For an emerging disease like COVID-19, systems immunology tools may quickly identify and quantitatively characterize cells associated with disease progression or clinical response. With repeated sampling, immune monitoring creates a real-time portrait of the cells reacting to a novel virus before disease specific knowledge and tools are established. However, single cell analysis tools can struggle to reveal rare cells that are under 0.1% of the population. Here, the machine learning workflow Tracking Responders Expanding (T-REX) was created to identify changes in both very rare and common cells in diverse human immune monitoring settings. T-REX identified cells that were highly similar in phenotype and localized to hotspots of significant change during rhinovirus and SARS-CoV-2 infections. Specialized reagents used to detect the rhinovirus-specific CD4+ cells, MHCII tetramers, were not used during unsupervised analysis and instead left out to serve as a test of whether T-REX identified biologically significant cells. In the rhinovirus challenge study, T-REX identified virus-specific CD4+ T cells based on these cells being a distinct phenotype that expanded by ≥95% following infection. T-REX successfully identified hotspots containing virus-specific T cells using pairs of samples comparing Day 7 of infection to samples taken either prior to infection (Day 0) or after clearing the infection (Day 28). Mapping pairwise comparisons in samples according to both the direction and degree of change provided a framework to compare systems level immune changes during infectious disease or therapy response. This revealed that the magnitude and direction of systemic immune change in some COVID-19 patients was comparable to that of blast crisis acute myeloid leukemia patients undergoing induction chemotherapy and characterized the identity of the immune cells that changed the most. Other COVID-19 patients instead matched an immune trajectory like that of individuals with rhinovirus infection or melanoma patients receiving checkpoint inhibitor therapy. T-REX analysis of paired blood samples provides an approach to rapidly identify and characterize mechanistically significant cells and to place emerging diseases into a systems immunology context.

中文翻译：

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无监督机器学习揭示了 COVID-19、鼻病毒感染和癌症治疗中的关键免疫细胞亚群。

对于像 COVID-19 这样的新兴疾病，系统免疫学工具可以快速识别和定量表征与疾病进展或临床反应相关的细胞。通过重复采样，免疫监测可以在建立疾病特异性知识和工具之前创建对新病毒反应的细胞的实时画像。然而，单细胞分析工具很难发现低于 0.1% 的稀有细胞。在这里，创建了机器学习工作流程跟踪响应者扩展 (T-REX)，以识别不同人类免疫监测环境中非常罕见和常见细胞的变化。T-REX 鉴定出表型高度相似的细胞，并定位于鼻病毒和 SARS-CoV-2 感染期间发生显着变化的热点。用于检测鼻病毒特异性 CD4+ 细胞 MHCII 四聚体的专用试剂在无监督分析期间未使用，而是用于测试 T-REX 是否识别出具有生物学意义的细胞。在鼻病毒攻击研究中，T-REX 鉴定出病毒特异性 CD4+ T 细胞，因为这些细胞是一种独特的表型，在感染后扩增 ≥95%。T-REX 使用成对的样本将感染第 7 天与感染前（第 0 天）或清除感染后（第 28 天）采集的样本进行比较，成功识别出含有病毒特异性 T 细胞的热点。根据变化的方向和程度在样本中进行成对比较，为比较传染病或治疗反应期间的系统水平免疫变化提供了一个框架。这表明，一些 COVID-19 患者全身免疫变化的幅度和方向与接受诱导化疗的急变期急性髓细胞白血病患者相当，并表征了变化最大的免疫细胞的特性。相反，其他 COVID-19 患者与接受检查点抑制剂治疗的鼻病毒感染者或黑色素瘤患者的免疫轨迹相匹配。配对血液样本的 T-REX 分析提供了一种快速识别和表征具有机械意义的细胞并将新出现的疾病置于系统免疫学环境中的方法。