Positive Droplets Research Articles

Utility of circulating plasma cell-free DNA for detection and quantification of brain arteriovenous malformations

Brain arteriovenous malformations (BAVMs) are primarily associated with somatic activating pathogenic KRAS variants. KRAS-targeting therapy is emerging as a potential treatment for BAVMs. However, current molecular diagnosis relies on surgically obtained tissue samples. Here, we investigated the feasibility of using cell-free DNA (cfDNA) from the blood for molecular diagnosis in sporadic BAVM patients. We included 31 BAVM patients and extracted genomic DNA from BAVM tissues, while paired cfDNA was isolated from plasma. Additionally, fifty plasma cfDNA samples from unaffected individuals served as controls. By utilizing droplet digital polymerase chain reaction (ddPCR), we tested KRAS c.35 G>A p.Gly12Asp (p.G12D) and c.35 G>T p.Gly12Val (p.G12V) variants in all samples. Among the 31 BAVM samples, KRAS somatic mutations were identified in 24 patients (77 %), comprising 79 % (19 out of 24) p.G12D and 21 % (5 out of 24) p.G12V variants. The variant frequencies (VFs) ranged from 0.227 % to 8.327 %, with positive droplets ranging from 17 to 1025. 63 % (15 out of 24) of patients with KRAS mutations had≥2 positive droplets in their cfDNA samples. In contrast, in none of the 50 control samples more than two positive droplets were detected. Specifically, 13 plasma samples (68 %) were positive for p.G12D mutation. The VFs in plasma cfDNA samples ranged from 0.042 % to 5.172 %. Furthermore, ddPCR demonstrated a sensitivity of 63 %, specificity of 100 %, positive predictive value of 100 %, and negative predictive value of 81 % for detecting plasma cfDNA. The VFs in mutant tissues had an inverse trend with the largest nidus sizes, volumes, and patient age, while an opposite trend was observed in plasma cfDNA. Taken together, we successfully detected pathogenic somatic activating KRAS variants in cfDNA obtained from the plasma of BAVM patients. The diagnostic utility of liquid biopsy for BAVMs will facilitate the development of personalized therapeutic approaches and offer opportunities for novel strategies to halt, slow, or delay disease progression.

Integrated Droplet-Based Digital Loop-Mediated Isothermal Amplification Microfluidic Chip with Droplet Generation, Incubation, and Continuous Fluorescence Detection.

This study integrated sample partition, incubation, and continuous fluorescence detection on a single microfluidic chip for droplet-based digital Loop-Mediated Isothermal Amplification (LAMP) of nucleic acids. This integration eliminated the need to transfer reactions between different platforms, avoiding sample contamination and loss. Prior to the reaction, filling the channels with an oil phase and adding a glass cover slip on top of the chip overcame the problem of bubble generation in the channels during the LAMP reaction due to heating. Additionally, using two fluorescence intensity thresholds enabled simultaneous detection and counting of positive and negative droplets within a single fluorescence detection channel. The chip can partition approximately 6000 droplets from a 5 µL sample within 10 min, with a droplet diameter of around 110 µm and a coefficient of variation (CV) value of 0.82%. Staphylococcus aureus was quantified via the proposed platform. The results demonstrated a highly accurate correlation coefficient (R = 0.9998), and the detection limit reached a concentration of 1.7 × 102 copies/µL. The entire process of the droplet digital LAMP reaction, from droplet generation to incubation to quantitative results, took a maximum of 70 min.

Evaluation of Droplet Digital PCR for the Detection of BRAF V600E in Fine-Needle Aspiration Specimens of Thyroid Nodules.

Droplet digital (dd)PCR is a new-generation PCR technique with high precision and sensitivity; however, the positive and negative droplets are not always effectively separated because of the "rain" phenomenon. We aimed to develop a practical optimization and evaluation process for the ddPCR assay and to apply it to the detection of BRAF V600E in fine-needle aspiration (FNA) specimens of thyroid nodules, as an example. We optimized seven ddPCR parameters that can affect "rain." Analytical and clinical performance were analyzed based on histological diagnosis after thyroidectomy using a consecutive prospective series of 242 FNA specimens. The annealing time and temperature, number of PCR cycles, and primer and probe concentrations were found to be more important considerations for assay optimization than the denaturation time and ramp rate. The limit of blank and 95% limit of detection were 0% and 0.027%, respectively. The sensitivity of ddPCR for histological papillary thyroid carcinoma (PTC) was 82.4% (95% confidence interval [CI], 73.6%-89.2%). The pooled sensitivity of BRAF V600E in FNA specimens for histological PTC was 78.6% (95% CI, 75.9%-81.2%, I2=60.6%). We present a practical approach for optimizing ddPCR parameters that affect the separation of positive and negative droplets to reduce rain. Our approach to optimizing ddPCR parameters can be expanded to general ddPCR assays for specific mutations in clinical laboratories. The highly sensitive ddPCR can compensate for uncertainty in cytological diagnosis by detecting low levels of BRAF V600E.

Highly precisive digital polymerase chain reaction based on inkjet printer

BackgroundTo eliminate the indeterminate droplet partitions that highly obfuscate the distinguishment of the positive and the negative partitions in digital polymerase chain reaction (dPCR), the highly precisive droplets array of dPCR was developed via the inkjet printer coupled with the X-Y stage. ResultsThe highly precisive droplets could be generated by the inkjet printer and the droplets array would be arranged by the movable X-Y stage at the same time. The highly precisive droplets array in a volume of at least 20,000 droplets could be obtained with a coefficients of variation (CV) of 1.57 % for the decided droplets in a diameter of 145.16 μm. The CV of the fluorescence intensities of droplets in array could be lowered to 3.17 % for the droplets of 5 μM fluorescein sodium solution, which guaranteed the clear identification of the positive droplets in the absolute quantification of dPCR. In addition, the thermal amplification in dPCR was designed to implement at the same sheet to avoid the delivery of the droplet partitions. The highly precisive dPCR was demonstrated through the wide linear range of more than 103 in the measurement of the model GAPDH gene with a low limitation of detection (LOD) of 0.60 copies/μL. Finally, the practicality of developed dPCR was confirmed by the measurement of the human mammaglobin (hMAM) gene in MCF-7 cell as well. Significance and noveltyThe successful development of the highly precisive droplet array through the inkjet printer coupled with X-Y stage eliminated the indeterminate positive droplet partitions in dPCR. It paved a way to develop the compact and highly sensitive dPCR.

Digital Sort-Enabled Counting Allows Absolute Electrical Quantification of Target Nucleic Acid.

Quantitative nucleic acid amplification tests are of great importance for diagnostics, but current approaches require complex and costly optical setups that limit their nonlaboratory applications. Herein we describe the implementation of a microfluidics platform that can perform binary DNA-amplification-activated droplet sorting. The digital sort-enabled counting (DISCO) platform enables label-free absolute quantification of the nucleic acid. This is achieved by provoking a pH change in droplets through a loop-mediated isothermal amplification (LAMP) reaction, followed by using sorting by interfacial tension (SIFT) to direct positive and negative droplets to different outlets. With the use of on-chip electrodes at both outlets, we demonstrate that the digital electrical counting of target DNA and RNA can be realized. DISCO is a promising approach for realizing sensitive nucleic acid quantification in point-of-care settings.

Abstract 3750: Dual isolation and profiling of circulating tumor cells and circulating tumor DNA from a single liquid sample using the Genesis Cell Isolation System and Droplet Digital PCR

Abstract Introduction: Recent advances in technology support the use of liquid biopsy as a noninvasive method for cancer monitoring and diagnosis. Circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA) are both regarded as key liquid biopsy biomarkers, offering significant disease-related molecular insights. Despite their potential, dual isolation and profiling have been challenged in a clinical setting by the lack of established platforms and optimized protocols that support this approach. The Genesis System with Celselect SlidesTM captures CTCs based on size and deformability, then the enriched cells can be recovered for subsequent downstream analysis. Concurrently, Droplet Digital PCR (ddPCRTM) can be utilized to interrogate cell-free DNA (cfDNA) to determine the presence of a range of DNA aberrations present in ctDNA. This study, using a model lung CTC-ctDNA sample, illustrates the feasibility of this dual-biomarker approach to detect cancer-associated nucleic acid mutations simultaneously with CTC enumeration, using commercially available kits and instruments. Methods: To model lung cancer samples, blood samples were prepared by spiking in two model circulating markers, cultured cancer cells (model CTCs, human ㅣlung cancer cell line A549) and fragmented genomic DNA containing EGFR mutations (model ctDNA, multiplexed 23 ctDNA [~145 bp band > 95%]), into whole blood from a healthy donor prior to enrichment/extraction. Plasma isolation from the whole blood model samples was performed using either two-step centrifugation or Ficoll-Paque PLUS solution for plasma/buffy coat separation. The extracted plasma layer was used for ctDNA separation, while the remaining cell layer was used for CTC isolation on the Genesis System with a modified protocol. Results: The Genesis System isolated 78-79% of spiked cancer cells in whole blood at a concentration of 40 cells/ml. Despite additional plasma separation steps and liquid handling, the system isolated CTCs spiked in with minimal cell loss (<6%). With an optimized ctDNA extraction, more than 70% of spiked ctDNA in blood samples in the size range of 120-220 bp was extracted. Using the Bio-Rad QX200 ddPCR System and ddPCR EGFR T790M Mutation Detection Assay, blood samples with the model ctDNA spiked in showed positive droplets (17 copies) for EGFR T790M while control blood samples without model ctDNA spiked in showed no positive droplets. Discussion and Conclusions: This work illustrates an optimized protocol for the dual isolation and profiling of CTCs and ctDNA from a single blood sample by using an automated CTC isolation platform and high-sensitivity ddPCR based ctDNA mutation detection. This protocol enables comprehensive liquid biopsy studies using a single blood sample from cancer patients with readily available instruments and kits, which can be applied to many other cancers. Citation Format: Yoon-Tae Kang, Abiodun Bodunrin, Errile Pusod, Kris Simonyi, Adam Corner, David Coe, Michelle Racey, Elizabeth J. Dreskin. Dual isolation and profiling of circulating tumor cells and circulating tumor DNA from a single liquid sample using the Genesis Cell Isolation System and Droplet Digital PCR [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3750.

A digital droplet PCR detection technique based on filter faster R-CNN

To propose a method for mitigate the impact of anomaly points (such as dust, bubbles, scratches on the chip surface, and minor indentations) in images on the results of digital droplet PCR (ddPCR) detection to achieve high-throughput, stable, and accurate detection. We propose a Filter Faster R-CNN ddPCR detection model, which employs Faster R-CNN to generate droplet prediction boxes followed by removing the anomalies within the positive droplet prediction boxes using an outlier filtering module (Filter). Using a plasmid carrying a norovirus fragment as the template, we established a ddPCR dataset for model training (2462 instances, 78.56%) and testing (672 instances, 21.44%). Ablation experiments were performed to test the effectiveness of 3 filtering branches of the Filter for anomaly removal on the validation dataset. Comparative experiments with other ddPCR droplet detection models and absolute quantification experiments of ddPCR were conducted to assess the performance of the Filter Faster R-CNN model. In low-dust and dusty environments, the Filter Faster R-CNN model achieved detection accuracies of 98.23% and 88.35% for positive droplets, respectively, with composite F1 scores reaching 99.15% and 99.14%, obviously superior to the other models. The introduction of the filtering module significantly enhanced the positive accuracy of the model in dusty environments. In the absolute quantification experiments, a regression line was plotted using the results from commercial flow cytometry equipment as the standard concentration. The results show a regression line slope of 1.0005, an intercept of -0.025, and a determination coefficient of 0.9997, indicating high consistency between the two results. The ddPCR detection technique using the Filter Faster R-CNN model provides a robust detection method for ddPCR under various environmental conditions.

Performance and Interlaboratory Reproducibility of Droplet Digital Polymerase Chain Reaction (ddPCR) and Real Time PCR for KIT D816V Mutation Detection: A Nationwide Pilot Study By the RIMA (Rete Italiana Mastocitosi) Association

Systemic Mastocytosis (SM) is a rare hematological neoplasm, but its incidence is probably underestimated because of the heterogeneity of clinical symptoms and presentation that may cause diagnostic difficulties especially outside reference centers. Detection of the activating D816V KIT mutation in the bone marrow or peripheral blood is one of the minor criteria for the diagnosis of SM, but it poses some challenges since in the indolent forms (the most frequent) the mast cell burden is very low. For this reason, the National Comprehensive Cancer Network and the European Competence Network on Mastocytosis (ECNM) recommend a high-sensitivity PCR-based assay, such as Allele-Specific Oligonucleotide-Real Time Quantitative PCR (ASO-qPCR) or ddPCR, while Sanger and Next Generation Sequencing are considered not adequate. Accurate quantitation of allele burden (AB) is also desirable since it offers diagnostic (an AB³10% qualifies as a B-finding according to the latest WHO classification) and prognostic information and might be used to monitor response to tyrosine kinase inhibitors. Despite the widespread routine availability of real time and digital PCR technologies, a map of labs across Italy offering KIT D816V testing with adequate sensitivity is lacking, nor have cooperative efforts aimed to check lab performance and reproducibility of diagnostic results ever been performed. Based on these premises, RIMA undertook, with the sponsorship of the GIMEMA Working Party on Chronic Myeloproliferative Neoplasms, a project aimed to: i) build a nationwide network of competent reference laboratories performing KIT D816V mutation testing; ii) promote harmonization of local procedures and ensure adequate proficiency and cross-comparability of results. The pilot phase of this project involved 7 labs (L) spread across the country (Milan, Bologna, Verona, Florence, Rome, Naples, Pavia) serving one or more reference clinical centers for the diagnosis and management of SM. First, a survey aimed to assess the techniques and procedures in use in each participating lab was conducted. Five labs (L2-L6) used a commercial ddPCR assay (KIT p.D816V c.2447A>T, Assay ID dHsaCP2000023; Bio-Rad); one lab (L7) used a home brew ASO-qPCR assay; one lab (L1) used a commercial semi-quantitative real time PCR-based assay (PlentiPlex Mastocytosis D816V kit; Pentabase). All labs performed analyses in duplicate or triplicate using DNA as input material, with amounts variable from 50 to 250ng/replicate. Reported limit of detection (LoD) was 0.0003% for the home brew method and 0.01% for the commercial methods. Subsequently, a round robin test to evaluate and compare performance in terms of sensitivity and inter-lab reproducibility was carried out. Mutated and wild-type DNA isolated from HMC-1.2 and HL-60 cells, respectively, was mixed and diluted to mimic different D816V ABs, from 10% down to 0.01%. Dilutions were externally assessed by the UK Wessex Genomics Laboratory Service, using an accredited ddPCR assay with a limit of detection (LoD) of 0.01%. Of note, the sixth dilution (D6) was scored as slightly below their LoD (0.008%; 3 positive droplets only). Identical batches of blinded vials were then distributed and analyzed in parallel by the 7 participating labs according to their own routine protocols. Positivity/negativity as scored by L1-7 and AB values as scored by L2-7 are detailed in Table 1. All the evaluated methods proved highly accurate in the detection and, for ddPCR and ASO-qPCR, in the quantitation of the KIT D816V (R 2 between 0.988 and 0.998). A very high degree of concordance was achieved, for all dilutions, across different labs and methods (CV between 0.07 and 0.8). D6 was scored borderline by L1 and positive by all other labs. This pilot experience demonstrates that the evaluated PCR-based methods may reliably identify and quantitate the KIT D816V mutation down to an AB of 0.01% with a high degree of cross-comparability of results, thus assisting clinicians in the diagnosis and monitoring of SM patients. To the best of our knowledge, this is the first cooperative effort aimed to assess the performance and reproducibility of KIT D816V mutation testing in SM. Involvement of additional Italian labs is already planned, and further implementation within the ECNM will be proposed. Definition of common SOPs, uniform sample requirements and web-based reporting following the GIMEMA LabNet model will be pursued.

THU235 Usefulness Of Digital Droplet Polymerase Chain Reaction (ddPCR) For Molecular Diagnosis Of McCune-Albright Syndrome In DNA From Peripheral Blood Leukocytes

Abstract Disclosure: A.G. de Faria: None. L.R. Montenegro: None. A.A. Jorge: None. R.S. Jallad: None. R.M. Martin: None. M.C. Fragoso: None. A.P. Canton: None. C.E. Seraphim: None. F.R. Tinano: None. N.C. Pinto: None. B.B. Mendonca: None. A. Latronico: None. V.N. Brito: None. Background: McCune-Albright Syndrome (MAS) is a rare congenital disorder caused by post-zygotic activating mutations in GNAS gene. Due to the mosaic pattern of this disease, mutation abundance is frequently low in several tissues, including blood cells. The emergence of digital droplet Polymerase Chain Reaction (ddPCR) is a breakthrough technology of quantitative PCR useful for a targeted detection and quantification of rare events. Objective: This study aims to identify the GNAS mutation in peripheral blood leukocytes of patients with MAS using ddPCR. Patients and Methods: Thirty-four patients (25 female and 9 male) with typical (classic triad) or atypical (one or two features) MAS were included. DNA was extracted from peripheral blood and was analyzed through ddPCR method. Two ddPCR mutation detection assays were used, each one targeting the p.R201C or p.R201H mutations. For each assay, the Taqman PCR system was used with two fluorescence probes: the first labeled with HEX, targeting de (WT allele), and the second labeled 6-FAM, targeting the (mutated allele). The mixture (reagents + DNA) was divided into 20,000 nanodrops, which was placed on a PCR plate and amplified by PCR. After amplification by PCR, the fluorescence of each droplet was read on the 200 Droplet Digital PCR system (Bio-Rad), being classified as positive (drop containing the target sequence) or negative. Results were analyzed using Quantasoft software (Bio-Rad) to determine the total amount of positive droplets in the original sample. The results were reported in fractional abundance (FA), corresponding to the percentage of mutated alleles within the total alleles. Results: Among all patients, the most common MAS feature was osteofibrous dysplasia (OFD) (73.5%), followed by peripheral precocious puberty (PPP) (64.7%), and café-au-lait skin spots (52.9%). Acromegaly or hyperthyroidism was diagnosed in 6 and 4 patients, respectively. Classical MAS triad, 2 clinical features, or 1 clinical feature (PPP or OFD) were present in 15, 9, and 10 patients, respectively. GNAS mutations were identified in 12/34 (35.3%) MAS patients: the p.R201C in 5 and the p.R201H in 7. Among them, 8 patients presented typical MAS and 4 patients with two MAS clinical features, respectively. Considering only typical MAS group, the positive molecular test rate was 53.3% (8/15). GNAS mutations were not identified in patients with only one MAS feature. There was a positive correlation between a positive molecular test and the number of MAS clinical features (p = 0.01). Median FA was 1.45%, ranged from 0.25% to 8.4%. There was no association between FA rate and the number of MAS clinical features (p=1.0). Conclusion: ddPCR representeds a more sensitive tool for identifying GNAS mutations in peripheral blood leukocytes, even with low abundance, in patients with classical and atypical MAS. Presentation: Thursday, June 15, 2023

Characterization of loss of chromosome Y in peripheral blood cells in male Han Chinese patients with schizophrenia

BackgroundSchizophrenia (SCZ) has a global prevalence of 1% and increases the risk of mortality, reducing life expectancy. There is growing evidence that the risk of this disorder is higher in males than in females and it tends to develop in early adulthood. The Y chromosome is thought to be involved in biological processes other than sex determination and spermatogenesis. Studies have shown that loss of chromosome Y (LOY) in peripheral blood cells is associated with a variety of diseases (including cancer) and increased all-cause mortality. An analysis of the relationship between LOY and schizophrenia is warranted.MethodsA total of 442 Chinese males (271 patients with schizophrenia vs. 171 controls) were included in this study. The copy numbers of the Y and X chromosomes were detected by positive droplets targeting the amelogenin gene (AMEL) on the Y chromosome and X chromosome (AMELY and AMELX, respectively), using droplet digital PCR (ddPCR). The LOY percentage was defined as the difference between the concentration of AMELX and the concentration of AMELY divided by the concentration of AMELX, denoted as (X - Y)/X.ResultsIn the Han Chinese population, the LOY percentage was higher in the schizophrenia group than in the control group (p < 0.05), although there was no significant difference in the presence of LOY between the two groups. A strong correlation was found between the average of the disease duration and the average of the LOY percentage (R2 = 0.506, p = 0.032). The logistic regression analysis implied that the risk of LOY increases by 0.058 and 0.057 per year according to age at onset and duration of disease, respectively (ponset = 0.013, pduration = 0.017).ConclusionsIn the Han Chinese population, the LOY percentage of the disease group was significantly different from that of the control group. The age of onset and duration of schizophrenia might be risk factors for LOY in peripheral blood cells. A larger sample size and expanded clinical information are needed for more in-depth and specific analyses.

Circulating tumor DNA in plasma as an early predictor for pathological lymph node involvement and (neo-)adjuvant chemotherapy in patients with colon cancer.

3532 Background: Postoperative chemotherapy is standard for colon cancer patients with pathological lymph node involvement (pN+). If the presence of pN+ could be accurately predicted prior to surgery, the initiation of neoadjuvant chemotherapy could potentially reduce the presence of micrometastasis and enhance survival. The objective of this study was to assess the utility of preoperative methylated circulating tumor DNA (meth-ctDNA) as a predictive marker for pN+ and its clinical value as a marker for the initiation of neoadjuvant chemotherapy. Methods: The clinical data of 203 colon cancer patients (stage I-III) was collected from the Colorectal Cancer Database established at Danish Colorectal Cancer Center South, Vejle Hospital, Denmark. Patients were randomized into a discovery and a validation cohort. Plasma collected preoperatively was analyzed for three different meth-ctDNAs (Neuropeptide Y, Galactose-3-O-Sulfotransferase 3, and KN Motif and Ankyrin Repeat Domains 1) by droplet digital PCR (ddPCR). Results: Based on analysis of the discovery cohort, samples were considered positive if two or more of the three methylation markers had at least three positive droplets in the ddPCR analysis. 40% of patients in the discovery cohort and 46% of patients in the validation cohort were considered positive. When using meth-ctDNA as a diagnostic tool to predict pN+, the sensitivity and specificity in the discovery cohort were 42% and 61% respectively. In the validation cohort the values were 54% and 59%, respectively. AUC was &lt; 60% in both cohorts. Meth-ctDNA was associated to clinical tumor (T)- and node (N)-category in both cohorts (p≤0.01). Four-year disease free survival (DFS) in patients with and without detectable meth-ctDNA was 60% and 90% in the discovery cohort (p = 0.01), and 62% and 78% in the validation cohort (p = 0.04). Meth-ctDNA remained the strongest prognostic factor of DFS in the multivariate analysis compared to clinical T- or N-category, gender and age (p = 0.065, HR 2.46, 95% CI 1.03-5.88), however not statistically significant. Four-year overall survival (OS) in patients with and without detectable meth-ctDNA was 75% and 95% in the discovery cohort (p = 0.006), and 78% and 91% in the validation cohort (p = 0.02). In the multivariate analysis, meth-ctDNA was the strongest prognostic factor of OS compared to clinical T- or N-category, gender and age (p = 0.016, HR 4.54, 95% CI 1.32-15.6). Conclusions: The preoperative measurement of meth-ctDNA was not found to be a clinically significant predictor of pN+ or a reliable indicator for the initiation of neoadjuvant chemotherapy. Nonetheless, the results showed that preoperative ctDNA is a powerful prognostic indicator, suggesting that randomized controlled trials should be conducted to determine the efficacy of neoadjuvant chemotherapy in colon cancer patients with preoperative detectable ctDNA.

MP33-15 COMPARISON OF SERUM miR-371a-3p ASSAY PERFORMANCE BY DIGITAL DROPLET PCR AND REVERSE TRANSCRIPTASE QUANTITATIVE PCR IN MALIGNANT GERM CELL TUMOR PATIENTS

You have accessJournal of UrologyCME1 Apr 2023MP33-15 COMPARISON OF SERUM miR-371a-3p ASSAY PERFORMANCE BY DIGITAL DROPLET PCR AND REVERSE TRANSCRIPTASE QUANTITATIVE PCR IN MALIGNANT GERM CELL TUMOR PATIENTS Michelle Nuño, John Lafin, Cinzia Scarpini, Anna Savelyeva, Zhaohui Wang, Jeffrey Gagan, Liwei Jia, Cheryl Lewis, Sarah Murray, Yun Cheng, Vitaly Margulis, Solomon Woldu, Douglas Strand, Nicholas Coleman, James Amatruda, Lindsay Frazier, Matthew Murray, and Aditya Bagrodia Michelle NuñoMichelle Nuño More articles by this author , John LafinJohn Lafin More articles by this author , Cinzia ScarpiniCinzia Scarpini More articles by this author , Anna SavelyevaAnna Savelyeva More articles by this author , Zhaohui WangZhaohui Wang More articles by this author , Jeffrey GaganJeffrey Gagan More articles by this author , Liwei JiaLiwei Jia More articles by this author , Cheryl LewisCheryl Lewis More articles by this author , Sarah MurraySarah Murray More articles by this author , Yun ChengYun Cheng More articles by this author , Vitaly MargulisVitaly Margulis More articles by this author , Solomon WolduSolomon Woldu More articles by this author , Douglas StrandDouglas Strand More articles by this author , Nicholas ColemanNicholas Coleman More articles by this author , James AmatrudaJames Amatruda More articles by this author , Lindsay FrazierLindsay Frazier More articles by this author , Matthew MurrayMatthew Murray More articles by this author , and Aditya BagrodiaAditya Bagrodia More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003266.15AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: MicroRNAs have shown promise as potential germ cell tumor (GCT) biomarkers. In particular, previous work has shown miR-371a-3p alone may serve as a biomarker for GCTs, demonstrating higher sensitivity than current biomarkers. Quantitative reverse transcriptase PCR (RT-qPCR) has been commonly used to measure circulating levels of miR-371a-3p. More recently, miR-371a-3p has also been measured using digital droplet PCR (ddPCR), which has the potential advantage of absolute quantification. We compared the performance of miR-371a-3p as quantified by RT-qPCR and ddPCR. METHODS: Patient samples from the University of Texas Southwestern (UTSW) Medical Center and the University of Cambridge (UoC) were evaluated using both RT-qPCR and ddPCR, as per current protocols (RT-qPCR) or as per standard manufacturer’s recommendations (ddPCR). A range of clinical scenarios (pre-orchiectomy, chemotherapy-naïve RPLND) were intentionally selected. We compared the performance of the two assays using receiver operating characteristic curves and area under the curve values. We also determined an optimal threshold for each procedure by maximizing the Youden Index and compared the corresponding estimated sensitivity and specificity. RESULTS: Data were available for 69 patients. Among these patients, 35 (50.7%) had malignant GCT (MGCT) and 34 (49.3%) had either non-MGCT (n=26) or no tumor (n=8). Patients with non-MCT or with no tumor were considered controls. Cq values were generally lower among patients with MGCT and the number of positive droplets was higher compared with controls. The AUC was 0.96 when using RT-qPCR and 0.82 when using ddPCR to classify patients based on circulating miR-371a-3p. The optimal threshold for ddPCR was determined to be 17.5 positive droplets with a corresponding estimated sensitivity of 71% and 100% specificity. For RT-qPCR, the optimal threshold was determined to be Cq=28.52, with a corresponding estimated sensitivity of 83% and specificity of 100%. CONCLUSIONS: RT-qPCR was more sensitive in identifying MGCT patients in our current cohort when compared with ddPCR. Further investigations are required to optimize PCR methodology, particularly for ddPCR, and determine whether tumor volume or clinical context affects ddPCR performance. Source of Funding: Cancer Prevention and Research Institute of Texas and St. Baldrick's Foundation. © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e456 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Michelle Nuño More articles by this author John Lafin More articles by this author Cinzia Scarpini More articles by this author Anna Savelyeva More articles by this author Zhaohui Wang More articles by this author Jeffrey Gagan More articles by this author Liwei Jia More articles by this author Cheryl Lewis More articles by this author Sarah Murray More articles by this author Yun Cheng More articles by this author Vitaly Margulis More articles by this author Solomon Woldu More articles by this author Douglas Strand More articles by this author Nicholas Coleman More articles by this author James Amatruda More articles by this author Lindsay Frazier More articles by this author Matthew Murray More articles by this author Aditya Bagrodia More articles by this author Expand All Advertisement PDF downloadLoading ...

Comparison of serum miR-371a-3p assay performance by digital droplet PCR and reverse transcriptase quantitative PCR in patients with malignant germ cell tumor.

426 Background: MicroRNAs have shown promise as potential germ cell tumor (GCT) biomarkers. In particular, previous work has shown miR-371a-3p alone may serve as a biomarker for GCTs, demonstrating higher sensitivity than current biomarkers. Quantitative reverse transcriptase PCR (RT-qPCR) has been commonly used to measure circulating levels of miR-371a-3p. More recently, miR-371a-3p has also been measured using digital droplet PCR (ddPCR), which has the potential advantage of absolute quantification. Here, we compared the performance of miR-371a-3p as quantified by RT-qPCR and ddPCR. Methods: Patient samples from the University of Texas Southwestern (UTSW) Medical Center and the University of Cambridge (UoC) were evaluated using both RT-qPCR and ddPCR, as per current protocols (RT-qPCR) or as per standard manufacturer’s recommendations (ddPCR). A range of clinical scenarios (pre-orchiectomy, chemotherapy-naïve RPLND) were intentionally selected. We compared the performance of the two assays using receiver operating characteristic (ROC) curves and area under the curve (AUC) values. We also determined an optimal threshold for each procedure by maximizing the Youden Index and compared the corresponding estimated sensitivity and specificity. Results: Data were available for 69 patients. Among these patients, 35 (50.7%) had malignant GCT (MGCT) and 34 (49.3%) had either non-MGCT (n=26) or no tumor (n=8). Patients with non-MGCT or with no tumor were considered controls. Cq values were generally lower among patients with MGCT and the number of positive droplets was higher compared with controls. The AUC was 0.96 when using RT-qPCR and 0.82 when using ddPCR to classify patients based on circulating miR-371a-3p. The optimal threshold for ddPCR was determined to be 17.5 positive droplets with a corresponding estimated sensitivity of 71% and 100% specificity. For RT-qPCR, the optimal threshold was determined to be Cq=28.52, with a corresponding estimated sensitivity of 83% and specificity of 100%. Conclusions: RT-qPCR was more sensitive in identifying MGCT patients in our current cohort when compared with ddPCR. Further investigations are required to optimize PCR methodology, particularly for ddPCR, and determine whether e.g., tumor volume and/or clinical context affects ddPCR performance. [Table: see text]

Genome concentration, characterization, and integrity analysis of recombinant adeno-associated viral vectors using droplet digital PCR.

Precise, reproducible characterization of AAV is critical for comparing preclinical results between laboratories and determining a safe and effective clinical dose for gene therapy applications. In this study, we systematically evaluated numerous parameters to produce a simple and robust ddPCR protocol for AAV characterization. The protocol uses a low ionic strength buffer containing Pluronic-F68 and polyadenylic acid to dilute the AAV into the ddPCR concentration range and a 10-minute thermal capsid lysis prior to assembling ddPCR reactions containing MspI. A critical finding is that the buffer composition affected the ITR concentration of AAV but not the ITR concentration of a double stranded plasmid, which has implications when using a theoretical, stoichiometric conversion factor to obtain the titer based on the ITR concentration. Using this protocol, a more comprehensive analysis of an AAV vector formulation was demonstrated with multiple ddPCR assays distributed throughout the AAV vector genome. These assays amplify the ITR, regulatory elements, and eGFP transgene to provide a more confident estimate of the vector genome concentration and a high-resolution characterization of the vector genome identity. Additionally, we compared two methods of genome integrity analysis for three control sample types at eight different concentrations for each sample. The genome integrity was independent of sample concentration and the expected values were obtained when integrity was determined based on the excess number of positive droplets relative to the number of double positive droplets expected by chance co-encapsulation of two DNA targets. The genome integrity was highly variable and produced unexpected values when the double positive droplet percentage was used to calculate the genome integrity. A protocol using a one-minute thermal capsid lysis prior to assembling ddPCR reactions lacking a restriction enzyme used the non-ITR assays in a duplex ddPCR milepost experiment to determine the genome integrity using linkage analysis.

A one-stage deep learning based method for automatic analysis of droplet-based digital PCR images.

Droplet-based dPCR offers many advantages over chip-based dPCR, such as lower processing cost, higher droplet density, higher throughput, while requiring less sample. However, the stochastic nature of droplet locations, uneven illuminations, and unclear droplet boundaries make automatic image analysis challenging. Most methods currently used to count a large amount of microdroplets rely on flow detection. Conventional machine vision algorithms cannot extract all information of the targets from complex backgrounds. Some two-stage methods, which first locate and then classify droplets according to their grayscale values, require high-quality imaging. In this study, we addressed these limitations by improving a one-stage deep learning algorithm named YOLOv5 and applying it to the detection task to realize one-stage detection. We introduced an attention mechanism module to increase the detection rate of small targets and used a new loss function to speed up the training process. Furthermore, we employed a network pruning method to facilitate the deployment of the model on mobile devices while preserving its performance. We validated the model with captured droplet-based dPCR images and found that the improved model accurately identified negative and positive droplets in complex backgrounds with an error rate of 0.65%. This method is characterized by its fast detection speed, high accuracy, and ability to be used on mobile devices or cloud platforms. Overall, the study presents a novel approach for detecting droplets in large-scale microdroplet images and provides a promising solution for accurate and efficient droplet counting in droplet-based dPCR.

A fluorescence-based sweat test sensor in a proof-of-concept clinical study for COVID-19 screening diagnosis.

During the corona virus disease 2019 (COVID-19) pandemic period, rapid screening of covid-19 patients has been of great interest by developing a fluorescent sensor for complexation with nonanal, which is a marker for Covid-19 detection in sweat. Solid phase micro-extraction gas chromatography-mass spectrometry (SPME GC-MS) was initially used to quantify nonanal in armpit sweat samples based on an external calibration curve. A sample containing a nonanal content above the threshold of 1.04 μL is expected to be COVID-19 positive with a sensitivity and specificity of 87% and 89%, respectively, validated by comparison with RT-PCR results. For more practical applications, helicene dye-encapsulated ethyl cellulose, namely EC@dyeNH, was applied to screen 140 sweat samples collected from the foreheads of volunteers. The mixed sensor and sweat solution droplets were then visualized and imaged under blacklight. The COVID-19 positive droplets exhibited yellow fluorescence emission, the brightness of which could be measured by using ImageJ in the grey scale. With the optimum color intensity of >73 for positive results, the screening performance was observed with a sensitivity and specificity of 96% and 93%, respectively. The overall test time of this method is approximately less than 15 min. This alternative method offers a promising practical screening approach for the diagnosis of COVID-19 in sweat.

A deep learning based method for automatic analysis of high-throughput droplet digital PCR images.

Droplet digital PCR (ddPCR) is a technique for absolute quantification of nucleic acid molecules and is widely used in biomedical research and clinical diagnosis. ddPCR partitions the reaction solution containing target molecules into a large number of independent microdroplets for amplification and performs quantitative analysis of target molecules by calculating the proportion of positive droplets by the principle of Poisson distribution. Accurate recognition of positive droplets in ddPCR images is of great importance to guarantee the accuracy of target nucleic acid quantitative analysis. However, hand-designed operators are sensitive to interference and have disadvantages such as low contrast, uneven illumination, low sample copy number, and noise, and their accuracy and robustness still need to be improved. Herein, we developed a deep learning-based high-throughput ddPCR droplet detection framework for robust and accurate ddPCR image analysis, and the experimental results show that our method achieves excellent performance in the recognition of positive droplets (99.71%) within a limited time. By combining the Hough transform and a convolutional neural network (CNN), our novel method can automatically filter out invalid droplets that are difficult to be identified by local or global encoding methods and realize high-precision localization and classification of droplets in ddPCR images under variable exposure, contrast, and uneven illumination conditions without the need for image pre-processing and normalization processes.

Deep learning enables accurate analysis of images generated from droplet-based digital polymerase chain reaction (dPCR)

Accurate analysis of digital polymerase chain reaction (dPCR) images is a key to the successful miniaturization of a dPCR system, which would allow point-of-care testing, but has been hampered by several issues, mainly out-of-focus images, and variations in shape, size, and fluorescence intensities of the droplets. Herein, we performed semantic segmentation by using a deep learning model combined with the circle Hough transform (CHT). For the deep learning model, Attention DeepLabV3+ was modified and used to analyze positive and negative droplets in the low-quality dPCR images, while CHT helps to count the droplets. The modified Attention DeepLabV3+ was trained with augmented 4224 dPCR images, which were sufficient for deep learning model training. As a proof of concept, encapsulated genomic DNA (gDNA) of Escherichia coli O157:H7, a foodborne pathogen, was amplified, and the corresponding droplets were analyzed with the deep learning model to demonstrate its rapid, absolute quantification and accurate identification of the target gDNA. Our analysis method achieved similar accuracy to the commercial, bulky dPCR system, which is based on continuous-flow analysis methods. Furthermore, the deep learning-based approach exhibited a great sensitivity for detecting pathogenic bacteria from low-quality images with a low detection limit of 4.0 × 101 copies/μL. Compared with the commercially available instrument, the deep learning method demonstrated a similar molecular analysis performance. The presented results may lay the ground for efficient absolute quantification of target nucleic acids from the dPCR images as well as the establishment of a point-of-care analysis platform.

Fabrication of planar monolayer microreactor array for visual statistical analysis and droplet-based digital quantitative analysis in situ.

Planar monolayer microreactor arrays (PMMRAs) make droplet-based numerical measurements and statistical analysis cheap and easy. However, PMMRAs are typically produced in complex microfluidic devices and, moreover, still requires stringent control to reduce droplet loss during heating. In this paper, a simple, reliable, and flexible method for fabricating PMMRAs in a 96-well plate is described in detail by using simple materials and low-cost equipment. The partitioned droplets spontaneously assemble into PMMRAs in the plates, and this distribution is maintained even after incubation. This is advantageous for in situ analysis based on an individual droplet in droplet digital loop-mediated isothermal amplification (ddLAMP) and does not require the transfer of positive droplets. Precise and reproducible quantification of classical swine fever virus (CSFV) extracts was executed in these PMMRAs to verify its availability. Our results demonstrate that the proposed approach not only provides a flexible and controllable execution scheme for droplet-based nucleic acid quantification in resource-limited laboratories but also opens new perspectives for numerous analytical and biochemical applications using droplets as versatile plastic microreactors.

PoDCall: positive droplet calling and normalization of droplet digital PCR DNA methylation data.

Droplet digital PCR (ddPCR) holds great promises for investigating DNA methylation with high sensitivity. Yet, the lack of methods for analyzing ddPCR DNA methylation data has resulted in users processing the data manually at the expense of standardization. PoDCall is an R package performing automated calling of positive droplets, quantification and normalization of methylation levels in ddPCR experiments. A Shiny application provides users with an intuitive and interactive interface to access PoDCall functionalities. The PoDCall R package is freely available on Bioconductor at https://bioconductor.org/packages/PoDCall/. The Shiny application can be executed from the R console using the wrapper function PoDCall::podcallShiny(). Supplementary data are available at Bioinformatics online.