Trade-offs between structural integrity and acquisition time in stochastic super-resolution microscopy techniques

Analysis of Phase Heterogeneity in Lipid Membranes Using Single-Molecule Tracking in Live Cells

In live cells, the plasma membrane is composed of lipid domains separated by hundreds of nanometers in dynamic equilibrium. Lipid phase separation regulates the trafficking and spatiotemporal organization of membrane molecules that promote signal transduction. However, visualizing domains with adequate spatiotemporal accuracy remains challenging because of their subdiffraction limit size and highly dynamic properties. Here, we present a single lipid-molecular motion analysis pipeline (lipid-MAP) for analyzing the phase heterogeneity of lipid membranes by detecting the instantaneous velocity change of a single lipid molecule using the excellent optical properties of nanoparticles, high spatial localization accuracy of single-molecule localization microscopy, and separation capability of the diffusion state of the hidden Markov model algorithm. Using lipid-MAP, individual lipid molecules were found to be in dynamic equilibrium between two statistically distinguishable phases, leading to the formation of small (∼170 nm), viscous (2.5× more viscous than surrounding areas), and transient domains in live cells. Moreover, our findings provide an understanding of how membrane compositional changes, i.e., cholesterol and phospholipids, affect domain formation. This imaging method can contribute to an improved understanding of spatiotemporal-controlled membrane dynamics at the molecular level.

A guide into the world of high-resolution 3D imaging: the case of soft X-ray tomography for the life sciences

In the world of bioimaging, every choice made determines the quality and content of the data collected. The choice of imaging techniques for a study could showcase or dampen expected outcomes. Synchrotron radiation is indispensable for biomedical research, driven by the need to see into biological materials and capture intricate biochemical and biophysical details at controlled environments. The same need drives correlative approaches that enable the capture of heterologous but complementary information when studying any one single target subject. Recently, the applicability of one such synchrotron technique in bioimaging, soft X-ray tomography (SXT), facilitates exploratory and basic research and is actively progressing towards filling medical and industrial needs for the rapid screening of biomaterials, reagents and processes of immediate medical significance. Soft X-ray tomography at cryogenic temperatures (cryoSXT) fills the imaging resolution gap between fluorescence microscopy (in the hundreds of nanometers but relatively accessible) and electron microscopy (few nanometers but requires extensive effort and can be difficult to access). CryoSXT currently is accessible, fully documented, can deliver 3D imaging to 25 nm resolution in a high throughput fashion, does not require laborious sample preparation procedures and can be correlated with other imaging techniques. Here, we present the current state of SXT and outline its place within the bioimaging world alongside a guided matrix that aids decision making with regards to the applicability of any given imaging technique to a particular project. Case studies where cryoSXT has facilitated a better understanding of biological processes are highlighted and future directions are discussed.

Autonomous illumination control for localization microscopy

Super-resolution fluorescence microscopy improves spatial resolution, but this comes at a loss of image throughput and presents unique challenges in identifying optimal acquisition parameters. Microscope automation routines can offset these drawbacks, but thus far have required user inputs that presume a priori knowledge about the sample. Here, we develop a flexible illumination control system for localization microscopy comprised of two interacting components that require no sample-specific inputs: a self-tuning controller and a deep learning-based molecule density estimator that is accurate over an extended range of densities. This system obviates the need to fine-tune parameters and enables robust, autonomous illumination control for localization microscopy.