of 16
A clinical microchip for evaluation of single immune cells
reveals high functional heterogeneity in phenotypically similar T
cells
Chao Ma
1,2,5
,
Rong Fan
1,2,4,5
,
Habib Ahmad
1,2
,
Qihui Shi
1,2
,
Begonya Comin-Anduix
3
,
Thinle Chodon
3
,
Richard C Koya
3
,
Chao-Chao Liu
2
,
Gabriel A. Kwong
1,2
,
Caius G. Radu
1,3
,
Antoni Ribas
1,3
, and
James R. Heath
1,2
1
NanoSystems Biology Cancer Center, California Institute of Technology, Pasadena, California,
USA
2
Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena,
California, USA
3
David Geffen School of Medicine, University of California–Los Angeles (UCLA), Los Angeles,
California, USA
Abstract
Cellular immunity has an inherent high level of functional heterogeneity. Capturing the full
spectrum of these functions requires analysis of large numbers of effector molecules from single
cells. We report a microfluidic platform designed for highly multiplexed (more than ten proteins),
reliable, sample-efficient (~1 × 10
4
cells) and quantitative measurements of secreted proteins from
single cells. We validated the platform by assessment of multiple inflammatory cytokines from
lipopolysaccharide (LPS)-stimulated human macrophages and comparison to standard
immunotechnologies. We applied the platform toward the
ex vivo
quantification of T cell
polyfunctional diversity via the simultaneous measurement of a dozen effector molecules secreted
from tumor antigen–specific cytotoxic T lymphocytes (CTLs) that were actively responding to
tumor and compared against a cohort of healthy donor controls. We observed profound, yet
focused, functional heterogeneity in active tumor antigen–specific CTLs, with the major
functional phenotypes quantitatively identified. The platform represents a new and informative
tool for immune monitoring and clinical assessment.
In response to infection or tissue dysfunction, immune cells develop into highly
heterogeneous repertoires with diverse functions
1–8
. A homeostatic makeup of these
functional phenotypes dictates the overarching effect of an immune response
4,9,10
. For
© 2011 Nature America, Inc. All rights reserved.
Correspondence should be addressed to: J.R.H. (heath@caltech.edu).
4
Present address: Department of Biomedical Engineering, Yale University, New Haven, Connecticut, USA.
5
These authors contributed equally to this work.
Note: Supplementary information is available on the Nature Medicine website.
AUTHOR CONTRIBUTIONS
C.M. conducted T cell experiments and analyzed data. R.F. conducted macrophage experiments. C.M. and R.F. performed validation
experiments and designed the chip. H.A. wrote Excel macros. Q.S., C.-C.L. and G.A.K. helped with experiments. B.C.-A., T.C. and
R.C.K. collected T cell samples and conducted flow cytometry phenotyping experiments. C.M., R.F. and J.R.H. conceived of the
experiments. C.M., R.F., C.G.R., A.R. and J.R.H. wrote the manuscript.
COMPETING FINANCIAL INTERESTS
The authors declare no competing financial interests.
Reprints and permissions information is available online at
http://www.nature.com/reprints/index.html
.
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Author Manuscript
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. Author manuscript; available in PMC 2013 June 13.
Published in final edited form as:
Nat Med
. 2011 June ; 17(6): 738–743. doi:10.1038/nm.2375.
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example, tumor-infiltrating macrophages are activated to be either proinflammatory or
regulatory depending on their interactions with other cells within the local
microenvironment
11–14
. Viral infection leads to activated T cells with a large variety of
effector functions, as reflected by different cytokine profiles
15,16
. Thus, a comprehensive
characterization of the multifunctional phenotypes of single immune cells could provide
both fundamental immunobiological information and clinically relevant data
5,17
.
Common techniques for single-cell protein assays include enzyme-linked immunosorbent
spot (Fluorospot) and intracellular cytokine staining (ICS) flow cytometry
6
. For enzyme-
linked immunosorbent spot assays, typically one to three secreted proteins are detected at
the single-cell level. The approach can be quantitative for detecting cytokine-secreting cells,
but it does not quantify amounts of the secreted proteins. ICS flow cytometry has enabled
the detection of up to five cytokines from single cells. Measurements of polyfunctionality
may provide a better indication of
in vivo
activity, relative to phenotypic classifications
based on cell surface markers
4
. This implies the need for measurements of increasing
numbers of functions, via multiplex protein assays, from single cells.
Here we report on the single-cell barcode chip (SCBC) for the high-content assessment of
functional heterogeneity at the single-cell level. The chip is comprised of 1,040 3-nl volume
microchambers, each loaded with single cells or small defined numbers of cells. Protein
concentrations are measured with immunosandwich assays from a spatially encoded
antibody barcode. A full barcode represents a complete panel of protein assays, and
duplicate barcodes per microchamber enable measurement statistics at the single-cell level.
The SCBC permits on-chip, highly multiplexed detection of subthousand copies of proteins
and requires only ~1 × 10
4
cells to perform the assay.
We validated the technique with a human macrophage cell line to demonstrate detection of
multiple cytokines from single cells. We then implemented it to assay the polyfunctionality
of tumor antigen–specific T cells in the setting of an adoptive cell transfer (ACT) therapy
clinical trial for melanoma. We observed focused, yet highly heterogeneous, functional
diversity compared to samples from healthy donor controls. The SCBC is a high-throughput,
low-cost and portable platform that can be used in a wide range of fundamental and clinical
applications.
RESULTS
Design rationale and detection limit of the SCBC
The SCBC system consists of four modules (Fig. 1): microchannels that contain cells,
control valves that isolate cells into microchambers, inlet and outlet ports for the
introduction and depletion of reagents and cells and a barcode-encoded glass substrate for
protein detection. The chip itself consists of two polydimethylsiloxane (PDMS) layers and
fits onto a microscope slide (Fig. 1a). The bottom PDMS layer has inlets for the loading of
reagents and cells that branch into 80 microchannels of 100
μ
m × 17
μ
m cross-sectional
size. Thirteen sets of vertical valves on the top PDMS layer divide those microchannels into
1,040-nl–volume microchambers. For a microchamber containing one to ten cells, the cell
density is 0.3 × 10
6
–3 × 10
6
cells ml
−1
, falling into the normal range for culture conditions
and physiological environments
18
.
The barcode array is a pattern of parallel stripes, each coated with a distinctive antibody.
The stripe width is 25
μ
m at a pitch of 50
μ
m. To achieve high and consistent antibody
loading, and to prevent antibody denaturation during microfluidics assembly, we used the
DNA-encoded antibody library (DEAL) approach
18
, coupled with microchannel-guided
flow patterning
19
(Supplementary Fig. 1). The chemistry and reproducibility of the DNA
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barcode patterning process has been previously described
19–21
. The SCBC bar-codes contain
13 stripes, 12 for assaying a dozen different proteins and one as a control and spatial
reference. Two sets of barcodes are included per microchamber. Cells are randomly loaded
so that each SCBC microchamber contains zero to a few cells, following a amma Poisson
distribution (goodness-of-fit test:
P
> 0.8. Supplementary Fig. 2). The numbers of cells in
each chamber are counted via imaging (Fig. 1b).
We determined the dynamic range and detection limit of our design by performing on-chip
immunoassays with recombinant proteins (Fig. 1c). The barcode array initially consists of
13 uniquely designed orthogonal DNA strands labeled in order as A through M
(Supplementary Table 1). Before loading of recombinant proteins, a cocktail containing all
capture antibodies conjugated to different complementary DNA strands (A
—L
) is used to
transform, via DNA hybridization, the DNA barcode into an antibody array (Fig. 1c). As
few as 100–1,000 copies (1 × 10
−22
–1 × 10
−21
mol) could be detected (3 s.d.), with a
dynamic range of three to four orders of magnitude (Fig. 1e and Supplementary Fig. 3),
which is compatible with single-cell secretion measurements
18
. Antibody loading, and thus
assay sensitivity, was uniform across the whole chip (coefficient of variation (CV) <10%)
(Fig. 1d,e).
Analysis of cytokine production by LPS-stimulated macrophages
We validated the SCBC by using the THP-1 human monocyte cell line. We differentiated
the THP-1 cells into cytokine-producing macrophages using phorbol 12-myristate 13-acetate
(PMA). Before loading into the device, we added LPS to activate Toll-like receptor 4
(TLR4) signaling
22,23
, a process that mimics the innate immune response to Gram-negative
bacteria (Supplementary Fig. 4). For these experiments, we designed the antibody barcode to
measure 12 proteins: tumor necrosis factor-
α
(TNF-
α
), interferon-
γ
(IFN-
γ
), interleukin-2
(IL-2), IL-1
α
, IL-1
β
, IL-6, IL-10, IL-12, granulocyte-macrophage colony–stimulating factor
(GM-CSF), chemokine (C-C motif) ligand-2 (CCL-2), transforming growth factor-
β
(TGF-
β
) and prostate-specific antigen (PSA) (Supplementary Table 2).
The microchambers contained between 0 and 40 cells so that both single-cell behavior and
signals from a population could be measured. Through an automated image processing
algorithm, we quantified the fluorescence intensities for each protein from each
microchamber. For chambers with cells centered between the two barcodes, measurements
from the barcode replicates were consistent (CV < 15%, Fig. 2). Uncentered cells
contributed to variance between replicates (CV up to 50%); however, the averages of the
barcode replicates from chambers with centered and uncentered cells were indistinguishable
(
P
> 0.2, Supplementary Fig. 5). Furthermore, measurements between chips showed good
consistency (
P
> 0.2).
The intensities of the individual proteins, averaged over many individual SCBC
microchamber measurements, agreed with measurements of those same proteins from cell
culture supernatants (Fig. 2a). Secretome data, when binned according to the numbers of
cells per chamber, yielded statistically distinct protein signals (
P
< 0.05, Fig. 2b and
Supplementary Fig. 6). However, the reference signal was insensitive to the numbers of cells
(
P
> 0.2, Fig. 2b and Supplementary Fig. 6). Most notably, single-cell protein signals could
be clearly detected (
P
< 0.0001, Fig. 2b and Supplementary Fig. 6). There was also clear
separation of secreting and nonsecreting cells, as visualized by multiple peaks in flow
cytometry histograms (Fig. 2c).
We gated the fraction of cells detected to secrete a given protein using background signals
from empty chambers. We measured the frequencies of cells producing TNF-
α
, IL-1
β
,
IL-10 and GM-CSF to be similar by both SCBC and by ICS flow cytometry (Fig. 2c and
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Supplementary Fig. 6). Furthermore, the measured fraction of cells in each quadrant also
showed a good similarity between the two techniques (Fig. 2c and Supplementary Fig. 6).
Thus, the SCBC platform can efficiently provide a functional profiling of single immune
cells. In general, these macrophages showed strongly coordinated functions
24
.
Analysis of polycytokine production by human CTLs
We next turned toward using the SCBC to functionally profile antigen-specific CTLs, which
are the main effector cell type of an adaptive immune response targeting intracellular
pathogens
25
. CTLs can show a great diversity of antigen specificities, phenotypic surface
proteins and functions, and capturing this diversity is a major challenge. Most data suggest
that the ability of CTLs to produce multiple cytokines (polyfunctionality) correlates with
protective immune responses
in vivo
4,26,27
.
We assayed the functional diversity of healthy donor CD8
+
T cells (
n
= 3), melanoma-
associated antigen recognized by T cells 1 (MART-1)–specific T cell receptor (TCR)-
transgenic cells collected from the peripheral blood of an individual with metastatic
melanoma participating in an ACT immunotherapy clinical trial, and
ex vivo
–expanded
tyrosinase-specific T cells. TCR engineering provides a means to generate large quantities of
antigen-specific T cells amenable to use for the therapy of cancer
28
. The TCR-transgenic
cells collected from the peripheral blood had been previously generated
in vitro
by retroviral
vector transduction to insert the two chains of a TCR specific for MART-1 and then
expanded
ex vivo
for 2 d followed by re-infusion into the patient after a lymphodepletion
conditioning regimen. The patient then received three vaccinations with dendritic cells
pulsed with a MART-1 peptide and a high dose of IL-2 to further activate and expand the
cells
in vivo
. Peripheral blood mononuclear cells (PBMCs) were collected on day 30 after
re-infusion by leukapheresis, at a time when multiple metastatic melanoma lesions were
responding to this therapy. The tyrosinase-specific T cell culture was generated
ex vivo
from
a tyrosinase-specific cell culture obtained by peptide–human leukocyte antigen A0201
(HLA-A0201) tetramer–based selection followed by nonspecific expansion with CD3-
specific antibody and IL-2 to obtain a population of nontransgenic but uniform antigen-
specific T cells.
We enriched the PBMCs for CD3 marker by negative magnetic bead selection before sorting
on DEAL-based CD8-specific antibody–coated or peptide–HLA-A0201 tetramer–coated
microarrays
29
. Then we released CD3
+
CD8
+
or CD3
+
tetramer
+
cells from the micro-arrays.
The released T cells underwent activation either by polyclonal TCR engagement of CD3 and
CD28-specific antibody binding or by antigen-specific TCR engagement via tetramer and
CD28-specific antibody. Activation and SCBC loading was completed within 5 min. To
maximize the on-percentage of single-cell measurement to 25–40%, we loaded ~1 × 10
4
sorted T cells in 5
μ
l medium into the device. Protein heat maps and plots that compare and
contrast these different T cell groups are presented in Figure 3. We included multiple
markers that indicate functions such as cytoxicity (perforin), T cell growth and
differentiation (IL-2), apoptosis promotion (TNF-
α
and IFN-
γ
), inflammation (IL-1
β
, IL-6
and TNF-
β
), anti-inflammation (IL-10) and the stimulation and recruitment of other immune
compartments (CCL-2, CCL-3CCL-5 and GM-CSF)
27
(Supplementary Table 3).
More than 20% (median) of the healthy donor CD3
+
CD8
+
T cells produced TNF-
α
, IL-6
and the chemokines CCL-2, CCL-3 and CCL-5. The MART-1–specific TCR–transgenic
cells that were inducing an objective tumor response
in vivo
showed a wide range of
positive functions demonstrated by the release of perforin, IL-1
β
, IL-10, IFN-
γ
, IL-2 and
CCL-5 upon
ex vivo
antigen re-stimulation with the MART-1–HLA-A0201 tetramer. The
functionality of the antigen-specific TCR-transgenic cells derived from the subject with
melanoma was higher compared to the healthy donor lymphocytes in terms of both signal
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intensity and fraction of positive cells (Fig. 3a,b,e). The frequency of cytokine-producing
cells among the subject-derived TCR transgenic cells was consistent with Fluorospot results
(Fig. 3d). Phenotyping results identified by flow cytometry of surface markers of T cell
phenotype (Fig. 3c) illustrated that the population of subject-derived TCR-transgenic,
MART-1–specific cells was mostly homogeneous; the principal (>90%) population of these
cells was CD8
+
and had a phenotype consistent with effector CCR7
-
CD45RA
+
T cells at a
late differentiation stage (Fig. 3c)
15
. In comparison, the
ex vivo
–expanded tyrosinase-
specific T cells showed decreased production of CCL-3, IL-6 and TNF-
α
(Fig. 3e).
Consistent with the notion that
ex vivo
expansion of T cells by IL-2 and CD3-specific
antibody results in terminal differentiation of T cells, the tyrosinase-specific T cell clone had
elevated release of perforin, IL-1
β
and IL-2, compared to healthy control samples, upon
activation (Fig. 3e and Supplementary Fig. 7).
Evaluation of polyfunctionality
We analyzed the multivariate features of these T cell populations by studying the protein-
protein correlations for two and three proteins. Pseudo–three-dimensional plots (Fig. 4a–c)
of markers representing various functions revealed that certain functions of the MART-1–
specific cell population were highly coordinated compared to the healthy donor cells. For
example, 70% of IL-6
+
cells produced CCL-5, whereas for CTLs from healthy donors the
frequency was around 50%. TNF-
α
and IFN-
γ
production were anticorrelated in MART-1–
specific cells (Fig. 4c), with >90% of the population expressing at most one of these effector
molecules. However, for the small fraction of MART-1– specific TCR–transgenic cells that
were TNF-
α
+
IFN-
γ
+
, secretion of IL-2 was often an additional function (75%, Fig. 4). A
full set of protein-protein correlations is provided in Supplementary Figure 8.
We defined functional subsets of the various T cells by identifying groups of cells that
secreted the same combination of proteins (Fig. 4d,e). There were at least 45 distinct
subpopulations that accounted for 60% of the MART-1–specific TCR–transgenic cells (Fig.
4e and Table 1). A similar accounting for the healthy donor CD8
+
T cells and the tyrosinase-
specific T cells yields 4–17 subpopulations (Fig. 4d and Table 1). Furthermore, for the
MART-1–specific TCR–transgenic cells, the major functional subsets averaged more than
five active functions, whereas both healthy donor and tyrosinase-specific cells averaged only
one or two functions (Table 1). This demonstrates the ability of SCBC to visualize and
discriminate different levels of functional heterogeneity.
DISCUSSION
The SCBC permits highly multiplexed (more than ten proteins) measurements of effector
molecules from single cells by detecting the natural protein secretome from macrophages
and T cells upon activation. The multiplex capacity can be further expanded beyond what
we explored here. The ability to use small sample size (~1 × 10
4
cells) implies that the
SCBC can be integrated with other upstream multiplexed analysis, such as flow cytometry
or (as we show here) microarray sorting, to enable a detailed functional study of
phenotypically defined sets of cells selected from heterogeneous populations. Analysis of
signals from chambers containing different numbers of cells may also provide information
relevant to cell-cell interactions.
The MART-1–specific TCR–transgenic CTLs had stronger perforin, IFN-
γ
and interleukin
secretion and more functional heterogeneity compared with the healthy donor CTL controls.
This functional status is also indicated by their identity as effector T cells
(CD45RA
+
CCL7
CD27
CD28
CD62L
)
15,30,31
. Previous vaccination studies identified
that polyfunctional T cells are better cytokine producers and that the quality of a
polyfunctional T cell response is a good predictor of clinical outcome
4,32
. We found that the
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MART-1–specific TCR–transgenic CTLs showed favorable features compared against
CTLs from healthy donors (for example, polyfunctional subset frequency 62% versus 6–
25%; Supplementary Fig. 9). These data are consistent with the observation that, at the time
the CTLs were collected, there was active inflammation and the tumors were responding to
the ACT therapy in this patient.
We observed signs of T cell terminal effector differentiation and exhaustion, as indicated by
the anticorrelation of TNF-
α
and IFN-
γ
secretion, high IL-10
+
cell frequency and high PD1
and CD127 expression
15
. However, the quality of the T cell response at a single time point
within a vaccination trial may not provide an indicator of long-term vaccination or therapy
response
4
. A similar multiparameter SCBC analysis carried out at multiple time points
throughout the course of a cancer immunotherapy treatment is currently underway.
We saw a high level of functional heterogeneity within a population defined as relatively
homogeneous by surface markers
25,33
, and that heterogeneity was also focused. For
example, 45 out of >4,000 possible functional subsets could account for 60% of all
MART-1–specific TCR-transgenic cells. The observed high level of polyfunctionality (up to
12 functions per cell, with an average of more than five functions) exceeds current
multiplexing capacity by most existing single-cell secretion assays. Moreover, none of the
proteins being profiled were interchangeable with others within the panel (with
R
2
< 0.6,
Supplementary Fig. 8). These findings indicate that a high dimensional analysis is in fact
required for comprehensively profiling of T cell effector functions.
The SCBC provides a new platform for analyzing the functional activity of immune cells
immediately after short-term
ex vivo
activation. This technology compares favorably to
current cellular immunoassays in terms of sensitivity, multiplexing capacity, quantification,
sample size, cost and infrastructure requirements and thus has potential for a thorough, cost-
effective characterization of human immune cell responses.
ONLINE METHODS
Microchip fabrication
The SCBCs were assembled from a DNA barcode microarray glass slide and a PDMS slab
containing a microfluidic circuit. The DNA barcode array was created with microchannel-
guided flow patterning (Supplementary Fig. 1). Each barcode was comprised of thirteen
stripes of uniquely designed single-stranded DNA molecules. The PDMS microfluidic chip
was fabricated using a two-layer soft lithography approach
34
(Supplementary Methods).
Human samples
Human samples were obtained from individuals with meta-static melanoma (males) enrolled
in a TCR-transgenic ACT protocol clinical trial (registration number NCT00910650). The
studies using human samples were approved by the appropriate human use committees
(UCLA Institutional Review Board 08-02-020, IND# 13859), and informed consent was
obtained from all individuals studied.
Isolation, purification and expansion of T cells
PBMCs were collected from individuals receiving TCR ACT immunotherapy by
leukapheresis and periodic peripheral blood draws as previously described
35
. Aliquots of
cryopreserved PBMCs were thawed and immediately diluted with RPMI complete medium
containing 5% human AB serum (Omega Scientific). Cells were washed and subjected to
enzymatic treatment with DNase (Sigma) for 1 h at 37 °C, washed and rested overnight in a
5% CO
2
incubator. Antigen-specific MART-1 T cells were purified sequentially by
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magnetic negative enrichment for CD3 (Stemcell) and by a MART-1–HLA-A0201 tetramer
microarray that have been previously described
29
(Supplementary Methods). Purified cells
were collected, washed, stimulated with MART-1 tetramer and CD28-specific antibody and
loaded into SCBC chip. PBMCs from healthy donors were negatively enriched in the same
way and were further purified by CD8-specific antibody microarray, followed by
stimulation with CD3/CD28–specific antibody. Sorted cells was checked to be >95% pure.
On-chip secretion profiling
Before loading cells onto the chip, the DNA barcode array was transformed into an antibody
microarray. The chip was then ready for cell loading. Chips with cells were incubated and
then assays were developed with secondary antibodies and fluorescent markers
(Supplementary Methods).
Intracellular cytokine staining of THP-1 cells
Brefeldin A (eBioscience) was added in the presence of PMA and LPS at the recommended
concentration in the final 4 h of stimulation. Standard intracellular staining was performed
as described by the supplier’s protocol (eBioscience) with additional blocking with human
serum (Sigma) and washes. Cells were fixed and permeabilized by using a fixation and
permeabilization kit (eBioscience) and then were stained intracellularly with antibody to
TNF-
α
(MAb11), antibody to IL-1
β
(H1b-98), antibody to IL-10 (JES3-9D7) and antibody
to GM-CSF (BVD2-21C11) (all from eBioscience). Isotype control staining was used as
negative control, and 2 × 10
4
events were collected for each condition. Samples were
analyzed on a FACSCalibur (BD Biosciences) machine with CellQuest Pro software (BD
Biosciences).
Flow cytometry analysis of antigen specific T cells
Cryopreserved PBMC samples from peripheral blood draws or leukapheresis were thawed
and analyzed by HLA-A*0201 tetramer assay (Beckman Coulter) with flow cytometric
analysis as previously described
35,36
. In brief, PBMCs were resuspended in 100
μ
l of adult
bovine serum (Omega Scientific) and stained for 15 min at room temperature (20 °C) using
a cocktail of antibodies to the following proteins in replicate aliquots: CD3 (UCHT1, BD
Bioscience), CD8 (3B5, Invitrogen), CD45RA (2H4LDHIILDB9, Beckman Coulter),
CD62L (DREG56, Beckman Coulter), CCR7 (150503, BD Bioscience), CD27 (MT271, BD
Bioscience), CD28 (CD28.2, BD Bioscience), CD127 (HIL-7R-M21, BD Bioscience) and
PD1 (MIH4, BD Bioscience). For all flow cytometry experiments, anti-mouse IgK isotype
control FBS compensation particles (BD Biosciences) were used for compensation purposes,
and 5 × 10
5
to 1 × 10
6
lymphocytes were acquired for each condition. To correctly gate the
flow cytometry data, the fluorescent minus one approach was used. Samples were acquired
on an LSR II system (BD Biosciences), and data were analyzed using FlowJo software
(TreeStar).
Fluorospot assay for sorted antigen-specific T cells
Antigen specific T cells were captured by tetramer microarray. Primary cytokine capture
antibody was co-localized on the same array. The captured cells were then incubated at 37
°C and 5% CO
2
for 12 h in 10% FBS in RPMI 1640 medium. Phycoerythrin-labeled
secondary antibody (eBioscience) was applied after incubation. Then slides were washed
and imaged by the EZ-C1 confocal microscope system (Nikon).
Data and statistical analyses
We used GenePix 4400 (Axon Instruments) to obtain the scanned fluorescent image for both
Cy3 and Cy5 channels. All scans were performed at constant instrument settings: laser
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power 80% (635 nm) and 15% (532 nm), optical gains 600 (635 nm) and 450 (532 nm),
brightness 80 and contrast 83 for T cell experiments; laser power 100% (635 nm) and 33%
(532 nm), optical gains 800 (635 nm) and 700 (532 nm), brightness 87 and contrast 88 for
macrophage experiments. All the barcodes were processed in PhotoShop (Adobe) and
ImageJ software (US National Institutes of Health) to generate fluorescence line profiles. A
home-developed Excel (MicroSoft) macro was employed for automatic extraction of
average fluorescence signal for all bars in each set of barcode, and all the barcode profiles
were compared to the number of cells by using the same program. On the basis of these data,
heat maps were generated by using the software Cluster and Treeview
37
. Flow cytometry
data were analyzed in FlowJo software.
P
values were calculated from two-tailed Student’s
t
tests assuming unequal variance.
Additional methods
Detailed methodology is described in the Supplementary Methods.
Supplementary Material
Refer to Web version on PubMed Central for supplementary material.
Acknowledgments
We thank B. Marzolf at the Institute for Systems Biology for printing DNA-spotted arrays and the UCLA nanolab
for photomask fabrication. We thank L. Yang, S. Wang, R. Diamond and H. Wu for valuable discussion. C.M.
acknowledges the support of the Benjamin M. Rosen Fellowship. R.F. is supported by the US National Institutes of
Health K99 Pathway to Independence Award (No. 1 K99 CA136759-01). This work was funded by the US
National Cancer Institute Grant No. 5U54 CA119347 (J.R.H.), by the Ivy Foundation and the Jean Perkins
Foundation (J.R.H.), by the California Institute for Regenerative Medicine New Faculty Award RN2-00902-1
(A.R.), by the Caltech/UCLA Joint Center for Translational Medicine (A.R. and J.R.H.) and the Melanoma
Research Alliance (A.R. and J.R.H.). The UCLA Flow Cytometry Core Facility is supported by the US National
Institutes of Health awards CA-16042 and AI-28697.
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Figure 1.
Design of the SCBC for single-cell protein secretome analysis. (
a
) Image of an SCBC in
which flow channels are shown in red and the control channels are shown in blue. Input and
output ports are labeled. Ab, antibody. (
b
) An optical micrograph showing cells loaded and
isolated within the microchambers, overlaid with the fluorescence micrograph of the
developed assay barcode for those same microchambers. Numbers of cells per
microchamber are indicated by the yellow numbers. (
c
) Drawing of the multiplex DEAL
primary antibody barcode array used for capture of secreted proteins from single cells and
then developed for the detection of those proteins. SA, Streptavidin. (
d
) Scanned fluorescent
images used for the antibody barcode calibration measurements using spiked recombinant
proteins. The protein concentrations (in numbers of molecules per chamber) are given to the
left of each row of images. The plot at the top is a line profile of the top row of images and
represents the reproducibility of the barcodes across the antibody array of an SCBC. (
e
)
Recombinant protein calibration curves for TNF-
α
, IL-1
β
, IL-6, IL-10 and GM-CSF.
Individual measurements (red) are shown for IL-1
β
. Other proteins measurements are
represented by average intensity values and s.d.
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