| 1. |
- Diamanti, Klev, 1987-, et al.
(författare)
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Single nucleus transcriptomics data integration recapitulates the major cell types in human liver
- 2021
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Ingår i: Hepatology Research. - : Wiley. - 1386-6346 .- 1872-034X. ; 51:2, s. 233-238
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Tidskriftsartikel (refereegranskat)abstract
- Hepatology Research published by John Wiley & Sons Australia, Ltd on behalf of Japan Society of Hepatology Aim: The aim of this study was to explore the benefits of data integration from different platforms for single nucleus transcriptomics profiling to characterize cell populations in human liver. Methods: We generated single-nucleus RNA sequencing data from Chromium 10X Genomics and Drop-seq for a human liver sample. We utilized state of the art bioinformatics tools to undertake a rigorous quality control and to integrate the data into a common space summarizing the gene expression variation from the respective platforms, while accounting for known and unknown confounding factors. Results: Analysis of single nuclei transcriptomes from both 10X and Drop-seq allowed identification of the major liver cell types, while the integrated set obtained enough statistical power to separate a small population of inactive hepatic stellate cells that was not characterized in either of the platforms. Conclusions: Integration of droplet-based single nucleus transcriptomics data enabled identification of a small cluster of inactive hepatic stellate cells that highlights the potential of our approach. We suggest single-nucleus RNA sequencing integrative approaches could be utilized to design larger and cost-effective studies.
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| 2. |
- Gustafsson, Johan, 1976, et al.
(författare)
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DSAVE: Detection of misclassified cells in single-cell RNA-Seq data
- 2020
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Ingår i: PLoS ONE. - : Public Library of Science (PLoS). - 1932-6203 .- 1932-6203. ; 15:12 December
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Tidskriftsartikel (refereegranskat)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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| 3. |
- Gustavsson, Mikael, et al.
(författare)
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Transformers enable accurate prediction of acute and chronic chemical toxicity in aquatic organisms
- 2024
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Ingår i: Sciences Advances. ; 10:10
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Tidskriftsartikel (refereegranskat)abstract
- Environmental hazard assessments are reliant on toxicity data that cover multiple organism groups. Generating experimental toxicity data is, however, resource-intensive and time-consuming. Computational methods are fast and cost-efficient alternatives, but the low accuracy and narrow applicability domains have made their adaptation slow. Here, we present a AI-based model for predicting chemical toxicity. The model uses transformers to capture toxicity-specific features directly from the chemical structures and deep neural networks to predict effect concentrations. The model showed high predictive performance for all tested organism groups—algae, aquatic invertebrates and fish—and has, in comparison to commonly used QSAR methods, a larger applicability domain and a considerably lower error. When the model was trained on data with multiple effect concentrations (EC50/EC10), the performance was further improved. We conclude that deep learning and transformers have the potential to markedly advance computational prediction of chemical toxicity.
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| 4. |
- Inda Díaz, Juan Salvador, 1984
(författare)
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Data-Driven Methods for Surveillance and Diagnostics of Antibiotic-Resistant Bacteria
- 2022
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Antimicrobial resistance is a rapidly growing challenge for the healthcare sector and multi-drug resistant bacterial infections are the cause of nearly a million deaths annually worldwide. Antibiotic resistance is conferred by either antibiotic resistance genes (ARGs) which can be acquired by horizontal gene transfer between bacterial cells or by mutations in pre-existing DNA. Large collections of ARGs are present in the bacterial communities hosted by humans and animals, and in the external environments. Most of these ARGs are uncharacterized and not well-studied. Furthermore, antimicrobial resistance has significantly hindered our ability to treat infections and novel diagnostic solutions are therefore needed to ensure efficient treatment. In paper I, the abundance and diversity of 24,074 ARGs of 17 classes were studied in metagenomic data. The majority of the ARGs were previously uncharacterized, of which several were commonly reoccurring and shared across the digestive system of humans and animals, suggesting that they are under strong selection pressures. The data-driven work in this paper showed that the analysis of all ARGs, including those that have previously not been described, is necessary to provide a comprehensive description of the resistance potential of bacterial communities. In paper II, an AI method for the prediction of bacterial susceptibility towards antibiotics is presented. The method is based on transformers and artificial neural networks and exploits the strong and highly non-trivial dependencies present in the resistance patterns of bacteria. The model was highly successful in predicting susceptibility for most antibiotics from the classes cephalosporins and quinolones but had a lower performance on penicillins and aminoglycosides. The AI-based methodology described in this paper may be used to improve the diagnostics chain of infectious diseases with the potential to reduce the morbidity and mortality of patients. This thesis provides methodologies for improved surveillance and diagnostics of antibiotic-resistant bacteria and, thereby, contributes to a more sustainable use of antibiotics.
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| 5. |
- Inda Diaz, Juan Salvador, 1984, et al.
(författare)
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Latent antibiotic resistance genes are abundant, diverse, and mobile in human, animal, and environmental microbiomes
- 2023
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Ingår i: Microbiome. - : Springer Science and Business Media LLC. - 2049-2618. ; 11:1, s. 44-
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Bacterial communities in humans, animals, and the external environment maintain a large collection of antibiotic resistance genes (ARGs). However, few of these ARGs are well-characterized and thus established in existing resistance gene databases. In contrast, the remaining latent ARGs are typically unknown and overlooked in most sequencing-based studies. Our view of the resistome and its diversity is therefore incomplete, which hampers our ability to assess risk for promotion and spread of yet undiscovered resistance determinants. RESULTS: A reference database consisting of both established and latent ARGs (ARGs not present in current resistance gene repositories) was created. By analyzing more than 10,000 metagenomic samples, we showed that latent ARGs were more abundant and diverse than established ARGs in all studied environments, including the human- and animal-associated microbiomes. The pan-resistomes, i.e., all ARGs present in an environment, were heavily dominated by latent ARGs. In comparison, the core-resistome, i.e., ARGs that were commonly encountered, comprised both latent and established ARGs. We identified several latent ARGs shared between environments and/or present in human pathogens. Context analysis of these genes showed that they were located on mobile genetic elements, including conjugative elements. We, furthermore, identified that wastewater microbiomes had a surprisingly large pan- and core-resistome, which makes it a potentially high-risk environment for the mobilization and promotion of latent ARGs. CONCLUSIONS: Our results show that latent ARGs are ubiquitously present in all environments and constitute a diverse reservoir from which new resistance determinants can be recruited to pathogens. Several latent ARGs already had high mobile potential and were present in human pathogens, suggesting that they may constitute emerging threats to human health. We conclude that the full resistome-including both latent and established ARGs-needs to be considered to properly assess the risks associated with antibiotic selection pressures. Video Abstract.
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| 6. |
- Inda Díaz, Juan Salvador, 1984
(författare)
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New AI-based methods for studying antibiotic-resistant bacteria
- 2023
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Antibiotic resistance is a growing challenge for human health, causing millions of deaths worldwide annually. Antibiotic resistance genes (ARGs), acquired through mutations in existing genes or horizontal gene transfer, are the primary cause of bacterial resistance. In clinical settings, the increased prevalence of multidrug-resistant bacteria has severely compromised the effectiveness of antibiotic treatments. The rapid development of artificial intelligence (AI) has facilitated the analysis and interpretation of complex data and provided new possibilities to face this problem. This is demonstrated in this thesis, where new AI methods for the surveillance and diagnostics of antibiotic-resistant bacteria are presented in the form of three scientific papers. Paper I presents a comprehensive characterization of the resistome in various microbial communities, covering both well-studied established ARGs and latent ARGs not currently found in existing repositories. A widespread presence of latent ARGs was observed in all examined environments, signifying a potential reservoir for recruitment to pathogens. Moreover, some latent ARGs exhibited high mobile potential and were located in human pathogens. Hence, they could constitute emerging threats to human health. Paper II introduces a new AI-based method for identifying novel ARGs from metagenomic data. This method demonstrated high performance in identifying short fragments associated with 20 distinct ARG classes with an average accuracy of 96. The method, based on transformers, significantly surpassed established alignment-based techniques. Paper III presents a novel AI-based method to predict complete antibiotic susceptibility profiles using patient data and incomplete diagnostic information. The method incorporates conformal prediction and accurately predicts, while controlling the error rates, susceptibility profiles for the 16 included antibiotics even when diagnostic information was limited. The results presented in this thesis conclude that recent AI methodologies have the potential to improve the diagnostics and surveillance of antibiotic-resistant bacteria.
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