A Proteomics Approach to Profiling the Temporal Translational Response to Stress and Growth

Comparative proteomic profiling of the ovine and human PBMC inflammatory response

Understanding the cellular and molecular mechanisms of inflammation requires robust animal models. Sheep are commonly used in immune-related studies, yet the validity of sheep as animal models for immune and inflammatory diseases remains to be established. This cross-species comparative study analyzed the in vitro inflammatory response of ovine (oPBMCs) and human PBMCs (hPBMCs) using mass spectrometry, profiling the proteome of the secretome and whole cell lysate. Of the entire cell lysate proteome (oPBMCs: 4217, hPBMCs: 4574 proteins) 47.8% and in the secretome proteome (oPBMCs: 1913, hPBMCs: 1375 proteins) 32.8% were orthologous between species, among them 32 orthologous CD antigens, indicating the presence of six immune cell subsets. Following inflammatory stimulation, 71 proteins in oPBMCs and 176 in hPBMCs showed differential abundance, with only 7 overlapping. Network and Gene Ontology analyses identified 16 shared inflammatory-related terms and 17 canonical pathways with similar activation/inhibition patterns in both species, demonstrating significant conservation in specific immune and inflammatory responses. However, ovine PMBCs also contained a unique WC1+γδ T-cell subset, not detected in hPBMCs. Furthermore, differences in the activation/inhibition trends of seven canonical pathways and the sets of DAPs between sheep and humans, emphasize the need to consider interspecies differences in translational studies and inflammation research.

Cellular control of protein levels: A systems biology perspective

How cells regulate protein levels is a central question of biology. Over the past decades, molecular biology research has provided profound insights into the mechanisms and the molecular machinery governing each step of the gene expression process, from transcription to protein degradation. Recent advances in transcriptomics and proteomics have complemented our understanding of these fundamental cellular processes with a quantitative, systems-level perspective. Multi-omic studies revealed significant quantitative, kinetic and functional differences between the genome, transcriptome and proteome. While protein levels often correlate with mRNA levels, quantitative investigations have demonstrated a substantial impact of translation and protein degradation on protein expression control. In addition, protein-level regulation appears to play a crucial role in buffering protein abundances against undesirable mRNA expression variation. These findings have practical implications for many fields, including gene function prediction and precision medicine.

An integrated workflow for quantitative analysis of the newly synthesized proteome

The analysis of proteins that are newly synthesized upon a cellular perturbation can provide detailed insight into the proteomic response that is elicited by specific cues. This can be investigated by pulse-labeling of cells with clickable and stable-isotope-coded amino acids for the enrichment and mass spectrometric characterization of newly synthesized proteins (NSPs), however convoluted protocols prohibit their routine application. Here we report the optimization of multiple steps in sample preparation, mass spectrometry and data analysis, and we integrate them into a semi-automated workflow for the quantitative analysis of the newly synthesized proteome (QuaNPA). Reduced input requirements and data-independent acquisition (DIA) enable the analysis of triple-SILAC-labeled NSP samples, with enhanced throughput while featuring high quantitative accuracy. We apply QuaNPA to investigate the time-resolved cellular response to interferon-gamma (IFNg), observing rapid induction of targets 2 h after IFNg treatment. QuaNPA provides a powerful approach for large-scale investigation of NSPs to gain insight into complex cellular processes.

Nascent Proteomics: Chemical Tools for Monitoring Newly Synthesized Proteins

Cellular proteins are dynamically regulated in response to environmental stimuli. Conventional proteomics compares the entire proteome in different cellular states to identify differentially expressed proteins, which suffers from limited sensitivity for analyzing acute and subtle changes. To address this challenge, nascent proteomics has been developed, which selectively analyzes the newly synthesized proteins, thus offering a more sensitive and timely insight into the dynamic changes of the proteome. In this Minireview, we discuss recent advancements in nascent proteomics, with an emphasis on methodological developments. Also, we delve into the current challenges and provide an outlook on the future prospects of this exciting field.

Determining DNA-Protein Binding Affinities and Specificities from Crude Lysates Using a Combined SILAC/TMT Labeling Strategy

In recent years, quantitative mass spectrometry-based interaction proteomics technology has proven very useful in identifying specific DNA-protein interactions using single pull-downs from crude lysates. Here, we applied a SILAC/TMT-based higher-order multiplexing approach to develop an interaction proteomics workflow called Protein-nucleic acid Affinity and Specificity quantification by MAss spectrometry in Nuclear extracts or PASMAN. In PASMAN, DNA pull-downs using a concentration range of specific and control DNA baits are performed in SILAC-labeled nuclear extracts. MS1-based quantification to determine specific DNA-protein interactions is then combined with sequential TMT-based quantification of fragmented SILAC peptides, allowing the generation of Hill-like curves and determination of apparent binding affinities. We benchmarked PASMAN using the SP/KLF motif and further applied it to gain insights into two CGCG-containing consensus DNA motifs. These motifs are recognized by two BEN domain-containing proteins, BANP and BEND3, which we find to interact with these motifs with distinct affinities. Finally, we profiled the BEND3 proximal proteome, revealing the NuRD complex as the major BEND3 proximal protein complex in vivo. In summary, PASMAN represents, to our knowledge, the first higher-order multiplexing-based interaction proteomics method that can be used to decipher specific DNA-protein interactions and their apparent affinities in various biological and pathological contexts.

Evaluating cPILOT Data toward Quality Control Implementation

Multiplexing enables the monitoring of hundreds to thousands of proteins in quantitative proteomics analyses and increases sample throughput. In most mass-spectrometry-based proteomics workflows, multiplexing is achieved by labeling biological samples with heavy isotopes via precursor isotopic labeling or isobaric tagging. Enhanced multiplexing strategies, such as combined precursor isotopic labeling and isobaric tagging (cPILOT), combine multiple technologies to afford an even higher sample throughput. Critical to enhanced multiplexing analyses is ensuring that analytical performance is optimal and that missingness of sample channels is minimized. Automation of sample preparation steps and use of quality control (QC) metrics can be incorporated into multiplexing analyses and reduce the likelihood of missing information, thus maximizing the amount of usable quantitative data. Here, we implemented QC metrics previously developed in our laboratory to evaluate a 36-plex cPILOT experiment that encompassed 144 mouse samples of various tissue types, time points, genotypes, and biological replicates. The evaluation focuses on the use of a sample pool generated from all samples in the experiment to monitor the daily instrument performance and to provide a means for data normalization across sample batches. Our results show that tracking QC metrics enabled the quantification of ∼7000 proteins in each sample batch, of which ∼70% had minimal missing values across up to 36 sample channels. Implementation of QC metrics for future cPILOT studies as well as other enhanced multiplexing strategies will help yield high-quality data sets.

Enhanced Multiplexing Technology for Proteomics

The identification of thousands of proteins and their relative levels of expression has furthered understanding of biological processes and disease and stimulated new systems biology hypotheses. Quantitative proteomics workflows that rely on analytical assays such as mass spectrometry have facilitated high-throughput measurements of proteins partially due to multiplexing. Multiplexing allows proteome differences across multiple samples to be measured simultaneously, resulting in more accurate quantitation, increased statistical robustness, reduced analysis times, and lower experimental costs. The number of samples that can be multiplexed has evolved from as few as two to more than 50, with studies involving more than 10 samples being denoted as enhanced multiplexing or hyperplexing. In this review, we give an update on emerging multiplexing proteomics techniques and highlight advantages and limitations for enhanced multiplexing strategies.

Global quantification of newly synthesized proteins reveals cell type- and inhibitor-specific effects on protein synthesis inhibition

Manipulation of protein synthesis is commonly applied to uncover protein functions and cellular activities. Multiple inhibitors with distinct mechanisms have been widely investigated and employed in bio-related research, but it is extraordinarily challenging to measure and evaluate the synthesis inhibition efficiencies of individual proteins by different inhibitors at the proteome level. Newly synthesized proteins are the immediate and direct products of protein synthesis, and thus their comprehensive quantification provides a unique opportunity to study protein inhibition. Here, we systematically investigate protein inhibition and evaluate different popular inhibitors, i.e. cycloheximide, puromycin, and anisomycin, through global quantification of newly synthesized proteins in several types of human cells (A549, MCF-7, Jurkat, and THP-1 cells). The inhibition efficiencies of protein synthesis are comprehensively measured by integrating azidohomoalanine-based protein labeling, selective enrichment, a boosting approach, and multiplexed proteomics. The same inhibitor results in dramatic variation of the synthesis inhibition efficiencies for different proteins in the same cells, and each inhibitor exhibits unique preferences. Besides cell type- and inhibitor-specific effects, some universal rules are unraveled. For instance, nucleolar and ribosomal proteins have relatively higher inhibition efficiencies in every type of cells treated with each inhibitor. Moreover, proteins intrinsically resistant or sensitive to the inhibition are identified and found to have distinct functions. Systematic investigation of protein synthesis inhibition in several types of human cells by different inhibitors provides valuable information about the inhibition of protein synthesis, advancing our understanding of inhibiting protein synthesis.

Dysfunctional tRNA reprogramming and codon-biased translation in cancer

Many cancers hijack translation to increase the synthesis of tumor-driving proteins, the messenger mRNAs of which have specific codon usage patterns. Termed 'codon-biased translation' and originally identified in stress response regulation, this mechanism is supported by diverse studies demonstrating how the 50 RNA modifications of the epitranscriptome, specific tRNAs, and codon-biased mRNAs are used by oncogenic programs to promote proliferation and chemoresistance. The epitranscriptome writers METTL1-WDR4, Elongator complex protein (ELP)1-6, CTU1-2, and ALKBH8-TRM112 illustrate the principal mechanism of codon-biased translation, with gene amplifications, increased RNA modifications, and enhanced tRNA stability promoting cancer proliferation. Furthermore, systems-level analyses of 34 tRNA writers and 493 tRNA genes highlight the theme of tRNA epitranscriptome dysregulation in many cancers and identify candidate tRNA writers, tRNA modifications, and tRNA molecules as drivers of pathological codon-biased translation.

Advances in stable isotope labeling: dynamic labeling for spatial and temporal proteomic analysis

The field of proteomics is continually improving, requiring the development of new quantitative methods. Stable isotope labeling in cell culture (SILAC) is a metabolic labeling technique originating in the early 2000s. By incorporating isotopically labeled amino acids into the media used for cell culture, unlabeled versus labeled cells can be differentiated by the mass spectrometer. Traditional SILAC labeling has been expanded to pulsed applications allowing for a new quantitative dimension of proteomics - temporal analysis. The complete introduction of Heavy SILAC labeling chased with surplus unlabeled medium mimics traditional pulse-chase experiments and allows for the loss of heavy signal to track proteomic changes over time. In a similar fashion, pulsed SILAC (pSILAC) monitors the initial incorporation of a heavy label across a period of time, which allows for the rate of protein label integration to be assessed. These innovative techniques have aided in inspiring numerous SILAC-based temporal and spatial labeling applications, including super SILAC, spike-in SILAC, spatial SILAC, and a revival in label multiplexing. This review reflects upon the evolution of SILAC and the pulsed SILAC application, introduces advances in SILAC labeling, and proposes future perspectives for this novel and exciting field.

Newly synthesized mRNA escapes translational repression during the acute phase of the mammalian unfolded protein response

Endoplasmic Reticulum (ER) stress, caused by the accumulation of misfolded proteins in the ER, elicits a homeostatic mechanism known as the Unfolded Protein Response (UPR). The UPR reprograms gene expression to promote adaptation to chronic ER stress. The UPR comprises an acute phase involving inhibition of bulk protein synthesis and a chronic phase of transcriptional induction coupled with the partial recovery of protein synthesis. However, the role of transcriptional regulation in the acute phase of the UPR is not well understood. Here we analyzed the fate of newly synthesized mRNA encoding the protective and homeostatic transcription factor X-box binding protein 1 (XBP1) during this acute phase. We have previously shown that global translational repression induced by the acute UPR was characterized by decreased translation and increased stability of XBP1 mRNA. We demonstrate here that this stabilization is independent of new transcription. In contrast, we show XBP1 mRNA newly synthesized during the acute phase accumulates with long poly(A) tails and escapes translational repression. Inhibition of newly synthesized RNA polyadenylation during the acute phase decreased cell survival with no effect in unstressed cells. Furthermore, during the chronic phase of the UPR, levels of XBP1 mRNA with long poly(A) tails decreased in a manner consistent with co-translational deadenylation. Finally, additional pro-survival, transcriptionally-induced mRNAs show similar regulation, supporting the broad significance of the pre-steady state UPR in translational control during ER stress. We conclude that the biphasic regulation of poly(A) tail length during the UPR represents a previously unrecognized pro-survival mechanism of mammalian gene regulation.

Mechanisms and minimization of false discovery of metabolic bio-orthogonal non-canonical amino acid proteomics

Metabolic proteomics has been widely used to characterize dynamic protein networks in many areas of biomedicine, including in the arena of tissue aging and rejuvenation. Bio-orthogonal non-canonical amino acid tagging (BONCAT) is based on mutant methionine-tRNA synthases (MetRS) that incorporates metabolic tags, e.g., azido-nor leucine, ANL, into newly synthesized proteins. BONCAT revolutionizes metabolic proteomics, because mutant MetRS transgene allows one to identify cell type specific proteomes in mixed biological environments. This is not possible with other methods, such as stable isotope labeling with amino acids in cell culture (SILAC), isobaric tags for relative and absolute quantitation (iTRAQ) and tandem mass tags (TMT). At the same time, an inherent weakness of BONCAT is that after click chemistry-based enrichment, all identified proteins are assumed to have been metabolically tagged, but there is no confirmation in Mass Spectrometry data that only tagged proteins are detected. As we show here, such assumption is incorrect and accurate negative controls uncover a surprisingly high degree of false positives in BONCAT proteomics. We show not only how to reveal the false discovery and thus improve the accuracy of the analyses and conclusions but also approaches for avoiding it through minimizing non-specific detection of biotin, biotin-independent direct detection of metabolic tags, and improvement of signal to noise ratio through machine learning algorithms.

Quantitative Translation Proteomics Using mePROD

Multiplexed enhanced protein dynamic mass spectrometry (mePROD MS) enables robust quantification of translation in cell culture. Tandem mass tags (TMT) are combined with pulsed stable isotope labeling in cell culture (pSILAC) to monitor newly synthesized proteins on a proteome wide scale. While approaches combining pSILAC and TMT typically require long labeling times to reach sufficient intensity of the newly synthesized peptides in the mass spectrometer, mePROD uses a carrier signal that boosts the survey scan intensity and strongly increases identification rates. Hence, this protocol provides an easy and cost-efficient method to profile proteome-wide translatome changes at a temporal resolution of minutes.

Differential Translation Activity Analysis Using Bioorthogonal Noncanonical Amino Acid Tagging (BONCAT) in Archaea

The study of protein production and degradation in a quantitative and time-dependent manner is a major challenge to better understand cellular physiological response. Among available technologies bioorthogonal noncanonical amino acid tagging (BONCAT) is an efficient approach allowing for time-dependent labeling of proteins through the incorporation of chemically reactive noncanonical amino acids like L-azidohomoalanine (L-AHA). The azide-containing amino-acid derivative enables a highly efficient and specific reaction termed click chemistry, whereby the azide group of the L-AHA reacts with a reactive alkyne derivate, like dibenzocyclooctyne (DBCO) derivatives, using strain-promoted alkyne-azide cycloaddition (SPAAC). Moreover, available DBCO containing reagents are versatile and can be coupled to fluorophore (e.g., Cy7) or affinity tag (e.g., biotin) derivatives, for easy visualization and affinity purification, respectively.Here, we describe a step-by-step BONCAT protocol optimized for the model archaeon Haloferax volcanii , but which is also suitable to harness other biological systems. Finally, we also describe examples of downstream visualization, affinity purification of L-AHA-labeled proteins and differential expression analysis.In conclusion, the following BONCAT protocol expands the available toolkit to explore proteostasis using time-resolved semiquantitative proteomic analysis in archaea .

Recent advancements in mass spectrometry-based tools to investigate newly synthesized proteins

Tight regulation of protein translation drives the proteome to undergo changes under influence of extracellular or intracellular signals. Despite mass spectrometry–based proteomics being an excellent method to study differences in protein abundance in complex proteomes, analyzing minute or rapid changes in protein synthesis and abundance remains challenging. Therefore, several dedicated techniques to directly detect and quantify newly synthesized proteins have been developed, notably puromycin-based, bio-orthogonal noncanonical amino acid tagging–based, and stable isotope labeling by amino acids in cell culture–based methods, combined with mass spectrometry. These techniques have enabled the investigation of perturbations, stress, or stimuli on protein synthesis. Improvements of these methods are still necessary to overcome various remaining limitations. Recent improvements include enhanced enrichment approaches and combinations with various stable isotope labeling techniques, which allow for more accurate analysis and comparison between conditions on shorter timeframes and in more challenging systems. Here, we aim to review the current state in this field.

An Overview of Biological and Computational Methods for Designing Mechanism-Informed Anti-biofilm Agents

Bacterial biofilms are complex and highly antibiotic-resistant aggregates of microbes that form on surfaces in the environment and body including medical devices. They are key contributors to the growing antibiotic resistance crisis and account for two-thirds of all infections. Thus, there is a critical need to develop anti-biofilm specific therapeutics. Here we discuss mechanisms of biofilm formation, current anti-biofilm agents, and strategies for developing, discovering, and testing new anti-biofilm agents. Biofilm formation involves many factors and is broadly regulated by the stringent response, quorum sensing, and c-di-GMP signaling, processes that have been targeted by anti-biofilm agents. Developing new anti-biofilm agents requires a comprehensive systems-level understanding of these mechanisms, as well as the discovery of new mechanisms. This can be accomplished through omics approaches such as transcriptomics, metabolomics, and proteomics, which can also be integrated to better understand biofilm biology. Guided by mechanistic understanding, in silico techniques such as virtual screening and machine learning can discover small molecules that can inhibit key biofilm regulators. To increase the likelihood that these candidate agents selected from in silico approaches are efficacious in humans, they must be tested in biologically relevant biofilm models. We discuss the benefits and drawbacks of in vitro and in vivo biofilm models and highlight organoids as a new biofilm model. This review offers a comprehensive guide of current and future biological and computational approaches of anti-biofilm therapeutic discovery for investigators to utilize to combat the antibiotic resistance crisis.

Translational remodeling by RNA-binding proteins and noncoding RNAs

Responsible for generating the proteome that controls phenotype, translation is the ultimate convergence point for myriad upstream signals that influence gene expression. System-wide adaptive translational reprogramming has recently emerged as a pillar of cellular adaptation. As classic regulators of mRNA stability and translation efficiency, foundational studies established the concept of collaboration and competition between RNA-binding proteins (RBPs) and noncoding RNAs (ncRNAs) on individual mRNAs. Fresh conceptual innovations now highlight stress-activated, evolutionarily conserved RBP networks and ncRNAs that increase the translation efficiency of populations of transcripts encoding proteins that participate in a common cellular process. The discovery of post-transcriptional functions for long noncoding RNAs (lncRNAs) was particularly intriguing given their cell-type-specificity and historical definition as nuclear-functioning epigenetic regulators. The convergence of RBPs, lncRNAs, and microRNAs on functionally related mRNAs to enable adaptive protein synthesis is a newer biological paradigm that highlights their role as "translatome (protein output) remodelers" and reinvigorates the paradigm of "RNA operons." Together, these concepts modernize our understanding of cellular stress adaptation and strategies for therapeutic development. This article is categorized under: RNA Interactions with Proteins and Other Molecules > Protein-RNA Interactions: Functional Implications Translation > Translation Regulation Regulatory RNAs/RNAi/Riboswitches > Regulatory RNAs.

One-shot analysis of translated mammalian lncRNAs with AHARIBO

A vast portion of the mammalian genome is transcribed as long non-coding RNAs (lncRNAs) acting in the cytoplasm with largely unknown functions. Surprisingly, lncRNAs have been shown to interact with ribosomes, encode peptides, or act as ribosome sponges. These functions still remain mostly undetected and understudied owing to the lack of efficient tools for genome-wide simultaneous identification of ribosome-associated and peptide-producing lncRNAs. Here, we present AHA-mediated RIBOsome isolation (AHARIBO), a method for the detection of lncRNAs either untranslated, but associated with ribosomes, or encoding small peptides. Using AHARIBO in mouse embryonic stem cells during neuronal differentiation, we isolated ribosome-protected RNA fragments, translated RNAs, and corresponding de novo synthesized peptides. Besides identifying mRNAs under active translation and associated ribosomes, we found and distinguished lncRNAs acting as ribosome sponges or encoding micropeptides, laying the ground for a better functional understanding of hundreds of lncRNAs.

Virus systems biology: Proteomics profiling of dynamic protein networks during infection

The host cell proteome undergoes a variety of dynamic changes during viral infection, elicited by the virus itself or host cell defense mechanisms. Studying these changes on a global scale by integrating functional and physical interactions within protein networks during infection is an important tool to understand pathology. Indeed, proteomics studies dissecting protein signaling cascades and interaction networks upon infection showed how global information can significantly improve understanding of disease mechanisms of diverse viral infections. Here, we summarize and give examples of different experimental designs, proteomics approaches and bioinformatics analyses that allow profiling proteome changes and host-pathogen interactions to gain a molecular systems view of viral infection.

An Engineered Escherichia coli Strain with Synthetic Metabolism for in-Cell Production of Translationally Active Methionine Derivatives

In the last decades, it has become clear that the canonical amino acid repertoire codified by the universal genetic code is not up to the needs of emerging biotechnologies. For this reason, extensive genetic code re-engineering is essential to expand the scope of ribosomal protein translation, leading to reprogrammed microbial cells equipped with an alternative biochemical alphabet to be exploited as potential factories for biotechnological purposes. The prerequisite for this to happen is a continuous intracellular supply of noncanonical amino acids through synthetic metabolism from simple and cheap precursors. We have engineered an Escherichia coli bacterial system that fulfills these requirements through reconfiguration of the methionine biosynthetic pathway and the introduction of an exogenous direct trans-sulfuration pathway. Our metabolic scheme operates in vivo, rescuing intermediates from core cell metabolism and combining them with small bio-orthogonal compounds. Our reprogrammed E. coli strain is capable of the in-cell production of l-azidohomoalanine, which is directly incorporated into proteins in response to methionine codons. We thereby constructed a prototype suitable for economic, versatile, green sustainable chemistry, pushing towards enzyme chemistry and biotechnology-based production.

Proteome Turnover in the Spotlight: Approaches, Applications, and Perspectives

In all cells, proteins are continuously synthesized and degraded to maintain protein homeostasis and modify gene expression levels in response to stimuli. Collectively, the processes of protein synthesis and degradation are referred to as protein turnover. At a steady state, protein turnover is constant to maintain protein homeostasis, but in dynamic responses, proteins change their rates of synthesis and degradation to adjust their proteomes to internal or external stimuli. Thus, probing the kinetics and dynamics of protein turnover lends insight into how cells regulate essential processes such as growth, differentiation, and stress response. Here, we outline historical and current approaches to measuring the kinetics of protein turnover on a proteome-wide scale in both steady-state and dynamic systems, with an emphasis on metabolic tracing using stable isotope-labeled amino acids. We highlight important considerations for designing proteome turnover experiments, key biological findings regarding the conserved principles of proteome turnover regulation, and future perspectives for both technological and biological investigation.

Decoupling Protein Production from Cell Growth Enhances the Site-Specific Incorporation of Noncanonical Amino Acids in E. coli

The site-specific incorporation of noncanonical amino acids (ncAAs) into proteins by amber stop codon suppression has become a routine method in academic laboratories. This approach requires an amber suppressor tRNA_CUA to read the amber codon and an aminoacyl-tRNA synthetase to charge the tRNA_CUA with the ncAA. However, a major drawback is the low yield of the mutant protein in comparison to the wild type. This effect primarily results from the competition of release factor 1 with the charged suppressor tRNA_CUA for the amber codon at the A-site of the ribosome. A number of laboratories have attempted to improve the incorporation efficiency of ncAAs with moderate results. We aimed at increasing the efficiency to produce high yields of ncAA-functionalized proteins in a scalable setting for industrial application. To do this, we inserted an ncAA into the enhanced green fluorescent protein and an antibody mimetic molecule using an industrial E. coli strain, which produces recombinant proteins independent of cell growth. The controlled decoupling of recombinant protein production from cell growth considerably increased the incorporation of the ncAA, producing substantially higher protein yields versus the reference E. coli strain BL21(DE3). The target proteins were expressed at high levels, and the ncAA was efficiently incorporated with excellent fidelity while the protein function was preserved.

Effective Method for Accurate and Sensitive Quantitation of Rapid Changes of Newly Synthesized Proteins

Protein synthesis is quickly and tightly regulated in cells to adapt to the ever-changing extracellular and intracellular environment. Accurate quantitation of rapid protein synthesis changes can provide insights into protein functions and cellular activities, but it is very challenging to achieve because of the lack of effective analysis methods. Here, we developed an effective mass spectrometry-based method named quantitative O-propargyl-puromycin tagging (QOT) by integrating O-propargyl-puromycin (OPP) labeling, bioorthogonal chemistry, and multiplexed proteomics for global and quantitative analysis of rapid protein synthesis. The current method enables us to accurately quantitate rapid changes of newly synthesized proteins because, unlike amino acids and their analogs, OPP can be utilized by the ribosome immediately without being activated and conjugated to tRNA, and thus cell starvation or pretreatment is not required. This method was applied to quantitate rapid changes of protein synthesis in THP-1 macrophages treated with lipopolysaccharide (LPS). For 15-min labeling, >3000 proteins were quantitated, and the synthesis of 238 proteins was significantly altered, including transcription factors and cytokines. The results demonstrated that protein synthesis was modulated to facilitate protein secretion in macrophages in response to LPS. Considering the importance of protein synthesis, this method can be extensively applied to investigate rapid changes of protein synthesis in the biological and biomedical research fields.

Fishing for newly synthesized proteins with phosphonate-handles

Bioorthogonal chemistry introduces affinity-labels into biomolecules with minimal disruption to the original system and is widely applicable in a range of contexts. In proteomics, immobilized metal affinity chromatography (IMAC) enables enrichment of phosphopeptides with extreme sensitivity and selectivity. Here, we adapt and combine these superb assets in a new enrichment strategy using phosphonate-handles, which we term PhosID. In this approach, click-able phosphonate-handles are introduced into proteins via 1,3-dipolar Huisgen-cycloaddition to azido-homo-alanine (AHA) and IMAC is then used to enrich exclusively for phosphonate-labeled peptides. In interferon-gamma (IFNγ) stimulated cells, PhosID enabled the identification of a large number of IFN responsive newly synthesized proteins (NSPs) whereby we monitored the differential synthesis of these proteins over time. Collectively, these data validate the excellent performance of PhosID with efficient analysis and quantification of hundreds of NSPs by single LC-MS/MS runs. We envision PhosID as an attractive and alternative tool for studying stimuli-sensitive proteome subsets.

Here the authors describe PhosID, an enrichment strategy using phosphonate-handles, that combines click chemistry and IMAC-based phospho-enrichment for quantitative proteomics analysis of newly synthesized proteins.

Uncovering an Organ's Molecular Architecture at Single-Cell Resolution by Spatially Resolved Transcriptomics

Revealing fine-scale cellular heterogeneity among spatial context and the functional and structural foundations of tissue architecture is fundamental within biological research and pharmacology. Unlike traditional approaches involving single molecules or bulk omics, cutting-edge, spatially resolved transcriptomics techniques offer near-single-cell or even subcellular resolution within tissues. Massive information across higher dimensions along with position-coordinating labels can better map the whole 3D transcriptional landscape of tissues. In this review, we focus on developments and strategies in spatially resolved transcriptomics, compare the cell and gene throughput and spatial resolution in detail for existing methods, and highlight the enormous potential in biomedical research.

Instrument Logic Increases Identifications during Mutliplexed Translatome Measurements

Pulsed Stable Isotope Labeling in Cell culture (SILAC) approaches allow measurement of protein dynamics, including protein translation and degradation. However, its use for quantifying acute changes has been limited due to low labeled peptide stoichiometry. Here, we describe the use of instrument logic to select peaks of interest via targeted mass differences (TMD) for overcoming this limitation. Comparing peptides artificially mixed at low heavy-to-light stoichiometry measured using standard data dependent acquisition with or without TMD revealed 2–3-fold increases in identification without significant loss in quantification precision for both MS² and MS³ methods. Our benchmarked method approach increased throughput by reducing the necessary machine time. We anticipate that all pulsed SILAC measurements, combined with tandem mass tagging (TMT) or not, would greatly benefit from instrument logic based approaches.

HyperQuant-A Computational Pipeline for Higher Order Multiplexed Quantitative Proteomics

Quantitative proteomics has evolved considerably over the last decade with the advent of higher order multiplexing (HOM) techniques. With the development of methods such as-multitagging, cPILOT, hyperplexing, BONPlex, and MITNCAT, the HOM technique is rapidly taking the center stage in multiplexed quantitative proteomics. These studies combined MS¹ and MS² labels in a single experiment enabling higher sample throughput. While HOM is highly promising, the computational analysis is still a big challenge, as the available tools cannot harness its power completely. We have developed a new quantitative pipeline, HyperQuant to aid in accurately quantitating complex HOM data. The pipeline uses identification results from either MaxQuant or any other search engine and quantitation results from QuantWiz^IQ. The Mapper and Combiner modules of HyperQuant allow facile integration of the labeled data, along with peptide spectrum match (PSM) intensity/ratio integration for proteins, respectively, for each PSM label combination. This also includes appropriate combination of replicates/fractions before summarizing the protein intensity/ratio, leading to robust quantitation. To the best of our knowledge, this is the first tool for the quantitation of HOM data with flexibility for any combination of MS¹ and MS² labels. We demonstrate its utility in analyzing two 18-plex data sets from the hyperplexing and the BONplex studies. The tool is open source and freely available for noncommercial use. HyperQuant is a highly valuable tool that will help in advancing the field of multiplexed quantitative proteomics.

Systematic quantification of the dynamics of newly synthesized proteins unveiling their degradation pathways in human cells

Proteins are continuously synthesized during cell growth and proliferation. At the same time, excessive and misfolded proteins have to be degraded, otherwise they are a burden to cells. Protein degradation is essential to maintain proteostasis in cells, and dysfunction of protein degradation systems results in numerous diseases such as cancer and neurodegenerative diseases. Despite the importance of protein degradation, the degradation pathways of many proteins remain to be explored. Here, we comprehensively investigated the degradation of newly synthesized proteins in human cells by integrating metabolic labeling, click chemistry, and multiplexed proteomics, and systematic and quantitative analysis of newly synthesized proteins first revealed the degradation pathways of many proteins. Bioinformatic analysis demonstrates that proteins degraded through two major pathways have distinct properties and functions. Proteins degraded through the ubiquitin-proteasome pathway contain more disordered structures, whereas those through the autophagy-lysosome pathway have significantly higher hydrophobicity. Systematic and quantitative investigation of the dynamics of newly synthesized proteins provides unprecedented and valuable information about protein degradation, which leads to a better understanding of protein properties and cellular activities.

Loss of Elongator- and KEOPS-Dependent tRNA Modifications Leads to Severe Growth Phenotypes and Protein Aggregation in Yeast

Modifications found in the Anticodon Stem Loop (ASL) of tRNAs play important roles in regulating translational speed and accuracy. Threonylcarbamoyl adenosine (t6A37) and 5-methoxycarbonyl methyl-2-thiouridine (mcm5s2U34) are critical ASL modifications that have been linked to several human diseases. The model yeast Saccharomyces cerevisiae is viable despite the absence of both modifications, growth is however greatly impaired. The major observed consequence is a subsequent increase in protein aggregates and aberrant morphology. Proteomic analysis of the t6A-deficient strain (sua5 mutant) revealed a global mistranslation leading to protein aggregation without regard to physicochemical properties or t6A-dependent or biased codon usage in parent genes. However, loss of sua5 led to increased expression of soluble proteins for mitochondrial function, protein quality processing/trafficking, oxidative stress response, and energy homeostasis. These results point to a global function for t6A in protein homeostasis very similar to mcm5/s2U modifications.

Defining the adult hippocampal neural stem cell secretome: In vivo versus in vitro transcriptomic differences and their correlation to secreted protein levels

Adult hippocampal neural stem and progenitor cells (NSPCs) secrete a variety of proteins that affect tissue function. Though several individual NSPC-derived proteins have been shown to impact key cellular processes, a broad characterization is lacking. Secretome profiling of low abundance stem cell populations is typically achieved via proteomic characterization of in vitro, isolated cells. Here, we identified hundreds of secreted proteins in conditioned media from in vitro adult mouse hippocampal NSPCs using an antibody array and mass spectrometry. Comparison of protein abundance between antibody array and mass spectrometry plus quantification of several key secreted proteins by ELISA revealed notable disconnect between methods in what proteins were identified as being high versus low abundance, suggesting that data from antibody arrays in particular should be approached with caution. We next assessed the NSPC secretome on a transcriptional level with single cell and bulk RNA sequencing (RNAseq) of cultured NSPCs. Comparison of RNAseq transcript levels of highly secreted proteins revealed that quantification of gene expression did not necessarily predict relative protein abundance. Interestingly, comparing our in vitro NSPC gene expression data with similar data from freshly isolated, in vivo hippocampal NSPCs revealed strong correlations in global gene expression between in vitro and in vivo NSPCs. Understanding the components and functions of the NSPC secretome is essential to understanding how these cells may modulate the hippocampal neurogenic niche. Cumulatively, our data emphasize the importance of using proteomics in conjunction with transcriptomics and highlights the need for better methods of unbiased secretome profiling.

Translational control of gene expression via interacting feedback loops

Translation is a key step in the synthesis of proteins. Accordingly, cells have evolved an intricate array of control mechanisms to regulate this process. By constructing a multicomponent mathematical framework we uncover how translation may be controlled via interacting feedback loops. Our results reveal that this interplay gives rise to a remarkable range of protein synthesis dynamics, including oscillations, step change, and bistability. This suggests that cells may have recourse to a much richer set of control mechanisms than was previously understood.