| 1. |
- Thrane, Kim, 1984-
(författare)
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Exploring Biological Systems using Spatial Transcriptomic Technologies
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The transcriptome and the cells’ spatial organization are important determinants for the functions of biological systems, such as a tumor, brain, or skin tissue. Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for profiling the transcriptome of individual cells. The nuanced characterization of cell types and states enabled by scRNA-seq has revolutionized our understanding of biological systems. However, these methods rely on the dissociation of tissues into single cells whereby spatial context is lost. Recent advancements have resulted in technologies that retain and associate spatial information with the gene expression of tissues, which has permitted the delineation of biological systems at an unprecedented level. The Spatial Transcriptomics (ST) technology offers transcriptome profiling across thousands of subareas of a tissue section by capturing mRNA in situ and sequencing ex situ.In Paper I, ST was used to explore heterogeneity in lymph node metastases of human cutaneous malignant melanoma. A data-driven analysis approach revealed inter- and intratumor heterogeneity in the examined tumor tissue, whereas the stromal tissue exhibited similar gene expression across patients. Paper II presents an integration of ST, scRNA-seq, and spatial protein analysis to characterize human cutaneous squamous cell carcinoma. The spatial resolution of ST is not at the single-cell level; however, this multimodal approach allowed for the identification of tumor subpopulations and revealed the niches in which they reside. In Paper III, ST and scRNA-seq data were generated to build an atlas of human skin. The combined data was used to map cell-type abundance and intercellular communications in homeostasis. Moreover, cell-of-origin analysis allowed for the identification of candidate cell types accountable for human genetic skin diseases. Paper IV introduces Spatial VDJ, a technique for spatial analysis of B and T cell antigen receptor transcripts, hence determining the position of lymphocyte clones. The spatial VDJ technique was applied to human tonsil and human breast cancer tissues, and this revealed enrichment of immunoglobulin clones in distinct spatial regions. Finally, Paper V explores an alternative protocol for ST that uses long-read sequencing to enable spatial isoform profiling in tissue sections. The protocol was applied to mouse brain and identified genes with spatially distinct alternative isoform expression. Additionally, the full-length transcript information was used to explore RNA editing events across different anatomical regions of the mouse brain.
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| 2. |
- Salmén, Fredrik, 1984-
(författare)
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Spatially resolved and single cell transcriptomics
- 2017
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In recent years, massive parallel sequencing has revolutionized the field of biology and has provided us with a vast number of new discoveries in fields such as neurology, developmental biology and cancer research. A significant area is deciphering gene expression patterns, as well as other aspects of transcriptome information, such as the impact of splice variants and mutations on biological functions and disease development. By applying RNA-sequencing, one can extract this type of information in a large-scale manner. The most recent approaches include high-resolution techniques such as single cell sequencing and in situ methods in order to circumvent the problems with gene expression averaging in homogenized samples, and loss of spatial information.The research in this thesis is focused on the development of a novel genome-wide spatial transcriptomics method. The technique is used for analysis of intact tissue sections as well as single cells from solution, with the aim to combine gene expression and morphological information. In Paper I, the method is described in detail, and it is shown that the method is able to generate spatial high quality data from mouse olfactory bulb tissue sections (a part of the forebrain) as well as from tissue sections from breast cancer samples. In Paper III, we adapt the library preparation method in order to be able to execute it on a robotic workstation, thus increasing the reproducibility and the throughput, and decreasing the hands-on time. In Paper IV, we generate 3D-data from breast cancer samples by serial sectioning. We show that the gene expression can be highly variable along all three axes of a tumor, and we track pathways with specific spatial activity, as well as perform subtype classification with three-dimensional resolution. In Paper II, we present a high-throughput method for single cell transcriptomics of cells in solution. The method is based on the same type of solid surface capture as the tissue protocol described in Papers I, III and IV. Again, we show that we can generate high-quality gene expression data, and connect this to morphological characteristics of the analyzed single cells; both using cultured cells and samples from patients with leukemia.
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| 3. |
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| 4. |
- 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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| 5. |
- Bergenstråhle, Joseph
(författare)
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Exploring the transcriptional space
- 2021
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Transcriptomics promises biological insight into gene regulation, cell diversity, and mechanistic understanding of dysfunction. Driven by technological advancements in sequencing technologies, the field has witnessed an exponential growth in data output. Not only has the amount of raw data increased tremendously but it’s granularity as well. From only being able to obtain aggregated transcript information from large tissue samples, we can now pinpoint the precise origin of transcripts within the tissue, sometimes even within the confines of individual cells. This thesis focuses on the different aspects of how to use these emergent technologies to obtain a greater understanding of biological mechanisms. The work conducted here spans only a few years of the much longer history of spatially resolved transcriptomics, which started with the early in situ hybridization techniques and will continue to a potential future with complete molecular profiling ofevery cell in their natural, active state. Thus, at the same time the workpresented here introduces and demonstrates the use of the latest techniques within spatial transcriptomics, it also deals with the shortcomings of the current state of the field, which undoubtedly will see extensive improvements in the not too distant future. Article I is part of a series of articles where we mechanistically examine the biological underpinnings of a serendipitous finding that single-stranded nucleic acids have immunomodulatory effects. In particular, we look at influenza-infected innate immune cells and the ability of the oligonucleotide to inhibit viral entry. The oligonucleotides prevent the cells from responding to certain types of pattern recognitionand cause a decrease in viral load. Our hypothesis is that the administration of oligonucleotides blocks certain endocytic routes. While the invivo experiments suggest that the influenza virus is still able to infect and promote disease in the host, changes in signaling response due to the inhibition of the endocytotic routes could represent an avenue for future therapeutics. The conclusions were drawn by combining protein labeling and conventional methods for RNA profiling in the form of quantitative realtime PCR and bulk RNA sequencing. As a transition into the concept of spatial RNA profiling, the thesis includes an Additional material review article on spatial transcriptomics, where we give an overview of the current state of the field, as it looked like in the beginning of 2020. In Article II, we report on the development of an R package for analyzing spatial transcriptomics datasets. The package offers visualization features and an automated pipeline for masking tissue images and aligning serially sectioned experiments. The tool is extensively used throughout the rest of the articles where spatial transcript information is analyzed and is available for all scientists that use the supported spatial transcriptomics platforms in their research. In Article III, we propose a method to spatially map long-read sequencing data. While previously described methods for high-throughput spatial transcriptomics produce short-read data, full-length transcript information allows us to spatially profile alternatively spliced transcripts. Using the proposed method, we find alternatively spliced transcripts and find isoforms of the same gene to be differentially expressed in different regions of the mouse brain. Furthermore, we profile RNA editing across the full-length transcripts and find certain parts of the mouse left hemisphere to display a substantially higher degree of editing events compared to the rest of the brain. The proposed method is based on readily available reagents and does not require advanced instrumentation. We believe full-length transcript information obtained in this manner could help scientists obtain a deeper understanding from transcriptome data. Finally, in Article IV, we explore how the latest technologies for spatial transcriptomics can be used to characterize the expression landscape of respiratory syncytial virus infections by comparing infected and non-infected mouse lungs. By integration of annotated single-cell data and spatially resolved transcriptomics, we map the location of the single cells onto the spatial grid to localize immune cell populations across the tissue sections. By correlating the locations to gene expression, we profile locally confined cellular processes and immune responses. We believe that high-throughput spatial information obtained without predefined targets will become an important tool for exploratory analysis and hypothesis generation, which in turn could unlock mechanistic knowledge of the differences between experimental models that are important for translational research.
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| 6. |
- 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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| 7. |
- 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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| 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. |
- Sandberg, Julia
(författare)
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Massively parallel analysis of cells and nucleic acids
- 2011
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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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| 10. |
- Wirta, Valtteri, 1977-
(författare)
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Mining the transcriptome - methods and applications
- 2006
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Regulation of gene expression occupies a central role in the control of the flow of genetic information from genes to proteins. Regulatory events on multiple levels ensure that the majority of the genes are expressed under controlled circumstances to yield temporally controlled, cell and tissue-specific expression patterns. The combined set of expressed RNA transcripts constitutes the transcriptome of a cell, and can be analysed on a large-scale using both sequencing and microarray-based methods. The objective of this work has been to develop tools for analysis of the transcriptomes (methods), and to gain new insights into several aspects of the stem cell transcriptome (applications). During recent years expectations of stem cells as a resource for treatment of various disorders have emerged. The successful use of endogenously stimulated or ex vivo expanded stem cells in the clinic requires an understanding of mechanisms controlling their proliferation and self-renewal. This thesis describes the development of tools that facilitate analysis of minute amounts of stem cells, including RNA amplification methods and generation of a cDNA array enriched for genes expressed in neural stem cells. The results demonstrate that the proposed amplification method faithfully preserves the transcript expression pattern. An analysis of the feasibility of a neurosphere assay (in vitro model system for study of neural stem cells) clearly shows that the culturing induces changes that need to be taken into account in design of future comparative studies. An expressed sequence tag analysis of neural stem cells and their in vivo microenvironment is also presented, providing an unbiased large-scale screening of the neural stem cell transcriptome. In addition, molecular mechanisms underlying the control of stem cell self-renewal are investigated. One study identifies the proto-oncogene Trp53 (p53) as a negative regulator of neural stem cell self-renewal, while a second study identifies genes involved in the maintenance of the hematopoietic stem cell phenotype. To facilitate future analysis of neural stem cells, all microarray data generated is publicly available through the ArrayExpress microarray data repository, and the expressed sequence tag data is available through the GenBank.
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