Building momentum through networks: Bioimaging across the Americas.

In September 2023, the two largest bioimaging networks in the Americas, Latin America Bioimaging (LABI) and BioImaging North America (BINA), came together during a 1-week meeting in Mexico. This meeting provided opportunities for participants to interact closely with decision-makers from imaging core facilities across the Americas. The meeting was held in a hybrid format and attended in-person by imaging scientists from across the Americas, including Canada, the United States, Mexico, Colombia, Peru, Argentina, Chile, Brazil and Uruguay. The aims of the meeting were to discuss progress achieved over the past year, to foster networking and collaborative efforts among members of both communities, to bring together key members of the international imaging community to promote the exchange of experience and expertise, to engage with industry partners, and to establish future directions within each individual network, as well as common goals. This meeting report summarises the discussions exchanged, the achievements shared, and the goals set during the LABIxBINA2023: Bioimaging across the Americas meeting.

Adaptive Optics in an Oblique Plane Microscope.

Adaptive optics (AO) can restore diffraction limited performance when imaging beyond superficial cell layers in vivo and in vitro, and as such is of interest for advanced 3D microscopy methods such as light-sheet fluorescence microscopy (LSFM). In a typical LSFM system, the illumination and detection paths are separate and subject to different optical aberrations. To achieve optimal microscope performance, it is necessary to sense and correct these aberrations in both light paths, resulting in a complex microscope system. Here, we show that in an oblique plane microscope (OPM), a type of LSFM with a single primary objective lens, the same deformable mirror can correct both the illumination and fluorescence detection. Besides reducing the complexity, we show that AO in OPM also restores the relative alignment of the light-sheet and focal plane, and that a projection imaging mode can stabilize and improve the wavefront correction in a sensorless AO format. We demonstrate OPM with AO on fluorescent nanospheres and by imaging the vasculature and cancer cells in zebrafish embryos embedded in a glass capillary, restoring diffraction limited resolution and improving the signal strength twofold.

The Vps13-like protein BLTP2 is pro-survival and regulates phosphatidylethanolamine levels in the plasma membrane to maintain its fluidity and function.

Lipid transport proteins (LTPs) facilitate nonvesicular lipid exchange between cellular compartments and have critical roles in lipid homeostasis1. A new family of bridge-like LTPs (BLTPs) is thought to form lipid-transporting conduits between organelles2. One, BLTP2, is conserved across species but its function is not known. Here, we show that BLTP2 and its homolog directly regulate plasma membrane (PM) fluidity by increasing the phosphatidylethanolamine (PE) level in the PM. BLTP2 localizes to endoplasmic reticulum (ER)-PM contact sites34, 5, suggesting it transports PE from the ER to the PM. We find BLTP2 works in parallel with another pathway that regulates intracellular PE distribution and PM fluidity6, 7. BLTP2 expression correlates with breast cancer aggressiveness8-10. We found BLTP2 facilitates growth of a human cancer cell line and sustains its aggressiveness in an in vivo model of metastasis, suggesting maintenance of PM fluidity by BLTP2 may be critical for tumorigenesis in humans.

navigate: an open-source platform for smart light-sheet microscopy.

navigate is a turnkey, open-source software solution designed to enhance light-sheet fluorescence microscopy (LSFM) by integrating smart microscopy techniques into a user-friendly framework. It enables automated, intelligent imaging with a Python-based control system that supports GUI-reconfigurable acquisition routines and the integration of diverse hardware sets. As a comprehensive package, navigate democratizes access to advanced LSFM capabilities, facilitating the development and implementation of smart microscopy workflows without requiring deep programming knowledge or specialized expertise in light-sheet microscopy.

Proteolysis-free amoeboid migration of melanoma cells through crowded environments via bleb-driven worrying.

In crowded microenvironments, migrating cells must find or make a path. Amoeboid cells are thought to find a path by deforming their bodies to squeeze through tight spaces. Yet, some amoeboid cells seem to maintain a near-spherical morphology as they move. To examine how they do so, we visualized amoeboid human melanoma cells in dense environments and found that they carve tunnels via bleb-driven degradation of extracellular matrix components without the need for proteolytic degradation. Interactions between adhesions and collagen at the cell front induce a signaling cascade that promotes bleb enlargement via branched actin polymerization. Large blebs abrade collagen, creating feedback between extracellular matrix structure, cell morphology, and polarization that enables both path generation and persistent movement.

Projective light-sheet microscopy with flexible parameter selection.

Projection imaging accelerates volumetric interrogation in fluorescence microscopy, but for multi-cellular samples, the resulting images may lack contrast, as many structures and haze are summed up. Here, we demonstrate rapid projective light-sheet imaging with parameter selection (props) of imaging depth, position and viewing angle. This allows us to selectively image different sub-volumes of a sample, rapidly switch between them and exclude background fluorescence. Here we demonstrate the power of props by functional imaging within distinct regions of the zebrafish brain, monitoring calcium firing inside muscle cells of moving Drosophila larvae, super-resolution imaging of selected cell layers, and by optically unwrapping the curved surface of a Drosophila embryo. We anticipate that props will accelerate volumetric interrogation, ranging from subcellular to mesoscopic scales.

Compression-dependent microtubule reinforcement enables cells to navigate confined environments.

Cells migrating through complex three-dimensional environments experience considerable physical challenges, including tensile stress and compression. To move, cells need to resist these forces while also squeezing the large nucleus through confined spaces. This requires highly coordinated cortical contractility. Microtubules can both resist compressive forces and sequester key actomyosin regulators to ensure appropriate activation of contractile forces. Yet, how these two roles are integrated to achieve nuclear transmigration in three dimensions is largely unknown. Here, we demonstrate that compression triggers reinforcement of a dedicated microtubule structure at the rear of the nucleus by the mechanoresponsive recruitment of cytoplasmic linker-associated proteins, which dynamically strengthens and repairs the lattice. These reinforced microtubules form the mechanostat: an adaptive feedback mechanism that allows the cell to both withstand compressive force and spatiotemporally organize contractility signalling pathways. The microtubule mechanostat facilitates nuclear positioning and coordinates force production to enable the cell to pass through constrictions. Disruption of the mechanostat imbalances cortical contractility, stalling migration and ultimately resulting in catastrophic cell rupture. Our findings reveal a role for microtubules as cellular sensors that detect and respond to compressive forces, enabling movement and ensuring survival in mechanically demanding environments.

A general algorithm for consensus 3D cell segmentation from 2D segmented stacks.

Cell segmentation is the fundamental task. Only by segmenting, can we define the quantitative spatial unit for collecting measurements to draw biological conclusions. Deep learning has revolutionized 2D cell segmentation, enabling generalized solutions across cell types and imaging modalities. This has been driven by the ease of scaling up image acquisition, annotation and computation. However 3D cell segmentation, which requires dense annotation of 2D slices still poses significant challenges. Labelling every cell in every 2D slice is prohibitive. Moreover it is ambiguous, necessitating cross-referencing with other orthoviews. Lastly, there is limited ability to unambiguously record and visualize 1000's of annotated cells. Here we develop a theory and toolbox, u-Segment3D for 2D-to-3D segmentation, compatible with any 2D segmentation method. Given optimal 2D segmentations, u-Segment3D generates the optimal 3D segmentation without data training, as demonstrated on 11 real life datasets, >70,000 cells, spanning single cells, cell aggregates and tissue.