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- Mezger, A, et al.
(author)
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High-throughput chromatin accessibility profiling at single-cell resolution
- 2018
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In: Nature communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 9:1, s. 3647-
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Journal article (peer-reviewed)abstract
- Here we develop a high-throughput single-cell ATAC-seq (assay for transposition of accessible chromatin) method to measure physical access to DNA in whole cells. Our approach integrates fluorescence imaging and addressable reagent deposition across a massively parallel (5184) nano-well array, yielding a nearly 20-fold improvement in throughput (up to ~1800 cells/chip, 4–5 h on-chip processing time) and library preparation cost (~81¢ per cell) compared to prior microfluidic implementations. We apply this method to measure regulatory variation in peripheral blood mononuclear cells (PBMCs) and show robust, de novo clustering of single cells by hematopoietic cell type.
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- Cooil, Simon P., et al.
(author)
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In Situ Patterning of Ultrasharp Dopant Profiles in Silicon
- 2017
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In: ACS Nano. - : American Chemical Society (ACS). - 1936-0851 .- 1936-086X. ; 11:2, s. 1683-1688
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Journal article (peer-reviewed)abstract
- We develop a method for patterning a buried two-dimensional electron gas (2DEG) in silicon using low kinetic energy electron stimulated desorption (LEESD) of a monohydride resist mask. A buried 2DEG forms as a result of placing a dense and narrow profile of phosphorus dopants beneath the silicon surface; a so-called δ -layer. Such 2D dopant profiles have previously been studied theoretically, and by angle-resolved photoemission spectroscopy, and have been shown to host a 2DEG with properties desirable for atomic-scale devices and quantum computation applications. Here we outline a patterning method based on low kinetic energy electron beam lithography, combined with in situ characterization, and demonstrate the formation of patterned features with dopant concentrations sufficient to create localized 2DEG states.
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- Mishra, K., et al.
(author)
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Genetically encoded photo-switchable molecular sensors for optoacoustic and super-resolution imaging
- 2022
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In: Nature Biotechnology. - : Springer Nature. - 1087-0156 .- 1546-1696. ; 40:4, s. 598-605
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Journal article (peer-reviewed)abstract
- Reversibly photo-switchable proteins are essential for many super-resolution fluorescence microscopic and optoacoustic imaging methods. However, they have yet to be used as sensors that measure the distribution of specific analytes at the nanoscale or in the tissues of live animals. Here we constructed the prototype of a photo-switchable Ca2+ sensor based on GCaMP5G that can be switched with 405/488-nm light and describe its molecular mechanisms at the structural level, including the importance of the interaction of the core barrel structure of the fluorescent protein with the Ca2+ receptor moiety. We demonstrate super-resolution imaging of Ca2+ concentration in cultured cells and optoacoustic Ca2+ imaging in implanted tumor cells in mice under controlled Ca2+ conditions. Finally, we show the generalizability of the concept by constructing examples of photo-switching maltose and dopamine sensors based on periplasmatic binding protein and G-protein-coupled receptor-based sensors.
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- Bekkhus, T., et al.
(author)
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Automated detection of vascular remodeling in tumor-draining lymph nodes by the deep-learning tool HEV-finder
- 2022
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In: Journal of Pathology. - : Wiley. - 0022-3417 .- 1096-9896. ; 258:1, s. 4-11
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Journal article (peer-reviewed)abstract
- Vascular remodeling is common in human cancer and has potential as future biomarkers for prediction of disease progression and tumor immunity status. It can also affect metastatic sites, including the tumor-draining lymph nodes (TDLNs). Dilation of the high endothelial venules (HEVs) within TDLNs has been observed in several types of cancer. We recently demonstrated that it is a premetastatic effect that can be linked to tumor invasiveness in breast cancer. Manual visual assessment of changes in vascular morphology is a tedious and difficult task, limiting high-throughput analysis. Here we present a fully automated approach for detection and classification of HEV dilation. By using 12,524 manually classified HEVs, we trained a deep-learning model and created a graphical user interface for visualization of the results. The tool, named the HEV-finder, selectively analyses HEV dilation in specific regions of the lymph nodes. We evaluated the HEV-finder's ability to detect and classify HEV dilation in different types of breast cancer compared to manual annotations. Our results constitute a successful example of large-scale, fully automated, and user-independent, image-based quantitative assessment of vascular remodeling in human pathology and lay the ground for future exploration of HEV dilation in TDLNs as a biomarker. (c) 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
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