从2日龄液体生长的培养分析和排序霉菌球团矿的过程中示意性地示于图1。被用于该方法的细节在下面给出。 1。增长（包括媒体和缓冲器的制备） <ol><li>制备1升的10倍的磷酸盐缓冲盐水（PBS，80克氯化钠，2克的KCl，14.4克Na 2 HPO 4，在1升蒸馏水2.4克KH 2 PO 4调节至pH 7.4）。在使用时加入100毫升10×PBS至900毫升蒸馏水中，得到1升PBS中。 </li><li>制备1升YEME培养基（3克Difco公司酵母提取物，5g细菌用胰蛋白胨，3克Oxoid公司麦芽提取物，10g葡萄糖340克蔗糖，蒸馏水至1升）和100毫升的2.5M的MgCl 2。通过高压灭菌消毒两种解决方案。请注意，还有其他链霉菌介质可用于4,14。 </li><li>加入100 ml YEME培养基和0.2ml的2.5M的MgCl 2，以一个无菌的250ml锥形烧瓶中的金属螺旋弹簧。准备尽可能多瓶的细菌样本来研究。 </li><li>接种每个烧瓶用10 8孢子链霉菌取得10 6孢子/ ml的孢子浓度。生长细菌2天，30℃，180转，而晃动。 </li></ol> 2。采样<ol><li>从每一种文化转移5毫升的样品用灭菌的5毫升吸管15毫升猎鹰管。轻轻摇动文化而采取的样品，确保样品的代表性整个文化。 </li><li>当样品立即用COPAS分析，无需样品制备步骤是必要的。保持样品置于冰上，然后继续步骤本协议3.1。当样品在稍后的时间点分析，确定在步骤本协议的2.3-2.5描述的颗粒。注意，固定也可以防止细菌在管道系统中生长时analys后不正确的清洁ES。确保固定与甲醛不妨碍下游分析。例如，RNA不能从真菌粒料固定用甲醛后分离。与此相反，RNA的固定，用70％乙醇12后成功地分离出来。 </li><li>添加600μl的37％的甲醛至5ml的样品和通过反转管3倍混合。固定在冰上的样品30分钟。 </li><li>离心机在2500×g离心以固定的转子在4℃下进行10分钟的样品。小心地取出上清液，并用5毫升的PBS洗涤这些颗粒。 </li><li>重复步骤2.4，以使颗粒用PBS洗涤2倍。样品可以储存在PBS中于4℃几周。 </li></ol> 3。 COPAS分析<ol><li>确保COPAS另外，泵，计算机和488 nm的氩激光被打开并启动Biosort软件。虽然导通时，激光将仍处于待机工作方式。 </li><li>检查鞘液瓶已满，废液瓶是空的。 </li><li>点击"开始"，然后"RUN"从待机状态切换488nm的氩激光对。经过大约60秒，当激光上，单击"完成"。 </li><li>检查压力表。压力为&#39;鞘&#39;，&#39;样品&#39;，&#39;分拣机&#39;和&#39;清洁&#39;应分别围绕阅读4.0，0.5，2.7和10.9。 </li><li>点击旁边的"压力OK"的复选框。然后，系统素数的流动池和准备使用。 </li><li>标准设置被认为与"延迟"11和"宽度"7。阈值设为50&#39;信号&#39;和40&#39;的TOF最小&#39;，与TOF表示时间 - 飞行。 （为了保持完整性:所有的&#39;收益&#39;被设置为1，触发设置为EXT（消光）和下的"配置"选项卡"选择扫描速率"2.50 MHz的选择巧合的是设置为"增强"）。 </li><li>从样品杯的移除剩余的水d从步骤2.2或2.5在杯中加入0.1毫升链霉菌样品用PBS（约50毫升）在一起。确保样品杯被正确关闭。 </li><li>点击"采集"开始收集数据。管理流动速度为每秒30-50事件之间获得。如果流量太高，PBS添加到样品杯。如果流动速度太低时，添加更多的样品。 </li><li>当至少2500事件收集点击"STOP"。要保存所有的数据点击"存储"，并保存你的文件进行后续的统计分析。该Biosort软件将数据保存在四个文件，其中仅txt文件将被用于后续分析。 </li><li>用50毫升的注射器和水洗除去2个样品清洁样品杯中。 </li><li>重复步骤3.7-3.10，直到所有的样本进行了分析。 </li></ol> 4。数据分析<ol><li>打开txt文件，并与EXT低于25（例如使用MS Excel）中丢弃数据。这对应于碎片和松散的菌丝片段（对于黑曲霉与EXT低于150的所有数据都将被丢弃）4,12。然后，保存所有的页头为止在一列在一个纯文本文件（对于许多数据文件，这在编程语言R，可从实现自动化:&#39;TXT&#39; <a href="http://www.r-project.org">http://www.r-project.org</a> ）。 </li><li>确保SCILAB（版本5.3.2或更新版本从http://www.scilab.org/）与"fittools"库，它是可用的，再加上人工，从一起安装: http://web.science.uu.nl/microbiology/images/fung/fittools.zip http://web.science.uu.nl/microbiology/images/fung/manual％20fittools.pdf </li><li> SCILAB启动并运行fittools库的必要的功能来模拟数据。总之:安装fittools库，如图手册（只需一次）。 <ol><li>加载fittools库。 </li><li>读取数据（&#39;。txt&#39;结尾的文件）导入到SCILAB。 </li><li>日志转换数据（自然对数是默认的日志功能）。 </li><li> 2人口的模型来拟合数据。 </li><li>绘制的直方图和模型（可选）的图表。 </li><li>引导数据（1,000重复）。 </li><li>该模型适合于自举数据集。 </li><li>得到的置信区间的5个参数的估计值。这将提供关于人口，其2装置（μ1和μ2）和附带的标准偏差（α1和α2）的参与比例（P）的信息。此外，95％置信区间均自举（1,000个重复）12,13,15后通过与模型改装来确定。当装置的置信区间不重叠，并且p的置信区间为0.025-0.975之间的数据集可以被解释为正态分布的两个群体的组合。 （注日在小球团矿的参与分数为p，而大的颗粒的参与是1 - P）TOF和链霉菌属的颗粒的直径之间的关系等于0.57×TOF + 159微米4。 </li></ol></li><li>在该情况下，两个群体被发现使用的平均页头为止，排序在COPAS参数的上界和下界的小和大颗粒后进行排序。请注意，还有其他排序参数可以根据用户的要求来选择。 </li></ol> 5。颗粒排序<ol><li>加载需要按照步骤3.7中所述要排序的链霉菌样品。 </li><li>设置&#39;排序限制"，并提供有关最小（LO）和最大（高）TOF值的详细信息。另外，通过选择&#39;地区&#39;，然后&#39;定义门区&#39;，选择具有所需的尺寸或性能的颗粒设置的区域。 </li><li>把50毫升的管收集符合排序参数多粒。可选地，在微滴定板可用于个人粒料进行排序。需要对DNA和蛋白质提取多个颗粒（10 4 -10 5）。一个单一的小球是足够用于基因表达的定量PCR分析，但需要对RNA测序多个粒料。 </li><li>决定并设置需要进行排序的颗粒的数量，然后点击"手动排序"排序为选定的参数。当达到颗粒的设定值，则该分拣自动结束。 </li><li>除去留下的样品与50ml的注射器从样品杯，并用水洗涤2个杯子。重复步骤5.1-5.5，如果其他样品需要进行排序。 </li><li>前关闭COPAS冲洗样品杯与水以除去剩余的粒料和清洁样品杯用70％乙醇和/或水2％次氯酸盐。运行的氯化水样本以清洁整个系统，并用水重复。空吨他溢出容器，清洗或更换驻留在这个容器中的过滤器。 </li><li>完成后，单击"停止"按钮来释放系统中的压力。当电机停止关闭该程序，然后单击"关机不清除"。然后关掉电脑，COPAS，激光，和泵。 </li></ol>

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作者宣称，他们有没有竞争的财务权益。

流式细胞仪已使大量单细胞的高速分析，这有助于我们异质性的认识在克隆群6。定期流式细胞仪是不可行的链霉菌和真菌的多细胞菌丝球的分析。我们的工作表明， 链霉菌粒料的高通量分析是可行的使用COPAS。这里列出的手续简单，速度快，重现性好。要牢记仪器的操作过程中的关键参数是流动的速度，应不超过100次/秒（步骤本协议的3.8）。如果颗粒的浓度，因此也流速度，变得太高，因为仪器无法检测个体粒料的TOF值将被错误地估计。充分地通过加入PBS稀释的样品克服了这个问题。 限制 该COPAS加上使用<html> ð此处为1毫米，这是适合于测量颗粒的尺寸范围为30-700微米的喷嘴直径。因此，该喷嘴能够测量由链霉菌形成的颗粒。在丝状真菌中的情况下，微菌落可能较大，从而限制了COPAS加的普遍适用性。该COPAS XL可以在大小最多测量颗粒以1500微米，但其在较低的范围内直径的灵敏度是少相比，该COPAS加的。无论是COPAS Plus和COPAS XL不能分析颗粒小于30微米更小。这意味着单个微生物孢子或细胞不能被分析。此外，COPAS可能不准确地分析和孢子细胞或非常小的微菌落的小聚集体。为此，常规的细胞分析仪应该被使用。这个限制是由Union Biometrica的Biosorter，它可以在1-1,500微米的范围内分析颗粒克服。然而，购买奖品是较高的。 T"&gt; 故障排除 该COPAS是一个强大的工具，易于操作。但是，有时颗粒未加载样品放入样品杯和开始测量后进行检测。其原因通常是堵塞入口管，它可以很容易地通过按下&#39;干净&#39;的命令来解决。这将迫使小球从管道系统返回到样品杯中。另一种原因可能是该盖没有正确放置在样品杯中。这导致压力损失和伴随故障检测粒料。 意义和未来的发展方向 我们在这里集中于丸粒尺寸的分析，但COPAS设置也能够分析和排序基于荧光和密度。荧光检测使我们能够分析的基础上记者如GFP基因的表达。此外，细胞的组合物可以进行评估。更厉害的是，根据这些参数来分离颗粒的选项TERS。排序粒料可用于下游分析，包括所有组学研究。事实上，我们预先分类大60,000 200,000和小颗粒，并表明，蛋白质是大，小颗粒4之间的显著差异。因此，该技术提供了新的线索，以提高链霉菌的细胞工厂。 该COPAS技术的主要优点是时间。使用显微镜进行的细胞沉淀之前的研究，而这限制了可以分析多达数百16粒料的数量。显微镜研究已经表明在液体生长霉菌培养16颗粒的两个种群的存在。事实上，检测粒料的两个群体，无论培养条件下在大量不同的链霉菌4。这个大小异质性不仅限于丝状链霉菌，但在丝状真菌中12中也观察到&lt;/ SUP&gt;。在所有情况下，异质性的基本机制尚不清楚。根据大小和荧光排序颗粒的可能性，使我们能够解开这样的机制。

链霉菌是丝状土壤细菌，是众所周知的熟练程度，使抗生素，以及可以用来作为免疫抑制剂或对抗真菌感染或癌症1,2化合物。此外，这些微生物产生的酶，是兴趣广泛的工业应用3。大部分的这些商业上令人感兴趣的化合物是生产生物反应器中。链霉菌的生物反应器中生长，其特征在于由互相连接的菌丝复杂的结构，被称为团块或球团的形成。这些多细胞结构是相对于尺寸为4高度异质性，并且可以达到的尺寸是比单细胞菌如大肠杆菌或枯草芽孢杆菌较大超过一百万次。虽然异质性被认为是自然生物系统5一个有益的特质，它被认为是行业生产的陷阱。生物反应器培养中必须是可重复和可控制的，以获得尽可能高的产率。因此，每一个生物反应器中的颗粒类型中的作用的详细的理解是至关重要的，以提高链霉菌的细胞工厂。 流式细胞仪通常用于分析单个细胞群体中的6。流式细胞仪可以同时测量细胞（如大小，密度，及多色荧光）的特性获得多参数信息。以这种方式，电池特性可以被相关从而有助于我们的文化中理解异质性的细胞和6的不同群体的存在。更专业的仪器已经可以根据用户定义的参数使人们有可能进行排序细胞。例如，突变体可以被筛选。下面的排序，这样突变的细胞可以培养作进一步鉴定。这已经被证明是有用的，其中包括，提高菌株7,8的生产力。流式细胞仪的喷嘴通常允许细胞与约10微米的最大直径的通道。因此，链霉菌的颗粒不能用常规的流式细胞仪分析。不过，他们可以用复杂对象的参数分析仪和分选机（COPAS）进行分析。像常规的流式细胞仪，COPAS可以获得粒子以高通量方式多参数数据。取决于COPAS的类型10-1,500微米尺寸的颗粒可以被分析。此外，它允许在可用于种植或下游分析单个颗粒，如DNA，RNA或蛋白质的分离的排序。 COPAS最初被设计为小的多细胞生物，如线虫 9，和果蝇胚胎和幼虫10的分析和分选。该工具也已用于斑马鱼11一第二对丝状真菌12,13。后者的生物也形成菌丝球的，甚至比那些由丝状菌形成更大。我们最近表明，使用COPAS的也是链霉菌4是可行的。我们在这里描述了用于使用COPAS，以评估在天蓝色链霉菌的颗粒的异质性，包括在方法的细节，以根据尺寸的颗粒进行排序的实验步骤。然而，请注意，也可以用于其它的颗粒形成的链霉菌的分析此方法。

<table border="0" cellpadding="0" cellspacing="0" width="992">
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:212px;">Copas PLUS</td>
			<td style="width:183px;">Union Biometrica</td>
			<td style="width:119px;">PLUS</td>
			<td style="width:479px;">Large particle flow cytometer including lasers and software</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">NaCl</td>
			<td style="width:183px;">Sigma Aldrich</td>
			<td>S3014</td>
			<td>PBS component</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">KCl</td>
			<td style="width:183px;">Sigma Aldrich</td>
			<td>P9541</td>
			<td>PBS component</td>
		</tr>
		<tr height="25">
			<td height="25" style="height:25px;">Na2HPO4</td>
			<td style="width:183px;">Sigma Aldrich</td>
			<td>S3264</td>
			<td>PBS component</td>
		</tr>
		<tr height="25">
			<td height="25" style="height:25px;">KH2PO4</td>
			<td style="width:183px;">Sigma Aldrich</td>
			<td>P9791</td>
			<td>PBS component</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Difco Yeast Extract</td>
			<td>BD Biosciences</td>
			<td>210933</td>
			<td>Media component</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Bacto Peptone</td>
			<td>BD Biosciences</td>
			<td>211677</td>
			<td>Media component</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Oxoid Malt Extract</td>
			<td>Fisher Scientific</td>
			<td>OXLP0039B</td>
			<td>Media component</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Glucose</td>
			<td>Sigma Aldrich</td>
			<td>G8270</td>
			<td>Media component</td>
		</tr>
		<tr height="23">
			<td height="23" style="height:23px;">Sucrose</td>
			<td>Sigma Aldrich</td>
			<td>S9378</td>
			<td>Media component</td>
		</tr>
		<tr height="25">
			<td height="25" style="height:25px;">MgCl2.6H2O</td>
			<td style="width:183px;">Sigma Aldrich</td>
			<td>M2670</td>
			<td>Media components. Add after autoclaving.</td>
		</tr>
		<tr height="24">
			<td height="24" style="height:24px;width:212px;">Formaldehyde Solution</td>
			<td style="width:183px;">Sigma Aldrich</td>
			<td>F8775</td>
			<td style="width:479px;">Fixation of pellets</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Sodium Hypochlorite Solution&#160;</td>
			<td>Sigma Aldrich</td>
			<td style="width:119px;">71696</td>
			<td>For cleaning of the instrument</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">EtOH</td>
			<td style="width:183px;">VWR</td>
			<td style="width:119px;">20816.367</td>
			<td>For cleaning of the instrument</td>
		</tr>
		<tr height="22">
			<td height="22" style="height:22px;width:212px;">Erlenmeyer Flask (250 ml)</td>
			<td style="width:183px;">Fisher Scientific</td>
			<td>214-1132</td>
			<td>Culture flask for growing Streptomyces</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:212px;">Springs</td>
			<td style="width:183px;">Verenfabriek De Spiraal</td>
			<td style="width:119px;">Custom-Made</td>
			<td>Used in culture flask. RVS1.4401/Length 210 mm/Diameter 17 mm/Pitch 5 mm</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:212px;">Sterile Centrifuge Tube (15 ml)</td>
			<td style="width:183px;">Sarstedt</td>
			<td>62.554.002</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:212px;">Syringes (50 ml)</td>
			<td style="width:183px;">Sigma</td>
			<td>Z683698&#160;</td>
			<td></td>
		</tr>
	</tbody>
</table>

sorting of streptomyces cell pellets using a complex object parametric analyzer and sorter

链霉菌是用于在工业上用于生产酶和抗生素的丝状土壤细菌。当在生物反应器中生长，这些微生物形成相互连接的菌丝，称为粒料，它们是异质的大小的网络。在这里，我们描述了一个分析方法，并使用一个复杂的对象参数分析仪和分选机（COPAS）排序菌丝球。详细的说明，给出了使用该仪器和数据的基本统计分析。我们还描述了小球如何进行排序，根据用户自定义设置，从而使下游加工，如RNA或蛋白质含量的分析。使用这种方法的内在机制异构增长可以解决。这将有助于改善链霉菌作为细胞工厂，考虑到效率与关联颗粒大小的事实。

链霉菌球团COPAS测量 链霉菌形成液体培养菌丝球具有多种尺寸。要分析的颗粒大小分布，2 -天龄的液体生长天蓝色链霉菌培养物进行大颗粒流式细胞仪用COPAS加探查配备有1毫米的喷嘴。一个典型的COPAS输出是一个散点图，如可视化图2A。 x轴表示时间飞行的（TOF），这与丸粒尺寸（ 即它需要较长时间较大的颗粒通过激光束）。 y轴示出了消光，它代表的对象的光学密度。在散点图中的每个点对应一个单独的事件， 即单颗粒通过激光束。重要的是，当样本过于集中（ 即当激光检测到超过100次/秒），该COPAS经常未能检测到单个pellets。这导致了过高的假TOF值。稀释样品降低了平均TOF值，到一个地步，进一步摊薄确实不再影响TOF。达到该点时约100粒料/秒进行了分析。 绘制数据点到一个直方图表示该尺寸不是正态分布的（ 图2B）。的分布似乎偏向右侧。日志转换数据集还没有导致一个正态分布（ 图2C）。以评估是否该粒度分布可以通过假设的两个正态分布的数据数学建模12,15的混合物进行说明。造型的确表明，该粒度分布可以通过假设粒料的两个不同的群体的存在来解释。小颗粒的人群包括具有248微米的平均大小的所有颗粒的92％，而大型像素的人口让（所有粒料的8％）的319微米的平均尺寸（请注意，两个种群被假定为存在时参与分数为2.5-97.5％）。 链霉菌粒料按大小排序 微菌落从大，小颗粒的人群进行了排序。为此，这两个群体的平均粒料尺寸被用来定义的边界进行分类。粒料比248微米更小被认为是由小的颗粒群，而丸粒比319微米的较大的被认为是从大的颗粒种群（ 图3），以便限制由两个大小的重叠部分排序粒料分布。排序的粒料的微观分析显示其独特的大小（ 图3）。所收集的颗粒，可以进一步用于DNA，RNA或蛋白质的隔离。 &lt;IMG ALT ="图1"FO:内容宽度="5英寸"FO:SRC ="/ files/ftp_upload/51178/51178fig1highres.jpg"SRC ="/ files/ftp_upload/51178/51178fig1.jpg"/&gt; 图1实验装置使用COPAS测量和分析链霉菌颗粒的示意图。有关详细信息，请参见实验程序。 <a href="https://jove.unicartagenaproxy.elogim.com/files/ftp_upload/51178/51178fig1highres.jpg" target="_blank">点击这里查看大图。</a> <img alt="图2" fo:content-width="5in" fo:src="/files/ftp_upload/51178/51178fig2highres.jpg" src="/files/ftp_upload/51178/51178fig2.jpg" /> 在液体培养链霉菌培养物图2。颗粒大小不均匀性。2日龄S的COPAS分析霉菌 YEME文化（ 一 ）表明，颗粒大小（表示为飞行价值观的时间）通常不分配D（B），和既不变得如此当数变换的（C）。相反，检测颗粒的两个群体是不同的TOF（因此大小）。 <a href="https://jove.unicartagenaproxy.elogim.com/files/ftp_upload/51178/51178fig2highres.jpg" target="_blank">点击这里查看大图。</a> <img alt="图3" fo:content-width="5in" fo:src="/files/ftp_upload/51178/51178fig3highres.jpg" src="/files/ftp_upload/51178/51178fig3.jpg" /> 图3。 链霉菌粒料按大小分级分离。颗粒，其尺寸小于248微米（在粉红色表示）被认为是小的颗粒，而颗粒饲料的尺寸小于319微米（以蓝色表示）较大被认为是大的颗粒。显微分析证实在尺寸上的差别。注意，这些尺寸是从对数变换的数据使用的TOF与d之间的关系，所述计算iameter 链霉菌颗粒，相当于0.57×TOF + 159微米。 <a href="https://jove.unicartagenaproxy.elogim.com/files/ftp_upload/51178/51178fig3highres.jpg" target="_blank">点击这里查看大图。</a>

Watch this Scientific Journal Video about Sorting of Streptomyces Cell Pellets Using a Complex Object Parametric Analyzer and Sorter at JoVE.com

Sorting of Streptomyces Cell Pellets Using a Complex Object Parametric Analyzer and Sorter

液体培养链霉菌培养物的特点是菌丝球是异构的大小。我们在这里描述了一种分析方法及排序，例如粒料在高通量方式进行。这些粒料可以用于进一步的分析，这将提供线索理解和控制生长的异质性。

排序<em&gt;链霉菌</em&gt;细胞沉淀用一个复杂的对象参数分析仪和分选机

Streptomycetes are filamentous soil bacteria that are used in industry for the production of enzymes and antibiotics. When grown in bioreactors, these ...

sorting-streptomyces-cell-pellets-using-complex-object-parametric

Universidad de Cartagena UDEC

Research

JoVE Journal

Biology

Sorting of Streptomyces Cell Pellets Using a Complex Object Parametric Analyzer and Sorter

10.9K 次观看.  Leiden University. 液体培养链霉菌培养物的特点是菌丝球是异构的大小。我们在这里描述了一种分析方法及排序，例如粒料在高通量方式进行。这些粒料可以用于进一步的分析，这将提供线索理解和控制生长的异质性。 

视频: Sorting of Streptomyces Cell Pellets Using a Complex Object Parametric Analyzer and Sorter

A Multi-Parametric Islet Perifusion System within a Microfluidic Perifusion Device

A microfluidic islet perifusion device was developed for the assessment of dynamic insulin secretion of multiple islets and simultaneous fluorescence imaging of calcium influx and mitochondrial potential changes. The device consists of three layers: first layer contains an array of microscale wells (500 &mu;m diameter and 150 &mu;m depth) that help to immobilize the islets while exposed to flow and maximize the exposed surface area of the islets; the second layer contains a circular perifusion chamber (3 mm deep, 7 mm diameter); and the third layer contains an inlet-mixing channel that fans out before injection into the perifusion chamber (2 mm in width, 19 mm in length, and 500 &mu;m in height) for optimizing the mixing efficiency prior to entering the perifusion chamber. The creation of various glucose gradients including a linear, bell shape, and square shapes also can be created in the microfluidic perifusion network and is demonstrated.

A microfluidic islet perifusion device was developed for the assessment of dynamic insulin secretion of multiple islets and simultaneous fluorescence imaging of calcium influx and mitochondrial potential changes.

一个多参数的胰岛内的微流控Perifusion装置Perifusion系统

A microfluidic islet perifusion device was developed for the assessment of dynamic insulin secretion of multiple islets and simultaneous fluorescence ...

科研

生物学

Fluorescence Activated Cell Sorting of Plant Protoplasts

High-resolution, cell type-specific analysis of gene expression greatly enhances understanding of developmental regulation and responses to environmental stimuli in any multicellular organism. In situ hybridization and reporter gene visualization can to a limited extent be used to this end but for high resolution quantitative RT-PCR or high-throughput transcriptome-wide analysis the isolation of RNA from particular cell types is requisite. Cellular dissociation of tissue expressing a fluorescent protein marker in a specific cell type and subsequent Fluorescence Activated Cell Sorting (FACS) makes it possible to collect sufficient amounts of material for RNA extraction, cDNA synthesis/amplification and microarray analysis.
An extensive set of cell type-specific fluorescent reporter lines is available to the plant research community. In this case, two marker lines of the Arabidopsis thaliana root are used: PSCR::GFP (endodermis and quiescent center) and PWOX5::GFP (quiescent center). Large numbers (thousands) of seedlings are grown hydroponically or on agar plates and harvested to obtain enough root material for further analysis. Cellular dissociation of plant material is achieved by enzymatic digestion of the cell wall. This procedure makes use of high osmolarity-induced plasmolysis and commercially available cellulases, pectinases and hemicellulases to release protoplasts into solution.
FACS of GFP-positive cells makes use of the visualization of the green versus the red emission spectra of protoplasts excited by a 488 nm laser. GFP-positive protoplasts can be distinguished by their increased ratio of green to red emission. Protoplasts are typically sorted directly into RNA extraction buffer and stored for further processing at a later time.
This technique is revealed to be straightforward and practicable. Furthermore, it is shown that it can be used without difficulty to isolate sufficient numbers of cells for transcriptome analysis, even for very scarce cell types (e.g. quiescent center cells). Lastly, a growth setup for Arabidopsis seedlings is demonstrated that enables uncomplicated treatment of the plants prior to cell sorting (e.g. for the cell type-specific analysis of biotic or abiotic stress responses). Potential supplementary uses for FACS of plant protoplasts are discussed.

A method for isolating specific cell types from plant material is demonstrated. This technique employs transgenic marker lines expressing fluorescent proteins in particular cell types, cellular dissociation and Fluorescence Activated Cell Sorting. Additionally, a growth setup is established here that facilitates treatment of Arabidopsis thaliana seedlings prior to cell sorting.

植物原生质体的荧光激活细胞分选

High-resolution, cell type-specific analysis of gene expression greatly enhances understanding of developmental regulation and responses to environmental ...

Lipid Vesicle-mediated Affinity Chromatography using Magnetic Activated Cell Sorting (LIMACS): a Novel Method to Analyze Protein-lipid Interaction

The analysis of lipid protein interaction is difficult because lipids are embedded in cell membranes and therefore, inaccessible to most purification procedures. As an alternative, lipids can be coated on flat surfaces as used for lipid ELISA and Plasmon resonance spectroscopy. However, surface coating lipids do not form microdomain structures, which may be important for the lipid binding properties. Further, these methods do not allow for the purification of larger amounts of proteins binding to their target lipids.
 To overcome these limitations of testing lipid protein interaction and to purify lipid binding proteins we developed a novel method termed lipid vesicle-mediated affinity chromatography using magnetic-activated cell sorting (LIMACS). In this method, lipid vesicles are prepared with the target lipid and phosphatidylserine as the anchor lipid for Annexin V MACS. Phosphatidylserine is a ubiquitous cell membrane phospholipid that shows high affinity to the protein Annexin V. Using magnetic beads conjugated to Annexin V the phosphatidylserine-containing lipid vesicles will bind to the magnetic beads. When the lipid vesicles are incubated with a cell lysate the protein binding to the target lipid will also be bound to the beads and can be co-purified using MACS. This method can also be used to test if recombinant proteins reconstitute a protein complex binding to the target lipid. 
 We have used this method to show the interaction of atypical PKC (aPKC) with the sphingolipid ceramide and to co-purify prostate apoptosis response 4 (PAR-4), a protein binding to ceramide-associated aPKC. We have also used this method for the reconstitution of a ceramide-associated complex of recombinant aPKC with the cell polarity-related proteins Par6 and Cdc42. Since lipid vesicles can be prepared with a variety of sphingo- or phospholipids, LIMACS offers a versatile test for lipid-protein interaction in a lipid environment that resembles closely that of the cell membrane. Additional lipid protein complexes can be identified using proteomics analysis of lipid binding protein co-purified with the lipid vesicles.

Lipid Vesicle-mediated Affinity Chromatography using Magnetic Activated Cell Sorting LIMACS: a Novel Method to Analyze Protein-lipid Interaction

To test the interaction of a protein with its target lipid we used MACS and Annexin V-conjugated magnetic beads and lipid vesicles synthesized from the target lipid and Annexin V-binding phosphatidylserine. Proteins bound to the target lipid are co-purified and analyzed after elution from the beads.

脂质囊泡介导的亲和层析法，使用磁性细胞分选（LIMACS）:一种新的方法来分析蛋白质 - 脂质相互作用

The analysis of lipid protein interaction is difficult because lipids are embedded in cell membranes and therefore, inaccessible to most purification ...

Cell Specific Analysis of Arabidopsis Leaves Using Fluorescence Activated Cell Sorting

After initiation of the leaf primordium, biomass accumulation is controlled mainly by cell proliferation and expansion in the leaves1. However, the Arabidopsis leaf is a complex organ made up of many different cell types and several structures. At the same time, the growing leaf contains cells at different stages of development, with the cells furthest from the petiole being the first to stop expanding and undergo senescence1. Different cells within the leaf are therefore dividing, elongating or differentiating; active, stressed or dead; and/or responding to stimuli in sub-sets of their cellular type at any one time. This makes genomic study of the leaf challenging: for example when analyzing expression data from whole leaves, signals from genetic networks operating in distinct cellular response zones or cell types will be confounded, resulting in an inaccurate profile being generated.
To address this, several methods have been described which enable studies of cell specific gene expression. These include laser-capture microdissection (LCM)2 or GFP expressing plants used for protoplast generation and subsequent fluorescence activated cell sorting (FACS)3,4, the recently described INTACT system for nuclear precipitation5 and immunoprecipitation of polysomes6.
FACS has been successfully used for a number of studies, including showing that the cell identity and distance from the root tip had a significant effect on the expression profiles of a large number of genes3,7. FACS of GFP lines have also been used to demonstrate cell-specific transcriptional regulation during root nitrogen responses and lateral root development8, salt stress9 auxin distribution in the root10 and to create a gene expression map of the Arabidopsis shoot apical meristem11. Although FACS has previously been used to sort Arabidopsis leaf derived protoplasts based on autofluorescence12,13, so far the use of FACS on Arabidopsis lines expressing GFP in the leaves has been very limited4. In the following protocol we describe a method for obtaining Arabidopsis leaf protoplasts that are compatible with FACS while minimizing the impact of the protoplast generation regime. We demonstrate the method using the KC464 Arabidopsis line, which express GFP in the adaxial epidermis14, the KC274 line, which express GFP in the vascular tissue14 and the TP382 Arabidopsis line, which express a double GFP construct linked to a nuclear localization signal in the guard cells (data not shown; Figure 2). We are currently using this method to study both cell-type specific expression during development and stress, as well as heterogeneous cell populations at various stages of senescence.

Cell Specific Analysis of Arabidopsis Leaves Using Fluorescence Activated Cell Sorting

A method for producing Arabidopsis leaf protoplasts that are compatible with fluorescence activated cell sorting (FACS), allowing for studies of specific cell populations. This method is compatible with any Arabidopsis line that expresses GFP in a subset of cells.

细胞特异性分析<em&gt;拟南芥</em&gt;叶用荧光激活细胞分选

After initiation of the leaf primordium, biomass accumulation is controlled mainly by cell proliferation and expansion in the leaves1. However, the ...

Optimized Staining and Proliferation Modeling Methods for Cell Division Monitoring using Cell Tracking Dyes

Fluorescent cell tracking dyes, in combination with flow and image cytometry, are powerful tools with which to study the interactions and fates of different cell types in vitro and in vivo.1-5 Although there are literally thousands of publications using such dyes, some of the most commonly encountered cell tracking applications include monitoring of:
<ol>
	<li>stem and progenitor cell quiescence, proliferation and/or differentiation6-8</li>
	<li>antigen-driven membrane transfer9 and/or precursor cell proliferation3,4,10-18 and</li>
	<li>immune regulatory and effector cell function1,18-21.</li>
</ol>
Commercially available cell tracking dyes vary widely in their chemistries and fluorescence properties but the great majority fall into one of two classes based on their mechanism of cell labeling. "Membrane dyes", typified by PKH26, are highly lipophilic dyes that partition stably but non-covalently into cell membranes1,2,11. "Protein dyes", typified by CFSE, are amino-reactive dyes that form stable covalent bonds with cell proteins4,16,18. Each class has its own advantages and limitations. The key to their successful use, particularly in multicolor studies where multiple dyes are used to track different cell types, is therefore to understand the critical issues enabling optimal use of each class2-4,16,18,24.
The protocols included here highlight three common causes of poor or variable results when using cell-tracking dyes. These are:
<ol>
	<li>Failure to achieve bright, uniform, reproducible labeling. This is a necessary starting point for any cell tracking study but requires attention to different variables when using membrane dyes than when using protein dyes or equilibrium binding reagents such as antibodies.</li>
	<li>Suboptimal fluorochrome combinations and/or failure to include critical compensation controls. Tracking dye fluorescence is typically 102 - 103 times brighter than antibody fluorescence. It is therefore essential to verify that the presence of tracking dye does not compromise the ability to detect other probes being used.</li>
	<li>Failure to obtain a good fit with peak modeling software. Such software allows quantitative comparison of proliferative responses across different populations or stimuli based on precursor frequency or other metrics. Obtaining a good fit, however, requires exclusion of dead/dying cells that can distort dye dilution profiles and matching of the assumptions underlying the model with characteristics of the observed dye dilution profile.</li>
</ol>
Examples given here illustrate how these variables can affect results when using membrane and/or protein dyes to monitor cell proliferation.

Successful use of cell tracking dyes to monitor immune cell function and proliferation involves several critical steps. We describe methods for: 1) obtaining bright, uniform, reproducible label-ing with membrane dyes; 2) selecting fluorochromes and data acquisition conditions; and 3) choosing a model to quantify cell proliferation based on dye dilution.

优化细胞分裂监测细胞示踪染料的染色和扩散建模方法

Fluorescent cell tracking dyes, in combination with flow and image cytometry, are powerful tools with which to study the interactions and fates of ...

The MultiBac Protein Complex Production Platform at the EMBL

Proteomics research revealed the impressive complexity of eukaryotic proteomes in unprecedented detail. It is now a commonly accepted notion that proteins in cells mostly exist not as isolated entities but exert their biological activity in association with many other proteins, in humans ten or more, forming assembly lines in the cell for most if not all vital functions.1,2 Knowledge of the function and architecture of these multiprotein assemblies requires their provision in superior quality and sufficient quantity for detailed analysis. The paucity of many protein complexes in cells, in particular in eukaryotes, prohibits their extraction from native sources, and necessitates recombinant production. The baculovirus expression vector system (BEVS) has proven to be particularly useful for producing eukaryotic proteins, the activity of which often relies on post-translational processing that other commonly used expression systems often cannot support.3 BEVS use a recombinant baculovirus into which the gene of interest was inserted to infect insect cell cultures which in turn produce the protein of choice. MultiBac is a BEVS that has been particularly tailored for the production of eukaryotic protein complexes that contain many subunits.4 A vital prerequisite for efficient production of proteins and their complexes are robust protocols for all steps involved in an expression experiment that ideally can be implemented as standard operating procedures (SOPs) and followed also by non-specialist users with comparative ease. The MultiBac platform at the European Molecular Biology Laboratory (EMBL) uses SOPs for all steps involved in a multiprotein complex expression experiment, starting from insertion of the genes into an engineered baculoviral genome optimized for heterologous protein production properties to small-scale analysis of the protein specimens produced.5-8 The platform is installed in an open-access mode at EMBL Grenoble and has supported many scientists from academia and industry to accelerate protein complex research projects.

Protein complexes catalyze key cellular functions. Detailed functional and structural characterization of many essential complexes requires recombinant production. MultiBac is a baculovirus/insect cell system particularly tailored for expressing eukaryotic proteins and their complexes. MultiBac was implemented as an open-access platform, and standard operating procedures developed to maximize its utility.

在EMBL的MultiBac蛋白质复杂的生产平台

Proteomics  research revealed the impressive complexity of eukaryotic proteomes in  unprecedented detail. It is now a commonly accepted notion that ...

Open-source Single-particle Analysis for Super-resolution Microscopy with VirusMapper

Super-resolution fluorescence microscopy is currently revolutionizing cell biology research. Its capacity to break the resolution limit of around 300 nm allows for the routine imaging of nanoscale biological complexes and processes. This increase in resolution also means that methods popular in electron microscopy, such as single-particle analysis, can readily be applied to super-resolution fluorescence microscopy. By combining this analytical approach with super-resolution optical imaging, it becomes possible to take advantage of the molecule-specific labeling capacity of fluorescence microscopy to generate structural maps of molecular elements within a metastable structure. To this end, we have developed a novel algorithm &#8212; VirusMapper &#8212; packaged as an easy-to-use, high-performance, and high-throughput ImageJ plugin. This article presents an in-depth guide to this software, showcasing its ability to uncover novel structural features in biological molecular complexes. Here, we present how to assemble compatible data and provide a step-by-step protocol on how to use this algorithm to apply single-particle analysis to super-resolution images.

This manuscript uses the Fiji-based open-source software package VirusMapper to apply single-particle analysis to super-resolution microscopy images in order to generate precise models of nanoscale structure.

与VirusMapper超分辨率显微镜开源单粒子分析

Super-resolution fluorescence microscopy is currently revolutionizing cell biology research. Its capacity to break the resolution limit of around 300 nm ...

Fluorescence-Activated Cell Sorting for the Isolation of Scleractinian Cell Populations

Coral reefs are under threat due to anthropogenic stressors. The biological response of coral to these stressors may occur at a cellular level, but the mechanisms are not well understood. To investigate coral response to stressors, we need tools for analyzing cellular responses. In particular, we need tools that facilitate the application of functional assays to better understand how cell populations are reacting to stress. In the current study, we use fluorescence-activated cell sorting (FACS) to isolate and separate different cell populations in stony corals. This protocol includes: (1) the separation of coral tissues from the skeleton, (2) creation of a single cell suspension, (3) labeling the coral cells using various markers for flow cytometry, and (4) gating and cell sorting strategies. This method will enable researchers to work on corals at the cellular level for analysis, functional assays, and gene expression studies of different cell populations.

Corals create biodiverse ecosystems important for both humans and marine organisms. However, we still do not understand the full potential and function of many coral cells. Here, we present a protocol developed for the isolation, labeling, and separation of stony coral cell populations.

荧光激活细胞分拣，用于分离瘦杆菌细胞群

Coral reefs are under threat due to anthropogenic stressors. The biological response of coral to these stressors may occur at a cellular level, but the ...

Assessment of Cellular Bioenergetics in Mouse Hematopoietic Stem and Primitive Progenitor Cells using the Extracellular Flux Analyzer

Under steady state, hematopoietic stem cells (HSCs) remain largely quiescent and are believed to be predominantly reliant on glycolysis to meet their energetic needs. However, under stress conditions such as infection or blood loss, HSCs become proliferative and rapidly produce downstream progenitor cells, which in turn further differentiate, ultimately producing mature blood cells. During this transition and differentiation process, HSCs exit from quiescence and rapidly undergo a metabolic switch from glycolysis to oxidative phosphorylation (OxPHOS). Various stress conditions, such as aging, cancer, diabetes, and obesity, can negatively impact mitochondrial function and thus can alter the metabolic reprogramming and differentiation of HSCs and progenitors during hematopoiesis. Valuable insights into glycolytic and mitochondrial functions of HSCs and progenitors under normal and stress conditions can be gained through the assessment of their extracellular acidification rate (ECAR) and oxygen consumption rate (OCR), which are indicators of cellular glycolysis and mitochondrial respiration, respectively. 
Here, a detailed protocol is provided to measure ECAR and OCR in mouse bone marrow-derived lineage-negative cell populations, which include both hematopoietic stem and primitive progenitor cells (HSPCs), using the extracellular flux analyzer. This protocol describes approaches to isolate lineage-negative cells from mouse bone marrow, explains optimization of cell seeding density and concentrations of 2-deoxy-D-glucose (2-DG, a glucose analog that inhibits glycolysis) and various OxPHOS-targeted drugs (oligomycin, FCCP, rotenone, and antimycin A) used in these assays, and describes drug treatment strategies. Key parameters of glycolytic flux, such as glycolysis, glycolytic capacity, and glycolytic reserve, and OxPHOS parameters, such as basal respiration, maximal respiration, proton leak, ATP production, spare respiratory capacity, and coupling efficiency, can be measured in these assays. This protocol allows ECAR and OCR measurements on non-adherent HSPCs and can be generalized to optimize analysis conditions for any type of suspension cells.

The method presented here summarizes optimized protocols for assessing cellular bioenergetics in non-adherent mouse hematopoietic stem and primitive progenitor cells (HSPCs) using the extracellular flux analyzer to measure the extracellular acidification rate (ECAR) and oxygen consumption rate (OCR) of HSPCs in real time.

使用细胞外通量分析仪评估小鼠造血干细胞和原始祖细胞中的细胞生物能量学

Under steady state, hematopoietic stem cells (HSCs) remain largely quiescent and are believed to be predominantly reliant on glycolysis to meet their ...

Using Real-Time Cell Metabolic Flux Analyzer to Monitor Osteoblast Bioenergetics

Bone formation by osteoblasts is an essential process for proper bone acquisition and bone turnover to maintain skeletal homeostasis, and ultimately, prevent fracture. In the interest to both optimize peak bone mass and combat various musculoskeletal diseases (i.e., post-menopausal osteoporosis, anorexia nervosa, type 1 and 2 diabetes mellitus), incredible efforts have been made in the field of bone biology to fully characterize osteoblasts throughout their differentiation process. Given the primary role of mature osteoblasts to secrete matrix proteins and mineralization vesicles, it has been noted that these processes take an incredible amount of cellular energy, or adenosine triphosphate (ATP). The overall cellular energy status is often referred to as cellular bioenergetics, and it includes a series of metabolic reactions that sense substrate availability to derive ATP to meet cellular needs. Therefore, the current method details the process of isolating primary, murine bone marrow stromal cells (BMSCs) and monitoring their bioenergetic status using the Real-time cell metabolic flux analyzer at various stages in osteoblast differentiation. Importantly, these data have demonstrated that the metabolic profile changes dramatically throughout osteoblast differentiation. Thus, using this physiologically relevant cell type is required to fully appreciate how a cell's bioenergetic status can regulate the overall function.

Real-time cell metabolic flux assay measures the oxygen consumption rate and extracellular acidification rate, which corresponds to mitochondrial and glycolytic adenosine triphosphate production, using pH and oxygen sensors. The manuscript explains a method to understand the energy status of osteoblasts and the characterization and interpretation of the cellular bioenergetic status.

使用实时细胞代谢通量分析仪监测成骨细胞生物能量学

Bone formation by osteoblasts is an essential process for proper bone acquisition and bone turnover to maintain skeletal homeostasis, and ultimately, ...

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