Aliénor Lahlou followed a multidisciplinary education in engineering (ESPCI), with hands-on experience in engineering, optics, acoustics, microfluidics, chemistry and biology. Her master’s degree in machine learning (MVA – ENS Paris Saclay) led her to an internship at the fundamental research laboratories of Sony (Sony CSL – Paris).

Sony CSL’s ROMI project develops accessible and open-source robotic tools to assist market gardeners with complex tasks. To extend the robot’s capabilities, she proposed finding a research laboratory to supervise her PhD in exploring plant stress level diagnostic methods. This led her to meet Ludovic Jullien and Thomas Le Saux (ENS-PSL), who had begun preliminary work on the possibility of monitoring the activity level of a fundamental metabolic process in plants, photosynthesis, using kinetic chemistry methods.

Photosynthetic organisms naturally emit fluorescence, which can be measured to obtain information about their health. In her PhD work, she introduced a multidisciplinary approach to studying this fluorescence, with the long-term goal of non-invasive plant health diagnostics. The first phase involved designing an automated microscope and a method for calibrating light intensity using light-sensitive molecules. Subsequently, an experimental protocol was established to apply light stress to unicellular microalgae and acquire temporal fluorescence data, which is a signature of the stress response. A new machine learning processing method was implemented to extract three scores reflecting three superimposed stress responses in single cells. Correlations between these stresses were highlighted, opening up interpretative perspectives on their interactions from a biophysical point of view. An open-source version of the instrument was created, and the results were successfully reproduced.