Identifying transcript-level differential expression in primary human immune cells

Abstract

Background: Multipotential hematopoietic stem cells differentiate into a wide variety of immune cells with a diversity of functions, including the ability to respond to a variety of stimuli. Importantly, numerous studies have demonstrated the importance of gene transcription in defining cell identity and functions. While these studies have primarily been performed at the level of the gene, it is known that key immune genes such as CD44 and CD45 generate multiple different transcripts that are differentially expressed across different immune cells, and that encode proteins with different sequences and functions. Prior genomic surveys have shown that the mechanisms for generating diversity in expressed transcripts (alternate splicing, alternate transcription start sites, etc.) are very active in immune cells, but have been lacking in terms of identifying genes with multiple transcripts, that are differentially expressed, and likely to affect cell functions.

Methods: We first identified the set of genes that had at least two transcripts expressed in our RNA sequencing dataset generated from purified populations of neutrophils, monocytes and five lymphocyte populations (B, NK, γδ T, CD4 + T and CD8 + T) from twelve healthy donors. Next, we developed a heuristic approach to identify genes where two or more transcripts have distinct expression patterns across lymphoid and/or myeloid populations. We then focused our annotation and interpretation on differentially expressed transcripts that affect the coding sequence. This process was repeated to identify transcripts that were differentially expressed between monocytes and populations of macrophages and LPS-stimulated macrophages derived from these monocytes in vitro.

Results: We found that over 55 % of genes had two or more expressed transcripts, with an average ∼3 transcripts per gene, and that 70 % of these had at least two of the transcripts that encoded proteins with different sequences. As expected, we identified a complex pattern of differential expression for multiple transcripts encoding the CD45 transmembrane protein, but we also found similar evidence for ten other genes (CD300A, FYB1, GPI, LITAF, PSMA1, PTMA, RPL32, SEPTIN9, SH3BP2, SH3KBP1) when comparing the expression patterns of transcripts within myeloid and lymphoid cells. We also identified five genes with differentially expressed transcripts associated with the transition from monocytes to macrophages (FNBP1, KLF6, and SEPTIN9) or between macrophages and LPS-stimulated macrophages (CD44, OAZ2, and SEPTIN9). For the most part, we found that the different transcripts of these genes are expected to impact specific biological functions, for example the different transcripts of SEPTIN9 likely regulate the cytoskeleton in immune cells via their interactions with actins filaments and microtubules.

Conclusions: This analytic approach successfully identified multi-transcript genes that are differentially expressed across immune cells and could be applied to other transcriptomic data.

Data availability statement: Researchers can request access to the individual-level data from the current study by contacting the Montreal Heart Institute ethics committee at the following institutional email address: cer.icm@icm-mhi.org.

Keywords: Differential transcript expression control; Primary human immune cells; Protein isoforms; RNA-Seq.