Cell Image Analysis Software from MIPAR

Compound microscopy paved the way for biologists to uncover a microscopic world occupied by miniscule living organisms. But there are limitations to the optical resolving power of visible light. Illuminating organic samples with light on the visible range (400—700nm) will enable resolutions approaching 200nm, which is suitable for observing animal and plant cells or bacteria with limited detail. Yet within those structures is a range of organelles which are far too small to detect with such poor resolution.

Here we will discuss some of the basics of high resolution life science imaging with a focus on the need for cell image analysis software. 

Challenges in High-Resolution Cell Imaging

High resolution cellular imaging refers to a range of techniques that can resolve small intracellular features and mechanisms (lipids, proteins, viral RNA, etc.). The most powerful of these often use cell image analysis software to ensure that any inferences drawn from cellular observations are accurate and useful to the researcher. Additionally, automated data analysis eliminates the margin for human error and allows for higher throughput analysis for greater scalability.

Fluorescence imaging, for example, benefits from statistical or data-driven analysis when it comes to complex classification issues such as cell polarization, colocalization, and subcellular localization.

The principle of fluorescence imaging involves the analysis of visible spectrum fluorescence emitted by light-sensitive reagents assayed with cellular structures. After excitation by a certain wavelength, bound fluorophores emit weak light signals with an intensity/wavelength distribution relevant to the concentration and/or identity of specific molecules.

Cell image analysis software empowers biologists to deploy fluorometric principles with polarized light microscopy to distinguish the organization of specific molecular populations and cytoskeletal architectures. This can resolve challenges associated with reliability due to human interpretation of complex imagery.

At the same time, cell image analysis software can improve scalability by allowing for high content imaging (HCI) of more assays with simultaneous data acquisition from multiple fluorophores. Software solutions designed to specifically resolve the issue of colocalization—the spectral overlap between two or more fluorescent labels—can improve cell count throughput and precision enormously.

Interested in cell image analysis software?

At MIPAR, we specialise in developing bespoke image analysis platforms for life science applications. Our cutting-edge cell image analysis software is designed specifically to resolve key pain points in the fluorescence imaging workflow. But we are also primed to supply analytical platforms suitable for the full spectrum of life science imaging techniques. Contact us today if you have any questions for the MIPAR team. 

Mitochondrial distribution within a differentiated PC12 cellFully automatic detection of mitochondria, separation of neurites and cell body, and measurement of mitochondrial fraction in each.
Cells expressing fluorescently-labeled HSP70 (green) and/or P73α (red). Fully automated count of nuclei, with the percentage expressing HSP70, P73α, or both.