Journal articles: 'Digital polymerase chain reaction'

1

Ahrberg, Christian D., Andreas Manz, and Bong Geun Chung. "Polymerase chain reaction in microfluidic devices." Lab on a Chip 16, no. 20 (2016): 3866–84. http://dx.doi.org/10.1039/c6lc00984k.

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2

Fan, H. Christina, and Stephen R. Quake. "Detection of Aneuploidy with Digital Polymerase Chain Reaction." Analytical Chemistry 79, no. 19 (October 2007): 7576–79. http://dx.doi.org/10.1021/ac0709394.

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3

Chang, Yi-Hsien, Gwo-Bin Lee, Fu-Chun Huang, Yi-Yu Chen, and Jr-Lung Lin. "Integrated polymerase chain reaction chips utilizing digital microfluidics." Biomedical Microdevices 8, no. 3 (May 20, 2006): 215–25. http://dx.doi.org/10.1007/s10544-006-8171-y.

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Roden, Anja C., Julie A. Vrana, Justin W. Koepplin, Angela E. Hudson, Andrew P. Norgan, Garrett Jenkinson, Satoko Yamaoka, et al. "Comparison of In Situ Hybridization, Immunohistochemistry, and Reverse Transcription–Droplet Digital Polymerase Chain Reaction for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Testing in Tissue." Archives of Pathology & Laboratory Medicine 145, no. 7 (March 15, 2021): 785–96. http://dx.doi.org/10.5858/arpa.2021-0008-sa.

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Context.— Small case series have evaluated severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) detection in formalin-fixed, paraffin-embedded tissue using reverse transcription–polymerase chain reaction, immunohistochemistry (IHC), and/or RNA in situ hybridization (RNAish). Objective.— To compare droplet digital polymerase chain reaction, IHC, and RNAish to detect SARS-CoV-2 in formalin-fixed, paraffin-embedded tissue in a large series of lung specimens from coronavirus disease 2019 (COVID-19) patients. Design.— Droplet digital polymerase chain reaction and RNAish used commercially available probes; IHC used clone 1A9. Twenty-six autopsies of COVID-19 patients with formalin-fixed, paraffin-embedded tissue blocks of 62 lung specimens, 22 heart specimens, 2 brain specimens, and 1 liver, and 1 umbilical cord were included. Control cases included 9 autopsy lungs from patients with other infections/inflammation and virus-infected tissue or cell lines. Results.— Droplet digital polymerase chain reaction had the highest sensitivity for SARS-CoV-2 (96%) when compared with IHC (31%) and RNAish (36%). All 3 tests had a specificity of 100%. Agreement between droplet digital polymerase chain reaction and IHC or RNAish was fair (κ = 0.23 and κ = 0.35, respectively). Agreement between IHC and in situ hybridization was substantial (κ = 0.75). Interobserver reliability was almost perfect for IHC (κ = 0.91) and fair to moderate for RNAish (κ = 0.38–0.59). Lung tissues from patients who died earlier after onset of symptoms revealed higher copy numbers by droplet digital polymerase chain reaction (P = .03, Pearson correlation = −0.65) and were more likely to be positive by RNAish (P = .02) than lungs from patients who died later. We identified SARS-CoV-2 in hyaline membranes, in pneumocytes, and rarely in respiratory epithelium. Droplet digital polymerase chain reaction showed low copy numbers in 7 autopsy hearts from ProteoGenex Inc. All other extrapulmonary tissues were negative. Conclusions.— Droplet digital polymerase chain reaction was the most sensitive and highly specific test to identify SARS-CoV-2 in lung specimens from COVID-19 patients.

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Sun, Chen, Leqian Liu, Harish N. Vasudevan, Kai-Chun Chang, and Adam R. Abate. "Accurate Bulk Quantitation of Droplet Digital Polymerase Chain Reaction." Analytical Chemistry 93, no. 29 (July 12, 2021): 9974–79. http://dx.doi.org/10.1021/acs.analchem.1c00877.

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White, A. K., K. A. Heyries, C. Doolin, M. VanInsberghe, and C. L. Hansen. "High-Throughput Microfluidic Single-Cell Digital Polymerase Chain Reaction." Analytical Chemistry 85, no. 15 (July 24, 2013): 7182–90. http://dx.doi.org/10.1021/ac400896j.

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Vynck, Matthijs, Wim Trypsteen, Olivier Thas, Linos Vandekerckhove, and Ward De Spiegelaere. "The Future of Digital Polymerase Chain Reaction in Virology." Molecular Diagnosis & Therapy 20, no. 5 (June 28, 2016): 437–47. http://dx.doi.org/10.1007/s40291-016-0224-1.

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Sundberg, Scott O., Carl T. Wittwer, Chao Gao, and Bruce K. Gale. "Spinning Disk Platform for Microfluidic Digital Polymerase Chain Reaction." Analytical Chemistry 82, no. 4 (February 15, 2010): 1546–50. http://dx.doi.org/10.1021/ac902398c.

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Byrnes, Samantha A., Tim C. Chang, Toan Huynh, Anna Astashkina, Bernhard H. Weigl, and Kevin P. Nichols. "Simple Polydisperse Droplet Emulsion Polymerase Chain Reaction with Statistical Volumetric Correction Compared with Microfluidic Droplet Digital Polymerase Chain Reaction." Analytical Chemistry 90, no. 15 (July 9, 2018): 9374–80. http://dx.doi.org/10.1021/acs.analchem.8b01988.

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Gaňová, Martina, Haoqing Zhang, Hanliang Zhu, Marie Korabečná, and Pavel Neužil. "Multiplexed digital polymerase chain reaction as a powerful diagnostic tool." Biosensors and Bioelectronics 181 (June 2021): 113155. http://dx.doi.org/10.1016/j.bios.2021.113155.

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FAN, Yi-Qiang, Mei WANG, Feng GAO, Jian ZHUANG, Gang TANG, and Ya-Jun ZHANG. "Recent Development of Droplet Microfluidics in Digital Polymerase Chain Reaction." Chinese Journal of Analytical Chemistry 44, no. 8 (August 2016): 1300–1307. http://dx.doi.org/10.1016/s1872-2040(16)60953-2.

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Sedlak, Ruth Hall, and Keith R. Jerome. "Viral diagnostics in the era of digital polymerase chain reaction." Diagnostic Microbiology and Infectious Disease 75, no. 1 (January 2013): 1–4. http://dx.doi.org/10.1016/j.diagmicrobio.2012.10.009.

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Sreejith, Kamalalayam Rajan, Chin Hong Ooi, Jing Jin, Dzung Viet Dao, and Nam-Trung Nguyen. "Digital polymerase chain reaction technology – recent advances and future perspectives." Lab on a Chip 18, no. 24 (2018): 3717–32. http://dx.doi.org/10.1039/c8lc00990b.

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14

Bhat, Somanath, and Kerry R. Emslie. "Digital polymerase chain reaction for characterisation of DNA reference materials." Biomolecular Detection and Quantification 10 (December 2016): 47–49. http://dx.doi.org/10.1016/j.bdq.2016.04.001.

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15

Sidstedt, Maja, Erica L. Romsos, Ronny Hedell, Ricky Ansell, Carolyn R. Steffen, Peter M. Vallone, Peter Rådström, and Johannes Hedman. "Accurate Digital Polymerase Chain Reaction Quantification of Challenging Samples Applying Inhibitor-Tolerant DNA Polymerases." Analytical Chemistry 89, no. 3 (January 20, 2017): 1642–49. http://dx.doi.org/10.1021/acs.analchem.6b03746.

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Yu, Yan, Ziqing Yu, Xufeng Pan, Lei Xu, Rui Guo, Xiaohua Qian, and Feng Shen. "Multiplex digital PCR with digital melting curve analysis on a self-partitioning SlipChip." Analyst 147, no. 4 (2022): 625–33. http://dx.doi.org/10.1039/d1an01916c.

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Multiplex digital polymerase chain reaction (digital PCR) with digital melting curve analysis (digital MCA) on a self-partitioning SlipChip can provide absolute quantification of different target nucleic acids by designed signature melting profiles.

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Jovanovski, Aleksandar, Jessica Petiti, Emilia Giugliano, Enrico Marco Gottardi, Giuseppe Saglio, Daniela Cilloni, and Carmen Fava. "Standardization of BCR-ABL1 p210 Monitoring: From Nested to Digital PCR." Cancers 12, no. 11 (November 6, 2020): 3287. http://dx.doi.org/10.3390/cancers12113287.

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The introduction of tyrosine kinase inhibitors in 2001 as a targeted anticancer therapy has significantly improved the quality of life and survival of patients with chronic myeloid leukemia. At the same time, with the introduction of tyrosine kinase inhibitors, the need for precise monitoring of the molecular response to therapy has emerged. Starting with a qualitative polymerase chain reaction, followed by the introduction of a quantitative polymerase chain reaction to determine the exact quantity of the transcript of interest-p210 BCR-ABL1, molecular monitoring in patients with chronic myeloid leukemia was internationally standardized. This enabled precise monitoring of the therapeutic response, unification of therapeutic protocols, and comparison of results between different laboratories. This review aims to summarize the steps in the diagnosis and molecular monitoring of p210 BCR-ABL1, as well as to consider the possible future application of a more sophisticated method such as digital polymerase chain reaction.

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18

Beck, Patrick, Kasen Reed Hutchings, Caroline Grant, Kevin Sheng, Joshua Aaron Morales, Robin Varghese, and Zhi Sheng. "A novel glioblastoma prognostic assay using droplet digital polymerase chain reaction." Journal of Clinical Oncology 40, no. 16_suppl (June 1, 2022): e14026-e14026. http://dx.doi.org/10.1200/jco.2022.40.16_suppl.e14026.

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e14026 Background: Glioblastoma (GBM) is an incurable brain tumor for which outcomes have seen little improvement in the past 30 years. Despite limited GBM therapies, accurate prognostic tools can be invaluable in informing best course of treatment. Recently, we have identified the GBM progression gene signature (GBM-PGS), a collection of 31 genes that shows superior accuracy at predicting GBM progression compared to existing biomarkers. From GBM-PGS expression, a GBM progression risk score can be calculated to provide clinicians with patient-tailored prognostic information. However, the question remains as to the best way to measure GBM-PGS to create a clinically applicable test. At our disposal are three approaches to quantify gene expression: quantitative polymerase chain reaction (qPCR), droplet digital PCR (ddPCR), and RNA sequencing (RNA seq). We hypothesize that the combination of high accuracy and sensitivity, while also providing absolute quantification, will deliver ddPCR as the best option for creating a rapid clinical test that is scalable for measuring GBM-PGS. Methods: A ddPCR protocol was developed, and cDNA and primer concentrations were optimized using samples from two GBM cell lines (SF295, LN18). To confirm assay accuracy, GBM-PGS was quantified using ddPCR and compared to GBM-PGS expression by qPCR, a protocol that has previously been thoroughly validated. Finally, GBM-PGS expression was measured in patient tumors using ddPCR, and the GBM-PGS risk score algorithm was trained using an RNA seq data set from the Tissue Cancer Genome Atlas (TCGA). Following training, risk scores were calculated for the patient samples using ddPCR-measured GBM-PGS expression. Risk scores were compared with clinical patient survival to determine if GBM-PGS expression measured by ddPCR could predict patient outcomes. Results: Optimal primer concentration (200 nM) and cDNA concentration (1.2 ng/μl) were identified. GBM-PGS expression measured by ddPCR absolute quantification and qPCR Ct value demonstrated a strong correlation in the SF295 (R2 = 0.91) and LN18 (R2 = 0.88) cell lines. GBM-PGS was measured in two patient samples, and the resulting risk scores were calculated as 26.6 and 29.8 (score > 0 = high progression risk; score < 0 = low progression risk) compared to disease free survival times of 12.9 and 10.5 months, respectively, demonstrating an inverse relationship between patient risk score and survival time. Conclusions: Our results demonstrate that we have developed an accurate ddPCR-based assay capable of measuring GBM-PGS in multiple GBM cell lines, and preliminary results suggest that GBM-PGS quantified using ddPCR accurately predicts patient survival.

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19

Lee, Seung Yong, and Seung Yong Hwang. "Application of digital polymerase chain reaction technology for noninvasive prenatal test." Journal of Genetic Medicine 12, no. 2 (December 31, 2015): 72–78. http://dx.doi.org/10.5734/jgm.2015.12.2.72.

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20

Men, Yongfan, Yusi Fu, Zitian Chen, Peter A. Sims, William J. Greenleaf, and Yanyi Huang. "Digital Polymerase Chain Reaction in an Array of Femtoliter Polydimethylsiloxane Microreactors." Analytical Chemistry 84, no. 10 (April 12, 2012): 4262–66. http://dx.doi.org/10.1021/ac300761n.

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21

Li, Huanan, Haoqing Zhang, Ying Xu, Alzbeta Tureckova, Pavel Zahradník, Honglong Chang, and Pavel Neuzil. "Versatile digital polymerase chain reaction chip design, fabrication, and image processing." Sensors and Actuators B: Chemical 283 (March 2019): 677–84. http://dx.doi.org/10.1016/j.snb.2018.12.072.

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22

Nie, Mengyue, Meng Zheng, Caiming Li, Feng Shen, Manhua Liu, Haibei Luo, Xiaohui Song, Ying Lan, Jian-Zhang Pan, and Wenbin Du. "Assembled Step Emulsification Device for Multiplex Droplet Digital Polymerase Chain Reaction." Analytical Chemistry 91, no. 3 (January 4, 2019): 1779–84. http://dx.doi.org/10.1021/acs.analchem.8b04313.

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23

Hua, Zhishan, Jeremy L. Rouse, Allen E. Eckhardt, Vijay Srinivasan, Vamsee K. Pamula, Wiley A. Schell, Jonathan L. Benton, Thomas G. Mitchell, and Michael G. Pollack. "Multiplexed Real-Time Polymerase Chain Reaction on a Digital Microfluidic Platform." Analytical Chemistry 82, no. 6 (March 15, 2010): 2310–16. http://dx.doi.org/10.1021/ac902510u.

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Zhu, Qiangyuan, Lin Qiu, Yanan Xu, Guang Li, and Ying Mu. "Single cell digital polymerase chain reaction on self-priming compartmentalization chip." Biomicrofluidics 11, no. 1 (January 2017): 014109. http://dx.doi.org/10.1063/1.4975192.

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25

Koizumi, Yuki, Daisuke Furuya, Teruo Endo, Kouichi Asanuma, Nozomi Yanagihara, and Satoshi Takahashi. "Quantification of Wilms’ tumor 1 mRNA by digital polymerase chain reaction." International Journal of Hematology 107, no. 2 (October 9, 2017): 230–34. http://dx.doi.org/10.1007/s12185-017-2336-8.

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26

Hu, Zhenming, Weibo Fang, Tong Gou, Wenshuai Wu, Jiumei Hu, Shufang Zhou, and Ying Mu. "A novel method based on a Mask R-CNN model for processing dPCR images." Analytical Methods 11, no. 27 (2019): 3410–18. http://dx.doi.org/10.1039/c9ay01005j.

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27

Coccaro, Nicoletta, Giuseppina Tota, Luisa Anelli, Antonella Zagaria, Giorgina Specchia, and Francesco Albano. "Digital PCR: A Reliable Tool for Analyzing and Monitoring Hematologic Malignancies." International Journal of Molecular Sciences 21, no. 9 (April 29, 2020): 3141. http://dx.doi.org/10.3390/ijms21093141.

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The digital polymerase chain reaction (dPCR) is considered to be the third-generation polymerase chain reaction (PCR), as it yields direct, absolute and precise measures of target sequences. dPCR has proven particularly useful for the accurate detection and quantification of low-abundance nucleic acids, highlighting its advantages in cancer diagnosis and in predicting recurrence and monitoring minimal residual disease, mostly coupled with next generation sequencing. In the last few years, a series of studies have employed dPCR for the analysis of hematologic malignancies. In this review, we will summarize these findings, attempting to focus on the potential future perspectives of the application of this promising technology.

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Hui, Yuan, Zhiming Wu, Zhiran Qin, Li Zhu, Junhe Liang, Xujuan Li, Hanmin Fu, et al. "Micro-droplet Digital Polymerase Chain Reaction and Real-Time Quantitative Polymerase Chain Reaction Technologies Provide Highly Sensitive and Accurate Detection of Zika Virus." Virologica Sinica 33, no. 3 (June 2018): 270–77. http://dx.doi.org/10.1007/s12250-018-0037-y.

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29

Ahrberg, Christian D., Ji Wook Choi, Jong Min Lee, Kyoung G. Lee, Seok Jae Lee, Andreas Manz, and Bong Geun Chung. "Plasmonic heating-based portable digital PCR system." Lab on a Chip 20, no. 19 (2020): 3560–68. http://dx.doi.org/10.1039/d0lc00788a.

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A miniaturized polymerase chain reaction (PCR) system is not only important for medical applications in remote areas of developing countries, but also important for testing at ports of entry during global epidemics, such as the current outbreak of the coronavirus.

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30

Ross, David M., and Susan Branford. "Minimal residual disease: the advantages of digital over analog polymerase chain reaction." Leukemia & Lymphoma 52, no. 7 (May 25, 2011): 1161–63. http://dx.doi.org/10.3109/10428194.2011.580481.

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31

Yin, Juxin, Zheyu Zou, Fangfang Yin, Hongxiao Liang, Zhenming Hu, Weibo Fang, Shaowu Lv, Tao Zhang, Ben Wang, and Ying Mu. "A Self-Priming Digital Polymerase Chain Reaction Chip for Multiplex Genetic Analysis." ACS Nano 14, no. 8 (August 14, 2020): 10385–93. http://dx.doi.org/10.1021/acsnano.0c04177.

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32

Li, Zipeng, Tsung-Yi Ho, and Krishnendu Chakrabarty. "Optimization of 3D Digital Microfluidic Biochips for the Multiplexed Polymerase Chain Reaction." ACM Transactions on Design Automation of Electronic Systems 21, no. 2 (January 28, 2016): 1–27. http://dx.doi.org/10.1145/2811259.

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33

Zhang, Weifei, Nan Li, Daisuke Koga, Yong Zhang, Hulie Zeng, Hizuru Nakajima, Jin-Ming Lin, and Katsumi Uchiyama. "Inkjet Printing Based Droplet Generation for Integrated Online Digital Polymerase Chain Reaction." Analytical Chemistry 90, no. 8 (March 29, 2018): 5329–34. http://dx.doi.org/10.1021/acs.analchem.8b00463.

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Cao, Lei, Xingye Cui, Jie Hu, Zedong Li, Jane Ru Choi, Qingzhen Yang, Min Lin, Li Ying Hui, and Feng Xu. "Advances in digital polymerase chain reaction (dPCR) and its emerging biomedical applications." Biosensors and Bioelectronics 90 (April 2017): 459–74. http://dx.doi.org/10.1016/j.bios.2016.09.082.

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35

Zhao, Shan, Hua Lin, Shijie Chen, Miao Yang, Qigui Yan, Caifang Wen, Zhongxiang Hao, et al. "Sensitive detection of Porcine circovirus-2 by droplet digital polymerase chain reaction." Journal of Veterinary Diagnostic Investigation 27, no. 6 (September 21, 2015): 784–88. http://dx.doi.org/10.1177/1040638715608358.

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36

Philibert, Robert, Meesha Dogan, Amanda Noel, Shelly Miller, Brianna Krukow, Emma Papworth, Joseph Cowley, April Knudsen, Steven R. H. Beach, and Donald Black. "Genome-wide and digital polymerase chain reaction epigenetic assessments of alcohol consumption." American Journal of Medical Genetics Part B: Neuropsychiatric Genetics 177, no. 5 (April 28, 2018): 479–88. http://dx.doi.org/10.1002/ajmg.b.32636.

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37

Tian, Qingchang, Qi Song, Yanan Xu, Qiangyuan Zhu, Bingwen Yu, Wei Jin, Qinhan Jin, and Ying Mu. "A localized temporary negative pressure assisted microfluidic device for detecting keratin 19 in A549 lung carcinoma cells with digital PCR." Analytical Methods 7, no. 5 (2015): 2006–11. http://dx.doi.org/10.1039/c4ay02604g.

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38

Du, Junfeng, Wenxian Guan, and Xiaofei Shen. "Circulating Tumor DNA Detection Using Digital Polymerase Chain Reaction—Promising But Needs Improvement." Gastroenterology 161, no. 1 (July 2021): 366–67. http://dx.doi.org/10.1053/j.gastro.2021.02.048.

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39

Wouters, Yannick, Daisy Dalloyaux, Anke Christenhusz, Hennie M. J. Roelofs, Heiman F. Wertheim, Chantal P. Bleeker‐Rovers, René H. Morsche, and Geert J. A. Wanten. "Droplet digital polymerase chain reaction for rapid broad‐spectrum detection of bloodstream infections." Microbial Biotechnology 13, no. 3 (May 2020): 657–68. http://dx.doi.org/10.1111/1751-7915.13491.

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40

Ookawa, Satoshi, Masahiko Wanibuchi, Yuko Kataoka-Sasaki, Masanori Sasaki, Shinichi Oka, Shunya Ohtaki, Shouhei Noshiro, et al. "Digital Polymerase Chain Reaction Quantification of SERPINA1 Predicts Prognosis in High-Grade Glioma." World Neurosurgery 111 (March 2018): e783-e789. http://dx.doi.org/10.1016/j.wneu.2017.12.166.

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41

Gerdes, Lars, Azuka Iwobi, Ulrich Busch, and Sven Pecoraro. "Optimization of digital droplet polymerase chain reaction for quantification of genetically modified organisms." Biomolecular Detection and Quantification 7 (March 2016): 9–20. http://dx.doi.org/10.1016/j.bdq.2015.12.003.

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42

Ko, Jina, Yongcheng Wang, Jonathan C. T. Carlson, Angela Marquard, Jeremy Gungabeesoon, Alain Charest, David Weitz, Mikael J. Pittet, and Ralph Weissleder. "Single Extracellular Vesicle Protein Analysis Using Immuno‐Droplet Digital Polymerase Chain Reaction Amplification." Advanced Biosystems 4, no. 12 (March 12, 2020): 1900307. http://dx.doi.org/10.1002/adbi.201900307.

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43

Prakash, Kasthuri, Simon B. Larsson, Gustaf E. Rydell, Maria E. Andersson, Johan Ringlander, Gunnar Norkrans, Heléne Norder, and Magnus Lindh. "Hepatitis B Virus RNA Profiles in Liver Biopsies by Digital Polymerase Chain Reaction." Hepatology Communications 4, no. 7 (March 29, 2020): 973–82. http://dx.doi.org/10.1002/hep4.1507.

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44

Park, Sol, Anita Rana, Way Sung, and Mariya Munir. "Competitiveness of Quantitative Polymerase Chain Reaction (qPCR) and Droplet Digital Polymerase Chain Reaction (ddPCR) Technologies, with a Particular Focus on Detection of Antibiotic Resistance Genes (ARGs)." Applied Microbiology 1, no. 3 (October 2, 2021): 426–44. http://dx.doi.org/10.3390/applmicrobiol1030028.

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With fast-growing polymerase chain reaction (PCR) technologies and various application methods, the technique has benefited science and medical fields. While having strengths and limitations on each technology, there are not many studies comparing the efficiency and specificity of PCR technologies. The objective of this review is to summarize a large amount of scattered information on PCR technologies focused on the two majorly used technologies: qPCR (quantitative polymerase chain reaction) and ddPCR (droplet-digital polymerase chain reaction). Here we analyze and compare the two methods for (1) efficiency, (2) range of detection and limitations under different disciplines and gene targets, (3) optimization, and (4) status on antibiotic resistance genes (ARGs) analysis. It has been identified that the range of detection and quantification limit varies depending on the PCR method and the type of sample. Careful optimization of target gene analysis is essential for building robust analysis for both qPCR and ddPCR. In our era where mutation of genes may lead to a pandemic of viral infectious disease or antibiotic resistance-induced health threats, this study hopes to set guidelines for meticulous detection, quantification, and analysis to help future prevention and protection of global health, the economy, and ecosystems.

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Wei, Chunyang, Chengzhuang Yu, Shanshan Li, Jiyu Meng, Tiejun Li, Jingmeng Cheng, Feng Pan, and Junwei Li. "Easy-to-Operate Co-flow Step Emulsification Device for Droplet Digital Polymerase Chain Reaction." Analytical Chemistry 94, no. 9 (February 24, 2022): 3939–47. http://dx.doi.org/10.1021/acs.analchem.1c04983.

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46

Tozaki, Teruaki, Aoi Ohnuma, Natasha A. Hamilton, Mio Kikuchi, Taichiro Ishige, Hironaga Kakoi, Kei‐ichi Hirota, Kanichi Kusano, and Shun‐ichi Nagata. "Low‐copy transgene detection using nested digital polymerase chain reaction for gene‐doping control." Drug Testing and Analysis 14, no. 2 (October 18, 2021): 382–87. http://dx.doi.org/10.1002/dta.3173.

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47

Pinheiro, Leonardo B., Victoria A. Coleman, Christopher M. Hindson, Jan Herrmann, Benjamin J. Hindson, Somanath Bhat, and Kerry R. Emslie. "Evaluation of a Droplet Digital Polymerase Chain Reaction Format for DNA Copy Number Quantification." Analytical Chemistry 84, no. 2 (December 21, 2011): 1003–11. http://dx.doi.org/10.1021/ac202578x.

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48

Paunescu, Daniela, Carlos A. Mora, Lorenzo Querci, Reinhard Heckel, Michela Puddu, Bodo Hattendorf, Detlef Günther, and Robert N. Grass. "Detecting and Number Counting of Single Engineered Nanoparticles by Digital Particle Polymerase Chain Reaction." ACS Nano 9, no. 10 (August 26, 2015): 9564–72. http://dx.doi.org/10.1021/acsnano.5b04429.

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49

Dorazio, Robert M., and Margaret E. Hunter. "Statistical Models for the Analysis and Design of Digital Polymerase Chain Reaction (dPCR) Experiments." Analytical Chemistry 87, no. 21 (October 13, 2015): 10886–93. http://dx.doi.org/10.1021/acs.analchem.5b02429.

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Miyaoka, Yuichiro, Amanda H. Chan, and Bruce R. Conklin. "Using Digital Polymerase Chain Reaction to Detect Single-Nucleotide Substitutions Induced by Genome Editing." Cold Spring Harbor Protocols 2016, no. 8 (June 1, 2016): pdb.prot086801. http://dx.doi.org/10.1101/pdb.prot086801.

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Journal articles on the topic 'Digital polymerase chain reaction'

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