Supplementary file 1_Simplified flow cytometry-based assay for rapid multi-cytokine profiling and machine-learning-assisted diagnosis of inflammatory diseases.pdf

IntroductionMultiple 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.
MethodsInspired 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.
ResultsWe 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.
DiscussionTogether, 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.

引言
多细胞因子检测相较于单一细胞因子检测，可更为稳健地预测疾病进展；基于流式细胞术(flow cytometry, FCM)的检测方法在全球范围内用于多细胞因子谱分析的应用日益普及。
材料与方法
本研究受酶联免疫吸附试验(Enzyme-Linked Immunosorbent Assay, ELISA)的单步操作理念启发，开发了一种基于流式细胞术的12重细胞因子检测方法，以缩短操作与反应时长。该方法将所有试剂（包括捕获抗体修饰微球与藻红蛋白标记的检测抗体）置于同一反应体系中混合，其检测性能与传统方法相当。此外，本研究采用冷冻干燥技术，无需对试剂进行冷链储存，进一步简化了检测流程。
结果
本研究利用所开发的技术，对新型冠状病毒肺炎（COVID-19）或乙型肝炎病毒(Hepatitis B Virus, HBV)感染患者的临床血清样本开展检测，并构建有监督或无监督机器学习模型，以预测疾病严重程度与病毒载量，进而深入解析此类疾病的特征与发病机制。
讨论
综上，本研究结果证实了一套可便捷分析细胞因子组的通用框架，该技术有望对该领域的医学研究与实际应用产生积极影响。