The Novo Nordisk Foundation Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, København 2200, Denmark.
Department of Bioscience, TUM School of Natural Sciences, Technical University of Munich, Garching (bei München) 85748, Germany.
Anal Chem. 2023 Jul 4;95(26):9881-9891. doi: 10.1021/acs.analchem.3c00842. Epub 2023 Jun 20.
A linear ion trap (LIT) is an affordable, robust mass spectrometer that provides fast scanning speed and high sensitivity, where its primary disadvantage is inferior mass accuracy compared to more commonly used time-of-flight or orbitrap (OT) mass analyzers. Previous efforts to utilize the LIT for low-input proteomics analysis still rely on either built-in OTs for collecting precursor data or OT-based library generation. Here, we demonstrate the potential versatility of the LIT for low-input proteomics as a stand-alone mass analyzer for all mass spectrometry (MS) measurements, including library generation. To test this approach, we first optimized LIT data acquisition methods and performed library-free searches with and without entrapment peptides to evaluate both the detection and quantification accuracy. We then generated matrix-matched calibration curves to estimate the lower limit of quantification using only 10 ng of starting material. While LIT-MS1 measurements provided poor quantitative accuracy, LIT-MS2 measurements were quantitatively accurate down to 0.5 ng on the column. Finally, we optimized a suitable strategy for spectral library generation from low-input material, which we used to analyze single-cell samples by LIT-DIA using LIT-based libraries generated from as few as 40 cells.
线性离子阱(LIT)是一种价格实惠、坚固耐用的质谱仪,具有快速扫描速度和高灵敏度,但与更常用的飞行时间或轨道阱(OT)质量分析仪相比,其主要缺点是质量精度较差。以前利用 LIT 进行低投入蛋白质组学分析的努力仍然依赖于内置 OT 来收集前体数据或基于 OT 的文库生成。在这里,我们展示了 LIT 作为一种独立的质量分析仪,在所有质谱(MS)测量中,包括文库生成,在低投入蛋白质组学中的潜在多功能性。为了测试这种方法,我们首先优化了 LIT 数据采集方法,并进行了有和没有捕获肽的无库搜索,以评估检测和定量准确性。然后,我们生成了基质匹配校准曲线,仅使用 10ng 的起始材料来估计定量下限。虽然 LIT-MS1 测量的定量准确性较差,但 LIT-MS2 测量在柱上低至 0.5ng 时具有定量准确性。最后,我们优化了一种从低投入材料生成光谱文库的合适策略,我们使用该策略通过 LIT-DIA 分析了单细胞样品,LIT 基于从低至 40 个细胞生成的文库。