Proteomic Techniques to Examine Neuronal Translational Dynamics

How does the neuronal proteostasis network react to cellular cues?

Even though neurons are post-mitotic cells, they still engage in protein synthesis to uphold their cellular content balance, including for organelles, such as the endoplasmic reticulum or mitochondria. Additionally, they expend significant energy on tasks like neurotransmitter production and maintaining redox homeostasis. This cellular homeostasis is upheld through a delicate interplay between mRNA transcription-translation and protein degradative pathways, such as autophagy and proteasome degradation. When faced with cues such as nutrient stress, neurons must adapt by altering their proteome to survive. However, in many neurodegenerative disorders, such as Parkinson's disease, the pathway and processes for coping with cellular stress are impaired. This review explores neuronal proteome adaptation in response to cellular stress, such as nutrient stress, with a focus on proteins associated with autophagy, stress response pathways, and neurotransmitters.

Combining offline high performance liquid chromatography fractionation of peptides and intact proteins to enhance proteome coverage in bottom-up proteomics

Offline peptide separation (PS) using high-performance liquid chromatography (HPLC) is currently used to enhance liquid chromatography-tandem mass spectrometry (LC-MS/MS) detection of proteins. In search of more effective methods for enhancing MS proteome coverage, we developed a robust method for intact protein separation (IPS), an alternative first-dimension separation technique, and explored additional benefits that it offers. Comparing IPS to the traditional PS method, we found that both enhance detection of unique protein IDs to a similar magnitude, though in diverse ways. IPS was especially effective in serum, which has a small number of extremely high abundance proteins. PS was more effective in tissues with fewer dominating high-abundance proteins and was more effective in enhancing detection of post-translational modifications (PTMs). Combining the IPS and PS methods together (IPS+PS) was especially beneficial, enhancing proteome detection more than either method could independently. The comparison of IPS+PS versus six PS fractionation pools increased total number of proteins IDs by nearly double, while also significantly increasing number of unique peptides detected per protein, percent peptide sequence coverage of each protein, and detection of PTMs. This IPS+PS combined method requires fewer LC-MS/MS runs than current PS methods would need to obtain similar improvements in proteome detection, and it is robust, time- and cost-effective, and generally applicable to various tissue and sample types.

Learning and memory formation in zebrafish: Protein dynamics and molecular tools

Research on learning and memory formation at the level of neural networks, as well as at the molecular level, is challenging due to the immense complexity of the brain. The zebrafish as a genetically tractable model organism can overcome many of the current challenges of studying molecular mechanisms of learning and memory formation. Zebrafish have a translucent, smaller and more accessible brain than that of mammals, allowing imaging of the entire brain during behavioral manipulations. Recent years have seen an extensive increase in published brain research describing the use of zebrafish for the study of learning and memory. Nevertheless, due to the complexity of the brain comprising many neural cell types that are difficult to isolate, it has been difficult to elucidate neural networks and molecular mechanisms involved in memory formation in an unbiased manner, even in zebrafish larvae. Therefore, data regarding the identity, location, and intensity of nascent proteins during memory formation is still sparse and our understanding of the molecular networks remains limited, indicating a need for new techniques. Here, we review recent progress in establishing learning paradigms for zebrafish and the development of methods to elucidate neural and molecular networks of learning. We describe various types of learning and highlight directions for future studies, focusing on molecular mechanisms of long-term memory formation and promising state-of-the-art techniques such as cell-type-specific metabolic labeling.

Extracellular matrix dynamics: tracking in biological systems and their implications

The extracellular matrix (ECM) constitutes the main acellular microenvironment of cells in almost all tissues and organs. The ECM not only provides mechanical support, but also mediates numerous biochemical interactions to guide cell survival, proliferation, differentiation, and migration. Thus, better understanding the everchanging temporal and spatial shifts in ECM composition and structure – the ECM dynamics – will provide fundamental insight regarding extracellular regulation of tissue homeostasis and how tissue states transition from one to another during diverse pathophysiological processes. This review outlines the mechanisms mediating ECM-cell interactions and highlights how changes in the ECM modulate tissue development and disease progression, using the lung as the primary model organ. We then discuss existing methodologies for revealing ECM compositional dynamics, with a particular focus on tracking newly synthesized ECM proteins. Finally, we discuss the ramifications ECM dynamics have on tissue engineering and how to implement spatial and temporal specific extracellular microenvironments into bioengineered tissues. Overall, this review communicates the current capabilities for studying native ECM dynamics and delineates new research directions in discovering and implementing ECM dynamics to push the frontier forward.

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.

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.

De novo proteomic methods for examining the molecular mechanisms underpinning long-term memory

Memory formation is a fundamental function of the nervous system that enables the experience-based adaptation of behaviour. The formation, recall and updating of long-term memory (LTM) requires new protein synthesis through its direct involvement in neuronal processes, such as long-term potentiation (LTP), long-term depression (LTD) and synaptic scaling. We discuss the advantages and limitations of several emerging techniques which enable the tagging of newly synthesised proteins, including stable isotope labelling with amino acids in cell culture (SILAC), puromycin labelling, and non-canonical amino acid (NCAA) labelling. We further present how these methods allow for the identification and visualisation of proteins which are newly synthesised during different stages of memory formation. These emerging techniques will continue to expand our understanding of how memories are formed, consolidated and retrieved.

Translational animal models for Alzheimer's disease: An Alzheimer's Association Business Consortium Think Tank

Over 5 million Americans and 50 million individuals worldwide are living with Alzheimer's disease (AD). The progressive dementia associated with AD currently has no cure. Although clinical trials in patients are ultimately required to find safe and effective drugs, animal models of AD permit the integration of brain pathologies with learning and memory deficits that are the first step in developing these new drugs. The purpose of the Alzheimer's Association Business Consortium Think Tank meeting was to address the unmet need to improve the discovery and successful development of Alzheimer's therapies. We hypothesize that positive responses to new therapies observed in validated models of AD will provide predictive evidence for positive responses to these same therapies in AD patients. To achieve this goal, we convened a meeting of experts to explore the current state of AD animal models, identify knowledge gaps, and recommend actions for development of next-generation models with better predictability. Among our findings, we all recognize that models reflecting only single aspects of AD pathogenesis do not mimic AD. Models or combinations of new models are needed that incorporate genetics with environmental interactions, timing of disease development, heterogeneous mechanisms and pathways, comorbidities, and other pathologies that lead to AD and related dementias. Selection of the best models requires us to address the following: (1) which animal species, strains, and genetic backgrounds are most appropriate; (2) which models permit efficient use throughout the drug development pipeline; (3) the translatability of behavioral-cognitive assays from animals to patients; and (4) how to match potential AD therapeutics with particular models. Best practice guidelines to improve reproducibility also need to be developed for consistent use of these models in different research settings. To enhance translational predictability, we discuss a multi-model evaluation strategy to de-risk the successful transition of pre-clinical drug assets to the clinic.

Tau-mediated dysregulation of RNA: Evidence for a common molecular mechanism of toxicity in frontotemporal dementia and other tauopathies

Frontotemporal dementias (FTDs) encompass several disorders commonly characterized by progressive frontotemporal lobar degeneration and dementia. Pathologically, TDP-43, FUS, dipeptide repeats, and tau constitute the protein aggregates in FTD, which in turn coincide with heterogeneity in clinical variants. The underlying molecular etiology explaining the formation of each type of protein aggregate remains unclear; however, dysregulated RNA metabolism rises as a common pathogenic factor. Alongside with TDP-43 and FUS, which bind to and regulate RNA dynamics, emerging data suggest that tau may also regulate RNA metabolism and translation. The complex mechanisms that drive translational selectivity in turn regulate the broad clinical presentation of FTDs. Here, we focus on the enigmatic relationship between tau and RNA and review the mechanisms of tau-mediated dysregulation of RNA in tauopathies such as FTD.

Review of Three-Dimensional Liquid Chromatography Platforms for Bottom-Up Proteomics

Proteomics is a large-scale study of proteins, aiming at the description and characterization of all expressed proteins in biological systems. The expressed proteins are typically highly complex and large in abundance range. To fulfill high accuracy and sensitivity of proteome analysis, the hybrid platforms of multidimensional (MD) separations and mass spectrometry have provided the most powerful solution. Multidimensional separations provide enhanced peak capacity and reduce sample complexity, which enables mass spectrometry to analyze more proteins with high sensitivity. Although two-dimensional (2D) separations have been widely used since the early period of proteomics, three-dimensional (3D) separation was barely used by low reproducibility of separation, increased analysis time in mass spectrometry. With developments of novel microscale techniques such as nano-UPLC and improvements of mass spectrometry, the 3D separation becomes a reliable and practical selection. This review summarizes existing offline and online 3D-LC platforms developed for proteomics and their applications. In detail, setups and implementation of those systems as well as their advances are outlined. The performance of those platforms is also discussed and compared with the state-of-the-art 2D-LC. In addition, we provide some perspectives on the future developments and applications of 3D-LC in proteomics.