Results

Device layout and principle

The present microfluidic system (Figure 3.15A) is intended for high-throughput single-cell cultivation and analysis of evolving isogenic microcolonies. Each device consists of a polydimethylsiloxane (PDMS) chip (3 mm thick, 15 mm wide and 20 mm long) with incorporated microfluidic channels bonded onto a 170 µm thick glass slide (cover glass) suitable for high-resolution microscopy. Since PDMS chips can be

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  Figure 3.15: Device layout and schematic overview of the microfluidic single-cell cultivation (MSCC) system. (A) CAD drawing of the MSCC system, containing ten arrays of monolayer growth chambers (MGC). (B) Close up view of one array of parallelized MGCs (top). Each MGC (orange) is 40 µm x 40 µm in size and 1 µm in height. Each MGC is connected to two main channels (blue) with a cross section of 30 µm x 10 µm. (C) Cultivation principle of the MSCC. Microcolonies are growing inside the MGC while medium is infused continuously through the main channel. This allows the cultivation at constant environmental conditions.

The chip incorporates 400 parallel monolayer growth chambers (MGC) (Figure 3.15B) connected with inlet channels to supply growth medium and a single outlet channel for fluid disposal. Each MGC of 40 µm x 40 µm x 1 µm (width x length x height) can accommodate one microcolony of approximately 750 individual bacteria (Appendix C.5). The uniform MGC height of 1 µm restricts microcolony growth to a cell monolayer, facilitating continuous image-based analysis of individual cells by time-lapse microscopy. MGCs are arranged in between two ten-fold deeper supply channels (10 µm depth x 30 µm width) with laterally interconnected micrometer sized channel arrays as depicted (Figure 3.15B). Throughout the cultivation, medium is fed continuously at identical flow-rates via each supply channel. Thus, zero pressure difference occurs across each MGC, leading to solely diffusive mass transport into and inside the MGC. Therefore, cultivated cells are not negatively affected by convective flow, shear stress or pressure gradients. Experimental flow tracer studies as well as computational fluid dynamics analysis were performed to characterize fluid dynamics inside the MGCs.

  Figure 3.16: Characterization of flow and trapping profile within the MGCs. (A) Fluorescence traces of 1 µm polystyrene beads illustrating the horizontal flow profile during cell trapping and (B) behavior of 200 nm beads during cultivation conditions, illustrating diffusion based transport. (C) Flow profile in the main channel compared to the MGC. (D) Flow profile within the cultivation chambers obtained by CFD simulations, showing a strong domination of diffusion based material transport compared to the main channel, where convection is the main transport phenomenon (color bar one magnitude smaller). (E) Nutrient supply after medium change. Within seconds the medium can be changed, guaranteeing excess of nutrients during MSCC. (F) Glucose concentration profile during medium change illustrated for 1 s and 8 s after medium change.

Flow tracer analysis

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fluorescent beads flowing through the monolayer cultivation chamber (blue traces) during cell loading. Some of the 1 µm beads are randomly trapped between glass and PDMS. In a typical experiment, the cells are seeded and the growth phase is then initiated with a medium switch from bacterial suspension to growth medium with a flow rate of 300 nl/min in total. This procedure was now emulated by 0.2 µm sized green fluorescent beads supplemented into the fluid flow. Figure 3.16B shows the trapped 1 µm beads (blue) and the 0.2 nm green fluorescence beads distributed inside the MGC. The green fluorescent beads inside the MGCs moved solely by diffusion, whereas the large beads remain trapped (no tumbling). This approach experimentally proved stable cultivation conditions compared to “open” monolayer growth chambers, where cells might get lost during the cultivation due to partial convective flow within the chamber (Appendix C.6).

CFD simulations

CFD simulations were performed to validate the experimental findings and optimize structure geometry. CFD simulation confirmed a solely diffusive flow behavior inside the MGCs with a negligible convective flow (Figures 3.16C and D). The characteristic time of diffusive media exchange (tdiffusion) between the main channels and the MGCs (assumed distance = 50 µm) for small molecules such as glucose is tdiffusion = 1.8 s (diffusion coefficient D 7 ∙ 10 at 25 °C). These estimations were confirmed by results of the CFD calculations, simulating the exchange of medium after initiating the growth phase. Within four seconds, half of the maximum concentration is reached. This indicates that cells are supplied continuously with substrate (Figures 3.16E and F). Compared to the glucose consumption of one single cell, which is in the range of atto- and femtomol per cell per second [228], the system offers excess nutrient supply and limitations can be excluded under standard cultivation conditions (CGXII; 30 °C). It is known from previous studies that cells actively change their environment and under glucose excess conditions (as predominant in the MSCC) cellular overflow metabolism leads to excretion of by-products such as ethanol, acetate or lactate [229]. Therefore, the removal of secreted products and by-products of the system was simulated additionally.

As demonstrated in Appendix C.7, secreted products and by-products are continuously removed, preventing accumulation, and remain at a low concentrations compared to the provided substrate. Cells are solely exposed to metabolite concentrations produced by neighboring cells, compared to a batch system, where by-product and product concentration can reach mM range [229].

  Figure 3.17: Analysis of growth heterogeneity of two isogenic microcolonies of C. glutamicum. (A+B) Lineage trees showing the overall growth and division behavior. (C+D) Division time distribution and (E+F) cell length distribution before (blue) and after division (green) derived from lineage trees showing in Figure A and B.

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Single-cell bacterial growth pattern analysis of isogenic microcolonies Standard growth experiments with C. glutamicum ATCC 13032 were performed.

Appendix C.8 shows a growth rate distribution of different colonies under the same growth conditions for 60 colonies. The overall microcolony growth behavior is comparable and shows a normal distribution. Observed variations within the microcolony growth could be related to differences of individual cells and cell clusters, potentially affecting the population’s performance and leading to differences in the overall growth rate.

Figure 3.17A, 3.17B and Appendix C.9 visualize three different colony lineage trees with comparable maximum colony growth rates (µmax, Col 1 = 0.58^-1, µmax, Col 2 = 0.61

-1, µmax, Col 3 = 0.65 h^-1). Figure 3.17C-F shows the corresponding single-cell doubling times as well as length before and after division. The distribution reveals that division times range from 10 minutes up to over 160 minutes with an average of 63 min (67 min) for the two illustrated examples. Similar results can be found for the length distribution before and after division, ranging from 2.6 µm to 5.8 µm and an average cell size of 3.2 µm before division. After division, cells have an average cell size of 1.95 µm, ranging from 1.2 µm in minimum to 3.7 µm as maximum.

  Figure 3.18: Overview of single-cell heterogeneity and rare events during C. glutamicum growth under standard conditions. (A) Probability distribution function, illustrating cell-to-cell heterogeneity as well as rare cellular events. (B+C) Asynchronous division led to a spread in the Gauss distribution in cell size and division time. (D+E) Rare events can be found in both, temporal and structural context. (D) Slow growing as well as dormant cells can be found (red arrow). (E) Elongation, as well as other morphological deformed cell shapes (see also Appendix C.10) can be detected.

Identifying rare cellular events in C. glutamicum

The distributions of cell size and cell division show variability between individuals, commonly referred to as “population noise” in the literature [19] (for schematic illustration see Figure 3.18A). Figure 3.18B and 3.18C display population noise caused by asynchronous division times as well as asynchronous division length. However, even elongated cells (Figure 3.18E), morphological deformed cells (Appendix Figure C.10A), branched cells (Appendix Figure C.10B) as well as slow growing or putative “dormant”

cells have been observed (Figure 3.18E). From here on, these “outliers” are referred to as

“rare events”. Especially, the latter case is difficult to identify during exponential growth conditions. These events are rarely seen and affect populations in less than 1% of the cells (e.g., one cellular division event with 160 min (cf. Figure 3.18D)).

  Figure 3.19: Dynamic SOS response of C. glutamicum cells. (A) Representative C. glutamicum colony, containing one single cell showing a SOS response during cultivation under normal conditions. (B) Lineage tree showing a homogeneous growth profile, except one cell that stops growth. (C) Fluorescence and cell area profile over time, which show a correlation between reduction in cell area/growth and reporter output.

(D) Microcolony containing a SOS positive cell cluster. Cells continue to grow, but with reduced growth rate and changed morphology.

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The reliable quantification of rare events as classified in Figure 3.19 requires a

“measurement output” which correlates to specific characteristics of the rare events.

Elongation or reduced growth can be indicators for the induction of cellular stress responses [230]. Thus, a genetically encoded reporter system which is able to perceive a cell’s response to DNA damage was used for further studies (further denoted as “SOS reporter”). The reporter gives a visual output for the transcription of the gene encoding the single-strand binding protein RecA. In response to DNA damage, RecA binds ssDNA, catalyzes the autoproteolytic cleavage of the repressor LexA and thus leads to its own activation [227] (further denoted as “SOS+ cells”).

C. glutamicum/pJC1-PrecA-e2-crimson was cultivated under the same environ-mental conditions as described in the previous section. Here, rare events showing the characteristics such as morphological changes or inhibition of cell division were observed. Typically, the identified cells showed a strong continuous SOS reporter signal.

Figure 3.19A displays an isogenic C. glutamicum/pJC1-PrecA-e2-crimson colony, where one single cell stops dividing, whereas all sister cells showed continued growth (see lineage tree Figure 3.19B). At the same time, the cell’s SOS reporter signal is increased (Figure 3.19C), allowing the direct identification of these cells inside the microcolony. In this particular case, the cell did not resume to grow, and seemed irreversibly damaged.

As expected, cells that grow and divide exhibit no irreversible SOS signal (Figure 3.19B).

Figure 3.19D illustrates stressed cells of another colony, that in fact resume growth, showed a pulse of the SOS signal but resumed growth, indicating that the cell was able to overcome this stress by the SOS-induced repair mechanisms despite having an increased SOS reporter output. As seen in the corresponding image sequence, most of the cells undergoing SOS response belong to a cell cluster originating from two common ancestors.

High-throughput screening of rare cellular events

Recently, intensive FACS studies were used to quantify the amount of cells that expressed a spontaneous SOS response in C. glutamicum [227]. Figure 3.20A shows the quantification of the SOS response of shaking flask cultivation combined with FACS analysis, resulting in 0.05-1.25% of spontaneously induced cells as reported, depending on gating and the number of analyzed cells (Appendix Figure C.11 and C.12). Again, C. glutamicum/pJC1-PrecA-e2-crimson was cultivated in the MGCs under standard conditions. An endpoint determination of spontaneously induced cells of 300 MGCs was performed after the chambers were filled. Similar to the shake flask experiments, 0.07-0.5% of cells showed an increased stress response. Figure 6B shows that the average number of cells per microcolony with an increased reporter output, which lies at around three to four cells per cultivation chamber. This can significantly vary, depending on the investigated chamber and the method of quantification (Appendix C.13).

  Figure 3.20: Quantification of SOS response during MSCC high-throughput screening. (A) FACS plot obtained by flow cytometric data of shaking flask cultivations. 0.07% of cells are showing spontaneous SOS response. (B) Number of cells that show SOS signal occurring in each of the 300 chambers. The distribution shows an average of approximately three to four spontaneously induced cells per chamber, corresponding to 0.07-0.5% percent of cells. (C) Image of three colonies at the end of the cultivation: (I) Colony containing no cell with increased SOS signal. (II) Colony shows two cells with an increased SOS signal. (III) Colony shows several cells with a positive SOS signal. (D) Area-fluorescence dot-plot obtained through MSCC of the three colonies shown in C.

In Figure 3.20D, three different subpopulations are shown. The cellular

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with two SOS+ cells resulting in 0.26% of stressed cells. In Population III, 25 cells show an increased signal. In this exceptional case, over 3% of spontaneously induced cells can be found under standard cultivation conditions. Appendix C.14 presents the difference in SOS+ cells for different quantification methods.