An inexpensive system for imaging the contents of multi-well plates.
Ontology highlight
ABSTRACT:
SUBMITTER: Bohm A
PROVIDER: S-EPMC6277959 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Described here are instructions for building and using an inexpensive automated microscope (AMi) that has been specifically designed for viewing and imaging the contents of multi-well plates. The X, Y, Z translation stage is controlled through dedicated software (AMiGUI) that is being made freely available. Movements are controlled by an Arduino-based board running grbl, and the graphical user interface and image acquisition are controlled via a Raspberry Pi microcomputer running Python. Images can be written to the Raspberry Pi or to a remote disk. Plates with multiple sample wells at each row/column position are supported, and a script file for automated z-stack depth-of-field enhancement is written along with the images. The graphical user interface and real-time imaging also make it easy to manually inspect and capture images of individual samples.
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Project description:Multi-well plates and cell arrays enable microscopy-based screening assays in which many samples can be analysed in parallel. Each of the formats possesses its own strengths and weaknesses, but reference comparisons between these platforms and their application rationale is lacking. We aim to fill this gap by comparing two RNA interference (RNAi)-mediated fluorescence microscopy-based assays, namely epidermal growth factor (EGF) internalization and cell cycle progression, on both platforms. Quantitative analysis revealed that both platforms enabled the generation of data with the appearance of the expected phenotypes significantly distinct from the negative controls. The measurements of cell cycle progression were less variable in multi-well plates. The result can largely be attributed to higher cell numbers resulting in less data variability when dealing with the assay generating phenotypic cell subpopulations. The EGF internalization assay with a uniform phenotype over nearly the whole cell population performed better on cell arrays than in multi-well plates. The result was achieved by scoring five times less cells on cell arrays than in multi-well plates, indicating the efficiency of the cell array format. Our data indicate that the choice of the screening platform primarily depends on the type of the cellular assay to achieve a maximum data quality and screen efficiency.
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Project description:The current drug development pipeline takes approximately fifteen years and $2.6 billion to get a new drug to market. Typically, drugs are tested on two-dimensional (2D) cell cultures and animal models to estimate their efficacy before reaching human trials. However, these models are often not representative of the human body. The 2D culture changes the morphology and physiology of cells, and animal models often have a vastly different anatomy and physiology than humans. The use of bioengineered human cell-based organoids may increase the probability of success during human trials by providing human-specific preclinical data. They could also be deployed for personalized medicine diagnostics to optimize therapies in diseases such as cancer. However, one limitation in employing organoids in drug screening has been the difficulty in creating large numbers of homogeneous organoids in form factors compatible with high-throughput screening (e.g., 96- and 384-well plates). Bioprinting can be used to scale up deposition of such organoids and tissue constructs. Unfortunately, it has been challenging to 3D print hydrogel bioinks into small-sized wells due to well-bioink interactions that can result in bioinks spreading out and wetting the well surface instead of maintaining a spherical form. Here, we demonstrate an immersion printing technique to bioprint tissue organoids in 96-well plates to increase the throughput of 3D drug screening. A hydrogel bioink comprised of hyaluronic acid and collagen is bioprinted into a viscous gelatin bath, which blocks the bioink from interacting with the well walls and provides support to maintain a spherical form. This method was validated using several cancerous cell lines, and then applied to patient-derived glioblastoma (GBM) and sarcoma biospecimens for drug screening.
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Project description:Protein-crystallization imaging and classification is a labor-intensive process typically performed either by humans or by instruments that currently cost well over $100?000. This cost puts the use of crystallization-trial imaging outside the reach of most academic laboratories, and also start-up biotechnology firms, where resources are scarce. An imaging system has been designed and prototyped which automatically captures images from multi-well protein-crystallization experiments using both standard and fluorescent imaging techniques at a cost 28 times lower than current market rates. The machine uses a Panowin F1 3D printer as a base and controls it using G-code commands sent from a Python script running on a desktop computer. A graphical user interface (GUI) was developed to enable users to control the machine and facilitate image capture, classification and editing. A 488?nm laser diode and a 525?nm filter were incorporated to allow in situ fluorescent imaging of proteins trace-labeled with a fluorophore, Alexa Fluor 488. The instrument was primarily designed using a 3D printer and augmented using commercially available parts, and this publication aims to serve as a guide for comparable in-laboratory robotics projects.
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