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Immune single-cell dynamics studied through optical label-free microscopy

通过光学无标记显微镜研究免疫单细胞动力学

基本信息

批准号：
21K12664
负责人：
パヴィヨンニコラ
金额：
$ 2.75万
依托单位：
Osaka University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2021
资助国家：
日本
起止时间：
2021-04-01 至 2023-03-31
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-21K12664/
关键词：
Label-free microscopy Immune response Live single-cell Cell dynamics Cell differentiation Raman spectroscopy Live cell Single-cell measurements Macrophage Lymphocyte Quantitative phase

项目摘要

During this part of second year, we extended our measurements of T cells to specifically study their early differentiation under stimulation. We could first very accurately detect activation, and by comparing different types of stimulations, we could determine the main molecular changes that allow for that classification.Then, by relying on recent non-linear algorithms such as uniform manifold approximation and projection (UMAP), we could observe the changes that occur after initial activation, where cells gradually differentiate into pre-effector cells. Purely based on the non-invasive signals of optical spectroscopy, it was even possible to observe differences between phenotypes (CD4 and CD8 T cells), and identify sub-populations after several days of differentiation.In addition to these results, we also applied our approach to the identification of rare lymphocytes phenotypes. We obtained promising results in the detection of regulatory T cells, a sub-type that is a key component in preventing auto-immune diseases, for example. Usually, destructive techniques are required to identify these cells, but despite the very strong similarity between regulatory T cells and conventional ones, our technique is able to distinguish them with high accuracy.

在第二年的这一部分，我们扩展了对T细胞的测量，专门研究它们在刺激下的早期分化。我们首先可以非常准确地检测激活，通过比较不同类型的刺激，我们可以确定允许分类的主要分子变化，然后，依靠最近的非线性算法，如均匀流形近似和投影（UMAP），我们可以观察初始激活后发生的变化，细胞逐渐分化为前效应细胞。基于光谱学的非侵入性信号，Pestival甚至可以观察表型（CD4和CD8 T细胞）之间的差异，并在分化几天后识别亚群。除了这些结果，我们还将我们的方法应用于罕见淋巴细胞表型的识别。例如，我们在检测调节性T细胞方面获得了有希望的结果，调节性T细胞是预防自身免疫疾病的关键组成部分。通常，需要破坏性技术来识别这些细胞，但尽管调节性T细胞和传统T细胞之间非常相似，我们的技术能够以高精度区分它们。