作者没有什么可透露的。

1. 准备生长培养基 注意: 最小介质 M63 的化学成分如下:62 mm K 2 HPO 4 , 39 mm, 2 PO 4 , 15 mm (NH 4 ) 2 因此 4 、1.8 和 #181; m FeSO 4 、15和 #181; m 硫胺素-HCl、0.2 mm MgSO 4 和 22 mm 葡萄糖。M63 是由混合三库存解决方案: 五 X 解决方案, 20% 葡萄糖和 MgSO 4 硫胺素溶液。将所有解决方案存储在4和 #176; C. 
<ol> <li> 准备五 X 解决方案 <ol> <li> 以准备 FeSO 4 解决方案, 请使用电子吸管和一次性血清吸管将 ddH 2 O 添加到50毫升离心管中。使用 P-200 添加0.06 毫升 hcl, 以获得0.01 米 hcl. 添加36毫克 FeSO 4 -7H 2 O 并混合好. </li>
		<li> 测量 160 ml ddH 2 O 在计量缸中, 并将其添加到500毫升的烧杯中。按以下顺序添加下列内容: 10.72 g K 2 HPO 4 , 5.24 g, 2 PO 4 , 2.0 g (NH 4 ) 2 所以 4 , 和 0.5 mL FeSO 4 解决方案 (在步骤1.1.1 中准备). </li>
		<li> 使用精密 ph 计, 将 ph 值调整到 7.0, 增加2米。将解决方案倒入测量缸中, 并将 ddH 2 O 添加到总体积为200毫升。使用250毫升过滤装置 (PVDF、0.22 和 #181; M) 对解决方案进行消毒. </li>
	</ol> </li> <li> 在测量缸中准备20% 葡萄糖溶液 <ol> <li> 测量 200 ml ddH 2 O, 并将其倒入一个500毫升的烧杯中。在混合使用磁力搅拌器的同时, 加入50克葡萄糖。稀释溶液在一个测量缸到总体积为250毫升。按照步骤1.1.3 中的说明对解决方案进行灭菌. </li>
	</ol> </li> <li> 准备 MgSO 4 硫胺素解决方案 <ol> <li> 向500毫升的烧杯添加 150 ml ddH 2 O。在与磁力搅拌器混合时, 加入1.0 克硫胺-HCl 和10克 MgSO 4 -7H 2 o. 将测量缸中的溶液稀释到总容积200毫升。按照步骤1.1.3 中的说明对解决方案进行灭菌. </li>
	</ol> </li> <li> 准备最小介质 M63 <ol> <li> 放置五 X 解决方案、20% 葡萄糖解决方案、MgSO 4 硫胺素解决方案、电子吸管、P-200 吸管和 200-#181; 在干净的长凳上的吸管提示. </li>
		<li> 测量 155.8 ml ddH 2 O 在计量缸中, 并将其倒入500毫升的烧杯中. </li>
		<li> 使用电子吸管和一次性血清吸管, 将40毫升的五 X 溶液和4毫升20% 葡萄糖溶液添加到测量的 ddH 2 O。然后, 添加0.2 毫升 MgSO 4 硫胺素溶液与 P-200。按照步骤1.1.3 中的说明对解决方案进行灭菌. </li>
	</ol> </li> </ol> 2。准备甘油库存 <ol> <li> 单元格区域性 注意: 细菌菌株 (如 、W3110 及其减少的基因组) 可从应变库组织中获得。菌株通常以琼脂板上的菌落的形式获得。
	<ol> <li> 将五消毒玻璃管与硅橡胶塞子、电子吸管、P-1,000、1000-#181; l 吸管提示, P-200, 200 和 #181; l 吸管提示, 和目标菌株 (板块上的殖民地) 在一个干净的长凳上. </li>
		<li> 在打开硅胶瓶塞之前, 将玻璃管的口暴露在本生燃烧器上。在管子打开后, 将硅橡胶塞子暴露在火焰中, 然后轻轻地将瓶盖放回玻璃管上. </li>
		<li> 使用电子吸管和一次性血清吸管将5毫升 M63 添加到其中一个玻璃管和4.5 毫升 M63 到其他四管. </li>
		<li> 使用 P-200 提示选择一个菌落, 并将其接种在含有5毫升 M63 的玻璃管中. </li>
		<li> 涡流管使其悬浮。然后, 通过将本解决方案的 0.5 ml 转换为包含 4.5 ml M63 的四管之一, 将溶液稀释10倍. </li>
		<li> 重复步骤2.1.5 中描述的剩余管的过程。五种不同浓度的稀释系列 (稀释1、10、100、1000和 1万) 现在已经准备就绪. </li>
		<li> 消毒玻璃管和硅橡胶塞子的嘴, 如步骤2.1.2 所述。用塞子把管子盖上。避免不起皱的硅橡胶塞子的污染. </li>
		<li> 将五根管子放在5ml 摇动孵化箱中, 在37和 #176; C, 在 200 rpm 处晃动。在夜间或10至30小时孵育养殖. </li>
	</ol> </li> <li> 选择甘油库存的区域性 <ol> <li> 将5ml 室温 M63 介质、P-1,000、1000和 #181; L 吸管提示和一次性试管在干净的长凳上. </li>
		<li> 添加1000和 #181; 我 M63 一个 P-1,000 的一次性试管。将一次性试管置于分光光度计中, 以 600 nm 的固定波长启动程序, 并测量空白. </li>
		<li> 将五玻璃管从摇动孵化器移到干净的长凳上. </li>
		<li> 丢弃 M63 从一次性试管, 并添加1000和 #181; L 文化到相同的一次性试管与 P-1,000。测量单元区域性的光学浊度 (OD 600 ), 如步骤2.2.2 中所述. 
		注意: 为避免任何污染, 并实现精确测量, 将玻璃管和塞子暴露在火焰中, 并在取样前对其进行涡流。为了确保可靠的结果, 建议重复测量, 特别是当细胞密度较低. </li>
		<li> 在五单元格区域性中, 选择一个位于早期的指数增长阶段 (OD 600 = 0.01-0.05), 用于甘油库存. 
		注意: 如果多个区域性在最佳范围内有密度, 则通常选择最接近0.05 的一个. </li>
	</ol> </li> <li> 为重复测试进行甘油储存. 
	注: 这是为准备十股票的描述。根据实验要求, 可以做较大或更小的量。
	<ol> <li> 将灭菌的60% 甘油溶液、十消毒1.5 毫升管内、P-1,000 和 P-200、1000-和 200-和 #181; L 吸管提示, 微站在干净的长凳上. </li>
		<li> 添加250和 #181; 我用60% 的甘油溶液和750和 #181 消毒; 选择的细胞培养微1.5 毫升的移. 
		注意: 库存量是可变的, 但始终保持1:3 的比率60% 甘油的细胞培养;这导致最终浓度为15% 甘油. </li>
		<li> 将剩余的九管内放在微支架上, 并分配100和 #181; 在步骤2.3.2 中准备的混合物的 L。现在有十相同的甘油库存供将来使用. </li>
		<li> 将库存存储在-80 和 #176 的深度冷冻库中; C. </li>
	</ol> </li> </ol> 3。获取增长曲线 <ol> <li> 设置微读取器. 
	注: 用引号显示的术语显示了在这里使用的平板阅读器的具体语法 (参见材料表)。
	<ol> <li> 打开该软件。开放和 #34;P rotocols 和 #34; #34; 任务管理器和 #34; 选择和 #34; 创建新的 #34;。选择和 #34; 标准协议和 #34;. </li>
		<li> 打开和 #34;P 程序和 #34;, 并调整设置。打开和 #34; 设置温度和 #34; 选择和 #34; 孵化器和 #34;设置和 #34; 温度和 #34; 37 和 #176; c 和 #34; 渐变和 #34; 到0和 #176; #34;P 再热 #34; 在继续下一步之前。打开和 #34; 启动动力学和 #34; 设置和 #34; 运行时间和 #34; 24:00:00 或 48:00:00, 和 #34; 间隔和 #34; 00:30:00 或 01:00:00. 
		注: 大约需要1分钟才能阅读整个96井板. </li>
		<li> 打开和 #34; 抖动和 #34; 设置和 #34; 抖动模式和 #34; #34; 线性和 #34;检查和 #34; Constitution 震动和 #34; 设置和 #34; 频率和 #34; 在 567 cpm。打开和 #34; 读取和 #34; 检查和 #34; 吸光度和 #34;, #34; 终点/动能和 #34; 和 #34; 单色 #34; 设置和 #34; 波长和 #34; 到 600. </li>
		<li> 单击和 #34; 验证和 #34; 确认程序正确。单击并 #34; 保存和 #34; 将其保存为新程序以备将来使用. </li>
	</ol> </li> <li> 实时增长记录 <ol> <li> 绘制一个96井板象形 (8 和 #215; 12 表), 以指示在96井板上接种的培养样品的位置。打印出表并将其用作实验的参考. 
		注: 位于微边缘的井只因蒸发而含有空白介质. </li>
		<li> 放置一个灭菌的96平底微, 盖子、P-1000、1000和 #181; 吸管提示, P-200, 200 和 #181; l 吸管提示, 数1.5 毫升管内, 8 多通道吸管, 灭菌试剂库, 室温 M63, 甘油库存 (在步骤2.3 中准备) 在一个干净的长凳上. </li>
		<li> 在试剂库中添加大约25毫升 M63。将此库存量用于以下所有步骤. </li>
		<li> 添加900和 #181; 我 M63 管内准备制作串口稀释. </li>
		<li> 在室温下解冻甘油库存。添加900和 #181; 我 M63 到解冻的甘油库存和涡流。这导致了10倍的稀释原甘油库存. </li>
		<li> 转移100和 #181; 将10倍稀释的 l 转到另一微, 其中包含900和 #181; l M63 和涡旋。这导致100倍的稀释. </li>
		<li> 重复步骤3.2.6 直到达到所需的稀释数. </li>
		<li> 用200和 #181 填充微边缘的井; M63 使用8通道吸管 (P-200). 
		注: 这些井可以用作空白. </li>
		<li> 加载200和 #181; 根据参考表 (步骤 3.2.1), 在步骤 3.2.4 3.2.7 对微井进行的每个稀释样品的 L。在板上不同位置的多个井中加载和装载相同样品之前, 先将稀释过的样品涡旋. </li>
		<li> 将96井微放在平板读卡器上. </li>
		<li> 打开和 #34; 现在阅读和 #34; #34; 任务管理器和 #34; 选择程序 (3.1 节)。单击并 #34; 确定 #34; 开始测量。将录制保存为数据分析的新实验文件 (4 节). </li>
	</ol> </li> </ol> 4。数据分析 <ol> <li> 将 real-time 增长速率数据 (第3.2 节) 的结果导出到 USB 内存棒作为文本文件. 
	注:96 个格式化的结果 (曲线) 将显示在 real-time 的平板阅读器上。每小时记录 (OD 值) 可以导出为表;行和列分别表示井号 ( 例如 、A1、B1) 和测量时间 ( 例如 , 00:00:59). </li>
	<li> 使用电子表格软件打开文本文件. </li>
	<li> 按小时 OD 600 将96井微的读数复制到新工作表中以进行进一步分析. </li>
	<li> 从每个样本的每小时读取中减去背景读取时间为零. 
	注: 为方便起见, 含 M63 (步 3.2.8) 的空井均值可作为背景值. </li>
	<li> 计算五连续 OD 的平均值 600 读取以估计最大人口密度. 
	注意: 五连续 od 600 读取的最大平均值定义为相应增长曲线的最大 od 600 。
	<li> 计算增长率, 和 #181; (h -1 ), 通过对 OD 600 的所有对连续的值应用下列公式: 
	<img alt="Equation" src="/files/ftp_upload/56197/56197eq1.jpg"/> 注意: 在这个等式中, c i 和 c i+1 表示任意两个连续时间点 (t i 和 t i+1 的 OD 600 值,分别)。LN 表示自然对数. </li>
	<li> 根据每小时 OD 600 按照步骤4.6 进行读取, 计算增长速率随时间的变化。计算五连续增长率的平均值和标准差, 以估计最大增长率. 
	注意: 最大的平均值与最小的标准偏差被定义为相应增长曲线的最大增长率. </li>
</ol> 5。确认96井读数 (可选) 的全局偏差 注意: 板读写器和可消耗的 96-井板都可能导致偏差测量。为了获得高精度和可重复性的定量结果, 强烈建议确认96井板的全球偏差. 
<ol> <li> 为偏压测试准备96井板和平板读卡器, 如第3.2.2 节所述. </li>
	<li> 添加20毫升 M63 到一个灭菌50毫升离心管。将甘油库存添加到相同的离心管和涡流中。将悬浮溶液转移到灭菌试剂库. </li>
	<li> 使用8通道吸管转移200和 #181; L 的悬浮解决方案的 96-井板. </li>
	<li> 将 96-井板放在平板读卡器上并开始测量. </li>
	<li> 记录每小时 OD 600 读取和分析, 如4节所述。比较各井的最大生长速率和人口密度, 以确定96井读数的位置偏差. </li>
</ol>

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E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=r-+and+K-selection+in+fluctuating+populations+is+determined+by+the+evolutionary+trade-off+between+two+fitness+measures:+Growth+rate+and+lifetime+reproductive+success.">r- and K-selection in fluctuating populations is determined by the evolutionary trade-off between two fitness measures: Growth rate and lifetime reproductive success.</a> Evolution. 71 (1), 167-173 (2017).</li></ol>

我们感谢平土提供 CFU 化验的例子。这项工作得到了部分财政支助, 其经费来自日本教育、文化、体育、科学和技术部 (补助金) 26506003 号 (BWY) 的科学研究。

协议中的关键步骤包括准备一股指数增长的细胞的普通股, 以及在微的不同位置的多个井中复制相同的样品。以前, 微生物学从一夜培养开始了文化。虽然这种方法可以减少细菌生长的滞后时间, 但很难获得可再生的生长曲线。如图 2所示, 使用普通甘油库存的独立测量导致几乎相同的生长曲线, 从而提供了重现性和精确的结果。如图 3所示, 不同位置的水井会略有不同的读数, 这被认为是测量的全局背景。对于细菌生长的定量评价, 应考虑这一背景。为实验性复制, 强烈建议将样品接种到多个不同的板块位置, 以此来解释全球偏见。
除了处理错误外, 还应考虑从实验设备和耗材中获得的修改和故障排除。一些先进的微读者在商业上是可利用的。它们的特性有很大的变化, 如调节光波长的方法, 加热或冷却的方式, 以及震动的方式和频率。根据我们的经验, 在高通量的研究中, 蒸发应该被处理, 因为液体体积的变化大大影响 OD600读取。蒸发是由微读者使用的加热方法和板的结构造成的。商业微读者普遍采用的两种供暖方式是暖空气加热和热板加热。本协议中使用的微读取器采用热板作为加热方式来减少蒸发, 因为微中的介质在吹暖的空气中会迅速干涸。板也应慎重选择。不同的96井板 (与盖子) 导致不同的蒸发水平。所述方法采用带有深盖的板格式, 可减少蒸发。
此外, 如果研究需要对生长曲线进行稳态相位分析, 则应考虑微读者的震动方式和频率。在培养基中晃动可以确保生长的细胞在培养基中均匀悬浮。不适当的震动或低频可能导致细胞在井边或中心处以高密度堆积, 导致误读或比实际读数更低或更高。所述方法采用线性、双向晃动。虽然8形的震动模式可能是足够的, 我们不建议的循环模式的晃动。
该方法的主要局限性是细胞培养基的蒸发。由于最大培养体积较小,即, 200 µL, 孵育在37° c 引起显著的蒸发。因此, 这种方法不宜培养长于 48 h. 此外, 应避免低密度的细胞培养, 因为光学检测 (OD600) 在这些密度下是有限的。当考虑到一起, 非常缓慢生长的细胞/菌株或非常低的初始浓度的文化是不可取的, 因为它们需要更长的时间和低于检测限制 OD600。
这种方法产生精确的高通量测量, 这对系统调查非常有利。作为一个代表性的结果, 此方法允许评估各种基因组序列和/或中等容易和性5的大肠杆菌菌株的分类。与传统的试管培养方法相比, 在不同时间点手工测量样品的方法比较少, 劳动强度大, 时间密集。该方法的未来应用和发展被认为涉及实验操作和计算分析的自动化。自动测量和机器人应用将导致一个高效和可靠的方法, 评估细胞生长和执行生存试验使用细菌和哺乳动物细胞。

微生物研究经常从细菌细胞的培养和细菌生长曲线的评估开始, 这代表细菌生理学的一个基本现象1,2,3。基本文化原则在已出版的研究文献和教科书中得到广泛应用, 因为细菌培养是一种基本的方法。在替补席上, 传统上一直关注于优化生长培养基和培养条件, 但控制生长速度, 这可能会提供更大的理解微生物生理学, 没有广泛研究了4。对于指数生长的细菌来说, 细胞状态的一个关键参数是生长速率, 据报告, 它与基因组、转录和蛋白质群相协调5,6,7,8.因此, 对细菌生长的定量评价是了解微生物生理学的关键。
为了评价细菌的生长, 用于估计生物量的实验方法已经建立了9,10 , 并基于检测生化、物理或生物参数, 如光学浊度。此外, 用于捕获生长变化动态特性的分析方法通常基于建立的非线性模型11,12,13, 例如, 逻辑方程。生长动力学通常通过测量光学浊度或进行菌落形成单元 (CFU) 测定来获得细胞生长的定时取样。这些培养和检测方法的局限性在于, 数据点不是人口动态的真实反映, 因为测量间隔通常是小时, 因为培养条件 (例如, 温度的变化和曝气) 在取样时受到干扰。必须利用技术和理解方面的最新发展来更新文化和分析技术。微读者的最新进展使 real-time 观察细菌生长, 并大大降低劳动力成本。利用这些先进的设备, 最新的细菌生长研究报告了高通量测量的分析方法14,15。
本议定书的目的是以高通量的方式评估精确的增长动态, 这对于最终解决增长率如何确定以及影响增长率的问题的定量研究将是有价值的。该议定书处理的所有因素, 应考虑到可重复和精确的定量细菌生长。本文主要对实验方法和分析进行了详细的介绍。这种方法允许以高通量的方式精确和重现地分析细菌的生长。微生物学可以利用这个协议从他们的实验证据中获得额外的定量结果。该协议也可用于系统生物学的研究, 试图得出概念性结论或实现对增长的理论概述。

<table><tbody><tr><td>K&lt;sub&gt;2&lt;/sub&gt;HPO&lt;sub&gt;4&lt;/sub&gt;</td><td>Wako</td><td>164-04295</td><td></td></tr><tr><td>KH&lt;sub&gt;2&lt;/sub&gt;PO&lt;sub&gt;4&lt;/sub&gt;</td><td>Wako</td><td>166-04255</td><td></td></tr><tr><td>(NH&lt;sub&gt;4&lt;/sub&gt;)&lt;sub&gt;2&lt;/sub&gt;SO&lt;sub&gt;4&lt;/sub&gt;</td><td>Wako</td><td>019-03435</td><td></td></tr><tr><td>MgSO&lt;sub&gt;4&lt;/sub&gt;-7H&lt;sub&gt;2&lt;/sub&gt;O</td><td>Wako</td><td>138-00415</td><td></td></tr><tr><td>Thiamine-HCl</td><td>Wako</td><td>201-00852</td><td></td></tr><tr><td>glucose</td><td>Wako</td><td>049-31165</td><td></td></tr><tr><td>HCl</td><td>Wako</td><td>080-01066</td><td></td></tr><tr><td>Iron (II) sulfate heptahydrate (FeSO&lt;sub&gt;4&lt;/sub&gt;-7H&lt;sub&gt;2&lt;/sub&gt;O)</td><td>Wako</td><td>094-01082</td><td></td></tr><tr><td>KOH</td><td>Wako</td><td>168-21815</td><td></td></tr><tr><td>Glycerol</td><td>Wako</td><td>075-00611</td><td></td></tr><tr><td>Centrifuge tube (50 mL, sterilized)</td><td>WATSON</td><td>1342-050S</td><td></td></tr><tr><td>Pipette Tips, 200 &amp;micro;L</td><td>WATSON</td><td>110-705Y</td><td></td></tr><tr><td>Pipette Tips, 1,000 &amp;micro;L</td><td>WATSON</td><td>110-8040</td><td></td></tr><tr><td>Microtube (1.5 mL)</td><td>WATSON</td><td>131-715C</td><td></td></tr><tr><td>8 multichannel-pipette</td><td>WATSON</td><td>NT-8200</td><td></td></tr><tr><td>PASORINA STIRRER</td><td>AS ONE</td><td>2-4990-02</td><td></td></tr><tr><td>Glass cylinder (200 mL)</td><td>AS ONE</td><td>1-8562-07</td><td></td></tr><tr><td>Precision pH mater</td><td>AS ONE</td><td>AS800 / 1-054-01</td><td></td></tr><tr><td>Pipetman P-200</td><td>GILSON</td><td>1-6855-05</td><td></td></tr><tr><td>Pipetman P-1000</td><td>GILSON</td><td>1-6855-06</td><td></td></tr><tr><td>Disposable Serolocical Pipettes (10 mL)</td><td>SANPLATEC</td><td>SAN27014</td><td></td></tr><tr><td>Disposable Serolocical Pipettes (25 mL)</td><td>SANPLATEC</td><td>SAN27015</td><td></td></tr><tr><td>Microtube stand</td><td>BM Bio</td><td>801-02Y</td><td></td></tr><tr><td>Vortex</td><td>BM Bio</td><td>BM-V1</td><td></td></tr><tr><td>Corning Costar 96-well microplate with lid (Flat bottom, Clear)</td><td>Sigma-Aldrich</td><td>Corning, 3370</td><td></td></tr><tr><td>Corning Costar reagent reservoir (50 mL)</td><td>Sigma-Aldrich</td><td>Corning, 4870</td><td></td></tr><tr><td>Stericup GV PVDF (250 mL, 0.22 &amp;micro;M)</td><td>Merck Millipore</td><td>SCGVU02RE</td><td></td></tr><tr><td>Pipet-Aid XP</td><td>DRUMMOND</td><td>4-000-101</td><td></td></tr><tr><td>Bioshaker (BR-23UM MR)</td><td>TAITEC</td><td>0053778-000</td><td></td></tr><tr><td>Disposal cell (1.5 mL)</td><td>Kartell</td><td>1938 / 2-478-02</td><td></td></tr><tr><td>DU 730 Life Science UV/Vis Spectrophotometer</td><td>Beckman Coulter</td><td>A23616</td><td></td></tr><tr><td>EPOCH2</td><td>BioTek</td><td>2014-EP2-002 / EPOCH2T</td><td></td></tr><tr><td>Beaker (500 mL)</td><td>IWAKI</td><td>82-0008</td><td></td></tr><tr><td>BIO clean bench</td><td>Panasonic</td><td>MCV-B131F</td><td></td></tr><tr><td>Glass tubes</td><td>NICHIDEN RIKA GLASS</td><td>P-10M~P-30 /101019</td><td></td></tr><tr><td>Silicone rubber stoppers</td><td>ShinEtsu Polymer</td><td>T-19</td><td></td></tr><tr><td>Bacterial strains</td><td>Strain bank organization; National Bio Resource Project (NBRP) in Japan</td><td></td><td></td></tr></tbody></table>

precise, high-throughput analysis of bacterial growth

细菌生长是现代微生物生理学发展的一个中心概念, 也是在系统层面上对细胞动力学的研究。最近的研究报告了细菌生长和全基因组事件之间的相关性, 如基因组减少和转录重组。正确分析细菌生长对理解生长依赖的基因功能和细胞成分的协调至关重要。因此, 需要以高通量的方式对细菌生长进行精确的定量评价。新兴的技术发展提供了新的实验工具, 允许更新用于研究细菌生长的方法。这里介绍的协议雇用了一个微的读者与一个高度优化的实验程序, 对细菌生长的可再生和精确的评估。此协议用于评估以前描述的几种大肠杆菌菌株的生长情况。该议定书的主要步骤如下: 在小瓶中制备大量的细胞储存, 用于重复试验, 具有重现性的结果, 使用 96-井板进行高通量增长评价, 并手工计算两个主要表示增长动态的参数 (即、最大增长率和人口密度)。与传统的菌落形成单元 (CFU) 测定法相比, 它对琼脂板上的玻璃管中培养的细胞进行了计数, 目前的方法更加有效, 提供了更详细的生长变化的时间记录, 但具有更严格低人口密度的检测限制。总之, 所描述的方法有利于精确和可重复的高通量分析细菌生长, 可用于绘制概念性结论或作出理论观察。

所描述的方法提供了一种手段, 以连续的, 高通量的方式捕捉动态细菌生长, 利用96井格式的阅读器, 在不同的时间间隔采取多种光学密度测量 (从分钟到小时到天)。在一个单一的实验中, 可以精确地获得表达各种基因组的大肠杆菌菌株的生长曲线 (图 1A)。与所描述的方法相比, 传统方法 (CFU 分析) 通常需要更长的采样时间间隔 (图 1B), 如果需要多种区域性, 则是劳动密集型的。此外, CFU 化验的每种培养取样时间都需要使用小体积的细胞培养, 而不能用于重复测量。此外, 检测的时间点数量有限可能会遗漏正确计算增长率和最大 OD600所需的取样点。然而, CFU 检测有一个较低的探测限制, 103 -104细胞/毫升, 使其至少两个数量级的灵敏度比光学浊度法 (OD600= 0.001, 其中有一个检测限制约 105-106单元格/mL)。所描述的方法为系统级的生长动力学研究提供了一个实用和有效的实验工具。
该协议的一个关键优点是它需要从一个普通的甘油库存重复测量。在早期指数增长阶段有多种不同稀释率的细胞培养是非常有用的, 因为它允许研究人员在任何时间尺度上确定培养期, 并避免由于不可预知的事件而产生饱和的文化。重复培养和测量的普通股, 这是从五种文化中的一个开始使用不同的稀释率, 如议定书2.2 节 (图 2A) 所述, 提出了非常相似的增长结果动力学分析。重复和独立测量的多条生长曲线 (N = 6) 重叠井 (图 2B, 顶部面板), 具有少许方差 (图 2B, 底部面板), 特别是在指数阶段 (图 2B, 阴影区域)。这些结果表明, 所描述的方法提供了高度重现性的结果。
强烈建议对96井读数的全局偏差进行估计。在本方法中, 通过监测5条中所述的野生型大肠杆菌株 W3110 的生长情况来估计全球偏倚。例如, 从增长曲线计算出的增长率在96口井 (图 3A) 中显示出显著的变化, 表示从数据操作和设备错误中产生的全局波动。这些错误可以通过将相同的样品装入多个不同位置的油井来掩盖。相比之下, 最大 od600显示了结果的明显位置依赖性偏差, 因为沿垂直方向观察到渐进的变化, 即最大 od600从2列的井逐渐增加到96-井板上的11列 (图 3B)。为了避免有偏差的结果, 建议将相同的样本加载到位于96孔板两侧的井中进行重复测量。最大 OD600值的位置偏差假定是由于蒸发速率的变化, 这是由加热和密封效率决定的。与96井微中间的油井相比, 位于边缘的油井往往显示出相对较高的数值, 反映出微的密封效果所产生的不同蒸发速率。此外, 由于其他96井板受到相同的偏倚测试显示类似的方向变化的基础上的油井是在板块上, 偏见的程度可能取决于板阅读器。因此, 在确定最大人口密度的研究中, 评估96井读数的全球偏差非常重要。商业版的读者都有其特定的全球偏见引起的加热和震动效率, 和96井板的主要区别是密封效率。
该方法是为系统分析细菌生长, 可用于分析两个主要参数,即, 生长率和细胞密度, 这是普遍适用于广泛的领域, 如系统生物学, 进化,生态学16,17。介绍了用电子表格进行手工计算的方法, 以显示原始读数的加工过程, 以产生生长速率和细胞密度的评价参数。每小时增长率首先根据协议4.6 节中的增长曲线 (图 4A) 的原始读取计算, 作为增长速率的连续变化 (图 4B) 的数据集。每五连续增长率的平均值和标准偏差依次获得 (图 4C-d), 并且最小标准偏差的最大平均增长率定义为增长的最大增长率曲线 (图 4C-d, 突出显示)。对最大单元格密度 (OD600) 进行了相似的分析 (图 4EF)。计算的生长速率和细胞密度随后进行进一步分析。请注意, 可以使用计算平台 (编程语言)、例如、R 和 Python 自动执行所描述的计算。
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/56197/56197fig1.jpg"> 
图1用不同方法获取的增长曲线。(A)多次获取增长曲线。以高通量的方式获得了表达不同基因组的四种不同的大肠杆菌菌株的生长曲线。大肠杆菌单元格以最小的介质 M63 生长, 并且在 OD600中的变化由微读取器以1小时的间隔记录。从左向右的菌株是野生类型的大肠杆菌W3110 (数字 0) 和其减少的基因组数 1, 10 和 18 (描述了以前的5), 分别。(B)使用 CFU 法获得的生长曲线。野生类型的大肠杆菌细胞 (数字 0) 被培养, 并在几个小时内被指定的时间点取样。用 CFU 法估计生长变化。开放圆圈表示记录或取样点。<a href="//ecsource-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/56197/56197fig1large.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/56197/56197fig2.jpg"> 
图2。重现重复测量.(A)不同稀释率的隔夜文化s. 如上所示, 在多个稀释率下, 在试管中接种了大肠杆菌细胞, 从1至1万倍不等。细胞被培养了在37° c 以摇晃在200转每分钟大约 12 h。五单元格区域性 (从左到右) 的最终 OD600值分别为1.51、0.74、0.05、0.008 和0.001。使用 OD600的0.05 的区域性用于普通甘油库存。(B)高度可重现的测量。六独立生长曲线 (顶部, 灰色线) 的相同的大肠杆菌菌株获得和计算从六位的微和两个小瓶的共同甘油库存。黑线代表六条增长曲线的平均值。对六重复测量的变异系数 (CV) 进行了计算 (下), 稳定的低点被阴影在粉红色。<a href="//ecsource-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/56197/56197fig2large.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/56197/56197fig3.jpg"> 
图 3 96-井格式的全局偏差.(A)增长速率的热图。大肠杆菌储存在最小介质 M63 中悬浮, 并在微的96口井中同样加载 (表示为 12 x 8 表)。如协议5节所述, 使用微读取器获得了 OD600的更改。从白色到红色的渐变表示从0.7 到 0.9 h-1的增长率。(B)最大 OD600的热图。从白色到红色的渐变表示最大 OD600值从0.9 到1.2。增长速率和最大 OD600的计算如图 4中所述。<a href="//ecsource-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/56197/56197fig3large.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/56197/56197fig4.jpg"> 
图4增长曲线的分析。(A)增长曲线的原始数据。每小时 OD600读取是根据区域性时间绘制的。开放圆圈表示取样时间。(B)增长速率的时间变化。根据4.6 中描述的等式计算两个连续 OD600读取的每小时增长率。(C)平均增长率。五连续增长率的平均值是为了给出平稳的增长率。(D)平均增长率的标准偏差。计算了同一五连续增长率的标准差。用红色突出显示最小标准差的最大生长速率。(E)表示 OD600。计算五连续 od600值的平均值以确定平滑 od600。(F)平均 OD600值的标准偏差。计算相同五连续 OD600值的标准偏差。最大 OD600以最小的标准偏差突出显示为红色。<a href="//ecsource-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/56197/56197fig4large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>

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Precise, High-throughput Analysis of Bacterial Growth

细菌生长的精确、高通量分析

细菌生长的定量评价是理解微生物生理学作为系统级现象的关键。本文介绍了一种实验操作和分析方法的协议, 允许对细菌生长进行精确、高通量的分析, 这是系统生物学研究的一个重要课题。

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

precise-high-throughput-analysis-of-bacterial-growth

Universidad San Sebastian

Research

JoVE Journal

Biology

23.7K 次观看.  University of Tsukuba. 细菌生长的定量评价是理解微生物生理学作为系统级现象的关键。本文介绍了一种实验操作和分析方法的协议, 允许对细菌生长进行精确、高通量的分析, 这是系统生物学研究的一个重要课题。

视频: Precise, High-throughput Analysis of Bacterial Growth

An Assay for Quantifying Protein-RNA Binding in Bacteria

In the initiation step of protein translation, the ribosome binds to the initiation region of the mRNA. Translation initiation can be blocked by binding of an RNA binding protein (RBP) to the initiation region of the mRNA, which interferes with ribosome binding. In the presented method, we utilize this blocking phenomenon to quantify the binding affinity of RBPs to their cognate and non-cognate binding sites. To do this, we insert a test binding site in the initiation region of a reporter mRNA and induce the expression of the test RBP. In the case of RBP-RNA binding, we observed a sigmoidal repression of the reporter expression as a function of RBP concentration. In the case of no-affinity or very low affinity between binding site and RBP, no significant repression was observed. The method is carried out in live bacterial cells, and does not require expensive or sophisticated machinery. It is useful for quantifying and comparing between the binding affinities of different RBPs that are functional in bacteria to a set of designed binding sites. This method may be inappropriate for binding sites with high structural complexity. This is due to the possibility of repression of ribosomal initiation by complex mRNA structure in the absence of RBP, which would result in lower basal reporter gene expression, and thus less-observable reporter repression upon RBP binding.

In this method, we quantify the binding affinity of RNA binding proteins (RBPs) to cognate and non-cognate binding sites using a simple, live, reporter assay in bacterial cells. The assay is based on repression of a reporter gene.

定量细菌中蛋白质-RNA结合的测定

In the initiation step of protein translation, the ribosome binds to the initiation region of the mRNA. Translation initiation can be blocked by binding ...

科研

遗传学

Digital PCR-based Competitive Index for High-throughput Analysis of Fitness in Salmonella

A competitive index is a common method used to assess bacterial fitness and/or virulence. The utility of this approach is exemplified by its ease to perform and its ability to standardize the fitness of many strains to a wild-type organism. The technique is limited, however, by available phenotypic markers and the number of strains that can be assessed simultaneously, creating the need for a great number of replicate experiments. Concurrent with large numbers of experiments, the labor and material costs for quantifying bacteria based on phenotypic markers are not insignificant. To overcome these negative aspects while retaining the positive aspects, we have developed a molecular-based approach to directly quantify microorganisms after engineering genetic markers onto bacterial chromosomes. Unique, 25 base pair DNA barcodes were inserted at an innocuous locus on the chromosome of wild-type and mutant strains of Salmonella. In vitro competition experiments were performed using inocula consisting of pooled strains. Following the competition, the absolute numbers of each strain were quantified using digital PCR and the competitive indices for each strain were calculated from those values. Our data indicate that this approach to quantifying Salmonella is extremely sensitive, accurate, and precise for detecting both highly abundant (high fitness) and rare (low fitness) microorganisms. Additionally, this technique is easily adaptable to nearly any organism with chromosomes capable of modification, as well as to various experimental designs that require absolute quantification of microorganisms.

Digital PCR-based Competitive Index for High-throughput Analysis of Fitness in Salmonella

This molecular-based approach for determining bacterial fitness facilitates precise and accurate detection of microorganisms using unique genomic DNA barcodes that are quantified via digital PCR. The protocol describes calculating the competitive index for Salmonella strains; however, the technology is readily adaptable to protocols requiring absolute quantification of any genetically-malleable organism.

基于数字PCR的沙门氏菌健康高通量分析竞争指数

A competitive index is a common method used to assess bacterial fitness and/or virulence. The utility of this approach is exemplified by its ease to ...

Enhanced Reproducibility and Precision of High-Throughput Quantification of Bacterial Growth Data Using a Microplate Reader

This study aimed to develop a repeatable, reliable, high-throughput protocol to monitor bacterial growth in 96-well plates and analyze the maximum growth rate. The growth curves and maximum growth rates of two bacterial species were determined. Issues including (i) lid condensation, (ii) pathlength correction, (iii) inoculation size, (iv) sampling time interval, and (v) spatial bias were investigated. The repeatability of the protocol was assessed with three independent technical replications, with a standard deviation of 0.03 between the runs. The maximum growth rates of Bacillus mycoides and Paenibacillus tundrae were determined to be&#160;(mean&#160;&#177; SD) 0.99 h&#8722;1 &#177;&#160; 0.03 h&#8722;1 and 0.85 h&#8722;1 &#177; 0.025 h&#8722;1, respectively. These bacteria are more challenging to monitor optically due to their affinity to clump together. This study demonstrates the critical importance of inoculation size, path length correction, lid warming, sampling time intervals, and well-plate spatial bias to obtain reliable, accurate, and reproducible data on microplate readers. The developed protocol and its verification steps can be expanded to other methods using microplate readers and high-throughput protocols, reducing the researchers&#39; innate errors and material costs.

Here, a high-throughput protocol is presented to measure growth data, including growth curves, growth rate, and maximum growth rate. The protocol was verified and validated using two biofilm-producing bacteria. The results and approach applied in this study can be expanded to other high-throughput protocols using microplate readers.

This study aimed to develop a repeatable, reliable, high-throughput protocol to monitor bacterial growth in 96-well plates and analyze the maximum growth ...

环境科学

ScanLag: High-throughput Quantification of Colony Growth and Lag Time

Growth dynamics are fundamental characteristics of microorganisms. Quantifying growth precisely is an important goal in microbiology. Growth dynamics are affected both by the doubling time of the microorganism and by any delay in growth upon transfer from one condition to another, the lag. The ScanLag method enables the characterization of these two independent properties at the level of colonies originating each from a single cell, generating a two-dimensional distribution of the lag time and of the growth time. In ScanLag, measurement of the time it takes for colonies on conventional nutrient agar plates to be detected is automated on an array of commercial scanners controlled by an in house application. Petri dishes are placed on the scanners, and the application acquires images periodically. Automated analysis of colony growth is then done by an application that returns the appearance time and growth rate of each colony. Other parameters, such as the shape, texture and color of the colony, can be extracted for multidimensional mapping of sub-populations of cells. Finally, the method enables the retrieval of rare variants with specific growth phenotypes for further characterization. The technique could be applied in bacteriology for the identification of long lag that can cause persistence to antibiotics, as well as a general low cost technique for phenotypic screens.

ScanLag is a high-throughput method for measuring the delay in growth, namely lag time, as well as the growth rate of colonies for thousands of cells in parallel. Screening using ScanLag enables the discrimination between long lag-time and slow growth at the level of a single variant.

ScanLag：高通量的菌落生长定量和滞后时间问题

Growth dynamics are fundamental characteristics of microorganisms. Quantifying growth precisely is an important goal in microbiology. Growth dynamics are ...

免疫与感染

Establishment of a High-throughput Setup for Screening Small Molecules That Modulate c-di-GMP Signaling in Pseudomonas aeruginosa

Bacterial resistance to traditional antibiotics has driven research attempts to identify new drug targets in recently discovered regulatory pathways. Regulatory systems that utilize intracellular cyclic di-GMP (c-di-GMP) as a second messenger are one such class of target. c-di-GMP is a signaling molecule found in almost all bacteria that acts to regulate an extensive range of processes including antibiotic resistance, biofilm formation and virulence. The understanding of how c-di-GMP signaling controls aspects of antibiotic resistant biofilm development has suggested approaches whereby alteration of the cellular concentrations of the nucleotide or disruption of these signaling pathways may lead to reduced biofilm formation or increased susceptibility of the biofilms to antibiotics. We describe a simple high-throughput bioreporter protocol, based on green fluorescent protein (GFP), whose expression is under the control of the c-di-GMP responsive promoter cdrA, to rapidly screen for small molecules with the potential to modulate c-di-GMP cellular levels in Pseudomonas aeruginosa (P. aeruginosa). This simple protocol can screen upwards of 3,500 compounds within 48 hours and has the ability to be adapted to multiple microorganisms.

Establishment of a High-throughput Setup for Screening Small Molecules That Modulate c-di-GMP Signaling in Pseudomonas aeruginosa

This article describes the high-throughput assay that has been successfully established to screen large libraries of small molecules for their potential ability to manipulate cellular levels of cyclic di-GMP in Pseudomonas aeruginosa, providing a new powerful tool for antibacterial drug discovery and compound testing.

高通量设置的建立筛选小分子能调节C-二GMP信令<em&gt;绿脓杆菌</em

Bacterial resistance to traditional antibiotics has driven research attempts to identify new drug targets in recently discovered regulatory pathways. ...

High Throughput, Real-time, Dual-readout Testing of Intracellular Antimicrobial Activity and Eukaryotic Cell Cytotoxicity

Traditional measures of intracellular antimicrobial activity and eukaryotic cell cytotoxicity rely on endpoint assays. Such endpoint assays require several additional experimental steps prior to readout, such as cell lysis, colony forming unit determination, or reagent addition. When performing thousands of assays, for example, during high-throughput screening, the downstream effort required for these types of assays is considerable. Therefore, to facilitate high-throughput antimicrobial discovery, we developed a real-time assay to simultaneously identify inhibitors of intracellular bacterial growth and assess eukaryotic cell cytotoxicity. Specifically, real-time intracellular bacterial growth detection was enabled by marking bacterial screening strains with either a bacterial lux operon (1st generation assay) or fluorescent protein reporters (2nd generation, orthogonal assay). A non-toxic, cell membrane-impermeant, nucleic acid-binding dye was also added during initial infection of macrophages. These dyes are excluded from viable cells. However, non-viable host cells lose membrane integrity permitting entry and fluorescent labeling of nuclear DNA (deoxyribonucleic acid). Notably, DNA binding is associated with a large increase in fluorescent quantum yield that provides a solution-based readout of host cell death. We have used this combined assay to perform a high-throughput screen in microplate format, and to assess intracellular growth and cytotoxicity by microscopy. Notably, antimicrobials may demonstrate synergy in which the combined effect of two or more antimicrobials when applied together is greater than when applied separately. Testing for in vitro synergy against intracellular pathogens is normally a prodigious task as combinatorial permutations of antibiotics at different concentrations must be assessed. However, we found that our real-time assay combined with automated, digital dispensing technology permitted facile synergy testing. Using these approaches, we were able to systematically survey action of a large number of antimicrobials alone and in combination against the intracellular pathogen, Legionella pneumophila.

A high throughput, real-time assay was developed to simultaneously identify (1) eukaryotic cell-penetrant antimicrobials targeting an intracellular bacterial pathogen, and (2) assess eukaryotic cell cytotoxicity. A variation on the same technology was thereafter combined with digital dispensing technology to enable facile, high-resolution, dose-response, and two- and three-dimensional synergy studies.

高通量，实时，细胞内的抗菌活性的双读出测试和真核细胞的细胞毒性

Traditional measures of intracellular antimicrobial activity and eukaryotic cell cytotoxicity rely on endpoint assays. Such endpoint assays require ...

Multi-scale Analysis of Bacterial Growth Under Stress Treatments

Analysis of the bacterial ability to grow and survive under stress conditions is essential for a wide range of microbiology studies. It is relevant to characterize the response of bacterial cells to stress-inducing treatments such as exposure to antibiotics or other antimicrobial compounds, irradiation, non-physiological pH, temperature, or salt concentration. Different stress treatments might disturb different cellular processes, including cell division, DNA replication, protein synthesis, membrane integrity, or cell cycle regulation. These effects are usually associated with specific phenotypes at the cellular scale. Therefore, understanding the extent and causality of stress-induced growth or viability deficiencies requires a careful analysis of several parameters, both at the single-cell and at the population levels. The experimental strategy presented here combines traditional optical density monitoring and plating assays with single-cell analysis techniques such as flow cytometry and real time microscopy imaging in live cells. This multiscale framework allows a time-resolved description of the impact of stress conditions on the fate of a bacterial population.

This protocol allows a time-resolved description of bacterial growth under stress conditions at the single-cell and the cell population levels.

压力处理下细菌生长的多尺度分析

Analysis of the bacterial ability to grow and survive under stress conditions is essential for a wide range of microbiology studies. It is relevant to ...

Single-cell Microfluidic Analysis of Bacillus subtilis

Microfluidic technology overcomes many of the limitations to traditional analytical methods in microbiology. Unlike bulk-culture methods, it offers single-cell resolution and long observation times spanning hundreds of generations; unlike agarose pad-based microscopy, it has uniform growth conditions that can be tightly controlled. Because the continuous flow of growth medium isolates the cells in a microfluidic device from unpredictable variations in the local chemical environment caused by cell growth and metabolism, authentic changes in gene expression and cell growth in response to specific stimuli can be more confidently observed. Bacillus subtilis is used here as a model bacterial species to demonstrate a &#34;mother machine&#34;-type method for cellular analysis. We show how to construct and plumb a microfluidic device, load it with cells, initiate microscopic imaging, and expose cells to a stimulus by switching from one growth medium to another. A stress-responsive reporter is used as an example to reveal the type of data that may be obtained by this method. We also briefly discuss further applications of this method for other types of experiments, such as analysis of bacterial sporulation.

Single-cell Microfluidic Analysis of Bacillus subtilis

We present a method for the microfluidic analysis of individual bacterial cell lineages using Bacillus subtilis as an example. The method overcomes shortcomings of traditional analytical methods in microbiology by allowing observation of hundreds of cell generations under tightly controllable and uniform growth conditions.

单细胞微流控芯片分析枯草芽孢杆菌

Microfluidic technology overcomes many of the limitations to traditional analytical methods in microbiology. Unlike bulk-culture methods, it offers ...

生物学

Using Microtiter Dish Radiolabeling for Multiple In Vivo Measurements Of Escherichia coli (p)ppGpp Followed by Thin Layer Chromatography

The (p)ppGpp nucleotide functions as a global regulator in bacteria in response to a variety of physical and nutritional stress. It has a rapid onset, in seconds, which leads to accumulation of levels that approach or exceed those of GTP pools. Stress reversal occasions a rapid disappearance of (p)ppGpp, often with a half-life of less than a minute. The presence of (p)ppGpp results in alterations of cellular gene expression and metabolism that counter the damaging effects of stress. Gram-negative and Gram-positive bacteria have different response mechanisms, but both depend on (p)ppGpp concentration. In any event, there is a need to simultaneously monitor many radiolabeled bacterial cultures at time intervals that may vary from 10 seconds to hours during critical stress transition periods. This protocol addresses this technical challenge. The method takes advantage of temperature- and shaker-controlled microtiter dish incubators that allow parallel monitoring of growth (absorbance) and rapid sampling of uniformly phosphate-radiolabeled cultures to resolve and quantitate nucleotide pools by thin-layer chromatography on PEI-cellulose. Small amounts of sample are needed for multiple technical and biological replicates of analyses. Complex growth transitions, such as diauxic growth and rapid (p)ppGpp turnover rates can be quantitatively assessed by this method.

Using Microtiter Dish Radiolabeling for Multiple In Vivo Measurements Of Escherichia coli pppGpp Followed by Thin Layer Chromatography

The growth of radiolabeled bacterial cultures in microtiter dishes facilitates high throughput sampling that allows multiple technical and biological replicate assays of nucleotide pool abundance, including that of (p)ppGpp. The effects of growth transitions provoked by sources of physiological stress as well as recovery from stress can be monitored.

使用微蒂特萝卜无线电标签进行多维欧大肠杆菌测量 (p) ppGpp,然后是薄层色谱

The (p)ppGpp nucleotide functions as a global regulator in bacteria in response to a variety of physical and nutritional stress. It has a rapid onset, in ...

High-Throughput Live Imaging of Microcolonies to Measure Heterogeneity in Growth and Gene Expression

Precise measurements of between- and within-strain heterogeneity in microbial growth rates are essential for understanding genetic and environmental inputs into stress tolerance, pathogenicity, and other key components of fitness. This manuscript describes a microscope-based assay that tracks approximately 105&#160;Saccharomyces cerevisiae microcolonies per experiment. After automated time-lapse imaging of yeast immobilized in a multiwell plate, microcolony growth rates are easily analyzed with custom image-analysis software. For each microcolony, expression and localization of fluorescent proteins and survival of acute stress can also be monitored. This assay allows precise estimation of strains&#39; average growth rates, as well as comprehensive measurement of heterogeneity in growth, gene expression, and stress tolerance within clonal populations.

Yeast growth phenotypes are precisely measured through highly parallel time-lapse imaging of immobilized cells growing into microcolonies. Simultaneously, stress tolerance, protein expression, and protein localization can be monitored, generating integrated datasets to study how environmental and genetic differences, as well as gene-expression heterogeneity among isogenic cells, modulate growth.

微殖器的高通量实时成像，测量生长和基因表达中的异质性

Precise measurements of between- and within-strain heterogeneity in microbial growth rates are essential for understanding genetic and environmental ...