High-Throughput, Algorithmic Determination of Nanoparticle Structure from Electron Microscopy Images

被引:59
|
作者
Laramy, Christine R. [1 ,3 ]
Brown, Keith A. [2 ,3 ]
O'Brien, Matthew N. [2 ]
Mirkin, Chad. A. [1 ,2 ]
机构
[1] Northwestern Univ, Dept Chem & Biol Engn, Evanston, IL 60208 USA
[2] Northwestern Univ, Dept Chem, Evanston, IL 60208 USA
[3] Northwestern Univ, Int Inst Nanotechnol, Evanston, IL 60208 USA
关键词
electron microscopy; nanoparticles; image analysis; high-throughput; automated; GOLD NANORODS; SHAPE CONTROL; OPTICAL-PROPERTIES; NANOSCALE FORCES; NANOCRYSTALS; SIZE; GROWTH; NANOSTRUCTURES; SUPERLATTICES; PLASMONICS;
D O I
10.1021/acsnano.5b05968
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Electron microscopy (EM) represents the most powerful tool to directly characterize the structure of individual nanoparticles. Accurate descriptions of nanoparticle populations with EM, however, are currently limited by the lack of tools to quantitatively analyze populations in a high-throughput manner. Herein, we report a computational method to algorithmically analyze EM images that allows for the first automated structural quantification of heterogeneous nanostructure populations, with species that differ in both size and shape. This allows one to accurately describe nanoscale structure at the bulk level, analogous to ensemble measurements with individual particle resolution. With our described EM protocol and our inclusion of freely available code for our algorithmic analysis, we aim to standardize EM characterization of nanostructure populations to increase reproducibility, objectivity, and throughput in measurements. We believe this work will have significant implications in diverse research areas involving nanomaterials, including, but not limited to, fundamental studies of structural control in nanoparticle synthesis, nanomaterial-based therapeutics and diagnostics, optoelectronics, and catalysis.
引用
收藏
页码:12488 / 12495
页数:8
相关论文
共 50 条
  • [21] High throughput microscopy: from raw images to discoveries
    Wollman, Roy
    Stuurman, Nico
    JOURNAL OF CELL SCIENCE, 2007, 120 (21) : 3715 - 3722
  • [22] Optimized Negative Staining: a High-throughput Protocol for Examining Small and Asymmetric Protein Structure by Electron Microscopy
    Rames, Matthew
    Yu, Yadong
    Ren, Gang
    JOVE-JOURNAL OF VISUALIZED EXPERIMENTS, 2014, (90):
  • [23] Accurate Prediction of Biological Assays with High-Throughput Microscopy Images and Convolutional Networks
    Hofmarcher, Markus
    Rumetshofer, Elisabeth
    Clevert, Djork-Arne
    Hochreiter, Sepp
    Klambauer, Guenter
    JOURNAL OF CHEMICAL INFORMATION AND MODELING, 2019, 59 (03) : 1163 - 1171
  • [24] Cell classification for high-throughput microscopy
    Heynen, S
    Price, JH
    CYTOMETRY, 2002, : 61 - 62
  • [25] High-Throughput Nonlinear Optical Microscopy
    So, Peter T. C.
    Yew, Elijah Y. S.
    Rowlands, Christopher
    BIOPHYSICAL JOURNAL, 2013, 105 (12) : 2641 - 2654
  • [26] Informatics challenges of high-throughput microscopy
    Zhou, XB
    Wong, STC
    IEEE SIGNAL PROCESSING MAGAZINE, 2006, 23 (03) : 63 - 72
  • [27] HIGH-THROUGHPUT SCREENING BY TRACTION MICROSCOPY
    Park, Chan Young
    Burger, Stephanie
    Frykenberg, Matthew
    Tambe, Dhananjay
    Zhou, Enhua
    Krishnan, Ramaswamy
    Marinkovic, Aleksander
    Tschumperlin, Daniel
    Butler, James
    Lavoie, Tera
    Dowell, Maria
    Chen, Bohao
    Gardel, Margaret
    Green, Geoffrey
    Solway, Julian
    Fredberg, Jeffrey
    FASEB JOURNAL, 2013, 27
  • [28] An automatic method for robust and fast cell detection in bright field images from high-throughput microscopy
    Buggenthin, Felix
    Marr, Carsten
    Schwarzfischer, Michael
    Hoppe, Philipp S.
    Hilsenbeck, Oliver
    Schroeder, Timm
    Theis, Fabian J.
    BMC BIOINFORMATICS, 2013, 14
  • [29] Accurate Classification of Protein Subcellular Localization from High-Throughput Microscopy Images Using Deep Learning
    Parnamaa, Tanel
    Parts, Leopold
    G3-GENES GENOMES GENETICS, 2017, 7 (05): : 1385 - 1392
  • [30] An automatic method for robust and fast cell detection in bright field images from high-throughput microscopy
    Felix Buggenthin
    Carsten Marr
    Michael Schwarzfischer
    Philipp S Hoppe
    Oliver Hilsenbeck
    Timm Schroeder
    Fabian J Theis
    BMC Bioinformatics, 14