Compound microscopy paved the way for biologists to discover a microscopic world that was occupied by minuscule living organisms. Sadly, the optical resolving power of visible light has limitations.
Resolutions approaching 200 nm are enabled by illuminating organic samples with light on the visible range (400—700 nm). This range is suitable for studying bacteria with limited detail or plant and animal cells.
Within those structures, there is a range of organelles that are far too minuscule to detect with such poor resolution.
This article will outline some of the basics of high resolution life science imaging with a spotlight on the requirement for cell image analysis software.
Image Credit: MIPAR Image Analysis
Challenges in high resolution cell imaging
High resolution cellular imaging refers to a variety of methods that can resolve small intracellular mechanisms and features (proteins, lipids, viral RNA, etc.).
Often, the most powerful of these utilize cell image analysis software to ensure that any inferences drawn from cellular studies are accurate and useful to the researcher.
The automated data analysis also enables higher throughput analysis for better scalability and eliminates the margin for human error. For instance, when it comes to complex classification challenges like cell polarization, colocalization and subcellular localization, fluorescence imaging benefits from statistical or data-driven analysis.
The principle of fluorescence imaging involves the analysis of visible spectrum fluorescence, which is emitted by light-sensitive reagents assayed with cellular structures.
Bound fluorophores emit weak light signals with an intensity/wavelength distribution after excitation by a particular wavelength, which are relevant to the identity and/or concentration of specific molecules.
In order to establish the organization of specific molecular populations and cytoskeletal architectures, cell image analysis software helps biologists to deploy fluorometric principles using polarized light microscopy.
This can resolve difficulties that are associated with reliability because of human interpretation of complex imagery.
By enabling high content imaging (HCI) of more assays with simultaneous data acquisition from multiple fluorophores, cell image analysis software can also enhance scalability.
Software solutions that are specifically designed to resolve the issue of the spectral overlap between two or more fluorescent labels, known as colocalization, can greatly enhance cell count precision and throughput.
Interested in cell image analysis software?
MIPAR specializes in developing bespoke image analysis platforms for life science applications. Their cutting-edge cell image analysis software is specifically designed to combat key pain points in the fluorescence imaging workflow.
They also supply analytical platforms that are suitable for the full spectrum of life science imaging methods.
About MIPAR Image Analysis
MIPAR Image Analysis is a world-leading algorithm development and image analysis software company. They specialize in efficiently, accurately, and reliably extracting measurements from complex images. From material and life sciences to aerospace and manufacturing solutions, our extensive portfolio can assist a variety of real-world applications.
Their flagship MIPAR product offers an intuitive user-experience, drag-and-drop custom algorithm development, and a powerful deep learning toolbox. Combined with expert consultative services, they offer clients an end-to-end solution that suits their particular project needs. MIPAR helps clients implement sophisticated algorithms that save time and cost while increasing accuracy and supervision over results.
MIPAR was founded in 2017 and is headquartered in Columbus, Ohio, United States. Additional software distributors and applications specialists are located in Australia, India, China, Europe, Japan, and Taiwan.
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