SUPPLEMENTARY DISCUSSION. REAP-seq method overview

SUPPLEMENTARY DISCUSSION REAP-seq method overview In the first step of the REAP-seq workflow, cells are labeled using standard flow cytometry methods but with antibodies conjugated to DNA in place of fluorophores. Antibody DNA labels consist of three parts: (i) a unique 8 bp antibody barcode providing up to 65,536 unique indices (B n, where B = any of the four nucleotides G,A,T,C, and n = length of the barcode sequence), (ii) a 25 bp poly (da) sequence, and (iii) a 33 bp universal Ab primer for amplification and sequencing (Supplementary Fig. 1a and 2). Excess unbound Ab-Barcodes (AbBs) are washed from the labeled cells before they are processed using the standard 1x Genomics scrna-seq platform 1, which is a droplet based system designed for 3 digital counting of mrna in thousands of single cells. Briefly, single cells are captured in droplets along with a gel-bead functionalized with a barcoded oligonucleotide that consists of: (i) a 14 bp cell barcode, (ii) a 1 bp unique molecular identifier (UMI), (iii) a 3 bp oligo-dt, and (iv) primers for PCR amplification and sequencing. Cells are lysed upon droplet formation and the polyadenylated AbB and mrna are primed with the poly (dt) oligonucleotides from the gel bead. REAP-seq leverages the DNA polymerase activity of the reverse transcriptase to simultaneously extend the primed AbB and synthesize complementary DNA from mrna in the same reaction. This reaction produces a non-covalent complex containing an antibody with a dsdna product consisting of an Ab barcode, a cell barcode, a UMI, and two PCR primer sequences that remains intact until the PCR amplification step. The droplets are then broken and the shorter dsdna products (~155 bp) are separated from the longer mrna-derived cdna (typically >5 bp) using a standard size selection procedure with SPRI beads. All downstream RNA-seq steps remain the same (1x Genomics standard protocol, v1) and the supernatant that is typically discarded from the SPRI size selection step is used for the proteomic part of the assay (Supplementary Fig. 3). Exonuclease I is used to degrade any excess unbound single-stranded oligonucleotides from the protein dsdna (~155 bp) products to prevent crosstalk between AbBs and cell barcodes from different cells (Supplementary Fig. 4). The dsdna product is then amplified using primers containing a sample index and an Illumina adapter sequence to create a final library product that can be sequenced in a multiplexed fashion with other samples. To reduce cost, protein libraries were sequenced separately from RNA-seq libraries using a shorter cycle sequencing kit (5 cycles). Following sequencing, AbB-derived reads are assigned using an AbB dictionary that associates each antibody with a unique 8 bp barcode. Next, reads are grouped by their cell barcodes and UMIs are used to avoid double counting sequence reads that arose from the same AbB molecule. Upon completion, a matrix of digital protein-expression measurements are generated analogous to how the matrix of digital gene-expression measurements are generated from the 1x Genomics scrna-seq pipeline 1, 2 (Supplementary Fig. 1b). acd27 ex-vivo CD8+ T cell assay CD27 (TNFRSF7) is a member of the tumor necrosis factor receptor super family and is a costimulatory receptor expressed on the majority of T cells (CD4 +, CD8 +, Treg), memory B cells, and a subset of NK cells. Upon interaction with its ligand, CD7, the CD27 costimulatory molecule has been found to play a key role in T cell activation, lymphocyte survival, proliferation, memory cell differentiation, and cytotoxicity 3-8. It has also been demonstrated that targeting CD27 with an agonist monoclonal antibody such as varlilumab provides costimulatory signals to human T cells in a TCR-dependent manner. Costimulation of T cells with varlilumab requires concurrent TCR signaling as pre-activated T cells are unable to produce cytokines with CD27 signaling alone 9. REAP-seq analysis of acd27 stimulated naïve CD8+ T cells The top five Gene Ontology (GO) biological processes that were enriched in the overlapping gene set (n=74, Fig. 2b, Supplementary Fig. 13) were interferon-gamma-mediated signaling pathway (p-value=2.2e-4), type 1 interferon signaling pathway (p-value=3.3e-3), glycolytic process (p-value=7.4e-3), positive regulation of cell killing (p-value=1.1e-2), and regulation of T cell mediated cytotoxicity (p-value=3.2e-2, using the Bonferroni correction for multiple testing). Enriched pathways were consistent with findings in literature showing production of IFNy in T cells with CD27 costimulation 9, 1. T cell cytotoxicity and positive regulation of cell killing are both

important pathways for effective immunotherapies. Previous reports have shown that triggering CD27 on T cells improves their cytotoxic capacity in addition to enhancing initial expansion and survival 8, 11, 12. In order to support cell division and expansion, metabolic pathways such as aerobic glycolysis have also been shown to be effected upon CD27 costimulation in CD8+ T cells 13, 14. When the overlapping differentially expressed genes (n=74) and proteins (n=16) were combined, the top GO biological processes enriched were T-cell costimulation (p-value=6.1e-5), interferon gamma mediated signaling pathway (p-value=6.e-4), positive regulation of T cell proliferation (p-value=5.5e-3), and regulation of hematopoiesis (p-value=5.3e-4). Ingenuity Pathway Analysis (IPA) showed high enrichment in icos-icosl Signaling in T Helper Cells (p-value=3.3e-8), Th1 and Th2 Activation Pathway (p-value=6.1e-8), and OX4 signaling pathway (p-value=6.8e-7, Fisher s exact test). Pathway analysis agreed with previous findings demonstrating that OX-4, ICOS, IFNy, Th1 and Th2 cytokines were upregulated in response to T cells costimulated with varlilumab and acd3 9. Differentially expressed protein markers Single cell data can provide insight into whether changes in expression are due to; 1) an increase or decrease in the absolute expression level per cell or 2) a change in the number of cells expressing the transcript or protein at the same level (cellular composition) (Supplementary Fig. 14a) 15. For example, since CD5 is constitutively expressed in T cells, there was no change in the percentage of CD5 positive cells for the different treatment conditions (ratio % positive cells treated/untreated). However, upon costimulation with acd27, cells expressed CD5 at higher levels, which can be seen in the right-shifted peak in the histogram (treated blue versus untreated orange) (Supplementary Fig. 14b). Conversely, the overall increase of CD7 expression was mainly determined by the increase of the percentage of CD7 expressing cells rather than the absolute level of expression per cell. Previous reports have shown that in vitro activation of T cells with CD3 and CD28 induces CD7 expression 16 and that the level of CD5 surface expression is directly related to TCR signaling intensity 17, 18. Our findings suggest that adding acd27 to acd3/acd28 stimulation can even further increase expression of CD5 and CD7 in naïve CD8+ T cells. Protein markers ICOS, OX-4, GITR, PD-1, and 4-1BB previously shown to be up-regulated in CD8+ T cells costimulated with acd27 and acd3, were also found with REAP-seq to have increased expression in acd27 treated cells (Supplementary Fig. 14b) 9. Flow cytometry confirmed REAP-seq data showing increased protein expression of CD4 and CD25 in acd27 treated cells (Supplementary Fig. 2). Interestingly, each donor had a significant percentage of their CD8+ T cells becoming double positive for both CD8 and CD4 expression upon costimulation with acd27. Previous studies have shown that activation of CD8 single positive (SP) T cells by costimulation resulted in the expression of CD4 and that naïve CD45RA+ CD8 SP cells respond to costimulation with greater expression of CD4 than do CD45RO+ CD8 SP cells suggesting CD4 may be involved in mature CD8 T cell function 19. In all three donors there was a significant decrease in CD27, CD73, IL7R, CD45RA, and CD69 protein expression (adjusted p-value<.1, Supplementary Fig. 15a). There was a decrease in CD27 AbB expression, which was confirmed by flow cytometry to originate from blocking by the acd27 drug (Supplementary Fig. 19). Based on flow cytometry data, CD69 upregulation was expected (Supplementary Fig. 15b) in acd27 treated cells, but the opposite was seen with REAP-seq data. Biacore analysis of CD69 AbB and unconjugated CD69 Ab showed that the conjugation did not reduce binding to recombinant CD69 (Supplementary Fig. 15c-e). It is possible, however, that the conjugated CD69 AbB has reduced binding affinity to CD69 in its native (or activated) form on the cell surface. Additional possibilities that could be affecting CD69 AbB binding, may include steric hindrances caused from the acd27 conjugated bead used for costimulation or other AbBs bound to the cell surface. This unexplained phenomena shows the importance of the continuation of testing and validation of the assay as it is scaled to include an unprecedented number of antibodies (>1). Comparison of protein and mrna data in Donor 2 Donor 2 had 23 differentially expressed proteins, where only 4 of these proteins (IL2RA, IL7R, CD7, HLA-E) also had differential expression at the transcriptional level (fold change >1.3, adjusted p-value <.1). Two of the

differentially expressed proteins were isoforms (CD45RA, CD45RO) that were not measured by scrna-seq. mrna and protein expression projected on the protein based t-sne plot allowed visualization of IL7R, NT5E, CD7, PD1, and CD4 expression across all cells (4,44 acd27 treated, 622 acd27 untreated cells) in Donor 2 (Supplementary Fig. 18a). Markers such as IL7R and CD7 had significant differential expression at both the protein and mrna level in the acd27 treated cells relative to untreated acd27 cells (**, red circle). However, other markers such as NT5E, PD1, and CD4 only show significant differences in protein expression levels. REAP-seq protein measurements of CD4 in Donor 2 were validated with flow cytometry, and both methods showed an increase in the percentage of double positive (CD8+/CD4+) cells, from ~2% in untreated acd27 cells to ~47% in acd27 treated cells (Supplementary Fig. 18b). This is an example of how protein expression data can help recover the information missed by the gene expression data due to drop-out events that occur when transcripts are expressed at low levels and are missed during the initial reverse transcription step 2.

Supplementary Figure 1 Schematic of REAP-seq (a) Cells are labeled with Ab-Barcodes (AbBs, Supplementary Fig. 2) before compartmentalization into discrete droplets containing a bead with cell-barcode primers. Upon droplet formation the cell is lysed and the polyadenylated mrna and AbBs hybridize to the poly(dt) cell barcoded primer. REAP-seq leverages the DNA polymerase activity of reverse transcriptase to simultaneously extend the hybridized AbB and synthesize complementary DNA from mrna in the same reaction. The droplet emulsion is then broken and the cell barcoded AbB sequences (~155 bp) are size fractionated from the cell barcoded cdna derived from mrna (>~5 bp). mrna and protein libraries are prepared and sequenced (see Methods). (b) Paired end sequencing reads for mrna and protein libraries are generated on a high-throughput sequencer. The mrna workflow is similar to previously published methods 1,2. Protein sequencing reads are first aligned using an antibody-barcode dictionary that associates each antibody with a unique 8 bp sequence (Supplementary Table 1). Next, reads are grouped by their cell barcodes, and sequences with unique UMIs are counted for each protein and gene in each cell. The result is a digital protein and gene expression matrix where each column corresponds to a cell, and each row corresponds to a different protein or gene. Each entry in this matrix is the integer number of detected genes or proteins per cell.

CD19 Nextera Read 1 (33 bp) Ab BC (8 bp) Poly(dA) (24-25 bp) CD3 Nextera Read 1 (33 bp) Ab BC (8 bp) Poly(dA) (24-25 bp). CD11b Nextera Read 1 (33 bp) Ab BC (8 bp) Poly(dA) (24-25 bp)... PD1 Nextera Read 1 (33 bp) Ab BC (8 bp) Poly(dA) (24-25 bp) Nextera Read 1: TCGTCGGCAGCGTCAGATGTGTATAAGAGACAG Supplementary Figure 2 Schematic of DNA barcoded antibodies. Antibodies were conjugated to oligonucleotides (65-66 bp) that consisted of 3 parts: 1) 33 bp Nextera Read 1 sequence that was used as a primer for amplification as well as sequencing, 2) a unique 8 bp antibody barcode (Ab BC) and 3) 24-25 bp poly(da) sequence. Due to complexity of manufacturing oligonucleotides with consecutive insertions of the same base, if the Ab BC ended in an A, then poly(da) was 24 bp to keep consecutive insertions of the same base <26 bp. All oligonucleotides were purchased from Integrated DNA Technologies with a 5 amine modification (/5AmMC6/).

RT Incubation 1x Genomics v1 single cell RNA assay Silane Bead cleanup.6x SPRI Cleanup cdna Amplification SPRI Cleanup Shearing SPRI Size Selection Discarded Supernatant Protein assay Discarded Supernatant: 1x BC-Ab BC (~155 bp) 1x BC (~114 bp) Cleanup excess single strand 1x BC via Exonuclease SPRI Cleanup Sample Index PCR, SPRI Cleanup End Repair & A-tailing Adapter Ligation, SPRI Cleanup Sample Index PCR, SPRI Cleanup Supplementary Figure 3 Overview of REAP-seq protein and mrna library preparation. REAP-seq leverages the DNA polymerase activity of reverse transcriptase to simultaneously synthesize complementary DNA from mrna and extend the hybridized AbB resulting in dsdna containing the Ab barcode, cell barcode, UMI, and two PCR primer sequences. The droplets are then broken and a silane bead cleanup step is used to purify the DNA from the oil emulsion. Then shorter dsdna molecule (~155 bp) is separated from the longer cdna molecules (typically >5 bp) using.6x SPRI bead enrichment step. All downstream scrna-seq steps remain the same (1x Genomics v1 standard protocol) and the supernatant that is typically discarded from the SPRI cleanup step is used for the protein part of the assay. Exonuclease I is used to degrade any excess unbound single-stranded oligonucleotides from the protein dsdna (~155 bp) product to prevent crosstalk between AbBs and cell barcodes from different cells. The dsdna product is then amplified using Illumina adapter sequence primers (P7 and P5) and a primer containing a sample index (P5-Sample_Index-Part_Rd1, Supplementary Table 6) to create a final library product that can be sequenced in a multiplexed fashion with other samples (Supplementary Table 7).

a b AbB 2 Counts tsne2 3 2 4 5 1 6 1 7 8 9 AbB 1 Counts tsne1 Supplementary Figure 4 Validation of REAP-seq protein assay using Anti-mouse IgG beads labeled with AbBs. (a) The protein part of the REAP-seq assay was conducted on a mixture of beads labeled with either AbB 1 (CD7) or AbB 2(CD13). The scatter plot shows the number of AbB counts associated with each bead. Blue dots indicate beads designated as AbB 1 specific (>7% AbB 1 counts); red dots indicate beads that are AbB 2 specific (>7% AbB 2 counts). Of the 574 beads identified (>1 counts), 4 (.7%) had a mixed phenotype, suggesting that the Exonuclease 1 step was successful at degrading excess unbound singlestranded barcodes and preventing crosstalk between AbBs and cell barcodes from different beads. (b) t-sne visualization of clusters identified among 1,82 beads that were labeled with one of the 1 different AbBs (CD127, TIGIT, CD27, CD8a,CD73, CD28, CD9, CD4, OX4, and Mouse IgG1 isotype control) and processed through the REAP-seq protein pipeline. Beads were assigned a color based on the AbB with the maximum number of counts. As expected, 1 clusters were identified for each of the 1 AbBs.

a No blocking buffer DNA Salmon Sperm Dextran sulfate Polyanionic competitor b Rat IgG1 Isotype Control Mouse IgG2b Isotype Control Mouse IgG1 Isotype Control % Total Cells % Total Cells % Total Cells Mouse_IgG1_ctrl 2.73 1.77.24 1.69 Ms IgG2b.57 1.55.14.33 Rat IgG1 2.59 2.45.2 1.51 No Blocking Buffer DS Blocking Buffer 1 1 8 6 4 2 1 55% 16% 14% 1% 2 5% 7% 3% 5 1 15 5 1 15 # UMI counts # UMI counts 8 6 4 2 9% 4% 5 1 15 # UMI counts 1 89% 8 6 4 2 7% 3% 5 1 15 # UMI counts % Total Cells 8 74% 6 4 85% 1 91% % Total Cells % Total Cells 8 6 4 2 7% 2% 5 1 15 # UMI counts 1 94% 8 6 4 2 5% 1% 5 1 15 # UMI counts Supplementary Figure 5 Evaluation of blocking buffer on non-specific binding of AbB to cells. (a) PBMCs were blocked with either DNA salmon sperm (1 mg/ml), dextran sulfate (.2 mg/ml), or polyanionic Inhibitor (1 µm). Bulk PBMCs were labeled with an AbB mix (n=28) and protein libraries were prepared for bulk cells rather than single cells (less expensive for initial optimization experiments). The table shows normalized counts (AbB counts/total AbB counts x 1x1 4 ) of DNA barcodes from isotype control antibodies; Mouse IgG1, Mouse IgG2b, and Rat IgG1. Dextran sulfate showed the best reduction in non-specific binding of the AbB isotype controls. (b) PBMCs were either blocked with dextran sulfate (.2 mg/ml) or not blocked and then labeled with an AbB mix (n=45). UMI count graphs showing the % total cells (# cells that had a specific number of UMI counts/ total # cells x 1) that are expressing a specific # of UMI counts. Single cells (without DS, n=3,158, with DS, n=4,33) were processed with REAP-seq and protein measurements show that dextran sulfate blocking helped reduce non-specific binding of the isotype controls and increased the % cells with UMI counts. All three isotype controls blocked with dextran sulfate had a background noise of <= 2 UMI counts in >96% of the cells.

a b 8 73% 14 R² =.95 12 1 8 6 4 2 5 1 15 CD8 AbB-2 (Clone SK1) 1 94% Mouse IgG1 Isotype Control Rat IgG1 Isotype Control CD8 AbB-9 (Clone RPA-TA) % Total Cells 6 4 2 8 6 2% 4 5% 2 5% 5 1 # UMI counts 15 5 # UMI counts 1 15 % Total Cells % Total Cells 1 8 6 4 2 1 8 6 4 2 92% 5 1 15 # UMI counts 91% 7% 8% Mouse IgG2a Isotype Control Mouse IgG2b Isotype Control 5 1 15 # UMI counts 1 8 6 4 2 93% 6% 5 1 15 # UMI counts Supplementary Figure 6 Evaluation of specificity and non-specific binding in the REAP-seq protein assay. (a) High correlation (R 2 =.95) between two different monoclonal antibodies against CD8 (Clone RPA-TA and SK1) was observed in PBMCs indicating high specificity and reproducibility of single cell protein measurements. (b) Evaluation of non-specific binding using isotype controls in acd27 treated and untreated cells from Donor 1 (n=5,196 cells). UMI count graphs showing the % total cells (# cells that had a specific number of UMI counts/ total # cells x 1) that are expressing a specific # of UMI counts. Isotype controls Mouse IgG2a, Mouse IgG2b, Rat IgG1, and Rat IgG2a have >9% cells with UMI counts and >98% of cells have <2 UMI counts. % Total Cells Rat IgG2a Isotype Control

Supplementary Figure 7 Benchmarking of REAP-seq on PBMCs. (a) CD3+ T cells (n=3,797), CD19+ B cells (n=1,533), and CD11b+ myeloid cells (n=2,883) were magnetically enriched from PBMCs and processed with REAP-seq. Gene expression matrices from the 3 magnetically enriched cell populations (CD3+,CD19+,CD11b+) were merged into one matrix and the nonlinear dimensionality reduction method, t-distributed Stochastic Neighbor Embedding (t-sne), was used to visualize the PCA-reduced dataset in two dimensional space. t-sne visualization of six clusters were identified using the top 9 significant principal components across 1,789 variable genes. (b) Cells are colored by the magnetic beads used for isolation: CD3+ (pink), CD11b+ (green), CD19+ (blue) and projected on the tsne plot from (a). There are three easily discernible purified populations of cells which can be used as a positive control to assess the sensitivity and specificity of REAP-seq mrna and protein measurements for canonical markers of these cell types. (c) mrna and protein signal for canonical markers expressed in myeloid cells (CD11b, CD33, CD14, CD155), B cells (CD19, CD2) and T cells (CD3, CD4, CD8) projected on the t-sne plot from (a). For each marker, the Pearson correlation coefficient (R) between mrna and protein expression across 8,213 single cells is displayed. Purple indicates high expression and grey indicates low expression. (d) mrna signal for markers expressed in FCGR3A+ Monocytes (FCGR3A) and mature B cells (TNFRSF17) were projected on the tsne plot from (a). Purple indicates high expression and grey indicates low expression.

Genes with low abundant cell expression Genes with high abundant cell expression CD33 6 CD3D Expression Level 4 2 scrna-seq REAP-seq Expression Level 4 2 scrna-seq REAP-seq HLA-DRA Expression Level 4 3 2 1 CD11b Expression Level 6 4 2 scrna-seq REAP-seq scrna-seq REAP-seq CD14 Expression Level 3 2 1 CD155 Expression Level 4 2 scrna-seq REAP-seq CD2 scrna-seq REAP-seq Expression Level 4 2 scrna-seq REAP-seq Supplementary Figure 8 REAP-seq gene expression compared to standard scrna-seq gene expression (a) Violin plots showing the distribution of gene expression in PBMCs that were processed with REAP-seq (n=4,113 ) versus standard scrna-seq (n=3,158 ) using the 1x Genomics platform. Genes on the left were those that had less abundant cell expression and genes on the right were those that were expressed in a larger number of cells. Distribution of gene expression between both platforms is comparable suggesting that the protein assay does not affect the scrna-seq standard assay.

a REAP-seq Gene Normalized counts 4 35 3 25 2 15 1 5 R² =.97 Variable: AbBs b REAP-seq with blocking buffer Gene Normalized counts 4 35 3 25 2 15 1 5 1 2 3 4 Gene Normalized counts scrna-seq Variable: Blocking Buffer R² =.96 1 2 3 4 Gene Normalized counts REAP-seq without blocking buffer Supplementary Figure 9 Evaluating the effect of different conditions on the transcriptome. (a) The REAP-seq transcriptomic assay was run on PBMCs labeled with or without AbBs (scrna-seq). Sequencing reads were processed with our bulk RNA-seq pipeline (Omicsoft, see Methods) and the 5, highest expressed genes were compared between the two conditions. High correlation (R 2 =.97) suggests that labeling cells with AbBs does not effect the transcriptomic signature. (b) PBMCs were either blocked with dextran sulfate (.2 mg/ml) or not blocked before labeling cells with AbBs and processed through the REAP-seq pipeline. Comparison between the top 5, expressed genes show good correlation (R 2 =.96) suggesting that blocking cells with dextran sulfate does not effect the transcriptomic signature. Gene counts were normalized by total counts and scaled by a factor of 1x1 6.

a b Donor 1 Donor 2 Donor 3 Protein mrna 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 tsne2 tsne1 1 2 3 4 5 6 1 2 3 4 5 6 7 1 2 3 4 5 6 7 Supplementary Figure 1 REAP-seq characterization of ex vivo activation of naïve CD8+ T cells with acd27 (a) Schematic of acd27 ex-vivo assay where naïve CD8+ enriched T cells were either treated with acd3 and acd28 (top, acd27 untreated) or acd3, acd28, and acd27 (bottom, acd27 treated). (b) t-sne visualization plots based on either gene or protein expression for each of the 3 donors where cells were treated with acd27 (Donor 1, 4,246; Donor 2, 4,44; Donor 3, 3,55 cells) or not treated with acd27 (Donor 1, 95; Donor2, 622; Donor 3, 46 cells). For t-sne visualization of mrna data, 6-7 clusters were identified using the top 1, 15, and 1 significant principal components across 1,452, 1,159, and 1,76 genes for Donor 1, 2, and 3 respectively. For t-sne visualization of protein data, 6-7 clusters were identified using the top 1 significant principal components across all Nature 8 antibodies Biotechnology for each doi:1.138/nbt.3973 donor. Cells are colored by cluster.

tsne2 Donor 3 Donor 2 Donor 1 a b tsne1 Untreated acd27 cells Treated acd27 cells Supplementary Figure 11 Unsupervised clustering of acd27 treated and untreated cells based on gene expression (a) t-sne plots based on gene expression for each of the 3 donors. Blue dots indicate cells treated with acd27 (Donor 1, 4,246; Donor 2, 4,44; Donor 3, 3,55 cells) and magenta indicates cells not treated with acd27 (Donor 1, 95; Donor2, 622; Donor 3, 46 cells). (b) t-sne plots based on a reduced set of genes (n=69) consisting of markers that were also measured for protein expression (excluding isotype controls and post translationally modified proteins such as CD45RO and CD45RA). For comparison, clustering based on protein expression is shown in Fig. 2a.

Supplementary Figure 12 Differentially expressed proteins and transcripts in acd27 treated naïve CD8+ T cells Volcano plots showing differential gene and protein expression between acd27 treated and untreated cells in three different donors. Cyan, genes and proteins with adjusted p-values <.1 (corrected for multiple testing using the Bonferroni correction) and fold changes greater than 1.3 (threshold used for differential expression). Magenta, remaining genes and proteins. Selected genes and proteins labeled were differentially expressed in all three donors.

DE genes in all three donors Donor 1 2 3 3 1 2 Untreated acd27 Treated acd27 IL7R SRSF5 EIF4A2 B2M MALAT1 ISG2 MIR155HG ISCU STAT1 IRF1 HSPA5 SLC3A2 HLA E TMEM66 GBP2 LTB SYNE2 DDX5 CIRBP HLA B CRTAM DNAJB6 HERPUD1 CCL5 CALM3 TK1 COX7B MYL6 CAPG GLRX DUT HMGB2 MT CO3 KIAA11 MT ND4 LSP1 GYPC GAPDH MKI67 TPI1 PGK1 DDIT4 SIT1 PGAM1 LDHA CCDC34 AIF1 LGALS1 HIST1H4C TMSB1 MT2A ATP5J2 RPA3 COX6A1 DBI ATPIF1 LST1 CCL22 H2AFZ CDKN3 PTTG1 1.5 1.5.5 1 1.5 Supplementary Figure 13 Heatmap showing normalized expression of genes (n=61) that were either upregulated or downregulated in the acd27 treated and untreated cells for all three donors. Red indicates high expression and blue indicates low expression. Expression levels are log normalized, first scaling each cell to a total of 1x1 4 molecules.

b Supplementary Figure 14 REAP-seq differential protein expression in acd27 stimulated cells (a) In addition to differential expression, single cell measurements can help distinguish if changes are due to gene or protein regulation versus those that arise due to compositional changes of different cellular states 15. (b) Histograms showing protein expression for markers that increased upon stimulation with acd27. (blue, acd27; orange, no acd27). Protein markers that had statistically significant differential expression (**) had an adjusted p-value <.1 (corrected for multiple testing using the Bonferroni correction) and a fold change > 1.3. For each marker, the fold change in the percentage of positive cells (right of red line) between the acd27 treated and untreated samples is shown in the table below the histogram.

a REAP-Seq Data: Proteins with Decreased Expression b Flow Cytometry Data: CD69 Expression CD27 CD73 IL7R CD45RA CD69 Donor 1 ** ** ** ** ** Donor 1 Frequency Donor 2 Donor 3 ** Expression Level ** ** ** ** Untreated/Treated % positive cells Donor CD27 CD73 CD127 (IL7R) CD45_RA CD69 1 1.18 1.57 2.9 1. 1.1 2 1.11 1.64 2.42 1. 1. 3 1.21 1.3 2.66 1. 1.1 Untreated acd27 ** ** ** ** ** Treated acd27 Frequency Donor 3 Donor 2 1 1 2 1 4 CD69-FITC Untreated acd27 Treated acd27 c CD69 dimer d Test Antibody Anti-CD69 mab anti-mfc capture mab Isotype Capture Level of Ab to surface (RU) Anti-CD69 Ms IgG1 13 Anti-CD69 + DNA barcode Ms IgG1 16 e Stability Binding Response (RU) Antibody Binding Comparison to CD69 Anti-CD69 Anti-CD69 DNA Barcode Recombinant CD69 (588 nm) Recombinant CD47 (neg. ctrl, 1 nm) Mouse IgG1 Isotype Ctrl Mouse IgG1 Isotype control Ms IgG1 15 Supplementary Figure 15 Differential protein expression in acd27 stimulated cells (a) Histograms showing REAP-seq protein expression for markers that decreased upon stimulation with acd27. (blue, acd27; orange, no acd27). Protein markers that had statistically significant differential expression (**) had an adjusted p-value <.1 (corrected for multiple testing using the Bonferroni correction) and a fold change > 1.3. For each marker the fold change in the percentage of positive cells (right of red line) between the acd27 untreated and treated samples is shown in the table below the histogram. (b) Flow cytometry histograms showed an increase in CD69 expression upon acd27 treatment of the CD8+ naïve T cells in all three donors. (c) Schematic of CD69 Ab characterization using the Biacore assay. First, rabbit anti-mouse FC polyclonal antibodies are immobilized to the flow cell. Then antibodies (anti-cd69, anti-cd69+dna Barcode, and Mouse IgG1 isotype control) are captured to the flow cell. Binding stability to either recombinant CD69 (dimer) or recombinant CD47 (negative control) is measured. (d) Anti-CD69, Anti- CD69+DNA barcode, and Mouse IgG1 Isotype control antibodies were captured to 13, 16, and 15 RU, respectively, on the flowcell. (e) Anti- CD69 and anti-cd69+dna barcode both demonstrated binding to recombinant human CD69 protein and did not bind to recombinant human CD47 Nature (negative Biotechnology control). doi:1.138/nbt.3973 The Mouse IgG1 isotype control did not exhibit any binding to either human CD69 or CD47 recombinant proteins.

Donor 1 Log2 (fold change protein) 2-2 HLA-DRA IL7R CD69 CD27 NT5E CD7 4-1BB CD25 GITR CD5 OX-4 R=.48 P value=9.5e 5-1. -.5..5 Donor 2 Log2 (fold change protein) 2-2 IL7R HLA-DRA CD4 CD155 CD69 NT5E CD27 4-1BB CD7 CD25 R=.37 P value=2.8e 3 -.5..5 Donor 3 Log2 (fold change protein) 1-1 -2-3 HLA-DRA IL7R CD27 CD274 NT5E CD5 CTLA-4 GITR R=.17 P value=2.1e 1 -.8 -.4..4 Log2 (fold change mrna) Supplementary Figure 16 Scatter plots looking at the relationship between the change in protein expression and the change in mrna expression between acd27 treated and untreated CD8+ naïve T cells for each of the three donors.

a Proteins increased expression in untreated acd27 b Proteins increased expression in treated acd27 Donor 1 NES=1.65 FDR<.1 NES=1.43 FDR=.3 IL7R HLA-DRA CD27 CD69 CD8A Proteins differentially expressed upon acd27 treatment Genes with increased expression in Control RNA-Seq Genes with increased expression in Treated Proteins differentially expressed upon acd27 treatment Genes with increased expression in Control RNA-Seq Genes with increased expression in Treated CD7 CD5 TNFRSF18 (GITR) CD9 TNFRSF4 (OX-4) NES=1.7 FDR<.1 NES=1.36 FDR=.9 Donor 2 IL7R HLA-DRA CD27 CD69 NT5E Proteins differentially expressed upon acd27 treatment Genes with increased expression in Control RNA-Seq Genes with increased expression in Treated Proteins differentially expressed upon acd27 treatment Genes with increased expression in Control RNA-Seq Genes with increased expression in acd27 Treated CD5 CD44 TNFRSF9 (4-1BB) CD7 IL2RA (CD25) Donor 3 NES=1.31 FDR=.14 NES=1.4 FDR=.41 Proteins differentially expressed upon acd27 treatment Proteins differentially expressed upon acd27 treatment IL7R CD27 NT5E CD4LG CD274 Genes with increased expression in Control RNA-Seq Genes with increased expression in Treated Genes with increased expression in Control RNA-Seq Genes with increased expression in Treated CD4 ICOS CTLA4 CD5 TNFRSF18 (GITR) Supplementary Figure 17 Comparative analysis of differentially expressed proteins and genes using Gene Set Enrichment Analysis (GSEA, Broad). (a,b) Differential gene expression between acd27 treated and untreated cells was used to generate a rank order list where red indicates genes with increased expression in acd27 untreated cells (control) and blue indicates genes with increased expression in acd27 treated cells. GSEA was then used to compare this rank order gene list to a rank order protein list (abs(log fold change)>.2, adjusted p-value <.5). Enrichment plots are shown for (a) proteins that had higher expression in the control acd27 untreated cells and for (b) proteins that had higher expression in the acd27 treated cells. The top 5 correlated proteins are shown for each donor. The horizontal scale bar from red (left) to blue (right) represents the DE genes ranked from highest expression in the acd27 untreated cells (left) to the high expression in the acd27 treated cells (right). The vertical black lines represent the projection of DE protein markers onto the ranked DE gene list. The curve in green corresponds to the calculation of the enrichment score (ES) where the green ES curve shifted to the upper left of the graph (a) indicates an enrichment in genes that show increased expression in acd27 untreated cells. Conversely, the green ES curve shifted to the lower right (b) indicates an enrichment of genes with increased expression in acd27 treated cells. Normalized enrichment scores (NES) and false discovery rate (FDR) q-values are indicated on the upper right corner of each plot.

a Donor 2 tsne2 tsne1 Untreated acd27 cells Treated acd27 cells b Supplementary Figure 18 REAP-seq comparison of mrna versus protein expression upon acd27 stimulation in Donor 2 (a) t-sne visualization plot (on left) based on protein expression for Donor 2 where blue dots indicate cells treated with acd27 (n=4,44) and magenta indicates cells not treated with acd27 (n=622) mrna and protein expression of IL7R, NT5E, CD7, PD1, and CD4 projected on the t-sne visualization plot (on right). Genes and proteins in the acd27 treated cells that had adjusted p-values <.1 and fold changes greater than 1.3 were considered significant (** with red circles). Purple indicates high expression and grey indicates low expression. (b) REAP-seq protein measurements showing increased CD4 expression in Donor 2 acd27 treated naïve CD8+ T cells was validated with flow cytometry.

a 26K Live 26K Singlets 26K PBMCs 2K 2K 2K SSC-A 14K 8K FSC-W 14K 8K SSC-A 14K 8K 2K 2K 2K 1 1 3 1 4 1 5 Live/Dead 26K T-cells 2K 14K FSC-H 1 5 26K Non B cell & Non monocytes 2K 14K FSC-A 26K SSC-A 2K 14K 8K CD14 1 4 1 3 2K 1 b 1 1 3 1 4 CD3 1 5 1 1 3 1 4 CD2 1 5 acd27 drug concentration: 1 ug/ml.25 ug/ml ug/ml 1 5 CD27+ T cells CD27+ T cells CD27+ T cells 1 5 1 5 1 4 1 4 1 4 CD8 BUV737 1 3 1 1 1 3 1 4 1 5 1 3 1 1 1 3 1 4 15 1 3 1 1 1 3 1 4 1 5 c CD27 (Clone M-T271) APC 1 5 T cells Negative Control CD8 BUV737 1 4 1 3 1 1 1 3 1 4 1 5 Mouse IgG1 Isotype control APC Supplementary Figure 19 Flow cytometry experiment showing acd27 drug partially blocks anti-cd27 (clone M-T271) from binding CD27 (a) A representative T cell gating strategy for flow cytometry where Rhesus blood was stained with a phenotypic panel of Abs (Supplementary Table 5). (b) Flow cytometry data showing that the T cells from Rhesus blood that are treated with acd27 drug (1 ug/ml and.25 ug/ml) partially block the CD27- APC monoclonal antibody clone (M-T271, BioLegend) used in the AbB panel compared to no acd27 drug ( ug/ml). (c) T cell staining of a Mouse IgG1 Isotype control conjugated to APC showed minimal background Nature fluorescence Biotechnology signal. doi:1.138/nbt.3973

a Flow Cytometry Data Donor 1 Donor 2 Donor 3 Frequency Frequency CD25-PE-Cy7 CD4-PE-CF594 acd27 treated acd27 untreated b Treated acd27 Donor 1 Donor 2 Donor 3 71.82% 23.6% 4.53% ~14x fold increase.59% 47.6% 47.17% 4.73% ~22x fold increase 1.4% 72.33% 18.55% 8.55% ~5x fold increase.57% Untreated acd27 CD8-APC-Cy7 1 5 1 4 1 3 1 93.5% 1.63% 5.2%.29% 1 1 3 1 4 1 5 94.9% 2.13% 3.59%.19% 93.22% 3.57% 2.94%.27% CD4-PE-CF594 Supplementary Figure 2 Validation of REAP-seq findings using flow cytometry analysis. (a) Flow cytometry histograms showed an increase in CD25 and CD4 expression upon acd27 treatment of the CD8+ naïve T cells in all three donors which is consistent with REAP-seq findings (Supplementary Fig. 14b). (b) Flow cytometry bivariate scatter plots show a 14, 22, and 5 fold increase in the percentage of double positive cells (CD8+/CD4+) in acd27 treated vs untreated naïve CD8+ T cells in Donor 1, 2, and 3, respectively.

a b Donor 1 Donor 2 Donor 3 Analyte mrna & Protein % of cells with both mrna & Protein counts > mrna % of cells with counts Protein % of cells with counts Donor 1 2 3 1 2 3 1 2 3 HLA-DRA 7 4 7 88 95 92 67 83 83 CD7 23 31 4 74 67 95 24 2 56 4-1BB 55 64 26 42 35 71 12 6 33 TIGIT 24 11 5 68 86 93 36 5 57 CD9 51 49 25 48 51 75 4 4 7 CD25 44 62 9 55 37 9 5 4 18 IL7R 3 2 3 91 91 91 84 88 88 ICOS 17 18 8 83 82 92 6 5 1 GITR 3 38 22 29 29 41 62 53 7 CD7 HLA-DRA CD9 TIGIT 4-1BB TNFRSF9 R=.36 R=.68 R=.19 R=.15 R=.29 R=.15 R=.2 R=.21 R=.31 R=.2 R=.7 R=.86 R=.42 R=.32 R=.49 R=.31 R=.36 R=.5 R=.49 R=.48 R=.86 R=.63 R=.22 R=.42 R=.41 R=.24 R=.21 R=.36 R=.31 R=.29 CD25 Supplementary Figure 21 mrna and protein correlations in acd27 treated and untreated naïve CD8+ T cells (a) Statistics of the percentage of cells with zero UMI counts and > UMI counts for mrna and protein expression in naïve CD8+ T cells in each donor. (b) Scatter plots looking at the relationship between protein and mrna expression in acd27 treated (turquoise) and untreated (red) CD8+ naïve T cells for each donor. Pearson R correlation scores including (black) or not including (red) cells with zero UMI protein or gene counts. Scatter plots shown include cells with zero protein or gene UMI counts. Protein (Normailzed Counts) Il7R ICOS GITR R=.21 R=.14 R=.17 R=.25 R=.8 R=.4 R=.11 R=.1 mrna (Normalized counts) R=.37 R=.25 R=.17 R=.59 R=.14 R=-.1 R=.15 R=.9 acd27 untreated acd27 treated R=.17 R=.21 R=.19 R=.45 R=.1 R=-.2 R=.15 R=.2

Supplementary Figure 22 Differential expression at both the protein and transcript level in the outlier cluster. Violin plots showing the expression distribution of the three markers (HLA-DRA, CD27, CD2) that had both differential gene and protein expression in the outlier cluster (purple) compared to the rest of the cells (grey) in all three donors. Expression levels are log transformed, first scaling each cell to a total of 1x1 4 molecules.

Development Transcriptional regulation of megakaryopoiesis pathway Up-regulated (+) in Donor Supplementary Figure 23 MetaCore pathway analysis of the outlier cluster in CD8+ naïve T cells (Fig. 2a). The development transcriptional regulation of megakaryopoiesis pathway was the most significant enriched pathway (FDR 1.2e-5, Clarivate Analytics) for genes and proteins upregulated in the outlier cluster compared to the rest of the cells (fold change >1.5, adjusted p value <.1). Red circles indicate genes that were upregulated in the outlier cluster in at least one of the donors and the red square indicates the two proteins (CD34 and CD38) Nature that Biotechnology were upregulated doi:1.138/nbt.3973 in the outlier cluster in all three donors.

a 26K 97% PBMCs 2K 14K 8K 2K SSC-A 1 1 3 1 4 1 5 Live/Dead ef56 b 26K 2K 14K Untreated acd27 CD8+ Naïve T cells Donor 1 Donor 2 Donor 3 43% 7% 77% 8K 2K SSC-A 1 1 3 1 4 1 5 Live/Dead ef56 Supplementary Figure 24 Representative live cell gating strategy for flow cytometry data (a) PBMCs were initially gated on live cells. (b) acd27 untreated CD8+ naïve T cells were initially gated on live cells. Representative live cell gating strategy for acd27 untreated and treated CD8+ naïve T cells for all three donors.

Supplementary Table 1 List of Antibody Barcodes (AbBs) Experiment Antibody target Ab Barcode Clone Company acd27 Blocking PBMCs CD8+ T buffer cell assay Ab formulation 1 CD45 ACGAGTAG H13 BioLegend x MaxPar Ready 2 CD8 CAATCCCT SK1 BioLegend x x x MaxPar Ready 3 CD4 GTCCAGGC OKT4 BioLegend x x x LEAF Purified 4 Control Mouse IgG1 TGTGTATA MOPC-21 BioLegend x x x LEAF Purified 5 CD3 AGGATCGA OKT3 BioLegend x x x Ultra LEAF Purified 6 CD4 CACGATTC OKT4 BioLegend x x x LEAF Purified 7 CD8 GTATCGAG SK1 BioLegend x x x MaxPar Ready 8 CD45 TCTCGACT H13 BioLegend x MaxPar Ready 9 CD8a ACCCGCAC RPA-TA BioLegend x x x LEAF Purified 1 TIGIT CATGCGTA MBSA43 Ebioscience x x PBS 11 CD45RA GTGATAGT HI1 BioLegend x x Purified 12 CD45RO TGATATCG UCHL1 BioLegend x x Purified 13 CD11b AGGGCGTT M1/7 BioLegend x x x LEAF Purified 14 CD19 CTATACGC HIB19 BioLegend x x x LEAF Purified 15 CD25 (IL2RA) GCTCGTCA PC61 BioLegend x x x LEAF Purified 16 CD56 (NCAM1) TACATAAG HCD56 BioLegend x x x LEAF Purified 17 CD14 AATTGAAC M5E2 BioLegend x x x LEAF Purified 18 CD33 CCAGTGGA WM53 BioLegend x x x LEAF Purified 19 CD152 (CTLA-4) GTCCATTG L3D1 BioLegend x x x LEAF Purified 2 CD223 (LAG3) TGGACCCT C9B7W BioLegend x x x LEAF Purified 21 CD273 (PD-L2) AGCAGTTA MIH18 BioLegend x x x LEAF Purified 22 CD274 (PD-L1) CTTGTACC 29E.2A3 BioLegend x x x LEAF Purified 23 CD279 (PD1) GAACCCGG EH12.2H7 BioLegend x x x LEAF Purified 24 CD357(GITR/ TNFRSF18) TCGTAGAT 621 BioLegend x x x LEAF Purified 25 CD2 ACGCGGAA 2H7 BioLegend x x x Purified 26 CD66b CGCTATCC G1F5 BioLegend x x x Purified 27 CD69 GTTGCATG FN5 BioLegend x x x Purified 28 CD155 TAAATCGT TX24 BioLegend x x x Purified 29 FOXP3 ATCGCCAT 26D BioLegend x x x LEAF Purified 3 Control Mouse IgG2b CATAAAGG MPC-11 BioLegend x x x LEAF Purified 31 Control Rat IgG1 TCACGGTA RTK271 BioLegend x x x LEAF Purified 32 CD9 CACTCAAC HI9a BioLegend x x Purified 33 CD27 (TNFRSF7) GCTGTGTA M-T271 BioLegend x x Purified 34 CD28 TTGCGTCG CD28.2 BioLegend x x LEAF Purified 35 CD4 ATATGAGA HB14 BioLegend x x Purified 36 CD68 CACCTCAG Y1/82A BioLegend x x Purified 37 CD73 (NT5E) GCTACTTC AD2 BioLegend x x Purified 38 CD127 (IL7R) TGGGAGCT A19D5 BioLegend x x Purified 39 CD134 (OX4/ TNFRSF4) ATCCGGCA Ber-ACT35 BioLegend x x Purified 4 CD137 (4-1BB) CCGTTATG 4B4-1 BioLegend x x Purified 41 CD154 (CD4L) GGTAATGT 24-31 BioLegend x x LEAF Purified Continued on next page

Continued Supplementary Table 1 List of Antibody Barcodes (AbBs) Antibody target Ab Barcode Clone Company Blocking buffer Experiment PBMCs acd27 CD8+ T cell assay Ab formulation 42 CD158E1 (KIR3DL1) TAAGCCAC DX9 BioLegend x x Purified 43 CD272 (BTLA) ACCGAACA MIH26 BioLegend x x LEAF Purified 44 CD278 (ICOS) CGACTCTT C398.4A BioLegend x x LEAF Purified 45 CD335 (NKP46) GTTTGTGG 9E2 BioLegend x x LEAF Purified 46 HLA-DR TAGACGAC L243 BioLegend x x LEAF Purified 47 CD197 (CCR7) ACGCTTGG G43H7 BioLegend x x LEAF Purified 48 CD11c CGCTACAT Clone 3.9 BioLegend x LEAF Purified 49 CD38 GAAAGACA HB7 BioLegend x Purified 5 CD85K (ILT3) TTTGCGTC ZM4.1 BioLegend x Purified 51 CD138 ATGGTCGC M115 BioLegend x Purified 52 CD141 CGACATAG M8 BioLegend x Purified 53 CD16 GATTCGCT 3G8 BioLegend x LEAF Purified 54 CD163 TCCAGATA RM3/1 BioLegend x Purified 55 CD366 (TIM-3) ACTACTGT F38-2E2 BioLegend x LEAF Purified 56 CLEC9A (CD37) CGGGAACG 8F9 BioLegend x Purified 57 CD357(GITR/ TNFRSF18) GACCTCTC MSD x PBS 58 HLA-ABC TTATGGAA W6/32 BioLegend x LEAF Purified 59 CD7 ACAGCAAC 113-16 BioLegend x Purified 6 HLA-G CGCAATTT 87G BioLegend x LEAF Purified 61 CD49b GAGTTGCG 12F1 BioLegend x LEAF Purified 62 CD16 TTTCGCGA BY55 BioLegend x Purified 63 Kappa ACCAGTCC MHK-49 BioLegend x Purified 64 Lambda CTTTCCTT MHL-38 BioLegend x Purified 65 CD25 (IL2RA) CTGGACGT M-A251 BioLegend x Purified 66 CD22 GAACGGTC HIB22 BioLegend x Purified 67 CD15 TGTTCACG H198 BioLegend x Purified 68 CD57 ATCTGATC HCD57 BioLegend x Purified 69 CD2 GAGAAGGG TS1/8 BioLegend x LEAF Purified 7 CD5 TCACTCCT UCHT2 BioLegend x Purified 71 CD7 AGATAACA CD7-6B7 BioLegend x Purified 72 CD123 CTTATTTG 6H6 BioLegend x Purified 73 HLA-E GCGGGCAT 3D12 BioLegend x Purified 74 CD13 TACCCGGC WM15 BioLegend x LEAF Purified 75 CD34 ATTGTTTC 561 BioLegend x Purified 76 CD117 CGCAGGAG A3C6E2 BioLegend x LEAF Purified 77 CD64 GCACCAGT 1.1 BioLegend x LEAF Purified 78 CD1 TAGTACCA HI1a BioLegend x Purified 79 CD23 ATTCGTGC EBVCS-5 BioLegend x Purified 8 EpCAM (Control Rat CGCGTGCA G8.8 IgG2a, Anti-Mouse) BioLegend x LEAF Purified 81 CD44 GAATACTG IM7 BioLegend x LEAF Purified 82 Control Mouse IgG2a TCGACAAT MOPC-173 BioLegend x Ultra LEAF Purified

Sample # Sample Description # mapped reads/cell (mrna) # mapped reads/cell (protein) Median genes per cell Median proteins per cell Inflection Point Threshold (Protein UMI counts) # cells (mrna) # cells ( mrna and protein overlap) 1 No AbB PBMCs 33,722 NA 57 NA NA 2,961 NA 2 No Blocking buffer PBMCs 38,236 17,689 553 19 51 3,241 3,158 3 Blocking buffer PBMCs 32,236 8,142 429 17 51 4,676 4,33 4 CD3+ T cell 36,22 7,639 55 13 17 3,821 3,797 5 CD11b+ Myeloid cell 45,797 26,814 524 22 21 2,891 2,883 6 CD19+ B cell 51,729 33,537 681 23 46 1,535 1,533 Supplementary Table 2 QC raw statistics of PBMC experiment. Number of reads before applying the Cell Ranger pipeline (1x Genomics, v1.3) to combine and normalize samples (4,5,& 6) to the same sequencing depth. Cell numbers shown are from before applying filters to remove low-quality cells (mitochondrial read rate >2% and expressed gene counts <25, see Methods).

a Gene # cells with > counts Max counts per cell Average across cells with > counts Percentage (%) of cells with > counts HLA-DRA 415 354 13.9 48.9 MS4A1 (CD2) 124 92 4.7 15.1 CD14 1613 79 2.6 19.6 CD3D 335 66 2.4 37. CD3E 2922 43 2.2 35.6 CD19 59 11 1.6 7.2 CD8A 425 16 1.4 5.2 CD33 456 61 1.4 5.6 CD4 736 24 1.3 9. ITGAM (CD11b) 293 3 1.1 3.6 PVR (CD155) 23 2 1..3 b Protein # cells with > 5 counts Max counts per cell Average across cells with >5 counts Percentage(%) of cells with >5 counts CD2 (MS4A1) 244 955 17.3 29.3 CD33 313 279 164.1 37.8 CD19 1646 4416 19.3 2. HLA-DR 3953 3276 62.7 48.1 CD14 2858 761 48.6 34.8 CD11b (ITGAM) 2884 1245 39.8 35.1 CD155 (PVR) 289 691 34.4 35.2 CD4 4289 1251 23.1 52.2 CD8 1543 252 22. 18.8 CD3 2121 231 11.1 25.8 Supplementary Table 3 Raw UMI count statistics for mrna and protein expression in PBMCs enriched for CD3+ T cells (n=3,797), CD11b+ myeloid cells (n=2,883), and CD19+ B cells (n=1,533). (a,b) Statistics for gene counts and protein counts for markers shown in Supplementary Figure 7c. The cutoff used for the label free mrna assay is >. The cutoff used for the protein assay is >5 due to a low background level from non-specific binding of AbB to cells during labeling step (Supplementary Fig. 5 and 6 ).

Sample Description # mapped reads/cell (mrna) # mapped reads/cell (protein) Median genes per cell Median protein per cell Inflection Point Threshold (Protein UMI counts) # cells detected (mrna) # cells (protein and mrna overlap) 1 Donor 1 no acd27 12,459 16,442 1,33 26 47 637 636 2 Donor 1 no acd27 141,349 22,372 718 26 91 51 58 3 Donor 1 with acd27 43,125 16,64 2,321 33 36 241 2,49 4 Donor 1 with acd27 5,962 9,312 1,993 29 46 1,891 1,891 5 Donor 2 no acd27 112,191 25,879 1,58 26 32 635 635 6 Donor 2 with acd27 42,98 19,81 2,298 31 46 1,954 1,954 7 Donor 2 with acd27 46,121 12,941 1,967 34 62 2,28 2,28 8 Donor 3 no acd27 87,581 19,138 843 2 16 547 543 9 Donor 3 with acd27 45,786 17,82 1,465 26 41 1,98 1,98 1 Donor 3 with acd27 61,56 13,151 976 27 36 1,775 1,775 Supplementary Table 4 QC raw statistics of acd27 samples. Number of reads before applying the Cell Ranger pipeline (1x Genomics, v1.3) to combine and normalize samples for Donor 1 (1,2,3,4), Donor 2 (5,6,7) and Donor 3 (8, 9,1). Cell numbers shown are before filtering was applied to remove low-quality cells (mitochondrial read rate >2% and expressed gene counts <5, see Methods).

Antibody Clone Fluorophore Company Experiment CD2 2H7 FITC BD PBMC CD4 L2 BUV395 BD PBMC CD8a SK1 BUV737 BD PBMC CD45RO UCHL1 AF7 BioLegend PBMC CD45RA 5H9 APC-H7 BD PBMC CD14 PBMC and Rhesus acd27 M5E2 PerCP-Cy5.5 BioLegend CD3 SP34-2 Pacific Blue BD blocking PBMC acd27 ex vivo assay CD69 FN5 FITC BD acd27 ex vivo assay CD4 L2 PE/CF594 BD acd27 ex vivo assay CD25 M-A251 PE/Cy7 BD acd27 ex vivo assay CD8a RPA-T8 APC/Cy7 BioLegend acd27 ex vivo assay CD2 2H7 FITC BD Rhesus acd27 blocking CD3 SP34-2 BUV395 BD Rhesus acd27 blocking CD8 SK1 BUV737 BD Rhesus acd27 blocking CD27 M-T271 APC BioLegend Rhesus acd27 blocking Ms IgG1 Isotype Ctrl MOPC-21 APC BioLegend Rhesus acd27 blocking Supplementary Table 5 List of antibodies used in flow cytometry experiments

Primer name Sequence Length (bp) P7 ATCTCGTATGCCGTCTTCTGCTTG 24 P5 AATGATACGGCGACCACCGAGATCTACAC 29 P5- Sample Index- Part Nextera Read 1(2 bp) AATGATACGGCGACCACCGAGATCTACAC - Sample Index - TCGTCGGCAGCGTCAGATGT 57 Sample Index S517 GCGTAAGA 8 S52 CTCTCTAT 8 S53 TATCCTCT 8 S54 AGAGTAGA 8 S55 GTAAGGAG 8 S56 ACTGCATA 8 S57 AAGGAGTA 8 S58 CTAAGCCT 8 Supplementary Table 6 Protein primer sequences for PCR amplification and library preparation

Sequencing Read Primer Number of cycles Primer Name Primer Sequence Purpose of read TruSeq Read 2 AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC Cell barcode Read 1 Custom 26 TruSeq Read 1 ACACTCTTTCCCTACACGACGCTCTTCCGATCT PhiX Control v3 for diversity Nextera Read 1 TCGTCGGCAGCGTCAGATGTGTATAAGAGACAG Ab barcode i7 Custom 8 TruSeq Read 1 ACACTCTTTCCCTACACGACGCTCTTCCGATCT PhiX Control v3 for diversity i5 Standard 8 N/A N/A Sample index Read 2 Standard 1 N/A N/A UMI 3) i5 (8 cycles) 2) i7 (8 cycles) Protein Library 1) Read 1 (26 cycles) 4) Read 2 (1 cycles) P5 (29 bp) Sample Index (8 bp) Nextera Read 1 (33 bp) Ab Barcode (8 bp) Oligo da (25 bp) UMI (1 bp) TruSeq Read 2 (34 bp) 1x Barcode (14 bp) P7 (24 bp) Supplementary Table 7 Primers used for Illumina paired end sequencing of the protein libraries. 1) Custom primers were used for the first 26 cycle read (TruSeq Read 2 and TruSeq Read 1 primers). The TruSeq Read 2 primer is used to sequence the 14 bp cell barcode. 12 bp of the P7 adapter is also sequenced since a minimum of 26 cycles is required for the first read. The 12 bp P7 adapter sequence is trimmed during downstream analysis. The TruSeq Read 1 primer is used to sequence PhiX to increase library diversity. 2) Then custom i7 primers are used (Nextera Read 1 and TruSeq Read 1) where the Nextera Read 1 primer is used to sequence the unique 8 bp antibody barcode and the TruSeq Read 1 primer is used to sequence PhiX to increase diversity. 3) Next, the standard Illumina i5 primer is used to sequence the 8 bp sample index. 4) Lastly, the standard Illumina Read 2 primer is used to sequence the 1 bp unique molecular identifier (UMI).

P7 (24 bp) N7X (8 bp) Read 2 (34 bp) UMI (1 bp) Poly dt (24 bp) 5 - -3 5 -CAAGCAGAAGACGGCATACGAGAT-N7X-GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT -NNNNNNNNNN-TTTTTTTTTTTTTTTTTTTTTTVN-3 P7 (rev comp) Read 2 (rev comp) UMI Poly dt N7X N71 (rev comp) N72 (rev comp) N73 (rev comp) N74 (rev comp) CAAGCAGAAGACGGCATACGAGAT GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT NNNNNNNNNN TTTTTTTTTTTTTTTTTTTTTTVN TCGCCTTA CTAGTACG TTCTGCCT GCTCAGGA Supplementary Table 8 Custom reverse transcriptase poly (dt) primers for REAP-seq protein assay on bulk samples.