Periodic Reporting for period 1 - NeuroProClick (Protein-engineering based approach to detect oxidative stress-induced changes in the neuronal proteome)
Reporting period: 2022-08-01 to 2024-07-31
Neurodegenerative disorders are characterised by the disruption of key cellular processes and neuronal loss. Injury via oxidative stress is also associated with the progression of these diseases. Identifying changes within neuronal cells at specific time points will help us better understand the role of oxidative stress in these diseases. These cellular changes are mediated by biomolecules known as proteins, which serve a variety of roles within cells, including structural support, catalysis, transport, and signalling. Quantitative proteomics refers to techniques used to determine protein amounts in biological samples. Such methods have been used to detect proteome changes in immortalised neuronal cells, primary cultured neurons and brain tissue. However, existing quantitative proteomic methods face limitations when examining proteome changes during specific time windows and in specific cells.
One method that can address these limitations is genetic code expansion (GCE). Normally, a cell’s genetic code is restricted to 20 standard and two rare amino acids (AAs). An expanded genetic code uses non-canonical amino acids (ncAAs) to give proteins novel properties. Like canonical AAs, these ncAAs need translational machinery that adds them into proteins in response to codons defined by the genetic code. Therefore, cells need orthogonal translational machinery specific for the ncAA and a certain codon. Usually, the amber stop codon is reprogrammed to introduce ncAAs into individual proteins. Alternatively, sense codons can be targeted, so the ncAA can be added across the whole proteome. This strategy has been exploited for proteomic studies.
This proteome-wide approach was first used in combination with bioorthogonal click chemistry for quantitative studies of newly synthesised proteins in fruit fly models. In this approach, the ncAAs were incorporated across the proteome, and bioorthogonal click chemistry, which refers to selective chemical reactions that occur under physiological conditions, was used to add a chemical handle for downstream enrichment and analysis. Incorporating these ncAAs can be timed to capture proteins synthesised after external factors and specific cell types or signalling pathways can be targeted.
So far, this technology has mainly been used at the proof-of-concept level and the number of studies in neurons is limited. The main aim of this project was to develop a methodology using this GCE-based approach for precise investigations of neuronal proteome changes. We aimed to use GCE to incorporate clickable ncAAs proteome-wide at defined time points relative to an oxidative injury to examine proteome changes in immortalised cell lines and human-derived neurons.
One method that can address these limitations is genetic code expansion (GCE). Normally, a cell’s genetic code is restricted to 20 standard and two rare amino acids (AAs). An expanded genetic code uses non-canonical amino acids (ncAAs) to give proteins novel properties. Like canonical AAs, these ncAAs need translational machinery that adds them into proteins in response to codons defined by the genetic code. Therefore, cells need orthogonal translational machinery specific for the ncAA and a certain codon. Usually, the amber stop codon is reprogrammed to introduce ncAAs into individual proteins. Alternatively, sense codons can be targeted, so the ncAA can be added across the whole proteome. This strategy has been exploited for proteomic studies.
This proteome-wide approach was first used in combination with bioorthogonal click chemistry for quantitative studies of newly synthesised proteins in fruit fly models. In this approach, the ncAAs were incorporated across the proteome, and bioorthogonal click chemistry, which refers to selective chemical reactions that occur under physiological conditions, was used to add a chemical handle for downstream enrichment and analysis. Incorporating these ncAAs can be timed to capture proteins synthesised after external factors and specific cell types or signalling pathways can be targeted.
So far, this technology has mainly been used at the proof-of-concept level and the number of studies in neurons is limited. The main aim of this project was to develop a methodology using this GCE-based approach for precise investigations of neuronal proteome changes. We aimed to use GCE to incorporate clickable ncAAs proteome-wide at defined time points relative to an oxidative injury to examine proteome changes in immortalised cell lines and human-derived neurons.
Before working with more complex neurons, we needed a simpler model to establish our methodology. For this, we selected the immortalised rodent neuronal cell line, ND7/23. When undifferentiated, these cells divide rapidly with high protein turnover, making them suitable to optimise the methodology. Additionally, they can be differentiated to provide a good neuronal model. Using undifferentiated ND7/23 cells that we transiently equipped with the translational machinery, we demonstrated that ncAAs could be incorporated across the proteome in place of canonical AAs. We also optimised what sense codon will be targeted and what ncAA was most suitable for follow-up proteomics. Another aim of screening different sense codons was to compare our technique to an established technique that replaces methionine with an ncAA.
Following this AA screen, we optimised conditions for high ncAA incorporation efficiency and a high-yielding click reaction. Additionally, we tested different clickable handles, including fluorophore handles for downstream microscopy and handles for downstream enrichment of labelled proteins. However, long-term studies were not possible when the translational machinery was only transiently in the cells.
To overcome this, ND7/23 cells were generated that stably expressed the translational machinery. Using these cell lines, we optimised our oxidative injury model. We used different compounds to model the acute or chronic injury phases of neurodegeneration. Changes to cell morphology and protein turnover following injury were validated, before we demonstrated that these changes could be followed using proteomics. Proteome changes were captured in ND7/27 cells in response to acute and chronic stress. Using our technique, we also demonstrated that we could achieve greater proteome coverage targeting other canonical AAs relative to methionine.
One major advantage of this GCE-based methodology is the ability to selectively target different subsets of the proteome. All proteins secreted by cells make up the secretome. They are very important in cell-to-cell communication and are typically lost during sample processing. Similarly, proteins located on the outer membrane of cells are essential to cell function but are lost in these steps. By using different clickable handles and labelling approaches, we were able to selectively target these subsets, enrich them and capture changes in expression.
Following the establishment in ND7/23 cells, we wanted to expand this technology into more complex human-derived neurons. Cells stably expressing the translational machinery were generated. However, following rounds of optimisation, we did not observe incorporation of the ncAA using downstream click chemistry with a fluorophore. Different strategies were tested to try to overcome these challenges, while in parallel we also tested a complementary method of proteome labelling. Using these technologies, we optimised our system for the future generation of stable cells. This will allow more biologically relevant studies, including in cells derived from human patients.
Following this AA screen, we optimised conditions for high ncAA incorporation efficiency and a high-yielding click reaction. Additionally, we tested different clickable handles, including fluorophore handles for downstream microscopy and handles for downstream enrichment of labelled proteins. However, long-term studies were not possible when the translational machinery was only transiently in the cells.
To overcome this, ND7/23 cells were generated that stably expressed the translational machinery. Using these cell lines, we optimised our oxidative injury model. We used different compounds to model the acute or chronic injury phases of neurodegeneration. Changes to cell morphology and protein turnover following injury were validated, before we demonstrated that these changes could be followed using proteomics. Proteome changes were captured in ND7/27 cells in response to acute and chronic stress. Using our technique, we also demonstrated that we could achieve greater proteome coverage targeting other canonical AAs relative to methionine.
One major advantage of this GCE-based methodology is the ability to selectively target different subsets of the proteome. All proteins secreted by cells make up the secretome. They are very important in cell-to-cell communication and are typically lost during sample processing. Similarly, proteins located on the outer membrane of cells are essential to cell function but are lost in these steps. By using different clickable handles and labelling approaches, we were able to selectively target these subsets, enrich them and capture changes in expression.
Following the establishment in ND7/23 cells, we wanted to expand this technology into more complex human-derived neurons. Cells stably expressing the translational machinery were generated. However, following rounds of optimisation, we did not observe incorporation of the ncAA using downstream click chemistry with a fluorophore. Different strategies were tested to try to overcome these challenges, while in parallel we also tested a complementary method of proteome labelling. Using these technologies, we optimised our system for the future generation of stable cells. This will allow more biologically relevant studies, including in cells derived from human patients.
We demonstrated that we could successfully quantify changes within the proteome of injured neuronal cells. Since these techniques provide a way to probe proteome changes with great temporal resolution and can be transferred into many other cell types, these results will be of great value across the scientific community to answer a variety of questions. Significantly, we also showed that different proteome subsets could be isolated and analysed for changes after these injuries. The subsets that we targeted, the secretome and plasma membrane proteome, are often lost during normal proteomic processing. Therefore, having the ability to selectively retain, enrich and quantify these subsets will be very valuable in future studies.
These results also lay the foundation for future work in more complex and clinically relevant systems such as human-derived neurons and animal models, in which the method can be used to target specific cell types. Studying how oxidative stress and other factors alter the proteome of specific cells at specific time points in these models will not only improve our understanding of neurological diseases but also be relevant to other areas of biology.
In conclusion, the project was overall successful. We successfully applied our methodology in neuronal cells and used it to track changes in the proteome in response to short and long term injury. We showed that specific proteome subsets could be targeted to probe changes in typically neglected protein populations. Finally, we laid a foundation for the application of the methodology in more complex cell types and animal models.
These results also lay the foundation for future work in more complex and clinically relevant systems such as human-derived neurons and animal models, in which the method can be used to target specific cell types. Studying how oxidative stress and other factors alter the proteome of specific cells at specific time points in these models will not only improve our understanding of neurological diseases but also be relevant to other areas of biology.
In conclusion, the project was overall successful. We successfully applied our methodology in neuronal cells and used it to track changes in the proteome in response to short and long term injury. We showed that specific proteome subsets could be targeted to probe changes in typically neglected protein populations. Finally, we laid a foundation for the application of the methodology in more complex cell types and animal models.