The Devil is in the Details . . . and the Data

by Bruce Windoffer and Rob Hall on September 18, 2014 in Genomics
Single cell sequencing

By Bruce Windoffer and Rob Hall

We’re often advised to stand back, examine something at a high level, and gain some perspective.  But sometimes the answers we need require us to do just the opposite – to stand closer, drill down, and study the details.

Such was the case at the recent Western Association of Core Directors (WACD) conference we attended in Davis California. Like the recent Next Generation Dx conference, many of the talks focused on single cell sequencing.

Single cell sequencing enables scientists and researchers to look at the genome of just one cell, providing a much more detailed, granular look at cellular differences and the function of an individual cell.

Recent advances in sequencing and amplification techniques are helping more labs offer single cell sequencing.  And according to Vincent Funari, PhD, and Genomics Core Lab Director at Cedars-Sinai, all core labs should brush up on what it will take to offer this as a service. In his talk, Funari focused on two sources of heterogeneity in data: QC and/or batch effects, or underlying biological diversity. It is imperative, from the core lab perspective, to adequately address and measure the effects of QC differences and batch processing to remove these potential sources of heterogeneity from datasets, particularly RNASeq, to reduce the possibility of reporting false positives in the data. Funari recommended further development and refinement of QC assessment when processing single cell assays and RNASeq in particular.

Indeed, labs that conduct single cell sequencing will meet with some interesting challenges.  For starters, the volume of samples and sample data will increase substantially when compared with traditional next generation sequencing. Additionally, investigators will need to gain a better understanding of these datasets.

Not surprisingly good informatics tools, such as BaseSpace Clarity LIMS, can help labs address these challenges.  It is a given that when sample volumes grow or new technologies and workflows are introduced in a lab, BaseSpace Clarity LIMS can help these labs adapt. Automation, improved accuracy, and better sample tracking help labs adjust to bigger sample volumes.  And preconfigured workflows for common instruments and the ability to easily configure many aspects of the system help labs adjust to new technology platforms.

More interestingly, however, BaseSpace Clarity LIMS can also help these labs make better sense of the data.  Single cell sequencing studies introduce a new level of complexity because investigators need to understand if the heterogeneity they see in resulting data is due to the underlying cell biology, or a result of sample quality or batch effects. BaseSpace Clarity LIMS includes quality control features that can help labs to determine the sources of heterogeneity. BaseSpace Clarity LIMS’ flexibility of configuration allows for tracking QC metrics at any step of a workflow, and enables downstream data analysts to easily correlate results with multiple upstream sample QC measures or batch processing data. With all of the QC and batch information available in BaseSpace Clarity LIMS, finding the source of heterogeneity in data sets can be much more easily accomplished, and the time and effort wasted chasing down false-positives drastically reduced.

By all accounts, single cell sequencing promises to give us greater insight into the function of individual cells and provide breakthrough applications in oncology, such as early detection of cancer cells and more personalized treatments. But with this potential comes the need for more refined analytical tools.

BaseSpace Clarity LIMS can help your lab deliver the newest technologies, such as single cell sequencing. For more information, contact us or request a demo.

References

  1. http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1004126
  2. http://www.biomedcentral.com/content/pdf/gm247.pdf
  3. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3947563/

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