Life-cycle-informed interpretation of microalgal bioproduct production and image-based cell-state analysis
Microalgae produce a variety of high-value bioproducts, such as neutral lipids, polyunsaturated fatty acids, fucoxanthin, astaxanthin, and other pigments. Production studies typically quantify biomass as dry mass or estimate growth based on cell density, alongside biochemical metrics like fatty acid profiles and pigment contents. However, these bulk measurements do not reveal which coexisting life-cycle stages or physiological states were responsible for observed changes. This review offers a structured synthesis of evidence related to life-cycle stages, physiological states, and population-level events that impact target compound allocation and overall process performance. The evidence was analyzed based on perturbation, observable cell state, image-derived feature groups, biochemical endpoints, process outcomes, and validation strength. Specific emphasis was placed on Haematococcus, Chlamydomonas, and Tisochrysis/Isochrysis, as these taxa have available stage-resolved evidence or validation from single-cell to bulk assessments. Cross-study comparisons indicate that similar bulk biochemical responses can emerge from different population state compositions, while seemingly similar phenotypes can yield varying process outcomes. For example, a selfsettling state of Tisochrysis combined harvestability with productivity in fucoxanthin and docosahexaenoic acid, whereas non-motile aggregated states of Haematococcus indicated stress adaptation, storage allocation, and trade-offs with rapid proliferation. These differing outcomes are attributed to factors such as state origin, reversibility, growth costs, target compound allocation, and harvest benefits, rather than aggregation alone. Imagederived variables were categorized into morphological, fluorescence, and populationevent indicators. While these indicators facilitate state tracking, they do not correspond directly to biological states and require validation through methods such as time-series microscopy, gas chromatography or lipidomics, high-performance liquid chromatography or mass spectrometry, pulse-amplitude-modulated fluorometry, and independent cell counts. This evidence-based framework connects cell state interpretation to strain screening, stress diagnosis, harvest timing, scale-up assessment, and bioproduct quality control.