Predicting cell properties with AI from 3D imaging flow cytometer data
Abstract Predicting the properties of tissues or organisms from the genomics data is widely accepted by the medical community. Here we ask a question: can we predict the properties of each individual cell? Single-cell genomics does not work because the RNA sequencing process destroys the cell, not a...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-02-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-80722-6 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849388066348728320 |
|---|---|
| author | Zunming Zhang Yuxuan Zhu Zhaoyu Lai Minhong Zhou Xinyu Chen Rui Tang William Alaynick Sung Hwan Cho Yu-Hwa Lo |
| author_facet | Zunming Zhang Yuxuan Zhu Zhaoyu Lai Minhong Zhou Xinyu Chen Rui Tang William Alaynick Sung Hwan Cho Yu-Hwa Lo |
| author_sort | Zunming Zhang |
| collection | DOAJ |
| description | Abstract Predicting the properties of tissues or organisms from the genomics data is widely accepted by the medical community. Here we ask a question: can we predict the properties of each individual cell? Single-cell genomics does not work because the RNA sequencing process destroys the cell, not allowing us to verify our predictions. To test the hypothesis, we investigate the approach of using AI to analyze single-cell images obtained from a 3D imaging flow cytometer. We analyze the cell image at day zero and make the AI-assisted cell property prediction. The prediction is then examined later when the cells continue to live and develop. Our preliminary results are promising, showing 88% accuracy in predicting cells that will have a high protein expression level. The technique can have strong ramifications and impact on preventive medicine, drug development, cell therapy, and fundamental biomedical research. |
| format | Article |
| id | doaj-art-3d97752d0d5c4795a4e643af971c9e12 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-3d97752d0d5c4795a4e643af971c9e122025-08-20T03:42:25ZengNature PortfolioScientific Reports2045-23222025-02-011511810.1038/s41598-024-80722-6Predicting cell properties with AI from 3D imaging flow cytometer dataZunming Zhang0Yuxuan Zhu1Zhaoyu Lai2Minhong Zhou3Xinyu Chen4Rui Tang5William Alaynick6Sung Hwan Cho7Yu-Hwa Lo8Department of Electrical and Computer Engineering, University of California, San DiegoDepartment of Electrical and Computer Engineering, University of California, San DiegoDepartment of Electrical and Computer Engineering, University of California, San DiegoDepartment of Electrical and Computer Engineering, University of California, San DiegoDepartment of Electrical and Computer Engineering, University of California, San DiegoNanoCellect Biomedical Inc.NanoCellect Biomedical Inc.NanoCellect Biomedical Inc.Department of Electrical and Computer Engineering, University of California, San DiegoAbstract Predicting the properties of tissues or organisms from the genomics data is widely accepted by the medical community. Here we ask a question: can we predict the properties of each individual cell? Single-cell genomics does not work because the RNA sequencing process destroys the cell, not allowing us to verify our predictions. To test the hypothesis, we investigate the approach of using AI to analyze single-cell images obtained from a 3D imaging flow cytometer. We analyze the cell image at day zero and make the AI-assisted cell property prediction. The prediction is then examined later when the cells continue to live and develop. Our preliminary results are promising, showing 88% accuracy in predicting cells that will have a high protein expression level. The technique can have strong ramifications and impact on preventive medicine, drug development, cell therapy, and fundamental biomedical research.https://doi.org/10.1038/s41598-024-80722-6 |
| spellingShingle | Zunming Zhang Yuxuan Zhu Zhaoyu Lai Minhong Zhou Xinyu Chen Rui Tang William Alaynick Sung Hwan Cho Yu-Hwa Lo Predicting cell properties with AI from 3D imaging flow cytometer data Scientific Reports |
| title | Predicting cell properties with AI from 3D imaging flow cytometer data |
| title_full | Predicting cell properties with AI from 3D imaging flow cytometer data |
| title_fullStr | Predicting cell properties with AI from 3D imaging flow cytometer data |
| title_full_unstemmed | Predicting cell properties with AI from 3D imaging flow cytometer data |
| title_short | Predicting cell properties with AI from 3D imaging flow cytometer data |
| title_sort | predicting cell properties with ai from 3d imaging flow cytometer data |
| url | https://doi.org/10.1038/s41598-024-80722-6 |
| work_keys_str_mv | AT zunmingzhang predictingcellpropertieswithaifrom3dimagingflowcytometerdata AT yuxuanzhu predictingcellpropertieswithaifrom3dimagingflowcytometerdata AT zhaoyulai predictingcellpropertieswithaifrom3dimagingflowcytometerdata AT minhongzhou predictingcellpropertieswithaifrom3dimagingflowcytometerdata AT xinyuchen predictingcellpropertieswithaifrom3dimagingflowcytometerdata AT ruitang predictingcellpropertieswithaifrom3dimagingflowcytometerdata AT williamalaynick predictingcellpropertieswithaifrom3dimagingflowcytometerdata AT sunghwancho predictingcellpropertieswithaifrom3dimagingflowcytometerdata AT yuhwalo predictingcellpropertieswithaifrom3dimagingflowcytometerdata |