Heavy water and 15N labeling with NanoSIMS analysis reveals growthrate dependent metabolic heterogeneity in chemostats

no page number (pc78)

EMI_12752 (hn_fanny)

END

Heavy water and 15N labeling with NanoSIMS analysis reveals growth-

rate 

dependent 

metabolic heterogeneity in chemostats

1

*Corresponding authors

Email addresses: skopf@caltech.edu (Sebastian H. Kopf), yunbin@gps.caltech.edu (Yunbin

Guan), dkn@caltech.edu (Dianne K. Newman), vorphan@gps.caltech.edu (Victoria J. Orphan)

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,

*, Victoria J. Orphan

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

Pseudomonas

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

1. Introduction

This article has been accepted for publication and undergone full peer review but has not been through the

copyediting, typesetting, pagination and proofreading process, which may lead to differences between this

version and the Version of Record. Please cite this article as doi: 10.1111/1462-2920.12752

Accepted

Article

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 application

with other enriched substrates (Wegener

et al., 2012

; Kellermann

et al., 2012). Heavy water

Accepted

Article

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 of

H (

0.0156

, 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

) 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:

lowest

max



























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



at 31.9738

and



at 31.9721

as well as their minor isotopes,



at 13.0034



at 27.0001

Accepted

Article

and



at 33.9679

. However, due to the low mass of hydrogen, simultaneous measurement



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 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 (



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

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

Accepted

Article

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.

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

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

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

Accepted

Article

coli

and spores of

Clostridia

(Davission

et al.,

2008

; Orphan

and House, 2009

; Dekas

and

Orphan, 2010

) 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 with

(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/



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. aeruginosa

(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

cal

ibration 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

’

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

Accepted

Article

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 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

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

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

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

S. 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.

Accepted

Article

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 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 o



6 hours,



1 day and



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

O isotope label (

water

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

label (



= 28.0 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





(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

Accepted

Article

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 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 4

B 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 increas

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

Accepted

Article

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 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



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

O as an additional isotope tracer allowed us to deconvolve the

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,

195

3; Koch and Levy, 1955

; Ernest

Borek,

1958

; Mandelstam

and

Accepted

Article

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 5

A 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 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



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,

-value < 10



). 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

ays) 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

in functional groups, disturbing biological macromolecules. Most organisms including mammals

Accepted

Article

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

O above



10%, because the (partial) inhibition of certain

organisms could severely bias activity measurements. We tested the susceptibility of

S. aureus

varying doses of

O 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 this study, we thus

focused specifically on testing

labeling methods at low

O 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

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

H enrichments at the single cell level

(

0.1



), 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

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,

Accepted

Article

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.

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 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 K

and 0.1 g/L

MgSO



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-225

9-7

0-1L), 10 mM ammonium chloride (spiked up to 10%

with 98% enriched

Cl, Sigma

-Aldrich, #299251) and 10

mM sodium succinate (spiked up

to 10% with 99% enriched succinic-1,

acid, Sigma-Aldrich, #491977).

S. aureus

was

grown in this medium with different amounts of D

O, 10 mM ammonium chloride (both spiked

identically to

P. aeruginosa

cultures) and 10

mM 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, 10

mL/L 50x 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



g/L

thiamine (B1), 100



g/L nicotinic acid (B3) and 10



g/L biotin (B7) for all experiments with this

organism.

All cell

s were grown in 50

mL 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 isoto

pic

composition. Cells were harvested by centrifugation at 4000

rpm for 10

min at 4

°C, and washed

5 times by resuspension in 1x 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 1x PBS and dehydrated in 50% ethanol.

3.2 Continuous culture

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

aureus

growing aerobically at 37°

C in a Sartorius Biostat QPlus autoclavable chemostat system

Accepted

Article

in the same medium used for the

S. aureus

isotope standards (without any isotope enrichment),

and amended with 500



L/L Antifoam 204 (Sigma Aldrich, #A6426). Chemostat vessels with



550

mL 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

ribos

omal RNA probe (5' to 3': GAAGCAAGCTTCTCGTCCG, Kempf

et al., 2000). After the

monitored physiological parameters reached steady-state (usually within 4

–

6 generations),

chemostat vessels were spiked with 2

mL 70%

O and 150

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



g of cell powder were weighed out into tin

capsules in duplicate, and the bulk carbon and nitrogen isotopic composition was determined by

-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



1 mg 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.

Accepted

Article

3.4

Single cell analysis



L 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 40x 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 Institute of

Technology. Whole cells on ITO were

analyzed using a



3.6pA primary Cs

beam current with a

nominal spot size of



300

nm 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/



(

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 10



m by 10



m for 15

min 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 14

ms/pixel. Pre-sputtering was

typically carried out on a larger region of at least 15



m by 15



m 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



minutes.

3.5 Quantification

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

-notation (



ref

sample



, with x

) relative to the reference materials VPDB

(

0.0112372

VPDB

), air (

0.003676

Air

) and VSMOW

(

0.00015576

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



ref

sample



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

Accepted

Article