Ensuring good quality cells for Single Cell Sequencing


Single-cell sequencing is a revolutionary technique that has shown great potential in research and clinical practice. To guarantee the best results, you need to start the experiments with good-quality cells. To find out if your cells are suitable for single-cell sequencing, you must perform a cell count and viability analysis.


This article discusses the main applications of single-cell sequencing, and how the Inish Analyser can help you perform your experiments with greater confidence.


Single Cell Sequencing Technology

Traditional sequencing is an excellent method to find out the exact nucleotide order within a DNA molecule. But this technology can only analyse a small number of cells at a time. That's why scientists use single-cell sequencing to obtain genomic, transcriptome, or other information about cell populations, (1).


Unlike traditional sequencing, Single-cell technology can detect heterogeneity among individual cells, distinguishing cells, and delineating cell maps. At first, the technology was too expensive for widespread use. But this reality is changing quickly, (1).


Single Cell Sequencing Applications

The technology has evolved considerably in the last number of years, lowering the costs and increasing the detection ability. Advanced single-cell sequencing methods can detect copy number variation (CNV) with high resolution. With the single-cell multiple sequencing technique (ssCOOL-seq), researchers can analyse CNV, chromatin state, and DNA methylation simultaneously, (1).


Using topographic single-cell sequencing (TSCS), scientists can describe the specific characteristics of individual tumor cells and study invasion and metastasis mechanisms, (1). Those are just a few examples of what you can do with this technology. The applications are endless and go from basic research to clinical practice and diagnostics. Examples include:

Cancer

Genetic variation originates in cells with different characteristics within tumor tissues. This heterogeneity may influence tumor development and metastasis. So, researchers can use single-cell sequencing to detect tumor cell heterogeneity ad draw tumor cell maps accurately. The method allows detection of specific markers within tumors to better explain the disease's mechanisms and develop new treatments and diagnostic procedures, (1).


Immunology

Single-cell sequencing detects and differentiates individual immune cells. With this method, researchers can discover new targets for disease treatment. They can also study highly heterogeneous immune cells originating from different pathogens by detecting their unique genetic material, (1).


Nervous System

Neurons are different from one another because of CNV within nerve cells. Before single-cell techniques, it was harder to study brain circuits and neuronal connections. The technology allows the study of different neurons and molecules within the central nervous system (CNS) to understand better how brain diseases develop, (1).


Reproductive medicine

This technology makes it possible to sequence and quantifies the whole genome of germ cells and embryonic cells. Single-cell sequencing aims to better understand, diagnose, and treat reproductive and genetic diseases, (1).


Single Cell Sequencing Quality Control

We've just talked about single-cell sequencing potentials and their application in different fields. If you work in any of these areas or plan to get started, it's fundamental to understand how to run proper quality control tests, (1).


These tests are vital since they allow the separation of technical artifacts from real biological variation within your cells, (2).


Also, poor-quality cells may show altered genomic integrity. Any damage that prevents amplification results in DNA loss and can cause amplification bias, (3). Therefore, it is important not to attempt SSC of damaged cells to ensure accurate downstream analysis, (2).

But traditional methods are laborious and not as cost-effective as you may think. This is because most assays use dyes that can compromise your cell's integrity. Besides, you end up adding extra steps to your workflow, increasing spending on reagents and disposables.


How Can The Inish Analyser Help?

The Inish Analyser is an automated method for cell counting and viability analysis based on impedance spectroscopy. The advantages of this method are:


  • Time-saving

Unlike manual cell counting, The Inish Analyser requires no cell staining, reducing the steps in your workflow.


  • Accuracy and Reproducibility

As an automated method, it helps to avoid user-to-user variability for more reliable results.


  • Cost-effective

The Inish Analyser doesn't require staining or expensive slides/chambers, offering a cost-effective option for your lab.


How does it Work?

Carrying out viability and cell count analysis with the Inish Analyser is straightforward.

After isolating your primary T-cells, pipette 40 μL of your sample into a tube, add 360 μL of the Inish Analyser buffer, and run the sample via 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, described as red dots.

  • Debris population

After that, you can select the population of interest for analysis – it's just so easy.

Would you like to see Cellix's Inish Analyser in action? Contact Cellix to book an online demo or request a quote.


References

  1. Tang X, Huang Y, Lei J, Luo H, Zhu X. The single-cell sequencing: new developments and medical applications. Cell Biosci. 2019;9(1):1–9.

  2. Jiang P. Quality Control of Single-Cell RNA-seq BT - Computational Methods for Single-Cell Data Analysis. In: Yuan G-C, editor. New York, NY: Springer New York; 2019. p. 1–9. Available from: https://doi.org/10.1007/978-1-4939-9057-3_1

  3. Bäumer C, Fisch E, Wedler H, Reinecke F, Korfhage C. Exploring DNA quality of single cells for genome analysis with simultaneous whole-genome amplification. Sci Rep [Internet]. 2018;8(1):7476. Available from: https://doi.org/10.1038/s41598-018-25895-7


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