| 1. |
- Zajac, Pawel, 1982-
(författare)
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Parallel target selection by trinucleotide threading
- 2009
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- DNA is the code for all life. Via intermediary RNA the information encoded by the genome is relayed to proteins executing the various functions in a cell. Together, this repertoire of inherently linked biological macromolecules determines all characteristics and features of a cell. Technological advancements during the last decades have enabled the pursuit of novel types of studies and the investigation of the cell and its constituents at a progressively higher level of detail. This has shed light on numerous cellular processes and on the underpinnings of several diseases. For the majority of studies focusing on nucleic acids, an amplification step has to be implemented before an analysis, scoring or interrogation method translates the amplified material into relevant biological information. This information can, for instance, be the genotype of particular SNPs or STRs, or the abundance level of a set of interesting transcripts. As such, amplification plays a significant role in nucleic acid assays. Over the years, a number of techniques – most notably PCR – has been devised to meet this amplification need, specifically or randomly multiplying desired regions. However, many of the approaches do not scale up easily rendering comprehensive studies cumbersome, time-consuming and necessitating large quantities of material.Trinucleotide threading (TnT) – forming the red thread throughout this thesis – is a multiplex amplification method, enabling simultaneous targeted amplification of several nucleic acid regions in a specific manner. TnT begins with a controlled linear DNA thread formation, each type of thread corresponding to a segment of interest, by a gap-fill reaction using a restricted trinucleotide set. The whole collection of created threads is subsequently subjected to an exponential PCR amplification employing a single primer pair. The generated material can thereafter be analyzed with a multitude of readout and detection platforms depending on the issue or characteristic under consideration.TnT offers a high level of specificity by harnessing the inherent specificities of a polymerase and a ligase acting on a nucleotide set encompassing three out of the four nucleotide types. Accordingly, several erroneous events have to occur in order to produce artifacts. This necessitates override of a number of control points.The studies constituting this thesis demonstrate integration of the TnT amplification strategy in assays for analysis of various aspects of DNA and RNA. TnT was adapted for expression profiling of intermediately-sized gene sets using both conventional DNA microarrays and massively parallel second generation 454 sequencing for readout. TnT, in conjunction with 454 sequencing, was also employed for allelotyping, defined as determination of allele frequencies in a cohort. In this study, 147 SNPs were simultaneously assayed in a pool comprising genomic DNA of 462 individuals. Finally, TnT was recruited for parallel amplification of STR loci with detection relying on capillary gel electrophoresis. In all investigations, the material generated with TnT was of sufficient quality and quantity to produce reliable and accurate biological information.Taken together, TnT represents a viable multiplex amplification technique permitting parallel amplification of genomic segments, for instance harboring polymorphisms, or of expressed genes. In addition to these, this versatile amplification module can be implemented in assays targeting a range of other features of genomes and transcriptomes.
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| 2. |
- Bergenstråhle, Ludvig
(författare)
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Computational Models of Spatial Transcriptomes
- 2024
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Spatial biology is a rapidly growing field that has seen tremendous progress over the last decade. We are now able to measure how the morphology, genome, transcriptome, and proteome of a tissue vary across space. Datasets generated by spatial technologies reflect the complexity of the systems they measure: They are multi-modal, high-dimensional, and layer an intricate web of dependencies between biological compartments at different length scales. To add to this complexity, measurements are often sparse and noisy, obfuscating the underlying biological signal and making the data difficult to interpret. In this thesis, we describe how data from spatial biology experiments can be analyzed with methods from deep learning and generative modeling to accelerate biological discovery. The thesis is divided into two parts. The first part provides an introduction to the fields of deep learning and spatial biology, and how the two can be combined to model spatial biology data. The second part consists of four papers describing methods that we have developed for this purpose. Paper I presents a method for inferring spatial gene expression from hematoxylin and eosin stains. The proposed method offers a data-driven approach to analyzing histopathology images without relying on expert annotations and could be a valuable tool for cancer screening and diagnosis in the clinics. Paper II introduces a method for jointly modeling spatial gene expression with histology images. We show that the method can predict super-resolved gene expression and transcriptionally characterize small-scale anatomical structures. Paper III proposes a method for learning flexible Markov kernels to model continuous and discrete data distributions. We demonstrate the method on various image synthesis tasks, including unconditional image generation and inpainting. Paper IV leverages the techniques introduced in Paper III to integrate data from different spatial biology experiments. The proposed method can be used for data imputation, super resolution, and cross-modality data transfer.
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| 3. |
- Hu, Yue, 1987-
(författare)
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Microbial DNA Sequencing in Environmental Studies
- 2017
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The field of microbial ecology has just entered a new era of rapid technological development and generation of big data. The high-throughput sequencing techniques presently available provide an opportunity to extensively inventorize the blueprints of life. Now, millions of microbes of natural microbial communities can be studied simultaneously without prior cultivation. New species and new functions (genes) can be discovered just by mining sequencing data. However, there is still a tremendous number of microorganisms not yet examined, nor are the ecosystem functions these carry out. The modern genomic technologies can contribute to solve environmental problems and help us understand ecosystems, but to most efficiently do so, methods need to be continuously optimised. During my Ph. D. studies, I developed a method to survey eukaryotic microbial diversity with a higher accuracy, and applied various sequencing-based approaches in an attempt to answer questions of importance in environmental research and ecology. In PAPER-I, we developed a set of 18S rRNA gene PCR primers with high taxonomic coverage, meeting the requirements of currently popular sequencing technologies and matching the richness of 18S rRNA reference sequences accumulated so far. In PAPER-II, we conducted the first sequencing-based spatial survey on the combined eukaryotic and bacterial planktonic community in the Baltic Sea to uncover the relationship of microbial diversity and environmental conditions. Here, the 18S primers designed in PAPER-I and a pair of broad-coverage 16S primers were employed to target the rRNA genes of protists and bacterioplankton for amplicon sequencing. In PAPER-III, we integrated metagenomic, metabarcoding, and metatranscriptomic data in an effort to scrutinise the protein synthesis potential (i.e., activity) of microbes in the sediment at a depth of 460 m in the Baltic Sea and, thus, disclosing microbial diversity and their possible ecological functions within such an extreme environment. Lastly, in PAPER-IV, we compared the performance of E. coli culturing, high-throughput sequencing, and portable real-time sequencing in tracking wastewater contamination in an urban stormwater system. From the aspects of cost, mobility and accuracy, we evaluated the usage of sequencing-based approaches in civil engineering, and for the first time, validated the real-time sequencing device in use within water quality monitoring. In summary, these studies demonstrate how DNA sequencing of microbial communities can be applied in environmental monitoring and ecological research.
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| 4. |
- Klevebring, Daniel, 1981-
(författare)
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On Transcriptome Sequencing
- 2009
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis is about the use of massive DNA sequencing to investigate the transcriptome. During recent decades, several studies have made it clear that the transcriptome comprises a more complex set of biochemical machinery than was previously believed. The majority of the genome can be expressed as transcripts; and overlapping and antisense transcription is widespread. New technologies for the interroga- tion of nucleic acids have made it possible to investigate such cellular phenomena in much greater detail than ever before. For each application, special requirements need to be met. The work presented in this thesis focuses on the transcrip- tome and the development of technology for its analysis. In paper I, we report our development of an automated approach for sample preparation. The procedure was benchmarked against a publicly available reference data set, and we note that our approach outperformed similar manual procedures in terms of reproducibility. In the work reported in papers II-IV, we used different massive sequencing technologies to investigate the transcriptome. In paper II we describe a concatemerization approach that increased throughput by 65% using 454 sequencing,and we identify classes of transcripts not previously described in Populus. Papers III and IV both report studies based on SOLiD sequencing. In the former, we investigated transcripts and proteins for 13% of the human gene and detected a massive overlap for the upper 50% transcriptional levels. In the work described in paper IV, we investigated transcription in non-genic regions of the genome and detected expression from a high number of previ- ously unknown loci.
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| 5. |
- Kvastad, Linda
(författare)
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The Spatial Context – through the lens of method development
- 2021
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In the present moment of time, we find ourselves in a period where the advancement of genomic tools is progressing at a fast pace. Of particular interest for this thesis is the study of gene activity. What patterns of genes are expressed? Where are they expressed? How can we use this knowledge to improve our quality of life? The research presented in this thesis focuses on developing and applying new tools for interrogating cells and tissues. In Paper I, we describe a protocol for transcript profiling of single cells, capable of measuring the relative expression levels for genes of interest. We successfully applied our method to cancer cells from metastatic breast cancer patients. Profiling 2 to 4 single cells per patient and measuring gene-specific expression from targets previously associated with metastatic breast cancer supports the use of our protocol as a diagnostic tool. In Paper II, we present an assay for spatial RNA quality evaluation, used to estimate the success for tissue specimens before proceeding with more expensive spatial sequencing methods. We showed that the method is capable of measuring high RNA quality in tissue areas of both high and low cell density and that the spatial RNA integrity patterns are reflected in spatial transcriptomics data. In Paper III, we present a protocol for performing spatial mRNA genome-wide expression profiling of FFPE tissue specimens. Thus, we bridge a gap between traditional tissue preservation methods and novel gene technologytools. We found a high Pearson correlation of 0.95 between formalin-fixation paraffin embedding (FFPE) and Fresh Frozen (FF) mouse brain datasets. Although the FPPE samples yielded fewer transcripts and genes compared to FF, there was a high agreement in gene expression between paired anatomical areas for FFPE and FF samples. In Paper IV, we present an approach to investigate in situ transcript derivedinferred copy number variation (iCNV) profiles based on spatial transcriptomics data. In a normal lymph node that displays both distinct gene expression patterns and histological landmarks, we observed a neutral iCNV profile. In contrast, we found huge variabilities investigating several malign tissue types ranging from homogenous (pediatric medulloblastoma) to highly variable genomes (ductal breast cancer and glioblastoma). Strikingly, we also observed similar iCNV profiles in both tumor and benign tissue areas from prostate and skin cancer. In Paper V, we explore the transcriptional and genomic landscape in pediatric tumors from 14 patients. Microglia cells have been implicated to play an important role in the tumor microenvironment, and we found spatial co-localization of microglia and epithelial-to-mesenchymal transition (EMT) signatures in our patient cohort. Furthermore, we found homogenous and recurrent iCNV profiles in the high-grade tumors of relapse patients and identified expression of gene SPP1 in the tumor stroma as a potential prognostic mRNA marker in pediatric brain tumor relapse patients.
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| 6. |
- Lindberg, Johan, 1977-
(författare)
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Transcriptional patterns in inflammatory disease
- 2008
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In the studies this thesis is based upon, microarrays were applied to profilemRNA populations in biological samples to gain insights into transcriptionalpatterns and their relation to inflammatory disease.Rheumatoid arthritis (RA) is a chronic inflammatory disease, which leads todegradation of cartilage and bone. RA is characterized by synovial inflammationwith varying levels of tissue heterogeneity. This was confirmed by microarrayanalyses of multiple biopsies from the joints of 13 patients, which showed interindividualvariation in transcript populations to be higher than intra‐individualvariationTherapeutic antibodies targeting TNF‐α have revolutionized treatment of RA,although some patients do not respond well. Identification of non‐responders isimportant, not only because anti‐TNF treatment elevates the risk of infections,but also because of the cost of treatment. A proof‐of‐concept study to investigatetranscriptional effects of anti‐TNF treatment demonstrated that differencesbetween response groups could be identified and that these differences revealedbiological themes related to inflammatory disease.A subsequent study was therefore initiated with a larger cohort of 62 patients toinvestigate gene expression patterns in the synovium prior to anti‐TNFtreatment. Here, the heterogeneity was even more pronounced, thetranscriptional patterns were confounded by the presence of synovial aggregatesand only a weak therapy‐correlated signature was detected. The presence oflymphocyte aggregates was found to correlate to response to therapy, which isconsistent with previous findings indicating a higher level of inflammation ingood responding patients.Periodontitis is an inflammatory disease with many similarities to RA. Both areincurable chronic auto‐immune diseases, characterized by tissue destructionwith common genetic associations. Individuals with RA are at higher risk ofaccumulating significant periodontal problems than the general population. PGE2(prostaglandin E2) is known to stimulate inflammation and bone resorption inperiodontitis. In further studies, microarrays were applied in a time seriesdesign on human gingival fibroblats to explore the signal transduction pathwayscontrolling TNF‐α induced PGE2 synthesis in order to identify novel therapeutictargets. The JNK and NF‐kb pathways were identified as being differentiallyaffected by TNF‐a treatment. The transcriptional patterns were further verifiedusing antibodies against phosphorylated JNK/NF‐kb molecules and specificinhibitors of the JNK and NF‐kb signaling cascades.
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| 7. |
- Marklund, Maja
(författare)
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Spatial transcriptome and epigenome analysis with focus on prostate cancer
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Each cancer is unique, and therefore the use of general treatments are often suboptimal. If we can understand the mechanisms of cancer development, we might be able to develop effective treatments tailored to each patient. Our bodies are complex three-dimensional structures and how things are organized correlate with proper functioning. Technologies for biological research have escalated enormously in the last years. Going from bulk analysis of tissues to the advent of single cell sequencing and spatially resolved transcriptomics has initiated a new era in biological research. The technology Spatial Transcriptomics (ST) combines histology with next-generation sequencing, making it possible to map which genes that are active at thousands of sub-areas in a tissue section. In Paper I, ST was combined with an in-house developed artificial intelligence method to explore the landscape of prostate cancer tissue. We identified a gene expression-based tumor signature in healthy tissue areas not possible to recognize through visual assessment, indicating that the genotype changes before phenotype. A gradient of the tumor microenvironment was also identified. In Paper II, prostate cancer tissue from three patients were investigated before and after androgen deprivation therapy using ST. All patients treated with this therapy long enough will reach a clinically defined stage called castration-resistant prostate cancer. We could see that only a set of cancer cells across the tissue responded to the treatment, which allowed comparison of gene expression program in responding versus non-responding cells. By understanding the underlying mechanisms to resistance, it might be possible to target these cells and decrease relapse risk. In Paper III, we inferred copy number variation from ST data allowing for the generation of genome integrity maps in cancerous tissue of prostate, breast, brain, and skin, and in a lymph node. This allowed us to identify tumor clones not recognizable histologically, indicating how genomic instability can be initiated and spread before visible for the naked eye. In Paper IV, we developed a method for spatial ATAC-seq by fusing the ST-technology with ATAC-seq, enabling the analyses of accessible chromatin while preserving histological information. The Visium platform by 10x Genomics was used and we demonstrate a similar capture efficiency to single-cell ATACseq.
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| 8. |
- Pradhananga, Sailendra
(författare)
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Towards understanding of complex disease etiology using sequence Capture Hi-C (HiCap) and associated high throughput functional assays
- 2020
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- A human genome laid out in a straight line would measure 2m from end to end, but in living cells it is folded into a compact structure contained in a nuclear space with a diameter of 2µm. This compact genome is hierarchically organized and spatiotemporally regulates distinct cellular gene expression mechanisms via promoter - enhancer chromatin loops. Additionally, large scale genomic studies have identified enhancer regions enriched with complex disease risk variants but uncovering their contribution to disease pathology remains challenging. This thesis reports the genome-wide generation and analysis of regulatory and functional regions in complex disease relevant cell types. High throughput sequencing methods including whole genome/exome sequencing, capture Hi-C (HiCap), RNA sequencing, and ChIP sequencing were used to create a snapshot of the functional and genomic landscape of cell types relevant to complex diseases. Paper I present a technical comparison of variant calls generated using two genotyping technologies: whole exome and whole genome sequencing (WGS and WES). This comparison unequivocally shows that variant quality from moderately sequenced WGS variant calls is stable and concordant with deeply sequenced WES calls. Papers II and III report the assignment of target genes to cardiovascular risk SNPs using capture Hi-C (HiCap) and other functional assays and identify both known and novel biological processes related to cardiovascular pathology. Finally, paper IV reports the use of whole genome sequencing and capture Hi-C data to identify rare variants in putative enhancer regions in a patient with a congenital heart defect and reveals a number of processes and genes relevant to the pathology. In conclusion, this thesis demonstrates that combining capture Hi-C with functional high throughput sequencing methods can improve our understanding of the etiology of complex diseases. We believe that the resulting mechanistic understanding of complex disease pathologies will enable effective intervention using drugs targeting regulatory processes.
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| 9. |
- Svedberg, Anna, 1988-
(författare)
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Toxicity and pharmacokinetic biomarkers for personalized non-small cell lung cancer treatment
- 2020
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Lung cancer is the leading cause of cancer-related deaths worldwide. Unfortunately, lung cancer is usually discovered at a late stage when the curative treatment options are limited. The treatment can include surgery, radiation, chemotherapy, targeted therapy and now also immunotherapy.The challenge in cancer treatment is to eradicate cancer by the use of harsh treatments, while still, keeping the patient alive. For this purpose, treatments with severe toxicities are usually accepted but regularly lead to dose reductions or postponed treatment. Large variations in response are generally observed between patients treated with the same drug at the same dose. The dose may be adequate in one patient while ineffective or cause severe adverse drug reactions in other patients. The occurrence of drug-induced toxicities can, however, also be a positive indicator of treatment response. In personalized treatment it is of importance to select the most suitable treatment option and give it at the most favorable dose, to enable the patients to stay on treatment during the time the treatment is able to affect cancer since the tumor commonly develops resistance towards the treatment eventually.In this thesis, inter-individual variability in pharmacokinetics and toxicity for the targeted therapy erlotinib, associated with the adverse events skin rash and diarrhea was studied. Inter-individual variability in toxicity was also studied for the chemotherapy treatment gemcitabine/carboplatin linked to the hematological toxicities neutropenia and leukopenia.Erlotinib was studied in papers I-IV. Erlotinib and its metabolite concentrations were determined using a validated LC-MS/MS method. Diarrhea was associated with erlotinib and the metabolite M13, while skin rash was associated with the activity of the erlotinib metabolizing enzyme CYP3A and the ABCG2 single nucleotide polymorphism rs10856870. CYP3A was also shown to be induced during treatment. Additionally, in vitro studies showed that genetic variability in ABCG2 contributes to differences in intracellular concentrations. Genes and gene variants were found to be associated with gemcitabine/carboplatininduced toxicity in paper V. The variants were partially validated, and two models were developed to estimate the risk of leukopenia or neutropenia based on a set of genetic variants.
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| 10. |
- Andersson, Alma, 1995-
(författare)
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Computational methods for analysis of spatial transcriptomics data : An exploration of the spatial gene expression landscape
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Transcriptomics techniques, whether in the form of bulk, single cell/nuclei, or spatial methods have fueled a substantial expansion of our knowledge about the biological systems within and around us. In addition, the rate of innovation has accelerated over the last decade, resulting in a multitude of technological advances and new methods for generation of transcriptomics data. In 2009, isolating and characterizing the transcriptome of a single cell was seen as a major achievement, ten years later, in 2019, studies surveying a hundred thousand cells were commonplace. The field of spatial transcriptomics went through an equally transformative phase; from struggling with simultaneous characterization of a few targets, to seamlessly provide spatially resolved maps of the full transcriptome. Inevitably, we’re approaching an inflection point where the generation of data is no longer the bottleneck, but rather its analysis. Alas, with standardized commercial products, high-quality spatial transcriptomics data can now be generated en masse. Hence, questions about data analysis have started to replace those of data generation. The work in this thesis seeks to address some of these emerging questions; the five articles it encompasses presents new methods for analysis of spatial transcriptomics data and examples of their application. Furthermore, it contains an introduction to current experimental and computational spatial transcriptomics techniques, as well as a section about data modeling. In Article I, a probabilistic model for integration of single cell/nuclei and spatial transcriptomics data is presented. In short, the method allows for mixed signals – present in certain spatial transcriptomics platforms – to be decomposed into contributions from biologically relevant cell types or states derived from single cell/nuclei data. The model was implemented in code as a software, stereoscope, which is open source and publicly available. The same policy of open source and high transparency holds true for all software or code associated with this thesis. The stereoscope method has been used in several studies, one example being Article II, where we examined the spatial transcriptomics landscape of HER2-positive breast cancer patients. By integrating single cell and spatial transcriptomics data, several intriguing co-localization signals emerged. These signals allowed us to identify a signature for tertiary lymphoid structures and evidence of a trifold interaction involving: type I interferon signals, a T-cell subset, and a macrophage subset. However, the work also included other forms of explorative data analysis, such as unsupervised expression-based clustering. The clusters from this analysis, once annotated, exhibited high concordance with annotations provided by a pathologist and the tissue morphology. Taken together, this makes a compelling case for the use of spatial transcriptomics in the age of “digital pathology.” Finally, we also derived “core signatures” from the expression-based clusters, representing common expression profiles shared across the patients.In Article III, we present a computational method, sepal, designed to identify genes with distinct spatial patterns, often referred to as “spatially variable genes.” The method uses Fick’s second law to simulate diffusion of transcripts in the tissue, measuring the time until convergence (a spatially uniform and homogeneous state). It then ranks the genes by their “diffusion time.” The assumption being that genes exhibiting strong spatial patterns will take longer time to converge compared to genes with no pattern, thus relating the diffusion time to the degree of spatial structure. Article IV constitutes a study of the mouse liver using spatial transcriptomics. As before, we employed stereoscope for the purpose of single cell integration, but realized more tailored computational tools – towards the specific tissue – were required to address certain questions. Thus, we developed two computational methods, one devoted to vein type identity prediction, the other enabling a change of data representation. In essence, to predict the vein identities, we first assembled spatially weighted composite expression profiles from – to the vein – neighboring observations. Then, a logistic classifier was trained using the composite profiles. Once the model was trained, it could be used to assign vein type identities to ambiguous or unannotated veins. In the second method, the two-dimensional spatial data was recast into a more informative one-dimensional representation by treating gene expression as a function of an observation’s distance to its nearest vein structure.The final work, Article V, expands the idea of recasting data into a more informative or helpful representation. More precisely, we present a method, eggplant, that allows the user to transfer spatial transcriptomics data from multiple sources to a common coordinate framework (CCF). Transfer of information to a CCF means spatial signals can be compared across conditions and time points, unlocking a plethora of valuable downstream analyses. For example, we perform spatiotemporal modeling of a synthetic system, and introduce the concept of “spatial arithmetics” to study local expression differences. With a growing corpus of spatial trancsriptomics data and ambitious international efforts like the Human Cell Atlas, we deem these sort of methods essential to leverage the data’s full potential.
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| 11. |
- Andrusivova, Zaneta
(författare)
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Development and application of spatial transcriptomics methods
- 2023
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Transcriptomics is one of the pivotal fields in molecular biology, enabling comprehensive analysis of gene expression patterns. Recent advancements in the biotechnology field have transformed the transcriptomics research, providing insights into the complexity of cellular processes in a greater detail. However, conventional transcriptomics methods such as bulk RNA sequencing or single-cell RNA sequencing rely on tissue dissociation and therefore lack spatial information, which limits our understanding of gene expression patterns within the tissue structures. The development of spatially resolved transcriptomics methods has revolutionized the study of transcriptomes, enabling analysis of gene expression patterns in the spatial context. The wide range of available transcriptomics technologies offer various levels of resolution and throughput, and combination of multiple techniques can be beneficial for studying biological systems and gain deeper understanding of their molecular processes. In this thesis, particular emphasis is given to the Visium spatial gene expression technology, which has gain widespread popularity in the research community over the recent years. In the article I, we expand the application of the Visium platform to fresh-frozen samples of lower RNA quality or otherwise challenging characteristics. To achieve this, we introduce specific modifications to the commercially available protocol and test its effectiveness across different tissue types of varying RNA quality, including pediatric brain tumors, human small intestine, and mouse bone and cartilage. By conducting comparative analysis, we demonstrate that the new protocol outperforms the standard Visium protocol when working with samples of moderate and lower RNA quality.Article II introduces a novel method that enhances the resolution of the Visium gene expression method through tissue expansion. We showcase the implementation of this new protocol on two regions of mouse brain, olfactory bulb and hippocampus. We demonstrate the ability of this approach to study smaller tissue structures that were previously beyond the resolution capabilities of the Visium platform.In the article III and IV, we demonstrate the practical application of the Visium approach and its combination with other methodologies in the field of developmental biology. We show how utilizing spatial transcriptomics methods help elucidate the spatial organization of cell types and cell states during organogenesis in the developing human spinal cord (article III) and developing lung tissue (article IV). By deploying single-cell RNA sequencing and spatial methods, we described the spatiotemporal gene expression profiles of various cell types as well as shared and unique events occurring during the spinal cord development in humans and rodents (article III). Applying this multimodal approach to lung tissue (article IV) allowed us to characterize novel cell states emerging during lung development and provided valuable insights into the structural organization of developing lungs. These studies highlight the findings and observations that can be gained by combining spatially resolved transcriptomics with other laboratory techniques to shed light on the spatial dynamics of cellular processes during organ development.
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| 12. |
- Asp, Michaela
(författare)
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Spatially Resolved Gene Expression Analysis
- 2018
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Spatially resolved transcriptomics has greatly expanded our knowledge of complex multicellular biological systems. To date, several technologies have been developed that combine gene expression data with information about its spatial tissue context. There is as yet no single spatial method superior to all others, and the existing methods have jointly contributed to progress in this field of technology. Some challenges presented by existing protocols include having a limited number of targets, being labor extensive, being tissue-type dependent and having low throughput or limited resolution. Within the scope of this thesis, many aspects of these challenges have been taken into consideration, resulting in a detailed evaluation of a recently developed spatial transcriptome-wide method. This method, termed Spatial Transcriptomics (ST), enables the spatial location of gene activity to be preserved and visually links it to its histological position and anatomical context. Paper I describes all the details of the experimental protocol, which starts when intact tissue sections are placed on barcoded microarrays and finishes with high throughput sequencing. Here, spatially resolved transcriptome-wide data are obtained from both mouse olfactory bulb and breast cancer samples, demonstrating the broad tissue applicability and robustness of the approach. In Paper II, the ST technology is applied to samples of human adult heart, a tissue type that contains large proportions of fibrous tissue and thus makes RNA extraction substantially more challenging. New protocol strategies are optimized in order to generate spatially resolved transcriptome data from heart failure patients. This demonstrates the advantage of using the technology for the identification of lowly expressed biomarkers that have previously been seen to correlate with disease progression in patients suffering heart failure. Paper III shows that, although the ST technology has limited resolution compared to other techniques, it can be combined with single-cell RNA-sequencing and hence allow the spatial positions of individual cells to be recovered. The combined approach is applied to developing human heart tissue and reveals cellular heterogeneity of distinct compartments within the complete organ. Since the ST technology is based on the sequencing of mRNA tags, Paper IV describes a new version of the method, in which spatially resolved analysis of full-length transcripts is being developed. Exploring the spatial distribution of full-length transcripts in tissues enables further insights into alternative splicing and fusion transcripts and possible discoveries of new genes.
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| 13. |
- Berglund, Emelie
(författare)
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Molecular and Spatial Profiling of Prostate Tumors
- 2020
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Every cancer tumor is unique, with characteristics that change over time. The evolution of a full-blown malignancy from a single cell that gives rise to a heterogeneous population of cancer cells is a complex process. The use of spatial information makes a big contribution to understanding the progression of tumors and how patients respond to treatment. Currently, the scientific community is taking a step further in order to understand gene expression heterogeneity in the context of tissue spatial organization to shed light on cell- to-cell interactions. Technological advances in recent years have increased the resolution at which heterogeneity can be observed. Spatial transcriptomics (ST) is an in situ capturing technique that uses a glass slide containing oligonucleotides to capture mRNAs while maintaining the spatial information of histological tissue sections. It combines histology and Illumina sequencing to detect and visualize the whole transcriptome information of tissue sections. In Paper I, an AI method was developed to create a computerized tissue anatomy. The rich source of information enables the AI method to identify genetic patterns that cannot be seen by the naked eye. This study also provided insights into gene expression in the environment surrounding the tumor, the tumor microenvironment, which interacts with tumor cells for cancer growth and progression. In Paper II, we investigate the cellular response of treatment. It is well known that virtually all patients with hormone naïve prostate cancer treated with GnRH agonists will relapse over time and that the cancer will transform into a castration-resistant form denoted castration-resistant prostate cancer. This study shows that by characterizing the non-responding cell populations, it may be possible to find an alternative way to target them in the early stages and thereby decrease the risk of relapse. In Paper III, we deal with scalability limitations, which in the ST method are represented by time- consuming workflow in the library preparation. This study introduces an automated library preparation protocol on the Agilent Bravo Automated Liquid Handling Platform to enable rapid and robust preparation of ST libraries. Finally, Paper IV expands on the first work and illustrates the utility of the ST technology by constructing, for the first time, a molecular view of a cross-section of a prostate organ.
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| 14. |
- Chelebian, Eduard, et al.
(författare)
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Morphological Features Extracted by AI Associated with Spatial Transcriptomics in Prostate Cancer
- 2021
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Ingår i: Cancers. - : MDPI AG. - 2072-6694. ; 13:19
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Tidskriftsartikel (refereegranskat)abstract
- Simple Summary Prostate cancer has very varied appearances when examined under the microscope, and it is difficult to distinguish clinically significant cancer from indolent disease. In this study, we use computer analyses inspired by neurons, so-called 'neural networks', to gain new insights into the connection between how tissue looks and underlying genes which program the function of prostate cells. Neural networks are 'trained' to carry out specific tasks, and training requires large numbers of training examples. Here, we show that a network pre-trained on different data can still identify biologically meaningful regions, without the need for additional training. The neural network interpretations matched independent manual assessment by human pathologists, and even resulted in more refined interpretation when considering the relationship with the underlying genes. This is a new way to automatically detect prostate cancer and its genetic characteristics without the need for human supervision, which means it could possibly help in making better treatment decisions. Prostate cancer is a common cancer type in men, yet some of its traits are still under-explored. One reason for this is high molecular and morphological heterogeneity. The purpose of this study was to develop a method to gain new insights into the connection between morphological changes and underlying molecular patterns. We used artificial intelligence (AI) to analyze the morphology of seven hematoxylin and eosin (H & E)-stained prostatectomy slides from a patient with multi-focal prostate cancer. We also paired the slides with spatially resolved expression for thousands of genes obtained by a novel spatial transcriptomics (ST) technique. As both spaces are highly dimensional, we focused on dimensionality reduction before seeking associations between them. Consequently, we extracted morphological features from H & E images using an ensemble of pre-trained convolutional neural networks and proposed a workflow for dimensionality reduction. To summarize the ST data into genetic profiles, we used a previously proposed factor analysis. We found that the regions were automatically defined, outlined by unsupervised clustering, associated with independent manual annotations, in some cases, finding further relevant subdivisions. The morphological patterns were also correlated with molecular profiles and could predict the spatial variation of individual genes. This novel approach enables flexible unsupervised studies relating morphological and genetic heterogeneity using AI to be carried out.
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| 15. |
- Divne, Anna-Maria, 1970-
(författare)
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Evaluation of New Technologies for Forensic DNA Analysis
- 2005
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- DNA samples from crime scenes or mass disasters are often limited and degraded which limits the possibility of successful traditional STR analysis. Moreover, there is a need to decrease the turnaround time in criminal investigations. These circumstances require a wider set of assays and technologies to be investigated for potential use in forensic DNA analysis, which has been explored in this thesis work. DNA analysis can also provide a useful tool in forensic pathology investigations. In a search for mutations involved in The Sudden Infant death Syndrome (SIDS), the entire mitochondrial genome was sequenced in six SIDS infants and shorter mtDNA regions were analysed in paraffin-embedded tissues from an additional 14 SIDS cases. In this sample material no mutations associated with SIDS were found that could explain the death of these infants. To reduce time, cost and effort related to sequencing of the mtDNA HVI/HVII regions in caseworks, a HVI/HVII mtDNA linear array assay was used as a pre-screening for exclusions of suspects or evidence samples. Using this assay, 56% of the samples involved in casework analysis could be excluded before sequencing was undertaken.The possibility to use the new array technology was explored in a SNP assay targeting both mtDNA and nuclear SNPs. The system relies on minisequencing in solution prior to hybridisation to tag arrays. Using this system, we demonstrate a rapid, highly multiplexable and flexible array-format for SNP analysis.The properties of the Pyrosequencing technology being a fast and user-friendly assay was utilised in a study to investigate the possibility to use this method for limited and degraded samples. Ten STR loci, overlapping with standardised kits, were genotyped in 114 Swedish individuals. We found additional variation and higher resolution of repeats at some of these loci that are not detected using standard fragment analysis.
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| 16. |
- Edlundh-Rose, Esther, 1976-
(författare)
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Molecular Signatures of Cancer
- 2006
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Cancer is an important public health concern in the western world, responsible for around 25% of all deaths. Although improvements have been made in the diagnosis of cancer, treatment of disseminated disease is inefficient, highlighting the need for new and improved methods of diagnosis and therapy. Tumours arise when the balance between proliferation and differentiation is perturbed and result from genetic and epigenetic alterations. Due to the heterogeneity of cancer, analysis of the disease is difficult and a wide range of methods is required. In this thesis, a number of techniques are demonstrated for the analysis of genetic, epigenetic and transcriptional alterations involved in cancer, with the purpose of identifying a number of molecular signatures. Pyrosequencing proved to be a valuable tool for the analysis of both point mutations and CpG methylation. Using this method, we showed that oncogenes BRAF and NRAS, members of the Ras-Raf-MAPK pathway, were mutated in 82% of melanoma tumours and were mutually exclusive. Furthermore, tumours with BRAF mutations were more often associated with infiltrating lymphocytes, suggesting a possible target for immunotherapy. In addition, methylation of the promoter region of the DNA repair gene MGMT was studied to find a possible correlation to clinical response to chemotherapy. Results showed a higher frequency of promoter methylation in non-responders as compared to responders, providing a possible predictive role and a potential basis for individually tailored chemotherapy. Microarray technology was used for transcriptional analysis of epithelial cells, with the purpose of characterization of molecular pathways of anti-tumourigenic agents and to identify possible target genes. Normal keratinocytes and colon cancer cells were treated with the antioxidant N-acetyl L-cysteine (NAC) in a time series and gene expression profiling revealed that inhibition of proliferation and stimulation of differentiation was induced upon treatment. ID-1, a secreted protein, was proposed as a possible early mediator of NAC action. In a similar study, colon cancer cells were treated with the naturally occurring bile acid ursodeoxycholic acid (UDCA) in a time series and analysed by microarray and FACS analysis. Results suggest a chemopreventive role of UDCA by G1 arrest and inhibition of cell proliferation, possibly through the secreted protein GDF15. These investigations give further evidence as to the diversity of cancer and its underlying mechanisms. Through the application of several molecular methods, we have found a number of potential targets for cancer therapy. Follow up studies are already in progress and may hopefully lead to novel methods of treatment.
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| 17. |
- Fernandez Navarro, Jose, 1982-
(författare)
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Computational methods for analysis and visualization of spatially resolved transcriptomes
- 2019
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Characterizing the expression level of genes (transcriptome) in cells and tis- sues is essential for understanding the biological processes of multicellular or- ganisms. RNA sequencing (RNA-seq) has gained traction in the last decade as a powerful tool that provides an accurate quantitative representation of the transcriptome in tissues. RNA-seq methods are, however, limited by the fact that they provide an average representation of the transcriptome across the tissue. Single cell RNA sequencing (scRNA-seq) provides quantitative gene expression levels of individual cells. This enables the molecular characteri- zation of cell types in health, disease and developmental tissues. However, scRNA-seq lacks the spatial context needed to understand how cells interact and their microenvironment. Current methods that provide spatially resolved gene expression levels are limited by a low throughput and the fact that the target genes must be known in advance.Spatial Transcriptomics (ST) is a novel method that combines high-resolution imaging with high-throughput sequencing. ST provides spatially resolved gene expression levels in tissue sections. The first part of the work presented in this thesis (Papers I, II, III and IV) revolves around the ST method and the development of the computational tools required to process, analyse and visualize ST data.Furthermore, the ST method was utilized to construct a three-dimensional (3D) molecular atlas of the adult mouse brain using 75 consecutive coronal sections (Paper V). We show that the molecular clusters obtained by unsu- pervised clustering of the atlas highly correlates with the Allen Brain Atlas. The molecular clusters provide new insights in the organization of regions like the hippocampus or the amygdala. We show that the molecular atlas can be used to spatially map single cells (scRNA-seq) onto the clusters and that only a handful of genes is required to define the brain regions at a molecular level.Finally, the hippocampus and the olfactory bulb of transgenic mice mim- icking the Alzheimer’s disease (AD) were spatially characterized using the ST method (Paper VI). Dierential expression analysis revealed genes central in areas highly cited as important in AD including lipid metabolism, cellular bioenergetics, mitochondrial function, stress response and neurotransmission.
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| 18. |
- Hagberg, Anette
(författare)
-
Expression profiling using manifold supports
- 2001
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Analyses of mRNA provides a condensed view of gene structure and quantitative analysis can reveal the induction of physiological or pathological gene expression programs. This thesis describes a new method for mRNA isolation, followed by sensitive real time detection via polymerase chain reaction (PCR), in order to quantitate transcripts of interest. Chimeric genes that result from chromosomal translocations can be used as disease-specific markers for the malignant clone to detect minimal residual diseases. It is important to detect an expanding clone as early as possible to increase the chance of a successful treatment. Accordingly, RT-PCR (reverse transcription PCR) of such chimeric transcripts has gained interest as a means to monitor patients due to its sensitivity. Expression of BCR-ABL in bone marrow or blood can be used as a measure of minimal residual disease (MRD) in patients with chronic myeloid leukemia. The newly described method for mRNA isolation was used to analyse the tumor burden in patient samples via real time detection using PCR. The proposed method constitues a promising, reproducible, and sensitive means to quantify BCR-ABL mRNA and it is suitable to monitor MRD in leukemic patients. Recombinant human erythropoietin (r-HuEpo) has an important role in the treatment of anemic patients. β-globin mRNA was monitored in order to elucidate if it could serve as a new marker for monitoring the response to r-HuEpo. Because of the high cost of EPO treatment, an early indicator of whether a patient responds to the therapy would be of great value. The response pattern for mRNA was compared to the reticulocyte count, levels of hemoglobin, transferrin receptor and ferritin in healty individuals receiving r-HuEpo or in controls. Following treatment, β-globin mRNA showed a more distinct increase compared to all other laboratory measurements and is therefore promising as a marker for the response to EPO treatment. The fourth project was undertaken to investigate fluctuations of mRNA expression levels for cytokines important for the rejection of xenotransplants. Porcine islet xenotransplantation could potentially solve the problem of the limited supply of suitable human donors for transplanation of islets, in order to offer a curative treatment of insulin dependent diabetes mellitus (IDDM). The rejection process was studied in the pig→rat model. Earlier studies reported a Th2 associated response. However, both morphological pattern and mRNA expression profiling supported the view that rejection is primarily due to a Th1 response.
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| 19. |
- Hasmats, Johanna, 1981-
(författare)
-
Analysis of genetic variations in cancer
- 2012
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The aim of this thesis is to apply recently developed technologies for genomic variation analyses, and to ensure quality of the generated information for use in preclinical cancer research.Faster access to a patients’ full genomic sequence for a lower cost makes it possible for end users such as clinicians and physicians to gain a more complete understanding of the disease status of a patient and adjust treatment accordingly. Correct biological interpretation is important in this context, and can only be provided through fast and simple access to relevant high quality data.Therefore, we here propose and validate new bioinformatic strategies for biomarker selection for prediction of response to cancer therapy. We initially explored the use of bioinformatic tools to select interesting targets for toxicity in carboplatin and paclitaxel on a smaller scale. From our findings we then further extended the analysis to the entire exome to look for biomarkers as targets for adverse effects from carboplatin and gemcitabine. To investigate any bias introduced by the methods used for targeting the exome, we analyzed the mutation profiles in cancer patients by comparing whole genome amplified DNA to unamplified DNA. In addition, we applied RNA-seq to the same patients to further validate the variations obtained by sequencing of DNA. The understanding of the human cancer genome is growing rapidly, thanks to methodological development of analysis tools. The next step is to implement these tools as a part of a chain from diagnosis of patients to genomic research to personalized treatment.
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| 20. |
- Jemt, Anders
(författare)
-
Library Preparation for High Throughput DNA Sequencing
- 2017
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Order 3 billion base pairs of DNA in the correct order and you get the blueprint of a human, the genome. Before the introduction of massively parallel sequencing a little more than a decade ago it would cost around $10 million to get this blueprint. Since then, sequencing throughput and cost have plummeted and now that figure is around $1000, and large sequencing centres such as the National Genomics Infrastructure in Stockholm is sequencing the equivalent of 25 human genomes per hour. The papers that form the basis of this thesis cover different aspects of the rapidly expanding DNA sequencing field. Paper I describes a model system that employ massively parallel sequencing to characterize the behaviour of type IIS restriction enzymes. Enzymes are biological macromolecules that catalyse chemical reactions in the cell. All commercially available sequencing systems use enzymes to prepare the nucleic acids before they are loaded on the machine. Thus, intimate knowledge of enzymes is vital not only when designing new sequencing protocols, but also for understanding the limitations of current protocols. Paper II covers the automation of a library preparation protocol for spatially resolved transcriptome sequencing. Automation increases the sample throughput and also minimises the risk of human errors that can introduce technical noise in the data. In paper III, the power of massively parallel sequencing is employed to describe the RNA content of the endometrium at two different time points during the menstrual cycle. Finally, paper IV covers the sequencing of highly degraded nucleic acids from formalin fixed, paraffin embedded samples. These samples often have a rich clinical background, making them extremely valuable for researchers. However, it is challenging to sequence these samples and this study looks at the impact that different preparation kits have on the quality of the sequencing data.
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| 21. |
- Lundin, Sverker, 1982-
(författare)
-
Methods to Prepare DNA for Efficient Massive Sequencing
- 2012
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Massive sequencing has transformed the field of genome biology due to the continuous introduction and evolution of new methods. In recent years, the technologies available to read through genomes have undergone an unprecedented rate of development in terms of cost-reduction. Generating sequence data has essentially ceased to be a bottleneck for analyzing genomes instead to be replaced by limitations in sample preparation and data analysis. In this work, new strategies are presented to increase both the throughput of library generation prior to sequencing, and the informational content of libraries to aid post-sequencing data processing. The protocols developed aim to enable new possibilities for genome research concerning project scale and sequence complexity.The first two papers that underpin this thesis deal with scaling library production by means of automation. Automated library preparation is first described for the 454 sequencing system based on a generic solid-phase polyethylene-glycol precipitation protocol for automated DNA handling. This was one of the first descriptions of automated sample handling for producing next generation sequencing libraries, and substantially improved sample throughput. Building on these results, the use of a double precipitation strategy to replace the manual agarose gel excision step for Illumina sequencing is presented. This protocol considerably improved the scalability of library construction for Illumina sequencing. The third and fourth papers present advanced strategies for library tagging in order to multiplex the information available in each library. First, a dual tagging strategy for massive sequencing is described in which two sets of tags are added to a library to trace back the origins of up to 4992 amplicons using 122 tags. The tagging strategy takes advantage of the previously automated pipeline and was used for the simultaneous sequencing of 3700 amplicons. Following that, an enzymatic protocol was developed to degrade long range PCR-amplicons and forming triple-tagged libraries containing information of sample origin, clonal origin and local positioning for the short-read sequences. Through tagging, this protocol makes it possible to analyze a longer continuous sequence region than would be possible based on the read length of the sequencing system alone. The fifth study investigates commonly used enzymes for constructing libraries for massive sequencing. We analyze restriction enzymes capable of digesting unknown sequences located some distance from their recognition sequence. Some of these enzymes have previously been extensively used for massive nucleic acid analysis. In this first high throughput study of such enzymes, we investigated their restriction specificity in terms of the distance from the recognition site and their sequence dependence. The phenomenon of slippage is characterized and shown to vary significantly between enzymes. The results obtained should favor future protocol development and enzymatic understanding.Through these papers, this work aspire to aid the development of methods for massive sequencing in terms of scale, quality and knowledge; thereby contributing to the general applicability of the new paradigm of sequencing instruments.
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| 22. |
- Lötstedt, Britta
(författare)
-
Spatial mapping of bacteria and transcriptomes
- 2022
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Novel insights into biological functions and mechanisms, cell networks and evolutionary relationships are gained through development of sequencing technologies and sequencing based applications. Massively parallel sequencing has enabled analysis of big data at gene and protein expression levels, but has also characterized bacterial communities. Additionally, different technological advancements enabled us to track those expression changes in single cells, to reveal insights into rare cell populations, or with added spatial resolution, to explore highly complex environments such as tissues. This thesis gives an overview of different technical, biological and computational methods used in genomics today with a specific focus on spatial techniques for detailed tissue characterization. This is followed by a chapter summarizing recent scientific contributions made by the author that have been included as part of this thesis. In Paper I, 16S sequencing was used to study the diversity and composition of bacterial communities with specific focus on the aerodigestive microbiome in children who had undergone a lung transplant. Potential connections between the microbiome and irregular gastric muscle movements were also examined. Patients with a lung transplant had significantly lower microbial diversity in the gastric and oropharyngeal sites as compared to controls, however, lung transplant recipients showed similar bacterial compositions, independent of motility status. Samples in the lung transplant patient group were in general dominated by Staphylococcaceae but Streptococcus, Prevotella and Veillonella were common in the gastric and oropharyngeal samples. Next, an automated method for simultaneous spatial analysis of both gene and antibodybased protein expression in tissue sections, named SM-Omics, was developed in Paper II. SM-Omics enabled simultaneous detection of proteins, by using either immunofluorescence or DNAbarcoded antibodies, and analysis of the spatial transcriptome in the same tissue section. SM-Omics was applied to the mouse brain and spleen and obtained correlated spatial patterns between respective gene and antibody measurements. The method allowed processing of up to 64 in situ spatial reactions or up to 96 sequencing-ready libraries, of high complexity, in a ~2 days process. The spatial host-microbiome sequencing method, presented in Paper III, was used to concurrently study the spatial environment created between bacteria and host cells within a tissue section. Using spatial host-microbiome sequencing, colonic sections from three different mouse models were examined by simultaneous in situ capture of both mRNA and 16S sequences, followed by sequencing and taxonomic assignment of bacterial 16S sequences using a deep learning model. ~17,000 genes and 39 bacteria genera across 16 different morphological regions were quantitatively assessed in the mouse colon. We reported specific genera in the interfold and lumen regions of the colon, as well as spatially variable genes across 100 tissue sections. To better understand genotype-relevant changes impacted by bacterial presence, we defined cell-type specific interactions described with sets of activated pathways. Finally, consecutive tissue sections of multiple synovial biopsies from patients suffering from rheumatoid arthritis were processed using the Spatial Transcriptomics method and sequenced in Paper IV. The alignment and transformation of the consecutive tissue sections enabled spatial profiling in 3D of genes and cell types within the biopsies. Spatially variable gene expression patterns revealed clusters radially distributed around organized structures of infiltrating leukocytes (TLOs). In patients with developed TLOs, these structures contained proinflammatory B cells, while the surrounding areas were high in fibroblasts.
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| 23. |
- Pettersson, Erik, 1980-
(författare)
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Interrogation of Nucleic Acids by Parallel Threading
- 2007
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Advancements in the field of biotechnology are expanding the scientific horizon and a promising era is envisioned with personalized medicine for improved health. The amount of genetic data is growing at an ever-escalating pace due to the availability of novel technologies that allow massively parallel sequencing and whole-genome genotyping, that are supported by the advancements in computer science and information technologies. As the amount of information stored in databases throughout the world is growing and our knowledge deepens, genetic signatures with significant importance are discovered. The surface of such a set in the data mining process may include causative- or marker single nucleotide polymorphisms (SNPs), revealing predisposition to disease, or gene expression signatures, profiling a pathological state. When targeting a reduced set of signatures in a large number of samples for diagnostic- or fine-mapping purposes, efficient interrogation and scoring require appropriate preparations. These needs are met by miniaturized and parallelized platforms that allow a low sample and template consumption. This doctoral thesis describes an attempt to tackle some of these challenges by the design and implementation of a novel assay denoted Trinucleotide Threading (TnT). The method permits multiplex amplification of a medium size set of specific loci and was adapted to genotyping, gene expression profiling and digital allelotyping. Utilizing a reduced number of nucleotides permits specific amplification of targeted loci while preventing the generation of spurious amplification products. This method was applied to genotype 96 individuals for 75 SNPs. In addition, the accuracy of genotyping from minute amounts of genomic DNA was confirmed. This procedure was performed using a robotic workstation running custom-made scripts and a software tool was implemented to facilitate the assay design. Furthermore, a statistical model was derived from the molecular principles of the genotyping assay and an Expectation-Maximization algorithm was chosen to automatically call the generated genotypes. The TnT approach was also adapted to profiling signature gene sets for the Swedish Human Protein Atlas Program. Here 18 protein epitope signature tags (PrESTs) were targeted in eight different cell lines employed in the program and the results demonstrated high concordance rates with real-time PCR approaches. Finally, an assay for digital estimation of allele frequencies in large cohorts was set up by combining the TnT approach with a second-generation sequencing system. Allelotyping was performed by targeting 147 polymorphic loci in a genomic pool of 462 individuals. Subsequent interrogation was carried out on a state-of-the-art massively parallelized Pyrosequencing instrument. The experiment generated more than 200,000 reads and with bioinformatic support, clonally amplified fragments and the corresponding sequence reads were converted to a precise set of allele frequencies.
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| 24. |
- Sandberg, Julia
(författare)
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Massively parallel analysis of cells and nucleic acids
- 2011
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Recent proceedings in biotechnology have enabled completely new avenues in life science research to be explored. By allowing increased parallelization an ever-increasing complexity of cell samples or experiments can be investigated in shorter time and at a lower cost. This facilitates for example large-scale efforts to study cell heterogeneity at the single cell level, by analyzing cells in parallel that also can include global genomic analyses. The work presented in this thesis focuses on massively parallel analysis of cells or nucleic acid samples, demonstrating technology developments in the field as well as use of the technology in life sciences. In stem cell research issues such as cell morphology, cell differentiation and effects of reprogramming factors are frequently studied, and to obtain information on cell heterogeneity these experiments are preferably carried out on single cells. In paper I we used a high-density microwell device in silicon and glass for culturing and screening of stem cells. Maintained pluripotency in stem cells from human and mouse was demonstrated in a screening assay by antibody staining and the chip was furthermore used for studying neural differentiation. The chip format allows for low sample volumes and rapid high-throughput analysis of single cells, and is compatible with Fluorescence Activated Cell Sorting (FACS) for precise cell selection. Massively parallel DNA sequencing is revolutionizing genomics research throughout the life sciences by constantly producing increasing amounts of data from one sequencing run. However, the reagent costs and labor requirements in current massively parallel sequencing protocols are still substantial. In paper II-IV we have focused on flow-sorting techniques for improved sample preparation in bead-based massive sequencing platforms, with the aim of increasing the amount of quality data output, as demonstrated on the Roche/454 platform. In paper II we demonstrate a rapid alternative to the existing shotgun sample titration protocol and also use flow-sorting to enrich for beads that carry amplified template DNA after emulsion PCR, thus obtaining pure samples and with no downstream sacrifice of DNA sequencing quality. This should be seen in comparison to the standard 454-enrichment protocol, which gives rise to varying degrees of sample purity, thus affecting the sequence data output of the sequencing run. Massively parallel sequencing is also useful for deep sequencing of specific PCR-amplified targets in parallel. However, unspecific product formation is a common problem in amplicon sequencing and since these shorter products may be difficult to fully remove by standard procedures such as gel purification, and their presence inevitably reduces the number of target sequence reads that can be obtained in each sequencing run. In paper III a gene-specific fluorescent probe was used for target-specific FACS enrichment to specifically enrich for beads with an amplified target gene on the surface. Through this procedure a nearly three-fold increase in fraction of informative sequences was obtained and with no sequence bias introduced. Barcode labeling of different DNA libraries prior to pooling and emulsion PCR is standard procedure to maximize the number of experiments that can be run in one sequencing lane, while also decreasing the impact of technical noise. However, variation between libraries in quality and GC content affects amplification efficiency, which may result in biased fractions of the different libraries in the sequencing data. In paper IV barcode specific labeling and flow-sorting for normalization of beads with different barcodes on the surface was used in order to weigh the proportion of data obtained from different samples, while also removing mixed beads, and beads with no or poorly amplified product on the surface, hence also resulting in an increased sequence quality. In paper V, cell heterogeneity within a human being is being investigated by low-coverage whole genome sequencing of single cell material. By focusing on the most variable portion of the human genome, polyguanine nucleotide repeat regions, variability between different cells is investigated and highly variable polyguanine repeat loci are identified. By selectively amplifying and sequencing polyguanine nucleotide repeats from single cells for which the phylogenetic relationship is known, we demonstrate that massively parallel sequencing can be used to study cell-cell variation in length of these repeats, based on which a phylogenetic tree can be drawn.
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| 25. |
- Sigurgeirsson, Benjamín
(författare)
-
Analysis of RNA and DNA sequencing data : Improved bioinformatics applications
- 2016
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Massively parallel sequencing has rapidly revolutionized DNA and RNA research. Sample preparations are steadfastly advancing, sequencing costs have plummeted and throughput is ever growing. This progress has resulted in exponential growth in data generation with a corresponding demand for bioinformatic solutions. This thesis addresses methodological aspects of this sequencing revolution and applies it to selected biological topics.Papers I and II are technical in nature and concern sample preparation and data anal- ysis of RNA sequencing data. Paper I is focused on RNA degradation and paper II on generating strand specific RNA-seq libraries.Paper III and IV deal with current biological issues. In paper III, whole exomes of cancer patients undergoing chemotherapy are sequenced and their genetic variants associ- ated to their toxicity induced adverse drug reactions. In paper IV a comprehensive view of the gene expression of the endometrium is assessed from two time points of the menstrual cycle.Together these papers show relevant aspects of contemporary sequencing technologies and how it can be applied to diverse biological topics.
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| 26. |
- Sountoulidis, Alexandros, et al.
(författare)
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A topographic atlas defines developmental origins of cell heterogeneity in the human embryonic lung
- 2023
-
Ingår i: Nature Cell Biology. - : Springer Nature. - 1465-7392 .- 1476-4679.
-
Tidskriftsartikel (refereegranskat)abstract
- Sountoulidis et al. provide a spatial gene expression atlas of human embryonic lung during the first trimester of gestation and identify 83 cell identities corresponding to stable cell types or transitional states. The lung contains numerous specialized cell types with distinct roles in tissue function and integrity. To clarify the origins and mechanisms generating cell heterogeneity, we created a comprehensive topographic atlas of early human lung development. Here we report 83 cell states and several spatially resolved developmental trajectories and predict cell interactions within defined tissue niches. We integrated single-cell RNA sequencing and spatially resolved transcriptomics into a web-based, open platform for interactive exploration. We show distinct gene expression programmes, accompanying sequential events of cell differentiation and maturation of the secretory and neuroendocrine cell types in proximal epithelium. We define the origin of airway fibroblasts associated with airway smooth muscle in bronchovascular bundles and describe a trajectory of Schwann cell progenitors to intrinsic parasympathetic neurons controlling bronchoconstriction. Our atlas provides a rich resource for further research and a reference for defining deviations from homeostatic and repair mechanisms leading to pulmonary diseases.
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| 27. |
- Stenbeck, Linnea, 1992-
(författare)
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Deconvolution of Spatial Gene Expression in Cancer
- 2022
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Cancer is the second leading cause of death in the world, claiming nearly 10 million lives in 2020 alone. One of the main issues in anti-cancer treatment is the heterogeneity of the tumor microenvironment (TME). The TME consists of different cells that are critical for cancer development. Understanding the interactions and identity of these cells is vital to discovering the mechanisms for tumorigenesis. To fundamentally understand the development and mechanisms of the disease will help us in designing novel treatments moving forward. To study the TME, we need methods that both provide extensive information about the cellular profiles and their spatial location, in order to understand how they interact with each other. Single-cell RNA-seq (scRNA-seq) has provided extensive insights into the cellular composition of tumors. However, it requires dissociation of the cells and thus does not retain spatial information. There are several methods to study spatially resolved gene expression in tissues, but one that allows for untargeted and whole-transcriptome wide analysis is the in situ capturing method, Spatial transcriptomics (ST). Although this method allows us to know the location of the gene expression, the resolution is too low for single-cell analysis. With an initial capturing area of 100 μm, 3-30 cells are captured in each spot resulting in a mixture of cells giving rise to the gene expression. At this resolution, it is challenging to confidentially profile the cells, thus making it difficult to explore the cellular interactions fully. To fundamentally explore the TME, improvements need to be made.In Paper I, we aimed to bridge the gap between ST and scRNA-seq by designing a new array with a capturing area of 2 μm. This new design increased the number of capture areas from 1007 to over 1.4 million and with over a 4000-fold improved resolution. We managed to get spatially resolved gene expression from mouse olfactory bulb (MOB) and breast tumor tissue at a sub-cellular resolution with this new design. Despite a low capture efficiency of around 1.3% per bead, we were able to identify differently expressed (DE) signatures specific to morphological layers, profile specific cell types and explore sub-cellular features. Paper II focuses on the information obtained from the widely available histological images. By integrating the spatial gene expression data from 23 different breast cancer patients with their morphological images via deep learning, we could predict gene expression on different samples solely from their histological images. This was further validated on external samples to ensure that it was applicable to other clinical data. In Paper III, we explored the biology of HER2-positive breast tumors by combining scRNA-seq with ST data from eight different HER2-positive patients. With this combinatorial approach, we studied the interactions of tumor-associated cell types and found tertiary lymphoid (TL)-like structures which have been shown to hold certain predictive power in treatment outcome. From this, we constructed a predictive model that could infer the presence of these TL-like structures across different tissue types and technical platforms. This was validated on external samples from breast cancer, rheumatoid arthritis and melanoma. Lastly, in Paper IV, we sought to improve upon the reproducibility and robustness of the method by automating the 10x Visium protocol on a robotic platform. To benchmark the protocol, we compared identical samples prepared both manually and with the automated approach and achieved high correlation scores of 0.995 and 0.990. By adapting the protocol on a Bravo Liquid Handling Platform, we were able to increase the throughput and robustness of the method and reduce hands-on time by over 80%.
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| 28. |
- Stranneheim, Henrik
(författare)
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Enabling massive genomic and transcriptomic analysis
- 2011
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In recent years there have been tremendous advances in our ability to rapidly and cost-effectively sequence DNA. This has revolutionized the fields of genetics and biology, leading to a deeper understanding of the molecular events in life processes. The rapid advances have enormously expanded sequencing opportunities and applications, but also imposed heavy strains on steps prior to sequencing, as well as the subsequent handling and analysis of the massive amounts of sequence data that are generated, in order to exploit the full capacity of these novel platforms. The work presented in this thesis (based on six appended papers) has contributed to balancing the sequencing process by developing techniques to accelerate the rate-limiting steps prior to sequencing, facilitating sequence data analysis and applying the novel techniques to address biological questions. Papers I and II describe techniques to eliminate expensive and time-consuming preparatory steps through automating library preparation procedures prior to sequencing. The automated procedures were benchmarked against standard manual procedures and were found to substantially increase throughput while maintaining high reproducibility. In Paper III, a novel algorithm for fast classification of sequences in complex datasets is described. The algorithm was first optimized and validated using a synthetic metagenome dataset and then shown to enable faster analysis of an experimental metagenome dataset than conventional long-read aligners, with similar accuracy. Paper IV, presents an investigation of the molecular effects on the p53 gene of exposing human skin to sunlight during the course of a summer holiday. There was evidence of previously accumulated persistent p53 mutations in 14% of all epidermal cells. Most of these mutations are likely to be passenger events, as the affected cell compartments showed no apparent growth advantage. An annual rate of 35,000 novel sun-induced persistent p53 mutations was estimated to occur in sun-exposed skin of a human individual. Paper V, assesses the effect of using RNA obtained from whole cell extracts (total RNA) or cytoplasmic RNA on quantifying transcripts detected in subsequent analysis. Overall, more differentially detected genes were identified when using the cytoplasmic RNA. The major reason for this is related to the reduced complexity of cytoplasmic RNA, but also apparently due (at least partly) to the nuclear retention of transcripts with long, structured 5’- and 3’-untranslated regions or long protein coding sequences. The last paper, VI, describes whole-genome sequencing of a large, consanguineous family with a history of Leber hereditary optic neuropathy (LHON) on the maternal side. The analysis identified new candidate genes, which could be important in the aetiology of LHON. However, these candidates require further validation before any firm conclusions can be drawn regarding their contribution to the manifestation of LHON.
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| 29. |
- Strömberg, Sara, 1979-
(författare)
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Antibody-based Profiling of Expression Patterns using Cell and Tissue Microarrays
- 2008
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In this thesis, methods to study gene and protein expression in cells and tissues were developed and utilized in combination with protein-specific antibodies, with the overall objective to attain greater understanding of protein function.To analyze protein expression in in vitro cultured cell lines, a cell microarray (CMA) was developed, facilitating antibody-based protein profiling of cell lines using immunohistochemistry (IHC). Staining patterns in cell lines were analyzed using image analysis, developed to automatically identify cells and immunohistochemical staining, providing qualitative and quantitative measurements of protein expression. Quantitative IHC data from CMAs stained with nearly 3000 antibodies was used to evaluate the adequacy of using cell lines as models for cancer tissue. We found that cell lines are homogenous with respect to protein expression profiles, and generally more alike each other, than corresponding cancer cells in vivo. However, we found variability between cell lines in regards to the level of retained tumor phenotypic traits, and identified cell lines with a preserved link to corresponding cancer, suggesting that some cell lines are appropriate model systems for specific tumor types. Specific gene expression patterns were analyzed in vitiligo vulgaris and malignant melanoma. Transcriptional profiling of vitiligo melanocytes revealed dysregulation of genes involved in melanin biosynthesis and melanosome function, thus highlighting some mechanisms possibly involved in the pathogenesis of vitiligo. Two new potential markers for infiltrating malignant melanoma, Syntaxin-7 and Discs large homolog 5, were identified using antibody-based protein profiling of melanoma in a tissue microarray format. Both proteins were expressed with high specificity in melanocytic lesions, and loss of Syntaxin-7 expression was associated with more high-grade malignant melanomas.In conclusion, the combination of antibody-based proteomics and microarray technology provided valuable information of expression patterns in cells and tissues, which can be used to better understand associations between protein signatures and disease.
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| 30. |
- Ståhl, Patrik L., 1981-
(författare)
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Methods for Analyzing Genomes
- 2010
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The human genome reference sequence has given us a two‐dimensional blueprint of our inherited code of life, but we need to employ modern‐day technology to expand our knowledge into a third dimension. Inter‐individual and intra‐individual variation has been shown to be larger than anticipated, and the mode of genetic regulation more complex. Therefore, the methods that were once used to explain our fundamental constitution are now used to decipher our differences. Over the past four years, throughput from DNA‐sequencing platforms has increased a thousand‐fold, bearing evidence of a rapid development in the field of methods used to study DNA and the genomes it constitutes. The work presented in this thesis has been carried out as an integrated part of this technological evolution, contributing to it, and applying the resulting solutions to answer difficult biological questions. Papers I and II describe a novel approach for microarray readout based on immobilization of magnetic particles, applicable to diagnostics. As benchmarked on canine mitochondrial DNA, and human genomic DNA from individuals with cystic fibrosis, it allows for visual interpretation of genotyping results without the use of machines or expensive equipment. Paper III outlines an automated and cost‐efficient method for enrichment and titration of clonally amplified DNA‐libraries on beads. The method uses fluorescent labeling and a flow‐cytometer to separate DNA‐beads from empty ones. At the same time the fraction of either bead type is recorded, and a titration curve can be generated. In paper IV we combined the highly discriminating multiplex genotyping of trinucleotide threading with the digital readout made possible by massively parallel sequencing. From this we were able to characterize the allelic distribution of 88 obesity related SNPs in a population of 462 individuals enrolled at a childhood obesity center. Paper V employs the throughput of present day DNA sequencingas it investigates deep into sun‐exposed skin to find clues on the effects of sunlight during the course of a summer holiday. The tumor suppressor p53 gene was targeted, only to find that despite its well‐documented involvement in the disease progression of cancers, an estimated 35,000 novel sun‐induced persistent p53 mutations are added and phenotypically tolerated in the skin of every individual every year. The last paper, VI, describes a novel approach for finding breast cancer biomarkers. In this translational study we used differential protein expression profiles and sequence capture to select and enrich for 52 candidate genes in DNA extracted from ten tumors. Two of the genes turned out to harbor protein‐altering mutations in multiple individuals.
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| 31. |
- Vazin, Tandis, 1979-
(författare)
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Generation of Dopaminergic Neurons from Human Embryonic Stem Cells
- 2008
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Since the first successful derivation of human embryonic stem cells (hESC), rapid progress has been attained in the development of strategies in differentiation of these cells into various neural lineages, with the fundamental objective of using these cells for replacement and repair of damaged neuronal circuits in the central nervous system (CNS). Of particular interest are midbrain dopaminergic (mDA) neurons, which play a central role in regulation of voluntary movement. Degeneration or loss of function of mDA neurons in the nigrostriatal pathway is associated with Parkinson disease (PD). Stromal-Derived Inducing Activity (SDIA) is recognized as one of the most efficient methods in restricting ESC differentiation to a dopaminergic lineage, and refers to the property of mouse stromal cell lines such as PA6 or MS5 to cause ESC to differentiate to DA neurons. Although this strategy has been extensively used to generate mDA neurons from hESC, the biochemical nature of SDIA is yet unknown. In the present study mDA neurons were generated from the BG01V2 hESC line by SDIA. To examine whether SDIA exerts its effect directly on hESC and is responsible for early dopaminergic induction, neural progenitor cells (NPC) were enyzmatically isolated from the co-cultures and allowed to differentiate in feeder-free conditions. The isolated cells were committed to a mesencephalic neural lineage, and were capable of maintaining their phenotype and developing into postmitotic mDA neurons in feeder-free conditions. The mDA neurons showed neuronal excitability and dopamine transporter function. The in vitro proliferation and differentiation of the NPC was also investigated by a BrDU incorporation assay. Next, the maintenance of cellular memory and capacity for proliferation of the mesencephalic NPC was assessed. The NPC could be expanded in vitro by five-fold as neurospheres for up to two weeks while retaining their DA differentiation potential, but did not retain a stable phenotype over extended periods of time. Preliminary transplantation experiments of neurospheres in striatal lesioned animals indicated, however, that these cells could survive and conserve their phenotype in vivo. To gain additional insight into the biochemical role of SDIA in early dopaminergic induction of hESC, the separate contributions of cell surface activity and secreted factors were examined. The data revealed that the PA6 cell surface activity promoted cell survival and was mainly responsible for enhanced neurogenesis of hESC, whereas secreted factors provided DA lineage-specific instructions. In order to identify the soluble factors responsible for the DA phenotype-inducing component of SDIA, the gene expression profile of PA6 cells was compared to that of cell lines lacking the DA-inducing property. A number of soluble factors known to be associated with CNS development that were highly expressed in PA6 cells were identified as potential DA differentiation-inducing candidates. These differentially-expressed genes included stromal cell-derived factor 1 (SDF-1/CXCL12), pleiotrophin (PTN), insulin-like growth factor 2 (IGF2), and ephrin B1 (EFNB1). When these factors, termed SPIE, were applied to the hESC, they induced dopaminergic neuronal differentiation of hESC line, BG01V2 and other karyotypically normal hESC lines in vitro. Thus, it appears that SPIE comprises the DA phenotype-inducing property of SDIA. This may provide a simple and direct means of differentiating hESC to form DA neurons in a single step, without a requirement for co-culture, animal cell lines, or animal products.
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| 32. |
- Werne Solnestam, Beata, 1983-
(författare)
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Interpreting the human transcriptome
- 2015
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The human body is made of billions of cells and nearly all have the same genome. However, there is a high diversity of cells, resulted from what part of the genome the cells use, i.e. which RNA molecules are expressed. Rapid advances within the field of sequencing allow us to determine the RNA molecules expressed in a specific cell at a certain time. The use of the new technologies has expanded our view of the human transcriptome and increased our understanding of when, where, and how each RNA molecule is expressed.The work presented in this thesis focuses on analysis of the human transcriptome. In Paper I, we describe an automated approach for sample preparation. This protocol was compared with the standard manual protocol, and we demonstrated that the automated version outperformed the manual process in terms of sample throughput while maintaining high reproducibility. Paper II addresses the impact of nuclear transcripts on gene expression. We compared total RNA from whole cells and from cytoplasm, showing that transcripts with long, structured 3’- and 5’-untranslated regions and transcripts with long protein coding sequences tended to be retained in the nucleus. This resulted in increased complexity of the total RNA fraction and fewer reads per unique transcript. Papers III and IV describe dynamics of the human muscle transcriptome. For Paper III, we systematically investigated the transcriptome and found remarkably high tissue homogeneity, however a large number of genes and isoforms were differentially expressed between genders. Paper IV describes transcriptome differences in response to repeated training. No transcriptome-based memory was observed, however a large number of isoforms and genes were affected by training. Paper V describes a transcript profiling protocol based on the method Reverse Transcriptase Multiplex Ligation-dependent Probe Amplification. We designed the method for a few selected transcripts whose expression patterns are important for detecting breast cancer cells, and optimized the method for single cell analysis. We successfully detected cells in human blood samples and applied the method to single cells, confirming the heterogeneity of a cell population.
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