Cancer Immunology, Immunotherapy â Gouttefangeas C et al. (submitted in 2014). Supplementary Figure 1. The two gating strategies compared in the study, ...
Cancer Immunology, Immunotherapy Gouttefangeas C et al. (submitted in 2014) Electronic Supplementary Materials
Supplementary Figure 1. The two gating strategies compared in the study, as illustrated with examples provided in the panel guideline Suppl. Figure 1a. Gating strategy 1 Step 1. Dot-plot showing forward scatter vs side scatter. Set a gate on the lymphocytes (gate 1) and give the number of events in this gate. Step 2. Dot-plot showing CD8 vs CD3 in “gate 1”. Gate the CD3+ events (gate 2) and the CD3+ CD8high events (gate 3). Give the numbers of CD3+ and of CD3+CD8high. Step 3. Dot-plot showing CD8 vs HLA-multimer in “gate 1 + gate 2”. Total number of events in this gate should be indicated. Give the number of CD8high and the number and frequency of multimer+ among CD8high cells. Use the same quadrants (or gates) for the FMO and multimer stainings in each donor. Indicate the number of events in each of the 4 quadrants. 1st dot-plot (all cells)
2nd dot-plot (in gate 1)
3rd dot-plot (in gates 1+2) 19
2
343
3
1
178.216
gate 1: 281.974 lymphocytes
gate 2: 209.571 CD3+ cells gate 3: 31.336 CD3+CD8+ (high) cells
30.993
343 CD3+CD8+Mult+ cells % CD8+Mult+ cells = 1.09
Suppl. Figure 1b. Gating strategy 2 Step 1. Dot-plot showing CD3 vs side scatter. Set a gate on the CD3+ showing a “low” side scatter (gate 1). Step 2. Dot-plot showing forward scatter vs side scatter in “gate 1”. Gate the lymphocytes (gate 2) and give the number of events in this gate. Step 3. Dot-plot showing CD8 vs HLA-multimer in “gate 1 + gate 2”. Give the number of CD3+CD8high cells and the number and frequency of multimer+ among CD8high cells. Use the same quadrants (or gates) for the FMO and multimer stainings in each donor. Indicate the number of events in each of the 4 quadrants. 1st dot-plot (all cells)
2nd dot-plot (in gate 1)
3rd dot-plot (in gates 1+2) 16
333
2 1 173.841
gate 1: 281.974 lymphocytes
gate 2: 204.189 CD3+ cells
29.999
333 CD3+CD8+Mult+ cells % CD8+Mult+ cells = 1.10
Cancer Immunology, Immunotherapy ‐ Gouttefangeas C et al. (submitted in 2014)
Supplementary Figure 2. Examples of data analyses obtained by five of the panel participants Dot plots are shown for FMO (left) and CMV-multimer (right) stainings for Donor 4 PBMC Suppl. Figure 2a. Gating strategy 1 Donor 4 FMO
ID01
ID03
ID07
ID11
ID25
Donor 4 CMV
Suppl Figure 2b. Gating strategy 2 Donor 4 FMO
Donor 4 CMV
ID01
ID03
ID07
ID11
ID25
v Cancer Immunology, Immunotherapy ‐ Gouttefangeas C et al. (submitted in 2014)
Supplementary Figure 3. Mean numbers of CD3+CD8+ lymphocytes counted per stain
250000
200000
150000
100000
Gating strategy 1
All centers
ID25
ID24
ID22
ID17
ID16
ID15
ID14
ID13
ID11
ID10
ID09
ID08
ID07
ID04
ID03
0
ID02
50000
ID01
Mean number of CD3+CD8+ cells / test
300000
Gating strategy 2
Mean number of cells and standard deviations are combined from stainings with CMV- and Flu-multimers (n=10 tests per lab). Results are shown for each of the n=17 participants and for the group.
Cancer Immunology, Immunotherapy ‐ Gouttefangeas C et al. (submitted in 2014)
Supplementary Figure 4. Automated analysis Suppl. Figure 4a. Examples of automated analysis of FCS files provided by three participants ID01
ID07
ID08
Pseudocolor-plots obtained by automated analysis for all stainings performed on donors 1-5 (FMO, CMVmultimer and Flu-multimer). Parameters are indicated on the axes. CD8+multimer+ cells are shown as red dots (right plots) and back-gated on CD3/CD8 and FSC/SSC plots (middle and left plots respectively).
Suppl. Figure 4b. Examples of dot-plots obtained by individual gating or automated analysis
ID10 D3 CMV ID11 D2 CMV
(I)
ID25 D1 Flu
(A)
ID07 D2 CMV
(I)
(A)
ID22 D3 Flu
ID15 D2 CMV
L12.FLU
100
101
102
103
104
Plots display CD8 staining (x-axis) vs HLA-multimer staining (y-axis) for 6 stainings provided by individual labs (I) or obtained by automated analysis (A). Lab ID and test are indicated. (I): after the central review process, tests D2 CMV performed by ID07, ID11 and ID15 were classified as negative. (A): a threshold of > 10 events was applied for positivity, therefore test D1 Flu performed by ID25 (1 positive event) was considered to be negative.
Suppl. Figure 4c. Automated analysis allows separation of CD8+multimer+ cells from background non-specific events
(A) True multimer+ cell cluster
(B) Non-specific background cluster
For some stainings (D4-CMV and D4-Flu performed by lab ID03), the statistical model partitioned PE-positive events into two clusters (shown in red) one that was compact across samples and is likely to contain antigen-specific multimer+ cells (A), and another that was diffuse and likely to contain non-specific multimer-binding events (B). An interesting difference between the two is that the antigen-specific cluster appears lower in CD3 expression than the non-specific cluster, likely indicating some degree of multimer-triggered T-cell receptor down-regulation.
Cancer Immunology, Immunotherapy ‐ Gouttefangeas C et al. (submitted in 2014)
Supplementary Table 1. Inter-laboratory variables for cell staining Table 1a. CD3 and CD8 mAb CD3 mAb Clone ID01 ID02 ID03 ID04 ID07 ID08 ID09 ID10 ID11 ID13 ID14 ID15 ID16 ID17 ID22 ID24 ID25
Fluorochrome Company
UCHT1 Pac-Blue UCHT1 FITC UCHT1 APC HIT3a FITC SK7 FITC OKT3 FITC SK7 APC SK7 PerCP SK7 FITC HIT3a FITC UCHT1 PE-Cy5 SK7 PerCP-Cy5.5 UCHT1 FITC OKT3 FITC SK7 PerCP SK7 FITC SK7
CD8 mAb
PE-Cy7
Clone
Fluorochrome
Company
BD BD eBio BD BD own BD BD BD BD Imm BD BD eBio BD BD
SK1 SK1 OKT8 RPA-T8 RPA-T8 SFCI21Thy2D3 SK1 SK1 SK1 SK1 B9.11 SK1 RPA-T8 RPA-T8 SK1 BW 135/80
FITC APC Pac-Blue APC APC PE-Cy7 FITC APC-H7 APC-Cy7 PerCP FITC FITC APC PE-Cy5 APC APC
BD BD eBio BD BD BC BD BD BD BD Imm BD BD BD BD Miltenyi
BD
SK1
PerCP-Cy5.5
BD
BD = BD Biosciences; BC = Beckman Coulter; eBio= eBiosciences; Imm= Immunotech
Table 1b. Other mAb, dead-cell markers and flow cytometers Dump channel ID01 ID02 ID03 ID04 ID07 ID08 ID09 ID10 ID11 ID13 ID14 ID15 ID16 ID17 ID22 ID24 ID25
Dead cell exclusion
Cell Flow fixation Cytometer
No Aqua Live/Dead (Invitrogen)* No No No No No No No PI No No No PI CD19 PerCP-Cy5.5 (SJ25C1, BD) EMA (Invitrogen) No No No No No No No No CD19 PE-Cy7 (SJ25C1, BD) PI No No No No No Dead Cell Kit (Miltenyi) CD4 Alexa700 (RPA-T4, BD)
No
Yes Yes Yes No No Yes No Yes No No Yes Yes No Yes Yes Yes
LSR II Calibur Cyan Aria Canto II Canto II Calibur LSR II Canto FACScan Canto Canto II LSR II LSR II Calibur Calibur
No
Canto II
LSR II, Calibur, Canto, FACScan and Aria: all BD Biosciences; Cyan from Dako *ID01 used a dye for dead cell exclusion but this was not included in the final analysis
Cancer Immunology, Immunotherapy ‐ Gouttefangeas C et al. (submitted in 2014)
Supplementary Table 2. Frequencies of antigen-specific cells in individual labs and statistics Results are given for the 7 specificities to be detected. Stains were declared as being positive (i.e. presence of CD3+CD8+multimer+ cells) after the central review process of all dot-plots as described in materials and methods. Frequencies are shown as 1 HLA-multimer+ cell/ x CD3+CD8+ lymphocytes and are calculated based on the cell numbers retrieved from the participants´report forms. “-“ = negative stain. n.a. = not analyzed. CV = % coefficient of variation. *Detection rates represent the percentage of labs having detected multimer+ cells (number of positive stainings / total number of stainings).
Table 2a. Analysis strategy 1 D1 Flu D2 CMV D2 Flu D3 CMV D3 Flu D4 CMV D4 Flu ID01 ID02 ID03 ID04 ID07 ID08 ID09 ID10 ID11 ID13 ID14 ID15 ID16 ID17 ID22 ID24 ID25
1762 1483 534 1561 1891 1539 1671 1248 2710 1172 2177
5901 2947 3941 414 n.a. 4085 3148 -
367 n.a. 331 528 895 551 611 1185 595 781 610 551 745 439 1127
38 27 31 33 30 19 57 43 32 28 29 20 39 35 32 38
n.a. 1286 492 8118 862 5282
68 42 42 54 36 31 30 74 44 34 49 61 44 53 27 49 59
1898 n.a. 1493 478 966 3364 977 3386 2104 1586 333 2101
Table 2b. Analysis strategy 2 ID01 ID02 ID03 ID04 ID07 ID08 ID09 ID10 ID11 ID13 ID14 ID15 ID16 ID17 ID22 ID24 ID25
D1 Flu
D2 CMV
D2 Flu
D3 CMV
D3 Flu
D4 CMV
D4 Flu
2038 1304 537 1367 1464 1482 1479 1526 2593
10517 2985 413 n.a. 6209
448 n.a. 283 638 904 546 521 675 529 156 770 588 161 866 512 969
42 28 27 36 31 19 55 35 29 n.a. 28 18 13 38 31 36
n.a. 446 707 -
72 47 42 51 38 31 30 44 40 36 48 56 15 50 27 47 57
1612 n.a. 748 2077 482 682 1694 840 n.a. 3516 934 1508 334 2183
Table 2c. Statistical analysis Strategy 1
D1 Flu
D2 CMV
D2 Flu
D3 CMV
D3 Flu
D4 CMV
D4 Flu
Mean Median CV Detection rate*
1613 1561 35
3406 3545 53
665 602 39
33 32 27
3208 1286 104
47 44 29
1699 1586 60
64,7 (11/17)
37,5 (6/16)
87,5 (14/16)
94,1(16/17)
31,2 (5/16)
100 (17/17)
68,7 (11/16)
Strategy 2
D1 Flu
D2 CMV
D2 Flu
D3 CMV
D3 Flu
D4 CMV
D4 Flu
Mean Median CV Detection rate*
1532 1479 36
5031 4597 87
571 546 43
31 31 33
577 577 n.a.
43 44 31
1384 1221 66
52,9 (9/17)
25 (4/16)
93,7 (15/16)
93,7 (15/16)
12,5 (2/16)
100 (17/17)
80 (12/15)
Cancer Immunology, Immunotherapy ‐ Gouttefangeas C et al. (submitted in 2014)
Supplementary Table 3. Frequencies of antigen-specific cells detected by automated analysis
ID01 ID02 ID03 ID04 ID07 ID08 ID09 ID10 ID11 ID13 ID14 ID15 ID16 ID17 ID22 ID24 ID25
D1 Flu
D2 CMV
D2 Flu
D3 CMV
D3 Flu
D4 CMV
D4 Flu
2913 n.t. 1320 2573 1925 3720 n.t. 2516 6083 9310 n.t. -
7330 5075 n.t. 7473 5470 n.a. 7311 12450 n.t. 4339 n.t. -
555 n.a. 2626 n.t. 624 1604 680 634 n.t. 779 871 1066 1482 2036 n.t. 1458
27 116 41 n.t. 26 20 67 464 25 n.t. 30 25 50 41 39 n.t. 51
n.a. n.t. 7040 670 n.t. 4801 n.t. -
46 22 42 n.t. 32 41 34 45 34 n.t. 45 99 74 49 58 n.t. 41
1364 n.a. 1516 n.t. 5853 2152 1670 2255 1388 n.t. 3708 5353 2012 4982 n.t. 1968
Results are given for the seven specificities to be detected. Analysis was performed on FCS files provided by the participants as described in materials and methods. Threshold for positivity was set at ≥ 10 CD8+HLA-multimer+ events and frequencies are shown as 1 multimer+ cell/ x CD3+CD8+ lymphocytes. n.a. = not analyzed. n.t = not tested
Cancer Immunology, Immunotherapy ‐ Gouttefangeas C et al. (submitted in 2014)
Cancer Immunology, Immunotherapy ‐ Gouttefangeas C et al. (submitted in 2014)