www.sciencemag.org/cgi/content/340/6131/475/DC1

Supplementary Materials for

Direct Proteomic Quantification of the Secretome of Activated Immune Cells

Felix Meissner, Richard A. Scheltema, Hans-Joachim Mollenkopf, Matthias Mann

*Corresponding author. E-mail: mmann@biochem.mpg.de

Published 26 April 2013, Science 340, 475 (2013)

DOI: 10.1126/science.1232578

This PDF file includes:

Materials and Methods Supplementary Text Figs. S1 to S9 Table S1 References

Other Supplementary Material for this manuscript includes the following: (available at www.sciencemag.org/cgi/content/full/340/6131/475/DC1)

Database S1 as Excel file

2

Materials and Methods Mice

MyD88-/- (11) and TRIF-/- (12) mice were kindly provided by Arturo Zychlinsky

(MPI of Infection Biology, Berlin). MyD88/TRIF-/- mice were obtained from H. Wagner

(TU, Munich). All mice were housed in the animal facility of the Max Planck Institute of

Biochemistry, Munich. Animal experiments were approved by the Regierung of

Oberbayern.

Tissue culture

Bone marrow was collected from femur and tibia of age and gender matched mice of

the indicated genotypes. Bone marrow cells were plated on sterile petridishes and

incubated in RPMI containing heat-inactivated FCS (10%) and equine serum (5%), 10

mM HEPES, 1 mM pyruvate, 10 mM L-glutamine, and 20% M-CSF–conditioned

medium at 37 °C and 7% CO2. M-CSF–conditioned medium was collected from an L929

M-CSF cell line. After 3 days medium was added and bone marrow derived macrophages

were harvested after 6 days and replated in tissue-culture treated dishes. Before the

experiment, cells were washed once with serum-free RPMI without phenolred or treated

as indicated. Cells were stimulated with 200 ng/mL LPS (Salmonella minessota,

ultrapure, Invivogen) in serum-free RPMI without phenolred or left untreated. At 1, 2, 4,

8 or 16 hours, cell supernatants were collected, passed through a 0.22 µm filter to remove

detached cells and immediately frozen in liquid nitrogen. Cells from the same wells were

lysed in Trizol for RNA preparations. Experiments were performed at least in biological

triplicates.

Secretome digestion

Cell supernatants were denatured with 2M urea in 10 mM HEPES pH 8.0 by

ultrasonication on ice. Proteins were reduced with 10 mM dithiotreitol for 40 min

followed by alkylation with 55 mM iodoacetamide for 40 min in the dark. Iodoacetamide

was quenched with 100 mM thiourea. Proteins were digested with 0.5 µg LysC (Wako)

for 3 h and digested with 0.5 µg trypsin (Promega) for 16 h at room temperature. The

digestion was stopped with 0.5 % trifluoracetic acid, 2 % acetonitril. Peptides were

desalted on reversed phase C18 StageTips. Peptides were eluted using 20 µl of 60%

acetonitrile in 0.5% acetic acid. The volume was reduced in a SpeedVac and the peptides

were acidified with 2% acetonitrile, 0.1% trifluoroacetic acid in 0.1% formic acid.

LC MS/MS analysis

A nanoflow UHPLC instrument (Easy nLC, Thermo Fisher Scientific) was coupled

on-line to a Q Exactive mass spectrometer (Thermo Fisher Scientific) with a

nanoelectrospray ion source (Thermo Fisher Scientific) (8). Chromatography columns

were packed in-house with ReproSil-Pur C18-AQ 1.8 µm resin (Dr. Maisch GmbH) in

MeOH. Peptides from the supernatants of 150.000 cells were loaded onto a C18-reversed

phase column (20 cm long, 75 µm inner diameter) and separated with a linear gradient of

5–60% buffer B (80% acetonitrile in 0.1% formic acid) at a flow rate of 250 nL/min over

170 min. Chromatography and column oven (Sonation GmbH) temperature were

3

controlled and monitored in real-time using SprayQC (25). MS data were acquired using

a data-dependent Top10 method dynamically choosing the most abundant precursor ions

from the survey scan (300–1650 Th) using HCD fragmentation. Survey scans were

acquired at a resolution of 70,000 at m/z 400. Unassigned precursor ion charge states as

well as singly charged species were rejected and peptide match was disabled. The

isolation window was set to 3 Th and fragmented with a normalized collision energies of

25. The maximum ion injection times for the survey scan and the MS/MS scans were 20

ms and 60 ms respectively and the ion target values were set to 3E6 and 1e6,

respectively. Selected sequenced ions were dynamically excluded for 30 seconds. Data

were acquired using Xcalibur software.

Bioinformatic Analysis

Mass spectra were analyzed using MaxQuant software version 1.2.6.1 using the

Andromeda search engine (9, 26). The initial maximum allowed mass deviation was set

to 6 ppm for monoisotopic precursor ions and 0.5 Da for MS/MS peaks. Enzyme

specificity was set to trypsin, defined as C-terminal to arginine and lysine excluding

proline, and a maximum of two missed cleavages were allowed.

Carbamidomethylcysteine was set as a fixed modification, N-terminal acetylation and

methionine oxidation as variable modifications. A time-dependent mass recalibration

algorithm was used to improve the mass accuracy of precursor ions. The spectra were

searched by the Andromeda search engine against the mouse Uniprot sequence database

combined with 248 common contaminants and concatenated with the reversed versions

of all sequences. Protein identification required at least one unique or razor peptide per

protein group. Quantification in MaxQuant was performed using the built in XIC-based

label free quantification (LFQ) algorithm (10) using fast LFQ. The required false positive

rate was set to 1% at the peptide and 1% at the protein level, and the minimum required

peptide length was set to 6 amino acids. Contaminants, reverse identification and proteins

only identified by site were excluded from further data analysis.

For each LPS treated sample of a given genotype and time point, ratios were

calculated from the individual protein LFQ intensities and the corresponding median

LFQ intensities of the untreated sample. Missing values were imputed only for untreated

samples by random sampling from a generated narrow normal distribution around the

detection limit for proteins. Conceptually, ratios are only generated in case a protein was

quantified upon LPS treatment; proteins without quantification after LPS stimulation had

no ratios and were indicated as missing values, respectively. The calculated ratios were

log10 normalized and from the 4976 identified proteins 4917 were quantified. In order to

retrieve those proteins with a reproducible dynamic (e.g. upregulation over time) we

employed a permutation-based FDR controlled filter based on the Kendall W statistic

(27). Shortly, the replicates were used to generate a p-value indicating their

reproducibility for each condition, for which the FDR was calculated by random

permutation of the ratios prior to calculation of the p-value. The p-value cut-off was

calculated at 2% FDR. To increase sensitivity, this approach was applied for each

treatment individually. Note that this does not capture proteins with no dynamic (i.e.

stable over time), which for this study were of no interest. From this analysis 1557

proteins were selected, which grouped by unsupervised clustering into 775 upregulated

and 782 downregulated. Principal component analysis (PCA) was performed on the 1557

4

regulated proteins, and missing values were replaced by 0, assuming that proteins that

were not quantified at a certain time point or genotype are not released. For statistical

analysis of signaling adaptor dominance [TRIF-ko – MyD88-ko], contribution [WT –

Myd88-ko and WT – TRIF-ko] and synergy [WT – (MyD88-ko + TRIF-ko)], we

performed 2 sample t-tests on the 1557 regulated proteins for each time point, using a

permutation-based FDR at 5% and S0=1 (21) (Fig. S8B to D). We tested multiplicative

and additive signaling models to describe adaptor interplay and both showed a time

dependent increase of synergy and redundancy, however increase of redundancy was

more pronounced in the former model (Figs. 4B & S8F). Proteins required to be

significantly regulated in at least 2 time points in each individual test, to be included for

further analysis. To rank significant regulations for adaptor dominance, adaptor

contributions or adaptor interplay, we used the maximal regulation from the median of

replicates of all time points (Figs. 3A and 4D). Presentation of time-dependent adaptor

contribution and interplay was performed on the median of replicates without imputations

(Figs. 3E, 4A-C, S5C to E).

RNA isolation, quantification and quality control

Total RNA was isolated by the TRIzol Reagent RNA preparation method

(Invitrogen, Karlsruhe, Germany) using Glycogen as carrier. Briefly, ~2e7cells were

resuspended in 1 ml TRIzol, shock frozen and stored at –80°C. Cells in Trizol were

thawed and further processed for total RNA isolation as described by the manufacturer.

The amount of RNA was determined by OD260/280 nm measurement using a NanoDrop

1000 spectrophotometer (Kisker, Steinfurt, Germany). The RNA size, integrity and the

amount of total RNA was measured with a Bioanalyzer 2100 (Agilent Technologies,

Waldbronn, Germany) using a RNA Nano 6000 microfluidics kit.

Microarray analysis

Microarray experiments were performed as dual-color hybridizations. RNA labeling

was accomplished with the two color Quick Amp Labeling Kit (Agilent Technologies).

In brief, mRNA was reverse transcribed and amplified using an oligo-dT-T7-promotor

primer. The second strand was amplified with T7 RNA Polymerase and labeled either

with Cyanine 3-CTP or Cyanine 5-CTP. After purification and quantification of the dye

incorporation, labeled aRNA samples were hybridized to 4x44K catalog whole mouse

genome microarrays V2 (Agilent-026655) according to the supplier’s protocol (Agilent

Technologies). Scanning of microarrays was performed with 5 µm resolution and

extended mode using a high resolution microarray laser scanner (G2505, Agilent

Technologies). Raw microarray image data were extracted and analyzed with the Image

Analysis / Feature Extraction software G2567AA (Version A.10.10.1.1, Agilent

Technologies). The extracted MAGE-ML files were further analyzed with the Rosetta

Resolver Biosoftware, Build 7.2.2.0 SP1.31 (Rosetta Biosoftware, Seattle, USA). For

genes which are covered with multiple probes the median ratio was calculated and used

for comparison with the proteome data. Correlation analysis was performed on genes

with significant and reproducible induction of protein secretion.

The data presented in this publication have been deposited in NCBIs Gene

Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE41490).

5

ELISA, Immunoblot and RT-PCR

ELISA Kits and primary antibodies were purchased from R&D or Santa Cruz

Biotechnology. ELISAs were performed according to the manufacturer’s instructions.

For western blot analysis, equivalent volumes of cell supernatants were precipitated with

aceton, resuspended and boiled 5 min with SDS sample buffer under reducing conditions,

resolved by SDS-PAGE and transferred to nitrocellulose membranes by electroblotting.

Membranes were probed for 4h at 4°C with the indicated antibodies and analyzed by

immunoblot. RNA was reversely transcribed using First Strand cDNA synthesis kit

(Thermo). Quantitative RT-PCR of cDNA was performed using the following primers:

Tpp1_f: GAGTCTCACTTTTGCGCTGAA ,

Tpp1_r: CTCCAGGGTTAGGTACTTTCCA;

Lgmn_f: TGGACGATCCCGAGGATGG, Lgmn_r: GTGGATGATCTGGTAGGCGT;

Ctsa_f: CCCTCTTTCCGGCAATACTCC, Ctsa_r: CGGGGCTGTTCTTTGGGTC;

Fuca1_f: CCAAGTTCGGGGTGTTCGT, Fuca1_r: GGGCGGGTAGTTTTCTGTCA;

Gapdh_f: TCACCACCATGGAGAAGGC, Gapdh_r: GCTAAGCAGTTGGTGGTGCA

SILAC labeling of BMMs

Bone marrow cells were isolated as described and enriched for hematopoietic stem

and progenitor cells using CD117 MicroBeads (Miltenyi) following the manufacturer’s

instructions. CD117+ cells were plated on sterile petridishes in SILAC RPMI containing

10% dialyzed and heat inactivated FCS, 10 mM HEPES, 1 mM pyruvate, 10 mM L-

glutamine, 50 µM β-mercaptoethanol, 10 ng/mL IL-3, 20 ng/mL M-CSF (Miltenyi), 0.2

mmol/L heavy Arginine, HCl U-13C6 U-15N4 and 0.32 mmol/L heavy Lysine, 2HCl U-

13C U-15N (Cambridge Isotope Labs) at 37 °C and 7% CO2. After 3 days, medium

(without IL-3) was added and bone marrow derived macrophages were harvested after 6

days and replated in tissue-culture treated dishes. For the experiment, cells were washed

once with serum-free RPMI without phenolred and incubated for 2h with light

supernatants from cells either left untreated or stimulated with LPS for 16h as described.

Metabolic incorporation rates of heavy amino acids into cellular proteins were > 95%.

Supplementary Text From the 1557 proteins which passed the Kendall W reproducibility filtering, 775

were induced and 782 repressed upon LPS stimulation (Fig. S4, A and B). To determine

significant signaling adaptor dependent regulations (dominance, contribution and

synergy) we applied t-tests on all 1557 proteins. Significantly induced proteins are

presented in Figs. 2 to 4 and Figs. S6 and S8. We analysed and plotted significantly

repressed proteins analogously to determine whether signaling adaptor specific

6

regulations can be extracted from this class of proteins. Our data indicate, that repressed

proteins do not show an adaptor specific dominance (Fig. S5 A and B). The signaling

adaptors also do not contribute differentially to the secretion by signal transduction

through both adaptors (Fig. S5 C and D). We further did not find any evidence for

signaling adaptor interplay, indicating that synergistic or redundant mechanisms do not

control repressed protein release (Fig. S5E). While proteins that are released upon LPS

treatment are expectedly annotated to localize predominantly to extracellular, membrane

or vesicular compartments and are de-enriched in nuclear origin, proteins that are

repressed upon TLR4 activation derive from diametrically opposed subcellular

localizations (Fig. S4C). Also glycosylation, signal peptide and membrane proportions of

this class of proteins are underrepresented and quite distinct from induced secretory

proteins (Fig. S5F and G). Taken together, these data suggest that the repressed protein

content is less likely to fulfill an extracellular function and may derive from protein

release in the absence of an activating signal (31, 32). In contrast to cells stimulated with

LPS, untreated cells show marginal signs of loss in cell membrane integrity of ~3% (in

contrast to <2% in LPS treated cells) and may be the origin of protein release in untreated

cells (Figs. S1C and S5H). Notably, there is no indication for a differential leakage of

cytoplasm to the supernatant in treated and untreated samples, arguing against overt cell

death and for the sensitivity of our technology (Fig. S2, A and B).

Supplementary References 25. R. A. Scheltema, M. Mann, SprayQc: A Real-Time LC-MS/MS Quality

Monitoring System To Maximize Uptime Using Off the Shelf Components. Journal of

proteome research, (May 11, 2012).

26. J. Cox et al., Andromeda: a peptide search engine integrated into the MaxQuant

environment. Journal of proteome research 10, 1794 (Apr 1, 2011).

27. A. Jankevics et al., Metabolomic analysis of a synthetic metabolic switch in

Streptomyces coelicolor A3(2). Proteomics 11, 4622 (Dec, 2011).

28. E. Lombardo, A. Alvarez-Barrientos, B. Maroto, L. Bosca, U. G. Knaus, TLR4-

mediated survival of macrophages is MyD88 dependent and requires TNF-alpha

autocrine signalling. Journal of immunology 178, 3731 (Mar 15, 2007).

29. Y. Ma, V. Temkin, H. Liu, R. M. Pope, NF-kappaB protects macrophages from

lipopolysaccharide-induced cell death: the role of caspase 8 and receptor-interacting

protein. The Journal of biological chemistry 280, 41827 (Dec 23, 2005).

7

Fig. S1. Experimental conditions for TLR4 stimulation induce secretion of known MyD88 and TRIF dependent cytokines and do not induce cell death.

Macrophages were stimulated with 200 ng/mL LPS or left untreated for the

indicated time points. Experiments were performed in the presence of the indicated

concentrations of serum or in serum-free medium after the indicated number of washes.

(A) Concentrations of the depicted cytokines in the supernatants were determined by

ELISA. (B) Relative cytokine release plotted in percent of induction in relation to 1%

serum. (C) Cell death was determined by loss of membrane integrity with the membrane

impermeable vital dye trypan blue. Experimental condition used for proteomic analysis is

indicated in yellow. Data are plotted as mean ± s.e.m of triplicate wells.

8

Fig. S2. Label-free Quantification (LFQ) intensities to evaluate released protein contents and as a ratiometric measure of relative protein abundances in cell supernatants.

9

WT, MyD88-deficient, TRIF-deficient and MyD88/TRIF-deficient macrophages

were stimulated with 200 ng/mL LPS or left untreated for the indicated time points. LFQ

intensities of (A) untreated and (B) LPS treated samples of the indicated genotypes. LFQ

intensities of all released proteins (upper panel), cytoplasmic proteins defined by gene

ontology of cellular component (GOCC slim) (upper middle panel), quantified annotated

cytokines (lower middle panel) and proteins derived from FBS (lower panel). Box and

whiskers are plotted as median with 10-90% percentile, dots represent the median. (C)

Heatmap of the average correlation of LFQ ratios of regulated secretory proteins for

experimental replicates and between all experimental conditions. (D,E) Proteins from

supernatants were quantified with MS and ELISA. For the indicated proteins, total

protein amounts determined by (D) ELISA were compared to (E) LFQ ratios. (F)

Correlation of values retrieved by MS and ELISA among genotypes and time points. (G)

Number of proteins with the indicated median fold release upon LPS treatment.

10

Fig. S3. Protein features based on sequence annotations of the identified peptides.

(A) Induced secretory proteins with annotated glycosylation sites. (B) Induced

secretory proteins with annotated signal peptide and/or transmembrane regions.

Differentiation of potential extracellular/intramembrane cleavages or membrane shedding

of the entire protein based on the identification of cytoplasmic peptides. (C) Kinetics of

proteolytic cleavage versus membrane shedding illustrated by the appearance of the

indicated peptide numbers in the supernatant form LPS stimulated and untreated cells.

Peptides detected in the double KOs were excluded from the analysis. (D) SILAC labeled

heavy wild-type BMMs were incubated with supernatants from LPS stimulated or

untreated light BMMs of the indicated genotypes. Occurence of heavy peptides from

transmembrane proteins in the resulting supernatants were analysed according to their

subcellular localization as indicated and plotted in percent LPS stimulated versus

untreated heavy peptides.

11

Fig. S4. Comparison of induced and repressed protein secretion.

(A) Number of induced and repressed proteins. (B) Magnitude of protein release

(upper panel: secretion induced upon LPS treatment; lower panel: secretion repressed

upon LPS treatment, plotted as median with 5-95% percentile. (C) Gene ontology

analysis of induced and repressed secretory proteins for their enrichment to cellular

component (GOCC slim) using Fisher’s exact test with a Benjamini-Hochberg FDR of

2%.

12

Fig. S5. Repressed protein secretion is not regulated by signaling adaptor dominance, contribution or interplay.

(A) Adaptor dominance ranked as maximal difference between MyD88 and TRIF

mediated repression of secretion. (B) Heatmap of differentially regulated proteins. (C)

Contribution of MyD88 and TRIF to the repressed secretory response induced by both

adaptors. Number of proteins plotted versus the strength of the contribution for the

indicated genotype. (D) Contribution of MyD88 and TRIF to the repressed secretory

output illustrated for redundant and synergistic adaptor interplay. (E) Frequency

distribution of redundantly and synergistically repressed regulation over time. Center of

the bins are indicated. (F) Repressed proteins with annotated glycosylation sites. (G)

Repressed proteins with annotated signal peptide and transmembrane regions.

Differentiation of potentially cleaved or shedded proteins based on the identification of

cytoplasmic peptides. (H) Cell death in the absence of LPS stimulation was determined

by loss of membrane integrity with the membrane impermeable vital dye trypan blue.

Data are plotted as mean ± s.e.m of triplicate wells

13

Fig. S6. Comparison of secretome and transcriptome.

(A) Histogram of the dynamic range of the secreted proteome versus transcriptome.

Center of the bins are indicated. (B-D) Heatmaps of (B,C) correlated and (D) anti-

correlated secreted proteins and transcripts.

14

Fig. S7. Confirmation of transcriptionally independent release of lysosomal proteins.

(A-C) Transcriptional regulation of the indicated lysosomal cargo proteins was

analyzed by RT-PCR. Ct values of the indicated genes in comparison to Gapdh from (A)

untreated and (B) LPS stimulated cells. (C) Transcriptional regulation of the indicated

genes plotted as ratio of treated to untreated. (D,E) Western blot analysis of the indicated

proteins in cell supernatants. (F,G) Total amounts of the indicated proteins in

supernatants of (F) untreated cells (G) LPS stimulated and were quantified by ELISA.

Values are mean ± s.e.m. *P < 0.05, two-tailed Student's t-test of 4h versus 16h time

points.

15

Fig. S8. Signaling adaptor contributions and interplay promotes synergy and redundancy.

16

(A) Terminology of regulations, transformations of logged data and applied t-test.

(B-D) Two sample t-tests for (B) dominance, (C) contribution and (D) synergy.

Significantly regulated proteins are indicated in black. (E) Heatmap of proteins with

significant contribution of TRIF or MyD88 to the secretion induced by both adaptors. (F)

Number of proteins with redundant and synergistic regulation over time calculated as

(WT-log10(10MyD88-ko

+10TRIF-ko

). Center of the bins are indicated. (G) Redundant protein

secretion of proteins with a maximal redundant regulation >1.5, upper panel: median with

interquartile range of synergistic proteins, lower panel: heatmap of regulated proteins.

*Theoretic adaptor interplay, not observed in this study.

17

Fig. S9. Confirmation of adaptor specific secretory regulations by ELISA.

(A,B) The absolute amounts of the indicated proteins in the supernatants of (A)

untreated and (B) LPS stimulated cells quantified by ELISA. Data are plotted as mean ±

s.e.m of triplicates.

18

Table S1. Annotated cytokines released upon TLR4 activation.

Fold increase of cytokine release for the indicated genotypes and time points. Data is

presented as mean and standard error of the mean.

19

Caption for database S1. Secreted proteins and corresponding transcribed mRNAs.

Fold increase of the indicated proteins and mRNAs as mean and s.e.m.; values as

log10 regulations. Colored headers are indicative as follows: blue - secreted proteins,

green - corresponding transcripts, purple - proteins with the indicated regulations as

defined in the text, amber - number of identified peptides, grey - annotations of identified

peptides.

20

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