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Segmentation of lipid nanoparticles from cryogenic electron microscopy images

Lipid nanoparticles (LNPs) were prepared as described (https://doi.org/10.1038/s42003-021-02441-2) using the lipids DLin-KC2-DMA, DSPC, cholesterol, and PEG-DMG2000 at mol ratios of 50:10:38.5:1.5. Four sample types were prepared: LNPs in the presence and absence of RNA, and with LNPs ejected into pH 4 and pH 7.4 buffer after microfluidic assembly. To prepare samples for imaging, 3 ?L of LNP formulation was applied to holey carbon grids (Quantifoil, R3.5/1, 200 mesh copper). Grids were then incubated for 30 s at 298 K and 100% humidity before blotting and plunge-freezing into liquid ethane using a Vitrobot Mark IV (Thermo Fisher Scientific). Grids were imaged at 200 kV using a Talos Arctica system equipped with a Falcon 3EC detector (Thermo Fisher Scientific). A nominal magnification of 45,000x was used, corresponding to images with a pixel count of 4096x4096 and a calibrated pixel spacing of 0.223 nm. Micrographs were collected as dose-fractionated ?movies? at nominal defocus values between -1 and -3 ?m, with 10 s total exposures consisting of 66 frames with a total electron dose of 12,000 electrons per square nanometer. Movies were motion-corrected using MotionCor2 (https://doi.org/10.1038/nmeth.4193), resulting in flattened micrographs suitable for downstream particle segmentation. A total of 38 images were manually segmented into particle and non-particle regions. Segmentation masks and their corresponding images are deposited in this data set.

About this Dataset

Updated: 2024-02-22
Metadata Last Updated: 2022-08-18 00:00:00
Date Created: N/A
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Lipid Nanoparticle
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Title Segmentation of lipid nanoparticles from cryogenic electron microscopy images
Description Lipid nanoparticles (LNPs) were prepared as described (https://doi.org/10.1038/s42003-021-02441-2) using the lipids DLin-KC2-DMA, DSPC, cholesterol, and PEG-DMG2000 at mol ratios of 50:10:38.5:1.5. Four sample types were prepared: LNPs in the presence and absence of RNA, and with LNPs ejected into pH 4 and pH 7.4 buffer after microfluidic assembly. To prepare samples for imaging, 3 ?L of LNP formulation was applied to holey carbon grids (Quantifoil, R3.5/1, 200 mesh copper). Grids were then incubated for 30 s at 298 K and 100% humidity before blotting and plunge-freezing into liquid ethane using a Vitrobot Mark IV (Thermo Fisher Scientific). Grids were imaged at 200 kV using a Talos Arctica system equipped with a Falcon 3EC detector (Thermo Fisher Scientific). A nominal magnification of 45,000x was used, corresponding to images with a pixel count of 4096x4096 and a calibrated pixel spacing of 0.223 nm. Micrographs were collected as dose-fractionated ?movies? at nominal defocus values between -1 and -3 ?m, with 10 s total exposures consisting of 66 frames with a total electron dose of 12,000 electrons per square nanometer. Movies were motion-corrected using MotionCor2 (https://doi.org/10.1038/nmeth.4193), resulting in flattened micrographs suitable for downstream particle segmentation. A total of 38 images were manually segmented into particle and non-particle regions. Segmentation masks and their corresponding images are deposited in this data set.
Modified 2022-08-18 00:00:00
Publisher Name National Institute of Standards and Technology
Contact mailto:thomas.cleveland@nist.gov
Keywords Lipid Nanoparticle , LNP , cryogenic electron microscopy , CryoEM , machine learning , AI , mRNA
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    "language": [
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    "title": "Segmentation of lipid nanoparticles from cryogenic electron microscopy images",
    "distribution": [
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            "downloadURL": "https:\/\/data.nist.gov\/od\/ds\/mds2-2753\/R111_S404_pH4_1p5pcPEG_noRNA.zip",
            "format": ".zip archive containing TIFF images",
            "description": "CryoEM images of lipid nanoparticles and their segmentation masks",
            "mediaType": "application\/x-zip-compressed",
            "title": "KC2 LNPs at pH 4, without RNA"
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    "issued": "2023-02-01",
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        "Lipid Nanoparticle",
        "LNP",
        "cryogenic electron microscopy",
        "CryoEM",
        "machine learning",
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    ]
}

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