Witting Lennart, Seiffarth Johannes, Stute Birgit, Schulze Tim, Hofer Jan Matthis, Nöh Katharina, Eisenhut Marion, Weber Andreas P M, von Lieres Eric, Kohlheyer Dietrich
IBG-1: Institute of Bio- and Geosciences, Forschungszentrum Jülich GmbH, Jülich, Germany.
Computational Systems Biotechnology (AVT.CSB), RWTH Aachen University, Aachen, Germany.
Lab Chip. 2025 Jan 28;25(3):319-329. doi: 10.1039/d4lc00567h.
Quantification of cell growth is central to any study of photoautotrophic microorganisms. However, cellular self-shading and limited CO control in conventional photobioreactors lead to heterogeneous conditions that obscure distinct correlations between the environment and cellular physiology. Here we present a microfluidic cultivation platform that enables precise analysis of cyanobacterial growth with spatio-temporal resolution. Since cyanobacteria are cultivated in monolayers, cellular self-shading does not occur, allowing homogeneous illumination and precise knowledge of the photon-flux density at single-cell resolution. A single chip contains multiple channels, each connected to several hundred growth chambers. In combination with an externally applied light gradient, this setup enables high-throughput multi-parameter analysis in short time. In addition, the multilayered microfluidic design allows continuous perfusion of defined gas mixtures. Transversal CO diffusion across the intermediate polydimethylsiloxane membrane results in homogeneous CO supply, with a unique exchange-surface to cultivation-volume ratio. Three cyanobacterial model strains were examined under various, static and dynamic environmental conditions. Phase-contrast and chlorophyll fluorescence images were recorded by automated time-lapse microscopy. Deep-learning trained cell segmentation was used to efficiently analyse large image stacks, thereby generating statistically reliable data. Cell division was highly synchronized, and growth was robust under continuous illumination but stopped rapidly upon initiating dark phases. CO-Limitation, often a limiting factor in photobioreactors, was only observed when the device was operated under reduced CO between 50 and 0 ppm. Here we provide comprehensive and precise data on cyanobacterial growth at single-cell resolution, accessible for further growth studies and modeling.
细胞生长的定量分析是光合自养微生物任何研究的核心。然而,传统光生物反应器中细胞的自我遮蔽和有限的CO控制导致条件不均一,模糊了环境与细胞生理学之间的明显关联。在此,我们展示了一种微流控培养平台,能够以时空分辨率精确分析蓝细菌的生长。由于蓝细菌以单层培养,不会发生细胞自我遮蔽,从而实现均匀照明,并能在单细胞分辨率下精确了解光子通量密度。单个芯片包含多个通道,每个通道连接数百个生长室。结合外部施加的光梯度,此设置能够在短时间内进行高通量多参数分析。此外,多层微流控设计允许连续灌注特定的气体混合物。横向CO穿过中间的聚二甲基硅氧烷膜扩散,实现均匀的CO供应,具有独特的交换表面与培养体积比。在各种静态和动态环境条件下对三种蓝细菌模式菌株进行了检测。通过自动延时显微镜记录相差和叶绿素荧光图像。使用深度学习训练的细胞分割来有效分析大型图像堆栈,从而生成统计上可靠的数据。细胞分裂高度同步,在连续光照下生长稳健,但在开始黑暗阶段后迅速停止。CO限制通常是光生物反应器中的一个限制因素,仅在装置在50至0 ppm的降低CO条件下运行时才观察到。在此,我们提供了单细胞分辨率下蓝细菌生长的全面而精确的数据,可供进一步的生长研究和建模使用。