Cell image analysis software is becoming increasingly popular in biochemistry applications, coupling with many imaging techniques such as fluorescence and electron microscopy to eliminate common sources of error or unreliability. Compound microscopy is often used for cell imaging and identification and it works at the very limit of the resolving power of optical light. The resolving power of the human eye is around 200 µm, whereas with the advanced compound microscopes you can reach the optical resolving power limitation of around 200 nm. Resolving power is an important parameter in microscopy as it defines the ability to optically distinguish adjacent objects that are close together.
At high magnification, it can still be difficult to identify cells using light based microscopy. High resolution cellular imaging can be used as a technique to identify small intracellular features such as proteins and lipids.
Fluorescence imaging is a method that biochemists largely rely upon, and they use a technique known as fluorescent tagging. This involves using known fluorophores such as green fluorescent protein (GFP), acridine orange (AO) and propidium iodide (PI), to bind to the cells, depending on the viability and composition. They will then emit characteristic signals when excited by light of a given wavelength. Biologists can use cell image analysis softwares with this method and utilise these fluorometric principles to aid with visually counting and identifying cells, and distinguishing specific molecular populations.
There are many difficulties associated with cell imaging and identification, such as the abundance of difficult to distinguish cells and weak fluorescence signals. These issues are key aspects where there can be a high risk of human error, a main cause is typically subjectiveness in deciding individual cells or the strength of a fluorescence signal. Cell image analysis softwares are commonly being adopted in order to overcome these. By introducing data-driven analysis methods, human error can be mostly eradicated with quantitative results obtained. Other complications with cell imaging and identification can include complex classification issues such as cell polarisation, subcellular localisation, and colocalisation (where there are spectral overlaps between 2 or more fluorescent labels) and software is being continuously developed to give accurate data without human error factors.
With the revolutionary MIPAR image analysis software, cell image analysis can be made easy and they specialise in developing bespoke recipes for life science applications. With its deep learning module, MIPAR can fully automate image analysis, eliminating the margin for human error in the data. Automation can also increase scalability, allowing high throughput analysis and high content imaging, enabling simultaneous data acquisition from multiple fluorophores if using fluorescence techniques. MIPAR is fully primed for supplying analytical solutions to the full range of life sciences imaging techniques.
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