SHANK3 depletion leads to ERK signalling overdose and cell death in KRAS-mutant cancers.

The KRAS oncogene drives many common and highly fatal malignancies. These include pancreatic, lung, and colorectal cancer, where various activating KRAS mutations have made the development of KRAS inhibitors difficult. Here we identify the scaffold protein SH3 and multiple ankyrin repeat domain 3 (SHANK3) as a RAS interactor that binds active KRAS, including mutant forms, competes with RAF and limits oncogenic KRAS downstream signalling, maintaining mitogen-activated protein kinase/extracellular signal-regulated kinase (MAPK/ERK) activity at an optimal level. SHANK3 depletion breaches this threshold, triggering MAPK/ERK signalling hyperactivation and MAPK/ERK-dependent cell death in KRAS-mutant cancers. Targeting this vulnerability through RNA interference or nanobody-mediated disruption of the SHANK3-KRAS interaction constrains tumour growth in vivo in female mice. Thus, inhibition of SHANK3-KRAS interaction represents an alternative strategy for selective killing of KRAS-mutant cancer cells through excessive signalling.

CellTracksColab is a platform that enables compilation, analysis, and exploration of cell tracking data.

In life sciences, tracking objects from movies enables researchers to quantify the behavior of single particles, organelles, bacteria, cells, and even whole animals. While numerous tools now allow automated tracking from video, a significant challenge persists in compiling, analyzing, and exploring the large datasets generated by these approaches. Here, we introduce CellTracksColab, a platform tailored to simplify the exploration and analysis of cell tracking data. CellTracksColab facilitates the compiling and analysis of results across multiple fields of view, conditions, and repeats, ensuring a holistic dataset overview. CellTracksColab also harnesses the power of high-dimensional data reduction and clustering, enabling researchers to identify distinct behavioral patterns and trends without bias. Finally, CellTracksColab also includes specialized analysis modules enabling spatial analyses (clustering, proximity to specific regions of interest). We demonstrate CellTracksColab capabilities with 3 use cases, including T cells and cancer cell migration, as well as filopodia dynamics. CellTracksColab is available for the broader scientific community at https://github.com/CellMigrationLab/CellTracksColab.

TLNRD1 is a CCM complex component and regulates endothelial barrier integrity.

We previously identified talin rod domain-containing protein 1 (TLNRD1) as a potent actin-bundling protein in vitro. Here, we report that TLNRD1 is expressed in the vasculature in vivo. Its depletion leads to vascular abnormalities in vivo and modulation of endothelial cell monolayer integrity in vitro. We demonstrate that TLNRD1 is a component of the cerebral cavernous malformations (CCM) complex through its direct interaction with CCM2, which is mediated by a hydrophobic C-terminal helix in CCM2 that attaches to a hydrophobic groove on the four-helix domain of TLNRD1. Disruption of this binding interface leads to CCM2 and TLNRD1 accumulation in the nucleus and actin fibers. Our findings indicate that CCM2 controls TLNRD1 localization to the cytoplasm and inhibits its actin-bundling activity and that the CCM2-TLNRD1 interaction impacts endothelial actin stress fiber and focal adhesion formation. Based on these results, we propose a new pathway by which the CCM complex modulates the actin cytoskeleton and vascular integrity.

Harnessing artificial intelligence to reduce phototoxicity in live imaging.

Fluorescence microscopy is essential for studying living cells, tissues and organisms. However, the fluorescent light that switches on fluorescent molecules also harms the samples, jeopardizing the validity of results - particularly in techniques such as super-resolution microscopy, which demands extended illumination. Artificial intelligence (AI)-enabled software capable of denoising, image restoration, temporal interpolation or cross-modal style transfer has great potential to rescue live imaging data and limit photodamage. Yet we believe the focus should be on maintaining light-induced damage at levels that preserve natural cell behaviour. In this Opinion piece, we argue that a shift in role for AIs is needed - AI should be used to extract rich insights from gentle imaging rather than recover compromised data from harsh illumination. Although AI can enhance imaging, our ultimate goal should be to uncover biological truths, not just retrieve data. It is essential to prioritize minimizing photodamage over merely pushing technical limits. Our approach is aimed towards gentle acquisition and observation of undisturbed living systems, aligning with the essence of live-cell fluorescence microscopy.

High-fidelity 3D live-cell nanoscopy through data-driven enhanced super-resolution radial fluctuation.

Live-cell super-resolution microscopy enables the imaging of biological structure dynamics below the diffraction limit. Here we present enhanced super-resolution radial fluctuations (eSRRF), substantially improving image fidelity and resolution compared to the original SRRF method. eSRRF incorporates automated parameter optimization based on the data itself, giving insight into the trade-off between resolution and fidelity. We demonstrate eSRRF across a range of imaging modalities and biological systems. Notably, we extend eSRRF to three dimensions by combining it with multifocus microscopy. This realizes live-cell volumetric super-resolution imaging with an acquisition speed of ~1 volume per second. eSRRF provides an accessible super-resolution approach, maximizing information extraction across varied experimental conditions while minimizing artifacts. Its optimal parameter prediction strategy is generalizable, moving toward unbiased and optimized analyses in super-resolution microscopy.

Live-cell imaging in the deep learning era.

Live imaging is a powerful tool, enabling scientists to observe living organisms in real time. In particular, when combined with fluorescence microscopy, live imaging allows the monitoring of cellular components with high sensitivity and specificity. Yet, due to critical challenges (i.e., drift, phototoxicity, dataset size), implementing live imaging and analyzing the resulting datasets is rarely straightforward. Over the past years, the development of bioimage analysis tools, including deep learning, is changing how we perform live imaging. Here we briefly cover important computational methods aiding live imaging and carrying out key tasks such as drift correction, denoising, super-resolution imaging, artificial labeling, tracking, and time series analysis. We also cover recent advances in self-driving microscopy.

Myosin-X recruits lamellipodin to filopodia tips.

Myosin-X (MYO10), a molecular motor localizing to filopodia, is thought to transport various cargo to filopodia tips, modulating filopodia function. However, only a few MYO10 cargoes have been described. Here, using GFP-Trap and BioID approaches combined with mass spectrometry, we identified lamellipodin (RAPH1) as a novel MYO10 cargo. We report that the FERM domain of MYO10 is required for RAPH1 localization and accumulation at filopodia tips. Previous studies have mapped the RAPH1 interaction domain for adhesome components to its talin-binding and Ras-association domains. Surprisingly, we find that the RAPH1 MYO10-binding site is not within these domains. Instead, it comprises a conserved helix located just after the RAPH1 pleckstrin homology domain with previously unknown functions. Functionally, RAPH1 supports MYO10 filopodia formation and stability but is not required to activate integrins at filopodia tips. Taken together, our data indicate a feed-forward mechanism whereby MYO10 filopodia are positively regulated by MYO10-mediated transport of RAPH1 to the filopodium tip.

Fast4DReg - fast registration of 4D microscopy datasets.

Unwanted sample drift is a common issue that plagues microscopy experiments, preventing accurate temporal visualization and quantification of biological processes. Although multiple methods and tools exist to correct images post acquisition, performing drift correction of three-dimensional (3D) videos using open-source solutions remains challenging and time consuming. Here, we present a new tool developed for ImageJ or Fiji called Fast4DReg that can quickly correct axial and lateral drift in 3D video-microscopy datasets. Fast4DReg works by creating intensity projections along multiple axes and estimating the drift between frames using two-dimensional cross-correlations. Using synthetic and acquired datasets, we demonstrate that Fast4DReg can perform better than other state-of-the-art open-source drift-correction tools and significantly outperforms them in speed. We also demonstrate that Fast4DReg can be used to register misaligned channels in 3D using either calibration slides or misaligned images directly. Altogether, Fast4DReg provides a quick and easy-to-use method to correct 3D imaging data before further visualization and analysis.

Mapping the Localization of Proteins Within Filopodia Using FiloMap.

Filopodia are fingerlike membrane protrusions extended by cells to sense their environment. Filopodia are widely used by migrating cells in vivo and directly contribute to several physiological processes and diseases. Due to the essential roles of filopodia in sensing the extracellular environment, there is a need to characterize their composition and ultrastructure further. This chapter highlights FiloMap, an image analysis pipeline that utilizes Fiji and R to map the localization of proteins within filopodia from microscopy images. I provide step-by-step protocols on (a) setting up FiloMap in Fiji and R, (b) extracting line intensity profiles from filopodia stainings in Fiji, (c) further analyzing line intensity profiles in R, and (d) creating filopodia maps to compare the localization of multiple proteins within filopodia. Notably, while FiloMap was written to analyze filopodia, the analysis pipeline described here can also analyze and compile any line intensity profiles.

MYO10-filopodia support basement membranes at pre-invasive tumor boundaries.

Ductal carcinoma in situ (DCIS) is a pre-invasive stage of breast cancer. During invasion, the encapsulating DCIS basement membrane (BM) is compromised, and tumor cells invade the surrounding stroma. The mechanisms that regulate functional epithelial BMs in vivo are poorly understood. Myosin-X (MYO10) is a filopodia-inducing protein associated with metastasis and poor clinical outcome in invasive breast cancer (IBC). We identify elevated MYO10 expression in human DCIS and IBC, and this suggests links with disease progression. MYO10 promotes filopodia formation and cell invasion in vitro and cancer-cell dissemination from progressively invasive human DCIS xenografts. However, MYO10-depleted xenografts are more invasive. These lesions exhibit compromised BMs, poorly defined borders, and increased cancer-cell dispersal and EMT-marker-positive cells. In addition, cancer spheroids are dependent on MYO10-filopodia to generate a near-continuous extracellular matrix boundary. Thus, MYO10 is protective in early-stage breast cancer, correlating with tumor-limiting BMs, and pro-invasive at later stages, facilitating cancer-cell dissemination.

DeepBacs for multi-task bacterial image analysis using open-source deep learning approaches.

This work demonstrates and guides how to use a range of state-of-the-art artificial neural-networks to analyse bacterial microscopy images using the recently developed ZeroCostDL4Mic platform. We generated a database of image datasets used to train networks for various image analysis tasks and present strategies for data acquisition and curation, as well as model training. We showcase different deep learning (DL) approaches for segmenting bright field and fluorescence images of different bacterial species, use object detection to classify different growth stages in time-lapse imaging data, and carry out DL-assisted phenotypic profiling of antibiotic-treated cells. To also demonstrate the ability of DL to enhance low-phototoxicity live-cell microscopy, we showcase how image denoising can allow researchers to attain high-fidelity data in faster and longer imaging. Finally, artificial labelling of cell membranes and predictions of super-resolution images allow for accurate mapping of cell shape and intracellular targets. Our purposefully-built database of training and testing data aids in novice users' training, enabling them to quickly explore how to analyse their data through DL. We hope this lays a fertile ground for the efficient application of DL in microbiology and fosters the creation of tools for bacterial cell biology and antibiotic research.

TrackMate 7: integrating state-of-the-art segmentation algorithms into tracking pipelines.

TrackMate is an automated tracking software used to analyze bioimages and is distributed as a Fiji plugin. Here, we introduce a new version of TrackMate. TrackMate 7 is built to address the broad spectrum of modern challenges researchers face by integrating state-of-the-art segmentation algorithms into tracking pipelines. We illustrate qualitatively and quantitatively that these new capabilities function effectively across a wide range of bio-imaging experiments.

Cargo-specific recruitment in clathrin- and dynamin-independent endocytosis.

Spatially controlled, cargo-specific endocytosis is essential for development, tissue homeostasis and cancer invasion. Unlike cargo-specific clathrin-mediated endocytosis, the clathrin- and dynamin-independent endocytic pathway (CLIC-GEEC, CG pathway) is considered a bulk internalization route for the fluid phase, glycosylated membrane proteins and lipids. While the core molecular players of CG-endocytosis have been recently defined, evidence of cargo-specific adaptors or selective uptake of proteins for the pathway are lacking. Here we identify the actin-binding protein Swiprosin-1 (Swip1, EFHD2) as a cargo-specific adaptor for CG-endocytosis. Swip1 couples active Rab21-associated integrins with key components of the CG-endocytic machinery-Arf1, IRSp53 and actin-and is critical for integrin endocytosis. Through this function, Swip1 supports integrin-dependent cancer-cell migration and invasion, and is a negative prognostic marker in breast cancer. Our results demonstrate a previously unknown cargo selectivity for the CG pathway and a role for specific adaptors in recruitment into this endocytic route.

SHANK3 conformation regulates direct actin binding and crosstalk with Rap1 signaling.

Actin-rich cellular protrusions direct versatile biological processes from cancer cell invasion to dendritic spine development. The stability, morphology, and specific biological functions of these protrusions are regulated by crosstalk between three main signaling axes: integrins, actin regulators, and small guanosine triphosphatases (GTPases). SHANK3 is a multifunctional scaffold protein, interacting with several actin-binding proteins and a well-established autism risk gene. Recently, SHANK3 was demonstrated to sequester integrin-activating small GTPases Rap1 and R-Ras to inhibit integrin activity via its Shank/ProSAP N-terminal (SPN) domain. Here, we demonstrate that, in addition to scaffolding actin regulators and actin-binding proteins, SHANK3 interacts directly with actin through its SPN domain. Molecular simulations and targeted mutagenesis of the SPN-ankyrin repeat region (ARR) interface reveal that actin binding is inhibited by an intramolecular closed conformation of SHANK3, where the adjacent ARR domain covers the actin-binding interface of the SPN domain. Actin and Rap1 compete with each other for binding to SHANK3, and mutation of SHANK3, resulting in reduced actin binding, augments inhibition of Rap1-mediated integrin activity. This dynamic crosstalk has functional implications for cell morphology and integrin activity in cancer cells. In addition, SHANK3-actin interaction regulates dendritic spine morphology in neurons and autism-linked phenotypes in vivo.

Improving the knock-in efficiency of the MOF-encapsulated CRISPR/Cas9 system through controllable embedding structures.

Appropriate tuning of robust artificial coatings can not only enhance intracellular delivery but also preserve the biological functions of genetic molecules in gene based therapies. Here, we report a strategy to synthesize controllable nanostructures in situ by encapsulating CRISPR/Cas9 plasmids into metal-organic frameworks (MOFs) via biomimetic mineralization. The structure-functionality relationship studies indicate that MOF-coated nanostructures dramatically impact the biological features of the contained plasmids through different embedding structures. The plasmids are homogeneously distributed within the heterogeneous nanoarchitecture and protected from enzymatic degradation. In addition, the plasmid-MOF structure exhibits excellent loading capability, pH-responsive release, and affinity for plasmid binding. Through in vitro assays it was found that the superior MOF vector can greatly enhance cellular endocytosis and endo/lysosomal escape of sheltered plasmids, resulting in successful knock-in of GFP-tagged paxillin genomic sequences in cancer cell lines with high transfection potency compared to our previous studies. Thus, the development of new cost-effective approaches for MOF-based intracellular delivery systems offers an attractive option for overcoming the physiological barriers to CRISPR/Cas9 delivery, which shows great potential for investigating paxillin-associated focal adhesions and signal regulation.

Imaging in focus: An introduction to denoising bioimages in the era of deep learning.

Fluorescence microscopy enables the direct observation of previously hidden dynamic processes of life, allowing profound insights into mechanisms of health and disease. However, imaging of live samples is fundamentally limited by the toxicity of the illuminating light and images are often acquired using low light conditions. As a consequence, images can become very noisy which severely complicates their interpretation. In recent years, deep learning (DL) has emerged as a very successful approach to remove this noise while retaining the useful signal. Unlike classical algorithms which use well-defined mathematical functions to remove noise, DL methods learn to denoise from example data, providing a powerful content-aware approach. In this review, we first describe the different types of noise that typically corrupt fluorescence microscopy images and introduce the denoising task. We then present the main DL-based denoising methods and their relative advantages and disadvantages. We aim to provide insights into how DL-based denoising methods operate and help users choose the most appropriate tools for their applications.