Amplification-free detection of SARS-CoV-2 with CRISPR-Cas13a and mobile phone microscopy.

The December 2019 outbreak of a novel respiratory virus, SARS-CoV-2, has become an ongoing global pandemic due in part to the challenge of identifying symptomatic, asymptomatic, and pre-symptomatic carriers of the virus. CRISPR diagnostics can augment gold-standard PCR-based testing if they can be made rapid, portable, and accurate. Here, we report the development of an amplification-free CRISPR-Cas13a assay for direct detection of SARS-CoV-2 from nasal swab RNA that can be read with a mobile phone microscope. The assay achieved ∼100 copies/µL sensitivity in under 30 min of measurement time and accurately detected pre-extracted RNA from a set of positive clinical samples in under 5 min. We combined crRNAs targeting SARS-CoV-2 RNA to improve sensitivity and specificity and directly quantified viral load using enzyme kinetics. Integrated with a reader device based on a mobile phone, this assay has the potential to enable rapid, low-cost, point-of-care screening for SARS-CoV-2.

Host-Viral Infection Maps Reveal Signatures of Severe COVID-19 Patients.

Viruses are a constant threat to global health as highlighted by the current COVID-19 pandemic. Currently, lack of data underlying how the human host interacts with viruses, including the SARS-CoV-2 virus, limits effective therapeutic intervention. We introduce Viral-Track, a computational method that globally scans unmapped single-cell RNA sequencing (scRNA-seq) data for the presence of viral RNA, enabling transcriptional cell sorting of infected versus bystander cells. We demonstrate the sensitivity and specificity of Viral-Track to systematically detect viruses from multiple models of infection, including hepatitis B virus, in an unsupervised manner. Applying Viral-Track to bronchoalveloar-lavage samples from severe and mild COVID-19 patients reveals a dramatic impact of the virus on the immune system of severe patients compared to mild cases. Viral-Track detects an unexpected co-infection of the human metapneumovirus, present mainly in monocytes perturbed in type-I interferon (IFN)-signaling. Viral-Track provides a robust technology for dissecting the mechanisms of viral-infection and pathology.

Extracellular Vesicle and Particle Biomarkers Define Multiple Human Cancers.

There is an unmet clinical need for improved tissue and liquid biopsy tools for cancer detection. We investigated the proteomic profile of extracellular vesicles and particles (EVPs) in 426 human samples from tissue explants (TEs), plasma, and other bodily fluids. Among traditional exosome markers, CD9, HSPA8, ALIX, and HSP90AB1 represent pan-EVP markers, while ACTB, MSN, and RAP1B are novel pan-EVP markers. To confirm that EVPs are ideal diagnostic tools, we analyzed proteomes of TE- (n = 151) and plasma-derived (n = 120) EVPs. Comparison of TE EVPs identified proteins (e.g., VCAN, TNC, and THBS2) that distinguish tumors from normal tissues with 90% sensitivity/94% specificity. Machine-learning classification of plasma-derived EVP cargo, including immunoglobulins, revealed 95% sensitivity/90% specificity in detecting cancer. Finally, we defined a panel of tumor-type-specific EVP proteins in TEs and plasma, which can classify tumors of unknown primary origin. Thus, EVP proteins can serve as reliable biomarkers for cancer detection and determining cancer type.

ORF8 and ORF3b antibodies are accurate serological markers of early and late SARS-CoV-2 infection.

The SARS-CoV-2 virus emerged in December 2019 and has caused a worldwide pandemic due to the lack of any pre-existing immunity. Accurate serology testing is urgently needed to help diagnose infection, determine past exposure of populations and assess the response to a future vaccine. The landscape of antibody responses to SARS-CoV-2 is unknown. In this study, we utilized the luciferase immunoprecipitation system to assess the antibody responses to 15 different SARS-CoV-2 antigens in patients with COVID-19. We identified new targets of the immune response to SARS-CoV-2 and show that nucleocapsid, open reading frame (ORF)8 and ORF3b elicit the strongest specific antibody responses. ORF8 and ORF3b antibodies, taken together as a cluster of points, identified 96.5% of COVID-19 samples at early and late time points of disease with 99.5% specificity. Our findings could be used to develop second-generation diagnostic tests to improve serological assays for COVID-19 and are important in understanding pathogenicity.

Cancer screening with multicancer detection tests: A translational science review.

Multicancer detection (MCD) tests use a single, easily obtainable biospecimen, such as blood, to screen for more than one cancer concurrently. MCD tests can potentially be used to improve early cancer detection, including cancers that currently lack effective screening methods. However, these tests have unknown and unquantified benefits and harms. MCD tests differ from conventional cancer screening tests in that the organ responsible for a positive test is unknown, and a broad diagnostic workup may be necessary to confirm the location and type of underlying cancer. Among two prospective studies involving greater than 16,000 individuals, MCD tests identified those who had some cancers without currently recommended screening tests, including pancreas, ovary, liver, uterus, small intestine, oropharyngeal, bone, thyroid, and hematologic malignancies, at early stages. Reported MCD test sensitivities range from 27% to 95% but differ by organ and are lower for early stage cancers, for which treatment toxicity would be lowest and the potential for cure might be highest. False reassurance from a negative MCD result may reduce screening adherence, risking a loss in proven public health benefits from standard-of-care screening. Prospective clinical trials are needed to address uncertainties about MCD accuracy to detect different cancers in asymptomatic individuals, whether these tests can detect cancer sufficiently early for effective treatment and mortality reduction, the degree to which these tests may contribute to cancer overdiagnosis and overtreatment, whether MCD tests work equally well across all populations, and the appropriate diagnostic evaluation and follow-up for patients with a positive test.

Direct Identification of Hundreds of Expression-Modulating Variants using a Multiplexed Reporter Assay.

Although studies have identified hundreds of loci associated with human traits and diseases, pinpointing causal alleles remains difficult, particularly for non-coding variants. To address this challenge, we adapted the massively parallel reporter assay (MPRA) to identify variants that directly modulate gene expression. We applied it to 32,373 variants from 3,642 cis-expression quantitative trait loci and control regions. Detection by MPRA was strongly correlated with measures of regulatory function. We demonstrate MPRA's capabilities for pinpointing causal alleles, using it to identify 842 variants showing differential expression between alleles, including 53 well-annotated variants associated with diseases and traits. We investigated one in detail, a risk allele for ankylosing spondylitis, and provide direct evidence of a non-coding variant that alters expression of the prostaglandin EP4 receptor. These results create a resource of concrete leads and illustrate the promise of this approach for comprehensively interrogating how non-coding polymorphism shapes human biology.

Partial coherence enhances parallelized photonic computing.

Advancements in optical coherence control1-5 have unlocked many cutting-edge applications, including long-haul communication, light detection and ranging (LiDAR) and optical coherence tomography6-8. Prevailing wisdom suggests that using more coherent light sources leads to enhanced system performance and device functionalities9-11. Our study introduces a photonic convolutional processing system that takes advantage of partially coherent light to boost computing parallelism without substantially sacrificing accuracy, potentially enabling larger-size photonic tensor cores. The reduction of the degree of coherence optimizes bandwidth use in the photonic convolutional processing system. This breakthrough challenges the traditional belief that coherence is essential or even advantageous in integrated photonic accelerators, thereby enabling the use of light sources with less rigorous feedback control and thermal-management requirements for high-throughput photonic computing. Here we demonstrate such a system in two photonic platforms for computing applications: a photonic tensor core using phase-change-material photonic memories that delivers parallel convolution operations to classify the gaits of ten patients with Parkinson's disease with 92.2% accuracy (92.7% theoretically) and a silicon photonic tensor core with embedded electro-absorption modulators (EAMs) to facilitate 0.108 tera operations per second (TOPS) convolutional processing for classifying the Modified National Institute of Standards and Technology (MNIST) handwritten digits dataset with 92.4% accuracy (95.0% theoretically).

Simultaneous profiling of multiple chromatin proteins in the same cells.

Methods derived from CUT&RUN and CUT&Tag enable genome-wide mapping of the localization of proteins on chromatin from as few as one cell. These and other mapping approaches focus on one protein at a time, preventing direct measurements of co-localization of different chromatin proteins in the same cells and requiring prioritization of targets where samples are limiting. Here, we describe multi-CUT&Tag, an adaptation of CUT&Tag that overcomes these hurdles by using antibody-specific barcodes to simultaneously map multiple proteins in the same cells. Highly specific multi-CUT&Tag maps of histone marks and RNA Polymerase II uncovered sites of co-localization in the same cells, active and repressed genes, and candidate cis-regulatory elements. Single-cell multi-CUT&Tag profiling facilitated identification of distinct cell types from a mixed population and characterization of cell-type-specific chromatin architecture. In sum, multi-CUT&Tag increases the information content per cell of epigenomic maps, facilitating direct analysis of the interplay of different chromatin proteins.

The oncomicropeptide APPLE promotes hematopoietic malignancy by enhancing translation initiation.

Initiation is the rate-limiting step in translation, and its dysregulation is vital for carcinogenesis, including hematopoietic malignancy. Thus, discovery of novel translation initiation regulators may provide promising therapeutic targets. Here, combining Ribo-seq, mass spectrometry, and RNA-seq datasets, we discovered an oncomicropeptide, APPLE (a peptide located in ER), encoded by a non-coding RNA transcript in acute myeloid leukemia (AML). APPLE is overexpressed in various subtypes of AML and confers a poor prognosis. The micropeptide is enriched in ribosomes and regulates the initiation step to enhance translation and to maintain high rates of oncoprotein synthesis. Mechanically, APPLE promotes PABPC1-eIF4G interaction and facilitates mRNA circularization and eIF4F initiation complex assembly to support a specific pro-cancer translation program. Targeting APPLE exhibited broad anti-cancer effects in vitro and in vivo. This study not only reports a previously unknown function of micropeptides but also provides new opportunities for targeting the translation machinery in cancer cells.

Mass cytometry: blessed with the curse of dimensionality.

Immunologists are being compelled to develop new high-dimensional perspectives of cellular heterogeneity and to determine which applications best exploit the power of mass cytometry and associated multiplex approaches.

RNA profiles reveal signatures of future health and disease in pregnancy.

Maternal morbidity and mortality continue to rise, and pre-eclampsia is a major driver of this burden1. Yet the ability to assess underlying pathophysiology before clinical presentation to enable identification of pregnancies at risk remains elusive. Here we demonstrate the ability of plasma cell-free RNA (cfRNA) to reveal patterns of normal pregnancy progression and determine the risk of developing pre-eclampsia months before clinical presentation. Our results centre on comprehensive transcriptome data from eight independent prospectively collected cohorts comprising 1,840 racially diverse pregnancies and retrospective analysis of 2,539 banked plasma samples. The pre-eclampsia data include 524 samples (72 cases and 452 non-cases) from two diverse independent cohorts collected 14.5 weeks (s.d., 4.5 weeks) before delivery. We show that cfRNA signatures from a single blood draw can track pregnancy progression at the placental, maternal and fetal levels and can robustly predict pre-eclampsia, with a sensitivity of 75% and a positive predictive value of 32.3% (s.d., 3%), which is superior to the state-of-the-art method2. cfRNA signatures of normal pregnancy progression and pre-eclampsia are independent of clinical factors, such as maternal age, body mass index and race, which cumulatively account for less than 1% of model variance. Further, the cfRNA signature for pre-eclampsia contains gene features linked to biological processes implicated in the underlying pathophysiology of pre-eclampsia.

Ferrobotic swarms enable accessible and adaptable automated viral testing.

Expanding our global testing capacity is critical to preventing and containing pandemics1-9. Accordingly, accessible and adaptable automated platforms that in decentralized settings perform nucleic acid amplification tests resource-efficiently are required10-14. Pooled testing can be extremely efficient if the pooling strategy is based on local viral prevalence15-20; however, it requires automation, small sample volume handling and feedback not available in current bulky, capital-intensive liquid handling technologies21-29. Here we use a swarm of millimetre-sized magnets as mobile robotic agents ('ferrobots') for precise and robust handling of magnetized sample droplets and high-fidelity delivery of flexible workflows based on nucleic acid amplification tests to overcome these limitations. Within a palm-sized printed circuit board-based programmable platform, we demonstrated the myriad of laboratory-equivalent operations involved in pooled testing. These operations were guided by an introduced square matrix pooled testing algorithm to identify the samples from infected patients, while maximizing the testing efficiency. We applied this automated technology for the loop-mediated isothermal amplification and detection of the SARS-CoV-2 virus in clinical samples, in which the test results completely matched those obtained off-chip. This technology is easily manufacturable and distributable, and its adoption for viral testing could lead to a 10-300-fold reduction in reagent costs (depending on the viral prevalence) and three orders of magnitude reduction in instrumentation cost. Therefore, it is a promising solution to expand our testing capacity for pandemic preparedness and to reimagine the automated clinical laboratory of the future.

A Cell-free DNA Blood-Based Test for Colorectal Cancer Screening.

BACKGROUND: Colorectal cancer is the third most diagnosed cancer in adults in the United States. Early detection could prevent more than 90% of colorectal cancer-related deaths, yet more than one third of the screening-eligible population is not up to date with screening despite multiple available tests. A blood-based test has the potential to improve screening adherence, detect colorectal cancer earlier, and reduce colorectal cancer-related mortality. METHODS: We assessed the performance characteristics of a cell-free DNA (cfDNA) blood-based test in a population eligible for colorectal cancer screening. The coprimary outcomes were sensitivity for colorectal cancer and specificity for advanced neoplasia (colorectal cancer or advanced precancerous lesions) relative to screening colonoscopy. The secondary outcome was sensitivity to detect advanced precancerous lesions. RESULTS: The clinical validation cohort included 10,258 persons, 7861 of whom met eligibility criteria and were evaluable. A total of 83.1% of the participants with colorectal cancer detected by colonoscopy had a positive cfDNA test and 16.9% had a negative test, which indicates a sensitivity of the cfDNA test for detection of colorectal cancer of 83.1% (95% confidence interval [CI], 72.2 to 90.3). Sensitivity for stage I, II, or III colorectal cancer was 87.5% (95% CI, 75.3 to 94.1), and sensitivity for advanced precancerous lesions was 13.2% (95% CI, 11.3 to 15.3). A total of 89.6% of the participants without any advanced colorectal neoplasia (colorectal cancer or advanced precancerous lesions) identified on colonoscopy had a negative cfDNA blood-based test, whereas 10.4% had a positive cfDNA blood-based test, which indicates a specificity for any advanced neoplasia of 89.6% (95% CI, 88.8 to 90.3). Specificity for negative colonoscopy (no colorectal cancer, advanced precancerous lesions, or nonadvanced precancerous lesions) was 89.9% (95% CI, 89.0 to 90.7). CONCLUSIONS: In an average-risk screening population, this cfDNA blood-based test had 83% sensitivity for colorectal cancer, 90% specificity for advanced neoplasia, and 13% sensitivity for advanced precancerous lesions. (Funded by Guardant Health; ECLIPSE ClinicalTrials.gov number, NCT04136002.).

Next-Generation Multitarget Stool DNA Test for Colorectal Cancer Screening.

BACKGROUND: A next-generation multitarget stool DNA test, including assessments of DNA molecular markers and hemoglobin level, was developed to improve the performance of colorectal cancer screening, primarily with regard to specificity. METHODS: In a prospective study, we evaluated a next-generation multitarget stool DNA test in asymptomatic adults 40 years of age or older who were undergoing screening colonoscopy. The primary outcomes were sensitivity of the test for colorectal cancer and specificity for advanced neoplasia (colorectal cancer or advanced precancerous lesions). Advanced precancerous lesions included one or more adenomas or sessile serrated lesions measuring at least 1 cm in the longest dimension, lesions with villous histologic features, and high-grade dysplasia. Secondary objectives included the quantification of sensitivity for advanced precancerous lesions and specificity for nonneoplastic findings or negative colonoscopy and comparison of sensitivities for colorectal cancer and advanced precancerous lesions between the multitarget stool DNA test and a commercially available fecal immunochemical test (FIT). RESULTS: Of 20,176 participants, 98 had colorectal cancer, 2144 had advanced precancerous lesions, 6973 had nonadvanced adenomas, and 10,961 had nonneoplastic findings or negative colonoscopy. With the next-generation test, sensitivity for colorectal cancer was 93.9% (95% confidence interval [CI], 87.1 to 97.7), and specificity for advanced neoplasia was 90.6% (95% CI, 90.1 to 91.0). Sensitivity for advanced precancerous lesions was 43.4% (95% CI, 41.3 to 45.6), and specificity for nonneoplastic findings or negative colonoscopy was 92.7% (95% CI, 92.2 to 93.1). With the FIT, sensitivity was 67.3% (95% CI, 57.1 to 76.5) for colorectal cancer and 23.3% (95% CI, 21.5 to 25.2) for advanced precancerous lesions; specificity was 94.8% (95% CI, 94.4 to 95.1) for advanced neoplasia and 95.7% (95% CI, 95.3 to 96.1) for nonneoplastic findings or negative colonoscopy. As compared with FIT, the next-generation test had superior sensitivity for colorectal cancer (P<0.001) and for advanced precancerous lesions (P<0.001) but had lower specificity for advanced neoplasia (P<0.001). No adverse events occurred. CONCLUSIONS: The next-generation multitarget stool DNA test showed higher sensitivity for colorectal cancer and advanced precancerous lesions than FIT but also showed lower specificity. (Funded by Exact Sciences; BLUE-C ClinicalTrials.gov number, NCT04144738.).

AI-based pathology predicts origins for cancers of unknown primary.

Cancer of unknown primary (CUP) origin is an enigmatic group of diagnoses in which the primary anatomical site of tumour origin cannot be determined1,2. This poses a considerable challenge, as modern therapeutics are predominantly specific to the primary tumour3. Recent research has focused on using genomics and transcriptomics to identify the origin of a tumour4-9. However, genomic testing is not always performed and lacks clinical penetration in low-resource settings. Here, to overcome these challenges, we present a deep-learning-based algorithm-Tumour Origin Assessment via Deep Learning (TOAD)-that can provide a differential diagnosis for the origin of the primary tumour using routinely acquired histology slides. We used whole-slide images of tumours with known primary origins to train a model that simultaneously identifies the tumour as primary or metastatic and predicts its site of origin. On our held-out test set of tumours with known primary origins, the model achieved a top-1 accuracy of 0.83 and a top-3 accuracy of 0.96, whereas on our external test set it achieved top-1 and top-3 accuracies of 0.80 and 0.93, respectively. We further curated a dataset of 317 cases of CUP for which a differential diagnosis was assigned. Our model predictions resulted in concordance for 61% of cases and a top-3 agreement of 82%. TOAD can be used as an assistive tool to assign a differential diagnosis to complicated cases of metastatic tumours and CUPs and could be used in conjunction with or in lieu of ancillary tests and extensive diagnostic work-ups to reduce the occurrence of CUP.

De novo design of modular and tunable protein biosensors.

Naturally occurring protein switches have been repurposed for the development of biosensors and reporters for cellular and clinical applications1. However, the number of such switches is limited, and reengineering them is challenging. Here we show that a general class of protein-based biosensors can be created by inverting the flow of information through de novo designed protein switches in which the binding of a peptide key triggers biological outputs of interest2. The designed sensors are modular molecular devices with a closed dark state and an open luminescent state; analyte binding drives the switch from the closed to the open state. Because the sensor is based on the thermodynamic coupling of analyte binding to sensor activation, only one target binding domain is required, which simplifies sensor design and allows direct readout in solution. We create biosensors that can sensitively detect the anti-apoptosis protein BCL-2, the IgG1 Fc domain, the HER2 receptor, and Botulinum neurotoxin B, as well as biosensors for cardiac troponin I and an anti-hepatitis B virus antibody with the high sensitivity required to detect these molecules clinically. Given the need for diagnostic tools to track the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)3, we used the approach to design sensors for the SARS-CoV-2 spike protein and antibodies against the membrane and nucleocapsid proteins. The former, which incorporates a de novo designed spike receptor binding domain (RBD) binder4, has a limit of detection of 15 pM and a luminescence signal 50-fold higher than the background level. The modularity and sensitivity of the platform should enable the rapid construction of sensors for a wide range of analytes, and highlights the power of de novo protein design to create multi-state protein systems with new and useful functions.

A pooled testing strategy for identifying SARS-CoV-2 at low prevalence.

Suppressing infections of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) will probably require the rapid identification and isolation of individuals infected with the virus on an ongoing basis. Reverse-transcription polymerase chain reaction (RT-PCR) tests are accurate but costly, which makes the regular testing of every individual expensive. These costs are a challenge for all countries around the world, but particularly for low-to-middle-income countries. Cost reductions can be achieved by pooling (or combining) subsamples and testing them in groups1-7. A balance must be struck between increasing the group size and retaining test sensitivity, as sample dilution increases the likelihood of false-negative test results for individuals with a low viral load in the sampled region at the time of the test8. Similarly, minimizing the number of tests to reduce costs must be balanced against minimizing the time that testing takes, to reduce the spread of the infection. Here we propose an algorithm for pooling subsamples based on the geometry of a hypercube that, at low prevalence, accurately identifies individuals infected with SARS-CoV-2 in a small number of tests and few rounds of testing. We discuss the optimal group size and explain why, given the highly infectious nature of the disease, largely parallel searches are preferred. We report proof-of-concept experiments in which a positive subsample was detected even when diluted 100-fold with negative subsamples (compared with 30-48-fold dilutions described in previous studies9-11). We quantify the loss of sensitivity due to dilution and discuss how it may be mitigated by the frequent re-testing of groups, for example. With the use of these methods, the cost of mass testing could be reduced by a large factor. At low prevalence, the costs decrease in rough proportion to the prevalence. Field trials of our approach are under way in Rwanda and South Africa. The use of group testing on a massive scale to monitor infection rates closely and continually in a population, along with the rapid and effective isolation of people with SARS-CoV-2 infections, provides a promising pathway towards the long-term control of coronavirus disease 2019 (COVID-19).

StratoLAMP: Label-free, multiplex digital loop-mediated isothermal amplification based on visual stratification of precipitate.

Multiplex, digital nucleic acid detections have important biomedical applications, but the multiplexity of existing methods is predominantly achieved using fluorescent dyes or probes, making the detection complicated and costly. Here, we present the StratoLAMP for label-free, multiplex digital loop-mediated isothermal amplification based on visual stratification of the precipitate byproduct. The StratoLAMP designates two sets of primers with different concentrations to achieve different precipitate yields when amplifying different nucleic acid targets. In the detection, deep learning image analysis is used to stratify the precipitate within each droplet and determine the encapsulated targets for nucleic acid quantification. We investigated the effect of the amplification reagents and process on the precipitate generation and optimized the assay conditions. We then implemented a deep-learning image analysis pipeline for droplet detection, achieving an overall accuracy of 94.3%. In the application, the StratoLAMP successfully achieved the simultaneous quantification of two nucleic acid targets with high accuracy. By eliminating the need for fluorescence, StratoLAMP represents a unique concept toward label-free, multiplex nucleic acid assays and an analytical tool with great cost-effectiveness.

Aerosolization of viable Mycobacterium tuberculosis bacilli by tuberculosis clinic attendees independent of sputum-Xpert Ultra status.

Potential Mycobacterium tuberculosis (Mtb) transmission during different pulmonary tuberculosis (TB) disease states is poorly understood. We quantified viable aerosolized Mtb from TB clinic attendees following diagnosis and through six months' follow-up thereafter. Presumptive TB patients (n=102) were classified by laboratory, radiological, and clinical features into Group A: Sputum-Xpert Ultra-positive TB (n=52), Group B: Sputum-Xpert Ultra-negative TB (n=20), or Group C: TB undiagnosed (n=30). All groups were assessed for Mtb bioaerosol release at baseline, and subsequently at 2 wk, 2 mo, and 6 mo. Groups A and B were notified to the national TB program and received standard anti-TB chemotherapy; Mtb was isolated from 92% and 90% at presentation, 87% and 74% at 2 wk, 54% and 44% at 2 mo and 32% and 20% at 6 mo, respectively. Surprisingly, similar numbers were detected in Group C not initiating TB treatment: 93%, 70%, 48% and 22% at the same timepoints. A temporal association was observed between Mtb bioaerosol release and TB symptoms in all three groups. Persistence of Mtb bioaerosol positivity was observed in ~30% of participants irrespective of TB chemotherapy. Captured Mtb bacilli were predominantly acid-fast stain-negative and poorly culturable; however, three bioaerosol samples yielded sufficient biomass following culture for whole-genome sequencing, revealing two different Mtb lineages. Detection of viable aerosolized Mtb in clinic attendees, independent of TB diagnosis, suggests that unidentified Mtb transmitters might contribute a significant attributable proportion of community exposure. Additional longitudinal studies with sputum culture-positive and -negative control participants are required to investigate this possibility.

Label-free and amplification-free viral RNA quantification from primate biofluids using a trapping-assisted optofluidic nanopore platform.

Viral outbreaks can cause widespread disruption, creating the need for diagnostic tools that provide high performance and sample versatility at the point of use with moderate complexity. Current gold standards such as PCR and rapid antigen tests fall short in one or more of these aspects. Here, we report a label-free and amplification-free nanopore sensor platform that overcomes these challenges via direct detection and quantification of viral RNA in clinical samples from a variety of biological fluids. The assay uses an optofluidic chip that combines optical waveguides with a fluidic channel and integrates a solid-state nanopore for sensing of individual biomolecules upon translocation through the pore. High specificity and low limit of detection are ensured by capturing RNA targets on microbeads and collecting them by optical trapping at the nanopore location where targets are released and rapidly detected. We use this device for longitudinal studies of the viral load progression for Zika and Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) infections in marmoset and baboon animal models, respectively. The up to million-fold trapping-based target concentration enhancement enables amplification-free RNA quantification across the clinically relevant concentration range down to the assay limit of RT-qPCR as well as cases in which PCR failed. The assay operates across all relevant biofluids, including semen, urine, and whole blood for Zika and nasopharyngeal and throat swab, rectal swab, and bronchoalveolar lavage for SARS-CoV-2. The versatility, performance, simplicity, and potential for full microfluidic integration of the amplification-free nanopore assay points toward a unique approach to molecular diagnostics for nucleic acids, proteins, and other targets.