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Quantum Dot Fluorescence Characterizes the Nanoscale Organizationof T Cell Receptors for Antigen

Sarah Boyle,† David L. Kolin,‡ Joan Glick Bieler,§ Jonathan P. Schneck,§ Paul W. Wiseman,‡ and Michael Edidin†*†Biology Department, The Johns Hopkins University, Baltimore, Maryland;  ‡Department of Physics, McGill University, Montreal, Canada;and   §Pathology Department, Johns Hopkins Medical Institutions, Baltimore, Maryland

ABSTRACT Changes in the clustering of surface receptors modulate cell responses to ligands. Hence, global measures of

receptor clustering can be useful for characterizing cell states. Using T cell receptor for antigen as an example, we show that

k-space image correlation spectroscopy of quantum dots blinking detects T cell receptor clusters on a scale of tens of nano-

meters and reports changes in clustering after T cell activation. Our results offer a general approach to the global analysis of

lateral organization and receptor clustering in single cells, and can thus be applied when the cell type of interest is rare.

Received for publication 2 August 2011 and in final form 13 October 2011.

*Correspondence: edidin@jhu.edu

Quantum dots (QDs) are stable, bright fluorescent labels

and, despite the fact that they exhibit intermittent fluores-

cence, i.e., blinking, they have been used in microscopyfor imaging of surface receptors and for tracking their diffu-

sion over the surface (1,2). We have found that QD blinking

itself is a useful parameter for characterizing the nanometer-

scale organization of the T-cell receptor (TCR) for antigen.

The blinking statistics differentiate small clusters of QDs

from a single QD or large clusters, and thus function as

reporters of nanometer-scale changes in QD-labeled TCR

organization after T cell activation by antigen.

Studies of QDs, on a surface using AFM to resolve QD

and fluorescence to image their blinking, show that the

blinking of a single QD shows persistent autocorrelation

over time due to power law emission statistics; there are

variable on- and off-periods of fluorescence emission that

span many orders of time. The blinking of clusters of 

many QDs also exhibits persistent autocorrelation in time.

On the other hand, blinking of small groups of QDs sepa-

rated by a few tens of nanometers is poorly correlated. There

are frequent transitions between fluorescent and nonfluores-

cent states, apparently due to Forster resonance energy

transfer interactions between individual dots (3–5).

It has been reported that TCRs of naıve, unstimulated

T cells are present as a mixture of monomers and small

oligomers (6,7), which form larger clusters when cells are

activated by a specific antigen (8,9). If this is correct, then

we expect that blinking of QD labeling TCRs will shiftfrom relatively uncorrelated (the signature of small numbers

of QD in nanometer proximity on labeled TCR oligomers)

to highly time-correlated (the signature of larger QD clus-

ters labeling clustered TCR).

QD blinking was characterized using k-space image

correlation spectroscopy (kICS) (10) (developed by D.K.

and P.W.W.). kICS characterizes the photophysics and

distribution of fluorescence for large regions or entire cells,

by calculating changes in k-space time correlation functions

calculated from image series. The kICS analysis yields

a temporal autocorrelation function for probe blinking,

hQ(t )  Q(t þt )i, where  Q  ¼   1 if a QD is fluorescent andQ ¼  0 if it is not (Fig. 1 A), averaged over the image series

in both space and time. The kICS correlation function

amplitude is directly proportional to the mean number of 

fluorescent entities per image area as well as the amplitude

of correlated photophysical fluctuations (10). The decay of 

the blinking autocorrelation is characterized in terms of a

Q-ratio, the ratio of the correlation amplitude for the longest

and shortest lag times (t ) observed.

We first used kICS to see if clustering QDs in buffer alters

their blinking in the sameway as described for QDs in plastic

(3). QDs in buffer, immobile and adhered on glass, were

imaged over a range of concentrations. Blinking of individual

fluorescent spots was easily seen, especially at the lowest

concentration of QDs. Quantifying this blinking by kICS,

we expected to see high Q-ratios for high concentrations of 

QDs, because at high concentrations, each fluorescent spot

contains many QDs, whereas at the lowest concentration

used, singlespots arelikely to represent a singleQD andagain

will yield high Q-ratios. Intermediate concentrations of QDs

will yield some spots that contain two or three QDs within

Forster resonance energy transfer proximity and so yield

low Q-ratios. This proved to be the case (Fig. 1 B). About 5

percent of the spots gave  Q-ratios <0.5. We also found the

same effect when T cells were labeled with saturating

amounts of biotin MHC/peptide and then with dilutions of QDs (Fig. 1,  C  and  D). Whatever the distribution of TCR,

we expect that as QDs are diluted, fewer and fewer clusters

of TCR will be labeled with multiple QDs until at a limiting

dilution there is only one (or no) QD per TCR cluster.

Editor: Susan Pierce.

2011 by the Biophysical Society

doi: 10.1016/j.bpj.2011.10.028

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We next used TCR transgenic T cells to test the model

prediction that a single QD will show highly correlated

blinking and high   Q-ratios (3). The TCRs of CD8þ   2C

T cells were specifically labeled to saturation with mono-

meric biotin-MHC/peptide H2K b /SIY, followed by dilu-

tions of streptavidin-conjugated QD655   (see   Methods,

below). Labeled cells were imaged at ~0.5 s intervals for~3 min. If the model holds for labeled T cells, then we

expect a progressive decrease in  Q-ratio, followed by an

increase when we reach the limit of one QD per TCR

cluster. This proved to be the case both for 2C TCR

CD8þ  T cells and for 5CC.7 CD4þ  T cells (labeled with

anti-CD3-biotin þQD). This reinforces our interpretation

that the rapid decay of   hQ(t )   Q(t   þ   t )i  autocorrelation

reports small clusters of TCR on naıve T cells and the

slow decay of  hQ(t )  Q(t  þ   t )i   is consistent with aggrega-

tion of receptor microclusters, or with their dispersion to

single receptors.

Naıve CD8þ 2C T cells stimulated by antigen in vitro acti-

vate, proliferate, and acquire lytic effector function over a

4–6 day time-course. We followed the changes in  Q-ratio

as a measure of TCR organization in a population of 2C

T cells after in vitro stimulation. The meanQ-ratio increased

significantly within a day of stimulation, remained high fordays 2–5, and dropped at day 6 (Fig. 2). The  Q-ratio rose

again after the cultures were stimulated with T cell growth

factors on day 6 (data not shown). Thelower values of Q-ratio

for naıve cells as compared to activated cells are consistent

with antigen-independent clustering of small numbers of 

TCR on a scale of tens of nanometers, an organization sug-

gested by electron microscopy (7), though not observable

using standard light microscopy techniques. kICS also

measures intensity and larger-scale clustering of label

(number of fluorescent spots per unitcell surfacearea), degree

of aggregation, and its diffusion. These changed as implied by

the higher  Q-ratios. Degree of aggregation increased ~20

from day 0 to day 3, whereas diffusion decreased ~5-foldfrom 2.5   10

3mm

2s

1to 0.5   10

3mm

2s

1. All these

values are also consistent with published reports of increased

TCR microclustering associated with T cell activation (8,9).

kICS and specific QD labels allow characterization of 

nanoscale TCR clustering and inferences about functional

activity by sampling thousands of spots of fluorescence on

as few as 20–50 cells. As noted above, the system also yields

data about larger-scale clustering. Although the range of 

measured   Q-ratios (0–1) is limited and a given   Q-ratio

may reflect clustering or a monomeric state, if all parameters

are used, the technique can be as a tool for distinguishing

T cell subsets differing in receptor sensitivity, organization,

and response to ligand. More generally, our approach offers

the possibility of monitoring many types of receptors and

a wide range of cell biological responses to stimuli.

FIGURE 1 (A)  Q-ratio compares the correlation amplitude at

long lag times to that at short lag times. (B ) The  Q-ratios for

blinking of QDs diluted in buffer, and adhered on glass. Dilu-tions are from the nominal stock concentration, 1   mM. Spots

blinking rapidly and chaotically, as expected from small

numbers of QDs in nanometer proximity, are found in the inter-

mediate dilutions. The Q-ratios for these rapidly blinking spots

lie below the horizontal line that defines the lowest 5% of all

values. (C ) Changes in   Q-ratio of CD8D   2C T cells labeled

with biotin-MHC/peptide followed by streptavidin QDs. The Q-

ratio for cells decreases with increasing dilutions of QDs but

at further dilution increases again. This is consistent with the

finding that small numbers of QDs within nanometer proximity

of one another blink more chaotically than large clusters of

QDs or a single QD. Data are pooled from two experiments. (D )

Changes in Q-ratio of CD4D 5CC.7 T cells labeled with biotin

anti-CD3 Fab followed by streptavidin QDs. The trend in Q-ratio

is the same as that seen for CD8D T cells. The high value of

Q-ratio at the greatest dilution is consistent with labeling by

a single QD. CD8D cells were labeled six days after stimulation.

CD4D cells were labeled three days after stimulation; they also

differ from CD8D in number of TCRs. Hence, the difference in

number of QDs bound at a given dilution.

FIGURE 2 Small-scale clustering of TCR after in vitro T cell

activation. The   Q-ratio for naıve cells is significantly (p    <

0.001) lower than Q-ratio for day-3 activated T cells. Also, there

is a significant (p   <  0.0001) linear trend of increasing  Q-ratio

over time.

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METHODS

In vitro activation

Naıve 2C T cells were purified from spleens of 2C trans-

genic mice using a Murine T Cell CD8 Subset Column

Kit (R&D Systems, Minneapolis, MN). T cells were acti-

vated on irradiated alloreactive splenic stimulator cells

from BALB/c mice, then cultured in RPMI media (Sigma-Aldrich, St. Louis, MO), supplemented with 10% fetal

calf serum and conditioned medium containing IL-2 and

growth factors. Fresh medium was at days 0, 3, and 6.

CD4þ   T cells were purified from spleens of 5C.C7 mice

(Taconic Farms, Germantown, NY) The studies have been

reviewed and approved by the Institutional Animal Care

and Use Committee at Johns Hopkins University.

Labeling CD8D

T cells were labeled using H2-SIY

K b

monomer (National

Institutes of Health Tetramer Facility, Atlanta, GA), which

has been shown to bind stably to 2C cells (11), at a concen-

tration of 2 mg/2–5   106

T cells in 100  ml phosphate-buff-

ered saline (PBS) for 30 min on ice and then washed once

with 2 ml PBS, followed by 10 nM streptavidin-coated

QD655

(Invitrogen, Carlsbad, CA) in 100  ml PBS incubated

for 30 min at 0C. Labeled cells were then washed 2 with

2 ml PBS before imaging. Labeling was specific; H2K b

monomer loaded with three different noncognate peptides

(TSINFVK, SSIEFARL, and SSYRRPVG) did not label

2C cells as detected by either microscopy or flow cytometry.

Although streptavidin QD655

is nominally polyvalent, a

comparison of QD bound to T cells with antibody labeling

of TCR (Stone and Stern (11) and our data, not shown) indi-

cated that most QD bound monovalently. CD4þ   T cellswere labeled with biotin-anti-CD3 mAb and streptavidin

QD655

.

Imaging

Cells were imaged using a 3-I Marianas Live Cell Imaging

Workstation (Intelligent Imaging Innovations, Denver, CO)

equipped with dual Cascade II 512 electron-multiplying

charge-coupled device cameras (Photometrics, Tucson,

AZ). Each image in a series of 300–500, taken 550 ms apart,

is fast Fourier-transformed in two dimensions to convert to

k-space followed by temporal correlation of the series.

Image processing

Each image series was preprocessed before kICS analysis.

Initially, the image series was corrected for background

intensity values using a top-hat transformation (12). Cells

were then segmented using the watershed transform (12).

Mean-intensity padding outside the cells was used to

maximize the analysis area (13). For the photophysical

measurements, the image series was filtered for immobile

components (as described in Hebert et al. (14)). For all of 

the image series, 25 time lags were calculated in the time

correlation function, which is   <10% of the length of the

image series. Changes in the  Q-ratio were insensitive to

the time point used for the calculation, as long as the corre-

lation functions had not decayed to zero. The k-space corre-

lation functions were fit up until  jk j2 ¼  20  mm2.

ACKNOWLEDGMENTS

We thank S. V. Desiderio, J. Hwang, J. Peters, J. Powell, R. Siliciano, and T.

Schroer for help, comments, and discussion.

This work was supported by National Institutes of Health grants AI14584

(to M.E.) and P01 AI072677, AI29575, AI44129, and CA108835 (to

J.P.S.); it was also supported by grants from the Natural Sciences and Engi-

neering Research Council (Canada), the Canadian Institutes of Health

Research (to P.W.W.), and a Natural Sciences and Engineering Research

Council (Canada) Postgraduate Scholarships fellowship (to D.K.).

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