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Cell-type specific polysome profiling from mammalian tissues - PubMed

  • ️Tue Jan 01 2019

Cell-type specific polysome profiling from mammalian tissues

Joseph Seimetz et al. Methods. 2019.

Abstract

The regulation of gene expression occurs through complex relationships between transcription, processing, turnover, and translation, which are only beginning to be elucidated. We know that at least for certain messenger (m) RNAs, processing, modifications, and sequence elements can greatly influence their translational output through recognition by translation and turn-over machinery. Recently, we and others have combined high-throughput sequencing technologies with traditional biochemical methods of studying translation to extend our understanding of these relationships. Additionally, there is growing importance given to how these processes may be regulated across varied cell types as a means to achieve tissue-specific expression of proteins. Here, we provide an in-depth methodology for polysome profiling to dissect the composition of mRNAs and proteins that make up the translatome from both whole tissues and a specific cell type isolated from mammalian tissue. Also, we provide a detailed computational workflow for the analysis of the next-generation sequencing data generated from these experiments.

Keywords: Cell-type isolation from tissues; Next-generation sequencing and bioinformatics; Polysome profiling; Ribosomal occupancy shift; Translation regulation.

Copyright © 2018 Elsevier Inc. All rights reserved.

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Figures

Figure 1.
Figure 1.. Experimental schematic.

The experimental workflow of polysome profiling from mammalian tissues or isolated cell-types following by next-generation sequencing analysis. Either whole tissue or single cell-types are isolated from the animal. The cells are lysed, nuclei and cell debris are removed, and the cytoplasm is layered onto a sucrose gradient. Following centrifugation, the gradient is flowed through an absorbance detector and the profile is collected. mRNA can then be isolated from fractions for downstream analysis.

Figure 2.
Figure 2.. Polysome profiles from different cells and tissues.

Representative polysome traces measured at 254 nm of (A) undifferentiated C2C12 cells, (B) adult mouse heart, (C) neonatal mouse heart, (D) freshly isolated mouse hepatocytes, and (E) adult mouse brain. (F) A representative negative control trace in which ribosomes are disassociated from RNA by the addition of EDTA.

Figure 3.
Figure 3.. Shirloc bioinformatic analysis pipeline.

Shirloc utilizes Kallisto and Sleuth to determine transcript abundance and calculate fold change of fractions relative to the monosome. A ribosomal occupancy factor is then applied to calculate Ribosomal Occupancy Shift (ROS) values. The final output is given in table form. The data can then be visualized in many different ways, including the examples provided.

Figure 4.
Figure 4.. Gene ontology analysis of cell-type specific ribosome occupancy.

Gene Ontology networks were created using Enrichment Maps plugin for Cytoscape. Networks were generated for 1000 transcripts with the A) lowest relative ribosomal occupancy (RO) in hepatocytes, B) highest RO in hepatocytes and C) lowest RO in cultured neurons. As expected, hepatocytes showed greatest RO for transcripts involved in various metabolic functions. Cultured neurons, on the other hand revealed lowest RO cluster of processes involving translation and cell division. No significant clustering or enrichment was observed for transcripts with high RO scores.

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