A multi-channel in situ light scattering instrument utilized for monitoring protein aggregation and liquid dense cluster formation

Advanced imaging techniques for studying protein phase separation in living cells and at single-molecule level

Protein-protein and protein-RNA interactions are essential for cell function and survival. These interactions facilitate the formation of ribonucleoprotein complexes and biomolecular condensates via phase separation. Such assembly is involved in transcription, splicing, translation and stress response. When dysregulated, proteins and RNA can undergo irreversible aggregation which can be cytotoxic and pathogenic. Despite technical advances in investigating biomolecular condensates, achieving the necessary spatiotemporal resolution to deduce the parameters that govern their assembly and behavior has been challenging. Many laboratories have applied advanced microscopy methods for imaging condensates. For example, single molecule imaging methods have enabled the detection of RNA-protein interaction, protein-protein interaction, protein conformational dynamics, and diffusional motion of molecules that report on the intrinsic molecular interactions underlying liquid-liquid phase separation. This review will outline advances in both microscopy and spectroscopy techniques which allow single molecule detection and imaging, and how these techniques can be used to probe unique aspects of biomolecular condensates.

Exploring Nucleation Pathways in Distinct Physicochemical Environments Unveiling Novel Options to Modulate and Optimize Protein Crystallization

The scientific discussion about classical and nonclassical nucleation theories has lasted for two decades so far. Recently, multiple nucleation pathways and the occurrence and role of metastable intermediates in crystallization processes have attracted increasing attention, following the discovery of functional phase separation, which is now under investigation in different fields of cellular life sciences, providing interesting and novel aspects for conventional crystallization experiments. In this context, more systematic investigations need to be carried out to extend the current knowledge about nucleation processes. In terms of the data we present, a well-studied model protein, glucose isomerase (GI), was employed first to investigate systematically the early stages of the crystallization process, covering condensing and prenucleation ordering of protein molecules in diverse scenarios, including varying ionic and crowding agent conditions, as well as the application of a pulsed electric field (pEF). The main method used to characterize the early events of nucleation was synchronized polarized and depolarized dynamic light scattering (DLS/DDLS), which is capable of collecting the polarized and depolarized component of scattered light from a sample suspension in parallel, thus monitoring the time-resolved evolution of the condensation and geometrical ordering of proteins at the early stages of nucleation. A diffusion interaction parameter, KD, of GI under varying salt conditions was evaluated to discuss how the proportion of specific and non-specific protein–protein interactions affects the nucleation process. The effect of mesoscopic ordered clusters (MOCs) on protein crystallization was explored further by adding different ratios of MOCs induced by a pEF to fresh GI droplets in solution with different PEG concentrations. To emphasize and complement the data and results obtained with GI, a recombinant pyridoxal 5-phosphate (vitamin B6) synthase (Pdx) complex of Staphylococcus aureus assembled from twelve monomers of Pdx1 and twelve monomers of Pdx2 was employed to validate the ability of the pEF influencing the nucleation of complex macromolecules and the effect of MOCs on adjusting the crystallization pathway. In summary, our data revealed multiple nucleation pathways by tuning the proportion of specific and non-specific protein interactions, or by utilizing a pEF which turned out to be efficient to accelerate the nucleation process. Finally, a novel and reproducible experimental strategy, which can adjust and facilitate a crystallization process by pEF-induced MOCs, was summarized and reported for the first time.

Molecular crowding and RNA synergize to promote phase separation, microtubule interaction, and seeding of Tau condensates

Biomolecular condensation of the neuronal microtubule‐associated protein Tau (MAPT) can be induced by coacervation with polyanions like RNA, or by molecular crowding. Tau condensates have been linked to both functional microtubule binding and pathological aggregation in neurodegenerative diseases. We find that molecular crowding and coacervation with RNA, two conditions likely coexisting in the cytosol, synergize to enable Tau condensation at physiological buffer conditions and to produce condensates with a strong affinity to charged surfaces. During condensate‐mediated microtubule polymerization, their synergy enhances bundling and spatial arrangement of microtubules. We further show that different Tau condensates efficiently induce pathological Tau aggregates in cells, including accumulations at the nuclear envelope that correlate with nucleocytoplasmic transport deficits. Fluorescent lifetime imaging reveals different molecular packing densities of Tau in cellular accumulations and a condensate‐like density for nuclear‐envelope Tau. These findings suggest that a complex interplay between interaction partners, post‐translational modifications, and molecular crowding regulates the formation and function of Tau condensates. Conditions leading to prolonged existence of Tau condensates may induce the formation of seeding‐competent Tau and lead to distinct cellular Tau accumulations.

Interpretable deep learning uncovers cellular properties in label-free live cell images that are predictive of highly metastatic melanoma

Deep learning has emerged as the technique of choice for identifying hidden patterns in cell imaging data but is often criticized as "black box." Here, we employ a generative neural network in combination with supervised machine learning to classify patient-derived melanoma xenografts as "efficient" or "inefficient" metastatic, validate predictions regarding melanoma cell lines with unknown metastatic efficiency in mouse xenografts, and use the network to generate in silico cell images that amplify the critical predictive cell properties. These exaggerated images unveiled pseudopodial extensions and increased light scattering as hallmark properties of metastatic cells. We validated this interpretation using live cells spontaneously transitioning between states indicative of low and high metastatic efficiency. This study illustrates how the application of artificial intelligence can support the identification of cellular properties that are predictive of complex phenotypes and integrated cell functions but are too subtle to be identified in the raw imagery by a human expert. A record of this paper's transparent peer review process is included in the supplemental information. VIDEO ABSTRACT.