BioSynth Trust: Developing understanding and confidence in flow cytometry benchmarking synthetic datasets to improve clinical and cell therapy diagnos

BioSynth Trust：发展对流式细胞仪基准合成数据集的理解和信心，以改善临床和细胞治疗诊断

• 批准号: 2796588
• 金额: --
• 依托单位: Loughborough University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2796588
• 关键词: BioSynth; Trust; Developing; understanding; confidence

Background Flow Cytometry (FC) is a ubiquitous biometrology tool for establishing the identity of cell populations within the clinical and biomanufacturing sectors. FC has three components that determine the metrological quality of the final answer - the upstream collection and preparation of the cell samples which may vary in integrity, the FC instrument itself and its inherent calibration/accuracy statement, and finally the subsequent data processing which typically involves some form of manual or automated cell cluster gating process.FC samples and analysis may range in dimensionality (the number of discrete unique cell types and clusters within a sample). Many practitioners would cite the FC instrument accuracy statements as evidence of metrology confidence. However, this ignores the subsequent data analysis and input of variation at this stage into the final cell count numbers.The analysis of the FC output data can be currently completed in two common manners - human manual gating and stratification of the clusters leading to final cell enumeration of the target cluster, and, by corollary, automated software solutions that purportedly remove the operator from most of the data analysis train thus reducing variation of output.The potential for variation in the data analysis is significant. Detailed work has already been completed that has specifically investigated the variation in human operators showing that as the complexity of the gating analysis increases, then the absolute variation of cell number output increases. A range of FC instrument manufacturers and third party automated unsupervised software solutions exist. Each software solution typically uses one of several different mathematical approaches for cluster identification, stratification and isolation. Recent research has specifically benchmarked different software solutions with the development and application of bespoke synthetic datasets. The benefit of synthetic datasets is that they allow for bespoke design of cell clusters; absolute cell number, distribution characteristics, skew, separation index, rare cell scenarios, etc, noting that the synthetic cell cluster design has been initially benchmarked against normal and rare live cell models.Project DetailThis project will be completed in collaboration with UK National External Quality Assessment Service for Leucocyte Immunophenotyping (UK NEQAS LI) - part of the Sheffield Teaching Hospitals NHS Foundation Trust. The aim of this new project is to develop enhanced confidence factors in the generation of synthetic cell cluster datasets and extend their performance capabilities and applicability. The project will consider and deliver to the following issues;- Development and benchmarking of synthetic datasets against specific disease states and live cell data.- Selection of disease states in collaboration with UK NEQAS LI - anticipating 2 to 3 different states to be used as exemplars.- Flow Cytometry based cell measurements at UK NEQAS LI to generate representative higher dimensionality cell cluster reference data (up to 12 FC fluorescence channels).- Design synthetic datasets with higher dimensionality.- Enhance confidence in the synthetic dataset generation process (including rare cell or Minimum Residual Disease) using manual and automated gating techniques.- Develop synthetic datasets that can be used for External Quality Assessment scheme (EQA) performance monitoring, matching and replacing live cell samples.- Monitor the use of synthetic cell cluster datasets within UK NEQAS LI participants during EQA testing rounds.- Explore the potential for using synthetic datasets for EQA participant training including rare disease states.- Develop a roadmap for synthetic dataset generation requirements to be used for the training of future potential artificial intelligence based FC data analysis systems

背景流式细胞仪（FC）是一种无处不在的生物量工学工具，用于在临床和生物制造领域建立细胞群体的身份。 FC has three components that determine the metrological quality of the final answer - the upstream collection and preparation of the cell samples which may vary in integrity, the FC instrument itself and its inherent calibration/accuracy statement, and finally the subsequent data processing which typically involves some form of manual or automated cell cluster gating process.FC samples and analysis may range in dimensionality (the number of discrete unique cell types and clusters within a sample).许多从业人员将FC仪器精度陈述作为计量学信心的证据。但是，这忽略了此阶段中随后的数据分析和变异的输入到最终的单元格数中。当前可以分别以两个通用的方式完成FC输出数据的分析 - 人工手动控和分层，导致目标群集的最终单元格列出，从而通过自动化的软件解决方案进行了验证，从而使运算型分析的变化介绍了运算的变化，从而将其置于了磁性分析中，从而将其置于了培训。很重要。详细的工作已经完成，该工作已专门研究了人类操作员的变化，表明随着门控分析的复杂性的增加，细胞数量输出的绝对变化也会增加。存在一系列FC仪器制造商和第三方自动化的无监督软件解决方案。每个软件解决方案通常使用几种不同的数学方法之一来进行集群识别，分层和隔离。最近的研究专门针对定制合成数据集的开发和应用对不同的软件解决方案进行了基准测试。合成数据集的好处是它们允许定制细胞簇设计。绝对细胞数，分配特征，偏斜，分离指数，罕见的细胞情景等，指出合成细胞群集设计最初是针对正常和罕见的实时细胞模型进行了基准的。项目将与英国国家外部质量评估服务合作完成，该项目将与Leucocyte Immunophenophenophenotyping（UKInqas lieq lieftirt nequild Trust）合作完成。这个新项目的目的是在生成合成细胞群集数据集的生成中发展增强的置信因素，并扩展其性能功能和适用性。该项目将考虑并交付到以下问题； - 合成数据集针对特定疾病状态和活细胞数据的开发和基准测试。-与英国Neqas li合作选择疾病状态 - 预计将有2至3个不同的状态用于范例。-示例用作示例性。 - 增强对合成数据集生成过程（包括稀有细胞或最小残留疾病）的信心，使用手动和自动化的门控技术。-开发可用于外部质量评估方案（eqa）性能监控，匹配和更换的实时域内的合成质量评估方案（EQA）ext ineq lie eq seq lie eq lie eq lie eq lie eq eq ex exas lie eq ex clust niq lie的exas lie eq lie eq lie neq lie eq ex clants neq li EQA参与者培训的合成数据集在包括罕见疾病状态。-为合成数据集生成要求开发路线图，用于培训未来潜在的基于人工智能的FC数据分析系统