DataSheet_1_On Deep Landscape Exploration of COVID-19 Patients Cells and Severity Markers.docx

COVID-19 is a disease with a spectrum of clinical responses ranging from moderate to critical. To study and control its effects, a large number of researchers are focused on two substantial aims. On the one hand, the discovery of diverse biomarkers to classify and potentially anticipate the disease severity of patients. These biomarkers could serve as a medical criterion to prioritize attention to those patients with higher prone to severe responses. On the other hand, understanding how the immune system orchestrates its responses in this spectrum of disease severities is a fundamental issue required to design new and optimized therapeutic strategies. In this work, using single-cell RNAseq of bronchoalveolar lavage fluid of nine patients with COVID-19 and three healthy controls, we contribute to both aspects. First, we presented computational supervised machine-learning models with high accuracy in classifying the disease severity (moderate and severe) in patients with COVID-19 starting from single-cell data from bronchoalveolar lavage fluid. Second, we identified regulatory mechanisms from the heterogeneous cell populations in the lungs microenvironment that correlated with different clinical responses. Given the results, patients with moderate COVID-19 symptoms showed an activation/inactivation profile for their analyzed cells leading to a sequential and innocuous immune response. In comparison, severe patients might be promoting cytotoxic and pro-inflammatory responses in a systemic fashion involving epithelial and immune cells without the possibility to develop viral clearance and immune memory. Consequently, we present an in-depth landscape analysis of how transcriptional factors and pathways from these heterogeneous populations can regulate their expression to promote or restrain an effective immune response directly linked to the patients prognosis.

COVID-19是一类临床应答谱覆盖中度至重度的疾病。为研究并管控其危害，大量研究者聚焦于两项核心目标。其一，发掘多样的生物标志物（biomarker）以分类并有望预判患者的疾病严重程度——此类生物标志物可作为临床判定标准，优先对更易出现重症应答的患者给予重点关注。其二，阐明免疫系统在该疾病严重程度谱中如何调控自身应答，这是设计新型优化治疗策略的基础性问题。本研究通过对9名COVID-19患者及3名健康对照者的支气管肺泡灌洗液（bronchoalveolar lavage fluid）开展单细胞RNA测序（single-cell RNAseq），在上述两方面均取得进展。第一，我们构建了高精度的有监督机器学习模型，可基于支气管肺泡灌洗液的单细胞数据对COVID-19患者的疾病严重程度（中度与重度）进行精准分类。第二，我们鉴定出肺部微环境异质性细胞群中与不同临床应答相关的调控机制。研究结果表明，表现为中度COVID-19症状的患者，其分析的细胞呈现激活/失活特征谱，最终引发有序且温和的免疫应答。相较之下，重症患者可能以系统性方式诱导细胞毒性与促炎应答，累及上皮细胞与免疫细胞，且无法实现病毒清除与免疫记忆。据此，我们深入解析了此类异质性细胞群中的转录因子及通路如何调控基因表达，进而促进或抑制与患者预后直接相关的有效免疫应答。