Novel methods for the integration of high dimensional single cell proteomic and RNA data to understand cell populations in development and disease.

整合高维单细胞蛋白质组和 RNA 数据以了解发育和疾病中的细胞群的新方法。

• 批准号: MR/S005471/1
• 负责人: Emmanouela Repapi
• 金额: $ 42.68万
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Fellowship
• 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=MR%2FS005471%2F1
• 关键词: Novel; methods; integration; dimensional; single

Each individual person started out as one single cell but an adult is made up of more than 30 trillion cells. These cells have been organized into complex tissues that work together to produce functioning organisms. Recent technological breakthroughs have allowed scientists to analyse the behaviour of millions of cells in order to better understand normal cell behaviour and how it is altered in disease. In order to achieve this, we will need to incorporate data from multiple techniques into statistical models, something that is technically quite challenging. My experience as a bioinformatician dealing with these complex datasets puts me in a perfect position to understand how to accomplish this. In my project, I aim to integrate data from single cell transcriptomics and proteomics; one studying the intermediate state of gene transcription from our genetic code and the other the resulting protein levels. Being able to use the available information from both technologies will undoubtedly help us better understand cell function in health and disease. However, currently, there are no methodological tools trying to accomplish this. I will use cutting edge single cell data generated by labs here at the MRC WIMM of the University of Oxford to produce models of cellular behaviour. This approach could be applicable to various biological fields, including: immunologists with an interest in understanding the relationship between immune cells and the tissues they infiltrate; haematologists aiming to understand the cellular environment in blood diseases; and oncology groups wishing to understand cellular organisation in tumours. Moreover, I will develop a visualisation tool to help visualise and interpret these models. The tools I develop will be made available to the wider scientific community to help them answer questions for a broad range of biological problems.

每个人最初都是一个细胞，但成年人是由超过30万亿个细胞组成的。这些细胞被组织成复杂的组织，共同工作以产生功能性生物体。最近的技术突破使科学家能够分析数百万个细胞的行为，以便更好地了解正常细胞行为以及疾病如何改变。为了实现这一目标，我们需要将来自多种技术的数据整合到统计模型中，这在技术上是相当具有挑战性的。我作为一名生物信息学家处理这些复杂数据集的经验使我处于一个完美的位置，了解如何实现这一目标。在我的项目中，我的目标是整合来自单细胞转录组学和蛋白质组学的数据;一个研究我们遗传密码中基因转录的中间状态，另一个研究由此产生的蛋白质水平。能够利用这两种技术提供的信息无疑将有助于我们更好地了解健康和疾病中的细胞功能。然而，目前还没有方法论工具试图实现这一目标。我将使用牛津大学MRC WIMM实验室产生的最先进的单细胞数据来制作细胞行为模型。这种方法可以适用于各种生物学领域，包括：免疫学家有兴趣了解免疫细胞和它们浸润的组织之间的关系;血液学家旨在了解血液疾病中的细胞环境;肿瘤学团体希望了解肿瘤中的细胞组织。此外，我将开发一个可视化工具来帮助可视化和解释这些模型。我开发的工具将提供给更广泛的科学界，以帮助他们回答广泛的生物学问题。

项目成果

Integration of single-cell RNA-Seq and CyTOF data characterises heterogeneity of rare cell subpopulations

• DOI: 10.12688/f1000research.121829.1
• 发表时间: 2022-05
• 期刊: F1000Research
• 作者: E. Repapi;D. Agarwal;G. Napolitani;David Sims;Stephen S. Taylor

Supplementary Figures and Table for Repapi et al. 2022

Repapi 等人的补充数据和表格。

• DOI: 10.5281/zenodo.6513603
• 发表时间: 2023
• 作者: Repapi E