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超高敏高分辨分析 COVID-19 患者早期血清转换。

Ultrasensitive high-resolution profiling of early seroconversion in patients with COVID-19.

机构信息

Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA.

Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA.

出版信息

Nat Biomed Eng. 2020 Dec;4(12):1180-1187. doi: 10.1038/s41551-020-00611-x. Epub 2020 Sep 18.

Abstract

Sensitive assays are essential for the accurate identification of individuals infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Here, we report a multiplexed assay for the fluorescence-based detection of seroconversion in infected individuals from less than 1 µl of blood, and as early as the day of the first positive nucleic acid test after symptom onset. The assay uses dye-encoded antigen-coated beads to quantify the levels of immunoglobulin G (IgG), IgM and IgA antibodies against four SARS-CoV-2 antigens. A logistic regression model trained using samples collected during the pandemic and samples collected from healthy individuals and patients with respiratory infections before the first outbreak of coronavirus disease 2019 (COVID-19) was 99% accurate in the detection of seroconversion in a blinded validation cohort of samples collected before the pandemic and from patients with COVID-19 five or more days after a positive nasopharyngeal test by PCR with reverse transcription. The high-throughput serological profiling of patients with COVID-19 allows for the interrogation of interactions between antibody isotypes and viral proteins, and should help us to understand the heterogeneity of clinical presentations.

摘要

敏感的检测对于准确识别感染严重急性呼吸综合征冠状病毒 2 (SARS-CoV-2) 的个体至关重要。在这里,我们报告了一种基于荧光的多重检测方法,用于检测从不到 1µl 的血液中感染个体的血清转化,并且可以在症状出现后的第一天的第一次阳性核酸检测时进行检测。该检测方法使用染料编码的抗原包被珠来定量针对 SARS-CoV-2 四种抗原的 IgG、IgM 和 IgA 抗体水平。使用大流行期间收集的样本和在 2019 年冠状病毒病 (COVID-19) 首次爆发前从健康个体和呼吸道感染患者收集的样本训练的逻辑回归模型,在对大流行前收集的样本和 COVID-19 患者的盲法验证队列进行检测时,检测血清转化的准确率达到了 99%,这些患者的鼻咽拭子 PCR 检测结果为阳性后 5 天或以上。对 COVID-19 患者进行高通量血清学分析,可以研究抗体同种型和病毒蛋白之间的相互作用,这应该有助于我们了解临床表现的异质性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2f37/7498988/6a73eb3d2495/41551_2020_611_Fig1_HTML.jpg

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