Technological advances have ushered in a new multi-omics era that has elucidated more granular information on cell types and functions at single-cell resolution. Integrating morphology as a multi-dimensional readout of cell identity, state, and function is an area of broad interest, since it has been historically difficult to quantify morphological properties. Here, we apply artificial intelligence (AI) and multi-dimensional morphology to characterize a variety of human cell types and states using the Deepcell platform. Captured brightfield images of cells flowing inside a microfluidic channel are analyzed by AI models in real-time and cells of interest can be sorted for downstream molecular or functional analyses.
We developed an AI model, termed ‘Human Foundation Model’ (HFM), for broad use that combines machine learning with computer vision for multi-dimensional morphology analysis to provide interpretable features on a broad range of human cells and diverse sample types. The HFM training and validation datasets, comprising 1.18 million and 4.05 million cell images, respectively, were chosen to capture a comprehensive range of visual features and cover a wide spectrum of biological diversity. Samples included 6μm polystyrene beads (as controls) and multiple immune and carcinoma cell lines. Subsets of these images were labeled for specific cell features then included in the supervised portion of the model training.
We apply the HFM as part of the Deepcell system to identify and characterize the morphological heterogeneity of tumor cells in different types of cancers such as melanoma, non-small cell lung carcinoma, and malignant pleural effusions. We further demonstrate the ability of the platform to distinguish different cell types (epithelial, stromal, immune, and endothelial cells) commonly found in tumors that typically require complex antibody panels.
With the Deepcell platform and the HFM, we determined cell identities using morphological features associated with distinct processes characterized by granules, vesicles, cell size, pigmentation and others. Furthermore, sorted cells of interest are unlabeled and can be retrieved for further analysis. This AI-powered technology can be applied to diverse areas including drug screenings/mechanism of action, tumor biology, and functional genomics.
Citation Format: Stephane C. Boutet, Anastasia Mavropoulos, Andreja Jovic, Jeanette Mei, Nianzhen Li, Kiran Saini, Senzeyu Zhang, Chassidy Johnson, Vivian Lu, Ryan Carelli, Kevin B. Jacobs, Mahyar Salek, Maddison Masaeli. A broad-use deep learning model based on multi-dimensional morphology to identify and characterize tumor cell heterogeneity. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5381.
