Farzad Rezaeianaran* and
Martin A. M. Gijs
Laboratory of Microsystems, Ecole Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland. E-mail: rezaeianaran.farzad@epfl.ch
First published on 8th June 2023
We study the presence within the worm Caenorhabditis elegans (C. elegans) of a fluorescent strain of the worm's bacterial food (Escherichia coli (E. coli) OP50) during early adulthood. Use of a microfluidic chip based on a thin glass coverslip substrate allows investigation of the intestinal bacterial load using a Spinning Disk Confocal Microscope (SDCM) equipped with a high-resolution objective (60×). High-resolution z-stack fluorescence images of the gut bacteria in adult worms, which were loaded in the microfluidic chip and subsequently fixed, were analyzed using IMARIS software and 3D reconstructions of the intestinal bacterial load in the worms were obtained. We present an automated bivariate histogram analysis of the volumes and intensities of the bacterial spots for each worm and find that, as the worms age, the bacterial load in their hindguts increases. We show the advantage of single-worm resolution automated analysis for bacterial load studies and anticipate that the methods described in our work can be easily implemented in existing microfluidic solutions to enable thorough studies of bacterial proliferation.
In order to discover the virulence determinants, different mutants of a pathogen of interest as well as the worms are generated and the interactions between the bacteria and the host (worm) are studied through survival curves and bacterial colonization assays. However, these assays typically cannot capture the interactions between the host and the bacteria on a microscopic scale. For example, the bacterial colonization itself can only measure the number of viable bacteria and it cannot determine whether the bacteria are merely accumulating in the gut or if they are proliferating there. Additionally, this assay cannot reveal the spatio-temporal dynamics of bacterial colonization in the gut as this requires real-time in vivo fluorescence imaging of the gut. Furthermore, the impact of pathogenic bacteria on the worm is not limited to the colonization of the gut. For example, it has been reported that both Salmonella typhimurium (S. typhimurium) and Pseudomonas aeruginosa (P. aeruginosa) reduce the motility and the rate of pharyngeal pumping of the worm over the course of infection.8,9 Intestinal infection with Serratia marcescens (S. marcescens) leads to the lysis and vacuolation of intestinal cells and a reduction of the volume of the epithelium.10 Therefore, a complete description of bacterial pathogenesis also requires imaging, to extract the relevant phenotypes, and the use of/designing of specific assays that target a certain interaction between the host and the pathogen (e.g., using competition and shifting assays to investigate whether a certain pathogen is capable of establishing a persistent infection8,11). While we mainly discussed the requirements for studying bacterial pathogenesis in the worm, it should be noted that the same requirements apply when it comes to investigating the beneficial aspects of commensal and probiotic bacteria or the role of the worm's native microbiome.
Based on the analysis above, it can be seen that a comprehensive characterization of the host–microbiota interactions, if done manually, is extremely laborious. However, this is exactly what makes microfluidics such a powerful approach. Microfluidic devices offer:12 (i) precise control over experimental conditions, (ii) facilitating manipulations of worms permitting high throughput screening studies, (iii) ease-of-doing long-term studies, (iv) reversible immobilization for both low- and high-resolution imaging, (v) laser microsurgery.13–15 As a result, the use of microfluidics for studying C. elegans has been extensively practiced16 and different devices for low-throughput long-term studies at single worm resolution17–21 as well as high-throughput and high-content screening studies have been reported.22–24
However, only a few microfluidic devices have been reported for studying the interactions between bacteria and C. elegans.25–28 Yang et al.,25 fabricated a microfluidic chip featuring 32 chambers arranged radially and 4 concentration gradient generators for evaluation of in vivo antimicrobial activity of several compounds. Each concentration generator supplies 8 unique chambers with 8 different concentrations of a given antibiotic. The device enables on-chip establishment of an infection model (in this case Staphylococcus aureus after 6 hours) and antibiotic treatment of the infection (in this case amoxicillin and some natural compounds for 48 hours) and, compared to conventional methods, requires less analysis time and less reagent consumption. However, the evaluation of antimicrobial activity was mostly limited to survival curves (within a 72 hours period). Fluorescence imaging of the worms were feasible but, since each chamber contained around 10 worms that were not in immobilized, neither high-resolution imaging nor single-worm resolution analysis were possible.
In another study by Lee et al.,26 a microfluidic chip for implementing a C. elegans killing assay was reported. The chip featured large chambers where the worm and the pathogen of interest (in this case P. aeruginosa) could be loaded. Survival curves were obtained with high accuracy and reproducibility. Due to lack of an immobilization mechanism on the chip, only low-resolution imaging could be performed. Imaging in the brightfield channel was used for the automated detection of dead worms and motility analysis, while fluorescence imaging was implemented for measuring the expression of irg-1::GFP (infection response gene 1). Since, the worms were freely moving in the chamber without any compartments, they could not be differentiated from each other, and thus individual worms could not be monitored over the entire course of the experiment.
In the more recent works by Viri et al.,27,28 the dynamics of bacterial transition, load and absorption in the gut of the worms are studied in an automated fashion through fluorescence imaging. The first microfluidic device27 contained 4 channels, each of which comprised 5 consecutive chambers, separated by filter structures. These chambers, which could hold 1–3 worms, included a culturing area where the worms could freely move and feed on fluorescent E. coli and tapered traps that immobilize the worm and stop it from eating (due to the application of strong fluid flow required for immobilization). Low-resolution fluorescent imaging over the entire body of the worms was performed, while they were freely moving and feeding on the fluorescent bacteria. Analyzing the resulting image sequence yielded the spatio-temporal distribution of the bacteria in the gut, albeit at a low resolution. In addition, the duration of a full cycle of bacterial ingestion until intestinal clearance was determined. Similar imaging and analysis were also performed while the worms were immobilized. In this case, the time constant of the transition of recently ingested bacteria could be obtained (as the worms could not eat while they were immobilized).
The second microfluidic device28 was similar in scope to the first one but had a different design. The device still consisted of 4 channels, but each channel had 4 parallel chambers instead. As before, these chambers included a culturing area where the worms were free to move and feed but the design of the trapping structure was modified so that the worm could continue eating while it was immobilized. As a result, the dynamics of bacterial load and digestion could be studied on immobilized worms using fluorescent bacteria at relatively higher imaging resolution. The higher resolution made it possible to distinguish the ingested intact bacteria from the ingested disrupted ones and as a result, and the time constant for food digestion was determined. As the immobilization could be maintained for 30 hours, long-term studies at single-worm resolution were also possible. While the newly improved immobilization and imaging procedure allows the spotting of intact bacteria, the bacterial dynamics load assay described was not capable of answering questions on bacterial colonization of the gut. Granting that the intact bacteria are indeed viable and have the potential to forming colonies, it is by no means guaranteed that this effectively happens in the gut; it is very much possible that the viable bacteria merely accumulate in the gut and do not proliferate.
Therefore, in this work, we intend to bring the analysis to a higher level of resolution, by acquiring for the first time high-resolution z-stack fluorescence images of fluorescent E. coli bacteria in the gut of fixed worms using a SPDM. The resulting images are subsequently imported in IMARIS software to allow visualization bacterial load of the gut in 3D. Such representation facilitates the investigation of eventual bacterial colonization of the gut at high-spatial resolution. We could study whether there are preferential regions in the gut for colonization and how intestinal bacterial load advanced with time.
E. coli OP50 was grown overnight in LB at 37 °C on a shaker. E. coli OP50 expressing Red Fluorescent Protein (RFP) was provided by the C. elegans Ageing Laboratory at the University College London. E. coli OP50 was transformed with plasmid pRZT3::dsRED to construct RFP-expressing E. coli OP50. The plasmid also contains genes for tetracycline resistance. As a result, LB RFP E. coli OP50 was grown in LB containing 10 μg ml−1 of tetracycline overnight at 37 °C on a shaker.
Before a worm population of interest can be loaded on the chip, the channels should be filled by S-medium. The chip therefore is placed on the stage of the Zeiss Axio Imager.M2 equipped with a 2.5X Zeiss objective to be observed in brightfield mode. To introduce S-medium in the chip, we connect its inlet tube to a neMESYS syringe pump (CETONI GmbH, Korbuβen, Germany) where a 1 ml BD l Luer-Lok syringe containing S-medium is present. When the chip is filled with S-medium for the first time, bubbles of air are bound to form in the channel. To remove the air bubbles, the tube of the outlet is clipped, 10 μl of S-medium is dispensed in the channel which pressurizes the air bubbles and thus facilitates their disappearance via the outward diffusion of air through the PDMS. In our case, the debubbling process takes around 15–20 minutes. Next, the worms suspended in S-medium (inside an Eppendorf tube) can be placed at the outlet of the chip and by aspirating with a flow rate of 500 nl s−1, the worms are loaded in the chip (Fig. 1d). It is preferred to have around 10–15 worms in the channel. In the case of overcrowding/undercrowding, further aspirations/dispensing of S-medium can be performed to adjust the number of worms present in the channel. In order to fix the worms, a 4% PFA solution is placed at the outlet and by aspiration with a flow rate of 80 nl s−1, it is introduced inside the channel. We use a relatively low flow rate to avoid excessive pushing of the worms against the filters. The worms are fixed within 5–10 minutes of being in contact with the 4% PFA solution. At this point, the inlet and outlet tubing are cut and clipped, and the worms are ready for the high-resolution imaging.
Our previous observations (not shown here) revealed that the hindgut is most often the location where bacterial presence is seen and, thus, we limit our observations to the hindgut (Fig. 3a). High resolution brightfield images, RFP channel and GFP channel images are shown in Fig. 3b, c and d, respectively. The imaging parameters (as mentioned in the Materials and Methods section) have been optimized such that signal/noise ratio is high enough in the fluorescence channels, photobleaching is limited and signal saturation is avoided. The z-range of the z-stacks (50 μm) is also large enough to cover the intestine entirely. We use the brightfield mode (Fig. 3b) to avoid photobleaching while locating the hindgut and adjusting the center of focus for z-stack imaging; afterwards fluorescence imaging is performed (Fig. 3c and d). The presence of fluorescent bacteria in hindgut can be clearly observed in Fig. 3c while the worm autofluorescence that originates mainly from the intracellular lysosome-derived granules within the intestinal cells33 is seen in Fig. 3d. As the field of view allows only a length of 150 μm of the worm to be imaged at a time, each worm needs to be imaged twice to observe the entire hindgut. In our case, the region closest to the tail was imaged first and it was followed by imaging the second half of the hindgut which is further away from the tail.
We can see in Fig. 4 several slices from z-stack fluorescence imaging of a typical fixed worm that has fed on RFP labelled E. coli OP50 for 4 days. As shown schematically in (Fig. 4a), the bacteria can be present in different focal planes (z numbers) within a cross-section of the intestinal lumen of the worm. In conventional widefield microscopy the light is received from many focal planes some of which are out of focus and thus leading to poor resolution (blurry image). In contrast, in SDCM, the pinholes block the signal from out-of-focus planes and as a result the bacteria in each focal plane are resolved.
Fig. 4b–d are slices with certain z numbers in the RFP channel, where each focal plane reveals the presence of bacteria in different positions (not only in plane but also along the z-axis) within the hindgut. Furthermore, while individual bacterial spots can be observed in Fig. 4a and c, in Fig. 4b, the bacteria appear to form clusters having high density. Fig. 4e–g show the same slices (the z numbers of e, f and g are the same as b, c and d, respectively) imaged in the GFP channel. It can be seen that, in the presence of strong worm autofluorescence, the signal appears both in the GFP (Fig. 4e and g) channel and the RFP channel (Fig. 4b and d). As a result, in these cases the latter RFP signals should be omitted from bacterial load analysis.
We use IMARIS for visualization and processing of our raw z-stack fluorescence images. IMARIS assembles all the slices belonging to the same channel (e.g. the RFP channel) of a given raw image and thereby creates a 3D image. In our case, since our raw images have two channels, namely, the RFP and the GFP channel, IMARIS creates two 3D images from each raw images-stacks. We can also display the two 3D images at the same time as shown in Fig. 5a. In this image, a worm that has fed on RFP E. coli OP50 for 3 days is displayed. The green signal originates from the autofluorescence of the worm, while the orange signal is due to RFP E. coli. A magnified view of the purple rectangle is shown in Fig. 5b. We can now observe more clearly the orange spots. We also see 3 yellow spots, as indicated by the white arrows, that are due to strong autofluorescence. The strong autofluorescence leads to the presence of signals both in the GFP and the RFP channels. The overlap of the stronger signal in the GFP channel with the weaker signal in the RFP channel results in the yellow spots. These signals should be omitted from our bacterial analysis.
By using the built-in spot detection algorithm of IMARIS, we can detect bacterial spots and obtain their sizes and intensities in a consistent manner. The algorithm begins with setting the RFP channel as the channel of interest for spot detection. Afterwards, we need to provide an initial estimate for the smallest spot size. Based on our observation of bacterial spots across different slices of the raw z-stack fluorescence images, we found the smallest bacteria spot to be 1 μm in the “X” and the “Y” directions. We assumed the bacterial spots to be spherical but due to the point spread function (PSF) of the microscope, the spherical spots become elongated in the “Z” direction and turn into ellipsoids. We thus used 2 μm in the “z” direction to account for the effect of PSF. IMARIS then uses the spot size estimation together with an internal parameter called “Quality” to detect spots. The “Quality” parameter is related to the intensity at the center of a spot in the RFP channel while including correction for the background intensity around that spot. This parameter sets a threshold on the detected spots, meaning spots with lower values are discarded and the spots with higher value are accepted. Based on one typical image, we optimized the value of this parameter such that the vast majority of the spots that we observe (in that one image) are detected by IMARIS and no false positives are present. We found the optimized value for the “Quality” to be 150 and we used this value for the detection of the spots in all the images to make sure that the detection of spots is consistent (i.e. the results obtained from different images can be meaningfully compared).
The spot detection algorithm also allows the additional filtering of the detected spots by other parameters such as the central intensity of the spots in the GFP channel. This is extremely useful, since, by setting an upper limit (3500–4000, depending on the image) for the central intensity we can discard those spots (in the RFP channel) that originate from the strong autofluorescence of the worm. Fig. 5c shows the detected spots after applying the said constraints i.e. estimation of smallest spot size, “Quality” factor, and central intensity in the GFP channel. As it can be seen, all the bacterial spots are detected while the yellow spots, as shown by the white arrows, are discarded from further analysis.
It should be noted that, at this step, only for the purpose of representation, the algorithm assigns to all the detected spots the same ellipsoid that we defined when we provided the dimensions of the smallest spot size. More importantly, it should be emphasized that up to this step, IMARIS treats these detected spots merely as seed points, meaning that it is only the centers of the spots that are meaningful, while their sizes still need to be determined in a consistent manner. The reason that those ellipsoids appear to have different sizes in Fig. 5c is due to the perspective projection.
Next, we used the “local contrast” method in IMARIS to find the spot size. In this method, the intensity distribution in the region around the center of a detected spot is first adjusted by subtracting from it the intensity of the background in the vicinity of that spot. After the adjustment, only the region around the center of that spot that has an intensity higher than a certain threshold value is considered to be belonging to the spot. If this threshold value is too low, the finalized detected spot will be larger than the actual bacterial spot while if the threshold value is too high, the finalized detected spot will be smaller than the actual bacterial spot. Based on analyzing typical images, we found the optimized value threshold value to be 80.4 in order that the finalized detected spot matched the actual bacterial spot. Fig. 5d shows the finalized detected spots where the sizes of the detected spots now match the size of the actual bacterial spots. The fine-tuning of this threshold value is subjective, as it is somewhat arbitrary to exactly determine where the border of the actual bacterial spot is and in turn how large the finalized detect spot should be. In addition, sometimes the actual bacterial spot is not perfectly ellipsoidal, which makes it impossible to have a perfect overlap between the actual bacterial spot and the finalized detected spot (Fig. 5d). As a result, in these cases, it is harder to find the exact value for the threshold parameter. In fact, in all cases, the diameters of the finalized spots are calculated such that the volumes of the finalized detected spots are equal to the volumes of the regions around the center of the spots that have higher intensity than the chosen threshold value. Despite these slight difficulties, we used the same threshold value (80.4) for all the images to ensure that the spot sizes are determined in a consistent manner.
The volume and the average intensity distribution across all the bacterial spots can be presented with histograms (Fig. 6b–d). While the histograms in Fig. 6b and c are simpler and easier to grasp, in principle, they exclude the possibility of revealing any trends or correlation between the volumes of the bacterial spots and their average intensities. Therefore, we prefer to use a bivariate histogram (Fig. 6d) to visualize the data in a more precise manner. This method of post-processing can be applied to each worm within an experiment to obtain single-worm resolution statistics of the bacterial presence within the hindgut. Additionally, the volumes and the average intensities of all the bacterial spots from all the worms belonging to one experiment, e.g., all the worms that have fed on RFP labelled E. coli OP50 for 4 days, can be pooled together. These data can be then visualized with a bivariate histogram normalized by the number of worms in that particular experiment (Fig. 7d). This bivariate histogram thus shows an average distribution of the volumes and the average intensities of the bacterial spots of worms in one experiment. Additionally, normalizing the sum of Vol · C. Int across all the pooled bacterial spots by the total number of worms in the experiment yields the average bacterial load per worm (Fig. 7e).
It can be seen that, as the worms age, the bacterial load in their hindguts increases (Fig. 7). This is in agreement with the general dynamics of intestinal bacterial load of the worm with age.34 It is known that the function of the grinder in the pharynx and the innate immunity of the worm decline with age and that older worms have less control over the number of bacteria present in their intestine, thus this trend is to be expected.34 Furthermore, over time, the number of bacterial spots, their sizes and their average intensity also increase. Therefore, not only there are more bacterial spots within the hindgut of the worm but also that each bacterial spot contains more bacteria. This suggests that there are preferential places in the hindgut where the bacteria accumulate over time, or it may also be possible that the ingested bacteria colonize the gut and proliferate there. It should be emphasized that these two phenomena are not mutually exclusive. Additionally, we can observe a significant increase in the number of small bacterial spots (volume ranging from 0 to 2 μm3) in day 3 and day 4. Perhaps due to the weakening of the innate immune system35 and the loss of function in the grinder in the pharynx36 in older worms, it becomes easier for the recently ingested bacteria to accumulate in the gut. As before, another possible explanation would be that, in older worms, due to the weakening of the immune system, the bacteria can more easily proliferate and spread in the gut.
The bacterial load in each individual worm in each day can be seen in Fig. 7e. A bacterial load of 1 was attributed to the worms for which no bacteria could be detected, so that the bacterial load can be displayed using a logarithmic axis. It can be seen that the average load in the hindgut of the worms is increasing with the age of worm similar to what was observed with the bivariate histograms. Even though the average bacterial load can be useful for identifying general trends, it does not provide an accurate representation of all the worms involved in a given experiment and in fact can be quite misleading. Additionally, by using average values to represent the entire population, the subpopulations that exhibit a significantly different phenotype (than the average of the entire population) will remain undiscovered. For example, while the average bacterial load for day 2 is quite high and comparable to that of day 3 and day 4, the large majority of the worms at day 2 have no or very little bacterial in their hindguts. Similar representation problems can also exist for the worms in day 1 and day 3. Even in the case of worms of day 4, which have the least worm-to-worm variations, the worm with highest bacterial population has 200 times more bacteria in hindgut than the one with the lowest bacterial population.
Previous studies done via conventional techniques on agar plates are affected by the same issue.8,11,34,37 In these studies, 10 worms were taken from a given experiment, washed, mechanically disrupted and number of colony forming bacteria were determined by culturing them on suitable agar plates. Therefore, this method inherently pools together the bacteria of all the worms and as a result can only provide an average bacterial load to represent the state of bacterial colonization in the gut of the worms. Moreover, the few worms with bacterial load that are greatly different from the majority of the worms (e.g. in day 2 the worms having bacterial load measuring around 106–107 compared with the majority that have few or no bacteria) cannot be simply discarded a priori. The experiments therefore need to be repeated and, in case of persistence of the “outlying” results, further systematic investigations should take place to discover causal relations/correlations.
Lastly, considering the bacterial loads in individual worms, there seems to be a saturating limit for the number of bacteria present in the hindgut. If the bacteria are simply accumulating in the gut, it is possible that the accumulation becomes balanced with defecation. In the case of proliferation, perhaps the bacterial population reaches an equilibrium with the immune system of the worm and/or with the hindgut milieu. Regardless of the exact explanation, this saturating limit has been previously reported by Portal and Blaser.11 Based on the discussions above, we also plotted the bivariate histograms (Fig. S1†) of selected worms that have fed on RFP-labelled E. coli OP50 for 4 days (highlighted by red circle in Fig. 7e). It can be seen in Fig. S1a† that the volumes, the intensities and the number of the bacterial spots in the gut are low, which implies a relatively small bacterial load. However, considering the worms in Fig. S1b and c,† the increase in bacterial load is accompanied by the formation of larger bacterial spots with higher intensities and the appearance of numerous small bacterial spots. This shows that, while with time the number of bacteria within the gut increases, an adequate explanation of the variation observed among the worms with the same feeding conditions requires further investigation.
The effect of the bacteria on the host is not only limited to the colonization of the gut. For example, P. aeruginosa and S. typhimurium reduce the motility and the rate of pharyngeal pumping of the worm overtime. Both of these phenotypes can be tracked in vivo using microfluidic solutions.21,28 Moreover, infection with bacteria can also damage the intestinal cells. For example, it has been reported that S. marcescens is a pathogen of the worm that over time causes vacuolation of the intestinal cells and reduces the volume of the intestinal epithelium.10 Overall, these examples show that interactions between the worm and the bacteria can have various aspects that need to be monitored.
In this work, for the first time to the best of our knowledge, we have carried out high-resolution z-stack imaging of fluorescent E. coli OP50 bacteria using a SDCM. Utilizing IMARIS, we constructed a 3D representation of the bacteria within the hindgut of each worm and managed to identify their sizes and their (fluorescent) intensities in an automated way. We showed that the number of bacterial spots, their sizes and their intensities increase as the worm ages, in agreement with previous studies.4,34 Interestingly, our single-worm bacterial analysis showed that the conventional sampling method for evaluating the bacterial presence within the gut of the worms may be less adequate and can be misleading in some cases. While the main innovation in our work is the establishment of a high-resolution imaging and analysis pipeline, our work also demonstrated that on-chip high-resolution imaging of intestinal bacteria is not hindered by the optical distortions that the PDMS layer and the liquid media can potentially introduce. Consequently, the methods we developed in this work can be readily implemented in existing microfluidic chips that are aimed at studying host–microbiota interactions.
To better highlight the novelty of our approach and the new possibilities offered by our imaging and analysis pipeline, we briefly compare our work with the previously reported microfluidic devices applied to host–microbiota interactions. The first reported microfluidic only considered the life span of the bacteria-infected worms that were being treated on-chip with antimicrobial compounds.25 The imaging possibilities were only briefly explored and were limited to fluorescence imaging at low magnifications. The HandKAchip microfluidic device for on chip hands-free killing assay improved on the previous work.26 Brightfield and fluorescence images, albeit with low-resolution and acquired at a low-magnification, were used to monitor the life span of the worms as well as their motility and infection response (irg-1::GFP) in an automated manner. However, similar to the imaging and analysis pipeline we presented in this work, the identities of the worms were lost in the analysis stage and therefore it was not possible to track and monitor the worms over an extended period of time. The latest microfluidic device for host–microbiota interactions allowed in vivo fluorescence imaging of C. elegans worms immobilized via traps at a moderate resolution for a duration of 30 hours while maintaining the identities of the worms.28 However, the immobilization is not sufficient to allow high-resolution z-stack fluorescence imaging. Indeed, in this study, a widefield microscope was used to perform time-lapse fluorescence images and no z-stack imaging was performed. However, widefield microscopes are inherently limited in resolution as the light signals from out-of-focus planes blur acquired images. This, in particular, is noticeable when bacterial cells are present in the intestine at high densities, which is typically the case in older worms. As a result, intact fluorescent bacteria in densely populated bacterial clusters will appear as diffusive fluorescent background when imaged using a widefield microscope and consequently, will be mistakenly interpreted as disrupted bacteria. This shows the necessity of utilizing a SDCM to accurately image and analyze the bacterial colonies in the intestine and thus the superior imaging resolution and accuracy afforded by our imaging and analysis pipeline.
As an outlook, a simple way to further improve the functionality of our chip would be to include traps for aligning the worms before performing fixing. This would allow us to not only to identify the bacterial spots but also to systematically characterize their positions within the gut and discover any preferential place for colonization. Our group has previously designed a microfluidic device that enables in vivo extraction of phenotypes such as length, area, motility and first egg release time in an automated fashion.21 The imaging analysis involved for the determination of these phenotypes could be modified such they can be used in the new device. Additionally, our group has also reported microfluidic devices for in vivo studying of bacterial load dynamics and absorption (e.g. measurement of the pharyngeal pumping rate, and the time evolution of bacterial load in the worm gut).28 The functions exhibited by the latter device could be complemented with image analysis techniques of the former device and combined with the ideas expressed in this work (high-resolution fluorescent z-stack imaging, fixing the worms on chip, shifting assays using fluorescent and non-fluorescent bacteria) to result in a more complete picture of bacterial proliferation and/or pathogenesis within the worm.
Footnote |
† Electronic supplementary information (ESI) available. See DOI: https://doi.org/10.1039/d3ra02934d |
This journal is © The Royal Society of Chemistry 2023 |