Reto Fiolka

Somatic oncogenic mutations are typically defined by their molecular alterations, yet how they reorganize cellular architecture within intact tissues remain largely unknown. Here, we demonstrate that distinct oncogenic drivers produce unique multiscale architectural phenotypes that can be quantitatively resolved in intact liver tissue. Using iterative expansion microscopy, multiscale light-sheet imaging, and three-dimensional morphometric analysis, we systematically mapped structural remodeling from single-cell morphology to mitochondrial architecture in mosaic mouse models of hepatocellular oncogene activation. NRAS and CTNNB1 induced fundamentally different morphological programs. NRAS activation drove extensive remodeling of cell morphology, membrane curvature, and surface irregularity, whereas CTNNB1 activation largely preserved global cell morphology while selectively altering mitochondrial organization and shape. We first established a zonation-aware reference state for interpreting oncogene-associated organelle remodeling by resolving mitochondrial differences between periportal and pericentral hepatocytes. Within this framework, CTNNB1 activation shifted mitochondrial features toward a pericentral-like state, consistent with the role of Wnt/β-catenin signaling in hepatic zonation and metabolic identity. Furthermore, integrating cellular morphology, membrane geometry, and mitochondrial architecture improved discrimination of oncogenic states beyond any individual structural feature, demonstrating that mutation-specific phenotypes arise through coordinated remodeling across multiple biological scales. Together, these findings establish multiscale structural phenotyping as a framework for linking oncogenic genotype to three-dimensional cellular organization and reveal that distinct oncogenic drivers remodel different architectural compartments during liver oncogene activation.

For over three decades, we have known that oncogenic RAS alters the actin cytoskeleton organization and cell surface morphology. RAS activates the GTPase RAC1, which triggers the growth of branched actin networks to promote cell membrane protrusions. In melanoma, the hyperactive RAC1 mutant, Rac1P29S, was recently shown to drive extended lamellipodia, which then empower cell proliferation through sequestration and localized inhibition of the merlin tumor suppressor. This discovery illustrates cell morphological programs not only as outputs but also as regulators of human oncogenic signals. Hence, we wondered whether the pronounced branched actin-driven membrane protrusions (BAMPs) downstream of oncogenic RAS are not mere outputs of RAS signaling but rather an active component in mediating the oncogenic penetrance of RAS mutants. We used volumetric light sheet microscopy and biochemical approaches to investigate the role of BAMPs in regulating the molecular signaling of oncogenic KRAS in pancreatic and lung cancer models. We found that elevated BAMP formation regulated the interaction of oncogenic KRAS with downstream effectors, specifically with the RAC1 GEF TIAM1. This implies that BAMPs amplify their own upstream regulators in a positive feedback. This meritorious cycle upregulates cyclin D1 expression by inactivating the merlin tumor suppressor, independently of the mitogen activated protein kinase pathway (MAPK). In the absence of BAMPs, cells carrying oncogenic KRAS mutations are unable to attain their full penetrance in proliferation. Overall, this work unveils the long-overlooked role of branched actin-driven cell morphology in the functionalization of KRAS mutants as potent oncogenes.

Altair-dvOPM is an open-access direct-view oblique plane microscope designed for large-field, three-dimensional imaging of cleared and expanded tissue sections. By combining photographic-lens-based detection, externally launched oblique illumination and precision-registered modular baseplates, the system achieves micrometer-scale lateral resolution over a ~5.4 mm field of view without custom objectives or highly specialized alignment procedures. We demonstrate imaging across scales, from subcellular structures in expanded cells to centimeter-scale expanded tissue sections, and provide documentation, CAD files, Zemax models and open-source control software to support replication and extension.

Mammals achieve the highest urine concentrations of any vertebrate, a feat that hinges on generating steep osmotic gradients within the renal medulla. Interestingly, the region with the highest osmolality, the inner medulla, is unique to mammals. Among the nephron’s segments, the ascending thin limb (aTL) is the sole element exclusive to this zone and is thought to mediate passive salt reabsorption. However, the architecture and functional impact of the aTL have remained obscure. Here we uncover an unexpected morphogenetic program in the aTL, characterized by extensive apical-junctional interdigitations that greatly increase cell-to-cell contact area. Integrating single-nucleus transcriptomics with high-resolution imaging, we identify claudin-10b, a tight junction protein and paracellular cation pore, as a central driver of this architecture. Inducible deletion of claudin-10b specifically in the aTL abolishes membrane interdigitations and markedly reduces urine-concentrating ability, thereby establishing a direct link between segment-specific epithelial morphology and whole-organ function. Claudin-10b proves necessary for interdigitation formation, acting through transcellular adhesion and interaction with the tight-junction scaffold ZO1. These findings offer definitive evidence that the inner medulla and aTL are essential for maximal urinary concentration, while revealing a non-canonical, morphogenetic role for claudin-10b.

Adaptive optics aims to restore diffraction limited resolution in an imaging system in the presence of optical aberrations. To this end, the wavefront error needs to be measured prior to its compensation. Traditional wavefront sensors typically measure the local gradient of the wavefront emitted from a guide star (or another known point source) using a microlens array in front of a camera. They are typically dedicated devices that need to be integrated into an imaging system. Here we have tested the concept of estimating the wavefront error directly from an image of a guide star by training a machine learning model. We produce a two-photon laser spot in a water fluorescein media, and introduce random, but known aberrations using a deformable mirror to form a large training set. After validation, we integrated the machine learning wavefront sensor in an AO feedback loop with the deformable mirror. We demonstrate proof of principle compensation of sample-introduced optical aberrations with this system.

Light-sheet fluorescence microscopy (LSFM) has found numerous applications in the life sciences through its gentle, rapid and efficient volumetric imaging capabilities. It would be advantageous if its rapid 3D imaging capabilities could be extended to non-fluorescent contrast mechanisms such as light-scattering. Yet, the coherent illumination with a light-sheet can cause cross-interference from multiple scatterers. Consequently, this can lead to an object-variant point-spread function, which complicates image analysis. Here we show that by combining axially swept illumination with sparse, but strong scatterers can lead to 3D imaging of comparable quality to traditional fluorescent light-sheet microscopy. We leverage axial sweeping of a light-sheet to both record fluorescence and scattered signals in expanded breast cancer spheroids seeded with gold nanoparticles.

Despite advances in fluorescence microscopy, spectral overlap and limited resolution hinder the dense mapping of the cellular ultrastructure. To overcome these challenges, we developed Cy-ExM, a high-plex imaging strategy that integrates optimized cryo-fixation for antigen preservation, expansion microscopy, and iterative immunofluorescence labeling. Using oblique plane microscopy, we perform three-dimensional super-resolution imaging of 20 biological targets encompassing the full cellular volume of individual mammalian cells.

Cell segmentation is the foundation of a wide range of microscopy-based biological studies. Deep learning has revolutionized two-dimensional (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, three-dimensional (3D) cell segmentation, requiring dense annotation of 2D slices, still poses substantial challenges. Manual labeling of 3D cells to train broadly applicable segmentation models is prohibitive. Even in high-contrast images annotation is ambiguous and time-consuming. Here we develop a theory and toolbox, u-Segment3D, for 2D-to-3D segmentation, compatible with any 2D method generating pixel-based instance cell masks. u-Segment3D translates and enhances 2D instance segmentations to a 3D consensus instance segmentation without training data, as demonstrated on 11 real-life datasets, comprising >70,000 cells, spanning single cells, cell aggregates and tissue. Moreover, u-Segment3D is competitive with native 3D segmentation, even exceeding when cells are crowded and have complex morphologies.

Super-resolution microscopy (SRM) has undeniable potential for scientific discovery, yet still presents many challenges that hinder its widespread adoption, including technical trade-offs between resolution, speed and photodamage, as well as limitations in imaging live samples and larger, more complex biological structures. Furthermore, SRM often requires specialized expertise and complex instrumentation, which can deter biologists from fully embracing the technology. In this Perspective, a follow-up to our recent Q&A article, we aim to demystify these challenges by addressing common questions and misconceptions surrounding SRM. Experts offer practical insights into how biologists can maximize the benefits of SRM while navigating issues such as photobleaching, image artifacts and the limitations of existing techniques. We also highlight recent developments in SRM that continue to push the boundaries of resolution. Our goal is to equip researchers with the crucial knowledge they need to harness the full potential of SRM.