NEW YORK – As the popularity of spatial proteomics continues to grow, a number of researchers are developing mass spectrometry-based approaches that allow for unbiased spatial analyses.

纽约——随着空间蛋白质组学的普及度持续增长，许多研究人员正在开发基于质谱的方法，以实现无偏见的空间分析。

These methods provide significantly greater depths of coverage than the antibody-based technologies that have dominated spatial proteomics research to date but face technical challenges such as sample preparation and throughput.

这些方法提供了显著更大的覆盖深度，相较于迄今为止在空间蛋白质组学研究中占主导地位的基于抗体的技术，但面临诸如样品制备和通量等技术挑战。

Named the 2024 'Method of the Year' by the journal

被评为该期刊2024年度“最佳方法”

Nature Methods

自然方法

, spatial proteomics has generated substantial commercial and research interest, with a number of companies, including Akoya Biosciences (recently acquired by Quanterix), Standard BioTools, Bruker, Ionpath, Bio-Techne, Thermo Fisher Scientific, and 10x Genomics bringing spatial proteomic platforms to market..

，空间蛋白质组学已经引起了大量的商业和研究兴趣，许多公司，包括Akoya Biosciences（最近被Quanterix收购）、Standard BioTools、Bruker、Ionpath、Bio-Techne、Thermo Fisher Scientific和10x Genomics，都已将空间蛋白质组学平台推向市场。

These systems are largely antibody-based, meaning researchers are limited to the reagents available for each platform. Typically, experiments top out at around 50 to 100 protein targets.

这些系统大多基于抗体，这意味着研究人员受限于每个平台可用的试剂。通常，实验最多达到大约50到100个蛋白质靶标。

Unbiased mass spec-based approaches, on the other hand, can measure thousands of proteins per experiment. Florian Rosenberger, an assistant professor in the department of medical biochemistry and biophysics at Karolinska Institute, said that the deep visual proteomics (DVP) method he helped develop while a postdoctoral fellow in the lab of Max Planck Institute of Biochemistry researcher Matthias Mann yields quantitative data for up to 8,000 proteins per sample..

另一方面，基于无偏见质谱的方法每次实验可以测量数千种蛋白质。卡罗林斯卡研究所医学生物化学与生物物理学系的助理教授弗洛里安·罗森贝格表示，他在马克斯普朗克生物化学研究所研究员马蒂亚斯·曼恩的实验室担任博士后期间帮助开发的深度视觉蛋白质组学（DVP）方法，每个样本可生成多达8000种蛋白质的定量数据。

Ruijun Tian, a professor at China's Southern University of Science and Technology (SUSTech), said that his lab is able to measure around 3,000 to 4,000 proteins using its Spatial and Cell-type Proteomics (SCPro) method, which, like DVP, is an unbiased mass spec-based approach.

南方科技大学教授田瑞军表示，其实验室能够通过空间和细胞类型蛋白质组学（SCPro）方法测量大约3000到4000种蛋白质，该方法与DVP一样，是一种基于无偏见质谱的方法。

In a

在一个

January

一月

paper

纸张

in

在

Nature Communications

自然通讯

, a team led by researchers at the Pacific Northwest National Laboratory demonstrated the ability of their wcSOP spatial proteomics approach to measure up to 4,600 proteins in human spleen tissue.

，由太平洋西北国家实验室研究人员领导的团队展示了他们的 wcSOP 空间蛋白质组学方法能够测量人类脾脏组织中多达 4,600 种蛋白质的能力。

Scientists at the State University of New York-Buffalo

纽约州立大学布法罗分校的科学家们

have

拥有

mapped

映射

roughly 5,000 proteins

大约5000种蛋白质

in mouse brain tissue using a spatial proteomics method developed by SUNY-Buffalo professor Jun Qu called micro-scaffold assisted spatial proteomics (MASP).

使用由纽约州立大学布法罗分校教授屈军开发的一种称为微支架辅助空间蛋白质组学（MASP）的空间蛋白质组学方法，在小鼠脑组织中进行。

Generally speaking, unbiased spatial proteomic approaches involve extracting and analyzing proteins from individual cells or small numbers of cells in tissue in such a way that those proteins can be traced back to the specific cells and their locations within the tissue. In recent years, advances in areas like artificial intelligence and laser microdissection have allowed researchers to characterize and select cells for analysis in a more rapid and streamlined manner..

一般来说，无偏空间蛋白质组学方法涉及从组织中的单个细胞或少量细胞中提取和分析蛋白质，这样就可以将这些蛋白质追溯到特定的细胞及其在组织中的位置。近年来，人工智能和激光显微切割等领域的进步使研究人员能够更快速、更有效地对细胞进行表征和选择以供分析。

The DVP method, for instance, combines conventional cell staining with artificial intelligence-based image analysis to identify cells or groups of cells of interest. Researchers then cut out those cells using automated laser microdissection (LMD) and analyze their proteomes via mass spec.

例如，DVP 方法结合了传统的细胞染色和基于人工智能的图像分析来识别目标细胞或细胞群。研究人员随后使用自动激光显微切割 (LMD) 切出那些细胞，并通过质谱分析它们的蛋白质组。

SCPro uses multicolor immunohistochemistry (IHC) to stain centimeter-scale tissue sections, which are then analyzed using algorithms for nuclei and cell membrane identification. Single cells are then isolated using automated LMD and analyzed with mass spec.

SCPro使用多色免疫组织化学（IHC）染色厘米级组织切片，然后使用细胞核和细胞膜识别算法进行分析。随后，使用自动激光显微切割（LMD）分离单个细胞，并通过质谱进行分析。

The depth of coverage enabled by unbiased approaches provides 'quite rich biology,' said Rosenberger. It comes with the trade-off of limited throughput, however.

罗森伯格说，无偏方法实现的覆盖深度提供了“相当丰富的生物学信息”。然而，这是以有限的通量为代价的。

'At the moment, we can only reasonably measure 80 samples per day,' he said. 'If you want to map a full tissue [at single-cell resolution], that is going to take forever.'

“目前，我们每天只能合理地测量80个样本，”他说。“如果你想绘制整个组织的图谱（以单细胞分辨率），那将需要永远的时间。”

Rosenberger said his lab is trying to address this issue by developing methods for clustering cells of interest.

罗森伯格说，他的实验室正试图通过开发聚类目标细胞的方法来解决这个问题。

'You don't have to measure every single cell [individually], but you can group cells based on morphological features that then stratify the biology in the tissue in a meaningful way,' he said.

“你不必逐个测量每一个细胞，但可以根据形态特征对细胞进行分组，从而以一种有意义的方式对组织中的生物学特性进行分层，”他说道。

Rosenberger cited the example of work he and his colleagues recently submitted for publication on alpha-1 antitrypsin deficiency, a genetic condition that can lead to lung and liver disease. The condition causes protein aggregates to form in hepatocytes, eventually killing the cells. In their study, the researchers imaged diseased tissue and used a convolutional neural network to cluster cells by whether or not aggregates were present and what structure those aggregates exhibited.

罗森伯格列举了他和同事最近提交发表的一项关于α-1抗胰蛋白酶缺乏症的研究实例，这是一种可能导致肺部和肝脏疾病的遗传病。该疾病会导致蛋白质聚集体在肝细胞中形成，最终杀死细胞。在他们的研究中，研究人员对病变组织进行了成像，并使用卷积神经网络根据聚集体是否存在及其结构特征对细胞进行聚类分析。

They then cut out those distinct clusters and profiled the proteome of each..

他们随后切下那些不同的簇，并分析了每个簇的蛋白质组。

'This gives us a new granularity of data … but it is not a full spatial mapping,' Rosenberger said. 'It is more a proteomic mapping based on histological features.'

“这为我们提供了新的数据粒度……但并不是完整的空间映射，”罗森伯格说。“它更多是基于组织学特征的蛋白质组学映射。”

In their SCPro approach, Tian and his team used IHC staining, cell morphology, and location data to select cells of interest from the centimeter-scale tissue sections they analyzed. They identified 'phenotype-matched' groups of 60 to 100 cells that they cut out and pooled for their mass spec experiments, allowing them to balance throughput, depth of coverage, and cellular and spatial resolution..

在他们的SCPro方法中，田和他的团队使用免疫组化染色、细胞形态和位置数据，从分析的厘米级组织切片中选择目标细胞。他们识别出“表型匹配”的60到100个细胞群，将其切割并汇集用于质谱实验，从而在通量、覆盖深度以及细胞和空间分辨率之间取得平衡。

'With those three categories [of data], you can capture all your favorite tissue heterogeneity features,' Tian said.

“通过这三类数据，你可以捕捉到你最喜欢的组织异质性特征，”田说。

The SUNY-Buffalo researchers' MASP method takes a different tack, using 3D-printed microscaffolds to cut tissue slices into separate microsections, each of which is then processed and analyzed via mass spec. In the group's initial paper describing the approach, they used microscaffolds consisting of 900 separate 400-μm microwells, though they are working to develop scaffolds providing higher spatial resolution..

纽约州立大学布法罗分校的研究人员的MASP方法采取了不同的策略，使用3D打印的微支架将组织切片分割成独立的微区段，每个微区段随后通过质谱进行处理和分析。在该团队描述这一方法的初步论文中，他们使用了由900个独立的400微米微孔组成的微支架，不过他们正在努力开发提供更高空间分辨率的支架。

Sample prep is also a major challenge for unbiased spatial proteomics approaches given the extremely small amounts of material typically involved in these experiments. Some researchers have leveraged sample prep methods commonly used in single-cell proteomics like the

样品制备也是无偏空间蛋白质组学方法面临的主要挑战，因为这些实验通常涉及的材料量极其微小。一些研究人员借鉴了单细胞蛋白质组学中常用的样品制备方法，例如

nanoPOTS

纳米POTS

(Nanodroplet Processing in One pot for Trace Samples)

（单锅痕量样品纳米液滴处理）

approach

方法

developed by Brigham Young University professor Ryan Kelly.

由杨百翰大学教授瑞安·凯利开发。

Simply collecting single cells after they have been isolated via LMD is also difficult, said Tujin Shi, a senior scientist at Pacific Northwest National Laboratory (PNNL). Shi and his colleagues developed their wcSOP (wet collection of single microscale tissue voxels and Surfactant-assisted One-Pot) sample collection and processing method to help address these challenges..

太平洋西北国家实验室 (PNNL) 的资深科学家史 Tucson 表示，即使在通过激光显微切割 (LMD) 分离后，单纯收集单个细胞也十分困难。史和他的同事开发了 wcSOP（湿法收集单个微尺度组织体素和表面活性剂辅助一锅法）样品收集与处理方法，以帮助应对这些挑战。

PCR tube caps are commonly used for collecting small tissue sections, or voxels, cut out via LMD. Typically, the voxel is captured in the top of the cap and then transferred to the bottom of the tube for sample processing. Shi and his colleagues found in their study, though, that in many cases voxels are not effectively transferred from the tube cap to the bottom of the tube.

PCR管盖常用于收集通过LMD切取的小组织块或体素。通常，体素被捕捉到管盖顶部，然后转移到管底进行样品处理。然而，史和他的同事在他们的研究中发现，在许多情况下，体素并没有有效地从管盖转移到管底。

Using a microscope to follow the path of voxels after capture, they found that they got stuck at many points around the PCR tube, leading to low reproducibility..

使用显微镜追踪捕获后的体素路径，他们发现这些体素在PCR管周围的许多点上被卡住，导致重现性较低。

To address this issue, they developed an optimized voxel collection process in which the buffers required for sample processing are placed in the tube cap prior to voxel collection. The sample is then processed in the tube cap, and the resulting digested peptides are moved to the bottom of the tube using centrifugation..

为了解决这个问题，他们开发了一种优化的体素收集流程，其中在体素收集之前，将样本处理所需的缓冲液放置在试管盖中。然后在试管盖中处理样本，并通过离心将生成的消化肽移至试管底部。

The approach allows for simple and robust sample collection and processing 'even for mass spec labs without much experience' in spatial proteomics, Shi said.

石毅表示，这种方法允许简单且稳健的样品收集和处理，“即使是对空间蛋白质组学没有太多经验的质谱实验室也能胜任”。

Mass spec sensitivity remains a limitation, Rosenberger said, particularly for measurements at the single-cell level. He noted that while his lab can measure around 4,000 proteins in large cells like hepatocytes, smaller cells like neutrophils are still a struggle.

罗森伯格说，质谱灵敏度仍然是一个限制，特别是在单细胞水平的测量中。他指出，虽然他的实验室可以测量大约 4,000 种蛋白质在肝细胞等大细胞中，但像中性粒细胞这样的小细胞仍然很有挑战性。

'We can isolate [these cells] very well, but getting meaningful data out of them is tricky because there is so little material,' he said.

“我们可以很好地分离[这些细胞]，但要从中获取有意义的数据却很棘手，因为材料非常少，”他说道。

Rosenberger said that recent mass spec releases like Thermo Fisher Scientific's Orbitrap Astral are helping in this regard.

罗森伯格表示，近期发布的质谱仪产品，如赛默飞世尔科技的Orbitrap Astral，正在这方面提供帮助。

'This is making a major difference,' he said. 'We are getting richer biological data from single cells nowadays. Inevitably there will be more technological development, and with better mass specs, we are going to get deeper [coverage].'

“这正在产生重大影响，”他说。“我们现在可以从单细胞中获得更丰富的生物学数据。不可避免地，会有更多的技术发展，随着更先进的质谱仪，我们将获得更深的[覆盖]。”

Tian noted that the formalin-fixed paraffin-embedded tissue commonly available for spatial proteomics studies is a challenging sample type for mass spectrometry. The SCPro method uses a solid-phase ion exchange-based protein aggregation capture (iPAC) workflow to improve extraction of proteins from these samples..

田指出，常用于空间蛋白质组学研究的福尔马林固定石蜡包埋组织对质谱分析而言是一种具有挑战性的样本类型。SCPro 方法采用基于固相离子交换的蛋白质聚集捕获 (iPAC) 工作流程，以改进从这些样本中提取蛋白质的效果。

Tian's lab is now working to automate the SCPro workflow, using well-annotated cancer tissue samples to train AI agents to recognize regions and cells of interest and then cut them out using LMD.

田的实验室现在正在努力实现SCPro工作流程的自动化，使用注释良好的癌症组织样本来训练人工智能代理识别感兴趣的区域和细胞，然后使用LMD将它们切割出来。

'We hope it will become a very efficient approach to do this type of analysis,' he said. 'It's a very aggressive goal, but we're hopeful that in the next five years we can achieve this kind of technology.'

“我们希望它将成为进行这种分析的非常有效的方法，”他说。“这是一个非常激进的目标，但我们希望在未来五年内能够实现这种技术。”

Tian is also working on a proximity labeling-based approach for spatial proteomics that could allow researchers to label proteins in particular cell types and then extract them for mass spec analysis.

田还在研究一种基于邻近标记的空间蛋白质组学方法，这种方法可以让研究人员标记特定细胞类型中的蛋白质，然后提取它们进行质谱分析。

Proximity labeling typically uses a target protein to tag other nearby proteins with a molecule, often biotin, that allows them to be extracted from a cell and analyzed. Tian suggested the approach could be used to, for instance, tag proteins in all the cells in a sample with a particular cellular marker and then pull them out to be measured by mass spec..

proximity labeling 通常利用一种目标蛋白用分子（通常是生物素）标记其他附近的蛋白质，以便将它们从细胞中提取出来进行分析。田建议这种方法可以用于，例如，用特定的细胞标记物标记样本中所有细胞中的蛋白质，然后将它们提取出来通过质谱进行测量。

'We could label, for instance, all the CD45-positive T cells on a whole centimeter-scale sample all at once,' he said.

他说：“例如，我们可以一次性标记整个厘米级样本中的所有CD45阳性T细胞。”

The approach is similar to the

方法类似于

Microscoop spatial proteomics platform

微尺度空间蛋白质组学平台

from

从

Taiwanese firm Syncell

台湾新讯公司

, which allows users to biotinylate all the proteins in cellular regions of interest and then pull them down for analysis by mass spec or another method.

，这使得用户可以生物素化目标细胞区域中的所有蛋白质，然后通过质谱或其他方法将它们提取出来进行分析。

With the Microscoop system, researchers identify a cellular region of interest, either by tagging a molecular marker of that region via immunofluorescence or by identifying an anatomical marker located in that region. The system then images the tagged sample and creates an image mask defining the specific region to be targeted for protein extraction..

通过Microscoop系统，研究人员可以通过免疫荧光标记该区域的分子标记，或者通过识别该区域中的解剖标记来确定感兴趣的细胞区域。随后，系统对标记的样本进行成像，并创建一个图像掩模，定义要进行蛋白质提取的目标特定区域。

Users then apply to the sample what Syncell calls its Synlight Rich kit, which consists of a photobiotinylation reagent that binds to proteins when exposed to light. Following application of this reagent, a laser exposes the region of interest defined by the image mask to light, binding the photobiotinylation reagent to the proteins in this portion of the sample.

用户随后将 Syncell 称为 Synlight Rich 试剂盒的应用于样本，该试剂盒包含一种光生物素化试剂，当暴露于光线下时会与蛋白质结合。在此试剂应用后，激光通过图像掩模定义的感兴趣区域暴露于光线，将光生物素化试剂与样本这部分中的蛋白质结合。

Those proteins can then be extracted using a streptavidin-biotin pull-down workflow..

那些蛋白质随后可以使用链霉亲和素-生物素下拉工作流程进行提取。

The company launched sales of the platform in 2023 and closed a $15 million Series A funding round in December of last year.

该公司于2023年推出了该平台的销售，并于去年12月完成了1500万美元的A轮融资。

While it is still early days for these spatial proteomics workflows, they have begun to have clinical impact. Last year, Max Planck researchers including Mann

虽然这些空间蛋白质组学工作流程尚处于早期阶段，但它们已经开始产生临床影响。去年，包括曼恩在内的马克斯·普朗克研究所的研究人员

used the CVP approach

使用了CVP方法

to

到

identify a linkage

识别一个联系

between increased activation of the inflammatory JAK/STAT pathway and the sometimes-fatal skin condition toxic epidermal necrolysis (TEN). Rosenberger, who was a coauthor on the study, said the findings have been used to successfully guide treatment in several patients suffering from the condition..

介于炎症JAK/STAT通路的激活增强与有时会致命的皮肤状况中毒性表皮坏死松解症（TEN）之间。罗森伯格是该研究的合著者，他表示这些发现已经成功指导了数名患有该病症的患者的治疗。

While the spread of unbiased spatial proteomics approaches remains limited by factors like the technical complexity of the workflows and the high cost of the equipment required, Rosenberger said he is seeing growing interest in the space. He said that with colleagues he is organizing a workshop on DVP and other methods that will take place this autumn in Vienna..

虽然无偏空间蛋白质组学方法的普及仍然受到诸如工作流程的技术复杂性和所需设备的高成本等因素的限制，但罗森伯格表示，他看到这个领域的兴趣正在增长。他说，他正在与同事组织一个关于DVP及其他方法的研讨会，将于今年秋天在维也纳举行。

'The community is growing, and it is now a good time to work on this more together,' he said. 'I think that for the spatial proteomics field this is a future avenue that will grow more and more.'

“社区正在成长，现在是时候更加齐心协力地开展这项工作了，”他说道。“我认为对于空间蛋白质组学领域来说，这是一个将会越来越发展的未来方向。”