Automated Crystal Detection
Automated Crystal Detection - Related Videos
11:20
Automated Protocols for Macromolecular Crystallization at the MRC Laboratory of Molecular Biology
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Cited by 14 •
2018
Automated systems and protocols for the routine preparation of a large number of screens and nanoliter crystallization droplets for vapor diffusion experiments are described and discussed.
08:58
Fully Automated Centrifugal Microfluidic Device for Ultrasensitive Protein Detection from Whole Blood
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Cited by 8 •
2016
This protocol demonstrates how to achieve femto molar detection sensitivity of proteins in 10 µL of whole blood within 30 min. This can be achieved by using electrospun nanofibrous mats integrated in a lab-on-a-disc, which offers high surface area as well as effective mixing and washing for enhanced signal-to-noise ratio.
09:15
Growing Protein Crystals with Distinct Dimensions Using Automated Crystallization Coupled with In Situ Dynamic Light Scattering
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Cited by 4 •
2018
Here we present a protocol for controlled production of protein microcrystals. The process uses an automated device allowing controlled manipulation of several crystallization parameters. The protein crystallization is carried-out by controlled and automated addition of crystallization solutions while monitoring and investigating the radius distribution of particles in the crystallization droplet.
07:39
Protein Crystallization
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2023
Protein crystallization, obtaining a solid lattice of biomolecules, elucidates protein structure and enables the study of protein function. Crystallization involves drying purified protein under a combination of many factors, including pH, temperature, ionic strength, and protein concentration. Once crystals are obtained, the protein structure can be elucidated by x-ray diffraction and computation of an electron density model. This video introduces protein crystallization and shows a general...
13:38
Synthesis of Biocompatible Liquid Crystal Elastomer Foams as Cell Scaffolds for 3D Spatial Cell Cultures
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Cited by 9 •
2017
This study presents a methodology to prepare 3D, biodegradable, foam-like cell scaffolds based on biocompatible side-chain liquid crystal elastomers (LCEs). Confocal microscopy experiments show that foam-like LCEs allow for cell attachment, proliferation, and the spontaneous alignment of C2C12s myoblasts.
FAQs
Automated crystal detection combines image-processing algorithms with, increasingly, machine-learning classifiers to interpret images from crystallization screens. The analysis separates likely protein crystals from precipitate, phase separation, dust, and other visual features. This classification focus matters because a visually complex well can be triaged according to the type of feature detected, rather than treated as a simple positive or negative observation.
Machine-learning classifiers can help recognize patterns associated with protein crystals across many screen images, complementing image-processing algorithms. Their value lies in reducing dependence on subjective visual judgments and applying a consistent analysis across wells. In a biochemistry workflow, this supports more reproducible identification of wells that may warrant closer inspection or further experimental attention.
Manual inspection requires researchers to examine wells individually and judge whether visible structures represent crystals or unrelated features. Automated analysis reduces the time required for that evaluation and applies the same computational criteria across a screen. The resulting consistency is especially useful when many experimental conditions must be compared, because promising wells can be prioritized more systematically.
The workflow uses images collected from crystallization-screen wells, applies computational image analysis, and distinguishes candidate protein crystals from precipitate, phase separation, dust, or other features. Results can then guide evaluation of many conditions, helping researchers prioritize wells that appear promising. This makes image interpretation part of a broader screening and experiment-optimization process.
By evaluating crystallization-screen images consistently, the method helps researchers identify which conditions contain features most suggestive of protein crystals. Those prioritized wells can inform subsequent optimization of experiments rather than requiring equal attention to every condition. In practice, this improves screening efficiency and helps focus structural-study efforts on the most promising parts of a crystallization campaign.
In biochemistry, protein crystals provide a basis for structural studies, while crystallization screens may contain many conditions requiring evaluation. Automated detection supports this process by improving screening efficiency, reproducibility, and prioritization. Its broader significance is that computational analysis can make high-throughput crystallography more practical, helping researchers progress from large image sets toward protein structure determination.