenarios26, 27. To favor the testing of SARS-CoV-2 at home, we designed a paper-loaded freeze-drying sInv-SHARK sensor with rapid readout by a PGM, which is easily accessible on the market (Fig. 3a). First, we constructed contrived SARS-Cov-2 and synthesized three crRNAs targeting the genes of replicase polyprotein 1ab (Orf1ab), nucleocapsid (N) and envelope protein (E) in SARS-CoV-2, respectively (Supplementary Fig. 4)27. Here, the combination of multiple crRNAs could activate multiple nuclease-inactive ribonucleoprotein complex23, and thus effectively increase the active Cas13a concentrations so as to improve sensitivity. When mixing the three crRNAs in the detecting solution containing contrived SARS-Cov-2 samples, the sInv-SHARK sensor accurately identified virus concentrations as low as 1 fM, outputting a signal value of 21.5 ± 0.9 with high reliability (CV = 4.18%, Fig. 3b). Considering the actual viral loads in clinic samples26, we serially diluted the pseudovirus on throat swab (from 10 to 7×108 copies per swab) with scrambled RNA as controls. The sInv-SHARK sensor outputted a decreased signal between 29.7 and 1.4, with a dose-dependent response from 20.2 to 5.3 at a concentration range of 10− 5-102 nM (i.e., ~103 to 107 copies/µL) with high accuracy and reliability, setting a cut-off value at 22.5 (~ 50 copies/µL) (Fig. 3c).
To facilitate the detection at home, we integrated sInv-SHARK with a PGM into a disposable device previously developed in our lab28, in which the users only need to drop the samples on the loading paper and press the start button, outputting a digital result within 1.5 h (Fig. 3d, Supplementary Fig. 5). We used qRT-PCR method to calibrate the sInv-SHARK-based detection, and several dilutions of pseudovirus at a range of 1.5×102 to 108 copies/µL were measured with three negative swabs as controls. The sInv-SHARK device generated reliable and easy-to-readout results between 5.3 and 22.5 that was highly consistent with those from the Ct values of qRT-PCR between 14 and 37 (Pearson r coefficient = 0.96) (Fig. 3e), meaning that the cut-off value of 22.5 was equivalent to Ct = 37 (the standard qRT-qPCR value from Disease Control and Prevention (CDC) of China). Further randomized double blind tests in laboratory confirmed that both the sInv-SHARK and qRT-PCR methods showed high coincidence in detecting positive samples (Supplementary Fig. 6), as well as in discriminating negative control samples (Supplementary Table 1).
Finally, we investigated the cross-reactivity between SARS-CoV-2 and other respiratory diseases related pathogens (e.g., MERS, human coronavirus HKU1, Influenza A virus (IVA), etc.). The heat map (Fig. 3f) reflected that the SHARK device could easily discriminate the pathogen RNA from others with the same concentration (1 nM). In turn, by designing cognate crRNA, our device could also detect the RNA targets from different pathogens (Fig. 3g, h). Taken together, sInv-SHARK-based device could sensitively and accurately sense the SARS-CoV-2 RNA and output digital signals with high reliability and robustness against matrix effect and interference (Supplementary Fig. 7a, b), presenting as a convenient and reliable strategy for rapid detection of SARS-CoV-2 at resource limited settings such as home or community.
Paper-based G6PD-SHARK sensor enables multiple miRNA detection for cancer staging. miRNA is a class of endogenous small noncoding RNA molecules, which serve as promising clinical biomarkers in various human diseases, including cancer29. However, the characteristics of miRNAs, such as short length, high sequence homology, and low abundance, make it a great challenge to develop sensitive and specific sensors30. CRISPR/Cas systems possess special capability in recognizing short gene targets (20–40 nucleotides) with high specificity and reliability, making it possible to sensitively detect miRNAs. Considering the flexibility of our SHARK platform, we designed a pattern pad to detect multiple miRNA targets in serum from the patients of non-small cell lung cancer (NSCLC), offering abundant information to evaluate the cancer staging.
We first built a data mining model to select biomarkers via bioinformatics tool, and seven miRNAs highly related to NSCLC (Fig. 4a) were selected with four upregulated (i.e., miR141, miR-30a, miR-20a, Let-7b) and three downregulated (i.e., miR-126, miR-328, miR-19b) compared with healthy people by analyzing the miRNA expression database of 915 NSCLC and 105 healthy individuals from The Cancer Genome Atlas (TCGA). Then we designed a G6PD-SHARK based paper origami device with two detection zone respectively for loading testing sample (T) and control standard sample (C). The G6PD-SHARK sensor was pre-packed in the top layer through a freeze-drying procedure, which was folded with the base layer together to assemble the device for sample loading and signal amplification, generating an easy-to-read visual output (Fig. 4b, Supplementary Fig. 8a, b). To easily extract the results, we quantified the colorimetric signals via Image J software and used the ratio of T to C (T/C) to reflect the miRNA concentrations (Fig. 4c). Using a synthetic miR-20a sample, we verified that the T/C signal was proportional to the miRNA concentration ranging from 10− 5 and 10− 1 nM with LOD values as low as 7.56 fM, and thus the sensor could be used to detect most miRNAs in serum (10− 5~10 nM)31, 32. Meantime, the sensor also showed excellent capability in discriminating different miRNA targets (Fig. 4d) even in the presence of 10-fold interferences with the signal deviation less than 5.21%. Especially, in the sensor targeting let-7b, which is highly overexpressed in most cancers33, we found other members of let-7 family (i.e., let-7a, let-7c, let-7d) with few mismatched bases had little interference (less than 4.97%) at the concentrations of 10, 50, and 100 pM (Supplementary Fig. 9), suggesting that the G6PD-SHARK platform guaranteed the detection of miRNAs with high sensitivity and specificity to distinguish single nucleotide mutations.
In clinic, more than 70% of NSCLC patients are diagnosed at advanced stages with a poor five-year survival rate (~ 19.40%) reported by the Surveillance, Epidemiology, and End Results program, and early diagnosis will greatly benefit the treatments34. We next used our G6PD-SHARK-based sensor to detect the serum samples from lung cancer patients (Supplementary Table 2). We observed the up-regulation of miR-30a (2.91 ± 0.12), miR-20a (2.82 ± 0.15), Let-7f (3.13 ± 0.15), and down-regulation of miR-126 (0.61 ± 0.05), miR-328 (0.24 ± 0.01) in the patients with moderate sensitivity (40.25%-70.71%) and specificity (29.28%-71.25%), showing good correlation to the bioinformatics profiles. ROC curve analysis showed that five miRNAs among them could be promising biomarkers for NSCLC, with AUC values from 0.64 to 0.76. Further, logistic regression analysis was used to construct combined models to evaluate these 5 candidates to verify the NSCLC signatures. Combining the five miRNAs (i.e., miR-30a (+), miR-20a (+), Let-7f (+), miR-126 (-), miR-328 (-)) enhanced the AUC to 0.88 (sensitivity = 84.24%, specificity = 86.51%), indicating that multiple miRNA patterns are more reliable in cancer diagnosis than the single biomarker (Fig. 4e). Considering that the miRNA signatures may vary with cancer progress, we sought to find the relationship between the miRNA profiles and TNM (tumor, nodes, and metastasis) stage of NSCLC. The miRNA signatures of different patients at different stages (I, II, III, IV) diagnosed by pathological section were profiled in Fig. 4f using G6PD-SHARK-based sensor and qRT-PCR. The miRNA profiles showed a high coincidence between our SHARK sensor and qRT-PCR method (Pearson r coefficient = 0.84) (Supplementary Fig. 10a). The patients at the same stage shares similar miRNA signatures, which also dynamically changed along with the cancer progression. That is, miR-126, miR-328, and miR-30a were positive at advanced stage, but negative at stage I (Supplementary Fig. 10b-d), while miR-20a exhibited higher expression at all stages compared with healthy people (Supplementary Fig. 10e). Overall, our G6PD-SHARK-based sensor showed high sensitivity and specificity in detecting fetamolar levels of short miRNAs in the serum of lung cancer patients, and provided unique miRNA patterns for cancer diagnosis and staging.
G6PD-SHARK-CPU sensor enables miRNA computation for cancer diagnosis. To realize early cancer diagnosis at home, we sought to optimize G6PD-SHARK sensor by outputting one simple diagnostic result from multiple miRNA detection through bio-computation (Fig. 5a). First, we built a computational classifier with a support-vector-machine (SVM) model according to the published work35, and trained the system using the miRNA profiles of NSCLC patient and healthy individuals from TCGA. To avoid over-fitting of machine learning, different models were evaluated to improve the classification performance (Supplementary Fig. 11, Supplementary Note 2) with a training database (105 NSCLC and 60 healthy samples), and generated an optimized model including seven miRNA inputs (miR-761 (-), Let-7f (-), miR-126 (-), miR-20a (+), miR-515 (+), Let-7b (+), miR-5688 (+)) associated with weights ranging from inter values of -5 to + 9, achieving identification accuracy of 85.72%, sensitivity of 87.11%, and specificity of 78.12% (Fig. 5b, Supplementary Table 3).
To facilitate the computation using the up-regulated and down-regulated miRNAs, we integrated a Cas9 system with the G6PD-SHARK to develop central processing unit (G6PD-SHARK-CPU) to transform miRNAs information. We combined the transcriptional and translational regulation systems together to tune the signal by using the up-regulated miRNAs related NSCLC to turn off the signal via G6PD-SHARK (OFF) and using the down-regulated miRNAs to turn on the signal via the Cas9-RsgRNA (ON) (Supplementary Fig. 12a, Supplementary Note 3). To test the ON/OFF system, we introduced G6PD reporter plasmids and measured the colorimetric signal as an output (Absorbance < 0.5 as TRUE). We observed 30-fold activation with high sensitivity (0.1 pM) and specificity when the target miRNA present in the ON system (Fig. 5c left, Supplementary Fig. 13), and also significant suppression in the OFF system (Fig. 5c right). Next, we designed an A NIMPLY B gate with miR-126 (-) and miR-5688 (+) as inputs, and only the input of (0, 1) could output a TRUE signal (1) (Fig. 5d). Finally, we tested G6PD-SHARK-CPU system with clinical serum samples from NSCLC and healthy individuals. The optimal diagnostic point was calculated at the probability score of 0.55 as cut-off value (Fig. 5e) from the receiver operating characteristic (ROC) curves (Fig. 5f). Through bio-computation, 28 of 38 NSCLC patients were identified with a sensitivity of 77.78% (95% CI: 64.92%, 88.28%), while 1 of 26 healthy samples was misclassified with specificity of 96.15% (95% CI: 81.11%, 99.81%), and accuracy of 86.42% (95% CI: 65.82%, 96.14%) (Fig. 5g). Despite that we simplified the miRNA transformation, G6PD-SHARK-CPU sensor still presented as a promising user-friendly platform to favor the routine early cancer screening.
Digital-Luc-SHARK device achieves absolute quantification of miRNA. Usually, the fold change of miRNAs is subtle at early stage of cancer. Inspired by the high sensitivity and specificity of SHARK in distinguishing miRNAs, we next integrated QuantStudio™ chip (20K data points) to design a digital-Luc-SHARK device with luciferase generating bioluminescence as reporters (Fig. 6a). To improve the signal, we constructed a fusion protein consisting of luciferase and yellow fluorescent protein (YFP), exhibiting a 7.45-fold enhancement over native luciferase via bioluminescence resonance energy transfer (BRET)36 (Fig. 6b). Then we designed a portable luminescence detection device consisting of an intensified CCD camera, a mirror and a filter with magnifier (Fig. 6c, Supplementary Fig. 14). After mixing the miRNA samples with the luc-SHARK reagents and dispersing it in the picoliter-scale reactors, we observed the increase of the positive spots on the chip with the increase of target miRNAs, which was consistent with Poisson distribution equation (Supplementary Fig. 15), presenting a dynamic range from 100 to 5 ×104 copies/µL (2 to 1000 aM) (Fig. 6d), and showing over 104-fold enhancement in sensitivity compared with the tube-based SHARK process. Further, by employing an IC Capture software, the full view of the chip was collected and analyzed, generating a detection linear range of 200 − 20,000 copies/µL (Supplementary Fig. 16a, b). By virtue of the strict recognition, the digital-Luc-SHARK device also showed high specificity in discriminating the members of Let-7 family with interference less than 0.51% (Fig. 6e).
We next evaluated the accuracy of digital-Luc-SHARK, digital PCR, and qRT-PCR in quantifying miR-20a in clinic serum samples from NSCLC patients. All tests generated positive results, and the relative expression results from digital SHARK showed similar variations to those measured with digital PCR (Fig. 6f, Supplementary Fig. 17). Finally, we used this handheld digital-Luc-SHARK device to precisely detect the miRNA markers for early diagnosis of NSCLC. Four miRNAs (i.e., let-7b, miR-126p, miR-20a and miR-30a), previously tested as NSCLC biomarkers, were picked for validation. Figure 6g reflected that the up-regulation of let-7b, miR-30a, and miR-20a was highly related to the early-stage patients (Stage I, II) with Let-7b and miR-30a four times higher than those in the healthy people. The accurate and reliable quantification of miRNAs using our handheld digital-Luc-SHARK device makes it promising to achieve convenient early NSCLC diagnosis at home.