Department of Clinical Laboratory, Chongqing General Hospital, Chongqing 400014, China.
Department of Chemistry, Fudan University, Shanghai 200438, China.
Anal Chem. 2021 Mar 23;93(11):4782-4787. doi: 10.1021/acs.analchem.0c04590. Epub 2021 Mar 3.
The outbreak of coronavirus disease 2019 (COVID-19) caused by SARS CoV-2 is ongoing and a serious threat to global public health. It is essential to detect the disease quickly and immediately to isolate the infected individuals. Nevertheless, the current widely used PCR and immunoassay-based methods suffer from false negative results and delays in diagnosis. Herein, a high-throughput serum peptidome profiling method based on matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is developed for efficient detection of COVID-19. We analyzed the serum samples from 146 COVID-19 patients and 152 control cases (including 73 non-COVID-19 patients with similar clinical symptoms, 33 tuberculosis patients, and 46 healthy individuals). After MS data processing and feature selection, eight machine learning methods were used to build classification models. A logistic regression machine learning model with 25 feature peaks achieved the highest accuracy (99%), with sensitivity of 98% and specificity of 100%, for the detection of COVID-19. This result demonstrated a great potential of the method for screening, routine surveillance, and diagnosis of COVID-19 in large populations, which is an important part of the pandemic control.
由 SARS-CoV-2 引起的 2019 年冠状病毒病（COVID-19）的爆发仍在持续，这对全球公共卫生构成了严重威胁。快速、立即检测出这种疾病并隔离感染者至关重要。然而，目前广泛使用的基于 PCR 和免疫测定的方法存在假阴性结果和诊断延迟的问题。在此，我们开发了一种基于基质辅助激光解吸/电离飞行时间质谱（MALDI-TOF MS）的高通量血清肽组分析方法，用于 COVID-19 的高效检测。我们分析了 146 例 COVID-19 患者和 152 例对照病例（包括 73 例具有相似临床症状的非 COVID-19 患者、33 例肺结核患者和 46 例健康个体）的血清样本。经过 MS 数据处理和特征选择，使用 8 种机器学习方法构建分类模型。具有 25 个特征峰的逻辑回归机器学习模型在 COVID-19 的检测中达到了最高的准确性（99%），其灵敏度为 98%，特异性为 100%。该结果表明该方法在大人群中进行 COVID-19 的筛查、常规监测和诊断方面具有巨大潜力，这是疫情控制的重要组成部分。
