作者感谢生物信息学解决方案公司的乔纳森·克里格博士为具有代表性的实验操作质谱仪，以及盖德斯-麦卡利斯特小组的成员在实验设置和手稿反馈方面给予的帮助。作者承认部分资金支持 来自班廷研究基金会-贾里斯洛夫斯基奖学金发现奖，新前沿研究基金-探索（NFRFE-2019-00425），加拿大创新基金会（JELF 38798）为J.G.M，以及NSERC加拿大研究生奖学金-硕士和安大略研究生奖学金的B.B，和女王伊丽莎白二世研究生奖学金为A.S.。

圭尔夫大学动物利用议定书4193批准的以下议定书使用了源自BALB/c小鼠的永生巨噬细胞系。值得注意的是，其他小鼠菌株或其他永生细胞来源可以应用于概述的协议，并有足够的测试，以优化详细的参数。以下协议将导航从大噬细胞的冷冻小瓶开始的步骤。细胞储存在10%FBS（胎儿牛血清），1%L-谷氨酰胺和5%笔/链球菌（青霉素-链霉素）混合物DMEM（杜尔贝科的改性鹰介质）和20%DMSO（二甲基硫氧化物）。
1. 培养 C.新福尔曼
<ol><li>使用甘油股票， 条纹野生类型 C. 新福尔曼菌 株 （H99） 到酵母提取肽德克斯特罗斯 （YPD） 藻类板分离单个殖民地.</li>
	<li>在静态孵化器中，在37°C下孵化16小时。</li>
	<li>在松散封顶的 10 mL 试管中，在 5 mL 的 YPD 汤中选择一个野生类型 C. 新福尔曼 菌株和文化的单一殖民地。以四倍为下执行。</li>
	<li>在 200 rpm 的摇晃孵化器中，在 37 °C 下过夜 16 小时。</li>
	<li>第二天亚文化每个通宵文化在1:100稀释成3 mL YPD汤。</li>
	<li>测量真菌培养的光学密度 （OD600nm）值，以确定中日志阶段。根据光谱仪 ，C. neoformans 野生型菌株在 YPD 中通常经过 6 到 8 小时的孵化后达到中日志阶段，一般 OD600nm 值在 1.0 到 1.5 之间。</li>
	<li>一旦细胞达到中日志阶段，将10 μL真菌细胞悬浮液放入清洁的1.5 mL微中心管中，在无菌的1x磷酸盐缓冲盐水中稀释1:100。使用血细胞计计算细胞数量。</li>
</ol>2. 培养巨噬细胞
注:确保工作环境在细胞培养工作之前进行消毒。
<ol><li>细胞培养介质的制备<ol>
<li>抗生素补充介质:加入10%FBS（胎儿牛血清），1%L-谷氨酰胺和5%笔/链球菌（青霉素-链霉素）混合物到DMEM（杜尔贝科的改性鹰介质）。通过 0.2 μm 完整滤镜系统过滤介质，并在 4 °C 下存储。</li>
		<li>无抗生素介质:遵循相同的协议，但省略笔/链球菌混合物。</li>
	</ol></li>
	<li>播种巨噬细胞 注:抗生素补充介质（2.1.1.）应在细胞培养工作之前加热至37°C。<ol>
<li>在37°C珠浴中快速解冻大噬细胞小瓶。</li>
		<li>通过在1mL抗生素补充介质中重新悬生细胞来清洗冷冻溶液的细胞。 注意:需要额外的预防措施，以确保细胞不会因为苛刻的管道而解洗。</li>
		<li>将重新悬念的细胞转移到无菌的15mL管中。</li>
		<li>在室温下以400× 克 离心5分钟，将颗粒细胞离心。</li>
		<li>用血清移液器或真空吸气器小心地去除超纳坦。</li>
		<li>在10 mL抗生素补充介质中轻轻重新悬生颗粒。</li>
		<li>轻轻移液器将细胞重新悬停在 60 x 15 mm 细胞培养处理的盘子中。</li>
		<li>在37°C下孵化菜肴，隔夜 含5%二氧化碳。</li>
	</ol></li>
	<li>巨噬细胞的初始通过 注:冷冻细胞库存通常每瓶含有500万至1000万个细胞（约1 mL）。第二天，在播种协议中幸存下来的巨噬细胞将粘附在细胞培养皿的底部，并准备通过。<ol>
<li>在细胞培养工作之前，加热抗生素补充介质（步骤2.1.1）至37°C。确保在细胞培养工作之前对工作环境进行消毒。使用光显微镜可视化细胞，以确保细胞的粘附和健康。</li>
		<li>用血清移液器或真空吸气器从盘子中取出细胞培养介质。</li>
		<li>轻轻将 5-7 mL 的无菌室温 PBS（磷酸盐缓冲盐水）添加到 60 x 15 mm 盘中。</li>
		<li>轻轻倾斜盘洗粘性细胞。</li>
		<li>使用血清学移液器或真空吸气器取出 PBS。</li>
		<li>将 1 mL 的冷 PBS（储存在 4 °C）添加到细胞中，倾斜盘将 PBS 分布在所有细胞上，并允许在室温下坐 1 分钟。</li>
		<li>通过轻轻敲击盘、将冷 PBS 吹到细胞上或使用细胞刮刀来释放细胞。</li>
		<li>在菜中加入 9 mL 的抗生素补充介质。</li>
		<li>在一个新的60×15毫米菜，添加9 mL的新鲜抗生素补充介质和1 mL的重新悬生细胞从原来的菜。</li>
		<li>一旦需要的通过板的数量被填充，从原来的菜的重新悬息细胞可以丢弃。</li>
		<li>在上述设置（步骤 2.2.8）中孵育新菜。</li>
		<li>当细胞达到 70-80% 的汇合时（大约每 2 天根据所使用的细胞系和介质执行后续传递）。 注:在感染实验之前，应至少通过五次巨噬细胞。根据细胞系的不同，感染实验应通过 25 到 30 个通道进行。在25到30个通道后，细胞应冻结或丢弃。第 2.4 节或第 2.5 节可以根据实验计划进行选择。第2.4节将用于以下部分。</li>
	</ol></li>
	<li>感染前播种大噬细胞<ol>
<li>执行传递协议步骤 2.3.1 到 2.3.7。</li>
		<li>使用血细胞计或自动细胞计数器确定细胞密度（细胞/mL）。</li>
		<li>将 0.3 x 106 微噬细胞转移到 6 井细胞培养板的单井中。</li>
		<li>使用抗生素补充介质（步骤 2.1.1）将井的总体积调整到 1 mL。</li>
		<li>重复步骤 2.4.3 和 2.4.4，直到 8 口油井被填充（4 口井填充在两个单独的板中）。</li>
		<li>可选地，在剩余的两口井中填充 1 mL 的室温 PBS，以在孵化期间保持水分水平。</li>
		<li>允许细胞在感染前2.2.8步2d的潜伏条件下生长。</li>
	</ol></li>
	<li>感染当天播种大噬细胞<ol>
<li>执行传递协议步骤 2.3.1 到 2.3.7。</li>
		<li>使用血细胞计或自动细胞计数器确定细胞密度（细胞/mL）。</li>
		<li>种子 1.2 x 106 巨噬细胞进入 6 井细胞培养板的单井。</li>
		<li>使用抗生素补充介质（2.1.1）将井的总体积调整到1 mL。</li>
		<li>重复 2.4.3 和 2.4.4，直到 8 口井被填充（4 口井填充在两个单独的板中）。</li>
		<li>可选地，在剩余的两口井中填充 1 mL 的室温 PBS，以在孵化期间保持水分水平。</li>
		<li>在步骤 2.2.8 中提及的 3 小时条件下孵化细胞，使细胞能够粘附在井上。</li>
	</ol></li>
</ol>3. 与C.新福尔曼的巨噬细胞感染
注:达到70-80%汇合后，每口井将有大约1.2 x 106 个巨噬细胞。要达到所需的100:1感染（MOI）的多重性，每个反应需要1.2 x 108 真菌细胞。文化必须在生物四倍体中相应地设置。
免责声明:100:1 的 MOI 在我们的研究组中取得了理想的成果，旨在向读者提供建议。对于更具传染性的 C. 新福尔曼 菌株或弹性较小的巨噬细胞系，可能需要较低的 MOI。感染验证（第3.5节）可用于确定特定 C.新福尔曼- 巨噬细胞组合的理想MOI。
<ol><li>真菌细胞的制备<ol>
<li>跟随第 1 步， 将 C. 新福尔曼人 增长到中日志阶段。</li>
		<li>收集和离心电池在1，500 x g 10分钟，轻轻洗颗粒与无菌室温PBS，并重复共三次洗涤。</li>
		<li>无抗生素细胞培养介质（步骤2.1.2）中的再悬生细胞，达到1.2 x 108 细胞/mL的浓度。</li>
	</ol>
</li>
	<li>大噬细胞的制备<ol>
<li>可视化 6 井板中的每口井，以确保细胞达到 70-80% 的汇合度。或者，可以测量细胞，以达到每口井约1.2×106 大噬细胞。</li>
		<li>按照步骤 2.3.1 到 2.3.4。</li>
	</ol>
</li>
	<li>C.新福尔曼和巨噬细胞的共同培养<ol>
<li>将重新悬念的 C.新福尔曼细胞 （步骤3.1.3）的1 mL添加到包含第3.2节中制备的巨噬细胞的4口井中。 注:在开始实验之前，需要计算所需的板数。在空井中加入 1 mL 的无抗生素介质（步骤 2.1.2）。</li>
		<li>允许细胞在列出的条件下孵育（步骤 2.2.8），持续 3 小时。</li>
		<li>使用血清学移液器或真空吸气器从盘子中取出细胞培养介质。</li>
		<li>轻轻添加 1 mL 的无菌室温 PBS。</li>
		<li>轻轻倾斜板洗非附着或非噬细胞外 C.新福尔曼细胞 。</li>
		<li>使用血清移液器或真空吸气器取出 PBS，重复共洗三次。重复 3.3.4 到 3.3.5 两次。</li>
	</ol>
</li>
	<li>未受感染的巨噬细胞<ol>
<li>同样，使用 6 井板的 4 口井作为仅限巨噬细胞的样品。在这些油井中加入1 mL的无抗生素介质（步骤2.1.2）。</li>
		<li>重复步骤 3.3.2 到 3.3.6。</li>
	</ol>
</li>
	<li>感染验证 注:使用细胞毒性检测，可以测量感染能力。以下协议将突出显示细胞毒性产品的应用，以测量 LDH（乳酸脱水酶）释放。其他细胞毒性产品也可用于。<ol>
<li>大噬细胞 C.新福尔曼感染 的准备<ol>
<li>重复步骤 1、2 和 3（最多 3.5）。如果愿意，LDH 检测可以分三次进行。</li>
			<li>继步骤 3.3.6 后，在 6 井板中每个井中添加 1 mL 的无抗生素介质 （2.1.2）。</li>
			<li>重复步骤 2.4.3 和 2.4.4，直到已填充 3 口井加 3n 口井（其中 n 是测量的时间点数）。</li>
			<li>在步骤 2.2.8 列出的条件下孵化细胞。</li>
			<li>在选定的时间点（例如，1、3、6、12 和 24 小时）收集超标剂，根据制造商的说明测量 LDH 释放</li>
			<li>同时，未受感染的巨噬细胞将被分析为最大细胞毒性的确定值。</li>
			<li>计算细胞毒性如下: <img alt="Equation 1" src="/files/ftp_upload/61881/61881equ01.jpg">
</li>
		</ol>
</li>
	</ol>
</li>
</ol>4. 样本收集
<ol><li>共同文化和未受感染的宏噬菌体收藏<ol>
<li>在细胞中加入1 mL的冷PBS（从3.3.6和3.4.2），并允许在室温下坐1分钟。</li>
		<li>通过轻轻敲击板或轻轻吹动冷 PBS 对细胞释放板中的细胞。 注意:应避免使用细胞刮刀，因为这可能导致细胞裂解。</li>
		<li>移液器将细胞重新插入15mL管中。</li>
		<li>离心机细胞在室温下在400 x g 下5分钟，并去除超纳坦。</li>
		<li>加工细胞（详见第 5 步）立即或闪烁冻结在液氮中，并储存在 -80 °C 以供以后处理。</li>
	</ol>
</li>
</ol>5. 细胞蛋白质组
注:必须针对所分析的细胞类型优化足够的裂解（即周期和振幅的数量取决于细胞颗粒大小和探针声波模型的功率百分比）。
<ol><li>受感染的巨噬细胞裂解<ol>
<li>100 mTris-HCl （pH 8.5） 的 300 μL 中重新悬生颗粒细胞（步骤 4.1.5），由新鲜溶解的蛋白酶抑制剂鸡尾酒片组成。 注:在开始实验之前，在10mL的冰冷100mTris-HCl（pH 8.5）中加入一种蛋白酶抑制剂鸡尾酒片剂。</li>
		<li>在冰浴中探测声波细胞15个周期，30个开启和30个关闭，以解冻细胞。</li>
		<li>离心机细胞在400 x g下短暂地为30s，小心不要形成颗粒，只是为了去除管子两侧的液体，然后将样品转移到2mL Lo-bind微中心管中。</li>
		<li>将 20% SDS 的 1:10 体积添加到 2% 的最终浓度中。</li>
		<li>将 1:100 体积的 1 M 二甲二醇 （DTT） 添加到 10 mM 的最终浓度中，并通过管道彻底混合样品，然后在 95 °C 的热加热块上孵育 10 分钟，在 800 rpm 搅拌时进行。接下来，冷却到室温（冰上可以冷却）。</li>
		<li>添加 1:10 体积 0.55 M 碘乙酰胺 （IAA）， 获得 55 mM 的最终浓度，并通过管道彻底混合样品。在室温下在黑暗中孵育20分钟。</li>
		<li>加入100%丙酮，获得80%丙酮的最终浓度，并在-20°C过夜储存样品，以沉淀蛋白质。</li>
	</ol></li>
	<li>蛋白质消化<ol>
<li>第二天，在10，000 x g 和4°C下，通过离心收集沉淀颗粒10分钟。 丢弃超纳坦剂，用 500μL 的 80% 丙酮清洗颗粒。重复共两次洗涤。洗涤后室温下空气干燥颗粒。</li>
		<li>在 8 M 尿素/40 mM HEPES 的 100 μL 中重新溶解蛋白颗粒，以确保在冰水浴中完全溶解、涡流或声波化 15 个周期的 30 s 开和 30 s 关闭。 注:尿素/HEPES 的体积调整取决于沉淀细胞颗粒的大小，如果发生改变，必须适当调整所有下游体积。</li>
		<li>根据制造商的说明，使用蛋白质检测（例如 BCA 蛋白质检测）量化蛋白质浓度，并通过 8 M 尿素/40 m M HEPES 的空白规范化调整背景测量。</li>
		<li>加入 300 μL 的 50 mM 碳酸氢铵，获得 2 M 尿素的最终浓度。 注:为下游测量实现蛋白质浓度正常化的机会，建议消化100微克蛋白质，并通过在液氮中闪光冻结来储存剩余未消化的样品，然后短期储存在-20°C或-80°C。</li>
		<li>在冰上加入2:50（v/w）的试金蛋白/Lys-C蛋白酶混合物的酶与蛋白质的比例，轻轻轻点管混合，在室温下孵育过夜。</li>
		<li>孵化后，通过在室温下加入1:10体积停止溶液（20%丙酮酸、6%三氟乙酸）和离心机样品，在10，000 x g 下停止消化5分钟。</li>
		<li>收集超纳坦（由消化的肽组成），并丢弃任何颗粒状碎片或沉淀物。</li>
	</ol></li>
	<li>肽脱盐<ol>
<li>通过在 1，000 x g 下添加 100 μL 的 100 μL 100% 丙酮和离心机，激活 C18 停止和去提取 （STAGE） 尖端（由 200 μL 移液器尖端中的 3 层 C18 树脂组成）。</li>
		<li>通过添加 50μL 的缓冲 B （80% （v/v） 丙酮、0.5% （v/v） 醋酸） 和离心机在 1，000 x g 2 分钟内，使 C18 STAGE 尖端保持平衡。</li>
		<li>通过添加 200 μL 的缓冲区 A （2% （v/v） 丙酮三酯、0.1% （v/v） 三氟乙酸、0.5% （v/v） 醋酸和离心机在 1，000 x g 3-5 分钟，使 C18 STAGE 尖端保持平衡。</li>
		<li>在 C18 STAGE 尖端和离心机中加入约 50μg 的消化样本，3-5 分钟，或直到样品通过自旋柱。 闪存将剩余的消化样本冻结在液氮中，并储存在 -20°C，直到需要为止。</li>
		<li>用 200μL 的缓冲区 A 和离心机在 1，000 x g 下清洗 C18 STAGE 尖端 3-5 分钟。</li>
		<li>在 C18 STAGE 尖端添加 50 μL 缓冲区 B，以 500 x g 的速度离心机 2 分钟。在 0.2 mL PCR 管中收集未清除的肽。</li>
		<li>在真空离心机中以最大速度干燥 30-40 分钟。完全干燥的样品可在室温下或 -20 °C 下储存，直至加工完毕。 注:干燥和脱盐肽是提交质谱处理设施的适当样品，可在环境温度下运输。</li>
	</ol></li>
</ol>6. 质谱
<ol><li>在 10 μL 缓冲区 A 中重组肽，并测量将 +1.5 至 3 μg 肽注入 MS 柱所需的浓度。样本量将取决于仪器仪表。</li>
	<li>使用预先确定的丙酮梯度（约 5-60%）在 0.5% 的醋酸在预期时间 （例如， 2 小时） 通过高性能液体色谱分离肽， 然后电喷电化到质谱仪。</li>
	<li>在数据依赖采集模式（m/z 300 至 1650）中使用高分辨率质谱仪获取 MS 扫描。 注意:梯度百分比和长度由实验和用户决定。精确的质谱仪设置取决于仪器仪表、实验和用户偏好。</li>
</ol>7. 数据分析
注:MS数据可以通过许多生物信息学管道进行处理。在此协议中，我们描述了使用公开的 MaxQuant 和 Perseus 平台的处理，但建议个人用户评估适合分析、偏好和使用的生物信息工具。
<ol><li>使用 MaxQuant 软件加载未处理的数据文件（直接来自 MS 仪器）。识别修改后的 MaxQuant 搜索参数下的蛋白质;使用目标诱饵方法识别蛋白质所需的至少两种独特的肽，假发现率为 1%，实现无标签量化与运行之间的匹配，将从 UniProt 数据库（即 新福尔曼斯 H99、 肌肉）获得的生物体 FASTA 文件合并，以识别和量化与仙女座搜索引擎的当前肽。咨询公共 MaxQuant 在线工具了解详细的教程（见 材料表）。</li>
	<li>将 MaxQuant 输出文件（"蛋白质组.txt"）上传到波斯。</li>
	<li>过滤包含潜在误报和污染物的行，以及仅通过站点肽与"基于分类列的过滤行"进行修改。</li>
	<li>在日志2 刻度上转换数据值。</li>
	<li>通过为行提供分类注释创建数据集组。</li>
	<li>按有效值筛选数据集，以定义蛋白质检测的截止点。 注意:对于严格而可靠的分析，建议提供 +50% 的识别率。例如，如果处理了四个副本，则将选择至少三个有效值。</li>
	<li>如果首选，则通过替换正常分布中的缺失值来将数据置换。 注:根据正常分布优化了被归责值，并提供了随机 LFQ 强度，以取代"NaN"占位符以模拟典型的丰度测量。此归责为需要可量化数据的下游统计分析提供了一个平台。</li>
	<li>在蛋白质行中添加注释（例如蛋白质名称、基因本体论术语）。 注:此 Perseus 工作流现已成为进一步生物信息处理、统计分析和数据可视化的强大框架，可咨询公共 Perseus 在线工具以获取详细的教程（见材料表）。</li>
</ol>

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作者声明没有利益冲突。

协议中的关键步骤包括制备巨噬细胞和收集用于蛋白质加工的共同培养样本，尽量减少对细胞的干扰。重要的是要执行步骤，清洗，接种，并删除粘附的巨噬细胞轻轻地和仔细，以防止不必要的裂解细胞之前收集。建立正确的实验MOI也至关重要，因为接种过高的MOI会导致快速的巨噬细胞死亡和MS的收集和处理样本的困难。 相反，低MOI数量将导致噬菌体真菌细胞减少，生物系统中真菌蛋白的检测有限。为了克服这些限制，我们建议在细胞死亡检测（例如 LDH 量化）的支持下，用不同的 MOI 进行测试实验，以定义启动宿主响应但不会在收集前杀死宿主细胞的真菌细胞数量。在实验中，我们的目标是识别与感染相关的真菌蛋白，需要高MOI（100:1）才能在大量宿主蛋白中充分检测真菌蛋白。在进行整个实验之前，我们定期进行巨噬细胞毒性检测，以评估MOI对宿主细胞死亡的影响。用巨噬细胞孵育C.新福尔曼细胞的时间也至关重要，因为真菌细胞可能含有大型多糖胶囊，因此，巨噬细胞需要更多的时间进行吞没。我们选择使用3小时的共同培养孵化时间进行概述的实验，因为我们发现此时真菌蛋白质组覆盖良好，并提供宿主响应的"快照":然而，研究人员可能希望探索早期和以后的时间点，并观察时间如何影响真菌和宿主蛋白的生产。或者，已经进行了手术的宏噬菌体，以帮助噬菌体过程17，18。
对于样品收集，如果不立即处理样品，在液氮中闪烁冻结将有助于防止样品中存在的蛋白酶对蛋白质进行不必要的降解。此外，对于基于MS的蛋白分析，在开始实验之前，应使用氮化物手套、实验室外衣和用70%乙醇清洗所有表面，限制灰尘或角蛋白（如皮肤和毛发细胞）污染的可能性。此外，上述协议是特定于共同培养样品集描述，但可以修改蛋白质提取工作流程优化，根据需要19，20。修改工作流程和优化特定细胞类型的机会包括选定的机械和化学干扰技术、酶消化的持续时间和温度以及样品分离。例如，C.新福尔曼细胞的裂解通常通过机械珠子跳动进行:然而，我们观察到探针声波后蛋白质组覆盖率增加，因此建议它对受感染的细胞19、21、22进行机械破坏。例如，通过高pH分馏或大小排除色谱将肽样品分馏成别名，可以降低样品的复杂性，提高质谱仪9的覆盖深度。此外，为了达到识别约8，000种宿主和真菌蛋白的覆盖深度，需要一个高分辨率质谱系统（例如，QExactive探索、融合流体、timsTOF Pro）。
使用LFQ来量化感染期间蛋白质水平的变化是基于MS的蛋白质组学12的可靠和具有成本效益的方法。它使蛋白质的量量化相对丰度，而无需额外的样品处理步骤。此外，分析是在实验完成后进行的，这为普遍应用和灵活的研究设计提供了经验。然而，LFQ的限制包括增加仪器时间，因为样品必须按顺序运行，不能组合，样品之间的可比性可能具有挑战性，而需要归责来取代缺失值可能很高。量化蛋白质丰度的替代方法包括代谢（例如，细胞培养中氨基酸的稳定同位素标签）和化学（例如串联质量标签）标签技术，这些技术允许结合样品以缩短仪器时间，在样品之间提供可靠的可比性，通常导致较少缺失值24，25。然而，这种方法需要额外的样品处理和处理步骤，增加湿实验室实验的复杂性和时间，以及需要固定的实验设计。为了选择最适合拟议实验的量化方法，用户应考虑研究样品的设计和可比性、湿实验室的复杂性和数据分析，以及仪器的可用性和成本。
所提出的协议的新颖性在于能够在单个实验中从宿主和病原体的角度定义蛋白质组的变化。两个蛋白质组的深度覆盖使人们能够对病原体如何引发感染以及宿主如何进行防御有新的认识。值得注意的是，该方法侧重于整个细胞的感染:然而，通过与空间定位技术（例如离心、浓缩、贴标签）的结合，存在定义对感染的亚蛋白质组或分门别类反应的机会。在这里，我们详细介绍了巨噬细胞与真菌病原体C.新福尔曼之间的相互作用:然而，这种方法是通用的，可以应用于各种生物系统之间的相互作用。例如，我们最近使用类似的工作流来发现从眼角膜炎28、29、30的murine模型中得出的中性粒细胞的一般和部位特定反应。此外，本议定书产生的感染体数据集可与病原体的体外蛋白质组和分泌物分析相结合，以检测宿主细胞存在时丰度变化的蛋白质。这种蛋白质，称为感染相关蛋白质，为进一步描述提供了大量已知和新颖的毒性因素，包括时间调节、本地化和与宿主的直接蛋白质-蛋白质相互作用。总体而言，概述的基于MS的蛋白组学工作流程提供了一个新的机会来研究宿主和病原体之间的复杂关系，在一个具有普遍性和理解性的实验中并不常见。

侵入性真菌感染的流行率正在大大增加，与不可接受的高死亡率有关，最常见的报告在有免疫缺陷倾向1的个人。隐球菌是一种臭名昭著的机会性真菌病原体，能够在宿主巨噬细胞内细胞内细胞内存活。抗真菌干预不足导致真菌传播和危及生命的隐球菌脑膜炎和脑膜炎2，3。全球免疫功能化地位的提高要求同时增加抗真菌剂的使用，其中许多真菌物种，包括C.新福曼，已经逐渐进化到4，5，6的抗药性。因此，必须实施有力和有效的技术，以回答有关宿主防御反应和微生物发病机制的重要生物问题。
质谱学技术进步的新时代，包括强大的计算和生物信息管道的产生，为大规模分析宿主病原体研究提供了基础。传统的发病机制驱动的蛋白质组分析通常从宿主或病原体的角度描述感染的观点，包括综合方法，如蛋白质相关性分析，亲和色谱结合蛋白质组学，和相互作用经济学9。对宿主系统中危险病原体毒性的调查具有巨大的临床意义:然而，在单个实验中应用双重透视分析以前被认为是无法实现的。例如，病原体对感染的看法往往被高度丰富的宿主蛋白所淹没，导致检测低富真菌蛋白7的敏感性降低。此外，高样本复杂性邀请许多目标在单个实验系统中进行调查，并证明很难阐明特定病原体蛋白的行动机制。
自下而上的蛋白组学是一种流行的MS技术，它使可管理的样品制备，其中肽产生序列特异性酶消化，然后液体色谱分离，识别和量化MS10，11。在这里，我们演示了一种基于数据的获取策略的方法，旨在实现基于感染的蛋白质组或"感染体"的公正覆盖。具体来说，无标签量化（LFQ）可以摆脱对化学或代谢标签的依赖，从而对多个蛋白质组的蛋白质水平变化进行可靠和准确的识别，从而减少样品处理和处理步骤12、13。这种普遍应用在细胞内的某一时刻询问产生的蛋白质，而该细胞独立于任何预期的蛋白质生产:因此，可能会发现对感染至关重要的新见解。
此处描述的工作流程进行了优化，以探索 C.新福尔曼在与 宿主免疫细胞的感染模仿条件下的蛋白质水平变化（图1）。这种方法不是依靠细胞类型的分离和分离，而是将宿主和病原体蛋白质组分离在一起，并利用生物信息分离利用两个生物特定的数据库来区分特定物种的蛋白质生产。这种方法为无限数量的样品处理提供了优势，无需同位素标签研究或分数所需的额外成本高昂的制备步骤。此外，此工作流程支持可转移到各种真菌和细菌病原体的优化蛋白质提取方案，这些病原体能够瞄准和感染宿主免疫细胞。总体而言，该协议概述了完成高分辨率MS的公正蛋白质提取和样品处理的步骤，然后是数据和统计分析，能够提供大量对感染具有重要意义的真菌蛋白知识，并结合对宿主防御反应的全面分析。

<table><tbody><tr><td>100 mM Tris-HCl, pH 8.5</td><td>Fisher Scientific</td><td>BP152-1</td><td>Maintain at 4&amp;deg;C</td></tr><tr><td>60 x 15 mm Dish, Nunclon Delta</td><td>ThermoFisher Scientific</td><td>174888</td><td></td></tr><tr><td>6-well cell culture plate</td><td>ThermoFisher Scientific</td><td>140675</td><td></td></tr><tr><td>Acetonitrile, MS grade</td><td>Pierce</td><td>TS-51101</td><td></td></tr><tr><td>Acetic Acid</td><td>Sigma Aldrich</td><td>1099510001</td><td></td></tr><tr><td>Acetone</td><td>Sigma Aldrich</td><td>34850-1L</td><td></td></tr><tr><td>Ammonium bicarbonate (ABC)</td><td>ThermoFisher Scientific</td><td>A643-500</td><td>Prepare a stock 50 mM ABC solution, stable at room temperature for up to one month.</td></tr><tr><td>Bel-Art&amp;trade; HiFlow Vacuum Aspirator Collection System</td><td>Fisher Scientific</td><td>13-717-300</td><td>Not essential, serological pipettes can be used to remove media.</td></tr><tr><td>C18 resin</td><td>3M Empore</td><td>3M2215</td><td></td></tr><tr><td>Cell Scrapers</td><td>VWR</td><td>10062-906</td><td>Not essential, other methods to release macrophage cells can be used.</td></tr><tr><td>Centrifugal vaccuum concentrator</td><td>Eppendorf</td><td>07-748-15</td><td></td></tr><tr><td>Complete Filtration Unit</td><td>VWR</td><td>10040-436</td><td></td></tr><tr><td>Conical falcon tubes (15 mL)</td><td>Fisher Scientific</td><td>05-539-12</td><td></td></tr><tr><td>Countess II Automated Cell Counter</td><td>ThermoFisher Scientific</td><td>AMQAX1000</td><td>Not essential, haemocytometer can be used as an alternative.</td></tr><tr><td>CytoTox 96 Non-Radioactive Cytotoxicity Assay</td><td>Promega</td><td>G1780</td><td></td></tr><tr><td>Dithiothreitol (DTT)</td><td>ThermoFisher Scientific</td><td>R0861</td><td>Prepare bulk stock solution of 1 M DTT, flash frozen and stored at -20 &amp;deg;C until use. Discard after each use (do not freeze-thaw repeatedly).</td></tr><tr><td>DMEM, high glucose, GlutaMAX Supplement</td><td>ThermoFisher Scientific</td><td>10566016</td><td></td></tr><tr><td>Fetal Bovine Serum (FBS)</td><td>ThermoFisher Scientific</td><td>12483020</td><td>Heat inactivate by incubating at 60&amp;deg;C for 30 minutes. Prepare 50 ml aliquots and flash freeze. Thaw prior to media preparation</td></tr><tr><td>Haemocytometer</td><td>VWR</td><td>15170-208</td><td></td></tr><tr><td>HEPES</td><td>Sigma Aldrich</td><td>H3375</td><td>Prepare 40 mM HEPES/8 M Urea in bulk stock solution, flash frozen, store at -20&amp;deg;C until use. Discard after each use (do not freeze-thaw repeatedly).</td></tr><tr><td>High-performance liquid chromatography system</td><td>ThermoFisher Scientific</td><td>LC140</td><td>Gradient length is based on sample complexity, recommended 120 min gradient for infectome samples.</td></tr><tr><td>High-resolution mass spectrometer</td><td>ThermoFisher Scientific</td><td>726042</td><td></td></tr><tr><td>Iodoacetamide (IAA)</td><td>Sigma Aldrich</td><td>I6125</td><td>Prepare 0.55 M bulk stock solution, flash frozen, store at -20&amp;deg;C until use. Discard after each use (do not freeze-thaw repeatedly).</td></tr><tr><td>L-glutamine</td><td>ThermoFisher Scientific</td><td>25030081</td><td>Can be aliquot and frozen for storage. Thaw prior to media preparation.</td></tr><tr><td>LoBind Microcentrifuge tubes</td><td>Eppendorf</td><td>13-698-794</td><td></td></tr><tr><td>MaxQuant</td><td>https://maxquant.org/</td><td></td><td>MaxQuant is a public platform that offers tutorials, such as the MaxQuant Summer School, outlining the computational analysis steps of large MS data sets</td></tr><tr><td>Microcentrifuge</td><td>Eppendorf</td><td>13864457</td><td></td></tr><tr><td>Penicillin : Streptomycin 10k/10k</td><td>VWR</td><td>CA12001-692</td><td>Can be aliquot and frozen for storage. Thaw prior to media preparation.</td></tr><tr><td>Peptide separation columns</td><td>ThermoFisher Scientific</td><td>ES803</td><td></td></tr><tr><td>Perseus Software</td><td>http://maxquant.net/perseus/</td><td></td><td></td></tr><tr><td>Phosphate Buffered Saline</td><td>VWR</td><td>CA12001-676</td><td>Puchase not required. PBS can also be prepared but sterile filteration must be performed before use.</td></tr><tr><td>Pierce BCA Protein Assay</td><td>ThermoFisher Scientific</td><td>&amp;nbsp;23225</td><td></td></tr><tr><td>Pipette, Disposable Serological (10 mL)</td><td>Fisher Scientific</td><td>13-678-11E</td><td></td></tr><tr><td>Pipette, Disposable Serological (25 mL) Basix</td><td>Fisher Scientific</td><td>14955235</td><td></td></tr><tr><td>Probe sonciator</td><td>ThermoFisher Scientific</td><td>100-132-894</td><td></td></tr><tr><td>Protease inhibitor cocktail tablet</td><td>Roche</td><td>4693159001</td><td></td></tr><tr><td>Sodium dodecyl sulfate</td><td>ThermoFisher Scientific</td><td>28364</td><td>20% (w/v)</td></tr><tr><td>Spectrophotometer (Nanodrop)</td><td>ThermoFisher Scientific</td><td>ND-2000</td><td></td></tr><tr><td>STAGE tipping centrifuge</td><td>Sonation</td><td>STC-V2</td><td></td></tr><tr><td>Thermal Shaker</td><td>VWR</td><td>NO89232-908</td><td></td></tr><tr><td>Trifluoroacetic acid</td><td>ThermoFisher Scientific</td><td>85183</td><td></td></tr><tr><td>Trypsin/Lys-C protease mix, MS grade</td><td>Pierce</td><td>A40007</td><td>Maintain at -20 &amp;deg;C.</td></tr><tr><td>Ultrasonic bath</td><td>Bransonic</td><td>A89375-450</td><td>Stored in cold room (4C)</td></tr><tr><td>Urea</td><td>Sigma Aldrich</td><td>U1250-1KG</td><td>Prepare 40 mM HEPES/8 M Urea in bulk stock solution, flash frozen, store at -20 &amp;deg;C until use. Discard after each use (do not freeze-thaw repeatedly).</td></tr><tr><td>Yeast-extract peptone dextrose broth</td><td>BD Difco</td><td>BM20</td><td></td></tr></tbody></table>

label-free quantitative proteomics workflow for discovery-driven host-pathogen interactions

基于质谱学（MS）的定量蛋白质组学的技术成就为分析生物体在不同条件下的全球蛋白质组开辟了许多未被发现的途径。这种强大的策略应用于微生物病原体与所需宿主的相互作用，全面地描绘了两种感染视角。在此，工作流描述了 密码球菌新福尔曼的 感染物的无标签量化（LFQ），这是一种真菌诱发性细胞内病原体，是致命疾病隐科病的致病剂，存在不朽的巨噬细胞。该协议在单个实验中详细说明了病原体和哺乳动物细胞的适当蛋白质制备技术，从而为液相色谱 （LC） -MS/MS 分析提交了适当的肽。LFQ 的高通量通用性允许广泛的蛋白质识别和量化动态范围，以及可转移到任何宿主病原体感染设置，保持极端敏感性。该方法进行了优化，以编目感染模仿条件下病原体的广泛、公正的蛋白质丰度配置文件。具体来说，这里演示的方法提供了关于 C.新福尔曼病 原体的基本信息，例如毒性所需的蛋白质生产，并识别出对微生物入侵作出反应的关键宿主蛋白。

上述协议允许在单个实验中识别和量化来自真菌病原体C.新福尔曼和宿主巨噬细胞的蛋白质。在共同培养之后，细胞被收集和处理在一起，并根据每个物种特有的肽谱进行生物信息分离。这是定义感染期间宿主-病原体关系相互作用的有力方法。从实验中识别出的蛋白质数量取决于启动材料、样品准备、梯度长度、MS 仪器和生物信息工作流程。使用此处描述的协议，我们通常从实验中识别出大约 8，000 种蛋白质，其中含有 1，500种 C. neoformans蛋白质和 6，500 种宿主蛋白。在处理数据集后，我们生成一个主要组件分析 （PCA） 图，以观察推动我们分析的关键因素 （图 2A）。在这里，我们观察到数据之间最大的分离成分是受感染的样品与未受感染的样本，正如我们从实验设计中预测的那样（组件 1，79.8%），样本的第二个显著特征是生物变异性（组件 2，5.7%）。接下来，Pearson 相关性与欧几里德距离的分层聚类相结合，对样本进行分组，并能够量化复制品之间的变异性（图 2B）。在我们的分析中，我们观察到受感染样本与未受感染样本的不同聚类，复制可重复性从 95-96% 到 95%不等，在复制品中具有良好的可重复性。最后，我们使用本杰明-霍奇伯格错误发现率 （FDR）（p值 ≤ 0.05）对多个假设测试进行学生t测试更正:罗斯福 =0.01:s0 = 1） 与未受感染的对照组相比，识别感染期间丰度差异显著的蛋白质（图 2C）。在这里，我们识别出760种富集显著变化的蛋白质，包括117种宿主蛋白，其中86种显著减少，31种在感染后显著增加。值得注意的是，我们也观察到真菌蛋白的丰度显著增加，正如在感染期间所预期的那样。有了这些数据，随后的分析，包括网络映射，在西里科特征，和后续实验进行验证数据，并探索分子机制的基础主机反应的毒性。
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/61881/61881fig1v4.jpg"> 图1:基于质谱学的蛋白组学工作流程，用于分析感染C.新福尔曼的巨噬细胞。工作流程从收集感染C.新福尔曼或未受感染控制的巨噬细胞开始。蛋白质通过机械和化学干扰提取，然后是减少和碱化、丙酮沉淀和酶消化。肽在C18 STAGE尖端进行纯化，通过高性能液相色谱分离，进行电喷雾电离，并在高分辨率质谱仪上测量。数据在公开的生物信息学平台MaxQuant（与仙女座）和Perseus14、15、16进行处理、分析和可视化。在生物四倍体中进行的实验。<a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/61881/61881fig1v3large.jpg" target="_blank">请点击这里查看此数字的较大版本。</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/61881/61881fig2v3.jpg"> 图2:大噬细胞 C.新福尔曼感染 的代表性数据。（A） 主要成分分析显示受感染与未受感染的巨噬细胞（组件1，79.8%）和生物复制品聚类（成分2，5.7%）之间的区别。（B） 由欧几里德距离分层聚类绘制的皮尔逊相关热图，以显示样本聚类（受感染与未受感染）和复制可重复性（+95%）。（C） 已识别出的蛋白质的火山图。蓝色 =真菌蛋白，富足有显著变化;黑色=大噬菌体蛋白，富足变化显著。学生 t-测试（p值≤0.05），罗斯福=0.01:s0=1。 <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/61881/61881fig2v3large.jpg" target="_blank">请点击这里查看此数字的较大版本。</a>

Watch this Scientific Journal Video about Label-Free Quantitative Proteomics Workflow for Discovery-Driven Host-Pathogen Interactions at JoVE.com

Label-Free Quantitative Proteomics Workflow for Discovery-Driven Host-Pathogen Interactions

在这里，我们提出了一个协议，以分析宿主和病原体之间的相互作用，在感染基于质谱学的蛋白组学。此协议使用无标签量化来测量宿主（例如巨噬细胞）和病原体（例如 新福尔曼隐球菌）的蛋白质丰度变化。

无标签定量蛋白经济学工作流程，用于发现驱动的宿主-病原体交互

The technological achievements of mass spectrometry (MS)-based quantitative proteomics opens many undiscovered avenues for analyzing an organism&#8217;s ...

label-free-quantitative-proteomics-workflow-for-discovery-driven-host

Universidad San Sebastian

Research

JoVE Journal

Immunology and Infection

6.6K 次观看.  University of Guelph. 在这里，我们提出了一个协议，以分析宿主和病原体之间的相互作用，在感染基于质谱学的蛋白组学。此协议使用无标签量化来测量宿主（例如巨噬细胞）和病原体（例如 新福尔曼隐球菌）的蛋白质丰度变化。

视频: Label-Free Quantitative Proteomics Workflow for Discovery-Driven Host-Pathogen Interactions

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

Many exceptional advances have been made in mass spectrometry (MS)-based proteomics, with particular technical progress in liquid chromatography (LC) coupled to tandem mass spectrometry (LC-MS/MS) and isobaric labeling multiplexing capacity. Here, we introduce a deep-proteomics profiling protocol that combines 10-plex tandem mass tag (TMT) labeling with an extensive LC/LC-MS/MS platform, and post-MS computational interference correction to accurately quantitate whole proteomes. This protocol includes the following main steps: protein extraction and digestion, TMT labeling, 2-dimensional (2D) LC, high-resolution mass spectrometry, and computational data processing. Quality control steps are included for troubleshooting and evaluating experimental variation. More than 10,000 proteins in mammalian samples can be confidently quantitated with this protocol. This protocol can also be applied to the quantitation of post translational modifications with minor changes. This multiplexed, robust method provides a powerful tool for proteomic analysis in a variety of complex samples, including cell culture, animal tissues, and human clinical specimens.

We present a protocol to accurately quantitate proteins with isobaric labelling, extensive fractionation, bioinformatics tools, and quality control steps in combination with liquid chromatography interfaced to a high-resolution mass spectrometer.

用等压标记、广泛的液相色谱法、质谱法和软件辅助定量技术进行深层蛋白质组分析

Many exceptional advances have been made in mass spectrometry (MS)-based proteomics, with particular technical progress in liquid chromatography (LC) ...

科研

生物化学

A Comparative Approach to Characterize the Landscape of Host-Pathogen Protein-Protein Interactions

Significant efforts were gathered to generate large-scale comprehensive protein-protein interaction network maps. This is instrumental to understand the pathogen-host relationships and was essentially performed by genetic screenings in yeast two-hybrid systems. The recent improvement of protein-protein interaction detection by a Gaussia luciferase-based fragment complementation assay now offers the opportunity to develop integrative comparative interactomic approaches necessary to rigorously compare interaction profiles of proteins from different pathogen strain variants against a common set of cellular factors.
This paper specifically focuses on the utility of combining two orthogonal methods to generate protein-protein interaction datasets: yeast two-hybrid (Y2H) and a new assay, high-throughput Gaussia princeps protein complementation assay (HT-GPCA) performed in mammalian cells.
A large-scale identification of cellular partners of a pathogen protein is performed by mating-based yeast two-hybrid screenings of cDNA libraries using multiple pathogen strain variants. A subset of interacting partners selected on a high-confidence statistical scoring is further validated in mammalian cells for pair-wise interactions with the whole set of pathogen variants proteins using HT-GPCA. This combination of two complementary methods improves the robustness of the interaction dataset, and allows the performance of a stringent comparative interaction analysis. Such comparative interactomics constitute a reliable and powerful strategy to decipher any pathogen-host interplays.

This article focuses on the identification of high-confident interaction datasets between host and pathogen proteins using a combination of two orthogonal methods: yeast two-hybrid followed by a high-throughput interaction assay in mammalian cells called HT-GPCA.

比较方法来表征景观的宿主 - 病原体蛋白 - 蛋白相互作用

Significant efforts were gathered to generate  large-scale comprehensive protein-protein interaction network maps. This is  instrumental to understand the ...

免疫与感染

Label-Free Immunoprecipitation Mass Spectrometry Workflow for Large-scale Nuclear Interactome Profiling

Immunoaffinity purification mass spectrometry (IP-MS) has emerged as a robust quantitative method of identifying protein-protein interactions. This publication presents a complete interaction proteomics workflow designed for identifying low abundance protein-protein interactions from the nucleus that could also be applied to other subcellular compartments. This workflow includes subcellular fractionation, immunoprecipitation, sample preparation, offline cleanup, single-shot label-free mass spectrometry, and downstream computational analysis and data visualization. Our protocol is optimized for detecting compartmentalized, low abundance interactions that are difficult to identify from whole cell lysates (e.g., transcription factor interactions in the nucleus) by immunoprecipitation of endogenous proteins from fractionated subcellular compartments. The sample preparation pipeline outlined here provides detailed instructions for the preparation of HeLa cell nuclear extract, immunoaffinity purification of endogenous bait protein, and quantitative mass spectrometry analysis. We also discuss methodological considerations for performing large-scale immunoprecipitation in mass spectrometry-based interaction profiling experiments and provide guidelines for evaluating data quality to distinguish true positive protein interactions from nonspecific interactions. This approach is demonstrated here by investigating the nuclear interactome of the CMGC kinase, DYRK1A, a low abundance protein kinase with poorly defined interactions within the nucleus.

Described is a proteomics workflow for identifying protein interaction partners from a nuclear subcellular fraction using immunoaffinity enrichment of a given protein of interest and label-free mass spectrometry. The workflow includes subcellular fractionation, immunoprecipitation, filter aided sample preparation, offline cleanup, mass spectrometry, and a downstream bioinformatics pipeline.

用于大规模核相互作用分析的无标签免疫沉淀质谱工作流程

Immunoaffinity purification mass spectrometry (IP-MS) has emerged as a robust quantitative method of identifying protein-protein interactions. This ...

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

With recent advances in mass spectrometry-based proteomics technologies, deep profiling of hundreds of proteomes has become increasingly feasible. However, deriving biological insights from such valuable datasets is challenging. Here we introduce a systems biology-based software JUMPn, and its associated protocol to organize the proteome into protein co-expression clusters across samples and protein-protein interaction (PPI) networks connected by modules (e.g., protein complexes). Using the R/Shiny platform, the JUMPn software streamlines the analysis of co-expression clustering, pathway enrichment, and PPI module detection, with integrated data visualization and a user-friendly interface. The main steps of the protocol include installation of the JUMPn software, the definition of differentially expressed proteins or the (dys)regulated proteome, determination of meaningful co-expression clusters and PPI modules, and result visualization. While the protocol is demonstrated using an isobaric labeling-based proteome profile, JUMPn is generally applicable to a wide range of quantitative datasets (e.g., label-free proteomics). The JUMPn software and protocol thus provide a powerful tool to facilitate biological interpretation in quantitative proteomics.

We present a systems biology tool JUMPn to perform and visualize network analysis for quantitative proteomics data, with a detailed protocol including data pre-processing, co-expression clustering, pathway enrichment, and protein-protein interaction network analysis.

JUMPn:蛋白质共表达聚类和网络分析在蛋白质组学中的简化应用

With recent advances in mass spectrometry-based proteomics technologies, deep profiling of hundreds of proteomes has become increasingly feasible. ...

Dissecting Multi-protein Signaling Complexes by Bimolecular Complementation Affinity Purification (BiCAP)

The assembly of protein complexes is a central mechanism underlying the regulation of many cell signaling pathways. A major focus of biomedical research is deciphering how these dynamic protein complexes act to integrate signals from multiple sources in order to direct a specific biological response, and how this becomes deregulated in many disease settings. Despite the importance of this key biochemical mechanism, there is a lack of experimental techniques that can facilitate the specific and sensitive deconvolution of these multi-molecular signaling complexes.
Here this shortcoming is addressed through the combination of a protein complementation assay with a conformation-specific nanobody, which we have termed Bimolecular Complementation Affinity Purification (BiCAP). This novel technique facilitates the specific isolation and downstream proteomic characterization of any pair of interacting proteins, to the exclusion of un-complexed individual proteins and complexes formed with competing binding partners. 
The BiCAP technique is adaptable to a wide array of downstream experimental assays, and the high degree of specificity afforded by this technique allows more nuanced investigations into the mechanics of protein complex assembly than is currently possible using standard affinity purification techniques.

Dissecting Multi-protein Signaling Complexes by Bimolecular Complementation Affinity Purification BiCAP

This manuscript describes the protocol for Bimolecular Complementation Affinity Purification (BiCAP). This novel method facilitates the specific isolation and downstream proteomic characterization of any two interacting proteins, while excluding un-complexed individual proteins as well as complexes formed with competing binding partners.

分子互补亲和纯化 (BiCAP) 对多蛋白信号络合物的解剖

The assembly of protein complexes is a central mechanism underlying the regulation of many cell signaling pathways. A major focus of biomedical research ...

A Streamlined Approach for Mass Spectrometry-Based Proteomics Using Selected Tissue Regions

Mass spectrometry (MS)-based proteomics enables comprehensive proteome analysis across a wide range of biological samples, including cells, tissues, and body fluids. Formalin-fixed, paraffin-embedded (FFPE) tissue sections, commonly used for long-term archiving, have emerged as valuable resources for proteomic studies. Beyond their storage benefits, researchers can isolate regions of interest (ROIs) from normal tissue regions through collaborative efforts with pathologists. Despite this potential, a streamlined approach for proteomic experiments encompassing ROI isolation, proteomic sample preparation, and MS analysis remains lacking. In this protocol, an integrated workflow that combines macrodissection of ROIs, suspension trapping-based sample preparation, and high-throughput MS analysis is presented. Through this approach, the ROIs of patients' FFPE tissues, consisting of benign serous cystic neoplasms (SCN) and precancerous intraductal papillary mucinous neoplasms (IPMN) diagnosed by pathologists, were macrodissected, collected, and analyzed, resulting in high proteome coverage. Furthermore, molecular differences between the two distinct pancreatic cystic neoplasms were successfully identified, thus demonstrating the applicability of this approach for advancing proteomic research with FFPE tissues.

Here, we establish a mass spectrometry-based proteomic method using isolated regions of interest in formalin-fixed, paraffin-embedded tissue sections. This protocol is used to analyze proteome from specific tissue areas in archived formalin-fixed, paraffin-embedded tissue sections.

Mass spectrometry (MS)-based proteomics enables comprehensive proteome analysis across a wide range of biological samples, including cells, tissues, and ...

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Pathogens can cause a wide variety of infectious diseases. The biological processes induced by the host in response to infection determine the severity of the disease.&#160;To study such processes, researchers can use high-throughput sequencing techniques (RNA-seq) that measure the dynamic changes of the host transcriptome at different stages of infection, clinical outcomes, or disease severity.This investigation can lead to a better understanding of the diseases, as well as uncovering potential drug targets and treatments. The protocol presented here describes a complete pipeline to analyze RNA-sequencing data from raw reads to functional analysis. The pipeline is divided into five steps: (1) quality control of the data; (2) mapping and annotation of genes; (3) statistical analysis to identify differentially expressed genes and co-expressed genes; (4) determination of the molecular degree of the perturbation of samples; and (5) functional analysis. Step 1 removes technical artifacts that may impact the quality of downstream analyses. In step 2, genes are mapped and annotated according to standard library protocols.&#160;The statistical analysis in step 3 identifies genes that are differentially expressed or co-expressed in infected samples, in comparison with non-infected ones. Sample variability and the presence of potential biological outliers are verified using the molecular degree of perturbation approach in step 4. Finally, the functional analysis in step 5 reveals the pathways associated with the disease phenotype. The presented pipeline aims to support researchers through the RNA-seq data analysis from host-pathogen interaction studies and drive future&#160;in vitro or in vivo experiments, that are essential to understand the molecular mechanism of infections.

The protocol presented here describes a complete pipeline to analyze RNA-sequencing transcriptome data from raw reads to functional analysis, including quality control and preprocessing steps to advanced statistical analytical approaches.

用于研究宿主-病原体相互作用的高通量转录组分析

Pathogens can cause a wide variety of infectious diseases. The biological processes induced by the host in response to infection determine the severity of ...

Characterization of Neuronal Lysosome Interactome with Proximity Labeling Proteomics

Lysosomes frequently communicate with a variety of biomolecules to achieve the degradation and other diverse cellular functions. Lysosomes are critical to human brain function, as neurons are postmitotic and rely heavily on the autophagy-lysosome pathway to maintain cellular homeostasis. Despite advancements in the understanding of various lysosomal functions, capturing the highly dynamic communications between lysosomes and other cellular components is technically challenging, particularly in a high-throughput fashion. Here, a detailed protocol is provided for the recently published endogenous (knock-in) lysosome proximity labeling proteomic method in human induced pluripotent stem cell (hiPSC)-derived neurons. 
Both lysosomal membrane proteins and proteins surrounding lysosomes within a 10-20 nm radius can be confidently identified and accurately quantified in live human neurons. Each step of the protocol is described in detail, i.e., hiPSC-neuron culture, proximity labeling, neuron harvest, fluorescence microscopy, biotinylated protein enrichment, protein digestion, LC-MS analysis, and data analysis. In summary, this unique endogenous lysosomal proximity labeling proteomics method provides a high-throughput and robust analytical tool to study the highly dynamic lysosomal activities in live human neurons.

A neuronal lysosome proximity labeling proteomics protocol is described here to characterize the dynamic lysosomal microenvironment in human induced pluripotent stem cell-derived neurons. Lysosomal membrane proteins and proteins that interact with lysosomes (stably or transiently) can be accurately quantified in this method with excellent intracellular spatial resolution in live human neurons.

具有邻近标记蛋白质组学的神经元溶酶体相互作用组表征

Lysosomes frequently communicate with a variety of biomolecules to achieve the degradation and other diverse cellular functions. Lysosomes are critical to ...

神经科学

<meta charset="utf8"></head><body>Galaxy-Enabled Metaprote组学:深入了解宿主-微生物组相互作用

A Clinical Metaproteomics Workflow Implemented within Galaxy Bioinformatics Platform to Analyze Host-Microbiome Interactions Underlying Human Disease

Clinical metaproteomics reveals host-microbiome interactions underlying diseases. However, challenges to this approach exist. In particular, the characterization of microbial proteins present in low abundance relative to host proteins is difficult. Other significant challenges are attributed to using very large protein sequence databases, which impedes sensitivity and accuracy during peptide and protein identification from mass spectrometry data in addition to retrieving taxonomy and functional annotations and performing statistical analysis. To address these problems, we present an integrated bioinformatics workflow for mass spectrometry-based metaproteomics that combines custom protein sequence database generation, peptide-spectrum match generation and verification, quantification, taxonomic and functional annotations, and statistical analysis. This workflow also offers characterization of human proteins (while prioritizing microbial proteins), thus offering insights into host-microbe dynamics in disease. The tools and workflow are deployed in the Galaxy ecosystem, enabling the development, optimization, and dissemination of these computational resources. We have applied this workflow for metaproteomic analysis of numerous clinical sample types, such as nasopharyngeal swabs and bronchoalveolar lavage fluid. Here, we demonstrate its utility via the analysis of residual fluid from cervical swabs. The complete workflow and accompanying training resources are accessible on the Galaxy Training Network to equip non-experts and experienced researchers with the necessary knowledge and tools to analyze their data.

<meta charset="utf8"></head><body>在 Galaxy Bioinformatics Platform 中实施的临床宏蛋白质组学工作流程，用于分析人类疾病背后的宿主-微生物组相互作用

Clinical metaproteomics offers insights into the human microbiome and its contributions to disease. We harnessed the computational power of the Galaxy platform to develop a modular bioinformatics workflow that facilitates complex mass spectrometry-based metaproteomic analysis and characterization of diverse clinical sample types relevant to studies of disease.