Identification of disease-specific cell subtypes (DSCSs) has profound implications for understanding disease mechanisms, preoperative diagnosis, and precision therapy. However, achieving unified annotation of DSCSs in heterogeneous single-cell datasets remains a challenge. In this study, we developed the gPRINT algorithm (generalized approach for cell subtype identification with single cell's voicePRINT). Inspired by the principles of speech recognition in noisy environments, gPRINT transforms gene position and gene expression information into voiceprints based on ordered and clustered gene expression phenomena, obtaining unique "gene print" patterns for each cell. Then, we integrated neural networks to mitigate the impact of background noise on cell identity label mapping. We demonstrated the reproducibility of gPRINT across different donors, single-cell sequencing platforms, and disease subtypes, and its utility for automatic cell subtype annotation across datasets. Moreover, gPRINT achieved higher annotation accuracy of 98.37% when externally validated based on the same tissue, surpassing other algorithms. Furthermore, this approach has been applied to fibrosis-associated diseases in multiple tissues throughout the body, as well as to the annotation of fibroblast subtypes in a single tissue, tendon, where fibrosis is prevalent. We successfully achieved automatic prediction of tendinopathy-specific cell subtypes, key targets, and related drugs. In summary, gPRINT provides an automated and unified approach for identifying DSCSs across datasets, facilitating the elucidation of specific cell subtypes under different disease states and providing a powerful tool for exploring therapeutic targets in diseases.
Humans ; Algorithms ; Single-Cell Analysis ; Databases, Genetic ; Molecular Sequence Annotation

OBJECTIVE To establish a rapid and accurate PCR method for detecting 24 strains of Burkholderia cepacia complex(Bcc) by comparing three detection methods of loop-mediated isothermal amplification(LAMP), SYTO 9 dye method based on polymerase chain reaction(PCR) and TaqMan probe real-time fluorescent quantitative PCR method( TaqMan probe method). METHODS According to the molecular biological information of 24 strains of Bcc in the NCBI database, multiple candidate sequence fragments unique to Bcc were screened out, and specific primer and probe that could simultaneously detect 24 strains of Bcc were designed. At the same time, the detection methods of LAMP, SYTO 9 dye method based on PCR and Taqman probe were explored, and the optimal annealing temperature was optimized and screened. The 39 experimental strains were used to verify the Bcc detection method. RESULTS LAMP method could not effectively detect Bcc, SYTO 9 dye method and TaqMan probe method could effectively detect more than 20 strains of Bcc, while TaqMan probe method had higher amplification effect, better detection sensitivity, repeatability and stability, which could meet the requirements of this study. CONCLUSION In this study, a TaqMan probe method for rapid detection of Bcc was established. Compared with LAMP method and SYTO 9 dye method, this method has the advantages of fast, simple and high sensitivity, and provides technical support for the rapid detectionof Bcc.

A novel gene, which was a homologue of Arabidopsis COI1 was isolated from rice (Oryza sativa L.) by RT-PCR and designated as OsCOI1. It encoded a protein of 595 amino acids. The similar F-box motif and 16 leucine-rich repeats were found in the deduced protein OsCOI1. OsCOI1 and COI1 showed high homology (74%) at amino acid level. Semi-quantitative RT-PCR and Northern blot analysis demonstrated that the expression of OsCOI1 in rice varied obviously after treatment with MeJA and ABA but was not affected by SA and ET, suggesting that the specific function of OsCOI1 in JA signal pathway and related ABA pathway.