Cell Identification: The Basic Principles

All living organisms are comprised of cells, from unicellular protozoa to the biggest cetaceans in the world’s oceans. Even the human body can be reduced to tens of trillions of structural components and this is one of the fundamental tenets of cell theory; now generally recognized as fact. This revolutionary concept began the work for modern cellular biology and a plethora of breakthroughs since.

​Biotechnology, molecular genetics, vaccinations, and many other concepts would not be achievable if not for the capability to distinguish structures and dynamics at the cellular level. The history of cell identification is consequently bound to that of microscopy.

Cell Identification: The Basic Principles

Image Credit: MIPAR Image Analysis

A brief history of cell identification

Resolving power is a vital metric for cell identification and life sciences. This characterizes the capacity to optically differentiate between adjacent objects that are in close proximity to one another.

The standard human eye has a resolving power close to 200 micrometers (μm), which makes it impossible to observe even the largest human cell – the ovum (approximately 10 μm) – with the naked eye. When Robert Hooke, a well-known and distinguished scientist, created a novel optical system founded on existing compound microscopes it meant that magnifications large enough to visualize the microscopic world could be achieved. Presently, advanced compound microscopes have reached the limits of visible light with a maximum resolving power of roughly 0.2 μm, or 200 nanometers (nm).

Not only are cells minuscule, they are also extremely complex. Even employing the strongest magnification, it can be challenging to reveal their molecular composition and determine their functions based only on light-based microscopy and visual cell identification. For that, biochemists mainly depend on fluorescent tagging with tried-and-tested fluorophores such as green fluorescent protein (GFP), propidium iodide (PI), and acridine orange (AO).

These distinctive reagents attach to cells depending on their structure, viability, and other factors, emitting characteristic fluorescent signals when excited by light in a given wavelength range. This means it is more simple to visually count and detect cells based on a broad spectrum of physicochemical attributes.

Challenges of cell identification

There are numerous challenges related to modern cell identification, but one of the most prevalent is the margin for human error. The large quantity of cells in a sample combined with the factor of subjectivity with visual characterization means it may still be challenging to correctly classify cells in solution with the level of quantitative assurance that modern life sciences require.

Biochemistry facilities are becoming more and more reliant on automated imaging systems to discern the weak fluorescent signals emitted by assayed samples and to reduce the margin for human error.

Cell identification with MIPAR

MIPAR Image Analysis is an industry-leading expert in algorithmic image analysis, specializing in effectively and consistently extracting data from complex imagery. MIPAR has leveraged its innovative image analysis software for fluorescence-based cell identification practices, including colocalization, sub-cellular localization, and cell polarization.

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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Last updated: Nov 24, 2020 at 5:02 AM


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