Integration of Image Pattern Recognition and Photon Sensor for Analyzing Cytokine Gene Expression Using πCode MicroDisc

Author:

Juntit On-anong12,Sornsuwan Kanokporn12,Yasamut Umpa234ORCID,Tayapiwatana Chatchai234ORCID

Affiliation:

1. Office of Research Administration, Chiang Mai University, Chiang Mai 50200, Thailand

2. Center of Biomolecular Therapy and Diagnostic, Faculty of Associated Medical Sciences, Chiang Mai University, Chiang Mai 50200, Thailand

3. Division of Clinical Immunology, Department of Medical Technology, Faculty of Associated Medical Sciences, Chiang Mai University, Chiang Mai 50200, Thailand

4. Center of Innovative Immunodiagnostic Development, Department of Medical Technology, Faculty of Associated Medical Sciences, Chiang Mai University, Chiang Mai 50200, Thailand

Abstract

Current quantitative gene expression detection in genomic and transcriptomic research heavily relies on quantitative real-time PCR (qPCR). While existing multiplex gene detection techniques offer simultaneous analysis of multiple targets, we present an alternative assay capable of detecting gene expression simultaneously within a single well. This highly sensitive method utilizes πCode MicroDiscs, featuring unique identification patterns and fluorescent detection. Our study compared this multiplex πCode platform with a qPCR platform for profiling cytokine gene expression. The πCode MicroDisc assay successfully demonstrated the expression of polymerization markers for M1- and M2-like macrophages generated from THP-1-derived macrophages in a qualitative assay. Additionally, our findings suggest a pattern agreement between the πCode assay and the qPCR assay, indicating the potential of the πCode technology for comparative gene expression analysis. Regarding the inherent sensitivity and linearity, the developed πCode assay primarily provides qualitative gene expression to discriminate the polarization of macrophages. This remarkable capability presents substantial advantages for researchers, rendering the technology highly suitable for high-throughput applications in clinical diagnosis and disease monitoring.

Funder

Chiang Mai University

Fundamental Fund 2024

Publisher

MDPI AG

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