Dataset Information

Estimating Real-Time qPCR Amplification Efficiency from Single-Reaction Data.

ABSTRACT: Methods for estimating the qPCR amplification efficiency E from data for single reactions are tested on six multireplicate datasets, with emphasis on their performance as a function of the range of cycles n₁-n₂ included in the analysis. The two-parameter exponential growth (EG) model that has been relied upon almost exclusively does not allow for the decline of E(n) with increasing cycle number n through the growth region and accordingly gives low-biased estimates. Further, the standard procedure of "baselining"-separately estimating and subtracting a baseline before analysis-leads to reduced precision. The three-parameter logistic model (LRE) does allow for such decline and includes a parameter E₀ that represents E through the baseline region. Several four-parameter extensions of this model that accommodate some asymmetry in the growth profiles but still retain the significance of E₀ are tested against the LRE and EG models. The recursion method of Carr and Moore also describes a declining E(n) but tacitly assumes E₀ = 2 in the baseline region. Two modifications that permit varying E₀ are tested, as well as a recursion method that directly fits E(n) to a sigmoidal function. All but the last of these can give E₀ estimates that agree fairly well with calibration-based estimates but perform best when the calculations are extended to only about one cycle below the first-derivative maximum (FDM). The LRE model performs as well as any of the four-parameter forms and is easier to use. Its proper implementation requires fitting to it plus a suitable baseline function, which typically requires four-six adjustable parameters in a nonlinear least-squares fit.

SUBMITTER: Tellinghuisen J

PROVIDER: S-EPMC8303528 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Real-time, isothermal, digital nucleic acid amplification is emerging as an attractive approach for a multitude of applications including diagnostics, mechanistic studies, and assay optimization. Unfortunately, there is no commercially available and affordable real-time, digital instrument validated for isothermal amplification; thus, most researchers have not been able to apply digital, real-time approaches to isothermal amplification. Here, we generate an approach to real-time digital loop-mediated isothermal amplification (LAMP) using commercially available microfluidic chips and reagents and open-source components. We demonstrate this approach by testing variables that influence LAMP reaction speed and the probability of detection. By analyzing the interplay of amplification efficiency, background, and speed of amplification, this real-time digital method enabled us to test enzymatic performance over a range of temperatures, generating high-precision kinetic and end-point measurements. We were able to identify the unique optimal temperature for two polymerase enzymes while accounting for amplification efficiency, nonspecific background, and time to threshold. We validated this digital LAMP assay and pipeline by performing a phenotypic antibiotic susceptibility test on 17 archived clinical urine samples from patients diagnosed with urinary tract infections. We provide all the necessary workflows to perform digital LAMP using standard laboratory equipment and commercially available materials. This real-time digital approach will be useful to others in the future to understand the fundamentals of isothermal chemistries, including which components determine amplification fate, reaction speed, and enzymatic performance. Researchers can also adapt this pipeline, which uses only standard equipment and commercial components, to quickly study and optimize assays using precise, real-time digital quantification, accelerating development of critically needed diagnostics.

Dataset Information

Estimating Real-Time qPCR Amplification Efficiency from Single-Reaction Data.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets