Journal of Investigative Dermatology Home

Research Techniques Made Simple: Volume Scanning Electron Microscopy

  • Ross Laws
    Electron Microscopy Research Services, Newcastle University, Newcastle upon Tyne, United Kingdom

    Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
    Search for articles by this author
  • David H. Steel
    Bioscience Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
    Search for articles by this author
  • Neil Rajan
    Correspondence: Neil Rajan, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne NE1 3BZ, United Kingdom.
    Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, United Kingdom

    Department of Dermatology, Royal Victoria Infirmary, Newcastle upon Tyne, United Kingdom
    Search for articles by this author
Published:November 08, 2021DOI:
      Volume scanning electron microscopy (VSEM) involves the serial sectioning and imaging of a sample using scanning electron microscopy (SEM), followed by segmentation and three-dimensional (3D) reconstruction using computer software packages to allow visualization of 3D structures. VSEM can reveal qualitative and quantitative properties of organelles and cells within tissues at nanoscale. The ability to visualize spatial relationships of structures of interest within and across cells in 3D space in particular sets VSEM apart from conventional SEM and transmission electron microscopy. Here, we provide an overview of VSEM platforms and image processing, highlighting characteristics that will aid selection of a method to address specific research questions in dermatological research.


      2D (two-dimensional), 3D (three-dimensional), ATUM-SEM (automatic tape-collecting ultramicrotome combined with scanning electron microscopy), CNN (convolutional neural network), DL (deep learning), FIB-SEM (focused ion beam milling combined with scanning electron microscopy), KC (keratinocyte), ML (machine learning), SBF-SEM (serial block face sectioning combined with scanning electron microscopy), SEM (scanning electron microscopy), TEM (transmission electron microscopy), TGN (trans-Golgi network), VSEM (volume scanning electron microscopy)
      To read this article in full you will need to make a payment
      Purchase one-time access
      Society Members (SID/ESDR), remember to log in for access.
      Subscribe to Journal of Investigative Dermatology
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect


        • Belevich I.
        • Jokitalo E.
        DeepMIB: user-friendly and open-source software for training of deep learning network for biological image segmentation.
        PLoS Comput Biol. 2021; 17e1008374
        • Berg S.
        • Kutra D.
        • Kroeger T.
        • Straehle C.N.
        • Kausler B.X.
        • Haubold C.
        • et al.
        ilastik: interactive machine learning for (bio)image analysis.
        Nat Methods. 2019; 16: 1226-1232
        • Buskin A.
        • Zhu L.
        • Chichagova V.
        • Basu B.
        • Mozaffari-Jovin S.
        • Dolan D.
        • et al.
        Disrupted alternative splicing for genes implicated in splicing and ciliogenesis causes PRPF31 retinitis pigmentosa.
        Nat Commun. 2018; 9: 4234
        • Eberle A.L.
        • Mikula S.
        • Schalek R.
        • Lichtman J.
        • Tate M.L.K.
        • Zeidler D.
        High-resolution, high-throughput imaging with a multibeam scanning electron microscope.
        J Microsc. 2015; 259: 114-120
        • Knoll M.
        • Ruska E.
        Das elektronenmikroskop [The electron microscope].
        Z Physik. 1932; 78 ([in German]): 318-339
        • Lindberg E.
        • Baumer Y.
        • Stempinski E.S.
        • Rodante J.A.
        • Powell-Wiley T.M.
        • Dey A.K.
        • et al.
        Nanotomography of lesional skin using electron microscopy reveals cytosolic release of nuclear DNA in psoriasis.
        JAAD Case Rep. 2021; 9: 9-14
        • Liu J.
        • Li L.
        • Yang Y.
        • Hong B.
        • Chen X.
        • Xie Q.
        • et al.
        Automatic reconstruction of mitochondria and endoplasmic reticulum in electron microscopy volumes by deep learning.
        Front Neurosci. 2020; 14: 599
        • Mizutani Y.
        • Yamashita M.
        • Hashimoto R.
        • Atsugi T.
        • Ryu A.
        • Hayashi A.
        • et al.
        Three-dimensional structure analysis of melanocytes and keratinocytes in senile lentigo.
        Microscopy (Oxf). 2021; 70: 224-231
        • Noh S.
        • Choi H.
        • Kim J.S.
        • Kim I.H.
        • Mun J.Y.
        Study of hyperpigmentation in human skin disorder using different electron microscopy techniques.
        Microsc Res Tech. 2019; 82: 18-24
        • Ronchi P.
        • Mizzon G.
        • Machado P.
        • D'Imprima E.
        • Best B.T.
        • Cassella L.
        • et al.
        High-precision targeting workflow for volume electron microscopy.
        J Cell Biol. 2021; 220e202104069
        • Spiers H.
        • Songhurst H.
        • Nightingale L.
        • de Folter J.
        • Zooniverse Volunteer Community
        • Hutchings R.
        • et al.
        Deep learning for automatic segmentation of the nuclear envelope in electron microscopy data, trained with volunteer segmentations.
        Traffic. 2021; 22: 240-253
        • Titze B.
        • Genoud C.
        Volume scanning electron microscopy for imaging biological ultrastructure.
        Biol Cell. 2016; 108: 307-323
        • Xu C.S.
        • Hayworth K.J.
        • Lu Z.
        • Grob P.
        • Hassan A.M.
        • García-Cerdán J.G.
        • et al.
        Enhanced FIB-SEM systems for large-volume 3D imaging.
        Elife. 2017; 6: e25916
        • Yamanishi H.
        • Soma T.
        • Kishimoto J.
        • Hibino T.
        • Ishida-Yamamoto A.
        Marked changes in lamellar granule and trans-Golgi network structure occur during epidermal keratinocyte differentiation.
        J Invest Dermatol. 2019; 139: 352-359