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RNA Sequencing for Identification of Differentially Expressed Non-Coding Transcripts During Adipogenic Differentiation of Adipose-Derived Stromal Cells
Anna Luan, M.S., Kevin J. Paik, A.B., Jiang Li, M.S., Elizabeth R. Zielins, M.D., David A. Atashroo, M.D., Andrew Spencley, B.S., Arash Momeni, M.D., Michael T. Longaker, M.D., M.B.A., Kevin C. Wang, M.D., Ph.D., Derrick C. Wan, M.D..
Stanford University School of Medicine, Stanford, CA, USA.
Purpose: Adipose-derived stromal cells (ASCs) represent a relatively abundant, readily-obtainable source of multipotent cells, with many potential applications in regenerative medicine. However, efficiency of in vivo differentiation of ASCs for tissue engineering remains an elusive goal, and sensitive technologies are needed to define new targets in ASC cellular behavior. The present study sought to demonstrate the use of RNA sequencing (RNA-Seq) in identifying differentially expressed transcripts, particularly long non-coding RNA (lncRNA), associated with adipogenic differentiation to gain a clearer picture of the mechanisms responsible for directing ASC fate toward the adipogenic lineage.
Methods: Human ASCs were cultured in adipogenic differentiation media (ADM) and total RNA was harvested at Days 0, 1, 3, 5, and 7. Directional RNA-Seq libraries were prepared using the Mondrian™ SP+ Workstation and the Encore SP+ Complete DR Multiplex System, and sequenced with the Illumina HiSeq System. Paired-end reads were mapped to the human genome reference sequence hg19 with TopHat. Transcriptome assembly was performed with Cufflinks and transcriptomes were merged. Fragments per kilobase of transcript per million mapped fragments (FPKM) for known transcripts were calculated, and significantly differentially expressed transcripts were identified. Gene Ontology (GO) term analysis was then performed to identify coding and non-coding transcripts with potential functional involvement in adipogenic differentiation. Differential expression of several identified lncRNA was verified by quantitative real-time polymerase chain reaction (qRT-PCR). RNA interference was used to knock-down a selected target lncRNA in ASCs, with si-PPARγ as a positive control and a scrambled-siRNA as a negative control. Knock-down was confirmed with qRT-PCR and transfected ASCs were cultured in ADM for 7 days. Adipogenic differentiation was assessed with qRT-PCR and Oil Red O staining.
Results: To visualize genome-mapped data, results were uploaded to the UCSC Genome Browser. Importantly, PPARG and FABP4 - two genes widely known to play integral roles in adipogenic differentiation - were found to significantly increase over the time course of ADM treatment. 3171 significantly differentially expressed transcripts were identified; 230 were non-coding. 1193 transcripts were upregulated and 1978 were downregulated. Enriched GO terms among upregulated coding transcripts notably reflected differentiation toward the adipogenic lineage. Enriched GO terms among downregulated coding transcripts reflected growth arrest, a key feature of differentiated adipocytes. Guilt-by-association analysis revealed non-coding RNA candidates with potential roles in the process of adipogenic differentiation. Knock-down of the selected lncRNA resulted in decreased adipogenic gene expression and lipid accumulation.
(a) Heatmap of DE non-coding transcripts. (b) Visual guide of guilt-by-association analysis used for prediction of non-coding RNA functions. (c) Heatmap of non-coding RNA candidates.
Conclusions: The precise mechanisms that guide lineage-specific differentiation in multipotent cells are not yet fully understood. Defining lncRNAs associated with adipogenic differentiation allows for potential manipulation of regulatory pathways in novel ways. Thus, we present RNA-Seq as a powerful tool for expanding our understanding of ASCs and for development of novel applications employing these cells in regenerative medicine.
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