Dynamic image analysis.
This master internship opportunity is centered in the field of single-cell resolution volumetric imaging, an expanding technique in biomedical research. This method enables the creation of intricate cellular maps within both healthy and tumoral human tissues, offering a profound expansion of our understanding of various diseases (Dekkers&Alieva et al, Nature Biotech 2022 and van Ineveld & Kleinnijenhuis, Nature Biotech 2021).
Through the application of timelapse dynamic imaging within 3D models, we aim to meticulously investigate dynamic processes occurring within tumors, such as aberrant cell divisions that lead to chromosome instability, a well-documented hallmark of cancer. This phenomenon results in heightened genetic adaptability among tumoral cells, which, in turn, fosters cancer aggressiveness and poor prognosis, demanding further scrutiny.
In this internship project, you will have the opportunity to utilize dynamic live imaging data of dividing tumor cells. You will employ tumor cell segmentation tools, conduct cell shape analysis, implement dimensionality reduction techniques, and apply classification methodologies with the primary objective of assessing how different drugs impact the mitotic processes of tumor cells.
Prerequisites for this internship include a fundamental knowledge of coding in R or Python, with added value of knowledge of dimensionality reduction techniques such as Principal Component Analysis (PCA). Your key takeaways from this experience will encompass an introductory understanding of biomedical image handling, expertise in cell segmentation, and proficiency in data analysis techniques.
Maria Alieva.
Correo electrónico: malieva@iib.uam.es.
Facultad de Medicina. Universidad Autónoma de Madrid. Calle del Arzobispo Morcillo 4. 28029 Madrid. Tel.: +34 914 975 486. Correo electrónico: informacion.medicina@uam.es
