SBIR/STTR Award attributes
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 lt 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.
