Rapid, High-Throughput Single-Cell Multiplex In Situ Tagging (MIST) Analysis of Immunological Disease with Machine Learning.
Rapid, High-Throughput Single-Cell Multiplex In Situ Tagging (MIST) Analysis of Immunological Disease with Machine Learning.
复制标题
DOI：
10.1021/acs.analchem.3c01157
复制
发表时间：
2023-05-16
影响因子：
7.4
通讯作者：
Wang, Jun
中科院分区：
文献类型：
--
作者：
Yang, Liwei;Dutta, Pratik;Davuluri, Ramana V.;Wang, Jun
免疫反应级联涉及多种免疫细胞的激活和大量细胞因子的释放，从而导致正常、平衡的炎症或过度炎症反应，甚至脓毒症引起的器官损伤。基于血清中多种细胞因子的免疫疾病的常规诊断具有不同的准确性，并且难以区分正常炎症和脓毒症。在此，我们提出了一种通过使用单细胞多重原位标记 (scMIST) 技术对 T 细胞进行快速、超高多重分析来检测免疫性疾病的新方法。 scMIST 无需特殊仪器的帮助即可同时检测单细胞中的 46 种标记物和细胞因子。建立了盲肠结扎穿刺 (CLP) 脓毒症模型，以提供两组小鼠的 T 细胞，这些小鼠在手术中幸存或一天后死亡。 scMIST 检测捕获了 T 细胞特征和恢复过程中的动态。与外周血中的细胞因子相比，T细胞标志物表现出不同的动态和细胞因子水平。我们将随机森林机器学习模型应用于来自两组小鼠的单个 T 细胞。通过训练，该模型已经能够通过 T 细胞分类和多数规则来预测小鼠群体，准确率高达 94%。我们的方法开创了单细胞组学的新方向，应该广泛适用于人类疾病。
The cascade of immune responses involves activation of diverse immune cells and release of large amount of cytokines, which leads to either normal, balanced inflammation or hyperinflammatory responses and even organ damage by sepsis. Conventional diagnosis of immunological disorders based on multiple cytokines in the blood serum has varied accuracy, and it is difficult to distinguish normal inflammation from sepsis. Herein, we present a new approach to detect immunological disorders through rapid, ultrahigh-multiplex analysis of T cells using a single-cell multiplex in situ tagging (scMIST) technology. scMIST permits simultaneous detection of 46 markers and cytokines from single cells without the assistance of special instruments. A cecal ligation and puncture (CLP) sepsis model was built to supply T cells from two groups of mice that survived the surgery or died after one day. The scMIST assays have captured the T cell features and the dynamics over the course of recovery. Compared with cytokines in the peripheral blood, T cell markers show different dynamics and cytokine levels. We have applied a random forest machine learning model to single T cells from two groups of mice. Through training, the model has been able to predict the group of mice through T cell classification and majority rule with 94% accuracy. Our approach pioneers a new direction of single-cell omics and should be widely applicable to human diseases.
登录
查看更多内容
绘制配对神经元与巨噬细胞单细胞之间分泌蛋白组介导的相互作用
DOI：
10.1073/pnas.2200944119
发表时间：
2022-11
影响因子：
11.1
作者：
通讯作者：
--
影响因子：
48
作者：
Amodio, Matthew;van Dijk, David;Srinivasan, Krishnan;Chen, William S.;Mohsen, Hussein;Moon, Kevin R.;Campbell, Allison;Zhao, Yujiao;Wang, Xiaomei;Venkataswamy, Manjunatha;Desai, Anita;Ravi, V.;Kumar, Priti;Montgomery, Ruth;Wolf, Guy;Krishnaswamy, Smita
通讯作者：
Krishnaswamy, Smita
DOI：
10.1038/nri3552
发表时间：
2013-12
期刊：
Nature reviews. Immunology
影响因子：
--
作者：
通讯作者：
--
DOI：
10.1126/science.1198704
发表时间：
2011-05-06
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Bendall SC;Simonds EF;Qiu P;Amir el-AD;Krutzik PO;Finck R;Bruggner RV;Melamed R;Trejo A;Ornatsky OI;Balderas RS;Plevritis SK;Sachs K;Pe'er D;Tanner SD;Nolan GP
通讯作者：
Nolan GP
影响因子：
7.4
作者：
Buchegger, Patricia;Preininger, Claudia
通讯作者：
Preininger, Claudia