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Related Concept Videos

Real Time RT-PCR02:57

Real Time RT-PCR

Real-time reverse transcription-polymerase chain reaction, or Real-time RT-PCR, is an analytical tool used to determine the expression level of target genes. The method involves converting mRNA to complementary DNA with the help of an enzyme known as reverse transcriptase, followed by the PCR amplification of the cDNA. These two processes can be performed simultaneously in a single tube or separately as a two-step reaction.
The real-time quantification of the number of amplified products is...

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Related Experiment Video

Updated: Jul 12, 2026

Visual Detection of Multiple Nucleic Acids in a Capillary Array
08:56

Visual Detection of Multiple Nucleic Acids in a Capillary Array

Published on: November 15, 2017

TimeSight: Kinetics-aware detection for digital nucleic acid amplification via spatiotemporal convolutional learning.

Yu Wang1, Zixiao Liao1, Yehong Gui1

  • 1Research Centre for Analytical Instrumentation, State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, Zhejiang Province, 310027, China.

Biosensors & Bioelectronics
|July 9, 2026
PubMed
Summary

TimeSight analyzes temporal fluorescence dynamics for accurate digital nucleic acid quantification in portable devices. This method improves point-of-care testing by distinguishing true amplification from noise, even with low signal quality.

Keywords:
Digital nucleic acid quantificationFluorescence image analysisPoint-of-care testing (POCT)Spatiotemporal deep learning

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Published on: February 3, 2021

Area of Science:

  • Biotechnology
  • Molecular Diagnostics
  • Bioinformatics

Background:

  • Digital nucleic acid amplification offers absolute quantification but current methods analyze single endpoint images, ignoring temporal data.
  • Portable point-of-care testing (POCT) instruments face challenges with image quality due to simplified optics and low signal-to-noise ratios.
  • Distinguishing true amplification from artifacts is difficult with traditional endpoint analysis, especially in noisy POCT environments.

Purpose of the Study:

  • To develop an end-to-end spatiotemporal detection framework, TimeSight, for analyzing time-resolved fluorescence image stacks from digital recombinase polymerase amplification (dRPA) on portable POCT instruments.
  • To improve the accuracy and reliability of absolute molecular quantification in resource-limited settings by leveraging temporal amplification kinetics.
  • To overcome limitations of single-frame analysis in low-signal and artifact-prone conditions inherent to miniaturized diagnostic devices.

Main Methods:

  • Implemented TimeSight, a framework using factorised 3D convolutions with a learned temporal collapse mechanism to jointly analyze spatial and kinetic information.
  • Developed a method that bypasses the need for partition segmentation, spatial registration, and manual threshold tuning.
  • Utilized time-resolved fluorescence image stacks acquired during dRPA on a portable POCT instrument.

Main Results:

  • TimeSight achieved superior performance (F1: 0.968, Precision: 0.977) compared to single-frame and multi-frame baselines on a large test set.
  • The framework effectively suppressed temporally invariant artifacts, maintaining performance across varying signal-to-noise ratios and artifact levels.
  • Demonstrated accurate matching of reference concentrations across four orders of magnitude and agreement with commercial digital PCR for S. aureus DNA detection.
  • Confirmed portable feasibility with rapid analysis (<2% parameter increase) at approximately 113 ms per image stack.

Conclusions:

  • Temporal amplification kinetics can compensate for spatial information loss in miniaturized hardware, enabling robust absolute quantification.
  • TimeSight provides a novel spatiotemporal detection paradigm for digital nucleic acid quantification, particularly beneficial for resource-limited settings and POCT.
  • The framework lowers the barrier for accurate molecular diagnostics at the point-of-care by effectively handling low-quality imaging data.