Improving detection of gene expression in microscopy, using Artificial Intelligence techniques in order to investigate how food affects ageing in C El

使用人工智能技术改进显微镜中基因表达的检测，以研究食物如何影响 C El 的衰老

• 批准号: 2187598
• 金额: --
• 依托单位: King's College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2187598
• 关键词: Improving; detection; gene; expression; microscopy

This project investigates how food affects ageing via conserved neuroendocrine factors. In C. elegans and humans, neuroendocrine factors such as insulin-like peptides, growth factors, and biogenic amines regulate each other in complex networks to modulate ageing, metabolism, and other physiological outputs. However, the information processing mechanisms in these networks for discriminating food inputs are unclear. We address this question by enhancing high-throughput C. elegans experiments with artificial intelligence and machine learning.These studies have two key limitations: first, the neurons are identified by hand, which is very labour intensive; and second, the need for highly standardised images means many images are discarded. The first issue offers an opportunity to automate cell identification, either by applying supervised machine learning (e.g. random forest classifier) to tens of thousands of annotated image stacks; or by using deep learning so that the algorithm can select the parameters to optimise. The second issue means that there are many unannotated image stacks that can be analysed by unsupervised deep learning and cross-validated against the annotated subset to check performance.We will then apply decoding analysis and machine learning to relate food inputs, combinatorial patterns of gene expression, and ageing phenotypes. Using these results, we will predict the lifespans of novel mutants based on their effects on gene expression, and verify these hypotheses experimentally by exploiting high-throughput imaging and the automated image analysis developed above. This work thus decodes food-sensing gene networks in the nervous system that impact ageing and other health-related outputs.

该项目研究食物如何通过保守的神经内分泌因子影响衰老。In C.在线虫和人类中，神经内分泌因子如胰岛素样肽、生长因子和生物胺在复杂的网络中相互调节，以调节衰老、代谢和其他生理输出。然而，在这些网络中的信息处理机制，以区分食物投入还不清楚。我们通过提高高通量C.这些研究有两个关键的局限性：首先，神经元是手工识别的，这是非常劳动密集型的;其次，需要高度标准化的图像意味着许多图像被丢弃。第一个问题提供了自动化细胞识别的机会，可以通过将监督机器学习（例如随机森林分类器）应用于数万个带注释的图像堆栈;或者通过使用深度学习，以便算法可以选择要优化的参数。第二个问题意味着有许多未注释的图像堆栈可以通过无监督深度学习进行分析，并与注释子集进行交叉验证以检查性能。然后，我们将应用解码分析和机器学习来关联食物输入，基因表达的组合模式和衰老表型。利用这些结果，我们将根据其对基因表达的影响预测新突变体的寿命，并通过利用高通量成像和上述开发的自动图像分析来验证这些假设。因此，这项工作解码了神经系统中影响衰老和其他健康相关输出的食物感应基因网络。

项目成果

3D Structure From 2D Microscopy Images Using Deep Learning.

• DOI: 10.3389/fbinf.2021.740342
• 发表时间: 2021
• 期刊: FRONTIERS IN BIOINFORMATICS
• 作者: Blundell, Benjamin;Sieben, Christian;Manley, Suliana;Rosten, Ed;Ch'ng, Queelim;Cox, Susan
• 通讯作者: Cox, Susan