The team was led by Aliénor Lahlou from École Normale Supérieure—PSL University, in collaboration with Sony CSL—Paris and the Institut de Biologie Physico-Chimique. The research results, published in New Phytologist, demonstrate how single-cell analysis can uncover biological strategies that traditional methods miss, which could help improve hypothetical models and select individual traits relevant for biotechnologies.

A new window into photosynthetic stress responses

Most knowledge about photosynthesis comes from measuring whole plants or large populations of microalgae. While such studies have provided fundamental insights, they average out the natural variation between individual cells—variation that can reveal how organisms orchestrate their responses to environmental stress.

The research team developed an automated fluorescence microscope capable of simultaneously tracking hundreds of individual Chlamydomonas reinhardtii cells as they respond to controlled light exposure. The system measures chlorophyll fluorescence—a widely used indicator of photosynthetic status and stress—with sufficient sensitivity to detect three distinct protective mechanisms called non-photochemical quenching (NPQ) components.

The technical challenge lies in separating these three NPQ components, which occur simultaneously over timescales ranging from seconds to hours, and create complex, overlapping fluorescence signatures. The team's solution leverages a key biological insight: By using genetic mutants and specific growth conditions, they created reference populations of algae expressing only one NPQ component at a time. These "training populations" taught machine learning algorithms—specifically dictionary learning and linear discriminant analysis—to recognize the characteristic fluorescence patterns of each component.