The ribosome is a large RNA-protein complex, comprising four ribosomal RNAs (rRNAs) and >80 ribosomal proteins (RPs), which coordinates mRNA-templated protein synthesis. In human cells, the 80S ribosome has a molecular weight of ~4.3 MDa and a diameter of 250–300 Å (Khatter et al., 2015), which is larger than ribosomes in either yeast cells (~3.3 MDa), or in bacteria (~2.3 MDa) (Melnikov et al., 2012). During translation, multiple ribosomes can simultaneously engage the same mRNA to form a ‘polysome’ (Noll, 2008; Warner and Knopf, 2002; Warner et al., 1963). In mammalian cells, it is likely that most protein translation takes place on polysomes, rather than via mRNA translation by a single ribosome.

The biochemical analysis of gene expression and regulation benefits from the ability to isolate and characterize these very large polysome structures. For example, polysome profiling has recently become a popular technique for the analysis of the ‘translatome’, that is the set of mRNA species being actively translated in polysomes (King and Gerber, 2016; Piccirillo et al., 2014). Polysome profiling involves the separation and isolation of polysomes away from free ribosomal subunits. This is typically achieved using a sucrose density gradient (SDG) step, prior to mRNA analysis with the enriched polysome fraction.

SDG fractionation, a method introduced in the 1960s (Britten and Roberts, 1960; Warner et al., 1963), remains the near ubiquitous approach currently used to isolate and analyse polysomes. However, SDG analyses have limitations and can be time-consuming to set up and perform. This includes the long ultracentrifugation step (typically 3–20 hr) required, which can affect the structure of the ribosome and result in loss of weakly associated proteins (Simsek et al., 2017). SDG analysis also requires dedicated hardware for profile generation and fraction collection (Gandin et al., 2014). Multiple steps in the SDG workflow can also introduce sources of technical variation (e.g. gradient formation between centrifugation tubes, differences in starting position and collection of fractions etc), leading to an overall decrease in reproducibility and affecting resolution and accuracy (Ingolia et al., 2009).

An alternative approach to SDG for isolation of polysomes and free ribosome subunits is to use affinity purification in extracts of cells expressing tagged RPs (Heiman et al., 2014; Inada et al., 2002; Simsek et al., 2017). However, polysomes and ribosomal subunits are co-isolated by this technique and cannot easily be separated. Moreover, the over-expression of RPs either with, or without tags, is known to induce formation of sub-complexes, because of inefficient assembly into ribosomes (Simsek et al., 2017). Unassembled ribosomal proteins are rapidly degraded by the ubiquitin-proteasome system (Lam et al., 2007; Sung et al., 2016a; Sung et al., 2016b; Warner, 1977).

Arguably, the most efficient and reproducible modern method for fractionation-based biochemical analysis is provided by ultra High-Pressure Liquid Chromatography (uHPLC). This provides exceptional reproducibility, in comparison with other methods, from the use of electronically controlled autosamplers for sample injection and automatic fraction-collection components. Despite these potential advantages, uHPLC methods have not been developed for the effective chromatographic separation of polysomes and ribosome subunits (Maguire et al., 2008; Trauner et al., 2011). In particular, the very large size of polysomes has been viewed as being outside the effective separation range of size exclusion chromatography methods.

To provide a more efficient and reproducible methodology that can facilitate the isolation of polysomes from mammalian cells and tissues, we have developed an uHPLC Size Exclusion Chromatography (SEC) method for studying very large intracellular structures and protein complexes (Mega-SEC). Here, we demonstrate the application of Mega-SEC to separate translation complexes extracted either from human cell lines, or from mouse liver tissue. We show efficient separation and collection of polysomes, 80S ribosomes, 60S and 40S ribosomal subunits using Ribo Mega-SEC uHPLC runs of ~15 min, with high reproducibility. We also combine Ribo Mega-SEC with downstream electron microscopy and high-throughput proteomic analysis to characterize isolated polysomes.

To prepare lysates for polysome separations, we employed the standard method of polysome extraction used in traditional SDG analysis. The lysis buffer contained the standard reagents used to stabilize polysomes (cycloheximide, RNase inhibitors and Mg²⁺) (Klein et al., 2004; Ruan et al., 1997; Schneider-Poetsch et al., 2010; Shi et al., 2017; Strezoska et al., 2000). In addition, the buffer needed to contain a non-denaturing (either non-ionic or zwitterionic) detergent to solubilize the cell membranes. The non-ionic detergent Triton X-100 is the most widely employed in biochemical studies on ribosomes, including SDG analyses (Ingolia et al., 2012; Ingolia et al., 2009; Olsnes, 1970; Shi et al., 2017). However, Triton X-100 has a low critical micelle concentration (CMC), and is therefore not ideal for use in an SEC separation buffer. Heparin has been widely used when preparing extracts for polysome analyses but is not always essential and as shown below is best avoided for specific applications, such as MS-based analysis of isolated complexes.

We therefore compared polysome profiles using SDG of lysates prepared in a buffer containing either Triton X-100, or the zwitterionic detergent CHAPS, which is also used for ribosome analysis and whose CMC is sufficiently high such that it can be effectively used in SEC separation buffers below its CMC (Hjelmeland, 1980). This showed a similar recovery of polysomes, including halfmers and ribosomal subunits from HeLa cells for both Triton X-100 and CHAPS (Figure 1—figure supplement 1). We therefore selected CHAPS for all subsequent experiments, based on its high CMC and because it is known to solubilize protein complexes without denaturation (Hjelmeland, 1980). We note that with the HeLa cells and culture conditions used here, which supported rapid cell growth, there was a high polysome:monosome (P/M) ratio, that is with relatively low levels of 80S ribosomes, as shown both by SDG and SEC analyses. We used these culture conditions in the experiments described below.

To efficiently resolve polysomes and ribosomal subunits from cell extracts by SEC, we next optimized the choice of pore size for the SEC column and the maximum flow rate. First, we compared the SEC chromatograms of HeLa cell lysates separated using three different SEC columns with pore sizes of either 300, 1,000, or 2,000 Å, respectively, but with the same flow rate and salt concentration. This showed that only the largest pore size, that is 2,000 Å SEC column, successfully resolved complexes in the polysome size range (Figure 1—figure supplement 2). To characterize the fractionation profile of the 2,000 Å SEC column and assign the resulting peaks, we injected onto the SEC column either ribosomal subunits, or polysomes, that had been previously isolated by SDG (Figure 1A). Using a column flow rate of 0.2 ml/min, the 40S subunit, 60S subunit, 80S monosome and a mixture of polysomes elute separately at ~50 min, 46 min, 43 min and between 33 and 41 min, respectively (Figure 1B and C). This showed that the first peak eluted from the SEC column contained a mixture of different n-mer polysomes, the second peak contained 80S ribosomes, the third peak contained 60S subunits, the fourth peak contained 40S subunits and the bulk of smaller protein complexes eluted in a broad peak at the end of chromatogram (Figure 1D). This also showed that the peak corresponding to di-somes (two ribosomes per mRNA) was also detectable (Figure 1D). As the 80S peak was clearly separated from the polysome peak, a P/M ratio can be calculated, similar to that achieved by SDG (Figure 1D). We note that the use of a 1,000 Å SEC column provided a higher resolution separation between 60S and 40S ribosomal subunits, as well as between the 40S subunit and smaller protein complexes, but resulted in the 80S peak almost overlapping the polysome peak (Figure 1—figure supplement 2).

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To achieve a higher resolution separation, extending from larger polysomes to smaller protein complexes, in a single shot analysis, we tested the use of two SEC columns connected in series using a column flow rate of 0.2 ml/min (Figure 1—figure supplement 3). This showed increased resolution of di-some and tri-some peaks, along with higher, n-mer polysome complexes and improved separation of both ribosomal subunits using two 2,000 Å SEC columns in series (Figure 1—figure supplement 3A). A similar improved separation of polysomes was achieved by the combination of a 1,000 Å SEC column and 2,000 Å SEC column in series, while this combination also provided more effective separation in the size range from 80S to smaller protein complexes (Figure 1—figure supplement 3B). Fractionation of extracts using two SEC columns run in series thus provides a flexible system for efficient separation across a large range of complexes of different sizes.

Next, we optimized the column flow rate using a single 2,000 Å SEC column to minimize the total elution time required, while maintaining resolution. To do this, we chose to focus on the separation of the 80S peak from the polysome peak, given their close proximity. Using a column flow rate of 0.2 ml/min and a separation time of 60 min, the polysome peak and the 80S peak were clearly resolved (Figure 1D and Figure 1—figure supplement 4). However, at a flow rate of 1.0 ml/min, the 80S peak overlapped the polysome peak. As a compromise between analysis time and resolution, we therefore selected a flow rate of 0.8 ml/min for all subsequent experiments (Figure 1—figure supplement 4). Importantly, this SEC-based analysis was achieved in only 15 min, which contrasts with the hours required for SDG analysis. We also analyzed lysates individually extracted from four human cell lines (i.e. HeLa, U2OS, HCT116 p53+/+ and HCT116 p53 -/- cells) and showed that the elution profiles from the 2,000 Å SEC column were similar (Figure 1—figure supplement 5). We term this SEC-based uHPLC polysome fractionation method, ‘Ribo Mega-SEC’.

To confirm that the first SEC peak contained polysomes, we compared the Ribo Mega-SEC chromatograms of control HeLa cell lysates with lysates that had been treated with EDTA. This treatment dissociates polysomes and 80S ribosomes into 60S and 40S subunits by chelating Mg²⁺ needed for complex formation (Klein et al., 2004; Nolan and Arnstein, 1969). Using SDG analysis, we confirmed this dissociation of polysomes and 80S ribosomes following EDTA treatment. We also observed the altered (earlier) elution of each dissociated 60S and 40S subunits due to the reduced subunit density resulting from EDTA treatment (Bengtson and Joazeiro, 2010; Blobel, 1971; Hamilton et al., 1971; Steitz et al., 1988) (Figure 2—figure supplement 1).

We analyzed these lysates by Ribo Mega-SEC, which showed that the polysome and 80S ribosome peaks both disappeared specifically from the EDTA-treated cell lysates. There was a simultaneous appearance of two new peaks, corresponding to the 60S and 40S subunits, respectively (Figure 2A). These fractions elute at slightly earlier times in the presence of EDTA, given that the density of each ribosomal subunit was decreased, and their effective size increased, following EDTA treatment, as noted above (Gesteland, 1966).

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We next collected 48 fractions, ranging from polysomes to smaller protein complexes (Figure 2—figure supplement 2) and characterized the SEC peaks further, using both western and northern blotting to compare the compositions of specific fractions (Figure 2A and Figure 2—figure supplement 2). The polysome fractions (fractions 1–6) in the untreated control lysate contained RPs, that is, uL10 (RPLP0) and eS10 (RPS10), and 28S and 18S rRNAs. The polysome peak was also highly enriched in PolyA(+) mRNA. For example, beta actin mRNA (ACTB) was enriched specifically in the polysome peak (Figure 2B). Moreover, the 60S and 40S subunit peak fractions were also enriched in the corresponding 60S and 40S-specific RPs and rRNAs. As expected, these RP, rRNA and mRNA signals were significantly reduced in the peak polysome fractions from EDTA-treated cell lysates (Figure 2B). Following EDTA treatment, uL10 accumulated in two fractions; that is, a dissociated 60S subunit fraction (fraction 7) and the fraction not co-eluted with 28S rRNA (fraction A). eS10 was neither detected in the dissociated 40S subunit in the fractions analysed by Ribo Mega-SEC, nor in the dissociated 40S fractions analysed by SDG, but was detected instead in the fractions containing smaller protein complexes (Figure 2B and Figure 2—figure supplements 1 and 3). This suggests that eS10 is one of the RPs released from ribosomal subunits after EDTA treatment, as reported previously for the 5S RNP (Blobel, 1971; Steitz et al., 1988).

We next examined the stability and activity of polysomes obtained by Ribo Mega-SEC, during sequential separations and high salt treatment. First, we tested polysome stability over time by taking three fractions in the polysome peak (highlighted fractions 1–3 in Figure 3A) and analyzing each fraction individually, both by a second round of Ribo Mega-SEC and by SDG analysis (Figure 3B). Earlier SEC fractions migrated in the heavier polysome region (>5 ribosomes per polysome) by SDG, and later SEC fractions migrated in the lighter polysome region (~3 ribosomes per polysome) by SDG. In addition, no dissociated ribosomal subunits from the Ribo Mega-SEC purified polysomes were detected by either the SEC, or SDG approaches. This demonstrates that the polysomes separated by Ribo Mega-SEC are stable and remain intact (Figure 3B).

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We also analyzed two sets of polysome fractions isolated by Ribo Mega-SEC using electron microscopy (EM); that is (a) material from earlier eluting polysome fractions (EM: a in Figure 3A) and (b) material from an intermediate eluting polysome fraction (EM: b in Figure 3A). The complexes from each fraction were applied directly onto an EM grid, without dialysis, and viewed in a JEOL 2010F with FEG (Field Emission Gun) electron microscope in TEM mode at a nominal magnification of 40,000X. This EM analysis showed that earlier eluting SEC fractions contain larger polysomes, with elongated and heterogeneous shapes (Figure 3C). These may correspond to ‘HMW and/or [HMW]ⁿ (n = 2) polysomes (i.e. assemblies of polysomes)’ (Viero et al., 2015). In contrast, polysomes composed of four ribosomes accounted for a major portion of the intermediate eluting SEC fraction and their shapes were more round and homogeneous (Figure 3B and C). These may correspond to ‘rounded’ polysomes (Viero et al., 2015). In addition, some larger polysomes were also found in this fraction, which is consistent with the data obtained by SDG analyses above (Figure 3B and C).

In a second round of stability tests, we examined SDG and SEC profiles of HeLa cell extracts prepared and separated under either normal salt concentrations, or high-salt concentrations. Using SDG analysis, we detected a decrease in intensity of the 80S peak and accumulation of free 60S and 40S subunits. This is likely due to vacant 80S couples dissociating into free subunits in the high-salt buffer (Figure 3—figure supplement 1A) (Martin and Hartwell, 1970; van den Elzen et al., 2014). We also detected a reduction of both the amount and resolution of polysome peaks in the SDG separations due to the increase of salt concentration (Figure 3—figure supplement 1A) (Strnadova et al., 2015). We then analyzed these same lysates by SEC, which also showed a similar decrease of polysome peak abundance (Figure 3—figure supplement 1B). Based on our observations that, under high-salt concentrations, (i) each peak analyzed by SDG elutes earlier and (ii) each peak analyzed by SEC elutes later, we infer that high salt reduces the size of ribosomal subunits, probably because of a loss of ribosomal proteins and/or proteins associated with ribosomal subunits.

Next, we examined whether the ribosomes in polysome fractions separated by Ribo Mega-SEC retained peptidyl transferase activity, which would provide a clear indicator of stability and retention of function after fractionation. To do this, we employed in vitro puromycin labeling (Figure 3D and Figure 3—figure supplement 2) (Aviner et al., 2013). As was true for all experiments in this study, we used lysates from cells treated with cycloheximide for this analysis. This was possible because short-term treatment of cells with cycloheximide has no significant effect on nascent polypeptide chain puromycylation (David et al., 2012). We detected nascent polypeptide chains linked with biotin-labeled puromycin specifically in the polysome fractions (Figure 3D). A streptavidin-HRP signal was not observed in the 60S subunit fractions, or when extracts were treated with unlabeled-puromycin (negative control) (Figure 3D). These data show that, using Ribo Mega-SEC, both intact and translation-active polysomes can be resolved from cell extracts efficiently (~11 min after injection).

An important distinction between density-gradient-based fractionation and uHPLC-based separation is the inherent improvement in reproducibility through the use of automated injection and fraction-collection systems. Many fields, including biochemistry and pharmacology, rely on the reproducible retention times and quantitation provided by automated uHPLC systems. We have evaluated reproducibility here for Ribo Mega-SEC through the analysis of three biological replicates of either untreated, or EDTA-treated, cell lysates. Statistical comparison of these chromatograms showed very high Pearson correlation coefficients of ~0.99 across the biological replicates (Figure 4A and Figure 4—figure supplement 1). Polysome profiles generated by SDG analysis from three biological replicates of untreated cell lysates also showed high Pearson correlation coefficients, but consistently lower than those from Ribo Mega-SEC (Figure 4B). Moreover, we found an ~5 to 10 s difference (equivalent to 80 μl to 160 μl difference) between the SDG replicates in the polysome region, possibly due to the variability in density of the sucrose gradients in each tube (Figure 4C). These data show that the Ribo Mega-SEC approach is highly reproducible and compares favourably in this regard with polysome isolation using SDG.

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We next took advantage of the optimized Ribo Mega-SEC method to examine whether we detect differences in the relative levels of polysomes and ribosomal subunits when extracts are analyzed from HeLa cells responding to growth under conditions of amino acid starvation. This showed dramatically decreased polysome levels in the lysates from amino acid starved cells, with a corresponding distinct increase in the peaks for 80S ribosome, and 60S and 40S subunits, consistent with previous reports (Caldarola et al., 2004) (Figure 5A). Quantitation of the peak areas and the P/M ratio confirmed the expected global reduction of translation in response to amino acid starvation (Figure 5B).

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Translation of 5’ TOP mRNAs has been reported to be repressed by amino acid starvation, due to the release of this class of mRNAs from polysomes (Damgaard and Lykke-Andersen, 2011). Our data here using Ribo Mega-SEC support these observations on the loss of 5’ TOP mRNAs from polysomes after starvation. Thus, we see that the uL23 (RPL23a) mRNA, a 5’ TOP mRNA, was specifically decreased in the polysome fractions of extracts isolated from amino acid starved cells, while the ACTB mRNA mostly remained associated with polysomes, although we detected a small portion of ACTB mRNA also redistributed into the ribosomal subunit fractions (Figure 5C). These data indicate that the Ribo Mega-SEC approach is well suited for use in studies to characterize changes in translational activity in cells, either under different growth conditions, or in response to drugs, physical, or genetic perturbations.

The previous examples involved analysis of polysomes in extracts isolated from human cell lines. We also evaluated how effectively Ribo Mega-SEC can be used to analyse polysomes in extracts prepared from mammalian tissue samples. In addition, we wished to test whether this approach can be used to perform MS-based proteomic analyses on the isolated polysome fractions. To do this, we applied Ribo Mega-SEC to analyze the proteins associated with polysomes and ribosomal subunits in mouse liver tissue. Extracts were prepared from mouse liver and fractionated by SEC, which showed a similar pattern of polysome separation as seen with extracts prepared from HeLa cells, also with very high Pearson correlation coefficients (~0.99) across biological replicates (Figure 6—figure supplement 1). This showed that translation complexes in liver tissue are mostly associated with polysomes, rather than 80S monosomes.

Next, we optimized the SEC running buffer to facilitate downstream proteomic analysis of the fractionated polysomes. For this, we found it was important to omit heparin from the SEC running buffer, as it interferes with MS analysis and is difficult to remove after SEC fractionation using conventional proteomics sample preparation techniques. We confirmed that the separation profiles were similar using SEC running buffer either with, or without, heparin, and also confirmed that polysomes and ribosomal subunits remained intact under both conditions (Figure 6—figure supplement 2). We then collected 24 SEC fractions, spanning the separation range from polysomes to smaller complexes (Figure 6A and B). Each fraction was digested with trypsin and the resulting peptides cleaned to remove the buffer components, prior to LC-MS/MS analysis on a QExactive plus mass spectrometer (Figure 6A) (Larance et al., 2016; Larance et al., 2015; Ly et al., 2015; Ly et al., 2017).

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The data from two biological replicates yielded >58,800 unique peptides, which were mapped to 5,158 protein groups, each identified with two or more peptides per protein. We used iBAQ intensity for label-free quantitation to estimate protein abundance and compare protein abundance across the fractions (Figure 6C and E and Figure 7) (Larance et al., 2016).

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We further analyzed the data to identify both polysome-associated and ribosomal subunit-associated proteins, using hierarchical clustering across the 24 fractions. To determine the number of protein clusters required, we tested the generation of 1–2,000 separate clusters from our dataset and calculated the mean Pearson correlation within each cluster (Figure 6—figure supplement 3A and B) (Kirkwood et al., 2013). We picked 400 protein clusters as an optimum, because this showed a high mean correlation coefficient (~0.95) for each cluster, while minimizing the subdivision of known protein complexes between different clusters (Figure 6—figure supplement 3A, Supplementary file 1). The large and small ribosomal subunit proteins predominantly clustered separately, with cluster 197 containing 39 proteins from the 60S subunit (Figure 6C and D). Cluster 454 contained 30 proteins, mostly from the 40S subunit, including the known accessory proteins Gnb2l1 (Link et al., 1999) (Figure 6E and F). Moreover, two proteins (Slc2a2 and Myl12a) were observed in the 40S cluster as candidate 40S-associated factors (Figure 6E and F).

Using mouse liver tissue, we detected in total 78 mouse RPs across the fractions, of which 33 were small ribosomal subunit proteins and detected in the polysomes/80S and 40S subunit fractions. The other 45 were large ribosomal subunit proteins and detected in polysomes/80S and 60S subunit fractions (Figure 7) (Nakao et al., 2004). We also detected multiple translation initiation factors (eIFs), which were mainly in 40S fractions, forming the 48S initiation complex. We also found that Poly(A) binding proteins 1 and 4 (PABPC1, PABPC4) were enriched in the polysome fractions, with a small portion in the fractions containing smaller protein complexes (Schäffler et al., 2010). Proteins from the Exon Junction Complex (EJC - EIF4A3, RBM8A, RNPS1, MAGOHB, CASC3) were detected specifically in the polysome fractions and ribosomal subunit fractions, consistent with a previous report showing that EJC proteins eluted in the void volume of a gel filtration column (>2 MDa) (Singh et al., 2012). We also detected UPF1, a protein involved in nonsense-mediated decay of mRNA, mainly in polysome fractions, as well as in 40S fractions (Min et al., 2013). In addition, we detected translation elongation factors (eEFs) and translation termination factors (eRFs), which were present in polysomes and 80S fractions, although the majority of these proteins were in the SEC fractions containing smaller complexes (Figure 7), consistent with previous studies (Eyler et al., 2013; Sivan et al., 2011; Sivan et al., 2007).

In summary, we conclude that Ribo Mega-SEC provides an efficient and flexible approach for isolating polysomes and ribosomal subunits that is compatible with downstream proteomic analysis on the isolated material. The method can be applied to study extracts from both cell lines and tissue samples and thereby provides a convenient new technology for characterizing polysome-associated proteins in cells under different growth and response conditions.

To maximize community access to the entire dataset described in this study, the proteomics data, including raw MS files and MaxQuant-generated output, are freely available via the ProteomeXchange PRIDE repository with the data set identifier PXD008913.

The Ribo Mega-SEC method described here provides a powerful new approach for enhancing the analysis of gene expression at the level of active translation complexes. We have shown that Ribo Mega-SEC provides a rapid and reproducible method for the isolation of polysomes and ribosomal subunits using uHPLC and is therefore an accessible alternative to the use of conventional SDG methods. In particular, the Ribo Mega-SEC approach is well suited to broadening access for researchers to analyze polysomes more widely in future studies on regulatory responses and disease mechanisms in mammalian cells and tissues.

Polysome analysis is currently performed almost exclusively using SDGs. This is a well-established and effective method but also is widely recognized to have some limitations. For example, it is comparatively slow to carry out, involving a long ultracentrifugation step, and requires specialist equipment and expertise to use reliably. In addition, the multiple handling steps involved in setting up SDG analyses, together with the subsequent fraction collection and analysis steps, all introduce potential sources of experimental and technical variation that overall can reduce detailed reproducibility of gradient profiles between SDG experiments, as previously recognised (Ingolia et al., 2009). Another practical issue arises from the sensitivity of the detectors commonly used for analyzing SDG fractions and the requirement for establishing a consistent signal to noise baseline. This can potentially complicate the accurate comparison of SDG polysome profiles between experiments, without additional normalization steps, particularly when analyzing samples with low RNA amounts (Figure 2—figure supplement 1). Moreover, due to the high viscosity of the sucrose solution used for SDG, either further purification and/or dialysis is needed if the separated polysomes and ribosome subunits are to be used for subsequent experiments, such as Cryo-EM analysis (Grassucci et al., 2007). In combination, all these issues outlined above have limited the wider application of polysome analyses for many types of gene expression studies addressing regulatory responses and disease mechanisms in human cells and model organisms.

The Ribo Mega-SEC approach we introduce here overcomes a number of the limitations associated with polysome analysis by SDG for studies on mammalian cells and tissues. For example, Ribo Mega-SEC involves a single, rapid uHPLC step; the process from injecting lysate to collecting fractions can be completed within 15 min, while keeping the sample at 4–5°C through all steps. This has obvious benefits for biochemical analyses. Moreover, the use of uHPLC provides exceptional reproducibility of retention time between experiments, due to the combination of automated, highly accurate sample injection, an automatic UV signal zeroing at the start of each sample run and high quality, robust components (pumps/valves) and columns. We also demonstrate that Ribo Mega-SEC can be used in conjunction with physiological buffers that are compatible with most subsequent analytical techniques, including MS-based proteomics, EM analysis and biochemical assays, for example translation activity assays (Figures 3, 6 and 7). Ribo Mega-SEC profiles can thus reflect the physiological status of polysomes and ribosomes in vivo, and the data they provide indicate that mRNA translation occurs predominantly in polysome complexes (as compared to 80S ‘monosome’ complexes) (Aspden et al., 2014; Noll, 2008; Warner and Knopf, 2002) in mouse liver and in human cancer cell lines.

Using a single uHPLC instrument, as many as 48 different samples can be automatically separated and fractionated by Ribo Mega-SEC within 16 hr. Ribo Mega-SEC is therefore suited for high-throughput experiments and for combining with techniques such as RNA-Seq analysis and proteomic analysis (Figures 6 and 7). We have also shown that the separate fractions in a single polysome peak have different sized n-mer polysomes (Figures 1 and 3), and these fractions as well as the ribosomal subunit fractions can also be re-analyzed and further separated by a second round of SEC. We note that, thanks to the rapid separation provided by uHPLC, even performing two sequential rounds of Ribo Mega-SEC analysis to achieve higher resolution separation of both different sizes of polysomes and ribosomal subunits can be achieved comfortably within 30–45 min total analysis time. This is faster than performing a single round of SDG fractionation, as well as delivering the polysomes and ribosomal subunits already in a physiological buffer.

The specific setup and conditions used for Ribo Mega-SEC fractionation can be customized in different experiments to optimize the analysis depending on whether speed, or resolution is most critical. For example, by employing a lower flow rate (e.g. 0.2 ml/min), higher resolution separation of large complexes can be achieved (Figure 1—figure supplement 4). Alternatively, employing two SEC columns connected in series, with a flow rate of 0.2 ml/min, provides a higher resolution separation of complexes from larger polysomes to smaller protein complexes in a single shot SEC analysis (Figure 1—figure supplement 3). In this case, the trade-off is that a longer separation time is required. For many experiments, therefore, using a single SEC column with a faster 0.8 ml/min flow rate, is optimal. The pore size of the SEC column can also be varied to help customize separation for specific applications. For example, we show that using a 1,000 Å SEC column with a flow rate of 0.2 ml/min improves the separation of 60S and 40S ribosomal subunits as well as 40S subunits from smaller protein complexes (Figure 1—figure supplement 2). This can be helpful for studying translation initiation steps. There is a clear scope for enhancing further the specific application of Mega-SEC fractionation methods based upon varying these parameters such as pore size, buffer, number and type of columns used in series and flow rates etc.

Polysome profiling is now a popular technique. It is used, for example, to assess changes in translation efficiency by examining a pool of efficiently translated mRNAs associated with more than three ribosomes, referred to as a ‘heavy polysome’, typically involving isolation of the heavy polysome by SDG (King and Gerber, 2016; Piccirillo et al., 2014). Ribosome profiling is another popular technique used to assess the changes in translation efficiency by identifying and quantifying the ribosome-protected fragments by RNAseq (Ingolia et al., 2012; Ingolia et al., 2009). However, polysome profiling offers technical advantages, because ribosome profiling can be biased toward the identification of abundant mRNAs that show large changes (Masvidal et al., 2017). A recent report has described an efficient method to enrich heavy polysomes in ~1 ml, using a non-linear sucrose gradient, which helps to overcome one of the issues when larger volumes are collected across several polysome fractions after a typical SDG analysis. Nonetheless, this method still involves the preparation of sucrose gradients and a long ultracentrifugation step (Liang et al., 2018).

The Ribo Mega-SEC approach shows that the heavy polysomes are enriched in the first half of the polysome peak, eluted between 9 and 10.25 min after injecting the lysates (Figure 3A–C), which is equivalent to ~1 ml in volume. However, it is thought that the bulk of 80S monosomes are involved in translation elongation. Therefore, pre-selection of heavy polysomes may bias the interpretation of an in vivo translation activity (Heyer and Moore, 2016). A more comprehensive picture of the translational flux in vivo may be provided therefore by isolating and quantifying mRNAs from fractions containing both polysomes and the 80S monosome, which can be collected within ~11.25 min of starting analysis using the Ribo Mega-SEC method (Figure 2A). We therefore suggest that Ribo Mega-SEC offers significant technical and practical advantages for future studies on post-transcriptional gene expression in mammalian cells and tissues involving the isolation of active translation complexes.

While we have demonstrated clear advantages provided by Ribo Mega-SEC, no single method is best suited to every situation and we acknowledge two potential features of the Ribo Mega-SEC method that may be relevant to whether it is used for specific applications. The first is that polysomes are predominantly eluted by a single column SEC in a single major peak, albeit distributed across multiple fractions. We show here that this resolution of polysomes can be improved by running two SEC columns in series in a single shot experiment. Nonetheless, if the separation of different higher order n-mer polysome species is critical for a specific experiment, it may still be preferable to perform SDG analysis, either instead of, or in addition to, Ribo Mega-SEC. However, while analysis of mRNAs with varying numbers of ribosomes is reported for ‘TrIP-Seq’ or ‘Poly-Ribo-Seq’ using SDG (Aspden et al., 2014; Floor and Doudna, 2016), our analyses indicate that the isolation of different polysome size classes by SDG might underestimate the true level of cross-contamination between fractions because of the increased baseline in the polysome peak region, which indicates that polysome ‘peaks’ are not homogeneous. Inevitably, in addition to the major species in the peak, they also contain mixtures of polysomes of different sizes. A second issue we have identified is the assessment of vacant 80S couples by high-salt treatment (Figure 3—figure supplement 1). This arises because SEC separation is sensitive to changes in mobile phase components, such as salts. However, we note this use of high-salt extracts represents a special case that is not involved in most routine studies of polysomes in cells and tissues. Instead, fractionation using high salt may be most relevant to specific biochemical studies, for example on the detailed mechanism of ribosome assembly. Therefore, the choice of whether best to use either conventional SDG methods, or the new Ribo Mega-SEC method, can be made by researchers in light of the required experimental design and mindful of the specific pros and cons of each approach. In many cases, it may be sensible to analyze extracts using both methods, as illustrated here.

In summary, we have demonstrated that the Ribo Mega-SEC method provides an efficient and powerful new approach for the biochemical analysis of mammalian polysomes and ribosome subunits, isolated from extracts of either cultured cell lines, or tissues. Furthermore, we note that this approach using large pore size (2,000 Å) SEC columns and uHPLC chromatography, potentially has many additional applications beyond the field of protein translation. For example, the ability to efficiently and rapidly separate very large structures by Mega-SEC could also be applied to the purification and biochemical analysis of other large sub-cellular complexes, viral particles and secreted vesicles such as exosomes. A particular advantage of using SEC is that the complexes are isolated rapidly and in buffers suitable for performing biochemical assays immediately on the isolated material without the need for further purification.

All of the RAW MS data and MaxQuant-generated output have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD008913.

1. 1. Larance M
   2. Harney D
   3. Yoshikawa H
   4. Ly T
   5. Lamond AI

   (2018) Ribo Mega-SEC Provides a Rapid, Efficient and Reproducible Analysis of Mammalian Polysomes and Ribosomal Subunits

   Publicly available at EBI PRIDE (accession no. PXD008913).

   https://www.ebi.ac.uk/pride/archive/projects/PXD008913

1. 1. Aspden JL
   2. Eyre-Walker YC
   3. Phillips RJ
   4. Amin U
   5. Mumtaz MA
   6. Brocard M
   7. Couso JP

   (2014) Extensive translation of small open reading frames revealed by Poly-Ribo-Seq

   eLife 3:e03528.

   https://doi.org/10.7554/eLife.03528

   • PubMed
   • Google Scholar
2. 1. Aviner R
   2. Geiger T
   3. Elroy-Stein O

   (2013) Novel proteomic approach (PUNCH-P) reveals cell cycle-specific fluctuations in mRNA translation

   Genes & Development 27:1834–1844.

   https://doi.org/10.1101/gad.219105.113

   • PubMed
   • Google Scholar
3. 1. Aviner R
   2. Geiger T
   3. Elroy-Stein O

   (2014) Genome-wide identification and quantification of protein synthesis in cultured cells and whole tissues by puromycin-associated nascent chain proteomics (PUNCH-P)

   Nature Protocols 9:751–760.

   https://doi.org/10.1038/nprot.2014.051

   • PubMed
   • Google Scholar
4. 1. Bengtson MH
   2. Joazeiro CA

   (2010) Role of a ribosome-associated E3 ubiquitin ligase in protein quality control

   Nature 467:470–473.

   https://doi.org/10.1038/nature09371

   • PubMed
   • Google Scholar
5. 1. Blobel G

   (1971) Isolation of a 5S RNA-protein complex from mammalian ribosomes

   PNAS 68:1881–1885.

   https://doi.org/10.1073/pnas.68.8.1881

   • PubMed
   • Google Scholar
6. 1. Britten RJ
   2. Roberts RB

   (1960) High-Resolution density gradient sedimentation analysis

   Science 131:32–33.

   https://doi.org/10.1126/science.131.3392.32

   • PubMed
   • Google Scholar
7. 1. Caldarola S
   2. Amaldi F
   3. Proud CG
   4. Loreni F

   (2004) Translational regulation of terminal oligopyrimidine mRNAs induced by serum and amino acids involves distinct signaling events

   Journal of Biological Chemistry 279:13522–13531.

   https://doi.org/10.1074/jbc.M310574200

   • PubMed
   • Google Scholar
8. 1. Cox J
   2. Mann M

   (2008) MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification

   Nature Biotechnology 26:1367–1372.

   https://doi.org/10.1038/nbt.1511

   • PubMed
   • Google Scholar
9. 1. Cox J
   2. Neuhauser N
   3. Michalski A
   4. Scheltema RA
   5. Olsen JV
   6. Mann M

   (2011) Andromeda: a peptide search engine integrated into the MaxQuant environment

   Journal of Proteome Research 10:1794–1805.

   https://doi.org/10.1021/pr101065j

   • PubMed
   • Google Scholar
10. 1. Damgaard CK
    2. Lykke-Andersen J

    (2011) Translational coregulation of 5'TOP mRNAs by TIA-1 and TIAR

    Genes & Development 25:2057–2068.

    https://doi.org/10.1101/gad.17355911

    • PubMed
    • Google Scholar
11. 1. David A
    2. Dolan BP
    3. Hickman HD
    4. Knowlton JJ
    5. Clavarino G
    6. Pierre P
    7. Bennink JR
    8. Yewdell JW

    (2012) Nuclear translation visualized by ribosome-bound nascent chain puromycylation

    The Journal of Cell Biology 197:45–57.

    https://doi.org/10.1083/jcb.201112145

    • PubMed
    • Google Scholar
12. 1. Eyler DE
    2. Wehner KA
    3. Green R

    (2013) Eukaryotic release factor 3 is required for multiple turnovers of peptide release catalysis by eukaryotic release factor 1

    Journal of Biological Chemistry 288:29530–29538.

    https://doi.org/10.1074/jbc.M113.487090

    • PubMed
    • Google Scholar
13. 1. Floor SN
    2. Doudna JA

    (2016) Tunable protein synthesis by transcript isoforms in human cells

    eLife 5:e10921.

    https://doi.org/10.7554/eLife.10921

    • PubMed
    • Google Scholar
14. 1. Gandin V
    2. Sikström K
    3. Alain T
    4. Morita M
    5. McLaughlan S
    6. Larsson O
    7. Topisirovic I

    (2014) Polysome fractionation and analysis of mammalian translatomes on a Genome-wide scale

    Journal of Visualized Experiments 87:e51455.

    https://doi.org/10.3791/51455

    • Google Scholar
15. 1. Gesteland RF

    (1966) Unfolding of Escherichia coli ribosomes by removal of magnesium

    Journal of Molecular Biology 18:356–371.

    https://doi.org/10.1016/S0022-2836(66)80253-X

    • PubMed
    • Google Scholar
16. 1. Grassucci RA
    2. Taylor DJ
    3. Frank J

    (2007) Preparation of macromolecular complexes for cryo-electron microscopy

    Nature Protocols 2:3239–3246.

    https://doi.org/10.1038/nprot.2007.452

    • PubMed
    • Google Scholar
17. 1. Hamilton MG
    2. Pavlovec A
    3. Petermann ML

    (1971) Molecular weight, buoyant density, and composition of active subunits of rat liver ribosomes

    Biochemistry 10:3424–3427.

    https://doi.org/10.1021/bi00794a017

    • PubMed
    • Google Scholar
18. 1. Heiman M
    2. Kulicke R
    3. Fenster RJ
    4. Greengard P
    5. Heintz N

    (2014) Cell type-specific mRNA purification by translating ribosome affinity purification (TRAP)

    Nature Protocols 9:1282–1291.

    https://doi.org/10.1038/nprot.2014.085

    • PubMed
    • Google Scholar
19. 1. Heyer EE
    2. Moore MJ

    (2016) Redefining the translational status of 80S monosomes

    Cell 164:757–769.

    https://doi.org/10.1016/j.cell.2016.01.003

    • PubMed
    • Google Scholar
20. 1. Hjelmeland LM

    (1980) A nondenaturing zwitterionic detergent for membrane biochemistry: design and synthesis

    PNAS 77:6368–6370.

    https://doi.org/10.1073/pnas.77.11.6368

    • PubMed
    • Google Scholar
21. 1. Hughes CS
    2. Foehr S
    3. Garfield DA
    4. Furlong EE
    5. Steinmetz LM
    6. Krijgsveld J

    (2014) Ultrasensitive proteome analysis using paramagnetic bead technology

    Molecular Systems Biology 10:757.

    https://doi.org/10.15252/msb.20145625

    • PubMed
    • Google Scholar
22. 1. Inada T
    2. Winstall E
    3. Tarun SZ
    4. Yates JR
    5. Schieltz D
    6. Sachs AB

    (2002) One-step affinity purification of the yeast ribosome and its associated proteins and mRNAs

    RNA 8:948–958.

    https://doi.org/10.1017/S1355838202026018

    • PubMed
    • Google Scholar
23. 1. Ingolia NT
    2. Ghaemmaghami S
    3. Newman JR
    4. Weissman JS

    (2009) Genome-wide analysis in vivo of translation with nucleotide resolution using ribosome profiling

    Science 324:218–223.

    https://doi.org/10.1126/science.1168978

    • PubMed
    • Google Scholar
24. 1. Ingolia NT
    2. Brar GA
    3. Rouskin S
    4. McGeachy AM
    5. Weissman JS

    (2012) The ribosome profiling strategy for monitoring translation in vivo by deep sequencing of ribosome-protected mRNA fragments

    Nature Protocols 7:1534–1550.

    https://doi.org/10.1038/nprot.2012.086

    • PubMed
    • Google Scholar
25. 1. Ishikawa H
    2. Yoshikawa H
    3. Izumikawa K
    4. Miura Y
    5. Taoka M
    6. Nobe Y
    7. Yamauchi Y
    8. Nakayama H
    9. Simpson RJ
    10. Isobe T
    11. Takahashi N

    (2017) Poly(A)-specific ribonuclease regulates the processing of small-subunit rRNAs in human cells

    Nucleic Acids Research 45:3437–3447.

    https://doi.org/10.1093/nar/gkw1047

    • PubMed
    • Google Scholar
26. 1. Khatter H
    2. Myasnikov AG
    3. Natchiar SK
    4. Klaholz BP

    (2015) Structure of the human 80S ribosome

    Nature 520:640–645.

    https://doi.org/10.1038/nature14427

    • PubMed
    • Google Scholar
27. 1. King HA
    2. Gerber AP

    (2016) Translatome profiling: methods for genome-scale analysis of mRNA translation

    Briefings in Functional Genomics 15:22–31.

    https://doi.org/10.1093/bfgp/elu045

    • PubMed
    • Google Scholar
28. 1. Kirkwood KJ
    2. Ahmad Y
    3. Larance M
    4. Lamond AI

    (2013) Characterization of native protein complexes and protein isoform variation using size-fractionation-based quantitative proteomics

    Molecular & Cellular Proteomics 12:3851–3873.

    https://doi.org/10.1074/mcp.M113.032367

    • PubMed
    • Google Scholar
29. 1. Klein DJ
    2. Moore PB
    3. Steitz TA

    (2004) The contribution of metal ions to the structural stability of the large ribosomal subunit

    RNA 10:1366–1379.

    https://doi.org/10.1261/rna.7390804

    • PubMed
    • Google Scholar
30. 1. Lam YW
    2. Lamond AI
    3. Mann M
    4. Andersen JS

    (2007) Analysis of nucleolar protein dynamics reveals the nuclear degradation of ribosomal proteins

    Current Biology 17:749–760.

    https://doi.org/10.1016/j.cub.2007.03.064

    • PubMed
    • Google Scholar
31. 1. Larance M
    2. Pourkarimi E
    3. Wang B
    4. Brenes Murillo A
    5. Kent R
    6. Lamond AI
    7. Gartner A

    (2015) Global proteomics analysis of the response to starvation in C. elegans

    Molecular & Cellular Proteomics 14:1989–2001.

    https://doi.org/10.1074/mcp.M114.044289

    • PubMed
    • Google Scholar
32. 1. Larance M
    2. Kirkwood KJ
    3. Tinti M
    4. Brenes Murillo A
    5. Ferguson MA
    6. Lamond AI

    (2016) Global membrane protein interactome analysis using in vivo crosslinking and mass Spectrometry-based protein correlation profiling

    Molecular & Cellular Proteomics 15:2476–2490.

    https://doi.org/10.1074/mcp.O115.055467

    • PubMed
    • Google Scholar
33. 1. Liang S
    2. Bellato HM
    3. Lorent J
    4. Lupinacci FCS
    5. Oertlin C
    6. van Hoef V
    7. Andrade VP
    8. Roffé M
    9. Masvidal L
    10. Hajj GNM
    11. Larsson O

    (2018) Polysome-profiling in small tissue samples

    Nucleic Acids Research 46:e3.

    https://doi.org/10.1093/nar/gkx940

    • PubMed
    • Google Scholar
34. 1. Link AJ
    2. Eng J
    3. Schieltz DM
    4. Carmack E
    5. Mize GJ
    6. Morris DR
    7. Garvik BM
    8. Yates JR

    (1999) Direct analysis of protein complexes using mass spectrometry

    Nature Biotechnology 17:676–682.

    https://doi.org/10.1038/10890

    • PubMed
    • Google Scholar
35. 1. Ly T
    2. Endo A
    3. Lamond AI

    (2015) Proteomic analysis of the response to cell cycle arrests in human myeloid leukemia cells

    eLife 4:e04534.

    https://doi.org/10.7554/eLife.04534

    • PubMed
    • Google Scholar
36. 1. Ly T
    2. Whigham A
    3. Clarke R
    4. Brenes-Murillo AJ
    5. Estes B
    6. Madhessian D
    7. Lundberg E
    8. Wadsworth P
    9. Lamond AI

    (2017) Proteomic analysis of cell cycle progression in asynchronous cultures, including mitotic subphases, using PRIMMUS

    eLife 6:e27574.

    https://doi.org/10.7554/eLife.27574

    • PubMed
    • Google Scholar
37. 1. Maguire BA
    2. Wondrack LM
    3. Contillo LG
    4. Xu Z

    (2008) A novel chromatography system to isolate active ribosomes from pathogenic bacteria

    RNA 14:188–195.

    https://doi.org/10.1261/rna.692408

    • PubMed
    • Google Scholar
38. 1. Mansour FH
    2. Pestov DG

    (2013) Separation of long RNA by agarose-formaldehyde gel electrophoresis

    Analytical Biochemistry 441:18–20.

    https://doi.org/10.1016/j.ab.2013.06.008

    • PubMed
    • Google Scholar
39. 1. Martin TE
    2. Hartwell LH

    (1970)

    Resistance of active yeast ribosomes to dissociation by KCl

    The Journal of Biological Chemistry 245:1504–1506.

    • PubMed
    • Google Scholar
40. 1. Masvidal L
    2. Hulea L
    3. Furic L
    4. Topisirovic I
    5. Larsson O

    (2017) mTOR-sensitive translation: Cleared fog reveals more trees

    RNA Biology 14:1299–1305.

    https://doi.org/10.1080/15476286.2017.1290041

    • PubMed
    • Google Scholar
41. 1. Melnikov S
    2. Ben-Shem A
    3. Garreau de Loubresse N
    4. Jenner L
    5. Yusupova G
    6. Yusupov M

    (2012) One core, two shells: bacterial and eukaryotic ribosomes

    Nature Structural & Molecular Biology 19:560–567.

    https://doi.org/10.1038/nsmb.2313

    • PubMed
    • Google Scholar
42. 1. Min EE
    2. Roy B
    3. Amrani N
    4. He F
    5. Jacobson A

    (2013) Yeast Upf1 CH domain interacts with Rps26 of the 40S ribosomal subunit

    RNA 19:1105–1115.

    https://doi.org/10.1261/rna.039396.113

    • PubMed
    • Google Scholar
43. 1. Nakao A
    2. Yoshihama M
    3. Kenmochi N

    (2004) RPG: the ribosomal protein gene database

    Nucleic Acids Research 32:168D–170.

    https://doi.org/10.1093/nar/gkh004

    • PubMed
    • Google Scholar
44. 1. Nolan RD
    2. Arnstein HR

    (1969) The dissociation of rabbit reticulocyte ribosomes with EDTA and the location of messenger ribonucleic acid

    European Journal of Biochemistry 9:445–450.

    https://doi.org/10.1111/j.1432-1033.1969.tb00629.x

    • PubMed
    • Google Scholar
45. 1. Noll H

    (2008) The discovery of polyribosomes

    BioEssays 30:1220–1234.

    https://doi.org/10.1002/bies.20846

    • PubMed
    • Google Scholar
46. 1. Olsnes S

    (1970) Characterization of protein bound to rapidly-labelled RNA in polyribosomes from rat liver

    European Journal of Biochemistry 15:464–471.

    https://doi.org/10.1111/j.1432-1033.1970.tb01029.x

    • PubMed
    • Google Scholar
47. 1. Pestov DG
    2. Lapik YR
    3. Lau LF

    (2008) Assays for ribosomal RNA processing and ribosome assembly

    Current Protocols in Cell Biology 39:22.11.1–22.1122.

    https://doi.org/10.1002/0471143030.cb2211s39

    • Google Scholar
48. 1. Piccirillo CA
    2. Bjur E
    3. Topisirovic I
    4. Sonenberg N
    5. Larsson O

    (2014) Translational control of immune responses: from transcripts to translatomes

    Nature Immunology 15:503–511.

    https://doi.org/10.1038/ni.2891

    • PubMed
    • Google Scholar
49. 1. Preti M
    2. O'Donohue MF
    3. Montel-Lehry N
    4. Bortolin-Cavaillé ML
    5. Choesmel V
    6. Gleizes PE

    (2013) Gradual processing of the ITS1 from the nucleolus to the cytoplasm during synthesis of the human 18S rRNA

    Nucleic Acids Research 41:4709–4723.

    https://doi.org/10.1093/nar/gkt160

    • PubMed
    • Google Scholar
50. 1. Rappsilber J
    2. Mann M
    3. Ishihama Y

    (2007) Protocol for micro-purification, enrichment, pre-fractionation and storage of peptides for proteomics using StageTips

    Nature Protocols 2:1896–1906.

    https://doi.org/10.1038/nprot.2007.261

    • PubMed
    • Google Scholar
51. 1. Ruan H
    2. Brown CY
    3. Morris DR

    (1997)

    mRNA Formation and Function

    305–321, Analysis of Ribosome Loading onto mRNA Species, mRNA Formation and Function, New York, Academic Press, 10.1016/B978-012587545-5/50017-3.

    • Google Scholar
52. 1. Schäffler K
    2. Schulz K
    3. Hirmer A
    4. Wiesner J
    5. Grimm M
    6. Sickmann A
    7. Fischer U

    (2010) A stimulatory role for the La-related protein 4B in translation

    RNA 16:1488–1499.

    https://doi.org/10.1261/rna.2146910

    • PubMed
    • Google Scholar
53. 1. Schneider-Poetsch T
    2. Ju J
    3. Eyler DE
    4. Dang Y
    5. Bhat S
    6. Merrick WC
    7. Green R
    8. Shen B
    9. Liu JO

    (2010) Inhibition of eukaryotic translation elongation by cycloheximide and lactimidomycin

    Nature Chemical Biology 6:209–217.

    https://doi.org/10.1038/nchembio.304

    • PubMed
    • Google Scholar
54. 1. Shi Z
    2. Fujii K
    3. Kovary KM
    4. Genuth NR
    5. Röst HL
    6. Teruel MN
    7. Barna M

    (2017) Heterogeneous ribosomes preferentially translate distinct subpools of mRNAs Genome-wide

    Molecular Cell 67:71–83.

    https://doi.org/10.1016/j.molcel.2017.05.021

    • PubMed
    • Google Scholar
55. 1. Simsek D
    2. Tiu GC
    3. Flynn RA
    4. Byeon GW
    5. Leppek K
    6. Xu AF
    7. Chang HY
    8. Barna M

    (2017) The mammalian Ribo-interactome reveals ribosome functional diversity and heterogeneity

    Cell 169:1051–1065.

    https://doi.org/10.1016/j.cell.2017.05.022

    • PubMed
    • Google Scholar
56. 1. Singh G
    2. Kucukural A
    3. Cenik C
    4. Leszyk JD
    5. Shaffer SA
    6. Weng Z
    7. Moore MJ

    (2012) The cellular EJC interactome reveals higher-order mRNP structure and an EJC-SR protein nexus

    Cell 151:750–764.

    https://doi.org/10.1016/j.cell.2012.10.007

    • PubMed
    • Google Scholar
57. 1. Sivan G
    2. Kedersha N
    3. Elroy-Stein O

    (2007) Ribosomal slowdown mediates translational arrest during cellular division

    Molecular and Cellular Biology 27:6639–6646.

    https://doi.org/10.1128/MCB.00798-07

    • PubMed
    • Google Scholar
58. 1. Sivan G
    2. Aviner R
    3. Elroy-Stein O

    (2011) Mitotic modulation of translation elongation factor 1 leads to hindered tRNA delivery to ribosomes

    Journal of Biological Chemistry 286:27927–27935.

    https://doi.org/10.1074/jbc.M111.255810

    • PubMed
    • Google Scholar
59. 1. Steitz JA
    2. Berg C
    3. Hendrick JP
    4. La Branche-Chabot H
    5. Metspalu A
    6. Rinke J
    7. Yario T

    (1988) A 5S rRNA/L5 complex is a precursor to ribosome assembly in mammalian cells

    The Journal of Cell Biology 106:545–556.

    https://doi.org/10.1083/jcb.106.3.545

    • PubMed
    • Google Scholar
60. 1. Strezoska Z
    2. Pestov DG
    3. Lau LF

    (2000) Bop1 is a mouse WD40 repeat nucleolar protein involved in 28S and 5. 8S RRNA processing and 60S ribosome biogenesis

    Molecular and Cellular Biology 20:5516–5528.

    https://doi.org/10.1128/MCB.20.15.5516-5528.2000

    • PubMed
    • Google Scholar
61. 1. Strnadova P
    2. Ren H
    3. Valentine R
    4. Mazzon M
    5. Sweeney TR
    6. Brierley I
    7. Smith GL

    (2015) Inhibition of translation initiation by protein 169: a vaccinia virus strategy to suppress innate and adaptive immunity and alter virus virulence

    PLoS Pathogens 11:e1005151.

    https://doi.org/10.1371/journal.ppat.1005151

    • PubMed
    • Google Scholar
62. 1. Sung MK
    2. Porras-Yakushi TR
    3. Reitsma JM
    4. Huber FM
    5. Sweredoski MJ
    6. Hoelz A
    7. Hess S
    8. Deshaies RJ

    (2016a) A conserved quality-control pathway that mediates degradation of unassembled ribosomal proteins

    eLife 5:e19105.

    https://doi.org/10.7554/eLife.19105

    • PubMed
    • Google Scholar
63. 1. Sung MK
    2. Reitsma JM
    3. Sweredoski MJ
    4. Hess S
    5. Deshaies RJ

    (2016b) Ribosomal proteins produced in excess are degraded by the ubiquitin-proteasome system

    Molecular Biology of the Cell 27:2642–2652.

    https://doi.org/10.1091/mbc.e16-05-0290

    • PubMed
    • Google Scholar
64. 1. Trauner A
    2. Bennett MH
    3. Williams HD

    (2011) Isolation of bacterial ribosomes with monolith chromatography

    PLoS One 6:e16273.

    https://doi.org/10.1371/journal.pone.0016273

    • PubMed
    • Google Scholar
65. 1. van den Elzen AM
    2. Schuller A
    3. Green R
    4. Séraphin B

    (2014) Dom34-Hbs1 mediated dissociation of inactive 80S ribosomes promotes restart of translation after stress

    The EMBO Journal 33:265–276.

    https://doi.org/10.1002/embj.201386123

    • PubMed
    • Google Scholar
66. 1. Viero G
    2. Lunelli L
    3. Passerini A
    4. Bianchini P
    5. Gilbert RJ
    6. Bernabò P
    7. Tebaldi T
    8. Diaspro A
    9. Pederzolli C
    10. Quattrone A

    (2015) Three distinct ribosome assemblies modulated by translation are the building blocks of polysomes

    The Journal of Cell Biology 208:581–596.

    https://doi.org/10.1083/jcb.201406040

    • PubMed
    • Google Scholar
67. 1. Warner JR
    2. Knopf PM
    3. Rich A

    (1963) A multiple ribosomal structure in protein synthesis

    PNAS 49:122–129.

    https://doi.org/10.1073/pnas.49.1.122

    • PubMed
    • Google Scholar
68. 1. Warner JR

    (1977) In the absence of ribosomal RNA synthesis, the ribosomal proteins of HeLa cells are synthesized normally and degraded rapidly

    Journal of Molecular Biology 115:315–333.

    https://doi.org/10.1016/0022-2836(77)90157-7

    • PubMed
    • Google Scholar
69. 1. Warner JR
    2. Knopf PM

    (2002) The discovery of polyribosomes

    Trends in Biochemical Sciences 27:376–380.

    https://doi.org/10.1016/S0968-0004(02)02126-6

    • PubMed
    • Google Scholar
70. 1. Yoshikawa H
    2. Ishikawa H
    3. Izumikawa K
    4. Miura Y
    5. Hayano T
    6. Isobe T
    7. Simpson RJ
    8. Takahashi N

    (2015) Human nucleolar protein Nop52 (RRP1/NNP-1) is involved in site 2 cleavage in internal transcribed spacer 1 of pre-rRNAs at early stages of ribosome biogenesis

    Nucleic Acids Research 43:5524–5536.

    https://doi.org/10.1093/nar/gkv470

    • PubMed
    • Google Scholar

Author details

1. Harunori Yoshikawa

   Centre for Gene Regulation and Expression, School of Life Sciences, University of Dundee, Dundee, United Kingdom

   Contribution

   Conceptualization, Data curation, Funding acquisition, Investigation, Methodology, Writing—original draft, Writing—review and editing

   Contributed equally with

   Mark Larance

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-3793-6219

2. Mark Larance

   1. Centre for Gene Regulation and Expression, School of Life Sciences, University of Dundee, Dundee, United Kingdom
   2. Charles Perkins Centre, School of Life and Environmental Sciences, University of Sydney, Sydney, Australia

   Contribution

   Conceptualization, Data curation, Investigation, Visualization, Funding acquisition, Methodology, Writing—original draft, Writing—review and editing

   Contributed equally with

   Harunori Yoshikawa

   Competing interests

   No competing interests declared

3. Dylan J Harney

   Charles Perkins Centre, School of Life and Environmental Sciences, University of Sydney, Sydney, Australia

   Contribution

   Data curation

   Competing interests

   No competing interests declared

4. Ramasubramanian Sundaramoorthy

   Centre for Gene Regulation and Expression, School of Life Sciences, University of Dundee, Dundee, United Kingdom

   Contribution

   Data curation, Writing—review and editing

   Competing interests

   No competing interests declared

5. Tony Ly

   1. Centre for Gene Regulation and Expression, School of Life Sciences, University of Dundee, Dundee, United Kingdom
   2. Wellcome Centre for Cell Biology, University of Edinburgh, Edinburgh, United Kingdom

   Contribution

   Conceptualization, Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8650-5215

6. Tom Owen-Hughes

   Centre for Gene Regulation and Expression, School of Life Sciences, University of Dundee, Dundee, United Kingdom

   Contribution

   Funding acquisition, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-0618-8185

7. Angus I Lamond

   Centre for Gene Regulation and Expression, School of Life Sciences, University of Dundee, Dundee, United Kingdom

   Contribution

   Conceptualization, Supervision, Funding acquisition, Project administration, Writing—review and editing

   For correspondence

   a.i.lamond@dundee.ac.uk

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6204-6045

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