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基于简化流式细胞术的检测方法，用于快速多细胞因子分析及炎症性疾病的机器学习辅助诊断。

Simplified flow cytometry-based assay for rapid multi-cytokine profiling and machine-learning-assisted diagnosis of inflammatory diseases.

作者信息

Quan Qiang, Ju Xuegui, Li Guangmei, Ye Lu, Ren Sichong, Yang Shuxin, Zhang Rui, Wang Hui, Lin Ruyue, Yu Luoting

机构信息

Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Children's Medicine Key Laboratory of Sichuan Province, Sichuan University, Sichuan, China.

The First Affiliated Hospital of Chengdu Medical College, Chengdu, China.

出版信息

Front Pharmacol. 2025 Jun 27;16:1594141. doi: 10.3389/fphar.2025.1594141. eCollection 2025.

DOI:10.3389/fphar.2025.1594141
PMID:40657638
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12245854/
Abstract

INTRODUCTION

Multiple cytokines detection represents a more robust way to predict the disease progression than a single cytokine, and flow cytometry (FCM)-based assays are increasingly used worldwide for multiple cytokines profile.

METHODS

Inspired by One-step concept of ELISA technology, here we reported the development of one-step FCM-based 12-plex cytokine assay to reduce operation and reaction times, in which all the reagents (including capture-antibody-modified beads and phycoerythrin-labeled detection antibodies) had mixed in the same reaction system and achieved similar performance to the conventional approach. Moreover, we used the lyophilization technique to remove the need for cold storage of reagents to further simplify the assay procedure.

RESULTS

We leveraged our technology to test clinical serum samples from patients with COVID-19 or HBV infectious diseases, and established supervised or unsupervised machine learning models to predict the severity or viral load and get deeper insights into the diseases.

DISCUSSION

Together, our results demonstrate a general and framework for convenient analysis of cytokine panel and have the potential to influence medical research and application in this field.

摘要

引言

与单一细胞因子相比，多种细胞因子检测是预测疾病进展的更有效方法，基于流式细胞术（FCM）的检测方法在全球范围内越来越多地用于多种细胞因子分析。

方法

受ELISA技术一步法概念的启发，我们在此报告了一种基于FCM的一步法12种细胞因子检测方法的开发，以减少操作和反应时间，其中所有试剂（包括捕获抗体修饰的微珠和藻红蛋白标记的检测抗体）在同一反应体系中混合，性能与传统方法相似。此外，我们使用冻干技术消除试剂冷藏需求，进一步简化检测程序。

结果

我们利用该技术检测COVID-19或HBV传染病患者的临床血清样本，并建立监督或无监督机器学习模型来预测疾病严重程度或病毒载量，深入了解这些疾病。

讨论

总之，我们的结果展示了一个方便分析细胞因子组的通用框架，有潜力影响该领域的医学研究和应用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5aea/12245854/942acd941088/fphar-16-1594141-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5aea/12245854/269aeb8ee6a3/fphar-16-1594141-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5aea/12245854/092296d2ad1a/fphar-16-1594141-g002.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5aea/12245854/3a47bd724681/fphar-16-1594141-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5aea/12245854/eb720a4bad62/fphar-16-1594141-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5aea/12245854/218300d4c07d/fphar-16-1594141-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5aea/12245854/942acd941088/fphar-16-1594141-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5aea/12245854/269aeb8ee6a3/fphar-16-1594141-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5aea/12245854/092296d2ad1a/fphar-16-1594141-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5aea/12245854/adaf95942fac/fphar-16-1594141-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5aea/12245854/21b18dabf10b/fphar-16-1594141-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5aea/12245854/3a47bd724681/fphar-16-1594141-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5aea/12245854/eb720a4bad62/fphar-16-1594141-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5aea/12245854/218300d4c07d/fphar-16-1594141-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5aea/12245854/942acd941088/fphar-16-1594141-g008.jpg

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