Archive by Author | Jay Patel

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.

Picture1.png

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.

Picture2

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.

Picture3

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.
References
  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: http://www.pnas.org/content/89/7/3010.short. 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.

BaseSpace Suite Summit

Join us for our BaseSpace® Suite Informatics Summit in Copenhagen, DK on 31 May and 1 June. Immediately after the European Society for Human Genetics (ESHG) annual meeting, attendance at the summit is FREE. Learn more about our informatics tools and how they’re designed to help you transform complex genomic data into meaningful insights quickly and easily.

basespace-suite-summit-copenhagen
Why attend a BaseSpace Suite Summit?

  • Share your perspectives on applying informatics tools in your lab
  • Attend informative sessions and learn how other customers use informatics
  • Get important product information for BaseSpace Clarity LIMS, BaseSpace Sequence Hub, BaseSpace Variant Interpreter (Beta), BaseSpace Cohort Analyzer, and BaseSpace Correlation Engine
  •  Learn best practices, including how an integrated approach to informatics can expedite workflows
  • Connect with your peers

Register here.

Learn more by clicking on the “Summit” dropdown above, or click here. 

 

BaseSpace Informatics Suite Summit 2016

transform-possibilities

You’re invited to an exclusive informatics event

Advancing Precision Medicine efforts relies on the ability to make sense of a growing body of genomic data. The need for robust informatics tools and an integrated approach when it comes to acquiring, storing, distributing, and analyzing data is essential.

Join us for our BaseSpace® Suite Summit in Rochester, MN on October 3 and 4. Taking place immediately before the Individualizing Medicine Conference, registration is free. Learn more about our informatics tools and how they’re designed to help you transform complex genomic data into meaningful insights quickly and easily.

  • Share your perspectives on applying informatics tools in your lab
  • Attend your choice of sessions on informatics topics
  • Learn best practices for laboratory information management, including how an integrated approach can expedite workflows
  • Connect with your peers

Venue and Format
Lodging and Summit activities take place at the Kahler Grand Hotel in Rochester, MN. All day sessions on October 3 and the morning of October 4 include a variety of hands-on, introductory, and training sessions.

More Information
If you have questions, please contact us.

register

Introducing Enrichment v3.0 with Enhanced Variant Calling

Enrichment

Enrichment v3.0

The new Enrichment v3.0 BaseSpace® App (formerly called Isaac Enrichment) introduces major improvements and new features including:

  • Improved small variant calling
  • Copy number variant (CNV) calling
  • Structural variant calling
  • Somatic/low-frequency variant calling
  • Ability to start from FASTQ or BAM
  • GRCh38 reference added
  • Variant table CSV file including variant frequencies
  • Improved variant annotation engine
  • Improved metrics engine

Read More…

Important Changes to the BaseSpace Sequence Hub Basic Tier Accounts

Early next week all BaseSpace Sequence Hub accounts will track storage and compute usage.

Our Basic tier users will continue to enjoy 1 Terabyte of storage for free. On top of that we’re crediting your account with 250 free iCredits to use over 30 days to make sure you have a chance to explore all the apps we offer*.

We offer several compute and storage options to fit your needs. Let us know if you’d like to learn more about BaseSpace Sequence Hub tiers.

* iCredits are used to pay for app  (compute and license fees) and additional storage charges. Any unused iCredits will expire at the end of this 30 day period.

Illumina Announces BaseSpace Correlation Engine Integration with Elsevier’s Pathway Studio

Accelerates discovery by demonstrating genetic functional relationships and validating results

 

Illumina and Elsevier R&D Solutions have announced a collaboration to integrate use of Pathway Studio, Elsevier’s database of experimental data and disease models, with Illumina’s BaseSpace Correlation Engine platform, offering them as one seamless solution. Under the terms of the agreement, all licensed Correlation Engine users have free access to Pathway Viewer, an application that enables researchers to quickly view a snapshot of the functional relationships among genes. In addition, researchers are able to find top scoring canonical pathways that correlate most highly with the genes list.

Correlation Engine is a suite of applications that lets users discover the relationships among their results, with billions of correlations derived from the world’s largest curated library of genomic knowledge. The applications contain over 130,000 experimental comparisons from more than a half million samples, drawn from NCBI’s Gene Expression Omnibus and other open-access or controlled-access databases.

Read More…

Human mtDNA Analysis in BaseSpace

Mitochondrial DNA (mtDNA) analysis enables forensic laboratories to extract genetic data from small biological samples, found in less than ideal condition. Mitochondria in humans cell contain about 1,000 copies of mtDNA. The ease-of-use of next-generation sequencing (NGS) and Nextera XT enables labs to speed up their workflow, reducing time and labor spent on prep, and generate deeper coverage data compared to Sanger sequencing at 1X coverage/rx.

The updated Illumina mtDNA demonstrated protocols provide a complete mitochondrial DNA solution – from targeted library prep and sequencing to bioinformatics analysis and report generation in BaseSpace – to help investigators draw conclusions in a straightforward and intuitive workflow.

The two new mtDNA apps in BaseSpace allow for variant analysis and easy visualization of mitochondrial sequence data. This workflow can analyze any part of the full circular genome, without any origin dead zone, using quality and coverage thresholds customized by the user (Figure 1).

Figure 1: mtDNA Analysis Workflow. Sequence data is streamed into BaseSpace. Initial processing is performed using the mtDNA Variant Processor app and the results are stored in your BaseSpace project. Use mtDNA Variant Analyzer to visualize the data and generate a downloadable Excel report.

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