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Identification, sorting and profiling of functional killer cells via the capture of fluorescent target-cell lysate

Nature Biomedical Engineering volume 8, pages 248–262 (2024)Cite this article

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Abstract

Assays for assessing cell-mediated cytotoxicity are largely target-cell-centric and cannot identify and isolate subpopulations of cytotoxic effector cells. Here we describe an assay compatible with flow cytometry for the accurate identification and sorting of functional killer-cell subpopulations in co-cultures. The assay, which we named PAINTKiller (for ‘proximity affinity intracellular transfer identification of killer cells’), relies on the detection of an intracellular fluorescent protein ‘painted’ by a lysed cell on the surface of the lysing cytotoxic cell (specifically, on cell lysis the intracellular fluorescein derivative carboxyfluorescein succinimidyl ester is captured on the surface of the natural killer cell by an antibody for anti-fluorescein isothiocyanate linked to an antibody for the pan-leucocyte surface receptor CD45). The assay can be integrated with single-cell RNA sequencing for the analysis of molecular pathways associated with cell cytotoxicity and may be used to uncover correlates of functional immune responses.

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Fig. 1: Schematic of PAINTKiller assay.
Fig. 2: Demonstration of PAINTKiller principles using NK-92MI as effector cells and K562 as target cells.
Fig. 3: PAINTKiller-sort reveals inheritable functional heterogeneity in NK cells.
Fig. 4: PAINTKiller enables sensitive identification of cytotoxic primary NK cells.
Fig. 5: PAINTKiller enables concurrent detection of cytotoxicity and cytokine secretion functionality of effector cells.
Fig. 6: PAINTKiller-seq characterizes primary NK cell cytotoxicity and associated molecular traits.
Fig. 7: PAINTKiller-seq reveals the cellular origins of cytotoxic NK cells.

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Data availability

Curated hallmark gene sets are publicly available from Molecular Signatures Database (MSigDB, v.7.4). Sequencing data from PAINTKiller-seq are available from the GEO database, with accession number GSE207508. All data needed to evaluate the conclusions described in this paper are included within the paper and its Supplementary Information. The raw and analysed datasets generated during the study are available for research purposes from the corresponding authors on reasonable request. Source data are provided with this paper.

Code availability

The R code to reproduce the analyses shown in the figures is available on Zenodo at https://doi.org/10.5281/zenodo.8004120.

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Acknowledgements

We acknowledge technical support from the Flow Cytometry Laboratory (NUS Medical Sciences Cluster) for cell sorting. L.F.C. was supported for the research described in this study by the National Medical Research Council (MOH-OFIRG18nov-003), the Ministry of Education of Singapore (MOE-T2EP30120-0008) and Singapore-MIT Alliance for Research and Technology Critical Analytics for Manufacturing of Personalized-Medicine (SMART CAMP) Interdisciplinary Research Group.

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Author notes

  1. These authors contributed equally: Yen Hoon Luah, Tongjin Wu.

Authors and Affiliations

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

    Yen Hoon Luah, Tongjin Wu & Lih Feng Cheow

  2. Critical Analytics for Manufacturing of Personalized-Medicine Interdisciplinary Research Group, Singapore-MIT Alliance in Research and Technology, Singapore, Singapore

    Yen Hoon Luah & Lih Feng Cheow

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

    Tongjin Wu & Lih Feng Cheow

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Contributions

L.F.C., Y.H.L. and T.W. conceived and designed the study. T.W. performed the experiments corresponding to Figs. 2, 4 and 7. Y.H.L. performed the experiments corresponding to Fig. 3. T.W. and L.F.C. performed the PAINTKiller-seq data analysis. L.F.C., T.W. and Y.H.L. wrote the manuscript. All authors commented on the manuscript and approved the submission.

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Correspondence to Lih Feng Cheow.

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Extended data

Extended Data Fig. 1 Sample demultiplexing of PAINTKiller-seq.

a, Unfiltered gene-expression data from two sequencing lanes (‘Exp. 1’ and ‘Exp. 2’). Cells with the following criteria were filtered for downstream analysis: gene numbers > 1,200 and < 8,000, gene-expression counts > 1,000 and < 40,000, mitochondrial genes < 10% of total genes detected. b, The cell samples, including NK-K562 co-culture group (‘NK + K562’), NK only group (‘NK only’) and isotype staining control (‘Isotype ctrl’), were demultiplexed by their staining intensity of anti-β2M hashtags. Cell numbers for each group were shown in bracket.

Extended Data Fig. 2 NK cell identification and cell clustering.

a, b, Cells were clustered by their transcriptional profiles (a), and non-NK cells were excluded according to the expression of lineage-associated genes such as CD3E for T cells and HBA1/HBA2 for K562 cells (b). c, NK cells were re-grouped by their transcriptional profiles. A total of 13 clusters were identified. Cluster 6 and 7 were mostly found in NK sample that has been co-cultured with K562 cells.

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Luah, Y.H., Wu, T. & Cheow, L.F. Identification, sorting and profiling of functional killer cells via the capture of fluorescent target-cell lysate. Nat. Biomed. Eng 8, 248–262 (2024). https://doi.org/10.1038/s41551-023-01089-z

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