Universal digital high-resolution melt: a novel approach to broad-based profiling of heterogeneous biological samples

Universal digital high-resolution melting for the detection of pulmonary mold infections

Invasive mold infections (IMIs) are associated with high morbidity, particularly in immunocompromised patients, with mortality rates between 40% and 80%. Early initiation of appropriate antifungal therapy can substantially improve outcomes, yet early diagnosis remains difficult to establish and often requires multidisciplinary teams evaluating clinical and radiological findings plus supportive mycological findings. Universal digital high-resolution melting (U-dHRM) analysis may enable rapid and robust diagnoses of IMI. A universal fungal assay was developed for U-dHRM and used to generate a database of melt curve signatures for 19 clinically relevant fungal pathogens. A machine learning algorithm (ML) was trained to automatically classify these pathogen curves and detect novel melt curves. Performance was assessed on 73 clinical bronchoalveolar lavage samples from patients suspected of IMI. Novel curves were identified by micropipetting U-dHRM reactions and Sanger sequencing amplicons. U-dHRM achieved 97% overall fungal organism identification accuracy and a turnaround time of ~4 hrs. U-dHRM detected pathogenic molds (Aspergillus, Mucorales, Lomentospora, and Fusarium) in 73% of 30 samples classified as IMI, including mixed infections. Specificity was optimized by requiring the number of pathogenic mold curves detected in a sample to be >8 and a sample volume to be 1 mL, which resulted in 100% specificity in 21 at-risk patients without IMI. U-dHRM showed promise as a separate or combination diagnostic approach to standard mycological tests. U-dHRM's speed, ability to simultaneously identify and quantify clinically relevant mold pathogens in polymicrobial samples, and detect emerging opportunistic pathogens may aid treatment decisions, improving patient outcomes.

IMPORTANCE

Improvements in diagnostics for invasive mold infections are urgently needed. This work presents a new molecular detection approach that addresses technical and workflow challenges to provide fast pathogen detection, identification, and quantification that could inform treatment to improve patient outcomes.

DNA melting analysis

Melting is a fundamental property of DNA that can be monitored by absorbance or fluorescence. PCR conveniently produces enough DNA to be directly monitored on real-time instruments with fluorescently labeled probes or dyes. Dyes monitor the entire PCR product, while probes focus on a specific locus within the amplicon. Advances in amplicon melting include high resolution instruments, saturating DNA dyes that better reveal multiple products, prediction programs for domain melting, barcode taxonomic identification, high speed microfluidic melting, and highly parallel digital melting. Most single base variants and small insertions or deletions can be genotyped by high resolution amplicon melting. High resolution melting also enables heterozygote scanning for any variant within a PCR product. A web application (uMelt, http://www.dna-utah.org) predicts amplicon melting curves with multiple domains, a useful tool for verifying intended products. Additional applications include methylation assessment, copy number determination and verification of sequence identity. When amplicon melting does not provide sufficient detail, unlabeled probes or snapback primers can be used instead of covalently labeled probes. DNA melting is a simple, inexpensive, and powerful tool with many research applications that is beginning to make its mark in clinical diagnostics.

Improving bacteria identification from digital melt assay via oligonucleotide-based temperature calibration

BACKGROUND

Bacterial infections, especially polymicrobial infections, remain a threat to global health and require advances in diagnostic technologies for timely and accurate identification of all causative species. Digital melt - microfluidic chip-based digital PCR combined with high resolution melt (HRM) - is an emerging method for identification and quantification of polymicrobial bacterial infections. Despite advances in recent years, existing digital melt instrumentation often delivers nonuniform temperatures across digital chips, resulting in nonuniform digital melt curves for individual bacterial species. This nonuniformity can lead to inaccurate species identification and reduce the capacity for differentiating bacterial species with similar digital melt curves.

RESULTS

We introduce herein a new temperature calibration method for digital melt by incorporating an unamplified, synthetic DNA fragment with a known melting temperature as a calibrator. When added at a tuned concentration to an established digital melt assay amplifying the commonly targeted 16S V1 - V6 region, this calibrator produced visible low temperature calibrator melt curves across-chip along with the target bacterial melt curves. This enables alignment of the bacterial melt curves and correction of heating-induced nonuniformities. Using this calibration method, we were able to improve the uniformity of digital melt curves from three causative species of bacteria. Additionally, we assessed calibration's effects on identification accuracy by performing machine learning identification of three polymicrobial mixtures comprised of two bacteria with similar digital melt curves in different ratios. Calibration greatly improved mixture composition prediction.

SIGNIFICANCE

To the best of our knowledge, this work represents the first DNA calibrator-supplemented assay and calibration method for nanoarray digital melt. Our results suggest that this calibration method can be flexibly used to improve identification accuracy and reduce melt curve variabilities across a variety of pathogens and assays. Therefore, this calibration method has the potential to elevate the diagnostic capabilities of digital melt toward polymicrobial bacterial infections and other infectious diseases.

Next generation multiplexing for digital PCR using a novel melt-based hairpin probe design

Digital PCR (dPCR) is a powerful tool for research and diagnostic applications that require absolute quantification of target molecules or detection of rare events, but the number of nucleic acid targets that can be distinguished within an assay has limited its usefulness. For most dPCR systems, one target is detected per optical channel and the total number of targets is limited by the number of optical channels on the platform. Higher-order multiplexing has the potential to dramatically increase the usefulness of dPCR, especially in scenarios with limited sample. Other potential benefits of multiplexing include lower cost, additional information generated by more probes, and higher throughput. To address this unmet need, we developed a novel melt-based hairpin probe design to provide a robust option for multiplexing digital PCR. A prototype multiplex digital PCR (mdPCR) assay using three melt-based hairpin probes per optical channel in a 16-well microfluidic digital PCR platform accurately distinguished and quantified 12 nucleic acid targets per well. For samples with 10,000 human genome equivalents, the probe-specific ranges for limit of blank were 0.00%-0.13%, and those for analytical limit of detection were 0.00%-0.20%. Inter-laboratory reproducibility was excellent (r 2 = 0.997). Importantly, this novel melt-based hairpin probe design has potential to achieve multiplexing beyond the 12 targets/well of this prototype assay. This easy-to-use mdPCR technology with excellent performance characteristics has the potential to revolutionize the use of digital PCR in research and diagnostic settings.

Next-generation molecular diagnostics: Leveraging digital technologies to enhance multiplexing in real-time PCR

Real-time polymerase chain reaction (qPCR) enables accurate detection and quantification of nucleic acids and has become a fundamental tool in biological sciences, bioengineering and medicine. By combining multiple primer sets in one reaction, it is possible to detect several DNA or RNA targets simultaneously, a process called multiplex PCR (mPCR) which is key to attaining optimal throughput, cost-effectiveness and efficiency in molecular diagnostics, particularly in infectious diseases. Multiple solutions have been devised to increase multiplexing in qPCR, including single-well techniques, using target-specific fluorescent oligonucleotide probes, and spatial multiplexing, where segregation of the sample enables parallel amplification of multiple targets. However, these solutions are mostly limited to three or four targets, or highly sophisticated and expensive instrumentation. There is a need for innovations that will push forward the multiplexing field in qPCR, enabling for a next generation of diagnostic tools which could accommodate high throughput in an affordable manner. To this end, the use of machine learning (ML) algorithms (data-driven solutions) has recently emerged to leverage information contained in amplification and melting curves (AC and MC, respectively) - two of the most standard bio-signals emitted during qPCR - for accurate classification of multiple nucleic acid targets in a single reaction. Therefore, this review aims to demonstrate and illustrate that data-driven solutions can be successfully coupled with state-of-the-art and common qPCR platforms using a variety of amplification chemistries to enhance multiplexing in qPCR. Further, because both ACs and MCs can be predicted from sequence data using thermodynamic databases, it has also become possible to use computer simulation to rationalize and optimize the design of mPCR assays where target detection is supported by data-driven technologies. Thus, this review also discusses recent work converging towards the development of an end-to-end framework where knowledge-based and data-driven software solutions are integrated to streamline assay design, and increase the accuracy of target detection and quantification in the multiplex setting. We envision that concerted efforts by academic and industry scientists will help advance these technologies, to a point where they become mature and robust enough to bring about major improvements in the detection of nucleic acids across many fields.

Interrogation of Functional miRNA-Target Interactions by CRISPR/Cas9 Genome Engineering

Posttranscriptional silencing by microRNAs (miRNAs) is a critical constituent of eukaryotic gene regulation. miRNAs are short (~22 nt) noncoding RNAs capable of specifically targeting the miRNA-induced silencing complex (miRISC) to transcripts bearing a complementary miRNA response element (MRE). Although recent methodological advances have greatly improved our understanding of miRNA biogenesis and the mechanisms by which miRNAs repress their cognate targets, exploring the physiological relevance of direct miRNA-target interactions in vivo has remained an outstanding challenge. Here we describe the experimental protocol underlying a novel approach, which allows direct in situ interrogation of specific miRNA-MRE interactions by CRISPR/Cas9-mediated genome engineering (Bassett G et al., Nat Commun 5, 4640, 2014). In this instance, the CRISPR/Cas9 system is first used to catalyze homology-directed replacement of candidate MREs with molecular barcodes at endogenous loci. Subsequently, the effect of MRE mutation on transcript abundance (i.e., MRE activity) can be rapidly evaluated by routine quantitative PCR. This strategy enables functional investigation of a putative miRNA-target pair in a pool of transiently transfected cells, obviating the need for generation of clonal cell lines or transgenic animals. This protocol can be implemented in any cell line in less than 2 weeks and can readily be scaled up for multiplex studies. To facilitate the conceptual workflow underlying this strategy, we also describe a genome-wide resource for automated design and computational evaluation of CRISPR/Cas9 guide RNAs targeting all predicted MREs in various species (miR-CRISPR).

SAMBA: A Multicolor Digital Melting PCR Platform for Rapid Microbiome Profiling

During the past decade, breakthroughs in sequencing technology have provided the basis for studies of the myriad ways in which microbial communities in and on the human body influence human health and disease. In almost every medical specialty, there is now a growing interest in accurate and quantitative profiling of the microbiota for use in diagnostic and therapeutic applications. However, the current next-generation sequencing approach for microbiome profiling is costly, requires laborious library preparation, and is challenging to scale up for routine diagnostics. Split, Amplify, and Melt analysis of BActeria-community (SAMBA), a novel multicolor digital melting polymerase chain reaction platform with unprecedented multiplexing capability is presented, and the capability to distinguish and quantify 16 bacteria species in mixtures is demonstrated. Subsequently, SAMBA is applied to measure the compositions of bacteria in the gut microbiome to identify microbial dysbiosis related to colorectal cancer. This rapid, low cost, and high-throughput approach will enable the implementation of microbiome diagnostics in clinical laboratories and routine medical practice.

Diagnosis of Bloodstream Infections: An Evolution of Technologies towards Accurate and Rapid Identification and Antibiotic Susceptibility Testing

Bloodstream infections (BSI) are a leading cause of death worldwide. The lack of timely and reliable diagnostic practices is an ongoing issue for managing BSI. The current gold standard blood culture practice for pathogen identification and antibiotic susceptibility testing is time-consuming. Delayed diagnosis warrants the use of empirical antibiotics, which could lead to poor patient outcomes, and risks the development of antibiotic resistance. Hence, novel techniques that could offer accurate and timely diagnosis and susceptibility testing are urgently needed. This review focuses on BSI and highlights both the progress and shortcomings of its current diagnosis. We surveyed clinical workflows that employ recently approved technologies and showed that, while offering improved sensitivity and selectivity, these techniques are still unable to deliver a timely result. We then discuss a number of emerging technologies that have the potential to shorten the overall turnaround time of BSI diagnosis through direct testing from whole blood—while maintaining, if not improving—the current assay’s sensitivity and pathogen coverage. We concluded by providing our assessment of potential future directions for accelerating BSI pathogen identification and the antibiotic susceptibility test. While engineering solutions have enabled faster assay turnaround, further progress is still needed to supplant blood culture practice and guide appropriate antibiotic administration for BSI patients.

Mapping human calreticulin regions important for structural stability

Calreticulin (CALR) is a highly conserved multifunctional chaperone protein primarily present in the endoplasmic reticulum, where it regulates Ca²⁺ homeostasis. Recently, CALR has gained special interest for its diverse functions outside the endoplasmic reticulum, including the cell surface and extracellular space. Although high-resolution structures of CALR exist, it has not yet been established how different regions and individual amino acid residues contribute to structural stability of the protein. In the present study, we have identified key residues determining the structural stability of CALR. We used a Saccharomyces cerevisiae expression system to express and purify 50 human CALR mutants, which were analysed for several parameters including secretion titer, melting temperature (T_m), stability and oligomeric state. Our results revealed the importance of a previously identified small patch of conserved surface residues, amino acids 166-187 ("cluster 2") for structural stability of the human CALR protein. Two residues, Tyr172 and Asp187, were critical for maintaining the native structure of the protein. Mutant D187A revealed a severe drop in secretion titer, it was thermally unstable, prone to degradation, and oligomer formation. Tyr172 was critical for thermal stability of CALR and interacted with the third free Cys163 residue. This illustrates an unusual thermal stability of CALR dominated by Asp187, Tyr172 and Cys163, which may interact as part of a conserved structural unit. Besides structural clusters, we found a correlation of some measured parameter values in groups of CALR mutants that cause myeloproliferative neoplasms (MPN) and in mutants that may be associated with sudden unexpected death (SUD).

Multiplexed digital polymerase chain reaction as a powerful diagnostic tool

The digital polymerase chain reaction (dPCR) multiplexing method can simultaneously detect and quantify closely related deoxyribonucleic acid sequences in complex mixtures. The dPCR concept is continuously improved by the development of microfluidics and micro- and nanofabrication, and different complex techniques are introduced. In this review, we introduce dPCR techniques based on sample compartmentalization, droplet- and chip-based systems, and their combinations. We then discuss dPCR multiplexing methods in both laboratory research settings and advanced or routine clinical applications. We focus on their strengths and weaknesses with regard to the character of biological samples and to the required precision of such analysis, as well as showing recently published work based on those methods. Finally, we envisage possible future achievements in this field.

Data-driven noise modeling of digital DNA melting analysis enables prediction of sequence discriminating power

MOTIVATION

The need to rapidly screen complex samples for a wide range of nucleic acid targets, like infectious diseases, remains unmet. Digital High-Resolution Melt (dHRM) is an emerging technology with potential to meet this need by accomplishing broad-based, rapid nucleic acid sequence identification. Here, we set out to develop a computational framework for estimating the resolving power of dHRM technology for defined sequence profiling tasks. By deriving noise models from experimentally generated dHRM datasets and applying these to in silico predicted melt curves, we enable the production of synthetic dHRM datasets that faithfully recapitulate real-world variations arising from sample and machine variables. We then use these datasets to identify the most challenging melt curve classification tasks likely to arise for a given application and test the performance of benchmark classifiers.

RESULTS

This toolbox enables the in silico design and testing of broad-based dHRM screening assays and the selection of optimal classifiers. For an example application of screening common human bacterial pathogens, we show that human pathogens having the most similar sequences and melt curves are still reliably identifiable in the presence of experimental noise. Further, we find that ensemble methods outperform whole series classifiers for this task and are in some cases able to resolve melt curves with single-nucleotide resolution.

AVAILABILITY

Data and code available on https://github.com/lenlan/dHRM-noise-modeling.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

High-Level Multiplexing in Digital PCR with Intercalating Dyes by Coupling Real-Time Kinetics and Melting Curve Analysis

Digital polymerase chain reaction (dPCR) is a mature technique that has enabled scientific breakthroughs in several fields. However, this technology is primarily used in research environments with high-level multiplexing, representing a major challenge. Here, we propose a novel method for multiplexing, referred to as amplification and melting curve analysis (AMCA), which leverages the kinetic information in real-time amplification data and the thermodynamic melting profile using an affordable intercalating dye (EvaGreen). The method trains a system composed of supervised machine learning models for accurate classification, by virtue of the large volume of data from dPCR platforms. As a case study, we develop a new 9-plex assay to detect mobilized colistin resistant genes as clinically relevant targets for antimicrobial resistance. Over 100,000 amplification events have been analyzed, and for the positive reactions, the AMCA approach reports a classification accuracy of 99.33 ± 0.13%, an increase of 10.0% over using melting curve analysis. This work provides an affordable method of high-level multiplexing without fluorescent probes, extending the benefits of dPCR in research and clinical settings.

Improving Quantitative Power in Digital PCR through Digital High-Resolution Melting

Applying digital PCR (dPCR) technology to challenging clinical and industrial detection tasks has become more prevalent because of its capability for absolute quantification and rare target detection. However, practices learned from quantitative PCR (qPCR) that promote assay robustness and wide-ranging utility are not readily applied in dPCR. These include internal amplification controls to account for false-negative reactions and amplicon high-resolution melt (HRM) analysis to distinguish true positives from false positives. Incorporation of internal amplification controls in dPCR is challenging because of the limited fluorescence channels available on most machines, and the application of HRM analysis is hindered by the separation of heating and imaging functions on most dPCR systems. We use a custom digital HRM platform to assess the utility of HRM-based approaches for mitigation of false positives and false negatives in dPCR. We show that detection of an exogenous internal control using dHRM analysis reduces the inclusion of false-negative partitions, changing the calculated DNA concentration up to 52%. The integration of dHRM analysis enables classification of partitions that would otherwise be considered ambiguous "rain," which accounts for up to ∼3% and ∼10% of partitions in intercalating dye and hydrolysis probe dPCR, respectively. We focused on developing an internal control method that would be compatible with broad-based microbial detection in dPCR-dHRM. Our approach can be applied to a number of DNA detection methods including microbial profiling and may advance the utility of dPCR in clinical applications where accurate quantification is imperative.

Emerging Analytical Techniques for Rapid Pathogen Identification and Susceptibility Testing

In the face of looming threats from multi-drug resistant microorganisms, there is a growing need for technologies that will enable rapid identification and drug susceptibility profiling of these pathogens in health care settings. In particular, recent progress in microfluidics and nucleic acid amplification is pushing the boundaries of timescale for diagnosing bacterial infections. With a diverse range of techniques and parallel developments in the field of analytical chemistry, an integrative perspective is needed to understand the significance of these developments. This review examines the scope of new developments in assay technologies grouped by key enabling domains of research. First, we examine recent development in nucleic acid amplification assays for rapid identification and drug susceptibility testing in bacterial infections. Next, we examine advances in microfluidics that facilitate acceleration of diagnostic assays via integration and scale. Lastly, recentdevelopments in biosensor technologies are reviewed. We conclude this review with perspectives on the use of emerging concepts to develop paradigm-changing assays.

A 'culture' shift: Application of molecular techniques for diagnosing polymicrobial infections

With the advancement of microbiological discovery, it is evident that many infections, particularly bloodstream infections, are polymicrobial in nature. Consequently, new challenges have emerged in identifying the numerous etiologic organisms in an accurate and timely manner using the current diagnostic standard. Various molecular diagnostic methods have been utilized as an effort to provide a fast and reliable identification in lieu or parallel to the conventional culture-based methods. These technologies are mostly based on nucleic acid, proteins, or physical properties of the pathogens with differing advantages and limitations. This review evaluates the different molecular methods and technologies currently available to diagnose polymicrobial infections, which will help determine the most appropriate option for future diagnosis.

Digital High-Resolution Melt Platform for Rapid and Parallelized Molecule-by-Molecule Genetic Profiling

This work presents a microfluidic Digital High-Resolution Melt platform for absolute quantitation and sensitive detection of locus-specific sequence variations on a molecule-by-molecule basis. The platform provides a facile means for assessment of hundreds to thousands of single DNA copies by digitizing template molecules in a 4096 1-nL array microfluidic device and observing the sequence-dependent fluorescence changes during temperature ramping. The analytical capability of this platform is demonstrated in several applications, such as digital assay characterization, detection and assessment of DNA methylation heterogeneity, and detection of rare biomarkers at frequencies as low as 0.0005% target to background molecules.

A "Culture" Shift: Broad Bacterial Detection, Identification, and Antimicrobial Susceptibility Testing Directly from Whole Blood

BACKGROUND

The time required for bloodstream pathogen detection, identification (ID), and antimicrobial susceptibility testing (AST) does not satisfy the acute needs of disease management. Conventional methods take up to 3 days for ID and AST. Molecular diagnostics have reduced times for ID, but their promise to supplant culture is unmet because AST times remain slow. We developed a combined quantitative PCR (qPCR)-based ID+AST assay with sequential detection, ID, and AST of leading nosocomial bacterial pathogens.

METHODS

ID+AST was performed on whole blood samples by (a) removing blood cells, (b) brief bacterial enrichment, (c) bacterial detection and ID, and (d) species-specific antimicrobial treatment. Broad-spectrum qPCR of the internal transcribed spacer between the 16S and 23S was amplified for detection. High-resolution melting identified the species with a curve classifier. AST was enabled by Ct differences between treated and untreated samples.

RESULTS

A detection limit of 1 CFU/mL was achieved for Acinetobacter baumannii, Escherichia coli, Klebsiella pneumoniae, and Staphylococcus aureus. All species were accurately identified by unique melting curves. Antimicrobial minimum inhibitory concentrations were identified with Ct differences of ≥1 cycle. Using an RNA target allowed reduction of AST incubation time from 60 min to 5 min. Rapid-cycle amplification reduced qPCR times by 83% to 30 min.

CONCLUSIONS

Combined, sequential ID+AST protocols allow rapid and reliable detection, ID, and AST for the diagnosis of bloodstream infections, enabling conversion of empiric to targeted therapy by the second dose of antimicrobials.

A High-Resolution Digital DNA Melting Platform for Robust Sequence Profiling and Enhanced Genotype Discrimination

DNA melting analysis provides a rapid method for genotyping a target amplicon directly after PCR amplification. To transform melt genotyping into a broad-based profiling approach for heterogeneous samples, we previously proposed the integration of universal PCR and melt analysis with digital PCR. Here, we advanced this concept by developing a high-resolution digital melt platform with precise thermal control to accomplish reliable, high-throughput heat ramping of microfluidic chip digital PCR reactions. Using synthetic DNA oligos with defined melting temperatures, we characterized sources of melting variability and minimized run-to-run variations. Within-run comparisons throughout a 20,000-reaction chip revealed that high-melting-temperature sequences were significantly less prone to melt variation. Further optimization using bacterial 16S amplicons revealed a strong dependence of the number of melting transitions on the heating rate during curve generation. These studies show that reliable high-resolution melt curve genotyping can be achieved in digital, picoliter-scale reactions and demonstrate that rate-dependent melt signatures may be useful for enhancing automated melt genotyping.

Emerging Technologies for Molecular Diagnosis of Sepsis

Rapid and accurate profiling of infection-causing pathogens remains a significant challenge in modern health care. Despite advances in molecular diagnostic techniques, blood culture analysis remains the gold standard for diagnosing sepsis. However, this method is too slow and cumbersome to significantly influence the initial management of patients. The swift initiation of precise and targeted antibiotic therapies depends on the ability of a sepsis diagnostic test to capture clinically relevant organisms along with antimicrobial resistance within 1 to 3 h. The administration of appropriate, narrow-spectrum antibiotics demands that such a test be extremely sensitive with a high negative predictive value. In addition, it should utilize small sample volumes and detect polymicrobial infections and contaminants. All of this must be accomplished with a platform that is easily integrated into the clinical workflow. In this review, we outline the limitations of routine blood culture testing and discuss how emerging sepsis technologies are converging on the characteristics of the ideal sepsis diagnostic test. We include seven molecular technologies that have been validated on clinical blood specimens or mock samples using human blood. In addition, we discuss advances in machine learning technologies that use electronic medical record data to provide contextual evaluation support for clinical decision-making.

Massively parallel digital high resolution melt for rapid and absolutely quantitative sequence profiling

In clinical diagnostics and pathogen detection, profiling of complex samples for low-level genotypes represents a significant challenge. Advances in speed, sensitivity, and extent of multiplexing of molecular pathogen detection assays are needed to improve patient care. We report the development of an integrated platform enabling the identification of bacterial pathogen DNA sequences in complex samples in less than four hours. The system incorporates a microfluidic chip and instrumentation to accomplish universal PCR amplification, High Resolution Melting (HRM), and machine learning within 20,000 picoliter scale reactions, simultaneously. Clinically relevant concentrations of bacterial DNA molecules are separated by digitization across 20,000 reactions and amplified with universal primers targeting the bacterial 16S gene. Amplification is followed by HRM sequence fingerprinting in all reactions, simultaneously. The resulting bacteria-specific melt curves are identified by Support Vector Machine learning, and individual pathogen loads are quantified. The platform reduces reaction volumes by 99.995% and achieves a greater than 200-fold increase in dynamic range of detection compared to traditional PCR HRM approaches. Type I and II error rates are reduced by 99% and 100% respectively, compared to intercalating dye-based digital PCR (dPCR) methods. This technology could impact a number of quantitative profiling applications, especially infectious disease diagnostics.

Microbial Typing by Machine Learned DNA Melt Signatures

There is still an ongoing demand for a simple broad-spectrum molecular diagnostic assay for pathogenic bacteria. For this purpose, we developed a single-plex High Resolution Melt (HRM) assay that generates complex melt curves for bacterial identification. Using internal transcribed spacer (ITS) region as the phylogenetic marker for HRM, we observed complex melt curve signatures as compared to 16S rDNA amplicons with enhanced interspecies discrimination. We also developed a novel Naïve Bayes curve classification algorithm with statistical interpretation and achieved 95% accuracy in differentiating 89 bacterial species in our library using leave-one-out cross-validation. Pilot clinical validation of our method correctly identified the etiologic organisms at the species-level in 59 culture-positive mono-bacterial blood culture samples with 90% accuracy. Our findings suggest that broad bacterial sequences may be simply, reliably and automatically profiled by ITS HRM assay for clinical adoption.

Interrogation of Functional miRNA-Target Interactions by CRISPR/Cas9 Genome Engineering

Post-transcriptional silencing by microRNAs (miRNAs) is a critical constituent of eukaryotic gene regulation. miRNAs are short (~22nt) noncoding RNAs capable of specifically targeting the miRNA-induced-silencing-complex (miRISC) to transcripts bearing a complementary miRNA response element (MRE). Although recent methodological advances have greatly improved our understanding of miRNA biogenesis and the mechanisms by which miRNAs repress their cognate targets, exploring the physiological relevance of direct miRNA-target interactions in vivo has remained an outstanding challenge. Here we describe the experimental protocol underlying a novel approach, which allows direct interrogation of specific miRNA-MRE interactions by CRISPR/Cas9-mediated genome engineering. In this instance, the CRISPR/Cas9 system is first used to catalyze homology-directed replacement of candidate MREs with molecular barcodes at endogenous loci. Subsequently, the effect of MRE mutation on transcript abundance (i.e., MRE activity) can be rapidly evaluated by routine quantitative PCR. This strategy enables functional investigation of a putative miRNA-target pair in a pool of transiently transfected cells, obviating the need for generation of clonal cell lines or transgenic animals. This protocol can be implemented in any cell line in less than 2 weeks, and can readily be scaled up for multiplex studies. To facilitate the conceptual workflow underlying this strategy, we also describe a genome-wide resource for automated design and computational evaluation of CRISPR/Cas9 guide RNAs targeting all predicted MREs in various species (miR-CRISPR).

Nested Machine Learning Facilitates Increased Sequence Content for Large-Scale Automated High Resolution Melt Genotyping

High Resolution Melt (HRM) is a versatile and rapid post-PCR DNA analysis technique primarily used to differentiate sequence variants among only a few short amplicons. We recently developed a one-vs-one support vector machine algorithm (OVO SVM) that enables the use of HRM for identifying numerous short amplicon sequences automatically and reliably. Herein, we set out to maximize the discriminating power of HRM + SVM for a single genetic locus by testing longer amplicons harboring significantly more sequence information. Using universal primers that amplify the hypervariable bacterial 16 S rRNA gene as a model system, we found that long amplicons yield more complex HRM curve shapes. We developed a novel nested OVO SVM approach to take advantage of this feature and achieved 100% accuracy in the identification of 37 clinically relevant bacteria in Leave-One-Out-Cross-Validation. A subset of organisms were independently tested. Those from pure culture were identified with high accuracy, while those tested directly from clinical blood bottles displayed more technical variability and reduced accuracy. Our findings demonstrate that long sequences can be accurately and automatically profiled by HRM with a novel nested SVM approach and suggest that clinical sample testing is feasible with further optimization.

Multi-template polymerase chain reaction

PCR is a formidable and potent technology that serves as an indispensable tool in a wide range of biological disciplines. However, due to the ease of use and often lack of rigorous standards many PCR applications can lead to highly variable, inaccurate, and ultimately meaningless results. Thus, rigorous method validation must precede its broad adoption to any new application. Multi-template samples possess particular features, which make their PCR analysis prone to artifacts and biases: multiple homologous templates present in copy numbers that vary within several orders of magnitude. Such conditions are a breeding ground for chimeras and heteroduplexes. Differences in template amplification efficiencies and template competition for reaction compounds undermine correct preservation of the original template ratio. In addition, the presence of inhibitors aggravates all of the above-mentioned problems. Inhibitors might also have ambivalent effects on the different templates within the same sample. Yet, no standard approaches exist for monitoring inhibitory effects in multitemplate PCR, which is crucial for establishing compatibility between samples.

Melting analysis on microbeads in rapid temperature-gradient inside microchannels for single nucleotide polymorphisms detection

A continuous-flow microchip with a temperature gradient in microchannels was utilized to demonstrate spatial melting analysis on microbeads for clinical Single Nucleotide Polymorphisms (SNPs) genotyping on animal genomic DNA. The chip had embedded heaters and thermometers, which created a rapid and yet stable temperature gradient between 60 °C and 85 °C in a short distance as the detection region. The microbeads, which served as mobile supports carrying the target DNA and fluorescent dye, were transported across the temperature gradient. As the surrounding temperature increased, the fluorescence signals of the microbeads decayed with this relationship being acquired as the melting curve. Fast DNA denaturation, as a result of the improved heat transfer and thermal stability due to scaling, was also confirmed. Further, each individual microbead could potentially bear different sequences and pass through the detection region, one by one, for a series of melting analysis, with multiplex, high-throughput capability being possible. A prototype was tested with target DNA samples in different genotypes (i.e., wild and mutant types) with a SNP location from Landrace sows. The melting temperatures were obtained and compared to the ones using a traditional tube-based approach. The results showed similar levels of SNP discrimination, validating our proposed technique for scanning homozygotes and heterozygotes to distinguish single base changes for disease research, drug development, medical diagnostics, agriculture, and animal production.

Trainable high resolution melt curve machine learning classifier for large-scale reliable genotyping of sequence variants

High resolution melt (HRM) is gaining considerable popularity as a simple and robust method for genotyping sequence variants. However, accurate genotyping of an unknown sample for which a large number of possible variants may exist will require an automated HRM curve identification method capable of comparing unknowns against a large cohort of known sequence variants. Herein, we describe a new method for automated HRM curve classification based on machine learning methods and learned tolerance for reaction condition deviations. We tested this method in silico through multiple cross-validations using curves generated from 9 different simulated experimental conditions to classify 92 known serotypes of Streptococcus pneumoniae and demonstrated over 99% accuracy with 8 training curves per serotype. In vitro verification of the algorithm was tested using sequence variants of a cancer-related gene and demonstrated 100% accuracy with 3 training curves per sequence variant. The machine learning algorithm enabled reliable, scalable, and automated HRM genotyping analysis with broad potential clinical and epidemiological applications.