Data för: A Morphology-Based Machine Learning Model for Scoring Epithelial-Mesenchymal Plasticity using Organelle Dynamics
https://doi.org/10.48723/m1cg-v223
Project Description:
This project is aimed at modelling dynamic organelle morphology changes in a cellular model of epithelial-mesenchymal transition (EMT). EMT is a developmental process, which is re-activated in cancer and promotes invasion, metastasis and resistance to various therapies.
The repository contains raw microscopy images (.nd2 files) and numerical data outputs of all the experiments performed on a variety of cell lines (breast cancer, lung cancer). Cells were stained with the CellPainting kit. Experiments codes are listed below.
Categories:
Translational and applied bioinformatics; Cellular interactions (incl. adhesion, matrix, cell wall)
Numerical Data Source_wet lab + database (782.78 KB)
(1 excel file with numerical data source for MS experiments; 1 pdf file with WB uncropped membranes images)
Numerical Source Data_all CP experiments (microscopy): (14.47 GB)
- CP015/016/018: NMuMG training set (cellprofiler outputs)
- CP017: NMuMG reversal experiment
- CP019:NMuMG model validation
- CP020: breast cancer cells
- CP_INT_03_Normoxia: A549 Normoxia
- CP_INT_03_Hypoxia: A549 Hypoxia
Images (.ND2 files):
- CP015_nd2 (37.24 GB): NMuMG training set.1
- CP016_nd2 (37.24 GB): NMuMG training set.2
- CP018_nd2 (37.24 GB): NMuMG training set.3
- CP017_nd2 (37.24 GB): NMuMG reversal experiment
- CP019_nd2 (37.24 GB): NMuMG model validation
- CP020_nd2 (37.24 GB): breast cancer cells
- CP_INT_03_Normoxia_nd2 (18.62 GB): A549 Normoxia
- CP_INT_03_Hypoxia_nd2 (18.62 GB): A549 Hypoxia
Image files are in nd2 format, which is Nikon's proprietary format, but can be viewed with open source tools such as ImageJ.
Datafiler
Datafiler
Citering och åtkomst
Citering och åtkomst
Tillgänglighetsnivå:
Skapare/primärforskare:
Forskningshuvudman:
Data innehåller personuppgifter:
Nej
Citering:
Språk:
Metod och utfall
Metod och utfall
Analysenhet:
Population:
Denna studie genomfördes med etablerade cellinjer och involverade inte människor eller djur.
Tidsdimension:
Studiedesign:
- Experimentell studie
- Preklinisk studie
Beskrivning av studiedesign:
A morphology-based machine learning approach was developed to score EMT based on changes in organelle dynamics. Using the Cell Painting assay and high-throughput microscopy, we trained a histogram gradient boosting classifier to identify stage-specific organelle remodeling during a time course of TGF-β1-induced EMT in mammary epithelial cells.
Tidsperiod(er) som undersökts:
Geografisk täckning
Geografisk täckning
Geografisk plats:
Administrativ information
Administrativ information
Ansvarig institution/enhet:
Institutionen för Laboratoriemedicin
Övriga forskningshuvudmän:
Medverkande:
Finansiering
Finansiering
Finansiär:
Referensnummer:
21 1739 Pj; 24 3842 Pj
Finansiär:
- Radiumhemmets forskningsfonder
Öppnar nytt fönster hos ror.org.
RORÖppnas i en ny tabb
Referensnummer:
211092; 231172
Ämnesområde och nyckelord
Ämnesområde och nyckelord
Standard för svensk indelning av forskningsämnen 2025:
Publikationer
Publikationer
Citering:
Fuxe, J., Slager, J., Gatto, F., Frey, B., Porebski, B., Carreras-Puigvert, J., Parniewska, M., & Shi, W. (2025). A Morphology-Based Machine Learning Model for Scoring Epithelial-Mesenchymal Plasticity using Organelle Dynamics. In Research Square. https://doi.org/10.21203/rs.3.rs-5859608/v1Öppnas i en ny tabb
