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Multiplexed transcriptomic profiling of the fate of human CAR T cells in vivo via genetic barcoding with shielded small nucleotides

Nature Biomedical Engineering volume 7, pages 1170–1187 (2023)Cite this article

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Abstract

The design of chimeric antigen receptor (CAR) T cells would benefit from knowledge of the fate of the cells in vivo. This requires the permanent labelling of CAR T cell products and their pooling in the same microenvironment. Here, we report a cell-barcoding method for the multiplexed longitudinal profiling of cells in vivo using single-cell RNA sequencing (scRNA-seq). The method, which we named shielded-small-nucleotide-based scRNA-seq (SSN-seq), is compatible with both 3′ and 5′ single-cell profiling, and enables the recording of cell identity, from cell infusion to isolation, by leveraging the ubiquitous Pol III U6 promoters to robustly express small-RNA barcodes modified with direct-capture sequences. By using SSN-seq to track the dynamics of the states of CAR T cells in a tumour-rechallenge mouse model of leukaemia, we found that a combination of cytokines and small-molecule inhibitors that are used in the ex vivo manufacturing of CAR T cells promotes the in vivo expansion of persistent populations of CD4+ memory T cells. By facilitating the probing of cell-state dynamics in vivo, SSN-seq may aid the development of adoptive cell therapies.

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Fig. 1: SSN group barcodes for multiplexed scRNA-seq.
Fig. 2: SSN-seq accurately demultiplexes cell subtypes, and identifies distinct CAR T exhaustion transcriptional programs.
Fig. 3: SSN-seq enables direct comparison of human CD19 CAR T cells manufactured ex vivo using various culture conditions.
Fig. 4: Longitudinal transcription profiling of CAR T cells in vivo using SSN-seq.
Fig. 5: Benchmarking SSN-seq in CAR T cells among different donors and across individual mice.
Fig. 6: SSN-seq is compatible with single-cell 5′ assays.

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Data availability

Raw and processed data of the scRNA-seq/SSN-seq experiments are available at the GEO under accession code GSE201647. Other public data used in this study include reference genomes for human (https://useast.ensembl.org, GRCh38) and RNA-seq/scRNA-seq datasets (GSE78220, GSE83978, GSE114631, GSE120575 and GSE150992). The raw and analysed datasets generated during the study are available for research purposes from the corresponding author on reasonable request. Source data are provided with this paper.

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Acknowledgements

We thank N. E. Navin for discussions; D. P. Pollock for technical assistance with scRNA-seq; and E. J. Thompson, Y. Zhu and Y. Chen for their help with scRNA-seq. scRNA-seq was performed at the University of Texas MD Anderson Cancer Center Advanced Technology Genomics Core (ATGC) Facility, which is supported in part by a Core Grant CA016672 (ATGC) and a NIH (1S10OD024977-01) award. Part of the figures were generated using BioRender. This work was supported in part by grants from the NIH to P.K.M. (R01CA236949, R01CA236118, R01CA278940, R01CA272843 and R01CA272844), CPRIT Individual Investigator Research Award (RP220391) and CPRIT Scholar in Cancer Research (RR160078), and the DoD PRCRP Career Development Award (CA181486). X.L. was supported by the CPRIT Training Award (RP210028). Y.Z. was supported by the Prostate Cancer Foundation Young Investigator Award, the CPRIT Training Award (RP210028), Odyssey fellowship by the Theodore N. Law Scientific Achievement Fund and Odyssey Program at The University of Texas MD Anderson Cancer Center.

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Authors and Affiliations

  1. Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Xiaoyin Lu, Shane M. Lofgren & Pawel K. Mazur

  2. Department of Genetics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Yuehui Zhao

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  1. Xiaoyin Lu
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Contributions

X.L. and P.K.M. conceived the project with input from S.M.L. X.L. designed and validated the SSNs and related constructs. X.L. performed all of the experiments, including proof-of-concept scRNA-seq, SSN optimization, CAR T generation and the corresponding SSN-seq. X.L. performed most of the computational data processing and bioinformatic analysis. S.M.L and Y.Z. assisted with data analysis. X.L. and P.K.M. wrote the manuscript, with input from all of the authors.

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Correspondence to Pawel K. Mazur.

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Competing interests

X.L., S.M.L. and P.K.M. have filed a patent application related to SSN-seq. P.K.M. is a scientific consultant and stockholder of Amplified Medicines, Ikena Oncology and Alternative Bio. The other authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Expression of standard direct capture sgRNA (STD.guide) as genetic barcodes yield low deconvolution rates across multiple cell lines.

a, Schematic overview of the multiplexed scRNA-seq experiment using standard direct-capture compatible sgRNA (STD.guide) as group barcode carrier. Cells in each experimental group were lentivirally transduced to express unique sgRNAs (denoted as sgRNA-1 to -5) and a cell surface marker NGFR. b, Quantification of indicated parameters in the multiplexed scRNA-seq experiments (as in (a)). sgRNA assignment rates for each cell type are shown. Lentivirus transduction efficiency for each group was determined by a cell surface marker NGFR+ expression using flow cytometry. c, Cell doublets prediction analysis (by DoubletFinder) of sgRNA-1 (human primary T cells) group indicates that the majority of misassigned cells are formed by transcriptionally distinct (heterotypic) cell doublets. d, Violin plots of mouse PDL1 expression levels in control KPC cells (KPC_WT) and PDL1-overexpressing KPC cells (KPC_PDL1). e, DEGs in control KPC (KPC_WT) and PDL1-overexpressing KPC cells (KPC_PDL1). Adjusted P values were calculated by two-sided Wilcoxon rank-sum test with Bonferroni’s correction. Red dots indicate significant genes with adjusted P-values < 0.05 and Log2(fold change) > 0.25.

Extended Data Fig. 2 Benchmarking performance of sgRNA- and F30-based SSN barcode designs across multiple cell types.

a, Schematics of all sgRNA- and F30-based SSN barcode designs tested in K562 cells. The control groups G01_STD.guide_mock and G02_STD.guide_Cas9 were transduced with lentiviral vectors expressing standard (without SSN) sgRNA (STD.guide) ± dCas9, respectively. G03_SSN.guide_A group expresses sgRNA-based SSN (SSN.guide) with 20 nt group barcode (GBC) whereas G04-G07 groups have 8 nt GBC. Cells in groups G08-G09 express SSN.guide cassette in the reverse orientation. G10_SSN.F30_A, cells express F30-based SSN barcode (SSN.F30) where the capture sequence is inserted into the Arm1 and in G11-G12 groups the capture sequence is inserted into the Arm2. G04-G07, G08-G09 or G11-G12 express GBC with different nucleotide sequences to assess the effect of barcode sequence on performance within the same barcode groups. b, The Pearson correlation analysis of transcription profiles between the indicated SSN-barcoded groups indicates that within the same cell type there are no signs of transcriptome perturbation due to the expression of different SSN barcodes. Of note, human HA.GD2.28z CAR T cells group (G20) exhibits a lower correlation with CD19.28z CAR T cells (G19) or primary human T cell groups (G17-G18) consistent with the observed enhanced exhaustion phenotype (see Fig. 2b). Group combining all identified cell multiples exhibits high transcriptome correlation with K562 groups suggesting that K562 cells are main contributors. The ‘not assigned’ group is highly correlated with human primary T cells congruent with the observed overall lower SSN-assignment rate (see Fig. 2e). c, Quantification of RNA content in each cell type as determined by the number of total RNA UMI counts per cell. Analysis for mouse KPC, human AsPC-1, mouse primary T and human primary T cells (as in Fig. 1c) and human K562, human AsPC-1, mouse EL4 and human primary T cells (as in Fig. 2d). The number above each violin plot indicates the median number of total RNA UMI counts. Note that primary T cells have significantly lower total RNA UMI counts per cell.

Extended Data Fig. 3 Quality control of SSN barcode sequencing library preparation using standard and optimized (enrichSSN) protocol.

a, Comparison of two strategies for SSN library preparation (the same cDNA input of SSN.guide-labelled human primary T cells). Schematics of the standard and optimized (enrichSSN) approach for library preparation with 10x Genomics Feature PCR or optimized enrichSSN amplification and the final library construction. The final SSN-seq libraries quality control was performed using Agilent Bioanalyzer. DNA electropherograms of the final libraries with indicated (arrows) expected product sizes: ~310 bp with 10x Feature PCR or ~280 bp with enrichSSN amplification (due to the omission of the TSO sequence). CBC, cell barcode. UMI, unique molecular identifier. CS1, 10x Capture Sequence 1. GBC, group barcode. TSO, template switch oligo. The TSO adds a common 5′ sequence to full-length cDNA after reverse transcription. b, Quantification of SSN UMI counts per cell and SSN assignment rates using standard Feature PCR or enrichSSN amplification for the final library preparation. Total sequencing reads and sequencing saturation were shown. Higher sequencing saturation indicates that a larger fraction of the library complexity has been captured. To enable fair comparison, only cells bearing the same cell barcodes were included. The enrichSSN strategies generated higher SSN UMI counts per cell. The center line indicates the median and the box marks the 25th and 75th percentiles. The whiskers extend to the smallest/largest value no further than 1.5 times the interquartile range. Outliers beyond the whisker ends are not plotted. The number to the right of each violin plot indicates the median number of UMIs counts. The enrichSSN amplification led to a higher barcode assignment rate compared to the standard PCR protocol.

Extended Data Fig. 4 The treatment with specific pathway inhibitors during the ex vivo manufacturing process modulates transcriptional profiles of the final CAR T cell infusion product.

a, Number of CAR T cells identified by SSN.guide and/or SSN.F30 barcoding (88% and 84%, respectively) indicate similar performances of both barcoding strategies. b, Expression analysis of selected markers of T cell effector (GZMB, GZMK, NKG7) and memory (CCR7, SELL, TCF7) functions for each CAR T cell treatment group (as in Fig. 3f). c, Distribution of CAR T cells from each treatment group in the identified gene clusters as in Fig. 3g based on SSN-seq analyses of pooled CAR T infusion product. The dot plots on the right indicate the corresponding expression of CD8A or CD4 in each cluster. The four CD8+ clusters (C01-C04) and the four CD4+ clusters (C07-C10) include approximately 91% of analysed cells. Of note, clusters C01+C07 contain mainly TWS119-treated, C03+C09 JQ1-treated, C02+C08 are dominated by Combo-treated and C04+C10 are mainly contributed by Mock control groups. d, Identification of active gene signatures in in vivo CAR T cell clusters using AUCell algorithm. AUCell analysis of curated gene sets from the MSigDB Immunologic Signatures database: genes down-regulated in naïve vs. effector CD8 T cells (GSE10239) and naïve vs. activated CD4 T cells (GSE28726) identified significant downregulation of naïve and elevation of activation/effector T cell signatures in clusters C04 and C10 containing mostly the mock-treated cells.

Extended Data Fig. 5 Conditions of ex vivo manufacturing impact long-term in vivo CAR T cells expansion and the ratio of CD4+ and CD8+ CAR T cells in NALM6 leukaemia mouse model.

a, Quantification of the in vivo expansion advantage of each CAR T cell treatment group normalized to Mock_IL2. The fold change is corrected by their input cell proportion (calculated by the distribution in the infusion product). b-c, Enrichment analysis (Fisher′s exact test) of each treatment group within the indicated CD4+ or CD8+ populations of CAR T cells in vivo (b) or the infusion product (c). The odds ratio indicates the ratio of two sets of odds: the odds of a treatment group presenting in one cluster shown in Fig. 4e versus the odds of the treatment group presenting in the remaining cells of all other clusters. Colour of the dots indicates the Log2-transformed odds ratio (blue: <0, underrepresented; red: >0, overrepresented). Dot size indicates the –Log10-transformed false discover rate (FDR) calculated using Benjamini-Hochberg correction. d, The CD8+/CD4+ composition changes in the CAR T infusion product (Day 0) versus persistent CAR T cells retrieved after in vivo tumour rechallenge (Day 42).

Extended Data Fig. 6 Culture conditions of ex vivo expansion impact long-term CAR T cells transcription profiles associated with cell lineage, exhaustion and anti-tumour activity in vivo.

a, Expression analysis of selected markers of T cell lineage (CD4, CD8A), memory (TCF7, CCR7) and effector (GZMB, GZMK, NKG7, PRF1) functions, chemokines (CXCL13), cytokines (IFNG), immune checkpoints (PDCD1, LAG3, HAVCR2, ENTPD1, TIGIT, CTLA4) and transcription factors (TOX, TOX2, EOMES, TBX21) for each cell cluster from the in vivo 8-plex CAR T cells experiment. b, Enrichment analysis (Fisher′ss exact test) of in vivo CAR T cells from each treatment group in the identified 12 phenotypic clusters. Colour of the dots indicates the Log2-transformed odds ratio (blue: <0, underrepresented; red: >0, overrepresented). Dot size indicates the –Log10-transformed false discover rate (FDR) calculated using Benjamini-Hochberg correction. c, The Scissor predicted anti-PD1 responses with inputs from four datasets56,58,59,60 are overlaid on UMAP visualization as in Fig. 4e. The dashed eclipse indicates cluster C01.

Supplementary information

Supplementary Information

Supplementary Figures and Notes, and descriptions of Supplementary Datasets 1–5.

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Supplementary Dataset 1

DEGs within each cluster (from Figs. 2h, 3f, 4f, 5d,f and 6e).

Supplementary Dataset 2

Summary of CAR T cell characteristics of the in vivo SSN-seq study in individual animals (from Fig. 5).

Supplementary Dataset 3

Annotated SSN-seq plasmid sequences.

Supplementary Dataset 4

Summary of quality metrics in all SSN-seq experiments.

Supplementary Dataset 5

PCR primers for SSN amplification.

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Lu, X., Lofgren, S.M., Zhao, Y. et al. Multiplexed transcriptomic profiling of the fate of human CAR T cells in vivo via genetic barcoding with shielded small nucleotides. Nat. Biomed. Eng 7, 1170–1187 (2023). https://doi.org/10.1038/s41551-023-01085-3

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