Single cell transcriptional analysis reveals novel innate immune cell types

• Type of Material: Article
• Authors: Linda E. Kippner, Georgia Institute of Technology; Jinhee Kim, Georgia Institute of Technology; Greg Gibson, Georgia Institute of Technology; Melissa Kemp, Emory University
• Language: English
• Date: 2014-06-24
• Publisher: PeerJ
• Publication Version: Final Published Version
• Copyright Statement: © 2014 Kippner et al.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.7717/peerj.452
• Title of Journal or Parent Work: PeerJ
• ISSN: 2167-8359
• Volume: 2
• Issue: 1
• Start Page: e452
• End Page: e452
• Grant/Funding Information: Funding for this work was provided to GG and MLK by the Petit Institute of Bioengineering and Bioscience at Georgia Institute of Technology and NIH award DP2OD006483 to MLK.
• Keywords: PROGRAMS; Fluidigm; LARGE GENE LISTS; EXPRESSION; Gene expression; Multidisciplinary Sciences; EMBRYONIC STEM-CELLS; REGULATORY HETEROGENEITIES; Single cell analysis; CANCER-CELLS; RESPONSES; Data processing; ANTIMITOTIC DRUGS; KINESIN-5; Science & Technology - Other Topics; APOPTOSIS; Science & Technology
• Research Categories: Biology, General; Engineering, Biomedical

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.