Characterizing Macrophages Diversity in COVID-19 Patients Using Deep Learning.
Characterizing Macrophages Diversity in COVID-19 Patients Using Deep Learning.
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DOI：
10.3390/genes13122264
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发表时间：
2022-12-01
期刊：
影响因子：
3.5
通讯作者：
Jin, Yufang
中科院分区：
文献类型：
--
作者：
Flores, Mario A.;Paniagua, Karla;Huang, Wenjian;Ramirez, Ricardo;Falcon, Leonardo;Liu, Andy;Chen, Yidong;Huang, Yufei;Jin, Yufang
严重急性呼吸道综合征冠状病毒2型（SARS-CoV-2）是导致2019冠状病毒病（COVID-19）的病原体，影响了数十亿人的生活，并导致数百万感染者死亡。这种病毒已被证明在个体之间有不同的结果，其中一些人表现出轻微的感染，而另一些人则表现出严重的症状甚至死亡。识别与COVID-19感染严重程度相关的分子状态对于理解关键免疫反应的差异至关重要。在这项研究中，我们计算处理了一组公开的单细胞RNA-Seq（scRNA-Seq）数据，这些数据来自12个被诊断为轻度、重度或无感染的支气管肺泡灌洗液（BALF）样本，并生成了一个高质量的数据集，该数据集由63，734个细胞组成，每个细胞有23，916个基因。我们扩展了细胞类型和亚型组成的鉴定，我们的分析表明，与正常组相比，轻度和重度组的细胞类型组成存在显着差异。重要的是，炎症反应在重度组中显著升高，这通过巨噬细胞的显著增加来证明，从正常组的10.56%到轻度组的20.97%和重度组的34.15%。作为免疫防御的指标，T细胞群体在轻度组中占24.76%，在重度组中降至7.35%。为了验证这些发现，我们开发了几种人工神经网络（ANN）和图卷积神经网络（GCNN）模型。我们表明，GCNN模型使用来自巨噬细胞亚型的数据达到了91.16%的感染预测准确率。总的来说，我们的研究表明严重感染患者的炎症反应和免疫细胞的基因表达谱存在显着差异。
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the etiological agent responsible for coronavirus disease 2019 (COVID-19), has affected the lives of billions and killed millions of infected people. This virus has been demonstrated to have different outcomes among individuals, with some of them presenting a mild infection, while others present severe symptoms or even death. The identification of the molecular states related to the severity of a COVID-19 infection has become of the utmost importance to understanding the differences in critical immune response. In this study, we computationally processed a set of publicly available single-cell RNA-Seq (scRNA-Seq) data of 12 Bronchoalveolar Lavage Fluid (BALF) samples diagnosed as having a mild, severe, or no infection, and generated a high-quality dataset that consists of 63,734 cells, each with 23,916 genes. We extended the cell-type and sub-type composition identification and our analysis showed significant differences in cell-type composition in mild and severe groups compared to the normal. Importantly, inflammatory responses were dramatically elevated in the severe group, which was evidenced by the significant increase in macrophages, from 10.56% in the normal group to 20.97% in the mild group and 34.15% in the severe group. As an indicator of immune defense, populations of T cells accounted for 24.76% in the mild group and decreased to 7.35% in the severe group. To verify these findings, we developed several artificial neural networks (ANNs) and graph convolutional neural network (GCNN) models. We showed that the GCNN models reach a prediction accuracy of the infection of 91.16% using data from subtypes of macrophages. Overall, our study indicates significant differences in the gene expression profiles of inflammatory response and immune cells of severely infected patients.
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通讯作者：
Regev A