AI-Aided Tool for Day Zero Selection of High Performing Cells for Biopharma Cell Line Development

用于生物制药细胞系开发的高性能细胞零日选择的人工智能辅助工具

• 批准号: 10672364
• 负责人: Sung Hwan Cho
• 金额: $ 87.3万
• 依托单位: NANOCELLECT BIOMEDICAL, INC.
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10672364
• 关键词: 3-Dimensional; Advanced Development; Antibodies; Artificial Intelligence; Atlases; Biology; Biomedical Research; Blood capillaries; Cell Line; Cell Proliferation; Cell Therapy; Cells; Characteristics; Chinese Hamster Ovary Cell; Classification; Color; Cost Savings; Deposition; Detection; Development; Disease; Environment; Evaluation; Evolution; Friends; Genetic; Image; Individual; Insulin; Investigation; Label; Maps; Modification; Monoclonal Antibodies; Normal Cell; Outcome; Performance; Pharmaceutical Preparations; Pharmacologic Substance; Phenotype; Population; Positioning Attribute; Process; Production; Proliferating; Property; Proteins; Resolution; Robot; Robotics; Savings; Side; Solid; Speed; Stem Cell Research; Stress; System; Techniques; Technology; Therapeutic Monoclonal Antibodies; Three-Dimensional Image; Three-Dimensional Imaging; Time; Training; Translating; Vaccine Production; Validation; Variant; artificial intelligence algorithm; autoencoder; bioinformatics tool; cancer cell; cell type; cellular imaging; convolutional neural network; design; disease diagnosis; disease prognosis; drug development; drug discovery; drug production; fluorescence activated cell sorter device; fluorescence imaging; genetic analysis; imaging modality; imaging system; improved; indexing; innovation; manufacture; personalized medicine; single cell analysis; single cell technology; therapeutic candidate; therapeutic protein; tool; transmission process

SUMMARY With the increasing number of protein therapeutic candidates, identifying and isolating single-cell derived colonies is a critical step that is conducted routinely and frequently in monoclonal antibody drug development and manufacture. Single cell technologies in cell line development (CLD) has gone through a few stages: first to place single-cells in wells by limiting dilution, then to use FACS, and more recently, to place high proliferation rate single-cells into wells of a microtiter plate, aided by time lapsed imaging and robotic tools. However, no system to date can identify and isolate those “high performance” cells, judged by cell proliferation rate and drug protein production rate at Day Zero. We propose to develop an innovative tool that can predict cell outgrowth characteristics immediately after genetic modification based on high throughput 2D/3D cell image and artificial intelligence (AI). The benefits of the system include: 1) shorten the time to clone selection from 6 weeks to 2-3 days, 2) increase the number of valuable clones analyzed by 50 times (from 200 to 10,000). These benefits will save drug companies hundreds of millions of dollars, and potentially save thousands of lives in the case of protein-based vaccine production. Our proposed tool possesses several unique capabilities, including (i) a 3D imaging flow cytometer (3D-IFC) to acquire 3D scattering and 2D transmission images (plus 3D images of up to 6 fluorescent colors) of each single cell, (ii) a cell placement module that places cells exiting the 3D IFC for subsequent outgrowth or genetic analysis, and (iii) convolutional neural network to classify individual cells immediately (Day Zero) into high-performance and average performance cells, healthy and diseased cells, cells of different phenotypes, normal and cancer cells, and different cell types. With these capabilities, our proposed system holds the promise of identifying the high performing cells at Day Zero in a unprecedent speed and throughput for CLD. The proposed tool and technique contain the following innovative features: (a) recording of 2D and 3D cell images on-the-fly to produce over 100K high information content single-cell images in < 20 minutes, (b) depositing every single cell exiting the imaging system onto a cell placement platform (CPP) consisting of a microcapillary array on a solid culture medium plate to keep each cell in a friendly and indexed environment, (c) using bioinformatic tools to detect any cell deletion and misplacement errors to assure high accuracy of mapping cell images to cell positions, and (d) using a fused convolutional neural network (f-CNN) from both 2D and 3D labelled and/or label-free images to classify cells. Besides CLD, the proposed tool can benefit drug discovery, personalized medicine, and fundamental biomedical research such as cell type/cell atlas discovery and spatial biology.

概括 随着蛋白质治疗候选数量的增加，识别和隔离单细胞得出 菌落是一个关键的步骤，在单克隆抗体药物开发中常规和经常进行 和制造。细胞系开发中的单细胞技术（CLD）已经经历了几个阶段：首先 通过限制稀释，然后使用FACS，将单细胞放入井中，而最近将其放置高增殖 将单细胞将单细胞置于微量盘板的井中，并由时间段的成像和机器人工具进行帮助。但是，不 迄今为止的系统可以识别和隔离那些以细胞增殖率和药物来判断的“高性能”细胞 零一天的蛋白质生产率。 我们建议开发一种创新工具，该工具可以在遗传之后立即预测细胞的生长特征 基于高吞吐量2D/3D单元格图像和人工智能（AI）的修改。系统的好处 包括：1）缩短选择克隆的时间从6周到2-3天，2）增加价值数量 克隆分析了50次（从200到10,000）。这些好处将为制药公司节省数百家 在基于蛋白质的疫苗生产的情况下，数百万美元，并有可能挽救数千美元的生命。 我们提出的工具具有多种独特的功能，包括（i）3D成像流式细胞仪（3D-IFC） 获取3D散射和2D变速箱图像（加上最多6个荧光颜色的3D图像） 每个单元，（ii）一个单元放置模块，该模块将细胞退出3D IFC以进行后续生长或 遗传分析和（iii）卷积神经网络立即对单个细胞进行分类（零天） 进入高性能和平均性能细胞，健康和解散的细胞，不同表型的细胞， 正常和癌细胞以及不同的细胞类型。有了这些功能，我们提议的系统持希望 在零天识别高性能细胞的速度和CLD的吞吐量。 提出的工具和技术包含以下创新特征：（a）记录2D和3D单元格 即时的图像在<20分钟内生成超过100k的高信息内容单细胞图像，（b） 沉积将成像系统退出的每个单元将其沉积到由A组成的单元格位置（CPP）上 在固体培养基板上的微毛细管阵列，以使每个单元保持在友好且索引的环境中，（c） 使用生物信息学工具来检测任何单元格删除和放置错误，以确保映射的高精度 细胞图像到细胞位置，（d）使用2D和3D的融合卷积神经网络（F-CNN） 标记和/或无标签图像以对单元进行分类。除CLD外，所提出的工具还可以使药物发现受益， 个性化医学以及基本的生物医学研究，例如细胞类型/细胞图集发现和空间 生物学。