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物数量的不断增加，鉴定和分离单细胞衍生的 菌落是单克隆抗体药物开发中常规且频繁进行的关键步骤 细胞系开发（CLD）中的单细胞技术经历了几个阶段：首先到 通过有限稀释将单细胞放入孔中，然后使用 FACS，最近又使用高增殖 在延时成像和机器人工具的帮助下，将单细胞放入微量滴定板的孔中。 迄今为止的系统可以根据细胞增殖率和药物来识别和分离那些“高性能”细胞 零天的蛋白质生产率。 我们建议开发一种创新工具，可以在遗传后立即预测细胞生长特征 基于高通量 2D/3D 细胞图像和人工智能 (AI) 的修改系统的优点。 包括：1）将克隆选择时间从6周缩短至2-3天，2）增加有价值的数量 对克隆进行 50 次分析（从 200 到 10,000 次），这些好处将为制药公司节省数百美元。 数百万美元，如果生产基于蛋白质的疫苗，可能会挽救数千人的生命。 我们提出的工具拥有多种独特的功能，包括 (i) 3D 成像流式细胞仪 (3D-IFC) 获取 3D 散射和 2D 透射图像（加上最多 6 种荧光颜色的 3D 图像） 每个单个细胞，(ii) 细胞放置模块，用于放置退出 3D IFC 的细胞以供后续生长或 遗传分析，以及 (iii) 卷积神经网络立即对单个细胞进行分类（零日） 分为高性能和平均性能细胞、健康细胞和患病细胞、不同表型的细胞， 凭借这些功能，我们提出的系统有望实现正常细胞和癌细胞以及不同的细胞类型。 在零天以前所未有的速度和吞吐量识别 CLD 的高性能细胞。 所提出的工具和技术包含以下创新功能：（a）2D 和 3D 细胞的记录 动态图像可在 20 分钟内生成超过 100K 高信息含量的单细胞图像，(b) 将离开成像系统的每个单细胞沉积到细胞放置平台（CPP）上，该平台由 固体培养基板上的微毛细管阵列，使每个细胞保持在友好且有索引的环境中，(c) 使用生物信息学工具检测任何细胞缺失和错位错误，以确保绘图的高精度 将细胞图像转换为细胞位置，以及 (d) 使用 2D 和 3D 融合卷积神经网络 (f-CNN) 除了 CLD 之外，所提出的工具还可以有利于药物发现、 个性化医疗以及基础生物医学研究，例如细胞类型/细胞图谱发现和空间 生物学。