Multi-omics Prediction from High-content Cellular Imaging with Deep Learning
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866913357298139136 |
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| author | Mehrizi, Rahil Mehrjou, Arash Alegro, Maryana Zhao, Yi Carbone, Benedetta Fishwick, Carl Vappiani, Johanna Bi, Jing Sanford, Siobhan Keles, Hakan Bantscheff, Marcus Nguyen, Cuong Schwab, Patrick |
| author_facet | Mehrizi, Rahil Mehrjou, Arash Alegro, Maryana Zhao, Yi Carbone, Benedetta Fishwick, Carl Vappiani, Johanna Bi, Jing Sanford, Siobhan Keles, Hakan Bantscheff, Marcus Nguyen, Cuong Schwab, Patrick |
| contents | High-content cellular imaging, transcriptomics, and proteomics data provide rich and complementary views on the molecular layers of biology that influence cellular states and function. However, the biological determinants through which changes in multi-omics measurements influence cellular morphology have not yet been systematically explored, and the degree to which cell imaging could potentially enable the prediction of multi-omics directly from cell imaging data is therefore currently unclear. Here, we address the question of whether it is possible to predict bulk multi-omics measurements directly from cell images using Image2Omics - a deep learning approach that predicts multi-omics in a cell population directly from high-content images of cells stained with multiplexed fluorescent dyes. We perform an experimental evaluation in gene-edited macrophages derived from human induced pluripotent stem cells (hiPSC) under multiple stimulation conditions and demonstrate that Image2Omics achieves significantly better performance in predicting transcriptomics and proteomics measurements directly from cell images than predictions based on the mean observed training set abundance. We observed significant predictability of abundances for 4927 (18.72%; 95% CI: 6.52%, 35.52%) and 3521 (13.38%; 95% CI: 4.10%, 32.21%) transcripts out of 26137 in M1 and M2-stimulated macrophages respectively and for 422 (8.46%; 95% CI: 0.58%, 25.83%) and 697 (13.98%; 95% CI: 2.41%, 32.83%) proteins out of 4986 in M1 and M2-stimulated macrophages respectively. Our results show that some transcript and protein abundances are predictable from cell imaging and that cell imaging may potentially, in some settings and depending on the mechanisms of interest and desired performance threshold, even be a scalable and resource-efficient substitute for multi-omics measurements. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2306_09391 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Multi-omics Prediction from High-content Cellular Imaging with Deep Learning Mehrizi, Rahil Mehrjou, Arash Alegro, Maryana Zhao, Yi Carbone, Benedetta Fishwick, Carl Vappiani, Johanna Bi, Jing Sanford, Siobhan Keles, Hakan Bantscheff, Marcus Nguyen, Cuong Schwab, Patrick Quantitative Methods Computer Vision and Pattern Recognition Machine Learning Genomics High-content cellular imaging, transcriptomics, and proteomics data provide rich and complementary views on the molecular layers of biology that influence cellular states and function. However, the biological determinants through which changes in multi-omics measurements influence cellular morphology have not yet been systematically explored, and the degree to which cell imaging could potentially enable the prediction of multi-omics directly from cell imaging data is therefore currently unclear. Here, we address the question of whether it is possible to predict bulk multi-omics measurements directly from cell images using Image2Omics - a deep learning approach that predicts multi-omics in a cell population directly from high-content images of cells stained with multiplexed fluorescent dyes. We perform an experimental evaluation in gene-edited macrophages derived from human induced pluripotent stem cells (hiPSC) under multiple stimulation conditions and demonstrate that Image2Omics achieves significantly better performance in predicting transcriptomics and proteomics measurements directly from cell images than predictions based on the mean observed training set abundance. We observed significant predictability of abundances for 4927 (18.72%; 95% CI: 6.52%, 35.52%) and 3521 (13.38%; 95% CI: 4.10%, 32.21%) transcripts out of 26137 in M1 and M2-stimulated macrophages respectively and for 422 (8.46%; 95% CI: 0.58%, 25.83%) and 697 (13.98%; 95% CI: 2.41%, 32.83%) proteins out of 4986 in M1 and M2-stimulated macrophages respectively. Our results show that some transcript and protein abundances are predictable from cell imaging and that cell imaging may potentially, in some settings and depending on the mechanisms of interest and desired performance threshold, even be a scalable and resource-efficient substitute for multi-omics measurements. |
| title | Multi-omics Prediction from High-content Cellular Imaging with Deep Learning |
| topic | Quantitative Methods Computer Vision and Pattern Recognition Machine Learning Genomics |
| url | https://arxiv.org/abs/2306.09391 |