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  • 51. Rubin, C.
    et al.
    Nathanaelsson, C.
    KTH.
    Lundeberg, Joakim
    KTH, Centra, Science for Life Laboratory, SciLifeLab. KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Genteknologi.
    Nilsson, O.
    Ljunggren, O.
    Kindmark, A.
    MicroRNA repertoire of primary human bone derived cells and MG63-cells - Polymorphic binding sites in putative target genes2007Inngår i: Calcified Tissue International, ISSN 0171-967X, E-ISSN 1432-0827, Vol. 80, s. S34-S35Artikkel i tidsskrift (Annet vitenskapelig)
  • 52.
    Salmén, Fredrik
    et al.
    KTH, Centra, Science for Life Laboratory, SciLifeLab. KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Genteknologi.
    Ståhl, Patrik
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Mollbrink, Annelie
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Navarro Fernandez, José Carlos
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Vickovic, Sanja
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Frisen, Jonas
    Karolinska Inst, Dept Cell & Mol Biol, Stockholm, Sweden..
    Lundeberg, Joakim
    KTH, Centra, Science for Life Laboratory, SciLifeLab. KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Genteknologi.
    Barcoded solid-phase RNA capture for Spatial Transcriptomics profiling in mammalian tissue sections2018Inngår i: Nature Protocols, ISSN 1754-2189, E-ISSN 1750-2799, Vol. 13, nr 11, s. 2501-2534Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Spatial resolution of gene expression enables gene expression events to be pinpointed to a specific location in biological tissue. Spatially resolved gene expression in tissue sections is traditionally analyzed using immunohistochemistry (IHC) or in situ hybridization (ISH). These technologies are invaluable tools for pathologists and molecular biologists; however, their throughput is limited to the analysis of only a few genes at a time. Recent advances in RNA sequencing (RNA-seq) have made it possible to obtain unbiased high-throughput gene expression data in bulk. Spatial Transcriptomics combines the benefits of traditional spatially resolved technologies with the massive throughput of RNA-seq. Here, we present a protocol describing how to apply the Spatial Transcriptomics technology to mammalian tissue. This protocol combines histological staining and spatially resolved RNA-seq data from intact tissue sections. Once suitable tissue-specific conditions have been established, library construction and sequencing can be completed in similar to 5-6 d. Data processing takes a few hours, with the exact timing dependent on the sequencing depth. Our method requires no special instruments and can be performed in any laboratory with access to a cryostat, microscope and next-generation sequencing.

  • 53.
    Smith, Bradley P.
    et al.
    Cent Queensland Univ, Sch Hlth Med & Appl Sci, Adelaide, SA 5034, Australia..
    Cairns, Kylie M.
    Univ New South Wales, Sch Biol Earth & Environm Sci, Ctr Ecosyst Sci, Sydney, NSW 2052, Australia..
    Adams, Justin W.
    Monash Univ, Dept Anat & Dev Biol, Melbourne, Vic 3800, Australia..
    Newsome, Thomas M.
    Univ Sydney, Sch Life & Environm Sci, Sydney, NSW 2006, Australia..
    Fillios, Melanie
    Univ New England, Humanities Arts & Social Sci, Armidale, NSW 2351, Australia..
    Deaux, Eloise C.
    Univ Neuchatel, Dept Comparat Cognit, CH-2000 Neuchatel, Switzerland..
    Parr, William C. H.
    Univ New South Wales, Surg & Orthopaed Res Lab, Sydney, NSW 2052, Australia..
    Letnic, Mike
    Univ New South Wales, Sch Biol Earth & Environm Sci, Ctr Ecosyst Sci, Sydney, NSW 2052, Australia..
    Van Eeden, Lily M.
    Univ Sydney, Sch Life & Environm Sci, Desert Ecol Res Grp, Sydney, NSW 2006, Australia..
    Appleby, Robert G.
    Griffith Univ, Environm Futures Res Inst, Nathan, Qld 4111, Australia..
    Bradshaw, Corey J. A.
    Flinders Univ S Australia, Coll Sci & Engn, Global Ecol, Adelaide, SA 5001, Australia..
    Savolainen, Peter
    KTH, Centra, Science for Life Laboratory, SciLifeLab. KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Genteknologi.
    Ritchie, Euan G.
    Deakin Univ, Sch Life & Environm Sci, Ctr Integrat Ecol, Burwood Campus, Geelong, Vic 3125, Australia..
    Nimmo, Dale G.
    Charles Sturt Univ, Sch Environm Sci, Albury, NSW 2650, Australia..
    Archer-Lean, Clare
    Univ Sunshine Coast, Sch Commun & Creat Ind, Maroochydore, Qld 4558, Australia..
    Greenville, Aaron C.
    Univ Sydney, Sch Life & Environm Sci, Desert Ecol Res Grp, Sydney, NSW 2006, Australia.;Univ Technol Sydney, Sch Life Sci, Ultimo, NSW 2007, Australia..
    Dickman, Christopher R.
    Univ Sydney, Sch Life & Environm Sci, Desert Ecol Res Grp, Sydney, NSW 2006, Australia..
    Watson, Lyn
    Australian Dingo Fdn, Gisborne, Vic 3437, Australia..
    Moseby, Katherine E.
    Univ New South Wales, Sch Biol Earth & Environm Sci, Ctr Ecosyst Sci, Sydney, NSW 2052, Australia..
    Doherty, Tim S.
    Deakin Univ, Sch Life & Environm Sci, Ctr Integrat Ecol, Burwood Campus, Geelong, Vic 3125, Australia..
    Wallach, Arian D.
    Univ Technol Sydney, Fac Sci, Ctr Compassionate Conservat, Ultimo, NSW 2007, Australia..
    Morrant, Damian S.
    Biosphere Environm Consultants, Cairns, Qld 4870, Australia..
    Crowther, Mathew S.
    Univ Sydney, Sch Life & Environm Sci, Sydney, NSW 2006, Australia..
    Taxonomic status of the Australian dingo: the case for Canis dingo Meyer, 17932019Inngår i: Zootaxa, ISSN 1175-5326, E-ISSN 1175-5334, Vol. 4564, nr 1, s. 173-197Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The taxonomic status and systematic nomenclature of the Australian dingo remain contentious, resulting in decades of inconsistent applications in the scientific literature and in policy. Prompted by a recent publication calling for dingoes to be considered taxonomically as domestic dogs (Jackson et al. 2017, Zootaxa 4317, 201-224), we review the issues of the taxonomy applied to canids, and summarise the main differences between dingoes and other canids. We conclude that (1) the Australian dingo is a geographically isolated (allopatric) species from all other Canis, and is genetically, phenotypically, ecologically, and behaviourally distinct; and (2) the dingo appears largely devoid of many of the signs of domestication, including surviving largely as a wild animal in Australia for millennia. The case of defining dingo taxonomy provides a quintessential example of the disagreements between species concepts (e.g., biological, phylogenetic, ecological, morphological). Applying the biological species concept sensu stricto to the dingo as suggested by Jackson et al. (2017) and consistently across the Canidae would lead to an aggregation of all Canis populations, implying for example that dogs and wolves are the same species. Such an aggregation would have substantial implications for taxonomic clarity, biological research, and wildlife conservation. Any changes to the current nomen of the dingo (currently Canis dingo Meyer, 1793), must therefore offer a strong, evidence-based argument in favour of it being recognised as a subspecies of Canis lupus Linnaeus, 1758, or as Canis familiaris Linnaeus, 1758, and a successful application to the International Commission for Zoological Nomenclature - neither of which can be adequately supported. Although there are many species concepts, the sum of the evidence presented in this paper affirms the classification of the dingo as a distinct taxon, namely Canis dingo.

  • 54.
    Stiller, Christiane
    et al.
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Proteinvetenskap, Protein Engineering.
    Aghelpasand, Hooman
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Genteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Frick, Tobias
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Genteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Westerlund, Kristina
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Proteinvetenskap, Protein Engineering.
    Ahmadian, Afshin
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Genteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Eriksson Karlström, Amelie
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Proteinvetenskap.
    Fast and Efficient Fc-Specific Photoaffinity Labeling To Produce Antibody-DNA Conjugates2019Inngår i: Bioconjugate chemistry, ISSN 1043-1802, E-ISSN 1520-4812, Vol. 30, nr 11, s. 2790-2798Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Antibody DNA conjugates are powerful tools for DNA-assisted protein analysis. Growing usage of these methods demands efficient production of high-quality conjugates. We developed an easy and fast synthesis route yielding covalent antibody-DNA conjugates with a defined conjugation site and low batch-to-batch variability. We utilize the Z domain from protein A, containing the unnatural amino acid 4-benzoylphenylalanine (BPA) for photoaffinity labeling of the antibodies' Fc region. Z(xBPA) domains are C-terminally modified with triple-glycine (G(3))-modified DNA-oligonucleotides enzymatic Sortase A coupling. We reliable modification of the most commonly used IgG's. To prove our conjugates' functionality, we detected antibody-antigen binding events in an assay called Droplet Barcode Sequencing for Protein analysis (DBS-Pro). It confirms not only retained functionality for both conjugate parts but also the potential of using DBS-Pro for quantifying protein abundances. As intermediates are easily storable and our approach is modular, it offers a convenient strategy for screening various antibody-DNA conjugates using the same starting material.

  • 55.
    The, Matthew
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Genteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Statistical and machine learning methods to analyze large-scale mass spectrometry data2018Doktoravhandling, med artikler (Annet vitenskapelig)
    Abstract [en]

    Modern biology is faced with vast amounts of data that contain valuable information yet to be extracted. Proteomics, the study of proteins, has repositories with thousands of mass spectrometry experiments. These data gold mines could further our knowledge of proteins as the main actors in cell processes and signaling. Here, we explore methods to extract more information from this data using statistical and machine learning methods.

    First, we present advances for studies that aggregate hundreds of runs. We introduce MaRaCluster, which clusters mass spectra for large-scale datasets using statistical methods to assess similarity of spectra. It identified up to 40% more peptides than the state-of-the-art method, MS-Cluster. Further, we accommodated large-scale data analysis in Percolator, a popular post-processing tool for mass spectrometry data. This reduced the runtime for a draft human proteome study from a full day to 10 minutes.

    Second, we clarify and promote the contentious topic of protein false discovery rates (FDRs). Often, studies report lists of proteins but fail to report protein FDRs. We provide a framework to systematically discuss protein FDRs and take away hesitance. We also added protein FDRs to Percolator, opting for the best-peptide approach which proved superior in a benchmark of scalable protein inference methods.

    Third, we tackle the low sensitivity of protein quantification methods. Current methods lack proper control of error sources and propagation. To remedy this, we developed Triqler, which controls the protein quantification FDR through a Bayesian framework. We also introduce MaRaQuant, which proposes a quantification-first approach that applies clustering prior to identification. This reduced the number of spectra to be searched and allowed us to spot unidentified analytes of interest. Combining these tools outperformed the state-of-the-art method, MaxQuant/Perseus, and found enriched functional terms for datasets that had none before.

  • 56.
    The, Matthew
    et al.
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Genteknologi.
    Käll, Lukas
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Genteknologi.
    Distillation of label-free quantification data by clustering and Bayesian modelingManuskript (preprint) (Annet vitenskapelig)
    Abstract [en]

    In shotgun proteomics, the amount of information that can be extracted from label-free quantification experiments is typically limited by the identification rate as well as the noise level of the quantitative signals. This generally causes a low sensitivity in differential expression analysis on protein level. Here, we present a new method, MaRaQuant, in which we reverse the typical identification-first workflow into a quantification-first approach. Specifically, we apply unsupervised clustering on both MS1 and MS2 level to summarize all analytes of interest without assigning identities. This ensures that no valuable information is discarded due to analytes missing identification thresholds and allows us to spend more effort on the identification process due to the data reduction achieved by clustering. Furthermore, we propagate error probabilities from feature level all the way to protein level and input these to our probabilistic protein quantification method, Triqler. Applying this methodology to an engineered dataset, we managed to identify multiple analytes of interest that would have gone unnoticed in traditional pipelines, specifically, through the use of open modification and de novo searches. MaRaQuant/Triqler obtains significantly more identifications on all levels compared to MaxQuant/Perseus, including differentially expressed proteins. Notably, we managed to identify differentially expressed proteins in a clinical dataset where previously none were discovered. Furthermore, our differentially expressed proteins allowed us to attribute multiple functional annotation terms to both clinical datasets that we investigated.

  • 57.
    The, Matthew
    et al.
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Genteknologi.
    Käll, Lukas
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Genteknologi.
    Integrated identification and quantification error probabilities for shotgun proteomicsManuskript (preprint) (Annet vitenskapelig)
    Abstract [en]

    Protein quantification by label-free shotgun proteomics experiments is plagued by a multitude of error sources. Typical pipelines for identifying differentially expressed proteins use intermediate filters in an attempt to control the error rate. However, they often ignore certain error sources and, moreover, regard filtered lists as completely correct in subsequent steps. These two indiscretions can easily lead to a loss of control of the false discovery rate (FDR). We propose a probabilistic graphical model, Triqler, that propagates error information through all steps, employing distributions in favor of point estimates, most notably for missing value imputation. The model outputs posterior probabilities for fold changes between treatment groups, highlighting uncertainty rather than hiding it. We analyzed 3 engineered datasets and achieved FDR control and high sensitivity, even for truly absent proteins. In a bladder cancer clinical dataset we discovered 35 proteins at 5% FDR, with the original study discovering none at this threshold. Compellingly, these proteins showed enrichment for functional annotation terms. The model executes in minutes and is freely available at https://pypi.org/project/triqler/.

  • 58.
    Thrane, Kim
    et al.
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Genteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Eriksson, Hanna
    Karolinska Inst, Dept Oncol Pathol, SE-17176 Stockholm, Sweden.;Karolinska Univ Hosp, Dept Oncol, SE-17176 Stockholm, Sweden..
    Maaskola, Jonas
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Genteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Hansson, Johan
    Karolinska Inst, Dept Oncol Pathol, SE-17176 Stockholm, Sweden.;Karolinska Univ Hosp, Dept Oncol, SE-17176 Stockholm, Sweden..
    Lundeberg, Joakim
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Genteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Spatially Resolved Transcriptomics Enables Dissection of Genetic Heterogeneity in Stage III Cutaneous Malignant Melanoma2018Inngår i: Cancer Research, ISSN 0008-5472, E-ISSN 1538-7445, Vol. 78, nr 20, s. 5970-5979Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Cutaneous malignant melanoma (melanoma) is characterized by a high mutational load, extensive intertumoral and intratumoral genetic heterogeneity, and complex tumor microenvironment (TME) interactions. Further insights into the mechanisms underlying melanoma are crucial for understanding tumor progression and responses to treatment. Here we adapted the technology of spatial transcriptomics (ST) to melanoma lymph node biopsies and successfully sequenced the transcriptomes of over 2,200 tissue domains. Deconvolution combined with traditional approaches for dimensional reduction of transcriptome-wide data enabled us to both visualize the transcriptional landscape within the tissue and identify gene expression profiles linked to specific histologic entities. Our unsupervised analysis revealed a complex spatial intratumoral composition of melanoma metastases that was not evident through morphologic annotation. Each biopsy showed distinct gene expression profiles and included examples of the coexistence of multiple melanoma signatures within a single tumor region as well as shared profiles for lymphoid tissue characterized according to their spatial location and gene expression profiles. The lymphoid area in close proximity to the tumor region displayed a specific expression pattern, which may reflect the TME, a key component to fully understanding tumor progression. In conclusion, using the ST technology to generate gene expression profiles reveals a detailed landscape of melanoma metastases. This should inspire researchers to integrate spatial information into analyses aiming to identify the factors underlying tumor progression and therapy outcome. Significance: Applying ST technology to gene expression profiling in melanoma lymph node metastases reveals a complex transcriptional landscape in a spatial context, which is essential for understanding the multiple components of tumor progression and therapy outcome. (C) 2018 AACR.

  • 59.
    Tiukova, Ievgeniia A.
    et al.
    Chalmers Univ Technol, Dept Biol & Biol Engn, Syst & Synthet Biol, Gothenburg, Sweden.;Swedish Univ Agr Sci, Dept Mol Sci, Uppsala, Sweden..
    Pettersson, Mats E.
    Uppsala Univ, Dept Med Biochem & Microbiol, Uppsala, Sweden..
    Hoeppner, Marc P.
    Uppsala Univ, Dept Med Biochem & Microbiol, Uppsala, Sweden.;NBIS, Uppsala, Sweden.;Christian Albrechts Univ Kiel, Inst Clin Mol Biol, Kiel, Germany.;Royal Inst Technol KTH, Sci Life Lab, Div Gene Technol, Sch Biotechnol, Solna, Sweden..
    Olsen, Remi-Andre
    KTH, Centra, Science for Life Laboratory, SciLifeLab. KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Genteknologi.
    Käller, Max
    KTH, Centra, Science for Life Laboratory, SciLifeLab. KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Genteknologi. Stockholm Univ, Dept Biochem & Biophys, SciLifeLab, Stockholm, Sweden..
    Nielsen, Jens
    Chalmers Univ Technol, Dept Biol & Biol Engn, Syst & Synthet Biol, Gothenburg, Sweden..
    Dainat, Jacques
    Uppsala Univ, Dept Med Biochem & Microbiol, Uppsala, Sweden.;NBIS, Uppsala, Sweden..
    Lantz, Henrik
    Uppsala Univ, Dept Med Biochem & Microbiol, Uppsala, Sweden.;NBIS, Uppsala, Sweden..
    Soderberg, Jonas
    Uppsala Univ, Dept Cell & Mol Biol, Mol Evolut, Uppsala, Sweden..
    Passoth, Volkmar
    Swedish Univ Agr Sci, Dept Mol Sci, Uppsala, Sweden..
    Chromosomal genome assembly of the ethanol production strain CBS 11270 indicates a highly dynamic genome structure in the yeast species Brettanomyces bruxellensis2019Inngår i: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 14, nr 5, artikkel-id e0215077Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Here, we present the genome of the industrial ethanol production strain Brettanomyces bruxellensis CBS 11270. The nuclear genome was found to be diploid, containing four chromosomes with sizes of ranging from 2.2 to 4.0 Mbp. A 75 Kbp mitochondrial genome was also identified. Comparing the homologous chromosomes, we detected that 0.32% of nucleotides were polymorphic, i.e. formed single nucleotide polymorphisms (SNPs), 40.6% of them were found in coding regions (i.e. 0.13% of all nucleotides formed SNPs and were in coding regions). In addition, 8,538 indels were found. The total number of protein coding genes was 4897, of them, 4,284 were annotated on chromosomes; and the mitochondrial genome contained 18 protein coding genes. Additionally, 595 genes, which were annotated, were on contigs not associated with chromosomes. A number of genes was duplicated, most of them as tandem repeats, including a six-gene cluster located on chromosome 3. There were also examples of interchromosomal gene duplications, including a duplication of a six-gene cluster, which was found on both chromosomes 1 and 4. Gene copy number analysis suggested loss of heterozygosity for 372 genes. This may reflect adaptation to relatively harsh but constant conditions of continuous fermentation. Analysis of gene topology showed that most of these losses occurred in clusters of more than one gene, the largest cluster comprising 33 genes. Comparative analysis against the wine isolate CBS 2499 revealed 88,534 SNPs and 8,133 indels. Moreover, when the scaffolds of the CBS 2499 genome assembly were aligned against the chromosomes of CBS 11270, many of them aligned completely, some have chunks aligned to different chromosomes, and some were in fact rearranged. Our findings indicate a highly dynamic genome within the species B. bruxellensis and a tendency towards reduction of gene number in long-term continuous cultivation.

  • 60.
    Vickovic, Sanja
    et al.
    KTH, Centra, Science for Life Laboratory, SciLifeLab. KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Genteknologi. Broad Inst MIT & Harvard, Klarman Cell Observ, Cambridge, MA 02142 USA..
    Eraslan, Gokcen
    Broad Inst MIT & Harvard, Klarman Cell Observ, Cambridge, MA 02142 USA..
    Salmen, Fredrik
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Genteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Klughammer, Johanna
    Broad Inst MIT & Harvard, Klarman Cell Observ, Cambridge, MA 02142 USA..
    Stenbeck, Linnea
    KTH, Centra, Science for Life Laboratory, SciLifeLab. KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Genteknologi.
    Schapiro, Denis
    Broad Inst MIT & Harvard, Klarman Cell Observ, Cambridge, MA 02142 USA.;Harvard Med Sch, Lab Syst Pharmacol, Boston, MA 02115 USA..
    Aijo, Tarmo
    Flatiron Inst, Ctr Computat Biol, New York, NY USA..
    Bonneau, Richard
    NYU, Ctr Data Sci, New York, NY USA.;Brigham & Womens Hosp, Dept Pathol, 75 Francis St, Boston, MA 02115 USA..
    Bergenstrahle, Joseph
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Genteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Fernandez Navarro, Jose
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Genteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Gould, Joshua
    Broad Inst MIT & Harvard, Klarman Cell Observ, Cambridge, MA 02142 USA..
    Griffin, Gabriel K.
    Broad Inst MIT & Harvard, Klarman Cell Observ, Cambridge, MA 02142 USA.;Brigham & Womens Hosp, Dept Pathol, 75 Francis St, Boston, MA 02115 USA..
    Borg, Ake
    Lund Univ, Dept Clin Sci Lund, Div Oncol & Pathol, Lund, Sweden..
    Ronaghi, Mostafa
    Illumina Inc, San Diego, CA USA..
    Frisen, Jonas
    Karolinska Inst, Dept Cell & Mol Biol, Stockholm, Sweden..
    Lundeberg, Joakim
    KTH, Centra, Science for Life Laboratory, SciLifeLab. KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Genteknologi. Stanford Univ, Dept Bioengn, Stanford, CA 94305 USA..
    Regev, Aviv
    Broad Inst MIT & Harvard, Klarman Cell Observ, Cambridge, MA 02142 USA.;MIT, Howard Hughes Med Inst, Cambridge, MA USA.;MIT, Dept Biol, Koch Inst Integrat Canc Res, Cambridge, MA USA..
    Ståhl, Patrik
    KTH, Centra, Science for Life Laboratory, SciLifeLab. KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Genteknologi.
    High-definition spatial transcriptomics for in situ tissue profiling2019Inngår i: Nature Methods, ISSN 1548-7091, E-ISSN 1548-7105, Vol. 16, nr 10, s. 987-+Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Spatial and molecular characteristics determine tissue function, yet high-resolution methods to capture both concurrently are lacking. Here, we developed high-definition spatial transcriptomics, which captures RNA from histological tissue sections on a dense, spatially barcoded bead array. Each experiment recovers several hundred thousand transcriptcoupled spatial barcodes at 2-mu m resolution, as demonstrated in mouse brain and primary breast cancer. This opens the way to high-resolution spatial analysis of cells and tissues.

  • 61.
    Wong, Kim
    et al.
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Genteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Navarro, Jose Fernandez
    KTH, Centra, Science for Life Laboratory, SciLifeLab. KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Genteknologi.
    Bergenstrahle, Ludvig
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Genteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Stahl, Patrik L.
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Genteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Lundeberg, Joakim
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Genteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    ST Spot Detector: a web-based application for automatic spot and tissue detection for spatial Transcriptomics image datasets2018Inngår i: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 34, nr 11, s. 1966-1968Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Motiviation: Spatial Transcriptomics (ST) is a method which combines high resolution tissue imaging with high troughput transcriptome sequencing data. This data must be aligned with the images for correct visualization, a process that involves several manual steps. Results: Here we present ST Spot Detector, a web tool that automates and facilitates this alignment through a user friendly interface.

  • 62.
    Zhang, Miao
    et al.
    KTH, Skolan för teknikvetenskap (SCI), Tillämpad fysik, Material- och nanofysik. KTH.
    Ngampeerapong, Chonmanart
    Redin, David
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Genteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Ahmadian, Afshin
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Genteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Sychugov, Ilya
    KTH, Skolan för teknikvetenskap (SCI), Tillämpad fysik, Material- och nanofysik.
    Linnros, Jan
    KTH, Skolan för teknikvetenskap (SCI), Tillämpad fysik, Material- och nanofysik.
    Thermophoresis-Controlled Size-Dependent DNA Translocation through an Array of Nanopores2018Inngår i: ACS Nano, ISSN 1936-0851, E-ISSN 1936-086X, Vol. 12, nr 5, s. 4574-4582Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Large arrays of nanopores can be used for high-throughput biomolecule translocation with applications toward size discrimination and sorting at the single-molecule level. In this paper, we propose to discriminate DNA length by the capture rate of the molecules to an array of relatively large nanopores (50–130 nm) by introducing a thermal gradient by laser illumination in front of the pores balancing the force from an external electric field. Nanopore arrays defined by photolithography were batch processed using standard silicon technology in combination with electrochemical etching. Parallel translocation of single, fluorophore-labeled dsDNA strands is recorded by imaging the array with a fast CMOS camera. The experimental data show that the capture rates of DNA molecules decrease with increasing DNA length due to the thermophoretic effect of the molecules. It is shown that the translocation can be completely turned off for the longer molecule using an appropriate bias, thus allowing a size discrimination of the DNA translocation through the nanopores. A derived analytical model correctly predicts the observed capture rate. Our results demonstrate that by combining a thermal and a potential gradient at the nanopores, such large nanopore arrays can potentially be used as a low-cost, high-throughput platform for molecule sensing and sorting.

  • 63.
    Åkerborg, Örjan
    et al.
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Genteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Spalinskas, Rapolas
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Genteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Pradhananga, Sailendra
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Genteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Anil, Anandashankar
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Genteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Höjer, Pontus
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Genteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Poujade, Flore-Anne
    Karolinska Inst, Cardiovasc Med Unit, Dept Med, Ctr Mol Med, Stockholm, Sweden..
    Folkersen, Lasse
    Tech Univ Denmark, Dept Bioinformat, Copenhagen, Denmark..
    Sahlén, Pelin
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Genteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Eriksson, Per
    Karolinska Inst, Cardiovasc Med Unit, Dept Med, Ctr Mol Med, Stockholm, Sweden..
    High-Resolution Regulatory Maps Connect Vascular Risk Variants to Disease-Related Pathways2019Inngår i: Circulation. Genomic and precision medicine, ISSN 2574-8300, Vol. 12, nr 3, artikkel-id e002353Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    BACKGROUND: Genetic variant landscape of coronary artery disease is dominated by noncoding variants among which many occur within putative enhancers regulating the expression levels of relevant genes. It is crucial to assign the genetic variants to their correct genes both to gain insights into perturbed functions and better assess the risk of disease. METHODS: In this study, we generated high-resolution genomic interaction maps (similar to 750 bases) in aortic endothelial, smooth muscle cells and THP-1 (human leukemia monocytic cell line) macrophages stimulated with lipopolysaccharide using Hi-C coupled with sequence capture targeting 25 429 features, including variants associated with coronary artery disease. We also sequenced their transcriptomes and mapped putative enhancers using chromatin immunoprecipitation with an antibody against H3K27Ac. RESULTS: The regions interacting with promoters showed strong enrichment for enhancer elements and validated several previously known interactions and enhancers. We detected interactions for 727 risk variants obtained by genome-wide association studies and identified novel, as well as established genes and functions associated with cardiovascular diseases. We were able to assign potential target genes for additional 398 genome-wide association studies variants using haplotype information, thereby identifying additional relevant genes and functions. Importantly, we discovered that a subset of risk variants interact with multiple promoters and their expression levels were strongly correlated. CONCLUSIONS: In summary, we present a catalog of candidate genes regulated by coronary artery disease-related variants and think that it will be an invaluable resource to further the investigation of cardiovascular pathologies and disease.

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