Label-Free Near-Infrared Image Cytometry for Chlorophyll Quantification and Carotenoid Prediction with Multiparametric Biophysicochemical Profiling

Abstract

Microalgae offer a promising platform for carotenoid manufacturing due to their productivity, tunable selectivity, and compatibility with large-scale bioprocessing. Yet, their cellular responses exhibit highly heterogeneous and asynchronous pigment and morphological transitions under fluctuating light stress, making precise prediction and process optimization challenging. Existing analytical methods cannot resolve these transitions in living cells because they either require destructive sampling or lack spectral specificity. In this study, we introduce near-infrared (nIR) image cytometry (NIC), a label-free, nondestructive, and high-throughput platform that quantifies biophysicochemical heterogeneity at single-cell resolution. By positioning chlorophyll detection within the nIR window, NIC structurally removes spectral interference from visible pigments and achieves precise quantification of chlorophyll content comparable to liquid chromatography. Simultaneously, NIC extracts biophysical parameters such as cell size and shape together with biochemical states including carotenoid accumulation in a fully label-free manner, enabling direct observation of their mechanistic coupling in living cells. Using representative industrial microalgae including D. salina and H. pluvialis as test beds, NIC resolves species-specific photoprotective strategies through 3D heterogeneity mapping. Furthermore, we customized a machine learning model that enables the prediction of cultivation stages for completely unknown batches with up to 89.0% stage-variable accuracy, driven by the single-cell data and multivariate biophysicochemical features quantified by NIC. This generalizable framework links cellular heterogeneity to process behavior and provides a basis for real-time monitoring in next-generation microalgal biomanufacturing.