Two-stage CD8+ CAR T-cell differentiation in patients with large B-cell lymphoma

Cao, Guoshuai; Hu, Yifei; Pan, Tony; Tang, Erting; Asby, Nicholas; Althaus, Thomas; Wan, Jun; Riedell, Peter A.; Bishop, Michael R.; Kline, Justin P.; Huang, Jun

doi:10.1038/s41467-025-59298-w

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Published: 06 May 2025

Two-stage CD8⁺ CAR T-cell differentiation in patients with large B-cell lymphoma

Nature Communications volume 16, Article number: 4205 (2025) Cite this article

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Abstract

Advancements in chimeric antigen receptor (CAR) T-cell therapy for treating diffuse large B-cell lymphoma (DLBCL) have been limited by an incomplete understanding of CAR T-cell differentiation in patients. Here, we show via single-cell, multi-modal, and longitudinal analyses, that CD8⁺ CAR T cells from DLBCL patients successfully treated with axicabtagene ciloleucel undergo two distinct waves of clonal expansion in vivo. The first wave is dominated by an exhausted-like effector memory phenotype during peak expansion (day 8–14). The second wave is dominated by a terminal effector phenotype during the post-peak persistence period (day 21–28). Importantly, the two waves have distinct ontogeny from the infusion product and are biologically uncoupled. Precursors of the first wave exhibit more effector-like signatures, whereas precursors of the second wave exhibit more stem-like signatures. We demonstrate that CAR T-cell expansion and persistence are mediated by clonally, phenotypically, and ontogenically distinct CAR T-cell populations that serve complementary clinical purposes.

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Introduction

Diffuse large B-cell lymphoma (DLBCL), the most common non-Hodgkin’s lymphoma in the United States, is characterized by diffusely proliferating and malignant B cells at nodal or extranodal sites¹. Although up-front chemoimmunotherapy is often curative, patients with DLBCL that is refractory to up-front treatment or relapse following remission (r/r DLBCL) have limited treatment options and poor outcomes^2,3. Effective treatment options were limited until the United States Food and Drug Administration approved autologous CD19-directed chimeric antigen receptor (CAR) T-cell therapies for r/r DLBCL in 2017. Autologous CD19-directed CAR T-cell therapy involves virally transducing a patient’s T cells ex vivo with a CD19-directed CAR—an engineered receptor consisting of an extracellular anti-CD19 single-chain variable fragment, a hinge/transmembrane region, and intracellular costimulatory (CD28 or 4-1BB) and activation (CD3ζ) domains. CAR-transduced T cells (i.e., CAR T cells) are cultured and infused into the patient, where they lyse CD19⁺ lymphoma cells⁴. CD19-directed CAR T-cell therapy has achieved complete response rates of 40–54%^5,6,7,8 for r/r DLBCL, but non-response rates^6,9 and treatment toxicities¹⁰ remain as challenges¹¹.

Development of CAR T-cell formulations with higher response rates and fewer toxicities requires a thorough understanding of how CAR T cells differentiate in patients with r/r DLBCL. The pioneering ZUMA-1 trial (NCT02348216) for axicabtagene ciloleucel (autologous CD28-costimulated CAR T cells) demonstrated that peripheral blood CAR T cells expand, contract, and sometimes persist.⁵ Greater expansion predicted higher response rates, but also higher likelihood of developing treatment toxicities^5,12. Longer persistence predicted durable remission and long-term immunosurveillance with leukemias, but its relevance for preventing DLBCL relapse remains obscure^9,13. Although many factors are associated with expansion and persistence (including CAR design^14,15 and CAR T-cell phenotypes^{16,17,18,19,20}, among others^21,22,23), how and why CAR T cells differentiate into expansive or persistent phenotypes in vivo is still an open question. Addressing this question requires a single-cell approach that integrates CAR T-cell phenotypes and clonal kinetics over longitudinal timepoints. However, existing studies have either focused on CAR T-cell phenotypes^19,24,25 or clonal kinetics²⁶ without integrating both data modalities, or lacked the temporal resolution required to decipher cell fates over longer periods^12,27. Consequently, a complete and longitudinal understanding of CAR T-cell differentiation in vivo has remained elusive.

To comprehensively elucidate CAR T-cell differentiation in vivo, we perform single-cell, multi-modal (paired RNA-seq/CITE-seq/TCR-seq), and longitudinal analyses of CD28-costimulated CAR T cells from infusion product and peripheral blood of seven patients with r/r DLBCL who were complete responders under treatment with axicabtagene ciloleucel. Peripheral blood CAR T cells are sorted using CD19 antigen-tetramers²⁸. Importantly, we report that the CD8⁺ CAR T cells observed during the peak expansion period (day 8–14) have distinct clonotypic repertoires compared to the later CD8⁺ CAR T cells observed during the post-peak persistence period (day 21–28). We further analyze how these two CAR T-cell populations differ with respect to phenotypes, transcriptional profiles, regulatory networks, and infusion product precursors. Our findings not only offer a finer understanding of CAR T-cell biology in vivo, but also inform efforts to develop CAR T cells with improved expansion and persistence.

Results

Study design and clinical findings

To interrogate CD28-costimulated CAR T-cell differentiation in vivo, we longitudinally interrogated the phenotypes and clonal dynamics of CAR T cells from seven patients (P1-7) who achieved complete responses under treatment with axicabtagene ciloleucel (Fig. 1a). Patients were diagnosed with r/r DLBCL and treated at the University of Chicago Medicine between 2019 and 2021 (Table 1). Clinical response was determined by positron emission tomography/computed tomography imaging 30 days after infusion product administration. Complete response was defined as no detectable lymphoma (Fig. S1). To capture phenotypic heterogeneity and clonal dynamics longitudinally, we performed single-cell multi-modal analyses (paired RNA-seq/CITE-seq/TCR-seq via the 10x Genomics platform) on infusion product and peripheral blood biospecimens at three timepoints: peak expansion (T_exp, day 8–14), early post-peak persistence (T_per1, day 21), and late post-peak persistence (T_per2, day 28) (Table 2). The three peripheral blood timepoints were available for all patients, except P3 (only T_exp and T_per1). CAR T cells were sorted from peripheral blood using CD19 antigen-tetramers (representative staining in Fig. 1b, Fig. S2)²⁸. Compared to CD19 antigen-tetramer-negative T cells, CD19 antigen-tetramer-positive T cells specifically expressed the CAR transgene, which validates our sorting strategy (Fig. 1c).

**Fig. 1: CD8⁺ CAR T cells from complete responders undergo a clonotypic shift in vivo.**

Table 1 Patient characteristics and relevant clinical data

Full size table

Table 2 Number of sequenced CD4⁺ and CD8⁺ CAR T cells for each sample

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CD8⁺ CAR T cells undergo a clonotypic shift between T_exp and T_per

To analyze CAR T-cell population dynamics, we tracked CAR abundance (% CAR⁺ of CD3⁺ T cells) in peripheral blood throughout the course of therapy. CAR abundance at peak expansion ranged from 11% to 73% (Fig. 1d). Peak expansion occurred at day 8–14, which is consistent with prior clinical findings⁵. For each timepoint, we quantified proportions of CD8⁺ and CD4⁺ T cells within the total CAR T-cell population by single-cell RNA-seq and CITE-seq. CAR T cells were predominantly CD8⁺ across most patients and timepoints (Fig. S3).

We next analyzed the dynamics of the CAR T-cell clonal repertoire across longitudinal timepoints (T_exp at day 8–14, T_per1 at day 21, T_per2 at day 28) using single-cell TCR-seq. Clone sizes were similar across timepoints (Fig. S4a). Clonotypes did not overlap between patients (Fig. S4b). Repertoire overlap analysis indicated that T_exp clonotypes were significantly distinct from T_per1 or T_per2 clonotypes (Fig. 1e, f, left). In sharp contrast, T_per1 and T_per2 clonotypes overlapped substantially more. As a control, we also analyzed endogenous (non-CAR) T cells from matched timepoints. Unlike with CAR T cells, endogenous T cells did not show distinctive clonotypic patterns (Fig. 1e, f, right), indicating that the shift in clonotypes between T_exp and T_per is CAR-specific. This CAR-specific clonotypic shift was consistent across patients (Fig. S4b). Moreover, the distinction between T_exp and T_per clonotypes is driven by CD8⁺ T cells, and not by CD4⁺ T cells (Fig. S4c). Collectively, these findings indicate that CD8⁺ CAR T cells undergo a clonotypic shift between T_exp and T_per.

CD8⁺ CAR T cells undergo a phenotypic shift from exhausted-like effector memory to terminal effector

Having shown a shift in CD8⁺ CAR T-cell clonotypes between T_exp and T_per, we hypothesized that unique CD8⁺ CAR T-cell phenotypes dominate T_exp and T_per. To test this hypothesis, we filtered CD8⁺ CAR T cells for Uniform Manifold Approximation and Projection (UMAP) and identified six T-cell clusters (Fig. 2a) based on gene and protein markers (Fig. 2b, expanded marker set in Fig. S5a). No cluster was patient-specific (Fig. S5b). All clusters expressed CAR transgene and CD8α, validating our sorting and filtering processes, respectively. All clusters expressed CXCR3, a chemokine receptor that demarks activated T cells.

**Fig. 2: Phenotypic heterogeneity of peripheral blood CD8⁺ and CD4⁺ CAR T cells.**

Examination of gene and protein markers revealed that one of the six clusters represented proliferating T cells (MKI67⁺TOP2A⁺). The remaining five non-proliferating clusters were annotated as central memory (CM, TCF7⁺TBX21^–), effector memory (EM, TCF7⁺TBX21⁺), or terminal effector (TE, TCF7^-TBX21⁺) T cells. The TCF7⁺TBX21^– CM cluster exhibited markers of stemness (IL7R, high CD127) and minimal markers of effectorness (GZMB, CX3CR1). The two TCF7⁺TBX21⁺ EM clusters occupied the lower half of the UMAP and uniquely expressed CXCR6, a chemokine receptor that facilitates trafficking into solid tumors²⁹. One of these EM clusters upregulated markers consistent with early exhaustion (NR4A2, TOX, GZMK, low TIM-3), hence it was designated “exhausted-like EM”. Lastly, the two TCF7^-TBX21⁺ TE clusters occupied the upper half of the UMAP and uniquely downregulated GZMK. One of these TE clusters upregulated markers consistent with late exhaustion (TOX, PDCD1, high TIM-3), hence it was designated “exhausted-like TE”. The other TE cluster was highly clonal (some clone sizes >100), suggesting expansion through proliferation (Fig. S7a). Overall, most CD8⁺ CAR T cells were TBX21⁺ EM or TE, which is consistent with the established link between CD28 costimulation and effector memory (rather than central memory) differentiation^14,15.

Phenotypic compositions of CD8⁺ CAR T cells at T_exp and T_per were compared. CAR T cells at T_exp were predominantly exhausted-like EM (64%) whereas CAR T cells at T_per were predominantly TE (63% for T_per1, 77% for T_per2) (Fig. 2c). These findings were statistically significant and consistent across all seven patients (Fig. 2d, Fig. S5c). Moreover, the large clone sizes within the T_per-specific TE cluster (Fig. S7a) suggest active TE proliferation at T_per. CAR T cells at T_per1 were enriched for EM. From T_per1 to T_per2, the EM proportion decreased while the TE proportion increased, suggesting progressive differentiation from EM to TE. However, the overall phenotypic compositions at T_per1 and T_per2 were more similar than different, which is concordant with findings from repertoire overlap analysis (Fig. 1e, f). Moreover, we observed decreasing proliferating proportions and increasing CM proportions over time (Fig. 2d), though this was not always statistically significant. These changing proportions may suggest some CAR T cells were returning from an activated to a resting phenotype. In conclusion, CD8⁺ CAR T cells phenotypically shifted from exhausted-like EM to TE between T_exp and T_per.

CD4⁺ CAR T cells maintain a memory phenotype with CAR Treg persistence

We next performed clustering, annotation, and longitudinal analyses of CD4⁺ CAR T cells to investigate phenotypes at T_exp and T_per. We identified five T-cell clusters, all expressing CAR transgene and CD4, based on gene and protein markers (Fig. 2e, f; expanded marker set in Fig. S6a). No cluster was patient-specific (Fig. S6b). All clusters expressed CXCR3, indicating T-cell activation and type 1 helper polarization.

One of the five clusters represented proliferating T cells (MKI67⁺TOP2A⁺). The remaining four non-proliferative clusters were annotated as memory (Mem, TBX21^–FOXP3^–), type 1 helper (Th1, TBX21⁺FOXP3^–), or regulatory (Treg, TBX21^–FOXP3⁺) T cells. The two TBX21^–FOXP3^– Mem clusters at the center of the UMAP comprised most of the cells. One of the Mem clusters upregulated IRF7 and interferon-stimulated genes (MX1, OAS1, ISG15), indicating response to type I interferon signaling. This signature suggests dynamic interferon secretion in vivo and is in concordance with type I interferon’s role in memory CD4⁺ T-cell differentiation³⁰. The TBX21⁺FOXP3^– Th1 cluster along the lower half of the UMAP upregulated cytolytic genes (PRF1, GNLY, GZMK, NKG7) and tissue-homing chemokine receptors (CX3CR1, CXCR6), resembling the cytotoxic CD4⁺ CAR T cells described by Melenhorst et al.¹⁹. Lastly, the TBX21^–FOXP3⁺ Treg cluster expressed classic Treg markers (IL2RA, CD25, low IL7R, low CD127) and upregulated IKZF2, indicating a suppressive phenotype³¹. CAR Tregs inhibit conventional CAR T cells^27,32 and portend progressive disease^12,27.

Phenotypic compositions of CD4⁺ CAR T cells at T_exp and T_per were compared. CD4⁺ CAR T cells predominantly exhibited a Mem phenotype (49–82%) across all timepoints (Fig. 2g). This observation was consistent across all patients (Fig. S6c). Between T_exp and T_per1, the Mem proportion significantly decreased while the Th1 proportion significantly increased, indicating that CD4⁺ CAR T cells may be increasingly polarized during the early contraction period (Fig. 2h). Moreover, across all patients, the Treg proportion steadily increased from T_exp (5%) to T_per1 (11%) to T_per2 (21%), indicating that CAR Tregs were maintained in peripheral blood after peak expansion. The CAR Treg cluster was predominantly non-clonal (Fig. S7b), suggesting CAR Tregs were maintained through persistence rather than proliferation. While previous studies have only investigated CAR Tregs at day 7^12,27, this current study affirms and extends the presence of CAR Tregs until at least day 28. Importantly, persistence of CAR Tregs points towards their possible involvement in decreasing acute inflammation and restoring immune homeostasis after peak expansion. In conclusion, we discovered that CD4⁺ CAR T cells did not exhibit an abrupt T_exp-to-T_per phenotypic shift. Rather, they consistently exhibited a memory phenotype with CAR Tregs persisting over the course of therapy.

Integration of clonotypic and phenotypic shifts in CD8⁺ CAR T cells supports a two-stage differentiation model

Having observed shifts in both CD8⁺ CAR T-cell clonotypes and phenotypes between T_exp and T_per, we hypothesized the existence of two distinct waves of in vivo clonal expansion. To test this hypothesis, we linked the clonotype and phenotype of single CD8⁺ CAR T cells in our dataset using their cell barcodes as unique indices. After this linking process, we confirmed that the proportions of T cells with exhausted-like EM (EM-exh) and terminal effector (TE) phenotypes (Fig. 3a) are consistent with prior results (Fig. 2c). A phenotype label was subsequently assigned to each CD8⁺ CAR T-cell clone based on its predominant phenotype at each timepoint. For each patient, we tracked the total abundances of clones sharing a common phenotype label across timepoints. Notably, the abundance of EM-exh clones at T_exp was significantly reduced at T_per1 and T_per2 (Fig. 3b, left), while the abundance of TE clones at T_per1 and T_per2 was significantly reduced at T_exp (Fig. 3b, middle and right). In addition to redemonstrating the shifts in CD8⁺ CAR T-cell clonotypes and phenotypes, these findings support the existence of two distinct waves of in vivo clonal expansion. (Fig. 3c).

**Fig. 3: Integration of clonotypic and phenotypic shifts supports a two-stage differentiation model.**

Next, we evaluated the explanatory power of our hypothesis for two distinct clonal waves. To minimize noise, we filtered for the top 500 largest clones (mean size 10.8, minimum size 4). Resulting clones were annotated with the following three definitions, based on our hypothesis for two waves: “Wave 1” (phenotypically EM-exh with highest abundance at T_exp), “Wave 2” (phenotypically TE with highest abundance at T_per1/T_per2), or “Other” (not Wave 1 or Wave 2). Wave 1 and Wave 2 clones together accounted for a significant majority (76%) of the top 500 clones (Fig. 3d). In accordance with our definitions, Wave 1 clones were maximally abundant at T_exp, while Wave 2 clones were maximally abundant at T_per (Fig. 3e). Wave 1 clones predominantly exhibited an EM-exh phenotype at T_exp and sharply decreased in abundance at T_per (Fig. 3f, left). In contrast, Wave 2 clones predominantly exhibited a TE phenotype at T_per and sharply decreased in abundance at T_exp (Fig. 3f, right). To investigate whether these findings are dependent on clone size, we expanded our analysis to the top 3000 largest clones (mean size 3.7, minimum size 2). Under this filter, Wave 1 and Wave 2 clones together continued to account for a significant majority (79%) of the top 3000 clones (Fig. S8a) and reaffirm the expected clonal and phenotypic dynamics (Fig. S8b, c). Therefore, our hypothesis for two distinct waves of in vivo clonal expansion has high explanatory power and is robust to clone size.

In conclusion, the two distinct waves of in vivo clonal expansion strongly substantiate a two-stage CD8⁺ CAR T-cell differentiation model (Fig. 3g). Under this model, some CAR T cells (Wave 1) with an exhausted-like EM phenotype expand earlier to dominate the peak expansion timeframe (T_exp), while other CAR T cells (Wave 2) with a TE phenotype expand later to dominate the post-peak persistence timeframe (T_per). Of note, the two-stage differentiation model uncouples CD8⁺ CAR T cells from peak expansion and post-peak persistence by designating these two waves as separate lineages. Moreover, these findings provide evidence against the intuitive idea that the post-peak contraction in CAR abundance is solely apoptosis or extravasation of short-lived CAR T cells from peak expansion. Rather, even as total CAR abundance contracts after peak expansion, a distinct subset of CAR T-cell clones simultaneously expands to eventually dominate the peripheral blood CAR T-cell repertoire.

T_exp- and T_per-specific transcriptional signatures and regulatory networks

We set out to identify the molecular determinants underlying CD8⁺ CAR T cells at T_exp and T_per using gene set enrichment analysis (GSEA), differential gene expression analysis (DGEA), and regulatory network analysis. For internal validation, the average expression of timepoint-specific genes (see Methods) for each sample (patient by timepoint) were hierarchically clustered and compared via correlation (Fig. 4a). Samples largely clustered by timepoint, validating the existence of patient-independent molecular signatures. We then validated our dataset against external data from Maus et al., which consisted of CD8⁺ CAR T cells from patients with large B-cell lymphoma (Fig. 4a, b, colored in tan and brown, six in total after filtering)²⁷. Based on their sample collection timing (day 7), external data from Maus et al. should resemble our samples at T_exp, rather than at T_per. Consistent with expectations, external data resembled T_exp transcriptomes on both pseudo-bulk (Fig. 4a, via correlation) and single-cell (Fig. 4b, via label transfer) levels, which increases the external validity of our findings.

**Fig. 4: Transcriptional signatures and regulatory networks of CD8⁺ CAR T cells at T_exp and T_per.**

To elucidate the immunological processes at each timepoint, CD8⁺ CAR T-cell transcriptomes at T_exp and T_per from our seven patients were compared via GSEA (Fig. 4c and data file S1 for single-cell method, Fig. S9a and data file S2 for pseudo-bulk method) and DGEA (Fig. 4d, Fig. S9b, data file S3-4). CAR T cells at T_exp upregulated gene sets for cell cycling and apoptosis, consistent with a short-lived phenotype. Upregulation of the cell cycling gene set remained true even when only comparing the proliferating cell cluster at T_exp and T_per (Fig. S9d). This indicates that, while proliferating cells are present at both T_exp and T_per, those at T_exp are more proliferative than those at T_per. We also observed T_exp-specific and cluster-independent upregulation of genes related to T-cell activation (including CD69, CD44, CD74) and exhaustion (including PRDM1, CTLA4, NR4A2), implicating antigen-engagement and CAR-mediated signaling during T_exp. On the other hand, CAR T cells at T_per upregulated gene sets for cytotoxicity and immune regulation. Expression of cytotoxicity-related genes (including KLRB1, FCGR3A, PRF1, GZMB, NKG7) was restricted to TE and exhausted-like TE clusters, indicating that this gene signature arises from T_per-specific preponderance of the terminal effector phenotype. We also observed T_per-specific upregulation of regulatory genes, including GTPase immune-associated proteins (GIMAP5, GIMAP7) which predict long-term persistence³³, as well as sphingosine-1-phosphate receptors (S1PR1, S1PR5) which indicate blood localization³⁴. These signatures suggest that CAR T cells at T_per are functional and capable of long-term persistence in peripheral blood. Lastly, downregulation of genes related to T-cell activation and exhaustion implicate decreased in vivo antigen load and/or CAR-mediated signaling during T_per.

Interestingly, some differentially expressed gene signatures between CD8⁺ CAR T cells at T_exp and T_per pertained to cytokine signaling (Fig. 4c, d, see Fig. S9c). CAR T cells at T_exp upregulated TNF response genes (including NFKBIA, NFKBIE, DUSP4, RELB). TNF can be secreted by CAR T cells for autocrine signaling²⁰, predicts efficacious CAR T cells²⁰, and contributes to CRS³⁵. On the other hand, CAR T cells at T_per upregulated type I interferon (IFN-I) response genes (including STAT1, IFITM1, IFITM2, IRF7). Many IFN-I response genes have known antiviral functions. The temporally specific and cluster-independent upregulation of TNF and IFN-I response genes indicates a dynamic in vivo cytokine environment during the CAR T-cell immune response.

Next, we investigated T_exp- and T_per-specific regulatory networks using a machine-learning model (Fig. 4e, see Methods). Single-cell transcriptomes were transformed into regulomes to calculate regulon scores. A machine-learning model was trained using the regulon scores to classify CD8⁺ CAR T cells at T_exp and T_per. This strategy resulted in high (~90%) classification accuracy across patients (Fig. S10a). Top T_exp- and T_per-determining regulons were identified based on each regulon’s importance (SHAP value) for the model’s prediction (Fig. 4f, data file S5). Sizes of timepoint-determining regulons span approximately 10–1000 genes (Fig. S10b). Regulon expression was compared between cell clusters and timepoints (Fig. 4g). T_exp- and T_per-determining regulons were broadly upregulated at T_exp and T_per, respectively. However, expression of some regulons was cluster-specific (e.g., FLI1 regulon among EM clusters).

The top three T_exp-determining regulons (for JUND, RELB, BHLHE40) exhibited higher expression among non-proliferating clusters and included co-regulated inflammation-associated genes (including NFKBIA, TNFAIP3, DUSP5) (Fig. 4h, see Fig. S11a)³⁶. The RELB and BHLHE40 genes themselves are included in the JUND regulon, indicating they may be directly upregulated by JUND. These three regulons were upregulated at T_exp and suggest response to pro-inflammatory cytokines (such as TNF, discussed above) or CAR signaling. BHLHE40 rewires mitochondrial metabolism for tissue residency³⁷, which may contribute to CAR T-cell fitness after entering the lymphoma. Shared genes between the JUND and BHLHE40 regulons included AP-1 transcription factors (JUN, JUNB, FOSL2), which have been implicated in T-cell and CAR T-cell exhaustion^38,39, and may contribute to the exhausted-like EM phenotype that predominates the first clonal wave at T_exp. Among remaining T_exp-determining regulons, FLI1 is notable for antagonizing effector T-cell differentiation⁴⁰. Consistent with known biology, expression of the FLI1 regulon was upregulated among EM clusters, downregulated among TE clusters, and did not significantly change between T_exp and T_per within TE clusters.

On the other hand, T_per-determining regulons were more obscure (Fig. 4i, see Fig. S11b). Among top T_per-determining regulons, KLF13 regulates T-cell apoptosis⁴¹, BPTF activates a stemness gene expression program⁴² and maintains peripheral T-cell homeostasis⁴³, and ELF4 promotes memory CD8⁺ T cells by regulating quiescence⁴⁴. Altogether, the putative functions of the KLF13, BPTF, and ELF4 regulons may underlie the delayed expansion and persistence of the second clonal wave at T_per. Moreover, these functions indicate restoration of immune homeostasis during T_per. The TBX21 regulon was notable for effector-associated genes (including NKG7, ZEB2) and was upregulated within non-CM clusters, consistent with preponderance of the TE phenotype at T_per. The TBX21 regulon also includes S1PR5, which is a known T-BET target⁴⁵ and retains T cells in peripheral blood³⁴. Lastly, the STAT1 regulon was notable for interferon-associated antiviral genes (including OAS2, ISG20), in concordance with upregulation of IFN-I signaling gene sets at T_per.

CD8⁺ CAR T cells at T_exp and T_per originate from distinct precursors in the infusion product

The distinct clonotypes, phenotypes, transcriptional signatures, and regulatory networks underlying the two waves of CD8⁺ CAR T cells led us to hypothesize that they originate from distinct precursors in the infusion product. To test this hypothesis, we first investigated infusion product phenotypes. After filtering for infusion product CD8⁺ T cells, we identified four clusters based on gene expression density (Fig. 5a, b, Fig. S12b) and levels (Fig. 5c, Fig. S12a): proliferating (high MKI67), activated naïve-like (naïve-act, high TCF7), type 1 effector (EFF-Tc1, high TBX21), and type 2 effector (EFF-Tc2, high GATA3) T cells. The TCF7^hi naïve-act cluster upregulated markers of naiveness (IL7R, LEF1, CCR7, CD45RA) and T-cell activation (CD38, CD95), consistent with an early activated or stem cell-like memory phenotype. The two EFF clusters upregulated cytolytic molecules (GZMB, PRF1, GNLY) and downregulated markers of naiveness (IL7R, LEF1). EFF-Tc1 and EFF-Tc2 were distinguished by expression density of lineage-specific transcription factors (TBX21 for Tc1, GATA3 for Tc2) and receptors (KLRG1 for Tc1, CCR4 for Tc2). The EFF-Tc1 cluster exhibited lower CAR transgene expression than the EFF-Tc2 cluster (Fig. S12b, c), which may impact their functional phenotypes in vivo^46,47. The three non-proliferating clusters upregulated IRF7 and anti-viral genes (ISG20, IFITM1, IFITM2), indicating type I interferon signaling during ex vivo transduction and/or expansion. Cluster proportions varied between patients (Fig. S12d). In general, proliferating T cells (54%) were the most prevalent, while proportions of non-proliferating phenotypes (naïve-act, 14%; EFF-Tc1, 11%; EFF-Tc2, 21%) were mutually comparable (Fig. 5e, top row).

**Fig. 5: Infusion product precursors of peripheral blood CD8⁺ CAR T cells.**

Having established infusion product phenotypes, we next performed TCR lineage tracing analysis to developmentally link infusion product CAR T cells with peripheral blood CAR T cells using their endogenous TCR clonotypes as unique indices. This method delineates infusion product precursors of CD8⁺ CAR T cells at T_exp (“Pre-T_exp”) and at T_per (“Pre-T_per”) (Fig. 5d). Both Pre-T_exp and Pre-T_per comprised all four infusion product clusters (Fig. 5e, bottom rows) and largely overlapped on the UMAP (Fig. 5f). Hence, we conclude that coarse cluster phenotype alone cannot accurately predict in vivo differentiation. Compared to T cells not linked to peripheral blood (“IP only”), Pre-T_exp and Pre-T_per cells upregulated expression of effectorness (including GZMB, TNF, GNLY) and type 1 polarization (including IFNG, KLRG1) genes, as well as lower expression of stemness (including SELL, IL7R) and type 2 polarization (including GATA3, IL4R) genes (Fig. 5g). The significance of type 1 versus type 2 polarized CD8⁺ CAR T cells is not well-understood in the literature and warrants future studies. Pathway analysis indicated that Pre-T_exp and Pre-T_per upregulated gene sets for T-cell effector function (“PD-1 signaling”, “T-cell-mediated cytotoxicity”), response to interferon γ (“interferon γ signaling”, “antigen processing and presentation”), and DNA replication, whereas non-linked CAR T cells upregulated gene sets for stemness (Fig. 5h, data file S6). Our discovery of CD8⁺ CAR T-cell precursors with an effector-like phenotype is corroborated by findings from Thomas et al., who also identified effector-like precursors from CAR T-cell patients treated for B-cell acute lymphoblastic leukemia⁴⁸. Possible fates for the non-linked, naïve-like, IP-only CAR T cells include extravasation into the lymphoma, expansion in peripheral blood outside the T_exp-T_per window (day 8–28), or failure to persist in vivo.

Lastly, we directly compared transcriptomic signatures of Pre-T_exp and Pre-T_per via GSEA. Although both Pre-T_exp and Pre-T_per broadly exhibited an effector phenotype (Fig. 5g, h), Pre-T_exp upregulated effectorness-associated gene sets (including TCR, PD-1, interferon γ, and IL-1 signaling), whereas Pre-T_per upregulated stemness-associated gene sets (Fig. 5i). In concordance, DGEA showed that Pre-T_exp upregulated effector molecules (GZMB, GZMK, FCGR3A, NKG7, KLRG1), AP-1 transcription factors (JUN, JUND, FOS), and MHC class II expression (HLA-DRB1, HLA-DRB5, HLA-DRA), whereas Pre-T_per upregulated naïve-like markers (SELL, IL7R, S1PR1) (Fig. S12e, data file S7). Upregulation of JUN in Pre-T_exp confers exhaustion resistance³⁸, potentially underlying their eventual efficacy at T_exp. Interestingly, Pre-T_per exhibited greater CAR transgene expression (Fig. S12e), which may reflect transduction differences among apheresis precursors or a link between CAR expression and in vivo differentiation⁴⁷. Next, we compared Pre-T_exp and Pre-T_per using transcriptomic signatures from a human CD8⁺ differentiation atlas from Giles et al.⁴⁹. Pre-T_exp more closely resembled an effector memory state, whereas Pre-T_per more closely resembled either a stem-cell memory or central memory state (Fig. 5j). Altogether, these patterns indicate that, while both Pre-T_exp and Pre-T_per exhibited an effector phenotype in the infusion product, Pre-T_exp were more differentiated with greater effectorness, whereas Pre-T_per were less differentiated with greater stemness.

In conclusion, TCR lineage tracing analysis supports our hypothesis that CD8⁺ CAR T cells at T_exp and T_per originate from different infusion product precursors. Integrating these findings into the two-stage differentiation model paints a more complete picture of CD8⁺ CAR T cells over the month following CAR T-cell administration (Fig. 5k). Effector CD8⁺ CAR T cells exist along a gradient of effectorness and stemness in the infusion product. Following infusion into the patient, effector CAR T cells with greater effectorness rapidly expand until peak expansion (T_exp, days 8–12), adopting a functional and cytotoxic EM phenotype with exhaustion-like characteristics upon antigen stimulation. Lymphoma infiltration and in vivo killing⁵⁰, CRS⁵, and ICANS⁵ coincide with the T_exp timeframe, suggesting that these CAR T cells mediate tumor clearance and side effects. Subsequently, this first wave of expanded CAR T cells diminishes through apoptosis or extravasation. Simultaneously, the remaining effector CAR T cells with greater stemness from the infusion product expand during the post-peak persistence timeframe (T_per, day 21–28). These newer and longer-lived CAR T cells adopt effector characteristics and persist in vivo, where they may ensure a durable response through long-term immunosurveillance.

CD8⁺ Exhausted-like EM CAR T cells at T_exp exhibit characteristics of early exhaustion

Discovery of the predominant exhausted-like EM phenotype in peripheral blood at T_exp may be surprising since exhaustion was conventionally defined in lymphoid organs or tumor-infiltrating lymphocytes⁵¹. To verify this annotation, we further characterized the molecular signatures of the exhausted-like EM cluster. The exhausted-like EM cluster was densely situated at the lower half of the UMAP (Fig. 6a, b). We used RNA-seq/CITE-seq to examine gene and protein expression density, respectively (Fig. 6c, Fig. S13a for additional markers). The exhausted-like EM cluster highly expressed exhaustion-associated transcription factors (NR4A2, TOX, IRF4) and inhibitory receptors (ENTPD1, PDCD1, TIGIT, LAG3, CTLA4). Intermediate expression of memory (TCF7, LEF1, CD27, IL7R) and effector (TBX21, GZMB, PRF1, GNLY, IFNG, NKG7) genes was also observed. Additionally, the cluster exhibited low expression of CX3CR1 (effector lineage marker) and B3GAT1 (senescence marker). CITE-seq measurements of protein expression for cell surface receptors (including CD39, PD-1, TIGIT, LAG3, CD27, CD57, CD127, TIM-3, CXCR3) were largely consistent with corresponding gene expression. Sequencing data indicated that high expression of CD39 (protein for ENTPD1) and low expression of CD57 (protein for B3GAT1) most clearly differentiated the exhausted-like EM cluster from other clusters. To validate these characteristics, flow cytometry was employed to analyze a set of longitudinal patient PBMCs (Fig. S13b for gating). Consistent with sequencing data, flow cytometry data demonstrated that the CD39⁺CD57^– phenotype was highest among CD8⁺ CAR T cells at T_exp (75%) compared to at T_per1 (33%) and T_per2 (18%) (Fig. 6d, top row). Furthermore, flow cytometry showed decreased CX3CR1 expression at T_exp, and increased expression at T_per1 and T_per2, in agreement with sequencing data (Fig. 6d, bottom row). This mixed expression pattern of exhaustion, memory, and effector markers resembles the circulating PD1⁺CD39⁺ T_ex cells described by the Wherry group’s human T-cell differentiation atlas⁴⁹.

**Fig. 6: Transcriptomic signatures of exhausted-like EM CD8⁺ CAR T cells.**

Next, we conducted GSEA using gene sets from Wherry et al. that identified T-cell differentiation states in the Armstrong/clone 13 LCMV model (Fig. 6e).⁵² These gene sets constitute comprehensive references for defining T-cell exhaustion because they (1) incorporate standing knowledge from the entire field, (2) integrate single-cell RNA-seq and ATAC-seq data from serial timepoints, and (3) originate from the Wherry group where comprehensive transcriptional signatures of T-cell exhaustion and most exhausted subsets were first defined. The two EM clusters (EM and exhausted-like EM) in our dataset correlated with different states from the Wherry group’s model. Our EM cluster resembled the “transitional I” T cells from the Armstrong model, indicating that these cells are not exhausted. Conversely, our exhausted-like EM cluster resembled the “precursor exhausted” T cells from the clone 13 model (Fig. 6f, left). Simultaneously, the exhausted-like EM cluster did not resemble the “terminally exhausted” T cells from the same model (Fig. 6f, right), which instead correlated with our exhausted-like TE cluster. Given clear differences between the EM and exhausted-like EM clusters, we directly compared their transcriptomic signatures through DGEA and GSEA. The exhausted-like EM cluster upregulated genes associated with T-cell activation (CD69, CD38, DUSP4, NFKBIA) and exhaustion (TOX, NR4A2, PRDM1, CTLA4) (Fig. 6g, data file S8). In contrast, the EM cluster upregulated genes associated with effector function (TBX21, CX3CR1, GZMB, PRF1, FCGR3A), memory (IL7R, S1PR1), and type I interferon signaling (STAT1, IFITM1, IFITM3). GSEA revealed that the exhausted-like EM cluster exhibited more exhaustion signatures and fewer effector signatures compared to the EM cluster (Fig. 6h, left column, Fig. S13c). GSEA also confirmed that the exhausted-like EM cluster resembled the “exhausted progenitor” and “exhausted precursor” subsets from Wherry et al. (whereas the EM cluster resembled the “transitional I” subset) (Fig. 6h, middle and right columns). Our findings suggest that the exhausted-like EM cluster exhibits robust gene and protein signatures of early exhaustion (justifying the “exhausted-like” annotation), yet is also phenotypically distinct from effector memory T cells.

Discussion

In this study, we use a combination of single-cell RNA-seq/CITE-seq/TCR-seq and longitudinal analyses to investigate CD28-costimulated CAR T-cell differentiation in seven patients with r/r DLBCL. Our findings are summarized in our two-stage model for CAR T-cell differentiation (Fig. 3g). Specifically, CD8⁺ CAR T cells undergo two distinct clonal expansion waves (at peak expansion and post-peak persistence timeframes), as revealed by clonotypic, phenotypic, and linked clonotypic-phenotypic analyses. The two waves are dominated by exhausted-like effector memory and terminal effector phenotypes, respectively. The exhausted-like effector memory annotation is supported by a CD39⁺CD57^- flow phenotype, low CX3CR1 expression, and an early exhaustion signature. We also identify transcription factors and regulatory networks associated with the first wave (including JUND, RELB, BHLHE40, FLI1 regulons) and second wave (including KLF13, BPTF, ELF4, TBX1, STAT1 regulons). Lastly, lineage tracing analysis determined that CD8⁺ CAR T cells from both waves derive from effector precursors in the infusion product. However, effector precursors of the first wave exhibit more effector-like signatures, whereas effector precursors of the second wave exhibit more stem-like signatures (Fig. 5k), suggesting that pre-infusion heterogeneity mediates two-stage differentiation. Our two-stage model implies that manipulating the phenotypic composition of the infusion product may allow more precise control over in vivo CAR T-cell differentiation for modulating therapeutic efficacy.

Although two-stage differentiation is a phenomenon that has not yet been reported in the literature, the individual elements of our model are consistent with current understanding in the field. Previous studies on 4-1BB-costimulated CAR T-cell differentiation described bursts of CAR T-cell clonal expansion that lead to significant changes in the CAR T-cell clonal repertoire over time^19,53. Our study not only generalizes this observation to CD28-costimulated CARs, but also directly pairs changes in the clonal repertoire with changes in T-cell phenotypes. Furthermore, previous reports have described CAR T-cell clones with memory-like phenotypes that exhibit delayed expansion after infusion²³, which corroborates with the second clonal expansion wave at T_per. Finally, the T_exp- and T_per-specific phenotypes described in our model are consistent with prior phenotyping studies^{25,27,48,54,55}. We also report agreement between our transcriptomes at T_exp and external data from Maus et al.²⁷. By integrating our findings from multiple data modalities with established studies in the CAR T-cell field, we present a more complete and coherent two-stage model for in vivo CD28-costimulated CAR T-cell differentiation.

The two-stage differentiation model we present in this study has an important implication. CD28-costimulated CD8⁺ CAR T cells from the peak expansion and post-peak persistence timeframes are biologically uncoupled. This uncoupling significantly informs how we understand CAR T-cell expansion and persistence. Expansion facilitates rapid tumor clearance but can also cause CRS and ICANS^5,17, while persistence facilitates long-term immunosurveillance but can also cause B-cell aplasia and hypogammaglobulinemia^9,13,56. Our findings suggest that expansion and persistence, both of which serve complementary clinical purposes, are mediated by distinct CD8⁺ CAR T-cell populations. Effective and personalized CAR T-cell therapies balance expansion and persistence, taking into consideration each patient’s tumor burden, tolerance for side effects, and risk of relapse. Our findings suggest that expansion and persistence may be independently tuned to meet a patient’s needs. On the other hand, our findings also suggest that engineering CD8⁺ CAR T cells that simultaneously expand and persist competently may be challenging, given that these characteristics originate from uncoupled CAR T-cell populations in vivo.

In addition to clonal kinetics and phenotypic heterogeneity, we characterized T_exp- and T_per-specific upregulation of TNF and IFN-I response gene sets and regulons, respectively. Notably, TNF-secreting CD8⁺ CAR T cells have been associated with complete responders²⁰, suggesting that TNF may be signaling in an autocrine or paracrine manner during T_exp. Upregulation of IFN-I response genes during T_per was intriguing. IFN-I response signatures in the apheresis of patients with B-cell acute lymphoblastic leukemia predict poor CAR T-cell persistence⁵⁷, but IFN-I signaling may also enhance CAR T-cell efficacy in vivo⁵⁸. Complicating this story further, we also identified IFN-I response signatures in the infusion product, which implies that interferons are secreted by an unknown source during ex vivo transduction and/or expansion. Hence, the possible roles of IFN-I in CAR T-cell differentiation are likely complex, context-dependent, or time-dependent, and warrant further mechanistic investigation.

We concluded our study by using single-cell TCR-seq to link CD8⁺ CAR T cells at T_exp and T_per with effector precursors in the infusion product that exhibit more effector-like or more stem-like signatures, respectively. Discovery of effector precursors is externally corroborated by findings from Thomas et al.⁴⁸. Importantly, this linkage suggests that the magnitude or duration of peak expansion and post-peak persistence can be modulated by manipulating the relative quantities of precursors in the infusion product. However, we cannot rule out the possibility that the in vivo peripheral blood environment in a patient with DLBCL also plays an instrumental role in determining whether a CAR T cell clonally expands at T_exp or T_per. Future studies can investigate this possibility by analyzing interactions between CAR T cells and other blood cells (such as myeloid cells, B cells, or NK cells) at T_exp and T_per.

The present study focuses on CD28-costimulated CAR T cells from complete responders, but two-stage differentiation may be more widely applicable. Others forms of adoptive cell therapy, such as 4-1BB-costimulated CAR T cells for B-cell leukemias⁵⁹ or systemic lupus erythematosus⁶⁰, may also exhibit a two-stage differentiation pattern in vivo. Indeed, our transcriptomic signatures at T_exp were highly consistent with external data from day 7 tisagenlecleucel CD8⁺ CAR T cells from Maus et al²⁷, hinting that two-stage differentiation may be more general. Different forms of adoptive cell therapy and disease contexts may exhibit different temporal dynamics or favor one wave over the other. Two-stage differentiation may also be aberrant in non-responders. Future studies are needed to explore potential differences in temporal dynamics, cell phenotypes, or major regulatory pathways at longitudinal timepoints in alternative therapeutic contexts.

Although this current study represents an advance for the CAR T-cell therapy field, we acknowledge four limitations. Firstly, we analyzed CAR T cells in two locations: peripheral blood and infusion product. However, CAR T cells are also found elsewhere, including lymph nodes and lymphoma foci. Importantly, CAR T-cell differentiation may be influenced by interactions with antigen-presenting cells in lymph nodes or with tumor cells in the lymphoma. Our study was ultimately limited by sample availability. Future studies can focus on less accessible locations and determine CAR T cell fates beyond peripheral blood. Secondly, we studied CAR T-cell intrinsic factors (RNAs, proteins, clonotypes) that can influence differentiation. However, differentiation may also depend on interactions between CAR T cells and a complex in vivo environment that includes other leukocytes (myeloid cells, B cells, NK cells), lymphoma cells, and cytokines. Thirdly, we focused on CAR T cells longitudinally between peak expansion and post-peak persistence timeframes. Hence, we cannot rule out additional waves of clonal expansion in the near (<1 week after infusion) or long (>1 month after infusion) terms. Lastly, we lack knowledge on whether two-stage differentiation applies to other contexts, such as non-responders, 4-1BB-costimulated CAR T cells, or TCR-transduced T cells. Future studies can explore alternative clinical contexts and test the generality of the two-stage differentiation model.

Methods

All methods complied with ethical guidelines established by the institutional review board at the University of Chicago Medicine.

Patient biospecimen collection

Deidentified biospecimens were obtained from a cellular therapy biobank in accordance with the institutional review board at the University of Chicago Medicine. Consent was obtained for biospecimen collection. Ethical guidelines were followed.

Longitudinal peripheral blood mononuclear cells (PBMCs) were collected from peripheral blood biospecimens by Ficoll-Paque PLUS (Cytiva, 95021-205) and stored in freezing media (RPMI, 10% FBS, 10% DMSO). Residual infusion product cells were collected from the patient’s spent infusion product bag and stored in CELLBANKER 1 (Amsbio, 11888). Cells were cryopreserved in liquid-phase nitrogen.

Generation of CD19-tetramers

Tetramers²⁸ were constructed from AviTag-biotinylated human His-tagged CD19 (Acro Biosystems, CD9-H82E9) and Alexa Fluor 647-labeled streptavidin (BioLegend, 405237). Biotinylated CD19 was added to the tetrameric streptavidin at a 4:1 molar ratio for 30 min at 4 °C in the dark. This mixture was diluted with PBS to convenient concentrations for staining. For each batch of biotinylated CD19, quality controls were performed by generating CD19-tetramers and quantifying their ability to stain anti-CD19 CAR-transduced Jurkat T cells.

Single-cell RNA-seq/CITE-seq/TCR-seq

Cryopreserved biospecimens were thawed (RPMI, 10% FBS) and washed with cold FACS buffer (PBS, 2% BSA, 0.05% sodium azide). Fc receptors were blocked with Human TruStain FcX (BioLegend, 422301) at 1:50 dilution for 5 min at 4 °C. Then, cells were incubated for 30 min at 4 °C in the dark with a staining solution containing CITE-seq antibodies (described below), BV421-labeled anti-CD3ε (clone SK7, BioLegend, 344833), and AF647-labeled CD19-tetramers (3 nM final concentration) for CAR T-cell phenotyping and detection. Subsequently, stained cells were conjugated with LIVE/DEAD Fixable Near-IR viability dye (Invitrogen, L34975) at 1:1000 dilution in PBS for 5 min at room temperature. Finally, cells were washed three times in cold cell media (RPMI, 10% FBS) before fluorescence-activated cell sorting (BD Biosciences, FACSAria Fusion). CAR⁺ sorting gates were drawn based on fluorescence of PBMCs from a similarly stained healthy donor.

Sorted endogenous T cells (CD3⁺CAR^-) and CAR T cells (CD3⁺CAR⁺) were separately partitioned into droplets for single-cell RNA-seq/CITE-seq/TCR-seq via Chromium Next GEM Single-Cell 5’Kit v2 (10x Genomics, 1000263). RNA-seq libraries were prepared according to manufacturer protocols. CITE-seq libraries were prepared via the 5’ Feature Barcode Kit (10x Genomics, 1000256). TCR-seq libraries were prepared via the Chromium Single-Cell Human TCR Amplification Kit (10x Genomics, 1000252). All libraries (RNA-seq, CITE-seq, TCR-seq) were quantified via the Qubit dsDNA HS Assay Kit (Invitrogen, Q32851), quality-checked for fragment sizes via high-sensitivity D5000 screentapes (Agilent, 5067-5592), pooled, and sequenced (Illumina, Novaseq-6000 and Nextseq-550).

CITE-seq antibody preparation

Thirty-one human “Cellular Indexing of Transcriptomes and Epitopes by Sequencing” (CITE-seq⁶¹) antibodies were obtained from BioLegend (TotalSeq-C reagents): anti-CD3ε (clone UCHT1, 300479), anti-CD5 (clone UCHT2, 300637), anti-TCRα/β (clone IP26, 306743), anti-TCRγ/δ (clone B1, 331231), anti-CD4 (clone SK3, 344651), anti-CD8α (clone SK1, 344753), anti-CD45RA (clone HI100, 304163), anti-CD45RO (clone UCHL1, 304259), anti-CCR7 (clone G043H7, 353251), anti-CD95 (clone DX2, 305651), anti-CD57 (clone QA17A04, 393321), anti-CD25 (clone BC96, 302649), anti-CD127 (clone A019D5, 351356), anti-CD103 (clone Ber-ACT8, 350233), anti-CXCR3 (clone G025H7, 353747), anti-CCR4 (clone L291H4, 359425), anti-CCR6 (clone G034E3, 353440), anti-PD-1 (clone EH12.2H7, 329963), anti-TIM-3 (clone F38-2E2, 345049), anti-LAG-3 (clone 11C3C65, 369335), anti-CD39 (clone A1, 328237), anti-TIGIT (clone A15153G, 372729), anti-CD27 (clone O323, 302853), anti-CD40L (clone 24-31, 310849), anti-GITR (clone 108-17, 371227), anti-OX40 (clone Ber-ACT35, 350035), anti-4-1BB (clone 4B4-1, 309839), anti-CD28 (clone CD28.2, 302963), mouse IgG1-κ isotype control (clone MOPC-21, 400187), mouse IgG2a-κ isotype control (clone MOPC-173, 400293), and mouse IgG2b-κ isotype control (clone MPC-11, 400381). All antibodies were diluted at a 1:100 ratio. Residual infusion product cells were stained with all 31 CITE-seq antibodies. Patient peripheral blood mononuclear cells were stained with all CITE-seq antibodies minus anti-CD3ε (30 in total) because T cells sorted from these samples were stained with BV421-labeled anti-CD3ε. To prepare the CITE-seq staining solution, the antibody pool was constructed and centrifuged at 14,000 × g for 10 min at room temperature in FACS buffer (PBS, 2% BSA, 0.05% sodium azide) to remove aggregates. The antibody supernatant was extracted and diluted with fresh FACS buffer to appropriate staining concentrations.

Analysis of CAR T-cell phenotype by flow cytometry

Cryopreserved biospecimens were thawed (RPMI, 10% FBS) and washed with cold FACS buffer (PBS, 2% BSA, 0.05% sodium azide). Fc receptors were blocked with Human TruStain FcX (BioLegend, 422301) at 1:50 dilution for 5 min at 4 °C. Then, cells were incubated for 30 min at 4 °C in the dark with a staining solution containing AF647-labeled CD19-tetramers (3 nM final concentration), BV421-labeled anti-CD3ε (clone SK7, BioLegend, 344833), AF488-labeled anti-CD8α (clone SK1, BioLegend, 344716), PE-labeled anti-CX3CR1 (clone 2A9-1, BioLegend, 341603), PE/Cy7-labeled anti-CD39 (clone A1, BioLegend, 328211), and BV605-labeled anti-CD57 (clone QA17A04, BioLegend, 393303). Subsequently, stained cells were conjugated with LIVE/DEAD Fixable Near-IR viability dye (Invitrogen, L34975) at 1:1000 dilution in PBS for 5 min at room temperature. Finally, cells were washed three times in cold FACS buffer (RPMI, 10% FBS) before flow cytometer analysis (BD Biosciences, FACSAria Fusion).

Single-cell RNA-seq data processing

RNA-seq and paired CITE-seq reads were aligned to the GRCh38 reference genome, which was modified with the CAR transgene sequence used in axicabtagene ciloleucel, and quantified using the cellranger count (10x Genomics, version 7.0.0)⁶². Only filtered gene/feature-barcode matrices that contained barcodes with unique molecular identifier (UMI) counts that passed the quality control were used for downstream analyses.

UMAP analysis and clustering on single-cell RNA-seq data

UMAP analysis and clustering were performed using the Seurat package (Version 4.3.0)⁶³. Raw count matrices were first converted to Seurat objects before being further merged into one Seurat object, with protein expression added as the antibody-derived tag (ADT) assay. Cells with <300 genes detected or >10% mitochondrial RNA content were excluded from further analysis.

The raw count was log-normalized using the NormalizeData function with default options. The top 5000 variable features were then identified using the FindVariableFeatures function with the default “vst” method. The data were centered and scaled using the ScaleData function, with additional regression against 1) the percent of mitochondrial RNA content and 2) difference in cell cycle S-phase score and G2M-phase score. Scaled data were then used as input for principal component analysis (PCA) based on variable genes using the RunPCA function. Data harmonization to remove patient-specific effects was performed on the principal components using the Harmony package (Version 1.0) through the RunHarmony function⁶⁴. Next, UMAP was constructed based on the first 30 harmony components. The same harmony components were used to construct the shared nearest neighbor (SNN) graph using the FindNeighbors function, which was then partitioned to identify clusters using the FindClusters function with default Louvain algorithm. These clusters were manually aggregated and classified as T-cell subsets based on known markers. CD8 and CD4 T-cell classifications were based on both gene and protein expression of CD4, CD8α, and CD8β.

For subset analysis, the corresponding subsets were extracted from the master Seurat object using the subset function. The above detailed preprocessing steps were repeated to generate the corresponding UMAP and subset annotations.

Gene expression visualization

Gene expression was visualized in three ways: violin plots, heatmaps, and density plots. Violin plots were made based on normalized expression. Heatmaps were made based on average scaled expression, both using Seurat internal functions. Density plots were constructed using the Nebulosa package (version 1.6.0) to enhance visibility and mitigate sparsity of gene expression on UMAP. This is necessary for datasets with large number of cells (e.g. our CD8⁺ CAR-T dataset has ~38k cells).

Differential gene expression (DEG) analysis on single-cell RNA-seq data

DEG analyses were by default performed using the FindMarkers function in Seurat package, with default parameters and the appropriate ‘ident.1” and “ident.2” set as contrast. Unless otherwise stated, the results were filtered with p_val_adj <0.05 and abs(avg_log2FC) > 0.25. Moreover, we also performed additional DEG analyses through a pseudo-bulk approach to better control for patient-specific effects, using the LibraDEG package (version 1.0.0)⁶⁵ with default parameters (edgeR with LRT method). This was mainly performed when extracting timepoint-specific genes. Areas where this pseudo-bulk method is used are clearly stated in the figure legend.

Timepoint-specific gene signature validation

To validate the timepoint-specific molecular signatures shared across patients and against external data, we adopted two independent and complementary approaches: (1) correlation of timepoint-specific gene expression, (2) label transfer from our data to external data.

For the correlation approach, we first conducted pseudo-bulk DEG analyses of T_exp vs T_per across all patients and kept all the statistically significant genes (padj <0.05) as timepoint-specific genes. Note that we did not filter by log fold-change to include even weak timepoint-specific signatures. Essentially, the goal here is to remove all non-timepoint-specific genes that may confound correlation analysis. The normalized gene expressions of these timepoint-specific genes were extracted from both our data and the external data. Following that, average expression was calculated for each sample (patient by timepoint). Correlations were then calculated pairwise for all samples.

For the label transfer approach, we adapted the Seurat standard data integration and label transfer workflow with default parameters. This is a complementary approach where, rather than at patient-level expression, individual cells in the external data and our data could be compared and similar timepoint-labels could be assigned.

GSEA and pathway enrichment analysis

Gene Set Enrichment Analysis (GSEA) and pathway enrichment analysis were carried out using the clusterProfiler (version 4.4.4)⁶⁶ package based on the msigdb database built in msigdbr package (version 7.5.1)⁶⁷.

Exhaustion signatures from reference datasets

Besides standard msigdb exhaustion-related pathways, we obtained T-cell subset-specific signatures from Wherry et al. (see Supplementary Table 1 in their study)⁵². The reference study provides the most updated and comprehensive CD8⁺ (exhausted) T cell subsets, in the standard LCMV/Cl13 mouse models where T_ex was first defined. To refine the signature, we converted the mouse genes to their human orthologous genes and filtered the list to retain only the top 500 (by descending avg_log2_FC) statistically significant (p_val_adj <0.05) genes for each subset.

Regulon analysis

To construct regulons in a specific cell subset, we first extracted the raw count matrix of that subset. Then, we supplied it as input for the pySCENIC package (version 0.10.4) and ran through the workflow as detailed in its documentation and publication^68,69. More specifically, the base script was adapted from the “PBMC10K” example script on the pyScenic website⁷⁰. In addition, the arboreto component was used to speed up analysis. Each resultant regulon is a gene list with a central transcription factor and all its putative target genes determined through the SCENIC algorithm, and each set of regulons is sample/subset-specific and represents a putative regulatory unit in that specific sample/subset.

Regulon or general gene set signature scoring

Individual cells were scored using the AUCell package (version 1.10.0)⁶⁸ for a particular gene set from the msigdb database or from a single-cell-derived regulon as follows. The normalized gene expression was first used as input into the AUCell_buildRankings function to score each cell for gene set enrichment and to build a ranking matrix. The signature score was then calculated as an AUC score using the AUCell_calcAUC function with all default parameters. In later revisions, Ucell package (version 2.0.1)⁷¹ was used in some re-analyses, as it is computationally more efficient and generates similar results as the AUCell package.

Machine learning classification on regulon scores

To classify CD8⁺ CAR T cells at T_exp and T_per, we first built a regulon set for each subset independently, assuming that regulatory networks for each timepoint may be different. Then, we merged all regulon sets into a master regulon set. We calculated the regulon scores using the master regulon set for all cells from the two subsets, resulting in a regulon score matrix. Each cell then served as an observation and each regulon as a feature for machine learning.

The machine learning classification models were built in the caret framework using the caret package (version 6.0–90)⁷². Briefly, we divided the input data into training (75%), validation (15%), and test sets (10%). We trained and optimized xgboost models⁷³ through grid hyperparameter search and with a 5-fold cross-validation until reaching a best accuracy of >85% (refer to figures for specific accuracies).

We interrogated the final models for feature importance evaluation using the Shapley Additive Explanation (SHAP) method⁷⁴ implemented in the SHAPforxgboost package (version 0.1.1)⁷⁵. Average SHAPley values were used to rank the regulons. Top timepoint-determining regulons were visualized and further analyzed.

Single-cell TCR-seq data processing

The TCR-seq reads from each sample were aligned to the 10x curated GRCh38 vdj reference genome and quantified using the cellranger vdj (10x Genomics, version 6.0.0). The results were then aggregated using the cellranger aggr function, with the source patient (donor) and timepoint (origin) of each sample supplied in the metadata to guide the clonotype assignment. The resultant clonotype and filtered contig annotation data were used for downstream analyses.

Single-cell TCR-seq tracing and phenotype linking

Each unique clonotype is defined by the amino acid and nucleotide sequence of CDR3 regions for paired productive TCRα and TCRβ chains. TCR clonality overlap and tracing analyses were carried out using the immunarch package (version 0.9.0)⁷⁶. Clones with the same TCR CDR3 sequences from the same patient at different timepoints were re-grouped and traced from infusion product to the post-infusion samples.

Phenotype-to-clonotype linking: To identify T_exp and T_per precursors, clonotypes among post-infusion T_exp and T_per cell populations were identified respectively and traced back to the infusion product cells. When linking the TCR clonotype to the corresponding cell phenotype, we keep only data with matching cell barcodes from both the TCR-seq and RNA-seq data.

Abundance tracing: Clonal abundance for each CAR T clone is calculated as the percentage of all CAR T cells occupied by a clone at a given timepoint. Each clone was assigned a phenotype at each timepoint based on the most frequently occurring phenotype. T_exp clones that were predominantly EM-exh had their abundances tracked at T_per1/T_per2. T_per1/T_per2 clones that were predominantly TE had their abundances tracked at T_exp.

CAR transgene mapping

The axicabtagene ciloleucel CAR design is documented⁷⁷. Its sequence was confirmed by Sanger sequencing of genomic DNA extracted from axicabtagene ciloleucel infusion products. The CAR sequence was then added to the GRCh38 FASTA and GTF files accordingly. A custom reference for cellranger was built from these annotation files using cellranger mkref (10x Genomics, version 7.0.0). The resultant custom reference was used for CAR transgene mapping through cellranger count.

Inclusion and ethics statement

Roles and responsibilities were agreed amongst collaborators in advance. Our findings are not expected to result in stigmatization, incrimination, or discrimination.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

All single-cell RNA-seq, CITE-seq, and TCR-seq data have been deposited in the Gene Expression Omnibus (GEO) and are available with the accession code GSE207192. Source data are provided with this paper.

Code availability

For reproducibility, code used for our analysis and figure-making is available on Zenodo: https://doi.org/10.5281/zenodo.15116870.

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Acknowledgements

We thank Phi Beta Psi, the Ullman Fund in Cancer Immunology, the Hoogland Lymphoma Research Pilot Projects, Chicago Immunoengineering Innovation Center, American Cancer Society Scholar Award (SG-22-136-01-1BCD) and NIH New Innovator award (1DP2AI144245) for financial support (to J.H.). Y.H. was supported by the University of Chicago MSTP Training Grant (T32GM007281). T.P. was supported by the University of Chicago CSTR training grant (T32HL007381). N.A. was supported by the University of Chicago MTCR training grant (T32CA009594). This project was supported by the National Center for Advancing Translational Sciences of the National Institutes of Health through Grant Number 5UL1TR002389-02 that funds the Institute for Translational Medicine. We thank the UChicago Human Immunologic Monitoring Facility for patient biospecimen cryopreservation, as well as the Pritzker School of Molecular Engineering Single-Cell Immunophenotyping Core and UChicago Genomics Facility for instrumentation to generate single-cell sequencing data. This work was completed in part with resources provided by the University of Chicago’s Research Computing Center. The authors thank Dr. Karen M. Watters for scientific editing of the manuscript.

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These authors contributed equally: Guoshuai Cao, Yifei Hu, Tony Pan.

Authors and Affiliations

Pritzker School of Molecular Engineering, University of Chicago, Chicago, IL, 60637, USA
Guoshuai Cao, Tony Pan, Erting Tang, Nicholas Asby & Jun Huang
Pritzker School of Medicine, University of Chicago, Chicago, IL, 60637, USA
Yifei Hu
The David and Etta Jonas Center for Cellular Therapy, University of Chicago, Chicago, IL, 60637, USA
Thomas Althaus, Peter A. Riedell, Michael R. Bishop & Justin P. Kline
Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
Jun Wan
Department of Medicine, University of Chicago, Chicago, IL, 60637, USA
Peter A. Riedell, Michael R. Bishop & Justin P. Kline
Committee on Cancer Biology, University of Chicago, Chicago, IL, 60637, USA
Peter A. Riedell, Justin P. Kline & Jun Huang
Committee on Immunology, University of Chicago, Chicago, IL, 60637, USA
Justin P. Kline & Jun Huang

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Guoshuai Cao
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Yifei Hu
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Tony Pan
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Erting Tang
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Thomas Althaus
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Jun Wan
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Peter A. Riedell
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Michael R. Bishop
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Justin P. Kline
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Jun Huang
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Contributions

Y.H., G.C., J.P.K., and J.H. conceived and designed all experiments. J.H. supervised the project. T.A., P.A.R., and M.R.B. coordinated patient biospecimens for these studies. G.C. and T.P. performed all single-cell and machine-learning analyses. Y.H. organized execution of all experiments. Y.H. and E.T. sorted CAR T cells and generated single-cell data. G.C. and Y.H. jointly interpreted data. N.A. and Y.H. designed cartoons. E.T. performed flow cytometry validation. Y.H. and G.C. prepared the manuscript. J.H. and J.P.K. edited the manuscript. All authors reviewed the manuscript.

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Correspondence to Jun Huang.

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Competing interests

P.A.R. reports Research Support/Funding: BMS, Kite Pharma, Inc./Gilead, MorphoSys, Calibr, Tessa Therapeutics, Fate Therapeutics, Xencor, and Novartis Pharmaceuticals Corporation. Speaker’s Bureau: Kite Pharma, Inc./Gilead; Consultancy on advisory boards: AbbVie, Novartis Pharmaceuticals Corporation, BMS, Janssen, BeiGene, Karyopharm Therapeutics Inc., Takeda Pharmaceutical Company, Kite Pharma, Inc./Gilead, Sana Biotechnology, Nektar Therapeutics, Nurix Therapeutics, Intellia Therapeutics, and Bayer. Honoraria: Novartis Pharmaceuticals Corporation. M.R.B. reports Membership on an Advisory Board or Consultancy for Kite/Gilead, Novartis, CRISPR Therapeutics, Autolus Therapeutics, BMS, Incyte, Sana Biotechnology, Iovance Biotherapeutics. He has served on a Speakers Bureau for BMS, Kite/Gilead, Agios, and Incyte. J.P.K. receives research support from Merck, Verastem, and iTeos; has served on a speaker’s bureau for Kite/Gilead; and has served on advisory boards for Verastem, Seattle Genetics, MorphoSys, and Karyopharm. The remaining authors declare no competing interests.

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Cao, G., Hu, Y., Pan, T. et al. Two-stage CD8⁺ CAR T-cell differentiation in patients with large B-cell lymphoma. Nat Commun 16, 4205 (2025). https://doi.org/10.1038/s41467-025-59298-w

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Received: 06 December 2024
Accepted: 16 April 2025
Published: 06 May 2025
DOI: https://doi.org/10.1038/s41467-025-59298-w

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