Computational Methods for Single Cell Biology

单细胞生物学的计算方法

• 批准号: RGPIN-2022-04378
• 负责人: Roth, Andrew
• 金额: $ 2.11万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=749898
• 关键词: Computational; Methods; Single; Cell; Biology

The biological sciences are undergoing a revolution driven by high throughput technologies which allow for the measurement of single cells. A prominent example is the rapid adoption of single cell RNA profiling using sequencing based approaches to explore all facets of biology. More recently other single cell sequencing approaches have been developed, allowing for the measurement of the DNA and epigenetic features of cells. The majority of approaches to date have required that cells be put into a liquid suspension for sequencing. This requires that tissue be broken apart and spatial information such as which cells were neighbouring be discarded. This makes it difficult to study how cells interact with each other. The cutting edge of single cell biology is removing this limitation by combining high throughput measurements with imaging or spatial barcoding. Spatial single cell technologies have enormous potential for advancing our understanding of biological systems. However, the data generated by these technologies are creating analytical challenges that are not adequately addressed by existing computational tools. These challenges range from low level problems in signal processing such as performing automated image analysis to identify cells and their expression profiles, to higher level problems such as extracting interpretable estimates of biologically interesting quantities from the data. Off the shelf machine learning approaches do not adequately address many of these problems. The two most important characteristics of this domain that challenge existing tools are the lack of well annotated datasets to train supervised learning approaches and the desire by biologists to have interpretable computational methods. In addition, the ability to leverage domain knowledge to impose additional structure on problems can significantly improve performance. I will develop novel computational and statistical models to analyze multi-modal single cell datasets to provide an integrated view of biological systems. Our approach will allow us to deeply probe the features of individual cells, while also measuring the spatial context and environment they exist in. The methods I develop will drive new biological discoveries by allowing spatial single cell profiling of complex systems such as tumours.

生物科学正在经历一场由高通量技术推动的革命，这种技术允许测量单细胞。一个突出的例子是使用基于测序的方法来探索生物学的所有方面，迅速采用单细胞RNA图谱。最近，已经开发了其他单细胞测序方法，允许测量细胞的DNA和表观遗传学特征。到目前为止，大多数方法都要求将细胞放入液体悬浮液中进行测序。这需要将组织分解，并丢弃空间信息，如哪些细胞相邻。这使得研究细胞如何相互作用变得困难。单细胞生物学的前沿正在通过将高通量测量与成像或空间条形码相结合来消除这一限制。空间单细胞技术在促进我们对生物系统的理解方面具有巨大的潜力。然而，这些技术产生的数据正在产生现有计算工具无法充分解决的分析挑战。这些挑战从信号处理中的低级问题，如执行自动图像分析来识别细胞及其表达谱，到高级问题，如从数据中提取可解释的生物学兴趣量估计。现成的机器学习方法不能充分解决其中的许多问题。该领域挑战现有工具的两个最重要的特征是缺乏良好注释的数据集来训练监督学习方法，以及生物学家希望拥有可解释的计算方法。此外，利用领域知识将额外的结构强加给问题的能力可以显著提高性能。我将开发新的计算和统计模型来分析多模式单细胞数据集，以提供生物系统的综合视图。我们的方法将使我们能够深入探索单个细胞的特征，同时也测量它们所存在的空间背景和环境。我开发的方法将通过允许对复杂系统(如肿瘤)进行空间单细胞图谱分析，推动新的生物学发现。