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The number of genetically modified organisms (GMOs) on the market is steadily increasing. Because of regulation of cultivation and trade of GMOs in several countries, there is pressure for their accurate detection and quantification. Today, DNA-based approaches are more popular for this purpose than protein-based methods, and real-time quantitative PCR (qPCR) is still the gold standard in GMO analytics. However, digital PCR (dPCR) offers several advantages over qPCR, making this new technique appealing also for GMO analysis. This critical review focuses on the use of dPCR for the purpose of GMO quantification and addresses parameters which are important for achieving accurate and reliable results, such as the quality and purity of DNA and reaction optimization. Three critical factors are explored and discussed in more depth: correct classification of partitions as positive, correctly determined partition volume, and dilution factor. This review could serve as a guide for all laboratories implementing dPCR. Most of the parameters discussed are applicable to fields other than purely GMO testing.

The value assignment for properties of six certified reference materials (ERM-AD623a–f), each containing a plasmid DNA solution ranging from 1 million to 10 copies per μL, by using digital PCR (dPCR) with the BioMark™ HD System (Fluidigm) has been verified by applying droplet digital PCR (ddPCR) using the QX100 system (Bio-Rad). One of the critical factors in the measurement of copy number concentrations by digital PCR is the partition volume. Therefore, we determined the average droplet volume by optical microscopy, revealing an average droplet volume that is 8 % smaller than the droplet volume used as the defined parameter in the QuantaSoft software version 1.3.2.0 (Bio-Rad) to calculate the copy number concentration. This observation explains why copy number concentrations estimated with ddPCR and using an average droplet volume predefined in the QuantaSoft software were systematically lower than those measured by dPCR, creating a significant bias between the values obtained by these two techniques. The difference was not significant anymore when the measured droplet volume of 0.834 nL was used to estimate copy number concentrations. A new version of QuantaSoft software (version 1.6.6.0320), which has since been released with Bio-Rad’s new QX200 systems and QX100 upgrades, uses a droplet volume of 0.85 nL as a defined parameter to calculate copy number concentration. Graphical Abstract Monolayer of droplets generated by the droplet generator and observed under an optical microscope

Coccidioidomycosis (Valley fever), caused by Coccidioides spp. fungi, is a reemerging, neglected fungal disease endemic to arid and semiarid regions of the Americas. Environmental detection remains challenging because of spatial heterogeneity, seasonal variability, low DNA abundance, PCR inhibitors, and lack of standardized methods. We conducted environmental surveillance in Baja California, Mexico, an understudied region near the US-Mexico border, by collecting 74 soil samples from active rodent burrows across 5 locations. We evaluated droplet digital PCR (ddPCR) for Coccidioides detection and compared ddPCR with nested PCR targeting the internal transcribed spacer 1 region. ddPCR demonstrated greater sensitivity, detecting Coccidioides spp. DNA at all sampling sites, whereas nested PCR detected Coccidioides spp. DNA from only 1 site. Although additional work is required to rigorously quantify sensitivity and specificity, ddPCR could help identify Coccidioides environmental hotspots, thus enabling public health interventions, such as warning communities of areas that pose higher risk for infection.

In most people living with HIV (PLWH) on effective antiretroviral therapy (ART), cell-associated viral transcripts are readily detectable in CD4+ T cells despite the absence of viremia. Quantification of HIV RNA species provides insights into the transcriptional activity of proviruses that persist in cells and tissues throughout the body during ART (‘HIV reservoir’). One such technique for HIV RNA quantitation, ‘HIV transcription profiling’, developed in the Yukl laboratory, measures a series of HIV RNA species using droplet digital PCR. To take advantage of advances in digital (d)PCR, we adapted the ‘HIV transcription profiling’ technique to Qiagen’s dPCR platform (QIAcuity) and compared its performance to droplet digital (dd)PCR (Bio-Rad QX200 system). Using RNA standards, the two technologies were tested in parallel and assessed for multiple parameters including sensitivity, specificity, linearity, and intra- and inter-assay variability. The newly validated dPCR assays were then applied to samples from PLWH to determine HIV transcriptional activity relative to HIV reservoir size. We report that HIV transcriptional profiling was readily adapted to dPCR and assays performed similarly to ddPCR, with no differences in assay characteristics. We applied these assays in a cohort of 23 PLWH and found that HIV reservoir size, based on genetically intact proviral DNA, does not predict HIV transcriptional activity. In contrast, levels of total DNA correlated with levels of most HIV transcripts (initiated, proximally and distally elongated, unspliced, and completed, but not multiply spliced), suggesting that a considerable proportion of HIV transcripts likely originate from defective proviruses. These findings may have implications for measuring and assessing curative strategies and clinical trial outcomes.

Hepatocellular carcinoma (HCC) circulating tumor cells (CTCs) exhibit significant phenotypic heterogeneity and diverse gene expression profiles due to epithelial‐mesenchymal transition (EMT). However, current detection methods lack the capacity for simultaneous quantification of multidimensional biomarkers, impeding a comprehensive understanding of tumor biology and dynamic changes. Here, the CTC Digital Simultaneous Cross‐dimensional Output and Unified Tracking (d‐SCOUT) technology is introduced, which enables simultaneous quantification and detailed interpretation of HCC transcriptional and phenotypic biomarkers. Based on self‐developed multi‐real‐time digital PCR (MRT‐dPCR) and algorithms, d‐SCOUT allows for the unified quantification of Asialoglycoprotein Receptor (ASGPR), Glypican‐3 (GPC‐3), and Epithelial Cell Adhesion Molecule (EpCAM) proteins, as well as Programmed Death Ligand 1 (PD‐L1), GPC‐3, and EpCAM mRNA in HCC CTCs, with good sensitivity (LOD of 3.2 CTCs per mL of blood) and reproducibility (mean %CV = 1.80–6.05%). In a study of 99 clinical samples, molecular signatures derived from HCC CTCs demonstrated strong diagnostic potential (AUC = 0.950, sensitivity = 90.6%, specificity = 87.5%). Importantly, by integrating machine learning, d‐SCOUT allows clustering of CTC characteristics at the mRNA and protein levels, mapping normalized heterogeneous 2D molecular profiles to assess HCC metastatic risk. Dynamic digital tracking of eight HCC patients undergoing different treatments visually illustrated the therapeutic effects, validating this technology's capability to quantify the treatment efficacy. CTC d‐SCOUT enhances understanding of tumor biology and HCC management. This study introduces the d‐SCOUT technology, which enables simultaneous quantification of mRNA and protein biomarkers in HCC circulating tumor cells. By integrating multi‐real‐time digital PCR and machine learning, d‐SCOUT provides detailed heterogeneous 2D molecular profiling for improved HCC diagnosis and treatment monitoring.

represents a new prognostic marker in chronic lymphocytic leukaemia (CLL). The low sensitivity of the current methods may increase the risk of false-negative results, particularly in patients with low allelic burden. This study compared two methods of the assessment including droplet digital PCR (ddPCR) and amplification-refractory mutation system PCR (ARMS-PCR) untreated CLL patients. This study included 319 untreated CLL patients. Two PCR-based methods; ddPCR and ARMS-PCR were performed to assess the mutational status of . The Mann-Whitney, Fisher's exact test, Kruskal-Wallis, Kaplan-Meier, Log rank tests and multivariate Cox proportional hazard regression model were used to analyze collected data. We proved that ddPCR increased the detectability of the compared to ARMS-PCR in CLL (18.55% vs 6%). We showed a shorter time to first treatment (TTFT) in the group of patients compared to the defined by ddPCR (1.5 vs 33 months, p=0.01). The TTFT survival curves analysis in subgroups divided according to the mutational status of and assessed by ddPCR discriminated group with the best prognosis: . Multivariate analysis revealed that the mutational status of represented an independent prognostic factor for TTFT, while determined by ddPCR constituted as a dependent prognostic factor for TTFT. The selection of the precise method of detection as ddPCR might significantly improve prognostic stratification of CLL patient. Assessment of might be relevant to more accurate discrimination of prognostic groups of CLL patients, especially in harboring irrespective of the quantity of allelic burden.

The evolution of antimicrobial resistance (AMR) presents substantial challenges to global medical health systems. Neisseria gonorrhoeae (N. gonorrhoeae), in particular, has developed resistance to all currently available antimicrobials. Addressing this issue necessitates not only discovering new antimicrobials but also deepening the understanding of bacterial responses to these agents, which can lead to new markers for rapid antimicrobial susceptibility testing (AST). Such advancements can enhance treatment outcomes and promote antimicrobial stewardship. In this study, single‐cell techniques, including live‐cell imaging, flow cytometry, and digital polymerase chain reaction (PCR) are utilized, to investigate the lysis dynamics and molecular features of N. gonorrhoeae upon exposure to β‐lactam antimicrobials. Distinct patterns of bacterial lysis and DNA fragmentation are uncovered in susceptible strains. Leveraging these discoveries, a microfluidic dual‐digital PCR approach that combines single‐cell and single‐molecule analyses, facilitate rapid and efficient phenotypic molecular AST for N. gonorrhoeae against β‐lactams is developed. This proof‐of‐concept validation demonstrates the effectiveness of the method in accessing antimicrobial susceptibility across a range of bacterial strains, contributing valuable insights for advancing the battle against AMR. Utilizing DNA fragmentation and abrupt bacterial lysis induced by β‐lactam antimicrobials, a microfluidic dual‐digital PCR that integrates single‐cell and single‐molecule analyses for rapid phenotypic molecular antimicrobial susceptibility testing of Neisseria gonorrhoeae to β‐lactams is developed.

Digital PCR (dPCR), as a new technology in the field of genetically modified (GM) organism (GMO) testing, enables determination of absolute target copy numbers. The purpose of our study was to test the transferability of methods designed for quantitative PCR (qPCR) to dPCR and to carry out an inter-laboratory comparison of the performance of two different dPCR platforms when determining the absolute GM copy numbers and GM copy number ratio in reference materials certified for GM content in mass fraction. Overall results in terms of measured GM% were within acceptable variation limits for both tested dPCR systems. However, the determined absolute copy numbers for individual genes or events showed higher variability between laboratories in one third of the cases, most possibly due to variability in the technical work, droplet size variability, and analysis of the raw data. GMO quantification with dPCR and qPCR was comparable. As methods originally designed for qPCR performed well in dPCR systems, already validated qPCR assays can most generally be used for dPCR technology with the purpose of GMO detection.

Water contamination by viruses has an increasing worldwide impact on human health, and has led to requirements for accurate and quantitative molecular tools. Here, we report the first one-step reverse-transcription droplet digital PCR-based absolute quantification of a RNA virus (rotavirus) in different types of surface water samples. This quantification method proved to be more precise and more tolerant to inhibitory substances than the benchmarking reverse-transcription real-time PCR (RT-qPCR), and needs no standard curve. This new tool is fully amenable for the quantification of viruses in the particularly low concentrations usually found in water samples.