| 1. |
- Ecke, Frauke, et al.
(författare)
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Sublethal Lead Exposure Alters Movement Behavior in Free-Ranging Golden Eagles
- 2017
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Ingår i: Environmental Science and Technology. - : American Chemical Society (ACS). - 0013-936X .- 1520-5851. ; 51:10, s. 5729-5736
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Tidskriftsartikel (refereegranskat)abstract
- Lead poisoning of animals due to ingestion of fragments from lead-based ammunition in carcasses and offal of shot wildlife is acknowledged globally and raises great concerns about potential behavioral effects leading to increased mortality risks. Lead levels in blood were correlated with progress of the moose hunting season. Based on analyses of tracking data, we found that even sublethal lead concentrations in blood (25 ppb, wet weight), can likely negatively affect movement behavior (flight height and movement rate) of free ranging scavenging Golden Eagles (Aquila chrysaetos). Lead levels in liver of recovered post-mortem analyzed eagles suggested that sublethal exposure increases the risk of mortality in eagles. Such adverse effects on animals are probably common worldwide and across species, where game hunting with lead-based ammunition is widespread. Our study highlights lead exposure as a considerably more serious threat to wildlife conservation than previously realized and suggests implementation of bans of lead ammunition for hunting.
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| 2. |
- He, B., et al.
(författare)
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Integrating spatial gene expression and breast tumour morphology via deep learning
- 2020
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Ingår i: Nature Biomedical Engineering. - : Nature Research. - 2157-846X. ; 4:8, s. 827-834
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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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| 3. |
- Vickovic, Sanja, et al.
(författare)
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High-definition spatial transcriptomics for in situ tissue profiling
- 2019
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Ingår i: Nature Methods. - : NATURE PUBLISHING GROUP. - 1548-7091 .- 1548-7105. ; 16:10, s. 987-
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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