YEAST ON A CHIP

Single-cell Analyses of MAPK Signaling Pathways in Saccharomyces Cerevisiae

using Cell Chips

Min Cheol Park, Moon Kyu Kwak, Hye Sung Cho, Kahp Y. Suh

School of Mechanical and Aerospace Engineering, Seoul National University, Seoul 151-742, Korea

Jae Young Hur, Sang-Hyun Park

Department of Biological Sciences, Seoul National University, Seoul 151-742, Korea

Keywords: Cell chip, single-cell, MAPK signaling pathway, stochastic kinetics, receding meniscus, florescent protein.

Abstract: The mitogen-activated protein kinase (MAPK) signaling pathways are essential for cell growth, cell

differentiation and survival in eukaryotes. The MAPK signaling pathways transmit signals from the cell

surface to nucleus. The mating and high osmolarity responses in the budding yeast, Saccharomyces

cerevisiae, depend on the MAPK signaling pathways. Here we analyzed the mating and high osmolarity

responses in the budding yeast, S. cerevisiae at single-cell level using cell chips. The cell chip analyses of

the mating and high osmolarity responses were performed using fluorescent proteins fused to genes whose

transcription is specifically upregulated by each signaling. Using the technique, we have determined the

real-time gene expression patterns of the mating and high osmolarity responses at single-cell level. In this

study, we observed that the mating and high osmolarity MAPK signaling showed a non-uniform, fluctuating

flux in the population of yeast cells analyzed.

1 INTRODUCTION

Cellular behavior has been typically investigated by

utilizing bulk-scale methods that measure average

values for a population of cells. For example,

commonly used methods for high-throughput, cell-

based assays are adapted to 96- and 384-well plate

(recently 1536-well plates) formats (Hertzberg &

Pope, 2000). Despite the success of these assays,

such population-wide studies mask the behavior of

individual cells and are often insufficient for

characterizing biological processes in which cellular

heterogeneity plays a key role (i.e., ensemble

averaging problem).

Single-cell measurements are necessary for

investigating the stochasticity of gene expression

because cell-to-cell variation cannot be quantified

using population measurements. Flow cytometry and

automated microscopy are some of the most widely

used techniques for single-cell measurements.

Owing to the stochastic nature of gene expression,

the optimal experimental setup for analyzing gene

expression dynamics will be capable of both

monitoring the behavior of a large population of

cells and of tracking individual cells. Flow

cytometry can be used to obtain gene expression

data for thousands of cells, but only provides a

snapshot of gene expression at single time points.

Traditional microscopy experiments can track gene

expression dynamics in individual cells, but can only

monitor a ralatively small population of cells.

Microfluidics or “lab-on-a-chip” technologies can be

used to track gene expression changes in individual

cells, enable large populations of cells to be

monitored, and allow the precise control of the

cellular microenvironment.

These microfluidic “lab-on-a-chip” technologies

offer the ability to work with smaller reagent

volumes, shorter reaction times, and the possibility

of high-throughput analysis (Figeys & Pinto, 2000;

Reyes, Iossifidis, Auroux, & Manz, 2002). Utilizing

these technologies, one possible approach to analyze

individual cells is based on cell-trapping including

hydrodynamic confinement (Wheeler et al., 2003),

negative dielectrophoresis (Voldman, Gray, Toner,

268

Cheol Park M., Kyu Kwak M., Sung Cho H., Y. Suh K., Young Hur J. and Park S. (2008).

YEAST ON A CHIP - Single-cell Analyses of MAPK Signaling Pathways in Saccharomyces Cerevisiae using Cell Chips.

In Proceedings of the First International Conference on Biomedical Electronics and Devices, pages 268-271

DOI: 10.5220/0001050602680271

 SciTePress

& Schmidt, 2002), optical tweezers (Ashkin, 1997),

and microwells etched at the tip of a fiber-optic

bundle (Biran & Walt, 2002). These methods,

however, would have some limitations for easy,

cheap, high-throughput microscopic studies of single

cells.

Recently, we reported highly improved version

of the soft lithographic approach using surface

tension driven cell seeding and subsequent cell

docking induced by receding meniscus (Park, Hur,

Kwon, Park, & Suh, 2006). Using this method,

single to multiple yeast cells can be accurately

deposited onto microwells depending on the size of

the microwell with a cheap, easy and high

throughput manner. Here, we incorporated the

receding meniscus induced docking method into

high-throughput automated fluorescent microscopy

for analyzing stochastic nature of the MAPK

signaling pathways in the budding yeast, S.

cerevisiae. Using the technique, we have determined

the real-time gene expression patterns of the mating

and high osmolarity responses at single-cell level. In

this study, we observed that the mating and high

osmolarity MAPK signaling showed a non-uniform,

fluctuating flux in the population of yeast cells

analyzed.

2 RESULTS AND DISCUSSION

2.1 Receding Meniscus Induced

Docking

Inside a microfluidic channel, receding meniscus can

be a powerful tool for arraying yeast cells at single-

cell level in a high-throughput manner. To utilize the

receding meniscus induced docking method, we

fabricated PUA microwells onto glass substrate

using capillary molding (Suh, Kim, & Lee, 2001),

and the patterned glass substrate was bonded to a

PDMS microfluidic mold (Khademhosseini et al.,

2004) (Fig. 1).

Figure 1: Fabrication of a patterned microfluidic channel.

Some representative SEM images of the

fabricated PUA microwells are shown in Fig. 2. The

pattern dimension of circular wells was 8 μm in

diameter, allowing for a feature density of 3906

wells/mm

which is similar to Affymetrix

GeneChip

. A higher-magnification (×3500) right

column SEM images shows the well-defined PUA

structures with good edge definition. The depth of

each PUA microstructure was measured to be 8 µm

corresponding to the original height of the silicon

master (not shown).

Figure 2: SEM images of PUA microwells.

As previously described (Park et al., 2006), yeast

cells were docked into the microwells at single-cell

level. As shown in Fig. 3, the docking efficiency is

more than 90 % that allows high-throughput and

high-content single-cell analysis.

Figure 3: Single-cell docking of yeast cells in large-area.

2.2 Monitoring Gene Expression

Using this cell chip platform, we monitored the

mating (α-factor) and high osmolarity (KCl)

responses in the budding yeast, S. cerevisiae at

single-cell level over time. The cell chip analyses of

the mating and high osmolarity responses were

performed using fluorescent proteins fused to genes

whose transcription is specifically upregulated by

each signaling. To do this, we constructed three

kinds of yeast strain such as SH129 (MATa, leu2,

trp1, met15, P

Fus1

-EGFP, P

Gpd1

-Tdimer2), SH133

(MATa, leu2, trp1, met15, P

Fus1

-EGFP-Cln2(PEST),

YEAST ON A CHIP - Single-cell Analyses of MAPK Signaling Pathways in Saccharomyces Cerevisiae using Cell Chips

269

Gpd1

-Tdimer2) and SH135 (MATa, leu2, trp1, met15,

Kar4_EGFP, P

Gpd1

-Tdimer2) using homologous

recombination.

For microscopic monitoring, we used

DeltaVision

system (Applied Precision, LLC.)

which provide real-time live cell imaging. In order

to image multiple fields of cells automatically and

repeatedly over time, we controlled the microscope

and camera with commercially available software,

softWorx

suite (Applied Precision, LLC.). We

monitored 20 image fields which contain 42 ~ 49

well so that the total number of monitored cells were

about one thousand. Some representative merged

fluorescent images of each yeast strain are shown in

Fig. 4 (scale bars are not shown).

Figure 4: Merged fluorescent images of yeast strains.

2.3 Analyses of Stochastic Gene Expression

The acquired time-course merged fluorescent images

were pre-processed for more accurate extraction of

single-cell expression level. The pre-processing

includes background subtraction, HiGauss filtering,

Sharpen filtering and Flatten filtering. After image

pre-processing, we extracted quantitative gene

expression information with ImagePro

software

(Media Cybernetics, Inc.) (Fig. 5).

Figure 5: Extraction of quantitative information.

Genetically identical cells exhibit remarkable

diversity even when they have identical histories of

environmental exposure (Elowitz, Levine, Siggia, &

Swain, 2002; Raser & O'Shea, 2004).

Figure 6: Analysis of stochastic gene expression in

SH129.

As expected, we observed that the mating and

high osmolarity MAPK signaling showed a non-

uniform, fluctuating flux in the population of yeast

cells analyzed. For example, when we used strain

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with EGFP and Tdimer2 reporters driven by the α-

factor-responsive P

Fus1

promoter or by the α-factor-

independent P

Gpd1

promoter (i.e., SH129), the total

fluorescence intensity, noise and noise strength upon

stimulation of 10 µM α-factor are characterized as

shown in Figure 6.

Figure 7 shows the stochastic gene expression in

SH133 strain which contain C-terminal residues

Cln2 (yeast G1 cyclin) PEST motifs. The Cln2

(PEST) destabilized EGFP so that it allows dynamic

monitoring of transcription over time. Figure 7a

shows dose-dependent gene expression of P

Fus1

EGFP upon stimulation of α-factor. Interestingly, the

mating MAPK signaling has different kinetic gene

expressions as increasing cellular area (Fig. 7b).

Figure 7: Analysis of stochastic gene expression in

SH133.

Similarly, the SH135 strain whose character is

protein localization exhibits the stochastic gene

expression (not shown).

3 CONCLUSIONS

We have presented an optimal experimental setup

for analyzing gene expression dynamics which

would be capable of both monitoring the behavior of

a large population of cells and of tracking individual

cells. It was composed of yeast strain construction,

single-cell docking, automated image acquisition,

extraction of quantitative information, analyzing and

modelling of the stochastic gene expression. Using

this cell chip platform, we could successfully have

an insight into the stochastic nature of gene

expression, so we hope that many other investigators

also will have such insight more easily aided this

high-throughput and high-content single-cell

analysis method.

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