Turning single cells into microarrays by super-resolution barcoding

被引:8
|
作者
Cai, Long [1 ]
机构
[1] CALTECH, Pasadena, CA 91125 USA
关键词
super-resolution microscopy; systems biology; single cells; single-molecule FISH; GENE-EXPRESSION; IN-VIVO; PROTEIN; MOLECULES; PCR; DROSOPHILA; MARKER;
D O I
10.1093/bfgp/els054
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
In this review, we discuss a strategy to bring genomics and proteomics into single cells by super-resolution microscopy. The basis for this new approach are the following: given the 10 nm resolution of a super-resolution microscope and a typical cell with a size of (10 mu m)(3), individual cells contain effectively 10(9) super-resolution pixels or bits of information. Most eukaryotic cells have 10(4) genes and cellular abundances of 10-100 copies per transcript. Thus, under a super-resolution microscope, an individual cell has 1000 times more pixel volume or information capacities than is needed to encode all transcripts within that cell. Individual species of mRNA can be uniquely identified by labeling them each with a distinct combination of fluorophores by fluorescence in situ hybridization. With at least 15 fluorophores available in super-resolution, hundreds of genes in can be barcoded with a three-color barcode (C-3(15) = 455). These calculations suggest that by combining super-resolution microscopy and barcode labeling, single cells can be turned into informatics platforms denser than microarrays and that molecular species in individual cells can be profiled in a massively parallel fashion.
引用
收藏
页码:75 / 80
页数:6
相关论文
共 50 条
  • [41] Super-resolution imaging of transcription in living cells
    Cisse, Ibrahim
    PROTEIN SCIENCE, 2023, 32 (12)
  • [42] Super-resolution microscopy of living bacterial cells
    Ponomareva, E. V.
    Vishnyakov, I. E.
    Morozova, N. E.
    Polinovskaya, V. S.
    Khodorkovskii, M. A.
    Vedyaykin, A. D.
    4TH INTERNATIONAL SCHOOL AND CONFERENCE ON OPTOELECTRONICS, PHOTONICS, ENGINEERING AND NANOSTRUCTURES (SAINT PETERSBURG OPEN 2017), 2017, 917
  • [43] Super-resolution advances imaging of living cells
    Verkhusha, Vladislav
    TRAC-TRENDS IN ANALYTICAL CHEMISTRY, 2009, 28 (03) : III - IV
  • [44] Super-Resolution Optical Imaging of Bacterial Cells
    Parisi, Miranda
    Lucidi, Massimiliano
    Visca, Paolo
    Cincotti, Gabriella
    IEEE JOURNAL OF SELECTED TOPICS IN QUANTUM ELECTRONICS, 2023, 29 (04)
  • [45] Super-Resolution Imaging of Chromosomal DNA in Cells
    Simonson, Paul D.
    Rothenberg, Eli
    Selvin, Paul R.
    BIOPHYSICAL JOURNAL, 2011, 100 (03) : 617 - 617
  • [46] Cytokinesis: Going Super-Resolution in Live Cells
    Liu, Yajun
    Wu, Jian-Qiu
    CURRENT BIOLOGY, 2016, 26 (21) : R1150 - R1152
  • [47] Single Image Super-resolution Based on Residual Learning
    Xie, Chao
    Lu, Xiaobo
    PROCEEDINGS OF 2017 INTERNATIONAL CONFERENCE ON VIDEO AND IMAGE PROCESSING (ICVIP 2017), 2017, : 124 - 129
  • [48] SRFeat: Single Image Super-Resolution with Feature Discrimination
    Park, Seong-Jin
    Son, Hyeongseok
    Cho, Sunghyun
    Hong, Ki-Sang
    Lee, Seungyong
    COMPUTER VISION - ECCV 2018, PT XVI, 2018, 11220 : 455 - 471
  • [49] Edge-Informed Single Image Super-Resolution
    Nazeri, Kamyar
    Thasarathan, Harrish
    Ebrahimi, Mehran
    2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW), 2019, : 3275 - 3284
  • [50] An Optimal Weight Model for Single Image Super-Resolution
    Dinh Hoan Trinh
    Luong, Marie
    Rocchisani, Jean-Marie
    Canh Duong Pham
    Huy Dien Pham
    Dibos, Francoise
    2012 INTERNATIONAL CONFERENCE ON DIGITAL IMAGE COMPUTING TECHNIQUES AND APPLICATIONS (DICTA), 2012,