Bridging live-cell imaging and next-generation cancer treatment

A plasmonic metasurface reveals differential motility of breast cancer cell lines at initial phase of adhesion

A plasmonic metasurface composed of a self-assembled monolayer of gold nanoparticles allows for fluorescence imaging with high spatial resolution, owing to the collective excitation of localized surface plasmon resonance. Taking advantage of fluorescence imaging confined to the nano-interface, we examined actin organization in breast cancer cell lines with different metastatic potentials during cell adhesion. Live-cell fluorescence imaging confined within tens of nanometers from the substrate shows a high actin density spanning < 1 μm from the cell edge. Live-cell imaging revealed that the breast cancer cell lines exhibited different actin patterns during the initial phase of cell adhesion (∼ 1 h). Non-tumorous MCF10A cells exhibited symmetric actin localization at the cell edge, whereas highly metastatic MDA-MB-231 cells showed asymmetric actin localization, demonstrating rapid polarization of MDA-MB-231 cells upon adhesion. The rapid actin organization observed by our plasmonic metasurface-based fluorescence imaging provides information on how quickly cancer cells sense the underlying substrate.

Artificial intelligence for high content imaging in drug discovery

Artificial intelligence (AI) and high-content imaging (HCI) are contributing to advancements in drug discovery, propelled by the recent progress in deep neural networks. This review highlights AI's role in analysis of HCI data from fixed and live-cell imaging, enabling novel label-free and multi-channel fluorescent screening methods, and improving compound profiling. HCI experiments are rapid and cost-effective, facilitating large data set accumulation for AI model training. However, the success of AI in drug discovery also depends on high-quality data, reproducible experiments, and robust validation to ensure model performance. Despite challenges like the need for annotated compounds and managing vast image data, AI's potential in phenotypic screening and drug profiling is significant. Future improvements in AI, including increased interpretability and integration of multiple modalities, are expected to solidify AI and HCI's role in drug discovery.

A Foundation Model for Cell Segmentation

Cells are a fundamental unit of biological organization, and identifying them in imaging data - cell segmentation - is a critical task for various cellular imaging experiments. While deep learning methods have led to substantial progress on this problem, most models in use are specialist models that work well for specific domains. Methods that have learned the general notion of "what is a cell" and can identify them across different domains of cellular imaging data have proven elusive. In this work, we present CellSAM, a foundation model for cell segmentation that generalizes across diverse cellular imaging data. CellSAM builds on top of the Segment Anything Model (SAM) by developing a prompt engineering approach for mask generation. We train an object detector, CellFinder, to automatically detect cells and prompt SAM to generate segmentations. We show that this approach allows a single model to achieve human-level performance for segmenting images of mammalian cells (in tissues and cell culture), yeast, and bacteria collected across various imaging modalities. We show that CellSAM has strong zero-shot performance and can be improved with a few examples via few-shot learning. We also show that CellSAM can unify bioimaging analysis workflows such as spatial transcriptomics and cell tracking. A deployed version of CellSAM is available at https://cellsam.deepcell.org/.

Tumor-immune metaphenotypes orchestrate an evolutionary bottleneck that promotes metabolic transformation

Introduction

Metabolism plays a complex role in the evolution of cancerous tumors, including inducing a multifaceted effect on the immune system to aid immune escape. Immune escape is, by definition, a collective phenomenon by requiring the presence of two cell types interacting in close proximity: tumor and immune. The microenvironmental context of these interactions is influenced by the dynamic process of blood vessel growth and remodelling, creating heterogeneous patches of well-vascularized tumor or acidic niches.

Methods

Here, we present a multiscale mathematical model that captures the phenotypic, vascular, microenvironmental, and spatial heterogeneity which shapes acid-mediated invasion and immune escape over a biologically-realistic time scale. The model explores several immune escape mechanisms such as i) acid inactivation of immune cells, ii) competition for glucose, and iii) inhibitory immune checkpoint receptor expression (PD-L1). We also explore the efficacy of anti-PD-L1 and sodium bicarbonate buffer agents for treatment. To aid in understanding immune escape as a collective cellular phenomenon, we define immune escape in the context of six collective phenotypes (termed "meta-phenotypes"): Self-Acidify, Mooch Acid, PD-L1 Attack, Mooch PD-L1, Proliferate Fast, and Starve Glucose.

Results

Fomenting a stronger immune response leads to initial benefits (additional cytotoxicity), but this advantage is offset by increased cell turnover that leads to accelerated evolution and the emergence of aggressive phenotypes. This creates a bimodal therapy landscape: either the immune system should be maximized for complete cure, or kept in check to avoid rapid evolution of invasive cells. These constraints are dependent on heterogeneity in vascular context, microenvironmental acidification, and the strength of immune response.

Discussion

This model helps to untangle the key constraints on evolutionary costs and benefits of three key phenotypic axes on tumor invasion and treatment: acid-resistance, glycolysis, and PD-L1 expression. The benefits of concomitant anti-PD-L1 and buffer treatments is a promising treatment strategy to limit the adverse effects of immune escape.

Extracellular vesicles and microvilli in the immune synapse

T cell receptor (TCR) binding to cognate antigen on the plasma membrane of an antigen-presenting cell (APC) triggers the immune synapse (IS) formation. The IS constitutes a dedicated contact region between different cells that comprises a signaling platform where several cues evoked by TCR and accessory molecules are integrated, ultimately leading to an effective TCR signal transmission that guarantees intercellular message communication. This eventually leads to T lymphocyte activation and the efficient execution of different T lymphocyte effector tasks, including cytotoxicity and subsequent target cell death. Recent evidence demonstrates that the transmission of information between immune cells forming synapses is produced, to a significant extent, by the generation and secretion of distinct extracellular vesicles (EV) from both the effector T lymphocyte and the APC. These EV carry biologically active molecules that transfer cues among immune cells leading to a broad range of biological responses in the recipient cells. Included among these bioactive molecules are regulatory miRNAs, pro-apoptotic molecules implicated in target cell apoptosis, or molecules triggering cell activation. In this study we deal with the different EV classes detected at the IS, placing emphasis on the most recent findings on microvilli/lamellipodium-produced EV. The signals leading to polarized secretion of EV at the synaptic cleft will be discussed, showing that the IS architecture fulfills a fundamental task during this route.