Singling out solutions for single-cell analysis

To date, most of what we know about our genome comes from studying populations of cells. Although few would argue with how far we have come to understand our genome, many researchers now realize that it may be just as important to fully examine the heterogeneity that exists within the population of cells. Evidence suggests that bulk sequencing methods can mask the contribution of individual cells. As a result, many researchers are turning to an evolving technique: single-cell sequencing.

Pioneered in the 1990s by James Eberwine2 and made more robust by the analytical sensitivity and specificity of next-generation sequencing (NGS) methods,3 single-cell sequencing enables researchers to examine the heterogeneity of cells, and promises to reveal what role individual cells play in disease and complex biological systems.

How? For every cell sequenced, researchers have a comprehensive map of the transcriptome that can be analyzed in several of different ways to characterize cells at single-cell resolution. Currently, 3 primary applications stand out:

  • Assessing cell-to-cell heterogeneity. In this application, researchers dissect cell subtypes in a heterogeneous population of cells using cell surface markers to characterize cell types within a population. Using this method, cells can be bioinformatically classified based on expression levels of thousands of genes using clustering approaches, such as principal component analysis (PCA). This process has even enabled discovery of new cell types that were not previously known.4
  • Mapping cell trajectories. Using this application, researchers can investigate cell lineage trajectories over time and possibly detect expression changes occurring in only a subset of cells or substates along a development path. Notably, in traditional bulk-cell sequencing approaches, these trajectories would be missed as they would be averaged across the population.
  • Dissecting transcriptional mechanics. Using this application, researchers can classify individual cells according to a gene’s transcription state, such as presence or absence of a transcription factor.

Yet researchers who conduct single-cell sequencing still face throughput and analysis challenges, so with the potential for this method comes the need for more refined sequencing and bioinformatics tools.

A scalable, high throughput, and straightforward solution

To deliver on the promise of single-cell biology, the Illumina® Bio-Rad® Single-Cell Sequencing Solution combines the Bio-Rad Droplet Digital™ Technology with Illumina NGS library preparation, sequencing, and analysis technologies. This new platform provides a comprehensive workflow for single-cell RNA-Seq that enables controlled experiments with multiple samples, treatment conditions, and time points.

This co-developed solution enables transcriptome analysis of hundreds to thousands of single cells in one experiment, enabling researchers to apply the sensitivity and precision of RNA-Seq to questions that can only be answered by interrogating individual cells.

Flowjo Workflow

After sequencing, the single-cell sequencing data can be instantly transferred, stored, and analyzed securely in BaseSpace Sequence Hub. There, users can access the SureCell RNA Single-Cell App, which was specifically designed to support data analysis for the Illumina Bio-Rad Single-Cell Sequencing Solution. This app enables streamlined data analysis for up to 96 samples across multiple sequencing runs and performs:

  • Read 2 alignment using the STAR aligner
  • Cell barcode and unique molecular identifier (UMI) identification
  • UMI counting for each gene and associated statistics
  • Identification of good barcodes corresponding to single cells
  • Calculation of alignment, cell, and gene metrics

The app generates a BAM, cell and gene counts table, and a report including analysis metrics and plots.


The UMI cell plot indicates the total number of cells passing filter; the vertical threshold (red line) must pass through the first knee. The defining features are the two distinct curves, or knees, and the threshold, which indicate the number of valid cells detected in the sample.


The t-Distributed Stochastic Neighbor Embedding (t-SNE) plot is a two-dimensional projection of cells illustrating potential clusters (populations) of neighboring cells with similar expression profiles.

Downstream analysis with FlowJo SeqGeq

We’ve worked with another one of our partners – FlowJo – to develop an integration between the SureCell RNA Single-Cell App and the SeqGeq toolset. SeqGeq is a set of tools for exploring single-cell NGS data with an intuitive drag-and-drop interface. Users of both systems can transfer files into SeqGeq for additional visualization and analysis, including gene tables, and heat maps.


Within SeqGeq, you can directly import data from BaseSpace Sequence Hub.

For more information, and to learn how Illumina instruments and bioinformatics are integrated with the solutions from Bio-Rad and FlowJo, download the technical note titled “Illumina® Bio-Rad® SureCell™ WTA 3′Library Prep Kit for the ddSEQ™ System” or visit the FlowJo website.

For Research Use Only.  Not for use in diagnostic procedures.
  1. Macaulay, Iain C. and Thierry Voet. “Single Cell Genomics: Advances And Future Perspectives”. PLoS Genet 10(1): e1004126. doi:10.1371/journal.pgen.1004126
  2. Eberwine J, Yeh H, Miyashiro K et al. Analysis of gene expression in single live neurons. Pnasorg. 2017. Available at: Accessed March 14, 2017.
  3. Liu STrapnell C. Single-cell transcriptome sequencing: recent advances and remaining challenges. 2017.
  4. Macosko E, Basu A, Satija R et al. Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets. 2017.

Leave a Reply

Fill in your details below or click an icon to log in: Logo

You are commenting using your account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s

%d bloggers like this: