Research publications
Here, we present reflective Fourier light field computed tomography (ReFLeCT), a high-speed volumetric fluorescence computational imaging technique. ReFLeCT synchronously captures entire tomograms of multiple unrestrained, unanesthetized model organisms across multi-millimeter 3D FOVs at 120 volumes per second. In particular, we applied ReFLeCT to reconstruct 4D videos of fluorescently labeled zebrafish and Drosophila larvae, enabling us to study their heartbeat, fin and tail motion, gaze, jaw motion, and muscle contractions with nearly isotropic 3D resolution while they are freely moving.
Patient-derived organoids (PDOs) are a valuable tool for investigations of intra-tumor and inter-site heterogeneity and patient-specific drug responsiveness. In this work, we employ the MCAM (Multi-Camera Array Microscope) Vireo™ system to rapidly acquire brightfield images of PDOs derived from gastrointestinal surgical resection samples in under 2 minutes per 24 well plate and a new machine learning model to automatically analyze this data.
We present a multi-camera array for capturing dynamic high-resolution videos of the human face. Compared to traditional single-camera configurations, our array of 54 individual cameras allows stitching of high-resolution composite video frames (709 megapixels total). In our novel multi-focus strategy, each camera in the array focuses on a unique object plane to resolve non-planar surfaces at a higher resolution than a standard single-lens camera design. Compared to a single-focus configuration, this is almost a 10-fold increase in effective DOF. We demonstrate how our multi-focus camera array can capture dynamic facial expressions at microscopic resolution with relevance in several biomedical applications.
We applied the Ramona Vireo to rapidly (< 1 min.) capture and process 3D image data of up to 96 cortical organoids grown within multi-well plates. This study aimed to longitudinally measure and analyze the growth of PGP1 line iPSC-derived cortical organoids at high throughput to evaluate the impact of morphogens (valproic acid and lithium) on cortical organoid growth. Results revealed a dose-dependentdecrease in organoid area with increasing concentrations of valproic acid, whereas lithium induced a moderate yet consistent increase. These findings underscore the power of high-throughput imaging andsegmentation for quantifying how drugs and dosages influence cortical organoid growth in vitro.
Here, we describe a cell viability assay and workflow using the Vireo by Ramona that offers a 10-fold decrease in processing time compared to traditional imaging-based viability assessments. It also offers a more accurate workflow compared to standard CellTiter-Glo (CTG) assays. This technology can revolutionize the field of drug discovery and toxicology by drastically decreasing the time between image acquisition and data analysis and allowing for high-throughput screening capabilities.
We demonstrate how the Ramona Kestrel can be used to capture information about zebrafish (D. rerio) larvae at various spatial and temporal scales from 1 to 5 days post fertilization. At these stages, the zebrafish larvae can be placed in 96 well plates where they be exposed to different chemicals in order to study their toxicological effects at high throughput prior to conducting studies in other animal models or humans.
Clinical diagnosis of cytology specimens is especially challenging given that samples are both spread over large areas and thick, which requires 3D capture. Here, we introduce a new parallelized microscope for scanning thick specimens across extremely wide fields-of-view (54 × 72 mm2) at 1.2 and 0.6 μm resolutions, accompanied by machine learning software to rapidly assess these 16 gigapixel scans. This Multi-Camera Array Scanner (MCAS) comprises 48 micro-cameras closely arranged to simultaneously image different areas. By capturing 624 megapixels per snapshot, the MCAS is significantly faster than most conventional whole-slide scanners.
We present a large-scale computational 3D topographic microscope that enables 6-gigapixel profilometric 3D imaging at micron-scale resolution across >110 cm2 areas over multi-millimeter axial ranges. We developed a self-supervised neural network-based algorithm for 3D reconstruction and stitching that jointly estimates an all-in-focus photometric composite and 3D height map across the entire field of view, using multi-view stereo information and image sharpness as a focal metric. Validation experiments on gauge blocks demonstrate a profilometric precision and accuracy of 10 µm or better, with subsequent experiments demonstrating the broad utility of our new computational microscope in applications ranging from cultural heritage to industrial inspection.
This study introduces a rapid, high-throughput approach using the Multi-Camera Array Microscope (MCAM™) to image and quantify neutrophils in zebrafish embryos—a key indicator of environmental impacts on immune function. Using advanced machine learning, the system is able to process and count individual fluorescent neutrophils across a 96-well plate in just over 5 minutes, offering a rapid and accurate alternative to traditional manual counting methods.
This article demonstrates how wide-field-of-view microscopy with Ramona’s Multi-Camera Array Microscope (MCAM™) can resolve three-dimensional information at high speed and spatial resolution. It then shows how this technology can serve as a powerful tool for studying the behavior of freely moving organisms, such as ants, fruit flies, and zebrafish larvae.
This publication presents results from three unique Multi-Camera Array Microscope (MCAM™) configurations for different use cases. These configurations include simultaneous capture with 3D object depth estimation, continuous video capture at high resolution over a large field of view, and a high-resolution configuration to produce 9.8 GP composites of large histopathology specimens.
This paper details how Ramona’s Multi-Camera Array Microscope (MCAM™) enables comprehensive high-resolution recording from multiple spatial scales simultaneously, ranging from cellular-scale structures to large-group behavioral dynamics. This allows researchers to observe the behavior and fine anatomical features of numerous freely moving model organisms on multiple spatial scales, including larval zebrafish, fruit flies, nematodes, carpenter ants, and slime mold.
This research demonstrates a machine learning technique for swift and precise insect egg identification using Ramona's Multi-Camera Array Microscope (MCAM™), distinguishing two crucial pest species with more than 99% accuracy. Validated with around 5500 images, the approach suggests new avenues for real-time agricultural pest diagnostics.
This paper details how Ramona’s Multi-Camera Array Microscope (MCAM™) can quantify morphological features in bacterial colonies across multi-well plates. It shows how the system can be used to augment high-throughput assays by synchronously capturing valuable phenotypic information throughout an acquisition and analysis pipeline.