Name: characterizing microbiome dynamics – flow cytometry based workflows from pure cultures to natural communities
Uploaded: 2018-07-12T15:00:00.000+00:00
Duration: 9 min 57 s
Description: Watch this Scientific Journal Video about Characterizing Microbiome Dynamics â€“ Flow Cytometry Based Workflows from Pure Cultures to Natural Communities at JoVE.com

我们感谢迈克尔 Jahn 和黄玉庭提供了纯文化和活性污泥群落的程序和数据集。我们进一步感谢 Katrin Mörters 对沼气群落样品的分析。这项工作由 Fachagentur Nachwachsende Rohstoffe e (FNR, 项目沼气-指纹 Nr) 资助. 22008313) 代表德国联邦粮食和农业部 (BMEL), 联邦部中小企业的中央创新方案 (星)经济事务和能源 (BMWi) (INAR-ABOS, 16KN043222), 德意志 Bundesstiftung Umwelt (DBU, 项目按需 Produktion 冯 Phosphatdünger 澳大利亚的 Reststoffen 和 Brauerei 33960/01-32), 德国联邦教育部和研究 (BMBF) (FZK 03XP0041G) 和亥姆霍兹协会的面向项目的资金 (光纤 III R31 主题3生物能源)。

<ol>
	<li>Xu, X., Hui, D., King, A. W., Song, X., Thornton, P. E., Zhang, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Convergence+of+microbial+assimilations+of+soil+carbon,+nitrogen,+phosphorus,+and+sulfur+in+terrestrial+ecosystems.">Convergence of microbial assimilations of soil carbon, nitrogen, phosphorus, and sulfur in terrestrial ecosystems.</a> Scientific Reports. 5 (1), 17445 (2015).</li><li>Fathepure, B. Z. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Recent+studies+in+microbial+degradation+of+petroleum+hydrocarbons+in+hypersaline+environments.">Recent studies in microbial degradation of petroleum hydrocarbons in hypersaline environments.</a> Frontiers in Microbiology. 5, 173 (2014).</li><li>Alshehrei, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Biodegradation+of+Synthetic+and+Natural+Plastic+by+Microorganisms.">Biodegradation of Synthetic and Natural Plastic by Microorganisms.</a> Journal of Applied &amp; Environmental Microbiology. 5 (1), 8-19 (2017).</li><li>Sarria, S., Kruyer, N. S., Peralta-Yahya, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Microbial+Synthesis+of+medium-chain+chemicals+from+renewables.">Microbial Synthesis of medium-chain chemicals from renewables.</a> Nature Biotechnology. 35 (12), 1158-1166 (2017).</li><li>G&#252;nther, S., Faust, K., Schumann, J., Harms, H., Raes, J., M&#252;ller, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Species-sorting+and+mass-transfer+paradigms+control+managed+natural+metacommunities:+Species-sorting+and+mass-transfer+paradigms+in+metacommunities.">Species-sorting and mass-transfer paradigms control managed natural metacommunities: Species-sorting and mass-transfer paradigms in metacommunities.</a> Environmental Microbiology. 18 (12), 4862-4877 (2016).</li><li>Lambrecht, J., Cichocki, N., H&#252;bschmann, T., Koch, C., Harms, H., M&#252;ller, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Flow+cytometric+quantification,+sorting+and+sequencing+of+methanogenic+archaea+based+on+F420+autofluorescence.">Flow cytometric quantification, sorting and sequencing of methanogenic archaea based on F420 autofluorescence.</a> Microbial Cell Factories. 16 (1), 180 (2017).</li><li>Huttenhower, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Structure,+function+and+diversity+of+the+healthy+human+microbiome.">Structure, function and diversity of the healthy human microbiome.</a> Nature. 486 (7402), 207-214 (2012).</li><li>Lloyd-Price, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Strains,+functions+and+dynamics+in+the+expanded+Human+Microbiome+Project.">Strains, functions and dynamics in the expanded Human Microbiome Project.</a> Nature. 550, 61-66 (2017).</li><li>Goodwin, S., McPherson, J. D., McCombie, W. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Coming+of+age:+ten+years+of+next-generation+sequencing+technologies.">Coming of age: ten years of next-generation sequencing technologies.</a> Nature Reviews Genetics. 17 (6), 333-351 (2016).</li><li>Liu, Z., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Ecological+Stability+Properties+of+Microbial+Communities+Assessed+by+Flow+Cytometry.">Ecological Stability Properties of Microbial Communities Assessed by Flow Cytometry.</a> mSphere. 3 (1), e00564-e00517 (2018).</li><li>Koch, C., G&#252;nther, S., Desta, A. F., H&#252;bschmann, T., M&#252;ller, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cytometric+fingerprinting+for+analyzing+microbial+intracommunity+structure+variation+and+identifying+subcommunity+function.">Cytometric fingerprinting for analyzing microbial intracommunity structure variation and identifying subcommunity function.</a> Nature Protocols. 8 (1), 190-202 (2013).</li><li>Koch, C., Fetzer, I., Schmidt, T., Harms, H., M&#252;ller, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Monitoring+Functions+in+Managed+Microbial+Systems+by+Cytometric+Bar+Coding.">Monitoring Functions in Managed Microbial Systems by Cytometric Bar Coding.</a> Environmental Science &amp; Technology. 47 (3), 1753-1760 (2013).</li><li>Lefort, T., Gasol, J. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Short-time+scale+coupling+of+picoplankton+community+structure+and+single-cell+heterotrophic+activity+in+winter+in+coastal+NW+Mediterranean+Sea+waters.">Short-time scale coupling of picoplankton community structure and single-cell heterotrophic activity in winter in coastal NW Mediterranean Sea waters.</a> Journal of Plankton Research. 36 (1), 243-258 (2014).</li><li>Besmer, M. D., Epting, J., Page, R. M., Sigrist, J. A., Huggenberger, P., Hammes, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Online+flow+cytometry+reveals+microbial+dynamics+influenced+by+concurrent+natural+and+operational+events+in+groundwater+used+for+drinking+water+treatment.">Online flow cytometry reveals microbial dynamics influenced by concurrent natural and operational events in groundwater used for drinking water treatment.</a> Scientific Reports. 6, 38462 (2016).</li><li>van Gelder, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+cytometric+approach+to+follow+variation+and+dynamics+of+the+salivary+microbiota.">A cytometric approach to follow variation and dynamics of the salivary microbiota.</a> Methods. 134, 67-79 (2018).</li><li>Jehmlich, N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Advanced+tool+for+characterization+of+microbial+cultures+by+combining+cytomics+and+proteomics.">Advanced tool for characterization of microbial cultures by combining cytomics and proteomics.</a> Applied Microbiology and Biotechnology. 88 (2), 575-584 (2010).</li><li>Jahn, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Accurate+Determination+of+Plasmid+Copy+Number+of+Flow-Sorted+Cells+using+Droplet+Digital+PCR.">Accurate Determination of Plasmid Copy Number of Flow-Sorted Cells using Droplet Digital PCR.</a> Analytical Chemistry. 86 (12), 5969-5976 (2014).</li><li>Koch, C., M&#252;ller, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Personalized+microbiome+dynamics+-+Cytometric+fingerprints+for+routine+diagnostics.">Personalized microbiome dynamics - Cytometric fingerprints for routine diagnostics.</a> Molecular Aspects of Medicine. 59, 123-134 (2018).</li><li>Jahn, M., Vorpahl, C., H&#252;bschmann, T., Harms, H., M&#252;ller, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Copy+number+variability+of+expression+plasmids+determined+by+cell+sorting+and+Droplet+Digital+PCR.">Copy number variability of expression plasmids determined by cell sorting and Droplet Digital PCR.</a> Microbial Cell Factories. 15 (1), 211 (2016).</li><li>Koch, C., Fetzer, I., Harms, H., M&#252;ller, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=CHIC-an+automated+approach+for+the+detection+of+dynamic+variations+in+complex+microbial+communities.">CHIC-an automated approach for the detection of dynamic variations in complex microbial communities.</a> Cytometry Part A. 83 (6), 561-567 (2013).</li><li>G&#252;nther, S., M&#252;ller, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Facilitated+gate+setting+by+sequential+dot+plot+scanning:+Facilitated+Gate+Setting+by+Sequential+Dot+Plot+Scanning.">Facilitated gate setting by sequential dot plot scanning: Facilitated Gate Setting by Sequential Dot Plot Scanning.</a> Cytometry Part A. 87 (7), 661-664 (2015).</li><li>Guo, Y., Baumgart, S., St&#228;rk, H. -. J., Harms, H., M&#252;ller, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mass+Cytometry+for+Detection+of+Silver+at+the+Bacterial+Single+Cell+Level.">Mass Cytometry for Detection of Silver at the Bacterial Single Cell Level.</a> Frontiers in Microbiology. 8, 1326 (2017).</li><li>Gasol, J. M., Del Giorgio, P. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Using+flow+cytometry+for+counting+natural+planktonic+bacteria+and+understanding+the+structure+of+planktonic+bacterial+communities.">Using flow cytometry for counting natural planktonic bacteria and understanding the structure of planktonic bacterial communities.</a> Scientia Marina. 64 (2), 197-224 (2000).</li><li>Gasol, J. M., Mor&#225;n, X. A. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Flow+Cytometric+Determination+of+Microbial+Abundances+and+Its+Use+to+Obtain+Indices+of+Community+Structure+and+Relative+Activity.">Flow Cytometric Determination of Microbial Abundances and Its Use to Obtain Indices of Community Structure and Relative Activity.</a> Hydrocarbon and Lipid Microbiology Protocols. , 159-187 (2016).</li><li>Props, R., Monsieurs, P., Mysara, M., Clement, L., Boon, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Measuring+the+biodiversity+of+microbial+communities+by+flow+cytometry.">Measuring the biodiversity of microbial communities by flow cytometry.</a> Methods in Ecology and Evolution. 7 (11), 1376-1385 (2016).</li><li>Kinet, R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Flow+cytometry+community+fingerprinting+and+amplicon+sequencing+for+the+assessment+of+landfill+leachate+cellulolytic+bioaugmentation.">Flow cytometry community fingerprinting and amplicon sequencing for the assessment of landfill leachate cellulolytic bioaugmentation.</a> Bioresource Technology. 214, 450-459 (2016).</li><li>De Roy, K., Clement, L., Thas, O., Wang, Y., Boon, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Flow+cytometry+for+fast+microbial+community+fingerprinting.">Flow cytometry for fast microbial community fingerprinting.</a> Water Research. 46 (3), 907-919 (2012).</li><li>Koch, C., Harnisch, F., Schr&#246;der, U., M&#252;ller, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cytometric+fingerprints:+evaluation+of+new+tools+for+analyzing+microbial+community+dynamics.">Cytometric fingerprints: evaluation of new tools for analyzing microbial community dynamics.</a> Frontiers in Microbiology. 5, 273 (2014).</li><li>Amalfitano, S., Fazi, S., Ejarque, E., Freixa, A., Roman&#237;, A. M., Butturini, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Deconvolution+model+to+resolve+cytometric+microbial+community+patterns+in+flowing+waters:+Deconvolving+Cytometric+Microbial+Subgroups.">Deconvolution model to resolve cytometric microbial community patterns in flowing waters: Deconvolving Cytometric Microbial Subgroups.</a> Cytometry Part A. 93 (2), 194-200 (2017).</li><li>Buysschaert, B., Kerckhof, F. -. M., Vandamme, P., De Baets, B., Boon, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Flow+cytometric+fingerprinting+for+microbial+strain+discrimination+and+physiological+characterization:+Flow+Cytometric+Fingerprinting.">Flow cytometric fingerprinting for microbial strain discrimination and physiological characterization: Flow Cytometric Fingerprinting.</a> Cytometry Part A. 93 (2), 201-212 (2017).</li><li>Besmer, M. D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Laboratory-Scale+Simulation+and+Real-Time+Tracking+of+a+Microbial+Contamination+Event+and+Subsequent+Shock-Chlorination+in+Drinking+Water.">Laboratory-Scale Simulation and Real-Time Tracking of a Microbial Contamination Event and Subsequent Shock-Chlorination in Drinking Water.</a> Frontiers in Microbiology. 8 (1900), (2017).</li><li>Zimmermann, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High-resolution+microbiota+flow+cytometry+reveals+dynamic+colitis-associated+changes+in+fecal+bacterial+composition:+Technical+comment.">High-resolution microbiota flow cytometry reveals dynamic colitis-associated changes in fecal bacterial composition: Technical comment.</a> European Journal of Immunology. 46 (5), 1300-1303 (2016).</li></ol>

作者没有什么可透露的。

对微生物种群和群落的成功分析需要经过良好调整的 cytometers, 适当的细胞参数选择, 以及从取样和固定到测量和数据评估的可靠工作流程。所选单元格参数必须与可用的激发波长匹配。我们使用和推荐 DAPI, 这是非常敏感的低浓度;但需要被紫外线激光激发, 这通常不包括在标准的细胞仪设置。其他染料, 如 SYBR 绿 I, 也玷污了整个人口或社区, 但一般提供劣质决议。我们不建议在微生物群落中使用鱼类程序或生存能力测试。这些方法无法量化、验证和控制, 因为它们对群落中个别物种的影响是不确定的。只要有相当一部分典型的社区仍然不能作为纯文化存在, 就无法对它们进行可靠的测试。
该协议的关键步骤包括取样和固定程序以及细胞分类。由于某些纯净的文化和微生物群落的倾向聚集到絮凝体或附着在样品基质中的微粒, 取样可能会变得复杂。在引入流细胞分析以保证可靠的结果之前, 必须在样品中消散这些聚集物并将细胞分离出来。对所提出的制备方案进行了优化, 以实现单细胞分析。最后50µm 过滤是在测量之前执行, 以去除任何残余骨料和防止70µm 喷嘴的细胞分拣器和流试管的分析仪从堵塞。污水处理厂的细胞絮凝体尤其丰富、健壮, 对系统的功能至关重要。对某大型污水处理厂不同储罐中的浮游污泥基微生物群落进行了研究, 对所建立的协议进行了测试。污泥形成细胞团聚体明显消散, 群落组成保持稳定。此外, 浮游和污泥为基础的群落在所有三取样罐中表现出显著的相似性 (图 8, 补充文件 1-S10)。这些结果通过比较 16S rDNA 扩增子测序的新鲜, 甲醛处理-, 甲醛和乙醇固定-, 和排序样本10验证。然而, 极具挑战性的环境样品, 如强的生物膜或在紧密相连的链条中生长的细菌, 可能无法消散。土壤样品可能是特别疑难的, 因为无处不在的微粒出现在直方图中, 并且可以通过纯粹的丰度掩盖细胞。在这种情况下, 需要采用传统的测序方法。
在设计实验前, 必须对每套新样品集的固定稳定性进行测试, 以保证结果的有效性。良好的固定稳定性也能在一天内使多样本时间点的汇集染色和测量。在实验结束和最后评价后, 它还能使复制测量和回溯细胞分类成为决定性的时间点。对纯培养物的固定稳定性进行了28天的验证 (图 9)。对于包括样品运输在内的现场试验, 应在较长时期内对固定稳定性进行试验 (在这种情况下, 活性污泥群落为60天, 沼气群落195天, 补充文件 1-S9)。
染色通常不是问题, 但需要控制与生物标准, 以允许应用的生物信息学评估工具。我们使用了模拟应变大肠杆菌BL21 (DE3), 这是根据 ASC 协议和储存, 以用于每一个染色批次。
除了 DAPI, SYBR 绿我已经成功地应用于解决微生物群落14 年,23,24,25,26。SYBR 绿色我可以用在线工作中的活细胞来解决社区变成高和低核酸子集 (海航和放大器)。然而, DAPI 在对 DNA 的结合上更具体, 并且在一个样本集合中能够区分超过50子社区, 但需要在染色前采取固定步骤。
细胞分类是这种方法的一个突出特点, 如果某些子社区有进一步的兴趣, 就可以应用。需要强调的是, 无论是粉煤灰 (仅执行30分钟), 还是乙醇处理或 DAPI 染色10都对随后的16S 扩增子测序产生负面影响。固定和染色程序对子社区 metagenome 分析的潜在影响仍需检验。
在涉及生物信息学工具的情况下, 许多关于社区功能和趋势动态的信息可以独立于分类方法中, 并通过细胞层次来解决虚拟细胞的社区特征, 并将这些特征参数。
最近, 一些新的生物信息学评估工具, 所有有用的 SYBR 绿色 I 和 DAPI 方法, 已开发解决细胞社区模式。这些是 FlowFP27, 这项研究中使用的包 (flowCHIC20, flowCyBar28), 一个与淡水社区29建立的反褶积模型和一个工具, 用于区分菌株根据其生理特征30。此外, 细胞多样性可以确定25 , 甚至微生物群落的稳定性特性现在可以遵循在线工具10。
因此, 在快速评价微生物群落和可能的非生物参数相关性方面, 流细胞分析具有巨大的优势。但是, 该方法仍然存在一些限制。由于有经验的基于门设置的程序, 它不能真正地在网上使用 flowCyBar 工具。此外, 研制定制、使用方便的流 cytometers 将极大地促进流细胞菌群分析方法的扩散。在这个方向上的第一步是进行28。
微生物流式细胞术的未来应用可以在微生物生态学中得到展望, 因为它允许在细菌产生时间范围内进行高频监测, 并表明生态学范式适用于细胞数据 5. ,10。该方法对在瑞士31的强制性饮水控制等自然环境的常规筛查非常有用。它也可以是一个宝贵的工具, 医疗应用, 如人类15或动物32微生物筛查。此外, 生物信息学的最新发展可能使微生物流式细胞术成为管理微生物系统过程控制中的一个整体传感器。微生物群落细胞术也为细胞分类提供了一个筛选工具, 它允许对特定群落子集进行更高分辨率的基因组调查。

微生物在人类生活的许多方面起着至关重要的作用。它们是行星碳、氮、磷和硫循环1的主要生物驱动因素, 其作用是降解2、3以及在各种工业 (如废水处理) 中合成4用于生物催化剂。5或生物技术6。它们甚至构成了人类的微生物菌群, 直接影响到人类的健康和新陈代谢7,8。因此, 如果我们的目标是充分理解和操纵这些系统, 那么关于微生物的结构、功能和动态行为的信息, 以响应它们的直接环境是必要的。下一代测序技术是解决菌群结构和功能9的既定方法。但是, 对内部数据的分析和评估不能产生量化信息, 而且成本高昂, 耗时, 而且在性能走廊内也没有准备好提供现场结果。有些细菌在1小时以下显示世代时间并且导致经常改变的社区结构在某些环境10。在这样的动态下, 使用排序方法将超过大多数科学实验室的财务和人力能力。
相比之式细胞术可以提供类似于数据的社区指纹 , 同时保留更高的时间、劳动和成本效率。在这里, 我们提出的流动细胞技术能够遵循微生物群落动力学在网上的单一细胞水平。与本研究不同, 流式细胞术并不提供功能基因的系统进化隶属关系或信息, 而是提供定量的细胞数量。利用流式细胞仪, 可以将微生物群落分解为子社区, 具有明显的光散射和荧光特性 (前向散射 (FSC) 和侧散射 (SSC), 分别提供细胞大小和粒度的指示)。所提出的方法主要利用细胞大小和与某些细胞类型和生理 (生长) 状态相关的 DNA 数量相关信息。dna 含量的量化使用紫外兴奋 4 ', 6-diamidino-2 '-苯基吲哚 (DAPI) 染料, 它绑定到一个 T 丰富的地区的 DNA, 甚至可以解决不同的染色体水平。通过结合 DAPI 荧光和 FSC 超过50子社区可以区分和监测, 以改变丰度随着时间的推移11。子社区丰度变化可与微环境环境的变化相关, 如 pH 值和产品效价12, 具有全球参数, 如天气13,14, 或肠道或唾液的情况下微生物与某些医疗治疗15。这些相关性揭示了关键子社区负责在整个社区的新陈代谢网络中的特定功能。然后, 通过改变周围的微环境, 或者为随后的测序15或蛋白质组调查16进行排序, 可以特别促进或抑制关键子社区。
然而, 微生物群落流式细胞术还没有得到广泛的建立, 因为大量的流动细胞装置能够正确地解决微生物种群或群落。此外, 缺乏经验、社区分析管道和有效的数据评估可能会给面临微生物分析挑战的研究人员带来进入障碍。我们已经建立了全面的工作流程来解决这些问题。为了说明它们的一般适用性, 我们将在三示范样本集 (补充文件 1-S1) 上展示它们, 即 i) 一种纯文化 (PC), 用于在明确的培养基中生长的生物技术应用 (单胞菌恶臭KT 2440 上葡萄糖), ii) 在透明介质 (合成废水活性污泥群落 (ASC)) 和 iii) 的一个复杂的实验室社区, 从一个稠密基质 (BC) 的天然环境中的一个复杂的社区 (玉米青贮)。
几个因素影响了每个样本集的协议选择。深冻放弃使用有毒化学品, 但主要限于实验室环境, 因为使用液态氮气的冲击霜。甲醛稳定和随后的乙醇固定允许散装取样, 不需要整除准备, 并已证明是稳定的长期。然而, 富含蛋白质的样品如人唾液15可能是问题, 因为蛋白质絮凝体, denaturized 由甲醛, 可能导致不赞同的信号到噪声比率。样品干燥将花费多数时间 (大约 1 h 在一共) 过程在这个比较, 但可以在现场执行, 不用毒物化学制品。它在管子中产生稳定的颗粒, 可以很容易地运送而不冷却或额外的危险预防措施。此外, 还提出了许多替代固定方法11。在引入新的样本集时, 我们强烈建议测试不同协议的分辨率和固定稳定性。
我们使用了两个不同的流 cytometers 来分析集合: i) 一个高度复杂和昂贵的, 研究中心的细胞分拣器和 ii) 一个应用重点的台顶分析仪, 似乎更适合现场应用5。BC 用工作台顶部分析仪测量, 而 PC 和 ASC 用细胞分拣机测量。分析三示范样本集需要不同的程序, 以优化重现性, 样品稳定性和工作流程的方便。下面的协议将指定三不同系统的各部分。

<table><tbody><tr><td>Milli-Q system Synthesis A10</td><td>Merck, Darmstadt, (GER)</td><td></td><td></td></tr><tr><td>BioPak Ultrafiltration Cartridge</td><td>Merck, Darmstadt, (GER)</td><td>CDUF BI0 01</td><td></td></tr><tr><td>MoFlo Legacy cell sorter</td><td>Beckman-Coulter, Brea, California (USA)</td><td></td><td></td></tr><tr><td>Innova 90C</td><td>Coherent, Inc , Santa Clara (USA)</td><td></td><td>334 nm - 364 nm, 100 mW</td></tr><tr><td>Innova 70C&amp;nbsp;</td><td>Coherent, Inc , Santa Clara (USA)</td><td></td><td>488 nm, 400 mW</td></tr><tr><td>Genesis MX488-500 STM OPS</td><td>Coherent, Inc , Santa Clara (USA)</td><td></td><td>488 nm, 400 mW</td></tr><tr><td>Xcyte CY-355-150</td><td>Lumentum, Milpitas, California, USA</td><td></td><td>355 nm, 150 mW</td></tr><tr><td>Photomultiplier tubes R928 and R3896</td><td>Hamamatsu Photonics, Hamamatsu City (Japan)</td><td></td><td></td></tr><tr><td>Summit 4.3 software</td><td>Beckman-Coulter, Brea, California (USA)</td><td></td><td></td></tr><tr><td>1 &amp;micro;m&amp;nbsp; FluoSpheres</td><td>Molecular Probes, Eugene, Oregon (USA)</td><td>F8815</td><td>350/440 blue fluorescent</td></tr><tr><td>2 &amp;mu;m YG FluoSpheres</td><td>Molecular Probes, Eugene, Oregon (USA)</td><td>F-8827</td><td>505/515 yellow-green fluorescent beads&amp;nbsp;</td></tr><tr><td>0.5 &amp;mu;m Fluoresbrite BB Carboxylate microspheres</td><td>PolyScience, Niles, Illinois (USA)</td><td>18339</td><td>360/407</td></tr><tr><td>1 &amp;mu;m Fluoresbrite BB Carboxylate microspheres</td><td>PolyScience, Niles, Illinois (USA)</td><td>17458</td><td>360/407 blue fluorescent</td></tr><tr><td>1 &amp;micro;mYG FluoSpheres</td><td>Molecular Probes, Eugene, Oregon (USA)</td><td>F-13081</td><td>505/515 yellow-green fluorescent beads&amp;nbsp;</td></tr><tr><td>KH&lt;sub&gt;2&lt;/sub&gt;PO&lt;sub&gt;4&lt;/sub&gt;</td><td>Sigma-Aldrich, St. Louis (USA)</td><td>CAS no. 7778-77-0</td><td></td></tr><tr><td>KCl</td><td>Sigma-Aldrich, St. Louis (USA)</td><td>CAS no. 7447-40-7</td><td></td></tr><tr><td>Cyflow Space&amp;nbsp;</td><td>Sysmex Corporation, Kobe (Japan)</td><td></td><td></td></tr><tr><td>UV Laser Genesis CX,</td><td>Coherent, Inc , Santa Clara (USA)</td><td></td><td>355 nm, 150 mW</td></tr><tr><td>H6779-32 series PMTs</td><td>Hamamatsu Photonics, Hamamatsu City (Japan)</td><td></td><td></td></tr><tr><td>FloMax software</td><td>Sysmex Partec GmbH, G&amp;ouml;rlitz, (GER)</td><td></td><td></td></tr><tr><td>all optical filters</td><td>Carl Zeiss, Jena (GER)</td><td></td><td></td></tr><tr><td>KH&lt;sub&gt;2&lt;/sub&gt;PO&lt;sub&gt;4&lt;/sub&gt;</td><td>Sigma-Aldrich, St. Louis (USA)</td><td>CAS no. 7778-77-0</td><td></td></tr><tr><td>KCl</td><td>Carl Roth, Karlsruhe (GER)</td><td>6781.3</td><td></td></tr><tr><td>FlowJo 10.0.8r1</td><td>FlowJo, LLC, Ashland, Oregon (USA)</td><td></td><td>Build number: 42398</td></tr><tr><td>R-software v.3.4.3</td><td>R Core Team</td><td></td><td></td></tr><tr><td>flowCHIC</td><td>&lt;a href=&quot;http://www.bioconductor.org/packages/release/bioc/html/flowCHIC.html&quot;&gt;http://www.bioconductor.org/packages/release/bioc/html/flowCHIC.html&lt;/a&gt;</td><td></td><td></td></tr><tr><td>flowCyBar</td><td>&lt;a href=&quot;http://www.bioconductor.org/packages/devel/bioc/html/flowCyBar.html&quot;&gt;http://www.bioconductor.org/packages/devel/bioc/html/flowCyBar.html&lt;/a&gt;</td><td></td><td></td></tr></tbody></table>

characterizing microbiome dynamics &#8211; flow cytometry based workflows from pure cultures to natural communities

对纯文化的调查和微生物群落动态监测对于了解和控制微生物驱动的自然生态系统和技术应用至关重要。下一代测序方法被广泛地用于解决微生物, 但它们通常是资源和时间密集型的, 提供的主要是质量信息。流细胞菌群分析不受这些缺点的影响, 可以提供相对子社区丰度和绝对细胞数的在线。虽然它不提供直接的系统系统的信息, 它可以提高分析的深度和解决的排序方法。流式细胞术在研究和常规设置中与医学应用形成鲜明对比, 目前仍未广泛应用于微生物菌群分析。缺少样本准备和数据分析管道的信息可能会为面临微生物分析挑战的研究人员带来进入障碍, 这通常是教科书流式细胞术的应用。在这里, 我们分别为纯文化、复杂社区在具有挑战性的矩阵中的清晰介质和复杂社区提供三个综合工作流。我们描述各自的抽样和固定程序和优化染色协议的各个样本集。本文以复杂的研究为中心, 以应用为重点的平台顶部设备, 描述了细胞分类程序, 并提出了数据分析包, 细胞分析。此外, 我们还提出了重要的实验控制, 并将所提出的工作流应用于相应的样本集。

以下代表的结果强调了在三样本集上复制和举例说明不同的数据可视化和分析技术的必要性。他们展示了可能的数据评估管道的结果适合回答标准研究问题关于各自集合。然而, 所提出的技术并不排斥它们所呈现的样本集。流式细胞仪原始数据是公开提供的 FlowRepository ID FR-FCM-ZY46。以前上载的 ASC 数据在 ID FR-FCM-ZYD7 下可用。
根据已发表的11号议定书, 进行了生物和技术复制, 编写和分析。从个人计算机和 ASC 的生物复制从三个平行的烧瓶被采取了。BC 的生物复制被采取了作为三后继抽样在一个地点在一个试验性规模植物。三整除数的样品进行固定、染色和分析, 以确保技术重现性。根据先前公布的程序11, 对重现性进行了评估。一个样本集的所有样本 (补充文件 1-S6) 的门模板用于计算每门的标准偏差 (%)。
对于 PC, 相对丰度的最大标准偏差为 3.44%, 平均标准偏差为 2.13% (图 1)。对于 ASC 和 BC, 相对丰度的最大标准偏差为1.27% 和 0.88%, 而标准偏差的平均值为2.13% 和 0.21% (补充文件 1-S8)。
一个标准的程序, 当调查纯应变培养, 是准备一个增长曲线, 以分析滞后阶段, 产生时间和细胞密度在特定条件下。我们将此过程与流程细胞分析工作流相结合, 以实现对系统的更深入的了解 (图 2)。FSC 与 DAPI 荧光图揭示了间歇培养的不同点的细胞周期状态。建立了主门模板 (补充文件 1-S6), 以量化细胞的比例与一个 (c1n), 两个 (c2n) 和多染色体 (航空公司,图 2B)。接种细胞的主要比例只有一条染色体 (93.7% 在 0 h)。这在指数增长期间发生了剧烈的变化, 几乎所有的细胞包含不止一个 (99.6%, 4 小时) 和超过一半的人口 (53.1%) 甚至包含两个以上的染色体。这说明P. 恶臭的染色体复制速度比它的生成时间快。
流细胞分析已被证明是非常有用的监测微生物群落的演变。它可以跟随社区动力学比更多资源密集的分子方法5,10更加接近。我们在一部电影中强调了这些动态, 并获得了随着时间的推移, 活性污泥群落变化的概述。每1/2 帧显示一个 FSC 与 DAPI 荧光图的样本点 (补充文件 2)。在0天和4天之间的一个非常明显的转变之后, 在7天之后建立了一个核心社区。另外的子社区在21天出现了。我们建立了一个主门模板 (补充文件 1-S6), 使评估微生物动力学的子社区水平。实验的不同阶段的显性子社区使用 CyBar 工具进行了清晰的识别 (图 3)。将它与相对子社区丰度的频率分布结合起来, 有助于选择有趣的门 (在关键时间点触发的大量的显著变化) 和可行 (超过5% 相对丰度) 进行排序和进一步分析22。
工业规模的生物反应器应用可以面对潜在的空间异质性由于搅拌限制。它们也经常接触到变化的操作参数, 如非合成基底质量的改变。BC 代表这样一个系统并且从不同的现场取样在一个动态地被驾驶的塞流反应器。FSC 与 DAPI 荧光地块 (图 4) 的模范抽样点显示只有很少的空间, 但明显的时间异质性。BC 是进一步调查使用, 快速自动化别致和更深入的主门模板基于 CyBar 方法。
它的自动化, 快速和无偏见的性质, 使别致特别有趣的现场控制发酵在一个工业环境。它比较了所有可用样本的原始. fcs 数据。其中的两个比较显示在图 5中。由此产生的不同值证实了以前关于空间和时间异质性的观察。NMDS 地块生成的别致工具不同矩阵 (图 6) 进一步证实这一结果, 并据此形象化。
此外, 我们计算了公元前23子社区的相对丰度, 使用主浇口模板 (补充文件 1-S6)。对48例一年期的样品进行相关分析, 了解微生物群落中的功能关系。这可以帮助确定感兴趣的门进一步调查 (4 细胞分类)。强正或负相关的非生物参数, 如产品的效价可以帮助了解和优化生态系统和生物技术的过程。这一相关性中包含的有机负载率 (图 7) 是这种非生物参数的例证。G5、G4 和 G10 具有较强的正相关性, 可能含有发酵类, 而 G8 中的负相关可能暗示甲烷古菌。
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/58033/58033fig1.jpg"> 图 1: 纯文化细胞分析的重现性.与控制珠 (A, B) 和条形图的3子社区丰度 [%] 的 DAPI 荧光地块, 其中误差条代表了一个标准偏差 (C, D)。相对丰度的确定与门模板显示在补充文件 1-S6。三生物复制 A, B 和 C 被采取了增长曲线在 4 h 和三技术复制 P1, P2, P3 从样品 A 和三次被测量了, 分别: M1, M2, M3, 并且给以他们各自的标准偏差。5万事件被记录在细胞门。<a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/58033/58033fig1large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/58033/58033fig2.jpg"> 图 2: 在 12 h (a) 的生长曲线实验过程中所采取的纯文化的细胞周期分析.FSC 与 DAPI 荧光图的双小时抽样在指数生长阶段表现出 DNA 含量的变化。这个测量系列不包括珠子 (参见补充文件 1-S3)。(B).基于光密度的生长曲线, 误差条代表了子社区的一个标准偏差和相对丰度。<a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/58033/58033fig2large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/58033/58033fig3.jpg"> 图 3: 活性污泥群落结构演化24天以上.(A) 在 boxplots 中, 根据趋势的相似性、相应的颜色键 (B) 以及在 NMDS 图 < 0.001 中的相似性分析, flowCyBar 与叠加频率分布可视化的, 分别为:C). 基础地块显示在补充文件2的电影序列中。使用辅助文件 1-S6 中的栅极模板确定了相对丰度。<a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/58033/58033fig3large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/58033/58033fig4.jpg"> 图 4: 工业级插头流沼气反应器中微生物群落结构的时空异质性.(A) 在223天, 对四个取样口的微生物群落结构进行比较。(B) 将0天至384日的时间依赖性社区结构变化与第二港口的比较。噪音已被切断, 以提高可视化。25万事件被测量了在细胞门 (补充文件 1-S6)。<a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/58033/58033fig4large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/58033/58033fig5.jpg"> 图 5: 细胞直方图图像比较-别致.通过比较两个表示空间和时间异质性的样本, 说明了别致算法的基本原理。(一) 在选择两个要显示的参数后, 从. fcs 文件创建别致图像 (FSC 与 DAPI 荧光)。灰度 (0–255) 在像素中对事件计数进行编码。(ii) 在指定包含单元格的区域后生成别致子集 (这里: 200 < x < 4000, 200 < y < 2200)。(iii) 通过比较子集之间的像素来创建 XOR 组合图像。没有区别等于 0, 并显示为黑色。最大差等于 200, 显示为白色。通过在 xor 图像中添加所有像素的灰度值来计算相应的 XOR 值。(iv) 叠加组合图像是通过添加子集像素的灰度值创建的。对应的叠加值是通过计算不等于零的叠加图像的像素并因此包含信息来计算的。(v) 不同值的计算方法是除以 XOR 和叠加值。这些是图 6中 NMDS 图的基础。不同的价值和 NMDS 的情节显示了一个微不足道的空间和明显的世俗群落异质性。<a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/58033/58033fig5large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/58033/58033fig6.jpg"> 图 6: 基于别致的沼气群落样本相似性分析.0天的样品被排除在这个情节由于它的极端不同的社区结构歪曲分析 (补充文件 1-S11)。NMDS 剧情证实了使用别致的工具做的低空间和更高的世俗异质性的假设并且解释了图 5。<a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/58033/58033fig6large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/58033/58033fig7.jpg"> 图 7: 沼气群落的相关分析.用长矛的秩阶系数计算矩阵, 并以辅助文件 1-S6 中主浇口模板确定的23子社区的相对丰度为基础。在一年的时间内, 它们与48份样品的有机负荷率 (OLR) 相关。<a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/58033/58033fig7large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<img alt="Figure 8" class="xfigimg" src="/files/ftp_upload/58033/58033fig8.jpg"> 图 8: 废水中浮游 (P) 和污泥基 (S) 群落的相似性分析.样品取自一个全尺寸污水处理厂的主澄清池 (PCL)、活性污泥池 (AS) 和消化池 (DT) (补充文件 1-S10 中的点地块)。使用辅助文件 1-S6 中显示的门模板确定了相对丰度。这些子社区丰度还与 flowCyBar 工具进行了分析, 以创建一个 CyBar (补充文件 1-S10) 和 NMDS 情节 (R < 0.01)。<a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/58033/58033fig8large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<img alt="Figure 9" class="xfigimg" src="/files/ftp_upload/58033/58033fig9.jpg"> 图 9: 纯文化的固定稳定性.在0小时的生长曲线实验样品中, 28 天内储存了15% 甘油的-80 摄氏度。对控制珠的 DAPI 荧光图进行了对比。测量系列不包括珠子 (补充文件 1-S3)。5万事件被记录在细胞门 (补充文件 1-S6)。<a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/58033/58033fig9large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
补充文件 1:<a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/58033/Supplementary-File-1.pdf" target="_blank">请点击这里下载此文件.</a>
补充文件 2:<a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/58033/Supplementary_File_2.gif" target="_blank">请点击这里下载此文件.</a>

Watch this Scientific Journal Video about Characterizing Microbiome Dynamics â€“ Flow Cytometry Based Workflows from Pure Cultures to Natural Communities at JoVE.com

Characterizing Microbiome Dynamics &#8211; Flow Cytometry Based Workflows from Pure Cultures to Natural Communities

流细胞分析已证明对研究纯文化和监测微生物群落动力学有价值。我们提供了三个全面的工作流程, 从抽样到数据分析, 到纯文化和复杂社区, 在清晰的介质中, 以及在挑战性的矩阵, 分别。

微生物动力学的特征-基于流式细胞仪的工作流从纯文化到自然群落

The investigation of pure cultures and monitoring of microbial community dynamics is vital to understand and control natural ecosystems and technical ...

characterizing-microbiome-dynamics-flow-cytometry-based-workflows

Universidad San Sebastian

Research

JoVE Journal

Environment

11.8K 次观看.  Helmholtz Centre for Environmental Research - UFZ. 流细胞分析已证明对研究纯文化和监测微生物群落动力学有价值。我们提供了三个全面的工作流程, 从抽样到数据分析, 到纯文化和复杂社区, 在清晰的介质中, 以及在挑战性的矩阵, 分别。

视频: Characterizing Microbiome Dynamics – Flow Cytometry Based Workflows from Pure Cultures to Natural Communities

Single-cell Analysis of Bacillus subtilis Biofilms Using Fluorescence Microscopy and Flow Cytometry

Biofilm formation is a general attribute to almost all bacteria 1-6. When bacteria form biofilms, cells are encased in extracellular matrix that is mostly constituted by proteins and exopolysaccharides, among other factors 7-10. The microbial community encased within the biofilm often shows the differentiation of distinct subpopulation of specialized cells 11-17. These subpopulations coexist and often show spatial and temporal organization within the biofilm 18-21.
Biofilm formation in the model organism Bacillus subtilis requires the differentiation of distinct subpopulations of specialized cells. Among them, the subpopulation of matrix producers, responsible to produce and secrete the extracellular matrix of the biofilm is essential for biofilm formation 11,19. Hence, differentiation of matrix producers is a hallmark of biofilm formation in B. subtilis.
We have used fluorescent reporters to visualize and quantify the subpopulation of matrix producers in biofilms of B. subtilis 15,19,22-24. Concretely, we have observed that the subpopulation of matrix producers differentiates in response to the presence of self-produced extracellular signal surfactin 25. Interestingly, surfactin is produced by a subpopulation of specialized cells different from the subpopulation of matrix producers 15.
We have detailed in this report the technical approach necessary to visualize and quantify the subpopulation of matrix producers and surfactin producers within the biofilms of B. subtilis. To do this, fluorescent reporters of genes required for matrix production and surfactin production are inserted into the chromosome of B. subtilis. Reporters are expressed only in a subpopulation of specialized cells. Then, the subpopulations can be monitored using fluorescence microscopy and flow cytometry (See Fig 1).
The fact that different subpopulations of specialized cells coexist within multicellular communities of bacteria gives us a different perspective about the regulation of gene expression in prokaryotes. This protocol addresses this phenomenon experimentally and it can be easily adapted to any other working model, to elucidate the molecular mechanisms underlying phenotypic heterogeneity within a microbial community.

Single-cell Analysis of Bacillus subtilis Biofilms Using Fluorescence Microscopy and Flow Cytometry

Microbial biofilms are generally constituted by distinct subpopulations of specialized cells. Single-cell analysis of these subpopulations requires the use of fluorescent reporters. Here we describe a protocol to visualize and monitor several subpopulationswithin B. subtilis biofilms using fluorescence microscopy and flow cytometry.

单细胞分析枯草芽孢杆菌生物膜，用荧光显微镜和流式细胞仪

Biofilm formation is a general attribute to almost all bacteria 1-6. When bacteria form biofilms, cells are encased in extracellular matrix that is mostly ...

科研

免疫与感染

Characterization of Aquatic Biofilms with Flow Cytometry

Biofilms are dynamic consortia of microorganism that play a key role in freshwater ecosystems. By changing their community structure, biofilms respond quickly to environmental changes and can be thus used as indicators of water quality. Currently, biofilm assessment is mostly based on integrative and functional endpoints, such as photosynthetic or respiratory activity, which do not provide information on the biofilm community structure. Flow cytometry and computational visualization offer an alternative, sensitive, and easy-to-use method for assessment of the community composition, particularly of the photoautotrophic part of freshwater biofilms. It requires only basic sample preparation, after which the entire sample is run through the flow cytometer. The single-cell optical and fluorescent information is used for computational visualization and biological interpretation. Its main advantages over other methods are the speed of analysis and the high-information-content nature. Flow cytometry provides information on several cellular and biofilm traits in a single measurement: particle size, density, pigment content, abiotic content in the biofilm, and coarse taxonomic information. However, it does not provide information on biofilm composition on the species level. We see high potential in the use of the method for environmental monitoring of aquatic ecosystems and as an initial biofilm evaluation step that informs downstream detailed investigations by complementary and more detailed methods.

Flow cytometry in combination with visual clustering offers an easy-to-use and fast method for studying aquatic biofilms. It can be used for biofilm characterization, detection of changes in biofilm community structure, and detection of abiotic particles embedded in the biofilm.

流式细胞仪对水生生物膜的表征

Biofilms are dynamic consortia of microorganism that play a key role in freshwater ecosystems. By changing their community structure, biofilms respond ...

环境科学

Combining Fluidic Devices with Microscopy and Flow Cytometry to Study Microbial Transport in Porous Media Across Spatial Scales

Understanding the transport, dispersion and deposition of microorganisms in porous media is a complex scientific task comprising topics as diverse as hydrodynamics, ecology and environmental engineering. Modeling bacterial transport in porous environments at different spatial scales is critical to better predict the consequences of bacterial transport, yet current models often fail to up-scale from laboratory to field conditions. Here, we introduce experimental tools to study bacterial transport in porous media at two spatial scales. The aim of these tools is to obtain macroscopic observables (such as breakthrough curves or deposition profiles) of bacteria injected into transparent porous matrices. At the small scale (10-1000 &#181;m), microfluidic devices are combined with optical video-microscopy and image processing to obtain breakthrough curves and, at the same time, to track individual bacterial cells at the pore scale. At larger scale, flow cytometry is combined with a self-made robotic dispenser to obtain breakthrough curves. We illustrate the utility of these tools to better understand how bacteria are transported in complex porous media such as the hyporheic zone of streams. As these tools provide simultaneous measurements across scales, they pave the way for mechanism-based models, critically important for upscaling. Application of these tools may not only contribute to the development of novel bioremediation applications but also shed new light on the ecological strategies of microorganisms colonizing porous substrates.

Breakthrough curves (BTCs) are efficient tools to study the transport of bacteria in porous media. Here we introduce tools based on fluidic devices in combination with microscopy and flow cytometric counting to obtain BTCs.

将流体设备与显微镜和流式细胞学相结合，研究跨空间尺度的多孔介质中的微生物传输

Understanding the transport, dispersion and deposition of microorganisms in porous media is a complex scientific task comprising topics as diverse as ...

Methods for Characterizing the Co-development of Biofilm and Habitat Heterogeneity

Biofilms are surface-attached microbial communities that have complex structures and produce significant spatial heterogeneities. Biofilm development is strongly regulated by the surrounding flow and nutritional environment. Biofilm growth also increases the heterogeneity of the local microenvironment by generating complex flow fields and solute transport patterns. To investigate the development of heterogeneity in biofilms and interactions between biofilms and their local micro-habitat, we grew mono-species biofilms of Pseudomonas aeruginosa and dual-species biofilms of P. aeruginosa and Escherichia coli under nutritional gradients in a microfluidic flow cell. We provide detailed protocols for creating nutrient gradients within the flow cell and for growing and visualizing biofilm development under these conditions. We also present protocols for a series of optical methods to quantify spatial patterns in biofilm structure, flow distributions over biofilms, and mass transport around and within biofilm colonies. These methods support comprehensive investigations of the co-development of biofilm and habitat heterogeneity.

Biofilms have complex interactions with their surrounding environment. To comprehensively investigate biofilm-environment interactions, we present here a series of methods to create heterogeneous chemical environment for biofilm development, to quantify local flow velocity, and to analyze mass transport in and around biofilm colonies.

方法表征的共同发展生物膜和生境异质性

Biofilms are surface-attached microbial communities that have complex structures and produce significant spatial heterogeneities. Biofilm development is ...

生物工程

Molecular Probe Optimization to Determine Cell Mortality in a Photosynthetic Organism (Microcystis aeruginosa) Using Flow Cytometry

Microbial subpopulations in field and laboratory studies have been shown to display high heterogeneity in morphological and physiological parameters. Determining the real time state of a microbial cell goes beyond live or dead categories, as microbes can exist in a dormant state, whereby cell division and metabolic activities are reduced. Given the need for detection and quantification of microbes, flow cytometry (FCM) with molecular probes provides a rapid and accurate method to help determine overall population viability. By using SYTOX Green and SYTOX Orange in the model cyanobacteria Microcystis aeruginosa to detect membrane integrity, we develop a transferable method for rapid indication of single cell mortality. The molecular probes used within this journal will be referred to as green or orange nucleic acid probes respectively (although there are other products with similar excitation and emission wavelengths that have a comparable modes of action, we specifically refer to the fore mentioned probes). Protocols using molecular probes vary between species, differing principally in concentration and incubation times. Following this protocol set out on M.aeruginosa the green nucleic acid probe was optimized at concentrations of 0.5 &#181;M after 30 min of incubation and the orange nucleic acid probe at 1 &#181;M after 10 min. In both probes concentrations less than the stated optimal led to an under reporting of cells with membrane damage. Conversely, 5 &#181;M concentrations and higher in both probes exhibited a type of non-specific staining, whereby &#39;live&#39; cells produced a target fluorescence, leading to an over representation of &#39;non-viable&#39; cell numbers. The positive controls (heat-killed) provided testable dead biomass, although the appropriateness of control generation remains subject to debate. By demonstrating a logical sequence of steps for optimizing the green and orange nucleic acid probes we demonstrate how to create a protocol that can be used to analyse cyanobacterial physiological state effectively.

Molecular Probe Optimization to Determine Cell Mortality in a Photosynthetic Organism Microcystis aeruginosa Using Flow Cytometry

Microbial populations contain substantial cell heterogeneity, which can dictate overall behavior. Molecular probe analysis through flow cytometry can determine physiological states of cells, however its application varies between species. This study provides a protocol to accurately determine cell mortality within a cyanobacterium population, without underestimating or recording false positive results.

分子探针的优化，以确定细胞死亡的光合生物（<em&gt;铜绿微囊藻</em&gt;）使用流式细胞仪

Microbial subpopulations in field and laboratory studies have been shown to display high heterogeneity in morphological and physiological parameters. ...

医学

Multicolor Flow Cytometry-based Quantification of Mitochondria and Lysosomes in T Cells

T cells utilize different metabolic programs to match their functional needs during differentiation and proliferation. Mitochondria are crucial cellular components responsible for supplying cell energy; however, excess mitochondria also produce reactive oxygen species (ROS) that could cause cell death. Therefore, the number of mitochondria must constantly be adjusted to fit the needs of the cells. This dynamic regulation is achieved in part through the function of lysosomes that remove surplus/damaged organelles and macromolecules. Hence, cellular mitochondrial and lysosomal contents are key indicators to evaluate the metabolic adjustment of cells. With the development of probes for organelles, well-characterized lysosome or mitochondria-specific dyes have become available in various formats to label cellular lysosomes and mitochondria. Multicolor flow cytometry is a common tool to profile cell phenotypes, and has the capability to be integrated with other assays. Here, we present a detailed protocol of how to combine organelle-specific dyes with surface markers staining to measure the amount of lysosomes and mitochondria in different T cell populations on a flow cytometer.

This article illustrates a powerful method to quantify mitochondria or lysosomes in living cells. The combination of lysosome- or mitochondria-specific dyes with fluorescently conjugated antibodies against surface markers allows the quantification of these organelles in mixed cell populations, like primary cells harvested from tissue samples, by using multicolor flow cytometry.

基于多色流式细胞仪的 t 细胞线粒体和溶酶体的定量

T cells utilize different metabolic programs to match their functional needs during differentiation and proliferation. Mitochondria are crucial cellular ...

Quantifying Human Norovirus Virus-like Particles Binding to Commensal Bacteria Using Flow Cytometry

Commensal bacteria are well established to impact infection of eukaryotic viruses. Direct binding between the pathogen and the host microbiome is responsible for altering infection for many of these viruses. Thus, characterizing the nature of virus-bacteria binding is a foundational step needed for elucidating the mechanism(s) by which bacteria alter viral infection. For human norovirus, commensal bacteria enhance B cell infection. The virus directly binds to these bacteria, indicating that this direct interaction is involved in the mechanism of infection enhancement. A variety of techniques can be used to quantify interactions between bacteria and viruses including scintillation counting of radiolabeled viruses and polymerase chain reaction (PCR). Both methods require the use of live virus, which may need to be generated in the laboratory. Currently, none of the established in vitro culture systems available for human norovirus are robust enough to allow for generation of highly concentrated viral stocks. In lieu of live virus, virus-like particles (VLPs) have been used to characterize the interactions between norovirus and bacteria. Herein a flow cytometry method is described with uses virus specific antibodies to quantify VLP binding to gram-negative and gram-positive bacteria. Inclusion of both bacteria only and isotype controls allowed for optimization of the assay to reduce background antibody binding and accurate quantification of VLP attachment to the bacteria tested. High VLP:bacterium ratios result in VLPs binding to large percentages of the bacterial population. However, when VLP quantities are decreased, the percent of bacteria bound also decreases. Ultimately, this method can be employed in future experiments elucidating the specific conditions and structural components that regulate norovirus:bacterial interactions.

The goal of this protocol is to quantify binding of the eukaryotic pathogen human norovirus to bacteria. After performing an initial virus-bacterium attachment assay, flow cytometry is used to detect virally-bound bacteria within the population.

使用流细胞测定量化人类诺如病毒样颗粒与准细菌结合

Commensal bacteria are well established to impact infection of eukaryotic viruses. Direct binding between the pathogen and the host microbiome is ...

Flow Cytometric Characterization of Murine B Cell Development

Extensive studies have characterized the development and differentiation of murine B cells in secondary lymphoid organs. Antibodies secreted by B cells have been isolated and developed into well-established therapeutics. Validation of murine B cell development, in the context of autoimmune prone mice, or in mice with modified immune systems, is a crucial component of developing or testing therapeutic agents in mice and is an appropriate use of flow cytometry. Well established B cell flow cytometric parameters can be used to evaluate B cell development in the murine peritoneum, bone marrow, and spleen, but a number of best practices must be adhered to. In addition, flow cytometric analysis of B cell compartments should also complement additional readouts of B cell development. Data generated using this technique can further our understanding of wild type, autoimmune prone mouse models as well as humanized mice that can be used to generate antibody or antibody-like molecules as therapeutics.

We describe herein a simple analysis of the heterogeneity of the murine immune B cell compartment in the peritoneum, spleen, and bone marrow tissues by flow cytometry. The protocol can be adapted and extended to other mouse tissues.

小鼠B细胞发育的流式细胞学表征

Extensive studies have characterized the development and differentiation of murine B cells in secondary lymphoid organs. Antibodies secreted by B cells ...

Flow Cytometry Approach to Characterize Phagocytic Properties of Acutely-Isolated Adult Microglia and Brain Macrophages In Vitro

Microglia and central nervous system (CNS)-infiltrating macrophages, collectively called CNS mononuclear phagocytes (CNS-MPs), play central roles in neurological diseases including neurodegeneration and stroke. CNS-MPs are involved in phagocytic clearance of pathological proteins, debris and neuronal synapses, each with distinct underlying molecular pathways. Characterizing these phagocytic properties can provide a functional readout that compliments molecular profiling of microglia using traditional flow cytometry, transcriptomics and proteomics approaches. Phagocytic profiling of microglia has relied on microscopic visualization and in vitro cultures of mouse neonatal microglia. The former approach suffers from limited sampling while the latter approach is inherently poorly reflective of the true in vivo state of adult CNS-MPs. This paper describes optimized protocols to phenotype phagocytic properties of acutely-isolated mouse CNS-MPs by flow cytometry. CNS-MPs are acutely isolated from adult mouse brain using mechanical dissociation followed by density gradient centrifugation, incubated with fluorescent microspheres or fluorescent A&#946; fibrils, washed, and then labeled with panels of antibodies against surface markers (CD11b, CD45). Using this approach, it is possible to compare phagocytic properties of brain-resident microglia with CNS-infiltrating macrophages and then assess the effect of aging and disease pathology on these phagocytic phenotypes. This rapid method also holds potential to functionally phenotype acutely-isolated human CNS-MPs from post-mortem or surgical brain specimens. Additionally, specific mechanisms of phagocytosis by CNS-MP subsets can be investigated by inhibiting select phagocytic pathways.

Assessment of phagocytic properties of microglia and brain macrophages can provide a valuable functional dimension to molecular profiling studies. We describe a validated protocol that uses flow cytometry to rapidly and reliably quantify phagocytosis of fluorescent microspheres and amyloid beta fibrils by acutely-isolated microglia and brain macrophages from mouse models.

Microglia and central nervous system (CNS)-infiltrating macrophages, collectively called CNS mononuclear phagocytes (CNS-MPs), play central roles in ...

神经科学

<meta charset="utf8"></head><body>与真菌伴侣共培养蔗糖分泌蓝细菌的方案

Assembly and Quantification of Co-Cultures Combining Heterotrophic Yeast with Phototrophic Sugar-Secreting Cyanobacteria

With the increasing demand for sustainable biotechnologies, mixed consortia containing a phototrophic microbe and heterotrophic partner species are being explored as a method for solar-driven bioproduction. One approach involves the use of CO2-fixing cyanobacteria that secrete organic carbon to support the metabolism of a co-cultivated heterotroph, which in turn transforms the carbon into higher-value goods or services. In this protocol, a technical description to assist the experimentalist in the establishment of a co-culture combining a sucrose-secreting cyanobacterial strain with a fungal partner(s), as represented by model yeast species, is provided. The protocol describes the key prerequisites for co-culture establishment: Defining the media composition, monitoring the growth characteristics of individual partners, and the analysis of mixed cultures with multiple species combined in the same growth vessel. Basic laboratory techniques for co-culture monitoring, including microscopy, cell counter, and single-cell flow cytometry, are summarized, and examples of nonproprietary software to use for data analysis of raw flow cytometry standard (FCS) files in line with FAIR (Findable, Accessible, Interoperable, Reusable) principles are provided. Finally, commentary on the bottlenecks and pitfalls frequently encountered when attempting to establish a co-culture with sugar-secreting cyanobacteria and a novel heterotrophic partner is included. This protocol provides a resource for researchers attempting to establish a new pair of co-cultured microbes that includes a cyanobacterium and a heterotrophic microbe.

<meta charset="utf8"></head><body>异养酵母与光养分泌糖的蓝细菌相结合的共培养物的组装和定量

This protocol provides a comprehensive guideline for the setup and quantitative monitoring of co-cultures, including photoautotrophic sugar-secreting cyanobacteria and heterotrophic yeasts.

异养酵母与光养分泌糖的蓝细菌相结合的共培养物的组装和定量

With the increasing demand for sustainable biotechnologies, mixed consortia containing a phototrophic microbe and heterotrophic partner species are being ...