Machine-learning-guided recognition of α and ß cells from label-free infrared micrographs of living human islets of Langerhans.

Human islets of Langerhans are composed mostly of glucagon-secreting α cells and insulin-secreting ß cells closely intermingled one another. Current methods for identifying α and ß cells involve either fixing islets and using immunostaining or disaggregating islets and employing flow cytometry for classifying α and ß cells based on their size and autofluorescence. Neither approach, however, allows investigating the dynamic behavior of α and ß cells in a living and intact islet. To tackle this issue, we present a machine-learning-based strategy for identification α and ß cells in label-free infrared micrographs of living human islets without immunostaining. Intrinsic autofluorescence is stimulated by infrared light and collected both in intensity and lifetime in the visible range, dominated by NAD(P)H and lipofuscin signals. Descriptive parameters are derived from micrographs for ~ 103 cells. These parameters are used as input for a boosted decision-tree model (XGBoost) pre-trained with immunofluorescence-derived cell-type information. The model displays an optimized-metrics performance of 0.86 (i.e. area under a ROC curve), with an associated precision of 0.94 for the recognition of ß cells and 0.75 for α cells. This tool promises to enable longitudinal studies on the dynamic behavior of individual cell types at single-cell resolution within the intact tissue.

Single-cell imaging of α and ß cell metabolic response to glucose in living human Langerhans islets.

Here we use a combination of two-photon Fluorescence Lifetime Imaging Microscopy (FLIM) of NAD(P)H free/bound ratio in living HIs with post-fixation, immunofluorescence-based, cell-type identification. FLIM allowed to measure variations in the NAD(P)H free/bound ratio induced by glucose; immunofluorescence data allowed to identify single α and ß cells; finally, matching of the two datasets allowed to assign metabolic shifts to cell identity. 312 α and 654 ß cells from a cohort of 4 healthy donors, 15 total islets, were measured. Both α and ß cells display a wide spectrum of responses, towards either an increase or a decrease in NAD(P)H free/bound ratio. Yet, if single-cell data are averaged according to the respective donor and correlated to donor insulin secretion power, a non-random distribution of metabolic shifts emerges: robust average responses of both α and ß cells towards an increase of enzyme-bound NAD(P)H belong to the donor with the lowest insulin-secretion power; by contrast, discordant responses, with α cells shifting towards an increase of free NAD(P)H and ß cells towards an increase of enzyme-bound NAD(P)H, correspond to the donor with the highest insulin-secretion power. Overall, data reveal neat anti-correlation of tissue metabolic responses with respect to tissue insulin secretion power.

Measuring Molecular Diffusion in Dynamic Subcellular Nanostructures by Fast Raster Image Correlation Spectroscopy and 3D Orbital Tracking.

Here we provide demonstration that fast fluorescence fluctuation spectroscopy is a fast and robust approach to extract information on the dynamics of molecules enclosed within subcellular nanostructures (e.g., organelles or vesicles) which are also moving in the complex cellular environment. In more detail, Raster Image Correlation Spectroscopy (RICS) performed at fast timescales (i.e., microseconds) reveals the fast motion of fluorescently labeled molecules within two exemplary dynamic subcellular nanostructures of biomedical interest, the lysosome and the insulin secretory granule (ISG). The measurement of molecular diffusion is then used to extract information on the average properties of subcellular nanostructures, such as macromolecular crowding or molecular aggregation. Concerning the lysosome, fast RICS on a fluorescent tracer allowed us to quantitatively assess the increase in organelle viscosity in the pathological condition of Krabbe disease. In the case of ISGs, fast RICS on two ISG-specific secreting peptides unveiled their differential aggregation propensity depending on intragranular concentration. Finally, a combination of fast RICS and feedback-based 3D orbital tracking was used to subtract the slow movement of subcellular nanostructures from the fast diffusion of molecules contained within them and independently validate the results. Results presented here not only demonstrate the acquired ability to address the dynamic behavior of molecules in moving, nanoscopic reference systems, but prove the relevance of this approach to advance our knowledge on cell function at the subcellular scale.

Probing Structural and Dynamic Properties of Trafficking Subcellular Nanostructures by Spatiotemporal Fluctuation Spectroscopy.

Imaging-derived mean square displacement (iMSD) is used to address the structural and dynamic properties of subcellular nanostructures, such as vesicles involved in the endo/exocytotic trafficking of solutes and biomolecules. iMSD relies on standard time-lapse imaging, is compatible with any optical setup, and does not need to dwell on single objects to extract trajectories. From each iMSD trace, a unique triplet of average structural and dynamic parameters (i.e., size, local diffusivity, anomalous coefficient) is calculated and combined to build the "iMSD signature" of the nanostructure under study. The potency of this approach is proved here with the exemplary case of macropinosomes. These vesicles evolve in time, changing their average size, number, and dynamic properties passing from early to late stages of intracellular trafficking. As a control, insulin secretory granules (ISGs) are used as a reference for subcellular structures that live in a stationary state in which the average structural and dynamic properties of the whole population of objects are invariant in time. The iMSD analysis highlights these peculiar features quantitatively and paves the way to similar applications at the sub-cellular level, both in the physiological and pathological states.