This work was financed in part by the Natural Sciences and Engineering Council of Canada. The authors acknowledge Jela Burkus, Donna Macey, and Jennifer Lloyd for their technical expertise and their contribution to the optimization of this method.

注:请始终确保正确的个人防护装备（PPE）戴在整个协议。玻璃器皿应该不能用手触摸。从手指，头发，油脂，油和烃脂质是所有潜在的污染物。处理干净的玻璃器皿时，请务必戴上丁腈手套和冲洗手套用70％酒精。 1.玻璃器皿的研制分析<ol><li> 一次性玻璃器皿（ 例如 ，离心管） <ol><li>在450ºC包裹在铝箔和热在马弗炉四个半小时。 </li></ol></li><li> 聚四氟乙烯（PTFE）-lined帽 <ol><li>泡帽在磷酸盐洗涤剂一小时，然后用刷子刷洗洗热水和磷酸盐洗涤剂。冲洗掉自来水肥皂。 </li><li>在酸浴的地方（5％HCl）的一小时只（不离开不再作为衬里可能会掉下来，如果他们浸泡时间过长的上限。冲洗三次在自来水中。冲洗在蒸馏水三次。干在40ºC烤箱。 </li></ol></li><li> 可重复使用的玻璃器皿（ 例如 ，将10毫升，15毫升45 mL样品瓶/罐） <ol><li>在热水和磷酸盐洗涤剂擦洗刷洗。冲洗掉用自来水和发生在酸浴（5％HCl）的过夜皂。在自来水中冲洗三次，然后在40ºC漂洗在蒸馏水洗涤三次，然后干燥烘箱中。 </li><li>在干净的铝箔包（50毫升罐子单独包装和其他玻璃器皿被包裹在样品批次， 例如 ，20），并在马弗炉中的热在450ºC四个半小时。 </li></ol></li><li> 容积玻璃器皿（ 例如 ，容量瓶） <ol><li>在热水和磷酸盐洗涤剂擦洗刷洗。冲洗掉用自来水和发生在酸浴（5％HCl）的过夜皂。在自来水中冲洗三次，用蒸馏水漂洗三次，然后在烘箱中在40℃干＃186℃。 </li><li>使用前用少量溶剂（ 如甲醇）冲洗3次-不要把容量瓶在马弗炉。 </li></ol></li></ol> 2.收集和土壤样品处理之前PLFA分析<ol><li>收集的土壤样品进入无菌袋，并且除非这些可立即进行分析，尽快冷冻。在冷冻室（-80℃），直到准备商店样品进行的样品冷冻干燥。冷冻后的冷冻干燥机的指示样品的干批次。 </li><li>每个冻干样品转移到新标记的无菌袋。从冻干材料称出样品中包入了磷脂脂肪酸萃取预标记的低沉的离心管中。 注:一般原则是把0.5克的有机材料（碳含量&gt; 17％的重量）和高达3.0克矿物的土壤样品。记录样品的重量每个样本。 </li><li>对于每10个样品，也掂量出一个额外重复进行分析，并为每20个样本包括一个空白（ 即 ，不具有任何样品离心管-用作对照，以确定在磷脂脂肪酸提取过程中的任何潜在的污染）。同时样品管的过程批次。 注意:一组20个样本将对应于一个批次23样品管（样品1至10，样品10的一个重复，样品12至22，样品22的一个重复，和一个空白=批次23样品管）。 注:为他们准备GC分析和运行它们放在一起之前进行萃取，再经分离，然后甲基化样品批次。这有助于确定是否有什么差错的地方失误发生，并可能有助于降低必要，请重复提取的数目。 </li></ol> 3. PLFA技术（步骤针对个别样品，但完整的一整批一次） 注:步骤描述的PLFA技术的所有步骤1-3下面应在通风橱中使用适当的PPE和以下实验室安全原则进行。 <ol><li> 提取（步骤1）: <ol><li>通过溶解在50毫升14克KOH蒸馏水，去离子水（DH 2 O） 的制备5.0微米的KOH。 </li><li>通过在400ml的dh 2 O溶解31.52克柠檬酸一水合物制备的0.15M柠檬酸盐缓冲液调节pH至4.00±0.02，加入5.0米KOH;大约45-50毫升5.0微米的KOH将需要将pH调节至4.00。当pH调节，用容量瓶稀释柠檬酸缓冲到1000毫升在冰箱在不使用时（长达一个月）存储柠檬酸盐缓冲液。 </li><li>每日准备在PC（19:0/19:0）nonadecanoate替代标准稀释250μl的在25毫升氯仿的储备溶液（10毫克/毫升）的（这提供了一批23个样品足够替代标准）。添加0.5的PC毫升（19:0/19:0）替代标准溶液的样品离心管中。 </li><li>一个DD的布莱和代尔萃取到土壤样品中的顺序如下:ⅰ）2.0毫升柠檬酸盐缓冲液，ⅱ）2.5毫升氯仿，以及iii）5.0毫升甲醇。用30秒的PTFE内衬帽和旋涡帽样品;放置在最终过端振动器2小时。 </li><li>离心机226 XG与帽15分钟。绘制上清液断用巴氏吸管，并转移到经标记45毫升玻璃管形瓶中。 </li><li>第二轮的Bligh和Dyer的萃取剂添加到每个样品中，并重复上述步骤4和5。绘制上清液断用巴氏吸管并转移到同一标记45毫升玻璃管形瓶中。 </li><li>添加到标记的含上清液45毫升玻璃小瓶:ⅰ）5.0毫升氯仿，以及ii）5.0毫升柠檬酸盐缓冲液。盖有白色聚四氟乙烯衬盖，涡旋玻璃小瓶30秒。让它在黑暗中室温下放置过夜（避免氧化）。 </li><li>小心真空断45的管瓶中的上层水相（如果没有真空可用，吸移管应通过aque降​​低OU的阶段非常仔细地为吹打有机相时不收取任何的吸管）。吸取下层有机相为标记15毫升小瓶。 </li><li>下被压缩的 N 2（以避免氧化）在室温下将15ml小瓶地方批次。蒸发氯仿关闭缓慢，设置的 N 2流中的液体的表面皱褶在小瓶但不爬小瓶的侧面。 </li><li>拧上在冷冻聚四氟乙烯内衬帽和存储样品在-20 C包裹在铝箔（缠绕分批而不是单独），直到准备与第2步进行。 </li></ol></li><li> 脂质分馏（步骤2） <ol><li>将固相萃取柱支架上的玻璃缸套管。插入新的SPE柱（二氧化硅，500毫克，6ml）中插入套管。必要的标签栏（建议，以避免混淆样本）。 </li><li>通过加入5毫升丙酮中，并让它漏极通过调节每一列，然后添加5毫升氯仿两个加法（总体积= 10毫升）中。允许第二氯仿洗流出熔块，直到约1毫米的位置，然后关闭水龙头。 </li><li>通过加入0.5毫升氯仿轻轻小瓶和涡流重新溶解样品（存储在第1步的端部15的管瓶中）。转移再溶解样品使用巴氏吸管在SPE柱;重复总共2转移（每个样品1毫升氯仿总传送量）。 </li><li>直接铺设脂质样品到塔的中心，并允许溶剂完全排入油箱。 </li><li>通过加入5毫升氯仿中，以每列洗脱中性脂质。允许溶剂完全排出到油箱。 </li><li>通过加入5毫升丙酮的每一列洗脱糖脂。允许溶剂完全排入收集箱。 </li><li>删除列从罐站，并与真空装置排水槽。插入机架干净，标离心管进入水箱。更换色谱柱站在坦克;上面标有列应该排队，标CENTR下面ifuge管。 </li><li>加入5毫升甲醇到每个列洗脱磷脂到离心管中。等待固相萃取柱他们处理之前在通风柜干燥。在室温下被压缩的 N 2干燥向下磷脂部分。 </li><li>用N 2.在冰箱内衬PTFE帽并确保样品螺杆清洗管在-20ºC铝箔包裹（套分批而不是单独），直到准备进行第3步。 </li></ol></li><li> 脂质甲基（步骤3）。 <ol><li>打开设置为37ºC热水浴。通过使用的1000ml容量瓶中溶解57.1毫升冰醋酸1,000ml中的dh 2 O制备1M乙酸（如果尚未制造）。这种溶液可在室温下储存长达三个月。 。 </li><li>准备一批甲醇氢氧化钾。通过在40ml甲醇0.45克KOH溶解制备的0.2M KOH溶液。根据调整的KOH体积和甲醇nticipated批量大小在步骤3中的日常准备这个解决方案;不存储更长的时间。 </li><li>从冰箱中取出样品（步骤2之后存储的离心管中），并允许样品来室温。加入0.5毫升氯仿和0.5毫升甲醇到每个样品中，随后加入1.0ml的KOH甲醇。盖管紧密地与聚四氟乙烯衬盖。涡流混合。 </li><li>放置密封样品在37ºC浴中30分钟。确保水水平是最小的1-2毫米的样品液体水平之上。删除并允许样品冷却。虽然样本在水中洗澡，用的标签样品ID（10毫升玻璃小瓶聚四氟乙烯衬里帽）的小玻璃瓶中。 </li><li>加入2.0毫升己烷至每个样品并乱舞。然后0.2毫升1.0M的乙酸添加到每个样品和漩涡再次混合;相分离应该变得可见。 </li><li>加入2.0毫升的卫生署2 O的每个样品突破阶段。涡旋样品30秒。然后在226×g离心2分钟离心样品。 </li><li>运用总之巴斯德吸管，转移前阶段清理标记10毫升小瓶。要小心，不要转移任何下层（水）相。 </li><li>加入2.0毫升己烷向每个样品的离心管并乱舞。涡旋样品30秒。然后在226×g离心2分钟离心样品。 </li><li>使用短巴斯德吸管，顶相再次添加到标记的10ml玻璃管形瓶中。 </li><li>下的 N 2蒸发在室温下标记10毫升玻璃小瓶中的溶剂。拧上在冷冻聚四氟乙烯内衬帽和存储样品在-20 C包裹在铝箔（缠绕分批而不是单独），直到准备用GC分析进行。一定要标记所有样本。 </li></ol></li><li> 气相色谱仪（GC）分析 注意:个别磷脂脂肪酸的鉴定和定量是使用连接到任何一个FID或MS检测器为GC完成。虽然下面的说明是GC-FID，样品制备将为GC-MS有效为WELL。一个例子设置为的GC-FID系统将包括一个25米×0.2毫米×0.33微米（5％ - 苯基）具有以下温度协议 - 甲基聚硅氧烷柱:初始温度190ºC，斜坡10℃/分钟至285ºC ，持有9.5分，斜坡60ºC/分钟至310ºC，持有0.42 31分钟。 <ol><li>加入MeC10一滴准备GC内标（ISTD）:0（甲基癸）至100毫升正己烷（记录续重0.1毫克）。打开气体GC，然后打开GC。确保H 2，N 2和空气气缸是开放的，即有足够的气体来运行分析（H 2不应该得到低于500磅）。 </li><li>检查一个良好校准的条件，通过运行包含的脂肪酸的混合物，接着己烷空白校准标准得到满足。个别脂肪酸的标识可以手动分配基于标准得到保留时间比较，或者这可能是AUTOMATICA使用可商购的软件31 LLY分配。在所有的情况下，明确地识别可使用的MS检测器2来实现。 注意:一个好的校准的其他症状包括平面基准和正己烷清洗没有污染。 </li><li>溶解各磷脂脂肪酸样品残余物中加入150μl内标溶液（由脂质甲基步骤3在10毫升玻璃小瓶中含有），并转移到GC小瓶中（可选地使用50到75微升，如果样本响应预期要弱，如在非常沙质土壤）。 </li><li>设置样品注射体积为2微升。将FID温度300ºC。运行使用250ºC的入口温度的样品中，用H 2作为载气（流速1.3毫升/分钟）和30分流比:1。 </li></ol></li></ol>

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作者什么都没有透露。

为了尽量减少和避免PLFA数据的误解，精心筛选的数据必须做的原因是土壤中的微生物群落发现了一些磷脂脂肪酸也存在于单一和多细胞真核生物，如植物根系，藻类和土壤动物。此外， 古细菌不含有磷脂脂肪酸;相反，古细菌膜是由磷脂醚脂（PLELs）的。因此，磷脂脂肪酸协议不能用于在土壤中表征古社区。 个体关联到磷脂脂肪酸特定的微生物群体应谨慎行使（见2011年出版物Frostegård和他的同事34的一些PLFA方法的局限性的一个很好的讨论）。相反，它可以是更合适的，正如在图2中做的那样，基磷脂脂肪酸基于它们的化学结构， 例如 ，i）的直链饱和磷脂脂肪酸，ⅱ）饱和磷脂脂肪酸与中期柴N（10 - 甲基）分支，三）末端支链饱和脂肪酸，ⅳ）单不饱和的磷脂脂肪酸，v）的多不饱和磷脂脂肪酸，以及vi）羟基脂肪酸。 样品的重量可能需要基于包含给定的土壤样品中提取磷脂脂肪酸的量进行调整。通过不调节在以下微生物活性的土壤提取土壤的量，用户在不获得足够数量的磷脂脂肪酸反应（峰），以精确地表示整体微生物群落的风险。在与高浓度磷脂脂肪酸的高活性微生物的土壤，用户是在超载的GC色谱柱，从而防止磷脂脂肪酸的精确定量的风险。在这两种情况下，土壤样品必须重新分析磷脂脂肪酸。一个很好的出发点是加0.5克的有机土壤样品（如森林地板）和矿物的土壤样品约3g。由于可萃取磷脂脂肪酸的量典型地关联到包含在土壤中，样品我们有机碳的量飞行能够根据土壤的碳含量来调节; 例如 ，将1克矿物土样可足以获得良好的磷脂脂肪酸量化时碳含量为10-15％，而如果碳含量为≤0.5&gt;5克可能是必要的％。它始终是一个好主意，做了几样试车运行整个集之前，以优化样品重量。 是的PLFA协议和气相色谱分析常见故障的排除策略如下。如果GC色谱基线太高，H 2气体筒应该改变。如果溶剂或ISTD峰从色谱丢失，则可能是样品注入一个问题（ 例如 ，堵塞注射器，具有自动进样器的问题，空或缺少小瓶中，误差小瓶定位）。如果在该方法/样品空白检测到三个以上的峰，有空白的污染，并有很高的概率是相同的污染物（S）将存在于样品的GCÇhromatograms。发现污染的来源（通常是水性介质），或者至少是，确保相应于污染物的峰被从样品色谱图，并且这些峰不包括在磷脂脂肪酸数据的任何统计分析中删除。此外，它是一个好主意，运行样品之间的正己烷​​冲洗时，被怀疑结转。在正己烷清洗附加峰将表明结转（ 即较重的部件从以前的运行洗脱）。 脂质是特别容易氧化，并且特别注意需要在整个协议行使以防止空气和光曝光的样品，如在黑暗中存储它们和使它们保持在氮气下。所有样品和空白，必须具有足够的C19:0代用标准。如果缺少C19:0高峰，无论是样品或空白的，表明一个贫穷的恢复或分析物的完全丧失。 0的样品中，比第m相当低:C19的响应ethod /样品坯料表示在所述磷脂脂肪酸方法论一些点的分析物的损失。当面对C19差:0的恢复，该过程的各个环节都必须认真考虑和研究，包括最初的提取，样品传输的所有阶段，固相萃取，脂肪酸甲基化，干燥，储存和样品处理，以隔离阶段或在分析丢失阶段。另一方面，这可能是因为一些土壤样品的提取可产生C19:0，其中，所述替代标准的情况下，恢复可能被高估。同时使用两种标准（（PC（19:0/19:0）和MeC10:0）不绝对要求，我们已发现这需要解决当是非常有用的。只要MeC10:0的反应是足够，故障排除可以专注于前GC分析的步骤。 如前面提到的，必须谨慎仅仅根据生物标志物派生解释生态学意义和生态系统的关系时，可以使用从PLFA数据D，因为纯培养的研究表明，孤立的细菌菌株将含有不同类别的磷脂脂肪酸的。相反，使更广泛的生态推论，当生物标志物磷脂脂肪酸应被看作是一套证据的有益补充。在文献中，从个人磷脂脂肪酸已经提出和作为生物标志物用于不同微生物组的利用。饱和磷脂脂肪酸通常用于表示革兰氏阳性菌和单不饱和的磷脂脂肪酸被用于革兰氏阴性菌。对于革兰氏阴性细菌识别的生物标志物包括单不饱和脂肪酸与在ω5或ω7位置，如C16的不饱和度:1ω7，C18:1ω7，和C18:1ω5。此外，环丙基脂肪酸也可以是代表性的革兰氏阴性细菌35。因此，从革兰氏阴性菌磷脂脂肪酸可以作为平等计算为A的总和:1ω5+ A:1ω7+ A:0cyclo。对革兰氏阳性菌识别生物标记末端支化山turated脂肪酸，异支链的，如C15:0I，C16:0I，C17:0I;或反异支链，如C15:0A和C17:0A。饱和的磷脂脂肪酸与中链（10 -甲基）分支，如10Me16:0和10Me18:0是在放线菌36特征存在。因此，从革兰氏阳性菌磷脂脂肪酸可被计算为等于A的总和:0（ISO，反异，10-甲基）。 真菌磷脂脂肪酸相关的许多生物标志物常常不饱和的，但有些真菌生物标志物的单不饱和。例如，在真菌的单不饱和的生物标记物而言，C16:1ω5已被确定为丛枝菌根真菌的指示器（AMF）37，C18:1ω9是腐生真菌常见，菌根通常含有C16:1ω9。二不饱和的C18的存在:2ω6,9常发生在与C18结合:1ω9并且还与真菌甾醇麦角甾醇，这表明C18良好相关:2ω6,9可以充分代表ectomycorrhizae和腐生菌38。三不饱和的C18:3ω6,9,12也被用作用于真菌10生物标志物;然而，C18:3ω6c可以在其它真核生物，包括植物和藻类中找到，尽管它通常不会在细菌中发现。在土壤原生生物的术语，C20:4ω6c已被确认为原生生物标志物39，虽然，其它工作未能检测C20:即使当可行原生生物种群光镜40目视确认4ω6c。 许多研究解决利用多元统筹技术作为PLFA数据分析工具，关系到整个土壤微生物群落的生态问题。当使用这种方法，一些作者已经选择通过除去存在于样品4的不到5％磷脂脂肪酸排除从数据集罕见磷脂脂肪酸。正常饱和C16:0和C18:0是所有土壤中的微生物丰富，包括原核生物和EUKaryotes。因此，一些研究人员选择的基础，它们可能不是土壤微生物群落的组合物的敏感指标统计分析之前除去这些磷脂脂肪酸 - 尽管C16:0和C18:0仍然求和所有磷脂脂肪酸时要包括微生物生物量的一个指标。在统计分析的术语，许多研究者选择进行非十进制多维尺度（基本数据）配合作为这种技术非常适合于可能不能正常分布33的数据。有关分类变量额外的分析可以包括指示种分析，这可以涉及特定磷脂脂肪酸到分类分组和多响应排列程序（MRPP），这可以帮助确定分配给分类群组的微生物群落之间的相似性或差异的发生。此外，多元回归树（MRT）特别有用当多个实验变量的影响或治疗需要评估作为潜在的解释变量5（ 例如，土壤质地，水分，回收的年龄）。 一旦建立，磷脂脂肪酸协议是一个相对简单的分析方法定量土壤微生物响应于环境变化和人为干扰时，可以是非常有用的。虽然分子技术，如基因指纹更适合微生物群落的细致刻画，该方法PLFA呈现总微生物量34提供定量信息的优势。在我们的研究实验室，一个人可以舒适地处理一组的20个样品4天以上，和我们已发现这里提出是一个强大的和可再现的技术评估土壤生物质量的协议。 该方法PLFA的功率可以通过它耦合到稳定同位素分析41，42大大延长。具体而言，除了ÒF 13 C标记的底物的土壤允许基板结合的量化到土壤中的微生物，虽然个别磷脂脂肪酸41同位素分析。此外，这种方法似乎是阐明土壤食物网42的营养链一个非常有前途的工具。

磷脂脂肪酸（磷脂脂肪酸）是从域细菌和真核生物的微生物细胞膜的一部分。微生物产生不同的链长和组合物的磷脂脂肪酸以保持响应于他们的直接环境条件细胞膜的完整性和细胞功能的装置;因而可以说，地理上分开，但都受到类似土壤条件的微生物群落将表达类似的磷脂脂肪酸。用14至20个碳原子之间的链长土壤磷脂脂肪酸通常被认为是主要的细菌和真菌来源1。在混合培养物，磷脂脂肪酸分析不能用于识别个体微生物物种，但它可以提供在土壤中发现的微生物群落的整体指纹。另外，由于磷脂脂肪酸正在迅速经细胞死亡退化，它们可以被认为是代表了可行的土壤微生物群落2。这TEChnique已被广泛用来描述在各种环境中发现的微生物群落，从林3-4 5-6草原和农田7的结构组成。它表征土壤针对土地管理的变化中得到成功应用，其中包括皆伐林8，9石灰，回收10-12，以及事件如火灾13，污染的金属14和碳氢化合物15，和昆虫爆发16 。 当代PLFA分析方法由多个研究小组通过几个关键的进步，过去60年中发展而来的。甲醇:水的比例在提取溶液到该混合物中，从一个单相转移到一个两相系统17在1958年，一个显著改善从调节氯仿出现。这种优化的脂质提取和订正隔离亲母育成为著名的布莱和代尔方法。该协议在未来的几十年里通过许多实验室，并在此期间，白和他的同事促成显著进步的方法;例如，它们改善了由水交换磷酸盐缓冲液提取步骤，并且它们的优化通过气相色谱（GC）通过增强的峰值识别和定量分析。或许更相关性土壤科学的，它们在1979年确定，所提取的脂类可以用作微生物结构的索引时，他们检验海洋沉积物18的微生物生物质。 进一步发展发生在20世纪80年代作为该方法变得更广泛地用于土壤科学，特别是相对于根际19。在那个时候，该方法包括甲基nonadecanoate（MeC19:0）作为内标19和脂质分馏21 &lt;用硅酸柱的/ SUP&gt;。在他与白19,21的工作，Tunlid回到瑞典，开始合作研究与复兴党和Frostegård。通过检查一组土壤中有机质含量不同的不同提取缓冲液的效率，该组显示，柠檬酸盐缓冲液增加萃取脂质磷酸盐的量相比，磷酸盐缓冲液22。此外，在石灰对土壤微生物群落的影响及其1993年出版23后来成为一个经典引文土壤生物与生物化学 24。研究人员使用的在它们的数据处理主要成分分析。 PLFA分析，由于该方法现在被称为，产生了大量的数据集和使用多元统计方法来处理这​​些数据是时间高度创新和鼓舞人心的很多。并发的工作正在做瑞典，修改到PLFA程序正在调查中德国通过Zelles和他的同事25-26。他们的程序的版本是著名的供其使用的固相萃取（SPE）柱代替硅酸柱，但是，总体而言，更为密集的实验室。 Frostegård的论文23，由白色和Ringelberg 27的详细方法一起，在使用PLFA技术来研究土壤科学的基本问题爆发奠定了基础。自那时起，通过火石及其同事的方法的精炼包括添加内部的GC标准（C10:0）和C19:0作为替代标准提高量化28和更换用分的漏斗为圆底小瓶，以简化提取29。最近，乔杜里和Dick 30研究了甲基化步骤，并报告说，在土壤科学文献中的KOH /甲醇方法IDENT使用的两个甲基化程序指明分数较大范围的脂肪酸。 所提出的方法基本上是根据由布莱和Dyer的开发的原始方法，并结合上​​述的修改，如使用甲醇的KOH的甲基化步骤。两个标准用于每个样品:，这是添加到之前的第一提取土壤样品1,2-dinonadecanoyl- SN -glycero -3-磷酸胆碱（:0/19 0）的PC（19）的替代标准评估效率和整个协议的恢复，以及甲基癸的仪器的标准（MeC10:0），这是以前鉴定和定量通过GC加入。 我们承认，PLFA方法通常被许多微生物生态学实验室全球，并已被证明多次，其中包括由国际标准化组织2。本文的目的是提出一种容易跟踪和健壮的协议，可以是土壤有用谁正在努力学习的PLFA技术的科学家。

<table><tbody><tr><td>Pierce Reacti-Vap III-evaporator gassing manifold with 27 ports</td><td></td><td></td><td>Used in Steps 1-3</td></tr><tr><td>Compressed Nitrogen</td><td>Praxair</td><td>Ni-T</td><td>Dangerous: Use only after training and minimize contact and use; Used in Steps 1-3</td></tr><tr><td>Disposable centrifuge tube and Teflon-lined cap</td><td>Fisher Scientific</td><td>05-569-3</td><td>Used for Step 1 (1 per sample) and Step 2 (1 per sample)</td></tr><tr><td>Disposable glass long-necked Pasteur pipettes</td><td>Fisher Scientific</td><td>13-678-20D</td><td>Used for Step 1 (2 per sample)</td></tr><tr><td>Disposable glass short-necked Pasteur pipettes</td><td>Fisher Scientific</td><td>13-678-4</td><td>Used for Step 2 (1 per sample), Step 3 (1 per sample), GC analysis (1 per sample), and one for each type of solution dispensed during PLFA methods (~10 per batch of samples)</td></tr><tr><td>Elastic band</td><td>Grand &amp;amp; Toy</td><td>42199</td><td>1 per sample for freeze drying</td></tr><tr><td>Freeze Dryer-Labconco 7806002 and 7753000</td><td></td><td></td><td>Used for preparing samples for PLFA analysis - use whatever model your lab has</td></tr><tr><td>Freezer (-20 &amp;deg;C, -80 &amp;deg;C)</td><td>Revco</td><td>ULT2586-5-A36</td><td>Minus 80 &amp;deg;C is used for freezing freshly collected soil samples, -20 &amp;deg;C is used for storing samples at end of each of 3 steps of PLFA analysis</td></tr><tr><td>Gas chromatograph - Agilent 6890 Series capillary gas chromatograph (GC; Wilmington, DE) equipped with a 50 m Ultra 2 (5%-phenyl)-methylpolysiloxane column</td><td>Agilent Technologies</td><td></td><td>Used for GC analysis, other models of GC could also be used (1 needed)</td></tr><tr><td>Analytical column</td><td>Agilent Technologies</td><td>19091B-105</td><td></td></tr><tr><td>Gas chromatograph insert</td><td>Agilent Technologies</td><td>5183-4692</td><td>Used for GC analysis (1 per sample)</td></tr><tr><td>Gas chromatograph vial and lid</td><td>Agilent Technologies</td><td>5188-6592</td><td>Used for GC analysis (1 per sample)</td></tr><tr><td>Hexanes</td><td>Fisher Scientific</td><td>H292-4</td><td>Hazardous: use only in fume hood, Read MSDS, minimize use and wear appropriate PPE; Total volume per sample = 4 ml (2.0 ml + 2.0 ml (step 3))</td></tr><tr><td>Hot water bath -Isotemp 220</td><td>Fisher Scientific</td><td></td><td>Used for Step 3 (1 per batch of samples)</td></tr><tr><td>Kimwipe</td><td>Fisher Scientific</td><td>06-666-2</td><td>Used for freeze drying samples</td></tr><tr><td>KOH, ACS grade</td><td>Fisher Scientific</td><td>P250-500</td><td>Hazardous: Use only in fume hood, Read MSDS, minimize use and wear appropriate PPE; Used for making citrate buffer (used in Step 1) and methanolic KOH (step 3)</td></tr><tr><td>Lab quake end-over-end shaker for centrifuge tubes</td><td>Barnstead/Thermolyne</td><td>3,625,485</td><td>Used for Step 1 (1 per batch of samples of appropriate holding capacity)</td></tr><tr><td>MeC10:0 methyl decanoate internal standard</td><td>Sigma-Aldrich</td><td>299030-2.5G</td><td>Used for GC analysis</td></tr><tr><td>Methanol</td><td>Fisher Scientific</td><td>A454-4</td><td>Hazardous: Use only in fume hood, Read MSDS, minimize use and wear appropriate PPE; Total volume per sample = 15.5 ml (5 ml + 5 ml (step 1) + 5 ml (step 2) + 0.5 ml (step 3))</td></tr><tr><td>MIDI Peak Identification Software</td><td>MIDI, Inc., Newark, DE</td><td></td><td>Used for interpreting GC analysis output</td></tr><tr><td>MIDI Calibration Standard Mix for Microbial Identification System</td><td>Lab Sphere Inc</td><td>1200-A</td><td>Used as a Calibration mix for TSBA 40</td></tr><tr><td>Nitrile gloves</td><td>Fisher Scientific</td><td>27-058-52</td><td>Used always when conducting PLFA lab work</td></tr><tr><td>pH Meter - Accumet XL200</td><td>Fisher Scientific</td><td></td><td>Used for making citrate buffer</td></tr><tr><td>Pipette (0.2 - 2 ml) - Socorex - Calibra Digital 832 Macropipette</td><td>Socorex</td><td>832.02</td><td>Used throughout Steps (1 per sample)</td></tr><tr><td>250 &amp;micro;l gastight syringe</td><td>Hamilton&amp;nbsp;</td><td>1725</td><td>Used for GC analysis (1 per sample)</td></tr><tr><td>silver shield gloves</td><td>Sigma-Aldrich</td><td>Z529575</td><td>For use when handling chloroform</td></tr><tr><td>solid phase extraction (SPE) column</td><td>Agilent Technologies</td><td>5982-2265</td><td>Used for Step 2 (1 per sample)</td></tr><tr><td>SPE glass column holder with spigots&amp;nbsp; and vacuum apparatus attached</td><td>Sigma-Aldrich</td><td></td><td>Used for Step 2 (1 per batch of samples)</td></tr><tr><td>Vortex - Barnstead International Maxi Mix II- M37615</td><td>Barnstead International&amp;nbsp;</td><td>M37615</td><td>Used in Steps 1-3</td></tr><tr><td>Water - ultra-pure and Chromosolv HPLC grade</td><td>Sigma-Aldrich</td><td>270733-4L</td><td>Used throughout analysis</td></tr><tr><td>Whirl-Pak&amp;reg; bags</td><td>Fisher Scientific</td><td>01-812-120</td><td>Used in sample collection - 2 bags required per sample collected</td></tr></tbody></table>

extraction and analysis of microbial phospholipid fatty acids in soils

磷脂脂肪酸（磷脂脂肪酸）是微生物细胞膜的重要组成部分。磷脂脂肪酸的从土壤中提取的分析可以提供关于陆地微生物群落的整体结构的信息。 PLFA分析已经在一系列的生态系统，整体土壤质量的生物指标被广泛使用，而作为土响应的量化指标，以土地管理和其他环境压力。 这里介绍的标准方法，概述了四个主要步骤:1.从土壤样品脂质提取与单相氯仿混合物，2.使用固相萃取柱以分离来自其他提取脂质磷脂分馏，3.磷脂，以产生脂肪酸的甲醇分解甲基酯（脂肪酸甲酯），和4通过使用火焰离子化检测器（GC-FID）毛细管气相色谱FAME分析。两个标准的使用，其中包括1,2- dinonadecanoyl- SN -glycero -3-磷酸胆碱（PC（19:0/19:0）），以评估提取方法的总回收率，和甲基癸（MeC10:0）作为内标（ISTD）用于GC分析。

脂肪酸被指定为X:YωZ，其中X表示碳原子数，Y表示双键的数目，和Z表示从分子的脂族（ω）端第一个双键的位置。后缀&#39;C&#39;和&#39;T&#39;表明顺反几何异构体。前缀和后缀"a"和"i"的指反异和异分支和Me和OH-指定甲基和羟基，分别。 后GC运行，检查样品获得足够的内标通过看10:0的峰值。同时检查的GC标准的反应， 即含正己烷添加了内标溶液瓶;这些应该没有其它峰。内部标准答案应该是在所有运行相似。 图1表示一个representa略去样品运行。大正己烷溶剂峰特征出现在约1.8分钟的保留时间（RT）。内标标准峰（C10:0）出现在3.4分钟RT，而C19:0有16.2分钟的RT。 GC分析分离基于它们的链长，具有更长链洗脱更慢的磷脂脂肪酸;例如，C18:0在洗脱而C16 14.4分:0洗脱为10.8分钟。此外，这种分析协议可以基于它们的不饱和度和其双键的位置的程度分离磷脂脂肪酸;例如，C18:0洗脱14.4分钟，而C18:1 9c中，和C18:14.0 1 7c的洗脱和14.1分钟，分别为（ 图1）。最后，类似链长度和饱和度，但不同的分支配置（反异与 ISO）的磷脂脂肪酸可以分开;例如，C15:0I和C15:在分别为8.6和8.7分钟，0A洗脱（ 图1）。 不同的GC峰的面积可以IM移植到电子表格，以进一步处理该气体色谱信息。每个识别的磷脂脂肪酸进行定量使用下述公式（纳摩尔克干土-1）: <img alt="式（1）" src="/files/ftp_upload/54360/54360eq1.jpg" />其中F为调整因子，考虑到的FID选择性和脂肪酸32之间的摩尔浓度的差异，areaPLFA为每个识别的磷脂脂肪酸，areaC10峰面积:0为内标峰面积（MeC10:0），C 10:0性病加入是GC的运行之前加入到每个样品中ISTD（纳摩尔）的量，比率（C19:0的std加入/ C19:0样品）对应于PC的恢复（C19:0 / C19:O）替代标准和样品重量的烘箱干燥的土壤（G）加入到原始样品离心管并用于提取磷脂脂肪酸的量。 注意:各色下面积NT峰表示为峰面积，反应或反应％取决于GC系统。内相关与土壤磷脂脂肪酸特征的范围内，区域可以被认为是线性比例的脂肪酸的权重;可选地，小的校正因子可以应用到占FID选择性32。另外，因为结果最初重量百分比的基础上表示，它们需要进行归一化，得到摩尔量。调整为摩尔浓度的差异被考虑的单个脂肪酸的分子量实现;已发布的表32和商业软件31也可用于正常化时，摩尔浓度帮助。 ISTD（纳摩尔）的量加入到每个样品可以进一步计算为: （STD添加）0std加= [ISTD]×V:C10 [ISTD]是浓度MeC10的（纳摩尔L -1）:溶于己烷0（甲基癸）（见步骤3.4.1）和V（STD添加）是体积制备的ISTD溶液（L）加入到每个样品之前，GC的运行（ 即 ，按照步骤3.4.4 150微升）。 C19的量:本GC分析在每个样品中的0（纳摩尔​​）对应于: <img alt="公式（2）" src="/files/ftp_upload/54360/54360eq2.jpg" />其中，areaC19:0是C19的峰面积:0，而C19的相应数额:0（纳摩尔​​）添加到每个样品的PLFA提取方法的开始（参见3.1.3步）为: <img alt="公式3" src="/files/ftp_upload/54360/54360eq3.jpg" />其中，[19:0] 标准 （毫克L-1 </SUP&gt;）是C19的浓度:0 nonadecanoate替代标准溶于氯仿（步骤3.1.3），V（19:0加入标准）是准备替代标准的在磷脂脂肪酸提取的开头添加到每个样品的体积方法（参见步骤3.1.3）中，M 19:0为1,2- dinonadecanoyl- SN -glycero -3-磷酸胆碱的分子量（PC（19:0/19:0））。 注:C19的一摩尔:0 nonadecanoate替代标准产量C19两摩尔:0以下的甲基化步骤，而C10:0标准的甲基化之后加入。 下面磷脂脂肪酸典型地排除在土壤的微生物群落的分析:ⅰ）是&lt;14℃和&gt; 20在长度C磷脂脂肪酸，以及ii）与峰面积总计小于0.5％磷脂脂肪酸。一旦这些磷脂脂肪酸已被排除，从所有剩余的磷​​脂脂肪酸的反应可以求和以获得总磷脂脂肪酸双omass（干土纳摩尔g -1）以上。磷脂脂肪酸数据的单因素分析（ 例如 ，方差分析，以下酌情满足正在执行的试验的假设数据变换）可被用于比较总磷脂脂肪酸生物量和/或样品组/处理间选择的组的磷脂脂肪酸生物量。例如， 图2呈现的结果为不同磷脂脂肪酸基团，如直链饱和磷脂脂肪酸，饱和磷脂脂肪酸与任一中链（10 -甲基）或末端支链的相对分布，和单-或多不饱和的磷脂脂肪酸，以及总磷脂脂肪酸的总和（干土纳摩尔g -1）以上。在这个特殊的例子中，树（其降低从站点1至站点3）的年龄被看作是同时影响总磷脂脂肪酸和不同的磷脂脂肪酸基团的相对分布。 为了评估样品中磷脂脂肪酸组成的整体模式，所有磷脂脂肪酸的多元分析可以condu反恐执行局33。所述磷脂脂肪酸数据需要选定多变量分析之前，要转化为所需的，满足统计测试和研究问题的假设被寻址， 例如 ，一个海林格变换经常被用来相对化的数据图 ，从非3所示的结果在图2中使用的相同的数据的-metric多维尺度（基本数据）的协调; NMDS是一个非参数，多元技术产生根据采样之间排名分数的相似性数据点的2-或3-维定位。 <img alt="图1" src="/files/ftp_upload/54360/54360fig1.jpg" /> 图1 代表性GC-FID色谱图。从培养布朗Chernozemic土壤获得的样品被用于此分析。保留时间和相应的磷脂脂肪酸（括号内）和它们的峰面积（PA）一重新标示在图中为代表的峰值。为了清楚起见，不是所有的峰标示在图中，虽然这种特定的样品产生33号磷脂脂肪酸。 <a href="https://jove.unicartagenaproxy.elogim.com/files/ftp_upload/54360/54360fig1large.jpg" target="_blank">请点击此处查看该图的放大版本。</a> <img alt="图2" src="/files/ftp_upload/54360/54360fig2.jpg" /> 图 2. 六种不同的类型磷脂脂肪酸（占总磷脂脂肪酸的百分比）的相对丰度和总生物量PLFA（纳摩尔摹-1 干燥的土壤 ，从森林Luvisolic 土壤）样品用于该分析。结果表明在站点1，它位于下较旧树（&gt; 50岁）的土壤更大的总磷脂脂肪酸，随后的中间年龄（25岁）的树木（站点2），最后是最年轻的（10年）的树木（网站3）。相对较多的饱和磷脂脂肪酸存在于最年轻现场，而更多的不饱和磷脂脂肪酸存在于最古老的站点。误差棒表示标准偏差。 <a href="https://jove.unicartagenaproxy.elogim.com/files/ftp_upload/54360/54360fig2large.jpg" target="_blank">请点击此处查看该图的放大版本。</a> <img alt="图3" src="/files/ftp_upload/54360/54360fig3.jpg" /> 磷脂脂肪酸的 图3. 非十进制多维尺度（基本数据）排序（使用总磷脂脂肪酸数据的％）对轴2和3维溶液3。这种配合用的是相同的数据与用于图2计算出来的。该最年轻的网站（站点3）分隔从旧两个网站的很清楚（站点2和3），显示在他们的PLFA微生物群落组成重叠。在由每个轴被包括在括号解释了磷脂脂肪酸社区数据的变化量;变异的72.5％是由这些两轴的3-扪解释简称AE，下同NMDS的解决方案。 <a href="https://jove.unicartagenaproxy.elogim.com/files/ftp_upload/54360/54360fig3large.jpg" target="_blank">请点击此处查看该图的放大版本。</a>

Watch this Scientific Journal Video about Extraction and Analysis of Microbial Phospholipid Fatty Acids in Soils at JoVE.com

Extraction and Analysis of Microbial Phospholipid Fatty Acids in Soils

磷脂脂肪酸提供有关土壤微生物群落结构的信息。我们提出了一个单相氯仿混合物，用固相萃取柱提取脂质的分馏，和甲醇，以产生脂肪酸甲酯，其通过毛细管气相色谱法分析从土壤样品萃取方法。

提取及土壤微生物磷脂脂肪酸分析

Phospholipid fatty acids (PLFAs) are key components of microbial cell membranes. The analysis of PLFAs extracted from soils can provide information about ...

extraction-and-analysis-of-microbial-phospholipid-fatty-acids-in-soils

Universidad de Cartagena UDEC

Research

JoVE Journal

Environment

30.8K 次观看.  University of Alberta. 磷脂脂肪酸提供有关土壤微生物群落结构的信息。我们提出了一个单相氯仿混合物，用固相萃取柱提取脂质的分馏，和甲醇，以产生脂肪酸甲酯，其通过毛细管气相色谱法分析从土壤样品萃取方法。 

视频: Extraction and Analysis of Microbial Phospholipid Fatty Acids in Soils

Analysis of Fatty Acid Content and Composition in Microalgae

A method to determine the content and composition of total fatty acids present in microalgae is described. Fatty acids are a major constituent of microalgal biomass. These fatty acids can be present in different acyl-lipid classes. Especially the fatty acids present in triacylglycerol (TAG) are of commercial interest, because they can be used for production of transportation fuels, bulk chemicals, nutraceuticals (&omega;-3 fatty acids), and food commodities. To develop commercial applications, reliable analytical methods for quantification of fatty acid content and composition are needed. Microalgae are single cells surrounded by a rigid cell wall. A fatty acid analysis method should provide sufficient cell disruption to liberate all acyl lipids and the extraction procedure used should be able to extract all acyl lipid classes.
With the method presented here all fatty acids present in microalgae can be accurately and reproducibly identified and quantified using small amounts of sample (5 mg) independent of their chain length, degree of unsaturation, or the lipid class they are part of.
This method does not provide information about the relative abundance of different lipid classes, but can be extended to separate lipid classes from each other.
The method is based on a sequence of mechanical cell disruption, solvent based lipid extraction, transesterification of fatty acids to fatty acid methyl esters (FAMEs), and quantification and identification of FAMEs using gas chromatography (GC-FID). A TAG internal standard (tripentadecanoin) is added prior to the analytical procedure to correct for losses during extraction and incomplete transesterification.

A method for the determination of fatty acid content and composition in microalgae based on mechanical cell disruption, solvent based lipid extraction, transesterification, and quantification and identification of fatty acids using gas chromatography is described. A tripentadecanoin internal standard is used to compensate for the possible losses during extraction and incomplete transesterification.

脂肪酸组分和含量微藻分析

A method to determine the content and composition of total fatty acids present in microalgae is described. Fatty acids are a major constituent of ...

科研

环境科学

Isolation and Chemical Characterization of Lipid A from Gram-negative Bacteria

Lipopolysaccharide (LPS) is the major cell surface molecule of gram-negative bacteria, deposited on the outer leaflet of the outer membrane bilayer. LPS can be subdivided into three domains: the distal O-polysaccharide, a core oligosaccharide, and the lipid A domain consisting of a lipid A molecular species and 3-deoxy-D-manno-oct-2-ulosonic acid residues (Kdo). The lipid A domain is the only component essential for bacterial cell survival. Following its synthesis, lipid A is chemically modified in response to environmental stresses such as pH or temperature, to promote resistance to antibiotic compounds, and to evade recognition by mediators of the host innate immune response. The following protocol details the small- and large-scale isolation of lipid A from gram-negative bacteria. Isolated material is then chemically characterized by thin layer chromatography (TLC) or mass-spectrometry (MS). In addition to matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) MS, we also describe tandem MS protocols for analyzing lipid A molecular species using electrospray ionization (ESI) coupled to collision induced dissociation (CID) and newly employed ultraviolet photodissociation (UVPD) methods. Our MS protocols allow for unequivocal determination of chemical structure, paramount to characterization of lipid A molecules that contain unique or novel chemical modifications. We also describe the radioisotopic labeling, and subsequent isolation, of lipid A from bacterial cells for analysis by TLC. Relative to MS-based protocols, TLC provides a more economical and rapid characterization method, but cannot be used to unambiguously assign lipid A chemical structures without the use of standards of known chemical structure. Over the last two decades isolation and characterization of lipid A has led to numerous exciting discoveries that have improved our understanding of the physiology of gram-negative bacteria, mechanisms of antibiotic resistance, the human innate immune response, and have provided many new targets in the development of antibacterial compounds.

Isolation and characterization of the lipid A domain of lipopolysaccharide (LPS) from gram-negative bacteria provides insight into cell surface based mechanisms of antibiotic resistance, bacterial survival and fitness, and how chemically diverse lipid A molecular species differentially modulate host innate immune responses.

隔离和脂质A的化学表征革兰氏阴性菌

Lipopolysaccharide (LPS) is the major cell surface molecule of gram-negative bacteria, deposited on the outer leaflet of the outer membrane bilayer. LPS ...

化学

A Simple Fractionated Extraction Method for the Comprehensive Analysis of Metabolites, Lipids, and Proteins from a Single Sample

Understanding of complex biological systems requires the measurement, analysis and integration of multiple compound classes of the living cell, usually determined by transcriptomic, proteomic, metabolomics and lipidomic measurements. In this protocol, we introduce a simple method for the reproducible extraction of metabolites, lipids and proteins from biological tissues using a single aliquot per sample. The extraction method is based on a methyl tert-butyl ether: methanol: water system for liquid: liquid partitioning of hydrophobic and polar metabolites into two immiscible phases along with the precipitation of proteins and other macromolecules as a solid pellet. This method, therefore, provides three different fractions of specific molecular composition, which are fully compatible with common high throughput 'omics' technologies such as liquid chromatography (LC) or gas chromatography (GC) coupled to mass spectrometers. Even though the method was initially developed for the analysis of different plant tissue samples, it has proved to be fully compatible for the extraction and analysis of biological samples from systems as diverse as algae, insects, and mammalian tissues and cell cultures.

A protocol for comprehensive extraction of lipids, metabolites and proteins from biological tissues using one sample is presented.

一种简单的分级提取方法，用于从单个样品中综合分析代谢物，脂质和蛋白质

Understanding of complex biological systems requires the measurement, analysis and integration of multiple compound classes of the living cell, usually ...

生物化学

A Lipid Extraction and Analysis Method for Characterizing Soil Microbes in Experiments with Many Samples

Microbial communities are important drivers and regulators of ecosystem processes. To understand how management of ecosystems may affect microbial communities, a relatively precise but effort-intensive technique to assay microbial community composition is phospholipid fatty acid (PLFA) analysis. PLFA was developed to analyze phospholipid biomarkers, which can be used as indicators of microbial biomass and the composition of broad functional groups of fungi and bacteria. It has commonly been used to compare soils under alternative plant communities, ecology, and management regimes. The PLFA method has been shown to be sensitive to detecting shifts in microbial community composition.
An alternative method, fatty acid methyl ester extraction and analysis (MIDI-FA) was developed for rapid extraction of total lipids, without separation of the phospholipid fraction, from pure cultures as a microbial identification technique. This method is rapid but is less suited for soil samples because it lacks an initial step separating soil particles and begins instead with a saponification reaction that likely produces artifacts from the background organic matter in the soil. 
This article describes a method that increases throughput while balancing effort and accuracy for extraction of lipids from the cell membranes of microorganisms for use in characterizing both total lipids and the relative abundance of indicator lipids to determine soil microbial community structure in studies with many samples. The method combines the accuracy achieved through PLFA profiling by extracting and concentrating soil lipids as a first step, and a reduction in effort by saponifying the organic material extracted and processing with the MIDI-FA method as a second step.

The article describes a method that increases throughput while balancing effort and accuracy for extraction of lipids from the cell membranes of microorganisms for use in characterizing both total lipids and the relative abundance of indicator lipids to determine soil microbial community structure in studies with many samples.

用于表征土壤微生物的脂质提取和分析方法在许多样品的实验中

Microbial communities are important drivers and regulators of ecosystem processes. To understand how management of ecosystems may affect microbial ...

Fatty Acid 13C Isotopologue Profiling Provides Insight into Trophic Carbon Transfer and Lipid Metabolism of Invertebrate Consumers

Fatty acids (FAs) are useful biomarkers in food web ecology because they are typically assimilated as a complete molecule and transferred into consumer tissue with minor or no modification, allowing the dietary routing between different trophic levels. However, the FA trophic marker approach is still hampered by the limited knowledge in lipid metabolism of the soil fauna. This study used entirely labelled palmitic acid (13C16:0, 99 atom%) as a tracer in fatty acid metabolism pathways of two widespread soil Collembola, Protaphorura fimata and Heteromurus nitidus. In order to investigate the fate and metabolic modifications of this precursor, a method of isotopologue profiling is presented, performed by mass spectrometry using single ion monitoring. Moreover, the upstream laboratory feeding experiment is described, as well as the extraction and methylation of dominant lipid fractions (neutral lipids, phospholipids) and the related formula and calculations. Isotopologue profiling does not only yield the overall 13C enrichment in fatty acids derived from the 13C labeled precursor but also produces the pattern of isotopologues exceeding the mass of the parent ion (i.e., the FA molecular ion M+) of each labeled FA by one or more mass units (M+1, M+2, M+3, etc.). This knowledge allows conclusions on the ratio of dietary routing of an entirely consumed FA in comparison to de novo biosynthesis. The isotopologue profiling is suggested as a useful tool for evaluation of fatty acid metabolism in soil animals to disentangle trophic interactions.

Fatty Acid 13C Isotopologue Profiling Provides Insight into Trophic Carbon Transfer and Lipid Metabolism of Invertebrate Consumers

The fatty acid trophic marker approach, i.e., the assimilation of fatty acids as entire molecule and transfer into consumer tissue with no or minor modification, is hampered by knowledge gaps in fatty acid metabolism of small soil invertebrates. Isotopologue profiling is provided as a valuable tool to disentangle trophic interactions.

脂肪酸13C Isotopologue 分析提供了对无脊椎动物消费者营养碳转移和脂质代谢的洞察力

Fatty acids (FAs) are useful biomarkers in food web ecology because they are typically assimilated as a complete molecule and transferred into consumer ...

Single-throughput Complementary High-resolution Analytical Techniques for Characterizing Complex Natural Organic Matter Mixtures

Natural organic matter (NOM) is composed of a highly complex mixture of thousands of organic compounds which, historically, proved difficult to characterize. However, to understand the thermodynamic and kinetic controls on greenhouse gas (carbon dioxide [CO2] and methane [CH4]) production resulting from the decomposition of NOM, a molecular-level characterization coupled with microbial proteome analyses is necessary. Further, climate and environmental changes are expected to perturb natural ecosystems, potentially upsetting complex interactions that influence both the supply of organic matter substrates and the microorganisms performing the transformations. A detailed molecular characterization of the organic matter, microbial proteomics, and the pathways and transformations by which organic matter is decomposed will be necessary to predict the direction and magnitude of the effects of environmental changes. This article describes a methodological throughput for comprehensive metabolite characterization in a single sample by direct injection Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS), gas chromatography mass spectrometry (GC-MS), nuclear magnetic resonance (NMR) spectroscopy, liquid chromatography mass spectrometry (LC-MS), and proteomics analysis. This approach results in a fully-paired dataset which improves statistical confidence for inferring pathways of organic matter decomposition, the resulting CO2 and CH4 production rates, and their responses to environmental perturbation. Herein we present results of applying this method to NOM samples collected from peatlands; however, the protocol is applicable to any NOM sample (e.g., peat, forested soils, marine sediments, etc.).

This protocol describes a single throughput for complementary analytical and omics techniques culminating in a fully-paired characterization of natural organic matter and microbial proteomics in different ecosystems. This approach permits robust comparisons for identifying metabolic pathways and transformations important for describing greenhouse gas production and predicting responses to environmental change.

用于表征复杂天然有机物混合物的单吞吐量互补高分辨率分析技术

Natural organic matter (NOM) is composed of a highly complex mixture of thousands of organic compounds which, historically, proved difficult to ...

Isolation of Lipoprotein Particles from Chicken Egg Yolk for the Study of Bacterial Pathogen Fatty Acid Incorporation into Membrane Phospholipids

Staphylococcus aureus and other Gram-positive pathogens incorporate fatty acids from the environment into membrane phospholipids. During infection, the majority of exogenous fatty acids are present within host lipoprotein particles. Uncertainty remains as to the reservoirs of host fatty acids and the mechanisms by which bacteria extract fatty acids from the lipoprotein particles. In this work, we describe protocols for enrichment of low-density lipoprotein (LDL) particles from chicken egg yolk and determining whether LDLs serve as fatty acid reservoirs for S. aureus. This method exploits unbiased lipidomic analysis and chicken LDLs, an effective and economical model for the exploration of interactions between LDLs and bacteria. The analysis of S. aureus integration of exogenous fatty acids from LDLs is performed using high-resolution/accurate mass spectrometry and tandem mass spectrometry, enabling the characterization of the fatty acid composition of the bacterial membrane and unbiased identification of novel combinations of fatty acids that arise in bacterial membrane lipids upon exposure to LDLs. These advanced mass spectrometry techniques offer an unparalleled perspective of fatty acid incorporation by revealing the specific exogenous fatty acids incorporated into the phospholipids. The methods outlined here are adaptable to the study of other bacterial pathogens and alternative sources of complex fatty acids.

This method provides a framework for studying incorporation of exogenous fatty acids from complex host sources into bacterial membranes, particularly Staphylococcus aureus. To achieve this, protocols for the enrichment of lipoprotein particles from chicken egg yolk and subsequent fatty acid profiling of bacterial phospholipids utilizing mass spectrometry are described.

从鸡蛋黄中分离脂蛋白颗粒,研究细菌病原体脂肪酸并入膜磷脂

Staphylococcus aureus and other Gram-positive pathogens incorporate fatty acids from the environment into membrane phospholipids. During infection, the ...

The MPLEx Protocol for Multi-omic Analyses of Soil Samples

Mass spectrometry (MS)-based integrated metaproteomic, metabolomic, and lipidomic (multi-omic) studies are transforming our ability to understand and characterize microbial communities in environmental and biological systems. These measurements are even enabling enhanced analyses of complex soil microbial communities, which are the most complex microbial systems known to date. Multi-omic analyses, however, do have sample preparation challenges, since separate extractions are typically needed for each omic study, thereby greatly amplifying the preparation time and amount of sample required. To address this limitation, a 3-in-1 method for the simultaneous extraction of metabolites, proteins, and lipids (MPLEx) from the same soil sample was created by adapting a solvent-based approach. This MPLEx protocol has proven to be both simple and robust for many sample types, even when utilized for limited quantities of complex soil samples. The MPLEx method also greatly enabled the rapid multi-omic measurements needed to gain a better understanding of the members of each microbial community, while evaluating the changes taking place upon biological and environmental perturbations.

A protocol is presented for simultaneously extracting metabolites, proteins, and lipids from a single soil sample, allowing reduced sample preparation times and enabling multi-omic mass spectrometry analyses of samples with limited quantities.

土壤样品多涨价可能分析的 MPLEx 协议

Mass spectrometry (MS)-based integrated metaproteomic, metabolomic, and lipidomic (multi-omic) studies are transforming our ability to understand and ...

Two-Dimensional Visualization and Quantification of Labile, Inorganic Plant Nutrients and Contaminants in Soil

We describe a method for two-dimensional (2D) visualization and quantification of the distribution of labile (i.e., reversibly adsorbed) inorganic nutrient (e.g., P, Fe, Mn) and contaminant (e.g., As, Cd, Pb) solute species in the soil adjacent to plant roots (the &#8216;rhizosphere&#8217;) at sub-millimeter (~100 &#181;m) spatial resolution. The method combines sink-based solute sampling by the diffusive gradients in thin films (DGT) technique with spatially resolved chemical analysis by laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). The DGT technique is based on thin hydrogels with homogeneously distributed analyte-selective binding phases. The variety of available binding phases allows for the preparation of different DGT gel types following simple gel fabrication procedures. For DGT gel deployment in the rhizosphere, plants are grown in flat, transparent growth containers (rhizotrons), which enable minimal invasive access to a soil-grown root system. After a pre-growth period, DGT gels are applied to selected regions of interest for in situ solute sampling in the rhizosphere. Afterwards, DGT gels are retrieved and prepared for subsequent chemical analysis of the bound solutes using LA-ICP-MS line-scan imaging. Application of internal normalization using 13C and external calibration using matrix-matched gel standards further allows for the quantification of the 2D solute fluxes. This method is unique in its capability to generate quantitative, sub-mm scale 2D images of multi-element solute fluxes in soil-plant environments, exceeding the achievable spatial resolution of other methods for measuring solute gradients in the rhizosphere substantially. We present the application and evaluation of the method for imaging multiple cationic and anionic solute species in the rhizosphere of terrestrial plants and highlight the possibility of combining this method with complementary solute imaging techniques.

This protocol presents a workflow for sub-mm 2D visualization of multiple labile inorganic nutrient and contaminant solute species using diffusive gradients in thin films (DGT) combined with mass spectrometry imaging. Solute sampling and high-resolution chemical analysis are described in detail for quantitative mapping of solutes in the rhizosphere of terrestrial plants.

土壤中实验室、无机植物养分和污染物的二维可视化和量化

We describe a method for two-dimensional (2D) visualization and quantification of the distribution of labile (i.e., reversibly adsorbed) inorganic ...

Quantitative Analysis of the Cellular Lipidome of Saccharomyces Cerevisiae Using Liquid Chromatography Coupled with Tandem Mass Spectrometry

Lipids are structurally diverse amphipathic molecules that are insoluble in water. Lipids are essential contributors to the organization and function of biological membranes, energy storage and production, cellular signaling, vesicular transport of proteins, organelle biogenesis, and regulated cell death. Because the budding yeast Saccharomyces cerevisiae is a unicellular eukaryote amenable to thorough molecular analyses, its use as a model organism helped uncover mechanisms linking lipid metabolism and intracellular transport to complex biological processes within eukaryotic cells. The availability of a versatile analytical method for the robust, sensitive, and accurate quantitative assessment of major classes of lipids within a yeast cell is crucial for getting deep insights into these mechanisms. Here we present a protocol to use liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) for the quantitative analysis of major cellular lipids of S. cerevisiae. The LC-MS/MS method described is versatile and robust. It enables the identification and quantification of numerous species (including different isobaric or isomeric forms) within each of the 10 lipid classes. This method is sensitive and allows identification and quantitation of some lipid species at concentrations as low as 0.2 pmol/&#181;L. The method has been successfully applied to assessing lipidomes of whole yeast cells and their purified organelles. The use of alternative mobile phase additives for electrospray ionization mass spectrometry in this method can increase the efficiency of ionization for some lipid species and can be therefore used to improve their identification and quantitation.

Quantitative Analysis of the Cellular Lipidome of Saccharomyces Cerevisiae Using Liquid Chromatography Coupled with Tandem Mass Spectrometry

We present a protocol using liquid chromatography coupled with tandem mass spectrometry to identify and quantify major cellular lipids in Saccharomyces cerevisiae. The described method for a quantitative assessment of major lipid classes within a yeast cell is versatile, robust, and sensitive.

使用液色谱结合串联质谱法对糖皮质酶细胞脂质的定量分析

Lipids are structurally diverse amphipathic molecules that are insoluble in water. Lipids are essential contributors to the organization and function of ...