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Träfflista för sökning "WFRF:(Maaskola Jonas) srt2:(2020-2022)"

Sökning: WFRF:(Maaskola Jonas) > (2020-2022)

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1.
  • Bergenstråhle, Ludvig, et al. (författare)
  • Super-resolved spatial transcriptomics by deep data fusion
  • 2022
  • Ingår i: Nature Biotechnology. - : Nature Research. - 1087-0156 .- 1546-1696. ; 40:4, s. 476-479
  • Tidskriftsartikel (refereegranskat)abstract
    • Current methods for spatial transcriptomics are limited by low spatial resolution. Here we introduce a method that integrates spatial gene expression data with histological image data from the same tissue section to infer higher-resolution expression maps. Using a deep generative model, our method characterizes the transcriptome of micrometer-scale anatomical features and can predict spatial gene expression from histology images alone. 
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2.
  • Erickson, A, et al. (författare)
  • Spatially resolved clonal copy number alterations in benign and malignant tissue
  • 2022
  • Ingår i: Nature. - : Springer Science and Business Media LLC. - 1476-4687 .- 0028-0836. ; 608:7922, s. 360-
  • Tidskriftsartikel (refereegranskat)abstract
    • Defining the transition from benign to malignant tissue is fundamental to improving early diagnosis of cancer1. Here we use a systematic approach to study spatial genome integrity in situ and describe previously unidentified clonal relationships. We used spatially resolved transcriptomics2 to infer spatial copy number variations in >120,000 regions across multiple organs, in benign and malignant tissues. We demonstrate that genome-wide copy number variation reveals distinct clonal patterns within tumours and in nearby benign tissue using an organ-wide approach focused on the prostate. Our results suggest a model for how genomic instability arises in histologically benign tissue that may represent early events in cancer evolution. We highlight the power of capturing the molecular and spatial continuums in a tissue context and challenge the rationale for treatment paradigms, including focal therapy.
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5.
  • He, B., et al. (författare)
  • Integrating spatial gene expression and breast tumour morphology via deep learning
  • 2020
  • Ingår i: Nature Biomedical Engineering. - : Nature Research. - 2157-846X. ; 4:8, s. 827-834
  • Tidskriftsartikel (refereegranskat)abstract
    • Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation. 
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  • Resultat 1-5 av 5

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