Supplementary Notes. 2. Nine cell surface markers, CD33, CD20, CD3, CD4, CD7, CD123, CD14, IgM, and HLA-DR,

Transcription

1 Supplementary Notes Supplementary Note 1: Mass-tag cellular multiplexing workflow First, the cellular state is preserved by formaldehyde cross-linking. Second, the cells are permeabilized with methanol, making intracellular thiol groups and epitopes accessible. Third, the cells are washed with phosphate buffered saline (PBS), and then labeled with a unique combination of lanthanide isotope masses to barcode each sample. Fourth, the cells are washed to remove unreacted mdota-lanthanide(iii) reagent and then pooled into a single tube for antibody staining and mass cytometry measurements. Supplementary Note 2: Time course analysis of PBMC signaling SPADE To identify PBMC populations and their signaling response, Spanning-tree Progression Analysis of Density-normalized Events (SPADE) 1, 2 was applied to the generated time course dataset. SPADE is a computational tool that hierarchically clusters high-dimensional single-cell data, and connects these clusters of cells by a minimum spanning tree for two-dimensional visualization 1, 2. Nine cell surface markers, CD33, CD20, CD3, CD4, CD7, CD123, CD14, IgM, and HLA-DR, were used to generate cell clusters from the time course dataset; minimum spanning trees colored according to the expression level of each marker are shown in Fig. 2b. In combination, the cell surface marker expression levels of these trees were used to define 14 immune cell populations within the PBMC cellular hierarchy (Fig. 2c). For example, the middle branch displayed high CD20 expression (Fig. 2b), specifying B cell identity, but only a subset of this branch shows IgM expression, allowing separation of naïve (IgM + ) and memory (IgM - ) B cell subsets (Fig. 2b-c). In addition to cell surface markers, the SPADE-generated minimum spanning tree can also be used to display the levels of the 14 measured signaling molecules over the entire 4-hour stimulation time course, revealing subpopulation-specific signaling states and the signaling network dynamics of each cell type and subpopulation (Fig. 2d-e; Supplementary Results 1). Supplementary Note 3: Time course analysis of PBMC signaling analysis of feedback regulation The quantitative measurement of signaling network dynamics can be exploited to identify timedependent phenomena such as feedback regulation. PMA/ionomycin stimulates protein kinase C (PKC) and other calcium-related signaling pathways, which in T cells activate several downstream arms of the T cell receptor (TCR) signaling network (Supplementary Fig. 6), including phosphorylation of ERK, p38, NF B, ZAP70, and S6. Among this group, S6 responded most rapidly to PMA/ionomycin stimulation with a 3-fold increase in phosphorylation after one minute. Levels of phosphorylated ERK, p38 and ZAP70 rose after 5 minutes, and 1

2 NF B showed induction after 15 minutes (Supplementary Fig. 7). Conversely, phosphorylation of AKT, PLC 2 and SLP76, all membrane-proximal proteins upon activation, decreased after 1 min (AKT) or 5 min (PLC 2, SLP76), indicating rapid negative feedback regulation of the TCR signaling pathway coinciding with the initial network induction via PKC (Supplementary Fig. 7). A Ca 2+ signaling dependent feedback loop inhibiting PLC 2 via sequestration by the nonphosphatase Sprouty was described recently in T cells 3 and might explain the observed changes by making the phosphorylation epitope inaccessible for the antibody. PLC 2 also interacts with the PKC-activated phosphatase SHP2 4, which alternatively might dephosphorylate and inactivate PLC 2 5, 6. A similar mechanism may also apply to SLP76, which is targeted by the PKC-dependent SHP1 phosphatase 7. AKT phosphorylation is likely down-regulated after PKC stimulation by an S6K-mediated feedback loop similar to that operating on IRS-1 8, 9. Supplementary Note 4 Jak2 Inhibitor III selectivity IL3 or GM-CSF-induced, JAK2-dependent STAT5 phosphorylation in monocytes was not inhibited by JAK2 inhibitor III (Supplementary Fig. 16a; Supplementary Fig. 16b, columns 4 and 7/rows 4 and 8, blue boxes), indicating a lack of JAK2 inhibition. The inhibitor s impact on IFN- stimulated STAT1 phosphorylation in B cells and monocytes (Supplementary Fig. 16a; Supplementary Fig. 16b, columns 13 and 14/row 6, red boxes / columns 4 and 7/row 6, red boxes) revealed little effect, indicating a lack of efficacy against both JAK1 and JAK2. Furthermore only a slight inhibition of phosphorylation on STAT5 in IL-2 stimulated T cells was observed (Supplementary Fig. 16a; Supplementary Fig. 16b, columns 10 and 11/row 7, green box), excluding JAK3 and JAK1 as the prime inhibitor targets. However, when TYK2 is recruited to the receptor by IFN- or G-CSF, inhibition of phosphorylation on STAT5 and STAT3 in T cells and CD14 + HLA-DR mid, and CD14 - HLA-DR mid monocytes (Supplementary Fig. 16a; Supplementary Fig. 16b, column 10 and 11/row 5, grey box / columns 4 and 7/rows 3 and 5, yellow boxes) was observed. Supplementary Note 5 Jak3 Inhibitor VI selectivity Here no effect on JAK2-dependent STAT5 phosphorylation levels in monocytes activated by GM-CSF or IL-3 (Supplementary Fig. 17a; Supplementary Fig. 17b, columns 4 and 7/row 4, yellow boxes / columns 4 and 7/row 8, red boxes) or IL-3, thereby excluding JAK2 as the primary target. In contrast, in the presence of JAK3 inhibitor VI, IL2-activated STAT5 phosphorylation was inhibited in T cells, NK cells CD14 - surface neg. cells (Supplementary Fig. 17a; Supplementary Fig. 17b, columns 10 and 11/row 7, blue boxes / column 12/row 7, blue box / columns 1 and 2/row 7, blue box), suggesting inhibition of JAK1, JAK3, or both. To test whether the inhibitor was indeed specific for JAK3 and to exclude a contribution to signaling by 2

3 JAK1, IFN- stimulated cells were further analyzed. Here, JAK3 inhibitor VI inhibited phosphorylation of STAT1 in B cells (Supplementary Fig. 17a; Supplementary Fig. 17b, columns 13 and 14/row 6, green box) and of STAT1 and STAT5 in monocytes (Supplementary Fig. 17a; Supplementary Fig. 17b, columns 4 and 7/row 6, green boxes). These phosphorylations were induced by JAK1 and JAK2, but because JAK2 was excluded as a target the observed inhibitory effects must be due to JAK1 inhibition. JAK1/TYK2 phosphorylation of STAT1, STAT3, and STAT5 after IFN- stimulation was also inhibited in various cell types (Supplementary Fig. 17a; Supplementary Fig. 17b, row 5, grey box), further evidencing a lack of pure JAK3 specificity. Comparison with the inhibitors in vitro profile shows that this compound is 4-fold more potent against JAK3 than TYK2 and ~12-fold more compared to JAK1 and JAK2. While the inhibition of TYK2 agrees with our analysis, the low inhibition of JAK1 does not agree with our findings. Supplementary Fig. 17b shows that phosphorylation of other signaling molecules was also inhibited (e.g., SYK, PLC 2; Supplementary Fig. 17b, rows 1 and 3). These data indicate that the JAK3 inhibitor VI also inhibits JAK1/TYK2 and kinases outside the JAK-STAT signaling pathway, in agreement with the broad in vitro inhibition profile observed by Anastassiadis et all 10. Supplementary Note 6 Potent inhibition by Go-6983 (CAS # ) 11, a broad spectrum PKC inhibitor 10, of phosphorylation on S6 in most cell types, including B cells, after PKC activation with PMA/ionomycin was visible (Fig. 5b, column 7/row 11, grey box; Supplementary Fig. 18; Supplementary Fig. 19, column 7, green box; Supplementary Results 6). The mtor inhibitor rapamycin and the PI3K inhibitor GDC-0941 (CAS # ) 12, 13 lacked inhibition on the same phosphorylation site, if B cells were stimulated with PMA/ionomycin (Fig. 5b, red boxes; Supplementary Fig. 19, column 17/rows 13 and 14, blue box; Supplementary Fig. 19, column 6/rows 13 and 14, red box; Supplementary Fig. 20), which is in agreement with a model, in which PKC contributes to phosphorylation on S6 via the RAF-ERK-p90RSK signaling branch, independently of the PI3K-AKT-mTOR-p70S6K signaling pathway (Supplementary Fig. 6). After BCR/FcR-XL, rapamycin (Fig. 5b, column 17/row 2, pink box) and GDC-0941 (Fig. 5b, column 6/row 2, blue box) both inhibit S6 phosphorylation in B cells (Supplementary Fig. 20), achieving a 50% and 80% inhibition, respectively, again in agreement with the current model in which rapamycin only inhibits S6 phosphorylation via mtor-p70s6k, but GDC-0941 inhibits the PKC- RAF-ERK-p90RSK pathway in addition to PI3K-AKT-mTOR-p70S6K, therefore achieving greater percent inhibition of S6 ps235/6 phosphorylation (Supplementary Fig. 6). On the contrary, Go-6983 nearly completely abolished phosphorylation of S6 protein after BCR/FcR-XL in B cells (Fig. 5b, column 7/row 2, green box; Supplementary Fig. 18), although the current 3

4 model only assumes a partial contribution of PKC in parallel to the PI3K-AKT-mTOR-p70S6K pathway (Supplementary Fig. 6), indicating additional targets besides PKC. Indeed, recent in vitro data suggest 10, that Go-6983 also potently inhibits p70s6k and p90rsk, both kinases known to phosphorylate ps235/6 on S6, giving a potential explanation for this observation. Supplementary Note 7 - Cell type selectivity Imatinib 14, 15, an inhibitor of the constitutively active tyrosine kinase BCR-ABL used to treat patients with CML 14, 15, affected in CD14 + HLA-DR mid monocytes compared to other cell types consistently phosphorylation of SHP2, SYK, and PLC 2 under most conditions (Supplementary Fig. 23, column 7, green box). This might be due to inhibition of Lyn kinase by imatinib which was shown in vitro 10, 13, and suggesting why imatinib inhibits normal monocyte development and function 16, 17 and causes cytopenia in non-tumor blood cells at higher concentrations 18. The c-jun N-terminal kinase (JNK) inhibitor SP had a distinct bias toward inhibition of phosphorylation of ZAP70, a critical kinase for T cell receptor signaling 20, in CD4 + T cells compared to CD8 + T cells under most conditions studied (Supplementary Fig. 24, column 11, green box). SP impairs CD4 + T cell function, so far assumed via the inhibition of JNK 19. However, these results indicate inhibition of additional kinase targets involved in TCR activation contributing to this observation. For Streptoningrin, in CD14 - HLA-DR mid and CD14 + HLA-DR mid monocytes, reproducible inhibition was observed of SYK, PLC and BLNK phosphorylation in the CD14 + HLA-DR mid cells (Supplementary Fig. 25a; Supplementary Fig. 25d, columns 4 and 7, green boxes) whereas in CD14 - HLA-DR mid monocytes inhibition of these phosphorylation sites was only detected after PMA/ionomycin stimulation (Supplementary Fig. 25b; Supplementary Fig. 25d, columns 4 and 7/row 11, red boxes). Supplementary Note 8 limitation of the method Several points have to be kept in mind when using MCB to analyze an experimental system: mdota reagent levels have to be titrated for each sample type, to ensure maximum separation of positively and negatively labeled cell populations for gating and deconvolution (Supplementary Fig. 3). Factors influencing the mdota cell labeling levels include cell number, availability of free thiol groups and cell size. The latter can be influenced by long-term change in growth condition, long-term drug treatment (e.g. using growth signaling inhibitors such as GDC- 0941, rapamycin or AKTi-1/2), induction of apoptosis or osmotic shock. Potential solutions to this caveat include cell type gating before deconvolution (as done in this study) and normalization of barcode channels with an orthogonal measure of cell size. 4

5 Furthermore, in our experiments only phosphorylation but not protein abundance levels were quantified. While this usually is not a limitation in short term stimulation experiments as constant protein levels can be assumed, it is possible that in the long-term experiments (such as the four hour time course experiment), changes in phosphorylation abundance might be due to changes in protein levels. In addition, many proteins function as signaling relays (e.g. p53, AKT...) with a multitude of phosphorylation sites which act in a combinatorial manner resulting in an enormous number of protein signaling states, which are not completely described if only a single phosphorylation site is quantified per protein as in our experiments. Finally, the approach taken here does not reveal differences between multi-target drug effects and indirect adaptive changes of the signaling network occurring over time. Here the generation of high-resolution time course data beginning with the drug treatment could differentiate the effects that are directly caused by a drug from those due to the adaptation of the cellular signaling network. Finally, as any method relying on antibodies, MCB is fully dependent on their quality and availability. Many signaling molecules of great biological interest exist at low copy number in the cell, and can be difficult to quantify accurately at the single cell level, especially with antibodies that display poor binding characteristics. For this study, multiple antibody clones were tested, and in some cases antibodies with high background binding or low pathway-induced shift in binding were chosen because they were the best available choice to measure a pathway of high biological interest, such as AKT activation. The antibody staining panel used in this study already provides a broad overview of cell signaling state, and this analysis of cell signaling by MCB will only improve as more measurement channels become available through improved antibody-labeling chemistries, and as more antibodies become commercially available, particularly as more antibodies are developed specifically for intracellular flow cytometry. Despite these limitations, MCB allows a comprehensive study of signaling networks and cellular responses in highly complex cell mixtures with unprecedented throughput. 5

6 Supplementary Tables Cell type Donor 1 Donor 2 Donor 3 Donor 4 Donor 5 Donor 6 Donor 7 Donor 8 CD4 + T cells 24% 30% 19% 21% 28% 34% 15% 26% CD8 + T cells 22% 21% 10% 11% 13% 13% 16% 20% CD14 + HLADR + Monocytes 4% 7% 12% 8% 6% 5% 5% 6% CD14 + HLADR - Monocytes 1% 2% 4% 4% 1% 1% 3% 3% CD14 - HLADR + Monocytes 8% 7% 6% 4% 8% 6% 3% 4% CD14 - HLADR - Monocytes 8% 9% 4% 5% 6% 7% 4% 4% Dendritic cells 1% 2% 2% 3% 3% 3% 2% 2% IgM + B cells 7% 4% 10% 7% 5% 7% 5% 4% IgM - B cells 1% 1% 1% 1% 1% 1% 2% 1% NK cells 11% 8% 13% 15% 10% 8% 21% 11% Surf - CD14 + cells 1% 1% 8% 9% 2% 2% 9% 6% Surf - CD14 - cells 12% 10% 10% 14% 18% 15% 16% 13% Supplementary Table 1. Cell type percentages from 8 different donors as determined by MCB. 6

7 Stimulus JAKs STATs phosphorylated IFN- JAK1 TYK2 STAT1 STAT3 STAT5 IFN- JAK1 JAK2 STAT1 STAT5 G-CSF JAK1 JAK2 TYK2 STAT1 STAT3 STAT5 GM-CSF JAK2 STAT5 IL-2 JAK1 JAK3 STAT1 STAT3 STAT5 IL-3 JAK2 STAT5 IL-12 JAK2 TYK2 STAT4 Supplementary Table 2: JAK-STAT relationships. Each row shows the JAK combination resulting from a given stimulus, and the STAT proteins phosphorylated 21, 22. 7

8 Inhibitor JAK1* JAK2 JAK3 TYK2* JAK (pan) inhibitor I % 1.8% 0.44% 1.5% JAK2 inhibitor III $ >10,000 >10,000 >10,000 >10,000 JAK3 inhibitor VI % 58% 4.8% 20.4% Ruxolitinib Ruxolitinib Tofacitinib Tofacitinib Tofacitinib 23 N/A Tofacitinib % 5.1% 1.9% 12% Lestauritinib SP VX VX N/A >10000 VX Supplementary Table 3: IC 50 values (nm or percent inhibition) of JAK inhibitors analyzed in this study as determined by in vitro kinase assays or by 10. (*) only JH1domain-catalytic domain results shown. $ Two different batches of JAK2 inhibitor III were tested. Inhibitors highlighted in grey/without reference were tested for this manuscript 10. 8

9 Supplementary Figures Tb159 Supplementary Figure 1. Time course analysis of cellular labeling using mdotalanthanide(iii) loaded with terbium 159. Unstimulated Stimulated Ir193 Yb174 Supplementary Figure 2. Slp76 induction by orthovanadate. Level of phosphorylation on Tyr- 696 on Slp76 before (left) and after (right) orthovanadate stimulation of K562 cells is shown. 9

10 Supplementary Figure 3. All seven channels used for barcoding are shown for a typical PBMC inhibitor experiment (AKTi-1/2 (CAS # )) analysis. The clear separation between positively (populations to the right) and unlabeled (populations to the left) cells allows a clean deconvolution of the barcoded cell populations. The cell events indicated outside the contour lines are below 2% of all cell events. Nevertheless, for the PBMC experiments separation between mdota-lanthanide(iii) positive and negative populations was reduced relative to that of K562 cells, likely due to cell size variability in the PBMC sample

11 pstat1 pstat1 pstat1 pstat1 pstat3 pstat3 pstat3 pstat5 pstat5 pstat5 Supplementary Figure 4. Time-resolved response of PBMCs to IFN- treatment. X-axis plots time in min, y-axis plots signal intensity of indicated marker. Blue dot and blue bars plot median and standard deviation, respectively. 11

12 Supplementary Figure 5. Histogram overlay of STAT5 induction over time after IFN- stimulation in T-cells. Only part of the CD4 - T cell population responds to the stimulus. 12

13 Supplementary Figure 6. Immune cell signaling network nodes and inhibitors studied. Signaling molecules on which a phosphorylation site was quantified in this study are indicated with a red circle. All inhibitors used in this study and their in vitro target or targets are shown in a cell type independent immune signaling network diagram. Many inhibitors (indicated by *) have additional known targets. General inhibitors without defined targets or targets outside the shown network are listed at the bottom. 13

14 perk p-p38 pzap70 pnf B ps6 pakt pslp76 pplc 2 Supplementary Figure 7. PMA/ionomycin-stimulated TCR signaling components. X-axis plots time in min, y-axis plots signal intensity of indicated marker. Blue dot and blue bars plot median and standard deviation, respectively. 14

15 p-p38 perk pnf B ps6 Supplementary Figure 8. Time-resolved induction of phosphorylation of different signaling proteins in monocytes after LPS stimulation. X-axis plots time in min, y-axis indicates signal intensity of indicated marker. Blue dot and blue bars plot median and standard deviation, respectively. 15

16 pstat3 pstat3 pstat3 pstat3 pstat5 pstat5 pstat5 pstat5 Supplementary Figure 9a. Time-resolved induction of phosphorylation on different signaling proteins and PBMC subtypes after LPS stimulation (left) and corresponding control (right). X- axis plots time in min, y-axis plots signal intensity of indicated marker. Blue dot and blue bars plot median and standard deviation, respectively. 16

17 pitk pitk pitk pitk pstat1 pstat1 Supplementary Figure 9b. Time-resolved induction of phosphorylation on different signaling proteins and PBMC subtypes after LPS stimulation (left) and corresponding control (right). X- axis plots time in min, y-axis plots signal intensity of indicated marker. Blue dot and blue bars plot median and standard deviation, respectively. 17

18 Supplementary Figure 10. PBMC signaling response comparison of 8 different donors in CD8 + T cells after IFN- stimulation. For each donor the control (left) and stimulation (right) condition are shown. 18

19 Supplementary Figure 11. PBMC signaling response comparison of 8 different donors in CD14 + HLA-DR + Monocytes after IFN- stimulation. For each donor the control (left) and stimulation (right) condition are shown. 19

20 IFN- / pstat1 IFN- / pstat1 IL-2 / pstat5 IL-3 / pstat5 G-CSF / pstat3 GM-CSF / pstat5 Supplementary Figure 12a. Effect of tofacenib on the indicated cell type, stimulus, and phosphorylation site combinations. X-axis plots dose in M, y-axis plots the signal intensity of indicated marker. Blue dot and blue bars plot median and standard deviation, respectively. 20

21 Supplementary Figure 12b. Effect of tofacinib inhibitor on indicated cell type, stimulus, and phosphorylation site combinations. 21

22 Supplementary Figure 13. Effect of ruxolitinib inhibitor on indicated cell type, stimulus, and phosphorylation site combinations. White circles indicate inhibition, but the cell number in the reference control was too low for determination of unstimulated control. 22

23 Supplementary Figure 14. Effect of lestauritinib inhibitor on indicated cell type, stimulus, and phosphorylation site combinations. 23

24 Supplementary Figure 15. Effect of Jak(pan) inhibitor on indicated cell type, stimulus and phosphorylation site combinations. 24

25 GM-CSF / pstat5 GM-CSF / pstat5 IL-3 / pstat5 IL-3 / pstat5 IFN- / pstat1 IFN- / pstat1 IL-2 / pstat5 IL-2 / pstat5 Supplementary Figure 16a. Continues next page. X-axis plots dose in M, y-axis plots signal intensity of indicated marker. Blue dot and blue bars plot median and standard deviation, respectively. 25

26 IFN- / pstat5 IFN- / pstat5 IFN- / pstat3 IFN- / pstat3 Supplementary Figure 16a. Effect of Jak2 inhibitor III on the indicated cell type, stimulus, and phosphorylation site combinations. X-axis plots dose in M, y-axis plots the signal intensity of indicated marker. Blue dot and blue bars plot median and standard deviation, respectively. 26

27 Supplementary Figure 16b. Effect of Jak2 inhibitor on indicated cell type, stimulus, and phosphorylation site combinations. 27

28 GM-CSF / pstat5 IL3 / pstat5 IL2 / pstat5 IL2 / pstat5 IL2 / pstat5 IFN- / pstat1 IFN- / pstat1 IFN- / pstat5 IFN- / pstat1 IFN- / pstat3 IFN- / pstat5 Supplementary Figure 17a. Analysis of Jak3 inhibitor VI specificity. X-axis plots dose in M, y- axis plots the signal intensity of indicated marker. Blue dot and blue bars plot median and standard deviation, respectively. 28

29 Supplementary Figure 17b. Effect of Jak3 inhibitor VI on indicated cell type, stimulus and phosphorylation site combinations. 29

30 PMA/Iono./pS6 PMA/Iono./pS6 BCR/FcR-XL/pS6 BCR/FcR-XL/pS6 Supplementary Figure 18. Go-6983 impact on ps6 phosphorylation after PMA/ionomycin or BCR-XL treatment. X-axis plots dose in M, y-axis indicates signal intensity of indicated marker. Blue dot and blue bars plot median and standard deviation, respectively. 30

31 Supplementary Figure 19. Signaling response of all PBMC subtypes to PMA/ionomycin stimulation for all analyzed inhibitors. 31

32 Rapamycin BCR/FcR-XL/pS6 PMA/Iono./pS6 GDC-0941 BCR/FcR-XL/pS6 PMA/Iono./pS6 Supplementary Figure 20. Impact of rapamycin and GDC-0941 on S6 phosphorylation after BCR-XL or PMA/ionomycin stimulation. X-axis plots dose in M, y-axis plots signal intensity of indicated marker. Blue dot and blue bars plot median and standard deviation, respectively. 32

33 Supplementary Figure 21. Overview of inhibitor impact on CD14 - HLA-DR mid monocytes under indicated stimulation conditions. Supplementary Figure 22. Overview of inhibitor impact on CD14 + HLA-DR mid monocytes under indicated stimulation conditions. 33

34 Supplementary Figure 23. Effect of imatinib on indicated cell type, stimulus, and phosphorylation site combinations. 34

35 Supplementary Figure 24. Effect of SP on indicated cell type, stimulus, and phosphorylation site combinations. 35

36 G-CSF / psyk G-CSF / psyk G-CSF / pplc 2 G-CSF / pplc 2 GM-CSF / pblnk GM-CSF / pblnk Supplementary Figure 25a. Overview of streptonigrin impact on indicated phosphorylation sites in CD14 + and CD14 - HLA-DR mid monocytes in indicated conditions. X-axis plots dose in M, y-axis plots the signal intensity of indicated marker. Blue dot and blue bars plot median and standard deviation, respectively.. 36

37 PMA-Iono. / psyk PMA-Iono. / psyk PMA-Iono. / pplc 2 PMA-Iono. / pplc 2 PMA-Iono. / pblnk PMA-Iono. / pblnk Supplementary Figure 25b. Overview of streptonigrin impact on indicated phosphorylation sites in CD14 + and CD14 - HLA-DR mid monocytes under indicated conditions. X-axis plots dose in M, y-axis plots the signal intensity of indicated marker. Blue dot and blue bars plot median and standard deviation, respectively. 37

38 G-CSF / ps6 G-CSF / ps6 IL-2 / pplc 2 IL-2 / pplc 2 G-CSF / pblnk G-CSF / pblnk IL-2 / pstat5 IL-2 / pstat5 Supplementary Figure 25c. Overview of streptonigrin impact on indicated phosphorylation sites in T and B cells under various conditions. X-axis plots dose in M, y-axis plots the signal intensity of indicated marker. Blue dot and blue bars plot median and standard deviation, respectively. 38

39 Supplementary Figure 25d. Effect of streptoningrin on indicated cell type, stimulus, and phosphorylation site combinations. 39

40 CD14 - HLA-DR high Monocytes CD14 + HLA-DR high Monocytes CD14 - HLA-DR mid Monocytes CD14 + HLA-DR mid Monocytes CD14 + HLA-DR low Monocytes CD14 - HLA-DR low Monocytes CD14 + Surface neg. Dendritic cells CD4 + T cells CD8 + T cells CD14 - Surface neg. NK cells IgM + B cells IgM - B cells Supplementary Figure 26. Clustergram of first 30 principle components of cell-type PCA analysis. 40

41 IFN- / pstat1 IFN- / pstat1 IFN- / pstat1 IFN- / pstat3 IFN- / pstat3 IFN- / pstat3 IFN- / pstat5 IFN- / pstat5 IFN- / pstat5 Supplementary Figure 27. Overview of SP effects on STAT signaling in indicated cell types after IFN- stimulation. X-axis plots dose in M, y-axis plots the signal intensity of indicated marker. Blue dot and blue bars plot median and standard deviation, respectively. 41

42 IFN- / pstat3 IFN- / pstat3 IFN- / pstat3 IFN- / pstat5 IFN- / pstat5 IFN- / pstat5 Supplementary Figure 28a. Overview of VX680 inhibition of STAT3 and STAT5 signaling in indicated cell types after IFN- stimulation. X-axis plots dose in M, y-axis plots the signal intensity of indicated marker. Blue dot and blue bars plot median and standard deviation, respectively. 42

43 Supplementary Figure 28b. Effect of VX680 on indicated cell type, stimulus, and phosphorylation site combinations. 43

44 Supplementary Figure 29. PBMC response to ruxolitinib (CAS # ) inhibition in 4 different donors. Donor 1 showed a broader inhibition profile compared to donor

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47 Supplementary Methods Kinase inhibitors The following list shows all inhibitors analyzed in this study. The highest concentration used in the dose-titrations and the companies from which they were purchased are shown. Inhibitor Concentration ( M) CAS number Bought from AKTi-1/ Chemdea BTK inhibitor III Calbiochem/EMD/Merck crassin (NCI DTP; index.html) dasatinib LC Laboratories GDC Chemdea Go Tocris H LC Laboratories IKK inhibitor X Calbiochem/EMD/Merck imatinib LC Laboratories JAK inhibitor I Calbiochem/EMD/Merck JAK2 inhibitor III Calbiochem/EMD/Merck JAK3 inhibitor VI Calbiochem/EMD/Merck LCK inhibitor Calbiochem/EMD/Merck lestauritinib LC Laboratories PP Calbiochem/EMD/Merck rapamycin LC Laboratories ruxolitinib (INCB018424) LC Laboratories SB LC Laboratories sorafenib LC Laboratories SP LC Laboratories staurosporine LC Laboratories streptoningrin Sigma sunitinib LC Laboratories SYK inhibitor IV Calbiochem/EMD/Merck tofacitinib (CP ) LC Laboratories U LC Laboratories VX LC Laboratories Kinase inhibitor stocks were prepared in DMSO. Supplementary Methods Table 1. Inhibitors used in this study. 1

48 96 well plate Short name Column 1 Sodium orthovanadate 2 IL-2 3 IL-3 4 IL-12 5 Reference 6 G-CSF 7 GM-CSF 8 BCR/FcR-XL 9 IFN- 10 IFN- 11 LPS 12 PMA/Ionomycin Supplementary Methods Table 2. Overview of the stimulations used for this study and their locations on the 96-well plate. Reference indicates no stimulus. 2

49 Isotope Antigen Clone Final concentration Supplier g/ml Cd110, 111, 112, 114 CD3 UCHT1 Invitrogen In115 CD45 HI30 2 Biolegend La139 BC Pr141 BC Nd142 NF B (ps529) K BD Nd144 p38 (pt180/py182) 36/ p38 2 BD Nd145 CD4 RPA-T4 3 Biolegend Nd146 BC Sm147 CD20 (intracellular) H1 3 BD Nd148 CD33 WM53 3 Biolegend Nd150 Stat5 (py694) 47 2 BD Eu151 CD123 9f5 3 BD Sm152 Akt (pt308) J BD Eu153 Stat1 (py701) 4a 3 BD Sm154 SHP2 (py580) polyclonal 2 CST Gd156 Zap70/Syk (py319/py352) 17a 1 BD Gd158 Stat3 (py705) 4 2 BD Tb159 BC Gd160 CD14 M5E2 3 Biolegend Dy164 Slp76 J141- (py128)/blnk (py 72) 1 BD Ho165 Er166 Er167 Er168 Tm169 BC pbtk (py551/py511)/pitk (py511) pplc (py759) Erk1/2 (pt202/ py204) BC 24a/BTK 2 BD K BD 20A 2 BD Er170 plat (py226) J BD Yb171 IgM G BD Yb172 S6 (ps235/ps236) N BD Yb174 HLA-DR L243 3 Biolegend Lu175 BC Yb176 CD7 M-T701 5 BD Supplementary Methods Table 3. Antibodies used in this study (BD is Becton Dickinson). 3

50 Supplementary Methods Figure 1. Strategy to determine fold-change and R2 cut-offs for each analyzed dose response curve. 4