Simplified Flow Cytometry-Based Assay for Rapid Multi-Cytokine Profiling and Machine-Learning-Assisted Diagnosis of Inflammatory Diseases

Qiang Quan^1,2Xuegui Ju³Guangmei Li^1,2Lu Ye³Sichong Ren³Shuxin Yang⁴Rui Zhang⁵Hui Wang³Ruyue Lin³Yu Luoting^1,2*

• ¹Children's Medicine Key Laboratory of Sichuan Province, sichuan, China
• ²Sichuan University, Chengdu, China
• ³First Affiliated Hospital of Chengdu Medical College, Chengdu, Sichuan Province, China
• ⁴Department of clinical laboratory of The General Hospital of Western Theater Command, Chegndu, China
• ⁵Department of clinical laboratory of The General Hospital of Western Theater Command, Chengdu, China

Multiple cytokine 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 cytokine profile. Inspired by the One-step concept of ELISA technology, here we report the development of One-step FCM-based 12-plex cytokine assay to reduce operation and reaction time, in which all the reagents (including capture-antibody modified beads and phycoerythrin-labeled detection antibodies) were mixed in the same reaction system and achieved similar performance with the conventional approach. Moreover, we used lyophilization technique to remove the need for cold storage of the reagents and further simplify the assay procedures. 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 of the diseases. Together, our results demonstrate a general and framework for convenient analysis of cytokine panel and has the potential to strongly influence medical research and application in this field.