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Biology subjects

Penedo, M.

Publications and source records attributed to Penedo, M..

2 recordsLinked to original sources

Non-contact direct sensing of material properties of biomolecular condensate using Scanning Ionic Conductance Microscopy

Biomolecular condensates are important regulators of cellular compartmentalization and biochemical processes. Understanding their material properties is critical to elucidate how they control molecular organization and dynamics within cells. However, quantitatively probing these properties remains challenging due to the wide range of length scales, concentrations, and timescales over which condensates operate, as well as the limited force ranges accessible to current nanoscale mechanical mapping methods. We explored the use of a non-contact 3D imaging tool Scanning Ion Conductance Microscopy (SICM) for stiffness measurements of liquid-liquid phase-separated biomolecular condensates. We focus on the Dhh1 protein, which is a regulator of cytoplasmic processing bodies (PBs) membrane-less cytoplasmic condensates that control the storage and degradation of untranslated mRNA. In our study, we investigate the properties of mCherry2- or His-mCherry2-tagged full-length Dhh1 and N- or C-terminus tail-deletion constructs, as well as the catalytically inactive mutant DQAD, under different pH and incubation times. We mapped both spatial and temporal changes in the material properties of the condensates, highlighting the capabilities of the instrument. We found that the removal of either of the two tails led to an increase in condensate stiffness upon shifting the pH from a stress-associated cellular environment (pH 6.5) to physiological conditions (pH 7.5). Additionally, the choice of protein tags led to vastly different results depending on the pH where mCherry2-Dhh1 exhibited a stiffening going from pH 6.0 to 6.5 while the double-tagged His-mCherry2 did not. Our measurements are verified and corroborated by established techniques such as optical tweezer-based fusion assays and fluorescence recovery after photobleaching (FRAP). Furthermore, we were able to track the same biomolecular condensate sample for up to 20 days getting insights on the ageing and evolution of the condensates. Overall, our study demonstrates the applicability of SICM for direct measurement of the material properties of biomolecular condensate.

biophysics↗

Deep-learning-assisted SICM for enhanced real-time imaging of nanoscale biological dynamics

Scanning Ion Conductance Microscopy (SICM) provides high-resolution, nanoscale imaging of living cells, but it is generally limited by a slow scan rate, making it challenging to capture dynamic processes in real time. To tackle this challenge, we propose an integrated data acquisition and computational framework that improves the temporal resolution of SICM by selectively skipping certain scan lines. A partial convolutional neural network (Partial-CNN) model is developed and trained on SICM images and their corresponding masks to reconstruct the complete images from the under-sampled data, ensuring the retention of structural integrity. This approach significantly reduces the image acquisition time (i.e., by 30-60%) without compromising quality, as validated through multiple quantitative metrics. Compared to conventional deep learning methods, the Partial-CNN demonstrates higher accuracy in reconstructing fine details and maintaining consistent height maps across skipped regions. We show that this method provides an increased temporal resolution and retains image fidelity, making it suitable for real-time dynamic SICM imaging and improving the smart scanning microscopy applications in time-resolved biological imaging. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=92 SRC="FIGDIR/small/663539v1_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@562d32org.highwire.dtl.DTLVardef@1b12dcaorg.highwire.dtl.DTLVardef@13535f3org.highwire.dtl.DTLVardef@6904f0_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗