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Single-cell real-time visualization and quantification of perylene bioaccumulation in microorganisms Xin Jin, Xuejun Guo, Deshu Xu, Yanna Zhao, Xinghui Xia, and Fan Bai Environ. Sci. Technol., Just Accepted Manuscript • Publication Date (Web): 17 May 2017 Downloaded from http://pubs.acs.org on May 19, 2017

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Environmental Science & Technology

3

Single-cell real-time visualization and quantification of perylene bioaccumulation in microorganisms

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Xin Jin†, Xuejun Guo†*, Deshu Xu†, Yanna Zhao‡, Xinghui Xia and Fan Bai‡*

5

†

6

Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China

7

‡

8

5 Yiheyuan Road, 100871, China

9

*Corresponding author

1 2

State Key Laboratory of Environment Simulation, School of Environment, Beijing

Biodynamic Optical Imaging Center, School of Life Sciences, Peking University, No.

10

Guo, X. Email: [email protected]; Phone: 86-10-5880-7808 Fax: 86-10-5880-7808;

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Bai, F. Email: [email protected]; Phone: 86-10-6275-6164 Fax: 86-10-6275-6164.

12

ABSTRACT

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Bioaccumulation of perylene in Escherichia coli (E. coli) and Staphylococcus

14

aureus (S. aureus) was visualized and quantified in real time with high sensitivity at

15

high temporal resolution. For the first time, single-molecule fluorescence microscopy

16

(SMFM) with a microfluidic flow chamber and temperature control has enabled us to

17

record the dynamic process of perylene bioaccumulation in single bacterial cells and

18

examine the cell-to-cell heterogeneity. Although with identical genomes, individual E.

19

coli cells exhibited a high degree of heterogeneity in perylene accumulation dynamics,

20

as shown by the high coefficient of variation (C.V=1.40). This remarkable

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heterogeneity was exhibited only in live E. coli cells. However, the bioaccumulation

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of perylene in live and dead S. aureus cells showed similar patterns with a low degree 1

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of heterogeneity (C.V=0.36). We found that the efflux systems associated with Tol C

24

played an essential role in perylene bioaccumulation in E. coli, which caused a

25

significantly lower accumulation and a high cell-to-cell heterogeneity. In comparison

26

with E. coli, the Gram-positive bacteria S. aureus lacked an efficient efflux system

27

against perylene. Therefore, perylene bioaccumulation in S. aureus was simply a

28

passive diffusion process across the cell membrane.

29 30

TOC

31 32 33

INTRODUCTION

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Hydrophobic organic chemicals (HOCs) are of special ecotoxicological concern

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among numerous anthropogenic chemicals because of their capacity to directly

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incorporate into the tissue of living organisms.1,

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bio-concentrated if a living organism is under continuous exposure.3 HOC

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bioaccumulation may occur in predators and humans through biomagnification along

39

the food chain because of the ingestion of contaminated foodstuffs.4, 5

2

Many HOCs can be

40

Microorganisms, which comprise the most diversified, fundamental component of

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the biosphere, significantly affect the mobility and bioavailability of HOCs in the 2


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environment via bioaccumulation and biotransformation.6-10 Because of convection

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and active processes in the cellular cytoplasm, microorganisms can accumulate a

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higher concentration of HOCs from water than particulates.11 Previous studies have

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shown that microorganisms can act as microbial carriers that enhance the mass

46

transfer of HOCs by up to a 100-fold through diffusive boundary layers.6 Furthermore,

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because it is efficient and economical, biodegradation has been the primary method to

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remove HOCs from the environment.10, 12-14

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The transmembrane transport of HOCs is the first step in the bioprocesses (e.g.,

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bioaccumulation, and biodegradation) between HOCs and microorganisms. The

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uptake of HOCs commonly determines the efficiency of bioaccumulation and

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biodegradation.7,

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transmembrane transport can significantly promote the degradation efficiency of

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HOCs.16 Nevertheless, the mechanisms underlying the entry of HOCs into

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microorganisms are unclear and are notably controversial. There are three hypotheses

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for the cross-membrane traffic of hydrocarbons in microorganisms: 1) passive

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diffusion; 19 2) facilitated diffusion; 20 and 3) energy-dependent transport.21, 22 The

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detailed molecular processes in microbial transmembrane transport of hydrocarbons

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remain poorly studied, mainly because there is no sensitive detection method to

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analyze the transmembrane process in vivo. In general, chromatography and mass

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spectrometry are used to analyze HOCs in microorganisms and other media. However,

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these ensemble measurements of cellular HOCs require a large number of samples

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and a series of complicated pretreatment procedures (e.g., extraction and

8, 15-18

It has been demonstrated that optimizing and enhancing

3



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concentration). These complex steps introduce errors and destroy biological samples;

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thus, it is difficult to determine the microscopic distribution and dynamic changes of

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cellular HOCs in situ. Moreover, these methods only measure the ensemble HOC

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behavior in a microbial population, which inevitably ignores the cell-to-cell

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heterogeneity. In fact, the phenotypic heterogeneity in a population of cells with an

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identical genome can be remarkable because of epigenetic modifications, stochastic

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gene expression, variable mRNA stabilities and protein activities.23-25 For example,

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the protein abundance in single cells may vary by more than 10-fold in an isogenic E.

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coli population, which causes a significant metabolic heterogeneity.26 In the case of

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free-fatty-acid (FFA) production, the FFA yield increases 5 times by selecting

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high-performance E. coli cells from an isogenic population.27 Therefore, we

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hypothesize that the capacity and kinetics of single bacterial cells in HOC

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bioaccumulation may also significantly vary among individuals. Real-time

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visualization and quantification of HOC accumulation at the single-cell level can

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reveal the heterogeneity of transmembrane transport of HOCs, which is required for

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our research purpose.

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In this paper, by implementing single-molecule fluorescence microscopy (SMFM)

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with a microfluidic device and temperature control, we monitored, in real time, the

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bioaccumulation and efflux of HOCs in microorganisms at the single-cell level. The

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Gram-negative bacteria Escherichia coli (E. coli) and Gram-positive bacteria

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Staphylococcus aureus (S. aureus) were selected as model organisms. These two types

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of bacteria have distinct membrane structures, which may lead to different features in 4


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bioaccumulation. We selected perylene (log KOW=5.82), which is a five-ring

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polycyclic aromatic hydrocarbon (PAH), as the research target. Perylene is an

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important and widely distributed HOC in the environment,28-32 and its high quantum

89

yield (0.94) and excitation spectra in the visible-light region make it an ideal probe for

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fluorescence microscopy. The high sensitivity of SMFM enabled us to detect low

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copy number changes of perylene molecules in live cells with a high temporal

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resolution.33

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MATERIALS AND METHODS

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Chemicals and Reagents

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Perylene,

phosphate

buffer

solution

(PBS),

carbonyl

cyanide

96

3-chlorophenylhydrazone (CCCP) and poly-L-lysine were purchased from Sigma

97

Aldrich Ltc. LB broth (Sangon Biotech, China) and LB agar (Sangon Biotech, China)

98

were used for routine bacterial cultivation. M9 glucose media supplemented with

99

amino acids (Thermo Fisher, USA) and vitamins (Thermo Fisher, USA) were used in

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the imaging process to reduce the fluorescence background from the culture medium.

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Propidium iodide (PI) from the LIVE/DEAD staining kit (Invitrogen, USA) was used

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to differentiate dead cells from live cells. A stock solution of 0.5 mM perylene was

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prepared using ethanol and stored at 4 °C in the dark. Before use, the perylene

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solution was prepared by diluting the stock solution to 1:500. Ultrapure water was

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used throughout this work.

106

Emission spectra of the perylene solution

107

The fluorescence spectra of 1 nM perylene in ethanol and water were recorded 5



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using a fluorescence spectrophotometer (Hitachi, F-4600, Japan). The solution (1.0

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mL) in a 4-mL tube was excited at 405 nm. The emission spectra at 438-486 nm were

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recorded.

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Bacterial strains and culture conditions

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The wild-type E. coli strain and its TolC knockout strain (∆tolC) were laboratory

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stocks (Biodynamic Optical Imaging Center, Peking University, Beijing, China). S.

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aureus was purchased from China Center of Industrial Culture Collection. Both

115

bacterial strains were cultured in LB broth in a 37 °C shaker overnight. Before the

116

experiments, these overnight cultures of bacteria were re-inoculated (1:200) into fresh

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LB and subsequently returned to the 37℃ shaker until OD600 reached 0.4.

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Microscopy

119

All imaging work was performed on an inverted microscope (Axio Observer,

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Zeiss, Germany). Perylene was excited by a 405-nm laser (OBIS 405, Coherent, USA)

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and monitored with a filter set (dichroic mirror 405 nm, emission 435-485 nm). The

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fluorescence emission was collected using a 100×oil-immersion objective with a

123

numerical aperture of 1.46 and imaged with an electron multiplying charge coupled

124

device camera (Evolve 512, Photometrics, USA). The exposure time and gain value of

125

the camera were set as 100 ms and 260, respectively, and maintained constant. The

126

microscope was mounted with a microfluidic flow chamber with temperature control

127

(Bioptech, FCS2).

128

Visualization of the accumulation and efflux process of perylene by E. coli and S.

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aureus 6


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The LB-cultured cells were washed twice using fresh M9 media and re-suspended

131

at a 2-fold dilution. Then, 200 µL of cells was added onto a coverslip, which was

132

coated with poly-L-lysine, for 15 min at room temperature for stable resting.

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Subsequently, the coverslip was placed on top of a Micro-aqueduct slide (Bioptechs,

134

FCS2) to sandwich the cells in between. The sample chamber was maintained at

135

37 °C during the experiments. After the temperature was stabilized, M9 medium

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containing 1 µM perylene was injected into the chamber though the Perfusion Tubes,

137

which were connected to the chamber. We began time-lapse epifluorescence imaging

138

as soon as the focal plane became stable. Bright-field and fluorescence images were

139

taken every 1 min for 30 min. Then, 0.85% NaCl was injected into the chamber to

140

wash away the M9 medium. PI in 0.85% NaCl was subsequently injected into the

141

chamber and incubated for 15 min to identify dead cells. Fresh M9 medium was

142

added to the chamber again. Then, we captured the fluorescence images of the PI dye.

143

Figure 1(A) schematically shows the experimental procedure to measure perylene

144

accumulation in bacteria.

145

To monitor the efflux process of E. coli, CCCP (50 µM) in PBS was injected into

146

the chamber for 10 min to dissipate the proton motive force after the chamber

147

temperature became stable. Then, PBS containing 1 µM perylene and CCCP was

148

injected into the chamber. Bright-field and fluorescence images were taken every 1

149

min for 30 min. Thereafter, fresh M9 medium was injected into the chamber.

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Bright-field and fluorescence images were taken every 1 min for 30 min. Then, PI

151

was used to distinguish dead cells. 7



152

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Image processing

153

Image analysis was performed using the ImageJ software (Fiji). The cell contour

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was constructed from bright-field images. The background intensity and cell

155

auto-fluorescence intensity were subtracted from the fluorescence intensity of each

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cell. The integrated fluorescence intensity over the entire cell area was obtained for

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each cell and normalized by the cell area.

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RESULTS AND DISCUSSION

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Calibration of the fluorescence intensity with the perylene concentration

160

161 162

Figure 1 (A) Schematically experimental procedures for measuring perylene

163

accumulation at single cell level using SMFM; (B) Calibration curve of perylene

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concentration and fluorescence intensity (R2=0.997). The illustration is an enlarged 8


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view of the low concentration area.; (C) Emission spectra of 1.0 nM perylene in water

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and ethanol (EX=405 nm).

167 168

Before the precise measurement of the dynamic transmembrane process of

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perylene in microorganisms, we established the relationship between the fluorescence

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intensity and perylene concentration. The fluorescence intensity of perylene in ethanol

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at different concentrations (0 nM, 10 nM, 50 nM, 100 nM, 250 nM, 500 nM, 1 µM, 2

172

µM, 3 µM, 4 µM and 5 µM) was measured. As shown in Figure 1(B), the relationship

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between the fluorescence intensity (I, AU·pixel-2) and perylene concentration (C, µM)

174

can be well fitted by a linear function as follows

‫ܫ‬ா௧ைு = 609.20 + 927.96‫ܥ‬ா௧ைு

175

(1)

176

Because the microorganism was in a water environment in our experiment, factor

177

α was introduced to convert the fluorescence intensity of perylene in ethanol to that in

178

water. The emission spectra of 1 nM perylene in ethanol and water were separately

179

measured (Figure 1(C)). We hypothesize that the conversion factor α can be calculated

180

as

181

஺ ಹమ ೀ

α=஺

=0.89 (2)

ಶ೟ೀಹ

182

where AH2O and AEtOH are the integration of the area (AU×nm) under emission

183

spectra from 438 nm to 486 nm, which is the wave range of the emission filter in the

184

microscope, in water and ethanol, respectively. Therefore, the relationship between

185

the fluorescence intensity and perylene concentration in water is calculated as follows:

‫ܫ‬ுమ ை = ߙ‫ܫ‬ா௧ைு 9



186 187

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= 542.19 + 825.88‫( ܥ‬3) C = 0.00121 × ‫ܪܫ‬2 ܱ − 0.66 (4)

188

The effect of the biota on the perylene fluorescence property, i.e., the quantum

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yield and excitation spectra, is not considered here. Perylene is insensitive to solvent

190

effect because it is a nonpolar aromatic hydrocarbon.34 In addition, most cellular

191

perylene molecules are in an aqueous environment (the water content of live cells is

192

more than 80%). Thereby, the effect of the biota on the perylene fluorescence property,

193

i.e., the quantum yield and excitation spectra, can be ignored and not considered here.

194

After the deduction of the background fluorescence intensity of microorganisms, the

195

cellular concentration of perylene was calculated based on Equation (4).

196

Heterogeneity in perylene accumulation by individual E. coli and S. aureus cells

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The dynamic accumulation of perylene by these two strains is presented in

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movies 1-2 (Supporting Information). Figure 2(A) shows representative images of

199

these processes. We calculated the cellular perylene concentration of the two strains

200

after a 30 min incubation with perylene (N=1000). Histograms of the cellular perylene

201

concentration are shown in Figure 2(B). Unlike the ensemble methods, which only

202

provide the overall mean value, SMFM can clearly display cell-to-cell heterogeneity.

203

In Figure 2(B), the perylene concentration in E. coli was notably heterogeneous. The

204

coefficient of variation (C.V) was calculated to evaluate the heterogeneity.

205

The C.V of cellular perylene accumulation in 1000 E. coli cells was up to 1.40.

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There was a small fraction of E. coli cells that presented much higher (20-fold)

207

fluorescence than the rest of the population. 10


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SMFM offered another advantage of differentiating the sub-population with

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different features using specific molecular markers. The fluorescent dye PI was

210

employed here to differentiate live and dead cells.35,

211

fluorescence intensity than the threshold value are identified as dead cells. As shown

212

in Figure 2(C), it was obvious that all dead E. coli cells accumulated to a higher

213

degree with perylene than the average level. The C.V value of perylene

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bioaccumulation in dead cells was 0.28, which was ~1/5 of that of live cells. It was

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interesting to observe that not all cells with high perylene accumulation were dead

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cells. These C.V values indicated a considerable heterogeneity in HOC

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bioaccumulation in live E. coli cells, but homogeneity in dead cells.

36

Cells with a higher PI

218

This high heterogeneity of perylene bioaccumulation by live E. coli cells was due

219

to biological processes because the dead cells showed a much lower heterogeneity. It

220

must be attributed to some phenotypic difference within the isogenic population of E.

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coli. Even though the cells had identical genomes, there can be often remarkable

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cell-to-cell heterogeneity among a total microbial population because of epigenetic

223

modifications, stochastic gene expression and variable mRNA stabilities and protein

224

activities.23-25 Because neither E. coli nor S. aureus we used here has perylene

225

degradation genes, the biodegradation pathway was excluded.37 We proposed that it

226

was the E. coli membrane-efflux system that not only led to significantly lower

227

perylene bioaccumulation in live E. coli cells than dead cells but also induced a high

228

heterogeneity of pollutant bioaccumulation in live E. coli. The important role of the

229

membrane-efflux system and related genes are presented in detail in later sections. 11



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On the contrary, perylene bioaccumulation in S. aureus was not correlated with

231

their survival status (Figure 2(D)). The C.V value and averaged perylene

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accumulation by live cells vs. dead cells were comparable: 0.35 vs. 0.31 and 3.06 µM

233

vs. 3.29 µM, respectively. This result indicates that bioaccumulation of perylene by

234

live and dead S. aureus cells is simply an energy-independent passive process.

12


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Figure 2 (A) Representative merged images of perylene accumulation by E.coli and S.

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aureus at different times; (B) The distributed histogram of cellular perylene

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accumulation after 30 min incubation (N=1000 for each strain); (C-D) Identification 13



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of dead bacteria from total population using fluorescence dye PI; (E-F) Kinetics of

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perylene accumulation in live cells of E.coli and S.aureus (N=100). Symbols: original

241

data from single cells. Lines: the mean cellular perylene concentration of the 100 cells.

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Shaded areas: standard deviation; (G-H) Kinetics of perylene accumulation in dead

243

cells of E.coli and S.aureus (N=10).

244 245

Single-cell kinetics of perylene accumulation in E. coli and S. aureus

246

In our SMFM method, the accumulation kinetics of single cells was recorded in

247

real time with high temporal resolution and high sensitivity. The single-cell kinetics of

248

perylene accumulation by E. coli and S. aureus are shown in Figures 2(E-H).

249

Consistent with the above results, Figures 2(E) and 2(G) show that the kinetics of

250

live E. coli cells were much more heterogeneous than those of dead E. coli cells. In

251

contrast, the kinetics of live and dead S. aureus cell are also alike (Figures 2(F) and

252

2(H)). The kinetics curves of perylene in dead E. coli cells were actually similar to

253

those of both live and dead S. aureus cells. More careful observation found that

254

perylene accumulation in live E. coli cells attained steady state much faster than the

255

latter three (dead E. coli cells, live and dead S. aureus cells). Most live E. coli cells

256

attained steady state after 10 min, but the latter three did not attain steady state over

257

the entire duration of perylene incubation (30 min). The phenomena of slow kinetics

258

and un-reached steady state even after a relatively long duration indicated that

259

perylene accumulation by the latter three obeys an identical mechanism: passive

260

trans-membrane diffusion. However, the membrane-efflux system in live E. coli cells 14


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can effectively resist perylene, which results in low perylene accumulation and a

262

rapidly steady state. In a later section, we show that knocking out the key genes of the

263

E. coli membrane-efflux system results in a completely contrary result.

264 265 266

The accumulation kinetics of perylene in individual cells was fitted using pseudo-first-order kinetic rate equation. The pseudo-first-order rate kinetics equation is: ௗ஼೟

267 268

ௗ௧

271

(5)

After integration, we have, ‫ܥ‬௧ = ‫ܥ‬௘ ሺ1 − ݁ ି௞భ ௧ ሻ

269 270

= ݇ଵ ሺ‫ܥ‬௘ − ‫ܥ‬௧ ሻ

(6)

Ce and Ct (µM) are the perylene accumulation at equilibrium and time t, respectively; k1 (min-1) is the rate constant of the pseudo-first order.

272 273

Figure 3 (A) Distributed histograms correlation coefficient of pseudo first kinetics

274

model; (B) Distributed histograms of initial perylene uptake rate over the first 5

275

minutes in E.coli and S.aureus.

276

Therefore, we plotted histogram distributions of the correlation coefficient (R2,

277

Figures 3A). For live E. coli cells, the resultant R2 from the two equations was 15



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distributed in a wide range from 0.60 to 0.95, with a few points with R2 < 0.6 (Figure

279

3A). This high dispersion of R2 not only demonstrated the high heterogeneity of live E.

280

coli cells, but also indicated that simple kinetic processes were inadequate to fit the

281

perylene accumulation dynamics in live E coli cells. However, for live S. aureus, the

282

resultant R2 was distributed in a concentrated zone of 0.92~1.0 (Figure 3B), which

283

indicated that perylene accumulation dynamics in live S. aureus were well fitted by

284

pseudo-first- and pseudo-second-order kinetic rate equations. This significant

285

difference of R2 in live E. coli and S. aureus was consistent with our previous

286

inference that the perylene accumulation in live E. coli was complex transport

287

processes which involved active process, while the uptake of perylene in live S.

288

aureus was simple passive diffusion. To compare the rate of perylene accumulation in

289

different cases, the initial perylene uptake rate within the first 5 minutes was

290

calculated (Figure 3B). The mean initial rate of live S. aureus cells was 0.22 µM/min,

291

which was 7.29 times higher than that of E. coli (0.03 µM/min). We also calculated

292

the mean initial accumulation rates of dead E. coli and S. aureus cells. The initial

293

perylene uptake rate was 0.31 µM/min for dead E. coli cells and 0.25 µM/min for

294

dead S. aureus cells. The initial rate in dead E. coli was ~10 times of that in live E.

295

coli. However, the initial rate of live and dead S. aureus were similar.

296

Dead E. coli cells (Figure 2G), live S. aureus cells (Figure 2F) and dead S. aureus

297

cells (Figure 2H) show a much stronger perylene accumulation ability than live E. coli

298

cells (Figure 2E). Previous studies have shown that the presence of polar

299

lipopolysaccharide (LPS) in the outer membrane of Gram-negative bacteria provides 16


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an efficient barrier for the passage of hydrophobic molecules.38 If this were the entire

301

truth, the perylene accumulation in live E. coli cells should reach the steady state

302

much more slowly, as shown in dead E. coli cells. However, our results for live E. coli

303

cells were notably the opposite and reached the steady state rapidly (Figure 2E)

304

despite a much lower rate of initial uptake (Figure 3B) . Thus, additional mechanisms

305

are required to explain this intrinsic resistance. It has been demonstrated that the

306

multidrug transporters of E. coli have multi-specificity for many relatively lipophilic,

307

planar molecules. 18, 39 Thus, the multidrug efflux pumps may play an important role

308

in the intrinsic resistance of E. coli to perylene.

309

An essential role of the E. coli efflux system in pumping out cellular perylene

310

To demonstrate whether a multidrug efflux system plays a role in perylene

311

accumulation in E. coli, a Tol C efflux pump knock out strain (∆tol C) and the proton

312

motive force (PMF) inhibitor CCCP were used. Tol C is a common channel protein of

313

both major and minor efflux systems, which enables interactions with many

314

translocase complexes.40 Deletion of the tol C gene in E. coli largely abolishes the

315

efflux activity.41 PMF is the driving force of multidrug efflux pumps in E. coli. 42 The

316

use of CCCP can dissipate PMF and block the efflux system. 43 This dissipation of

317

PMF is reversible by removing CCCP and supplying a carbon source.

17



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318 319

Figure 4 (A) The distributed histogram of cellular perylene accumulation in E.coli,

320

∆tol C and CCCP pre-incubated E.coli (N=1000 for each strain); (B-C) Kinetics of

321

perylene accumulation in live cells ofΔtol C and CCCP pre-incubated E.coli; (D)

322

Kinetics of perylene efflux in E.coli after elimination of CCCP inhibition.

323 324

Figure 4(A) shows histograms of cellular perylene accumulation of wild-type E.

325

coli, ∆tol C and CCCP pre-incubated E. coli after 30 min of incubation (N=1000). The

326

cellular perylene concentration of ∆tol C and CCCP-pre-incubated E. coli was

327

significantly higher than that shown in wild-type E. coli, which demonstrates that the

328

efflux system played an important role in excluding perylene. The cellular perylene

329

concentration of ∆tol C (0.42) had a much lower C.V than that of E. coli (1.40). This

330

situation may occur because the accumulation of perylene is a passive process after

331

the blocking the efflux system. The C.V of cellular perylene accumulation in 18


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CCCP-pre-incubated E. coli (0.90) is higher than that of ∆tol C, which was probably

333

because of the heterogeneous CCCP sensitivity of live E. coli cells.

334

The dynamic perylene accumulation process by ∆tol C and CCCP-treated E. coli

335

is shown in movies 3-4 (Supporting Information). The kinetics of perylene

336

accumulation by the two strain is shown in Figures 4 (B-C) (live cells, N=100). In

337

contrast to wild-type E. coli, the kinetics of ∆tol C and CCCP-treated E. coli showed a

338

linear relationship with time. Neither ∆tol C nor CCCP-treated E. coli attained steady

339

state after 30 min of incubation. The accumulation kinetics of ∆tol C were fitted to a

340

zero-order rate kinetic equation. The mathematical representations of the models are

341

shown in Eqs. (7). ‫ܥ‬௧ = ݇௱௧௢௟஼ ‫ݐ‬

342 343 344

(7)

Ct (µM) is the cellular perylene concentration at time t; k∆tolC (µM /min) is the rate constant of kinetics of ∆tol C.

345

The accumulation of perylene by CCCP-treated E. coli lagged by a few minutes

346

after injection of the perylene solution, which might result from the incomplete

347

dissipation of PMF in the first few minutes. Thus, we added a new parameter, tlag, to

348

the zero-order rate kinetic equation. The equation is

349 350 351

‫ܥ‬௧ = ݇஼஼஼௉ ሺ‫ ݐ‬− ‫ݐ‬௟௔௚ ሻ

(8)

kCCCP (µM/min) is the rate constant of CCCP-pre-incubated E. coli; tlag is the lag time of bioaccumulation of perylene in CCCP-pre-incubated E. coli.

352

The parameters obtained from the two kinetic models are shown in Figure 5. As a

353

result of the heterogeneous sensitivity of live E. coli cells to CCCP, the fitting 19



Page 20 of 32

354

parameters of CCCP-treated E. coli have a higher C.V than those of ∆tol C. The

355

higher values of k∆tol C (0.060 µM/min) than kCCCP (0.043 µM/min) indicate a faster

356

perylene accumulation rate in ∆tol C than in CCCP-treated E. coli.

357

Unlike dead E. coli cells, the rate of perylene accumulation in ∆tol C and

358

CCCP-treated E. coli is constant during the entire 30 min of incubation. We also

359

found that the perylene accumulation rate of both ∆tol C (0.060 µM/min) and

360

CCCP-treated E. coli (0.043 µM/min) was much lower than that of dead E. coli cells

361

(0.31 µM/min). This can be explained by the different membrane structure of live and

362

dead cells. The cytoplasmic membranes of dead bacteria are generally considered to

363

be partly damaged,35,

364

difference in the accumulation velocity between efflux-blocked live cells and dead

365

cells was likely the result of the different membrane structures of live and dead cells.

366

Although the active efflux of perylene was intercepted in ∆tol C and CCCP-treated E.

367

coli, the integrated membrane structure of live cells (e.g., LPS) could still

368

significantly slow the accumulation of perylene molecules.

36, 44

whereas live cells have integrated membranes. This

369

20


Page 21 of 32


370 371

Figure 5 (A-E) Distributed histograms of fitted kinetics parameters for perylene

372

accumulation inΔtol C and CCCP pre-incubated E.coli. (F-H) Distributed histograms

373

of fitted kinetics parameters for perylene efflux by E.coli after elimination of CCCP

374

inhibition.

375 376

After CCCP-pre-incubated E. coli with perylene were incubated for 30 min,

377

CCCP and the perylene solution were removed by injecting fresh M9 medium. Then,

378

monitored efflux of perylene by E. coli. The dynamic process of perylene efflux by E.

379

coli after eliminating the effects of CCCP is shown in movie 5 (Supporting

380

Information). Figure 4(D) shows the efflux kinetics of perylene (N=100). Intracellular

381

perylene was rapidly pumped out of bacteria within 10 min. A small fraction of

382

cellular perylene remained in cells after 30 min, which could be irreversibly 21



Page 22 of 32

383

sequestered in the hydrophobic region of the cellular proteins and lipophilic plasma

384

membrane.

385

The kinetics of perylene efflux was fitted to the following equation ‫ܥ‬௧ = ‫ܥ‬଴ × ݁ ି௞೐೑೑೗ೠೣ ௧

386 387 388

(9)

where Ct (µM) is the cellular perylene concentration at time t; kCCCP (µM /min) is the rate constant of efflux; and C0 is the initial cellular perylene concentration.

389

The parameters obtained from this kinetics models are shown in Figures 5 (F, G,

390

H). The R2 of the efflux model had a wide distribution of 0.04-0.99, with a C.V of

391

0.22. The rate constant of efflux (kCCCP) varied in the range of 0.08-0.23 min-1, with a

392

C.V. of 0.55. The initial perylene concentration (C0) also showed a broad distribution

393

from 0.28 µM to 3.88 µM, which resulted in a C.V. of 0.72. The high heterogeneity of

394

the initial perylene accumulation (when pre-incubated with CCCP) and subsequent

395

efflux process demonstrated the phenotypic difference in the isogenic population of E.

396

coli. More comprehensively, this high heterogeneity can be attributed to the combined

397

phenotypic differences in cellular sensitivity to the CCCP treatment, individual

398

capability of recovering from CCCP and individual membrane-efflux activity.

399

Environmental Implications

400

In this study, we visualized the accumulation and efflux processes of perylene in

401

Gram-negative E. coli and Gram-positive S. aureus at the single-cellular level in real

402

time. The accumulation of perylene by live S. aureus cells is significantly higher than

403

that of live E. coli cells. We wondered whether this distinct pattern of HOC

404

accumulation was ubiquitously present in Gram-negative and Gram-positive bacteria. 22


Page 23 of 32


405

We also visualized the perylene accumulation by some other Gram-positive and

406

Gram-negative bacteria (B. subtilis (G+), V. alginolyticus (G-) and P. vulgaris (G-))

407

(data not shown). Gram-positive B. subtilis showed similarity to S. aureus for

408

perylene accumulation. Gram-negative V. alginolyticus and P. vulgaris also efficiently

409

excluded perylene. Stringfellow et al. investigated phenanthrene accumulation by nine

410

bacteria strains. Except for one strain, Micrococcus luteus (G+), they found that all

411

Gram-positive bacteria had better accumulation than Gram-negative bacteria

412

Thereby, our observation of significantly higher HOC accumulation in Gram-positive

413

bacteria than Gram-negative bacteria is consistent with the ensemble observation from

414

the previous study 14.

14

.

415

The difference in the amount of HOC accumulation between Gram-positive

416

bacteria and Gram-negative bacteria was inaccurately interpreted to be due to the

417

different membrane structure and components between them. In this study, we

418

demonstrated that a multidrug efflux system associated with Tol C played a primary

419

role in the accumulation of perylene in E. coli. Driven by the PMF, the multidrug

420

efflux system of E. coli can efficiently expel perylene molecules as soon as they enter

421

cells. By contrast, perylene accumulation in Gram-positive bacteria S. aureus is from

422

simple passive diffusion, which depends less on cellular phenotypic differences. If the

423

E. coli efflux system is blocked by knocking out the involved genes or eliminating the

424

PMF, the kinetics of perylene accumulation become comparable to that of S. aureus.

425

However, Gram-positive bacteria also have numerous membrane transporters that

426

promote the efflux of various drugs from the cells to the outer medium.45 Questions 23



Page 24 of 32

427

arise regarding the free diffusion of a typical HOC into a Gram-positive bacterium

428

(visualized here). Why did they exhibit no significant ability to expel perylene, even

429

though it has been proven to be a growth inhibitor in most bacteria ?46

430

In this study, dead E. coli cells were distinguished from living cells according to

431

their significantly higher perylene accumulation. Ensemble methods cannot determine

432

the mortality of a microbial population, so they always ignore the effect of the

433

metabolic status of cells. In fact, mortality is considerable in practical applications

434

(e.g., bacteria in the stationary phases or active sludge). The ensemble measurement

435

of only the average change of cellular HOCs will false results regarding the real

436

exposure and bioaccumulation of pollutants in a living cell. Our single-cellular

437

approach reveals a detailed heterogeneity of pollutant exposure and bioaccumulation

438

for each individual cell, which offers a better understanding of the transmembrane

439

transport of HOCs.

440

As demonstrated above, microorganisms are the fundamental component in

441

biosphere, acting as the microbial carriers which enhance the mass transfer of HOCs

442

by two orders of magnitude. The bioaccumulation and biotransformation via

443

microorganisms significantly affect the mobility and bioavailability of HOCs in the

444

environment 6-10. We imaged the transmembrane transport (both the uptake and efflux)

445

of perylene in microorganisms with a high temporal resolution, which revealed a high

446

degree of heterogeneity in both the uptake and efflux of this HOC pollutant by live E.

447

coli cells. Similar to the bacterial resistance to the antibiotics, the heterogeneity in

448

phenotype expression of efflux systems (TolC observed here) would ultimately affect 24


Page 25 of 32


449

the adaptive process of E. coli to this HOC polluted environment. However, some

450

unclear pathways to reduce intracellular perylene toxicity in S. aureus (gram-positive

451

bacteria) warrants further investigation.

452

Our observations here also have potential implications in environmental

453

remediation. The results of this study, such as the efflux-system-mediated HOCs

454

resistance in Gram-negative bacteria, difference between live/dead cells, and notable

455

heterogeneity in live cells, will provide important guidance for the selection and

456

optimization of bio-sorption and biodegradation systems. Through the genetic

457

modification that disables the efflux associated gene, one might enhance the

458

microbial-availability of a targeted pollutant, and hence potentially improve the

459

efficiency of HOC’s biodegradation.

460

To the best of our knowledge, this study for the first time visualized and

461

quantified accumulation of a typical HOC pollutant in microorganisms at the

462

single-cell level by implementing SMFM. Overcoming the disadvantage of general

463

ensemble based methods, such as chromatography and mass spectrometry, SMFM

464

presented a sensitive detection method to visualize the transmembrane process in vivo,

465

which enabled us to achieve a real-time visualization of molecular processes in

466

microbial transmembrane transport of hydrocarbons. Although the accuracy of SMFM

467

quantification cannot be as good as traditional chromatographic analysis and mass

468

spectrometry. But bio-imaging approach used here provided a film-projection mode to

469

real-time visualize and quantify the accumulation of pollutants in cells at the

470

single-cell level, which also cannot be done by traditional methods. Taking the 25



Page 26 of 32

471

advantage of SMFM, many environment-induced biological processes can be detected,

472

sorted, and quantitatively compared at individually cellular level. The real-time

473

visualization of pollutants at the single-cellular level will add a vital extra dimension

474

to further investigate the mutual interactions among various pollutants and even

475

multicellular organisms.

476

SUPPORTING INFORMATION AVAILABLE

477

Movies of perylene bioaccumulation in E. coli, S. aureus, ∆tolC and

478

CCCP-pre-incubated E. coli; and the movie of perylene efflux in CCCP-pre-incubated

479

E. coli; table of initial perylene uptake rates of different strains; emission spectra of

480

perylene in ethanol and in ethanol supplemented with 1 mM palmitic acid. These

481

materials are available free of charge via the Internet at http://pubs.acs.org.

482

ACKNOWLEDGMENTS

483

This work was supported by the National Natural Science Foundation of China

484

(grant no. 41371440, no. 91547207, no. 31370847, no. 31327901) and the National

485

Key Basic Research Program of China (2013CB430406). F.B. also acknowledges

486

financial support from the Recruitment Program of Global Youth Experts.

487

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