
中国科学院微生物研究所,中国微生物学会
文章信息
- 卢维来, 许添顺, 汤晖, 蒋靖坤, 付钰. 2022
- LU Weilai, XU Tianshun, TANG Hui, JIANG Jingkun, FU Yu.
- 微生物气溶胶检测技术的研究进展
- Technologies for microbial aerosol sampling and identification: a review and current perspective
- 微生物学报, 62(4): 1345-1361
- Acta Microbiologica Sinica, 62(4): 1345-1361
-
文章历史
- 收稿日期:2021-08-31
- 修回日期:2021-12-09
- 网络出版日期:2022-01-14
2. 河北大学生命科学学院, 河北 保定 071002;
3. 清华大学环境学院, 北京 100084;
4. 中国科学院大学存济医学院, 北京 100049
2. School of Life Sciences, Hebei University, Baoding 071002, Hebei, China;
3. School of Environment, Tsinghua University, Beijing 100084, China;
4. Savaid Medical School, University of Chinese Academy of Sciences, Beijing 100049, China
气溶胶(aerosols)一般指悬浮于气体介质中的固体或液体颗粒物体系,颗粒物尺寸通常为1 nm–100 μm。含有生物物质的气溶胶被称为生物气溶胶(bioaerosols),常见的生物微粒包括细菌、古菌、真菌孢子、病毒、藻类、虫卵和花粉颗粒等[1–2],其中含有微生物(细菌、真菌和病毒)的气溶胶也被称为微生物气溶胶。生物气溶胶不仅具备气溶胶的特性还具有其独特的生物特性,对生态系统和人类健康具有重要的影响,是近些年研究的热点之一。微生物气溶胶具有大范围快速传播疾病的潜在危害性而备受关注。有效地收集和准确地分析微生物气溶胶中所含的微生物是研究微生物如何通过气溶胶传播疾病的前提所在,本综述着重介绍微生物气溶胶采集和分析鉴定的研究进展。
1 微生物气溶胶的生物特性及潜在危害性随着世界范围内,布鲁氏菌感染、流感、重急性呼吸系统综合征(severe acute respiratory syndrome,SARS)、中东呼吸综合征(middle East respiratory syndrome,MERS)和新型冠状病毒(coronavirus disease 2019,COVID-19)等病原微生物的传播,微生物气溶胶的危害被引起广泛的关注。微生物气溶胶的微生物浓度、种类和丰度极大地受到地域、季节、湿度、温度、风向和风速等因素的影响。一般认为微生物气溶胶中微生物的数量在101–106个细胞/m3[3–5]。由于环境的动态变化、微生物气溶胶的采集和鉴定方法的差异致使不同研究报道的微生物的种类和数量差异巨大。细菌、真菌或病毒可以以独立个体或聚集体的形式在空气中扩散[6],真菌通常以孢子的形式悬浮于空气中,细菌一般吸附于颗粒物表面[7],当生物形成聚集体(例如形成生物膜结构)时可以有力地抵制大气中的紫外辐照和臭氧等外界环境的压力。气溶胶的微环境为微生物个体或聚集体提供一定的保护作用,而气溶胶微环境的营养成分、水分含量、酸碱度、盐离子成分和温度变化也将影响微生物的活力。
气溶胶中的微生物致敏性、感染性和毒性等是人们关注的重点。有研究报道,大气颗粒物中可以检测到23种人类病原菌的基因序列,在大气PM2.5时病原菌的相对丰度最高[8],可吸入病原菌的相对丰度随PM污染程度而增加[9]。吸入的生物气溶胶沉积在呼吸道表面或肺部,当达到病原菌感染剂量时可能引发诸如过敏性疾病、呼吸道疾病、神经系统疾病、癌症和COVID-19等疾病[10–12]。尽管微生物气溶胶的化学成分、老化程度、粒子形状、直径大小、带电性、密度、温度和吸湿性等因素会影响微生物气溶胶的感染力和致病力,但感染力和毒力的决定性因素是其所携带的微生物种类。含有不同菌株的微生物气溶胶在相似条件下,感染剂量可相差若干个数量级。因此,研究与监测微生物气溶胶中微生物的种类、浓度、分布及其变化规律对防控有害微生物气溶胶,促进人类健康具有重要的意义。
2 微生物气溶胶的收集与鉴定气溶胶中微生物的检测和鉴定是研究其性质和评估其危害性的前提和依据,相关新技术和新方法的研发目前已成为微生物气溶胶研究中的重要研究方向之一,下面将对微生物气溶胶检测所涉及的采样和分析鉴定两个关键步骤的技术方法进展进行综述。
2.1 微生物气溶胶的采样方法高效、全覆盖地收集微生物气溶胶是准确获取气溶胶中微生物种类、组成和丰度信息的基础前提。微生物气溶胶与非生物气溶胶的采样原理和方法基本一致,即根据气溶胶的尺寸、重量和电荷等物理特性设计采样方法。常用的采样方法有重力沉降、惯性冲击、过滤、静电采集和冷凝采集等[13]。与非生物气溶胶不同的是微生物气溶胶的研究聚焦于微生物,一般从细胞活力、可培养性和遗传物质完整性等方面评估微生物气溶胶的采样方法,根据研究内容和目的选择合适的采样方法[14]。目前,微生物气溶胶研究领域常采用以下取样方法(表 1)。
Collection methods | Characteristics | Samplers | Environments, targets, or biomass | References |
Sedimentation | Passive collection, easy for large particles Culturable microorganism Influence by the environment |
Solid medium | Office, laboratory: bacteria 8.9×104 CFU/m3, fungi 5.0×104 CFU/m3 |
[15] |
Solid medium | Hospital wastewater treatment plant: bacteria 100–400 CFU/m3, fungi < 100 CFU/m3 | [16] | ||
Impinger | Liquid medium, high sampling and retention efficiency Low airflow Collection efficiency was affect by bubbles |
BioSampler | Office, laboratory, 104–105 CFU/m3 | [15] |
SKC BioSampler, AGI-30 | Bacillus subtilis, B. atrophaeus, Penicillium chrysogenum test strains | [19] | ||
Impactor | Solid medium Single stage or cascade impactor, measurement of size-fractionation of particles Low airflow or high airflow |
Impactor of multi-slit nozzles | Particles (0.02–2 µm) | [21] |
Andersen impactor | Influenza virus: 6.09 log10 copies of RNA/L | [33] | ||
Cyclone | High collection efficiency for large particles High airflow High mechanical stress |
Wet cyclone | H1N1, H3N2 | [26] |
NIOSH | H1N1, H3N2: 3.84 log10 copies of RNA/L | [33] | ||
Filtration | Collection on the surface of dry filters Low airflow Portable but low efficiency for viruses |
Glass fibre, polycarbonate, gelatin | DNA yield | [30] |
Polytetrafluoroethylene filter | H1N1, H3N2: 3.66 log10 copies of RNA/L | [33] | ||
Gelatin filters, polytetrafluoroethylene, polycarbonate | Dust Sampler | [34] | ||
Mixed cellulose ester, polyethersulfone, polyamide, polytetrafluorethylene and polyvinylidene fluoride |
Comparison of five membrane filter | [35] | ||
Electrostatic precipitator | Suitable for collecting virus aerosol Collection the particles sensitive to electricity Low airflow, high collection efficiency Corona damages RNA or protein |
HAFES | Virus aerosols (H1N1, H3N2, MS2) | [36] |
TB Hotspot DetectOR (THOR) | Mycobacterium tuberculosis | [37] | ||
TracB | particles (0.01–10 mm), Bacillus thuringiensis var. kurtsaki | [38] | ||
PEBS | B. atrophaeus, P. chrysogenum | [40] | ||
Condensation | Suitable for small size particles Collection for virus aerosol High efficiency for capturing live cells or viruses |
Condensation growth tube | B. subtilis | [42] |
Viable virus aerosol sampler (VIVAS) |
MS2, collection efficiency 74% | [44] | ||
VIVAS | Viable H1N1, H3N2 and influenza B viruses | [45] | ||
Spot sampler | Bacteriophages and influenza virus | [46] | ||
VIVAS | SARS-CoV-2, TCID50 6–74 units/L |
[47] |
2.1.1 重力沉降法
重力沉降法是通过将固体培养基(沉降板)放置于空气环境中,借助气溶胶中微生物自身重力沉降的收集方法,是最简单经济的采样方法。但是该采样方法属于被动采集,误差大,收集效率低,易受颗粒质量、沉降速率、温度、气流和微生物是否可培养等因素影响,难以准确反映空间环境中的微生物总量。由于该方法易于收集较大颗粒的气溶胶,所以仍然被广泛用于室内和室外空气质量评估[15],以及污水处理厂气溶胶暴露风险评估[16]等研究。若将沉降板和其他主动采样器结合,所得数据将能更准确地反映气溶胶中微生物的总量[17]。
2.1.2 惯性撞击法惯性撞击法是指利用气溶胶微粒的惯性撞击,将其收集到固体基质或液体中。目前基于惯性撞击的取样设备主要包括冲击采样器(impinger)、撞击采样器(impactor)和旋风分离器(cyclone)三类。冲击采样器,例如AGI-30和BioSampler,含有气溶胶的空气流通过喷嘴加速后使微生物粒子撞击收集液界面,通过收集腔内的收集液捕获气溶胶中微生物(图 1A)。冲击采样器的流速一般为10–20 L/min[18]。冲击采样器的喷嘴结构决定了气溶胶颗粒在收集界面的入射角度和速度;收集腔室的结构也会影响气流的旋转和运动方向;空气流速、采集时间和收集液类型等影响气溶胶的总收集量和总蒸发量,以上因素都将影响气溶胶中微生物的收集效率、保留效率和微生物活性。因此,在采集生物气溶胶样本时需要考虑收集对象的尺寸范围,调整空气流速,优化收集液体积和收集时间来提高收集效率和微生物的生物活力[19]。收集的生物气溶胶样本可以直接用于细胞培养、计数和分子实验等;然而冲击采样器的空气流速较低,收集过程产生的剧烈气泡和液体蒸发将降低收集效率[20]。
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图 1 惯性撞击法采集器原理图 Figure 1 Schematic diagram of inertial impact collector. A: the particles in the incoming airstream were impacted into the liquid collection medium inside the bottle[28]; B: the aerosol particles were classified and collected onto different size stages by successively decreasing nozzle size[28]; C: the particles were collected into an inner-wall liquid film of the wet-cyclone by the particles centrifugal force[29]. |
撞击采样法将微生物气溶胶引导至喷嘴并提高粒子惯性,气溶胶中粒子撞击于琼脂培养板、滤膜或其他固体基质[13]。撞击采集器可以由多层级串联,构成级联撞击采样器,喷嘴直径和截留的颗粒尺寸随层级的增加而逐渐变小(图 1B),可以获得气溶胶的粒径分布信息。撞击器的收集效率主要取决于颗粒直径和密度,喷嘴的数量和直径,喷射口的空气速度和收集时间[20]。撞击采样器的空气流速范围一般在2–170 L/min[18, 21],新型撞击采集器的空气速度甚至可以达到1 200 L/min[22]。然而高速撞击压力往往会降低微生物活力,撞击板表面的粘合力也会影响微生物的收集效率[23]。
旋风采样器的原理是将带有气溶胶的空气通过螺旋、旋涡等形状的收集室后形成螺旋形旋流。在旋流中,气溶胶颗粒受到与其直径、密度和速度成比例的离心力,该离心力将颗粒带至旋风分离器壁,在分离器壁处颗粒与气流分离并被收集。旋风采样器的入口角度和旋风筒长度影响粒子的收集效率[24–25]。旋风采集器的空气流速可以达到1 000 L/min[26],然而高流速往往会损伤微生物细胞,降低微生物纯培养率。湿氏旋风采集器可以提高气溶胶中微生物粒子的捕获效率和生物活性(图 1C),对直径小于1 μm的颗粒物的收集效率略低,对大于3 μm的颗粒物的收集效率可达到100%[27]。本实验室在液体撞击法的基础上设计研发了旋转液腔微生物气溶胶采样器,通过收集液的旋转增加液体的收集面积和颗粒物的离心力,当空气中微生物气溶胶颗粒与旋转液面接触后,水的旋转力会相对柔和并快速地富集微生物气溶胶颗粒。该方法的收集效率与旋风采样器相仿,且在微生物的可培养性上有大幅的提高。
2.1.3 过滤收集法微生物气溶胶通过由纳米孔结构(例如聚碳酸酯膜)、复杂空隙结构(例如聚氯乙烯膜、凝胶膜)或随机排列结构(例如玻璃纤维膜)构成的滤膜时大颗粒物将被截留于过滤器上(图 2A)[30]。空气通过膜的气流速度通常在1–133 L/min[18]。研究过程中须根据微生物尺寸大小和实验目的选择不同孔径或材质的滤膜。滤器上的微生物可以转移至固体或液体培养基,也可以用于显微观测、计数、鉴定、DNA提取和高通量测序分析等[31–32]。过滤器的滤膜材质有聚碳酸酯膜、凝胶、聚氯乙烯膜、聚四氟乙烯膜和玻璃纤维膜等[30, 33]。滤膜的材质和结构影响微生物收集效率,例如,聚碳酸酯膜收集细菌的效率明显优于聚四氟乙烯膜和凝胶膜[34];聚醚砜膜回收细菌、真菌DNA的比例高于混合纤维素膜、聚酰胺纤维膜、聚四氟乙烯膜和聚偏二氟乙烯膜[35]。过滤收集可用于依赖培养或不依赖培养的研究,具有成本低和便携等优点,其主要缺陷是膜空隙小易被堵塞,对病毒的回收效率较低,干燥的膜表面可能降低微生物活性。
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图 2 过滤、静电和冷凝采集原理图 Figure 2 Schematic diagrams of filtration, electrostatic precipitator and condensation sampler. A: particles larger than the diameter of nanopore were collected onto filter media; B: the charged particles were drew to collection plates by electrostatic attraction; C: the particles were introduced into a cold "conditioner", warm "initiator", and cool "moderator" region[42]. |
2.1.4 静电采集法
静电采集(electrostatic precipitator,EP)已经越来越多地应用于病毒和细菌气溶胶收集[36–38]。微生物气溶胶通过静电采集器时首先在静电场入口处被带上电荷,在电场的作用下颗粒与空气流分离并沉积在带电板上(图 2B)。收集效率随空气流速的增加而降低,在低流速下静电采集器可以高效地收集不同尺寸的颗粒物,且机械应力损伤小。在相同温度、湿度和收集时间下静电采集法富集微生物菌落数和总DNA的效率均明显高于过滤和液体撞击法[39]。与BioSampler相比,Han等[40]开发的生物气溶胶静电捕集装置(personal electrostatic bioaerosol sampler,PEBS),捕获活的微生物总量基本一致,而收集气溶胶化的Bacillus atrophaeus和Penicillium chrysogenum真菌孢子的总量更高。在静电捕获微生物时还需要防止微生物RNA或蛋白质由电晕电离空气产生活性氧造成的损伤等[41]。静电采集法更适合捕获气溶胶中带电敏感或较小尺寸的微生物,其收集效率与电压、流速、除尘器尺寸形状和颗粒带电量等因素密切相关。因其功耗低,可为低功率监测技术提供可行的解决方法。
2.1.5 冷凝采集法冷凝采集(condensation sampler)使微生物气溶胶颗粒经加湿器潮湿化和升温后注入冷却器,含有过饱和蒸汽的微生物气溶胶颗粒充当凝结核凝结于冷却器表面(图 2C)。冷凝采集器的升温区与冷凝区温差越大捕获的微生物总量越多,但是高温可能会造成微生物细胞的损伤。与冲击采集器和撞击采集器相比,冷凝采集器的流速相对较低,在收集较小尺寸的微生物方面更具优势,可以实现微生物气溶胶遗传物质的高效率、高保真回收[42]。因此,冷凝采集法多应用于病毒气溶胶的收集。冷凝采集器收集MS2病毒的效率比BioSampler高10倍以上[43],收集感染性H1N1效率比BioSampler高13倍[44]。VIVAS冷凝采集器可以在病人2 m以外的距离高效地捕获H1N1和H3N2等呼吸道病毒,而BioSampler分离病毒的频率远低于冷凝采集器[45]。冷凝采集器(spot sampler)回收感染性MS2、PhiX174、Phi6等噬菌体和气溶胶化H1N1流感病毒的效率高于0.8 μm聚碳酸酯滤膜[46]。同样地,VIVAS冷凝采集器收集COVID-19患者病房空气气溶胶样品,经Vero E6细胞培养后细胞发生病变,结果证明了气溶胶中分离的SARS-CoV-2基因组序列与COVID-19病人的基因组序列一致[47]。
2.2 微生物气溶胶分析鉴定微生物气溶胶广泛存在于大气中,成分十分复杂,同时受制于采样技术,从气溶胶中完整无损地分离微生物存在一定的难度,加之气溶胶中存在不可培养微生物,且存在提取单个微生物细胞核酸困难等难题,分析和鉴定一直是微生物气溶胶研究的重点发展方向,因此亟需发展新的分析鉴定技术,以便更加深入地了解微生物气溶胶。本文主要从依赖和不依赖培养以及单细胞分析鉴定方面阐述气溶胶微生物的分析鉴定研究(表 2)。
Identification methods | Characteristics | Methods | Collection parameters | Environments | Target or biomass | References |
Dependent on culture method | Culturable microorganisms Time consuming, low efficiency |
BD Phoenix- 100 |
Andersen Cascade Impactor, 28.3 L/min, 10 min | Intensive care unit | 168 CFU/m3 | [48] |
Catalase test | Polytetrafluoroethylen filter, 28.3 L/min, 5 min |
Hospital | 43–243 CFU/m3 | [49] | ||
VITEK 2 | Ventilation system, total 200 L | Animal clinic | Bacteria 1 000± 800 CFU/m3, fungi 324±245 CFU/m3 | [50] | ||
MALDI-TOF MS | Sedimentation | Hospital PM2.5 and PM10 | Staphylococcus, Micrococcus, Corynebacterium et al. | [53] | ||
Sanger sequencing | Whatman filter, 30 min |
Hospital | 459–1 392 CFU/m3 | [52] | ||
Sanger sequencing | DLPI+, Polycarbonate filter, HiVo sampler | Livestock facility | 18–38 species | [58] | ||
Independent culture method | Base on microbial nucleic acid High throughput, accurate assessment of biomass Unable to distinguish the physiological state of cells |
DAPI, LIVE/DEAD BacLight | Polycarbonate filter, 28.3 L/min, 30 min | Air quality index (AQI) | 104–105 CFU/m3 | [3] |
DAPI | Polycarbonate filter, 5 L/min, 24 h | Outdoor PM2.5–PM10 | 104–105 CFU/m3 | [32] | ||
FISH | Polycarbonate filter, 9 L/min, 23 h | Amazon rain forest | 1–5×105 CFU/m3 | [60] | ||
Illumina | Quartz filter, 100 L/min, 24 h | Wastewater, submicron aerosols | 7 036 virus species, 32 antibiotic- resistance genes | [65] | ||
Illumina |
HighBioTrap, 1 000 L/min, 20 min |
13 different land types | 103–105 CFU/m3 | [64] | ||
Illumina | Polycarbonate filter,15–60 min | Mountainous and urban areas | 103–105 CFU/m3 | [62] | ||
Illumina | Quartz filter, 7 d | Global | 104–106 CFU/m3 | [5] | ||
Illumina | Andersen sampler, 28.3 L/min, 5 min | Indoor | 2.3×103–8.5×103 CFU/m3 | [63] | ||
Illumina and Oxford Nanopore | HVAC filter | Indoor | Diversity at genus and species level | [66] | ||
Single cell identification | Dependent on the substance within cells In the experimental stage |
Surface- enhanced Raman spectroscopy, SERS |
Optofluidic SERS | 5 test strains | Limit of detection is 102 CFU/mL | [76] |
Klarite SERS | Andersen sampler 28.3 L/min, 10 min | Restaurant | E. coli | [78] |
2.2.1 依赖于培养的分析鉴定
微生物的总量、种类和具有活性的微生物是生物气溶胶研究的重要内容之一。气溶胶样品通过转移至合适的固体或液体中培养和分离纯化,通过统计菌落形成单位(colony forming units,CFU)即可粗略评估气溶胶中可培养微生物的生物量,确定气溶胶活细菌和真菌丰度信息等。基于培养的分析鉴定一般包括纯培养、分离、宏观描述、显微描述和生理生化测试等一系列实验操作。例如,基于培养鉴定原理的全自动细菌鉴定仪(BD Phoenix-100)监测到病人探视活动会增高重症监护室空气中病原微生物总量和耐药菌比例[48]。虽然该方法被用于检测医疗机构[49]、动物诊所[50]等环境中微生物气溶胶,但其耗时长、实验操作复杂。目前气溶胶中微生物的鉴定较多采用分子手段,即提取单菌落DNA,通过细菌16S rRNA基因或真菌18S rRNA基因等序列相似性初步判定细菌种类,揭示与污染环境相关的细菌多样性差异[51],评估医院空气质量,监测空气中人类病原菌等[52]。MALDI-TOF MS也可以分析空气中微生物种类信息,例如MALDI-TOF分析鉴定医院病房和校园宿舍空气中微生物的复杂性[53]。Jäckel等从生长的菌落中提取总DNA并扩增16S rRNA基因,最后将混合PCR产物用于后续的克隆和测序[54],该方法可以克服目前基于培养鉴定技术的一些缺陷。然而由于培养条件的限制,并非气溶胶中所有的微生物都可以被纯培养,地球上的微生物目前大概只有1% 在实验室中培养成功。另外,气溶胶的收集过程可能造成细胞损伤,死亡或处于活的但无法培养(viable but nonculturable,VBNC)状态的微生物也无法纯培养。据估计,灰尘中细菌总量不超过总颗粒物的10%[55],可培养的微生物数量不超过总颗粒物的1%[56],基于培养的方法获得的细菌总量比非培养方法预测的细菌总量低100–1 000倍[57–58]。尽管依赖培养的分析鉴定方法具有一定的局限性,但是培养是深入研究微生物生理生化特性、侵染力、毒力、抗逆、耐药性和基因变异等研究的基础。利用多学科和多技术的交叉实现难培养微生物的纯培养对气溶胶微生物的研究意义极其重大,针对气溶胶微生物纯培养技术的开发也将是气溶胶微生物技术发展的重点方向之一。
2.2.2 不依赖于培养的分析鉴定基于微生物核酸的研究能够规避微生物的纯培养。气溶胶中活的微生物,不可培养的或具有完整形态的死细胞也均可以通过荧光方法定量分析。通过DAPI (4′, 6-diamidino-2-phenylindole)、LIVE/DEAD® BacLight等荧光染料对细胞膜或细胞核染色和计数可以估测气溶胶中微生物的总量和死活细胞比例等[3]。利用荧光原位杂交技术(FISH)可以原位靶向细胞内DNA或RNA,快速获得细菌和真菌的高阶分类元信息,进而评估室内或自然环境气溶胶中细菌、真菌和古菌的总量及空间变化等[59–60]。利用核酸片段多样性和梯度凝胶电泳(例如qPCR-DGGE)可以低成本粗略估计气溶胶中高丰度的微生物类群[61],但无法精确获得微生物种类信息。从气溶胶样品中获取全部微生物、高质量DNA和扩增合适的目标基因是获得气溶胶微生物准确信息的关键。生物气溶胶的宏基因组分析通常采用的目标基因为细菌16S rRNA基因高变区(例如V3–V4),真菌转录间隔区的高变区(例如ITS1和ITS2)。高通量二代测序技术可以更加全面地揭示生物气溶胶的微生物组,获得比培养方法更高的生物多样性,发现未培养类群和新分类群,揭示气溶胶中微生物的群落构成、传播和变化规律,评估生物气溶胶暴露风险等[5, 62–65]。但是二代测序也存在一些缺陷,例如PCR扩增过程中易形成嵌合序列导致测序错误,引物偏好性导致优先扩增某些类群,二代测序读长较短致使种或亚种分类元鉴定难度大。纳米孔三代测序的读长比Illumina测序的读长更长,可以获得更丰富的微生物属或种水平信息[66],但是三代测序还需要提高测序精度并降低测序成本。基于气溶胶测序的方法无法判断微生物的生理状态和活性,因此培养和测序方法的结合将更加全面地认识不同环境中生物气溶胶微生物特点和变化规律。
2.2.3 新兴的单细胞水平检测方法气溶胶颗粒直径分布研究表明,微生物单个细胞可以自由漂浮于空气中[55, 67],这为利用介电泳(dielectrophoresis,DEP)、微流控等技术开展微生物气溶胶单细胞水平分选研究成为可能。如果能够进一步结合一系列新兴的单细胞分析技术,在单细胞水平研究微生物气溶胶不仅有助于全面地解析气溶胶中微生物的组成,还可以实时动态地分析微生物的生理代谢特征。利用介电泳在空间产生不均匀电场使可极化的Botrytis cinerea、Pseudoperonospora cubensis和Podosphaera xanthii真菌孢子发生偏移,进而实现温室作物气传病害孢子平均75%以上的分离和富集效率,有助于温室作物气传病害菌的实时监测[68]。微流控芯片是另一种近年兴起的单细胞分离手段,精准控制微流控芯片内空气动力是气溶胶微生物分离的关键,双平行空气鞘流为空气气流中的微生物颗粒提供稳定的水平动力,然后利用径向空气鞘流为粒子提供侧向平移力,进而实现混合霉菌孢子的分离和富集[69]。微流控芯片与气溶胶毛细管构建的采集装置,能够实现气溶胶颗粒高效的微液滴包裹和98%以上的收集效率,最后通过染色在20 s内区分气溶胶中的微生物和非生物物质[70–71]。虽然这些收集技术取得一定的进展,但是微生物气溶胶单细胞分离技术还不够成熟,污染和设备的小型化等问题亟待解决。
此外,当前的研究主要集中于样品采集,对于后续单细胞水平的检测和表征技术研究较少。期待通过微生物单细胞测序技术弥补宏基因组学水平研究的不足,直接从气溶胶微生物个体基因组水平阐释其种类、生理特性、代谢特点和毒力情况等一系列重要信息。因为测序等侵入性分析方法无法判断气溶胶中微生物是否存活,对微生物气溶胶的致病性会产生较大的误判,无损的单细胞分析方法在微生物气溶胶研究中逐渐引起人们的关注。拉曼光谱是一种产生于分子或晶格振动能级的光子非弹性散射光谱,通过拉曼光谱可以鉴定和分析化学物质,因而被称作物质的“指纹图谱”。拉曼光谱作为一种无损、非接触、非标记和水干扰性小的快速检测技术,在微生物鉴定和表征上已经有大量的应用[72–75]。拉曼光谱技术与气溶胶采集技术的结合为实现气溶胶中微生物的快速捕获和实时检测成为可能。例如由空气收集流和液体收集流构成的两相弯曲管道芯片直接收集空气中微生物,微生物在惯性作用下进入液相并与表面增强材料结合,最后获取其拉曼光谱(图 3A)。Jung研究团队构建了光流体表面增强拉曼光谱平台,实现了1 μm颗粒物99%的收集效率,100 CFU/mL的最低检测限[76]。利用单粒子光镊可以在捕获气溶胶化Bacillus globigii,Yersinia rhodei和Bacillus subtilis等颗粒的同时获得其拉曼光谱,随后通过主成分分析判断气溶胶颗粒中的生物种类[77]。陈建民团队发明了具有表面增强功能的Klarite基底(图 3B),利用该基底可以非培养、快速地检测自助餐厅空气环境中大肠杆菌(Escherichia coli)[78]。由于拉曼分析的无损性,分析后的微生物单细胞可以同后续的培养或其他操作无缝对接。同时,可以通过拉曼光谱谱征结合人工智能算法预测细胞是否存活,而不需要通过培养验证,能在极短时间内初步判断微生物气溶胶的危害性,具有很大的优势。拉曼光谱的无损性和指纹表征性适合与微流控等单细胞分离技术联合使用,预期在未来的微生物气溶胶单细胞水平检测中将呈现出更多微生物气溶胶拉曼检测的新技术和新方法。
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图 3 拉曼光谱检测气溶胶微生物 Figure 3 Schematic diagram of Raman spectroscopy detecting microorganisms in bioaerosols. A: optofluidic SERS platform for continuous characterization of airborne microorganisms[76]; B: Klarite SERS for the rapid detection of Escherichia coli in bioaerosols without laborious culture processes[78]. |
3 气溶胶中病毒的鉴定方法
病毒是微生物气溶胶重要的成分之一,而且SARS、MERS、COVID-19等疫情的传播同病毒气溶胶密切关联。病毒的大小在纳米级别,远远小于细菌的尺寸,相比于细菌气溶胶的收集,病毒气溶胶的采集更有挑战性。由于病毒生命周期的特殊性,无法与细胞形态的微生物一样直接进行培养,而是需要转染特定的细胞系才能判断其活性,过程十分烦琐,因此气溶胶病毒的检测通常是检测其核酸的存在与否。病毒基因组不具有类似16S rRNA基因和18S rRNA基因的通用标记,对于气溶胶中病毒核酸的检测通常采用两种方法。一种是根据特定病毒的基因组设计特异性荧光探针,通过RT-PCR检测。例如,RT-PCR检测A型流感病毒的M基因,B型流感病毒的NS基因表达量来判断学校、医院和养殖场等环境中的病毒量[79–81]。在气溶胶中的病毒种类不明确,没有可用的分子或血清学诊断分析时,通过宏基因组学对空气中病毒进行基因组识别是开发生物检测工具的重要策略[82]。宏基因组分析揭示托儿所空气中人类相关DNA和RNA病毒群落具有季节性变化[83],为冬季室内病毒传播预防提供重要的科学依据。污水处理厂气溶胶中人类腺病毒、轮状病毒、甲型肝炎病毒和1型单纯疱疹病毒等DNA病毒和少量的Retroviridae RNA病毒等也被宏基因组分析发现[84]。需要指出的是,空气中病原体基因浓度低对宏基因组分析带来极大挑战,测序的方法只能显示病毒核酸片段的存在,可能无法提供相关疾病传播和感染性的直接评估。
自2019年新型冠状病毒肺炎暴发以来,新型冠状病毒的扩散、传播与环境的关系受到人们极大的关注。SARS-CoV-2通过飞沫还是气溶胶传播存在较大的争议[85–86]。越来越多的科学家和研究证据表明,新型冠状病毒可以通过气溶胶传播[86–89],在密闭或拥挤的空间将增加病毒传播的风险。蓝柯团队收集了武汉两家医院的空气样本,通过数字PCR检测SARS-CoV-2的ORF1a/b和N基因,均检测到气溶胶中SARS-CoV-2的RNA[90],Van Doremalen实验研究证明SARS-CoV-2在气溶胶中的活力和侵染性可以保持数小时,这种活力和侵染性在物体表面甚至可以保持数天,证明了SARS-CoV-2通过气溶胶传播的可能性[91]。类似的感染事件及新型冠状病毒感染家庭的时间和空间分布以及环境变量的研究证明SARS-CoV-2可以通过气溶胶传播[92]。然而,有研究者采用NIOSH旋风采集器收集了普通医疗室以及COVID-19重症监护室护士站和走廊的气溶胶样品,然后通过RT-PCR检测N基因,即使在每平方米8个病毒拷贝的检测极限下仍未检测到新冠病毒核酸[93]。生物气溶胶样品采集的挑战首先是气溶胶中病毒含量非常低,其次是保持病毒颗粒的完整性和活性[94],然而在气溶胶采集过程中,由于采集器的构造和采集方式等可能损伤病毒颗粒的脂膜或蛋白外壳,进而损害病毒颗粒的活力和侵染性。目前对于新型冠状病毒的生物采集和分析等缺乏统一的标准,因此也缺乏比较性[95],最终分析结果易受到样品采集条件和检测灵敏性的影响。
快速、便携和灵敏的检测病毒方法对预防和控制病毒的传播至关重要。新技术的交叉有助于气溶胶中病毒的实时、原位、快速和高灵敏检测,例如,将抗体、靶病毒和碱性磷酸酶(alkaline phosphatases,ALPs)设计成三明治结构的表面放大纳米生物传感器可以实现空气中甲型H1N1流感病毒亚PFU/mL检测水平[96]。利用病毒抗原-抗体识别原理结合静电颗粒收集(electrostatic particle concentrator,EPC)可以快速、便捷、特异性地富集气溶胶中的H1N1病毒,检测能力与qPCR相当[97]。集气溶胶收集和检测于一体的病毒检测技术将是气溶胶病毒检测的重要发展方向,例如具有气溶胶收集、病毒裂解、RNA富集和环介导恒温扩增(loop-mediated isothermal amplification,LAMP)功能的病毒检测技术[98],集气溶胶富集和侧向免疫层析分析(lateral flow immunochromatographic assay,LFA)的检测技术[99]等。目前这些集成采样与检测的实时设备通常可以检测到较高浓度的人工气溶胶病毒,对于气溶胶中载量很低的病毒粒子检测灵敏度还有待进一步提高。
4 展望自1833年查尔斯·达尔文在佛得角群岛首次发现霉菌孢子气溶胶以来,微生物气溶胶领域已经历了将近300年的研究历史,近几十年来,微生物气溶胶领域出现了研究热潮,在微生物气溶胶取样和分析技术方面取得了里程碑式的进展。但是,微生物气溶胶的研究方法目前难以全覆盖地收集所有样本,微生物种类鉴定的高不确定性,不同实验室评估误差较大,收集方式对气溶胶中微生物的活性具有不同程度的损害性,可能导致低估了生物气溶胶的风险[100]。首先,推动建立相对统一的生物气溶胶采集器性能评价标准和体系,通过标准和规范的体系降低不同仪器设备参数和收集条件等因素造成的研究结果差异性,进而提高各类研究之间的可比性。其次,建议在研究中采用多种收集器交叉联用,尽可能提高微生物的收集效率和覆盖率。最后,机械应力小、微生物活培养效率高的气溶胶采集装置的研发依然是该领域的重要发展方向。研发微生物气溶胶收集与原位鉴定集成化高灵敏性设备可提升气溶胶病原体快速响应和预警。随着不同学科的交叉,难培养微生物先进培养技术、第三代测序技术、拉曼光谱技术以及人工智能等领域的发展融合,相信微生物气溶胶的研究方法将日新月异。这些新技术新方法也将会大大增加收集与分析的全面性和准确性,加强我们对于微生物气溶胶的微生物组成、活力变化以及传播方式等的深入了解,从而让微生物气溶胶对人类健康的影响降至最低。
[1] | Després V, Huffman JA, Burrows SM, Hoose C, Safatov A, Buryak G, Fröhlich-Nowoisky J, Elbert W, Andreae M, Pöschl U, Jaenicke R. Primary biological aerosol particles in the atmosphere: a review. Tellus B: Chemical and Physical Meteorology, 2012, 64(1): 15598. DOI:10.3402/tellusb.v64i0.15598 |
[2] | Xu ZQ, Wu Y, Shen FX, Chen Q, Tan MM, Yao MS. Bioaerosol science, technology, and engineering: past, present, and future. Aerosol Science and Technology, 2011, 45(11): 1337-1349. DOI:10.1080/02786826.2011.593591 |
[3] | Gong J, Qi JH, E BB, Yin YD, Gao DM. Concentration, viability and size distribution of bacteria in atmospheric bioaerosols under different types of pollution. Environmental Pollution, 2020, 257: 113485. DOI:10.1016/j.envpol.2019.113485 |
[4] | Šantl-Temkiv T, Sikoparija B, Maki T, Carotenuto F, Amato P, Yao MS, Morris CE, Schnell R, Jaenicke R, Pöhlker C, DeMott PJ, Hill TCJ, Huffman JA. Bioaerosol field measurements: challenges and perspectives in outdoor studies. Aerosol Science and Technology, 2020, 54(5): 520-546. DOI:10.1080/02786826.2019.1676395 |
[5] | Tignat-Perrier R, Dommergue A, Thollot A, Keuschnig C, Magand O, Vogel TM, Larose C. Global airborne microbial communities controlled by surrounding landscapes and wind conditions. Scientific Reports, 2019, 9: 14441. DOI:10.1038/s41598-019-51073-4 |
[6] | Stetzenbach LD. Airborne infectious microorganisms. Encyclopedia of Microbiology. Amsterdam: Elsevier, 2009: 175-182. |
[7] | Li YP, Lu R, Li WX, Xie ZS, Song Y. Concentrations and size distributions of viable bioaerosols under various weather conditions in a typical semi-arid city of Northwest China. Journal of Aerosol Science, 2017, 106: 83-92. DOI:10.1016/j.jaerosci.2017.01.007 |
[8] | Liu H, Zhang X, Zhang H, Yao XW, Zhou M, Wang JQ, He ZF, Zhang HH, Lou LP, Mao WH, Zheng P, Hu BL. Effect of air pollution on the total bacteria and pathogenic bacteria in different sizes of particulate matter. Environmental Pollution, 2018, 233: 483-493. DOI:10.1016/j.envpol.2017.10.070 |
[9] | Cao C, Jiang WJ, Wang BY, Fang JH, Lang JD, Tian G, Jiang JK, Zhu TF. Inhalable microorganisms in Beijing's PM2.5 and PM10 pollutants during a severe smog event. Environmental Science & Technology, 2014, 48(3): 1499-1507. |
[10] | Douwes J, Eduard W, Thorne PS. Bioaerosols. International Encyclopedia of Public Health. Amsterdam: Elsevier, 2017: 210-218. |
[11] | Kim KH, Kabir E, Jahan SA. Airborne bioaerosols and their impact on human health. Journal of Environmental Sciences, 2018, 67: 23-35. DOI:10.1016/j.jes.2017.08.027 |
[12] | Jiang JK, Vincent Fu Y, Liu L, Kulmala M. Transmission via aerosols: plausible differences among emerging coronaviruses. Aerosol Science and Technology, 2020, 54(8): 865-868. DOI:10.1080/02786826.2020.1769020 |
[13] | Mainelis G. Bioaerosol sampling: classical approaches, advances, and perspectives. Aerosol Science and Technology, 2020, 54(5): 496-519. DOI:10.1080/02786826.2019.1671950 |
[14] | Haddrell AE, Thomas RJ. Aerobiology: experimental considerations, observations, and future tools. Applied and Environmental Microbiology, 2017, 83(17): e0080917. DOI:10.1128/AEM.00809-17 |
[15] | Mortazavi R, Ariya PA. The impact of renovation on indoor airborne bacterial and fungal populations. Indoor and Built Environment, 2017, 26(10): 1351-1361. DOI:10.1177/1420326X15610396 |
[16] | Yazdanbakhsh A, Ghazi M, Sahlabadi F, Teimouri F. Data on airborne bacteria and fungi emission from a conventional hospital wastewater treatment plant. Data in Brief, 2020, 28: 105019. DOI:10.1016/j.dib.2019.105019 |
[17] | Pasquarella C, Vitali P, Saccani E, Manotti P, Boccuni C, Ugolotti M, Signorelli C, Mariotti F, Sansebastiano GE, Albertini R. Microbial air monitoring in operating theatres: experience at the University hospital of Parma. Journal of Hospital Infection, 2012, 81(1): 50-57. DOI:10.1016/j.jhin.2012.01.007 |
[18] | Grinshpun SA, Buttner MP, Mainelis G, Willeke K. Sampling for airborne microorganisms. //Yates MV, Nakatsu CH, Miller RV, Pillai SD. Manual of Environmental Microbiology. Washington, DC, USA: ASM Press, 2015: 3.2.2-1–3.2.2-17. |
[19] | Zheng YH, Yao MS. Liquid impinger BioSampler's performance for size-resolved viable bioaerosol particles. Journal of Aerosol Science, 2017, 106: 34-42. DOI:10.1016/j.jaerosci.2017.01.003 |
[20] | Haig CW, Mackay WG, Walker JT, Williams C. Bioaerosol sampling: sampling mechanisms, bioefficiency and field studies. Journal of Hospital Infection, 2016, 93(3): 242-255. DOI:10.1016/j.jhin.2016.03.017 |
[21] | Lim JH, Park D, Yook SJ. Development of a multi-slit virtual impactor as a high-volume bio-aerosol sampler. Separation and Purification Technology, 2020, 250: 117275. DOI:10.1016/j.seppur.2020.117275 |
[22] | Chen HX, Yao MS. A high-flow portable biological aerosol trap (HighBioTrap) for rapid microbial detection. Journal of Aerosol Science, 2018, 117: 212-223. DOI:10.1016/j.jaerosci.2017.11.012 |
[23] | Griffin DW. Atmospheric movement of microorganisms in clouds of desert dust and implications for human health. Clinical Microbiology Reviews, 2007, 20(3): 459-477. DOI:10.1128/CMR.00039-06 |
[24] | Misiulia D, Andersson AG, Lundström TS. Effects of the inlet angle on the collection efficiency of a cyclone with helical-roof inlet. Powder Technology, 2017, 305: 48-55. DOI:10.1016/j.powtec.2016.09.050 |
[25] | Brar LS, Sharma RP, Elsayed K. The effect of the cyclone length on the performance of Stairmand high-efficiency cyclone. Powder Technology, 2015, 286: 668-677. DOI:10.1016/j.powtec.2015.09.003 |
[26] | Sung G, Ahn C, Kulkarni A, Shin WG, Kim T. Highly efficient in-line wet cyclone air sampler for airborne virus detection. Journal of Mechanical Science and Technology, 2017, 31(9): 4363-4369. DOI:10.1007/s12206-017-0835-4 |
[27] | Sung G, Kim HU, Shin D, Shin WG, Kim T. High efficiency axial wet cyclone air sampler. Aerosol and Air Quality Research, 2018, 18(10): 2529-2537. DOI:10.4209/aaqr.2017.12.0596 |
[28] | Pan M, Lednicky JA, Wu CY. Collection, particle sizing and detection of airborne viruses. Journal of Applied Microbiology, 2019, 127(6): 1596-1611. DOI:10.1111/jam.14278 |
[29] | Cho YS, Hong SC, Choi J, Jung JH. Development of an automated wet-cyclone system for rapid, continuous and enriched bioaerosol sampling and its application to real-time detection. Sensors and Actuators B: Chemical, 2019, 284: 525-533. DOI:10.1016/j.snb.2018.12.155 |
[30] | Ferguson RMW, Garcia-Alcega S, Coulon F, Dumbrell AJ, Whitby C, Colbeck I. Bioaerosol biomonitoring: sampling optimization for molecular microbial ecology. Molecular Ecology Resources, 2019, 19(3): 672-690. DOI:10.1111/1755-0998.13002 |
[31] | Vestlund AT, Al-Ashaab R, Tyrrel SF, Longhurst PJ, Pollard SJT, Drew GH. Morphological classification of bioaerosols from composting using scanning electron microscopy. Waste Management, 2014, 34(7): 1101-1108. DOI:10.1016/j.wasman.2014.01.021 |
[32] | Chen LWA, Zhang M, Liu T, Fortier K, Chow JC, Alonzo F, Kolberg R, Cao JJ, Lin G, Patel TY, Cruz P, Buttner MP, Watson JG. Evaluation of epifluorescence methods for quantifying bioaerosols in fine and coarse particulate air pollution. Atmospheric Environment, 2019, 213: 620-628. DOI:10.1016/j.atmosenv.2019.05.051 |
[33] | Bekking C, Yip L, Groulx N, Doggett N, Finn M, Mubareka S. Evaluation of bioaerosol samplers for the detection and quantification of influenza virus from artificial aerosols and influenza virus-infected ferrets. Influenza and Other Respiratory Viruses, 2019, 13(6): 564-573. DOI:10.1111/irv.12678 |
[34] | Wang CH, Chen BT, Han BC, Liu ACY, Hung PC, Chen CY, Chao HJ. Field evaluation of personal sampling methods for multiple bioaerosols. PLoS One, 2015, 10(3): e0120308. DOI:10.1371/journal.pone.0120308 |
[35] | Jeong SY, Kim TG. Comparison of five membrane filters to collect bioaerosols for airborne microbiome analysis. Journal of Applied Microbiology, 2021, 131(2): 780-790. DOI:10.1111/jam.14972 |
[36] | Kim HR, An S, Hwang J. High air flow-rate electrostatic sampler for the rapid monitoring of airborne coronavirus and influenza viruses. Journal of Hazardous Materials, 2021, 412: 125219. DOI:10.1016/j.jhazmat.2021.125219 |
[37] | Rufino De Sousa N, Sandström N, Shen L, Håkansson K, Vezozzo R, Udekwu KI, Croda J, Rothfuchs AG. A fieldable electrostatic air sampler enabling tuberculosis detection in bioaerosols. Tuberculosis, 2020, 120: 101896. DOI:10.1016/j.tube.2019.101896 |
[38] | Priyamvada H, Kumaragama K, Chrzan A, Athukorala C, Sur S, Dhaniyala S. Design and evaluation of a new electrostatic precipitation-based portable low-cost sampler for bioaerosol monitoring. Aerosol Science and Technology, 2021, 55(1): 24-36. DOI:10.1080/02786826.2020.1812503 |
[39] | Mescioglu E, Paytan A, Mitchell BW, Griffin DW. Efficiency of bioaerosol samplers: a comparison study. Aerobiologia, 2021, 37(3): 447-459. DOI:10.1007/s10453-020-09686-0 |
[40] | Han TT, Thomas NM, Mainelis G. Performance of personal electrostatic bioaerosol sampler (PEBS) when collecting airborne microorganisms. Journal of Aerosol Science, 2018, 124: 54-67. DOI:10.1016/j.jaerosci.2018.07.004 |
[41] | Piri AM, Kim HR, Park DH, Hwang J. Increased survivability of coronavirus and H1N1 influenza virus under electrostatic aerosol-to-hydrosol sampling. Journal of Hazardous Materials, 2021, 413: 125417. DOI:10.1016/j.jhazmat.2021.125417 |
[42] | Nieto-Caballero M, Savage N, Keady P, Hernandez M. High fidelity recovery of airborne microbial genetic materials by direct condensation capture into genomic preservatives. Journal of Microbiological Methods, 2019, 157: 1-3. DOI:10.1016/j.mimet.2018.12.010 |
[43] | Pan M, Eiguren-Fernandez A, Hsieh H, Afshar-Mohajer N, Hering SV, Lednicky J, Fan ZH, Wu CY. Efficient collection of viable virus aerosol through laminar-flow, water-based condensational particle growth. Journal of Applied Microbiology, 2016, 120(3): 805-815. DOI:10.1111/jam.13051 |
[44] | Lednicky J, Pan MH, Loeb J, Hsieh H, Eiguren-Fernandez A, Hering S, Fan ZH, Wu CY. Highly efficient collection of infectious pandemic influenza H1N1 virus (2009) through laminar-flow water based condensation. Aerosol Science and Technology, 2016, 50(7): i-iv. DOI:10.1080/02786826.2016.1179254 |
[45] | Pan MH, Bonny TS, Loeb J, Jiang X, Lednicky JA, Eiguren-Fernandez A, Hering S, Fan ZH, Wu CY. Collection of viable aerosolized influenza virus and other respiratory viruses in a student health care center through water-based condensation growth. mSphere, 2017, 2(5): e0025117. DOI:10.1128/mSphere.00251-17 |
[46] | Degois J, Dubuis ME, Turgeon N, Veillette M, Duchaine C. Condensation sampler efficiency for the recovery and infectivity preservation of viral bioaerosols. Aerosol Science and Technology, 2021, 55(6): 653-664. DOI:10.1080/02786826.2021.1889960 |
[47] | Lednicky JA, Lauzardo M, Fan ZH, Jutla A, Tilly TB, Gangwar M, Usmani M, Shankar SN, Mohamed K, Eiguren-Fernandez A, Stephenson CJ, Alam MM, Elbadry MA, Loeb JC, Subramaniam K, Waltzek TB, Cherabuddi K, Morris JG Jr, Wu CY. Viable SARS-CoV-2 in the air of a hospital room with COVID-19 patients. International Journal of Infectious Diseases, 2020, 100: 476-482. DOI:10.1016/j.ijid.2020.09.025 |
[48] | Tsay MD, Tseng CC, Wu NX, Lai CY. Size distribution and antibiotic-resistant characteristics of bacterial bioaerosol in intensive care unit before and during visits to patients. Environment International, 2020, 144: 106024. DOI:10.1016/j.envint.2020.106024 |
[49] | Jalili D, Dehghani M, Fadaei A, Alimohammadi M. Assessment of airborne bacterial and fungal communities in shahrekord hospitals. Journal of Environmental and Public Health, 2021, 2021: 8864051. |
[50] | Kim MH, Baek KO, Park GG, Jang JY, Lee JH. A study on concentration, identification, and reduction of airborne microorganisms in the military working dog clinic. Safety and Health at Work, 2020, 11(4): 517-525. DOI:10.1016/j.shaw.2020.09.002 |
[51] | Pan YM, Ren QQ, Chen P, Wu JG, Wu ZD, Zhang GX. Insight into microbial community aerosols associated with electronic waste handling facilities by culture-dependent and culture-independent methods. Frontiers in Public Health, 2021, 9: 657784. DOI:10.3389/fpubh.2021.657784 |
[52] | Sivagnanasundaram P, Amarasekara RWK, Madegedara RMD, Ekanayake A, Magana-Arachchi DN. Assessment of airborne bacterial and fungal communities in selected areas of teaching hospital, Kandy, Sri Lanka. BioMed Research International, 2019, 2019: 7393926. |
[53] | Ling S, Hui L. Evaluation of the complexity of indoor air in hospital wards based on PM2.5, real-time PCR, adenosine triphosphate bioluminescence assay, microbial culture and mass spectrometry. BMC Infectious Diseases, 2019, 19(1): 646. DOI:10.1186/s12879-019-4249-z |
[54] | Jäckel U, Martin E, Schäfer J. Heterogeneity in cultivation-based monitoring of airborne bacterial biodiversity in animal farms. Annals of Work Exposures and Health, 2017, 61(6): 643-655. DOI:10.1093/annweh/wxx039 |
[55] | Hu W, Murata K, Fan CL, Huang S, Matsusaki H, Fu PQ, Zhang DZ. Abundance and viability of particle-attached and free-floating bacteria in dusty and nondusty air. Biogeosciences, 2020, 17(17): 4477-4487. DOI:10.5194/bg-17-4477-2020 |
[56] | Tham KW, Zuraimi MS. Size relationship between airborne viable bacteria and particles in a controlled indoor environment study. Indoor Air, 2005, 15(Suppl 9): 48-57. |
[57] | Nehme B, Létourneau V, Forster RJ, Veillette M, Duchaine C. Culture-independent approach of the bacterial bioaerosol diversity in the standard swine confinement buildings, and assessment of the seasonal effect. Environmental Microbiology, 2008, 10(3): 665-675. DOI:10.1111/j.1462-2920.2007.01489.x |
[58] | Riccardi C, Di Filippo P, Pomata D, Simonetti G, Castellani F, Uccelletti D, Bruni E, Federici E, Buiarelli F. Comparison of analytical approaches for identifying airborne microorganisms in a livestock facility. Science of the Total Environment, 2021, 783: 147044. DOI:10.1016/j.scitotenv.2021.147044 |
[59] | Wei DJ, Liu WT, Chin HT, Lin CH, Chen IC, Chang YT. An investigation of airborne bioaerosols and endotoxins present in indoor traditional wet markets before and after operation in Taiwan: a case study. International Journal of Environmental Research and Public Health, 2021, 18(6): 2945. DOI:10.3390/ijerph18062945 |
[60] | Prass M, Andreae MO, De Araùjo AC, Artaxo P, Ditas F, Elbert W, Förster JD, Franco MA, Hrabe De Angelis I, Kesselmeier J, Klimach T, Kremper LA, Thines E, Walter D, Weber J, Weber B, Fuchs BM, Pöschl U, Pöhlker C. Bioaerosols in the Amazon rain forest: temporal variations and vertical profiles of eukarya, bacteria, and archaea. Biogeosciences, 2021, 18(17): 4873-4887. DOI:10.5194/bg-18-4873-2021 |
[61] | Anedda E, Carletto G, Gilli G, Traversi D. Monitoring of air microbial contaminations in different bioenergy facilities using cultural and biomolecular methods. International Journal of Environmental Research and Public Health, 2019, 16(14): 2546. DOI:10.3390/ijerph16142546 |
[62] | Mu FF, Li YP, Lu R, Qi YZ, Xie WW, Bai WY. Source identification of airborne bacteria in the mountainous area and the urban areas. Atmospheric Research, 2020, 231: 104676. DOI:10.1016/j.atmosres.2019.104676 |
[63] | Ye J, Qian H, Zhang JS, Sun F, Zhuge Y, Zheng XH. Combining culturing and 16S rDNA sequencing to reveal seasonal and room variations of household airborne bacteria and correlative environmental factors in Nanjing, southeast China. Indoor Air, 2021, 31(4): 1095-1108. DOI:10.1111/ina.12807 |
[64] | Li XY, Chen HX, Yao MS. Microbial emission levels and diversities from different land use types. Environment International, 2020, 143: 105988. DOI:10.1016/j.envint.2020.105988 |
[65] | Han YP, Yang T, Chen TZ, Li L, Liu JX. Characteristics of submicron aerosols produced during aeration in wastewater treatment. Science of the Total Environment, 2019, 696: 134019. DOI:10.1016/j.scitotenv.2019.134019 |
[66] | Nygaard AB, Tunsjø HS, Meisal R, Charnock C. A preliminary study on the potential of nanopore MinION and Illumina MiSeq 16S rRNA gene sequencing to characterize building-dust microbiomes. Scientific Reports, 2020, 10: 3209. DOI:10.1038/s41598-020-59771-0 |
[67] | Galès A, Bru-Adan V, Godon JJ, Delabre K, Catala P, Ponthieux A, Chevallier M, Birot E, Steyer JP, Wéry N. Predominance of single bacterial cells in composting bioaerosols. Atmospheric Environment, 2015, 107: 225-232. DOI:10.1016/j.atmosenv.2015.02.035 |
[68] | Wang YF, Zhang XD, Yang N, Ma GX, Du XX, Mao HP. Separation-enrichment method for airborne disease spores based on microfluidic chip. International Journal of Agricultural and Biological Engineering, 2021, 14(5): 199-205. DOI:10.25165/j.ijabe.20211405.6375 |
[69] | Xu PF, Zhang RB, Yang N, Kwabena Oppong P, Sun J, Wang P. High-precision extraction and concentration detection of airborne disease microorganisms based on microfluidic chip. Biomicrofluidics, 2019, 13(2): 024110. DOI:10.1063/1.5086087 |
[70] | Damit B. Droplet-based microfluidics detector for bioaerosol detection. Aerosol Science and Technology, 2017, 51(4): 488-500. DOI:10.1080/02786826.2016.1275515 |
[71] | Choi J, Hong SC, Kim W, Jung JH. Highly enriched, controllable, continuous aerosol sampling using inertial microfluidics and its application to real-time detection of airborne bacteria. ACS Sensors, 2017, 2(4): 513-521. DOI:10.1021/acssensors.6b00753 |
[72] | Cui L, Yang K, Li HZ, Zhang H, Su JQ, Paraskevaidi M, Martin FL, Ren B, Zhu YG. Functional single-cell approach to probing nitrogen-fixing bacteria in soil communities by resonance Raman spectroscopy with 15N2 labeling. Analytical Chemistry, 2018, 90(8): 5082-5089. DOI:10.1021/acs.analchem.7b05080 |
[73] | Lu WL, Chen XQ, Wang L, Li HF, Fu YV. Combination of an artificial intelligence approach and laser tweezers Raman spectroscopy for microbial identification. Analytical Chemistry, 2020, 92(9): 6288-6296. DOI:10.1021/acs.analchem.9b04946 |
[74] | Yu SX, Li HF, Li X, Fu YV, Liu FH. Classification of pathogens by Raman spectroscopy combined with generative adversarial networks. Science of the Total Environment, 2020, 726: 138477. DOI:10.1016/j.scitotenv.2020.138477 |
[75] | Heidari Baladehi M, Hekmatara M, He YH, Bhaskar Y, Wang ZB, Liu L, Ji YT, Xu J. Culture-free identification and metabolic profiling of microalgal single cells via ensemble learning of ramanomes. Analytical Chemistry, 2021, 93(25): 8872-8880. DOI:10.1021/acs.analchem.1c01015 |
[76] | Choi J, Lee J, Jung JH. Fully integrated optofluidic SERS platform for real-time and continuous characterization of airborne microorganisms. Biosensors and Bioelectronics, 2020, 169: 112611. DOI:10.1016/j.bios.2020.112611 |
[77] | Ai YK, Alali H, Pan YL, Videen G, Wang CJ. Single-particle optical-trapping Raman spectroscopy for the detection and identification of aerosolized airborne biological particles. Measurement Science and Technology, 2021, 32(5): 055207. DOI:10.1088/1361-6501/abd5f1 |
[78] | Tahir MA, Zhang XL, Cheng HY, Xu D, Feng YQ, Sui GD, Fu HB, Valev VK, Zhang LW, Chen JM. Klarite as a label-free SERS-based assay: a promising approach for atmospheric bioaerosol detection. The Analyst, 2019, 145(1): 277-285. |
[79] | Scoizec A, Niqueux E, Thomas R, Daniel P, Schmitz A, Le Bouquin S. Airborne detection of H5N8 highly pathogenic avian influenza virus genome in poultry farms, France. Frontiers in Veterinary Science, 2018, 5: 15. DOI:10.3389/fvets.2018.00015 |
[80] | Coleman KK, Sigler WV. Airborne influenza a virus exposure in an elementary school. Scientific Reports, 2020, 10: 1859. DOI:10.1038/s41598-020-58588-1 |
[81] | Chamseddine A, Soudani N, Kanafani Z, Alameddine I, Dbaibo G, Zaraket H, El-Fadel M. Detection of influenza virus in air samples of patient rooms. Journal of Hospital Infection, 2021, 108: 33-42. DOI:10.1016/j.jhin.2020.10.020 |
[82] | Fulci V, Carissimi C, Laudadio I. COVID-19 and preparing for future ecological crises: hopes from metagenomics in facing current and future viral pandemic challenges. Omics: a Journal of Integrative Biology, 2021, 25(6): 336-341. DOI:10.1089/omi.2021.0058 |
[83] | Prussin Ⅱ AJ, Torres PJ, Shimashita J, Head SR, Bibby KJ, Kelley ST, Marr LC. Seasonal dynamics of DNA and RNA viral bioaerosol communities in a daycare center. Microbiome, 2019, 7(1): 53. DOI:10.1186/s40168-019-0672-z |
[84] | Brisebois E, Veillette M, Dion-Dupont V, Lavoie J, Corbeil J, Culley A, Duchaine C. Human viral pathogens are pervasive in wastewater treatment center aerosols. Journal of Environmental Sciences, 2018, 67: 45-53. DOI:10.1016/j.jes.2017.07.015 |
[85] | Morawska L, Milton DK. It is time to address airborne transmission of coronavirus disease 2019 (COVID-19). Clinical Infectious Diseases, 2020, 71(9): 2311-2313. |
[86] | Wilson NM, Norton A, Young FP, Collins DW. Airborne transmission of severe acute respiratory syndrome coronavirus-2 to healthcare workers: a narrative review. Anaesthesia, 2020, 75(8): 1086-1095. DOI:10.1111/anae.15093 |
[87] | Tang S, Mao YX, Jones RM, Tan QY, Ji JS, Li N, Shen J, Lv YB, Pan LJ, Ding P, Wang XC, Wang YB, MacIntyre CR, Shi XM. Aerosol transmission of SARS-CoV-2? Evidence, prevention and control. Environment International, 2020, 144: 106039. DOI:10.1016/j.envint.2020.106039 |
[88] | Kenarkoohi A, Noorimotlagh Z, Falahi S, Amarloei A, Mirzaee SA, Pakzad I, Bastani E. Hospital indoor air quality monitoring for the detection of SARS-CoV-2 (COVID-19) virus. Science of the Total Environment, 2020, 748: 141324. DOI:10.1016/j.scitotenv.2020.141324 |
[89] | Wang CC, Prather KA, Sznitman J, Jimenez JL, Lakdawala SS, Tufekci Z, Marr LC. Airborne transmission of respiratory viruses. Science, 2021, 373(6558): eabd9149. DOI:10.1126/science.abd9149 |
[90] | Liu Y, Ning Z, Chen Y, Guo M, Liu YL, Gali NK, Sun L, Duan YS, Cai J, Westerdahl D, Liu XJ, Xu K, Ho KF, Kan HD, Fu QY, Lan K. Aerodynamic analysis of SARS-CoV-2 in two Wuhan hospitals. Nature, 2020, 582(7813): 557-560. DOI:10.1038/s41586-020-2271-3 |
[91] | Van Doremalen N, Bushmaker T, Morris DH, Holbrook MG, Gamble A, Williamson BN, Tamin A, Harcourt JL, Thornburg NJ, Gerber SI, Lloyd-Smith JO, De Wit E, Munster VJ. Aerosol and surface stability of SARS-CoV-2 as compared with SARS-CoV-1. The New England Journal of Medicine, 2020, 382(16): 1564-1567. DOI:10.1056/NEJMc2004973 |
[92] | Li YG, Qian H, Hang J, Chen XG, Cheng P, Ling H, Wang SQ, Liang P, Li JS, Xiao SL, Wei JJ, Liu L, Cowling BJ, Kang M. Probable airborne transmission of SARS-CoV-2 in a poorly ventilated restaurant. Building and Environment, 2021, 196: 107788. DOI:10.1016/j.buildenv.2021.107788 |
[93] | Lane MA, Brownsword EA, Babiker A, Ingersoll JM, Waggoner J, Ayers M, Klopman M, Uyeki TM, Lindsley WG, Kraft CS. Bioaerosol sampling for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in a referral center with critically ill coronavirus disease 2019 (COVID-19) patients March-May 2020. Clinical Infectious Diseases, 2021, 73(7): e1790-e1794. DOI:10.1093/cid/ciaa1880 |
[94] | Borges JT, Nakada LYK, Maniero MG, Guimarães JR. SARS-CoV-2: a systematic review of indoor air sampling for virus detection. Environmental Science and Pollution Research International, 2021, 28(30): 40460-40473. DOI:10.1007/s11356-021-13001-w |
[95] | Robotto A, Quaglino P, Lembo D, Morello M, Brizio E, Bardi L, Civra A. SARS-CoV-2 and indoor/outdoor air samples: a methodological approach to have consistent and comparable results. Environmental Research, 2021, 195: 110847. DOI:10.1016/j.envres.2021.110847 |
[96] | Shin J, Kim HR, Bae PK, Yoo H, Kim J, Choi Y, Kang A, Yun WS, Shin YB, Hwang J, Hong S. Reusable surface amplified nanobiosensor for the sub PFU/mL level detection of airborne virus. Scientific Reports, 2021, 11: 16776. DOI:10.1038/s41598-021-96254-2 |
[97] | Bhardwaj J, Kim MW, Jang J. Rapid airborne influenza virus quantification using an antibody-based electrochemical paper sensor and electrostatic particle concentrator. Environmental Science & Technology, 2020, 54(17): 10700-10712. |
[98] | Jiang X, Loeb JC, Pan MH, Tilly TB, Eiguren-Fernandez A, Lednicky JA, Wu CY, Fan ZH. Integration of sample preparation with RNA- amplification in a hand-held device for airborne virus detection. Analytica Chimica Acta, 2021, 1165: 338542. DOI:10.1016/j.aca.2021.338542 |
[99] | Lee I, Seok Y, Jung H, Yang B, Lee J, Kim J, Pyo H, Song CS, Choi W, Kim MG, Lee J. Integrated bioaerosol sampling/monitoring platform: field- deployable and rapid detection of airborne viruses. ACS Sensors, 2020, 5(12): 3915-3922. DOI:10.1021/acssensors.0c01531 |
[100] | Li MX, Wang L, Qi WZ, Liu YJ, Lin JH. Challenges and perspectives for biosensing of bioaerosol containing pathogenic microorganisms. Micromachines, 2021, 12(7): 798. DOI:10.3390/mi12070798 |