Ready for your all-in-one single cell sequencing solution?
Contact us

Quality controls after tissue dissociation for single cell RNA sequencing

8’
Tissue dissociation

When cells come together, they form tissues and organs. These building blocks exhibit diverse cell-type-specific traits and gene expression patterns that dictate a tissue’s developmental pathway, stress responses, and physiological behavior. Single cell RNA sequencing (scRNA-seq) can characterize these responses by monitoring gene expression within individual cells. But to properly elucidate single cell phenotypes, their tissues must be dissociated (know-how) in such a way that the cells of interest preserve a cell’s viability and structural integrity.

This task is not an easy feat.

On the one hand, overly aggressive approaches can induce cell death and stress-related transcriptional responses that compromise the cells you’re trying to characterize. On the other hand, insufficient tissue dissociation also produces clumps that create noise in scRNA-seq data.

Therefore, for scRNA- and snRNA-seq to produce reliable single cell transcriptomics data, quality control (QC) steps must be implemented after tissue dissociation.

Cell viability: Keeping your cells alive

Cell viability determines how many single cells are still alive after dissociating the tissues of interest. This value can either be reported as a viability percentage or as a ratio of live to dead cells.

Why it’s important

Cell viability assesses whether your protocols may contribute to cell stress and death. Tissue dissociation protocols use chemicals, enzymes, or mechanical methods to isolate individual cells. These methods can kill cells when performed too long or too harshly. In such cases, your cells’ phenotypes may change to the point they do not reflect their physiological state in vivo.

The presence of debris can also compromise the success of a scRNA-seq run. Debris can be intracellular contents released upon lysis, extracellular matrix components, or fibrous biomolecules. For each case, debris can be mistaken as viable cells, becoming false positives that artificially raise cell numbers when preparing scRNA-seq libraries.

What to do

Before proceeding with library preparation, make sure you keep as many cells viable as possible. You can do this using Singleron’s PythoN i tissue dissociation machine. Although researchers can expect to achieve ~70% viability, Singleron’s PythoN i can dissociate tissues to yield cells at 90% viability.

Regardless of whether you use the PythoN i, you should have a single cell suspension immediately after tissue dissociation. Use dyes that distinguish viable (live) and non-viable (dead) cells. Trypan Blue stains under a brightfield microscope can identify dead cells and debris by staining blue. For a more quantitative approach, consider using a combination of acridine orange or SYTO9 and propidium iodide.

Trypan Blue: Trypan Blue is an azo dye and is one of the first dyes to distinguish between viable and non-viable cells. The dye is membrane-impermeable and emits a blue color when it intercalates with intracellular proteins in membrane-compromised cells. Although the dye is an excellent quality control indicator, it also stains debris, compromising its quantitative capabilities. For a basic Trypan Blue staining protocol, click here.
Propidium iodide: Propidium iodide (PI) is a membrane-impermeable nucleic acid dye. When it binds to the DNA of membrane-compromised cells, the dye emits a red color. The dye can be used alone, but it can also be used in a mixture with other dyes such as SYTO9 (see below).
Acridine Orange: Acridine orange is a planar fused-ring acridine molecule that binds to DNA and RNA. The dye is cell-permeable but adopts distinct emission properties when it binds to DNA and RNA. The dye emits an orange color when it binds to RNA, but a green color when it binds to DNA. This property has helped researchers distinguish between cells at different phases of the cell cycle1 and identify viable cells.
SYTO9: SYTO9 is a green, fluorescent dye that binds to DNA in viable and non-viable cells. The dye is most used in combination with PI to distinguish viable cells that stain green and non-viable cells that stain red. The cell populations can then be sorted using flow cytometry and fluorescence-activated cell sorting (FACS).

Also consider using Singleron’s PythoN i machine. It dissociates most tissues within 15 minutes and skin within 60 minutes. It can also integrate quality controls seamlessly while retaining 90% cell viability for downstream protocols.

The PythoN i instrument (right) and a graph (left) demonstrating that PythoN i enables tissue dissociation with high cell viability and yield

Learn more about PythoN ITM: Intelligent Tissue Dissociation system

Cell clumping: Isolating your cells, one by one

Cell clumps arise when more than one cell is associated together. Clumping of these cells most commonly happens when tissues are not sufficiently detached from the rest of the tissue.

Why it’s important

The success of a scRNA-seq study depends on successfully loading individual viable cells into wells, a trait critical for producing high-quality scRNA-seq libraries. Cell clumping can cause too many cells to be loaded on a microwell chip. Failing to minimize cell clumping will increase the probability of detecting a doublet or multiplet in single cell isolation protocols2. The multiplets can produce hybrid transcriptional profiles that would then be misinterpreted as a novel lineage discovery in single cell data analyses3.

What to do

Brightfield or confocal microscopy remains the best way to determine whether your cells are clumping. In addition, use cell counters to determine the precise number of cells you have. Accurate cell counts reduce the risk of overloading chips during cell capture. This ensures optimal input and reduces the risk of clumping for downstream sequencing.

You can also use Singleron’s chips, which capture between 3000-20000 cells at standard density. If you’re seeking to identify rare cell populations or comprehensively characterize a tissue’s cellular heterogeneity, you can also use Singleron’s high-density chips. They can load as many as 30000 cells for a single scRNA-seq run.

Learn more about SCOPE-chip micro-well microfluidics system

Cell stress: Keeping the in vivo phenotypes of your cells

Cell stress during tissue dissociation occurs because tissues are exposed to conditions that encourage single cell separation. Prolonged incubation at 37oC with enzymes, combined with mechanical shearing, introduces stressors not typically encountered in vivo.

Why it’s important

Retaining in vivo cellular phenotypes from tissues is a top priority for generating robust scRNA-seq data. Stress responses during tissue dissociation confound efforts to distinguish physiological processes from dissociation-induced biological artefacts. For instance, tissue dissociation induces the expression of heat shock proteins for several cell types4. Phenotypic changes have also arisen among single cells, such as artificial microglia activation after the dissociation of mouse hippocampus5. Most damningly,

What to do

The more time tissues spend outside the body, the more time cells have to respond to the change in environment. If you’re not using a preservative, any tissue dissociation protocol you develop must hence minimize the time between sample extraction and library preparation. Consider identifying marker genes of cell stress induced by tissue dissociation, such as heat shock protein induction. Screen for the expression of these genes using qPCR or in your sequencing pipeline.

In both cases, the PythoN i can dissociate tissues within an hour after the protocol begins. Quick dissociation minimizes the risk of stress-related transcriptional responses from adversely affecting the in vivo transcriptional phenotypes of the dissociated cells. This reduces biological noise induced by stress-related expression artefacts.

Conclusion

Dissociating tissues is the first step in a single cell transcriptomics study, one that isolates single cells to characterize their gene expression. For single cell transcriptomes to demonstrate biological relevance, QCs must be implemented. These QCs include checking for cell viability, the presence of debris, and the expression of stress genes associated with tissue dissociation. They ensure that the dissociated cells remain alive, retain in vivo phenotypes and transcriptomes, and are not clumped. Accounting for these parameters produces robust scRNA-seq that facilitates the discovery and validation of biological processes in health and disease.

For each QC parameter, Singleron Biotechnologies’ PythoN i integrates them all with ease and produces high-quality single cell suspensions suitable for downstream library preparation and RNA sequencing. Furthermore, PythoN i has eight channels so that multiple experiments can be run at the same time, extending the breadth of a single scRNA-seq study. Most importantly, the single cells that PythoN i produce reflect in vivo phenotypes thanks to the short tissue dissociation times that it offers. Put together, PythoN i, the newest product in Singleron’s suite of single cell transcriptomics, bursts open a path for scientists to obtain viable single cells from any type of tissue from the human body.

References

Darzynkiewicz Z. Chapter 27 Differential Staining of DNA and RNA in Intact Cells and Isolated Cell Nuclei with Acridine Orange. In: Darzynkiewicz Z, Crissman HA, eds. Methods in Cell Biology. Vol 33. Flow Cytometry. Academic Press; 1990:285-298.
https://doi.org/10.1016/S0091-679X(08)60532-4

DePasquale EAK, Schnell DJ, Van Camp PJ, et al. DoubletDecon: Deconvoluting Doublets from Single-Cell RNA-Sequencing Data. Cell Reports. 2019;29(6):1718-1727.e8.
https://doi.org/10.1016/j.celrep.2019.09.082

Xin H, Lian Q, Jiang Y, et al. GMM-Demux: sample demultiplexing, multiplet detection, experiment planning, and novel cell-type verification in single cell sequencing. Genome Biol. 2020;21:188. https://doi.org/10.1186/s13059-020-02084-2

Denisenko E, Guo BB, Jones M, et al. Systematic assessment of tissue dissociation and storage biases in single-cell and single-nucleus RNA-seq workflows. Genome Biology. 2020;21(1):130.
https://doi.org/10.1186/s13059-020-02048-6

Neuschulz A, Bakina O, Badillo‐Lisakowski V, et al. A single‐cell RNA labeling strategy for measuring stress response upon tissue dissociation. Molecular Systems Biology. 2023;19(2):e11147. https://doi.org/10.15252/msb.202211147

A post by Salih Yilmaz

Check out our latest blog posts

Learn more

What is Tissue Dissociation? 

Tissue Dissociation: The First Step Toward Single Cell Analysis Tissue dissociation is a crucial preparatory step in many biological and clinical research workflows, involving the…

Read more