Cell lines offer a world of opportunities in life science research. But you have to work to the highest quality standards to ensure your results are reliable and reproducible. To help you with that, we have the Inish Analyser, for automated cell counting and viability analysis.
In this article, we'll take a closer look at how to analyse your cells for the best possible results.
Why are Cell Lines Important in Research?
Cell lines are cultures that derive from primary cell cultures and can be propagated repeatedly and indefinitely. Some primary cultures can be immortalized and become continuous cell lines, (1).
Cell lines have a massive value in life sciences as they enable researchers to more easily study several physiological, pathophysiological, and differentiation cellular processes. Scientists can also use cell lines to examine changes in the structure, biology, or genetic makeup of cells under controlled environments, (2).
Here, we explore a popular immortalized cell line with several applications in basic and applied sciences, the Jurkat cell line.
Jurkat T-cell Lines
Jurkat cells are immortalized T lymphocytes derived from acute T-cell leukemia. Researchers use them to study T-cell-induced signaling pathways, cytokines, and receptor expression, (3).
The Jurkat cell model mimics T lymphocyte's function, helping researchers study molecular and cellular events such as the HIV life cycle and other infectious diseases, (3).
Additionally, the pharmaceutical industry uses Jurkat cells in the screening of various medical agents. This involves testing drug sensitivity, immunomodulatory effect, and the ability to induce cellular death. It facilitates researchers studying the mechanism of action of potentially cardioprotective medications, increasing the treatment's efficacy, (4).
Scientists also use Jukart cells in the quality control of biologics such as vaccines, antibodies, and therapeutic proteins. For example, these cells can be used to assess chimeric antigen receptor (CAR)-induced T cell activation when developing CAR-T therapies, (5).
Assessing Cells Quality
As we discussed, cell lines are essential in the research and development of biological products and medicines. And as you may know, these products must be manufactured following the highest experimental standards to ensure reliable and reproducible results.
That's when quality control comes into play. Quality control of cell lines for biomedical research involves numerous steps and procedures, including, (6):
Only using media and reagents from reputable sources
Ensuring all laboratory equipment and items are working correctly
Keeping media preparation separate from cell procedures
When working with cell cultures, researchers will often examine the cells under an inverted microscope before any manipulation and will regularly asses the cell's viability and appearance, (6). It is also essential to ensure that the cells are free from contamination and relevant pathogens. There are specific tests that can detect mycoplasma and other microorganisms in cell cultures, (6).
Cell counting is another crucial metric to determine cell growth rates accurately and set up reproducible experiments, (6).
The Challenges in Cell Analysis
Cell counting and viability are vital metrics in cell analysis for R&D applications. However, manual cell counting using a hemocytometer can be extremely laborious and time-consuming.
Additionally, traditional cell counting and viability assays typically involve using stains and dyes. Because of that, you add up more steps in your workflow, meaning you spend more money on reagents and disposables.
The Solution
The Inish Analyser is an automated instrument for cell counting and viability analysis based on impedance spectroscopy. This technology has numerous advantages, including:
It saves you time
There is no need to use stains or dyes, which reduces the steps in your workflow.
It's accurate and reproducible
This automated method helps you avoid user-to-user variability, ensuring you get reliable results.
It's cost-effective
The Inish Analyser doesn't require expensive slides/chambers, providing good value for money.
How does it Work?
Carrying out viability and cell count analysis with The Inish Analyser is pretty simple.
After isolating your cells, pipette 40 uL of your sample into a tube, add 360 uL of the Inish Analyser buffer, select and run the sample into the Inish Analyser's touchscreen. The results are available to you in a few seconds.
Depending on your results, you'll see 3 populations:
Live cells, represented as green dots.
Dead cells, represented as red dots.
Debris population
After that, you can select the population of interest for analysis – easy as pie.
Would you like an online demonstration of the Inish Analyser? Contact Cellix to book an online demo or request a quote.
References
Ulrich AB, Pour PM. Cell Lines. In: Maloy S, Hughes KBT-BE of G (Second E, editors. San Diego: Academic Press; 2001. p. 481–2. Available from: https://www.sciencedirect.com/science/article/pii/B9780123749840002126
Bols NC, Kawano A, Lee LEJ. CELLULAR, MOLECULAR, GENOMICS, AND BIOMEDICAL APPROACHES | Culture of Fish Cell Lines. In: Farrell APBT-E of FP, editor. San Diego: Academic Press; 2011. p. 1965–70. Available from: https://www.sciencedirect.com/science/article/pii/B9780123745538002537
Chen J-L, Nong G-M. [Advances in application of Jurkat cell model in research on infectious diseases]. Zhongguo Dang Dai Er Ke Za Zhi. 2018 Mar;20(3):236–42.
Ratiani L, Sanikidze T, Sulakvelidze M, Bejitashvili N, Meladze K. Jurkat cell as an appropriate model for drug investigation. Georgian Med News. 2009 Mar;(168):117–20.
Bloemberg D, Nguyen T, MacLean S, Zafer A, Gadoury C, Gurnani K, et al. A High-Throughput Method for Characterizing Novel Chimeric Antigen Receptors in Jurkat Cells. Mol Ther - Methods Clin Dev [Internet]. 2020 Mar 13;16:238–54. Available from: https://doi.org/10.1016/j.omtm.2020.01.012
Geraghty RJ, Capes-Davis A, Davis JM, Downward J, Freshney RI, Knezevic I, et al. Guidelines for the use of cell lines in biomedical research. Br J Cancer [Internet]. 2014/08/12. 2014 Sep 9;111(6):1021–46. Available from: https://pubmed.ncbi.nlm.nih.gov/25117809