Articles
Trans-SILAC: sorting out the non-cell-autonomous
proteome
© 2010 Nature America, Inc. All rights reserved.
Oded Rechavi1,5, Matan Kalman2, Yuan Fang3, Helly Vernitsky1,4, Jasmine Jacob-Hirsch4, Leonard J Foster3,
Yoel Kloog1,6 & Itamar Goldstein4,6
Non-cell-autonomous proteins are incorporated into cells that
form tight contacts or are invaded by bacteria, but identifying
the full repertoire of transferred proteins has been a challenge.
Here we introduce a quantitative proteomics approach to sort
out non-cell-autonomous proteins synthesized by other cells or
intracellular pathogens. Our approach combines stable-isotope
labeling of amino acids in cell culture (SILAC), high-purity cell
sorting and bioinformatics analysis to identify the repertoire of
relevant non-cell-autonomous proteins. This ‘trans-SILAC’ method
allowed us to discover many proteins transferred from human B
to natural killer cells and to measure biosynthesis rates of
Salmonella enterica proteins in infected human cells. Trans-SILAC
should be a useful method to examine protein exchange between
different cells of multicellular organisms or pathogen and host.
The dual nature of the cell, being both an autonomous entity and
a vital component in the construction of multicellular organisms
is at the foundation of the modern cell theory1. But the recent
recognition of extensive transfer of proteins among interacting
cells, particularly immune cells, supports the notion that under
certain circumstances, cellular autonomy is compromised2,3.
The poorly understood mechanisms that lymphocytes use to
acquire proteins from target cells have been termed ‘trogocytosis’,
‘internalization’, ‘absorption’, ‘acquisition’, ‘snatching’, ‘stripping’,
‘shaving’ and ‘trapping’2–7. The most studied trogocytic event
is a fast process that is cell contact– and actin cytoskeleton–
dependent2,3,5, and the proteins that transfer by trogocytosis can
remain intact and functional8–10. To date, only membrane-associated
proteins have been described to transfer; Ras proteins, which interact only with the inner leaflet of the plasma membrane, transfer
very efficiently when cell-cell contact is established8,11. Moreover,
the whole repertoire of proteins that can transfer in vitro among
lymphocytes during culture has not been defined yet.
Cells can also be forced to accept non-cell-autonomous proteins,
as in the cases of bacteria that infect host cells or deliver proteins
via secretion systems, including various virulence factors that are
detrimental to host cells12. Some intracellular bacteria rely on continuous production of proteins in host cells, and their patterns of
expression can be regulated by the host’s environment12.
Global identification of non-cell-autonomous proteins has been
challenging. Previous studies using flow cytometry–based ana­l­
ysis have only identified a limited number of plasma membrane–
­associated proteins that transfer among lymphocytes2,3,5. Flow
cytometry and other methods, including microscopy and protein
biotinylation9,13, are time consuming and typically can be used to
detect only a few non-cell-autonomous proteins per experiment.
Here we describe a quantitative proteomics method, trans–
stable-isotope labeling of amino acids in cell culture (trans-SILAC),
which can be used to differentiate non-cell-autonomous proteins,
acquired during cell-cell contact, from the endogenous proteome.
We took advantage of the SILAC method in which cell lines are
metabolically labeled with heavy-isotope amino acids and compared to an unlabeled reference cell sample; differences in protein abundance between the samples can be detected by liquid
chromatography–tandem mass spectrometry (LC-MS/MS)14,15.
Here we labeled one cell type (protein donor) with ‘heavy’ amino
acids and left the other cell type (protein recipient) unlabeled, such
that the transferred non-cell-autonomous proteins can be clearly
identified in the recipient cell by their mass shift. We used digital
fluorescence-activated cell sorting (FACS) to carefully sort out the
protein-recipient cell population before LC-MS/MS analysis and a
stringent bioinformatics approach to identify non-cell-autonomous
proteins. Using trans-SILAC we scanned the non-cell-autonomous
proteome exchanged among conjugated lymphocytes. In another
application, we followed the translation rate of Salmonella enterica–
derived non-cell-autonomous proteins labeled with ‘heavy’ amino
acids in host cells during the early phase of the bacterial infection.
RESULTS
Experimental system to probe cell-cell protein transfer
We used a well-defined cellular system8,9 to study cell contact–
and actin cytoskeleton–dependent protein transfer. We used the
1Department of Neurobiology, The George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel. 2Department of Biochemistry, The George S. Wise Faculty
of Life Sciences, Tel Aviv University, Tel Aviv, Israel. 3Centre for High-Throughput Biology, Department of Biochemistry and Molecular Biology, University of British
Columbia, Vancouver, Canada. 4Cancer Research Center, Chaim Sheba Medical Center, Tel Hashomer and Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel.
5Present address: Howard Hughes Medical Institute, Department of Biochemistry and Molecular Biophysics, Columbia University Medical Center, New York, New York,
USA. 6These authors contributed equally to this work. Correspondence should be addressed to Y.K. ([email protected]) or I.G. ([email protected]).
Received 11 May; accepted 10 August; published online 10 October 2010; doi:10.1038/nmeth.1513
nature methods | VOL.7 NO.11 | NOVEMBER 2010 | 923
© 2010 Nature America, Inc. All rights reserved.
Articles
B cell line 721.221 (B721), a classical natural killer (NK) cell target,
as the heavy isotope–labeled protein donor and freshly isolated
‘light’ human NK cells as the acquiring cell population (Fig. 1).
As untagged enhanced GFP (EGFP) does not transfer among
cells8, we used B721 cells stably expressing EGFP (B721-EGFP) to
identify these cells. Previously we had shown that H-Ras transfers
robustly from B721 to NK cells and that this transfer correlates
with the transfer of the transmembrane protein CD86 (ref. 8)
(Supplementary Fig. 1). To identify only non-cell-autonomous
proteins that transfer by an actin cytoskeleton–dependent mechanism, we added a control condition in which we disturbed the
actin cytoskeleton by latrunculin-B (LatB) treatment. Calibration
of LatB inhibition showed that 1 μM LatB efficiently abolished
the transfer of H-Ras and CD86. Therefore, we used LatB-treated
co-cultures as controls for actin cytoskeleton–independent cellcell protein transfer in this experimental system (Fig. 2 and
Supplementary Figs. 1 and 2).
First, to use trans-SILAC to identify ‘heavy’ B721-derived
­proteins that transfer into the ‘light’ NK cells, we grew the B721EGFP cells for at least seven cell divisions with heavy isotopologs
of lysine and arginine, for a labeling efficiency of ~98%. We then
grew the ‘heavy’ B721-EGFP cells with NK cells for 1.5 h with
or without LatB. To assess transfer, we monitored the transfer
of CD86 from B721 to NK cells. We used a FACSAria digital cell
sorter and a stringent multiparameter duplet-discrimination
algorithm to sort EGFP+ NK single-cell events with high purity
(>99.5%) from the combined cultures (Fig. 2). Then we lysed the
sorted NK cells, digested the extracted proteins, fractionated them
and analyzed them by LC-MS/MS. Using MaxQuant16 to analyze
the data, we identified 2,426 proteins in total and quantified their
‘trans-SILAC ratios’ (the ratio of labeled to nonlabeled peptides;
Supplementary Table 1) and spectral counts (Supplementary
Table 2) before subsequent bioinformatics analysis.
Defining the NK-cell non-cell-autonomous proteome
Our initial analysis of the data included only annotated proteins
that contained heavy peptides, namely with a trans-SILAC ratio >0.
Whereas we detected heavy peptides for 1,635 proteins in the
untreated samples, we detected much fewer heavy peptides (representing 1,144 proteins) in the presence of LatB (Supplementary
Table 1). There was considerable intersection between the peptide
sets identified among the two experimental conditions (1,068 of
1,711 proteins; 62%), but a much greater percentage of proteins
exclusively contained B cell–derived ‘heavy’ peptides in the untreated
medium (567 of 1,711 proteins; 33%) versus LatB treatment condition (76 of 1,711 proteins; 4.4%) (Supplementary Fig. 3a).
We then compared the distribution of the trans-SILAC ratios
for all of the detected proteins in LatB-treated and untreated samples. The overall distribution of trans-SILAC ratio for the proteins
in the experimental condition was significantly shifted as compared to proteins in LatB-treated cells (P = 1.7 × 10−14, MannWhitney-Wilcoxon test; Supplementary Fig. 3b). As ‘heavy’
proteins could have only originated from the B721 cells, these
results suggested that significantly more proteins were transferred
from B to NK cells when the actin cytoskeleton was undisturbed.
In agreement with previous studies8,11, we detected transfer of
Ras proteins in the absence of LatB, confirming the sensitivity
of trans-SILAC in detecting the transfer of proteins expressed in
physiological amounts.
924 | VOL.7 NO.11 | NOVEMBER 2010 | nature methods
‘Light’
TransSILAC
‘Heavy’
Culture
1.5 h
m/z
m/z
LatB
LatB
LatB
Transfer
–
m/z
FACS
Transfer
LatB
m/z
=
m/z
Non-cellautonomous
proteins
Figure 1 | Detecting non-cell-autonomous translated proteins by
trans-SILAC. Flow diagram of a typical trans-SILAC experiment designed
to detect cell-to-cell protein transfer. LatB-treated cells served as control
to allow the exclusion of actin cytoskeleton–independent transfer.
To assess which proteins were transferred via an actin
cyto­skeleton–dependent mechanism, we used a stringent
­bioinformatics approach to analyze the trans-SILAC data
(Supplementary Fig. 4 and Online Methods). The final and intermediate lists as well as the results of all analysis steps performed
are listed in Supplementary Table 3.
We generated two initial lists: the ‘transfer set’ included
­proteins assumed to transfer by an actin cytoskeleton–
­dependent mechanism (with a high trans-SILAC ratio in the
experi­mental condition and a relatively low trans-SILAC ratio
in LatB-treated cells; Supplementary Table 3b), and the ‘high
LatB set’ included proteins assumed to transfer by an actin cyto­
skeleton–­independent mechanism (high trans-SILAC ratio in
the LatB condition; Supplementary Table 3a). We next analyzed the lists using the database for annotation, visualization
and integrated discovery (DAVID) bioinformatics resources 17
to cluster these protein lists into enriched biological functions
and examined whether proteins with similar biological functions have, as a group, significantly higher trans-SILAC ratios
(P < 0.05). Only two biological functions, containing just five
proteins, were significantly enriched in LatB-treated cells (P
< 0.05; Supplementary Table 3d). In contrast, proteins in
the ‘transfer set’ ­clustered into eight annotation source terms
(Supplementary Table 3e), such as ‘integral to plasma membrane’ (trans-SILAC ratio = 1.23, P = 0.0051, 21 proteins), ‘trans­
membrane ­ receptor activity’ (trans-SILAC = 1.72, P = 0.01,
8 proteins) and ‘MHC class II protein complex’ (trans-SILAC
= 3.98, P = 0.0013, 3 proteins). Together, these observations
supported our hypo­thesis that groups of proteins with a distinct
biological function ­ transfer among lymphocytes by an actin
cyto­skeleton–­dependent process.
Next, we filtered the ‘transfer set’ using a heuristic approach to
include only the proteins with high likelihood for transfer by an
actin cytoskeleton–dependent process. Each filtering step was
Articles
B721
cells
128
64
NK cells,
initial
64
128
192
FSC-A (×1,000) (a.u.)
128
64
10
103
0
CD86-APC-A (a.u.)
0.18
10
0
256
256
192
128
64
10
104
103
0
0
h
3
103 104
GFP-A (a.u.)
64
128
192
FSC-W (×1,000) (a.u.)
105
105
0
64
5
CD86-APC-A (a.u.)
CD86-APC-A (a.u.)
4
104
128
0
103
104
105
GFP-A (×1,000) (a.u.)
f
5
103
104
GFP-A (a.u.)
192
256
10
103
104
GFP-A (a.u.)
105
105
104
0.45
3
10
102
0
0 102 103 104
GFP-A (a.u.)
105
105
accompanied by a functional analysis to evaluate the identity
of the proteins that were removed from or remained in the list
(Supplementary Table 3f–i). This procedure eventually yielded
a final list of 172 non-cell-autonomous proteins that transfer
5.83
105 94.17
104
103
2
10
0
101 0
101 102 103 104 105
CD11a-PE-A (a.u.)
Untreated
CD45-APC-A
(a.u.)
LatB treatment
4.25
105 95.75
104
103
102
0
101 0
101 102 103 104 105
CD43-PE-A (a.u.)
105 21.62
104
103
102
101 0
78.38
b
Figure 2 | Sorting of highly purified human CD56+ NK cells. (a–c) The
plots depict the various analysis and sorting steps of the stringent dupletdiscrimination algorithm used to detect and sort NK single-cell events
based on their light-scatter characteristics and EGFP signals. Shown are
side scatter area (SSC-A) versus forward scatter area (FSC-A) (a), and FSC-H
(height) versus FSC-W (width) of all viable cells in B721 cell (green) and
initial NK cell (red) gates (b) and of the cells included in the initial NK
single-cell gate (c). (d) The events included in the initial NK single-cell
gate were additionally purged of EGFP+ events to obtain the final EGFP−
NK single-cell gate. (e,f) Analysis of cells collected from LatB-treated
(e) or untreated (f) cultures of B721 and NK cells for cell-surface CD86
before cell sorting. Green dots depict B721-EGFP cell and NK-B721 cell
doublets, and red dots denote only events that represent single NK cells
with high probability. (g,h) For quality control, the single NK cells sorted
from either the LatB-treated (g) or untreated (h) cultures of B721 and NK
cells were analyzed for purity. Percentages of EGFP+ events in the sorted
cells is indicated. CD86-APC-A, CD86 allophycocyanin signal area; GFP-A,
EGFP signal area. Results shown are from a typical experiment of more
than five experiments.
upon cell contact by actin cytoskeleton–dependent mechanisms:
the ‘final transfer set’ (Supplementary Table 3j).
To validate the cell-cell transfer of selected proteins from this
‘final transfer set’, we used a FACS-based B cell to NK cell transfer
assay8,18. We labeled 17 candidate proteins only in donor cells
(HEK293 or B721 cells) either by transfecting HEK293 cells with
plasmids encoding EGFP-tagged intracellular proteins, or by tagging cell-surface proteins on B721 cells by specific fluorochromeconjugated monoclonal antibodies. Then we grew these donor cells
with NK cells, with or without LatB. The results of the FACS-based
experiments were in agreement with the LC-MS/MS data (Fig. 3
and Supplementary Table 4). Moreover, proteins that had high
(>0.18) trans-SILAC ratios in both the initial ‘transfer’ and ‘LatB’
sets did not transfer, confirming that purging proteins with high
trans-SILAC ratios in the LatB-treatment condition prevented the
inclusion of false positive actin cytoskeleton–independent events.
Furthermore, we found that proteins with low trans-SILAC ratios
in either treatment condition did not transfer. Note that the comparison among the LC-MS/MS and FACS derived data is inherently qualitative because we transfected target cells with plasmids
encoding EGFP-tagged proteins for the FACS validation.
Transfer of intracellular proteins
4,419.35
2,945.67
2,409.03
1,248.96
109.42
0
101 102 103 104 105
CD43-PE-A (a.u.)
CD45-APC-A
(a.u.)
CD45-APC-A
(a.u.)
a
CD45-APC-A
(a.u.)
© 2010 Nature America, Inc. All rights reserved.
g
d
192
0
256
256
256
64
128
192
FSC-W (×1,000) (a.u.)
e
FSC-A (×1,000) (a.u.)
192
FSC-A (×1,000) (a.u.)
FSC-A (×1,000) (a.u.)
c
b
256
CD86-APC-A (a.u.)
SSC-A (×1,000) (a.u.)
a
18.23
105 81.77
104
103
102
0
101 0
101 102 103 104 105
CD11a-PE-A (a.u.)
RalB
RalA
H-Ras
GFP
NK cells
101 102 103 104 105
GFP-A (a.u.)
c
Transfer of cell-surface proteins
95.51
3.76
3.51
3.41
3.38
100
725.34
188.94
173.45
100.91
52.77
61.15
65.03
Arf4
Rab11a
Rab10
H-Ras
ADAR
GFP
NK cells
CD10
PDGFR
CD14
CD49d
NK cells
101
102
103
PE-A (a.u.)
16.58
CD70
HLA-DR
CD58
NK cells
7.82
8.16
Figure 3 | FACS-based validation of the trans-SILAC results.
2.28
(a) Analysis of the transfer of CD11a and CD43 from B721 to
101 102 103 104 105
100
101
102
103
NK cells in LatB-treated (left) and untreated cultures (right;
PE-A (a.u.)
GFP-A (a.u.)
numbers indicate percentage of events in each quadrant). Axis values
represent arbitrary logarithmic fluorescent units of the indicated fluorescently tagged mAb. (b,c) Overlapping, vertically displaced histograms
(FlowJo software) for the analysis of the transfer of indicated GFP-tagged intracellular proteins from HEK293 to NK cells (b) and of cell-surface proteins
labeled by fluorochrome-conjugated monoclonal antibodies from B721 to NK cells (c) relative to NK-cells-alone cultures. Numbers to the right of
histograms represent the mean fluorescence intensity for that analysis. Data were collected from ~10,000 single cell events.
nature methods | VOL.7 NO.11 | NOVEMBER 2010 | 925
Articles
© 2010 Nature America, Inc. All rights reserved.
Figure 4 | Non-cell-autonomous proteins
that form the ‘cancer, immunological disease,
hematological disease’ network. The image
was created using the Ingenuity Pathways
Analysis (IPA) platform (Ingenuity Systems) by
overlaying the transferring proteins detected
by trans-SILAC (red) onto a global molecular
network from the Ingenuity knowledgebase.
Red indicates high trans-SILAC ratios, and
white indicates proteins that were not in the
‘transfer set’ but form part of this network. For
each transferring protein, the bottom number
corresponds to the trans-SILAC ratio in the
‘transfer’ condition and the top number is
the difference between the ratios of the two
experimental conditions (trans-SILAC ratio in
‘transfer’ condition minus trans-SILAC ratio
in ’control’ condition). For cases in which no
trans-SILAC ratio was available for the ‘control’
condition, both numbers are equal.
EHD4
0.261
0.261
RAB10
CNP
0.176
0.305
HLA-DRA
0.239
0.239
ITGA4
ARHGAP17
3.389
6.295
Nfat (family)
DOK2
0.182
0.182
0.213
0.213
HLA-DRB1
0.51
0.51
LCK
4.451
4.451
0.291
0.291
KHDRBS1
TCR
0.140
0.249
CD58
ERK
0.824
0.824
Ap1
Insulin
Sos
Ras
RALA
0.184
0.184
Pkc(s)
PI3K
To validate that the transfer was cell
contact–dependent we used a transwell
MTCH2
assay system9 and found that the transfer
0.128
of the selected proteins, positively vali0.230
dated by the FACS-based assay, was indeed
halted when we separated the B721-cell
from the NK-cell cultures by a transwell
membrane (Supplementary Table 4).
BDH1
The DAVID-based functional analysis
of the ‘final transfer set’ (Supplementary
0.319
0.319
Table 3k) revealed that 70% (120 of 172
proteins) of the transferring proteins were
membrane associated (gene ontology (GO) terms ‘intrinsic to
membrane’ or ‘intracellular ­membrane-bound organelle’). To identify functional protein networks in the ‘final transfer set’, we used
the network explorer feature of the Ingenuity Pathways Analysis
(IPA) platform (Ingenuity Systems). IPA revealed that the transferring proteins accumulate in ­specific networks within the adopting
NK cells with a high degree of interconnectivity (Supplementary
Table 5). One such network identified by IPA with a high degree of
interconnectivity (IPA score = 37, n = 23 proteins) was ‘cancer, immuno­
logical disease, hemato­logical disease’ (Fig. 4 and Supplementary
Table 5). We validated the transfer of eight proteins within this network by FACS: HLA-DRA, CD58, ITGA4 (CD49d), K-Ras, H-Ras,
Rab10, RALA and RALB (Fig. 3 and Supplementary Table 4).
Note that the final ‘transfer’ list of 172 proteins that transfer by
contact and actin cytoskeleton–dependent mechanisms probably
does not contain all the proteins that can transfer, and additional
proteins may transfer in other contexts.
Finding Salmonella protein translation rate in host cells
Salmonella infecting host cells represent an interspecies transfer of
intracellular proteins. Once Salmonella adhere to host cells, they
secrete proteins into the host cytosol sequentially via two type-3
secretion systems (T3SS; encoded on the Salmonella pathogenicity islands19). After Salmonella modulate their own internalization
into the host, the bacteria may survive for more than a day, during
which time they can replicate inside the Salmonella-containing
vacuoles and adjust as needed the composition of the bacterial
proteome in several stages19.
926 | VOL.7 NO.11 | NOVEMBER 2010 | nature methods
UFD1L
0.251
0.251
SLC2A1
Mapk
1.142
1.142
ERK1/2
Akt
KRAS
0.323
0.323
PLAA
BSG
PITPNA
0.501
0.904
0.941
0.941
SLC3A2
0.936
0.936
0.383
0.383
UBXN7
0.230
0.230
TPD52
0.284
0.284
To apply trans-SILAC to measure the expression rates of
Salmonella proteins while the bacteria survive and multiply inside
the host, we used Salmonella that had been fully SILAC-labeled
to infect host HeLa cells and then maintained this culture for
2 h. During this time, we collected aliquots of the cells every
20 min, lysed them and analyzed the samples by LC-MS/MS.
In this application, trans-SILAC was not required to identify
which proteins are actually transferred, as Salmonella proteins
are easily distinguished from human proteins via their unique
sequence, but rather the trans-SILAC allowed us to measure how
the Salmonella reprogrammed their proteome while inside the
host. We used the increase in ‘light’ Salmonella proteins to estimate the change in expression of any given protein over time.
During infection, bacterial protein synthesis was substantially
delayed in the first 40 min of infection compared to the control
log-phase growth of Salmonella alone. However, after 40 min, protein synthesis resumed its regular turnover rate (Supplementary
Fig. 5 and Supplementary Table 6), suggesting that Salmonella
shuts down its replication machinery during invasion. We also
used trans-SILAC to detect specifically Salmonella pathogeni­city
island (SPI)-1 and SPI-2 secreted effector proteins in various subcellular fractions of infected cells (data not shown).
DISCUSSION
Our trans-SILAC method allowed us to scan the entire proteome
for non-cell-autonomous proteins. The particular heuristic analysis we used allowed us to sort out the positive hits with high fidelity.
The strategy could be fine tuned when applied to different
© 2010 Nature America, Inc. All rights reserved.
Articles
cellular systems and other types of datasets. Such developments
should facilitate the use of less stringent bioinformatics filtering
algorithms to follow up the positive hits.
Although most work on cell-cell protein transfer has focused
on immune cells, this process also occurs outside the immune
system, including between cancer and tumor-associated
stromal cells20. A recent study even describes cell-cell transfer,
via tunneling nanotubes 21 , of the disease-specific prion
­protein, PrPsc (ref. 22). And we have observed that T cells also
acquire small regulatory RNAs from target cells that function
in a transcellular mode to regulate target protein translation23.
Trans-SILAC could be used to explore whether conjugated cells
exchange, in addition to regulatory RNAs, multiple non-cellautonomous proteins that jointly may target specific cellular
pathways. Our DAVID-based analysis of the ‘final transfer set’
indeed revealed significant enrichment for the annotation term
‘MHC class II protein complex’ and the proteins in this family
had the highest trans-SILAC ratio as a group (trans-SILAC
ratio = 3.98, P = 0.0013 for the full DAVID analysis). Similar
transfer has been previously described to alter the effector
functions of adopting lymphocytes9,24,25.
We also applied trans-SILAC to study previously inaccessible
aspects of invading bacteria and host interactions. The approach
could be optimized to monitor intracellular bacterial protein
biosynthesis at higher resolution by fractionation to compare
bacterial protein synthesis rates in specific compartments inside
host cells. More detailed time-course experiments can be done
to track bacterial proteome reprogramming events at different
stages of infection.
As no specialized instruments are required, we predict that
trans-SILAC should become a routine method to study the
non-cell-autonomous proteome exchanged among cells of multicellular organisms or pathogens and infected host cells.
Methods
Methods and any associated references are available in the online
version of the paper at http://www.nature.com/naturemethods/.
Accession codes. Proteomics identifications database (PRIDE):
13639, 13640, 13641 and 13642.
Note: Supplementary information is available on the Nature Methods website.
Acknowledgments
O.R. was supported by a scholarship from the Clore Israel Foundation. M.K.
was supported by the Edmond J. Safra Program in Bioinformatics at Tel Aviv
University. Operating funds for this work came, in part, from the Prajs-Drimmer
Institute for the Development of Anti-degenerative Disease Drugs to Y.K., from
the Israel Cancer Association to I.G. and Y.K. and from a Canadian Institutes
of Health Research Operating grant (MOP-77688) to L.J.F. Mass spectrometry
infrastructure used in this project was supported by the Canadian Foundation
for Innovation, the British Columbia Knowledge Development Fund and the
British Columbia Proteomics Network. Y.F. is supported by a studentship from
the Genome Sciences and Technologies graduate program. Expression vectors
encoding for EGFP-tagged RALA and RALB proteins were a gift from A. Cox
(The University of North Carolina at Chapel Hill) and vectors for Arf4, Rab10 and
Rab11a were a gift from D. Cassel (Technion, Israel Institute of Technology).
AUTHOR CONTRIBUTIONS
O.R. jointly conceived the study with I.G., designed experiments, performed
experiments, analyzed data and wrote the paper; M.K. developed analytical tools
and analyzed data; Y.F. designed and performed experiments and analyzed data;
H.V. performed experiments and analyzed data; J.J.-H. analyzed data; L.J.F.
designed experiments, developed analytical tools, analyzed data and wrote
the paper; Y.K. and I.G. jointly supervised the project, designed experiments,
analyzed data and wrote the paper.
COMPETING FINANCIAL INTERESTS
The authors declare no competing financial interests.
Published online at http://www.nature.com/naturemethods/. Reprints and permissions information is available online at http://npg.nature.
com/reprintsandpermissions/.
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11. Daubeuf, S. et al. Preferential transfer of certain plasma membrane
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of host cell signaling pathways by bacterial pathogens. Int. J. Biochem.
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13. Vanherberghen, B. et al. Human and murine inhibitory natural killer cell
receptors transfer from natural killer cells to target cells. Proc. Natl. Acad.
Sci. USA 101, 16873–16878 (2004).
14. Ong, S.E. et al. Stable isotope labeling by amino acids in cell culture,
SILAC, as a simple and accurate approach to expression proteomics.
Mol. Cell. Proteomics 1, 376–386 (2002).
15. Ong, S.E., Foster, L.J. & Mann, M. Mass spectrometric-based approaches in
quantitative proteomics. Methods 29, 124–130 (2003).
16. Cox, J. & Mann, M. MaxQuant enables high peptide identification rates,
individualized p.p.b.-range mass accuracies and proteome-wide protein
quantification. Nat. Biotechnol. 26, 1367–1372 (2008).
17. Dennis, G., Jr. et al. DAVID: database for annotation, visualization, and
integrated discovery. Genome Biol. 4, 3 (2003).
18. Daubeuf, S., Puaux, A.L., Joly, E. & Hudrisier, D. A simple trogocytosisbased method to detect, quantify, characterize and purify antigen-specific
live lymphocytes by flow cytometry, via their capture of membrane
fragments from antigen-presenting cells. Nat. Protoc. 1, 2536–2542 (2006).
19. Hansen-Wester, I. & Hensel, M. Salmonella pathogenicity islands encoding
type III secretion systems. Microbes Infect. 3, 549–559 (2001).
20. Rafii, A. et al. Oncologic trogocytosis of an original stromal cells induces
chemoresistance of ovarian tumours. PLoS ONE 3, e3894 (2008).
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nature methods | VOL.7 NO.11 | NOVEMBER 2010 | 927
ONLINE METHODS
Subjects. This study was approved by the Institutional Ethics
Committee at the Chaim Sheba Medical Center. Signed written
informed consent was obtained from all subjects. All peripheral blood samples were obtained from healthy individuals who
donated blood.
© 2010 Nature America, Inc. All rights reserved.
Antibodies and reagents. Fluorochrome-conjugated monoclonal
antibodies to CD86, CD70, CD49d, CD43 and PDGFR were purchased from BD Biosciences, monoclonal antibodies to CD11a,
CD56, CD14 and CD58 from Beckman Coulter, and monoclonal
antibody to CD10 from Dako. The monoclonal antibody to HLAE was a gift from E. Gazit (Chaim Sheba Medical Center, Israel).
Isotopologs of arginine and lysine were purchased from Sigma.
Dialyzed serum and media lacking lysine and arginine were purchased from Bet-H’aemek.
Plasmids and transfections. HEK293 cells were transfected as
described elsewhere8. The expression vectors containing cDNA encoding for EGFP-tagged Ras proteins have been described elsewhere8.
Isolation of NK cells. Peripheral blood lymphocytes (PBLs) were
isolated by density-gradient centrifugation on Histopaque 1077
(Sigma), as previously described26. Primary CD56+ NK cells were
isolated from the PBLs by the use of anti-CD56 microbeads and the
MACS cell separation system (Miltenyi Biotec), as described8.
NK cell cultures. Cells were cultured in RPMI-1640 medium supplemented with 10% FBS (FBS), 2 mM L-glutamine, 100 U ml−1
penicillin and 100 μg ml−1 streptomycin (all from Gibco) and
maintained at 37 °C in a humidified 5% CO2 incubator. NK cells
were typically grown for 24–48 h before experiments in medium
supplemented with 100 international units (IU) of rhIL-2.
Cell lines. The human B lymphoblastoid cell line 721.221 (B721)
was obtained from the American Type Culture Collection.
B721 stable transfectants and HEK293 cells were grown as
previously described8.
SILAC labeling. B721 cells were cultured through at least seven
cell divisions in SILAC I media lacking normal isotopic abundance arginine and lysine but supplemented with 1.15 mM
[13C615N4]arginine and 0.274 mM [13C615N2]lysine (Sigma). The
medium also contained 10% dialyzed serum.
B721 and primary human CD56+ NK cells cultures. NK cells
(1 × 106 cells per tube) were grown for 1.5 h with B721 cells (0.5 ×
106 cells per tube) stably expressing EGFP in round-bottom tubes
cells in 1 ml of culture medium at 37 °C. The culture tubes were
centrifuged for 2 min at 200g to promote cell-conjugate formation before co-culturing. After co-culture the cells were treated
to disturb cell conjugates. Then single NK cells were sorted on a
FACSAria instrument (BD Biosciences).
FACS analysis and cell sorting. Cell samples were analyzed
on a FACSCalibur using Cellquest software or on a FACSAria
using FACSDiva software (BD Biosciences). FACS data were
analyzed using FlowJo 7.2.1 software (Tree Star Inc.). All cellsorting experiments were performed on FACSAria. Viable
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lymphocytes were identified by their distinct FSC and SSC
(including pulse width, height and area), propidium iodide
exclusion and expression of a distinct cell marker as indicated
(for example, CD56).
Formation of cell conjugates and analysis of cell-to-cell
transfer. To validate the cell-to-cell transfer of tagged proteins,
the various B721 or HEK293 transfectants were distributed into
U-bottom 96-well plates (30,000 cells per well in 100 ml) to which
we added NK cells (60,000 cells per well in 100 ml) to obtain an
effector to target ratio of 2:1. The culture plates were centrifuged
for 2 min at 200g to promote cell-conjugate formation and then
incubated for 1.5 h at 37 °C. The collected cells were resuspended
vigorously in 5 mM EDTA-PBS and kept on ice for 30 min to allow
cell conjugates to dissociate. Immunofluorescence staining with
anti-CD56 or CD45-allophycocyanin monoclonal antibodies, as
appropriate, was performed for 30 min at 4 °C. After labeling the
cells were washed and again resuspended in 5 mM PBS-EDTA.
Data collected from 10,000 single-cell events were then analyzed
by multiparametric FACS. Primary NK cells were distinguished
from target cells by their smaller size (as defined by their FSC/
SSC) and fluorescence (specific monoclonal antibody staining and
by not expressing EGFP, in the case of culture with B721-EGFP
cells). To exclude NK-target cell conjugates from the analysis
we used a very stringent state-of-the-art doublet discrimination
algorithm using fluorescence height versus area and fluorescence
width versus area pulse measurements to distinguish single NK
cells from NK-B721 conjugates, as previously described8,23.
This doublet discrimination model is considered very precise in
cells, such as lymphocytes, as they are rather homogenous and
spherical in shape.
LatB treatments. ‘Donor’ and ‘acceptor’ cells were pretreated for 1 h with 1 μM LatB before and during culture, as
previously described8.
Bacterial infection. S. enterica serovar Typhimurium strain
SL1344 auxotrophic for arginine and lysine biosynthesis
(ΔargH:ΔlysA) was grown to stationary phase in minimal medium
labeled with 175 mg l−1 [13C6]arginine and 300 mg l−1 [2H4]lysine.
Bacteria were collected at mid–log phase to infect HeLa cells at
a multiplicity of infection of 200 for 2 h. After a 30 min infection, cells were washed twice with PBS and were incubated with
50 μg ml−1 gentamicin for 1 h to kill extracellular bacteria. During
2 h infection, we collected aliquots of the cells every 20 min. The
first sample was collected at 0 min of infection. Similarly, aliquots
of log-phase bacteria culture were collected every 20 min for 2 h.
Lysates were tryptically digested in a solution containing 1%
sodium deoxycholate and 50 mM NH4HCO3. Five micrograms
of peptides were injected into an LTQ-OrbitrapXL and peak lists
were searched against a database containing all human protein
sequences in the International Proteome Index plus the sequences
of Salmonella strain SL1344. Peptides were quantified in MSQuant27 by comparing the peak intensity between the heavy form
and light from of tryptic peptides and proteins were quantified by
integrating peptide data. Bacteria intracellular protein synthesis
rates were determined by the percentage of newly synthesized
proteins as: percentage = L / (L + H) × 100%, in which L is the
abundance of light proteins and H represents the abundance of
doi:10.1038/nmeth.1513
© 2010 Nature America, Inc. All rights reserved.
heavy proteins). Because, by default, proteins were quantified
as a ratio of heavy to light, the percentage of synthesis equals
1/(1 + ‘SILAC ratio’). The s.d. values for protein SILAC ratios were
calculated from peptide ratios and s.d. for percentage of synthesis
was calculated as 1/(1 + ’SILAC ratio’ ± s.d.).
Mass spectrometry and proteomic data analysis. Sorted cell pellets were lysed in 1% deoxycholate, 50 mM NH4HCO3 (pH 8) and
digested in solution as described28. For each replicate of each condition, peptide digested from 100 μg protein was resolved by pI
using an OFFGEL system (Agilent). Each fraction collected from
the OFFGEL was then subjected to LC-MS/MS on a ­linear trapping
Fourier transform mass spectrometer (ThermoFisher Scientific)
exactly as described29. Proteins were identified by searching the
fragment spectra against the human IPI database (v3.47, 144,389
sequences) using MaxQuant16, allowing a 1% false discovery
rate at the ­protein level. Identifications were based on at least 2
­peptides unique by sequence as can be seen in Supplementary
Table 1. Out of the proteins identified, all proteins whose IPI
could be mapped to an Entrez gene ID were included in the analysis (2,418 out of 2,426). In the few cases in which different IPIs
were mapped to the same Entrez gene identifier, we left in only
proteins that were included in either the ‘high LatB’ or ‘transfer’
sets as described below. Absolute protein expression ratios were
calculated as described30 using 0.99 for the protein identification
probability (Pi value) for each hit and approximating the expected
number of unique tryptic peptides for a protein (Oi value) with
between 6 and 30 amino acids in each protein.
Extracting a confident set of transferring proteins. The ‘transfer set’ was built using the following six-step protocol. (i) First,
we considered only the proteins whose trans-SILAC ratio in the
experimental condition was very high (top 30%). (ii) From these
proteins, we considered only proteins that either had no transSILAC ratio in the control condition, or whose trans-SILAC ratio
was very low compared to the experimental condition (top 30%
difference between the two trans-SILAC ratios). (iii) We filtered
out the proteins that were annotated with GO functions that
were enriched in the control condition that were annotated with
the GO function ‘biosynthetic process’. (iv) We filtered out the
proteins that were annotated to localize to the cell compartment
‘endomembrane system’ or ‘organelle lumen’ and had a very
high trans-SILAC ratio in the control condition (top 15%).
(v) We filtered out the top 10% most abundant proteins according
to the spectral count. (vi) We filtered out proteins whose actindependent transfer was inconsistent among the different replicates performed.
The trans-SILAC ratio must be considered a measure of how
much of a given protein was transferred relative to the amount of
that protein in the receiving cell but since the endogenous levels
of any given protein are likely to be different between donor and
acceptor cells, the ratio does not represent an exact quantification
of the amount of transferred protein. This means that a ratio of
0.10 for a highly abundant protein such as actin would represent an enormous movement of protein from donor to acceptor,
whereas a ratio of 100 for a transcription factor may only represent a few molecules transferred per cell since that protein might
be present at very endogenous low levels in the acceptor. For this
reason, it is virtually impossible to apply the conventional rules
doi:10.1038/nmeth.1513
used in other proteomics experiments, such as a ratio cut-off or
significance versus a ‘control’ ratio. Despite this, trans-SILAC still
allowed us to use the measured ratios to extract a set of proteins
that have a high likelihood to transfer by following the heuristic
approach described in this section.
We also created a second set of proteins (‘high LatB set’), to
characterize the proteins that are very likely to transfer by an
actin-independent mechanism. This set served to highlight proteins identified in the experimental condition that might comprise the background (false positive detection) and moreover to
understand patterns common to this ‘biological noise’. It initially
contained all of the proteins that have a very high trans-SILAC
ratio in the control condition (top 15%), and subsequently underwent the same filtering steps (steps iii–v) as the ‘transfer’ set, to
enable spotting the differences between the two sets that are not
a direct outcome of the filtering method.
Below we describe and explain each filtering step and the analysis that accompanied it. The final sets, intermediate sets and results
of all the analyses performed are available in Supplementary
Table 3 and Supplementary Software.
Building the initial ‘transfer’ and ‘high LatB’ sets. For the
‘transfer’ set, we included all proteins that had a high trans-SILAC
ratio in the experimental condition (top 30%, trans-SILAC ratio
>0.18). For the ‘high LatB’ set, we included the proteins that had
a high trans-SILAC ratio in the LatB condition (top 15%, transSILAC ratio >0.2, Supplementary Table 3a).
Subtracting the proteins that transfer independently of actin.
We compared the trans-SILAC ratios of the two conditions and
identified within the ‘experimental’ condition 200 proteins that
had no detectable trans-SILAC ratio in the ‘LatB treatment’ condition (Supplementary Table 3b). These proteins were included
in our following sets for further analysis as they were considered as having a high likelihood to transfer by an actin-dependent mechanism. Moreover, 303 proteins that had trans-SILAC
ratios in both conditions were further analyzed to identify individual proteins that had, nevertheless, a high likelihood of actindependent transfer. We assessed these ‘overlapping’ proteins by
examining a scatter plot of the ‘LatB’ versus ‘transfer’ transSILAC ratios for all these 303 proteins (Supplementary Fig.
6). The Pearson correlation coefficient of the plot was 0.8156,
indicating overall a positive correlation between the two conditions (see also best-fitting regression line), namely between
the actin-dependent and independent modes of transfer. Three
possible models are consistent with this observation, as follows: (i) existence of a weaker transfer process that transfers
the same proteins in LatB-dependent and -independent manner; (ii) there is a tendency for abundant proteins to transfer
among lymphocytes in both LatB-dependent and -independent
processes; and (iii) abundant proteins create more false positive background ‘noise’ in our experimental system. At present
we could not distinguish between the above noted possibilities.
Therefore, to avoid with high likelihood inclusion of proteins
that transfer by actin-independent mechanisms, we included
annotated proteins with the top 30% highest difference in the
trans-SILAC ratios between the two conditions (difference >
0.117; Supplementary Fig. 6). This method revealed 91 different
proteins that were added to the list of 200 proteins detected only
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© 2010 Nature America, Inc. All rights reserved.
in the ‘experiment’ condition as described above thus yielding a
set of 291 proteins (Supplementary Table 3b).
We alternatively tried to neutralize the effect of protein abundances on transfer rate by selecting only proteins that were below
the linear regression line in the scatter plot with the top 30% distance from the line (distance > 0.03755; Supplementary Fig. 4).
This strategy yielded a very similar list (62 of 91 overlapping proteins) with similar enriched functions according to the DAVID
functional analysis.
Two rounds of analysis and filtering were preformed based on
protein set enrichment analysis using the DAVID Bioinformatics
Resources available online that use multiple heterogeneous functional gene annotation database sources17. First we examined
the ‘experimental’ set and discovered that many biological functions were significantly enriched (expression analysis systematic
explorer (EASE) score, P < 0.01; a modified Fisher exact test used
by DAVID) compared to the background that consisted of all
the proteins that appear in the raw mass spectrometry dataset
(Supplementary Table 3c). As mostly membrane-associated proteins have been previously described to transfer from cell-to-cell
by trogocytosis when an immunological synapse (IS) is formed5,
some of these enriched functions seemed highly relevant such
as the term ‘intrinsic to membrane’ (GO: 0031224, 47 proteins),
‘integral to membrane’ (GO: 0016021, 17 proteins), ‘plasma membrane part’ (GO: 0044459, 26 proteins) and ‘membrane-bound
organelle’ (GO: 0043227, 136 proteins). The full DAVID analysis
for this set is available in Supplementary Table 3c.
Next, based on these DAVID data, we analyzed each of these
enriched functions to determine whether sets of proteins possessing a similar function tend to have significantly high (P < 0.05)
average trans-SILAC values. Empirical P values were calculated for
each such set by generating 10,000 random lists of the same size.
In the control (LatB) this analysis revealed two enriched functions
containing only five proteins that have higher than background
trans-SILAC ratios: ‘pyridoxal phosphate binding’ (trans-SILAC
ratio = 10.28, P = 0.0005) and ‘vitamin binding’ (trans-SILAC
ratio = 8.35, P = 0.0015; Supplementary Table 3d).
This DAVID-based analysis of the ‘transfer’ set revealed significant enrichment for eight annotation source terms, all of which
refer to antigen presentation, membrane localization or receptor activities (Supplementary Table 3e). For example, ‘MHC
class II protein complex’ (trans-SILAC ratio = 3.98, P = 0.0013),
‘Integral to plasma membrane’ (trans-SILAC ratio = 1.23,
P = 0.0051), and ‘Transmembrane receptor activity’ (transSILAC ratio = 1.72, P = 0.01).
Filtering out proteins annotated with the function ‘biosynthetic
process’. Different functional networks that are known to be
highly abundant and over-represented in mass spectrometry data
in general were also very common in our datasets. To formally
test this possibility we checked for each GO function the median
of its spectral count in the experimental and control conditions
(Supplementary Fig. 7). Only GO functions that include more
than 150 proteins in each condition (to examine relatively general
functions) were included. The analysis revealed that the most
abundant terms are translation (GO: 0006412), macromolecule
biosynthetic process (GO: 0009059), cellular biosynthetic process (GO: 0044249) and biosynthetic process (GO: 0009058). All
these overrepresented functions are subterms of the ‘biosynthetic
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pro­cess’ GO term and are also enriched in the ‘high LatB’ set (EASE
score = 4 × 10−4). Therefore, proteins that belong to this annotation
source term were suspected as ‘background’ and filtered out. This
rather conservative filtering strategy was adopted to reduce the
risk of including ‘experimental noise’ (Supplementary Table 3f).
This and all subsequent filtering steps were applied also to the
‘High LatB’ set.
Filtering proteins by their localization to specific cell compartments. In another filtering step we used the GO annotation
database to examine the cellular localization of the proteins in
each of our sets. We wanted to filter out proteins that are located
in distinctive cellular compartments which host proteins that
were transferred by an actin-independent mechanism, namely,
compartments that are enriched in the ‘high LatB’ set. We thus
determined what percentage of proteins localized within each
compartment in each set (‘experimental’ and ‘high LatB’). The
results of the analysis suggested that two cell components contained many proteins with high trans-SILAC ratio only in the
‘high LatB’ set: ‘endomembrane system’ and ‘organelle lumen’.
Therefore we filtered out from the ‘transfer’ set 12 proteins that
localize into these cell components and had a high (>0.2) transSILAC ratio in the LatB condition (Supplementary Fig. 8 and
Supplementary Table 3g).
Filtering out the most abundant proteins. We filtered out
the top 10% most abundant proteins according to the spectral
count (top 10% out of all detected proteins, spectral count above
1.294; Supplementary Table 3h). This was done to reduce the
amount of false positives by minimizing the chance of including overrepresented proteins, picked up simply because of their
abundance.
Filtering out proteins that were inconsistent among the different replicates. Finally, a protein was filtered out from the final
transfer set if its trans-SILAC ratios in both duplicates of the
experimental condition were substantially lower than the overall
trans-SILAC ratio in that condition, or if the trans-SILAC ratio of
either triplicate of the LatB condition was substantially higher than
the overall trans-SILAC ratio in that condition (Supplementary
Table 3i). For this case, ‘substantially lower’ was defined to be
at least 50% lower than the original ratio. ‘Substantially higher’
was defined to be at least 50% higher, the only exception being
when the overall control ratio was zero, in which case substantially higher was defined to be of ratio at least 0.2, the cut-off used
for the ‘high LatB’ set.
Analysis of the final ‘transfer’ set. To evaluate the nature of the
proteins in the final ‘transfer’ set that remained after all the filtering (Supplementary Table 3j) we performed another round
of DAVID-based analysis (Supplementary Table 3k), which
revealed that 70% (120/172) of the transferring proteins interact
with membranes or intracellular membrane-bound organelles
(GO terms ‘intrinsic to membrane’ or ‘intracellular membranebound organelle’).
The dataset containing protein identifiers and corresponding values was uploaded into IPA. Each identifier was mapped
to its corresponding gene object in the Ingenuity knowledgebase. The proteins were overlaid onto a global molecular
doi:10.1038/nmeth.1513
network developed from information contained in the Ingenuity
knowledgebase. Networks of these focus genes were then algorithmically generated based on their connectivity (Fig. 4 and
Supplementary Table 5).
We also roughly assessed the abundance of B cell–derived
proteins (B values) in the NKs according to the spectral counts
and the trans-SILAC ratios. This was calculated by the equation
(r/(r + 1))s, where r is the trans-SILAC ratios and s is the spectral
count (Supplementary Table 3l). The equation is based on the
fact that the r is the ratio between the abundance of proteins in the
B and the NK cell, and s is roughly the sum of the abundances.
26. Goldstein, I. et al. alpha1beta1 Integrin+ and regulatory Foxp3+ T cells
constitute two functionally distinct human CD4+ T cell subsets oppositely
modulated by TNFalpha blockade. J. Immunol. 178, 201–210 (2007).
27. Mortensen, P. et al. MSQuant, an open source platform for mass
spectrometry-based quantitative proteomics. J. Proteome Res. 9, 393–403
(2009).
28. Rogers, L.D. & Foster, L.J. The dynamic phagosomal proteome and the
contribution of the endoplasmic reticulum. Proc. Natl. Acad. Sci. USA 104,
18520–18525 (2007).
29. Chan, Q.W., Howes, C.G. & Foster, L.J. Quantitative comparison of caste
differences in honeybee hemolymph. Mol. Cell. Proteomics 5, 2252–2262
(2006).
30. Lu, P., Vogel, C., Wang, R., Yao, X. & Marcotte, E.M. Absolute protein
expression profiling estimates the relative contributions of transcriptional
and translational regulation. Nat. Biotechnol. 25, 117–124 (2007).
© 2010 Nature America, Inc. All rights reserved.
Statistical analysis. P values were calculated by either the nonparametric Wilcoxon rank-sum test or empirically by generating
a large random population as appropriate. A P-value of 0.05 or
less was considered significant.
Software. The in-house Perl and R scripts used to process the
datasets and a readme file with detailed instructions for sorting
out the positive hits are available as Supplementary Software.
doi:10.1038/nmeth.1513
nature methods