Abstract
Single-cell analysis has the potential to provide us with a host of new knowledge about biological systems, but it comes with the challenge of correctly interpreting the biological information. While emerging techniques have made it possible to measure inter-cellular variability at the transcriptome level, no consensus yet exists on the most appropriate method of data analysis of such single cell data. Methods for analysis of transcriptional data at the population level are well established but are not well suited to single cell analysis due to their dependence on population averages. In order to address this question, we have systematically tested combinations of methods for primary data analysis on single cell transcription data generated from two types of primary immune cells, neutrophils and T lymphocytes. Cells were obtained from healthy individuals, and single cell transcript expression data was obtained by a combination of single cell sorting and nanoscale quantitative real time PCR (qRT-PCR) for markers of cell type, intracellular signaling, and immune functionality. Gene expression analysis was focused on hierarchical clustering to determine the existence of cellular subgroups within the populations. Nine combinations of criteria for data exclusion and normalization were tested and evaluated. Bimodality in gene expression indicated the presence of cellular subgroups which were also revealed by data clustering. We observed evidence for two clearly defined cellular subtypes in the neutrophil populations and at least two in the T lymphocyte populations. When normalizing the data by different methods, we observed varying outcomes with corresponding interpretations of the biological characteristics of the cell populations. Normalization of the data by linear standardization taking into account technical effects such as plate effects, resulted in interpretations that most closely matched biological expectations. Single cell transcription profiling provides evidence of cellular subclasses in neutrophils and leukocytes that may be independent of traditional classifications based on cell surface markers. The choice of primary data analysis method had a substantial effect on the interpretation of the data. Adjustment for technical effects is critical to prevent misinterpretation of single cell transcript data.
Highlights
A growing body of evidence indicates that cell populations, even those comprised of genetically identical cells, can be highly phenotypically heterogeneous (Enver et al, 2009; Niepel, Spencer & Sorger, 2009; Spencer et al, 2009; Spencer & Sorger, 2011), and that these differences between individual cells can have functional consequences (Feinerman et al, 2010)
Our results show that analysis and correct interpretation of single cell gene expression data is dependent on the method chosen for primary data analysis, on the method chosen for data normalization
In order to determine whether the existence of genes with bimodal expression patterns signaled the existence of cellular subclasses, the data was clustered based on shared gene expression patterns
Summary
A growing body of evidence indicates that cell populations, even those comprised of genetically identical cells, can be highly phenotypically heterogeneous (Enver et al, 2009; Niepel, Spencer & Sorger, 2009; Spencer et al, 2009; Spencer & Sorger, 2011), and that these differences between individual cells can have functional consequences (Feinerman et al, 2010). Non-genetic variations in response to pro-apoptotic stimuli have been found across several cell lines and stimuli, resulting in phenotypically different subgroups even within clonal cell populations (Cohen et al, 2008; Gascoigne & Taylor, 2008; Geva-Zatorsky et al, 2006; Huang, Mitchison & Shi, 2010; Orth et al, 2008; Sharma et al, 2010; Shi, Orth & Mitchison, 2008; Spencer et al, 2009) In light of this evidence, it is apparent that single cell resolution is needed in order to achieve systems level understanding of functionality
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