Single Molecule Microscopy in Living Cells

8/14/2019 Single Molecule Microscopy in Living Cells

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Single Molecule Microscopy in Living Cells:

Subtraction of Autofluorescence Based onTwo Color Recording

Manuel MrtelmaierA), Eva J. KglerB), Jan Hesse A), Max SonnleitnerC), Lukas A.HuberB) and Gerhard J. Schtz A)

A) Biophysics Institute

Johannes Kepler University Linz

Altenbergerstr.69

A-4040 Linz, Austria

B) IMP, Research Institute of Molecular Pathology

A-1030 Vienna, Austria

C) Center for Biomedical Nanotechnology

Upper Austrian Research GmbH

Scharitzerstr.6-8

A-4020 Linz, Austria

Correspondence to

Gerhard J. Schtz

Biophysics Institute, Johannes Kepler University Linz,

Altenbergerstr.69, A-4040 Linz, Austria

phone +43-732-2468-9265

fax +43-732-2468-9280

email [email protected]

submitted 02 May 2002

acepted 17 Jul 2002

published 02 Aug 2002

keywords: single fluorophore detection, image processing,CD44, spectroscopy, singular value decomposition

Abstract

A significant limitation of ultra-sensitive microscopy on living

cells is set by background signal arising from cellular

autofluorescence. Up to now, most strategies to circumvent

this limitation were based on choosing long-wavelength dyes

and selecting cell lines with reduced metabolism. In this

article, we present a new strategy to identify and eliminate

signal arising from autofluorescence. Two images are

recorded simultaneously in distinct spectral channels. An

algorithm, based on singular value decomposition, separates

the contributions by the fluorophore of interest and

autofluorescence. A first application of the method for imaging

CD44-YFP in living cells is given.

Introduction

In recent years, single fluorophore detection has become a

standard technique for the investigation of a wide range of

molecular properties (for reviews see [1,2]). In synthetic

environments, the identification of single molecule signals can

be achieved routinely due to full control over environmental

parameters. The study of biomolecules, however, requires

experiments under physiological conditions; the ultimate goal

would be the investigation of biological processes in the living

cell. The frequent occurrence of many species of endogenous

fluorescent molecules inside cells makes such studies

difficult. Up to now, successful detection of single molecules

in vivo has been achieved through the deliberate choice of celltype, metabolic state, and excitation wavelength [3-11]. Such

RESEARCH PAPER Molecules

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Single Mol. 3 (2002) 4, 225-231

WILEY-VCH Verlag Berlin GmbH, 13086 Berlin, 2002 1438-5163/01/0408-0225 $17.50+.50/0

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a strategy severely limits the range of addressable biological

questions; most standard cell lines and primary cells show a

significant level of background fluorescence ([12]; see also

Fig.2).Cellular autofluorescence has been well characterized in

terms of spectral properties [12], lifetime [13], and spatial

distribution [14]. In the visible regime, flavins [12] and

lipofuscin [15] are currently regarded as the major source of

endogenous fluorescence. Flavins are mainly located in

mitochondria, while lipofuscins predominantly reside in

lysosomes. In fluorescence images, both organelles appear as

diffraction-limited spots randomly distributed in the cytoplasm

of the cell. The high variability of the fluorescence intensity of

such spots, even within one cell, makes unambiguous

distinction between fluorophores and autofluorescence a

challenging task.Up to now, the most prominent strategy has been based

on choosing long excitation wavelengths. It has been shown

that the signal of cellular autofluorescence in HASM cells was

low enough for single fluorophore detection upon excitation at

633nm and detection at typical Cy5 filter settings [5]. Still,

lipofuscin-like autofluorescent pigments were shown to be

visible under fluorescent filters for UV, fluorescein, rhodamine

and Cy5 [15]. In general, autofluorescence spectra are broad,

RESEARCH PAPERMolecules

Single Single Mol. 3 (2002) 4226

Fig. 1. a) Schematic overview of

the microscope. Laser light withwavelengths of 633nm (red)

and 514nm (green) is combined

to a single beam using a

dichroic beamsplitter. Electronic

shutter elements and

Acousto-Optical-Modulators

(AOMs) allow for precise control

of illumination times. Excitation

of the target molecules in the

sample takes place in a spot of

approximately 20m in

diameter (wide field

illumination). This beam

geometry is achieved by placing

a defocusing lens between the

lasers and the objective.

Emitted fluorescence light

originating from the sample is

collected via an oil immersion

objective (100x, NA=1.4), and,

after appropriate filters,

reflected by a dichroic wedge.

Light with l630nm is

reflected from the rear surface

(red channel). The tilt angle of

~1 between front and rear

surface leads to two separate

images on the CCD camera

one for each spectral region

with a distance of ~100m on

the chip. b) Reflection coef-

ficient of the dichroic wedge for

the green channel and the

red channel. Superimposed isthe emission spectrum of YFP

(dashed line).


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ranging from 500nm to 700nm upon excitation at 488nm

[12], a wavelength typically used for fluorescein or GFP

imaging. The broad emission cannot be blocked by filters,

making ratiometric techniques the method of choice for

correction of contributions due to autofluorescence.

In this article, we present a method which allows

unambiguous distinction of ultra-low signals arising from

YFP-molecules and cellular background fluorescence. The

method is based on parallel imaging in two distinct spectral

channels: a green channel, containing signals from both YFP

and autofluorescence, and a red channel, with only

autofluorescence contributions. Even at signal levels typicalfor single molecule microscopy, clear discrimination was

feasible. The method was used to image CD44-YFP in living

EpH4 and cos7 cells [16,17].

Experimental

Cell Culture

EpH4 cells are a spontaneously immortalized mouse

mammary epithelial cell which displays a fully polarized

epithelial cell phenotype [18, 19]. Cells were cultivated in high

Glucose DMEM (Dulbecos modified Eagles medium)

supplemented with 10mM Hepes pH 7.3, 1%

Penicillin/Streptomycin and 5% FCS (Gibco BRL and

Boehringer Mannheim Corp.) at 37C, 5% CO2, and 98%humidity. For transfections, Lipofectamine Plus Reagent was

obtained from Life Technologies. Transfected cells express the

standard murine CD44-EYFP (cytoplasmic tagged) receptor

under a PGK promotor. All experiments were performed at

room temperature in PBS (phosphate buffered saline)

supplemented with 1mM MgCl2 and 1mM CaCl2.

Microscopy

The apparatus for two color microscopy is shown in Fig.1a.

Samples were illuminated for 5-20ms by 514nm light from an

Ar+-laser (Model 2020, Spectra Physics) or 633nm light from

a dye laser (Model 375B, Spectra Physics), using a 100-times

objective (PlanApochromat, NA=1.4, Zeiss) in an

epi-fluorescence microscope (Axiovert 135TV, Zeiss). The

laser beam was defocused to an area of 255m2 at a mean

intensity of 40W/cm2. Rayleigh scattered light was effectively

blocked by appropriate filter combinations (custom TRITC/Cy5

dichroic and emission filter, Chroma). Images were obtained

by a liquid-nitrogen cooled slow-scan CCD-camera system

(ST-138, Roper Scientific, N.J., equipped with an EEV

1340x1300-chip) and stored on a PC. A dichroic wedge (1

separation, Chroma) was mounted in the parallel beam path,

which allows simultaneous recording of two color channels. The spectral properties of the two channels are depicted in

Fig.1b. Effectively all emission from YFP (dashed line) falls into

the green channel.

Results

We present here a method for the decomposition of optical

signals originating from two distinct spectral components: a

specific fluorophore, and cellular autofluorescence. It is based

on the more general algorithm of singular valuedecomposition [20]. The method allows to distinguish


SingleM. Mrtelmaier et al.Single Molecule Microscopy in Living Cells:Subtraction of Autofluorescence

227

Fig. 2. Autofluorescence image of a cos7 cell. (a) The light

image shows a living, unstained cos7 cell. The red square

indicates the region of observation for subsequent

fluorescence images. b and c show false-color fluorescence

images of the red (b) and green (c) channel. Both

images were obtained simultaneously with the apparatus

described in figure 1, using excitation at 514nm. The

fluorescence images show close resemblance, indicating a

spatially homogenous spectral composition of

autofluorescence. Scale bar 5m.


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between image features originating from the fluorophore andfrom autofluorescence. In addition, the relative contribution of

the two signal sources to the overall signal strength in each

pixel can be quantified, thus allowing to obtain an optimum

estimation of the background-free image.

Each fluorescence image was recorded simultaneously in

two spectral channels, which are sensitive for emission up to

~650nm (green channel), and for emission above 660nm

(red channel). Fig.2 shows the light image and a

two-channel fluorescence image of a cos7 cell. In both

channels, structures with a size beyond the diffraction limit are

visible. The spatial patterns of autofluorescence appear to be

very similar in both wavelength regions. This agrees well withprevious reports of broad spectral profiles of cellular

autofluorescence [12, 15]. Moreover, the signal of each pixelin the green channel, Sg, is highly correlated with the

respective signal of the same pixel in the red channel, Sr, as

shown in Fig.3a. When F1 denotes the total autofluorescence

emission in a pixel, andr

= ( , )g r1 1 the detection efficiencies

for autofluorescence in the green and red channel,

respectively,r

S S Sg r= ( , ) can be written asr

r

S F= 1 , with

r g r1 1 1= =const; here, the index in uppercase specifies

autofluorescence as the source for the signal. In other words,

a specific color can be ascribed to cellular

autofluorescence, for the chosen settings characterized by a

slope r1 1 . The following method is based on thedetermination of the characteristic color of a fluorophore,



Fig. 3. YFP image of a cos7

cell. The false-color

fluorescence images show a

cos7 cell overexpressing

CD44-YFP in the red (a) and

green (b) channel upon

excitation at 514nm. Clearly,

the signal in the green,

short-wavelength channel is

much more pronounced than its

counterpart on the left. For

illustration, the scaling of the

red channel was increased by

a factor of 10. Effectively all

emission from CD44-YFP falls

into the green channel. Scale

bar 5m.

Fig. 4. Pixel-per-pixel

correlation between spectral

channels for autofluorescence

(a) and CD44-YFP (b). The plots

were generated using the

images depicted in figure 2 and

3, respectively.

Autofluorescence signal in thered channel Sr (see main text)

is highly correlated with its

counterpart in the green, Sg.

YFP fluorescence, however, is

present almost exclusively in the

green channel. The difference in

the slope of the data was

exploited for subsequent image

correction.


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which is in general different from the color of

autofluorescence.

Fig. 3. shows the fluorescence image of a cell

overexpressing the YFP-labeled transmembrane protein

CD44. The detail shows the targeting of the molecule to the

plasma membrane of the cell. Due to the high signal

amplitude, no significant contribution of autofluorescence to

the signal was observed; the red image appears dark when

compared to the green image. The color of YFP is

characterized by a large slope r2 20> (Fig. 4b). This figure willbe used as a standard for YFP-fluorescence inside a cell.

In general, both emission from the fluorophore and

autofluorescence contribute to the signal obtained in one

pixel. In this case, the signal is given byr

r r

S F F = 1 1 2 2 . Forconvenience, we introduce here vector notation:

r rt

S F= , withr

F F F= ( , )1 2 andt

=

g r

g r

1 1

2 2 . In order to decompose the

contributions of the individual fluorescent species,r

F, the

matrix has to be inverted:r r

t

F S= 1. The matrixt

can bemeasured on purified substances, or, if such purification is not

feasible, can be determined iteratively by minimizing the

correlation between the two resulting estimations ofr

F. Fig.5

shows an arbitrary region of an EpH4 cell expressing

CD44-YFP. Fig.5a and b show the original red (Sr) and

green channel (Sg), respectively. The corrected image of the

YFP-channel, F2, is shown in Fig.5c. Evidently, the most

intensive spots have been identified as autofluorescence by

the algorithm and have vanished in the corrected image.

Besides these bright spots, Fig.5b contains a number of spots

with an intensity similar to that expected for CD44-YFP

clusters. However, even within such spots of heterogeneous

origin, the algorithm clearly identified those corresponding to

CD44-YFP clusters.

The most challenging test case for the presented

methodology is the regime of single fluorophore detection. It is

evident that signals arising from single fluorophores can onlybe identified in the presence of autofluorescent background


SingleM. Mrtelmaier et al. 229

Fig. 5. Fluorescence images of

EpH4 cells expressing

CD44-YFP. The false-color

fluorescence images show a

EpH4 cell expressing CD44-YFP

in the red (a) and green (b)

channel upon excitation at

514nm. The image in the

green channel contains many

structures indicative of

CD44-YFP clustering. These

structures are of highly variable

size and brightness, and show

no specific distributional

pattern, leaving only their

spectral profile as a practical

discriminatory feature. Indeed,some of those structures are

also observed in the red

channel, and can thus be

unmasked as autofluorescence.

(c) shows the reconstructed

YFP-signal. Contributions of

autofluorescence to the overall

signal have been removed

successfully, thereby

dramatically increasing the

information content of the

image. d and e further illustratethe working principle of the

decomposition algorithm. (d) shows the pixel-per-pixel correlation between the two images a and b. The contributions of both

CD44-YFP and autofluorescence become obvious as two branches of the data set. The different colors of fluorophore

emission and autofluorescence are reflected by the different slopes of the branches (green lines). Application of the algorithm

minimizes the correlation between the resulting images, as shown in (e). Now, the green and red channel represent the

color of the two fluorescence contributions. Scale bar 5m.

Single Molecule Microscopy in Living Cells:Subtraction of Autofluorescence


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when their amplitude exceeds the shot noise of the

background. Moreover, since the source of autofluorescence

is heterogeneous and might vary from cell to cell, even from

organelle to organelle, the ratio S Sg r

shows slight variations

within an image, rendering the identification of weak signals

on a high background difficult. However, for most studies the

major problem is not the detection of weak signals on high

background, but the unambiguous assignment of individual

fluorescent spots to either autofluorescence or the

fluorophores of interest. Fig.6a and b show fluorescence

images of EpH4 cells expressing CD44-YFP, which have been

extensively photobleached to a level where individual

fluorophores can be resolved. A multitude of fluorescence

spots can be observed in the green channel. It is likely that

those spots are of heterogeneous origin: some represent

CD44-YFP clusters, while other spots might occur due to the

presence of autofluorescence. Indeed, some of the structures

observed in Fig.6b are also clearly visible in the red channel

(Fig.6a). Application of the above algorithm yields the image

of the genuine fluorophore distribution, shown in Fig.6c. The

picture improved in two ways: first, autofluorescent structures

have been removed, and second, the homogenous

background in the interior of the cell has been lowered

remarkably. Now the preferential location of CD44-YFP in the

plasma membrane is clearly visible.

The methodical framework described here can easily be

generalized to a higher number of fluorophores and recording

channels. In general, images consisting of pixels recorded in n

channels can be regarded as a dataset of points in ann-dimensional space. The spectra of m fluorophores with

distinct spectral properties this includes the endogenous

fluorophores responsible for autofluorescence span an

m-dimensional subspace within this n-dimensional space. For

each pixel, the best estimate of the intensity contributed by

each fluorophore can be obtained by orthogonal subspace

projection [20]. This methodological extension will allow for

the simultaneous discrimination of several fluorophores

against autofluorescence, and for unambiguous quantification

of the concentration of fluorophores, even with similar

emission and absorption spectra, e.g. GFP and YFP.

Acknowledgement This work was funded by the Austrian

Research Funds, grant P15053.

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Fig. 6. Fluorescence images of EpH4 cells expressing CD44-YFP at a low signal level. The false-color fluorescence images

show a EpH4cell expressing CD44-YFP in the red (a) and green (b) channel upon excitation at 514nm. In comparison to

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autofluorescence have been clearly identified and removed (c). In addition, the overall image quality was improved due to

subtraction of a homogenous autofluorescence background.


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Single Molecule Microscopy in Living Cells