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

背景流式细胞术（FC）是一种普遍存在的生物计量学工具，用于在临床和生物制造领域建立细胞群的身份。FC有三个组成部分决定了最终答案的质量-上游细胞样本的收集和制备，其完整性可能会有所不同，FC仪器本身及其固有的校准/准确度声明，以及最后的后续数据处理，其通常涉及某种形式的手动或自动细胞簇门控过程。（样品内离散的独特细胞类型和簇的数量）。许多从业者会引用FC仪器精度声明作为计量置信度的证据。然而，这忽略了随后的数据分析和在此阶段将变化输入到最终细胞计数中的数量。FC输出数据的分析目前可以以两种常见的方式完成-人类手动门控和聚类分层，导致目标聚类的最终细胞计数，并且，通过推论，自动化软件解决方案，据称将操作员从大部分数据分析训练中删除，从而减少输出的变化。数据分析中的变化潜力很大。已经完成了详细的工作，专门研究了人类操作员的变化，表明随着门控分析复杂性的增加，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聚类识别、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，它们允许定制设计细胞簇;绝对细胞数、分布特征、偏斜、分离指数、稀有细胞情况等，注意到合成细胞簇设计最初是以正常和罕见的活细胞模型为基准的。项目详细信息该项目将与英国国家白细胞免疫表型外部质量评估服务合作完成（英国NEQAS LI）-谢菲尔德教学医院NHS基金会信托基金的一部分。这个新项目的目的是在合成细胞簇数据集的生成中开发增强的置信因子，并扩展其性能和适用性。该项目将考虑并解决以下问题：-针对特定疾病状态和活细胞数据的合成数据集的开发和基准测试。与英国NEQAS LI合作选择疾病状态-预计将使用2到3个不同的状态作为范例。在UK NEQAS LI进行基于流式细胞术的细胞测量，以生成代表性的高维细胞簇参考数据（多达12个FC荧光通道）。设计具有更高维度的合成数据集。使用手动和自动门控技术增强合成数据集生成过程（包括罕见细胞或最小残留疾病）的信心。开发可用于外部质量评估计划（EQA）性能监测，匹配和替换活细胞样本的合成数据集。在EQA测试期间，监测英国NEQAS LI参与者中合成细胞簇数据集的使用。探索使用合成数据集进行EQA参与者培训的潜力，包括罕见疾病状态。制定合成数据集生成要求的路线图，用于培训未来潜在的基于人工智能的FC数据分析系统