The field of mechanobiology is delivering remarkable insights into fundamental biological processes, such as the role of proteins in cellular functioning, signaling and binding events, and molecular, cell-cell and cell-surface interactions.
Atomic force microscopy (AFM) has emerged as a key platform for studying the morphological and nanomechanical properties of living biological systems and is making a vital contribution towards understanding various pathological disorders and the development of innovative therapeutic approaches.
The NanoWizard V AFM provides a degree of automation and ease-of-use second to none:
In this webinar recording, Prof Etienne Dague provides insight into his work using AFM, with a particular focus on the importance of automated measurements for life science and biomedical research. Dr Thomas Henze, Head of Applications at Bruker BioAFM, also gives an overview of the new AFM developments enabling automated quantitative nanomechanical and high-throughput data acquisition measurements of large samples. This webinar concludes with a live demo from our laboratories in Berlin, Germany.
Find out more about the technology featured in this webinar or our other solutions for Mechanobiology Research:
|Welcome and Introduction||Carmen Pettersson, European Marcom Manager BNSM, Bruker|
|Mechanobiological Measurements with AFM||Prof. Etienne Dague, Ph.D., LAAS-CNRS, University of Toulouse, France|
|AFM Developments Enabling Automated Analysis of Large Sample Areas||Thomas Henze, Ph.D., Head of Applications, Bruker BioAFM|
|Live Demo NanoWizard V AFM||Tanja Neumann, Ph.D., Senior Applications Scientist, Bruker BioAFM|
Prof Etienne Dague, LAAS-CNRS, University of Toulouse, CNRS, France
The paradigm in bio-AFM is to draw fundamental biophysical conclusions from measurements performed on only a few dozen cells. In order to apply these results in the fields of biology or medicine, it is essential to improve the statistical significance by increasing the number of cells measured and by automating the measurements. In this context, it is necessary to optimize the measurements, reduce variation of the results, and increase the speed of the experiment. Once this has been achieved, it will become possible to generate enough data sets to enable machine learning and an analysis that can decipher between malignant and normal cells.
Prof. Etienne Dague, Ph.D., LAAS-CNRS, University of Toulouse, CNRS, France
Tanja Neumann, Ph.D., Senior Applications Scientist, Bruker BioAFM