Advancing quantitative PCR with color cycle multiplex amplification.

Quantitative PCR (qPCR) is the gold standard for detection and quantitation of known DNA targets, but the scarcity of spectrally distinct fluorophores and filter sets limits the number of detectable targets. Here, we introduce color cycle multiplex amplification (CCMA) to significantly increase the number of detectable DNA targets in a single qPCR reaction using standard instrumentation. In CCMA, presence of one DNA target species results in a pre-programmed pattern of fluorescence increases. This pattern is distinguished by cycle thresholds (Cts) through rationally designed delays in amplification. For example, we design an assay wherein Staphylococcus aureus sequentially induces FAM, then Cy5.5, then ROX fluorescence increases with more than 3 cycles between each signal. CCMA offers notably higher potential for multiplexing because it uses fluorescence permutation rather than combination. With 4 distinct fluorescence colors, CCMA theoretically allows the detection of up to 136 distinct DNA target sequences using fluorescence permutation. Experimentally, we demonstrated a single-tube qPCR assay screening 21 sepsis-related bacterial DNA targets in samples of blood, sputum, pleural effusion and bronchoalveolar lavage fluid, with 89% clinical sensitivity and 100% clinical specificity, showing its potential as a powerful tool for advanced quantitative screening in molecular diagnostics.

Microsatellite Instability Detection in Cancer: A Multiplex qPCR Approach that Obviates the Need for Matching Normal Samples.

BACKGROUND: Microsatellite instability (MSI) indicates DNA mismatch repair deficiency in certain types of cancer, such as colorectal cancer. The current gold standard technique, PCR-capillary electrophoresis (CE), requires matching normal samples and specialized instrumentation. We developed VarTrace, a rapid and low-cost quantitative PCR (qPCR) assay, to evaluate MSI using solely the tumor sample DNA, obviating the requirement for matching normal samples. METHODS: One hundred and one formalin-fixed paraffin-embedded (FFPE) tumor samples were tested using VarTrace and compared with the Promega OncoMate assay utilizing PCR-CE. Tumor percentage limit of detection was evaluated on contrived samples derived from clinical high MSI (MSI-H) samples. Analytical sensitivity, specificity, limit of detection, and input requirements were assessed using synthetic commercial reference standards. RESULTS: VarTrace successfully analyzed all 101 clinical FFPE samples, demonstrating 100% sensitivity and 98% specificity compared to OncoMate. It detected MSI-H with 97% accuracy down to 10% tumor. Analytical studies using synthetic samples showed a limit of detection of 5% variant allele frequency and a limit of input of 0.5 ng. CONCLUSIONS: This study validates VarTrace as a swift, accurate, and economical assay for MSI detection in samples with low tumor percentages without the need for matching normal DNA. VarTrace's capacity for highly sensitive MSI analysis holds potential for enhancing the efficiency of clinical work flows and broadening the availability of this test.

Validation of the Burn Survivor Fear-Avoidance Questionnaire and Its Association With Pain Intensity, Catastrophizing, and Disability.

According to the Fear-avoidance (FA) model, FA beliefs can lead to disability due to avoidance of activities expected to result in pain or further injury. Extensive research on the relationship of FA, pain, catastrophizing, and disability has been generated with patients suffering from chronic neck and back pain, but little research has been conducted with burn survivors. To address this need, the Burn Survivor FA Questionnaire (BSFAQ) was developed (Langlois J, Vincent-Toskin, S, Duchesne, P et al. Fear-avoidance beliefs and behaviors of burn survivors: A mixed-methods approach. Burns 2021;47:175-89.) but has not been validated. Thus, the primary objective of this study was to investigate the construct validity of the BSFAQ among burn survivors. The secondary objective was to examine the relationship between FA and 1) pain intensity and 2) catastrophizing at baseline, 3 months and 6 months postburn, and 3) disability among burn survivors at 6 months postburn. A prospective mixed-methods approach was used to examine the construct validity by comparing the quantitative scores of the BSFAQ to independently performed qualitative interviews of burn survivors (n = 31) that explored their lived experiences, to determine if the BSFAQ discriminated those who had, from those who did not have FA beliefs. Data for the secondary objective, scores of burn survivors (n = 51) pain intensity (numeric rating scale), catastrophizing (pain catastrophizing scale), and disability (Burn Specific Health Scale-brief), were collected through a retrospective chart review. For the primary objective, Wilcoxon rank sum test results showed a statistically significant difference (P = .015) between the BSFAQ scores of participants who were identified from the qualitative interviews as fear avoidant compared to those who were identified as non fear avoidant, with a receiver operating characteristic curve indicating that the BSFAQ correctly predicted FA 82.4% of the time. For the secondary objective, Spearman correlation test results showed a moderate correlation between FA and 1) pain at baseline (r = .466, P = .002), 2) catastrophizing thoughts over time (r = .557, P = .000; r = .470, P = .00; r = .559, P = .002 respectively at each time point), and 3) disability at 6 months postburn (r = -.643, P = .000). These results support that the BSFAQ is able to discriminate which burn survivors are experiencing FA beliefs. It is also consistent with the FA model since burn survivors who express FA are more likely to report higher levels of pain early during their recovery that correlates with persistently elevated catastrophizing thoughts and ultimately results in higher self-reported disability. The BSFAQ demonstrates construct validity and is able to correctly predict fear-avoidant burn survivors; however, additional research is required to further examine the BSFAQ's clinimetric properties.

Ensemble of nucleic acid absolute quantitation modules for copy number variation detection and RNA profiling.

Current gold standard for absolute quantitation of a specific DNA sequence is droplet digital PCR (ddPCR), which has been applied to copy number variation (CNV) detection. However, the number of quantitation modules in ddPCR is limited by fluorescence channels, which thus limits the CNV sensitivity due to sampling error following Poisson distribution. Here we develop a PCR-based molecular barcoding NGS approach, quantitative amplicon sequencing (QASeq), for accurate absolute quantitation scalable to over 200 quantitation modules. By attaching barcodes to individual target molecules with high efficiency, 2-plex QASeq exhibits higher and more consistent conversion yield than ddPCR in absolute molecule count quantitation. Multiplexed QASeq improves CNV sensitivity allowing confident distinguishment of 2.05 ploidy from normal 2.00 ploidy. We apply multiplexed QASeq to serial longitudinal plasma cfDNA samples from patients with metastatic ERBB2+ (HER2+ ) breast cancer seeking association with tumor progression. We further show an RNA QASeq panel for targeted expression profiling.

Cost-Efficient Sequence-Based Nonextensible Oligonucleotide in Real-Time PCR and High-Throughput Sequencing.

Molecular detection of disease-associated mutations, especially those with low abundance, is essential for academic research and clinical diagnosis. Certain variant detection methods reach satisfactory sensitivity and specificity in detecting rare mutations based on the introduction of blocking oligos to prevent the amplification of wild-type or unwanted templates, thus selectively amplifying and enriching the mutations. These blocking oligos usually suppress PCR amplification through the 3' chemical modifications, with high price, slow synthesis, and reduced purity. Herein, we introduce chemistry-free designs to block enzymatic extension during PCR by the steric hindrance from the secondary structures attached to the 3' end of the oligos (nonextensible oligonucleotide, NEO). We demonstrated that NEO efficiently prohibited the extension of both Taq and high-fidelity DNA polymerases. By further applying NEO as blockers in blocker displacement amplification (BDA) qPCR, multiplex BDA (mBDA) NGS, and quantitative BDA (QBDA) NGS methods, we showed that NEO blockers had performance comparable with previously validated chemical modifications. Comparison experiments using QBDA with NEO blockers and droplet digital PCR (ddPCR) on clinical formalin-fixed paraffin-embedded (FFPE) samples exhibited 100% concordance. Lastly, the ability of NEO to adjust plex uniformity through changes of PCR amplification efficiency was demonstrated in an 80-plex NGS panel.

Limitations and opportunities of technologies for the analysis of cell-free DNA in cancer diagnostics.

Cell-free DNA (cfDNA) in the circulating blood plasma of patients with cancer contains tumour-derived DNA sequences that can serve as biomarkers for guiding therapy, for the monitoring of drug resistance, and for the early detection of cancers. However, the analysis of cfDNA for clinical diagnostic applications remains challenging because of the low concentrations of cfDNA, and because cfDNA is fragmented into short lengths and is susceptible to chemical damage. Barcodes of unique molecular identifiers have been implemented to overcome the intrinsic errors of next-generation sequencing, which is the prevailing method for highly multiplexed cfDNA analysis. However, a number of methodological and pre-analytical factors limit the clinical sensitivity of the cfDNA-based detection of cancers from liquid biopsies. In this Review, we describe the state-of-the-art technologies for cfDNA analysis, with emphasis on multiplexing strategies, and discuss outstanding biological and technical challenges that, if addressed, would substantially improve cancer diagnostics and patient care.

Calibration-free NGS quantitation of mutations below 0.01% VAF.

Quantitation of rare somatic mutations is essential for basic research and translational clinical applications including minimal residual disease (MRD) detection. Though unique molecular identifier (UMI) has suppressed errors for rare mutation detection, the sequencing depth requirement is high. Here, we present Quantitative Blocker Displacement Amplification (QBDA) which integrates sequence-selective variant enrichment into UMI quantitation for accurate quantitation of mutations below 0.01% VAF at only 23,000X depth. Using a panel of 20 genes recurrently altered in acute myeloid leukemia, we demonstrate quantitation of various mutations including single base substitutions and indels down to 0.001% VAF at a single locus with less than 4 million sequencing reads, allowing sensitive MRD detection in patients during complete remission. In a pan-cancer panel and a melanoma hotspot panel, we detect mutations down to 0.1% VAF using only 1 million reads. QBDA provides a convenient and versatile method for sensitive mutation quantitation using low-depth sequencing.

Highly multiplexed rapid DNA detection with single-nucleotide specificity via convective PCR in a portable device.

Assays for the molecular detection of nucleic acids are typically constrained by the level of multiplexing (this is the case for the quantitative polymerase chain reaction (qPCR) and for isothermal amplification), turnaround times (as with microarrays and next-generation sequencing), quantification accuracy (isothermal amplification, microarrays and nanopore sequencing) or specificity for single-nucleotide differences (microarrays and nanopore sequencing). Here we show that a portable and battery-powered PCR assay performed in a toroidal convection chamber housing a microarray of fluorescently quenched oligonucleotide probes allows for the rapid and sensitive quantification of multiple DNA targets with single-nucleotide discrimination. The assay offers a limit of detection of 10 DNA copies within 30 min of turnaround time and a dynamic range spanning 4 orders of magnitude of DNA concentration, and we show its performance by detecting 20 genomic loci and 30 single-nucleotide polymorphisms in human genomic DNA samples, and 15 bacterial species in clinical isolates. Portable devices for the fast and highly multiplexed detection of nucleic acids may offer advantages in point-of-care diagnostics.

A deep learning model for predicting next-generation sequencing depth from DNA sequence.

Targeted high-throughput DNA sequencing is a primary approach for genomics and molecular diagnostics, and more recently as a readout for DNA information storage. Oligonucleotide probes used to enrich gene loci of interest have different hybridization kinetics, resulting in non-uniform coverage that increases sequencing costs and decreases sequencing sensitivities. Here, we present a deep learning model (DLM) for predicting Next-Generation Sequencing (NGS) depth from DNA probe sequences. Our DLM includes a bidirectional recurrent neural network that takes as input both DNA nucleotide identities as well as the calculated probability of the nucleotide being unpaired. We apply our DLM to three different NGS panels: a 39,145-plex panel for human single nucleotide polymorphisms (SNP), a 2000-plex panel for human long non-coding RNA (lncRNA), and a 7373-plex panel targeting non-human sequences for DNA information storage. In cross-validation, our DLM predicts sequencing depth to within a factor of 3 with 93% accuracy for the SNP panel, and 99% accuracy for the non-human panel. In independent testing, the DLM predicts the lncRNA panel with 89% accuracy when trained on the SNP panel. The same model is also effective at predicting the measured single-plex kinetic rate constants of DNA hybridization and strand displacement.

Predicting DNA hybridization kinetics from sequence.

Hybridization is a key molecular process in biology and biotechnology, but so far there is no predictive model for accurately determining hybridization rate constants based on sequence information. Here, we report a weighted neighbour voting (WNV) prediction algorithm, in which the hybridization rate constant of an unknown sequence is predicted based on similarity reactions with known rate constants. To construct this algorithm we first performed 210 fluorescence kinetics experiments to observe the hybridization kinetics of 100 different DNA target and probe pairs (36 nt sub-sequences of the CYCS and VEGF genes) at temperatures ranging from 28 to 55 °C. Automated feature selection and weighting optimization resulted in a final six-feature WNV model, which can predict hybridization rate constants of new sequences to within a factor of 3 with ∼91% accuracy, based on leave-one-out cross-validation. Accurate prediction of hybridization kinetics allows the design of efficient probe sequences for genomics research.