High-throughput hyperdimensional vertebrate phenotyping

被引:0
|
作者
Carlos Pardo-Martin
Amin Allalou
Jaime Medina
Peter M. Eimon
Carolina Wählby
Mehmet Fatih Yanik
机构
[1] Massachusetts Institute of Technology (MIT),Department of Electrical Engineering and Computer Science
[2] 77 Massachusetts Avenue,Division of Health Sciences and Technology
[3] Cambridge,Department of Biological Engineering
[4] Massachusetts 02139,undefined
[5] USA,undefined
[6] MIT,undefined
[7] 77 Massachusetts Avenue,undefined
[8] Cambridge,undefined
[9] Massachusetts 02139,undefined
[10] USA,undefined
[11] School of Engineering and Applied Sciences,undefined
[12] Harvard University,undefined
[13] Centre for Image Analysis,undefined
[14] Science for Life Laboratory,undefined
[15] Uppsala University,undefined
[16] Box337,undefined
[17] Uppsala SE-75105,undefined
[18] Sweden,undefined
[19] Imaging Platform,undefined
[20] Broad Institute of Massachusetts Institute of Technology and Harvard,undefined
[21] 7 Cambridge Center,undefined
[22] MIT,undefined
[23] 77 Masschusetts Avenue,undefined
[24] Cambridge,undefined
[25] Massachusetts 02139,undefined
[26] USA,undefined
来源
关键词
D O I
暂无
中图分类号
学科分类号
摘要
Most gene mutations and biologically active molecules cause complex responses in animals that cannot be predicted by cell culture models. Yet animal studies remain too slow and their analyses are often limited to only a few readouts. Here we demonstrate high-throughput optical projection tomography with micrometre resolution and hyperdimensional screening of entire vertebrates in tens of seconds using a simple fluidic system. Hundreds of independent morphological features and complex phenotypes are automatically captured in three dimensions with unprecedented speed and detail in semitransparent zebrafish larvae. By clustering quantitative phenotypic signatures, we can detect and classify even subtle alterations in many biological processes simultaneously. We term our approach hyperdimensional in vivo phenotyping. To illustrate the power of hyperdimensional in vivo phenotyping, we have analysed the effects of several classes of teratogens on cartilage formation using 200 independent morphological measurements, and identified similarities and differences that correlate well with their known mechanisms of actions in mammals.
引用
收藏
相关论文
共 50 条
  • [31] High-throughput Phenotyping of Lung Cancer Somatic Mutations
    Berger, Alice H.
    Brooks, Angela N.
    Wu, Xiaoyun
    Shrestha, Yashaswi
    Chouinard, Candace
    Piccioni, Federica
    Bagul, Mukta
    Kamburov, Atanas
    Imielinski, Marcin
    Hogstrom, Larson
    Zhu, Cong
    Yang, Xiaoping
    Pantel, Sasha
    Sakai, Ryo
    Watson, Jacqueline
    Kaplan, Nathan
    Campbell, Joshua D.
    Singh, Shantanu
    Root, David E.
    Narayan, Rajiv
    Natoli, Ted
    Lahr, David L.
    Tirosh, Itay
    Tamayo, Pablo
    Getz, Gad
    Wong, Bang
    Doench, John
    Subramanian, Aravind
    Golub, Todd R.
    Meyerson, Matthew
    Boehm, Jesse S.
    CANCER CELL, 2016, 30 (02) : 214 - 228
  • [32] Automatic Quantification of Stomata for High-throughput Plant Phenotyping
    Bhugra, Swati
    Mishra, Deepak
    Anupama, Anupama
    Chaudhury, Santanu
    Lall, Brejesh
    Chugh, Archana
    2018 24TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR), 2018, : 3904 - 3910
  • [33] GiNA, an Efficient and High-Throughput Software for Horticultural Phenotyping
    Diaz-Garcia, Luis
    Covarrubias-Pazaran, Giovanny
    Schlautman, Brandon
    Zalapa, Juan
    PLOS ONE, 2016, 11 (08):
  • [34] Toward high-throughput biomechanical phenotyping of single molecules
    David Alsteens
    Savaş Tay
    Daniel J Müller
    Nature Methods, 2015, 12 : 45 - 46
  • [35] The Informatics of High-Throughput Mouse Phenotyping: EUMODIC and Beyond
    Hancock, John M.
    Gates, Hilary
    MOUSE AS A MODEL ORGANISM: FROM ANIMALS TO CELLS, 2011, : 77 - 87
  • [36] High-throughput phenotyping for crop improvement in the genomics era
    Mir, Reyazul Rouf
    Reynolds, Mathew
    Pinto, Francisco
    Khan, Mohd Anwar
    Bhat, Mohd Ashraf
    PLANT SCIENCE, 2019, 282 : 60 - 72
  • [37] Leveraging Image Analysis for High-Throughput Plant Phenotyping
    Choudhury, Sruti Das
    Samal, Ashok
    Awada, Tala
    FRONTIERS IN PLANT SCIENCE, 2019, 10
  • [38] Fenomica: A Computer Vision System for High-Throughput Phenotyping
    dos Santos, Marcos Roberto
    Madalozzo, Guilherme Afonso
    Cunha Fernandes, Jose Mauricio
    Rieder, Rafael
    INTERNATIONAL JOURNAL OF AGRICULTURAL AND ENVIRONMENTAL INFORMATION SYSTEMS, 2020, 11 (01) : 1 - 22
  • [39] Mouse Eye Enucleation for Remote High-throughput Phenotyping
    Mahajan, Vinit B.
    Skeie, Jessica M.
    Assefnia, Amir H.
    Mahajan, MaryAnn
    Tsang, Stephen H.
    JOVE-JOURNAL OF VISUALIZED EXPERIMENTS, 2011, (57):
  • [40] A bioimage informatics platform for high-throughput embryo phenotyping
    Brown, James M.
    Horner, Neil R.
    Lawson, Thomas N.
    Fiegel, Tanja
    Greenaway, Simon
    Morgan, Hugh
    Ring, Natalie
    Santos, Luis
    Sneddon, Duncan
    Teboul, Lydia
    Vibert, Jennifer
    Yaikhom, Gagarine
    Westerberg, Henrik
    Mallon, Ann-Marie
    BRIEFINGS IN BIOINFORMATICS, 2018, 19 (01) : 41 - 51