Lung cancer intravasation-on-a-chip: Visualization and machine learning-assisted automatic quantification
During lung cancer metastasis, tumor cells undergo epithelial-to-mesenchymal transition (EMT), enabling them to intravasate through the vascular barrier and enter the circulation before colonizing secondary sites. Here, a human in vitro microphysiological model of EMT-driven lung cancer intravasatio...
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co., Ltd.
2025-09-01
|
| Series: | Bioactive Materials |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2452199X25002567 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849700494904131584 |
|---|---|
| author | Christy Wing Tung Wong Joyce Zhi Xuen Lee Anna Jaeschke Sammi Sze Ying Ng Kwok Keung Lit Ho-Ying Wan Caroline Kniebs Dai Fei Elmer Ker Rocky S. Tuan Anna Blocki |
| author_facet | Christy Wing Tung Wong Joyce Zhi Xuen Lee Anna Jaeschke Sammi Sze Ying Ng Kwok Keung Lit Ho-Ying Wan Caroline Kniebs Dai Fei Elmer Ker Rocky S. Tuan Anna Blocki |
| author_sort | Christy Wing Tung Wong |
| collection | DOAJ |
| description | During lung cancer metastasis, tumor cells undergo epithelial-to-mesenchymal transition (EMT), enabling them to intravasate through the vascular barrier and enter the circulation before colonizing secondary sites. Here, a human in vitro microphysiological model of EMT-driven lung cancer intravasation-on-a-chip was developed and coupled with machine learning (ML)-assisted automatic identification and quantification of intravasation events.A robust EMT-inducing cocktail (EMT-IC) was formulated by augmenting macrophage-conditioned medium with transforming growth factor-β1. When introduced into microvascular networks (MVNs) in microfluidic devices, EMT-IC did not affect MVN stability and physiologically relevant barrier functions.To model lung cancer intravasation on-a-chip, EMT-IC was supplemented into co-cultures of lung tumor micromasses and MVNs. Wihin 24 h of exposure, EMT-IC facilitated the insertion of membrane protrusions of migratory A549 cells into microvascular structures, followed by successful intravasation. EMT-IC reduced key basement membrane and vascular junction proteins - laminin and VE-Cadherin - rendering vessel walls more permissive to intravasating cells. ML-assisted vessel segmentation combined with co-localization analysis to detect intravasation events confirmed that EMT induction significantly increased the number of intravasation events.Introducing metastatic (NCI-H1975) and non-metastatic (BEAS-2B) cell lines demonstrated that both, baseline intravasation potential and responsiveness to EMT-IC, are reflected in the metastatic predisposition of lung cancer cell lines, highlighting the model's universal applicability and cell-specific sensitivity.The reproducible detection of intravasation events in the established model provides a physiologically relevant platform to study processes of cancer metastasis with high spatio-temporal resolution and short timeframe. This approach holds promise for improved drug development and informed personalized patient treatment plans. |
| format | Article |
| id | doaj-art-448bfc5f8fb74ef4baaba40b618191f8 |
| institution | DOAJ |
| issn | 2452-199X |
| language | English |
| publishDate | 2025-09-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Bioactive Materials |
| spelling | doaj-art-448bfc5f8fb74ef4baaba40b618191f82025-08-20T03:18:15ZengKeAi Communications Co., Ltd.Bioactive Materials2452-199X2025-09-015185887510.1016/j.bioactmat.2025.06.028Lung cancer intravasation-on-a-chip: Visualization and machine learning-assisted automatic quantificationChristy Wing Tung Wong0Joyce Zhi Xuen Lee1Anna Jaeschke2Sammi Sze Ying Ng3Kwok Keung Lit4Ho-Ying Wan5Caroline Kniebs6Dai Fei Elmer Ker7Rocky S. Tuan8Anna Blocki9Institute for Tissue Engineering and Regenerative Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China; School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China; Center for Neuromusculoskeletal Restorative Medicine (CNRM), Hong Kong Science Park, Shatin, New Territories, Hong Kong SAR, ChinaInstitute for Tissue Engineering and Regenerative Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China; School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, ChinaInstitute for Tissue Engineering and Regenerative Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China; School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China; Center for Neuromusculoskeletal Restorative Medicine (CNRM), Hong Kong Science Park, Shatin, New Territories, Hong Kong SAR, ChinaInstitute for Tissue Engineering and Regenerative Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China; School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, ChinaInstitute for Tissue Engineering and Regenerative Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China; School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China; Center for Neuromusculoskeletal Restorative Medicine (CNRM), Hong Kong Science Park, Shatin, New Territories, Hong Kong SAR, ChinaInstitute for Tissue Engineering and Regenerative Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China; School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China; Center for Neuromusculoskeletal Restorative Medicine (CNRM), Hong Kong Science Park, Shatin, New Territories, Hong Kong SAR, ChinaInstitute for Tissue Engineering and Regenerative Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China; School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China; Department of Biohybrid and Medical Textiles (BioTex), AME - Institute of Applied Medical Engineering, Helmholtz Institute, RWTH Aachen University, Aachen, GermanyInstitute for Tissue Engineering and Regenerative Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China; School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China; Center for Neuromusculoskeletal Restorative Medicine (CNRM), Hong Kong Science Park, Shatin, New Territories, Hong Kong SAR, China; Department of Orthopaedics & Traumatology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China; Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, ChinaInstitute for Tissue Engineering and Regenerative Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China; School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China; Center for Neuromusculoskeletal Restorative Medicine (CNRM), Hong Kong Science Park, Shatin, New Territories, Hong Kong SAR, China; Department of Orthopaedics & Traumatology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, ChinaInstitute for Tissue Engineering and Regenerative Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China; School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China; Center for Neuromusculoskeletal Restorative Medicine (CNRM), Hong Kong Science Park, Shatin, New Territories, Hong Kong SAR, China; Department of Orthopaedics & Traumatology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China; Corresponding author. Institute for Tissue Engineering and Regenerative Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China.During lung cancer metastasis, tumor cells undergo epithelial-to-mesenchymal transition (EMT), enabling them to intravasate through the vascular barrier and enter the circulation before colonizing secondary sites. Here, a human in vitro microphysiological model of EMT-driven lung cancer intravasation-on-a-chip was developed and coupled with machine learning (ML)-assisted automatic identification and quantification of intravasation events.A robust EMT-inducing cocktail (EMT-IC) was formulated by augmenting macrophage-conditioned medium with transforming growth factor-β1. When introduced into microvascular networks (MVNs) in microfluidic devices, EMT-IC did not affect MVN stability and physiologically relevant barrier functions.To model lung cancer intravasation on-a-chip, EMT-IC was supplemented into co-cultures of lung tumor micromasses and MVNs. Wihin 24 h of exposure, EMT-IC facilitated the insertion of membrane protrusions of migratory A549 cells into microvascular structures, followed by successful intravasation. EMT-IC reduced key basement membrane and vascular junction proteins - laminin and VE-Cadherin - rendering vessel walls more permissive to intravasating cells. ML-assisted vessel segmentation combined with co-localization analysis to detect intravasation events confirmed that EMT induction significantly increased the number of intravasation events.Introducing metastatic (NCI-H1975) and non-metastatic (BEAS-2B) cell lines demonstrated that both, baseline intravasation potential and responsiveness to EMT-IC, are reflected in the metastatic predisposition of lung cancer cell lines, highlighting the model's universal applicability and cell-specific sensitivity.The reproducible detection of intravasation events in the established model provides a physiologically relevant platform to study processes of cancer metastasis with high spatio-temporal resolution and short timeframe. This approach holds promise for improved drug development and informed personalized patient treatment plans.http://www.sciencedirect.com/science/article/pii/S2452199X25002567Cancer intravasationLung cancerEpithelial-to-mesenchymal transition (EMT)MacrophagesTransforming growth factor-beta 1 (TGF-β1)Microfluidic devices |
| spellingShingle | Christy Wing Tung Wong Joyce Zhi Xuen Lee Anna Jaeschke Sammi Sze Ying Ng Kwok Keung Lit Ho-Ying Wan Caroline Kniebs Dai Fei Elmer Ker Rocky S. Tuan Anna Blocki Lung cancer intravasation-on-a-chip: Visualization and machine learning-assisted automatic quantification Bioactive Materials Cancer intravasation Lung cancer Epithelial-to-mesenchymal transition (EMT) Macrophages Transforming growth factor-beta 1 (TGF-β1) Microfluidic devices |
| title | Lung cancer intravasation-on-a-chip: Visualization and machine learning-assisted automatic quantification |
| title_full | Lung cancer intravasation-on-a-chip: Visualization and machine learning-assisted automatic quantification |
| title_fullStr | Lung cancer intravasation-on-a-chip: Visualization and machine learning-assisted automatic quantification |
| title_full_unstemmed | Lung cancer intravasation-on-a-chip: Visualization and machine learning-assisted automatic quantification |
| title_short | Lung cancer intravasation-on-a-chip: Visualization and machine learning-assisted automatic quantification |
| title_sort | lung cancer intravasation on a chip visualization and machine learning assisted automatic quantification |
| topic | Cancer intravasation Lung cancer Epithelial-to-mesenchymal transition (EMT) Macrophages Transforming growth factor-beta 1 (TGF-β1) Microfluidic devices |
| url | http://www.sciencedirect.com/science/article/pii/S2452199X25002567 |
| work_keys_str_mv | AT christywingtungwong lungcancerintravasationonachipvisualizationandmachinelearningassistedautomaticquantification AT joycezhixuenlee lungcancerintravasationonachipvisualizationandmachinelearningassistedautomaticquantification AT annajaeschke lungcancerintravasationonachipvisualizationandmachinelearningassistedautomaticquantification AT sammiszeyingng lungcancerintravasationonachipvisualizationandmachinelearningassistedautomaticquantification AT kwokkeunglit lungcancerintravasationonachipvisualizationandmachinelearningassistedautomaticquantification AT hoyingwan lungcancerintravasationonachipvisualizationandmachinelearningassistedautomaticquantification AT carolinekniebs lungcancerintravasationonachipvisualizationandmachinelearningassistedautomaticquantification AT daifeielmerker lungcancerintravasationonachipvisualizationandmachinelearningassistedautomaticquantification AT rockystuan lungcancerintravasationonachipvisualizationandmachinelearningassistedautomaticquantification AT annablocki lungcancerintravasationonachipvisualizationandmachinelearningassistedautomaticquantification |