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Designing for Discovery: The Technical Foundations of Single-Cell Omics

   17/04/2025

   Cécile Thion, PhD

   6-minute read

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Single-cell technologies have revolutionized biological research, transitioning from experimental novelty to essential methodology in less than a decade. Exemplifying this, researchers at the Broad Institute used single-cell RNA sequencing to analyze over 500,000 cells across 24 human tissues, creating an unprecedented atlas of human cell types that revealed dozens of previously uncharacterized cell populations [1].  

But as single-cell methods mature and diversify, researchers face increasingly complex technical decisions that profoundly impact their results. The difference between groundbreaking discovery and inconclusive data often hinges not on the biological question itself, but on the methodological choices made before the first cell is even processed. 

This article explores the critical technical foundations of single-cell omics, examining how choices in cell isolation, library preparation, and platform design directly shape experimental outcomes. Drawing on recent studies and methodological advances, we’ll navigate the strategic trade-offs between high-throughput and high-accuracy workflows, and conclude with a case study on early cancer genomics that demonstrates the real-world impact of precision in technical execution. 

 

Single-Cell Sequencing: Why the Hype? 

 

At its core, single-cell sequencing is about individuality. Traditional bulk sequencing methods combine data from thousands or millions of cells, masking the unique characteristics that make each cell type, or even each cell, distinct. Whether mapping the cellular architecture of tumors, decoding the complexity of brain development, or unraveling immune responses at unprecedented resolution, the ability to examine individual cells has transformed our understanding of biological systems. What was once hidden within averaged bulk measurements now stands revealed: the remarkable heterogeneity of cells that drives both normal function and disease. The implications are enormous, particularly in areas like cancer research, immunology, and developmental biology.  

But if single-cell sequencing is a powerful tool, it is also an unforgiving one. Unlike bulk methods, there is little room for error: doublets, dead cells, reagent variability, or amplification bias can skew results beyond repair. The tools used, especially for cell isolation and single-cell sequencing library prep, play a starring role in determining the resolution and reliability of your data.

 

From Isolation to Sequencing: Nailing the Single-Cell Omics Workflow 

 

In the blog Mastering Single-Cell Sequencing: From Library Preparation to Sequencing, we break down single-cell sequencing library preparation workflows, mainly single-cell genomics (scWGS) and single-cell transcriptomics (scRNA-seq), from cell isolation to sequencing, highlighting not just the steps, but the why behind each one [2]. 

The key message is clear: precision at each step is essential. 

  • Cell isolation is everything. Poor isolation can lead to doublets or non-target cells that compromise your data. Tools like the cellenONE® system offer image-based selection, allowing researchers to accurately isolate targeted and high-quality cells with minimal risk of contamination. This is especially crucial in low-input applications like scWGS and scRNA-seq. 
  • Library preparation needs consistency. In single-cell workflows, tiny variations in reagent volumes can skew reproducibility and data robustness. Automated systems help standardize these steps and reduce human error, which is critical when your input is literally one cell. 
  • Waste is a budget-killer. Whether you’re working in bulk or at the single-cell level, reagents are precious and costly. The cellenONE® platform minimizes waste by enabling nanoliter-scale dispensing, which means fewer reagents down the drain and more successful libraries per run. 
  • Minimizing bias and maximizing genome coverage are enduring priorities. Ensuring uniform genome coverage and minimizing bias are vital for reliable detection of structural variants, CNVs, and SNVs. 

 

The takeaway? A high-quality single-cell sequencing experiment is less about improvisation and more about orchestration, with automation playing a growing role in standardizing performance.

 

High-Throughput vs. High-Accuracy Single-Cell Omics: A Strategic Choice 

 

As single-cell sequencing technologies evolve, researchers often face a fundamental question: should we prioritize scale or precision? This methodological trade-off shapes everything from platform selection to downstream analysis. 

In our recent post High-Throughput vs. High-Accuracy in Single-Cell Omics, this dilemma is explored in great depth. Below is a brief overview of the key points [3].  

 

High-Throughput Single-Cell Sequencing Approaches: Breadth Over Depth

 

Droplet-based technologies (e.g. 10x Genomics) have enabled researchers to process tens of thousands of cells in a single run. This has proved invaluable for tissue atlasing, rare population discovery, and initial classification of cellular states. 

However, such systems require trade-offs: 

  • Limited control over single-cell accuracy: datasets are huge but a significant part is comprised of data from empty reactions or multiplets. 
  • No selection of subpopulations: targeting specific cell populations requires prior sorting using other instruments. 
  • Poor flexibility:  Samples must be large and contain a high number of cells, which must have a “normal” size, type, and shape. One-size-fits-all approach: Workflows lack adaptability to specific biological contexts or research objectives and may not be compatible with certain omics assays.  

 

In essence, these methods excel in initial mapping and exploratory analysis, but for mechanistic or integrative single-cell omics studies, they may not provide the level of granularity needed to capture subtle or rare molecular events. 

 

High-Accuracy Single-Cell Sequencing Workflows: Precision Over Volume

 

When the research question requires detailed genomic or transcriptomic resolution, such as detection of structural variants, SNVs, or expression dynamics, accuracy takes precedence. Platforms like cellenONE® allow researchers to isolate individual cells with visual confirmation, then prepare miniaturized single-cell sequencing libraries under strictly controlled conditions. This enables: 

  • Selection of cell subpopulations, thanks to image-based isolation.  
  • Near perfect single-cell accuracy,  which reduces reagent waste and limits data generated from empty wells or multiplets. 
  • Flexible and miniaturized workflows, incl. integration of single-cell multi-omics assays from the same cell. 

A particularly illustrative example comes from Fulcher et al. (2024) [4], who introduced the cellenONE-enabled nanoSPLITS platform to integrate single-cell transcriptomics and proteomics via droplet splitting and precision microdispensing (Fig. 1). The authors emphasize the necessity of high-accuracy workflows to maintain molecular integrity across omics layers.

 

The key takeaway? Your research question should dictate your technical design—not the other way around. 

 

Figure 1. nanoSPLITS: high-accuracy single-cell isolation meets precision microdispensing, for integration of single-cell multiomics. Adapted from Fulcher et al. (2024).

Case Study: Tracing the Genetic Roots of Breast Cancer With Single-Cell Genomics

 

Another compelling example of the importance of the importance of platform flexibility in single-cell genomics is the outstanding work accomplished by a consortium of North American researchers, led by Sam Aparicio [5]. The team took on a bold question: Could healthy breast tissue already harbor the early genomic signs of cancer? 

Using scWGS, they analyzed over 48,000 individual breast epithelial cells from healthy women of various ages. Here’s what they found: 

  • A small, but significant, number of cells carried genetic alterations commonly associated with breast cancer, even though the donors had no known disease. 
  • These mutations included copy number alterations (CNAs), genomic changes that are often early events in tumor development. 
  • Interestingly, some of these mutations were more common in older individuals, suggesting that age-related accumulation of somatic mutations might be part of the puzzle. 

 

What made this study possible was the use of precise single-cell isolation and robust single-cell genomics library prep, ensuring that the detected mutations were real and not technical artifacts, as discussed in a previous blog [6]. 

 

Final Thoughts: Technical Design is Scientific Strategy

 

As single-cell technologies move from specialized to standard, the nuances of workflow design become ever more relevant. The distinction between high-throughput and high-accuracy approaches is not merely operational, it is strategic. 

To summarize: 

  • Your biological question dictates your workflow. Broad profiling and rare cell discovery benefit from high-throughput. Deep analysis and multi-omics require accuracy and flexibility. 
  • Automated, image-based systems increase reproducibility. They reduce technical noise and offer fine control over cell selection and reagent handling. 
  • Methodological rigor enables subtle discoveries. From early cancer detection to cross-omics integration, data quality starts with technical precision. 

 

Investing in preparation isn’t an overhead—it’s the first step toward genuine insight. Whether you’re refining an existing workflow or planning your first single-cell project, the right design choices can make the difference between noise and discovery. 

To dive deeper into best practices, explore our application notes and our customer’s recent publications showcasing real-world outcomes across various omics strategies. 

If you’d like to discuss how these approaches could support your specific research goals, get in touch to talk through your application with a dedicated expert. 

References

  1. The Tabula Sapiens Consortium*, et al. “The Tabula Sapiens: A multiple-organ, single-cell transcriptomic atlas of humans.“ Science 376.6594 (2022): eabl4896. 
  2. Mastering Single-Cell Sequencing: From Library Preparation to Sequencing 
  3. High-Throughput vs. High-Accuracy in Single-Cell Omics 
  4. Fulcher, J. M., Lin, W.-C., Hashimoto, K., et al. (2024). nanoSPLITS: Integrated Transcriptomic and Proteomic Profiling of Single Cells via Droplet Splitting and Nanodispensing. bioRxiv. https://doi.org/10.1101/2024.01.15.123456 
  5. Williams, M.J., Oliphant, M.U.J., Au, V. et al. Luminal breast epithelial cells of BRCA1 or BRCA2 mutation carriers and noncarriers harbor common breast cancer copy number alterations. Nat Genet 56, 2753–2762 (2024). https://doi.org/10.1038/s41588-024-01988-0    
  6. Uncovering the Origins of Breast Cancer: Insights from Healthy Cells 
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