Single-Cell Ecophysiology of Microbes as Revealed by Raman ...

ANRV387-MI63-20

ARI

ANNUAL REVIEWS

4 August 2009

21:54

Further

Annu. Rev. Microbiol. 2009.63:411-429. Downloaded from arjournals.annualreviews.org by University of Vienna - Central Library for Physics on 02/09/10. For personal use only.

Click here for quick links to Annual Reviews content online, including: • Other articles in this volume • Top cited articles • Top downloaded articles • Our comprehensive search

Single-Cell Ecophysiology of Microbes as Revealed by Raman Microspectroscopy or Secondary Ion Mass Spectrometry Imaging Michael Wagner University of Vienna, Department of Microbial Ecology, 1090 Vienna, Austria; email: [email protected]

Annu. Rev. Microbiol. 2009. 63:411–29

Key Words

First published online as a Review in Advance on June 10, 2009

isotope labeling, assimilation, microbial community, host-microbe interaction, NanoSIMS, structure and function

The Annual Review of Microbiology is online at micro.annualreviews.org This article’s doi: 10.1146/annurev.micro.091208.073233 c 2009 by Annual Reviews. Copyright All rights reserved 0066-4227/09/1013-0411$20.00

Abstract An astonishing diversity of microorganisms thrives on our planet and their activities are fundamental for the functioning of all ecosystems including the human body. Consequently, detailed insights into the functions performed by microorganisms in their natural environment are required to understand human biology and the biology of the world around us and to lay the foundations for targeted manipulation of microbial communities. Isotope-labeling techniques combined with molecular detection tools are frequently used by microbial ecologists to directly link structure and function of microbial communities and to monitor metabolic properties of uncultured microbes at the single-cell level. However, only the recent combination of such techniques with Raman microspectroscopy or secondary ion mass spectrometry enables functional studies of microbes on a single-cell level by using stable isotopes as labels. This review provides an overview of these new techniques and their applications in microbial ecology, which allow us to investigate the ecophysiology of uncultured microbes to an extent that was unimaginable just a few years ago.

411

ANRV387-MI63-20

ARI

4 August 2009

21:54

Contents


INTRODUCTION . . . . . . . . . . . . . . . . . . RAMAN MICROSPECTROSCOPIC ANALYSES OF MICROBES . . . . . . Brief Introduction to Raman Spectroscopy . . . . . . . . . . . . Applications of Raman Microspectroscopy in Microbial Ecology . . . . . . . . . . . . ANALYTICAL IMAGING OF MICROBES BY SECONDARY ION MASS SPECTROMETRY . . . Brief Introduction to SIMS . . . . . . . . . Applications of SIMS Imaging in Microbial Ecology . . . . . . . . . . . .

412 414 414

416

418 418 420

INTRODUCTION

Metagenomics: genomic analyses of microbial communities without isolation of individual species

412

Prerequisites for understanding the ecology of an ecosystem are the identification of the organisms that populate the system, the quantification of their abundances, and the deciphering of their interactions with other organisms and the abiotic environment. For decades, botanists and zoologists could cope with these tasks much more easily than microbiologists could. In contrast to microorganisms, most plants and animals can be identified by their morphology and geographic occurrence, their abundances can be reliably estimated by direct counting, and important ecophysiological traits of many eukaryotic community members are known. However, with the implementation of molecular tools that exploit rRNA for the identification and quantification of microorganisms within environmental samples, detailed descriptions of microbial community structures have become feasible for microbial ecologists during the past 20 years (48, 71, 78, 85, 91). These methodological advancements have led to a census phase in microbial ecology, and today we begin to understand basic patterns of the composition and dynamics of microbial communities in many complex engineered and natural systems, including the human body (54, 76, 86, 94). Wagner

Complementary to these efforts, the environmental genomics approach provides access to genome fragments and, in some cases, even entire genomes of microbes without the requirement of their cultivation in the laboratory (41). Huge metagenomics data sets have been established from various ecosystems (72, 75, 79, 88), and these projects have led to the discovery of thousands of new protein families with no homologues from cultured organisms in the database (93). Because this newly discovered diversity is just the tip of the iceberg regarding the total diversity of protein families on our planet (93) and because of dramatic progress in the development of ever faster and cheaper sequencing technologies (8), microbiologists are now confronted with a data storm (74) of genome fragment sequences from microbes for which in many cases no other information is available. Despite this wealth of metagenomic information, inference of metabolic traits of the microbes represented in these data sets is still difficult. This is because annotation by homology (which is not even possible for a high percentage of the detected open reading frames) is notoriously imprecise and often leads to incomplete or incorrect conclusions regarding the metabolic potential of the respective organisms (25, 37). Furthermore, the mere presence of a gene does not demonstrate that it is actually used, and even if transcription and/or translation is demonstrated in the environment by technically challenging approaches like metatranscriptomics (23, 82) and metaproteomics (84, 90), respectively, the encoded enzyme can still be inactive, for example, owing to inhibition. Owing to these limitations, our knowledge of the functional contribution of specific microbes to many ecosystems is still surprisingly scarce. For most of the more than 80 bacterial phyla that were discovered by the application of molecular tools during the past 20 years (1), not a single member has been characterized on a functional level. Even for environmentally highly abundant phyla, such as the Acidobacteria, which numerically dominate many terrestrial ecosystems (35) and are also frequently found


ANRV387-MI63-20

ARI

4 August 2009

21:54

in many other systems, we have only just begun to understand their metabolic potential (6, 39, 62). Indeed, for most Acidobacteria subdivisions we are not even in a position to speculate about their ecophysiology. Vice versa, for many systems it remains a mystery which microbes catalyze well-known key transformations of the global element cycles. For example, there are heated debates about whether bacterial or recently discovered archaeal ammonia oxidizers are functionally more important for nitrification in soil systems (46, 66). Functional genes encoding key enzymes of defined metabolic guild members are frequently used to analyze the identity and diversity of those microbes catalyzing a certain process in the environment and to quantify their abundances (21, 26, 68). This approach certainly provides useful information but is in many cases restricted by the specificity of primers and probes used for the detection of these genes, so that novel gene variants are often overlooked. Furthermore, microorganisms that recently adapted to a new lifestyle can still encode a functional gene in their genomes even though they have already lost the ability to catalyze the respective process (33). In addition, neither documented transcription or translation of a functional gene in an environment proves that the respective function is actually performed. While the molecular toolbox described above is irreplaceable for formulating hypotheses on the functional properties of uncultured microorganisms, there remains a big need to directly observe and quantify the metabolic activity of microorganisms in their natural environment. This can allow us to verify such hypotheses and to understand the relative contributions of different groups of microbes to major microbially catalyzed processes. Isotopelabeling techniques combined with molecular identification tools provide this urgently required direct link between the structure of a microbial community and the function of its members. For such experiments, microbial communities are exposed to substrates labeled with stable or radioactive isotopes under conditions designed to mimic as closely as

possible the natural environment. Substrateconsuming microorganisms incorporate the heavier isotopes into their cellular components. This feature is then either exploited by stable-isotope-probing (SIP) techniques or by fluorescence in situ hybridization– microautoradiography (FISH-MAR) to identify those microbial populations that consumed the added labeled compound. Both approaches, which were developed about 10 years ago (5, 45, 51, 60, 69), have found widespread application in microbial ecology (Supplementary Figure 1; follow the Supplemental Material link from the Annual Reviews home page at http://www.annualreviews.org) but rely on different principles. SIP-based techniques use extracted biomarkers (phospholipid-derived fatty acids, DNA, or RNA) for label detection and identification and can be nicely combined with metagenomic approaches (38, 55). In contrast, FISH-MAR is a microscopic technique that simultaneously visualizes identity and specific activity of microorganisms at the single-cell level. Both approaches, which have been reviewed exhaustively (18, 87), enable microbial ecologists to investigate which microbes “eat what, where and when” (56), and thus provide the methodological premise to more closely integrate microbial ecology with general ecological theory, which usually focuses on active organisms. Ecological concepts such as functional redundancy, referring to different organisms performing the same functional role in an ecosystem (42), can now be addressed in situ experimentally in microbial communities. This paves the way for testing existing and developing novel ecological theory based on data generated in microbial systems. In functional studies of microbial communities, methods that provide single-cell resolution are particularly valuable. Metabolic heterogeneities of individual cells within highly similar or even genetically identical populations can be detected by FISH-MAR (57) and linked to the location of the analyzed cell in its habitat. The latter insights will be particularly powerful if obtained by modern imageanalysis software packages designed to extract www.annualreviews.org • Single-Cell Ecophysiology

SIP: stable isotope probing Fluorescence in situ hybridization (FISH): a widely used molecular method that exploits fluorescently labeled rRNA-targeted oligonucleotide probes to identify microbes in the environment in a cultivationindependent manner Microautoradiography (MAR): a microscopic method that enables researchers to observe radioactive labeling of cells by silver grain formation in an emulsion placed on top of the cells after labeling RNA- or DNA-stable isotope probing: techniques based on the physical separation of isotope-labeled microbial community nucleic acids from nonlabeled nucleic acids by density gradient centrifugation

Supplemental Material

413

ANRV387-MI63-20

ARI

4 August 2009


SIMS: secondary ion mass spectrometry

414

21:54

quantitative information from FISH images (16). Whether the presence of another defined microbial community member in the vicinity of the analyzed population stimulates or represses a certain ecophysiological activity, indicating mutualistic or antagonistic interactions, respectively, can now be addressed in situ and complemented by quantitative coaggregation or repulsion data of different populations in biofilms and flocs (49). However, until recently several limitations of the FISH-MAR technique prevented microbiologists from fully exploiting the potential of single-cell functional analyses. For example, FISH-MAR cannot be used to detect several isotopes simultaneously and does not always offer single-cell resolution in dense microbial aggregates. Furthermore, FISH-MAR requires the use of radioactive isotopes, which cannot be readily applied in the field or in humans. Isotopes with a suitable half-life for MAR are not available for several elements including nitrogen or oxygen, and thus FISH-MAR cannot be applied, for example, to monitor nitrogen fixation in microbial communities. In addition, quantification of the amount of incorporated isotope per cell by FISH-MAR is tedious and requires an internal standard of bacteria with known radioactive isotope composition (57), and it remains unclear which cellular compounds are isotope labeled. Finally, cells analyzed by FISH-MAR have been fixed with ethanol or formaldehyde and are covered by the autoradiography emulsion and are thus difficult to manipulate for downstream analyses such as single-cell genomics. Recently, a new generation of single-cell approaches for structure-function analyses of stable-isotope-labeled complex microbial communities has entered the field. These approaches either employ confocal Raman microspectroscopy or secondary ion mass spectrometry (SIMS) and overcome many of the abovementioned limitations of FISH-MAR. Although these new analytical tools are rather expensive (a confocal Raman microspectrometer coupled with an epifluorescence microscope costs about 200,000 and a NanoSIMS about 2.5 million), they hold enormous promise for Wagner

giving a detailed understanding of the functions and interactions of microbes in their natural environment. It is the aim of this review to provide a comprehensive overview of the pioneering Raman and SIMS applications in microbial ecology and to discuss potential future applications of these innovative methods in the field.

RAMAN MICROSPECTROSCOPIC ANALYSES OF MICROBES Raman microspectroscopy is a rapid and nondestructive vibrational spectroscopic method for obtaining information on the molecular composition of a sample. Crucially, it can be applied for microbial cells on a single-cell level and can be combined with fluorescence staining and epifluorescence microscopy (31, 40). In microbiology, this technique has been evaluated, for example, for the characterization of the chemical composition of microbial biofilms (34) and for rapid spectroscopic identification of bacteria (36, 63). This review focuses exclusively on the use of Raman microspectroscopy to detect and quantify the assimilation of radioactivelabeled or stable-isotope-labeled substrates in single microbial cells to study their ecophysiology. Although this approach was developed for microbial ecology, it has also been adopted by cell biologists for imaging amino acid incorporation into proteins in human cells (83).

Brief Introduction to Raman Spectroscopy In modern Raman spectroscopy (81) a sample is illuminated with monochromatic light generated by a laser. While some incident photons are transmitted or absorbed, other photons are scattered after interaction with molecules belonging to the sample. Most of the scattered photons have the same energy as the incident photons. In this case, which is called elastic Rayleigh scattering, the molecule excited by the incident photon returns after a short transition to a virtual energy level back to its vibrational ground state, thereby emitting photons with the same wavelength (Figure 1a). However, a very

ANRV387-MI63-20

ARI

4 August 2009

21:54

a

D C Ei

Ei

ER = Ei

ES < Ei

EAS > Ei

Ei

Figure 1

B A Stokes scattering

Anti-Stokes scattering

b 13 13

Raman intensity (a.u.)

C

400

13C

C

15

13

C

13 13C

C

13

C

NH2

13C

O

13C

OH

600

800

1000

1200

1400

1600

1800

c 597 cm–1 967 cm–1 1003 cm–1 1171 cm–1

1530 cm–1

120 h

Raman intensity (a.u.)


Rayleigh scattering

72 h

24 h

0h 400

600

800

1000

1200

Raman wave number (cm–1)

small fraction of the scattered photons (between 10−6 and 10−8 ) are shifted in wavelength relative to the incident photons by inelastic Stokes and anti-Stokes Raman scattering (Figure 1a).

1400

1600

1800

(a) Jablonski energy diagram showing Rayleigh, Stokes Raman, and antiStokes Raman scattering. Rayleigh scattering does not change the wavelength of the photon, whereas Stokes and anti-Stokes scattering lead to longer and shorter wavelengths, respectively. A is the vibrational ground state, B is first excited vibrational state, C and D are virtual energy states. (b) Raman spectra of unlabeled (black) and labeled (red ) phenylalanine. (c) Raman spectra of an obligate intracellular chlamydial symbiont living in an amoebal cell. Labeled phenylalanine was added to the growth medium of the amoebae, and after the indicated time periods the amoebae were lysed and the Raman spectra of the symbionts were recorded. Red parts of the spectra were caused by the uptake of phenylalanine into the symbionts cells. a.u., artificial units.

Stokes scattering occurs when a molecule that is excited from its vibrational ground state by a photon to a virtual energy level does not return to its vibrational ground state, but rather www.annualreviews.org • Single-Cell Ecophysiology

415

ARI

4 August 2009

21:54

to the first excited vibrational state. In this event the vibrational energy of the molecule is increased and a photon with a lower energy is emitted. Anti-Stokes scattering occurs when a molecule already situated on the first excited vibrational level is excited by a photon to a virtual energy level and then returns to the vibrational ground state. In this event, the vibrational energy of the molecule is decreased and the emitted photon has a higher energy than the incident photon. Raman spectrometers efficiently separate the weak inelastically scattered light from the intense Rayleigh scattered light. Generally, Stokes scattered photons are measured in Raman spectroscopy because they are more frequent than anti-Stokes scattered photons owing to the higher abundance of molecules in the vibrational ground state than at excited vibrational levels. Stokes scattered photons have a longer wavelength (λ) than the incident laser beam (λ0 ), and the Raman wave number (in cm−1 ) of the scattered photon is calculated according to the formula:


ANRV387-MI63-20

−1 7 Raman wave number = (λ−1 0 − λ ) × 10 .

Consequently, the higher the Raman wave number of a certain band is, the lower the energy (and higher the wavelength) of the respective Stokes scattered photon. A Raman spectrum of a certain molecule consists of several sharp bands specific for the chemical bonds in this molecule (Figure 1b). The spectrum of an entire microbial cell is obviously much more complex but still allows the identification of several compound classes as well as molecules with pronounced peaks such as phenylalanine (89) or cytochrome c (64). The intensity of a Raman band depends on the excitation wavelength and power of the laser, the temperature, the polarizability of the respective molecule, and the concentration of the Raman active molecule. Therefore, Raman band intensities of different molecules occurring at the same concentration in a sample are different, but the Raman band intensity of a certain molecule is (in the absence of absorption and selfabsorption) linearly correlated with its 416

Wagner

concentration. The decrease in energy of Raman scattered photons is among other factors dependent on the mass of the atoms in the molecule with which the incident photon interacts. The higher the mass of these atoms, the higher the energy of the Stokes scattered photons. Consequently, incorporation of heavy isotopes into a molecule leads to a shift of the respective Raman bands toward shorter wave numbers (Figure 1b). Confocal Raman spectroscopy can be combined with light or epifluorescence microscopy, leading to lateral and depth resolutions that allow the spectral analysis of single microbial cells even if they are incorporated into a complex matrix. Water does not generally interfere with Raman analysis, and microbial cells can be measured untreated on quartz or CaF2 slides. However, in contrast to CaF2 slides, use of quartz slides results in strong background peaks in a spectral region typically used for fingerprinting of microbial cells (73). In contrast to SIMS, Raman spectroscopy is a nondestructive technique (although sample degradation can occur if the photon flux is too high) and the cells can thus be processed further after analysis (32).

Applications of Raman Microspectroscopy in Microbial Ecology Confocal Raman spectroscopy (using a LabRAM 300 instrument from HORIBA Scientific) was applied to Pseudomonas fluorescens cells grown on different media, and the singlecell spectra obtained were compared with spectra retrieved from single isogenic P. fluorescens cells that were incubated on sterilized leaves of sugar beets (28). A comparison of the Raman spectra revealed pronounced differences between the cells grown on different media, reflecting the differences in the metabolic history of the cells. The Raman spectra obtained from cells grown in planta were significantly different from the spectra obtained from cells cultured in vitro, and no indications for carbon limitation or carbon-catabolite repression were detected for those cells grown in planta (28).


ANRV387-MI63-20

ARI

4 August 2009

21:54

However, the major potential of Raman spectroscopy for microbial ecology undoubtedly lies in the analysis of uncultured cells. Huang et al. (30) showed that cells of P. fluorescens grown in media containing different proportions of 13 C-glucose as the sole carbon source showed peak shifts in their Raman spectra. These shifts, which affected the major phenylalanine peak among others, were more pronounced as more 13 C was incorporated into the biomass. Band shifts in Raman spectra were also observed in Escherichia coli labeled with 15 N (K. Stoecker & M. Wagner, unpublished data). Stimulated by these observations, confocal Raman microspectroscopy was combined with FISH and epifluorescence microscopy to record Raman spectra of probeidentified bacterial cells in complex microbial communities (31). Probe-conferred labeling of different microbial cells with the fluorescent dyes Fluos or Cy5 did not interfere with Raman spectroscopy using the 532-nm Nd:YAG laser of the LabRAM 300 instrument. In contrast, for Cy3-labeled cells a short bleaching step was required before meaningful Raman spectra could be recorded. When the ratio of phenylalanine peak heights at 1003 cm−1 (unlabeled) and 967 cm−1 (labeled) (Figure 1c) was used as a determinative marker, an almost linear correlation between the known 13 C content of pure culture cells and the phenylalanine peak ratio was observed for living and for FISH-analyzed cells. For cells with a minimum labeling level of 10 atom% 13 C, this spectral shift could be reproducibly detected from spectra of unlabeled cells. This detection limit is better or comparable to those reported for DNA- or RNA-SIP (52, 70). The described pure culture standard curves can then be used to infer the cellular 13 C content from Raman spectra of FISH-stained microbial cells in environmental samples. By using this approach, in situ–detected Pseudomonas cells were shown to contribute to naphthalene degradation in a groundwater biofilm after pulse-labeling of the sample with 300 μM 13 C-naphthalene. Interestingly, strong differences in the in situ cellular 13 C content were observed among individual Pseudomonas

cells, showing that FISH-Raman is capable of revealing metabolic heterogeneities within bacterial populations (31). A follow-up study (29) using FISH-Raman demonstrated that, in addition to Pseudomonas sp., an uncultured Acidovorax sp. also contributed to 13 Cnaphthalene degradation in this system. If the naphthalene concentration added to the system was lowered to 3.8 μM, only Acidovorax cells contained significant amounts of 13 C in their biomass, demonstrating that the two different degrader populations have adapted to different niches, reflecting the often highly variable naphthalene concentration in this system. As a nondestructive technique, Raman microspectroscopy can be combined with optical trapping, using laser tweezers to capture and manipulate bacterial cells prior to and after Raman analysis (92). Using an infrared laser (1064 nm) for optical trapping to minimize thermal damage to the cells and a 514-nm laser for Raman spectra acquisition, Huang et al. (32) detected in a capillary tube 13 C-labeled P. fluorescens cells in an artificial cell mixture and separated them from the other cells. For this purpose single cells with a Raman spectrum characteristic for 13 C incorporation were moved with the trapping laser to a cell-free part of the capillary tube before this part of the tube was cleaved. These sorted single cells were then used as inocula for cultivation or for single-cell genome amplification, although for both approaches only a fraction of the sorted cells gave positive results (32). It remains to be seen whether single-cell amplification of sorted cells after Raman microspectroscopy also works for fixed and FISH-identified cells. This option would provide direct access to the genomes of single cells of probe-identified uncultured microbes with an experimentally confirmed ecophysiology—a possibility that microbial ecologists would still have declared as science fiction some years ago. As for SIMS imaging, stable isotope Raman microspectroscopic analyses of the ecophysiology of bacteria are promising not only for environmental studies, but also well suited for medical microbiology. For example, the metabolism www.annualreviews.org • Single-Cell Ecophysiology

417

ANRV387-MI63-20

ARI

4 August 2009

21:54


of facultative and obligate intracellular bacteria while growing in their host cells can be examined (Figure 1c) (S. Haider, unpublished data), and the ecophysiology of microbes living in the human body can be investigated. In the future such studies might further benefit from evaluating the many available modifications of the standard Raman approach such as surfaceenhanced Raman spectroscopy (36), coherent anti-Stokes Raman scattering (95), and stimulated Raman scattering (22), each of which offers pronounced increases in sensitivity.

ANALYTICAL IMAGING OF MICROBES BY SECONDARY ION MASS SPECTROMETRY Analytical SIMS imaging is perfectly suited to measure and visualize the distribution of stable or radioactive isotopes in microbial cells. It is one of the most promising techniques for studying the cellular functions of multicellular microorganisms such as filamentous cyanobacteria in pure culture (65) as well as the ecophysiology of microorganisms in their natural habitat.

Brief Introduction to SIMS SIMS (or ion microprobing) is an extremely sensitive mass spectrometric technique that determines the elemental, isotopic, or molecular composition of a solid sample surface (4). SIMS uses an energetic primary ion beam generated by an ion gun to produce and expel under high vacuum secondary particles (primarily atoms or molecules depending on the SIMS mode) from the sample surface (Figure 2). These particles can be neutral or positively or negatively charged. Charged particles of one polarity are extracted from the sputtering area by an electric field. These secondary ions are then focused by an extraction lens and the secondary ion beam is analyzed by mass spectrometry. Although virtually all elements can be analyzed by SIMS, different elements possess different secondary ion yields and consequently their SIMS detection limit varies dramatically. SIMS is applied in two 418

Wagner

different modes to bombard the surface of the sample. Dynamic SIMS uses a continuous ion beam at a high dose so that the number of incident ions is higher than the number of surface atoms of the sample. Dynamic SIMS instruments are commonly equipped with a duoplasmatron source producing a negatively (or positively) charged primary oxygen ion beam and a cesium source for generating a Cs+ primary ion beam for the enhanced generation of positively and negatively charged secondary ions, respectively. Owing to the high dose of primary ions, almost all molecules at the sample surface are completely fragmented and the sample surface erodes rapidly (up to several tens of microns per hour, dependent on the instrument and analytical conditions), permitting depth analysis of the sample composition (10). Thus, dynamic SIMS provides information on the elemental and isotopic composition of a sample with high sensitivity by using sector field or quadrupole mass analyzers for the measurement of the secondary ions. In modern instruments such as the NanoSIMS 50LTM (CAMECA) up to seven elements or isotopes of elements can be measured simultaneously, and this feature also enables researchers to determine precise isotope ratios of the sample. In contrast, static SIMS uses short-pulsed low-dosed ion beams typically produced by a liquid metal ion source (e.g., Ga+ or Bi3 + ). In this mode, each primary ion is intended to hit a fresh, untouched sample surface area, and molecular as well as elemental secondary ions are produced only from the first one or two molecular layers of the sample. These released molecular secondary ions are separated by a time-of-flight (TOF) mass analyzer, which unlike the mass analyzers used in dynamic SIMS permits the detection of all secondary ions simultaneously. However, because of the lower ionization yield of molecular fragments, relatively larger spot sizes of the primary beam are required, leading to a lower lateral resolution than in dynamic SIMS. Furthermore, owing to the application of a pulsed beam in static SIMS much longer acquisition times are generally required than for dynamic SIMS.

ANRV387-MI63-20

ARI

4 August 2009

21:54

Primary ion beam Secondary ions

12C– 13C– 12C14N– 12C15N– 13C14N– 13C15N–

Cs+


e– I–

Figure 2 Schematic illustration of a dynamic secondary ion mass spectrometry (SIMS) analysis of a bacterial community (not to scale). The bacterial cell sputtered with the primary ion beam is labeled with 15 N and 13 C. Only selected secondary ions are displayed. Note that nitrogen is detected as cyanide ion and that isobaric ions are formed (e.g., 12 C15 N− and 13 C14 N− ), whose differentiation requires high mass resolution as offered, for example, by modern NanoSIMS instruments. In addition, the analyzed rod-shaped cell is also labeled with an iodized oligonucleotide probe targeting the ribosomal RNA. If sufficient cellular material is removed by sputtering to allow secondary ion formation from the ribosomes (displayed in the cell lumen), iodine ions (I− ) can also be detected in the secondary ions. Figure by Gerhard Pucher.

In 1962 ion microscopy based on SIMS was introduced by Castaing & Slodzian (9). They built an instrument that preserved the lateral position of the desorbed secondary ions from the sample surface to the detector and thus allowed them to image the distribution of the detected ions. Alternatively, ion images can be produced by rasterizing a finely focused primary ion beam across the sample surface and by recording the intensities of the secondary ions of interest for each beam position (61). This ion microprobe imaging mode has been combined with static TOF-SIMS as well as dynamic sector field SIMS. The lateral resolution of the images produced by these machines is dependent on the diameter of the primary ion beam, and therefore the sensitivity of the instrument decreases with increasing lateral resolution. Modern dynamic SIMS imaging instruments like the

NanoSIMS 50L achieve a lateral resolution of about 50 and 150 nm with Cs+ and O− used as the primary ion source, respectively. Modern TOF-SIMS imaging has a lateral resolution of about 100 nm, but at such high lateral resolution the achievable mass resolution of TOFSIMS is insufficient for direct isotope-labeling studies in microbial ecology. For a more detailed description of SIMS ion imaging, I recommend the excellent reviews by Pacholski & Winograd (61), Guerquin-Kern et al. (24), and Lechene et al. (43). Because of the characteristics of the instruments, TOF-SIMS is ideally suited for imaging the molecular composition of the outermost surface of a sample, while the newest generation of dynamic SIMS instruments, which combine high mass with high lateral resolution, are the first choice if quantification and visualization www.annualreviews.org • Single-Cell Ecophysiology

419

ANRV387-MI63-20

ARI

4 August 2009


δ13 C value: (13 C/12 C sample −13 C/12 C standard) divided by 13 C/12 C standard × 1000 with a belemnite sample from the PeeDee rock formation as standard

21:54

of elements or isotopes in a microbial cell are desired. During the last few years, static and dynamic SIMS imaging have been applied to study different ecophysiological aspects of microorganisms in the environment. For this purpose, microbes must be fixed and dried or embedded appropriately to prevent their destruction under the high SIMS vacuum. In addition, the microorganisms should be immobilized on conducting surfaces such as silicon wafers (43) or gold-palladium sputtered nucleopore filters (53) or embedded in gold sputtered epoxy-resin sections (27) to minimize charging of the sample during bombardment with the primary ion beam. In addition to microbes, other sample material can be analyzed by SIMS to reveal microbial activities. For example, dynamic NanoSIMS has been applied to measure 32 S and 34 S captured as Ag2 S by introducing silver foil into a hypersaline cyanobacterial mat (20). Quantitative millimeter-scale 2D distribution maps of sulfide concentration and δ 34 S composition across the oxycline during photosynthesis were generated by systematic NanoSIMS analyses of the silver foil. These analyses revealed pronounced spatial heterogeneities of sulfate-reducing activity in the mat and provided indications for active sulfur disproportionation (20).

Applications of SIMS Imaging in Microbial Ecology SIMS imaging has been successfully applied to study the ecophysiology of microbes in several environments by either exploiting natural abundance measurements of carbon or by quantifying the amount of isotope in microbial cells after exposure to experimentally added isotopelabeled substrates. SIMS imaging to identify anaerobic methane oxidizers in marine systems. The first applications of dynamic SIMS imaging for inferring ecophysiological properties of microbes were published by Orphan et al. (58, 59). These authors used an IMS 1270 ion

420

Wagner

microprobe from CAMECA with a Cs+ primary ion beam to determine the δ13 C values of archaeal and bacterial cells in anoxic, methanerich marine sediments and to examine whether they are involved in the anaerobic oxidation of methane. Biologically produced methane is isotopically significantly lighter than other possible substrates, and owing to this unusual feature, natural abundance measurements of carbon isotopes are useful for monitoring methane assimilation in microbes. Orphan and colleagues pioneered the field by indirectly combining the identification of microbes in the sediments via FISH with subsequent SIMS analysis of the identified cells. By using this FISH-SIMS, they found that specific archaeal groups (ANME-1 and ANME-2), as well as some sulfate-reducing bacteria physically associated with ANME-2 cells, were strongly depleted in 13 C, suggesting their involvement in anaerobic methanotrophy. Furthermore, by making use of the depth analysis option of dynamic SIMS, they showed that, in structured microbial aggregates consisting of a shell of deltaproteobacterial sulfate-reducing bacteria and a core of ANME-2 cells, the surface layers are less depleted in 13 C than the central biomass is. These findings indicated that in these aggregates the centrally located ANME2 cells are the primary methane consumers that excrete methane-derived products to their bacterial partners. In 2007, Treude et al. (77) investigated reefforming methanotrophic microbial mats above gas seeps in the Black Sea with the same instrument. δ13 C values were determined in different areas of the mat, and the isotopic signatures were compared with (a) 14 CH4 and 14 CO2 assimilation data as measured by beta microimaging of the same mat sections and (b) spatial distribution data of ANME-1 and ANME-2 anerobic methanotrophs stained using catalyzed reporter deposition (CARD)-FISH. The δ13 C values of the mat and the emanating methane from the seep were similar, reflecting that most of the biomass in the mat was produced by anaerobic methanotrophs. However,


ANRV387-MI63-20

ARI

4 August 2009

21:54

relatively pronounced variations in δ13 C values were observed for SIMS-measured spots in closely located mat areas, demonstrating that at a microscale anaerobic methanotrophs (isotopically lighter) coexist with microbes assimilating other substrates containing heavier carbon (isotopically heavier). Mapping of the δ13 C values showed no obvious correlation with high- or low-activity zones of 14 CH4 and 14 CO2 assimilation in the mat. However, areas deeper in the mat, which were dominated by ANME-2 archaea, had significantly lower δ13 C values than zones closer to the surface where ANME-1 cells were abundant (77). SIMS imaging of nitrogen fixation in a bacterial shipworm symbiont. In their seminal paper on SIMS imaging of biological samples with a NanoSIMS prototype, Lechene et al. (43) also presented a proof-of-principle application of dynamic NanoSIMS for analyzing nitrogen fixation in microbial cells. Teredinibacter turnerae, a nitrogen-fixing marine microbe, and the gram-positive bacterium Enterococcus faecalis, which is unable to assimilate nitrogen gas, were grown in the presence of 15 N2 . Nitrogen fixation by T. turnerae was visualized in mixtures of both species by mass images of 12 14 − C N and 12 C15 N− ions (note that N− ions do not form in sufficient quantities and N is thus measured as its cyanide ions). Images visualizing the 12 C15 N− /12 C14 N− ratio unambiguously demonstrated that T. turnerae fixed labeled nitrogen gas, whereas E. faecalis remained unlabeled. Furthermore, heterogeneities in the amount of nitrogen fixation within the labeled T. turnerae population were observed. This experiment nicely illustrates one of the major advantages of multi-isotope imaging. Several secondary ion masses can be measured simultaneously, and therefore isotope ratios from the same sample location can be calculated and visualized by color coding for every pixel in the image. In contrast to absolute data on the amount of a labeled isotope in a sample, these ratios directly prove labeling of the sample if the ratio is above the natural abundance level, and

provide quantitative information on the excess of the labeled isotope. Excitingly, nitrogen fixation of T. turnerae was detected not only in artificially mixed culture but also in its natural habitat, the gill bacteriocytes of the shipworm Lyrodus pepedicellatus. In a follow-up study (44) nitrogen fixation by this shipworm symbiont was analyzed in detail using multi-isotope imaging. After exposure of the animals in their intact wood borrows to 15 N2 labeling, the symbionts in the gill bacteriocytes had 15 N/14 N ratios of up to factor 68 over the natural ratio, which indicates strong labeling. Furthermore, when transmission electron microscopy images of the bacteriocytes with their symbionts were precisely superimposed with ion images, indications for transfer of compounds containing fixed nitrogen from the symbionts to the host cells were found. Consistent with this observation, symbiont-cell-free regions of the shipworm showed 15 N labeling, strongly suggesting that the fixed nitrogen from the symbionts is used for biosynthesis of cellular material by the shipworm, a feature urgently required by this animal, which feeds on an almost nitrogen-free diet of wood. SIMS imaging of nitrogen assimilation and phenol degradation in soil. Cliff et al. (12– 14) published a series of manuscripts exploring the use of static TOF-SIMS for investigating nitrogen assimilation and mineralization in soil systems. These studies were performed with the TRIFT-II TOF-SIMS instrument from Physical Electronics, Inc. The analyzed microorganisms were transferred from experimental soil microcosms or native soils, which had been exposed to stable-isotope-labeled compounds, onto Si contact slides and subsequently sputtered with a Ga+ primary ion beam. In these studies no attempts were made to identify the labeled microorganisms, but fungal hyphae were included in the analyses in addition to cells with a typical bacterial morphology. Cliff and coworkers showed that the atom percents of 15 N in microbial pure cultures labeled with known amounts of 15 N can be determined

www.annualreviews.org • Single-Cell Ecophysiology

421

ARI

4 August 2009

21:54

with their TOF-SIMS instrument at a relative accuracy of 6% by measuring cyanide ions of mass 26 (12 C14 N− ) and 27 (12 C15 N− ). Using this approach, they detected and quantified ammonia assimilation in bacterial cells and (to a lesser degree) in fungal hyphae in native soil. Furthermore, they showed that fungal hyphae growing in a model soil system amended with 15 NO3 − and containing pieces of straw and manure had a lower 15 N abundance if physically associated with manure than if they grew in straw. These results suggested that either (unlabeled) ammonium was produced by mineralization of organic N in manure and assimilated by the hyphae or that higher denitrification rates in manure regions decreased the amount of labeled nitrate available for assimilation (13). Furthermore, isobaric interferences of 27 Al− and 13 C14 N− with 12 C15 N− were observed if the analytical conditions were optimized for high spatial resolution, which is required for the analyses of environmental samples. These problems could be diminished by peak fitting under some conditions, but no general solution was obtained (14). Using the developed peak fitting algorithm, the same authors visualized by using TOF-SIMS the influence of a layer of dead bacteria as a hot spot of highly labile organic N on 15 NH4 + assimilation of microorganisms in a soil microcosm. Steep gradients of 15 N assimilation at the millimeter scale around the highly labeled organic N source were observed, demonstrating that the influence of microsite heterogeneities in soil on nitrogen cycling can be measured by TOF-SIMS (12). However, interpretation of TOF-SIMSbased isotope ratio analyses of microorganisms in the environment is complicated because this mass spectrometric method only measures the isotopic composition of the outside of the target cells. Therefore, contaminating organic matter from the soil might influence the isotope composition of the cell surfaces and lead to biased results (13). Furthermore, the distribution of the isotopic label in the cells might not be homogenous, for example, if the label is incorporated into storage compounds or associated with rRNA. In these cases, exclusive anal-


ANRV387-MI63-20

422

Wagner

ysis of the cell surface provides incorrect data. These problems can be solved if 15 N-labeled microbes are detected in soil by using dynamic NanoSIMS. Herrmann et al. (27) labeled P. fluorescens with 15 N in pure culture and added the labeled cells to soil fixed with glutaraldehyde and embedded in a vacuum-compatible epoxy resin. After sectioning, the labeled cells were readily detected by NanoSIMS 50TM using Cs+ as the primary ion source and measuring [12 C14 N]− and [12 C15 N]− , and their 15/14 N ratio was comparable to the ratio measured for the bulk pure culture biomass in an isotope ratio mass spectrometer. In this study, NanoSIMS imaging was also nicely combined with scanning electron microscopy and energy-dispersive spectroscopy X-ray microanalysis of the soil sections to select suitable sample regions for SIMS analysis. Madsen and coworkers (11, 17, 67) studied phenol-degrading microorganisms in soil that were exposed to 12 C- or 13 C-phenol in the field with a IMS-3f (CAMECA) ion microscope for dynamic SIMS imaging with an O2 + primary ion beam. Mass 27 and mass 26 were measured to quantify [13 C14 N]− and [12 C14 N]− , but under the instrument settings required for microbial imaging in soil, this instrument (in contrast to, for example, the NanoSIMS 50) does not provide the mass resolution to avoid isobaric interferences. Therefore, for example, 13 C2 − and [12 C14 N]− could not be distinguished. To overcome this problem, a standard curve of the percent mass 27 was generated by using pure culture cells of Pseudomonas putida grown with glucose with different 13 C proportions (67). This standard curve showed an almost linear correlation and was used to estimate the 13 C enrichment from labeled soil microbes. About 30% of the soil microbes, which remained unidentified, were more than 90% 13 C labeled in soil that was repeatedly dosed with 13 Cphenol. In addition to these microbes apparently living exclusively on phenol, several microbes with intermediate levels of labeling were observed that could originate from general activity differences, unevenness in phenol availability, cross-feeding, and mixotrophy.


ANRV387-MI63-20

ARI

4 August 2009

21:54

Simultaneous identification and isotopic analysis of single microbial cells. In 2008 three research teams developed NanoSIMS 50 imaging approaches for simultaneous identification of microbes at the single-cell level by in situ hybridization with rRNA-targeted probes and measurement of their isotope ratios of selected elements for functional analyses (3, 47, 53). For SIMS detection of microbial cells containing specifically hybridized oligonucleotide probes, each team exploited halogen labeling because these elements have high ionization yields and in general relatively low natural background concentrations. Another advantage of this approach is that, unlike for FISH, autofluorescence of biological samples does not hamper these analyses of natural microbial communities. Li et al. (47) utilized iodized probes for in situ hybridization, which were synthesized by using the deoxyribonucleoside analogue 5-iodo-2 deoxycytidine instead of 2 deoxycytidine. In principle this strategy can be extended by using probes labeled with different halogens so that simultaneous SIMS detection of more than one probe should become feasible. To demonstrate the suitability of the SIMS in situ hybridization approach (for which they use the acronym SIMSISH) for environmental application, Li and coworkers quantified incorporation of 13 C-labeled methanol into probe-labeled bacterial and archaeal cells in a municipal solid waste batch bioreactor. However, it remains to be seen whether the iodine signal introduced by the SIMSISH probes is sufficient for detection of microbes with a low ribosome content and whether the chemically modified oligonucleotides change the melting behavior of probe-target duplexes. Behrens et al. (3) introduced the halogen label selectively into probe-targeted cells by applying horseradish peroxidase-conjugated oligonucleotide probes and signal amplification with customized tyramides labeled with fluorine- or bromine-containing fluorophores. Consequently, cells treated with the EL-FISH (enhanced elemental labeling FISH) approach can be detected by fluorescence microscopy and by NanoSIMS imaging. Dependent on the

specific tyramide applied, SIMS halogen signals were up to 180-fold above the natural halogen background and signal-to-noise ratios were 10- to 100-fold higher compared with the use of halogen-containing oligonucleotides. EL-FISH was then applied to confirm that a heterotrophic Rhizobium strain living physically associated with a cyanobacterial Anabaena strain assimilates carbon and nitrogen fixed by the cyanobacterium. With the same technique (termed HISHSIMS, or halogen in situ hybridization-SIMS) Musat and colleagues (53) identified three different anaerobic phototrophic bacteria in an oligotrophic meromictic lake and quantified their 15 N-ammonium and inorganic carbon (13 CO2 ) assimilation. Large differences in the amounts of assimilates were observed within each of the three populations, confirming metabolic heterogeneity of closely related or genetically identical microorganisms. Together, these three species were responsible for more than 50% of the total ammonium and 80% of the total inorganic carbon uptake of the system. Surprisingly, most of the uptake of these compounds could be attributed to Chromatium okenii, which represented less than 1% of the total cell number in the lake and was the least abundant of the three species. This finding is a beautiful example that the functional importance of a microbe in an ecosystem is not always linked to its numerical abundance. Taken together, SIMS imaging and in particular the advent of NanoSIMS-based structurefunction analyses enable microbiologists to study the ecophysiology of microbes in their natural environment in astonishing detail not achievable by any other existing approach. For comparison, NanoSIMS has an imaging resolution about 10-fold higher than MAR and is roughly 1000-fold more sensitive than MAR regarding the detection of labeled carbon (43). However, SIMS-based quantification of isotopes in microbes is still in its infancy and measured isotope ratios of a cell might have been unintentionally modified by sample preparation. For example, chemical fixation and in situ hybridization of microbes introduce www.annualreviews.org • Single-Cell Ecophysiology

423

ANRV387-MI63-20

ARI

4 August 2009

a

21:54

b

402

0.036 0.036 0.034

0.037


5 µm

2 µm

0

Figure 3 Morphological and metabolic analyses of obligate intracellular chlamydial symbionts exposed to 13 C-labeled phenylalanine. (a) Scanning electron microscopic image of the chlamydial cells after release from their intracellular niche achieved by mechanical lysis of the amoebal host cells. Note the amoebal cell debris next to the chlamydial cells. (b) 12 C14 N− -NanoSIMS image of the same sample showing the released coccoid chlamydial symbionts (encircled ) and the host cell debris. Color bar indicates the number of 12 C14 N− counts. The 13 C/12 C ratio as measured by NanoSIMS is indicated for each chlamydial cell. The measured ratios are much higher than the natural abundance ratio of 13 C/12 C (0.0112), demonstrating uptake of labeled phenylalanine.

unlabeled hydrogen, carbon, oxygen, and nitrogen into the cell. This effect is more pronounced if tyramide signal amplification is applied. Furthermore, chemical fixation with formaldehyde or glutaraldehyde cross-links proteins, purines, and pyrimidines and thus renders them insoluble and immobile. However, this fixation does not stabilize sugars and other soluble molecules that might thus get redistributed or lost during sample preparation or in

the high vacuum required for SIMS measurements. This problem might be ameliorated if cryofixation instead of chemical fixation is used for effective immobilization of any cellular material (15, 24). NanoSIMS-based ecophysiological analyses also have an enormous potential for shedding light on the metabolic activities of obligate intracellular bacteria thriving in eukaryotic host cells (Figure 3). Among these difficult to study bacteria are many pathogens and it is thus safe to predict that NanoSIMS will become a powerful tool for medical microbiologists too. The importance of the human microbiome for health and disease (80) is increasingly well recognized. Whereas the community composition of the human microbiome can be precisely determined (19, 94), studying in situ the specific functions of microbial populations colonizing our gut system and the oral cavity remains a major research frontier. The first proof-of-principle experiments showing uptake of 13 C-labeled amino acids by Cytophaga-Flavobacterium species in a complex oral biofilm via EL-FISH (3) indicate the potential of stable isotope NanoSIMS studies for revealing microbe-microbe and microbehuman interactions in our body. Furthermore, future NanoSIMS applications might also allow the specific detection of mRNA and proteins (via stable isotope or element-labeled antibodies) at the single-cell level, thereby providing a direct link between metabolic, translational, and transcriptional activity.

SUMMARY POINTS 1. Raman microspectroscopy and SIMS imaging are suitable for detecting and quantifying stable isotope labeling of single microbial cells in complex microbial communities and can be combined with in situ hybridization to identify the active cells. 2. Raman microspectroscopy and SIMS imaging are suitable for investigating the metabolism of obligate intracellular bacteria. 3. In contrast to SIMS, Raman microspectroscopy is a nondestructive technique and single isotope-labeled cells can be identified and physically separated by an optical tweezer for subsequent cultivation or single-cell genomics approaches.

424

Wagner

ANRV387-MI63-20

ARI

4 August 2009

21:54

4. In contrast to conventional Raman microspectroscopy, modern NanoSIMS instruments are significantly more sensitive, allow the quantification of several elements or isotopes of elements in a microbial cell simultaneously, and have a higher spatial resolution of ∼10–20 × , enabling subcellular analyses.

FUTURE ISSUES


1. It should be explored whether RNA labeled with stable isotopes and hybridized to phylochips can be detected with Raman microspectroscopy or NanoSIMS to make the isotope array approach (2) compatible with stable isotope labeling. 2. Innovative substrate application techniques such as Bio-Traps (7) need to be further developed to mimic as closely as possible natural conditions during exposure of microbial communities to labeled substrates. 3. Live in situ NMR techniques (50) for inferring the metabolism of (low-diversity) microbial communities should be combined with Raman- or SIMS-based ecophysiological studies to address the functioning of these communities with a systems biology approach.

DISCLOSURE STATEMENT The author is not aware of any affiliations, memberships, funding, or financial holdings that might be perceived as affecting the objectivity of this review.

ACKNOWLEDGMENTS I would like to apologize in advance to all the researchers whose publications could not be cited owing to space limitations. I thank Marcel Kuypers, Tomas Vagner, Francois Hillion, and Markus Schmid for help with the NanoSIMS data, and Susanne Haider for providing unpublished Raman microspectroscopy data. Kilian Stoecker is acknowledged for helpful advice regarding Raman microspectroscopy. I extend a special thanks to Susanne Haider and Gerhard Pucher for help with preparing figures. Mike Taylor, Andreas Richter, and Francois Horreard are greatly acknowledged for providing comments on the manuscript. I thank Per Nielsen and his team for a very stimulating long-term collaboration on microbial single-cell ecophysiology, and Andrew Whiteley for our collaboration on Raman microspectroscopy. I also thank Matthias Horn, Holger Daims, and Alex Loy from the Department of Microbial Ecology for their enthusiasm and support over many years. LITERATURE CITED 1. Achtman M, Wagner M. 2008. Microbial diversity and the genetic nature of microbial species. Nat. Rev. Microbiol. 6:431–40 2. Adamczyk J, Hesselsoe M, Iversen N, Horn M, Lehner A, et al. 2003. The isotope array, a new tool that employs substrate-mediated labeling of rRNA for determination of microbial community structure and function. Appl. Environ. Microbiol. 69:6875–87 3. Behrens S, Losekann T, Pett-Ridge J, Weber P, Ng WO, et al. 2008. Linking microbial phylogeny to metabolic activity at the single-cell level by using enhanced element labeling-catalyzed reporter deposition fluorescence in situ hybridization (EL-FISH) and NanoSIMS. Appl. Environ. Microbiol. 74:3143–50 www.annualreviews.org • Single-Cell Ecophysiology

3. Smart modification of the CARD-FISH approach for high sensitivity detection of oligonucleotide probe labeled cells by NanoSIMS.

425

ARI

4 August 2009

21:54

¨ 4. Benninghoven A, Rudenauer FG, Werner HW. 1987. Secondary Ion Mass Spectrometry: Basic Concepts, Instrumental Aspects, Applications, and Trends. New York: Wiley 5. Boschker HTS, Nold SC, Wellsbury P, Bos D, de Graaf W, et al. 1998. Direct linking of microbial populations to specific biogeochemical processes by 13 C-labelling of biomarkers. Nature 392:801–5 6. Bryant DA, Costas AM, Maresca JA, Chew AG, Klatt CG, et al. 2007. Candidatus Chloracidobacterium thermophilum: an aerobic phototrophic acidobacterium. Science 317:523–26 7. Busch-Harris J, Sublette K, Roberts KP, Landrum C, Peacock AD, et al. 2008. Bio-traps coupled with molecular biological methods and stable isotope probing demonstrate the in situ biodegradation potential of MTBE and TBA in gasoline-contaminated aquifers. Ground Water Monit. Remediat. 28:47–62 8. Cardenas E, Tiedje JM. 2008. New tools for discovering and characterizing microbial diversity. Curr. Opin. Biotechnol. 19:544–49 9. Castaing R, Slodzian G. 1962. Microanalyse par e´ mission ionique secondaire. J. Microsc. 1:395–410 10. Chandra S. 2001. Studies of cell division (mitosis and cytokinesis) by dynamic secondary ion mass spectrometry ion microscopy: LLC-PK1 epithelial cells as a model for subcellular isotopic imaging. J. Microsc. 204:150–65 11. Chandra S, Pumphrey G, Abraham JM, Madsen EL. 2008. Dynamic SIMS ion microscopy imaging of individual bacterial cells for studies of isotopically labeled molecules. Appl. Surf. Sci. 255:847–51 12. Cliff JB, Bottomley PJ, Gaspar DJ, Myrold DD. 2007. Nitrogen mineralization and assimilation at millimeter scales. Soil Biol. Biochem. 39:823–26 13. Cliff JB, Gaspar DJ, Bottomley PJ, Myrold DD. 2002. Exploration of inorganic C and N assimilation by soil microbes with time-of-flight secondary ion mass spectrometry. Appl. Environ. Microbiol. 68:4067–73 14. Cliff JB, Gaspar DJ, Bottomley PJ, Myrold DD. 2004. Peak fitting to resolve CN− isotope ratios in biological and environmental samples using TOF-SIMS. Appl. Surf. Sci. 231–32:912–16 15. Clode PL, Stern RA, Marshall AT. 2007. Subcellular imaging of isotopically labeled carbon compounds in a biological sample by ion microprobe (NanoSIMS). Microsc. Res. Technol. 70:220–29 16. Daims H, Luecker S, Wagner M. 2006. daime, a novel image analysis program for microbial ecology and biofilm research. Environ. Microbiol. 8:200–13 17. DeRito CM, Pumphrey GM, Madsen EL. 2005. Use of field-based stable isotope probing to identify adapted populations and track carbon flow through a phenol-degrading soil microbial community. Appl. Environ. Microbiol. 71:7858–65 18. Dumont MG, Murrell JC. 2005. Stable isotope probing: linking microbial identity to function. Nat. Rev. Microbiol. 3:499–504 19. Eckburg PB, Bik EM, Bernstein CN, Purdom E, Dethlefsen L, et al. 2005. Diversity of the human intestinal microbial flora. Science 308:1635–38 20. Fike DA, Gammon CL, Ziebis W, Orphan VJ. 2008. Micron-scale mapping of sulfur cycling across the oxycline of a cyanobacterial mat: a paired NanoSIMS and CARD-FISH approach. ISME J. 2:749–59 21. Foster RA, Subramaniam A, Zehr JP. 2009. Distribution and activity of diazotrophs in the Eastern Equatorial Atlantic. Environ. Microbiol. 11:741–50 22. Freudiger CW, Min W, Saar BG, Lu S, Holtom GR, et al. 2008. Label-free biomedical imaging with high sensitivity by stimulated Raman scattering microscopy. Science 322:1857–61 23. Frias-Lopez J, Shi Y, Tyson GW, Coleman ML, Schuster SC, et al. 2008. Microbial community gene expression in ocean surface waters. Proc. Natl. Acad. Sci. USA 105:3805–10 24. Guerquin-Kern JL, Wu TD, Quintana C, Croisy A. 2005. Progress in analytical imaging of the cell by dynamic secondary ion mass spectrometry (SIMS microscopy). Biochim. Biophys. Acta 1724:228–38 25. Haferkamp I, Schmitz-Esser S, Wagner M, Neigel N, Horn M, Neuhaus HE. 2006. Tapping the nucleotide pool of the host: novel nucleotide carrier proteins of Protochlamydia amoebophila. Mol. Microbiol. 60:1534–45 26. He Z, Gentry TJ, Schadt CW, Wu L, Liebich J, et al. 2007. GeoChip: a comprehensive microarray for investigating biogeochemical, ecological and environmental processes. ISME J. 1:67–77 27. Herrmann AM, Clode PL, Fletcher IR, Nunan N, Stockdale EA, et al. 2007. A novel method for the study of the biophysical interface in soils using nano-scale secondary ion mass spectrometry. Rapid Commun. Mass Spectrom. 21:29–34


ANRV387-MI63-20

426

Wagner


ANRV387-MI63-20

ARI

4 August 2009

21:54

28. Huang WE, Bailey MJ, Thompson IP, Whiteley AS, Spiers AJ. 2007. Single-cell Raman spectral profiles of Pseudomonas fluorescens SBW25 reflects in vitro and in planta metabolic history. Microb. Ecol. 53:414–25 29. Huang WE, Ferguson A, Singer AC, Lawson K, Thompson IP, et al. 2009. Resolving genetic functions within microbial populations: in situ analyses using rRNA and mRNA stable isotope probing coupled with single-cell Raman-fluorescence in situ hybridization. Appl. Environ. Microbiol. 75:234–41 30. Huang WE, Griffiths RI, Thompson IP, Bailey MJ, Whiteley AS. 2004. Raman microscopic analysis of single microbial cells. Anal. Chem. 76:4452–58 31. Huang WE, Stoecker K, Griffiths R, Newbold L, Daims H, et al. 2007. Raman-FISH: combining stable-isotope Raman spectroscopy and fluorescence in situ hybridization for the single cell analysis of identity and function. Environ. Microbiol. 9:1878–89 32. Huang WE, Ward AD, Whiteley AS. 2009. Raman tweezers sorting of single microbial cells. Environ. Microbiol. 1(1):44–49 33. Imachi H, Sekiguchi Y, Kamagata Y, Loy A, Qiu YL, et al. 2006. Non-sulfate-reducing, syntrophic bacteria affiliated with Desulfotomaculum cluster I are widely distributed in methanogenic environments. Appl. Environ. Microbiol. 72:2080–91 34. Ivleva NP, Wagner M, Horn H, Niessner R, Haisch C. 2009. Towards a nondestructive chemical characterization of biofilm matrix by Raman microscopy. Anal. Bioanal. Chem. 393:197–206 35. Janssen PH. 2006. Identifying the dominant soil bacterial taxa in libraries of 16S rRNA and 16S rRNA genes. Appl. Environ. Microbiol. 72:1719–28 36. Jarvis RM, Goodacre R. 2004. Discrimination of bacteria using surface-enhanced Raman spectroscopy. Anal. Chem. 76:40–47 37. Kalyuzhnaya MG, Hristova KR, Lidstrom ME, Chistoserdova L. 2008. Characterization of a novel methanol dehydrogenase in representatives of Burkholderiales: implications for environmental detection of methylotrophy and evidence for convergent evolution. J. Bacteriol. 190:3817–23 38. Kalyuzhnaya MG, Lapidus A, Ivanova N, Copeland AC, McHardy AC, et al. 2008. High-resolution metagenomics targets specific functional types in complex microbial communities. Nat. Biotechnol. 26:1029–34 39. Kalyuzhnaya MG, Lidstrom ME, Chistoserdova L. 2008. Real-time detection of actively metabolizing microbes by redox sensing as applied to methylotroph populations in Lake Washington. ISME J. 2:696– 706 40. Krause M, Radt B, Rosch P, Popp J. 2007. The investigation of single bacteria by means of fluorescence staining and Raman spectroscopy. J. Raman Spectrosc. 38:369–72 41. Kunin V, Copeland A, Lapidus A, Mavromatis K, Hugenholtz P. 2008. A bioinformatician’s guide to metagenomics. Microbiol. Mol. Biol. Rev. 72:557–78 42. Lawton JH, Brown VK. 1994. Redundancy in ecosystems. In Biodiversity and Ecosystem Function, ed. E-D Schulze, HA Mooney, pp. 255–70. Berlin: Springer 43. Lechene C, Hillion F, McMahon G, Benson D, Kleinfeld AM, et al. 2006. High-resolution quantitative imaging of mammalian and bacterial cells using stable isotope mass spectrometry. J. Biol. 5:20 44. Lechene CP, Luyten Y, McMahon G, Distel DL. 2007. Quantitative imaging of nitrogen fixation by individual bacteria within animal cells. Science 317:1563–66 45. Lee N, Nielsen PH, Andreasen KH, Juretschko S, Nielsen JL, et al. 1999. Combination of fluorescent in situ hybridization and microautoradiography—a new tool for structure-function analyses in microbial ecology. Appl. Environ. Microbiol. 65:1289–97 46. Leininger S, Urich T, Schloter M, Schwark L, Qi J, et al. 2006. Archaea predominate among ammoniaoxidizing prokaryotes in soils. Nature 442:806–9 47. Li T, Wu TD, Mazeas L, Toffin L, Guerquin-Kern JL, et al. 2008. Simultaneous analysis of microbial identity and function using NanoSIMS. Environ. Microbiol. 10:580–88 48. Loy A, Kusel K, Lehner A, Drake HL, Wagner M. 2004. Microarray and functional gene analyses of sulfate-reducing prokaryotes in low-sulfate, acidic fens reveal cooccurrence of recognized genera and novel lineages. Appl. Environ. Microbiol. 70:6998–7009 49. Maixner F, Noguera DR, Anneser B, Stoecker K, Wegl G, et al. 2006. Nitrite concentration influences the population structure of Nitrospira-like bacteria. Environ. Microbiol. 8:1487–95 www.annualreviews.org • Single-Cell Ecophysiology

30. First observation of Raman band shifts in isotope-labeled bacterial cells.

31. First report showing the combination of FISH and Raman microspectroscopy for structure-function analysis of microbial communities.

43. A very inspiring overview of application examples of NanoSIMS imaging in biology.

47. First direct combination of in situ hybridization and NanoSIMS for the identification of isotope-labeled cells.

427

ANRV387-MI63-20

ARI

4 August 2009


53. Beautiful application example of NanoSiMS revealing an anoxygenic phototroph as key stone species in an alpine lake.

58. A pioneering study providing the first application example of SIMS for inferring the ecophysiology of an uncultured microbe.

428

21:54

50. Majors PD, McLean JS, Scholten JC. 2008. NMR bioreactor development for live in situ microbial functional analysis. J. Magn. Reson. 192:159–66 51. Manefield M, Whiteley AS, Griffiths RI, Bailey MJ. 2002. RNA stable isotope probing, a novel means of linking microbial community function to phylogeny. Appl. Environ. Microbiol. 68:5367–73 52. Manefield M, Whiteley AS, Ostle N, Ineson P, Bailey MJ. 2002. Technical considerations for RNAbased stable isotope probing: an approach to associating microbial diversity with microbial community function. Rapid Commun. Mass Spectrom. 16:2179–83 53. Musat N, Halm H, Winterholler B, Hoppe P, Peduzzi S, et al. 2008. A single-cell view on the ecophysiology of anaerobic phototrophic bacteria. Proc. Natl. Acad. Sci. USA 105:17861–66 54. Musat N, Werner U, Knittel K, Kolb S, Dodenhof T, et al. 2006. Microbial community structure of sandy intertidal sediments in the North Sea, Sylt-Romo Basin, Wadden Sea. Syst. Appl. Microbiol. 29:333–48 55. Neufeld JD, Chen Y, Dumont MG, Murrell JC. 2008. Marine methylotrophs revealed by stable-isotope probing, multiple displacement amplification and metagenomics. Environ. Microbiol. 10:1526–35 56. Neufeld JD, Wagner M, Murrell JC. 2007. Who eats what, where and when? Isotope-labelling experiments are coming of age. ISME J. 1:103–10 57. Nielsen JL, Christensen D, Kloppenborg M, Nielsen PH. 2003. Quantification of cell-specific substrate uptake by probe-defined bacteria under in situ conditions by microautoradiography and fluorescence in situ hybridization. Environ. Microbiol. 5:202–11 58. Orphan VJ, House CH, Hinrichs KU, McKeegan KD, DeLong EF. 2001. Methane-consuming archaea revealed by directly coupled isotopic and phylogenetic analysis. Science 293:484–87 59. Orphan VJ, House CH, Hinrichs KU, McKeegan KD, DeLong EF. 2002. Multiple archaeal groups mediate methane oxidation in anoxic cold seep sediments. Proc. Natl. Acad. Sci. USA 99:7663–68 60. Ouverney CC, Fuhrman JA. 1999. Combined microautoradiography-16S rRNA probe technique for determination of radioisotope uptake by specific microbial cell types in situ. Appl. Environ. Microbiol. 65:1746–52 61. Pacholski ML, Winograd N. 1999. Imaging with mass spectrometry. Chem. Rev. 99:2977–3005 62. Pankratov TA, Serkebaeva YM, Kulichevskaya IS, Liesack W, Dedysh SN. 2008. Substrate-induced growth and isolation of Acidobacteria from acidic Sphagnum peat. ISME J. 2:551–60 63. Patel IS, Premasiri WR, Moir DT, Ziegler LD. 2008. Barcoding bacterial cells: a SERS-based methodology for pathogen identification. J. Raman Spectrosc. 39:1660–72 64. Pätzold R, Keuntje M, Theophile K, Muller J, Mielcarek E, et al. 2008. In situ mapping of nitrifiers and anammox bacteria in microbial aggregates by means of confocal resonance Raman microscopy. J. Microbiol. Methods 72:241–48 65. Popa R, Weber PK, Pett-Ridge J, Finzi JA, Fallon SJ, et al. 2007. Carbon and nitrogen fixation and metabolite exchange in and between individual cells of Anabaena oscillarioides. ISME J. 1:354–60 66. Prosser JI, Nicol GW. 2008. Relative contributions of archaea and bacteria to aerobic ammonia oxidation in the environment. Environ. Microbiol. 10:2931–41 67. Pumphrey GM, Hanson BT, Chandra S, Madsen EL. 2009. Dynamic secondary ion mass spectrometry imaging of microbial populations utilizing C13 -labelled substrates in pure culture and in soil. Environ. Microbiol. 11:220–29 68. Purkhold U, Pommerening-Roser A, Juretschko S, Schmid MC, Koops HP, Wagner M. 2000. Phylogeny of all recognized species of ammonia oxidizers based on comparative 16S rRNA and amoA sequence analysis: implications for molecular diversity surveys. Appl. Environ. Microbiol. 66:5368–82 69. Radajewski S, Ineson P, Parekh NR, Murrell JC. 2000. Stable-isotope probing as a tool in microbial ecology. Nature 403:646–49 70. Radajewski S, McDonald IR, Murrell JC. 2003. Stable-isotope probing of nucleic acids: a window to the function of uncultured microorganisms. Curr. Opin. Biotechnol. 14:296–302 71. Rappe MS, Giovannoni SJ. 2003. The uncultured microbial majority. Annu. Rev. Microbiol. 57:369–94 72. Rusch DB, Halpern AL, Sutton G, Heidelberg KB, Williamson S, et al. 2007. The Sorcerer II Global Ocean Sampling expedition: northwest Atlantic through eastern tropical Pacific. PLoS Biol. 5:e77 73. Schuster KC, Reese I, Urlaub E, Gapes JR, Lendl B. 2000. Multidimensional information on the chemical composition of single bacterial cells by confocal Raman microspectroscopy. Anal. Chem. 72:5529–34 Wagner


ANRV387-MI63-20

ARI

4 August 2009

21:54

74. Strous M. 2007. Data storm. Environ. Microbiol. 9:10–11 75. Strous M, Pelletier E, Mangenot S, Rattei T, Lehner A, et al. 2006. Deciphering the evolution and metabolism of an anammox bacterium from a community genome. Nature 440:790–94 76. Taylor MW, Radax R, Steger D, Wagner M. 2007. Sponge-associated microorganisms: evolution, ecology, and biotechnological potential. Microbiol. Mol. Biol. Rev. 71:295–347 77. Treude T, Orphan V, Knittel K, Gieseke A, House CH, Boetius A. 2007. Consumption of methane and CO2 by methanotrophic microbial mats from gas seeps of the anoxic Black Sea. Appl. Environ. Microbiol. 73:2771–83 78. Tringe SG, Hugenholtz P. 2008. A renaissance for the pioneering 16S rRNA gene. Curr. Opin. Microbiol. 11:442–46 79. Tringe SG, von Mering C, Kobayashi A, Salamov AA, Chen K, et al. 2005. Comparative metagenomics of microbial communities. Science 308:554–57 80. Turnbaugh PJ, Ley RE, Mahowald MA, Magrini V, Mardis ER, Gordon JI. 2006. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature 444:1027–31 81. Turrell G, Corset J. 1996. Raman Microscopy: Developments and Applications. San Diego, CA: Academic 82. Urich T, Lanzén A, Qi J, Huson DH, Schleper C, Schuster SC. 2008. Simultaneous assessment of soil microbial community structure and function through analysis of the meta-transcriptome. PLoS ONE 3:e2527 83. van Manen HJ, Lenferink A, Otto C. 2008. Noninvasive imaging of protein metabolic labeling in single human cells using stable isotopes and Raman microscopy. Anal. Chem. 80:9576–82 84. Verberkmoes NC, Denef VJ, Hettich RL, Banfield JF. 2009. Systems biology: functional analysis of natural microbial consortia using community proteomics. Nat. Rev. Microbiol. 7:196–205 85. Wagner M, Horn M, Daims H. 2003. Fluorescence in situ hybridisation for the identification and characterisation of prokaryotes. Curr. Opin. Microbiol. 6:302–9 86. Wagner M, Loy A. 2002. Bacterial community composition and function in sewage treatment systems. Curr. Opin. Biotechnol. 13:218–27 87. Wagner M, Nielsen PH, Loy A, Nielsen JL, Daims H. 2006. Linking microbial community structure with function: fluorescence in situ hybridization-microautoradiography and isotope arrays. Curr. Opin. Biotechnol. 17:83–91 88. Warnecke F, Luginbuhl P, Ivanova N, Ghassemian M, Richardson TH, et al. 2007. Metagenomic and functional analysis of hindgut microbiota of a wood-feeding higher termite. Nature 450:560–65 89. Wei F, Zhang D, Halas NJ, Hartgerink JD. 2008. Aromatic amino acids providing characteristic motifs in the Raman and SERS spectroscopy of peptides. J. Phys. Chem. B 112:9158–64 90. Wilmes P, Bond PL. 2006. Metaproteomics: studying functional gene expression in microbial ecosystems. Trends Microbiol. 14:92–97 91. Wilson KH, Wilson WJ, Radosevich JL, DeSantis TZ, Viswanathan VS, et al. 2002. High-density microarray of small-subunit ribosomal DNA probes. Appl. Environ. Microbiol. 68:2535–41 92. Xie C, Mace J, Dinno MA, Li YQ, Tang W, et al. 2005. Identification of single bacterial cells in aqueous solution using conflocal laser tweezers Raman spectroscopy. Anal. Chem. 77:4390–97 93. Yooseph S, Sutton G, Rusch DB, Halpern AL, Williamson SJ, et al. 2007. The Sorcerer II Global Ocean Sampling expedition: expanding the universe of protein families. PLoS Biol. 5:432–66 94. Zoetendal EG, Rajilic-Stojanovic M, de Vos WM. 2008. High-throughput diversity and functionality analysis of the gastrointestinal tract microbiota. Gut 57:1605–15 95. Zumbusch A, Holtom GR, Xie XS. 1999. Three-dimensional vibrational imaging by coherent anti-Stokes Raman scattering. Phys. Rev. Lett. 82:4142–45

www.annualreviews.org • Single-Cell Ecophysiology

429

AR387-FM

ARI

5 August 2009

13:23


Contents

Annual Review of Microbiology Volume 63, 2009

Frontispiece Lars G. Ljungdahl p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p xii A Life with Acetogens, Thermophiles, and Cellulolytic Anaerobes Lars G. Ljungdahl p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 1 Regulation of Translation Initiation by RNA Binding Proteins Paul Babitzke, Carol S. Baker, and Tony Romeo p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p27 Chemotaxis-Like Regulatory Systems: Unique Roles in Diverse Bacteria John R. Kirby p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p45 Aminoacyl-tRNA Synthesis and Translational Quality Control Jiqiang Ling, Noah Reynolds, and Michael Ibba p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p61 Resurrected Pandemic Influenza Viruses Terrence M. Tumpey and Jessica A. Belser p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p79 Interspecies Chemical Communication in Bacterial Development Paul D. Straight and Roberto Kolter p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p99 Lipid Signaling in Pathogenic Fungi Ryan Rhome and Maurizio Del Poeta p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 119 Biological Insights from Structures of Two-Component Proteins Rong Gao and Ann M. Stock p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 133 Role of GTPases in Bacterial Ribosome Assembly Robert A. Britton p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 155 Gene Transfer and Diversification of Microbial Eukaryotes Jan O. Andersson p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 177 Malaria Parasite Development in the Mosquito and Infection of the Mammalian Host Ahmed S.I. Aly, Ashley M. Vaughan, and Stefan H.I. Kappe p p p p p p p p p p p p p p p p p p p p p p p p p p p p 195 How Sweet it is! Cell Wall Biogenesis and Polysaccharide Capsule Formation in Cryptococcus neoformans Tamara Lea Doering p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 223 v

AR387-FM

ARI

5 August 2009

13:23

Mitochondrial Evolution and Functions in Malaria Parasites Akhil B. Vaidya and Michael W. Mather p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 249 Probiotic and Gut Lactobacilli and Bifidobacteria: Molecular Approaches to Study Diversity and Activity Michiel Kleerebezem and Elaine E. Vaughan p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 269 Global Emergence of Batrachochytrium dendrobatidis and Amphibian Chytridiomycosis in Space, Time, and Host Matthew C. Fisher, Trenton W.J. Garner, and Susan F. Walker p p p p p p p p p p p p p p p p p p p p p p p p p 291 Annu. Rev. Microbiol. 2009.63:411-429. Downloaded from arjournals.annualreviews.org by University of Vienna - Central Library for Physics on 02/09/10. For personal use only.

Anaerobic Oxidation of Methane: Progress with an Unknown Process Katrin Knittel and Antje Boetius p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 311 The Trypanosoma brucei Flagellum: Moving Parasites in New Directions Katherine S. Ralston, Zakayi P. Kabututu, Jason H. Melehani, Michael Oberholzer, and Kent L. Hill p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 335 Plants, Mycorrhizal Fungi, and Bacteria: A Network of Interactions Paola Bonfante and Iulia-Andra Anca p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 363 Evolutionary Roles of Upstream Open Reading Frames in Mediating Gene Regulation in Fungi Heather M. Hood, Daniel E. Neafsey, James Galagan, and Matthew S. Sachs p p p p p p p p p 385 Single-Cell Ecophysiology of Microbes as Revealed by Raman Microspectroscopy or Secondary Ion Mass Spectrometry Imaging Michael Wagner p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 411 Microbiology of the Atmosphere-Rock Interface: How Biological Interactions and Physical Stresses Modulate a Sophisticated Microbial Ecosystem Anna A. Gorbushina and William J. Broughton p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 431 What Sets Bacillus anthracis Apart from Other Bacillus Species? Anne-Brit Kolst, Nicolas J. Tourasse, and Ole Andreas Økstad p p p p p p p p p p p p p p p p p p p p p p p p p p p 451 The Expanding World of Methylotrophic Metabolism Ludmila Chistoserdova, Marina G. Kalyuzhnaya, and Mary E. Lidstrom p p p p p p p p p p p p p p 477 Genomics, Genetics, and Cell Biology of Magnetosome Formation Christian Jogler and Dirk Schüler p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 501 Predatory Lifestyle of Bdellovibrio bacteriovorus Renee Elizabeth Sockett p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 523 Plant-Growth-Promoting Rhizobacteria Ben Lugtenberg and Faina Kamilova p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 541

vi

Contents

AR387-FM

ARI

5 August 2009

13:23

Photorhabdus and a Host of Hosts Nick R. Waterfield, Todd Ciche, and David Clarke p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 557 Management of Oxidative Stress in Bacillus Peter Zuber p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 575 Sociobiology of the Myxobacteria Gregory J. Velicer and Michiel Vos p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 599


Index Cumulative Index of Contributing Authors, Volumes 59–63 p p p p p p p p p p p p p p p p p p p p p p p p p p p 625 Errata An online log of corrections to Annual Review of Microbiology articles may be found at http://micro.annualreviews.org/

Contents

vii