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Time-resolved assessment of single-cell protein secretion by sequencing

Nature Methods volume 20, pages 723–734 (2023)Cite this article

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Abstract

Secreted proteins play critical roles in cellular communication. Methods enabling concurrent measurement of cellular protein secretion, phenotypes and transcriptomes are still unavailable. Here we describe time-resolved assessment of protein secretion from single cells by sequencing (TRAPS-seq). Released proteins are trapped onto the cell surface and probed by oligonucleotide-barcoded antibodies before being simultaneously sequenced with transcriptomes in single cells. We demonstrate that TRAPS-seq helps unravel the phenotypic and transcriptional determinants of the secretion of pleiotropic TH1 cytokines (IFNγ, IL-2 and TNF) in activated T cells. In addition, we show that TRAPS-seq can be used to track the secretion of multiple cytokines over time, uncovering unique molecular signatures that govern the dynamics of single-cell cytokine secretions. Our results revealed that early central memory T cells with CD45RA expression (TCMRA) are important in both the production and maintenance of polyfunctional cytokines. TRAPS-seq presents a unique tool for seamless integration of secretomics measurements with multi-omics profiling in single cells.

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Fig. 1: Schematic representation of TRAPS-seq workflow.
Fig. 2: TRAPS-seq enables integrated profiling of single-cell protein secretion, phenotypes and transcriptomes.
Fig. 3: TRAPS-seq identifies TCMRA cells as the most effective multiple-cytokine-secreting T cells.
Fig. 4: Differential gene signatures associated with cytokine secretion pattern.
Fig. 5: Secretion tracing reveals secretion strength-associated acquisition and maintenance of cytokine-releasing polyfunctionality.
Fig. 6: Secretion tracing with TRAPS-seq identifies cellular and molecular determinants of cytokine secretion dynamics.

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Data availability

Curated hallmark gene sets are publicly available from MSigDB (v7). Single-cell gene expression, surface marker and secreted cytokine abundance data have been deposited to the Gene Expression Omnibus (GEO) under accession number GSE200690. Source data are provided with this paper.

Code availability

The R code to reproduce the analyses in the figures is available from the Zenodo repository (https://doi.org/10.5281/zenodo.7515637).

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Acknowledgements

We thank M. Birnbaum (MIT) and J.Y.-H. Loh (A*STAR) for the generous gifts of Raji and Jurkat cell lines, respectively, used in this study. We acknowledge technical support from the Flow Cytometry Laboratory (NUS Medical Sciences Cluster) for cell sorting and thank K.C. Tang from iHealthtech for assistance in the generation of silicon mold. This work was supported by funding from the National Medical Research Council Individual Research Grant (MOH-000219), Singapore Ministry of Education Academic Research Fund Tier 2 (MOE-000063) and Institute for Health Innovation and Technology (iHealthtech), NUS.

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Authors and Affiliations

  1. Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore, Singapore

    Tongjin Wu & Lih Feng Cheow

  2. Institute for Health Innovation and Technology, National University of Singapore, Singapore, Singapore

    Tongjin Wu, Howard John Womersley & Lih Feng Cheow

  3. Singleron Biotechnologies Pte. Ltd., Singapore, Singapore

    Jiehao Ray Wang & Jonathan Scolnick

  4. Healthy Longevity Translational Research Program, Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

    Jonathan Scolnick

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Contributions

T.W. and L.F.C. conceived and designed the study. T.W. performed the experiments and data analysis, with input from L.F.C. and J.S. in data analysis and assistance from H.J.W. and J.R.W. in sequencing. T.W. and L.F.C. wrote the paper with feedback from J.S. All authors approved the submission.

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Correspondence to Lih Feng Cheow.

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L.F.C. and T.W. are listed as co-inventors on a patent application related to this work (patent application no. PCT/SG2022/050812). The other authors declare no competing interests.

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Nature Methods thanks Xiangjie Li and the other, anonymous, reviewers for their contribution to the peer review of this work. Primary Handling Editor: Madhura Mukhopadhyay, in collaboration with the Nature Methods team. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Characterization of the accuracy of cytokine secretion capture assay.

(A) CapAb-modified PBMC (concurrently for 3 cytokines) were pulsed with saturated concentrations of purified IFN-γ, IL-2 and TNF-α (200 ng/ml each), or subjected to secretion capture assay after 6-hour activation with 50 ng/ml PMA and 1 µg/ml ionomycin. Flow cytometry data shows that CapAb cytokine binding sites are not saturated under physiological secretion conditions. (B, C) Pre-stimulated PBMC (6 hours) and unstimulated Jurkat cells were modified with CapAb and mixed together before subjected to secretion capture measurements (B). Cells with significant cytokine secretion can be found within the stimulated CD3+ lympbocyte population. Instead, negligible cytokine capture was observed on the unstimulated Jurkat cells in this mixture experiment (C). (D) The percentage of cytokine-secreting cells among CD3+ lymphocytes are comparable with or without Jurkat cells added. Data are mean ± S.E.M of three independent experiments.

Source data

Extended Data Fig. 2 Cell clustering and annotation.

(A–C) Unsupervised cell clustering based on the expression of a panel of surface protein markers as listed in B. The ‘negative population’ (blue arrow) was determined as the cluster with the lowest specific protein expression. Shown are UMAP plots for all cells from integrated time points (A) as well as for cells from individual time points (C). (D) Manual cell type annotation based on the expression profile of surface protein markers and a panel of genes relating to immune cell activation/differentiation. (E) Cells from all time points were integrated and clustered by their gene expression profiles (left) and colored by cell subtypes (right) as annotated in D.

Source data

Extended Data Fig. 3 Refined T-cell clustering and annotation.

(A) T cells from Fig. 2F (left) were re-clustered by their surface marker expression (cell numbers for each new T-cell cluster indicated in bracket, middle). For comparison, the new UMAP plot was colored by the original cell annotation (right). (B) The new T-cell clusters were annotated according to surface protein expression and a panel of selected genes associated with T-cell activation/differentiation. (C) Sankey diagram showing the correspondence between the annotations of the original and new clustering.

Source data

Extended Data Fig. 4 Characterization of cell types associated with cytokine secretion pattern over time.

The cytokine secretion patterns of different T-cell subpopulations were shown as Sankey plots (left panel) and heatmap matrices (right panel) for time point of 1 hour, 6 hours, and 16 hours respectively post stimulation. T-cell clusters with less than 40 cells were omitted.

Source data

Extended Data Fig. 5 Measurement of single-cell cytokine secretion in microengraved arrays.

(A, B) Celltrace Far Red-labelled T cells were loaded into the nanowell array at 20,000 cells (A) or 40,000 cells (B). The observed cell occupancies in wells from two independent experiments were presented (A, B). (C) Naïve/memory composition of resting (from resting PBMC) or effector (from extensively expanded T cells) T cells that were used for the testing of on-array SCA. (D) The cells were pre-stimulated with 50 ng/ml PMA and 1 µg/ml ionomycin (‘P + I’) for 3 hours before CapAbs labelling. SCA was carried out in both tubes and arrays as described in Methods. (E) Side-by-side comparison between tube- and array-based SCA using T-cell samples shown in (C). (F) Summary of cytokine capture efficiency using tube- and array-based SCA. Shown were percentage of cells identified to be secreting cytokines and relative intensity of positive signal versus background staining (signal-to-noise ratio). Data are mean of three (array-based) and two (tube-based) independent experiments with individual data points presented.

Source data

Extended Data Fig. 6 Scatter plots of T-cell secretion-transition pattern over time.

Shown were 6-color FACS analyses for the measurement of three cytokines simultaneously over two time periods, relating to Fig. 5. A total of four secretion windows (0, 1, 6, and 16 hour) with two consecutive secretion capture each were performed to track the secretion dynamics of single activated T cells. After the 1st-round of SCA, cells were stained in-situ with a first-set of fluorescent antibodies against IFN-γ, IL-2 and TNF-α to label cytokines secreted during this first period. After buffer exchange and supplementation with additional CapAbs, the cells were further incubated in the sealed nanowells for additional 45 minutes, and stained in situ with a second set of differently-labeled fluorescent antibodies to detect cytokines secreted during the second period.

Source data

Extended Data Fig. 7 Characterization of secretory transition states.

(A) Cell clustering based on single cell transcriptome in secretion tracing experiment with cell numbers each cluster indicated in bracket. (B) Identification of non-T cells (clusters 8 and 11) according to the positive expression of non-T cell-related genes including CD19, MS4A1 and CST3 (encoding CD19, CD20 and cystatin C respectively) but absent expression of other T cell-associated genes. (C) Unsupervised T-cell clustering based on cytokine secretion dynamics derived from two consecutive rounds of secretion capture. The cells initially without any secretion detected were omitted. (D) Cell cluster annotation according to their secretion transition states.

Source data

Extended Data Fig. 8 Characterization of dynamic cytokine state transitions associated with T-cell subtypes.

(A, B) Unsupervised T-cell clustering by surface marker expression with cell numbers each cluster bracketed (A) and annotated according to the expression profile of both cell surface proteins and selected genes associated with T-cell activation/differentiation. (C) UMAP plot as shown in A was colored by secretion transitions in Extended Data Fig. 7C. (D) Distribution of T-cell subpopulations among different secretion transitions.

Source data

Extended Data Fig. 9 Concurrent measurement of the release of cytokine and cytotoxic molecule by SCA.

(A) HyNic-4FB chemistry-based covalent conjugation of anti-CD45 and anti-granzyme B (GrB) to form bispecific antibody. (B) T cells modified with CD45-GrB bispecific antibodies are able to capture extracellular GrB molecules with high efficiency. (C, D) Secretion capture of GrB, IFN-γ and TNF-α over a period of 10 hours in CD8+ T cells (C) and CD8− T cells (D) after stimulation with anti-CD3/CD28 Dynabeads (beads-to-cell ratio = 1:1) in 96-well U-bottom plate.

Source data

Extended Data Fig. 10 Characterization of CD45-independent secretion capture assay in non-immune cells.

(A) Schematic diagram shows the principle of cell-surface biotinylation with Sulfo-NHS-biotin as a universal method to create anchor sites for capture antibodies on cell surfaces when a specific surface marker is not available. Biotinylated cell surfaces can be modified with streptavidin to immobilize biotin-conjugated capture antibodies of any kind. (B) The density of capture antibody binding sites on modified HeLa (cervical epithelial cancer) and A549 cells (lung epithelial cancer) can be tuned by varying the concentrations of Sulfo-NHS-biotin as detected by Streptavidin-PE. (C) Optimization of streptavidin amount needed to maximize the binding of biotinylated capture antibodies on cell surface. (D) IL-8 secretion capture matrices were shown to successfully capture purified IL-8 in staining buffer. (E) IL-8 intracellular cytokine staining for HeLa and A549 cells that were stimulated with 5 ng/ml IL-1β for 4 hours, with the presence of Brefeldin A during the last hour. (F) IL-8 secretion capture assay using HeLa and A549 cells that were pre-stimulated with 5 ng/ml IL-1β for 4 hours. Data are mean ± S.E.M of three independent experiments, two-tailed t-test (E, F).

Source data

Supplementary information

Supplementary Information

Supplementary Methods, Figs. 1–10 and Tables 1 and 2.

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Wu, T., Womersley, H.J., Wang, J.R. et al. Time-resolved assessment of single-cell protein secretion by sequencing. Nat Methods 20, 723–734 (2023). https://doi.org/10.1038/s41592-023-01841-y

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