Name: odelay: a large-scale method for multi-parameter quantification of yeast growth
Uploaded: 2017-07-03T14:00:00.000+00:00
Duration: 11 min 19 s
Description: Watch this Scientific Journal Video about ODELAY: A Large-scale Method for Multi-parameter Quantification of Yeast Growth at JoVE.com

作者通过授予U54 RR022220和P50 GM076547向美国国家卫生研究院的JDA承认对此项工作的支持。 FDM是加拿大卫生研究所的博士后研究员。我们还感谢卢森堡系统生物医学中心和卢森堡大学的支持。

琼脂糖凝胶库的制备<ol><li>称量2 g高纯度琼脂糖。 </li><li>将琼脂糖加入500 mL瓶中，并记录其合并质量。 </li><li>用150克超纯水计算瓶子的目标质量和2克琼脂糖，然后加入150克超纯水至该目标物质的0.1克内。 </li><li>注意瓶子的质量加上琼脂糖和水。 </li><li>将瓶子放入微波炉中，确保将顶盖上的松散盖子装入瓶子，以尽量减少水分蒸发，同时加热琼脂糖。 </li><li>微波瓶子在15 - 20秒之间爆发，然后短暂地旋转瓶子来混合琼脂糖和水。重复此过程直到溶液沸腾并且混合物是均匀的。 小心:注意避免烫伤皮肤，避免蒸汽从瓶中逸出。此外，瓶子会变得很热，需要适当的保护，如高压灭菌手套，以避免受伤。</li><li>在熔融的琼脂糖均匀后，再次称重瓶子，并将超纯水加入原始质量的0.1克内。这将取代由于蒸发而损失的任何水分。 </li><li>在熔融的琼脂糖凝固之前，旋转以在加入的水中混合以确保均匀性。 </li><li>将15.2g琼脂糖等分成9-50mL塑料管。 </li><li>冷却管直到需要。 </li></ol>准备ODELAY琼脂糖介质<ol><li>加热400毫升去离子水，在一个盖烧杯中煮沸，在电热板上用一个磁力搅拌棒轻轻搅拌。 </li><li>在步骤1.10的50mL锥形管中加入2mL 10X培养基（ 例如酵母提取物胨（YEP）或Complete Supplement Mixture（CSM））至15.2g琼脂糖等分试样。 注意:CSM介质倾向于粘贴玻璃片。如果使用CSM培养基配方，在无菌水中加入5μL50％wt聚乙二醇（PEG）至培养基制剂。 PEG防止agar在脱模期间粘附到玻片上，并且不抑制酵母生长。 </li><li>如果需要，添加额外的100X营养补充剂，并使用水将总添加量从该步骤提升到1 mL。 </li><li>在将50mL锥形管置于步骤2.1的沸水中之前，称量琼脂糖等分试样加入培养基和补充剂。 </li><li> 16分钟后，取出50mL管，并用涡旋匀浆混合物。将盖子紧紧地紧固在50mL锥形管上。 注意:烧杯上的盖子有助于均匀加热整个管，否则会形成固体琼脂糖膜，也可能不熔化。此外，在此期间，组装琼脂糖模具是方便的（ 图1 ）。 </li><li>使用涡流使混合物均质化。 </li><li>煮沸另外2分钟，以确保所有琼脂混合和融化。 </li><li>称重管，并用超纯无菌水代替任何损失的物质。 </li><li>加入2 mL 10X碳酸ce试剂（ 例如 ，20％w / v葡萄糖）并涡旋以获得琼脂糖培养基溶液。 注意:玻璃滑块的分离:组装模具时，必须注意确保长的垫片以正确的方向放置在模具中。这是因为当激光切割丙烯酸时，它倾向于作为锥体切割，离开具有稍微倾斜的侧面的部分而不是恰好90°的侧面。然后当模具放置在台面上以从琼脂中释放玻璃时，间隔物的边缘应与琼脂成锐角。当模具间隔件的外边缘围绕下边缘枢转时，锐角有助于压缩琼脂的边缘远离上部玻璃板（ 见图1 ）。结果是琼脂与顶部玻璃片的分离更为一致。 注意:不要使用应用于玻璃的脱模剂，如油，硅喷雾剂甚至商业窗户处理，因为它们会污染s<html>琼脂的表面，可能抑制生长。这些方法采取一些做法是一致的。 </li><li>用70％乙醇清洗x 1 mm厚的玻璃载玻片中的4个2×2，并用强制空气干燥。 </li><li>用70％乙醇清洁丙烯酸模具，干燥并组装， 如图1所示。如图所示，拿起三个底部的零件，并组装它们，使两个相同的零件夹在第三个零件上（ 图1B ）。使用两个小夹子夹紧每侧的基片（ 图1C ）。将立柱放入模具中，确保激光切口正确定位（ 图1E ）。 </li><li>将清洁干燥的玻璃滑块放在模具上，并将其固定在另一侧。用较大的夹子夹紧组件（ 图1D ）。用较大的活页夹夹住两张幻灯片（lass ="xfig"&gt;图1D）。确保上粘结夹与玻璃滑块重叠与丙烯酸接触。 </li><li>添加剩余的剪贴簿夹。再次，确保夹具接触与丙烯酸重叠的滑块（ 图1D ）。 注意:在用熔融琼脂填充组装的模具之前，通过沿着模具内侧吸取约70μL的熔融琼脂介质来确保边缘被密封。这种琼脂将迅速凝固并防止任何泄漏。 </li><li>用熔融琼脂填充模具，确保避免在模具中捕获气泡。 注意:这可以通过沿着模具边缘慢慢吸取熔融琼脂来实现。填充后，允许模具在约23°C环境温度下冷却40分钟至1小时。如果允许冷却太久，琼脂可能在模具中断裂。 注意:模具分离:从浇注琼脂中正确移除模具对于确保均匀的固体至关重要琼脂垫用于酵母生长。不能确保在该步骤中模具的有效分离将导致由于固体琼脂垫中的缺陷而引起的生长差异。推荐几步练习。 </li><li>从底部夹子夹取下来。拆下由三个夹片组成的模具的底部。压缩幻灯片，然后取下夹子夹。拆卸夹子时，请勿碰到模具侧面。这会使媒体变形。 </li><li>将模具放在台面的边缘，以使具有蓝点的模具侧面朝上，并在左下角（ 图1E ）。将拇指放在丙烯酸酯的下方，将第一根手指放在模具的内边缘上。 </li><li>慢慢地，小心地用拇指向上推动模具，好像将模具隔片围绕其下边缘旋转（ 图1F ）。应用常数但缓慢增加的压力。用户将看到一个断裂，气泡出现在顶部玻璃覆盖模具间隔物的线上。 </li><li>看完初始断线后，继续用拇指向上推，施加恒压。此时琼脂应该从玻璃开始脱落（ 图1F ）。继续向上枢转模具。这样可以提起玻璃而不会滑动玻璃。琼脂从玻璃上剥离的线应该继续离开初始断裂线。 注意:此时，琼脂粘在玻璃的底部和顶部。这将偶尔会发生在最左边缘。只要变形区域不在发现酵母的地方，这应该是罚款。 </li><li>当玻璃完全自由时，抓住它并将其完全从模具中取出。然后使用类似的动作取出另一个模具，以便不移动琼脂来提升碎片。将幻灯片放在无菌尖端盒底部有一些无菌高纯度水。关闭盒子并储存在4°CO / N，以备第二天使用。如果储存更长时间，增长率将变得不一致。 </li></ol> ODELAY文化准备<ol><li>将菌株置于96孔板中进行O / N生长。 </li><li>第二天，将20μL的过夜培养物稀释到200μL的培养基中，在一个新的板中达到220μL的总体积。 </li><li>在平板阅读器中测量每种培养物的600nm（OD600）的光密度。使用读板机和液体处理机器人，遵循步骤3.3.1 - 3.3.6的自动稀释方案。将板中的培养物手动稀释至约0.1 OD600（可选）。 <ol><li>在读板器上，点击"创新"下的"实验"。选择ODELAYDilution.exp并运行实验。输入实验名称为ODELAY"日期""时间""实验名称"迭代。 </li><li>点击s纹身选项卡，然后单击Excel图标。将数据保存到拇指驱动器，并将其传输到液体处理机器人计算机。 </li><li>打开机器人的布局和方法编辑器。打开方法文件"ODELAYDilution_v1.med"。运行可执行文件"Convert_SynergyFiles.exe"。为目标OD600输入0.09。 </li><li>点击"Glu校正"和"Gal校正"为0.05。测量相同板材中的空白介质。点击"生成文件"。在方法编辑器中选择文件。点击停车图标启动打开运行时间稀释程序的方法。 </li><li>确保管子未封盖，并且移除板盖，并从顶端除去防尘盖。装载板，管和尖端到液体处理机器人的甲板上，如甲板布局所示。 </li><li>点击"播放"按钮运行稀释程序。选择正确数量的50μL提示。选择正确数量的300μL提示。等等12分钟进行运行。 </li></ol></li><li>从机器人取稀释板，盖上尖端，并回顾15 mL介质管。在30℃培养板5-6小时。 </li><li>开始第二次稀释前约1-2小时，请务必打开显微镜的孵育室。允许它们在℃或实验所需的温度下平衡。 </li><li>重复稀释步骤3.3 - 3.4，但将培养物稀释至0.01至0.02的OD 600，以便在琼脂糖载玻片上观察培养物。 </li><li>用金属冷冻密封盖住稀释板。 </li><li>使用离心机金属桶配件将稀释板在冰浴中超声处理30秒，使板悬浮在冰水中，以固定板（ 图2A ）。将超声处理的培养物从稀释板转移到平底板。标记4×96孔平底板1,2,3和4（ 图2B ）。 <ol><li>转让150＆＃181;将来自孔A01-D06的稀释板的L加入标记为1的板的孔C04-F09中。然后将150μL稀释板从孔A07-D12转移到标记为2的板的孔C04-F09中。然后转移150μL将孔E01-H06的稀释板放入标记为3的板的C04-F09孔中。最后将150μL的稀释板从孔E07-H12转移到标有4的板的C04-F09孔中。 </li></ol></li><li>转到步骤4，点燃琼脂。 </li></ol> 4.使用自动液体检测机器人检测琼脂<ol><li>从其加湿的无菌吸管盒中取出以4°CO / N（来自步骤2.19）储存的琼脂糖载玻片。 </li><li>如果需要，用干净的剃刀刀片修剪琼脂糖培养基的角落，以确保它们适合滑动室。 </li><li>从支架上卸下滑块。小心地将琼脂板放入舞台室底座的凹陷区域。 注意:确保或幻灯片的方法从实验到实验是一致的。 </li><li>检查以确保夹具与腔室底部齐平。如果弯曲，则幻灯片可能未完全就位在凹进的区域。将调平间隔件放入点样机器人的中心板位置。该间隔件为调平螺钉提供接触，使得滑动室可以与尖端平齐。 </li><li>将板放在桌子上，按照它们的象限。板1，2，3和4从左到右排列（ 图2B ）。 </li><li>布置提示，使内部24口井C04 - F09被占用4个空的提示盒。第五个盒子需要有一个尖端在C10位置，以及4个尖端，其端点在A01，A12，H01和H12位置被切断。这4个切断尖端为尖端板夹具提供了稳定性（ 图2C 和2D ）。 </li><li>从sl上卸下顶盖ide室安装。 </li><li>将第一个尖头冲头放入机器人控制点测试程序的起始位置。这应该在琼脂糖上打孔，以后将用于对齐原点坐标。将液位处理机器人上的板1放在第一象限位置。确保盖子已拆除，并继续检测程序。 </li><li>检查以确保所有斑点都存在。另外，等待〜30秒，让他们干燥。当斑点直径约为1mm时，程序可能会继续。将用过的尖端清空到生物危害容器中，并将新鲜的提示放在机器人上。换出第2象限盘1。 </li><li>重复第三个象限。并重复第四个象限。 </li><li>当斑点干燥时，更换滑盖室盖并翻转设备。 </li><li>用空气过滤器安装管路连接。 </li><li>将玻璃滑块放在腔室的顶部。 注意:此幻灯片对于reduci至关重要将热通量传递到琼脂中，并使冷凝物形成在室的客体盖滑动侧。不要忘记这个封面。根据显微镜结构，该盖滑动防止来自照明侧的加热在物体侧引起冷凝。 </li><li>放置风扇将热空气吹到室的物体侧。该风扇防止在盖板上形成冷凝。将通过起泡器的空气流量设定为10mL / min。 </li></ol> 5.在显微镜上运行ODELAY <ol><li>运行脚本"ODELAY_Microscope_Control.m"。 </li><li>点击"快门"打开传输的光快门，然后点击"对焦"按钮启动高相机速率（ 图3A ，红色箭头）。 </li><li>点击"去原点"，移动舞台，找到在琼脂中冲孔的原点。 </li><li>然后向右移动专注于区域E07发现的酵母细胞。 </li><li>返回到原点打孔并将其居中在视野中。 </li><li>通过按"设置"按钮（ 图3A ，蓝色箭头）将原点设置为该值。现在移动到位置H18并使用最靠近该位置的六角螺丝进行对焦。然后移动到位置L07并使用最靠近该位置的六角螺丝进行对焦。然后移动到位置E07并使用距离该位置最近的六角螺丝进行对焦。 </li><li>根据需要重复步骤5.5 - 5.7，直到这些位置保持对焦。 </li><li>检查中心的焦点和位于E12，H12，L12处的边缘。如果由Z值表示的焦点Z值大于±自动对焦范围（ 例如 ，将自动对焦范围设置为60μm，中心点的Z值大于±），则调整自动对焦范围40μm）。 </li><li>按复位按钮，这将激活按钮属性，然后按ODELAY（ 图3A ，绿色箭头）。选择保存数据的目录。确保有足够的驱动器空间可用。 注意:显微镜收集数据48小时，或直到程序关闭。 </li></ol> 6.处理ODELAY数据<ol><li>打开ODELAY_IPT.exe或使用脚本ODELAY_Image_Processing_Tool_v7.m脚本（ 图3C ）。 </li><li>准备一个* ODELAYExpDisc.xlsx excel电子表格。 </li><li>在单元格B1中命名实验。 </li><li>选择存储图像文件E07 - H18的图像目录。 </li><li>选择要写入数据的数据目录。 </li><li>将实验开始的日期写入单元格B04。使用格式MM / DD / YYYY。 </li><li>在C04单元中写入稀释时间。这个时间大约是在收集第一张图像之前的五分钟。使用格式HH:MMpm，其中HH为h，MM为最小。 </li><li>根据t加入菌株名称o源板（步骤3.1）。由于菌株被发现的方式，它们被重新排列在ODELAY琼脂糖载玻片上。电子表格B31-B126中的单元是源极板，而细胞C31 - C126是ODELAY琼脂平板中的斑点位置。 </li><li>然后按"过程数据"按钮，然后选择刚准备好的* ODELAYExpDisc.xlsx。 </li><li>根据用于处理数据的计算机系统等待16-24小时。 </li><li>处理图像时，会出现名为"ODELAY Well Data"的目录和文件"* _Index_ODELAYData.mat"。按"加载数据"按钮，选择刚生成的"* _Index_ODELAYData.mat"文件。这将加载刚刚处理的数据集。文件名中的"*"将根据输入到excel电子表格中的实验名称进行列出。 </li><li>加载数据后，使用底部找到的时间栏进行检查，点击图像正方形以查看生长曲线f或那个地点，或在左边的列表中找到一个很好的兴趣，然后加载该列表的图像。 </li><li>按"小提琴图"按钮生成人口增长参数的直方图。这将产生如图4或图6所示的曲线。 </li></ol>

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	<li>Zwietering, M. H., Jongenburger, I., Rombouts, F. M., van't Riet, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Modeling+of+the+bacterial+growth+curve.">Modeling of the bacterial growth curve.</a> Appl Environ Microbiol. 56 (6), 1875-1881 (1990).</li><li>Sellick, C. A., Campbell, R. N., Reece, R. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Galactose+metabolism+in+yeast-structure+and+regulation+of+the+leloir+pathway+enzymes+and+the+genes+encoding+them.">Galactose metabolism in yeast-structure and regulation of the leloir pathway enzymes and the genes encoding them.</a> Int Rev Cell Mol Biol. 269, 111-150 (2008).</li><li>Yoshikawa, K., 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=Comprehensive+phenotypic+analysis+for+identification+of+genes+affecting+growth+under+ethanol+stress+in+Saccharomyces+cerevisiae.">Comprehensive phenotypic analysis for identification of genes affecting growth under ethanol stress in Saccharomyces cerevisiae.</a> FEMS Yeast Res. 9 (1), 32-44 (2009).</li><li>Bryan, A. K., Goranov, A., Amon, A., Manalis, S. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Measurement+of+mass,+density,+and+volume+during+the+cell+cycle+of+yeast.">Measurement of mass, density, and volume during the cell cycle of yeast.</a> Proc Natl Acad Sci U S A. 107 (3), 999-1004 (2010).</li><li>Baryshnikova, A., 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=Quantitative+analysis+of+fitness+and+genetic+interactions+in+yeast+on+a+genome+scale.">Quantitative analysis of fitness and genetic interactions in yeast on a genome scale.</a> Nat Methods. 7 (12), 1017-1024 (2010).</li><li>Costanzo, 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=The+genetic+landscape+of+a+cell.">The genetic landscape of a cell.</a> Science. 327 (5964), 425-431 (2010).</li><li>Collins, S. 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=Functional+dissection+of+protein+complexes+involved+in+yeast+chromosome+biology+using+a+genetic+interaction+map.">Functional dissection of protein complexes involved in yeast chromosome biology using a genetic interaction map.</a> Nature. 446 (7137), 806-810 (2007).</li><li>Zackrisson, 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=Scan-o-matic:+High-Resolution+Microbial+Phenomics+at+a+Massive+Scale.">Scan-o-matic: High-Resolution Microbial Phenomics at a Massive Scale.</a> G3 (Bethesda). 6 (9), 3003-3014 (2016).</li><li>Bean, G. J., Jaeger, P. A., Bahr, S., Ideker, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Development+of+ultra-high-density+screening+tools+for+microbial+'omics'.">Development of ultra-high-density screening tools for microbial 'omics'.</a> PloS One. 9 (1), e85177 (2014).</li><li>Levy, S. F., Ziv, N., Siegal, M. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Bet+hedging+in+yeast+by+heterogeneous,+age-correlated+expression+of+a+stress+protectant.">Bet hedging in yeast by heterogeneous, age-correlated expression of a stress protectant.</a> PLoS Biol. 10 (5), e1001325 (2012).</li><li>Herricks, T., 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=One-Cell+Doubling+Evaluation+by+Living+Arrays+of+Yeast.">One-Cell Doubling Evaluation by Living Arrays of Yeast.</a> ODELAY! G3 (Bethesda). 7 (1), 279-288 (2017).</li><li>Mast, F. D., Ratushny, A. V., Aitchison, J. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Systems+cell+biology.">Systems cell biology.</a> J Cell Biol. 206 (6), 695-706 (2014).</li><li>Edelstein, A. 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=Advanced+methods+of+microscope+control+using+&#181;Manager+software.">Advanced methods of microscope control using &#181;Manager software.</a> J Biol Methods. 1 (2), e10 (2014).</li></ol>

作者没有什么可以披露的。

ODELAY测定法具有确保可重现和可靠的表型测量的几个关键点。第一个关键点是酵母培养物的一致准备。必须注意从对数生长收获酵母细胞。如果文化饱和，那么他们的群体异质性将会增加，这可能会混淆由遗传或环境（ 例如碳源）因素11引起的异质性。第二个关键点是媒体的一致准备。一般来说，应该产生大量的10X股票媒体解决方案，然后随着时间推移使库存效应最小化。通过重量制备培养基，尽可能通过确保琼脂糖的密度和琼脂糖的总体含水量得以密切监测，有助于提高培养基随时间的一致性。第三个关键点是最小化或消除琼脂糖我的任何机械变形直径。介质的机械变形通常会在分离琼脂糖与玻璃片时发生。与许多实验室技术一样，需要实践来掌握这一步骤。 如图4所示的滞后时间的变化通常与三个因素之一有关:琼脂糖介质的机械变形，模制琼脂厚度的变化或不稳定的光源。如果琼脂糖介质在斑点阵列上的Z高度变化，则高度变化可能会压倒自动对焦程序的范围，导致初始图像稍微偏离焦点。因此，检查中心点和点阵列边缘的多个点处的对焦高度，以确保自动对焦程序具有足够的Z范围来找到焦点。如果需要，使用自动对焦面板增加对焦范围并增加对焦步骤的数量。 第三个可能的条件可能导致焦点不足的离子是不稳定或闪烁的光源，这可能会破坏特定Z高度的计算焦点得分。在灯泡烧坏之前，钨卤素灯泡会很好地闪烁。在其中生长曲线在第一和第二时间点（ 图5A ）之间下降的一个实例中观察到不良焦点的影响，而相邻点不具有相同的浸渍（ 图 5B ）。在这种情况下，通过更换卤素卤素光源可以减轻对焦条件差。 在实践中，作者发现，为了减少100W钨卤素灯泡的闪烁，当显微镜被大量使用时，灯泡需要每500小时或大约每2个月更换一次。为了避免闪烁的灯泡引起的焦点不足，请经常更换钨卤素光源或用二极管光源替换卤素灯泡。一个在图6中示出了在倍增时间中显示低变化以及更均匀滞后时间的数据集的示例。该数据集采用二极管照明器，在执行自动对焦时可以随时间提供更稳定的照明。 虽然这里提到的优化媒体准备的许多观点可能看起来是显而易见的，但在文献中大多数大屏幕都不能很好地复制8,11 。因此，我们仔细地描述了培养物和琼脂糖介质的制备，以便可能产生更多可重现的表型筛选。 与基于固定的测定（如合成遗传数组）或Scan-O-Matic测定相比，ODELAY测定目前的吞吐量受到限制。虽然这些方法增加了测量的菌株数量，但它们缺乏解决单个细胞的能力因此不能测量我们在克隆酵母菌株内观察到的种群异质性。这个人口异质性的起源目前尚未得到理解，但是这里所示的技术和计算的合并提供了客观地解决潜在的细胞机制12的机会。 作者希望注意到，ODELAY目前仅针对特定的显微镜品牌和身体类型进行了优化。为其他显微镜系统修改ODELAY是直接的，但需要了解开源API 13 。然而，API和ODELAY脚本都被编写成易于适应于不同的系统和实验测定。 虽然ODELAY最初是为酵母开发的，但是我们已经能够在不改变的情况下利用它来观察耻垢分枝杆菌的生长。观察其他集落形成微生物是可能对提供的源代码进行修改11 。一般来说，ODELAY是一种强大且灵活的工具，用于比较在不同环境条件下生长的微生物和遗传扰动。

微生物的生长表型是对其给定环境条件的潜在遗传适应性的强指标。生长经典地分为3种不同的生长方式:滞后期，对数期和稳定期生长1 。每个生长阶段可以揭示依赖于各种环境和遗传条件的适应性的不同方面。例如，延迟时间或生物体在指数生长开始之前在滞后阶段中花费的时间长度可以指示生物体对改变的环境条件作出反应的能力2 。对数期生长期间的双倍增长是细胞适应度最常见的指标，揭示了生物体通过代谢和利用环境材料进行复制分裂能力的总体效率。固定阶段，在对数阶段之后的增长迅速减少，是健身的另一个指标，这是正常的在基于斑点的酵母生长测定中被用作生长终点。 目前有几种酵母生长测定可用，并且被认为是在酵母3,4,5中评价生长表型的标准方法。这些测定主要基于在固体或液体培养基中生长酵母的方法。在固体培养基上，菌落钉扎测定将少量细胞转移到具有针的固体琼脂上，使酵母细胞生长一段规定的时间。然后将殖民地成像，并在终端6比较其大小。这些集落钉扎测定已被证明是鲁棒和可扩展的，以产生全基因组屏幕。最近，使用平板扫描仪和单镜反射（SLR）相机的定期成像已被纳入这些测定中，以记录随时间的集落增长7,8， 9 。然而，这些装置的分辨率阻止它们检测单个细胞，因此，这些集落钉扎测定法不直接观察滞后时间，并且不能观察到生长到菌落中的各个细胞之间的变化。 基于液体的生长测定也已被用于进行全基因组筛选3 。用延时显微镜耦合液体生长测定显示遗传相同的个体细胞倍增时间的群体异质性，这为理解遗传调节和环境适应提供了重要的视角。然而，该测定不测量生长的其它方面，例如滞后时间和承载能力10 。在这里，我们提出一种方法来表征在固体培养基上形成菌落的微生物的所有三个生长阶段，使用我们的术语ODELAY 11 。 ODELAY由utili组成zing高通量延时显微镜记录在固体培养基上生长成菌落的单细胞的图像。生长在殖民地的单个细胞群体揭示了潜在的种群异质性，其不被其他较不敏感的测量（例如终端终点评分）检测到。我们展示酵母的方法，但是ODELAY可以应用于在明场显微镜中显示对比度的任何生物体。

<table><tbody><tr><td>Agarose UltraPure</td><td>ThermoFisher</td><td>16500500</td><td>Gel Temp 36C, Gel Strength (1%) 1.2 g/sq cm</td></tr><tr><td>Yeast Extract Peptone (YEP)</td><td>Fisher Scientific</td><td>BP1422-2</td><td></td></tr><tr><td>Complete Suplement Mixture (CSM)</td><td>Fisher Scientific</td><td>MP114560222</td><td></td></tr><tr><td>Polyethylene Glycol 3350 (av. mol. wt. 3000-3700)</td><td>SigmaAldrich</td><td>P2906</td><td></td></tr><tr><td>Yeast Strain BY4741</td><td>ThermoFisher</td><td>95400.BY4741</td><td></td></tr><tr><td>Yeast Strain BY4742</td><td>ThermoFisher</td><td>95400.BY4742</td><td></td></tr><tr><td>50 mL Falcon tubes</td><td>Corning</td><td>430291</td><td>1 case</td></tr><tr><td>15 mL Falcon tubes</td><td>Corning</td><td>352096</td><td></td></tr><tr><td>2 x 3 inch 1.0 mm thick slides 1/2 gross</td><td>VWR</td><td>48382-179</td><td></td></tr><tr><td>96-well plate flat bottom</td><td>Corning</td><td>353072</td><td></td></tr><tr><td>Hydra liquid handleing robot</td><td>Thermo</td><td>1096-DT-100</td><td></td></tr><tr><td>Hamilton Microlab Star Liquid Handleing Robot</td><td>Hamilton</td><td></td><td></td></tr><tr><td>hydra 100 mL tips Extended Length DARTS</td><td>Thermo</td><td>5527</td><td></td></tr><tr><td>Synergy H4 Plate Reader</td><td>Biotek</td><td>H4MLFAD</td><td></td></tr><tr><td>Leica DMI6000 B Microscope</td><td>Leica</td><td></td><td></td></tr><tr><td>Leica 10X/0.3NA objective</td><td>Leica</td><td>11506289</td><td></td></tr><tr><td>Hamamatsu ORCA Flash 4.0 Camera</td><td>Hamamatsu</td><td>C11440-22CU</td><td></td></tr><tr><td>MATLAB with image processing tool box</td><td>Mathworks</td><td></td><td></td></tr><tr><td>MicroManager</td><td>Open Imaging</td><td></td><td>https://micro-manager.org/</td></tr><tr><td>ODELAY Microscope Control (MATLAB scripts and GUI)</td><td></td><td></td><td>www.aitchisonlab.com\ODELAY for Matlab scripts and software</td></tr><tr><td>ODELAY Microscope Chamber</td><td></td><td></td><td>www.aitchisonlab.com\ODELAY for Mechanincal Drawings</td></tr><tr><td>ODELAY Agar Molds</td><td></td><td></td><td>www.aitchisonlab.com\ODELAY for mold drawings</td></tr></tbody></table>

odelay: a large-scale method for multi-parameter quantification of yeast growth

微生物的生长表型是其潜在遗传适应性的强指标，可以分为3个生长方式:滞后期，对数期和稳定期。每个生长阶段可以揭示与各种环境和遗传条件相关的适应性的不同方面。通常难以获得所有3个生长阶段的高分辨率和定量测量。在这里，我们提供一种详细的方法来表征固体培养基上所有3个生长阶段，使用称为酵母生活阵列的单细胞倍增评估（ODELAY）的测定法。 ODELAY使用延时显微镜量化生长在固体培养基上的菌落中的个体细胞的生长表型。这种方法可以直接观察生长在殖民地的遗传相同细胞中的每个生长参数的种群异质性。这种群体异质性为理解遗传和表观遗传调控以及对其的反应提供了独特的视角遗传和环境扰动。虽然使用酵母证明了ODELAY方法，但它可以用于通过明视野显微镜可见的任何集落形成微生物。

<html>在延时显微镜中生长的酵母的实例图像显示在图3B中 。处理延时图像后，比较酵母菌株BY4741＆BY4742的代表性数据集显示在图4中 。在该示例数据集中，板上不同位置之间的倍增时间几乎没有变化。如果琼脂糖培养基的制备不好，则在与琼脂糖凝胶的变形区域重合的斑点位置上，倍增时间和滞后时间都会明显的偏差。虽然倍增时间似乎相对均匀，但该示例显示了滞后时间测量的变化。更一致的数据集如图6所示。在该数据集中，滞后时间和倍增时间均匀。 / ftp_upload / 55879 / 55879fig1.jpg"/&gt; 图1:琼脂模组装。 琼脂模具的组分如（ A ）所示。如（ B ）所示组装底座，然后用小的夹子夹住底座。将更长的立柱放在基座（ C ）的空腔中，然后将滑块固定在模具上，如（ D ）所示。侧视图示出了用于使琼脂与玻片（ E ）一致分离所需的模具的角度和模切口的取向。注意拇指和食指的位置，以及与幻灯片（ F ）分离的琼脂直线，注意水平箭头。分离线应沿垂直方向均匀移动。 <a href="http://ecsource.jove.com/files/ftp_upload/55879/55879fig1large.jpg" target="_blank">请点击此处查看此图的较大版本。</a> ="jove_content"fo:keep-together.within-page ="1"&gt; <img alt="图2" class="xfigimg" src="/files/ftp_upload/55879/55879fig2.jpg" /> 图2:超声处理和检测方法。 在冰水中超声波处理板，并使用离心机斗架来帮助支撑板（ A ）。将平板从步骤3.8.1排出，以便点样到琼脂糖板（ B ）上。此外，安排这些尖端使得最左边的尖端盒在位置C10处具有一个尖端，然后将四个其它尖端切断，使得它们不会撞击板支架（ C ）。将剩余的提示放在四个盒子中，使内部24个提示位置被占用（ D ）。 <a href="http://ecsource.jove.com/files/ftp_upload/55879/55879fig2large.jpg" target="_blank">请点击此处查看此图的较大版本。</a> 55879fig3.jpg"/&gt; 图3:ODELAY图形用户界面。 "ODELAY_Microscopecontrol.m"（ A ）的图形用户界面的屏幕截图。该界面允许监控摄像机并调整显微镜照明设置，以实现荧光和亮场模式。红色箭头指向对焦和传输按钮，激活相机快速获取图像并分别打开传输的光闸。蓝色箭头用于移动原点的舞台，然后使用"Go Origin"按钮和"Set"按钮设置原点。绿色箭头指向"重置"和"ODELAY !!!"将ODELAY图像模式重置为当前条件并启动ODELAY图像采集的按钮。点样后0,3,6和9小时在固体培养基上生长的酵母的延时图像（ B ）。 "ODELAY_IPT.m"的图形用户界面的屏幕截图<html> e ODELAY图像处理工具（ C ）。 <a href="http://ecsource.jove.com/files/ftp_upload/55879/55879fig3large.jpg" target="_blank">请点击此处查看此图的较大版本。</a> <img alt="图4" class="xfigimg" src="/files/ftp_upload/55879/55879fig4.jpg" /> 图4:ODELAY输出示例。 该数据集在YPD培养基上比较菌株BY4741和BY4742。这个数字是一个精心准备的琼脂糖幻灯片的例子;但是，自动对焦设置不是最佳的。从左到右的每一列中提供的数据是:殖民地区Log 2的承载能力;倍增时间，以分钟为单位;和滞后时间，以分钟为单位。在这个例子中，琼脂上的所有斑点的倍增时间都很顺利，少量的加倍的时间朝向色谱柱。然而，这个数据集的滞后时间有很大差异。_upload / 55879 / 55879fig4large.jpg"target ="_ blank"&gt;请点击此处查看此图的较大版本。 <img alt="图5" class="xfigimg" src="/files/ftp_upload/55879/55879fig5.jpg" /> 图5:增长曲线示例。 这个例子说明了较差的初始焦点可能导致估计的滞后时间（t lag ）增加（ A ），而相邻位置显示较短的滞后时间（ B ）。 t d是以min为单位的加倍时间，t lag是以分钟为单位的滞后时间。 <a href="http://ecsource.jove.com/files/ftp_upload/55879/55879fig5large.jpg" target="_blank">请点击此处查看此图的较大版本。</a> <img alt="图6" class="xfigimg" src="/files/ftp_upload/55879/55879fig6.jpg" /> 图6:执行试验实验的例子。 一个e在使用二极管照明器替换钨卤素灯泡后测试BY4742应变的样本，并确保自动对焦设置正确。所有倍增时间看起来都很好重叠，滞后时间似乎是一致的。 <a href="http://ecsource.jove.com/files/ftp_upload/55879/55879fig6large.jpg" target="_blank">请点击此处查看此图的较大版本。</a>

Watch this Scientific Journal Video about ODELAY: A Large-scale Method for Multi-parameter Quantification of Yeast Growth at JoVE.com

ODELAY: A Large-scale Method for Multi-parameter Quantification of Yeast Growth

我们提出了一种定量单个酵母细胞的生长表型的方法，它们使用称为酵母生活阵列的单细胞倍增评估（ODELAY）的延时显微镜在固体培养基上生长成菌落。可以直接观察和量化生长到菌落中的遗传相同细胞的群体异质性。

ODELAY:用于酵母生长多参数定量的大规模方法

Growth phenotypes of microorganisms are a strong indicator of their underlying genetic fitness and can be segregated into 3 growth regimes: lag-phase, ...

odelay-large-scale-method-for-multi-parameter-quantification-yeast

Research

JoVE Journal

Biology

8.1K 次观看.  Institute for Systems Biology. 我们提出了一种定量单个酵母细胞的生长表型的方法，它们使用称为酵母生活阵列的单细胞倍增评估（ODELAY）的延时显微镜在固体培养基上生长成菌落。可以直接观察和量化生长到菌落中的遗传相同细胞的群体异质性。 

视频: ODELAY: A Large-scale Method for Multi-parameter Quantification of Yeast Growth

Saccharomyces cerevisiae Metabolic Labeling with 4-thiouracil and the Quantification of Newly Synthesized mRNA As a Proxy for RNA Polymerase II Activity

Global defects in RNA polymerase II transcription might be overlooked by transcriptomic studies analyzing steady-state RNA. Indeed, the global decrease in mRNA synthesis has been shown to be compensated by a simultaneous decrease in mRNA degradation to restore normal steady-state levels. Hence, the genome-wide quantification of mRNA synthesis, independently from mRNA decay, is the best direct reflection of RNA polymerase II transcriptional activity. Here, we discuss a method using non-perturbing metabolic labeling of nascent RNAs in Saccharomyces cerevisiae (S. cerevisiae). Specifically, the cells are cultured for 6 min with a uracil analog, 4-thiouracil, and the labeled newly transcribed RNAs are purified and quantified to determine the synthesis rates of all individual mRNA. Moreover, using labeled Schizosaccharomyces pombe cells as internal standard allows comparing mRNA synthesis in different S. cerevisiae strains. Using this protocol and fitting the data with a dynamic kinetic model, the corresponding mRNA decay rates can be determined.

Saccharomyces cerevisiae Metabolic Labeling with 4-thiouracil and the Quantification of Newly Synthesized mRNA As a Proxy for RNA Polymerase II Activity

The protocol described here is based on the genome-wide quantification of newly synthesized mRNA purified from yeast cells labeled with 4-thiouracil. This method allows to measure mRNA synthesis uncoupled from mRNA decay and, thus, provides an accurate measurement of RNA polymerase II transcription.

酿酒酵母4-硫尿嘧啶代谢标记及新合成 mRNA 作为 RNA 聚合酶 II. 活性的一种代理

Global defects in RNA polymerase II transcription might be overlooked by transcriptomic studies analyzing steady-state RNA. Indeed, the global decrease in ...

科研

遗传学

Quantifying Yeast Chronological Life Span by Outgrowth of Aged Cells

The budding yeast Saccharomyces cerevisiae has proven to be an important model organism in the field of aging research 1. The replicative and chronological life spans are two established paradigms used to study aging in yeast. Replicative aging is defined as the number of daughter cells a single yeast mother cell produces before senescence; chronological aging is defined by the length of time cells can survive in a non-dividing, quiescence-like state 2. We have developed a high-throughput method for quantitative measurement of chronological life span. This method involves aging the cells in a defined medium under agitation and at constant temperature. At each age-point, a sub-population of cells is removed from the aging culture and inoculated into rich growth medium. A high-resolution growth curve is then obtained for this sub-population of aged cells using a Bioscreen C MBR machine. An algorithm is then applied to determine the relative proportion of viable cells in each sub-population based on the growth kinetics at each age-point. This method requires substantially less time and resources compared to other chronological lifespan assays while maintaining reproducibility and precision. The high-throughput nature of this assay should allow for large-scale genetic and chemical screens to identify novel longevity modifiers for further testing in more complex organisms.

Chronological aging in yeast refers to the loss of cell viability associated with time in stationary phase. Here we describe a high-throughput method for quantitatively determining yeast chronological life span.

量化老化细胞生长的酵母年谱跨度

The budding yeast Saccharomyces cerevisiae has proven to be an important model organism in the field of aging research 1. The replicative and ...

生物学

Measuring Replicative Life Span in the Budding Yeast

Aging is a degenerative process characterized by a progressive deterioration of cellular components and organelles resulting in mortality. The budding yeast Saccharomyces cerevisiae has been used extensively to study the biology of aging, and several determinants of yeast longevity have been shown to be conserved in multicellular eukaryotes, including worms, flies, and mice 1. Due to the lack of easily quantified age-associated phenotypes, aging in yeast has been assayed almost exclusively by measuring the life span of cells in different contexts, with two different life span paradigms in common usage 2. Chronological life span refers to the length of time that a mother cell can survive in a non-dividing, quiescence-like state, and is proposed to serve as a model for aging of post-mitotic cells in multicellular eukaryotes. Replicative life span, in contrast, refers the number of daughter cells produced by a mother cell prior to senescence, and is thought to provide a model of aging in mitotically active cells. Here we present a generalized protocol for measuring the replicative life span of budding yeast mother cells. The goal of the replicative life span assay is to determine how many times each mother cell buds. The mother and daughter cells can be easily differentiated by an experienced researcher using a standard light microscope (total magnification 160X), such as the Zeiss Axioscope 40 or another comparable model. Physical separation of daughter cells from mother cells is achieved using a manual micromanipulator equipped with a fiber-optic needle. Typical laboratory yeast strains produce 20-30 daughter cells per mother and one life span experiment requires 2-3 weeks.

In this article we present a general protocol for measuring the replicative life span of yeast mother cells.

在芽殖酵母中的自我复制寿命测量

Aging is a degenerative process characterized by a progressive deterioration of cellular components and organelles resulting in mortality. The budding ...

A Quantitative Fitness Analysis Workflow

Quantitative Fitness Analysis (QFA) is an experimental and computational workflow for comparing fitnesses of microbial cultures grown in parallel1,2,3,4. QFA can be applied to focused observations of single cultures but is most useful for genome-wide genetic interaction or drug screens investigating up to thousands of independent cultures. The central experimental method is the inoculation of independent, dilute liquid microbial cultures onto solid agar plates which are incubated and regularly photographed. Photographs from each time-point are analyzed, producing quantitative cell density estimates, which are used to construct growth curves, allowing quantitative fitness measures to be derived. Culture fitnesses can be compared to quantify and rank genetic interaction strengths or drug sensitivities. The effect on culture fitness of any treatments added into substrate agar (e.g. small molecules, antibiotics or nutrients) or applied to plates externally (e.g. UV irradiation, temperature) can be quantified by QFA.
The QFA workflow produces growth rate estimates analogous to those obtained by spectrophotometric measurement of parallel liquid cultures in 96-well or 200-well plate readers. Importantly, QFA has significantly higher throughput compared with such methods. QFA cultures grow on a solid agar surface and are therefore well aerated during growth without the need for stirring or shaking.
QFA throughput is not as high as that of some Synthetic Genetic Array (SGA) screening methods5,6. However, since QFA cultures are heavily diluted before being inoculated onto agar, QFA can capture more complete growth curves, including exponential and saturation phases3. For example, growth curve observations allow culture doubling times to be estimated directly with high precision, as discussed previously1.
Here we present a specific QFA protocol applied to thousands of S. cerevisiae cultures which are automatically handled by robots during inoculation, incubation and imaging. Any of these automated steps can be replaced by an equivalent, manual procedure, with an associated reduction in throughput, and we also present a lower throughput manual protocol. The same QFA software tools can be applied to images captured in either workflow.
We have extensive experience applying QFA to cultures of the budding yeast S. cerevisiae but we expect that QFA will prove equally useful for examining cultures of the fission yeast S. pombe and bacterial cultures.

Quantitative Fitness Analysis (QFA) is a complementary series of experimental and computational methods for estimating microbial culture fitnesses. QFA estimates the effect of genetic mutations, drugs or other applied treatments on microbe growth. Experiments scaling from focussed analysis of single cultures to thousands of parallel cultures can be designed.

定量健身分析工作流程

Quantitative Fitness Analysis (QFA) is an experimental and computational workflow for comparing fitnesses of microbial cultures grown in parallel1,2,3,4. ...

Analysis of Lipid Droplet Content in Fission and Budding Yeasts using Automated Image Processing

Lipid metabolism and its regulation are of interest to both basic and applied life sciences and biotechnology. In this regard, various yeast species are used as models in lipid metabolic research or for industrial lipid production. Lipid droplets are highly dynamic storage bodies and their cellular content represents a convenient readout of the lipid metabolic state. Fluorescence microscopy is a method of choice for quantitative analysis of cellular lipid droplets, as it relies on widely available equipment and allows analysis of individual lipid droplets. Furthermore, microscopic image analysis can be automated, greatly increasing overall analysis throughput. Here, we describe an experimental and analytical workflow for automated detection and quantitative description of individual lipid droplets in three different model yeast species: the fission yeasts Schizosaccharomyces pombe and Schizosaccharomyces japonicus, and the budding yeast Saccharomyces cerevisiae. Lipid droplets are visualized with BODIPY 493/503, and cell-impermeable fluorescent dextran is added to the culture media to help identify cell boundaries. Cells are subjected to 3D epifluorescence microscopy in green and blue channels and the resulting z-stack images are processed automatically by a MATLAB pipeline. The procedure outputs rich quantitative data on cellular lipid droplet content and individual lipid droplet characteristics in a tabular format suitable for downstream analyses in major spreadsheet or statistical packages. We provide example analyses of lipid droplet content under various conditions that affect cellular lipid metabolism.

Here, we present a MATLAB implementation of automated detection and quantitative description of lipid droplets in fluorescence microscopy images of fission and budding yeast cells.

使用自动图像处理分析裂变和萌芽酵母中的脂滴含量

Lipid metabolism and its regulation are of interest to both basic and applied life sciences and biotechnology. In this regard, various yeast species are ...

免疫与感染

Temporal Quantification of MAPK Induced Expression in Single Yeast Cells

The quantification of gene expression at the single cell level uncovers novel regulatory mechanisms obscured in measurements performed at the population level. Two methods based on microscopy and flow cytometry are presented to demonstrate how such data can be acquired. The expression of a fluorescent reporter induced upon activation of the high osmolarity glycerol MAPK pathway in yeast is used as an example. The specific advantages of each method are highlighted. Flow cytometry measures a large number of cells (10,000) and provides a direct measure of the dynamics of protein expression independent of the slow maturation kinetics of the fluorescent protein. Imaging of living cells by microscopy is by contrast limited to the measurement of the matured form of the reporter in fewer cells. However, the data sets generated by this technique can be extremely rich thanks to the combinations of multiple reporters and to the spatial and temporal information obtained from individual cells. The combination of these two measurement methods can deliver new insights on the regulation of protein expression by signaling pathways.

Two complementary methods based on flow cytometry and microscopy are presented which enable the quantification, at the single cell level, of the dynamics of gene expression induced by the activation of a MAPK pathway in yeast.

在单酵母细胞MAPK诱导表达的时空定量

The quantification of gene expression at the  single cell level uncovers novel regulatory mechanisms obscured in measurements  performed at the population ...

Reporter-based Growth Assay for Systematic Analysis of Protein Degradation

Protein degradation by the ubiquitin-proteasome system (UPS) is a major regulatory mechanism for protein homeostasis in all eukaryotes. The standard approach to determining intracellular protein degradation relies on biochemical assays for following the kinetics of protein decline. Such methods are often laborious and time consuming and therefore not amenable to experiments aimed at assessing multiple substrates and degradation conditions. As an alternative, cell growth-based assays have been developed, that are, in their conventional format, end-point assays that cannot quantitatively determine relative changes in protein levels.
Here we describe a method that faithfully determines changes in protein degradation rates by coupling them to yeast cell-growth kinetics. The method is based on an established selection system where uracil auxotrophy of URA3-deleted yeast cells is rescued by an exogenously expressed reporter protein, comprised of a fusion between the essential URA3 gene and a degradation determinant (degron). The reporter protein is designed so that its synthesis rate is constant whilst its degradation rate is determined by the degron. As cell growth in uracil-deficient medium is proportional to the relative levels of Ura3, growth kinetics are entirely dependent on the reporter protein degradation.
This method accurately measures changes in intracellular protein degradation kinetics. It was applied to: (a) Assessing the relative contribution of known ubiquitin-conjugating factors to proteolysis (b) E2 conjugating enzyme structure-function analyses (c) Identification and characterization of novel degrons. Application of the degron-URA3-based system transcends the protein degradation field, as it can also be adapted to monitoring changes of protein levels associated with functions of other cellular pathways.

Here we describe a robust biological assay for quantifying the relative rate of proteolysis by the ubiquitin-proteasome system. The assay readout is yeast growth rate in liquid culture, which is dependent on the cellular levels of a reporter protein comprising a degradation signal fused to an essential metabolic marker.

记者的增长分析的蛋白质降解的系统分析

Protein degradation by the ubiquitin-proteasome system (UPS) is a major regulatory mechanism for protein homeostasis in all eukaryotes. The standard ...

Growth-based Determination and Biochemical Confirmation of Genetic Requirements for Protein Degradation in Saccharomyces cerevisiae

Regulated protein degradation is crucial for virtually every cellular function. Much of what is known about the molecular mechanisms and genetic requirements for eukaryotic protein degradation was initially established in Saccharomyces cerevisiae. Classical analyses of protein degradation have relied on biochemical pulse-chase and cycloheximide-chase methodologies. While these techniques provide sensitive means for observing protein degradation, they are laborious, time-consuming, and low-throughput. These approaches are not amenable to rapid or large-scale screening for mutations that prevent protein degradation. Here, a yeast growth-based assay for the facile identification of genetic requirements for protein degradation is described. In this assay, a reporter enzyme required for growth under specific selective conditions is fused to an unstable protein. Cells lacking the endogenous reporter enzyme but expressing the fusion protein can grow under selective conditions only when the fusion protein is stabilized (i.e. when protein degradation is compromised). In the growth assay described here, serial dilutions of wild-type and mutant yeast cells harboring a plasmid encoding a fusion protein are spotted onto selective and non-selective medium. Growth under selective conditions is consistent with degradation impairment by a given mutation. Increased protein abundance should be biochemically confirmed. A method for the rapid extraction of yeast proteins in a form suitable for electrophoresis and western blotting is also demonstrated. A growth-based readout for protein stability, combined with a simple protocol for protein extraction for biochemical analysis, facilitates rapid identification of genetic requirements for protein degradation. These techniques can be adapted to monitor degradation of a variety of short-lived proteins. In the example presented, the His3 enzyme, which is required for histidine biosynthesis, was fused to Deg1-Sec62. Deg1-Sec62 is targeted for degradation after it aberrantly engages the endoplasmic reticulum translocon. Cells harboring Deg1-Sec62-His3 were able to grow under selective conditions when the protein was stabilized.

Growth-based Determination and Biochemical Confirmation of Genetic Requirements for Protein Degradation in Saccharomyces cerevisiae

This article describes a yeast growth-based assay for the determination of genetic requirements for protein degradation. It also demonstrates a method for rapid extraction of yeast proteins, suitable for western blotting to biochemically confirm degradation requirements. These techniques can be adapted to monitor degradation of a variety of proteins.

成长型测定和遗传需求生化确认的蛋白质降解<em&gt;酿酒酵母</em

Regulated protein degradation is crucial for virtually every cellular function. Much of what is known about the molecular mechanisms and genetic ...

Precise, High-throughput Analysis of Bacterial Growth

Bacterial growth is a central concept in the development of modern microbial physiology, as well as in the investigation of cellular dynamics at the systems level. Recent studies have reported correlations between bacterial growth and genome-wide events, such as genome reduction and transcriptome reorganization. Correctly analyzing bacterial growth is crucial for understanding the growth-dependent coordination of gene functions and cellular components. Accordingly, the precise quantitative evaluation of bacterial growth in a high-throughput manner is required. Emerging technological developments offer new experimental tools that allow updates of the methods used for studying bacterial growth. The protocol introduced here employs a microplate reader with a highly optimized experimental procedure for the reproducible and precise evaluation of bacterial growth. This protocol was used to evaluate the growth of several previously described Escherichia coli strains. The main steps of the protocol are as follows: the preparation of a large number of cell stocks in small vials for repeated tests with reproducible results, the use of 96-well plates for high-throughput growth evaluation, and the manual calculation of two major parameters (i.e., maximal growth rate and population density) representing the growth dynamics. In comparison to the traditional colony-forming unit (CFU) assay, which counts the cells that are cultured in glass tubes over time on agar plates, the present method is more efficient and provides more detailed temporal records of growth changes, but has a stricter detection limit at low population densities. In summary, the described method is advantageous for the precise and reproducible high-throughput analysis of bacterial growth, which can be used to draw conceptual conclusions or to make theoretical observations.

Quantitative evaluation of bacterial growth is essential to understanding microbial physiology as a systems-level phenomenon. A protocol for experimental manipulation and an analytical approach are introduced, allowing for precise, high-throughput analysis of bacterial growth, which is a key subject of interest in systems biology.

细菌生长的精确、高通量分析

Bacterial growth is a central concept in the development of modern microbial physiology, as well as in the investigation of cellular dynamics at the ...

Saccharomyces cerevisiae Exponential Growth Kinetics in Batch Culture to Analyze Respiratory and Fermentative Metabolism

Saccharomyces cerevisiae cells in the exponential phase sustain their growth by producing ATP through fermentation and/or mitochondrial respiration. The fermentable carbon concentration mainly governs how the yeast cells generate ATP; thus, the variation in fermentable carbohydrate levels drives the energetic metabolism of S. cerevisiae. This paper describes a high-throughput method based on exponential yeast growth to estimate the effects of concentration changes and nature of the carbon source on respiratory and fermentative metabolism. The growth of S. cerevisiae is measured in a microplate or shaken conical flask by determining the optical density (OD) at 600 nm. Then, a growth curve is built by plotting OD versus time, which allows identification and selection of the exponential phase, and is fitted with the exponential growth equation to obtain kinetic parameters. Low specific growth rates with higher doubling times generally represent a respiratory growth. Conversely, higher specific growth rates with lower doubling times indicate fermentative growth. Threshold values of doubling time and specific growth rate are estimated using well-known respiratory or fermentative conditions, such as non-fermentable carbon sources or higher concentrations of fermentable sugars. This is obtained for each specific strain. Finally, the calculated kinetic parameters are compared with the threshold values to establish whether the yeast shows fermentative and/or respiratory growth. The advantage of this method is its relative simplicity for understanding the effects of a substance/compound on fermentative or respiratory metabolism. It is important to highlight that growth is an intricate and complex biological process; therefore, preliminary data from this method must be corroborated by the quantification of oxygen consumption and accumulation of fermentation byproducts. Thereby, this technique can be used as a preliminary screening of compounds/substances that may disturb or enhance fermentative or respiratory metabolism.

Saccharomyces cerevisiae Exponential Growth Kinetics in Batch Culture to Analyze Respiratory and Fermentative Metabolism

Here we present a protocol to estimate the respiratory and fermentative metabolism by fitting the exponential growth of Saccharomyces cerevisiae to the exponential growth equation. Calculation of the kinetic parameters allows for the screening of influences of substances/compounds on fermentation or mitochondrial respiration.

酿酒酵母间歇培养中的指数生长动力学分析呼吸和发酵代谢

Saccharomyces cerevisiae cells in the exponential phase sustain their growth by producing ATP through fermentation and/or mitochondrial respiration. The ...