Designing sensitive viral diagnostics with machine learning

CRISPR-Based Assays for Point-of-Need Detection and Subtyping of Influenza

The high disease burden of influenza virus poses a significant threat to human health. Optimized diagnostic technologies that combine speed, sensitivity, and specificity with minimal equipment requirements are urgently needed to detect the many circulating species, subtypes, and variants of influenza at the point of need. Here, we introduce such a method using Streamlined Highlighting of Infections to Navigate Epidemics (SHINE), a clustered regularly interspaced short palindromic repeats (CRISPR)-based RNA detection platform. Four SHINE assays were designed and validated for the detection and differentiation of clinically relevant influenza species (A and B) and subtypes (H1N1 and H3N2). When tested on clinical samples, these optimized assays achieved 100% concordance with quantitative RT-PCR. Duplex Cas12a/Cas13a SHINE assays were also developed to detect two targets simultaneously. This study demonstrates the utility of this duplex assay in discriminating two alleles of an oseltamivir resistance (H275Y) mutation as well as in simultaneously detecting influenza A and human RNAse P in patient samples. These assays have the potential to expand influenza detection outside of clinical laboratories for enhanced influenza diagnosis and surveillance.

A versatile automated pipeline for quantifying virus infectivity by label-free light microscopy and artificial intelligence

Virus infectivity is traditionally determined by endpoint titration in cell cultures, and requires complex processing steps and human annotation. Here we developed an artificial intelligence (AI)-powered automated framework for ready detection of virus-induced cytopathic effect (DVICE). DVICE uses the convolutional neural network EfficientNet-B0 and transmitted light microscopy images of infected cell cultures, including coronavirus, influenza virus, rhinovirus, herpes simplex virus, vaccinia virus, and adenovirus. DVICE robustly measures virus-induced cytopathic effects (CPE), as shown by class activation mapping. Leave-one-out cross-validation in different cell types demonstrates high accuracy for different viruses, including SARS-CoV-2 in human saliva. Strikingly, DVICE exhibits virus class specificity, as shown with adenovirus, herpesvirus, rhinovirus, vaccinia virus, and SARS-CoV-2. In sum, DVICE provides unbiased infectivity scores of infectious agents causing CPE, and can be adapted to laboratory diagnostics, drug screening, serum neutralization or clinical samples.

Electrochemical Sensors and Biosensors for Identification of Viruses: A Critical Review

Due to their life cycle, viruses can disrupt the metabolism of their hosts, causing diseases. If we want to disrupt their life cycle, it is necessary to identify their presence. For this purpose, it is possible to use several molecular-biological and bioanalytical methods. The reference selection was performed based on electronic databases (2020-2023). This review focused on electrochemical methods with high sensitivity and selectivity (53% voltammetry/amperometry, 33% impedance, and 12% other methods) which showed their great potential for detecting various viruses. Moreover, the aforementioned electrochemical methods have considerable potential to be applicable for care-point use as they are portable due to their miniaturizability and fast speed analysis (minutes to hours), and are relatively easy to interpret. A total of 2011 articles were found, of which 86 original papers were subsequently evaluated (the majority of which are focused on human pathogens, whereas articles dealing with plant pathogens are in the minority). Thirty-two species of viruses were included in the evaluation. It was found that most of the examined research studies (77%) used nanotechnological modifications. Other ones performed immunological (52%) or genetic analyses (43%) for virus detection. 5% of the reports used peptides to increase the method's sensitivity. When evaluable, 65% of the research studies had LOD values in the order of ng or nM. The vast majority (79%) of the studies represent proof of concept and possibilities with low application potential and a high need of further research experimental work.

Prediction of on-target and off-target activity of CRISPR-Cas13d guide RNAs using deep learning

Transcriptome engineering applications in living cells with RNA-targeting CRISPR effectors depend on accurate prediction of on-target activity and off-target avoidance. Here we design and test ~200,000 RfxCas13d guide RNAs targeting essential genes in human cells with systematically designed mismatches and insertions and deletions (indels). We find that mismatches and indels have a position- and context-dependent impact on Cas13d activity, and mismatches that result in G-U wobble pairings are better tolerated than other single-base mismatches. Using this large-scale dataset, we train a convolutional neural network that we term targeted inhibition of gene expression via gRNA design (TIGER) to predict efficacy from guide sequence and context. TIGER outperforms the existing models at predicting on-target and off-target activity on our dataset and published datasets. We show that TIGER scoring combined with specific mismatches yields the first general framework to modulate transcript expression, enabling the use of RNA-targeting CRISPRs to precisely control gene dosage.

New design strategies for ultra-specific CRISPR-Cas13a-based RNA detection with single-nucleotide mismatch sensitivity

An increasingly pressing need for clinical diagnostics has required the development of novel nucleic acid-based detection technologies that are sensitive, fast, and inexpensive, and that can be deployed at point-of-care. Recently, the RNA-guided ribonuclease CRISPR-Cas13 has been successfully harnessed for such purposes. However, developing assays for detection of genetic variability, for example single-nucleotide polymorphisms, is still challenging and previously described design strategies are not always generalizable. Here, we expanded our characterization of LbuCas13a RNA-detection specificity by performing a combination of experimental RNA mismatch tolerance profiling, molecular dynamics simulations, protein, and crRNA engineering. We found certain positions in the crRNA-target-RNA duplex that are particularly sensitive to mismatches and establish the effect of RNA concentration in mismatch tolerance. Additionally, we determined that shortening the crRNA spacer or modifying the direct repeat of the crRNA leads to stricter specificities. Furthermore, we harnessed our understanding of LbuCas13a allosteric activation pathways through molecular dynamics and structure-guided engineering to develop novel Cas13a variants that display increased sensitivities to single-nucleotide mismatches. We deployed these Cas13a variants and crRNA design strategies to achieve superior discrimination of SARS-CoV-2 strains compared to wild-type LbuCas13a. Together, our work provides new design criteria and Cas13a variants to use in future easier-to-implement Cas13-based RNA detection applications.

CRISPR-Cas system: A promising tool for rapid detection of SARS-CoV-2 variants

COVID-19, caused by SARS-CoV-2, remains a global health crisis. The emergence of multiple variants with enhanced characteristics necessitates their detection and monitoring. Genome sequencing, the gold standard, faces implementation challenges due to complexity, cost, and limited throughput. The CRISPR-Cas system offers promising potential for rapid variant detection, with advantages such as speed, sensitivity, specificity, and programmability. This review provides an in-depth examination of the applications of CRISPR-Cas in mutation detection specifically for SARS-CoV-2. It begins by introducing SARS-CoV-2 and existing variant detection platforms. The principles of the CRISPR-Cas system are then clarified, followed by an exploration of three CRISPR-Cas-based mutation detection platforms, which are evaluated from different perspectives. The review discusses strategies for mutation site selection and the utilization of CRISPR-Cas, offering valuable insights for the development of mutation detection methods. Furthermore, a critical analysis of the clinical applications, advantages, disadvantages, challenges, and prospects of the CRISPR-Cas system is provided.

Deep learning and CRISPR-Cas13d ortholog discovery for optimized RNA targeting

Effective and precise mammalian transcriptome engineering technologies are needed to accelerate biological discovery and RNA therapeutics. Despite the promise of programmable CRISPR-Cas13 ribonucleases, their utility has been hampered by an incomplete understanding of guide RNA design rules and cellular toxicity resulting from off-target or collateral RNA cleavage. Here, we quantified the performance of over 127,000 RfxCas13d (CasRx) guide RNAs and systematically evaluated seven machine learning models to build a guide efficiency prediction algorithm orthogonally validated across multiple human cell types. Deep learning model interpretation revealed preferred sequence motifs and secondary features for highly efficient guides. We next identified and screened 46 novel Cas13d orthologs, finding that DjCas13d achieves low cellular toxicity and high specificity-even when targeting abundant transcripts in sensitive cell types, including stem cells and neurons. Our Cas13d guide efficiency model was successfully generalized to DjCas13d, illustrating the power of combining machine learning with ortholog discovery to advance RNA targeting in human cells.

RNA structure modulates Cas13 activity and enables mismatch detection

The RNA-targeting CRISPR nuclease Cas13 has emerged as a powerful tool for applications ranging from nucleic acid detection to transcriptome engineering and RNA imaging1-6. Cas13 is activated by the hybridization of a CRISPR RNA (crRNA) to a complementary single-stranded RNA (ssRNA) protospacer in a target RNA1,7. Though Cas13 is not activated by double-stranded RNA (dsRNA) in vitro, it paradoxically demonstrates robust RNA targeting in environments where the vast majority of RNAs are highly structured2,8. Understanding Cas13's mechanism of binding and activation will be key to improving its ability to detect and perturb RNA; however, the mechanism by which Cas13 binds structured RNAs remains unknown9. Here, we systematically probe the mechanism of LwaCas13a activation in response to RNA structure perturbations using a massively multiplexed screen. We find that there are two distinct sequence-independent modes by which secondary structure affects Cas13 activity: structure in the protospacer region competes with the crRNA and can be disrupted via a strand-displacement mechanism, while structure in the region 3' to the protospacer has an allosteric inhibitory effect. We leverage the kinetic nature of the strand displacement process to improve Cas13-based RNA detection, enhancing mismatch discrimination by up to 50-fold and enabling sequence-agnostic mutation identification at low (<1%) allele frequencies. Our work sets a new standard for CRISPR-based nucleic acid detection and will enable intelligent and secondary-structure-guided target selection while also expanding the range of RNAs available for targeting with Cas13.

Development of Cas13a-based assays for Neisseria gonorrhoeae detection and gyrase A determination

Neisseria gonorrhoeae is one of the most common bacterial sexually transmitted infections. The emergence of antimicrobial-resistant N. gonorrhoeae is an urgent public health threat. Currently, the diagnosis of N. gonorrhoeae infection requires expensive laboratory infrastructure, while antimicrobial susceptibility determination requires bacterial culture, both of which are infeasible in low-resource areas where the prevalence of infection is highest. Recent advances in molecular diagnostics, such as specific high-sensitivity enzymatic reporter unlocking (SHERLOCK) using CRISPR-Cas13a and isothermal amplification, have the potential to provide low-cost detection of pathogen and antimicrobial resistance. We designed and optimized RNA guides and primer sets for SHERLOCK assays capable of detecting N. gonorrhoeae via the porA gene and of predicting ciprofloxacin susceptibility via a single mutation in the gyrase A (gyrA) gene. We evaluated their performance using both synthetic DNA and purified N. gonorrhoeae isolates. For porA, we created both a fluorescence-based assay and lateral flow assay using a biotinylated fluorescein reporter. Both methods demonstrated sensitive detection of 14 N. gonorrhoeae isolates and no cross-reactivity with 3 non-gonococcal Neisseria isolates. For gyrA, we created a fluorescence-based assay that correctly distinguished between 20 purified N. gonorrhoeae isolates with phenotypic ciprofloxacin resistance and 3 with phenotypic susceptibility. We confirmed the gyrA genotype predictions from the fluorescence-based assay with DNA sequencing, which showed 100% concordance for the isolates studied. We report the development of Cas13a-based SHERLOCK assays that detect N. gonorrhoeae and differentiate ciprofloxacin-resistant isolates from ciprofloxacin-susceptible isolates. IMPORTANCE Neisseria gonorrhoeae, the cause of gonorrhea, disproportionately affects resource-limited settings. Such areas, however, lack the technical capabilities for diagnosing the infection. The consequences of poor or absent diagnostics include increased disease morbidity, which, for gonorrhea, includes an increased risk for HIV infection, infertility, and neonatal blindness, as well as an overuse of antibiotics that contributes to the emergence of antibiotic resistance. We used a novel CRISPR-based technology to develop a rapid test that does not require laboratory infrastructure for both diagnosing gonorrhea and predicting whether ciprofloxacin can be used in its treatment, a one-time oral pill. With further development, that diagnostic test may be of use in low-resource settings.

Model-directed generation of CRISPR-Cas13a guide RNAs designs artificial sequences that improve nucleic acid detection

Generating maximally-fit biological sequences has the potential to transform CRISPR guide RNA design as it has other areas of biomedicine. Here, we introduce model-directed exploration algorithms (MEAs) for designing maximally-fit, artificial CRISPR-Cas13a guides-with multiple mismatches to any natural sequence-that are tailored for desired properties around nucleic acid diagnostics. We find that MEA-designed guides offer more sensitive detection of diverse pathogens and discrimination of pathogen variants compared to guides derived directly from natural sequences, and illuminate interpretable design principles that broaden Cas13a targeting.

Highly multiplexed mRNA quantitation with CRISPR-Cas13

RNA quantitation tools are often either high-throughput or cost-effective, but rarely are they both. Existing methods can profile the transcriptome at great expense or are limited to quantifying a handful of genes by labor constraints. A technique that permits more throughput at a reduced cost could enable multi-gene kinetic studies, gene regulatory network analysis, and combinatorial genetic screens. Here, we introduce quantitative Combinatorial Arrayed Reactions for Multiplexed Evaluation of Nucleic acids (qCARMEN): an RNA quantitation technique which leverages the programmable RNA-targeting capabilities of CRISPR-Cas13 to address this challenge by quantifying over 4,500 gene-sample pairs in a single experiment. Using qCARMEN, we studied the response profiles of interferon-stimulated genes (ISGs) during interferon (IFN) stimulation and flavivirus infection. Additionally, we observed isoform switching kinetics during epithelial-mesenchymal transition. qCARMEN is a simple and inexpensive technique that greatly enhances the scalability of RNA quantitation for novel applications with performance similar to gold-standard methods.

CRISPR-Cas-Based Biomonitoring for Marine Environments: Toward CRISPR RNA Design Optimization Via Deep Learning

Almost all of Earth's oceans are now impacted by multiple anthropogenic stressors, including the spread of nonindigenous species, harmful algal blooms, and pathogens. Early detection is critical to manage these stressors effectively and to protect marine systems and the ecosystem services they provide. Molecular tools have emerged as a promising solution for marine biomonitoring. One of the latest advancements involves utilizing CRISPR-Cas technology to build programmable, rapid, ultrasensitive, and specific diagnostics. CRISPR-based diagnostics (CRISPR-Dx) has the potential to allow robust, reliable, and cost-effective biomonitoring in near real time. However, several challenges must be overcome before CRISPR-Dx can be established as a mainstream tool for marine biomonitoring. A critical unmet challenge is the need to design, optimize, and experimentally validate CRISPR-Dx assays. Artificial intelligence has recently been presented as a potential approach to tackle this challenge. This perspective synthesizes recent advances in CRISPR-Dx and machine learning modeling approaches, showcasing CRISPR-Dx potential to progress as a rising molecular tool candidate for marine biomonitoring applications.

New design strategies for ultra-specific CRISPR-Cas13a-based RNA-diagnostic tools with single-nucleotide mismatch sensitivity

The pressing need for clinical diagnostics has required the development of novel nucleic acid-based detection technologies that are sensitive, fast, and inexpensive, and that can be deployed at point-of-care. Recently, the RNA-guided ribonuclease CRISPR-Cas13 has been successfully harnessed for such purposes. However, developing assays for detection of genetic variability, for example single-nucleotide polymorphisms, is still challenging and previously described design strategies are not always generalizable. Here, we expanded our characterization of LbuCas13a RNA-detection specificity by performing a combination of experimental RNA mismatch tolerance profiling, molecular dynamics simulations, protein, and crRNA engineering. We found certain positions in the crRNA-target-RNA duplex that are particularly sensitive to mismatches and establish the effect of RNA concentration in mismatch tolerance. Additionally, we determined that shortening the crRNA spacer or modifying the direct repeat of the crRNA leads to stricter specificities. Furthermore, we harnessed our understanding of LbuCas13a allosteric activation pathways through molecular dynamics and structure-guided engineering to develop novel Cas13a variants that display increased sensitivities to single-nucleotide mismatches. We deployed these Cas13a variants and crRNA design strategies to achieve superior discrimination of SARS-CoV-2 strains compared to wild-type LbuCas13a. Together, our work provides new design criteria and new Cas13a variants for easier-to-implement Cas13-based diagnostics.

KEY POINTS

Certain positions in the Cas13a crRNA-target-RNA duplex are particularly sensitive to mismatches.Understanding Cas13a's allosteric activation pathway allowed us to develop novel high-fidelity Cas13a variants.These Cas13a variants and crRNA design strategies achieve superior discrimination of SARS-CoV-2 strains.

GRAPHICAL ABSTRACT

Leveraging artificial intelligence in the fight against infectious diseases

Despite advances in molecular biology, genetics, computation, and medicinal chemistry, infectious disease remains an ominous threat to public health. Addressing the challenges posed by pathogen outbreaks, pandemics, and antimicrobial resistance will require concerted interdisciplinary efforts. In conjunction with systems and synthetic biology, artificial intelligence (AI) is now leading to rapid progress, expanding anti-infective drug discovery, enhancing our understanding of infection biology, and accelerating the development of diagnostics. In this Review, we discuss approaches for detecting, treating, and understanding infectious diseases, underscoring the progress supported by AI in each case. We suggest future applications of AI and how it might be harnessed to help control infectious disease outbreaks and pandemics.

Generative and predictive neural networks for the design of functional RNA molecules

RNA is a remarkably versatile molecule that has been engineered for applications in therapeutics, diagnostics, and in vivo information-processing systems. However, the complex relationship between the sequence and structural properties of an RNA molecule and its ability to perform specific functions often necessitates extensive experimental screening of candidate sequences. Here we present a generalized neural network architecture that utilizes the sequence and structure of RNA molecules (SANDSTORM) to inform functional predictions. We demonstrate that this approach achieves state-of-the-art performance across several distinct RNA prediction tasks, while learning interpretable abstractions of RNA secondary structure. We paired these predictive models with generative adversarial RNA design networks (GARDN), allowing the generative modelling of novel mRNA 5' untranslated regions and toehold switch riboregulators exhibiting a predetermined fitness. This approach enabled the design of novel toehold switches with a 43-fold increase in experimentally characterized dynamic range compared to those designed using classic thermodynamic algorithms. SANDSTORM and GARDN thus represent powerful new predictive and generative tools for the development of diagnostic and therapeutic RNA molecules with improved function.

Development of Cas13a-based Assays for Neisseria gonorrhoeae Detection and Gyrase A Determination

Background

Neisseria gonorrhoeae is one of the most common bacterial sexually transmitted infections. The emergence of antimicrobial-resistant N. gonorrhoeae is an urgent public health threat. Currently, diagnosis of N. gonorrhoeae infection requires expensive laboratory infrastructure, while antimicrobial susceptibility determination requires bacterial culture, both of which are infeasible in low-resource areas where prevalence is highest. Recent advances in molecular diagnostics, such as Specific High-sensitivity Enzymatic Reporter unLOCKing (SHERLOCK) using CRISPR-Cas13a and isothermal amplification, have the potential to provide low-cost detection of pathogen and antimicrobial resistance.

Methods and Results

We designed and optimized RNA guides and primer-sets for SHERLOCK assays capable of detecting N. gonorrhoeae via the por A gene and of predicting ciprofloxacin susceptibility via a single mutation in the gyrase A ( gyr A) gene. We evaluated their performance using both synthetic DNA and purified N. gonorrhoeae isolates. For por A, we created both a fluorescence-based assay and lateral flow assay using a biotinylated FAM reporter. Both methods demonstrated sensitive detection of 14 N. gonorrhoeae isolates and no cross-reactivity with 3 non-gonococcal Neisseria isolates. For gyr A, we created a fluorescence-based assay that correctly distinguished between 20 purified N. gonorrhoeae isolates with phenotypic ciprofloxacin resistance and 3 with phenotypic susceptibility. We confirmed the gyr A genotype predictions from the fluorescence-based assay with DNA sequencing, which showed 100% concordance for the isolates studied.

Conclusion

We report the development of Cas13a-based SHERLOCK assays that detect N. gonorrhoeae and differentiate ciprofloxacin-resistant isolates from ciprofloxacin-susceptible isolates.

Machine learning for design of degenerate Cas13a crRNAs using lassa virus as a model of highly variable RNA target

The design of minimum CRISPR RNA (crRNA) sets for detection of diverse RNA targets using sequence degeneracy has not been systematically addressed. We tested candidate degenerate Cas13a crRNA sets designed for detection of diverse RNA targets (Lassa virus). A decision tree machine learning (ML) algorithm (RuleFit) was applied to define the top attributes that determine the specificity of degenerate crRNAs to elicit collateral nuclease activity. Although the total number of mismatches (0-4) is important, the specificity depends as well on the spacing of mismatches, and their proximity to the 5' end of the spacer. We developed a predictive algorithm for design of candidate degenerate crRNA sets, allowing improved discrimination between "included" and "excluded" groups of related target sequences. A single degenerate crRNA set adhering to these rules detected representatives of all Lassa lineages. Our general ML approach may be applied to the design of degenerate crRNA sets for any CRISPR/Cas system.

CRISPR techniques and potential for the detection and discrimination of SARS-CoV-2 variants of concern

The continuing evolution of the SARS-CoV-2 virus has led to the emergence of many variants, including variants of concern (VOCs). CRISPR-Cas systems have been used to develop techniques for the detection of variants. These techniques have focused on the detection of variant-specific mutations in the spike protein gene of SARS-CoV-2. These sequences mostly carry single-nucleotide mutations and are difficult to differentiate using a single CRISPR-based assay. Here we discuss the specificity of the Cas9, Cas12, and Cas13 systems, important considerations of mutation sites, design of guide RNA, and recent progress in CRISPR-based assays for SARS-CoV-2 variants. Strategies for discriminating single-nucleotide mutations include optimizing the position of mismatches, modifying nucleotides in the guide RNA, and using two guide RNAs to recognize the specific mutation sequence and a conservative sequence. Further research is needed to confront challenges in the detection and differentiation of variants and sublineages of SARS-CoV-2 in clinical diagnostic and point-of-care applications.

Capturing nucleic acid variants with precision using CRISPR diagnostics

CRISPR/Cas systems have the ability to precisely target nucleotide sequences and enable their rapid identification and modification. While nucleotide modification has enabled the therapeutic correction of diseases, the process of identifying the target DNA or RNA has greatly expanded the field of molecular diagnostics in recent times. CRISPR-based DNA/RNA detection through programmable nucleic acid binding or cleavage has been demonstrated for a large number of pathogenic and non-pathogenic targets. Combining CRISPR detection with nucleic acid amplification and a terminal signal readout step allowed the development of numerous rapid and robust nucleic acid platforms. Wherever the Cas effector can faithfully distinguish nucleobase variants in the target, the platform can also be extended for sequencing-free rapid variant detection. Some initial PAM disruption-based SNV detection reports were limited to finding or integrating mutated/mismatched nucleotides within the PAM sequences. In this review, we try to summarize the developments made in CRISPR diagnostics (CRISPRDx) to date emphasizing CRISPR-based SNV detection. We also discuss the applications where such diagnostic modalities can be put to use, covering various fields of clinical research, SNV screens, disease genotyping, primary surveillance during microbial infections, agriculture, food safety, and industrial biotechnology. The ease of rapid design and implementation of such multiplexable assays can potentially expand the applications of CRISPRDx in the domain of affinity-based target sequencing, with immense possibilities for low-cost, quick, and widespread usage. In the end, in combination with proximity assays and a suicidal gene approach, CRISPR-based in vivo SNV detection and cancer cell targeting can be formulated as personalized gene therapy.

Simplified Cas13-based assays for the fast identification of SARS-CoV-2 and its variants

The widespread transmission and evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) call for rapid nucleic acid diagnostics that are easy to use outside of centralized clinical laboratories. Here we report the development and performance benchmarking of Cas13-based nucleic acid assays leveraging lyophilised reagents and fast sample inactivation at ambient temperature. The assays, which we named SHINEv.2 (for 'streamlined highlighting of infections to navigate epidemics, version 2'), simplify the previously reported RNA-extraction-free SHINEv.1 technology by eliminating heating steps and the need for cold storage of the reagents. SHINEv.2 detected SARS-CoV-2 in nasopharyngeal samples with 90.5% sensitivity and 100% specificity (benchmarked against the reverse transcription quantitative polymerase chain reaction) in less than 90 min, using lateral-flow technology and incubation in a heat block at 37 °C. SHINEv.2 also allows for the visual discrimination of the Alpha, Beta, Gamma, Delta and Omicron SARS-CoV-2 variants, and can be run without performance losses by using body heat. Accurate, easy-to-use and equipment-free nucleic acid assays could facilitate wider testing for SARS-CoV-2 and other pathogens in point-of-care and at-home settings.

Recent Advances in Clustered Regularly Interspaced Short Palindromic Repeats-Based Biosensors for Point-of-Care Pathogen Detection

Infectious pathogens are pressing concerns due to their heavy toll on global health and socioeconomic infrastructure. Rapid, sensitive, and specific pathogen detection methods are needed more than ever to control disease spreading. The fast evolution of clustered regularly interspaced short palindromic repeats (CRISPR)-based diagnostics (CRISPR-Dx) has opened a new horizon in the field of molecular diagnostics. This review highlights recent efforts in configuring CRISPR technology as an efficient diagnostic tool for pathogen detection. It starts with a brief introduction of different CRISPR-Cas effectors and their working principles for disease diagnosis. It then focuses on the evolution of laboratory-based CRISPR technology toward a potential point-of-care test, including the development of new signaling mechanisms, elimination of preamplification and sample pretreatment steps, and miniaturization of CRISPR reactions on digital assay chips and lateral flow devices. In addition, promising examples of CRISPR-Dx for pathogen detection in various real samples, such as blood, saliva, nasal swab, plant, and food samples, are highlighted. Finally, the challenges and perspectives of future development of CRISPR-Dx for infectious disease monitoring are discussed.

Rapid, adaptable and sensitive Cas13-based COVID-19 diagnostics using ADESSO

During the ongoing COVID-19 pandemic, PCR testing and antigen tests have proven critical for helping to stem the spread of its causative agent, SARS-CoV-2. However, these methods suffer from either general applicability and/or sensitivity. Moreover, the emergence of variant strains creates the need for flexibility to correctly and efficiently diagnose the presence of substrains. To address these needs we developed the diagnostic test ADESSO (Accurate Detection of Evolving SARS-CoV-2 through SHERLOCK (Specific High Sensitivity Enzymatic Reporter UnLOCKing) Optimization) which employs Cas13 to diagnose patients in 1 h without sophisticated equipment. Using an extensive panel of clinical samples, we demonstrate that ADESSO correctly identifies infected individuals at a sensitivity and specificity comparable to RT-qPCR on extracted RNA and higher than antigen tests for unextracted samples. Altogether, ADESSO is a fast, sensitive and cheap method that can be applied in a point of care setting to diagnose COVID-19 and can be quickly adjusted to detect new variants.

SARS-CoV-2 antigen tests are commonly used point-of-care tests and provide rapid results but lack sensitivity. Here, the authors present a new point-of-care approach for COVID-19 diagnosis, “ADESSO”, which outperforms antigen tests on clinical samples and can be quickly adapted for different variants.

Multiplexed CRISPR-based microfluidic platform for clinical testing of respiratory viruses and identification of SARS-CoV-2 variants

The coronavirus disease 2019 (COVID-19) pandemic has demonstrated a clear need for high-throughput, multiplexed and sensitive assays for detecting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and other respiratory viruses and their emerging variants. Here, we present a cost-effective virus and variant detection platform, called microfluidic Combinatorial Arrayed Reactions for Multiplexed Evaluation of Nucleic acids (mCARMEN), which combines CRISPR-based diagnostics and microfluidics with a streamlined workflow for clinical use. We developed the mCARMEN respiratory virus panel to test for up to 21 viruses, including SARS-CoV-2, other coronaviruses and both influenza strains, and demonstrated its diagnostic-grade performance on 525 patient specimens in an academic setting and 166 specimens in a clinical setting. We further developed an mCARMEN panel to enable the identification of 6 SARS-CoV-2 variant lineages, including Delta and Omicron, and evaluated it on 2,088 patient specimens with near-perfect concordance to sequencing-based variant classification. Lastly, we implemented a combined Cas13 and Cas12 approach that enables quantitative measurement of SARS-CoV-2 and influenza A viral copies in samples. The mCARMEN platform enables high-throughput surveillance of multiple viruses and variants simultaneously, enabling rapid detection of SARS-CoV-2 variants.

Current progress and open challenges for applying deep learning across the biosciences

Deep Learning (DL) has recently enabled unprecedented advances in one of the grand challenges in computational biology: the half-century-old problem of protein structure prediction. In this paper we discuss recent advances, limitations, and future perspectives of DL on five broad areas: protein structure prediction, protein function prediction, genome engineering, systems biology and data integration, and phylogenetic inference. We discuss each application area and cover the main bottlenecks of DL approaches, such as training data, problem scope, and the ability to leverage existing DL architectures in new contexts. To conclude, we provide a summary of the subject-specific and general challenges for DL across the biosciences.

Multiplexed detection of bacterial nucleic acids using Cas13 in droplet microarrays

Rapid and accurate diagnosis of infections is fundamental to individual patient care and public health management. Nucleic acid detection methods are critical to this effort, but are limited either in the breadth of pathogens targeted or by the expertise and infrastructure required. We present here a high-throughput system that enables rapid identification of bacterial pathogens, bCARMEN, which utilizes: (1) modular CRISPR-Cas13-based nucleic acid detection with enhanced sensitivity and specificity; and (2) a droplet microfluidic system that enables thousands of simultaneous, spatially multiplexed detection reactions at nanoliter volumes; and (3) a novel preamplification strategy that further enhances sensitivity and specificity. We demonstrate bCARMEN is capable of detecting and discriminating 52 clinically relevant bacterial species and several key antibiotic resistance genes. We further develop a simple proof of principle workflow using stabilized reagents and cell phone camera optical readout, opening up the possibility of a rapid point-of-care multiplexed bacterial pathogen identification and antibiotic susceptibility testing.