Investigating Cellular Trajectories in the Severity of COVID-19 and Their Transcriptional Programs Using Machine Learning Approaches.
Investigating Cellular Trajectories in the Severity of COVID-19 and Their Transcriptional Programs Using Machine Learning Approaches.
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DOI：
10.3390/genes12050635
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发表时间：
2021-04-24
期刊：
影响因子：
3.5
通讯作者：
Zhao Z
中科院分区：
文献类型：
--
作者：
Jeong HH;Jia J;Dai Y;Simon LM;Zhao Z
对新冠肺炎患者的支气管肺泡灌洗液样本进行单细胞核酸测序，使我们能够检测人体组织在感染SARS-CoV-2病毒后基因表达的变化。然而，新冠肺炎在单细胞分辨率下的潜在致病机制、其转录驱动因素和动力学需要进一步研究。在这项研究中，我们应用机器学习算法来推断细胞变化的轨迹，并识别它们的转录程序。我们的研究产生的细胞轨迹显示了新冠肺炎对巨噬细胞和T细胞的健康到中度及健康到重度的发病机制，并且我们在巨噬细胞中观察到比T细胞更多样化的轨迹。此外，我们的深度学习算法DrivAER发现了几条可能是新冠肺炎发病机制转录变化的潜在驱动因素的途径(例如，异生途径和补体途径)和转录因子(例如，MITF和GATA3)，以及新冠肺炎严重程度的标志。此外，与T细胞相关的功能相比，巨噬细胞相关的功能更符合疾病的严重程度。我们的发现更熟练地剖析了导致新冠肺炎感染严重程度的转录变化。
Single-cell RNA sequencing of the bronchoalveolar lavage fluid (BALF) samples from COVID-19 patients has enabled us to examine gene expression changes of human tissue in response to the SARS-CoV-2 virus infection. However, the underlying mechanisms of COVID-19 pathogenesis at single-cell resolution, its transcriptional drivers, and dynamics require further investigation. In this study, we applied machine learning algorithms to infer the trajectories of cellular changes and identify their transcriptional programs. Our study generated cellular trajectories that show the COVID-19 pathogenesis of healthy-to-moderate and healthy-to-severe on macrophages and T cells, and we observed more diverse trajectories in macrophages compared to T cells. Furthermore, our deep-learning algorithm DrivAER identified several pathways (e.g., xenobiotic pathway and complement pathway) and transcription factors (e.g., MITF and GATA3) that could be potential drivers of the transcriptomic changes for COVID-19 pathogenesis and the markers of the COVID-19 severity. Moreover, macrophages-related functions corresponded more to the disease severity compared to T cells-related functions. Our findings more proficiently dissected the transcriptomic changes leading to the severity of a COVID-19 infection.
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影响因子：
4.7
作者：
El-Ghiaty, Mahmoud A.;Shoieb, Sherif M.;El-Kadi, Ayman O. S.
通讯作者：
El-Kadi, Ayman O. S.
影响因子：
4.6
作者：
Lee SH;Kwon JY;Kim SY;Jung K;Cho ML
通讯作者：
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影响因子：
9.8
作者：
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通讯作者：
Pavan WJ
DOI：
10.1073/pnas.2010540117
发表时间：
2020-10-06
影响因子：
11.1
作者：
Holter JC;Pischke SE;de Boer E;Lind A;Jenum S;Holten AR;Tonby K;Barratt-Due A;Sokolova M;Schjalm C;Chaban V;Kolderup A;Tran T;Tollefsrud Gjølberg T;Skeie LG;Hesstvedt L;Ormåsen V;Fevang B;Austad C;Müller KE;Fladeby C;Holberg-Petersen M;Halvorsen B;Müller F;Aukrust P;Dudman S;Ueland T;Andersen JT;Lund-Johansen F;Heggelund L;Dyrhol-Riise AM;Mollnes TE
通讯作者：
Mollnes TE
影响因子：
14.9
作者：
Han H;Cho JW;Lee S;Yun A;Kim H;Bae D;Yang S;Kim CY;Lee M;Kim E;Lee S;Kang B;Jeong D;Kim Y;Jeon HN;Jung H;Nam S;Chung M;Kim JH;Lee I
通讯作者：
Lee I