Ribonuclease selection for ribosome profiling

Abstract

Ribosome profiling has emerged as a powerful method to assess global gene translation, but methodological and analytical challenges often lead to inconsistencies across labs and model organisms. A critical issue in ribosome profiling is nuclease treatment of ribosome-mRNA complexes, as it is important to ensure both stability of ribosomal particles and complete conversion of polysomes to monosomes. We performed comparative ribosome profiling in yeast and mice with various ribonucleases including I, A, S7 and T1, characterized their cutting preferences, trinucleotide periodicity patterns and coverage similarities across coding sequences, and showed that they yield comparable estimations of gene expression when ribosome integrity is not compromised. However, ribosome coverage patterns of individual transcripts had little in common between the ribonucleases. We further examined their potency at converting polysomes to monosomes across other commonly used model organisms, including bacteria, nematodes and fruit flies. In some cases, ribonuclease treatment completely degraded ribosome populations. Ribonuclease T1 was the only enzyme that preserved ribosomal integrity while thoroughly converting polysomes to monosomes in all examined species. This study provides a guide for ribonuclease selection in ribosome profiling experiments across most common model systems.

Figure 1.

(A) Schematic of a sucrose gradient fractionation of ribosomes. Cells are lysed, and the lysate is loaded on top of the tube filled with a sucrose gradient solution. Ultracentrifugation at high G leads to separation of mRNA–ribosome complexes based on how many ribosomes are bound to a particular mRNA molecule. Ribosomes appear as UV absorbing peaks when the content of the tube is passed through the UV detector. Monosomal and polysomal peaks correspond to one or more ribosomes bound by mRNA (green arrows on the left panel). (B) Sucrose gradient profiles of ribosomes from model organisms. Each row represents a species, and columns correspond to treatments with different ribonucleases. Equal amounts of ribosomes were taken for nuclease digestion and the control. Mouse (15 weeks old) is represented by liver samples, and fruit fly by whole embryos (2 h post laying). Absorbance was measured at 254 nm. Yeast monosomes display resistance to any ribonuclease tested. Mouse and Drosophila ribosomes show signs of degradation by RNases I and A in particular. The profile corresponding to mouse liver RNase I treatment was obtained with the low amount of the nuclease; increasing the amount or incubation time would further degrade monosomes producing profiles similar to RNase A in mouse or S7 in Drosophila. RNase T1 performs consistently well in all model organisms; it is also lenient in terms of incubation times and concentrations. The number in each panel is the recovery of ribosomes relative to the control (it does not reflect the extent of the polysomes to monosomes conversion).

Figure 2.

Sucrose gradient profiles of ribosomes from mouse spleen and pancreas. (A) Pancreatic ribosomes do not require the addition of ribonuclease inhibitors. Splenic ribosomes require the presence of heparin (800 μg/ml) as a stabilizing agent in order to collect the monosomal peak. Equal amounts of splenic ribosomes were incubated for 30 min at room temperature. Curiously, RNase T1 is not affected by heparin (Supplementary Figure S7) unlike RNase I or A, hence it can be used when other nucleases are inhibited. (B) Comparison of footprint yield between different RNase treatments. No additional methods for rRNA depletion were applied (such as subtractive hybridization). Samples with multiple biological replicates are drawn with SD error bars. RNases I, S7 and T1 perform reasonably well, while RNase A generated the highest contamination with rRNA (proportion of reads aligned to ribosomal RNAs in the total pool of reads).

Figure 3.

Estimation of gene expression produces similar results for all ribonucleases. (A) Gene expression levels in yeast lysates treated with different RNases. Left panel shows correlation between biological replicates treated with RNase I. (B) Pearson correlation matrix for yeast and mouse Ribo-seq libraries. (C) Gene expression levels in mouse liver lysates treated with T1, micrococcal S7 nucleases or both.

Figure 4.

Characteristics of footprints produced by different ribonucleases. (A) Nuclease cutting preference and periodic nature of ribosome profiling footprints. Sequencing reads with specified lengths were lined up by their 3′ termini. Full-length pattern is shown for guanine, patterns for other nucleotides are shown partially to address cutting preference of the nucleases. Note: no mapping to a transcriptome is necessary at this point. Nuclease T1 has a strong preference for guanine residue with over 90% of all footprints having G at the 3′ terminus. Nuclease A cuts exclusively after U and C. Refer to Supplementary Figures S1–4 for footprints with other lengths. (B) Reference pattern typically observed in mRNA-seq. Codon periodicity is weak but noticeable. (C) Read length distribution among ribosomal footprints produced by different nucleases.

Figure 5.

Ribosome coverage profiles. (A) Aggregate ribosome occupancy profiles of all yeast genes longer than 1000 nt. Note: RNase A, S7 and T1 treatments were applied to the exact same yeast harvest, whereas RNase I control comes from a separate harvest. This explains the slight difference between line slopes. The line designated NAR 2014 represents one of the control samples from our previous study (9) and illustrates variability of slope in nuclease I treated samples. (B) Ribosomal coverage profiles of TPI1 gene (YDR050C) produced by various ribonucleases. Left-side panels show the coverage when the entire footprint length is used. Right-side panels show the coverage when only a single 3′ end nucleotide of each read is mapped. Two biological replicates for RNase I demonstrate remarkable similarity of coverage patterns (Pearson's correlation coefficient 0.99).

Figure 6.

Similarity scores of transcript coverage patterns produced by different RNases. (A) Comparison of gene coverage patterns between two yeast Ribo-seq replicates. A cutoff is the average number of reads supporting each nucleotide position within a gene's open reading frame. Increasing the cutoff leads to omission of genes with low similarity of their patterns. (B) Sequencing depth raises correlations between patterns for every gene. Ideally, at the very high coverage, all correlation coefficients would be close to 1. (C) Spread on Spearman's rank correlation coefficients in a set of six yeast Ribo-seq biological replicates. Fifteen possible pairwise comparisons were done for each gene's coverage pattern. Replicates have different sequencing depth, as can be seen in the panel B. (D) The more footprints per pattern a gene has, the higher the correlation is. At the rpk cutoff > 2000 (red dashed line), the Spearman's correlation reaches its maximum. Genes that passed this cutoff can be further compared across studies or across samples. (E) Coverage patterns of the 87 genes (Supplementary Table S1) with rpk > 2000 were compared across Ribo-seq samples. Spearman's rank correlation coefficient was chosen as a similarity score metric. This metric requires a high number of reads per pattern, therefore we limited our analysis to genes with a large number of footprints. The ‘pattern’ is defined as footprint coverage when the entire footprint length is used (Figure 5B, left panel). Complete linkage hierarchical clustering was performed using Spearman correlation distance. RNase I is represented by two biological replicates to show nearly perfect reproducibility of coverage pattern when the experiment is performed at the same conditions. None of the other RNases has the pattern similar to the one of RNase I. Notably, RNase S7 and RNase A patterns share some common features.