RAMONA
We built Ramona to advance human health through live imaging that delivers unrivaled precision and scale

Our goals


G-quadruplexes (G4s) are four-stranded nucleic acid structures that regulate key cellular processes and represent promising therapeutic targets in oncology. To investigate the therapeutic potential of three G4 ligands—pidnarulex, APTO-253, and BRACO-19—a high-throughput drug combination screen was conducted in thirty-one multi-cell type tumor spheroids derived from patient tumors and established cancer cell lines. These 3D spheroids mimic key features of the tumor microenvironment, comprising malignant, endothelial, and mesenchymal cell populations. Compounds selected for combination screening included agents with mechanistic relevance to G4 biology, such as inhibitors of DNA damage response (DDR), replication stress, and chromatin regulation, based on the proposed roles of G4s in replication and genome stability. Combination responses were assessed using cell viability assays and supported by longitudinal brightfield imaging to monitor spheroid morphology and growth dynamics. Drug interactions were quantified using Bliss independence scores and the volume under the viability surface, providing complementary metrics of synergy and overall response. Among the G4 ligands, pidnarulex demonstrated the broadest single-agent activity, while APTO-253 and BRACO-19 showed limited effects. Model-specific synergy was observed from combinations with inhibitors of PARP, DDR kinases (ATM, ATR, DNA-PK), and cell cycle regulators (WEE1, PIM1). Interestingly, pidnarulex exhibited consistent synergy in one of eight pancreatic adenocarcinoma models (966289-007-R4-J1) across multiple DDR-targeted combinations. Combination interactions were also observed with HDAC inhibitors in a subset of models. Brightfield imaging corroborated enhanced spheroid growth suppression from synergistic combinations. These findings underscore the context-dependent activity of G4 ligands and support the use of integrated functional and imaging-based approaches to characterize potential therapeutic combinations in physiologically relevant 3D cancer models.
Using machine learning, we developed models that rigorously detect and classify larval zebrafish spontaneous and stimulus-evoked behaviors in various well plate formats. Zebrafish are an ideal model system for investigating the neural substrates underlying behavior due to their simple nervous system and well-documented responses to environmental stimuli. To track movement, we utilized an 8 key point pose estimation model, allowing precise capture of zebrafish kinematics. Using this kinematic data, we trained two random forest classifiers in a semi-supervised learning framework to classify various discreet behavioral outputs including stationary, scoot, turn, acoustic-startle like behavior, and visual-startle like behavior. The classifiers were trained on a manually labeled dataset, and their accuracy was validated showing high precision. To validate our machine learning models, we analyzed behavioral outputs during various stimulus evoked responses and during spontaneous behavior. For additional validation, and to show the utility of our recording and analysis pipeline, we investigated the locomotor effects of several established drugs with well-defined impacts on neurophysiology. Here we show that machine learning model development, enabled by semi-supervised learning developed classification models, provide detailed insights into the behavioral phenotypes of zebrafish, offering a powerful, high throughput method for studying neural control of behavior.
Large-area microscopy with submicron resolution is limited by tradeoffs between field of view (FOV), resolution, and imaging speed. Samples are rarely flat across centimeter-scale FOV, which often requires existing solutions to use mechanical scanning to ensure focused capture at reduced throughput. Here, we present PANORAMA, a single-shot, re-imaging microscope that achieves seamless, gigapixel imaging over a 16.3 18.8 mm FOV at 0.84 µm half-pitch resolution without mechanical scanning. By using a telecentric photolithography lens, a large-aperture tube lens, and a flat micro-camera array with adaptive per-camera focus control, PANORAMA maintains submicron focus across flat, curved, or uneven samples that span centimeters. This approach improves imaging throughput and adaptability, enabling gigapixel multi-modal microscopy of large flat and non-flat samples in one shot, thus broadening its applications in biomedical and materials imaging.
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