Periodic Reporting for period 3 - METACELL (Metabolism of a cell pictured by single-cell approach)
Okres sprawozdawczy: 2021-07-01 do 2022-12-31
Metabolism is a collection of carefully regulated chemical reactions within cells and organisms aimed to generate molecules called metabolites serving as energy sources, building blocks, and regulatory molecules for essential cellular programs. Reprogrammed and disbalanced metabolism is a hallmark of various diseases including cancer, diabetes, fatty liver disease, and autoimmune diseases. Lately, there is a rapidly emerging interest in metabolomics, a field of -omics sciences and technology focused on detecting and interpreting metabolites. At the same time, there is a growing demand in understanding cellular heterogeneity by so-called single-cell technologies able to decode cellular programs in every individual cell. In METACELL, we are developing novel technologies for single-cell metabolomics, able to detect and interpret metabolites in every cell.
The main gap considered in METACELL is the lack of single-cell metabolomics technologies allowing to understand metabolism on the single-cell level. This gap is critical for fundamental biology, as well as for medicine where the fate of one cell can affect the outcome of disease, as well as for drug discovery and development. The growing spread of cancer, diabetes, autoimmune diseases, and metabolic diseases known for metabolic reprogramming happening in different cell types creates an urgent societal demand for novel technologies able to provide scientists with the ultimate answers about the metabolism or chemical reactions happening within every cell. The overall objectives of the project are to fill this gap by creating cheap, robust, and high-throughput technologies for single-cell metabolomics and evaluate them in critical biomedical applications.
The key achievement in the first reporting period was the development of SpaceM, a method for detecting metabolites from single cultured cells. It integrates microscopy and mass spectrometry and can detect hundreds of metabolites from various cell types. We have demonstrated the capacities of the SpaceM method by investigating in vitro model of fatty liver disease and non-alcoholic steatohepatitis, a disease characterized by inflammation in the liver and by reprogrammed lipid metabolism. We have discovered metabolic states and markers of steatohepatitis that in the future can be used for diagnostics purposes and drug development. The results were submitted for publication. Moreover, we filed a patent covering the key aspects of the method.
We have developed various supporting methods, including experimental and computational methods needed for single-cell metabolomics and its applications: protocols for cell culturing and preparation, protocols for imaging mass spectrometry, image registration methods for microscopy and imaging mass spectrometry, metabolite identification methods for imaging mass spectrometry, methods for single-cell metabolomics data integration, differential analysis, and visualization. They will be essential in the second reporting period to apply SpaceM to biologically and medically relevant problems on a large scale.
We have multiplied the impact of our developments through dissemination efforts and through launching several other funded projects which build on top of the METACELL project. We have presented the results at tens of various symposia, conferences, and workshops as well as during seminar talks at top-level universities and research organisations worldwide. Besides, we are exploring collaborations with several companies which are interested in technologies developed in METACELL.
Overall, the exceptional funding from ERC has enabled us to transform our research team to focus on the emerging field of single-cell metabolomics and allowed us to start contributing to the cutting-edge developments in the field.
The main gap considered in METACELL is the lack of single-cell metabolomics technologies allowing to understand metabolism on the single-cell level. This gap is critical for fundamental biology, as well as for medicine where the fate of one cell can affect the outcome of disease, as well as for drug discovery and development. The growing spread of cancer, diabetes, autoimmune diseases, and metabolic diseases known for metabolic reprogramming happening in different cell types creates an urgent societal demand for novel technologies able to provide scientists with the ultimate answers about the metabolism or chemical reactions happening within every cell. The overall objectives of the project are to fill this gap by creating cheap, robust, and high-throughput technologies for single-cell metabolomics and evaluate them in critical biomedical applications.
The key achievement in the first reporting period was the development of SpaceM, a method for detecting metabolites from single cultured cells. It integrates microscopy and mass spectrometry and can detect hundreds of metabolites from various cell types. We have demonstrated the capacities of the SpaceM method by investigating in vitro model of fatty liver disease and non-alcoholic steatohepatitis, a disease characterized by inflammation in the liver and by reprogrammed lipid metabolism. We have discovered metabolic states and markers of steatohepatitis that in the future can be used for diagnostics purposes and drug development. The results were submitted for publication. Moreover, we filed a patent covering the key aspects of the method.
We have developed various supporting methods, including experimental and computational methods needed for single-cell metabolomics and its applications: protocols for cell culturing and preparation, protocols for imaging mass spectrometry, image registration methods for microscopy and imaging mass spectrometry, metabolite identification methods for imaging mass spectrometry, methods for single-cell metabolomics data integration, differential analysis, and visualization. They will be essential in the second reporting period to apply SpaceM to biologically and medically relevant problems on a large scale.
We have multiplied the impact of our developments through dissemination efforts and through launching several other funded projects which build on top of the METACELL project. We have presented the results at tens of various symposia, conferences, and workshops as well as during seminar talks at top-level universities and research organisations worldwide. Besides, we are exploring collaborations with several companies which are interested in technologies developed in METACELL.
Overall, the exceptional funding from ERC has enabled us to transform our research team to focus on the emerging field of single-cell metabolomics and allowed us to start contributing to the cutting-edge developments in the field.
In the first reporting period, we have focused on the core method development. Our aims were to develop a method that requires no custom instrumentation (to ensure a broad impact of the developments), can work with most common types of cultured cells (to ensure applicability in a broad range of applications), has high throughput (to obtain large numbers of analyzed cells for high statistical power and for discovering cell subpopulations), and can detect a large number of metabolites (to provide biologically-meaningful metabolic profiles). The result of these developments is the method SpaceM (Rappez et al 2019 BioRxiv, in revision).
Moreover, we have performed the essential validation of the properties of the SpaceM method, in particular we validated its single-cell resolution. This required substantial work since currently there are no established approaches or models for this question. So, we have developed a cellular model that represents spatially-heterogeneous co-cultured cells of two types, where each cell has an inherent fluorescent readout corresponding to its cell type. Using this model allowed us to validate that SpaceM can detect metabolites with a single-cell resolution. Moreover, based on the SpaceM single-cell metabolic profiles we were able to predict the cell types thus demonstrating the biological relevance of the detected single-cell metabolic profiles.
Once we have developed the method and validated its relevance, we have focused on applications. Together with collaborators, we have performed a thorough investigation of in vitro model of fatty liver disease and non-alcoholic steatohepatitis. This resulted in one manuscript (Rappez et al 2019 BioRxiv, in revision) and another in preparation. Moreover, we have initiated applications of SpaceM to other cellular models and biomedical questions.
In addition, we have started exploring approaches to improve the throughput of SpaceM to enable analysis of multiple samples simultaneously (work in progress).
We also started developing software that would streamline SpaceM method, would shorten the data processing times, and would enable broader applicability of the method.
Moreover, we have performed the essential validation of the properties of the SpaceM method, in particular we validated its single-cell resolution. This required substantial work since currently there are no established approaches or models for this question. So, we have developed a cellular model that represents spatially-heterogeneous co-cultured cells of two types, where each cell has an inherent fluorescent readout corresponding to its cell type. Using this model allowed us to validate that SpaceM can detect metabolites with a single-cell resolution. Moreover, based on the SpaceM single-cell metabolic profiles we were able to predict the cell types thus demonstrating the biological relevance of the detected single-cell metabolic profiles.
Once we have developed the method and validated its relevance, we have focused on applications. Together with collaborators, we have performed a thorough investigation of in vitro model of fatty liver disease and non-alcoholic steatohepatitis. This resulted in one manuscript (Rappez et al 2019 BioRxiv, in revision) and another in preparation. Moreover, we have initiated applications of SpaceM to other cellular models and biomedical questions.
In addition, we have started exploring approaches to improve the throughput of SpaceM to enable analysis of multiple samples simultaneously (work in progress).
We also started developing software that would streamline SpaceM method, would shorten the data processing times, and would enable broader applicability of the method.
The method SpaceM represents an advance in the field. Moreover, we have developed various supporting methods (reviewed earlier in this report), each of them represents a novel technological advance.
Applying SpaceM to hepatocytes, we have revealed co-existing metabolic states among isogenic cell populations. Although this was earlier reported based on single-cell proteomics (e.g. metal-tagged antibodies) or transcriptomics analyses, we have shown it for the first time with single-cell metabolomics.
In the second reporting period, we expect to develop high-throughput single-cell metabolomics method allowing for analysis of over 50 samples in the same array format, essential for any study with over 10 conditions (assuming the requirement of 3-5 technical replicates) e.g. when investigating perturbations with 3 drugs where for each drug 3 concentrations are considered; to provide assessment of the quantitative properties of SpaceM; to apply SpaceM and high-throughput SpaceM to selected biological questions.
Applying SpaceM to hepatocytes, we have revealed co-existing metabolic states among isogenic cell populations. Although this was earlier reported based on single-cell proteomics (e.g. metal-tagged antibodies) or transcriptomics analyses, we have shown it for the first time with single-cell metabolomics.
In the second reporting period, we expect to develop high-throughput single-cell metabolomics method allowing for analysis of over 50 samples in the same array format, essential for any study with over 10 conditions (assuming the requirement of 3-5 technical replicates) e.g. when investigating perturbations with 3 drugs where for each drug 3 concentrations are considered; to provide assessment of the quantitative properties of SpaceM; to apply SpaceM and high-throughput SpaceM to selected biological questions.