Segmentation-guided photon pooling enables robust single cell analysis and fast fluorescence lifetime imaging microscopy

Abstract

Fluorescence lifetime imaging microscopy (FLIM) can probe the metabolic environment of living cells in a label-free and non-invasive manner. However, endogenous fluorophores have low absorption and quantum yields, which necessitates long integration times to acquire the high photon counts needed for accurate pixel-wise multi-exponential decay fitting. Here, we present a region-of-interest photon pooling technique to expedite label-free, single cell FLIM acquisition and analysis. As a result, we achieved single-cell metabolic information at intervals as low as one second and acquired large FLIM mosaics 15 times faster than would be possible with conventional pixel-level analysis. This technique is computationally light, does not require machine learning algorithms, and has been integrated with commonly used analysis software and file types.