Ilieva Mirolyuba, Tschaikowski Max, Vandin Andrea, Uchida Shizuka
Center for RNA Medicine, Department of Clinical Medicine Aalborg University Copenhagen Denmark.
Department of Computer Science Aalborg University Aalborg Denmark.
Clin Transl Discov. 2022 Sep;2(3):e104. doi: 10.1002/ctd2.104. Epub 2022 Jul 17.
The global pandemic of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has swept through every part of the world. Because of its impact, international efforts have been underway to identify the variants of SARS-CoV-2 by genome sequencing and to understand the gene expression changes in COVID-19 patients compared to healthy donors using RNA sequencing (RNA-seq) assay. Within the last two and half years since the emergence of SARS-CoV-2, a large number of OMICS data of COVID-19 patients have accumulated. Yet, we are still far from understanding the disease mechanism. Further, many people suffer from long-term effects of COVID-19; calling for a more systematic way to data mine the generated OMICS data, especially RNA-seq data.
By searching gene expression omnibus (GEO) using the key terms, COVID-19 and RNA-seq, 108 GEO entries were identified. Each of these studies was manually examined to categorize the studies into bulk or single-cell RNA-seq (scRNA-seq) followed by an inspection of their original articles.
The currently available RNA-seq data were generated from various types of patients' samples, and COVID-19 related sample materials have been sequenced at the level of RNA, including whole blood, different components of blood [e.g., plasma, peripheral blood mononuclear cells (PBMCs), leukocytes, lymphocytes, monocytes, T cells], nasal swabs, and autopsy samples (e.g., lung, heart, liver, kidney). Of these, RNA-seq studies using whole blood, PBMCs, nasal swabs and autopsy/biopsy samples were reviewed to highlight the major findings from RNA-seq data analysis.
Based on the bulk and scRNA-seq data analysis, severe COVID-19 patients display shifts in cell populations, especially those of leukocytes and monocytes, possibly leading to cytokine storms and immune silence. These RNA-seq data form the foundation for further gene expression analysis using samples from individuals suffering from long COVID.
由严重急性呼吸综合征冠状病毒2（SARS-CoV-2）引起的2019年冠状病毒病（COVID-19）全球大流行席卷了世界各个角落。由于其影响，国际社会一直在努力通过基因组测序来识别SARS-CoV-2的变体，并通过RNA测序（RNA-seq）分析来了解COVID-19患者与健康供体相比的基因表达变化。自SARS-CoV-2出现后的过去两年半时间里，积累了大量COVID-19患者的组学数据。然而，我们对疾病机制仍知之甚少。此外，许多人遭受COVID-19的长期影响；这就需要一种更系统的方法来挖掘生成的组学数据，尤其是RNA-seq数据。
通过使用关键词COVID-19和RNA-seq搜索基因表达综合数据库（GEO），识别出108个GEO条目。对这些研究中的每一项进行人工检查，将其分类为批量或单细胞RNA-seq（scRNA-seq），然后检查其原始文章。
目前可用的RNA-seq数据来自各种类型的患者样本，与COVID-19相关的样本材料已在RNA水平上进行了测序，包括全血、血液的不同成分[如血浆、外周血单核细胞（PBMC）、白细胞、淋巴细胞、单核细胞、T细胞]、鼻拭子和尸检样本（如肺、心脏、肝脏、肾脏）。其中，对使用全血、PBMC、鼻拭子和尸检/活检样本的RNA-seq研究进行了综述，以突出RNA-seq数据分析的主要发现。
基于批量和scRNA-seq数据分析，重症COVID-19患者的细胞群体发生了变化，尤其是白细胞和单核细胞，这可能导致细胞因子风暴和免疫沉默。这些RNA-seq数据为使用长期COVID患者的样本进行进一步的基因表达分析奠定了基础。
