Stem cells and cancer cells have enough molecular similarities that the former can be used to trigger immunity against the latter.
Updated classics and new techniques help microbiologists get up close and quantitative.
May 1, 2015|
COURTESY OF ESCHEL BEN-JACOB/INA BRAINISEver since Antonie van Leeuwenhoek espied the cavorting, swiftly swimming tiny critters he called animalcules through a small sphere of glass held in a metal frame, microscopes have figured into microbiological advances.
The stunning diversity of microbes, whether harvested from the human gut or scraped from the ocean floor, has increasingly led researchers to explore microbial behavior. As research entered the age of DNA, microscopes fell out of favor, and gaps in understanding the twitching, swimming, or creeping movements of microbes individually and as a colony have persisted.
Studying bacterial behavior requires techniques to view, track, and analyze these organisms in motion. Today, this involves new tools, such as genetically encoded fluorescent reporters, and improvements on old ones, such as quantitative methods for analyzing the complex swirls and spirals of bacterial colonies growing on agar plates. Even microscopes have made a comeback over the past two decades, thanks to the advent of small, relatively inexpensive cameras and increasingly sophisticated image-analysis programs, explains biologist Nicolas Biais of Brooklyn College.
The Scientist sought out some creative solutions for studying microbes on the move, en masse or one by one.
ANALYZING BACTERIAL SWARM PATTERNS
Researchers: Eshel Ben-Jacob, professor of physics, Tel Aviv University, and adjunct professor of biochemistry and cell biology, Rice University; Colin Ingham, chief scientific officer, MicroDish, Utrecht, Netherlands
Challenge: Early microbiologists quickly recognized that the swirls and streaks in their petri dishes differed among bacterial strains. The simplest interactions between strains were easy to decode: when competing strains grown in the same dish meet, for example, the boundary region—called the Dienes line—is starkly visible. But the human eye tends to misinterpret the patterns in more complex dynamics, says Ingham.
Solution: For Ingham, formerly a senior scientist at Wageningen University, describing bacterial colony patterns quantitatively meant bringing in mathematicians and physicists. He teamed up with Ben-Jacob to study the swarming behavior of a pattern-forming bacterium that Ben-Jacob’s lab group had discovered in the 1990s called Paenibacillus vortex. Together they returned to more traditional methods of observing patterns at the colony level through a microscope. Bacterial swarming has practical applications in health-care settings, where you’d want to prevent it from happening. The coordinated, almost intelligent movement of many agents that aren’t considered intelligent can also inspire cybernetics. (See “Crowd Control,” The Scientist, July 2013.)
Under controlled growing conditions, the researchers subject their bacterial colonies to different stressors, such as nutrient deprivation or changes in humidity. Then they stain and image the colonies to figure out how those challenges affect bacterial motility and pattern formation. “These are very straightforward techniques,” says Ben-Jacob. “But you need to look at the population level to see how [the microbes] talk to each other.”
Through their collaboration, Ingham and Ben-Jacob uncovered traffic-like organization patterns in the swarms of P. vortex snaking across soft agar. On harder agar surfaces, the bacteria form complex colonies studded with vortices from which the colonies expand—features that inspired P. vortex’s name. By tracking mutant cells across the frames of time-lapse photos and measuring values such as the bend angle of the snake-like arms and the speed of different subpopulations in the colony, the researchers came up with the first quantitative description of swarming dynamics by the bacterium (BMC Microbiol, 8:36, 2008).
Further work elucidated the simple rules that can give rise to “decisions” about the direction of colony movement (PLOS Comput Biol, 7: e1002177, 2011), and revealed P. vortex’s apparent cooperation with the nonmotile fungus Aspergillus fumigatus. The moving bacteria carried fungal spores along with the swarm and crossed air gaps in the agar, which normally form an impediment to bacterial movement, over bridges made from fungal mycelia (PNAS, 108:19731-36, 2011).
DIY: Ingham highly recommends ImageJ, a public-domain image-processing and analysis program developed at the National Institutes of Health (NIH). The program can be run online or downloaded and includes a wide variety of plug-ins for many applications. Microbiologists use it to analyze shapes, scan frame by frame through a video for movement, and more. The user base is active and committed. Questions and problems can be posted online at the NIH site and “the chances of finding someone who can answer are pretty good,” Ingham says.
TRACKING MANY BACTERIA AT ONCE
COURTESY OF GABRIEL ROSSERResearchers: Alexander Fletcher, applied mathematician, Oxford University; Gabriel Rosser, research associate in civil, environmental, and geomatic engineering, University College London
COURTESY OF GABRIEL ROSSERChallenge: Typically, researchers laboriously track the movements of individual bacteria across their field of view. Enough single tracks can yield general conclusions about the species’ movements. However, advances in imaging and computing now allow the capture and analysis of multiple tracks at a time. After a colleague generated a way to gather hundreds of bacterial tracks in one go—based on a program intended to track submarine movements—Rosser wondered what could be done with all the data. “I knew he had a valuable resource,” he says. “But he wasn’t really sure where to begin.”
Solution: Instead of choosing a subset of tracks to analyze, Rosser, then a PhD student in Fletcher’s lab, developed algorithms that would take all the tracks, throw out ones too messy or too improbable to be real, and analyze the remainder. “The minute you choose a bacterium, you have to ask what kind of bias are you introducing,” Fletcher says. You can choose more tracks that are ‘good’ tracks—straighter, longer and easier to analyze—than shorter, more tortuous tracks that nevertheless are part of the average movement of the population. In their method, the computer does the choosing.
They studied Rhodobacter sphaeroides, free-swimming, rod-shaped bacteria that use a single flagellum to move about, as do many liquid-dwelling microbes. Because the organisms are so tiny, the random Brownian motion of molecules surrounding them keeps them from achieving straight trajectories. Instead, they employ a strategy dubbed “run-and-tumble,” where a quick spin or flick of the flagellum sends the bacterium on a short, straight run before it comes to a halt. A tumble, presumably influenced only by that Brownian jostle, sends the bacterium spinning before it can run again. However, by mathematically extracting out the runs and tumbles of many bacterial tracks, Rosser and his colleagues determined that the tumble phase wasn’t random, as previously thought, but somehow mediated by the bacterium (J R Soc Interface, 11:20140320, 2014). “They change angle too quickly for it to be a passive process,” says Rosser.
The breakthrough came after computer algorithms processed data from two mutant strains. One couldn’t run at all and tumbled in place, buffeted by its environment. The other ran and never stopped. Together the two mutants modeled the two phases of the wild-type bacterium and a way to analyze the tracks.
DIY: The analysis method Rosser worked on, along with instructions for its use, is freely available for download in their report’s supplementary information (PLOS Comput Biol, 9: e1003276, 2013). Bacterial tracks from typically used image-capture programs can be fed into the tools. Some of the apps Rosser wrote for cleaning up the data might need a few tweaks depending on the bacterial species, he says, “but the analysis should be fairly generic.”
DETECTING SMALL NUMBERS OF BACTERIA DURING INFECTIONS
M. CHANG ET AL., PLOS ONE, DOI:10.1371/JOURNAL.PONE.0108341, 2014.Researcher: Jeffrey Cirillo, professor of microbial pathogenesis and immunology, Texas A&M Health Science Center
Challenge: The slow initial growth rate of many pathogens—particularly tuberculosis-causing mycobacteria—stymies efforts to understand how they infect their hosts. Researchers are still unsure exactly where Mycobacterium tuberculosis gains its foothold in the human body— the alveoli or the nasal pharynx—or what cues the pathogen to spread throughout the body. “Being able to track in vivo is a huge step forward for us,” says Cirillo.
Solution: To pinpoint such low numbers and selectively signal the presence of tuberculosis in samples that might be swarming with other bacteria, Cirillo’s group developed a fluorescent reporter that only activates when the TB bacterium is near. Typically, organism-specific fluorescence reporter genes are engineered into the pathogen’s genome for study in cell culture and in lab animals. However, this method promotes energy expenditure that wild-type bacteria don’t employ in nature. “It may be a small difference, but it can impact the overall pathogenesis,” Cirillo says, and thus experimental results. Instead, Cirillo’s group embeds the fluorescent reporter within the chemical structure of nutrients the bacteria like to consume. The reporter-nutrient combination can be mixed into cell culture plates to identify clinical samples, or administered to lab animals, to help track moving infections. When the bacteria secrete a digestive enzyme, called BlaC, to break down those nutrients, the enzyme also cleaves the reporter in such a way that a fluorescing molecule is released and starts glowing green (Nat Chem, 4:802-09, 2012).
The system can detect as few as 10 bacteria in a sample of human sputum (Angew Chem Int Ed Engl, 53:9360-64, 2014). Cirillo is also working on another system that uses bioluminescent molecules from the firefly and click beetle to track infections in living animals (PLOS ONE, 9:e108341, 2014). After tracking which organ is infected, the researchers can watch the infection grow and spread, Cirillo says.
DIY: The reporter system is based on genes that other groups discovered—Cirillo’s team just made some tweaks. The plasmids for making the reporter-substrate are available in the open-source Addgene database.
MEASURING FORCES EXERTED BY BACTERIA
N. BIAIS ET AL., PLOS BIOL, DOI:10.1371/JOURNAL.PBIO.OO60087, 2008.Researcher: Nicolas Biais, assistant professor of biology, Brooklyn College
Challenge: The various structures that microbes use to get around are intricate molecular machines whose parts have been well characterized physically and genetically. But how different bacteria use these structures to propel themselves requires measurement of forces at microscopic scales.
Flagella in swimming bacteria have been well studied, but “we don’t know squat about how [bacteria] move on surfaces,” Biais says. His group studies long structures that bacteria can extend and retract called Type IV pili, which can be found on many infectious bacteria including Vibrio cholerae and Neisseria gonorrhoeae. Biais calls them “small-scale spidermen,” because these microbes use their pili like grappling hooks to pull themselves along surfaces, interact with other bacteria, and attach to the epithelial cells of organisms they infect.
Solution: Biais’s lab group uses three methods for manipulating bacteria. All three involve giving the bacterium—usually a specimen from the team’s model system of choice, N. gonorrhoeae—something with which to interact.
Optical tweezers produce a gradient of electromagnetic energy with the help of a highly focused laser beam. That energy can trap small objects and manipulate them as if using microscopic tweezers. Biais uses the tweezers to offer protein-coated beads to bacteria, which grab on and pull. By measuring a bead’s displacement, the researchers can determine the force that the bacterium exerts. A camera mounted on a microscope captures the images for analysis in a program such as ImageJ or MATLAB.
However, the light of the laser produces heat that can change bacterial behavior. For a less disruptive manipulation, Biais builds a microscopic field of polymer pillars, using lithography methods similar to those that mold computer chips. The bacteria can move across the micropillars, like Spiderman across a cityscape, and Biais measures the displacement of each super-bendable “skyscraper.”
Magnetic tweezers are cheaper, easier to use than their optical counterparts, and don’t heat up cells. They work similarly, holding a magnetic bead in place to allow researchers to measure its displacement to determine force. However, these tweezers can’t move as nimbly as the optical ones and are mostly useful for applying known forces and calibrating.
Using a combination of these tools, Biais’s group has studied the pili of the Neisseria genus. The researchers found that a single fiber just 6 nanometers in diameter but extending up to 20 micrometers in length can exert forces up to 100 piconewtons. Bundled together in groups of 8 to 10, pili can maintain a 1 nanonewton tug for an hour (PLOS Biol, 6:e87, 2008). That force is equivalent to 100,000 times the bacterium’s weight. Because such disturbances on mammalian cells trigger a signaling cascade to protect the cell from infection, insights into bacterial-epithelial cell interactions could offer new targets for antibiotics to attack.
DIY: Lasers are expensive, but either optical tweezers or magnetic tweezers are needed to calibrate measurements on the micropillars. Biais has coauthored a book chapter that serves as a guide to using all three of these tools (Methods Mol Biol, 799:197-216, 2012), including fabrication methods for molding the micropillars. In another chapter, Biais delves into the details of magnetic tweezers (Methods Cell Biol, 83:473-493, 2007). However, given the range of forces bacteria can exert, tweaks may be needed depending on your organism.