NEW YORK – Researchers at the Max Planck Institute of Biochemistry are using mass spectrometry to analyze the plasma proteomes of 50,000 individuals to identify biomarkers linked to maternal and fetal health.

纽约——马克斯·普朗克生物化学研究所的研究人员正在利用质谱分析50,000名个体的血浆蛋白质组，以识别与母婴健康相关的生物标志物。

Detailed in a

详细信息见

BioRxiv

生物预印本仓库

preprint

预印本

published in March, the effort is one of the largest to date to use mass spectrometry for population-scale plasma proteomics analysis.

今年3月发表的这项研究是迄今为止使用质谱进行大规模血浆蛋白质组学分析的最大努力之一。

Over the last half decade, researchers have conducted a number of large-scale plasma proteomic studies measuring thousands of proteins in tens of thousands of individuals, assessing their links to a variety of health conditions. These studies have not used mass spectrometry, however, but rather affinity-based platforms from Olink (now part of Thermo Fisher Scientific) and SomaLogic (now part of Standard BioTools), which until recently were the only technologies that offered the combination of depth of coverage and throughput required to analyze such large sample cohorts..

在过去的五年里，研究人员开展了多项大规模血浆蛋白质组学研究，测量了数万人中数千种蛋白质，并评估了它们与各种健康状况的关联。然而，这些研究并未使用质谱技术，而是采用了基于亲和力的平台，如Olink（现为赛默飞世尔科技的一部分）和SomaLogic（现为Standard BioTools的一部分），直到最近，这些技术仍是唯一能够提供分析如此庞大样本队列所需的覆盖深度和通量的技术。

Advances in mass spec instrumentation and plasma protein enrichment have made feasible studies analyzing tens of thousands of samples, but, to date, few if any mass spec studies on this scale have been undertaken.

质谱仪器和血浆蛋白富集方面的进步使得分析数以万计的样本成为可能，但迄今为止，很少甚至没有进行过如此规模的质谱研究。

'It's kind of the mass spec answer to the UK Biobank,' said Vincent Albrecht, a graduate student in the lab of Max Planck researcher Matthias Mann and first author on the preprint.

“它有点像是质谱领域对英国生物样本库的回答，”马克斯·普朗克研究所研究员马蒂亚斯·曼恩实验室的研究生文森特·阿尔布雷希特说道，他是该预印本的第一作者。

The UK Biobank's Pharma Proteomics Project (PPP) has generated one of the largest population proteomics datasets, releasing in 2023 a set consisting of roughly 3,000 proteins measured in blood samples from 54,000 UKB participants produced using Olink's Explore platform. In January, the biobank said it has .

英国生物样本库的制药蛋白质组学项目（PPP）生成了其中一个最大规模的人群蛋白质组学数据集，并于2023年发布了一组数据，包含使用Olink的Explore平台在来自54,000名英国生物样本库参与者血液样本中测量的大约3,000种蛋白质。今年一月，该生物样本库表示它已经 。

launched a study

启动了一项研究

that will measure up to 5,400 proteins in 600,000 blood samples.

那将对60万个血液样本中的多达5400种蛋白质进行测量。

The samples used in the Max Planck study come from the Multi-Omics for Mothers and Infants (MOMI) Consortium, which is supported by the Bill and Melinda Gates Foundation. The project aims to define the underlying biological risks for adverse pregnancy outcomes and prioritize early interventions.

马克斯·普朗克研究所的研究中使用的样本来自多组学母婴联盟（MOMI），该联盟由比尔和梅琳达·盖茨基金会支持。该项目旨在确定不良妊娠结局的潜在生物学风险，并优先进行早期干预。

Albrecht said he and his colleagues have analyzed around three-quarters of the 50,000-sample cohort. Running all 50,000 samples will take around a year total using two mass spectrometry systems, he said.

阿尔布雷希特说，他和他的同事已经分析了大约四分之三的50,000个样本队列。他说，使用两台质谱仪系统运行所有50,000个样本总共需要大约一年的时间。

He said that advances in sample preparation as well as improved instrumentation, including the Evosep One liquid chromatography system and the Thermo Fisher Scientific Orbitrap Astral, had enabled the researchers to tackle a cohort of this size.

他说，样品制备技术的进步以及包括 Evosep One 液相色谱系统和赛默飞世尔科技 Orbitrap Astral 在内的仪器改进，使研究人员能够应对如此规模的队列研究。

For the study, the Max Planck team is using a perchloric acid-based workflow called PCA-N. The approach is based on

针对这项研究，马克斯·普朗克团队正在使用一种名为PCA-N的高氯酸基工作流程。该方法基于

a method

一种方法

developed by Boston Children's Hospital researchers Judith and Hanno Steen in which plasma samples are treated with perchloric acid to deplete high abundance proteins, allowing for improved depth of coverage.

由波士顿儿童医院研究人员Judith和Hanno Steen开发，其中血浆样品用高氯酸处理以去除高丰度蛋白质，从而提高了覆盖深度。

Albrecht said that he and his colleagues evaluated a number of plasma proteomic workflows before settling on the perchloric acid-based workflow. They chose the workflow in large part due to its robustness and its robustness to sample contamination in particular, Albrecht said.

阿尔布雷希特说，他和他的同事评估了多种血浆蛋白质组学工作流程，最终选择了基于高氯酸的工作流程。阿尔布雷希特说，他们选择这个工作流程主要是因为它具有强大的稳定性，特别是对样品污染的强大适应性。

'We know the plasma is collected at different sites and different clinics, and a key driver for variance is contamination based on platelets, and leukocytes, and coagulation,' he said. 'If you have different nurses or different phlebotomists, they may take the blood in different ways, and when you start getting, for instance, platelets in your sample, the biology is masked by this sort of contamination.'.

“我们知道血浆是在不同的地点和诊所采集的，差异的一个关键驱动因素是基于血小板、白细胞和凝血的污染，”他说。“如果你有不同的护士或不同的采血师，他们可能以不同的方式采血，当你开始在样本中得到例如血小板时，这种污染就会掩盖生物学特性。”

The MOMI samples were collected at different sites in Zambia, Tanzania, Pakistan, and Bangladesh over multiple years.

MOMI 样本是在多年间从赞比亚、坦桑尼亚、巴基斯坦和孟加拉国的不同地点收集的。

Albrecht said that the workflow's low sample requirement, which the researchers enabled by adding a neutralization step, was also key as it made sample prep compatible with a high-throughput 384-well plate (and, potentially, a 1,536-well) format. They were able to process all 50,000 samples in seven days..

阿尔布雷希特说，工作流程的低样本需求也是关键，研究人员通过增加中和步骤实现了这一点，这使得样本制备与高通量的384孔板（以及潜在的1536孔板）格式兼容。他们能够在七天内处理所有50,000个样本。

Achieving low marginal costs was also important to the team, Albrecht said, noting that they hope for the method to be widely accessible. The cost per sample of the PCA-N workflow is essentially the same as the cost per sample of a neat plasma analysis.

阿尔布雷希特表示，实现低边际成本对团队来说也很重要，他们希望这种方法能够被广泛使用。PCA-N 工作流程的每个样本的成本与纯血浆分析的每个样本的成本基本相同。

Albrecht said that in the MOMI cohort he and his colleagues are measuring close to 2,000 proteins per sample using the PCA-N approach. The researchers also included in the

阿尔布雷希特说，在MOMI队列中，他和同事们使用PCA-N方法对每个样本测量了近2000种蛋白质。研究人员还纳入了

BioRxiv

生物预印本（BioRxiv）

preprint data on 1,500 quality control samples interspersed among the MOMI cohort samples, reporting intraplate CVs of 17.7 percent across the study's duration of nearly one year.

预印本数据包含了分布在MOMI队列样本中的1500个质量控制样本，报告了在近一年的研究期间内板内变异系数为17.7%。

A number of new mass spec-based plasma proteomic workflows have become available in recent years. Companies including Seer, Biognosys, and PreOmics have developed particle-based enrichment methods as has the lab of University of Washington researcher Michael MacCoss. A

近年来，出现了许多基于质谱的新血浆蛋白质组学工作流程。包括Seer、Biognosys和PreOmics在内的公司已经开发出基于颗粒的富集方法，华盛顿大学研究员迈克尔·麦克考斯的实验室也同样如此。

recent study

最近的研究

by University of Wisconsin-Madison researcher Josh Coon compared several of these approaches, finding that Seer's Proteograph XT platform, PreOmics EnrichPlus product, and University of Washington approach (called Mag-Net) outperformed a perchloric acid-depletion approach both in terms of depth of coverage and reproducibility.

威斯康星大学麦迪逊分校的研究员乔什·库恩比较了其中几种方法，发现Seer的Proteograph XT平台、PreOmics的EnrichPlus产品以及华盛顿大学的方法（称为Mag-Net）在覆盖深度和可重复性方面均优于高氯酸耗竭方法。

(The Biognosys system was not included among the approaches evaluated.) In an email, Seer Senior VP and Scientific Fellow Asim Siddiqui said that the company is in some cases measuring more than 9,000 proteins per plasma sample..

（Biognosys系统未被列入评估方法之中。）Seer高级副总裁兼科学研究员阿西姆·西迪基在一封电子邮件中表示，在某些情况下，该公司每个血浆样本测量的蛋白质数量超过9000种。

Sara Ahadi, associate director of multiomics and integrative analysis at Grifols subsidiary Alkahest, said that while perchloric acid depletion does not offer the depth of coverage of other methods, its low cost and ease of use make it an attractive option.

Grifols子公司Alkahest的多组学与整合分析副主管Sara Ahadi表示，虽然高氯酸耗竭法无法提供其他方法那样的深度覆盖，但其低成本和易用性使其成为一个有吸引力的选择。

'We use it in our lab, and it's a favorite [approach] even though it is not the deepest,' she said. 'We have seen in our data that it is more robust than nanoparticle or bead enrichment' methods.

“我们在实验室里使用它，虽然它不是最深的，但却是最喜欢的方法之一，”她说。“我们在数据中看到，它比纳米粒子或珠粒富集方法更稳健。”

She added that the neutralization step incorporated by the Max Planck researchers further streamlines the method while also making it more amenable to high-throughput automation.

她补充说，马克斯·普朗克研究所的研究人员采用的中和步骤进一步简化了该方法，同时也使其更适合高通量自动化。

In March, Ahadi, who is not involved in the Max Planck MOMI work, and her colleagues published results from

今年 3 月，阿哈迪和她的同事发表了研究成果，阿哈迪并未参与马克斯·普朗克 MOMI 工作。

a study

一项研究

comparing several commercially available plasma proteomic workflows, though they did not include a perchloric acid-depletion approach.

比较了几种商业上可用的血浆蛋白质组学工作流程，尽管他们没有包括高氯酸耗竭方法。

Ahadi also applauded the Max Planck team's demonstration of its workflow's stability over the course of many samples and months of analysis.

阿哈迪还赞扬了马克斯·普朗克团队在许多样本和数月的分析过程中展示其工作流程的稳定性。

'You have multiple levels of [QC] information — interplate, intraplate, a large number of samples over a long time,' she said. 'They have to look at all of it in detail. It really shows how you would be able to maintain this quality, [addressing] any factor that could cause robustness to go off.'

“你拥有多层次的[QC]信息——板间、板内、长时间内的大量样本，”她说。“他们必须详细查看所有这些信息。这确实展示了如何能够维持这种质量，[解决]任何可能导致稳健性下降的因素。”

'I think their attention to detail and the quality of data is very important,' Ahadi said. 'They basically didn't play the number game, they played the quality game, and that will give us advantages we will see in the biological data that is going to come out of this.'

“我认为他们对细节的关注和数据的质量非常重要，”阿哈迪说。“他们基本上没有玩数字游戏，而是玩质量游戏，这将为我们带来优势，我们将会在接下来的生物学数据中看到。”

While population proteomics studies have thus far relied almost exclusively on affinity-based methods, the Max Planck-MOMI work suggests that mass spec is now a viable option for such work. Perhaps as important, it indicates that organizations like MOMI who possess large sample cohorts are ready to give mass spec approaches a look..

虽然迄今为止，群体蛋白质组学研究几乎完全依赖于基于亲和力的方法，但马克斯·普朗克-MOMI的研究表明，质谱技术现在已成为这类研究的可行选择。或许同样重要的是，它表明像MOMI这样拥有大规模样本队列的机构已准备好考虑采用质谱方法。

Ahadi noted that among mass spec's advantages is its ability to provide peptide-level information that can help identify specific protein isoforms present in a sample as well as its ability to provide protein posttranslational modification data that is not typically available with affinity-based approaches.

Ahadi指出，质谱的一大优势在于它能够提供肽段水平的信息，有助于鉴定样品中存在的特定蛋白质异构体，还能够提供通常基于亲和力的方法无法获得的蛋白质翻译后修饰数据。

.

。

Effectively generating and making use of that data takes significant expertise, however, which Ahadi suggested has slowed mass spec's move into population studies.

有效地生成和利用这些数据需要相当的专业知识，然而，Ahadi认为这减缓了质谱技术在群体研究中的应用。

'Mass spectrometry data is not as easy to interpret as what you get from affinity-based readouts,' she said. 'It has a lot of complexity and levels that, if you can take advantage of them, you can benefit from them a lot … but it requires a high level of understanding of the mass spectrometry data to take advantage of those extra levels.'.

“质谱数据不像基于亲和力的读数那样容易解释，”她说。“它有很多复杂性和层次，如果能够利用它们，就能从中获得很大益处……但要充分利用这些额外的层次，需要对质谱数据有很高的理解水平。”