Enabling Reliable Testing Of SMLM Datasets

实现 SMLM 数据集的可靠测试

• 批准号: BB/X01858X/1
• 负责人: Susan Cox
• 金额: $ 79.65万
• 依托单位: King's College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FX01858X%2F1
• 关键词: Enabling; Reliable; Testing; SMLM; Datasets

Fluorescence microscopy is a key technology for biomedical research due to its ability to image the distribution of specific proteins in cells. Over the last twenty years the resolution achievable with fluorescence microscopy has been improved from around 200nm down to 20nm. The most popular of these methods, single molecule light microscopy (SMLM), collects a series of images and then processes it to create a reconstructed image of the sample with improved resolution. While this method is experimentally simple and can achieve very good resolution, it has a major drawback: the image processing it relies on can lead to artifacts (false structures) in the reconstructed image that are hard to spot, leading biologists to draw incorrect conclusions about their data. It is extremely difficult to test for these artifacts, and methods to do so have only recently been developed. Here the two leading developers of data assessment for SMLM want to combine forces to create a new tool which enables testing using both of their approaches.We will create an integrated software package that tests for resolution (using a metric improved in accuracy compared to the standard one used) and two types of artifact. The first, artificial sharpening, is common issue encountered in multicolour and live cell SMLM, and any reconstruction method that doesn't find positions of fluorophores, but instead modifies the input images. The second examines how well the SMLM reconstructed image agrees with a widefield image of the sample, and is good at spotting missing structure, variation in the proportion of fluorophores being detected across the image, and issues caused by the background. By testing for all of these different types of artifact, users will be able to have confidence that their data analysis is not introducing errors into their images.Code will be developed on a Github repository and source code will be made available. This is in keeping with the long track record of the applicants in releasing open source software. The software will be released in two different packages: as an ImageJ/Fiji plugin (the most common system used by microscopists and biological/biomedical researchers) and also in napari (a new python-based image processing package, which is increasingly used by methods developers). We will seek to establish our method as a standard test before publication, and encourage the field to develop further extensions as our understanding of SMLM improves, with a process of code review ultimately determining which modifications are accepted.We will also assess a range of publicly available data, and acquire new exemplar datasets to allow us to illustrate to potential users likely artifacts and under what circumstances they might be encountered. While there is an increasing amount of SMLM data available, this usually focuses on high quality raw data which others might want to process. We also want to generate datasets in which raw data likely to produce errors is deliberately generated. The lack of consistent standards and tests contributes to the reproducibility crisis in science: it is impossible to benchmark or validate the data analysis in SMLM studies because there are no agreed standards to which such studies should adhere. Our approach, uniting the best of the current tests available, has the support and confidence of the SMLM developer community and support from facility managers and researchers who rely on SMLM. Setting such a standard for testing will allow journals to finally put in place a consistent set of requirements for SMLM data verification, which is both actually possible for non-specialist researchers to fulfill, and provides rigorous quality checks.

荧光显微镜是生物医学研究的关键技术，因为它能够成像细胞中特定蛋白的分布。在过去的二十年中，可以通过荧光显微镜实现的分辨率从200nm左右提高到20nm。这些方法中最流行的单分子光显微镜（SMLM）收集一系列图像，然后对其进行处理以创建带有改进分辨率的样品的重建图像。尽管该方法在实验上很简单，并且可以实现很好的分辨率，但它具有一个主要的缺点：它所依赖的图像处理可能会导致重建图像中很难发现的伪影（错误结构），这使生物学家得出有关其数据的错误结论。对于这些文物的测试非常困难，并且直到最近才开发出来的方法。在这里，SMLM数据评估的两个领先开发人员希望组合力来创建一个新工具，该工具可以使用两种方法进行测试。我们将创建一个集成的软件包，该软件包测试解决方案（使用与使用标准的标准相比，准确性提高了准确性）和两种类型的工件。第一个是人造锐化，是多色和活细胞SMLM中遇到的常见问题，以及任何找不到荧光团位置但可以修改输入图像的重建方法。第二个研究了SMLM重建图像与样品的广阔图像的一致，并且擅长发现缺失的结构，在整个图像中检测到的荧光团比例的变化以及背景引起的问题。通过测试所有这些不同类型的工件，用户将能够确信他们的数据分析不会在其图像中引入错误。代码将在github存储库上开发，并将提供源代码。这与申请人的长期记录保持一致，以释放开源软件。该软件将以两种不同的软件包发布：作为ImageJ/Fiji插件（显微镜家和生物/生物医学研究人员使用的最常见系统）以及Napari（一种新的基于Python的图像处理软件包，方法开发人员越来越多地使用它）。我们将寻求在出版前建立我们的方法作为标准测试，并鼓励该领域在我们对SMLM的理解改进的情况下开发进一步的扩展，并通过代码审查的过程最终确定接受了哪些修改。我们还将评估一系列可公开可用的数据，并获得新的示例数据集，并获得潜在的用户来说明他们可能会在哪些情况下批准的潜在用户。尽管可用的SMLM数据越来越多，但这通常集中于其他人可能想要处理的高质量原始数据。我们还想生成数据集，其中故意生成可能产生错误的原始数据。缺乏一致的标准和测试会导致科学的可重复性危机：不可能基准或验证SMLM研究中的数据分析，因为没有某些协议的标准应遵守此类研究。我们的方法将当前可用的最佳测试结合在一起，具有SMLM开发人员社区的支持和信心，并得到了依靠SMLM的设施经理和研究人员的支持。设定这样的测试标准将使期刊最终达到SMLM数据验证的一致要求，实际上，非专业研究人员可以实现这一要求，并且提供了严格的质量检查。