Microbial heterogeneity affects bioprocess robustness: Dynamic single-cell analysis contributes to understanding of microbial populations

被引:101
|
作者
Delvigne, Frank [1 ]
Goffin, Philippe [1 ]
机构
[1] Univ Liege, Gembloux Agrobio Tech, Unite Bioind, Gembloux, Belgium
关键词
Bioreactor heterogeneity; Microbial stress; Scale-up; Single cell; Stress biosensor; RECOMBINANT ESCHERICHIA-COLI; STOCHASTIC GENE-EXPRESSION; GREEN FLUORESCENT PROTEIN; MULTIPARAMETER FLOW-CYTOMETRY; SCALE-DOWN; REAL-TIME; PHYSIOLOGICAL HETEROGENEITY; PHENOTYPIC HETEROGENEITY; SACCHAROMYCES-CEREVISIAE; VISUALIZING EVOLUTION;
D O I
10.1002/biot.201300119
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Heterogeneity or segregation of microbial populations has been the subject of much research, but the real impact of this phenomenon on bioprocesses remains poorly understood. The main reason for this lack of knowledge is the difficulty in monitoring microbial population heterogeneity under dynamic process conditions. The main concepts resulting in microbial population heterogeneity in the context of bioprocesses have been summarized by two distinct hypotheses. The first involves the individual history of microbial cells or the "path" followed during their residence time inside the process equipment. The second hypothesis involves a coordinated response by the microbial population as a bet-hedging strategy, in order to cope with process-related stresses. The respective contribution of each hypothesis to microbial heterogeneity in bioprocesses is still unclear. This illustrates the fact that, although microbial phenotypic heterogeneity has been thoroughly investigated at a fundamental level, the implications of this phenomenon in the context of microbial bioprocesses are still subject to debate. At this time, automated flow cytometry is the best technique for investigating microbial heterogeneity under process conditions. However, dedicated software and relevant biomarkers are needed for the proper integration of flow cytometry as a bioprocess control tool.
引用
收藏
页码:61 / 72
页数:12
相关论文
共 50 条
  • [41] Microbial single-cell analysis in picoliter-sized batch cultivation chambers
    Kaganovitch, Eugen
    Steurer, Xenia
    Dogan, Deniz
    Probst, Christopher
    Wiechert, Wolfgang
    Kohlheyer, Dietrich
    NEW BIOTECHNOLOGY, 2018, 47 : 50 - 59
  • [42] A critical review of NanoSIMS in analysis of microbial metabolic activities at single-cell level
    Gao, Dawen
    Huang, Xiaoli
    Tao, Yu
    CRITICAL REVIEWS IN BIOTECHNOLOGY, 2016, 36 (05) : 884 - 890
  • [43] Single-Cell Isolation and Size Sieving Using Microenclosure Array for Microbial Analysis
    Matsutani, Akihiro
    Takada, Ayako
    SENSORS AND MATERIALS, 2015, 27 (05) : 383 - 390
  • [44] Microfluidic single-cell analysis links boundary environments and individual microbial phenotypes
    Dusny, Christian
    Schmid, Andreas
    ENVIRONMENTAL MICROBIOLOGY, 2015, 17 (06) : 1839 - 1856
  • [45] Integrating microbial GWAS and single-cell transcriptomics reveals associations between host cell populations and the gut microbiome
    Li, Jingjing
    Ma, Yunlong
    Cao, Yue
    Zheng, Gongwei
    Ren, Qing
    Chen, Cheng
    Zhu, Qunyan
    Zhou, Yijun
    Lu, Yu
    Zhang, Yaru
    Deng, Chunyu
    Chen, Wei-Hua
    Su, Jianzhong
    NATURE MICROBIOLOGY, 2025,
  • [46] Streamlined analysis of heterogeneity in cell populations using single-cell gene expression profiling.
    May, A. P.
    Lebofsky, R.
    Leyrat, A.
    Fowler, B.
    Shuga, J.
    Chen, P.
    Wang, J.
    Toppani, D.
    Thu, M.
    Wang, M.
    West, J.
    Weaver, S.
    Jones, B.
    Kemp, D.
    Norris, M.
    Unger, M.
    Charn, T-H.
    Jones, B.
    MOLECULAR BIOLOGY OF THE CELL, 2012, 23
  • [47] Massively parallel single-cell sequencing of diverse microbial populations (vol 21, pg 228, 2024)
    Lan, Freeman
    Saba, Jason
    Ross, Tyler D.
    Zhou, Zhichao
    Krauska, Katie
    Anantharaman, Karthik
    Landick, Robert
    Venturelli, Ophelia S.
    NATURE METHODS, 2025, 22 (02) : 446 - 446
  • [48] Paper Single-cell transcriptomic analysis uncovers diverse and dynamic senescent cell populations
    Wechter, Noah
    Rossi, Martina
    Anerillas, Carlos
    Tsitsipatis, Dimitrios
    Piao, Yulan
    Fan, Jinshui
    Martindale, Jennifer L.
    De, Supriyo
    Mazan-Mamczarz, Krystyna
    Gorospe, Myriam
    AGING-US, 2023, 15 (08): : 2824 - 2851
  • [49] SINGLE-CELL ANALYSIS AND MODELLING OF CELL POPULATION HETEROGENEITY
    Samusik, Nikolay
    Aghaeepour, Nima
    Bendall, Sean
    PACIFIC SYMPOSIUM ON BIOCOMPUTING 2017, 2017, : 557 - 563
  • [50] Quantitation and Comparison of Phenotypic Heterogeneity Among Single Cells of Monoclonal Microbial Populations
    Calabrese, Federica
    Voloshynoyska, Iryna
    Musat, Florin
    Thullner, Martin
    Schloemann, Michael
    Richnow, Hans H.
    Lambrecht, Johannes
    Mueller, Susann
    Wick, Lukas Y.
    Musat, Niculina
    Stryhanyuk, Hryhoriy
    FRONTIERS IN MICROBIOLOGY, 2019, 10