- Key takeaways
- What is single-cell sequencing?
- Why single-cell sequencing matters in genomics research
- Single-cell sequencing vs traditional bulk sequencing
- The single-cell sequencing workflow explained
- Why tissue dissociation matters in single-cell analysis
- Common challenges in single-cell sample preparation
- How tissue processing impacts scRNA-seq data quality
- Manual vs automated tissue dissociation methods
- Improving cell viability and yield in single-cell workflows
- Applications of single-cell sequencing in oncology and omics research
- The future of single-cell sequencing and multiomics
- FAQs
Key takeaways: Improve single-cell sequencing results with efficient sample preparation workflows
As a researcher, you understand how critical tissue processing and workflow decisions are to single-cell sequencing data quality. This article shows how optimizing tissue dissociation and sample preparation can improve outcomes while enabling faster, more consistent workflows that preserve cell integrity. By applying these strategies, you can avoid poor viability, biased cell representation, and unreliable results, which ultimately improve data accuracy, consistency, and the biological insights generated from your experiments (Fig. 1).
Figure 1. Overview of stepwise approach to designing single cell analysis workflow. Key considerations across single-cell sequencing workflows, from tissue dissociation and cell capture methods to sequencing strategies and data analysis, highlight how biological variation, handling, throughput, and analytical choices collectively influence cell viability, representation, and data quality.
Here are the key considerations to keep in mind:
- Single-cell sequencing reveals cellular heterogeneity that bulk methods miss, enabling deeper insight into disease biology.
- Tissue dissociation quality directly impacts data accuracy, influencing cell viability and representation.
- High-quality sample preparation is critical to preserve cell integrity and ensure reliable sequencing results.
- Automated workflows improve consistency and reproducibility by reducing operator variability.
- Processing methods can alter sequencing outcomes, affecting yield, transcriptional profiles, and downstream analysis.
What is single-cell sequencing?
Single-cell sequencing is a powerful genomics approach that analyzes the genetic or transcriptional content of individual cells, rather than averaging signals across entire populations, enabling researchers to uncover cellular heterogeneity and identify rare cell types. By isolating and sequencing thousands of individual cells from a single sample, this technique provides a detailed view of how cells differ in function, state, and response to disease, making it a critical tool for understanding complex biological systems and advancing diagnostics, therapeutics, and precision medicine.
Why single-cell sequencing matters in genomics research
Single-cell sequencing matters in genomics research because it reveals cell-to-cell differences that bulk methods miss, enabling deeper insight into cellular function, disease mechanisms, and treatment responses. By capturing this level of detail especially when combined with multi-omics it helps researchers uncover rare populations, understand complex biological systems, and accelerate the discovery of new biomarkers and therapeutic targets.
Single-cell analysis offers researchers an unprecedented opportunity to understand individual cell populations and their behavior in health and disease. In contrast to traditional bulk analysis, which provides an average analysis of mixed-cell populations, single-cell studies allow for the examination of tens of thousands of individual cells from a single tissue sample. Consequently, single-cell analysis technologies have become a powerful tool for understanding cellular function, disease diagnosis, therapy response prediction, and treatment selection.
A crucial element of single-cell workflows is the dissociation of tissues into single cells. This process must be handled quickly and carefully to ensure high cell viability and accurate representation of the tissue’s cellular composition. To learn more about how tissue dissociation can affect the quality and reliability of downstream analyses, read our case study.
The VIA Extractor™ tissue disaggregator provides a standardized, semi-automated approach to tissue dissociation. Designed for high-throughput omics research, the VIA Extractor ensures high-quality single-cell suspensions with minimal cell damage.
Single-cell sequencing versus traditional bulk sequencing
Bulk sequencing analyzes all cells in a sample together, producing an average signal that can mask important biological differences, while single-cell sequencing examines individual cells to reveal the diversity within a population. By capturing cell-to-cell variation and identifying rare or functionally distinct subpopulations, single-cell sequencing provides deeper insights into biological processes and disease mechanisms that bulk methods cannot resolve.
Single-cell sequencing expands genomics research horizons
Traditional sequencing methods lack the sensitivity to analyze individual cell differences, resulting in a loss of cellular heterogeneity information. In contrast, single-cell sequencing characterizes individual cells, uncovering small sub-populations within larger groups. (1)
Multi-omics approach harnesses big data to accelerate discovery
The power of single-cell sequencing is even greater when combined with multi-omics. Integrating genomics, transcriptomics, and proteomics provides a comprehensive view of cellular functions and interactions. Researchers can better understand the complexities of biological systems by uncovering how genetic mutations, gene expression changes, and protein dynamics contribute to disease progression and treatment responses.
Multi-omics allows for a detailed analysis of the tumor microenvironment, revealing how different cell types interact and influence each other. It can identify mechanisms of drug resistance, track cell differentiation pathways and map the complex network of molecular interactions within and across cell populations.
Using big data and advanced analytical tools, multi-omics enhances the precision and depth of single-cell research, accelerating discovery of new biomarkers, therapeutic targets and diagnostic tools.
The single-cell sequencing workflow explained
Initially limited by high costs and technical challenges, single-cell sequencing is now robust and accessible throughout the research community. The most important factor is high-quality starting material, as it ensures accurate and reliable results. Preparing whole tissue samples for data analysis involves several steps, including disaggregating tissues into viable single cells, extracting nucleic acids, amplifying individual cells, and sequencing (Fig. 2). However, tissue disaggregation remains a significant challenge.
Figure 2. Overview of the single-cell sequencing workflow, including collection, isolation, amplification sequencing. and data analysis.
Why tissue dissociation matters in single-cell analysis
Tissue dissociation matters in single-cell analysis because it directly determines cell viability, integrity, and how accurately the original cellular composition of a tissue is preserved. Poor or inconsistent dissociation can introduce stress or bias, leading to distorted sequencing data and unreliable biological insights.
Common challenges in single-cell sample preparation
The disaggregation or dissociation of tissue samples into viable single cells is a critical step in single-cell sequencing. Manual tissue processing is time-consuming and highly variable, depending on an operator’s skills. Researchers typically prefer a mild disaggregation process that ensures high cell yield and integrity while maintaining the context of the parent sample.
Key aspects of an optimal disaggregation process include:
- Minimal time between tissue collection and disaggregation into single cells for downstream applications.
- Protocol consistency to minimize variability throughout the entire workflow.
- High yield and viability of the disaggregated single cells.
- Representative and consistent cell yield and viability relative to the original tissue sample.
Transitioning from a manual to an automated dissociation system greatly enhances consistency, improves reproducibility, and increases efficiency, making it a desirable advancement for researchers working with single cells.
How tissue processing impacts scRNA-seq data quality
scRNA-seq, or single-cell RNA sequencing, is a technique that analyzes gene expression in individual cells to reveal how cells function and differ within a sample. Tissue processing directly impacts scRNA-seq data quality because harsh or inefficient handling can reduce cell viability, alter gene expression profiles, and distort the true cellular composition of the sample. Optimized processing preserves cell integrity and minimizes stress-induced artifacts, resulting in more accurate and biologically relevant sequencing data. Advances in tissue processing—including automated dissociation, improved handling of diverse sample types, and optimized cryopreservation strategies—are helping researchers further enhance cell viability and maintain biological context in high-throughput workflows.
Manual versus automated tissue dissociation methods
Comparing VIA Extractor and gentleMACS™ for scRNA-seq
Single-cell sequencing is a powerful tool for studying cellular heterogeneity. Advances in single-cell RNA sequencing (scRNA-seq) provide new insights into transcription profiles, allowing for identification of rare cell populations. High sensitivity in single-cell analyses requires careful handling and processing of tissue samples to minimize process-induced effects that could skew results.
This study investigated the ability of the VIA Extractor™ disaggregator (Fig. 2) to generate high-quality single-cell suspensions from fresh tissue for scRNA-seq analysis, comparing its performance to the gentleMACS™ dissociator (Miltenyi Biotec) in the Cytiva case study(2). The data were generated from a minimum of three independent experiments with equal numbers of replicates per condition, conducted under identical treatment conditions according to each manufacturer’s recommended protocols.
How do the protocols compare?
Murine liver tissue was weighed, washed, and equally divided between the VIA Extractor cell processor and gentleMACS™ to ensure sample consistency. The tissue was then disaggregated using the gentleMACS™ or VIA Extractor protocol (Fig 3). Notably, the VIA Extractor cell processor completed the dissociation process in 10 minutes, while gentleMACS™ required 30 minutes.
How do the results compare?
The VIA Extractor™ disaggregator produced higher results of viable single cells compared to gentleMACS™ (Fig 4). Observations under a microscope, cell counting, and viability assays indicated superior performance for VIA Extractor disaggregator, with a higher percentage of viable cells (72% ± 7%) compared to gentleMACS™ (52% ± 3.6%). The VIA Extractor™ demonstrates higher viable cell recovery than gentleMACS™, suggesting that selecting a gentler, more standardized dissociation method can improve cell viability and overall single-cell workflow quality.
UMAP clustering showed more cells represented in the sequencing data from VIA Extractor disaggregator, with fewer cells displaying stress-related transcription profiles compared to gentleMACS™ (Fig 5). The fraction of reads in cells was also higher for VIA Extractor™ disaggregator (59.7% ± 5.7%) compared to gentleMACS™ (49.8% ± 7.4%). Selecting an optimized tissue dissociation method can improve downstream sequencing quality by increasing the number of cells captured, reducing stress-induced transcriptional artifacts, and enhancing overall data richness and usability.
Figure 3. (A) The VIA Extractor tissue disaggregator provides fast, low-impact tissue dissociation into single cell suspensions. (B) The Omics pouch placed into the VIA Extractor disaggregator and held in place with the Omics clamp. (C) The VIA Extractor disaggregator placed into the top of the VIA Freeze Uno.
Figure 4. The complete workflow for both the gentleMACS dissociation method and the VIA Extractor disaggregator dissociation method.
Figure 5. Cell viability. Cell counts as determined after sequencing from Cell Ranger software (10x Genomics) were significantly higher when using the VIA Extractor disaggregator for tissue dissociation.
What can we learn from this?
This study demonstrates that the VIA Extractor™ disaggregator provides a higher yield of viable cells in a shorter time, with fewer cells displaying stress-related transcription profiles compared to gentleMACS™. This highlights the importance of choosing low-impact technologies for generating single-cell suspensions in omics workflows.
Figure 6. Clusters identified with cell types
Figure 7. UMAP clustering of cells and differential abundance of cells. (A) UMAP clustering of all cells by sample set (gentleMACS and VIA Extractor™ disaggregator). While the clustering is similar, there are significantly more cells in each cluster from tissue processed using the VIA Extractor disaggregator. (B) Proportional representation of cells from both sample sets in each of the clusters identified. This indicates that for samples processed using the VIA Extractor disaggregator there are significantly more cells present in most clusters except cluster 10 and 14.
Single-cell discussion guide
Figure 8. Single-cell discussion guide designed to help identify workflow challenges, understand tissue dissociation approaches, and explore opportunities to improve cell viability, reproducibility, and throughput.
Improving cell viability and yield in single-cell analysis
Researchers can improve cell viability and yield in single-cell workflows by using gentle, low-impact tissue dissociation methods that preserve cell integrity and maintain the original biological context. Standardized, semi-automated approaches also reduce variability and enable faster, more consistent processing, helping generate high-quality single-cell suspensions suitable for downstream analysis. The features of the VIA Extractor™ tissue disaggregator combines mild processing, rapid turnaround, parallel sample handling, and reduced contamination risk to enable more reproducible, efficient workflows that support reliable, high-quality single-cell data generation.
Gentle, efficient tissue disaggregation for optimized cell viability and yield
The VIA Extractor tissue disaggregator is a novel device for the disaggregation of human and animal solid tissue and tumor samples into viable single cells. The VIA Extractor disaggregator uses a mild processing approach for consistently high cell viability, yield, and preservation of cell integrity relative to the parent sample. The standardized, closed system provides a semi-automated process for use in high-throughput omics research (genomics, proteomics, metabolomics, etc.) giving reliable results in single-cell sequencing and flow cytometry applications.
Key features:
- Gentle: Optimizes cell viability and yield with low-impact disaggregation.
- Standardized: Consistent process reduces sample-to-sample variation.
- Semi-automated: Simplifies tissue dissociation with fewer steps.
- Fast: Processes tissue to single cell suspension in as little as 10 minutes.
Comparative performance of tissue dissociation methods
To compare the efficacy of the VIA Extractor disaggregator with gentleMACS and manual disaggregation methods, we conducted experiments on mouse liver, lung, kidney, and brain tissues. Across all tissue types, the VIA Extractor disaggregator demonstrated higher yields and viability with fewer undissociated clumps compared to gentleMACS and manual methods (Fig 6).
Figure 9. Omics pouch placed into the VIA Extractor and held in place with the Omics clamp.
Comparison tissue dissociation workflows in single-cell sequencing
Understanding how different tissue dissociation methods operate can help you choose the most efficient and reliable approach for your workflow. The following summaries highlight key differences in processing time, level of automation, and overall workflow complexity across common methods.
VIA Extractor protocol: Tissue is weighed and washed, enzymatically processed in a pouch using the VIA Extractor for 10 minutes at 37 °C, then centrifuged to remove red blood cells and normalized for downstream analysis.
gentleMACS™ protocol: Tissue is washed, combined with enzyme cocktail in a tube, processed for approximately 30 minutes at 37 °C with additional dissociation steps, then centrifuged and prepared for analysis.
Manual disaggregation protocol: Tissue is physically minced and filtered before centrifugation and cleanup, followed by dilution to prepare cells for downstream applications.
Before comparing performance outcomes, it’s important to understand how these workflows differ in their core processing time, handling steps, and level of automation, as these differences can directly influence cell viability, consistency, and the quality of downstream single-cell sequencing data. Table 1 provides the results of a comparator study of the VIA Extractor tissue disaggregator and the gentleMACS™. The VIA Extractor™ tissue disaggregator and its accessories enable gentle, efficient, and semi-automated tissue dissociation for high cell viability and yield in single-cell workflows, while its mild massaging within a multicompartment pouch contrasts with gentleMACS™ rotor blending, illustrating the difference in mechanical dissociation approaches. Mouse liver, lung, and kidney tissues processed with the VIA Extractor™ consistently show more complete digestion, fewer particulates, higher cell viability, greater yield per mg of tissue, and purer single-cell suspensions with less debris compared to gentleMACS™ and manual methods. These results highlight the superior dissociation performance and efficiency of the VIA Extractor™, which also achieves equally high or higher cell viability and yield in kidney samples, supports efficient cell isolation in sensitive tissues like brain, and demonstrates consistently high yields and viability percentages across a range of tissue types.
Figure 10. Comparative performance of tissue dissociation methods.
Applications of single-cell sequencing in oncology and omics research
Single-cell sequencing is a powerful tool in oncology and omics research that enables researchers to uncover tumor heterogeneity, identify rare or therapy-resistant cell populations, and understand cell-to-cell interactions within the tumor microenvironment, while multi-omics approaches further connect genetic, transcriptional, and protein-level insights. Understanding these applications is critical because it helps researchers generate deeper biological insights that drive biomarker discovery, therapeutic development, and more precise, personalized treatment strategies.
The future of single-cell sequencing and multi-omics
Single-cell sequencing is a critical tool in oncology and omics research because it enables researchers to dissect tumor heterogeneity, identify rare or therapy-resistant cell populations, and map interactions within the tumor microenvironment at single-cell resolution, while multi-omics integration further links genomic mutations, gene expression, and protein dynamics to provide a comprehensive view of disease biology. By delivering this level of detail, it supports more accurate biomarker discovery, improves understanding of disease progression and treatment response, and helps advance targeted therapies and personalized medicine strategies, ultimately driving more precise and impactful research outcomes.
Conclusion
In this article, you learned how optimizing tissue dissociation and workflow design can improve single-cell sequencing outcomes by enhancing cell viability, consistency, and data quality. Adopting more standardized, low-impact approaches can help reduce variability and preserve cellular integrity, enabling more reliable and biologically meaningful results in downstream analysis.
Cytiva supports researchers with advanced, semi-automated solutions designed to streamline tissue dissociation and improve reproducibility in single-cell workflows. By combining efficient processing with proven expertise in genomics and sample preparation, Cytiva helps enable high-quality, consistent results that drive deeper biological insights—learn more about our genomics solutions.
Explore strategies for more reliable single-cell sample preparation. Read our case study.
About the author
|
|
Angeliki Achimastou is the product manager for single cell within the Genomics and Diagnostic solutions business at Cytiva. Angeliki has been working in the genomics sector for over a decade in academic, research, and commercial settings, in varying roles including Field Application Specialist, Modality Specialist and Life Science Specialist. She completed her BSc at King’s College London and her MSc at Imperial College in London. Angeliki went on to gain her PhD and post-doctorate in Molecular Neurobiology at the National Institute for Medical Research in the UK. |
References
Method of the Year 2013. Nat Methods. 2014;11(1):1-1. DOI:10.1038/nmeth.2801. [Date accessed: [30DEC2013 https://pubmed.ncbi.nlm.nih.gov/24524124/
Cytiva case study. Document number: CY11624-19Sep23-DF. [Date accessed: 01JAN2023]. digi-50500-pdf
FAQs
What is the difference between bulk RNA sequencing and single-cell RNA sequencing?
Bulk RNA sequencing analyzes all cells in a sample together, producing an average gene expression profile that can mask important differences between individual cells, while single-cell RNA sequencing examines each cell individually, revealing cellular heterogeneity, rare cell populations, and distinct functional states that bulk methods cannot detect.
What is single-cell transcriptomics used for?
Single-cell transcriptomics is used to analyze gene expression in individual cells, enabling researchers to identify distinct cell types, uncover cellular heterogeneity, study developmental processes, and understand disease mechanisms at a higher resolution than bulk methods.
What technologies are used for single-cell analysis?
Technologies used for single-cell analysis include microfluidics devices, droplet-based and microwell-based platforms, tube-based methods (often after FACS sorting), automated tissue dissociation systems, and next-generation sequencing (NGS) instruments such as Illumina™ platforms, all of which enable isolation, processing, and transcriptomic profiling of individual cells.
What is the VIA Extractor tissue disaggregator?
The VIA Extractor is a device that breaks down human and animal tissues, including tumors, into a uniform mix of live single cells. It’s a closed system that’s semi-automated, making it great for high-throughput research like genomics, proteomics, and metabolomics.
What kinds of tissue samples can the system handle?
The system is designed to break down mammalian tissues, tumors, and small organs. It’s been tested on various mouse organs like the kidney, liver, muscle, lungs, brain, and spleen, as well as on human renal cell carcinoma.
What settings can be adjusted on the device to control tissue breakdown?
You can tweak several settings depending on the tissue type:
- Time: Decide how long you want the digestion process to run.
- Speed: You can set the paddle rotation speed anywhere between 50 to 240 rpm.
- Mode: Pick between continuous paddle beating or an on-and-off rotation (one minute on, one minute off).
- Temperature: You can set the temperature for digestion from 0°C to 37°C, depending on what you need.
What’s the minimum and maximum amount of tissue the system can handle?
The system can handle tissue samples as small as 30 mg and as large as 1.2 g, depending on the tissue type.
Besides cells, can we also isolate nuclei?
That hasn’t been tested yet.
How does the system optimize cell viability?
The VIA Extractor uses a gentle, semi-automated process that helps keep cells intact and preserves their content, similar to the original tissue.
Can the cells be preserved for use at a later stage?
Yes, you can preserve the cells using the VIA Freeze™ Uno controlled-rate freezer, which maintains cell viability for future applications.
Lastly, if I go ahead and get the VIA Extractor and VIA freezer for my lab, what else will I need to buy?
Here’s a list of everything you’ll need:
- VIA Extractor tissue disaggregator
- VIA Freeze Uno controlled-rate freezer
- Omics clamp
- Omics pouch
- Omics applicator
- Heat sealer (to prevent sample contamination)
- Forceps or tweezers
- Luer-lock 5 mL syringe (helps with getting the sample in and protects both the sample and the bag)
If you want to keep it simple, there’s also the Omics bundle, which comes with the VIA Extractor, VIA Freeze Uno, and the Omics clamp all together.