CausalXtract, a flexible pipeline to extract causal effects from live-cell time-lapse imaging data
Live-cell microscopy routinely provides massive amounts of time-lapse images of complex cellular systems under various physiological or therapeutic conditions. However, this wealth of data remains difficult to interpret in terms of causal effects. Here, we describe CausalXtract, a flexible computati...
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| Format: | Article |
| Language: | English |
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eLife Sciences Publications Ltd
2025-01-01
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| Series: | eLife |
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| Online Access: | https://elifesciences.org/articles/95485 |
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| author | Franck Simon Maria Colomba Comes Tiziana Tocci Louise Dupuis Vincent Cabeli Nikita Lagrange Arianna Mencattini Maria Carla Parrini Eugenio Martinelli Herve Isambert |
| author_facet | Franck Simon Maria Colomba Comes Tiziana Tocci Louise Dupuis Vincent Cabeli Nikita Lagrange Arianna Mencattini Maria Carla Parrini Eugenio Martinelli Herve Isambert |
| author_sort | Franck Simon |
| collection | DOAJ |
| description | Live-cell microscopy routinely provides massive amounts of time-lapse images of complex cellular systems under various physiological or therapeutic conditions. However, this wealth of data remains difficult to interpret in terms of causal effects. Here, we describe CausalXtract, a flexible computational pipeline that discovers causal and possibly time-lagged effects from morphodynamic features and cell–cell interactions in live-cell imaging data. CausalXtract methodology combines network-based and information-based frameworks, which is shown to discover causal effects overlooked by classical Granger and Schreiber causality approaches. We showcase the use of CausalXtract to uncover novel causal effects in a tumor-on-chip cellular ecosystem under therapeutically relevant conditions. In particular, we find that cancer-associated fibroblasts directly inhibit cancer cell apoptosis, independently from anticancer treatment. CausalXtract uncovers also multiple antagonistic effects at different time delays. Hence, CausalXtract provides a unique computational tool to interpret live-cell imaging data for a range of fundamental and translational research applications. |
| format | Article |
| id | doaj-art-81f580d13d384a5db373f76f9e8ef165 |
| institution | Kabale University |
| issn | 2050-084X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | eLife Sciences Publications Ltd |
| record_format | Article |
| series | eLife |
| spelling | doaj-art-81f580d13d384a5db373f76f9e8ef1652025-01-17T13:11:19ZengeLife Sciences Publications LtdeLife2050-084X2025-01-011310.7554/eLife.95485CausalXtract, a flexible pipeline to extract causal effects from live-cell time-lapse imaging dataFranck Simon0https://orcid.org/0000-0002-6952-0819Maria Colomba Comes1Tiziana Tocci2Louise Dupuis3Vincent Cabeli4Nikita Lagrange5Arianna Mencattini6Maria Carla Parrini7https://orcid.org/0000-0002-7082-9792Eugenio Martinelli8Herve Isambert9https://orcid.org/0000-0001-9638-8545CNRS UMR168, Institut Curie, Université PSL, Sorbonne Université, Paris, FranceDepartment of Electronic Engineering, University of Rome Tor Vergata, Rome, ItalyCNRS UMR168, Institut Curie, Université PSL, Sorbonne Université, Paris, France; Department of Electronic Engineering, University of Rome Tor Vergata, Rome, ItalyCNRS UMR168, Institut Curie, Université PSL, Sorbonne Université, Paris, FranceCNRS UMR168, Institut Curie, Université PSL, Sorbonne Université, Paris, FranceCNRS UMR168, Institut Curie, Université PSL, Sorbonne Université, Paris, FranceDepartment of Electronic Engineering, University of Rome Tor Vergata, Rome, ItalyINSERM U830, Institut Curie, Université PSL, Paris, FranceDepartment of Electronic Engineering, University of Rome Tor Vergata, Rome, ItalyCNRS UMR168, Institut Curie, Université PSL, Sorbonne Université, Paris, FranceLive-cell microscopy routinely provides massive amounts of time-lapse images of complex cellular systems under various physiological or therapeutic conditions. However, this wealth of data remains difficult to interpret in terms of causal effects. Here, we describe CausalXtract, a flexible computational pipeline that discovers causal and possibly time-lagged effects from morphodynamic features and cell–cell interactions in live-cell imaging data. CausalXtract methodology combines network-based and information-based frameworks, which is shown to discover causal effects overlooked by classical Granger and Schreiber causality approaches. We showcase the use of CausalXtract to uncover novel causal effects in a tumor-on-chip cellular ecosystem under therapeutically relevant conditions. In particular, we find that cancer-associated fibroblasts directly inhibit cancer cell apoptosis, independently from anticancer treatment. CausalXtract uncovers also multiple antagonistic effects at different time delays. Hence, CausalXtract provides a unique computational tool to interpret live-cell imaging data for a range of fundamental and translational research applications.https://elifesciences.org/articles/95485causal inferencetime-lapse image analysislive-cell imagingtumor on chipcausal discoverygranger causality |
| spellingShingle | Franck Simon Maria Colomba Comes Tiziana Tocci Louise Dupuis Vincent Cabeli Nikita Lagrange Arianna Mencattini Maria Carla Parrini Eugenio Martinelli Herve Isambert CausalXtract, a flexible pipeline to extract causal effects from live-cell time-lapse imaging data eLife causal inference time-lapse image analysis live-cell imaging tumor on chip causal discovery granger causality |
| title | CausalXtract, a flexible pipeline to extract causal effects from live-cell time-lapse imaging data |
| title_full | CausalXtract, a flexible pipeline to extract causal effects from live-cell time-lapse imaging data |
| title_fullStr | CausalXtract, a flexible pipeline to extract causal effects from live-cell time-lapse imaging data |
| title_full_unstemmed | CausalXtract, a flexible pipeline to extract causal effects from live-cell time-lapse imaging data |
| title_short | CausalXtract, a flexible pipeline to extract causal effects from live-cell time-lapse imaging data |
| title_sort | causalxtract a flexible pipeline to extract causal effects from live cell time lapse imaging data |
| topic | causal inference time-lapse image analysis live-cell imaging tumor on chip causal discovery granger causality |
| url | https://elifesciences.org/articles/95485 |
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