作者感谢所有其他实验室和设施成员进行有益的讨论。这项工作得到了 R01GM114260、R01AG047928、R01AG053987 和 ALSAC 的部分支持。MS 分析是在 St. 裘德儿童的研究医院蛋白质组学, 部分支持的 NIH 癌症中心支持补助金 P30CA021765。作者感谢莎 Badders 对编辑手稿的帮助。

警告: 请在使用前查阅所有相关的安全数据表 ( 即 、MSDS)。执行此协议时, 请使用所有适当的安全做法. 
 注意: 在本协议中, 一个 TMT 10 丛等压标签试剂集用于10样本的蛋白质组定量. 
 1. 细胞/组织的制备 注意: 在低温下收集样品以保持蛋白质的原始状态是至关重要的. 
<ol> <li> 在 10 cm 板上两次用10毫升冰冷磷酸盐缓冲盐水 (PBS) 清洗附着细胞 (如 、HEK 293 细胞)。在1毫升 PBS 中, 刮和收集细胞 (~ 2 x 10 6 细胞)。转移到1.5 毫升管和删除 PBS 后, 离心在 600 x g 为5分钟, 在4和 #176; c. 在-80 和 #176 处储存细胞颗粒, 直到溶解为止. 
	注: 通常进行试验研究蛋白质的产量。大约1毫克的蛋白质可以提取从 10 x 10 6 哺乳动物细胞或从15厘米板块与80% 汇合. </li>
	<li> 或者, 在其板上溶解附着的细胞, 在洗涤后直接加入裂解缓冲液。将裂解收集到1.5 毫升的管子中, 并在步骤2.5 中使用协议描述. </li>
	<li> 收集15毫升管中的悬浮细胞, 用10毫升的冰冷 pbs 冲洗两次, 将其转换为1.5 毫升管, 并在 600 x g 离心机上去除 pbs。将 PBS 从洗涤步骤中完全除去, 以保持所需的裂解缓冲成分浓度。将细胞小球储存在-80 和 #176; C 直到裂解. </li>
	<li> 快速收集和称量组织。解剖后立即将组织保存在液氮中, 贮存在-80 和 #176; C. 
	注: 组织的蛋白质产量约为5% 至10% 的组织重量 </li> <li> 使用均匀的组织大小和解剖区域为10样本, 以减少样品的异质性。通过冲洗样品两次, 用1毫升冰冷 PBS 去除血液污染, 以避免高丰度的血液蛋白影响蛋白质定量和下游数据分析. </li>
</ol> 2。蛋白质提取、质量控制西部印迹、溶出消化和多肽脱盐 注意: 在所有的10样本中, 以同样的方式处理每一个步骤之前, 汇集的 TMT 标记的样本是必不可少的, 以减少变体. 
<ol> <li> 进行裂解缓冲 (50 毫米 HEPES [pH 8.5]、8米尿素和0.5% 钠胆酸) 在实验的当天. 
	注意: 在样品裂解过程中部分蛋白质变性影响蛋白质消化效率. </li>
	<li> 添加裂解缓冲液, 使缓冲液与样品体积的比值为10:1 和20% 的玻璃珠 (0.5 mm 直径) 的最终体积为细胞球团或组织 ( 例如 , 10: 1 的容积比之间的缓冲和样本), 以达到最终的蛋白质浓度5到10毫克/毫升。通过考虑每个样品的细胞数量或组织重量, 保持10样品的浓度相同. </li>
	<li> 在样品中加入固态尿素以维持8米的尿素浓度。记住在计算固体尿素添加到样品的数量时, 要考虑细胞颗粒或组织的体积, 以达到8米的浓度. 
	注: 非新鲜尿素缓冲液可以引入人工化学修饰 ( 即 , 蛋白质 carbamylation), 并对识别出的肽的数量产生负面影响。尿素占有相当大的容积 (100 毫克尿素占73和 #181; L 在溶液中). </li>
	<li> 将不溶性碎片保存在裂解物中, 以允许消化不溶蛋白 23 . </li>
	<li> 溶解4和 #176 的搅拌器中的样品; C 在速度8三十年代, 休息5秒, 重复5次或直到样品均匀。样品也可以裂解的涡流三十年代在室温 (RT) 与三十年代冷却期间在冰为〜10周期。根据需要调整溶解条件, 具体取决于样品类型. </li>
	<li> 在没有离心的情况下将裂解井拌匀, 使2小等分 (15 和 #181;使用1分测量蛋白质浓度和1分进行阳性对照验证通过执行西方印迹分析。保留剩余的大分进行蛋白质组分析. </li>
	<li> 通过标准蛋白质定量测定或由马斯亮染色的短十二烷基硫酸钠聚丙烯酰胺凝胶 (10%) 22 以牛血清白蛋白 (BSA) 作为标准测定蛋白质浓度。使用具有高度已知浓度准确度的 BSA 标准。为了达到最高的准确度, 对 BSA 标准进行氨基酸分析. 
	注: 可提供其他准确标准。蛋白质浓度也可以通过细胞数或组织重量来估计。虽然建议使用1毫克的蛋白质 (100 和 #181; g/样本), 但分析的结果可能只有100和 #181; 蛋白质 g (10 和 #181; g/样本). </li>
	<li> 添加100% 乙腈 (ACN) 以达到 10% ACN 和 LysC 蛋白酶的最终浓度, enzyme-to-substrate 率为 1:100 (w/w), 以在 RT 为 2 h 1 的高度变性条件下消化蛋白质。在最后浓度为1毫米时加入糖 () 以减少蛋白质中的二硫化键, 并孵育 1 h. 在最终浓度为7.2 米的尿素中进行 LysC 消化. </li>
	<li> 稀释样品到 2 M 尿素与50毫米 HEPES (pH 8.5) 和进一步消化样品与胰蛋白酶在 RT 为 3 h 或隔夜在胰蛋白酶到蛋白质比率 1:50 (w/w)。使用大约2和 #181; g 的胰蛋白酶为100和 #181; g 的蛋白质和胰蛋白酶浓度约 10 ng/和 #181; L. 
	注: 可能导致胰蛋白酶消化效率低下的因素是 pH 值问题、不活泼的胰蛋白酶, 或使用不适当的酶与基质的比值. </li>
	<li> 在 RT 中添加1毫米的 "数码", 并进一步减少2小时的多肽. </li>
	<li> 添加碘 (IAA) 以实现 10 mM。在 RT 孵育30分钟在黑暗中烷基化胱氨酸含肽. </li>
	<li> 淬火未 IAA 通过添加的德勤, 以达到30毫米和孵化30分钟在 RT. </li>
	<li> 通过检查每个样品的小分来检测胰蛋白酶消化的效率。淡化使用10和 #181; 根据制造商和 #39 的协议, 在死区中嵌入了色谱介质。通过 lc-ms/毫秒来分析每个样品. lc-ms/毫秒参数与步骤5相同, 但渐变为 10 min 23 . </li>
	<li> 对 MS 原始数据执行数据库搜索 (在步骤6中查看更多详细信息), 方法是使用4丢失的分裂作为搜索参数. 
	注: 如果漏分裂的数量是和 #62; 10%, 可能表示不完全胰蛋白酶消化。这将对下游结果产生负面影响。这是一个关键的质量控制 (QC) 步骤. </li>
	<li> 如果漏掉的是和 # 62 , 则再次对样品进行消化 ; 10% 通过添加一个额外的 10 和 # 181 ; L 胰蛋白酶对样品 . </li>
	<li> 通过添加乙酸酸 (TFA) 酸化样品, 达到1% 的浓度。使用 ph 值试纸测量 ph 值。验证 pH 值是否匹配之间2和3。添加更多的 TFA 下降明智的 (1 和 #181; L) 根据需要, 直到正确的 pH 值达到. </li>
	<li> 在室温下离心 2万 x g, 10 分钟, 并收集上清液. </li>
	<li> 洗涤0.5 到60和 #181; g 容量自旋列, 含有0.25 毫升甲醇的 C18 反相树脂, 如果使用100和 #181; g 样品。洗涤0.03 到30和 #181; g 容量自旋列0.25 毫升 60% ACN/0.1% TFA, 如果使用10和 #181; g 样本。离心机自旋柱在 500 x g 为三十年代. </li>
	<li> 平衡列的0.5 毫升 0.1% TFA 和删除离心在 500 x g 三十年代. </li>
	<li> 将样本加载到列上, 并通过离心在 100 x g 处将样本绑定到列上3分钟, 或者直到所有示例都经过该列. </li>
	<li> 增加0.5 毫升 0.1% TFA 的列和离心机在 500 x g 三十年代. </li>
	<li> 添加125和 #181; L 60% ACN/0.1% TFA 到每列, 洗由离心在 100 x g 处为 3 min. 确保所有解决方案都已通过该列. </li>
	<li> 在真空浓缩机中烘干剂。确保样品完全干燥, 管内不存在液体。存储在-80 和 #176; C 直到进一步分析. </li>
</ol> 3。多肽的 tmt 标记 注意: 确保所有样品都被 tmt 试剂完全标记是至关重要的。有几个因素 ( 例如 、所用的 tmt 试剂量、pH 值和蛋白质定量的准确度) 会影响 tmt 标记效率, 这将对所有下游结果产生负面的改变. 
<ol> <li> 在50和 #181 中重建每个盐水多肽样本; 50 mM HEPES 缓冲区 (pH 8.5)。检查 pH 值并确保它介于7和8之间. 
	注: 样品可能是酸性的, 如果没有完全干燥后的脱盐步骤, 这将影响标签的效率. </li>
	<li> 保持〜1和 #181; g 的每个未标记的样本, 随后 TMT 标记效率测试, 如步骤3.3 所述. </li>
	<li> 根据制造商和 #39 的说明, 将 TMT 试剂溶于无水 ACN 和标签肽样品中。在 RT 中孵育1小时的试剂 </li>
	<li> 通过使用10和 #181 对每个 tmt 标记和未标记的样品进行1和 #181, 以执行 QC 的 tmt 标记效率测试; 根据制造商和 #39 的协议, 采用嵌入式色谱介质吸管提示。通过 LC/ms 分析 TMT 标记和未标记的样品。
	注: LC-ms/毫秒参数与5节相同, 但渐变为 10 min. </li>
	<li> 检查原始数据以确保标记的样本中未检测到未标记的多肽, 确保完整的标签效率 ( 图 2 )。这样做6到10单独的多肽验证的标签效率. </li>
	<li> 通过加入4和 #181 来淬火反应; 5% 羟胺的 L, 孵育15分钟. </li>
	<li> 混合一个小的和相等的容量 (2 和 #181; L) 分每个 TMT 标记的样品。淡化使用10和 #181; L 吸管提示与嵌入色谱介质。如步骤3.4 所述, 按 LC-ms/毫秒分析样品。使用 "跳转软件" 程序 (一种开源数据库搜索算法, 将 MS 原始文件转换为多肽和蛋白质的列表), 通过所有识别的多肽的 TMT 标记强度来确定所有10样本的相对浓度比率. 
	注: 此 QC 预混料比试验有助于确定在最终池前相等混合的正确比例, 因为移误差会发生, 会改变浓度, 蛋白质的定量步骤可能并不总是准确的。这也是确保最终组合所用的10样本中的每一个都是相等的比例的最佳方法, 如步骤3.5 所述. </li>
	<li> 根据需要重复多次, 以便在所有10示例中实现1:1 的比率. </li>
	<li> 根据预混料比测试的结果, 将10样品均匀地混合。使用最低浓度的样品作为基线来调整其他9样本的体积. </li>
	<li> 通过脱盐去除被汇集的 TMT 标记样品的淬火反应的副产品. </li>
	<li> 在使用100和 #181 的情况下, 用1毫升/0.25 毫升的甲醇洗涤1毫升固相萃取盒, 每列含有50毫克吸附剂。洗涤 0.5-到 60-和 #181; g 容量 C18 自旋列1毫升/0.25 毫升 60% ACN/0.1% TFA, 如果使用10和 #181; g 样本。离心机柱在 500 x g 为三十年代. </li>
	<li> 平衡列的0.5 毫升 0.1% TFA 和删除离心在 500 x g 三十年代. </li>
	<li> 将样本加载到列上, 并通过离心在 100 x g 处进行3分钟的绑定, 或者直到所有样本都经过该列. </li>
	<li> 增加0.5 毫升 0.1% TFA 的列和离心机在 500 x g 三十年代. </li>
	<li> 添加125和 #181; L 60% ACN/0.1% TFA 到每列, 洗由离心在 100 x g 处为 3 min. 确保所有解决方案都已通过该列. </li>
	<li> 在真空浓缩机中烘干剂。确保样品完全干燥, 管内不存在液体。存储在-80 和 #176; C 直到进一步分析. </li>
</ol> 4。广泛的高分辨率, 碱性 pH lc Prefractionation <ol> <li> 设置2连接的 C18 列, 其中包含桥接的乙烯混合粒子 (4.6 毫米 x 25 厘米, 总计50厘米; 3.5 和 #181; m 粒度), 并使用升流高性能 lc 泵分馏. 
	注: 2 柱的使用增加了肽的负荷, 并不一定改善色谱。对于含有和 #8804 的样品; 200 和 #181; g 肽, 1 柱足够. </li>
	<li> 清洗 100 #181; 随后添加300和 #181 的循环; l 每个甲醇, 水和缓冲 A (10 毫米甲酸铵, pH 8.0). </li>
	<li> 用100和 #181 清洗列; 同样混合异丙醇, 甲醇, ACN, 和水和平衡在95% 缓冲区 A 的列 2 h. </li>
	<li> 溶解65和 #181 中的盐水合并的 TMT 标记的多肽样品; 缓冲区 a L 确认样品的 pH 值是 ~ 8.0。如果仍然酸性, 使用氢氧化铵调整 pH 值为 8.0. </li>
	<li> 分级样品通过使用以下梯度与缓冲 B (缓冲 A 加 90% ACN): 15% 到20% 为 15 min, 20% 到35% 为 100 min 和35% 到50% 为 30 min. 设置分数收集器以收集分数, 每2分钟包括加载时间。将流量设置为0.4 毫升/分钟。有关代表性色谱的参考 29 和 图 1 。
	<li> 收集80馏分和干40馏分 (每其他分数) 与真空浓缩器完成干燥. </li>
	<li> 使用40干燥样品进行 LC/ms 分析. </li>
	<li> 通过 LC-ms/毫秒分析所有80分数以实现超的蛋白质组覆盖率. </li>
</ol> 5。LC-质谱/质谱准备和参数 注意: 在 TMT-based 定量中, 多肽离子是等压, 在 MS1 扫描中显示为1质量。然而, 他们是定量根据记者离子的强度 (10 独特的记者离子) 的 ms 扫描后, 多肽离子已破碎与高能碰撞离解 (HCD)。tmt 记者的离子比率可能会被 co-elution 的 tmt 标记的离子 24 所抑制。缩小离子隔离窗口 25 、气相净化 26 、MultiNotch MS3 方法 27 或具有多维 LC 和长渐变 (4-8 h) 的广泛分馏 28 是备选方法. 
<ol> <li> 包75和 #181; m 内径 (ID) 空列, 具有1.9 和 #181; C18 树脂为30至40厘米长 (〜1.3 和 #181; L 床容积). </li>
	<li> 将柱形加热到65和 #176; C 带有蝶形组合加热器, 以减少背压, 防止超高压的压力液相色谱 (UPLC) 系统。在蝶式加热器内卷2到3次, 以确保整个柱床的长度被加热。将该柱贴在加热器内部, 注意不要将加热器内的温度传感器带上胶带. 
	注: 蝶式组合加热器安装在仪表的 XYZ 舞台上的 custom-made 一块有机玻璃上. </li>
	<li> 准备缓冲 a (3% 二甲基亚砜和0.2% 甲酸) 和缓冲 b (缓冲 a 加 67% ACN), 并用95% 缓冲 b 彻底清洗列。然后, 在95% 缓冲区 A (至少3列卷) 中完全平衡列。使用0.25 和 #181 的流量; L/分钟和反背压力280到 320 bar. </li>
	<li> 在分析 TMT 标记的样本之前, 通过运行 100 ng 大鼠脑肽 (或我们选择的标准), 检查 LC-ms 系统的质量。经常检查以下参数以确保高质量的数据收集: 测量 ms 信号强度 (e8 和 e9 之间的基准峰值强度)、质谱/ms 频谱 (良好的 y 和 b 离子表示)、峰值宽度 (12-22 秒)、总保留时间、LC 系统压力 (压力上升和 #62; 50 条表示脏的列或堵塞的发射器尖端) 和运行的可变性 (相同的峰值应该和 #60; 三十年代在奔跑之间). 
	注: 要解决10丛 TMT 试剂 (6.32 mDa 差异) 的近等压记者离子, 必须将 ms/毫秒分辨率设置为至少 3万 400 m/z 。HCD 通常用于 TMT10-plex 记者的离子破碎, 因为它不受1/3 规则适用于碰撞诱导离解 (CID) 在离子阱仪器. </li>
	<li> 定期清洗和校准 MS 仪器, 并使用新的 LC 缓冲器来提高系统性能. </li>
	<li> 在 5% TFA 中从碱性 pH 分离中重组干肽. </li>
	<li> 在流5% 缓冲区 A 时加载0.2 和 #181; g 在列上. </li>
	<li> 洗与15% 到45% 的缓冲 B 在150分钟。作为起点, 使用以下渐变: 时间 0, 缓冲 B 5%;时间 2, 缓冲器 B 15%;时间 135, 缓冲器 b 45%, 时间 145, 缓冲器 b 70%;和时间 150, 缓冲器 B 95%。在复习基础峰值谱后, 对早期和晚期碱性 pH 色分数稍加调整渐变. </li>
	<li> 在离子阱质量分析仪 (400-1600 m/z ; 6万分辨率; 1 x 10 6 自动增益控制 (AGC) 目标; 50 毫秒最大离子时间; 和质心模式), 在数据相关模式下操作质谱仪。执行20毫秒/ms 高分辨率扫描 (6万分辨率、1 x 10 5 AGC 目标、100毫秒最大离子时间、HCD 正常化的碰撞能量、35个 m/z 隔离窗口和二十年代动态排除). 
	注: 不推荐 TMT10-plex 试剂的电子转移离解 (约), 因为离地的裂解部位不同于 HCD, 导致报告者离子重叠。此外, HCD 因为胰肽通常产生 +2 的带电状态, 而在较高的电荷状态下更有效。
</ol> 6。MS 数据分析 注意: 我们用跳跃软件程序描述数据分析。但是, 数据分析可以与其他商业可用或免费程序一起执行. 
<ol> <li> 处理来自质谱仪的原始文件与基于混合搜索引擎跳转 21 , 它结合了模式数据库搜索和基于 de 从头排序, 以提高灵敏度和特异性. 
	<li> 将原始数据转换为 mzXML 格式, 并根据目标诱饵 UniProt 人类数据库 (或其他适当的 species-specific 数据库) 来搜索 MS2 频谱, 以计算虚假发现率 (罗斯福). </li>
	<li> 对前体和片段离子使用 10 ppm 质量公差进行搜索。设置其他搜索参数, 以完全胰限制与2最大漏分裂, 3 最大修改网站每肽, 并分配 a , b , 和 y 离子, 以评分的肽。设置对在赖氨酸残留物和 N 总站 (+ 229.162 da) 和 carbamidomethylation 胱氨酸残留物 (+ 57.021 da) 的 TMT 标签的静态修改。设置动态修改, 包括符合氧化 (+ 15.994 Da), 这是一个常见的多肽工件由样品处理产生. </li>
	<li> 按以下标准筛选数据: 7 氨基酸最小肽长度, m/z 精确度 ( 例如 、5 ppm) 和匹配分数 (J 分数和 deltaCn)。根据肽的长度、trypticity 和电荷状态来分组肽. </li>
	<li> 通过匹配分数以将罗斯福的蛋白质减少到1% 以下, 使用额外的过滤级别。由单个光谱计数确定的蛋白质需要更高的匹配分数. </li>
	<li> 对于由蛋白质家族的多个成员共享的多肽, 将匹配的蛋白质成员聚集成1组. 
	注: 根据简约原则, 该组以所分配的多肽和其他蛋白质匹配的最高数量的蛋白质为代表, 由独特的肽 1 . </li>
	<li> 定量蛋白通过在所有匹配的肽谱匹配 (psm) 中通过使用内置在跳转软件套件中的程序来汇总报告器的离子计数. </li>
	<li> 对于每个被接受的 psm, 根据标记试剂的同位素分布和负载偏倚, 提取和改正 TMT 记者的离子强度, 这是从全球 psm 定量数据中计算出来的。在定量的过程中, 具有低强度和高噪声水平的过滤 psm. </li>
	<li> 计算每个报告员之间的相对信号和所有10记者离子的平均值。对所确定的蛋白质的 psm 的相对信号进行求和。通过将前 3 psm 的平均报告离子强度乘以相应的蛋白质, 将这些相对信号转换为绝对信号. </li>
	<li> 使用后 MS 计算方法来纠正干扰。假设 y 1 离子强度与报告者的离子强度成正比, 则计算出从清洁扫描中得到的线性关系, 并从受污染的 y1ion 强度中推导出干扰级别, 在噪声扫描中 23 . 
	注: 对于 tmt 标记的胰肽, 在 MS2 扫描中, K tmt 和 R 残滓是2种类型的 y 1 离子 (分别为 376.27574 da 和 175.11895 da)。如果检测到仅 1 y 1 离子并且与识别的肽一致, 则 MS2 被视为干净扫描 ( 图 3A )。如果检测到两个 y1ions, MS2 将被视为一个嘈杂的扫描. </li>
	<li> 将蛋白质定量值导出到电子表格软件程序中进行进一步分析。使用配对比较或方差分析来识别差异表达的蛋白质. </li>
</ol> 7。ms 数据验证 注意: 为了评估 ms 数据的质量, 在进行耗时的生物实验之前, 至少应执行1验证方法. 
<ol> <li> 手动检查感兴趣的蛋白质的 ms/毫秒光谱, 以验证肽序列和 TMT 报告者的离子定量. </li>
	<li> 使用已知的蛋白质定量数据来确认 MS 结果. </li>
	<li> 使用抗体方法验证蛋白质的变化 ( 如 , 中西部rn 和免疫组织化学分析). </li>
	<li> 对感兴趣的肽进行化学合成, 并将其作为内部标准来确认本地多肽的识别. 
	注: 在 LC-ms/毫秒中, 其 ms/ms 模式和保留时间应相同. </li>
	<li> 使用目标 MS 方法验证蛋白质的变化. </li>
</ol>

<ol>
	<li>Pagala, V. R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantitative+protein+analysis+by+mass+spectrometry.">Quantitative protein analysis by mass spectrometry.</a> Methods Mol Biol. 1278, 281-305 (2015).</li><li>Altelaar, A. F., Munoz, J., Heck, A. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Next-generation+proteomics:+towards+an+integrative+view+of+proteome+dynamics.">Next-generation proteomics: towards an integrative view of proteome dynamics.</a> Nat Rev Genet. 14 (1), 35-48 (2013).</li><li>Rauniyar, N., Yates, J. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Isobaric+labeling-based+relative+quantification+in+shotgun+proteomics.">Isobaric labeling-based relative quantification in shotgun proteomics.</a> J Proteome Res. 13 (12), 5293-5309 (2014).</li><li>Ross, P. L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Multiplexed+protein+quantitation+in+Saccharomyces+cerevisiae+using+amine-reactive+isobaric+tagging+reagents.">Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents.</a> Mol Cell Proteomics. 3 (12), 1154-1169 (2004).</li><li>Aebersold, R., Mann, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mass-spectrometric+exploration+of+proteome+structure+and+function.">Mass-spectrometric exploration of proteome structure and function.</a> Nature. 537 (7620), 347-355 (2016).</li><li>Bai, B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Deep+Profiling+of+Proteome+and+Phosphoproteome+by+Isobaric+Labeling.">Deep Profiling of Proteome and Phosphoproteome by Isobaric Labeling.</a> Extensive Liquid Chromatography, and Mass Spectrometry. Methods Enzymol. 585, 377-395 (2017).</li><li>McAlister, G. C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Increasing+the+multiplexing+capacity+of+TMTs+using+reporter+ion+isotopologues+with+isobaric+masses.">Increasing the multiplexing capacity of TMTs using reporter ion isotopologues with isobaric masses.</a> Anal Chem. 84 (17), 7469-7478 (2012).</li><li>Everley, R. A., Kunz, R. C., McAllister, F. E., Gygi, S. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Increasing+throughput+in+targeted+proteomics+assays:+54-plex+quantitation+in+a+single+mass+spectrometry+run.">Increasing throughput in targeted proteomics assays: 54-plex quantitation in a single mass spectrometry run.</a> Anal Chem. 85 (11), 5340-5346 (2013).</li><li>Bai, B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Integrated+approaches+for+analyzing+U1-70K+cleavage+in+Alzheimer's+disease.">Integrated approaches for analyzing U1-70K cleavage in Alzheimer's disease.</a> J Proteome Res. 13 (11), 4526-4534 (2014).</li><li>Bai, B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=U1+small+nuclear+ribonucleoprotein+complex+and+RNA+splicing+alterations+in+Alzheimer's+disease.">U1 small nuclear ribonucleoprotein complex and RNA splicing alterations in Alzheimer's disease.</a> Proc Natl Acad Sci U S A. 110 (41), 16562-16567 (2013).</li><li>Thongboonkerd, V., LaBaer, J., Domont, G. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Recent+advances+of+proteomics+applied+to+human+diseases.">Recent advances of proteomics applied to human diseases.</a> J Proteome Res. 13 (11), 4493-4496 (2014).</li><li>Churchman, M. L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Efficacy+of+Retinoids+in+IKZF1-Mutated+BCR-ABL1+Acute+Lymphoblastic+Leukemia.">Efficacy of Retinoids in IKZF1-Mutated BCR-ABL1 Acute Lymphoblastic Leukemia.</a> Cancer Cell. 28 (3), 343-356 (2015).</li><li>Wang, X., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Joint+mouse-human+phenome-wide+association+to+test+gene+function+and+disease+risk.">Joint mouse-human phenome-wide association to test gene function and disease risk.</a> Nat Commun. 7, 10464 (2016).</li><li>Mertz, J. L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Sequential+elution+interactome+analysis+of+the+Mind+bomb+1+ubiquitin+ligase+reveals+a+novel+role+in+dendritic+spine+outgrowth.">Sequential elution interactome analysis of the Mind bomb 1 ubiquitin ligase reveals a novel role in dendritic spine outgrowth.</a> Mol Cell Proteomics. 14 (7), 1898-1910 (2015).</li><li>Yang, F., Shen, Y., Camp, D. G., Smith, R. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High-pH+reversed-phase+chromatography+with+fraction+concatenation+for+2D+proteomic+analysis.">High-pH reversed-phase chromatography with fraction concatenation for 2D proteomic analysis.</a> Expert Rev Proteomics. 9 (2), 129-134 (2012).</li><li>Wang, H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+off-line+high+pH+reversed-phase+fractionation+and+nano-liquid+chromatography-mass+spectrometry+method+for+global+proteomic+profiling+of+cell+lines.">An off-line high pH reversed-phase fractionation and nano-liquid chromatography-mass spectrometry method for global proteomic profiling of cell lines.</a> J Chromatogr B Analyt Technol Biomed Life Sci. 974, 90-95 (2015).</li><li>Batth, T. S., Francavilla, C., Olsen, J. V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Off-line+high-pH+reversed-phase+fractionation+for+in-depth+phosphoproteomics.">Off-line high-pH reversed-phase fractionation for in-depth phosphoproteomics.</a> J Proteome Res. 13 (12), 6176-6186 (2014).</li><li>Song, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Reversed-phase-reversed-phase+liquid+chromatography+approach+with+high+orthogonality+for+multidimensional+separation+of+phosphopeptides.">Reversed-phase-reversed-phase liquid chromatography approach with high orthogonality for multidimensional separation of phosphopeptides.</a> Anal Chem. 82 (1), 53-56 (2010).</li><li>Wang, H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Systematic+optimization+of+long+gradient+chromatography+mass+spectrometry+for+deep+analysis+of+brain+proteome.">Systematic optimization of long gradient chromatography mass spectrometry for deep analysis of brain proteome.</a> J Proteome Res. 14 (2), 829-838 (2015).</li><li>Hebert, A. S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+one+hour+yeast+proteome.">The one hour yeast proteome.</a> Mol Cell Proteomics. 13 (1), 339-347 (2014).</li><li>Wang, X., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=JUMP:+a+tag-based+database+search+tool+for+peptide+identification+with+high+sensitivity+and+accuracy.">JUMP: a tag-based database search tool for peptide identification with high sensitivity and accuracy.</a> Mol Cell Proteomics. 13 (12), 3663-3673 (2014).</li><li>Xu, P., Duong, D. M., Peng, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Systematical+optimization+of+reverse-phase+chromatography+for+shotgun+proteomics.">Systematical optimization of reverse-phase chromatography for shotgun proteomics.</a> J Proteome Res. 8 (8), 3944-3950 (2009).</li><li>Niu, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Extensive+Peptide+Fractionation+and+y1+Ion-Based+Interference+Detection+Method+for+Enabling+Accurate+Quantification+by+Isobaric+Labeling+and+Mass+Spectrometry.">Extensive Peptide Fractionation and y1 Ion-Based Interference Detection Method for Enabling Accurate Quantification by Isobaric Labeling and Mass Spectrometry.</a> Anal Chem. 89 (5), 2956-2963 (2017).</li><li>Ting, L., Rad, R., Gygi, S. P., Haas, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MS3+eliminates+ratio+distortion+in+isobaric+multiplexed+quantitative+proteomics.">MS3 eliminates ratio distortion in isobaric multiplexed quantitative proteomics.</a> Nat Methods. 8 (11), 937-940 (2011).</li><li>Savitski, M. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Delayed+fragmentation+and+optimized+isolation+width+settings+for+improvement+of+protein+identification+and+accuracy+of+isobaric+mass+tag+quantification+on+Orbitrap-type+mass+spectrometers.">Delayed fragmentation and optimized isolation width settings for improvement of protein identification and accuracy of isobaric mass tag quantification on Orbitrap-type mass spectrometers.</a> Anal Chem. 83 (23), 8959-8967 (2011).</li><li>Wenger, C. D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Gas-phase+purification+enables+accurate,+multiplexed+proteome+quantification+with+isobaric+tagging.">Gas-phase purification enables accurate, multiplexed proteome quantification with isobaric tagging.</a> Nat Methods. 8 (11), 933-935 (2011).</li><li>McAlister, G. C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MultiNotch+MS3+enables+accurate,+sensitive,+and+multiplexed+detection+of+differential+expression+across+cancer+cell+line+proteomes.">MultiNotch MS3 enables accurate, sensitive, and multiplexed detection of differential expression across cancer cell line proteomes.</a> Anal Chem. 86 (14), 7150-7158 (2014).</li><li>Zhou, F., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genome-scale+proteome+quantification+by+DEEP+SEQ+mass+spectrometry.">Genome-scale proteome quantification by DEEP SEQ mass spectrometry.</a> Nat Commun. 4, 2171 (2013).</li><li>Savitski, M. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Delayed+fragmentation+and+optimized+isolation+width+settings+for+improvement+of+protein+identification+and+accuracy+of+isobaric+mass+tag+quantification+on+Orbitrap-type+mass+spectrometers.">Delayed fragmentation and optimized isolation width settings for improvement of protein identification and accuracy of isobaric mass tag quantification on Orbitrap-type mass spectrometers.</a> Anal. Chem. 83 (23), 8959-8967 (2011).</li><li>Ahrne, E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Evaluation+and+Improvement+of+Quantification+Accuracy+in+Isobaric+Mass+Tag-Based+Protein+Quantification+Experiments.">Evaluation and Improvement of Quantification Accuracy in Isobaric Mass Tag-Based Protein Quantification Experiments.</a> J Proteome Res. 15 (8), 2537-2547 (2016).</li><li>Xu, P., Duong, D. M., Peng, J. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Systematical+Optimization+of+Reverse-Phase+Chromatography+for+Shotgun+Proteomics.">Systematical Optimization of Reverse-Phase Chromatography for Shotgun Proteomics.</a> J Proteome Res. 8 (8), 3944-3950 (2009).</li><li>Tan, H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Integrative+Proteomics+and+Phosphoproteomics+Profiling+Reveals+Dynamic+Signaling+Networks+and+Bioenergetics+Pathways+Underlying+T+Cell+Activation.">Integrative Proteomics and Phosphoproteomics Profiling Reveals Dynamic Signaling Networks and Bioenergetics Pathways Underlying T Cell Activation.</a> Immunity. 46 (3), 488-503 (2017).</li><li>Wu, Z., Na, C. H., Tan, H., Peng, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Global+ubiquitination+analysis+by+SILAC+in+mammalian+cells.">Global ubiquitination analysis by SILAC in mammalian cells.</a> Methods Mol Biol. 1188, 149-160 (2014).</li><li>Tan, H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Refined+phosphopeptide+enrichment+by+phosphate+additive+and+the+analysis+of+human+brain+phosphoproteome.">Refined phosphopeptide enrichment by phosphate additive and the analysis of human brain phosphoproteome.</a> Proteomics. 15 (2-3), 500-507 (2015).</li><li>Li, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=JUMPg:+an+Integrative+Proteogenomics+Pipeline+Identifying+Unannotated+Proteins+in+Human+Brain+and+Cancer+Cells.">JUMPg: an Integrative Proteogenomics Pipeline Identifying Unannotated Proteins in Human Brain and Cancer Cells.</a> J Proteome Res. 17 (7), 2309-2320 (2016).</li><li>Yuan, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Assessing+the+clinical+utility+of+cancer+genomic+and+proteomic+data+across+tumor+types.">Assessing the clinical utility of cancer genomic and proteomic data across tumor types.</a> Nat Biotechnol. 32 (7), 644-652 (2014).</li><li>Nesvizhskii, A. I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Proteogenomics:+concepts,+applications+and+computational+strategies.">Proteogenomics: concepts, applications and computational strategies.</a> Nat Methods. 11 (11), 1114-1125 (2014).</li><li>Fagerberg, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Analysis+of+the+human+tissue-specific+expression+by+genome-wide+integration+of+transcriptomics+and+antibody-based+proteomics.">Analysis of the human tissue-specific expression by genome-wide integration of transcriptomics and antibody-based proteomics.</a> Mol Cell Proteomics. 13 (2), 397-406 (2014).</li></ol>

作者没有什么可透露的。

我们描述了一个高通量协议的蛋白质定量与10丛等压标记策略, 已经成功地实现了在几个出版物12,13,14,32.在本协议中, 我们可以在1实验中分析多达10种不同的生物蛋白样本。我们可以定期识别和定量超过1万的蛋白质, 并有很高的信心。虽然等压标记是一种有效的定量蛋白质的技术, 但它受比率压缩的限?...

下一代测序技术的进步已经为研究生物系统和人类疾病带来了新的前景。这使得大量的基因组、转录、蛋白质、代谢和其他分子系统的测量变得有形。质谱 (MS) 是分析化学中最敏感的方法之一, 其在蛋白质组学中的应用在人类基因组测序之后迅速扩大。在蛋白质组学领域, 在过去的几年中取得了重大的技术进步 MS-based 定量分析, 包括等压标记和复用能力结合广泛的液相色谱法, 除了仪器仪表进展, 允许更快, 更准确的测量, 并要求较少的样品材料。定量蛋白质组学已经成为在高度复杂的生物样品中分析数以万计的蛋白质和后修饰的主流方法1,2,3,4,5,6。
多路等压标记方法, 如等压标签的相对和绝对定量 (即, iTRAQ) 和串联质量标签 (TMT) MS 大大提高了样本吞吐量, 并增加了样本数, 可以分析在一个单一的实验1,6,7,8。与其他 MS-based 定量方法, 例如无标签定量和稳定同位素标记与氨基酸在细胞培养 (即, SILAC), 这些技术的潜力在蛋白质组学领域是可观的9 ,10,11。例如, TMT 方法允许在1实验中使用10丛试剂对10蛋白质样品进行分析。这些结构相同的 TMT 标签具有相同的整体质量, 但重同位素是差异分布的碳或氮原子, 导致一个独特的记者离子在 ms/毫秒碎片的每个标签, 从而使相对定量在10样本之间TMT 策略通常用于研究生物通路、疾病进展和细胞过程12、13、14。
大量的技术改进有增强的液相色谱 (lc) –MS/ms 系统, 无论是在 LC 分离和 ms 参数, 以最大限度地提高蛋白质的鉴定, 而不牺牲定量的准确性。在这种类型的猎枪蛋白质组学方法中, 采用高正交分离技术对肽的一维分离是至关重要的, 从而达到最大结果20。高 pH 反相液相色谱 (色) 比传统的强阳离子交换色谱法20具有更好的性能。当 high-pH 色与 low-pH 色的第二维度相结合时, 分析的动态范围和蛋白质覆盖率都得到提高, 从而能够在执行全蛋白质组分析时识别表达的蛋白质的大部分15 ,16,17,18。其他技术进步包括小 C18 微粒 (1.9 µm) 和延长长的专栏 (~ 1 m)19。此外, 其他显著的改进包括具有快速扫描速率的质谱仪的新版本、改进的灵敏度和分辨率20以及用于 MS 数据挖掘的复杂生物信息学管道21。
在这里, 我们描述了一个详细的协议, 结合了最新的方法和修改, 以提高灵敏度和吞吐量, 同时侧重于质量控制机制的整个实验。该协议包括蛋白质提取和消化, TMT 10 丛标记, 碱性 ph 和酸 ph 色分馏, 高分辨率 ms 检测, 和 ms 数据处理 (图 1)。此外, 我们还实施了一些质量控制步骤, 用于诊断和评估实验变化。这一详细的协议旨在帮助研究人员在新的领域定期识别和准确定量数以千计的蛋白质从一个裂解或组织。

<table><tbody><tr><td>1220 LC system</td><td>Agilent</td><td>G4288B</td><td></td></tr><tr><td>50% Hydroxylamine</td><td>Thermo Scientific</td><td>90115</td><td></td></tr><tr><td>Acetonitrile</td><td>Burdick &amp;amp; Jackson</td><td>AH015-4</td><td></td></tr><tr><td>Bullet Blender</td><td>Next Advance</td><td>BB24-AU</td><td></td></tr><tr><td>Butterfly Portfolio Heater</td><td>Phoenix S&amp;amp;T</td><td>PST-BPH-20</td><td></td></tr><tr><td>C18 tips</td><td>Harvard Apparatus</td><td>74-4607</td><td></td></tr><tr><td>Dithiothreitol (DTT)</td><td>Sigma</td><td>D5545</td><td></td></tr><tr><td>DMSO</td><td>Sigma</td><td>41648</td><td></td></tr><tr><td>Formic acid</td><td>Sigma</td><td>94318</td><td></td></tr><tr><td>Fraction Collector</td><td>Gilson</td><td>FC203B</td><td></td></tr><tr><td>Glass Beads</td><td>Next Advance</td><td>GB05</td><td></td></tr><tr><td>HEPES</td><td>Sigma</td><td>H3375</td><td></td></tr><tr><td>Iodoacetamide (IAA)</td><td>Sigma</td><td>I6125</td><td></td></tr><tr><td>Lys-C</td><td>Wako</td><td>125-05061</td><td></td></tr><tr><td>Methanol</td><td>Burdick &amp;amp; Jackson</td><td>AH230-4</td><td></td></tr><tr><td>Pierce BCA Protein Assay kit</td><td>Thermo Scientific</td><td>23225</td><td></td></tr><tr><td>Mass Spectrometer</td><td>Thermo Scientific</td><td>Q Exactive HF</td><td></td></tr><tr><td>nanoflow UPLC</td><td>Thermo Scientific</td><td>Ultimate 3000</td><td></td></tr><tr><td>ReproSil-Pur C18 resin, 1.9um</td><td>Dr. Maisch GmbH</td><td>r119.aq.0003</td><td></td></tr><tr><td>Self Pck Columns</td><td>New Objective</td><td>PF360-75-15-N-5</td><td></td></tr><tr><td>Sodium deoxycholate</td><td>Sigma</td><td>30970</td><td></td></tr><tr><td>Speedva</td><td>Thermo Scientific</td><td>SPD11V</td><td></td></tr><tr><td>TMT 10plex Isobaric label reagent</td><td>Thermo Scientific</td><td>90110</td><td></td></tr><tr><td>Trifluoroacetic acid (TFA)</td><td>Applied Biosystems</td><td>400003</td><td></td></tr><tr><td>Trypsin</td><td>Promega</td><td>V511C</td><td></td></tr><tr><td>Urea</td><td>Sigma</td><td>U5378</td><td></td></tr><tr><td>Xbridge Column C18 column</td><td>Waters</td><td>186003943</td><td></td></tr><tr><td>Ziptips C18</td><td>Millipore</td><td>ZTC18S096</td><td></td></tr><tr><td>SepPak 1cc 50mg</td><td>Waters</td><td>WAT054960</td><td></td></tr></tbody></table>

deep proteome profiling by isobaric labeling, extensive liquid chromatography, mass spectrometry, and software-assisted quantification

在质谱 (ms) 的蛋白质组学中, 有许多特殊的进展, 特别是在液相色谱 (lc) 与串联质谱 (lc-ms) 和等压标记复用能力的技术进展。在这里, 我们引入了一个 deep-proteomics 的分析协议, 结合10丛串联质量标签 (TMT) 标签与广泛的 lc/lc/ms 平台, 和后 MS 计算干扰校正, 以准确定量整个蛋白质。该协议包括以下主要步骤: 蛋白质提取和消化, TMT 标签, 2 维 (2D) LC, 高分辨率质谱, 和计算数据处理。质量控制步骤包括在故障排除和评估实验变化。在哺乳动物样本中, 有超过1万的蛋白质可以自信地定量这个协议。这一协议也可以应用于量化的后平移修改与小的变化。这种多路复用、健壮的方法为蛋白质组分析提供了强有力的工具, 包括细胞培养、动物组织和人体临床标本。

我们使用先前描述的物种肽组合系统地分析了比压缩在3主要协议步骤中的影响, 包括前 ms 分馏、ms 设置和后 ms 校正23。采用碱性 ph 色和酸性 ph 色结合的方法对预 MS 分馏进行了评价和优化。对于后质谱分析, 只考虑 species-specific 肽。我们使用此干涉模型检查 lc/lc-ms/毫秒中的多个参数, 包括 MS2 隔离窗口、在线 lc 分辨率、在线酸性 pH 色的加载量以及脱机 hi...

Watch this Scientific Journal Video about Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification at JoVE.com

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

我们提出了一个协议, 以准确定量蛋白质与等压标签, 广泛的分馏, 生物信息学工具, 和质量控制步骤, 结合液相色谱与高分辨率质谱仪接口。

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) ...

deep-proteome-profiling-isobaric-labeling-extensive-liquid

Universidad San Sebastian

Research

JoVE Journal

Biochemistry

11.8K 次观看.  St. Jude Children's Research Hospital. 我们提出了一个协议, 以准确定量蛋白质与等压标签, 广泛的分馏, 生物信息学工具, 和质量控制步骤, 结合液相色谱与高分辨率质谱仪接口。

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

Quantitative Proteomics Using Reductive Dimethylation for Stable Isotope Labeling

Stable isotope labeling of peptides by reductive dimethylation (ReDi labeling) is a method to accurately quantify protein expression differences between samples using mass spectrometry. ReDi labeling is performed using either regular (light) or deuterated (heavy) forms of formaldehyde and sodium cyanoborohydride to add two methyl groups to each free amine. Here we demonstrate a robust protocol for ReDi labeling and quantitative comparison of complex protein mixtures. Protein samples for comparison are digested into peptides, labeled to carry either light or heavy methyl tags, mixed, and co-analyzed by LC-MS/MS. Relative protein abundances are quantified by comparing the ion chromatogram peak areas of heavy and light labeled versions of the constituent peptide extracted from the full MS spectra. The method described here includes sample preparation by reversed-phase solid phase extraction, on-column ReDi labeling of peptides, peptide fractionation by basic pH reversed-phase (BPRP) chromatography, and StageTip peptide purification. We discuss advantages and limitations of ReDi labeling with respect to other methods for stable isotope incorporation. We highlight novel applications using ReDi labeling as a fast, inexpensive, and accurate method to compare protein abundances in nearly any type of sample.

Stable isotope labeling of peptides by reductive dimethylation (ReDi labeling) is a rapid, inexpensive strategy for accurate mass spectrometry-based quantitative proteomics. Here we demonstrate a robust method for preparation and analysis of protein mixtures using the ReDi approach that can be applied to nearly any sample type.

定量蛋白质组学使用还原二甲基化的稳定同位素标记

Stable isotope labeling of peptides by reductive dimethylation (ReDi labeling) is a method to accurately quantify protein expression differences between ...

科研

化学

Enhanced Sample Multiplexing of Tissues Using Combined Precursor Isotopic Labeling and Isobaric Tagging (cPILOT)

There is an increasing demand to analyze many biological samples for disease understanding and biomarker discovery. Quantitative proteomics strategies that allow simultaneous measurement of multiple samples have become widespread and greatly reduce experimental costs and times. Our laboratory developed a technique called combined precursor isotopic labeling and isobaric tagging (cPILOT), which enhances sample multiplexing of traditional isotopic labeling or isobaric tagging approaches. Global cPILOT can be applied to samples originating from cells, tissues, bodily fluids, or whole organisms and gives information on relative protein abundances across different sample conditions. cPILOT works by 1) using low pH buffer conditions to selectively dimethylate peptide N-termini and 2) using high pH buffer conditions to label primary amines of lysine residues with commercially-available isobaric reagents (see Table of Materials/Reagents). The degree of sample multiplexing available is dependent on the number of precursor labels used and the isobaric tagging reagent. Here, we present a 12-plex analysis using light and heavy dimethylation combined with six-plex isobaric reagents to analyze 12 samples from mouse tissues in a single analysis. Enhanced multiplexing is helpful for reducing experimental time and cost and more importantly, allowing comparison across many sample conditions (biological replicates, disease stage, drug treatments, genotypes, or longitudinal time-points) with less experimental bias and error. In this work, the global cPILOT approach is used to analyze brain, heart, and liver tissues across biological replicates from an Alzheimer&#39;s disease mouse model and wild-type controls. Global cPILOT can be applied to study other biological processes and adapted to increase sample multiplexing to greater than 20 samples.

Enhanced Sample Multiplexing of Tissues Using Combined Precursor Isotopic Labeling and Isobaric Tagging cPILOT

Combined precursor isotopic labeling and isobaric tagging (cPILOT) is a quantitative proteomics strategy that enhances sample multiplexing capabilities of isobaric tags. This protocol describes the application of cPILOT to tissues from an Alzheimer's disease mouse model and wild-type controls.

增强组织样品使用组合前体同位素标记和标记等压的复用（cPILOT）

There is an increasing demand to analyze many biological samples for disease understanding and biomarker discovery. Quantitative proteomics strategies ...

生物化学

Resolving Affinity Purified Protein Complexes by Blue Native PAGE and Protein Correlation Profiling

Most proteins act in association with others; hence, it is crucial to characterize these functional units in order to fully understand biological processes. Affinity purification coupled to mass spectrometry (AP-MS) has become the method of choice for identifying protein-protein interactions. However, conventional AP-MS studies provide information on protein interactions, but the organizational information is lost. To address this issue, we developed a strategy to unravel the distinct functional assemblies a protein might be involved in, by resolving affinity-purified protein complexes prior to their characterization by mass spectrometry. Protein complexes isolated through affinity purification of a bait protein using an epitope tag and competitive elution are separated through blue native electrophoresis. Comparison of protein migration profiles through correlation profiling using quantitative mass spectrometry allows assignment of interacting proteins to distinct molecular entities. This method is able to resolve protein complexes of close molecular weights that might not be resolved by traditional chromatographic techniques such as gel filtration. With little more work than conventional AP-geLC-MS/MS, we demonstrate this strategy may in many cases be adequate for obtaining protein complex topological information concomitantly to identifying protein interactions.

Here we present protocols for affinity purification of protein complexes and their separation by blue native PAGE, followed by protein correlation profiling using label free quantitative mass spectrometry. This method is useful to resolve interactomes into distinct protein complexes.

解决由蓝变性PAGE和蛋白质相关剖析亲和纯化的蛋白复合物

Most proteins act in association with others; hence, it is crucial to characterize these functional units in order to fully understand biological ...

Proteome-wide Quantification of Labeling Homogeneity at the Single Molecule Level

Cell proteomes are often characterized using electrophoresis assays, where all species of proteins in the cells are non-specifically labeled with a fluorescent dye and are spotted by a photodetector following their separation. Single molecule fluorescence imaging can provide ultrasensitive protein detection with its ability for visualizing individual fluorescent molecules. However, the application of this powerful imaging method to electrophoresis assays is hampered by the lack of ways to characterize the homogeneity of fluorescent labeling of each protein species across the proteome. Here, we developed a method to evaluate the labeling homogeneity across the proteome based on a single molecule fluorescence imaging assay. In our measurement using a HeLa cell sample, the proportion of proteins labeled with at least one dye, which we termed &#8216;labeling occupancy&#8217; (LO), was determined to range from 50% to 90%, supporting the high potential of the application of single molecule imaging to sensitive and precise proteome analysis.

Here, we present a protocol to assess the labeling homogeneity for each protein species in a complex protein sample at the single molecule level.

单分子水平标记均匀性的全蛋白质定量

Cell proteomes are often characterized using electrophoresis assays, where all species of proteins in the cells are non-specifically labeled with a ...

High-Resolution Complexome Profiling by Cryoslicing BN-MS Analysis

Proteins generally exert biological functions through interactions with other proteins, either in dynamic protein assemblies or as a part of stably formed complexes. The latter can be elegantly resolved according to molecular size using native polyacrylamide gel electrophoresis (BN-PAGE). Coupling of such separations to sensitive mass spectrometry (BN-MS) has been well-established and theoretically allows for exhaustive assessment of the extractable complexome in biological samples. However, this approach is rather laborious and provides limited complex size resolution and sensitivity. Also, its application has remained restricted to abundant mitochondrial and plastid proteins. Thus, for a majority of proteins, information regarding integration into stable protein complexes is still lacking. Presented here is an optimized approach for complexome profiling comprising preparative-scale BN-PAGE separation, sub-millimeter sampling of broad gel lanes by cryomicrotome slicing, and mass spectrometric analysis with label-free protein quantification. The procedures and tools for critical steps are described in detail. As an application, the report describes complexome analysis of a solubilized endosome-enriched membrane fraction from mouse kidneys, with 2,545 proteins profiled in total. The results demonstrate identification of uniform, low-abundance membrane proteins such as intracellular ion channels as well as high resolution, complex protein assembly patterns, including glycosylation isoforms. The results are in agreement with independent biochemical analyses. In summary, this methodology allows for comprehensive and unbiased identification of protein (super)complexes and their subunit composition, providing a basis for investigating stoichiometry, assembly, and interaction dynamics of protein complexes in any biological system.

A versatile cryoslicing BN-MS protocol using a microtome is presented for high-resolution complexome profiling.

通过低温BN-MS分析进行高分辨率复杂分析

Proteins generally exert biological functions through interactions with other proteins, either in dynamic protein assemblies or as a part of stably formed ...

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 ...

TMT Sample Preparation for Proteomics Facility Submission and Subsequent Data Analysis

Proteomic technologies are powerful methodologies that can aid our understanding of mechanisms of action in biological systems by providing a global view of the impact of a disease, treatment, or other condition on the proteome as a whole. This report provides a detailed protocol for the extraction, quantification, precipitation, digestion, labeling, and subsequent data analysis of protein samples. Our optimized TMT labeling protocol requires a lower tag-label concentration and achieves consistently reliable data. We have used this protocol to evaluate protein expression profiles in a variety of mouse tissues (i.e., heart, skeletal muscle, and brain) as well as cells cultured in vitro. In addition, we demonstrate how to evaluate thousands of proteins from the resulting dataset.

We present an optimized tandem mass tag (TMT) labeling protocol that includes detailed information for each of the following steps: protein extraction, quantification, precipitation, digestion, labeling, submission to a proteomics facility, and data analyses.

蛋白质组学设施提交和后续数据分析的TMT样品制备

Proteomic technologies are powerful methodologies that can aid our understanding of mechanisms of action in biological systems by providing a global view ...

Automated Sample Multiplexing by using Combined Precursor Isotopic Labeling and Isobaric Tagging (cPILOT)

We have introduced a high throughput quantitative proteomics workflow, combined precursor isotopic labeling and isobaric tagging (cPILOT) capable of multiplexing up to 22 or 24 samples with tandem mass tags or isobaric N,N-dimethyl leucine isobaric tags, respectively, in a single experiment. This enhanced sample multiplexing considerably reduces mass spectrometry acquisition times and increases the utility of the expensive commercial isobaric reagents. However, the manual process of sample handling and pipetting steps in the strategy can be labor intensive, time consuming, and introduce sample loss and quantitative error. These limitations can be overcome through the incorporation of automation. Here we transferred the manual cPILOT protocol to an automated liquid handling device that can prepare large sample numbers (i.e., 96 samples) in parallel. Overall, automation increases feasibility and reproducibility of cPILOT and allows for broad usage by other researchers with comparable automation devices.

Automated Sample Multiplexing by using Combined Precursor Isotopic Labeling and Isobaric Tagging cPILOT

Combined precursor isotopic labeling and isobaric tagging (cPILOT) is an enhanced sample multiplexing strategy that is capable of increasing the number of samples that can be analyzed simultaneously with available isobaric tags. Incorporation of a robotic platform has greatly increased experimental throughput, reproducibility, and quantitative accuracy.

使用组合前体同位素标签和同位素标记 （cPILOT） 实现自动样品多路复用

We have introduced a high throughput quantitative proteomics workflow, combined precursor isotopic labeling and isobaric tagging (cPILOT) capable of ...

Quantitative Mass Spectrometric Profiling of Cancer-cell Proteomes Derived From Liquid and Solid Tumors

In-depth analyses of cancer cell proteomes are needed to elucidate oncogenic pathomechanisms, as well as to identify potential drug targets and diagnostic biomarkers. However, methods for quantitative proteomic characterization of patient-derived tumors and in particular their cellular subpopulations are largely lacking. Here we describe an experimental set-up that allows quantitative analysis of proteomes of cancer cell subpopulations derived from either liquid or solid tumors. This is achieved by combining cellular enrichment strategies with quantitative Super-SILAC-based mass spectrometry followed by bioinformatic data analysis. To enrich specific cellular subsets, liquid tumors are first immunophenotyped by flow cytometry followed by FACS-sorting; for solid tumors, laser-capture microdissection is used to purify specific cellular subpopulations. In a second step, proteins are extracted from the purified cells and subsequently combined with a tumor-specific, SILAC-labeled spike-in standard that enables protein quantification. The resulting protein mixture is subjected to either gel electrophoresis or Filter Aided Sample Preparation (FASP) followed by tryptic digestion. Finally, tryptic peptides are analyzed using a hybrid quadrupole-orbitrap mass spectrometer, and the data obtained are processed with bioinformatic software suites including MaxQuant. By means of the workflow presented here, up to 8,000 proteins can be identified and quantified in patient-derived samples, and the resulting protein expression profiles can be compared among patients to identify diagnostic proteomic signatures or potential drug targets.

In-depth analyses of cancer cell proteomes facilitate identification of novel drug targets and diagnostic biomarkers. We describe an experimental workflow for quantitative analysis of (phospho-)proteomes in cancer cell subpopulations derived from liquid and solid tumors. This is achieved by combining cellular enrichment strategies with quantitative Super-SILAC-based mass spectrometry.

对源自液体和固体肿瘤癌症细胞定量蛋白质组质谱剖析

In-depth analyses of cancer cell proteomes are needed to elucidate oncogenic pathomechanisms, as well as to identify potential drug targets and diagnostic ...

医学

High-throughput and Deep-proteome Profiling by 16-plex Tandem Mass Tag Labeling Coupled with Two-dimensional Chromatography and Mass Spectrometry

Isobaric tandem mass tag (TMT) labeling is widely used in proteomics because of its high multiplexing capacity and deep proteome coverage. Recently, an expanded 16-plex TMT method has been introduced, which further increases the throughput of proteomic studies. In this manuscript, we present an optimized protocol for 16-plex TMT-based deep-proteome profiling, including protein sample preparation, enzymatic digestion, TMT labeling reaction, two-dimensional reverse-phase liquid chromatography (LC/LC) fractionation, tandem mass spectrometry (MS/MS), and computational data processing. The crucial quality control steps and improvements in the process specific for the 16-plex TMT analysis are highlighted. This multiplexed process offers a powerful tool for profiling a variety of complex samples such as cells, tissues, and clinical specimens. More than 10,000 proteins and posttranslational modifications such as phosphorylation, methylation, acetylation, and ubiquitination in highly complex biological samples from up to 16 different samples can be quantified in a single experiment, providing a potent tool for basic and clinical research.

Presented here is an optimized high-throughput protocol developed with 16-plex tandem mass tag reagents, enabling quantitative proteome profiling of biological samples. Extensive basic pH fractionation and high-resolution LC-MS/MS mitigate ratio compression and provide deep proteome coverage.

Isobaric tandem mass tag (TMT) labeling is widely used in proteomics because of its high multiplexing capacity and deep proteome coverage. Recently, an ...

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

摘要

探索更多视频

此视频中的章节

定量蛋白质组学使用还原二甲基化的稳定同位素标记

增强组织样品使用组合前体同位素标记和标记等压的复用（cPILOT）

解决由蓝变性PAGE和蛋白质相关剖析亲和纯化的蛋白复合物

单分子水平标记均匀性的全蛋白质定量

通过低温BN-MS分析进行高分辨率复杂分析

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

蛋白质组学设施提交和后续数据分析的TMT样品制备

使用组合前体同位素标签和同位素标记（cPILOT）实现自动样品多路复用

对源自液体和固体肿瘤癌症细胞定量蛋白质组质谱剖析

High-throughput and Deep-proteome Profiling by 16-plex Tandem Mass Tag Labeling Coupled with Two-dimensional Chromatography and Mass Spectrometry

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

摘要

探索更多视频

此视频中的章节

定量蛋白质组学使用还原二甲基化的稳定同位素标记

增强组织样品使用组合前体同位素标记和标记等压的复用（cPILOT）

解决由蓝变性PAGE和蛋白质相关剖析亲和纯化的蛋白复合物

单分子水平标记均匀性的全蛋白质定量

通过低温BN-MS分析进行高分辨率复杂分析

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

蛋白质组学设施提交和后续数据分析的TMT样品制备

使用组合前体同位素标签和同位素标记 （cPILOT） 实现自动样品多路复用

对源自液体和固体肿瘤癌症细胞定量蛋白质组质谱剖析

High-throughput and Deep-proteome Profiling by 16-plex Tandem Mass Tag Labeling Coupled with Two-dimensional Chromatography and Mass Spectrometry

使用组合前体同位素标签和同位素标记（cPILOT）实现自动样品多路复用