PRM-PASEF

Abstract

与标准选择和并行反应监测（SRM和PRM）相比，PRM-PaseF增加了可以以单个采集方法为目标的肽的数量，而不会损害选择性或灵敏度。使用原型采集软件，我们针对201个同位素标记的合成肽（Aqua肽），被刺激到100 ng的人类HeLa细胞系消化中。PRM-PASEF使用采集方法的某些肽的量化限制为17,2 AMOL的限制，该方法可以在30分钟的LC梯度中监测216个前体。新的PRM-PASEF方法在注射之间具有很高的可重复性，并且可以准确定量。

作者

Antoine Metur¹，Pierre-Olivier Schmit²，克里斯托弗·亚当斯³, Gunnar Dittmar¹;
1 Lih，Strassen，卢森堡；2法国布鲁克法国Wissembourg；3美国圣何塞Bruker Daltonics Inc.

关键字：

4D-Proteomics, prm-PASEF, timsTOF Pro, PASEF, LFQ, absolute quantitation, targeted proteomics

靶向质谱是一种强大的技术，可以支持假设驱动的蛋白质组学实验，例如，在大型样本队列中验证生物标志物候选者。它减轻了样本之间缺失值的问题，但与数据依赖性（DDA）和数据无关（DIA）采集方法相比，总体敏感性增加了。靶向蛋白质组学使用合成肽作为内标，以使MS信号正常化，并以最大的置信度确认内源性肽的检测。

靶向蛋白质组学的一个主要局限性是必须在单个运行中测量的目标数量，液相色谱分离的持续时间和整体灵敏度之间找到折衷。当前一代（包括SRM和PRM）的有针对性的采集方法仍然如此。因此，只有通过使用更长的色谱分离或妥协MS灵敏度和选择性来实现大量靶向肽的完整数据完整性。诸如DIA之类的替代方法可以部分解决此问题，并依赖于使用广泛的M/Z隔离窗口对洗脱肽的系统共隔离和共裂。这种方法测量了大多数肽，但是在可实现的MS循环期间（分析仪的扫描速度必须最小化），具有有限的特异性和灵敏度（所有片段都混合在一起），并且与短色谱梯度不易结合。

In this application note, we present the prm-PASEF method, an innovative implementation of the PASEF acquisition strategy that overcomes the constraints associated with the current targeted proteomics techniques.

如图1所述，用201个重的水上肽和15个光合成肽升高了人类细胞系消化物（100 ng/µl），使用15个浓度的浓度曲线，使用15个浓度的浓度曲线，使用15次重aqua产生了5.5至50,000 amol/µl肽及其轻度对应物。这组肽是内标。在所有样品中，其他186种水肽的恒定浓度为2 fmol/µl。

Samples and controls were injected in technical triplicate and separated on a nano HPLC (nanoElute, Bruker Daltonics) using 250 mm pulled emitter columns (IonOpticks, Australia) with a 30 min gradient stepped gradient ranging from 2-30% acetonitrile. Peptides were analyzed on a timsTOF Pro mass spectrometer (Bruker Daltonics) operated in prm- PASEF mode. A single prm-PASEF method was used for this study: the tims accumulation time was fixed to 50 ms while the tims separation duration was set to 100 ms. The range of mobility values was 0.65-1.3 1/K₀and the covered m/z range was 100-1700 m/z. Data were processed with Skyline-daily (20.1.1.83).

The quantification performance was evaluated measuring the 15 heavy/light ratios at each concentration level and by determining the respective concentration accuracy and relative standard deviation. As a quality control, the concentrations of two curves were back-calculated with the linear regression of the first curve. Limits of quantification (LOQ) were defined as the lowest concentration point within 80% < accuracy < 120% associated with a signal higher than the mean (blanks)+3×SD(blanks).

通过测量以恒定浓度尖刺的186个重型水上肽的PRM-PaseF痕迹的区域来评估多路复用性能。在30个PRM-PASEF运行中计算了面积的相对标准偏差（RSD）和整个色谱峰的数据点数量（使用最高的PRM过渡）。

PRM-PASEFtakes advantage of the trapped ion mobility spectrometry (tims) device to dramatically increase the multiplexing capability of the method. This allowed for massive parallelization of targeted acquisitions with no detrimental effect on the sensitivity and greater flexibility of implementation with fast LC and UHPLC. The capture and elution principle of the tims cell (Figure 2) allows all the targeted peptides, that sequentially elute in the ion mobility dimension, to be acquired during a 100 ms tims scan event. In addition, the time focusing effect increases the sensitivity because peptides accumulated for 100 ms are then eluted in 5 ms peaks to the MS.

216个前体的洗脱时间在30分钟的梯度中分布在22分钟内。在PRM-PASEF中，采集源是三维的，必须根据色谱洗脱时间，离子迁移率和Quadrupole隔离M/z来定义隔离窗口。当我们将保留窗口设置为每个前体40s时，它在LC保留时间维度上产生了重叠。但是，IMS尺寸减少了最终的采集窗口重叠，如图5a所示。具有重叠采集窗口的前体离子，在离子迁移率维度中仍然可以分开的前体离子在同一PRM-PASEF框架内测量（图3）。

通过研究15个尖刺肽对的重/光比评估PRM-PaseF的绝对定量潜力。选择性和灵敏度的组合也可以良好的信号检测，即使在低浓度下（图7）。对ATVVYQGER重/光对的详细分析表明，信号响应可以通过线性回归拟合在2900的浓度系数（从17.2到50,000 AMOL注射柱）上。此外，基于用第一条建立的校准曲线，两份三份三份的浓度的后计数证实，可以在所有浓度下以±20％的精度确定定量（表1）。定量限（LOQ）定义为80％<精度<准确度<120％的最低浓度点与高于平均值（空白） + 3x SD（空白）相关的信号相关。

在这个实验中,平均4针对我ons was measured within each prm-PASEF frame (Figure 5b) without affecting the sensitivity nor the cycle time. When several compounds were overlapping in both IMS and LC dimensions, they were measured in successive PASEF frames (figure 4). On average, there were 4 distinct prm-PASEF frames in a prm-PASEF cycle. At the highest precursor density, up to 10 frames were used per MS cycle (Figure 5c). The number of frames per MS cycle does not impact the sensitivity but the number of data points that define the chromatographic peak. Despite this, there was no under-sampling of chromatographic peaks (Figure 5d), the median number of data points per chromatographic peak was 25, suggesting that the experiment could be performed with an even shorter gradient. Good peak sampling and preserved sensitivity allowed measuring all peak areas with a correct RSD even without internal standard normalization (Figure 6).