Utilization of single-cell RNA-Seq and genome-scale modeling for investigating cancer metabolism
Doctoral thesis, 2022
Cancer remains a leading cause of death worldwide, and its dysregulated metabolism is a promising target for therapy. However, metabolism is complex to study – the metabolism of a cell involves the interplay of thousands of chemical reactions that are combined in different ways across tissues and cell types. Genome-scale metabolic models (GEMs), where the reaction networks of cells are described using a mathematical formulation, have been developed to help in such studies.
In this thesis, methods were developed for determining the active metabolic network (the context-specific model) in individual cell types, followed by studies of cancer metabolism. To enable identification of the active metabolic network per cell type, single-cell RNA sequencing (scRNA-Seq) was employed to detect the presence of individual genes. However, the technical and biological variation in scRNA-Seq data poses a major challenge to the identification of the active reaction network in a cell type. The variability of gene expression due to technical and biological factors was therefore examined, concluding that data from thousands of cells is often required to provide enough stability for robust model generation. An improved quantification method for scRNA-Seq data, called BUTTERFLY, was also developed and implemented as part of the kallisto-bustools scRNA-Seq workflow. A new optimized version of tINIT, which enables generation of context-specific models, was also developed. It allowed for generation of models based on bootstrapped cell populations, which were used to acquire the statistical uncertainty of models generated from scRNA-Seq data. Finally, the method was applied to a lung cancer dataset, identifying both known and unknown features of cancer metabolism.
To further explore cancer metabolism, a study was conducted to investigate the most optimal metabolic behavior under different degrees of hypoxia. To this end, a diffusion-based model for estimating nutrient availability was developed, as well as a light-weight version of the tool GECKO that enables constraining the total enzyme usage in the model. The model could explain the glutamine addiction phenomenon in cancers and was used to show that metabolic collaboration between cell types in tumors is likely not important for growth.
In this thesis, methods were developed for determining the active metabolic network (the context-specific model) in individual cell types, followed by studies of cancer metabolism. To enable identification of the active metabolic network per cell type, single-cell RNA sequencing (scRNA-Seq) was employed to detect the presence of individual genes. However, the technical and biological variation in scRNA-Seq data poses a major challenge to the identification of the active reaction network in a cell type. The variability of gene expression due to technical and biological factors was therefore examined, concluding that data from thousands of cells is often required to provide enough stability for robust model generation. An improved quantification method for scRNA-Seq data, called BUTTERFLY, was also developed and implemented as part of the kallisto-bustools scRNA-Seq workflow. A new optimized version of tINIT, which enables generation of context-specific models, was also developed. It allowed for generation of models based on bootstrapped cell populations, which were used to acquire the statistical uncertainty of models generated from scRNA-Seq data. Finally, the method was applied to a lung cancer dataset, identifying both known and unknown features of cancer metabolism.
To further explore cancer metabolism, a study was conducted to investigate the most optimal metabolic behavior under different degrees of hypoxia. To this end, a diffusion-based model for estimating nutrient availability was developed, as well as a light-weight version of the tool GECKO that enables constraining the total enzyme usage in the model. The model could explain the glutamine addiction phenomenon in cancers and was used to show that metabolic collaboration between cell types in tumors is likely not important for growth.
genome-scale metabolic modeling
cancer
metabolism
single-cell RNA-Seq
Author
Johan Gustafsson
Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology
Sources of variation in cell-type RNA-Seq profiles
PLoS ONE,;Vol. 15(2020)p. e0239495-
Journal article
DSAVE: Detection of misclassified cells in single-cell RNA-Seq data
PLoS ONE,;Vol. 15(2020)
Journal article
BUTTERFLY: addressing the pooled amplification paradox with unique molecular identifiers in single-cell RNA-seq
Genome Biology,;Vol. 22(2021)
Journal article
Generation and analysis of context-specific genome-scale metabolic models derived from single-cell RNA-Seq data
Proceedings of the National Academy of Sciences of the United States of America,;Vol. 120(2023)
Journal article
Gustafsson J, Roshanzamir F, Hagnestål A, Robinson J.L, Nielsen J Cellular limitation of enzymatic capacity explains glutamine addiction in cancers
Mathematical models can help explain cancer
Metabolism describes the complex processes by which living cells acquire energy and building blocks from their surroundings to sustain cellular function and growth. Human metabolism involves thousands of different chemical reactions that form a complex reaction network, and in this thesis, mathematical modeling of the reaction network was used to study the metabolic behavior of cells. Methods were developed for utilizing a technology called single-cell RNA sequencing to detect the presence of enzymes that can catalyze reactions, enabling specialization of the reaction network for individual cell types. Furthermore, a method for improving the quantification of single-cell RNA sequencing data was developed. Mathematical modeling was then applied to study the metabolism of the environment within tumors, which unraveled why many cancers use amino acids instead of glucose as an energy source. Furthermore, it has been proposed that healthy cell types within a tumor assist cancer cells metabolically by supplying them with resources. Our models showed that such metabolic collaboration scenarios likely have a small or non-existent positive effect on cancer cell growth. The results from this thesis show that mathematical modeling is a powerful tool for investigating human health and disease, and that combining such modeling with single-cell RNA sequencing shows promise to advance our understanding of human metabolism even further.
Metabolism describes the complex processes by which living cells acquire energy and building blocks from their surroundings to sustain cellular function and growth. Human metabolism involves thousands of different chemical reactions that form a complex reaction network, and in this thesis, mathematical modeling of the reaction network was used to study the metabolic behavior of cells. Methods were developed for utilizing a technology called single-cell RNA sequencing to detect the presence of enzymes that can catalyze reactions, enabling specialization of the reaction network for individual cell types. Furthermore, a method for improving the quantification of single-cell RNA sequencing data was developed. Mathematical modeling was then applied to study the metabolism of the environment within tumors, which unraveled why many cancers use amino acids instead of glucose as an energy source. Furthermore, it has been proposed that healthy cell types within a tumor assist cancer cells metabolically by supplying them with resources. Our models showed that such metabolic collaboration scenarios likely have a small or non-existent positive effect on cancer cell growth. The results from this thesis show that mathematical modeling is a powerful tool for investigating human health and disease, and that combining such modeling with single-cell RNA sequencing shows promise to advance our understanding of human metabolism even further.
Subject Categories
Cell and Molecular Biology
Bioinformatics (Computational Biology)
Bioinformatics and Systems Biology
Cancer and Oncology
Areas of Advance
Life Science Engineering (2010-2018)
ISBN
978-91-7905-651-3
Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 5117
Publisher
Chalmers