nihms694809.pdf

Heavy water and

N labeling with NanoSIMS analysis reveals

growth-rate dependent metabolic heterogeneity in chemostats

Sebastian H. Kopf

a,b,c

Shawn E. McGlynn

Abigail Green-Saxena

Yunbin Guan

a,c

Dianne K. Newman

a,b,c

, and

Victoria J. Orphan

Sebastian H. Kopf: skopf@caltech.edu; Yunbin Guan: yunbin@gps.caltech.edu; Dianne K. Newman: dkn@caltech.edu;

Victoria J. Orphan: vorphan@gps.caltech.edu

Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA,

USA

Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA,

USA

Howard Hughes Medical Institute, Pasadena, CA, USA

Abstract

To measure single cell microbial activity and substrate utilization patterns in environmental

systems, we employ a new technique using stable isotope labeling of microbial populations with

heavy water (a passive tracer) and

N ammonium in combination with multi-isotope imaging

mass spectrometry. We demonstrate simultaneous NanoSIMS analysis of hydrogen, carbon and

nitrogen at high spatial and mass resolution, and report calibration data linking single cell isotopic

compositions to the corresponding bulk isotopic equivalents for

Pseudomona

aeruginosa

and

Staphylococcus aureus

. Our results show that heavy water is capable of quantifying in situ single

cell microbial activities ranging from generational time scales of minutes to years, with only light

isotopic incorporation (

∼

0.1 atom %

H). Applying this approach to study the rates of fatty acid

biosynthesis by single cells of

S. aureus

growing at different rates in chemostat culture (

∼

6 hours,

1 day and 2 week generation times), we observe the greatest anabolic activity diversity in the

slowest growing populations. By using heavy water to constrain cellular growth activity, we can

further infer the relative contributions of ammonium vs. amino acid assimilation to the cellular

nitrogen pool. The approach described here can be applied to disentangle individual cell activities

even in nutritionally complex environments.

Keywords

stable isotope labeling; NanoSIMS; single cell analysis; multi isotope imaging; metabolic

heterogeneity

Correspondence to: Sebastian H. Kopf,

skopf@caltech.edu

; Yunbin Guan,

yunbin@gps.caltech.edu

; Dianne K.

Newman,

dkn@caltech.edu

; Victoria J. Orphan,

vorphan@gps.caltech.edu

HHS Public Access

Author manuscript

Environ Microbiol

. Author manuscript; available in PMC 2016 July 01.

Published in final edited form as:

Environ Microbiol

. 2015 July ; 17(7): 2542–2556. doi:10.1111/1462-2920.12752.

Author Manuscript

1. Introduction

A fundamental challenge in environmental microbiology is discerning what microorganisms

are doing in diverse habitats. Being able to answer this question quantitatively is an even

more elusive goal, yet necessary to predict the effects of microbial activity on environmental

processes. Today it is well recognized that microbial communities are diverse; often it is

necessary to parse them at the single-cell level in order to understand how heterogeneous

populations operate. Though it is possible to gain valuable insights from measuring

microbial activities in bulk, without single cell resolution, important features of population

biology may be missed. Particularly for individual cells, net anabolic activity for growth and

maintenance is a key physiological parameter that reflects use of all available resources, and

allows specific activities–such as substrate use–to be properly contextualized. Traditional

techniques involving isotopically enriched substrates (e.g. with 13C, 15N, 34S, etc.) have

the potential to provide such insight, if their utilization at the single cell level can be

measured in combination with that of a universal tracer for general cellular activity.

Multi-isotope secondary ion imaging mass spectrometry (MIMS or NanoSIMS) provides

one of the most sensitive and precise analytical methods available to study elemental and

isotopic composition at high spatial resolution. This technique has been broadly applied in

the field of microbial ecology to study the spatiometabolic activity of diverse microbial

communities (

Popa et al., 2007

;

Musat et al., 2008

;

Orphan et al., 2009

;

Dekas et al., 2009

;

Morono et al., 2011

;

Woebken et al., 2012

), soil microbe-mineral co-localization (

Herrmann

et al., 2006

), and symbiotic interactions (

Lechene et al., 2007

;

Foster et al., 2011

;

Pernice et

al., 2012

;

Thompson et al., 2012

) using various

N labeled isotope tracers. The non-toxic

nature of stable isotope labels combined with the high sensitivity and spatial resolution of

NanoSIMS has great potential to quantitatively study metabolic processes even within

model organisms ranging from microbes to humans (

Steinhauser and Lechene, 2013

Examples include the application of isotopically labeled

N-thymidine to trace stem cell

division and nuclear metabolism in mice (

Steinhauser et al., 2012

;

Gormanns et al.,

2012

C-oleic acid to study fatty acid transport in lipid droplets,

N-leucine to trace

protein renewal in kidney cells (

Lechene et al., 2006

), various

N labeled amino acids to

study protein turnover in hair-cell stereocilia (

Zhang et al., 2012

O-trehalose penetration

into the nucleus of mouse sperm (

Lechene et al., 2012

), dual

C and

N-labeled substrate

in microbial activity studies of oral biofilms (

Spormann et al., 2008

) and the utilization of

host-derived substrates by intestinal microbiota

Berry et al. (2013)

. As evidenced by this

accelerating body of work, secondary ion mass spectrometry increasingly finds application

in disciplines as diverse as geobiology, biogeochemistry, host-microbe interactions and

biomedical research (see

Wagner (2009)

Orphan and House (2009)

Steinhauser and

Lechene (2013)

, and

Hoppe et al. (2013)

for recent reviews). However, many of these

disciplines frequently address questions in nutritionally complex environments where

substrate specific

C and

N based isotopic tracers often capture only a subset of the

microbial population.

To overcome the limitations of substrate-specific isotope labels, heavy water (

O) was

recently shown to be a powerful new tracer in environmental systems due to its advantages

as a chemically and nutritionally passive isotopic label, and its potential for combined

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application with other enriched substrates (

Wegener et al., 2012

;

Kellermann et al., 2012

Heavy water constitutes a general isotopic tracer because it is utilized by all organisms and

provides a unique tool for measuring microbial activity in a diverse range of environments.

This is particularly useful in complex systems with slow-growing organisms, numerous

carbon and nitrogen sources, and diverse heterotrophic populations, because the introduction

of substrate-specific tracers can disturb the concentration and availability of autochthonous

nutrients and inflate or bias species-specific measures of activity. In contrast, heavy water is

easy to administer both in environmental and medical contexts, does not distort substrate

availability to the benefit of some organisms over others, and is stably incorporated during

metabolism into fatty acids and other cellular components with stable C-H bonds (and

transiently into exchangeable hydrogen bonds). Additionally, the low natural abundance

H (

= 0.0156 at%,

de Laeter et al., 2003

) enables relatively small isotopic spikes to

capture a wide range of microbial activity (hours to months) in a short time span, with

higher tracer concentrations enabling detection even of slow environmental populations with

generation times of tens to hundreds of years (

Hoehler and Jørgensen, 2013

). Figure 1

illustrates an example of the theoretically estimated minimal incubation times required to

achieve a fatty acid enrichment signal of

= 5500‰ (or

= 0.1 at%) with

different

O isotopic spikes for a wide range of microbial populations doubling over the

course of an hour to 100 years (see Supplemental Information G for details).

Despite the potential of

O as a tracer for microbial activity in environmental

microbiology, its application in multi-tracer NanoSIMS studies has been fundamentally

limited by the typical limitations in dynamic mass range encountered in multi-collector

SIMS instruments. The CAMECA NanoSIMS 50L, for example, is a widely used

multicollector secondary ion mass spectrometer equipped with 7 electron multiplier

detectors or faraday cups that provide simultaneous detection of up to 7 masses at a fixed

magnetic field strength. Secondary ion mass spectrometry (SIMS) is a destructive technique

that uses a the primary ion beam to gradually ablate the analytical target and generate

secondary ions. The destructive nature of SIMS can be particularly problematic in the

analysis of organic targets that can be sputtered away quickly and are sometimes in short

supply. The parallel detection of all ions of interest is thus an important feature of the

NanoSIMS 50L, and its large magnet and multi-collection assemblage typically allow

parallel detection of ions with vastly different mass to charge

ratios up to

∼

22:1 (i.e.

the maximum

can be 22 times larger than the lowest mass:

This allows, for example, routine parallel detection of several of the most important

biological ions with

at 12.0000

for measuring N at 26.0031

31.9738

and

at 31.9721

as well as their minor isotopes,

at 13.0034

at 27.0001

and

at 33.9679

. However, due to the low mass of

hydrogen, simultaneous measurement of

at 1.0078

and

at 2.0141

can only be

combined with other ions up to a mass to charge ratio of

∼

22.2, which allows multi-isotope

imaging for H and C in parallel, but not H and N in parallel. This restriction provides a

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serious impediment to the use of hydrogen labeled isotopic tracers in combination with

nitrogen (both an important isotopic tracer and identifying ion for biomass).

One approach to this problem is to use the instrument in magnetic field switching mode,

which requires alternating magnetic field strengths for various ions in subsequent frames of

the same analysis. However, this approach precludes simultaneous detection of all ions and

is significantly more time-consuming because of the need for sequential analyses and

frequent cycling of the magnetic field. An alternative approach was employed by

Lozano et

al. (2013)

to measure the

vs.

ions with a NanoSIMS 50L in experiments with

highly

H enriched sphingomeylin lipids (

≈

40 at %) as tracers, with corrections for

isobaric interferences from

and

. Although further improved by modifying the

entrance slit (

Slodzian et al., 2014

), the typical abundance sensitivity achievable on a

NanoSIMS 50L is limited in resolving these interferences for environmental tracer

experiments with relatively small enrichments close to natural abundance

H (

Doughty et

al., 2014

). Another potential method proposed by

Slodzian et al. (2014)

takes advantage of

the deflection plates located in front of the electron multipliers to use electrostatic peak

switching for quasi-simultaneous detection of

and

(both nominally at 26

Da) without magnetic field switching. However, truly simultaneous detection is not possible

and significant isobaric interferences include

and

In this study, we present an approach for the simultaneous analysis of three biologically

relevant isotope systems (hydrogen, carbon and nitrogen) in microbial populations by

NanoSIMS. We establish the necessary calibration for the use of

O in single-cell stable

isotope tracer work with native and embedded microorganisms (

Staphylococcus aureus

as a

model gram-positive, and

Pseudomonas aeruginosa

as a model gram-negative organism) at

environmentally relevant levels of

N and

H enrichment. We demonstrate the

combined application of heavy water and

N ammonium isotope tracers in a study of

microbial activity and population heterogeneity of

S. aureus

during growth in continuous

culture with generation times ranging from hours to weeks.

2. Results and Discussion

2.1. Simultaneous NanoSIMS analysis of H, C and N isotopes

The combination of heavy water labeling with other commonly used C and N based isotope

tracers for NanoSIMS analysis is technically challenging because of the typical limitations

in dynamic mass range encountered in multi-collector SIMS instruments (

∼

22:1). For this

study, we extended the position of detector trolley #1 past its official maximum

configuration in our CAMECA NanoSIMS 50L multi-collection assemblage, gaining an

effective mass range of 28:1. This configuration precluded the need for any magnetic or

electric field switching and allowed for truly simultaneous detection of

the

−

and

−

ions with key isobaric interferences well-

resolved (MRP for

−

and

−

was 4560, 7530 and 8800, respectively).

Because there are no isobaric interferences for

and

with this analytical setup, small

isotopic enrichments of

H can be detected and quantified.

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Measurement of the

H and

H ions requires a strong primary beam current because of the

low ionization efficiency of hydrogen. In balancing primary beam current, pre-sputtering

and analysis time, additional complications arise from the destructive nature of the technique

and the faster detection of nitrogen. Pre-sputtering is a process where the sample is

bombarded with a higher primary beam current for a short amount of time prior to data

collection in order to embed primary ions (Cs

) in the sample matrix (

Hoppe et al., 2013

This greatly improves ionization efficiency, and consequently provides higher secondary ion

counts during analysis, but also degrades the sample and changes ionization efficiency

differentially depending on the ions. Figure 2 illustrates the change in ion counts per milli-

second, both quantitatively (A) and visually (B), for the major isotopes' ions

(

) as a function of exposing the sample surface (here, a cluster of single

whole cells of

P. aeruginosa

on conductive indium tin oxide (ITO) coated glass) to the

primary ion beam. The figure shows how Cs

beam sputtering increases ionization

efficiency up to a maximum, at which point the sample is increasingly degraded, and ion

counts drop as the organic material disappears. The major ion maps in Figure 2 illustrate this

visually and also highlight the faster detection of nitrogen. This effect requires adapting

analytical conditions to optimally capture secondary ions prior to cellular degradation. The

corresponding analytical window targeted in this study for all analyses of whole single cells

is indicated by the gray band in Figure 2A. Corresponding data on ionization efficiency and

sample ablation during analysis of plastic embedded cells appears in Supplemental

Information C.

2.2. Single cell calibration

To calibrate the simultaneously acquired measurements of hydrogen, carbon and nitrogen

isotopic composition of single cells by NanoSIMS, we compared single cell values of

isotopically labeled homogenous cultures of

S. aureus

and

P. aeruginosa

to their

independently measured bulk isotopic composition (see Table I.7 for details). This

calibration step is particularly important for hydrogen due to the high capacity for H

exchange in organic material and the potential mass fractionation effects expected in the

SIMS analysis for H isotopes. We elected to calibrate single cell H isotope measurements

against their respective bulk membrane fatty acid isotope composition because fatty acids

represent a key non-exchangeable cellular H reservoir that can be measured rigorously at

low

H enrichment. The calibration curve itself reflects all combined isotopic effects

associated with sample preparation and analysis (loss, exchange, mass fractionation,

combined H pools from all cellular components, etc.) and thus allows an empirical

conversion of a single cell NanoSIMS measurement into the representative enrichment of a

cell component (the membrane) that is quantitatively interpretable. In light of this being the

first application of multi-isotope imaging mass spectrometry with H, C and N

simultaneously, it seemed prudent to also calibrate single cell C and N isotope

measurements against their respective bulk cell equivalents. Although natural abundance

cells of Escherichia coli and spores of Clostridia (

Davission et al., 2008

;

Orphan and House,

2009

;

Dekas and Orphan, 2011

) as well as highly

N enriched (

∼

50%

N) cells of P.

fluorescences (

Herrmann et al., 2006

) have been used as reference materials for isotopic

analysis of whole single cells, to our knowledge, a multi-point calibration curve with

enriched isotopic standards has only been reported previously in TOF-SIMS experiments

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with

N (

Cliff et al., 2002

) and does not exist for carbon or nitrogen analysis of free whole

cells in NanoSIMS.

Calibration parameters were calculated from 1/x weighted linear regression of the average

isotopic composition of all single cells for a given bacterial isotope standard vs. the

measured bulk isotopic composition, and are summarized in table 1 for all isotopic standards

tested in this study. In calculations of the average isotopic composition of all single cells for

a given standard, individual cells were weighted inversely by the predicted Poisson error

in their isotopic measurement (see Supplemental Information B and

Fitzsimons et al. (2000

);

Hayes (2001)

for details) to offset the influence of highly imprecise measurements from

small ROIs and low ion counts.

Figure 3 shows the calibration curves for single whole cell analyses of fixed

P. aeruginos

(172 ROIs) and

S. aureus

(222 ROIs), respectively. As expected, the nitrogen isotope

compositions of single cells for both organisms mirror the bulk isotopic composition, with

near perfect linear correlation and slope close to 1. However, it is important to note that both

slopes fall slightly short of 1.0 (0.94±0.06 and 0.91±0.03), suggesting systematic dilution of

the cellular isotopic signal from trace nitrogen on ITO coated glass, systematic isotope

fractionation in the analytical process (mass fractionation effects in SIMS analysis typically

deplete isotope ratios by 1-10% in the heavier isotope,

Fitzsimons et al. (2000

)), or potential

variability of the cellular components due to preparation for SIMS analysis (fixation,

dehydration and storage in ethanol). Background analysis of nitrogen ion counts on ITO

coated glass indicates that this component, while present, contributes only negligible

amounts of nitrogen to the signal (data not shown). Since isotope ratios and fractional

abundances of single cells are derived here directly from NanoSIMS ion count

measurements without comparison to an authentic reference standard, fractionating effects

during ionization and analysis likely contribute to the observed discrepancy. The relative

standard deviation (RSD, in % of the measurement) of the single cell measurements of each

calibration curve provides an estimate of the measurement uncertainty from the combination

of both analytical error as well as biological variation in the standards (Table 1). In the case

of nitrogen, the RSD for the

S. aureus

standards (5.7%) suggests higher biological

variability in single cell nitrogen than for

P. aeruginosa

(RSD of 1.6%), which is consistent

with the wider range of potential nitrogen sources available in the

S. aureus

medium because

of the organism's auxotrophy for several amino acids (see Experimental Procedures).

The carbon isotope composition of single

P. aeruginosa

cells (no

C standards were

prepared for

S. aureus

), closely follows the bulk isotopic composition but also falls short,

with a slope of 0.73±0.07. This also suggests a combination of systematic dilution and

isotopic fractionation during analysis. Background analysis also indicates a maximal

contribution of organic carbon adhered to the ITO coated glass of

∼

3%. However, in the

case of carbon, fixed single cells are expected to be slightly offset isotopically from the

unfixed bulk population due to the introduction of near natural abundance carbon in

formaldehyde.

Musat et al. (2014)

recently reported this effect to account for a

∼

4% dilution

of cellular carbon in experiments with

Pseudomonas

putida, which could explain part of the

observed offset in the calibration. Even stronger isotope dilution effects have been observed

in more elaborate pre-treatment procedures, such as catalyzed reporter deposition

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fluorescence

in situ

hybridization (CARD-FISH) (

Woebken et al., 2014

), and must be taken

into consideration for experiments involving this approach.

Finally, the hydrogen isotope composition of single cells for both organisms show a robust

linear dependence on the bulk membrane fatty acid isotopic composition. The slope is

substantially lower than unity (0.67±0.05 for

P. aeruginosa

and 0.59±0.05 for

S. aureus

This is consistent with the expected effects of hydrogen exchange. While the measured bulk

isotopic composition is based on non-exchangeable hydrogen incorporated into membrane

fatty acids, the

H content of individual cells measured by NanoSIMS is necessarily based

on the integrated signal from all cellular hydrogen. Here, we employed a strict multi-step

washing protocol for all cultures, with the goal of exchanging all readily exchangeable

hydrogen with natural abundance H in the washing solutions. The calibration should allow

for conversion of single cell measurements to bulk fatty acid

H for cells treated identically.

The calibration parameters inferred for

P. aeruginosa

and

S. aureus

suggest, however, that

there can be a substantial degree of variability between individual organisms. The observed

pattern indicates that

S. aureus

cells contain a higher proportion of hydrogen that exchanges

during these washing steps (lower slope) than

P. aeruginosa

(higher slope), which is

consistent with the gram-positive (one lipid membrane instead of two), spherical (lower

surface to volume ratio)

S. aureus

containing a lower proportion of lipid bound hydrogen

than the gram-negative (two lipid membranes), rod-shaped

P. aeruginosa

. In the absence of

any isotope fractionation in the detection of hydrogen during NanoSIMS analysis, the

observed slopes would indicate that about

∼

40% of the hydrogen was exchanged with water

during the washing steps for

S. aureus

, and

∼

30% for

P. aeruginosa

. Lastly, single cell

isotopic measurements of hydrogen show substantial variability around the mean for both

aureus

(RSD of 19%) and

P. aeruginosa

(RSD of 22%), which likely reflects both the

statistical uncertainty in the measurements for each single cell from low ion counts of

H, as

well as random variation in the exact cellular components (highly exchangeable vs. non-

exchangeable parts of the cell) sampled by the ion beam during analysis. This aspect of

hydrogen isotope measurements of single cells by secondary ion mass spectrometry is a

fundamental constraint that limits the ability to resolve small isotopic differences between

individual cells, and requires the analysis of many cells (10s to 100s) within a microbial

population if isotopically similar communities need to be distinguished.

This calibration provides the empirical parameters for inferring the bulk (whole membrane)

hydrogen isotopic composition from the analysis of single whole cells of

S. aureus

and

aeruginosa

, with the statistical caveats outlined above. While this calibration is likely

applicable to other gram-negative and gram-positive cells of similar morphology that are

prepared identically for NanoSIMS analysis, extrapolation to other microorganisms has to

be interpreted with care. We applied this calibration to study the distribution of single cell

growth rates in continuous culture of

S. aureus

as described in section 2.3 below.

We also measured all bacterial isotopic standards in plastic embedded thin section to provide

a calibration for NanoSIMS measurements of sectioned samples. Multi-isotope imaging

mass spectrometry in thin section vastly expands the range of applicability of this technique

to large complex systems, such as highly structured microbial communities (e.g., biofilms

and microbial mats,

Fike et al. (2008

);

Woebken et al. (2012)

;

Wilbanks et al. (2014)

) or

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communities associated with plants and animals (e.g. commensal and symbiotic

relationships,

Lechene et al. (2007

);

Berry et al. (2013)

). The structural support lent by the

plastic matrix provides thin-sections with a flat surface that enables high spatial resolution in

imaging mass spectrometry due to the lack of strong topological features. It also retards

sample destruction by the ion beam. However, plastic resins also contribute significant

amounts of carbon and hydrogen that dilute the isotopic signal of enriched cells. It is thus

imperative to calibrate and correct any isotopic measurements of single cells embedded in

plastic and we present details on this calibration in Supplemental Information C, which

should be useful for future NanoSIMS studies requiring embedding prior to analysis.

2.3. Single cell growth diversity in continuous culture

Both environmental and laboratory populations exhibit significant metabolic variation at the

single cell level. This heterogeneity is a fundamental aspect of the ecological function and

metabolic versatility of microbial communities, yet it is notoriously difficult to capture

analytically at the population level. Here, we demonstrate the combined use of hydrogen and

nitrogen isotope labeling with secondary ion mass spectrometry to study the activity,

heterogeneity and substrate preferences of individual cells in microbial populations growing

at different growth rates under controlled conditions in a chemostat. We focused on the

common nocosomial pathogen

S. aureus

for these extended growth experiments to minimize

the risk of population heterogeneity by physical differentiation through the formation of

biofilms, which is of considerable concern with

P. aeruginosa. S. aureus

was grown in

continuous culture with three different dilution rates (corresponding to generation times of

∼

6 hours,

∼

1 day and

∼

2 weeks), and spiked at steady state simultaneously with both

O isotope label (

water

= 0.248 at%, 0.246 at% and 0.275 at%) as well as a

label (

at%, 24.9 at%, 25.0 at%) as described in the Experimental

Procedures. Samples were withdrawn at regular intervals within 1/2 of a generation time for

each experimental setup. The hydrogen and nitrogen isotopic composition of individual cells

was measured by multi-isotope NanoSIMS and single cell isotopic values were converted to

their corresponding bulk population equivalents using the calibrations for

S. aureus

cells

presented earlier (overview of single cell data in Figure I.12). The growth activity rate

act

μ +

(representing the combined cellular replication rate

and fatty acid turnover

)

was calculated from the hydrogen isotope measurements for each cell using the equations

outlined in Supplemental Information F.

Table 2 summarizes the results and Figure 4A shows the aggregated data for single cell

growth activity rates

act

measured from the three continuous culture experiments in

comparison with the experimentally set dilution rates for each culture (representing the

expected average replication rate

). Overall population growth is slightly underestimated by

single cell data in the fastest growing culture (6.38 hours), and overestimated at the

intermediate (1.24 days) and slowest (13.34 days) generation times. Overestimates at slower

growth rates are potentially a consequence of the turnover component (

. Turnover

represents the rate of molecular replacement, that is fatty acid degradation and production

that is in excess of the biosynthetic rate required purely for cellular replication (

Hydrogen from the water isotope tracer is incorporated in both processes and only their

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combined effect, the overall biosynthetic/growth activity rate (

act

), can be captured at the

single cell level. Bacteria are known to modulate the fatty acid composition of their

membranes in response to physical and chemical changes in their environment (

Zhang and

Rock, 2008

), but little is known about fatty acid turnover in bacteria growing at steady-state

in a chemically stable environment.

The most striking observation, however, is the diversity in cellular activity rates revealed by

our data. For comparison, Table 2 includes a simulated data set produced from all single cell

measurements of the bacterial standards (Table 1) scaled to the mean of the fastest

continuous culture condition (generation time of 6.38 hours). The RSDs of the single cell

hydrogen isotope measurements themselves suggest an increase in heterogeneity from the

standards, which reflect exponential growth in batch culture, to the chemostat cultures.

Typically, well mixed steady-state chemostat cultures are considered to be amongst the most

homogenous microbial populations in any experimental system due to the constant medium

composition and lack of chemical gradients. While the possibility of spatial differentiation in

the chemostat vessels cannot be ruled out,

S. aureus

does not typically form biofilms in this

medium, and careful inspection of the vessels after termination of each experiment revealed

no cellular attachment. This implies either phenotypic or genotypic differentiation. The

observed diversity would not be possible to detect in bulk physiological or isotopic

measurements and is revealed here only by inspection at the single cell level. Single cell

activity rates appear to be log-normally, rather than normally distributed (illustrated in

Figure H.11). Figure 4B shows the histograms and estimated probability density functions

for the log-transformed data together with the best fit approximation to log-normal

distributions. Differences in the heterogeneity between the simulated standards data set and

the chemostat cultures is evaluated statistically by comparing the variances of the growth

rate distributions, as listed in Table 2. The observed pattern suggests that the fastest

continuous culture condition (generation time of 6.38 hours) is significantly more

heterogeneous than the standards (p-value < 0.01) with a less significant increase from the

fastest to the next slower (1.24 days) continuous culture (p-value < 0.5). The most

significant increase in heterogeneity is seen in the slowest growth condition (p-value <

0.0001).. It is noteworthy that the distribution of single cell growth rates at the slowest

growth condition hints at a possible bimodal activity pattern with two distinct sub-

populations. No clear pattern as to a dependence of growth activity on cell size could be

distinguished and it remains an open question what physiological differences precipitate the

diversity of activity rates observed for

S. aureus

, and what mechanisms give rise to the

diversification. Slight differences in metabolic strategy, for example, could be a potential

source of single cell diversity, and could arise from either genetic diversification, or through

stochastic gene expression and other purely phenotypic diversification mechanisms.

Previous work on mixed culture chemostats has highlighted microbial assemblages that are

functionally stable yet can undergo dramatic phylogenetic variation within the community

(

Fernández et al., 1999

), but genotypic and phenotypic diversification do not require mixed

cultures, both have been observed even for single species in homogenous conditions.

Maharjan et al. (2007)

, for example, measured metabolic diversification of initially clonal,

single-species populations of

Escherichia coli

grown in glucose limited chemostats after 90

generations, and showed evidence for differentially evolving subpopulations adopting

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different metabolic strategies for substrate use.

Nikolic et al. (2013)

on the other hand,

studied gene expression of clonal populations of

E. coli

in similar glucose limited

chemostats after

∼

7 generations, and showed evidence for heterogenous expression of

metabolic genes. While both mechanisms do likely occur in parallel, the latter (phenotypic

diversification) is more likely to play a dominant role at the generational time scales of this

study (4-6 generations). Lastly, it is also noteworthy that observations in long term

experiments of stationary phase cultures suggest that at any point in time, the population

consists of genetically distinct subpopulations that are dynamically increasing and declining

over time (

Finkel, 2006

). While this growth advantage in stationary phase (GASP)

phenotype of different subpopulations is mostly reported in the context of batch cultures

where chemical conditions are not constant, it is possible that a slow growing chemostat

provides an environment that similarly supports increased heterogeneity within the culture.

2.4. Single cell ammonium utilization

In the continuous culture experiments in this study,

S. aureus

has access to both inorganic

and organic nitrogen sources in the form of ammonium and various amino acids (details in

Experimental Procedures). A single isotopic tracer based on nitrogen (here,

) thus

integrates a mixed signal that is affected both by growth, as well as the cells' nitrogen

preference. The RSDs of the nitrogen measurements reported at the end of table 2 already

suggest a stronger diversification in nitrogen (from 5.7% RSD in the standards to 93% in the

slowest growing culture) compared to hydrogen (from 19% to 51%), pointing to pronounced

differences in single cell nitrogen preferences. Constraining growth activity independently

by using

H2O as an additional isotope tracer allowed us to deconvolve the

N signal and

infer ammonium utilization on a single cell basis. For each cell, the fraction of nitrogen

assimilated from ammonium (

was estimated from the

N isotope labeling strength

and the hydrogen-derived growth rate, as detailed in Supplemental Information F. It is

important to note that the hydrogen-derived growth activity rates (

act

) include a component

of fatty acid turnover (

) that is unlikely to be representative of cellular nitrogen turnover

(

). To account for this difference, we estimated cellular nitrogen turnover from known

rates of protein turnover, considering the vast majority of cellular nitrogen is bound in

proteins (

Bertilsson et al., 2003

), and assuming ammonium incorporation to occur during

turnover. Protein turnover in bacteria has been studied most extensively in experiments with

Escherichia coli

, which suggest turnover rates can range from less than 0.25%/hr for rapidly

growing batch cultures to 4-5%/hr for non-growing cells (

Podolsky, 1953

;

Koch and Levy,

1955

;

Ernest Borek, 1958

;

Mandelstam and Halvorson, 1960

;

Mandelstam, 1960

;

Marr et

al., 1963

), with similar ranges observed in studies with

Bacillus cereus

(

Urbá, 1959

) and

yeast (

Pratt et al., 2002

Pine (1970

;

1972)

studied protein turnover specifically in

continuous culture at varying generation times and found turnover rates to be fairly constant

between 2.5%/hr (glucose based growth) and 3%/hr (acetate based growth), independent of

growth rate. For this study, we thus used

=2.75%/hr as an estimate for turnover during

steady-state growth with a mixture of carbon sources.

Figure 5A shows the aggregated data for single cell ammonium assimilation, indicating the

range and median value of nitrogen assimilation from ammonium (vs. amino acids) in each

continuous culture experiment. The data indicate that the majority of all cells derive less

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than

∼

50% of their nitrogen from ammonium. Such a significant contribution of amino-acid

derived nitrogen (

) is consistent with known nutritional requirements of

S. aureus

(

Mah et al., 1967

;

Lincoln et al., 1995

). The precise values of the average nitrogen

assimilation depend on the exact protein turnover rate, which is estimated from literature

values. Future work that further constrains protein turnover will enable more quantitative

assessment of the exact partitioning between N assimilation from inorganic and organic

sources both in laboratory and environmental studies.

The most striking observation, however, is the correlation between cell-specific use of

ammonium and growth activity illustrated in Figure 5B. Ammonium utilization in the fastest

growing culture is negatively correlated (Spearman correlation = -0.63, p-value < 10

-30

whereas the intermediate growth condition does not show a statistically significant

correlation, and the slowest growing culture is instead positively correlated (Spearman

correlation = 0.80, p-value < 10

-30

). This indicates that cells within a population of

S. aureus

that is growing relatively fast on average (generation time of 6.38 hours) assimilate more

nitrogen from amino acids when they are growing above the median rate (gray area in panel

1, Figure 5B), whereas cells within a population that is growing relatively slow on average

(generation time of 13.34 days) assimilate more nitrogen from

ammonium

when they are

growing faster than their peers (gray area in panel 3). These data do not reveal the nature of

any potential causal relationship underlying this correlation, but they potentially suggest that

utilizing amino acids over ammonium is the optimal strategy for

S. aureus

only in relatively

fast growing cultures (generation time of

∼

6 hours, panel 1) and becomes less advantageous

(generation time of

∼

1.2 days, panel 2), or even disadvantageous at slower growth and

nutrient fluxes (generation time of

∼

13.3 days, panel 3). This overall pattern of a transition

from positive to negative correlation of ammonium uptake with cellular growth activity

between the slower growth and fast growth conditions, holds up independent of precise

protein turnover rates, although we are only just beginning to uncover these single-cell

metabolic differences.

2.5. Important constraints on the quantitative use of

O labels

For practical application, it is important to note that labeling concentrations above the

maximum of 20%

O pictured in Figure 1 are not recommended, because at higher

concentrations the heavy isotope starts to significantly affect the solvent properties of water

and substitutes for

H in functional groups, disturbing biological macromolecules. Most

organisms including mammals and insects are usually unaffected by doses of up to

∼

10%.

Although microorganisms can be unaffected by concentrations as high as 20-30% (

Kushner

et al., 1999

), it is important to assess potential inhibitory effects for all targeted

microorganisms in an environmental sample when using elevated concentrations of

above

∼

10%, because the (partial) inhibition of certain organisms could severely bias

activity measurements. We tested the susceptibility of

S. aureus

to varying doses of

and observed the toxicity threshold observed to fall between 15-20%

O (see

Supplemental Information E for details). In the absence of representative laboratory cultures,

toxicity thresholds could be assessed directly in environmental samples by monitoring

metabolic output (for example, the rate of sulfate reduction or oxygen respiration) in

response to increasing

O spikes. However,

O spikes are best used at low doses. In

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this study, we thus focused specifically on testing

O labeling methods at low

concentrations (< 0.3 at%) with the goal of environmental application in mind.

For quantitative assessments of

H enrichment, it is also important to consider that the

anabolic incorporation of hydrogen from water can vary significantly in response to

physiological differences (especially autotrophic vs. heterotrophic growth strategies) and

substrate preferences (

Sessions and Hayes, 2005

;

Zhang et al., 2009

;

Valentine, 2009

) and

should also be assessed carefully for all targeted microorganisms in an environmental

sample. For this study, we determined the key physiological parameters for water hydrogen

assimilation into fatty acids specifically for

S. aureus

following the approach of

Zhang et al.

(2009)

, and report the results in detail in Figure F.10 and Supplemental Information F.

2.6. Conclusion

As we have demonstrated, single-cell multi-isotope labeling with heavy water in

combination with

C and

N stable isotope tracers can complement traditional

spatiometabolic stable isotope studies by providing the context of baseline microbial

activity. The use of heavy water as a non-toxic (at concentrations < 10%), fast-diffusing,

chemically conservative tracer, combined with the ability to measure relatively small

enrichments at the single cell level (

≈

0.1 at%), allows microbial activity measurement

in a wide range of environmental systems. These range from natural habitats where

microorganisms are growing very slowly to

in-vivo

studies where microbial activity is set in

response to host-derived growth factors, toxins or other effectors.

Our data reveal heterogeneity in cellular activity at the single cell level even in populations

grown under tightly controlled laboratory conditions in continuous culture. Moreover, at this

level of resolution, differences in nitrogen assimilation patterns are evident within

populations growing at different rates. Future studies will have to determine whether this

phenomenon simply reflects random variation between

S. aureus

populations, or perhaps is

indicative of a much broader physiological adaptation that is causally linked to turnover

rates. Regardless, it is clear that this approach could provide insight into important

physiological processes that elude batch analysis. Going forward, the combination of heavy

water labeling with specific

C and

N labeled substrates, in particular, will enable studies

to measure the assimilation of a target substrate at the single-cell level even in organic-rich

environments (for example, soils, biofilms, microbial mats and tissues) while also providing,

at the same time, a clear context for activity of all members of the community independent

of their use of the target substrate. Similarly, substrate assimilation studies in oligotrophic

environmental systems can be understood in the context of overall community activity and

can shed light on the direct and indirect impacts of added nutrients.

3. Experimental Procedures

3.1. Bacterial isotope standards

Bacterial hydrogen, carbon and nitrogen isotopic standards for single cell analysis were

created by growing a gram-positive organism,

Staphylococcus aureus

(MN8,

Kreiswirth et

al., 1983

), and a gram-negative organism,

Pseudomonas aeruginosa

(PA14,

Rahme et al.,

1995

), with nutrients of different isotopic composition. A phosphate buffered minimal

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medium at pH 7.2 containing 2.5 g/L NaCl, 13.5 g/L K

HPO

, 4.7 g/L KH

, 1 g/L

and 0.1 g/L MgSO

·7H

O served as the basis for all experiments.

P. aeruginosa

was

grown in this medium with different amounts of

O (up to 1%, prepared from a 70%

stock solution, Cambridge Isotope Laboratories, #DLM-2259-70-1L), 10 mM ammonium

chloride (spiked up to 10%

N with 98% enriched

Cl, Sigma-Aldrich, #299251) and

10mM sodium succinate (spiked up to 10% with 99% enriched succinic-1,2-

acid,

Sigma-Aldrich, #491977).

S. aureus

was grown in this medium with different amounts of

O, 10 mM ammonium chloride (both spiked identically to

P. aeruginosa

cultures) and

10mM glycerol (

C labeling experiments were not the focus of the study and isotopically

labeled glycerol was unavailable for the standards at the time).

S. aureus

further exhibits

auxotrophy for several amino acids and vitamins (

Mah et al., 1967

;

Lincoln et al., 1995

;

Aldeen and Hiramatsu, 2004

) and the medium was amended with 11.5 mg/L proline,

10mL/L 50× MEM Amino Acid solution (Sigma-Aldrich, #M5550, final amino acid

concentrations: 63.2 mg/L arginine, 15.6 mg/L cysteine, 21 mg/L histidine, 26.35 mg/L

isoleucine, 26.2 mg/L leucine, 36.3 mg/L lysine, 7.6 mg/L methionine, 16.5 mg/L

phenylalanine, 23.8 mg/L threonine, 5.1 mg/L tryptophan, 18.0 mg/L tyrosine, 23.4 mg/L

valine) and 100 μg/L thiamine (B1), 100 μg/L nicotinic acid (B3) and 10 μg/L biotin (B7)

for all experiments with this organism.

All cells were grown in 50mL batch cultures aerobically at 37°C, and were inoculated from

fresh (exponential) cultures grown on the same medium. Cultures were harvested in mid-

exponential phase to ensure as homogenous a population as possible for consistent isotopic

composition. Cells were harvested by centrifugation at 4000rpm for 10min at 4°C, and

washed 5 times by resuspension in 1× phosphate buffered saline (PBS) solution to remove

all residual nutrients. Before the last washing step, samples were split into separate aliquots

for bulk isotopic analysis and single cell analysis. Aliquots for bulk analysis were pelleted,

frozen and stored at -80°C until further processing. Aliquots for single cell analysis were

fixed in 1% freshly prepared formaldehyde in PBS (Paraformaldehyde, Electron Microscopy

Sciences, #15713) for 2 hours at room temperature, washed once more in 1× PBS and

dehydrated in 50% ethanol.

3.2. Continuous culture

Carbon-limited continuous culture experiments at different growth rates were carried out

with

S. aureus

growing aerobically at 37°C in a Sartorius Biostat QPlus autoclavable

chemostat system in the same medium used for the

S. aureus

isotope standards (without any

isotope enrichment), and amended with 500μL/L Antifoam 204 (Sigma Aldrich, #A6426).

Chemostat vessels with

∼

550mL working volume were inoculated from a single colony pre-

grown in the same medium and continuous supply of medium was started upon reaching

early stationary phase. Overflow from the vessels was continuously removed to maintain a

fixed volume, and for each experiment, the exact dilution rate was determined

gravimetrically from the total vessel content and medium flow rate. Redox potential, pH and

dissolved oxygen were monitored continuously and optical density was measured in aliquots

withdrawn aseptically from vessel overflow. Purity of the culture was checked periodically

by light and once by epifluorescence microscopy using fluorescent

in situ

hybridization

(FISH,

Amann et al., 1990

) with an

S. aureus

-specific 16S ribosomal RNA probe (5

′

to 3

′

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GAAGCAAGCTTCTCGTCCG,

Kempf et al., 2000

). After the monitored physiological

parameters reached steady-state (usually within 4-6 generations), chemostat vessels were

spiked with 2mL 70%

O and 150mg

Cl isotope tracers, and samples for single cell

analysis were withdrawn directly from the vessel at regular intervals depending on the

dilution rate. Dilution of the tracers from the continuous supply of fresh medium during

incubation (to maintain steady-state growth conditions) was accounted for during data

evaluation (see Supplemental Information F for details). Samples were washed and fixed the

same way as the bacterial isotope standards for single cell analysis. Dehydrated cells were

stored in 100% ethanol at 4°C and returned to 50% ethanol prior to analysis. The effective

water isotopic composition of the medium in the chemostat vessels after the spike was

monitored using a Los Gatos Research DLT-100 liquid water isotope analyzer; the

ammonium concentration was monitored using a Dionex DX-500 ion chromatography

system with a 5 · 250 mm IonPac CS16 cation-exchange column and isocratic elution with

38% methanesulfonic acid at a flow rate of 2 mL/min.

3.3. Bulk analysis

All bulk analyses were carried out on homogenized dry biomass from lyophilized cell

pellets. For nitrogen and carbon isotope analysis, 300 to 800 μg of cell powder were

weighed out into tin capsules in duplicate, and the bulk carbon and nitrogen isotopic

composition was determined by EA-ir-MS at the UC Davis Stable Isotope Facility (Davis,

CA).

For hydrogen isotope analysis, the average membrane fatty acid hydrogen isotopic

composition was used as a measure of bulk

H incorporation because, unlike labile N-H or

O-H bonds (

Katz, 1960

;

Thomas, 1971

), C-H bonds do not exchange hydrogen

spontaneously (

Sessions et al., 2004

). Lyophilized cell pellets were weighed out into

∼

1mg

aliquots of dry cell mass, transesterified in the presence of acetyl chloride in anhydrous

methanol (1:20 v/v) at 100°C for 10 min (

Lepage and Roy, 1986

;

Rodríguez-Ruiz et al.,

1998

), extracted into hexane, and concentrated under a stream of N

at room temperature.

Fatty acid methyl esters (FAMEs) were identified by gas chromatography/mass

spectrometry on a Thermo-Scientific Trace DSQ, and analyzed in triplicate for their isotopic

composition by GC/pyrolysis/isotope-ratio mass spectrometry on a Thermo-Scientific Delta

Plus XP. All data were corrected for the addition of methyl hydrogen during derivatization.

Reported bulk hydrogen isotope compositions represent the mass balance weighted average

isotopic composition of all major membrane fatty acids.

3.4. Single cell analysis

1μL aliquots of fixed whole cells suspended in 50% ethanol (both isotopic standards and

continuous culture samples) were spotted onto custom-cut, conductive indium tin oxide

(ITO) coated glass (TEC15, Pilkington Building Products, Greensboro, NC, USA) and air-

dried at room temperature. ITOs were mapped microscopically with a 40× air objective for

later orientation and sample identification during secondary ion mass spectrometry.

All samples were analyzed with a CAMECA NanoSIMS 50L (CAMECA, Gennevilliers,

France) housed in the Division of Geological and Planetary Sciences at the California

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Institute of Technology. Whole cells on ITO were analyzed using a

∼

3.6pA primary Cs

beam current with a nominal spot size of

∼

300nm and were pre-sputtered with a

∼

23pA

primary Cs

beam current (

pre

) for 3 to 6 minutes (

), depending on the size of the pre-

sputtering area (

), to a cumulative charge density of

∼

20pC/μm

(

pre

t/A

). Seven masses

were collected in parallel (

−

) using electron

multipliers. Individual samples were located using the NanoSIMS CCD camera, and random

analytical spots were chosen within a sample area. For all analyses, the beam was rastered

over a square region of 10μm by 10μm for 15min per analytical plane/frame. At least two

frames were collected per analysis, and all ion images were recorded at 256×256 pixel

resolution with a dwell time of 14ms/pixel. Pre-sputtering was typically carried out on a

larger region of at least 15μm by 15μm to make sure that the analytical frame was fully

within the pre-sputtered area. Analytical parameters including primary beam focus,

secondary beam centering and mass resolution for all ions were verified every

∼

30 minutes.

3.5. Quantification

Bulk carbon, nitrogen and hydrogen isotope measurements were recorded in the

conventional

-notation (

, with x=

) relative to the reference

materials VPDB (

), air (

) and

VSMOW (

), respectively (

de Laeter et al., 2003

). To allow

consistent reporting and exact mass balance calculations (see Supplemental Information A

for details), all measurements were converted to fractional abundances

using the relation

. Fractional abundances of single cell analyses were

calculated directly from raw ion counts and calibrated against bulk measurements (Section

2.2). In this study, the fractional abundance values of most isotopically enriched standards

and samples fall into the percent (10

-2

) range, and are thus reported in atom percent (at%).

Raw data from all acquired ion images was processed using the open-source MATLAB

plugin Look@NanoSIMS (

Polerecky et al., 2012

). Ion images from multiple frames were

corrected for dead time and QSA effect, aligned, and discrete regions of interest (ROIs)

were hand-drawn using the

ion images to identify the cellular outline of individual

cells. All ROIs in this study represent individual single cells.

With the primary beam currents and analytical parameters employed in this study, single

cells of

S. aureus

and

P. aeruginosa

typically supported the collection of up to three

sequential frames before the primary ion beam ablated the cells. Two frames were collected

routinely, and individual ROIs were screened for consistency between the isotopic values of

two subsequent frames to control for higher quality data not distorted by sample destruction.

ROIs with isotopic value

in any frame deviating by more than twice the shot noise 2 ·

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