Microbial Motility in 3-D Extending the reach of phase contrast microscopy to track the three- dimensional motility of microbes from Woods Hole, MA.

Max Villa, Microbial Diversity 2016, Marine Biological Lab, Woods Hole, MA, Home Institution: Duke University, Durham, NC

Abstract

Bacteria solve complex 3D optimization problems to search for energy sources in oligotrophic environments. However, until now, accessing their 3D motility patterns has been limited to expensive and complex microscopy systems. Herein, a recent algorithm that using the diffraction patterns of out-of-focus bacteria to determine their Z-location is implemented and applied to a diverse collection of marine bacteria isolated from around Woods Hole, MA.

Introduction

We fundamentally rely on the concept of search. Google, Yahoo, Baidu and others have made search a modern necessity and changed our vernacular (“you can google it”). Microbial chemo- and phototaxis (“search”) in oligotrophic marine environments is essential to survival and to our understanding of how microbes influence the ecology of their environment.

Patterns of microbial motility can help us better understand how bacteria sense and respond to chemical cues in their microenvironment.1,2, 3 Studying motility in the context of populations can provide insights into variations in their behavior across the population, so-called ‘bacterial individuality’.4,5 Finally, few models exist to quantitatively connect genetics to behavior. 6 High throughput tracking of bacteria is an ideal system for efficiently producing large datasets to probe the connection between behavior and genetics. A deep understanding of diverse microbial motility should lead to more predictive models of how microbes shape our environment. Furthermore, insight into biological solutions to hard optimization problems could yield new algorithms for robotics and computation.7

Microbial motility exists in three-dimensional space. Observation of full 3D movement would lead to more accurate track measurements such as velocity and turning angle. However capturing three-dimensional motility must overcome the limitation of shallow depth-of-field inherent in high NA objectives. Many microbiologists are familiar with the experience of observing motile cells move in and out of the focal plane, capturing movement for a few seconds and then zipping out of focus. This challenge was first overcome in 1972 by Berg and Brown using a custom microscope that automatically tracked the focal position of a microbe as it moved throughout a sample chamber.1 This led to the first glimpse of true 3D microbial motility in a set of chemotaxis experiments. While a technical tour-de-force, this method could only track one microbe at a time. By comparison, later methods such as digital holographic microscopy (DHM) could track the 3D position of multiple microbes simultaneously.8 DHM works by illuminating the sample with a collimated light source, typically using a laser, and using the 2D interference patterns from the incoming and scattered light to calculate an image in 3D space. Yet another approach to track bacteria in 3D is a so-called “de-focussed” imaging method. This approach follows from the observation that a microbe observed by phase contrast outside of the focal plane produces a specific diffraction pattern that reflects its z-position away from the focal plane. Taute et al. recently developed an algorithm that translates the diffraction rings into 3D motility tracks with improved z- resolution than previous de-focussed methods.4 This approach combines high throughput acquisition of multiple bacteria simultaneously with a setup most labs are familiar with – phase contrast microscopy.

The goals of this project are to (i) implement a 3D tracking method with phase at MBL, (ii) apply tracking method to a diverse sample of marine bacteria, and (iii) compare motility patterns with quantitative metrics. Many bacterial taxa have not been well characterized in terms of their 3D motility. It was therefore hypothesized that if enough diverse taxa were tracked in 3D, perhaps novel motility patterns would be observed.

Methods Isolation and culture A variety of strains and environmental samples from the surrounding Woods Hole were investigated (Table 1). The Bacillus and Vibrio strains originated from the isolations performed during the laboratory section of the course following the spore-forming and bioluminescent enrichments, respectively. Putative Thiovulum strains (SWT) were found in a section of sediment that was placed in the seawater table. An unknown sulfur oxidizing bacteria (GC1) was isolated from sediment found in Sippewissett marsh after a biofilm formed on the side of the culture tube.

Genetic Analysis Colony PCR was performed on isolates following the laboratory manual. Forward and Reverse reads were aligned using CLUSTAL Omega and concatenated manually before a BLAST search was performed.

Microscopy All microscopy was performed on a Zeiss Observer.Z1 inverted scope with a N-Acroplan 40x 0.65 NA Ph2 (420961-9911). Imaging chambers were created by adding three layers of parafilm on either side of a microslide and adding a 500 µm thick coverslip on top. The slide-parafilm-coverslip chamber was heated briefly on a hotplate on low to seal the coverslip to the parafilm. After allowing to chamber to cool, a dilute cell suspension (OD ~0.001) was added to the chamber and sealed with Valap (equal parts vaseline, lanolin, and paraffin) to prevent evaporation. The objective lens is optimized for a coverslip thickness of 170 microns. Therefore the thicker 500 micron coverslip will introduce spherical aberrations that result in an asymmetric diffraction pattern about the focal plane. This is important so the algorithm can distinguish between cells above and below the focal plane. A reference stack was generated by taking a z-stack of 1 µm beads embedded in a 1% polyacrylamide gel. However, the averaged reference stack that was provided with the software worked well and was used instead. The reference stack will depend on the optical components and size of bacteria to be imaged and is thus ideally generated by the user for his/her experimental conditions. Time-lapse videos of bacterial motility were recorded at the maximal frame rate in ‘burst mode’ with the Zen imaging software, translating to an imaging interval of 0.0078 seconds. One thousand frames were recorded per each time lapse and exported as a .tif stack.

Chemotaxis experiments Briefly, the Vibrio samples were resuspended in seawater and added to an imaging chamber. Then a flattened glass capillary tube was filled with SWC medium and inserted into the side of the chamber. Time lapse acquisition proceeded as described above.

Motility Tracking Stacks were resized and converted to 16-bit using a custom ImageJ macro (provided in the appendix). Then they were loaded into MATLAB and the 3D Tracking software (available through K. Taute on request) background corrects the stack, and performs tracking. At each time step, bacteria are located in the x-y plane and each 2D diffraction pattern is compared to a diffraction pattern from the reference stack via a cross-correlation function. If threshold values are met, a new microbe position in x-y-z is added to a table for each track. The resultant output is tables of x-y-z values as a function of frame for each bacterial track. Velocity calculation from 3-D positional information was performed using a custom MATLAB script (provided in the appendix).

Results

Several different patterns of microbial motility were observed. All taxa displayed beautiful arcing dives and ascents. Variation in motility behavior within a population was also apparent in nearly all runs. The characteristic “flicks” of Vibrios was present; where bacteria make a sharp turn, reverse motion along the same path, and then redirection along another arcing turn. Vibrios also would occasionally display false and complete looping behavior (Fig. G). Some of the Vibrio runs were much longer than the field of view. As expected, but cool nonetheless, when Vibrios near a chemical gradient the frequency of the redirection increases, resulting in fast switching forward and reverse motility (Figure 3L and 3M). By contrast the Bacillus species had a more “run and tumble” motility (Fig. A), wherein longer runs are terminated by bacterial tumbling and redirection. Interestingly, the large sulfur bacteria displayed a motility pattern wherein runs were terminated by sharp redirects at what appears to be 90° angles (Fig. E). Furthermore, the large sulfur bacteria displayed a helical procession about the velocity vector, visualized by the helical motion blur in the images – despite a 10 millisecond frame interval. The sulfur bacteria were also the fastest of the bacteria examined, moving at a rate of more than 400 microns/second.

Sample Code BLAST hit Source Enrichment Culture Medium Cult. Temp. [° C] MV6 Bacillus altitudinis Trunk River pond Pasteurization 5YE 30 MV8 Bacillus stratosphericus Trunk River pond Pasteurization 5YE 30 HHM1 Vibrio Cholerae Trunk river outflow Plating on SWC SWC 30 GC1 - Sippewissett Sediment - Sippewissett - seawater SWT - Trunk river pond - Environmental Seawater table sample Table 1 | Strain information and culture conditions.

Figure 1 | Principle of 3D tracking using Phase Contrast microscopy. (A) Z-stack of a 1 um bead viewed side-on. The asymmetric diffraction pattern can be observed. (B) Extracted slice from the bead stack shown in A above the focal plane (FP). (C) Extracted slice from the bead stack shown in A below the focal plane (FP). A reference stack such as this is used to compare with the diffraction patterns of a similiarly sized bacterium and deduce the z-position of the bacterium with respect to the focal plane.

Figure 2 | Tracking validation in x-y-z. (A) Inverted maximum intensity Z-projection of motile Vibrios. (B) 3D tracking output of the stack shown in A, viewed in the x-y plane. (C) Overlay of tracking output from (B) on the Z-projection in (A) showing good correlation between calculated and actual tracks in x-y. (D) Z validation – to determine the software was tracking well in Z, a bead Z-stack was analyzed. The software expects the stack to be frames throughout time (not in Z) and produces an output of beads translating in the Z-direction, confirming Z-tracking as expected.

Figure 3 | 2D Z-projections and corresponding 3D tracks for: Bacillus Altitudinus (A) and (B). Bacillus Stratosphericus (C) and (D). Sample GC1 (E) and (F). Vibrio (G) and (H). Putative thiovulum (I) and (J). Vibrios in a nutrient gradient (L) and (M), edge of capillary tube with attractant is shown at right. The scale (150 by 100 microns in x-y) is shared by all images here.

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● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● 400 ● ● ●

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Velocity [microns/sec] Velocity ● ●

200 ● ● ● ● ●

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● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●●●● ●●● ● ● ● ● ● ● ● ● ● ● ●● ●●● ● ● ● ● ● ● ● ● 0 Bacillus altitudinis Bacillus stratosphericus Grays purps Thiovulum Vibrio Sample Figure 4 | Purple sulfur bacteria are extraordinarily fast. Collection of velocity data compared across putative bacterial taxa. The purples sulfur bacteria are the fastest, followed by the second fastest: an environmental sample thought to contain Thiovulum (K. Hanselman).

Discussion

A purple sulfur bacteria stood out with some very novel features, which is tempting to speculate about a lifestyle of transitioning to different trophic zones during a light-dark cycle. Notable was the high velocity and helical procession about it’s velocity direction. Reports on what may a similar bacteria also describe this helical procession.9 The procession was easily visualized under the microscope due to the relatively large size of this bacterium (~ 10 microns). Liu et al. have recently reported on the helical procession of Caulobacter by combining a focus tracking approach with an algorithm to reconstruct the microbial body shape at each step during motility. 10 This group proposes that in fact the body must also rotate in the opposite direction of the flagellum in order to balance the net torque. It is believed that the body rotation introduces a drag force in the transverse direction, resulting in helical motion about the velocity direction. Furthermore, it is thought that this type of motility actually enhances translational motion, in effect increasing velocity. This type of motility is not yet well understood, and if tractable in culture, these purple sulfur bacteria represent a novel type of motility behavior that should be studied further. How does the extra size of these bacteria influence their motility? When did this large size occur in during evolution? Since the helical procession was studied in the smaller Caulobacter, how does the helical procession change with a larger size and shape of the purple sulfur bacteria?

The tracking algorithm worked well once established. Improvements to this approach would be to implement a reference library taken on the in house scope. The bottleneck that was encountered here was the process of aligning and merging many 3D bead stacks, but ideally could be streamlined further. To analyze the large sulfur bacteria more accurately, a reference stack generated from 10 µm beads would be much better than the stack used here. An attempt was made to generate a reference stack from immobilized bacteria, but they lysed upon addition to hot polyacrylamide gel. Secondly, rich 3D datasets were generated, however another challenge was writing scripts to analyze motility metrics such as velocity, turning angle, acceleration, radius of curvature, etc. It would be helpful to produce and share more scripts for analyzing and comparing aspects of 3D motility patterns. Ultimately, analysis could be rapidly performed from generated scripts and the user limited only by the ability to find and image novel microbes from the environment.

The simplicity of this approach is very powerful. In the future, one might imagine a ‘microbial critter cam’ where a small portable phase microscope is place in situ in a marine environment and pipes images back to a central server for analysis. Such a tool could aid in the discovery of novel microbial behaviors. Furthermore, the pairing of this method with genetic screens, such as transposon or chemical mutagenesis, should shed light on the connections between genotype and behavioral phenotype.

Ultimately, tools that make it faster and more simple to study microbes in situ and in three-dimensions are expected to catalyze our understanding of how microbes sense, react and shape our shared planet.

Conclusion

In summary, diverse microbes were tracked in 3D dimensions using only a phase contrast microscope and a desktop computer. Large 3D datasets of x-y-z positions were generated and should a rich source for data mining and analysis. The velocity of several microbes was measured based on 3D data and should be more accurate than 2D metrics, owing to 2D errors from surface interactions and perspective bias. Novel right-angle motility and a helical processional motion was observed in a large purple sulfur bacterium. This work should provide a baseline for further study of microbial motility in 3D within the context of the Microbial Diversity course.

Acknowledgments

This work would not have been possible without the help of a great number of passionate and brilliant people. First and foremost, I must thank Katja Taute for sharing the 3D tracking code that she invented. Sean Crossen mentioned that this project was possible and helped me get in touch with Katja. Lots of helpful advice came from George O’Toole, Kyle Costa, Ethan Garner, Jessica Polka, Jared Leadbetter and Dianne Newman. Jim, the MBL Zeiss rep was extremely helpful in providing microscope objectives and help with the Zen microscopy software. Elise Cowley was always at the ready to help with my benchtop microscope when it needed repairs. Bacteria were kindly provided by Hannah Holland-Moritz and Grayson Chadwick (who endured multiple tick bites to find them). I would also like to thank all the course instructors, TAs and participants for teaching me lots of new tricks in constant good humor. No of this would be possible without funding for my participation in this course provided by the Simons Foundation, the Howard Hughes Medical Institute, and Duke University.

References

1. Berg, H. C. & Brown, D. A. Chemotaxis in Escherichia coli analysed by Three-dimensional Tracking. Nature 239, 500–504 (1972). 2. Alon, U., Surette, M. G., Barkai, N. & Leibler, S. Robustness in bacterial chemotaxis. Nature 397, 168– 171 (1999). 3. Masson, J.-B., Voisinne, G., Wong-Ng, J., Celani, A. & Vergassola, M. Noninvasive inference of the molecular chemotactic response using bacterial trajectories. Proc. Natl. Acad. Sci. U.S.A. 109, 1802– 1807 (2012). 4. Taute, K. M., Gude, S., Tans, S. J. & Shimizu, T. S. High-throughput 3D tracking of bacteria on a standard phase contrast microscope. Nature Communications 6, 1–9 (2016). 5. Spudich, J. L. & Koshland, D. E. Non-genetic individuality: chance in the single cell. Nature 262, 467– 471 (1976). 6. Hu, C. K. & Hoekstra, H. E. Peromyscus burrowing: A model system for behavioral evolution. Semin. Cell Dev. Biol. (2016). doi:10.1016/j.semcdb.2016.08.001 7. Cohen, J. Models and Simulations of Collective Motion in Biomimetic Robots and Bacteria. 1–124 (2007). 8. Cheong, F. C. et al. Rapid, High-Throughput Tracking of Bacterial Motility in 3D via Phase-Contrast Holographic Video Microscopy. Biophysj 108, 1248–1256 (2015). 9. Fenchel, T. & Thar, R. ‘“Candidatus Ovobacter propellens”’: a large conspicuous prokaryote with an unusual motility behaviour. FEMS Microbiology Ecology 48, 1–8 (2016). 10. Liu, B. et al. Helical motion of the cell body enhances Caulobacter crescentus motility. Proceedings of the National Academy of Sciences 111, 1–5 (2016).

Appendix

Stack preprocessing code – ImageJ Macro // Pre-process stacks for 3D tracking, Max Villa 8/8/16 MBL Microbial Diversity setBatchMode(true); input = "/Volumes/MAXDRIVE/8816_balt2/"; //note can't have spaces in paths, add brackets? output = "/Volumes/MAXDRIVE/8816_balt2/"; list = getFileList(input); function preprocess(input, output, filename) { //print(input); //print(filename); //Array.print(list); //print(i); //print("open="+input + filename+"] sort"); //run("Image Sequence...", "open=/Volumes/MAXDRIVE/8816_Bstrat_2/Experiment-166- Image-Export-01 sort"); run("Image Sequence...", "open="+input + filename+" use"); //use -virtual stack option run("Set Scale...", "distance=0 known=0 pixel=1 unit=pixel"); run("Size...", "width=960 height=608 constrain average interpolation=Bilinear"); //optional resize run("32-bit"); run("16-bit"); saveAs("Tiff", output+i); //naming output files based on index for now, issue naming based on folder close(); }

for (i = 6; i < list.length; i++) { preprocess(input, output, list[i]); }

//setMinAndMax(3031, 41889); //run("Apply LUT", "stack");

Velocity calculations – MATLAB m-file

%Velocity Calculator M. Villa 8.15.16 clc clear all dt = 0.0128; %seconds - time interval C = 0;

%load data set_microbe = 'Grays_purps'; load('/Volumes/MAXDRIVE/Tracking_Output/Chromatium/Bugs.mat')

%filter out short tracks d = 20; ScaleXY=1; ScaleZ=1; %Bugs=B.Bugs; Bugs=BugStruct.Bugs; TrackDistances = cellfun(@(x) ( sqrt( ScaleXY^2*var(x(:,2))+ScaleXY^2*var(x(:,3)) + ScaleZ^2*var(x(:,4))) ), Bugs); l=TrackDistances>d; %logical expression Bugsl=Bugs(l); %captures only long tracks

%calculate mean velocity i=0; for i=1:length(Bugsl) A = Bugsl{i,1}; k=0; for k=1:(length(A)-1) %iterates through array containg position info x0 = A(k,2); x1 = A(k+1,2); y0 = A(k,3); y1 = A(k+1,3); z0 = A(k,4); z1 = A(k+1,4); C(k) = sqrt((x1-x0)^2+(y1-y0)^2+(z1-z0)^2)/(dt)*0.147; % 40x calibration is 0.147 microns/pixel end Mean_Vel(i) = mean(C); end

Global_mean = mean(Mean_Vel, 2) Total_tracks = length(Bugsl) histogram(Mean_Vel, 10) xlim([0 1000]) ylim([0 30]) xlabel('Velocity (microns/sec)'); ylabel('Number of Tracks'); title(set_microbe)

Mean_Vel = Mean_Vel'; filepath=sprintf('/Volumes/MAXDRIVE/Tracking_Output/Velocity_Data/%s.txt',set_microbe); save(filepath,'Mean_Vel','-ascii')