A rapid, high-throughput, and sensitive PEG-precipitation method for SARS-CoV-2 wastewater surveillance

废水 离心 吞吐量 色谱法 环境科学 化学 计算机科学 环境工程 电信 无线
作者
Xiawan Zheng,Mengying Wang,Yu Deng,Xiaoqing Xu,Danxi Lin,Yulin Zhang,Shuxian Li,Jiahui Ding,Xianghui Shi,Chung In Yau,Leo L. M. Poon,Tong Zhang
出处
期刊:Water Research [Elsevier]
卷期号:230: 119560-119560 被引量:32
标识
DOI:10.1016/j.watres.2022.119560
摘要

The effective application of wastewater surveillance is dependent on testing capacity and sensitivity to obtain high spatial resolution testing results for a timely targeted public health response. To achieve this purpose, the development of rapid, high-throughput, and sensitive virus concentration methods is urgently needed. Various protocols have been developed and implemented in wastewater surveillance networks so far, however, most of them lack the ability to scale up testing capacity or cannot achieve sufficient sensitivity for detecting SARS-CoV-2 RNA at low prevalence. In the present study, using positive raw wastewater in Hong Kong, a PEG precipitation-based three-step centrifugation method was developed, including low-speed centrifugation for large particles removal and the recovery of viral nucleic acid, and medium-speed centrifugation for the concentration of viral nucleic acid. This method could process over 100 samples by two persons per day to reach the process limit of detection (PLoD) of 3286 copies/L wastewater. Additionally, it was found that the testing capacity could be further increased by decreasing incubation and centrifugation time without significantly influencing the method sensitivity. The entire procedure uses ubiquitous reagents and instruments found in most laboratories to obtain robust testing results. This high-throughput, cost-effective, and sensitive tool will promote the establishment of nearly real-time wastewater surveillance networks for valuable public health information.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
孙波完成签到,获得积分20
1秒前
落寞的棒棒糖完成签到,获得积分10
1秒前
无花果应助兴奋的平松采纳,获得10
2秒前
单纯凝丹发布了新的文献求助10
2秒前
Hello应助早起吃饱多运动采纳,获得10
3秒前
3秒前
YONG发布了新的文献求助10
3秒前
小蘑菇应助coffee采纳,获得10
3秒前
4秒前
ovc发布了新的文献求助10
4秒前
我是老大应助松松松采纳,获得10
5秒前
我是老大应助KX2024采纳,获得50
6秒前
6秒前
syy发布了新的文献求助10
6秒前
LX发布了新的文献求助10
7秒前
852应助标致的浩天采纳,获得10
8秒前
8秒前
8秒前
斯文败类应助今今采纳,获得10
9秒前
嘿嘿完成签到 ,获得积分10
10秒前
小小发布了新的文献求助30
10秒前
YC发布了新的文献求助10
10秒前
阔达的海完成签到,获得积分10
10秒前
阔达樱桃发布了新的文献求助20
11秒前
鬲木发布了新的文献求助10
12秒前
完美世界应助鲤鱼安青采纳,获得10
15秒前
15秒前
彭于晏应助LEON采纳,获得10
16秒前
研友_VZG7GZ应助弋yi采纳,获得10
17秒前
18秒前
林强完成签到,获得积分10
18秒前
18秒前
18秒前
李玲玲完成签到,获得积分10
19秒前
舟舟完成签到,获得积分10
19秒前
此生不换完成签到,获得积分10
19秒前
21秒前
搜集达人应助淡然的夜柳采纳,获得10
21秒前
好运连连完成签到,获得积分10
22秒前
CodeCraft应助dd123采纳,获得10
22秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Kinesiophobia : a new view of chronic pain behavior 2000
The Social Psychology of Citizenship 1000
Streptostylie bei Dinosauriern nebst Bemerkungen über die 540
Signals, Systems, and Signal Processing 510
Discrete-Time Signals and Systems 510
Brittle Fracture in Welded Ships 500
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 计算机科学 有机化学 物理 生物化学 纳米技术 复合材料 内科学 化学工程 人工智能 催化作用 遗传学 数学 基因 量子力学 物理化学
热门帖子
关注 科研通微信公众号,转发送积分 5923534
求助须知:如何正确求助?哪些是违规求助? 6933303
关于积分的说明 15821492
捐赠科研通 5051169
什么是DOI,文献DOI怎么找? 2717633
邀请新用户注册赠送积分活动 1672445
关于科研通互助平台的介绍 1607786