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The collective behavior of oscillators is a venerable subject in Physics since Huygens’ seminal contributions. Living systems, from simple unicellular bacteria to multicellular plants and mammals also display oscillatory dynamics, the most conspicuous of which are circadian rhythms, coupling the biology of these organisms to day/night cycles on Earth.
While considerable headway has been made in understanding the behavior of individual circadian clocks and their molecular components, the behavior of a large collection of clocks is still poorly understood, constituting a fertile ground of inquiry.

We studied at the single-cell level the collective behavior of one-dimensional arrays of clocks in Anabaena, a cyanobacterial organism of ancient origin, as a model system. Anabaena filaments display remarkable synchrony and spatial coherence at the organismal scale, despite considerable and yet inevitable fluctuations in each cell –demographic noise-, stemming from the stochastic nature of biochemical reactions. Furthermore, we provide experimental evidence supporting the notion that spatio-temporal coherence is largely due to the coupling of clocks by cell-cell communication, and that the clock controls other cellular processes such as cell division. A stochastic, one-dimensional toy model of coupled clocks shows that demographic noise can seed stochastic oscillations outside the region where deterministic limit cycles with circadian periods occur. The model reproduces the observed spatio-temporal coherence along filaments and provides a robust description of coupled circadian clocks in a multicellular organism.

Living cells are densely packed conglomerate of macromolecules, where diffusion is essential for their function. The crowded conditions may affect diffusion both through hard (occluded space) and soft (weak, non-specific) interactions. Multiple-methods have been developed to measure diffusion coefficients at physiological protein concentrations within cells, each with its limitations. Here, we show that Line-FRAP, a method based on measuring recovery of photobleaching under a confocal microscope that allows diffusion coefficient measurements in a variety of environments, from in vitro to in vivo. The use of Line mode greatly improves the time resolution in of FRAP data acquisition, from 20-50 Hz in the classical mode to 800 Hz in the line mode. We also introduce an updated method for data analysis to obtain diffusion coefficients in various environments, with the number of pixels bleached at the first frame after bleaching being a critical parameter. We evaluated the method using different proteins either chemically labelled or by fusion to YFP. The calculated diffusion rates were comparable to literature data as measured in vitro, in HeLa cells and in E.coli. Diffusion coefficients in HeLa was ~2.5-fold slower and in E.coli 15-fold slower than measured in buffer and were comparable to previously published data. Moreover, we show that increasing the osmotic pressure on E.coli further decreases diffusion, to the point at which proteins virtually stop moving. Next, we investigated the diffusion behavior of small organic molecule drugs. The diffusion rates of these molecules differed greatly in crowding conditions and living cells from the expected, pointing towards interactions of the small molecules with the surrounding. Micrographs have shown many of these molecules to accumulate in the lysosomes of cells, explaining their extremely slow diffusion. These findings are relevant for drug design, as the observed phase separation would make the small molecules not accessible to their targets. The method presented here requires a confocal microscope equipped with dual scanners, can be applied to study a large range of molecules with different sizes, and provides robust results in a wide range of environments and protein concentrations for fast diffusing molecules.
Reference: [1] D. Dey, S. Marciano, A. Nunes-Alves, V. Kiss, R. C. Wade and G. Schreiber, Line-FRAP, a versatile method to measure diffusion rates in vitro and in vivo, Journal of Molecular Biology, 433, 9, 2021, 166898.