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Single cell transcriptional analysis reveals novel innate immune cell types.

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.

SUBMITTER: Kippner LE

PROVIDER: S-EPMC4081288 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Single cell transcriptional analysis reveals novel innate immune cell types.

Kippner Linda E LE Kim Jinhee J Gibson Greg G Kemp Melissa L ML

PeerJ 20140624

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 ...[more]

PMID: 25024920

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Project description:Adipose tissue (AT) is an endocrine organ with a central role on whole-body energy metabolism and development of metabolic diseases. Single-cell and single-nuclei RNA sequencing (scRNA-seq and snRNA-seq, respectively) analyses in mice and human AT have revealed vast cell heterogeneity and functionally distinct subtypes that are potential therapeutic targets to metabolic disease. In periparturient dairy cows, AT goes through intensive remodeling and its dysfunction is associated with metabolic disease pathogenesis and decreased productive performance. The contributions of depot-specific cells and subtypes to the development of diseases in dairy cows remain to be studied. Our objective was to elucidate differences in cellular diversity of visceral (VAT) and subcutaneous (SAT) AT in dairy cows at the single-nuclei level. We collected matched SAT and VAT samples from three dairy cows and performed snRNA-seq analysis. We identified distinct cell types including four major mature adipocytes (AD) and three stem and progenitor cells (ASPC) subtypes, along with endothelial cells (EC), mesothelial cells (ME), immune cells, and pericytes and smooth muscle cells. All major cell types were present in both SAT and VAT, although a strong VAT-specificity was observed for ME, which were basically absent in SAT. One ASPC subtype was defined as adipogenic (PPARG+) while the other two had a fibro-adipogenic profile (PDGFRA+). We identified vascular and lymphatic EC subtypes, and different immune cell types and subtypes in both SAT and VAT, i.e., macrophages, monocytes, T cells, and natural killer cells. Not only did VAT show a greater proportion of immune cells, but these visceral immune cells had greater activation of pathways related to immune and inflammatory response, and complement cascade in comparison with SAT. There was a substantial contrast between depots for gene expression of complement cascade, which were greatly expressed by VAT cell subtypes compared to SAT, indicating a pro-inflammatory profile in VAT. Unprecedently, our study demonstrated cell-type and depot-specific heterogeneity in VAT and SAT of dairy cows. A better understanding of depot-specific molecular and cellular features of SAT and VAT will aid in the development of AT-targeted strategies to prevent and treat metabolic disease in dairy cows, especially during the periparturient period.

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Single cell transcriptional analysis reveals novel innate immune cell types.

Publications

Single cell transcriptional analysis reveals novel innate immune cell types.

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