High-throughput colorimetric LAMP detection of <i>Mycoplasma gallisepticum</i> with intelligent algorithm-assisted analysis
<i>Mycoplasma gallisepticum</i> (MG) is a common and harmful pathogen in the poultry farming industry. Early detection is essential for effective prevention and control of MG. To meet the demand for high-throughput detection in resource-limited testing environments, a colorimetric loop-mediated isothermal amplification (LAMP) assay based on the cresol red indicator was developed. This method enables visual analysis by detecting hydrogen ions produced during the LAMP reaction, causing a color shift from magenta to yellow. The assay successfully detected MG at concentrations as low as 1.07 × 10<sup>2</sup> copies per µL within 70 min, with no cross-reactivities against other avian respiratory pathogens. It demonstrated stability and reliability, with a coefficient of variation (CV) below 5% across repeated experiments. Additionally, an intelligent software program based on the LAB color space algorithm was developed to complement this assay. The digital analysis function enhances detection precision and facilitates high-throughput analysis by processing up to 64 samples simultaneously. Validation with clinical samples from intensive farms showed a 98% concordance rate with qPCR results (<i>κ</i> > 0.9), confirming the method's accuracy. Consequently, the colorimetric LAMP assay in this study not only accurately identifies MG but also provides an intelligent, high-throughput detection platform for MG detection.
Authors
- Wuyang Jing
- Yucen Liang
- Renhang Xiao
- Haiyan Shen
- Chenggang Xu
- Ruidong Liu
- Jianmin Zhang (ORCID: https://orcid.org/0000-0003-4765-0812)
- X. George Xu (ORCID: https://orcid.org/0000-0003-0605-3878)
- Qijie Lin
- Qianyi Cai
- Hongchao Gou
- Ming Liao
Institutions
- South China Agricultural University (CN)
- Huazhong Agricultural University (CN)
- Guangdong Academy of Agricultural Sciences (CN)
- Zhongkai University of Agriculture and Engineering (CN)
Publication Details
- Journal
- Analytical Methods
- Published
- 2026-01-01
- DOI
- https://doi.org/10.1039/d6ay00198j
- Primary Topic
- Microbial infections and disease research
- Type
- article
- Field-Weighted Citation Impact
- 0.00