| 1. |
- Abarenkov, Kessy, et al.
(author)
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Annotating public fungal ITS sequences from the built environment according to the MIxS-Built Environment standard – a report from a May 23-24, 2016 workshop (Gothenburg, Sweden)
- 2016
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In: MycoKeys. - : Pensoft Publishers. - 1314-4057 .- 1314-4049. ; 16, s. 1-15
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Journal article (peer-reviewed)abstract
- Recent molecular studies have identified substantial fungal diversity in indoor environments. Fungi and fungal particles have been linked to a range of potentially unwanted effects in the built environment, including asthma, decay of building materials, and food spoilage. The study of the built mycobiome is hampered by a number of constraints, one of which is the poor state of the metadata annotation of fungal DNA sequences from the built environment in public databases. In order to enable precise interrogation of such data – for example, “retrieve all fungal sequences recovered from bathrooms” – a workshop was organized at the University of Gothenburg (May 23-24, 2016) to annotate public fungal barcode (ITS) sequences according to the MIxS-Built Environment annotation standard (http://gensc.org/mixs/). The 36 participants assembled a total of 45,488 data points from the published literature, including the addition of 8,430 instances of countries of collection from a total of 83 countries, 5,801 instances of building types, and 3,876 instances of surface-air contaminants. The results were implemented in the UNITE database for molecular identification of fungi (http://unite.ut.ee) and were shared with other online resources. Data obtained from human/animal pathogenic fungi will furthermore be verified on culture based metadata for subsequent inclusion in the ISHAM-ITS database (http://its.mycologylab.org).
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| 2. |
- Dejby, Ellen, et al.
(author)
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Left-sided valvular heart disease and survival in out-of-hospital cardiac arrest: a nationwide registry-based study.
- 2023
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In: Scientific reports. - 2045-2322. ; 13:1
-
Journal article (peer-reviewed)abstract
- Survival in left-sided valvular heart disease (VHD; aortic stenosis [AS], aortic regurgitation [AR], mitral stenosis [MS], mitral regurgitation [MR]) in out-of-hospital cardiac arrest (OHCA) is unknown. We studied all cases of OHCA in the Swedish Registry for Cardiopulmonary Resuscitation. All degrees of VHD, diagnosed prior to OHCA, were included. Association between VHD and survival was studied using logistic regression, gradient boosting and Cox regression. We studied time to cardiac arrest, comorbidities, survival, and cerebral performance category (CPC) score. We included 55,615 patients; 1948 with AS (3,5%), 384 AR (0,7%), 17 MS (0,03%), and 704 with MR (1,3%). Patients with MS were not described due to low case number. Time from VHD diagnosis to cardiac arrest was 3.7years in AS, 4.5years in AR and 4.1years in MR. ROSC occurred in 28% with AS, 33% with AR, 36% with MR and 35% without VHD. Survival at 30days was 5.2%, 10.4%, 9.2%, 11.4% in AS, AR, MR and without VHD, respectively. There were no survivors in people with AS presenting with asystole or PEA. CPC scores did not differ in those with VHD compared with no VHD. Odds ratio (OR) for MR and AR showed no difference in survival, while AS displayed OR 0.58 (95% CI 0.46-0.72), vs no VHD. AS is associated with halved survival in OHCA, while AR and MR do not affect survival. Survivors with AS have neurological outcomes comparable to patients without VHD.
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| 3. |
- Folkersen, Lasse, et al.
(author)
-
Genomic and drug target evaluation of 90 cardiovascular proteins in 30,931 individuals.
- 2020
-
In: Nature metabolism. - : Springer Science and Business Media LLC. - 2522-5812. ; 2:10, s. 1135-1148
-
Journal article (peer-reviewed)abstract
- Circulating proteins are vital in human health and disease and are frequently used as biomarkers for clinical decision-making or as targets for pharmacological intervention. Here, we map and replicate protein quantitative trait loci (pQTL) for 90 cardiovascular proteins in over 30,000 individuals, resulting in 451 pQTLs for 85 proteins. For each protein, we further perform pathway mapping to obtain trans-pQTL gene and regulatory designations. We substantiate these regulatory findings with orthogonal evidence for trans-pQTLs using mouse knockdown experiments (ABCA1 and TRIB1) and clinical trial results (chemokine receptors CCR2 and CCR5), with consistent regulation. Finally, we evaluate known drug targets, and suggest new target candidates or repositioning opportunities using Mendelian randomization. This identifies 11 proteins with causal evidence of involvement in human disease that have not previously been targeted, including EGF, IL-16, PAPPA, SPON1, F3, ADM, CASP-8, CHI3L1, CXCL16, GDF15 and MMP-12. Taken together, these findings demonstrate the utility of large-scale mapping of the genetics of the proteome and provide a resource for future precision studies of circulating proteins in human health.
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| 4. |
- Otto, Maximilian, 1991, et al.
(author)
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Expansion of the Yeast Modular Cloning Toolkit for CRISPR-Based Applications, Genomic Integrations and Combinatorial Libraries
- 2021
-
In: ACS Synthetic Biology. - : American Chemical Society (ACS). - 2161-5063. ; 10:12, s. 3461-3474
-
Journal article (peer-reviewed)abstract
- Standardisation of genetic parts has become a topic of increasing interest over the last decades. The promise of simplifying molecular cloning procedures, while at the same time making them more predictable and reproducible has led to the design of several biological standards, one of which is modular cloning (MoClo). The Yeast MoClo toolkit provides a large library of characterised genetic parts combined with a comprehensive and flexible assembly strategy. Here we aimed to (1) simplify the adoption of the standard by providing a simple design tool for including new parts in the MoClo library, (2) characterise the toolkit further by demonstrating the impact of a BglII site in promoter parts on protein expression, and (3) expand the toolkit to enable efficient construction of gRNA arrays, marker-less integration cassettes and combinatorial libraries. These additions make the toolkit more applicable for common engineering tasks and will further promote its adoption in the yeast biological engineering community.
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| 5. |
- Wang, Hao, 1975, et al.
(author)
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Genome-scale metabolic network reconstruction of model animals as a platform for translational research
- 2021
-
In: Proceedings of the National Academy of Sciences of the United States of America. - : Proceedings of the National Academy of Sciences. - 0027-8424 .- 1091-6490. ; 118:30
-
Journal article (peer-reviewed)abstract
- Genome-scale metabolic models (GEMs) are used extensively for analysis of mechanisms underlying human diseases and metabolic malfunctions. However, the lack of comprehensive and high-quality GEMs for model organisms restricts translational utilization of omics data accumulating from the use of various disease models. Here we present a unified platform of GEMs that covers five major model animals, including Mouse1 (Mus musculus), Rat1 (Rattus norvegicus), Zebrafish1 (Danio rerio), Fruitfly1 (Drosophila melanogaster), and Worm1 (Caenorhabditis elegans). These GEMs represent the most comprehensive coverage of the metabolic network by considering both orthology-based pathways and species-specific reactions. All GEMs can be interactively queried via the accompanying web portal Metabolic Atlas. Specifically, through integrative analysis of Mouse1 with RNA-sequencing data from brain tissues of transgenic mice we identified a coordinated up-regulation of lysosomal GM2 ganglioside and peptide degradation pathways which appears to be a signature metabolic alteration in Alzheimer’s disease (AD) mouse models with a phenotype of amyloid precursor protein overexpression. This metabolic shift was further validated with proteomics data from transgenic mice and cerebrospinal fluid samples from human patients. The elevated lysosomal enzymes thus hold potential to be used as a biomarker for early diagnosis of AD. Taken together, we foresee that this evolving open-source platform will serve as an important resource to facilitate the development of systems medicines and translational biomedical applications.
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| 6. |
- Berglund, Sara, et al.
(author)
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Cardiorenal function and survival in in-hospital cardiac arrest : A nationwide study of 22,819 cases
- 2022
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In: Resuscitation. - : Elsevier BV. - 0300-9572 .- 1873-1570. ; 172, s. 9-16
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Journal article (peer-reviewed)abstract
- Background: We studied the association between cardiorenal function and survival, neurological outcome and trends in survival after in-hospital Methods: We included cases aged 18 years in the Swedish Cardiopulmonary Resuscitation Registry during 2008 to 2020. The CKD-EPI equation was used to calculate estimated glomerular filtration rate (eGFR). A history of heart failure was defined according to contemporary guideline criteria. Logistic regression was used to study survival. Neurological outcome was assessed using cerebral performance category (CPC). Results: We studied 22,819 patients with IHCA. The 30-day survival was 19.3%, 16.6%, 22.5%, 28.8%, 39.3%, 44.8% and 38.4% in cases with eGFR < 15, 15-29, 30-44, 45-59, 60-89, 90-130 and 130-150 ml/min/1.73 m2, respectively. All eGFR levels below and above 90 ml/min/1.73 m2 were associated with increased mortality. Probability of survival at 30 days was 62% lower in cases with eGFR < 15 ml/min/1.73 m2, compared with normal kidney function. At every level of eGFR, presence of heart failure increased mortality markedly; patients without heart failure displayed higher mortality only at eGFR below 30 ml/min/1.73 m2. Among survivors with eGFR < 15 ml/min/1.73 m2, good neurological outcome was noted in 87.2%. Survival increased in most groups over time, but most for those with eGFR < 15 ml/min/1.73 m2, and least for those with normal eGFR. Conclusions: All eGFR levels below and above normal range are associated with increased mortality and this association is modified by the presence of heart failure. Neurological outcome is good in the majority of cases, across kidney function levels and survival is increasing.
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| 7. |
- Bjursell, Cecilia, 1970-, et al.
(author)
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Lifelong Learning Through Context Collapse : Higher education Teachers’ Narratives About Online education During The Pandemic
- 2022
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In: Proceedings of INTED2022 Conference 7th-8th March 2022. - : IATED. - 9788409377589 ; , s. 2632-2641
-
Conference paper (peer-reviewed)abstract
- The COVID-19 pandemic has elicited a shift from campus classrooms to distance education in higher education worldwide, shaping not only students’ experiences, but also those of teachers, especially those who never have taught online. In addition, the pandemic created a meta-context that has positioned distance education as something different from previous efforts. This study aimed to investigate higher education teachers’ experiences during the transition from classroom to online teaching by using a collective auto-ethnography method based on 13 personal stories from Swedish faculty. For the abductive approach in the analysis, a framework that combines lifelong learning theory with the context collapse concept has been applied. The disjuncture that the pandemic has elicited created a situation in which teachers had to make sense of the fact that their previous experiences did not completely fit the new situation. Context collapse, a term used to describe encounters with many audiences in social media, has been introduced to highlight the clash between professional and private contexts in online educational platforms. Based on lifelong learning theories, we suggest that context collapse should be examined in terms of how it can help improve higher education, as it holds the potential to include the entire person – body and mind – in education.
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| 8. |
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| 9. |
- Chen, Yu, 1990, et al.
(author)
-
Reconstruction, simulation and analysis of enzyme-constrained metabolic models using GECKO Toolbox 3.0
- 2024
-
In: Nature Protocols. - 1754-2189 .- 1750-2799. ; 19:3, s. 629-667
-
Journal article (peer-reviewed)abstract
- Genome-scale metabolic models (GEMs) are computational representations that enable mathematical exploration of metabolic behaviors within cellular and environmental constraints. Despite their wide usage in biotechnology, biomedicine and fundamental studies, there are many phenotypes that GEMs are unable to correctly predict. GECKO is a method to improve the predictive power of a GEM by incorporating enzymatic constraints using kinetic and omics data. GECKO has enabled reconstruction of enzyme-constrained metabolic models (ecModels) for diverse organisms, which show better predictive performance than conventional GEMs. In this protocol, we describe how to use the latest version GECKO 3.0; the procedure has five stages: (1) expansion from a starting metabolic model to an ecModel structure, (2) integration of enzyme turnover numbers into the ecModel structure, (3) model tuning, (4) integration of proteomics data into the ecModel and (5) simulation and analysis of ecModels. GECKO 3.0 incorporates deep learning-predicted enzyme kinetics, paving the way for improved metabolic models for virtually any organism and cell line in the absence of experimental data. The time of running the whole protocol is organism dependent, e.g., ~5 h for yeast.
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| 10. |
- Chen, Yu, 1990, et al.
(author)
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Single-cell omics analysis with genome-scale metabolic modeling
- 2024
-
In: Current Opinion in Biotechnology. - 0958-1669 .- 1879-0429. ; 86
-
Research review (peer-reviewed)abstract
- Single-cell technologies have been widely used in biological studies and generated a plethora of single-cell data to be interpreted. Due to the inclusion of the priori metabolic network knowledge as well as gene–protein–reaction associations, genome-scale metabolic models (GEMs) have been a powerful tool to integrate and thereby interpret various omics data mostly from bulk samples. Here, we first review two common ways to leverage bulk omics data with GEMs and then discuss advances on integrative analysis of single-cell omics data with GEMs. We end by presenting our views on current challenges and perspectives in this field.
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| 11. |
- Felton, Adam, et al.
(author)
-
Correction to: Keeping pace with forestry : Multi-scale conservation in a changing production forest matrix (vol 49, pg 1050, 2020)
- 2020
-
In: Ambio. - : Springer. - 0044-7447 .- 1654-7209. ; 49:5, s. 1065-1066
-
Journal article (peer-reviewed)abstract
- In the original published article, the sentence “Nevertheless, semi-natural forest remnants continue to be harvested and fragmented (Svensson et al. 2018; Jonsson et al. 2019), and over 2000 forest-associated species (of 15 000 assessed) are listed as threatened on Sweden’s red-list, largely represented by macro-fungi, beetles, lichens and butterflies (Sandström 2015).”under the section Introduction was incorrect. The correct version of the sentence is “Nevertheless, semi-natural forest remnants continue to be harvested and fragmented (Svensson et al. 2018; Jonsson et al. 2019), and approximately 2000 forest-associated species (of 15 000 assessed) are on Sweden’s red-list, largely represented by macro-fungi, beetles, lichens and butterflies (Sandström 2015).”
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| 12. |
- Felton, Adam, et al.
(author)
-
Keeping pace with forestry : Multi-scale conservation in a changing production forest matrix
- 2020
-
In: Ambio. - : Springer. - 0044-7447 .- 1654-7209. ; 49:5, s. 1050-1064
-
Journal article (peer-reviewed)abstract
- The multi-scale approach to conserving forest biodiversity has been used in Sweden since the 1980s, a period defined by increased reserve area and conservation actions within production forests. However, two thousand forest-associated species remain on Sweden's red-list, and Sweden's 2020 goals for sustainable forests are not being met. We argue that ongoing changes in the production forest matrix require more consideration, and that multi-scale conservation must be adapted to, and integrated with, production forest development. To make this case, we summarize trends in habitat provision by Sweden's protected and production forests, and the variety of ways silviculture can affect biodiversity. We discuss how different forestry trajectories affect the type and extent of conservation approaches needed to secure biodiversity, and suggest leverage points for aiding the adoption of diversified silviculture. Sweden's long-term experience with multi-scale conservation and intensive forestry provides insights for other countries trying to conserve species within production landscapes.
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| 13. |
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| 14. |
- Gustafsson, Johan, 1976, et al.
(author)
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Brain energy metabolism is optimized to minimize the cost of enzyme synthesis and transport
- 2024
-
In: Proceedings of the National Academy of Sciences of the United States of America. - 0027-8424 .- 1091-6490. ; 121:7
-
Journal article (peer-reviewed)abstract
- The energy metabolism of the brain is poorly understood partly due to the complex morphology of neurons and fluctuations in ATP demand over time. To investigate this, we used metabolic models that estimate enzyme usage per pathway, enzyme utilization over time, and enzyme transportation to evaluate how these parameters and processes affect ATP costs for enzyme synthesis and transportation. Our models show that the total enzyme maintenance energy expenditure of the human body depends on how glycolysis and mitochondrial respiration are distributed both across and within cell types in the brain. We suggest that brain metabolism is optimized to minimize the ATP maintenance cost by distributing the different ATP generation pathways in an advantageous way across cell types and potentially also across synapses within the same cell. Our models support this hypothesis by predicting export of lactate from both neurons and astrocytes during peak ATP demand, reproducing results from experimental measurements reported in the literature. Furthermore, our models provide potential explanation for parts of the astrocyte-neuron lactate shuttle theory, which is recapitulated under some conditions in the brain, while contradicting other aspects of the theory. We conclude that enzyme usage per pathway, enzyme utilization over time, and enzyme transportation are important factors for defining the optimal distribution of ATP production pathways, opening a broad avenue to explore in brain metabolism.
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| 15. |
- Gustafsson, Johan, 1976, et al.
(author)
-
BUTTERFLY: addressing the pooled amplification paradox with unique molecular identifiers in single-cell RNA-seq
- 2021
-
In: Genome Biology. - : Springer Science and Business Media LLC. - 1474-760X .- 1474-7596. ; 22:1
-
Journal article (peer-reviewed)abstract
- The incorporation of unique molecular identifiers (UMIs) in single-cell RNA-seq assays makes possible the identification of duplicated molecules, thereby facilitating the counting of distinct molecules from sequenced reads. However, we show that the naïve removal of duplicates can lead to a bias due to a “pooled amplification paradox,” and we propose an improved quantification method based on unseen species modeling. Our correction called BUTTERFLY uses a zero truncated negative binomial estimator implemented in the kallisto bustools workflow. We demonstrate its efficacy across cell types and genes and show that in some cases it can invert the relative abundance of genes.
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| 16. |
- Gustafsson, Johan, 1976, et al.
(author)
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Cellular limitation of enzymatic capacity explains glutamine addiction in cancers
- 2022
-
Journal article (other academic/artistic)abstract
- Metabolism within the tumor microenvironment, where a complex mixture of different cell types resides in a nutrient-deprived surrounding, is not fully understood due to difficulties in measuring metabolic fluxes and exchange of metabolites between different cell types in vivo. Genome-scale metabolic modeling enables estimation of such exchange fluxes as well as an opportunity to gain insight into the metabolic behavior of individual cell types. Here, we estimated the availability of nutrients and oxygen within the tumor microenvironment using concentration measurements from blood together with a metabolite diffusion model. In addition, we developed an approach to efficiently apply enzyme usage constraints in a comprehensive metabolic model of human cells. The combined modeling reproduced severe hypoxic conditions and the Warburg effect, and we found that limitations in enzymatic capacity contribute to cancer cells’ preferential use of glutamine as a substrate to the citric acid cycle. Furthermore, we investigated the common belief that some stromal cells are exploited by cancer cells to produce metabolites useful for the cancer cells. We identified a total of 233 potential metabolites that could support collaboration between cancer cells and cancer associated fibroblasts, but when limiting to metabolites previously identified to participate in such collaboration, no growth advantage was observed. Our work highlights the importance of enzymatic capacity limitations for cell behaviors and exemplifies the utility of enzyme constrained models for accurate prediction of metabolism in cells and tumor microenvironments.
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| 17. |
- Gustafsson, Johan, 1976, et al.
(author)
-
DSAVE: Detection of misclassified cells in single-cell RNA-Seq data
- 2020
-
In: PLoS ONE. - : Public Library of Science (PLoS). - 1932-6203 .- 1932-6203. ; 15:12 December
-
Journal article (peer-reviewed)abstract
- Single-cell RNA sequencing has become a valuable tool for investigating cell types in complex tissues, where clustering of cells enables the identification and comparison of cell populations. Although many studies have sought to develop and compare different clustering approaches, a deeper investigation into the properties of the resulting populations is lacking. Specifically, the presence of misclassified cells can influence downstream analyses, highlighting the need to assess subpopulation purity and to detect such cells. We developed DSAVE (Down-SAmpling based Variation Estimation), a method to evaluate the purity of single-cell transcriptome clusters and to identify misclassified cells. The method utilizes down-sampling to eliminate differences in sampling noise and uses a log-likelihood based metric to help identify misclassified cells. In addition, DSAVE estimates the number of cells needed in a population to achieve a stable average gene expression profile within a certain gene expression range. We show that DSAVE can be used to find potentially misclassified cells that are not detectable by similar tools and reveal the cause of their divergence from the other cells, such as differing cell state or cell type. With the growing use of single-cell RNA-seq, we foresee that DSAVE will be an increasingly useful tool for comparing and purifying subpopulations in single-cell RNA-Seq datasets.
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| 18. |
- Gustafsson, Johan, 1976, et al.
(author)
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Generation and analysis of context-specific genome-scale metabolic models derived from single-cell RNA-Seq data
- 2023
-
In: Proceedings of the National Academy of Sciences of the United States of America. - : Proceedings of the National Academy of Sciences. - 0027-8424 .- 1091-6490. ; 120:6
-
Journal article (peer-reviewed)abstract
- Single-cell RNA sequencing combined with genome-scale metabolic models (GEMs) has the potential to unravel the differences in metabolism across both cell types and cell states but requires new computational methods. Here, we present a method for generating cell-type-specific genome-scale models from clusters of single-cell RNA-Seq profiles. Specifically, we developed a method to estimate the minimum number of cells required to pool to obtain stable models, a bootstrapping strategy for estimating statistical inference, and a faster version of the task-driven integrative network inference for tissues algorithm for generating context-specific GEMs. In addition, we evaluated the effect of different RNA-Seq normalization methods on model topology and differences in models generated from single-cell and bulk RNA-Seq data. We applied our methods on data from mouse cortex neurons and cells from the tumor microenvironment of lung cancer and in both cases found that almost every cell subtype had a unique metabolic profile. In addition, our approach was able to detect cancer-associated metabolic differences between cancer cells and healthy cells, showcasing its utility. We also contextualized models from 202 single-cell clusters across 19 human organs using data from Human Protein Atlas and made these available in the web portal Metabolic Atlas, thereby providing a valuable resource to the scientific community. With the ever-increasing availability of single-cell RNA-Seq datasets and continuously improved GEMs, their combination holds promise to become an important approach in the study of human metabolism.
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| 19. |
- Gustafsson, Johan, 1976, et al.
(author)
-
Generation and analysis of context-specific genome-scale metabolic models derived from single-cell RNA-Seq data
- 2022
-
Journal article (other academic/artistic)abstract
- Single-cell RNA sequencing has the potential to unravel the differences in metabolism across cell types and cell states in both the healthy and diseased human body. The use of existing knowledge in the form of genome-scale metabolic models (GEMs) holds promise to strengthen such analyses, but the combined use of these two methods requires new computational methods. Here, we present a method for generating cell-type-specific genome-scale models from clusters of single-cell RNA-Seq profiles. Specifically, we developed a method to estimate the number of cells required to pool to obtain stable models, a bootstrapping strategy for estimating statistical inference, and a faster version of the tINIT algorithm for generating context-specific GEMs. In addition, we evaluated the effect of different RNA-Seq normalization methods on model topology and differences in models generated from single-cell and bulk RNA-Seq data. We applied our methods on data from mouse cortex neurons and cells from the tumor microenvironment of lung cancer and in both cases found that almost every cell subtype had a unique metabolic profile, emphasizing the need to study them separately rather than to build models from bulk RNA-Seq data. In addition, our approach was able to detect cancer-associated metabolic differences between cancer cells and healthy cells, showcasing its utility. With the ever-increasing availability of single-cell RNA-Seq datasets and continuously improved GEMs, their combination holds promise to become an important approach in the study of human metabolism.
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| 20. |
- Gustafsson, Johan, 1976, et al.
(author)
-
Metabolic collaboration between cells in the tumor microenvironment has a negligible effect on tumor growth
- 2024
-
In: Innovation. - 2666-6758. ; 5:2
-
Journal article (peer-reviewed)abstract
- The tumor microenvironment is composed of a complex mixture of different cell types interacting under conditions of nutrient deprivation, but the metabolism therein is not fully understood due to difficulties in measuring metabolic fluxes and exchange of metabolites between different cell types in vivo. Genome-scale metabolic modeling enables estimation of such exchange fluxes as well as an opportunity to gain insight into the metabolic behavior of individual cell types. Here, we estimated the availability of nutrients and oxygen within the tumor microenvironment using concentration measurements from blood together with a metabolite diffusion model. In addition, we developed an approach to efficiently apply enzyme usage constraints in a comprehensive metabolic model of human cells. The combined modeling reproduced severe hypoxic conditions and the Warburg effect, and we found that limitations in enzymatic capacity contribute to cancer cells’ preferential use of glutamine as a substrate to the citric acid cycle. Furthermore, we investigated the common hypothesis that some stromal cells are exploited by cancer cells to produce metabolites useful for the cancer cells. We identified over 200 potential metabolites that could support collaboration between cancer cells and cancer-associated fibroblasts, but when limiting to metabolites previously identified to participate in such collaboration, no growth advantage was observed. Our work highlights the importance of enzymatic capacity limitations for cell behaviors and exemplifies the utility of enzyme-constrained models for accurate prediction of metabolism in cells and tumor microenvironments.
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| 21. |
- Gustafsson, Johan, 1976, et al.
(author)
-
Sources of variation in cell-type RNA-Seq profiles
- 2020
-
In: PLoS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 15:9
-
Journal article (peer-reviewed)abstract
- Cell-type specific gene expression profiles are needed for many computational methods operating on bulk RNA-Seq samples, such as deconvolution of cell-type fractions and digital cytometry. However, the gene expression profile of a cell type can vary substantially due to both technical factors and biological differences in cell state and surroundings, reducing the efficacy of such methods. Here, we investigated which factors contribute most to this variation. We evaluated different normalization methods, quantified the variance explained by different factors, evaluated the effect on deconvolution of cell type fractions, and examined the differences between UMI-based single-cell RNA-Seq and bulk RNA-Seq. We investigated a collection of publicly available bulk and single-cell RNA-Seq datasets containing B and T cells, and found that the technical variation across laboratories is substantial, even for genes specifically selected for deconvolution, and this variation has a confounding effect on deconvolution. Tissue of origin is also a substantial factor, highlighting the challenge of using cell type profiles derived from blood with mixtures from other tissues. We also show that much of the differences between UMI-based single-cell and bulk RNA-Seq methods can be explained by the number of read duplicates per mRNA molecule in the single-cell sample. Our work shows the importance of either matching or correcting for technical factors when creating cell-type specific gene expression profiles that are to be used together with bulk samples.
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| 22. |
- Gustafsson, Johan, 1976
(author)
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Utilization of single-cell RNA-Seq and genome-scale modeling for investigating cancer metabolism
- 2022
-
Doctoral thesis (other academic/artistic)abstract
- 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.
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| 23. |
- Gustafsson, Linnea, et al.
(author)
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Characteristics, survival and neurological outcome in out-of-hospital cardiac arrest in young adults in Sweden : A nationwide study.
- 2023
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In: Resuscitation Plus. - 2666-5204. ; 16
-
Journal article (peer-reviewed)abstract
- AIM: The aim of this study was to present a comprehensive overview of out-of-hospital cardiac arrests (OHCA) in young adults.METHODS: The data set analyzed included all cases of OHCA from 1990 to 2020 in the age-range 16-49 years in the Swedish Registry of Cardiopulmonary Resuscitation (SRCR). OHCA between 2010 and 2020 were analyzed in more detail. Clinical characteristics, survival, neurological outcomes, and long-time trends in survival were studied. Logistic regression was used to study 30-days survival, neurological outcomes and Utstein determinants of survival.RESULTS: Trends were assessed in 11,180 cases. The annual increase in 30-days survival during 1990-2020 was 5.9% with no decline in neurological function among survivors. Odds ratio (OR) for heart disease as the cause was 0.55 (95% CI 0.44 to 0.67) in 2017-2020 compared to 1990-1993. Corresponding ORs for overdoses and suicide attempts were 1.61 (95% CI 1.23-2.13) and 2.06 (95% CI 1.48-2.94), respectively. Exercise related OHCA was noted in roughly 5%. OR for bystander CPR in 2017-2020 vs 1990-1993 was 3.11 (95% CI 2.57 to 3.78); in 2020 88 % received bystander CPR. EMS response time increased from 6 to 10 minutes.CONCLUSION: Survival has increased 6% annually, resulting in a three-fold increase over 30 years, with stable neurological outcome. EMS response time increased with 66% but the majority now receive bystander CPR. Cardiac arrest due to overdoses and suicide attempts are increasing.
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| 24. |
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| 25. |
- Hjort, Filip, 1991, et al.
(author)
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Optical microprism cavities based on dislocation-free GaN
- 2020
-
In: Applied Physics Letters. - : AIP Publishing. - 0003-6951 .- 1077-3118. ; 117:23
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Journal article (peer-reviewed)abstract
- Three-dimensional growth of nanostructures can be used to reduce the threading dislocation density that degrades III-nitride laser performance. Here, nanowire-based hexagonal GaN microprisms with flat top and bottom c-facets are embedded between two dielectric distributed Bragg reflectors to create dislocation-free vertical optical cavities. The cavities are electron beam pumped, and the quality (Q) factor is deduced from the cavity-filtered yellow luminescence. The Q factor is similar to 500 for a 1000nm wide prism cavity and only similar to 60 for a 600nm wide cavity, showing the strong decrease in Q factor when diffraction losses become dominant. Measured Q factors are in good agreement with those obtained from quasi-3D finite element frequency-domain method and 3D beam propagation method simulations. Simulations further predict that a prism cavity with a 1000nm width will have a Q factor of around 2000 in the blue spectral regime, which would be the target regime for real devices. These results demonstrate the potential of GaN prisms as a scalable platform for realizing small footprint lasers with low threshold currents.
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