Single-Cell Multiomics Analysis for Drug Discovery

被引:17
|
作者
Nassar, Sam F. [1 ,2 ]
Raddassi, Khadir [3 ]
Wu, Terence [4 ]
机构
[1] Brandeis Univ, Dept Biol, Waltham, MA 02453 USA
[2] IsoPlexis, Branford, CT 06405 USA
[3] Yale Sch Med, Dept Neurol, New Haven, CT 06511 USA
[4] Yale Univ, West Campus Analyt Core, West Haven, CT 06516 USA
关键词
multiomics; genomics; metabolomics; proteomics; transcriptomics; single-cell; mass spectrometry; IsoLight; COVID-19; DECISION-MAKING PROCESS; MASS CYTOMETRY; STRUCTURAL MODIFICATION; RECENT INNOVATIONS; FLOW-CYTOMETRY; PROTEOMICS; RESPONSES; SUPPORT; METABOLOMICS; SPECTROMETRY;
D O I
10.3390/metabo11110729
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Given the heterogeneity seen in cell populations within biological systems, analysis of single cells is necessary for studying mechanisms that cannot be identified on a bulk population level. There are significant variations in the biological and physiological function of cell populations due to the functional differences within, as well as between, single species as a result of the specific proteome, transcriptome, and metabolome that are unique to each individual cell. Single-cell analysis proves crucial in providing a comprehensive understanding of the biological and physiological properties underlying human health and disease. Omics technologies can help to examine proteins (proteomics), RNA molecules (transcriptomics), and the chemical processes involving metabolites (metabolomics) in cells, in addition to genomes. In this review, we discuss the value of multiomics in drug discovery and the importance of single-cell multiomics measurements. We will provide examples of the benefits of applying single-cell omics technologies in drug discovery and development. Moreover, we intend to show how multiomics offers the opportunity to understand the detailed events which produce or prevent disease, and ways in which the separate omics disciplines complement each other to build a broader, deeper knowledge base.
引用
下载
收藏
页数:14
相关论文
共 50 条
  • [41] Single-cell sequencing techniques from individual to multiomics analyses
    Yukie Kashima
    Yoshitaka Sakamoto
    Keiya Kaneko
    Masahide Seki
    Yutaka Suzuki
    Ayako Suzuki
    Experimental & Molecular Medicine, 2020, 52 : 1419 - 1427
  • [42] Single-cell multiomics sequencing and analyses of human colorectal cancer
    Bian, Shuhui
    Hou, Yu
    Zhou, Xin
    Li, Xianlong
    Yong, Jun
    Wang, Yicheng
    Wang, Wendong
    Yan, Jia
    Hu, Boqiang
    Guo, Hongshan
    Wang, Jilian
    Gao, Shuai
    Mao, Yunuo
    Dong, Ji
    Zhu, Ping
    Xiu, Dianrong
    Yan, Liying
    Wen, Lu
    Qiao, Jie
    Tang, Fuchou
    Fu, Wei
    SCIENCE, 2018, 362 (6418) : 1060 - +
  • [43] Protein velocity and acceleration from single-cell multiomics experiments
    Gorin, Gennady
    Svensson, Valentine
    Pachter, Lior
    GENOME BIOLOGY, 2020, 21 (01)
  • [44] Single-cell sequencing techniques from individual to multiomics analyses
    Kashima, Yukie
    Sakamoto, Yoshitaka
    Kaneko, Keiya
    Seki, Masahide
    Suzuki, Yutaka
    Suzuki, Ayako
    EXPERIMENTAL AND MOLECULAR MEDICINE, 2020, 52 (09): : 1419 - 1427
  • [45] Deciphering the generation of heterogeneity in esophageal squamous cell carcinoma metastasis via single-cell multiomics analysis
    Kaiwen Sheng
    Jun Chen
    Ruitang Xu
    Haoqiang Sun
    Ran Liu
    Yongjie Wang
    Wenwen Xu
    Jiao Guo
    Miao Zhang
    Shuai Liu
    Juan Lei
    Yawen Sun
    Yang Jia
    Dianhao Guo
    Journal of Translational Medicine, 23 (1)
  • [46] The Evolution of Single-Cell Analysis and Utility in Drug Development
    Shibani Mitra-Kaushik
    Anita Mehta-Damani
    Jennifer J. Stewart
    Cherie Green
    Virginia Litwin
    Christèle Gonneau
    The AAPS Journal, 23
  • [47] The Evolution of Single-Cell Analysis and Utility in Drug Development
    Mitra-Kaushik, Shibani
    Mehta-Damani, Anita
    Stewart, Jennifer J.
    Green, Cherie
    Litwin, Virginia
    Gonneau, Christele
    AAPS JOURNAL, 2021, 23 (05):
  • [48] A multiplex single-cell RNA-Seq pharmacotranscriptomics pipeline for drug discovery
    Alice Dini
    Harlan Barker
    Emilia Piki
    Subodh Sharma
    Juuli Raivola
    Astrid Murumägi
    Daniela Ungureanu
    Nature Chemical Biology, 2025, 21 (3) : 432 - 442
  • [49] A mixture-of-experts deep generative model for integrated analysis of single-cell multiomics data
    Minoura, Kodai
    Abe, Ko
    Nam, Hyunha
    Nishikawa, Hiroyoshi
    Shimamura, Teppei
    CELL REPORTS METHODS, 2021, 1 (05):
  • [50] Tensor-Decomposition-Based Unsupervised Feature Extraction in Single-Cell Multiomics Data Analysis
    Taguchi, Y-h
    Turki, Turki
    GENES, 2021, 12 (09)