Digital Droplet PCR Method for the Quantification of AAV Transduction Efficiency in Murine Retina

Author(s):  
Iskalen Cansu Topcu Okan ◽  
Mehri Ahmadian ◽  
Yesim Tutuncu ◽  
Halit Yusuf Altay ◽  
Cavit Agca
2021 ◽  
Author(s):  
Ryo Ariyasu ◽  
Ken Uchibori ◽  
Takaaki Sasaki ◽  
Mika Tsukahara ◽  
Kazuma Kiyotani ◽  
...  

2021 ◽  
Vol 16 (3) ◽  
pp. S417
Author(s):  
V. Lamberts ◽  
M. Aldea ◽  
L. Mezquita ◽  
C. Jovelet ◽  
D. Vasseur ◽  
...  

2020 ◽  
Vol 78 (1) ◽  
Author(s):  
Romain Laurian ◽  
Cécile Jacot-des-Combes ◽  
Fabiola Bastian ◽  
Karine Dementhon ◽  
Pascale Cotton

ABSTRACT During Candida macrophage interactions, phagocytosed yeast cells feed in order to grow, develop hyphae and escape. Through numerous proteomic and transcriptomic studies, two metabolic phases have been described. A shift to a starvation mode is generally identified as early as one-hour post phagocytosis, followed by a glycolytic growth mode after C. albicans escaped from the macrophage. Healthy macrophages contain low amounts of glucose. To determine if this carbon source was sensed and metabolized by the pathogen, we explored the transcription level of a delimited set of key genes expressed in C. albicans cells during phagocytosis by macrophages, at an early stage of the interaction. This analysis was performed using a technical digital droplet PCR approach to quantify reliably the expression of carbon metabolic genes after 30 min of phagocytosis. Our data confirm the technique of digital droplet PCR for the detection of C. albicans transcripts using cells recovered after a short period of phagocytosis. At this stage, carbon metabolism is clearly oriented towards the use of alternative sources. However, the activation of high-affinity glucose transport system suggests that the low amount of glucose initially present in the macrophages is detected by the pathogen.


2020 ◽  
Author(s):  
Lisa Oberding ◽  
Jia Hu ◽  
Byron Berenger ◽  
Abu Naser Mohon ◽  
Dylan R. Pillai

AbstractSaliva samples were collected through a simple mouth wash procedure and viral load quantified using a technology called digital droplet PCR. Data suggest ddPCR allows for precise quantification of viral load in clinical samples infected with SARS-CoV-2.


2021 ◽  
Author(s):  
Ling Xu ◽  
Xiangying Zhang ◽  
Yaling Cao ◽  
Zihao Fan ◽  
Yuan Tian ◽  
...  

Abstract Background & Aims The prevalence of hepatitis delt virus (HDV) far exceeds our expected level, there remains a lack of reliable quantitative assays for HDV RNA detection. We sought to develop a new method based on digital droplet PCR (ddPCR) for HDV RNA quantitative detection. Methods With plasmid (pMD19T) containing HDV full-genome, we determined the method for ddPCR-based HDV RNA quantification. To compare various assays for HDV detection, 30 cases diagnosed hepatitis D and 14 controls were examined by ELISA, RT-PCR and ddPCR. 728 HBV-related patients including 182 chronic hepatitis B (CHB), 182 liver cirrhosis (LC), 182 hepatocellular carcinoma (HCC) and 182 liver failure (LF) were screened for HDV infection. Results The limit of detection of ddPCR for HDV is significantly low, which lower limit of detection (LLoD) and lower limit of quantitation (LLoQ) to be 5.51 copies/reaction (95% CI: 1.15–6.4*105) and 0.18 copies/reaction (95% CI: 0.0012151- 0.76436), respectively. Among the 44 samples, ELISA detected 30 cases positive for anti-HDV, ddPCR reported 24 samples and RT-PCR reported 10 samples positive for HDV RNA. Moreover, the positive rates of anti-HDV IgG were 1.1%, 3.3%, 2.7% and 7.1% in patients with CHB, LC, HCC, and LF; the detection rates of RT-PCR in HDV RNA were 0%, 16.67%, 15.4% and 20%, however, the detection rates of ddPCR were 0%, 33.33%, 30.77% and 60%. Conclusion We establish a high sensitivity and high specificity quantitative HDV RNA detection method based on ddPCR compared to RT-PCR. HBV-related end-stage liver disease, especially liver failure, are associated with a remarkably high rate of HDV infection.


2018 ◽  
Vol 245 (2) ◽  
pp. 499-509 ◽  
Author(s):  
W. Mayer ◽  
M. Schuller ◽  
M. C. Viehauser ◽  
R. Hochegger

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