High efficiency CRISPR knock-in demonstrates that TCF1 is insufficient to reverse T cell exhaustion

Domain: chemistry
Published: 2026-02-17
Editorial outcome: minor_revision

Abstract
CD8+ T cell exhaustion is a regulatory state triggered by chronic antigen stimulation in both cancer and persistent infection. The less differentiated stem-like sub-populations of exhausted T cells have been heavily studied given their importance to the efficacy of current immunotherapies. While the transcription factor TCF1 is both necessary and sufficient for formation and maintenance of these stem-like populations, it remains unclear whether TCF1 can actively de-differentiate more terminally exhausted subsets back into a stem-like state. To address this question, here we utilize and optimize a high efficiency CRISPR knock-in methodology, compatible with mouse in vivo exhaustion models, to engineer T cells that either constitutively over-express TCF1, or conditionally over-express TCF1 following differentiation of the cells into a CX3CR1+ intermediate-exhausted state. Strikingly, we find that only constitutive, and not conditional, TCF1 over-expression can increase the size of the stem-like T cell pool. Thus, while TCF1 can slow stem-like T cell differentiation, it is insufficient to revert more differentiated cells back into a stem-like state.

Introduction
Chronic antigen stimulation in the context of viral infection and cancer drives CD8+ T cells into an exhausted state1,2. T cell exhaustion is characterized by progressive loss of proliferative capacity, diminished cytokine production and upregulation of inhibitory receptors, such as PD-12–7. Exhausted T cells encompass a phenotypically and functionally heterogeneous pool of cells, the least differentiated of which are precursor of exhausted T cells (Tpex)8,9. Tpex have elevated stem-like properties compared to other exhausted subsets, such as enhanced proliferative and differentiation potential, and thus play important roles in sustaining the exhausted T cell response10. Tpex constitutively seed and sustain the more differentiated intermediate (Tex-int) sub-population that is important for tumor and virus control, after which they ultimately become terminally exhausted (Tex-term)9,11–21. Chronic antigen stimulation in the context of viral infection and cancer also have clinical relevance as they mediate the proliferative response to immune checkpoint blockade (ICB) therapy in both viral and cancer models15,17–19,22, with elevated intratumoral Tpex predicting better response to ICB therapy in cancer patients17,18,23–26.

Due to the clinical relevance of Tpex, defining the pathways responsible for Tpex formation and maintenance is a major focus of the field. The transcription factor TCF1 is viewed as the “master regulator” of Tpex as it is both necessary and sufficient for Tpex formation and, consequently, for sustaining the exhausted CD8+ T cell response to chronic viral infection and within tumors15,19,27,28. TCF1 directly induces the transcriptional program required for Tpex formation21, and its expression is lost once Tpex differentiate15,16,20. TCF1 gene deletion results in loss of Tpex alongside chromatin accessibility changes associated with differentiation into Tex-int15,19,27. Conversely, TCF1 over-expression in both tumor and viral models enhances stem-like features, increases Tpex number and improves viral and tumor control21,27,29,30. Notably, though, these latter studies involved constitutive TCF1 over-expression in all exhausted cell subsets. It thus remains unclear whether TCF1 can actively reverse exhaustion by de-differentiating TCF1 negative subsets (i.e. Tex-int and Tex-term) back into Tpex, or whether TCF1 over-expression simply slows Tpex differentiation. This is an important unanswered question with clinical relevance, as reverting differentiated exhausted subsets back into stem-like states could restore ICB responsiveness in treatment refractory patients. However, addressing this question is challenging as it requires conditional TCF1 over-expression within the more differentiated TCF1 negative exhausted cells. Engineering conditional over-expression of a transgene typically requires the time-consuming and costly generation of specialized transgenic or gene knock-in mice, and for this reason, questions such as this one have frequently remained unaddressed.

Recent progress in CRISPR-Cas9 based gene knock-in technologies have provided an alternative and potentially rapid approach to achieving conditional gene over-expression. Cas9-sgRNA ribonucleotide protein (RNP) complexes induce site-specific DNA double-strand breaks, which trigger DNA repair through either non-homologous end joining (NHEJ) or homology-directed repair (HDR). Whereas NHEJ causes indels, HDR can lead to precise integration of donor DNA into the targeted genomic locus when the provided DNA template is flanked by the appropriate homology arms. CRISPR HDR technology has now been employed to generate human CAR T cells31, to knock-in transgenes into human T cells32,33, and to correct point mutations in hematopoietic stem cells as a treatment alternative for sickle cell disease32,34. HDR knock-in can also be used for conditional gene expression by knocking the transgene into an endogenous gene locus with lineage-specific expression patterns35–37. Nevertheless, HDR is a much lower efficiency process than NHEJ, and HDR editing within mouse T cells in particular has often failed to achieve the high efficiency levels seen within their human counterparts38,39. While progress has recently been made to improve knock-in efficacy within mouse T cells40–42, the bulk of prior studies were conducted in vitro meaning that the applicability of these methods to the study of in vivo T cell biology remains unclear. This has limited the capacity of CRISPR knock-in approaches to address research questions within primary murine CD8+ T cells. In contrast, NHEJ-based CRISPR gene knock-out approaches have been successfully and broadly applied to mouse T cells and have typically achieved high knock-out efficacy in a range of contexts, including naïve T cells and in sophisticated inducible systems39,43–45.

Here, we describe a methodology for high efficiency CRISPR HDR-based gene knock-in within primary murine CD8+ T cells that is suitable for both in vitro and in vivo studies. Using this approach, we achieve highly specific, conditional transgene over-expression within Tex-int through transgene knock-in into the Cx3cr1 gene locus, which is selectively expressed within this cell subset. We leverage this approach to conditionally over-express TCF1 selectively within Tex-int cells and compare the resulting phenotype to that induced by constitutive TCF1 over-expression. While conditional TCF1 over-expression causes mild epigenetic, transcriptional and phenotypic changes in edited Tex-int cells, it ultimately fails to fully reprogram these cells back into a Tpex state and, unlike constitutive TCF1 expression, does not increase Tpex numbers. These data indicate that TCF1 is insufficient to reverse exhaustion.

Results
Optimal mouse T cell CRISPR HDR knock-in requires in vitro and in vivo rest before in vivo re-activation
We previously observed low HDR editing efficiency and high DNA template toxicity when using double-stranded DNA templates for HDR editing in primary mouse T cells39. As previous studies have indicated that using AAV DNA repair templates for HDR editing can both reduce DNA toxicity and improve knock-in efficiency36,40, we decided to trial AAV repair templates instead. Different AAV serotypes can successfully transduce murine cells42,46–48, and AAV6 has demonstrated some capacity to infect primary murine CD8+ T cells37,40,41,49. To test the efficiency of AAV6 templates for HDR, we employed templates targeting the GFP gene to the start codon of the Thy1 gene (Thy1-GFP). As the Thy1 gene is constitutively expressed in mouse T cells, this editing strategy will lead to constitutive GFP expression alongside loss of Thy1 expression. For these experiments, we used lymphocytic choriomeningitis virus (LCMV)-specific CD8+ T cell receptor transgenic T cells (P14 cells) to enable optimization of HDR knock-in for downstream in vivo studies within the well characterized chronic LCMV T cell exhaustion model. Previous work has suggested that HDR editing is most efficient within cycling cells50,51 so purified P14 cells were first activated using anti-CD3 and anti-CD28 antibodies for 24 hours. Cells were then electroporated with Cas9-sgRNA RNPs targeting the Thy1 repair site prior to immediate incubation with AAV6 containing the Thy1-GFP repair template for 4 hours at a high multiplicity of infection (MOI; 100K). These MOI and incubation conditions were based on previously published optimized parameters for AAV6 template editing in other cell types37,52. The excess AAV was then washed off the cells, after which cells were cultured in IL-2 and IL-7 for a further 72h (Fig. 1a). Using this protocol, we achieved nearly 80% GFP knock-in within P14 cells, with a corresponding loss of Thy1 protein indicating that GFP targeting was occurring specifically at the Thy1 locus (Fig. 1b, c). Of note, we also observed that a proportion of cells were edited by NHEJ, resulting in loss of Thy1 protein (presumably due to indels in the Thy1 gene) with no GFP insertion (Fig. 1b).

We next tested whether this high knock-in efficiency was preserved after in vivo transfer of P14 cells and subsequent activation by chronic LCMV-Clone 13 (Cl.13) infection. To minimize any impact of cell culture on in vivo differentiation, CRISPR-edited P14 cells were initially transferred into mice immediately after the 4h AAV6 incubation period (“no rest”—Fig. 1a). While we still observed relatively high editing efficiency (~40% GFP+ cells), the results were more variable than that observed in vitro, with a trend towards reduced efficiency (Fig. 1d, e). The reasons for this reduced efficiency of in vivo versus in vitro knock-in are unclear but given that the method was identical prior to transfer into mice, we hypothesized that edited cells had a survival disadvantage when immediately activated in vivo after editing. To test whether a period of rest after HDR editing could potentially overcome this problem, we either cultured CRISPR-edited P14 cells in IL-2 and IL-7 for 48h (“in vitro rest”) prior to transfer and immediate LCMV infection, or immediately transferred the cells into mice but waited for 6 days before LCMV infection (“in vivo rest”) (Fig. 1a). Both in vitro and in vivo rest led to less variability in GFP+ percentage (Fig. 1e) and, consistent with our hypothesis, splenic P14 cell recovery was higher when cells were rested in vivo (Fig. 1f). We finally tested whether combining in vitro and in vivo rest before LCMV infection improved P14 yield and/or editing efficiency (Fig. 1g). While editing efficiency was comparable across all conditions (~60%), 24h in vitro rest plus 5 days in vivo rest resulted in the highest P14 yield among the conditions tested (Fig. 1j). Prior in vitro activation is expected to alter in vivo T cell differentiation, however, cell number, cytokine function and subset proportions were largely unaltered (Supplementary Fig. 1a-d), indicating that this protocol minimally impacts P14 cell exhaustion in vivo and can therefore be used to study exhausted T cell differentiation. Thus, we successfully optimized a protocol for high efficiency CRISPR knock-in within mouse T cells compatible with in vivo studies of T cell differentiation.

High efficiency CRISPR HDR editing requires T cell activation and is influenced by insert size
We and others have developed protocols that enable CRISPR gene deletion by NHEJ-induced indels with equally high efficiency in both naïve and activated T cells39,43. We thus next tested whether the in vitro activation step was necessary for high efficiency CRISPR knock-in. Naïve P14 cells were either immediately edited without prior culture or cultured for 24h in IL-7 to enable survival without activation prior to editing. Cells were then cultured for 72h in vitro, with editing efficiency then compared to that seen in cells activated prior to editing. Editing efficiency was equally poor in both naïve conditions compared to pre-activated cells in vitro (~5% versus ~60% respectively; Supplementary Fig. 2a), and after in vivo transfer and LCMV challenge (Supplementary Fig. 2b). Thus, unlike CRISPR-induced NHEJ, optimal HDR editing requires that cells are actively in cycle, consistent with previous work39,50.

We next examined how insert size impacts editing efficiency. AAV6 has a maximum DNA packaging capacity of 4.7 kb, which restricts HDR template size to a maximum of ~3 kb when accounting for the additional space required for the homology arms. The Thy1-GFP AAV6 construct from Fig. 1 was modified by adding non-coding regions (ncr) after the polyA tail of the GFP sequence to sequentially increase the insert size close to the maximum possible insert size that could be accommodated within the AAV6 genome (the homology arms were left unaltered) (Fig. 2a). A mock control electroporated with Cas9 protein, but no sgRNA, was also included. Increasing the insert size dramatically diminished editing efficiency (Fig. 2b-d) in line with previous findings53. Thus, editing efficiency is highly dependent on the insert size.

High efficiency knock-in does not require endogenous gene disruption for the Thy1-GFP [end] construct
While CRISPR HDR knock-in can leverage endogenous regulatory elements to control transgene expression, this is often at the expense of the endogenous gene given that the transgene is typically inserted at the start codon. To trial approaches that preserve endogenous gene expression, we designed a new HDR template for GFP insertion at the Thy1 gene stop codon (Thy1-GFP [end]), with a self-cleaving P2A peptide sequence added before the GFP sequence to promote GFP cleavage from Thy1 protein during translation54 (Fig. 3a). Similar editing efficiencies were achieved using both constructs (Fig. 3b, c) and, importantly, the GFP-expressing cells were still largely Thy1.2+ in the Thy1-GFP [end] condition (Fig. 3d). However, GFP expression levels were significantly lower when GFP was inserted at the end of the gene (Fig. 3e), possibly due to inefficient P2A protein splicing55. Thus, transgenes can be inserted in a manner that preserves endogenous gene expression using this methodology, albeit at the expense of transgene expression level.

To develop an alternative CRISPR knock-in approach that would enable constitutive transgene expression without disrupting an endogenous coding gene, we designed HDR templates for GFP insertion into the Rosa26 locus (Fig. 3f). CRISPR gene knock-in into the Rosa26 locus has been reported in murine hematopoietic stem and progenitor cells (HSPCs)48, but not murine primary CD8+ T cells. A ~40% knock-in efficiency was achieved in P14 cells both in vitro and in vivo (Fig. 3g, h). This provides an alternative approach for constitutive transgene over-expression by CRISPR knock-in that leaves endogenous gene expression intact.

The Cx3cr1 locus can be leveraged for conditional transgene over-expression within intermediate exhausted T cells
To enable conditional over-expression of transgenes after Tpex differentiation, we decided to target the Cx3cr1 gene locus. CX3CR1 is not expressed within Tpex, but is instead induced after Tpex downregulate TCF1 and differentiate into a Tex-int state11,12,20. We initially used a GFP transgene to validate the specificity of transgene expression. To preserve Cx3cr1 gene expression, the GFP transgene was inserted at the end of the Cx3cr1 gene separated by a P2A sequence (as for the Thy1-GFP [end] construct in Fig. 3) (Fig. 4a). While ~40% of CX3CR1+ P14 cells were GFP+ on day 8 p.i., this doubled by day 20 p.i. (Fig. 4b, c). Of note, while the proportion of CX3CR1+ P14 cells was comparable between days 8 and 20 p.i. (Fig. 4d), CX3CR1 MFI within CX3CR1+ cells increased 3-fold between these time points (Fig. 4e). In line with this increase in endogenous CX3CR1 expression level, we similarly observed an increase in GFP MFI within the GFP+ population at day 20 relative to day 8 (Fig. 4b, f). Importantly, GFP expression was restricted to the CX3CR1+ Tex-int population, with negligible expression within the other subsets (Fig. 4g, h). Thus, this targeting strategy enables highly specific Tex-int transgene over-expression while leaving endogenous CX3CR1 expression intact, with high transgene expression observed particularly at later stages of chronic LCMV infection. Since HDR can result in both mono- and bi-allelic gene targeting35 we investigated the extent of homozygous versus heterozygous knock-in within the Cx3cr1 locus. To assess mono-allelic editing frequencies, AAV HDR templates that targeted GFP or mCherry to the Cx3cr1 locus were used either separately or in combination during the in vitro editing step. Using this approach, we observed that on average ~20% of cells were GFP and mCherry double positive, indicating that a proportion of cells undergo mono-allelic editing (Fig. 4i, j). These data indicate that transgene positive cells are likely a mix of mono- and bi-allelically edited cells.

Conditional TCF1 over-expression is not sufficient to de-differentiate Tex-int back into Tpex
Previous studies have suggested that TCF1 over-expression can increase Tpex numbers21,27,29, but it is unclear whether this is because TCF1 is slowing Tpex differentiation versus actively de-differentiating TCF1- cells back into a Tpex state. To test whether TCF1 is sufficient to de-differentiate non-Tpex back into a Tpex state, HDR templates were designed targeting the full-length (p45) TCF1 isoform to the Cx3cr1 locus to enable TCF1 over-expression selectively within Tex-int cells (Fig. 5a). If TCF1 is capable of de-differentiating Tex-int into Tpex, then this editing strategy should increase Tpex numbers. As a positive control to mimic previous constitutive TCF1 over-expression approaches known to boost Tpex numbers, TCF1 was also knocked-in to the Thy1 locus. Given that TCF1 was being over-expressed, Tpex were defined as Slamf6+ cells based on prior studies identifying Slamf6 as a specific Tpex marker12,16,17,19,20. There was a substantial (~5–10 fold) increase in the frequency of TCF1+ cells in both CRISPR-edited groups when compared to mock control cells, with significant increases in TCF1+ cell frequency on both days 8 and 20 for Thy1-TCF1, but only on day 20 p.i. for CX3CR1-TCF1 (Fig. 5b, c). Despite the lower TCF1+ cell frequency in CX3CR1-TCF1 cells at day 20, TCF1 MFI in the TCF1+ population within CX3CR1-TCF1 edited cells was similar to the levels found in endogenous Tpex cells, and higher in this group relative to TCF1+ cells within the Thy1-TCF1 edited group, indicating that the Cx3cr1 locus can drive higher per cell TCF1 expression within edited cells (Fig. 5d). Importantly, CX3CR1-TCF1 cells only over-expressed TCF1 within the Tex-int populations in contrast to the Thy1-TCF1 cells, which over-expressed TCF1 in all cells (Fig. 5e), including Tpex, which had a higher TCF1 MFI than control in the Thy1-TCF1 but not the CX3CR1-TCF1 group (Supplementary Fig. 3a). While Thy1-TCF1 cells exhibited the expected increase in Slamf6+ Tpex numbers at day 8 and 20 p.i., strikingly CX3CR1-TCF1 edited cells failed to demonstrate any Tpex increase at either time point (Fig. 5f, g). Similar trends were seen when measuring the number of less differentiated cells using different markers (Granzyme-B neg and PD-1 lo TOX lo cells) (Supplementary Fig. 3b-e). Thus, unlike constitutive TCF1 over-expression, conditional TCF1 over-expression was not sufficient to increase Tpex despite higher per cell TCF1 expression.

Previous work showing that TCF1 can increase Tpex have used retroviral over-expression approaches21,27,29,30, raising the possibility that failed re-programming was due to weaker TCF1 over-expression by CRISPR knock-in. We thus compared TCF1 over-expression by our CRISPR knock-in approach to traditional retroviral over-expression approaches. As the retroviral vector drives constitutive TCF1 over-expression, cells retrovirally over-expressing TCF1 were compared to cells CRISPR-edited to similarly constitutively over-express TCF1 via the Thy1-TCF1 HDR template. Strikingly, TCF1 MFI within edited cells was higher in cells edited by CRISPR knock-in compared to retroviral transduction (Fig. 5h). Thus, our CRISPR knock-in approach can match, or even out-perform, conventional retroviral over-expression approaches. As CX3CR1-TCF1 knock-in led to even higher TCF1 levels than Thy1-TCF1 knock-in (Fig. 5d), these data suggest that failed reprogramming is not due to insufficient TCF1 over-expression.

To test if using a different TCF1 isoform would change outcomes, we examined the effect of different TCF1 isoforms on Tpex frequencies. However, consistent with prior work21, there was no difference in Tpex frequencies when either the p45 or p33 TCF1 isoforms were retrovirally over-expressed, with Tpex frequencies comparable to those seen in Thy1-TCF1 (p45) edited cells (Supplementary Fig. 3f, g). Thus, different TCF1 isoforms do not have different potencies in their capacity to increase Tpex. It was also possible that limiting amounts of the TCF1 co-factor β-catenin may be preventing reprogramming. While retroviral β-catenin over-expression alone boosted Tpex frequencies, combining conditional TCF1 over-expression with β-catenin over-expression failed to increase Tpex beyond that seen with β-catenin over-expression alone (Supplementary Fig. 3h, i). Thus, insufficient β-catenin does not explain failed reprogramming consistent with previous work indicating that combined TCF1 and β-catenin over-expression does not synergistically increase Tpex29.

Collectively, these results indicate that while TCF1 over-expression can cause mild changes in Tex-int gene expression, epigenetic state and phenotype, TCF1 is insufficient to de-differentiate Tex-int cells back into Tpex.

TCF1 over-expression causes subtle transcriptional and epigenetic changes in Tex-int cells
Given prior findings showing that TCF1 is a master regulator of the Tpex fate, the lack of phenotype within CX3CR1-TCF1 cells was unexpected. To investigate the impact of TCF1 over-expression within CX3CR1+ cells at the transcriptional and epigenetic level, we performed combined single-cell RNA-sequencing (scRNA-seq) and assay for transposase-accessible chromatin sequencing (scATAC-seq) on sorted P14 cells from the Thy1-TCF1, CX3CR1-TCF1 and mock groups at day 20 p.i. We identified 10 clusters closely associated with known exhausted T cell differentiation states56 (Fig. 6a, b and Supplementary Fig. 4a-d). TCF1 over-expression in the Thy1-TCF1-edited cells caused expansion of Tpex in addition to increased effector-like cells (Clusters 9 and 5, respectively), with the CX3CR1-TCF1 cells demonstrating milder effector-like and/or intermediate cell expansion (Clusters 5 and 0) with little to no increase in Tpex (Cluster 9) (Fig. 6b, c). Thus, constitutive TCF1 expands Tpex and potentially cells in the early stages of differentiation, while conditional TCF1 over-expression has only mild impacts on subset proportions.

We next analyzed whether TCF1 over-expression within CX3CR1+ cells leads to gain or loss of any molecular features of Tpex. Analysis of the differentially accessible chromatin regions (DACR) and differentially expressed genes (DEG) between mock Tpex, and CX3CR1+ cells from all 3 groups, revealed an overall subtle, but detectable, gain of Tpex features and loss of CX3CR1+ cell features downstream of TCF1 over-expression (Fig. 6d, e, red boxes). Strikingly, CX3CR1+ cells from both TCF1 over-expressing groups had a significant enrichment of a Tpex transcriptional signature, and a significant depletion of a CX3CR1+ transcriptional signature, relative to mock (Fig. 6f). In contrast, there was no differential enrichment of these signatures between CX3CR1+ cells from the Thy1-TCF1 versus CX3CR1-TCF1 groups, indicating comparable TCF1-associated transcriptional changes in both conditions. Furthermore, a mild enrichment of TCF1 family binding motifs was seen among the DACRs across all non-Tpex cell clusters within the Thy1-TCF1 group, with a milder enrichment seen within the CX3CR1-TCF1 group (Fig. 6g). Thus, TCF1 over-expression within CX3CR1+ cells causes a mild transcriptional and epigenetic acquisition of Tpex features, however these changes appear insufficient to de-differentiate these cells back into Tpex.

We next examined if these mild phenotypic changes were evident by flow cytometry through phenotypic analysis of the TCF1+ and TCF1- Tex-int populations (Fig. 5e) in the CX3CR1-TCF1 and Thy1-TCF1 conditions. Consistent with our multi-ome analysis, the TCF1+ Tex-int cells exhibited a mild gain of Tpex features relative to their TCF1- counterparts, such as slightly reduced Granzyme B and PD-1 expression and slightly increased CXCR5 levels (Fig. 6h-j), suggesting partial reprogramming of the TCF1+ subset. However, TOX levels were unchanged between subsets, indicating no difference in commitment to exhaustion (Fig. 6k). These phenotypical changes were detected in both Thy1-TCF1 and CX3CR1-TCF1 groups.

Collectively, these results indicate that while TCF1 over-expression can cause mild changes in Tex-int gene expression, epigenetic state and phenotype, TCF1 is insufficient to de-differentiate Tex-int cells back into Tpex.

Methods
Mice
CD45.2+ C57BL/6 (B6) mice (Mus musculus) were purchased from the Walter and Eliza Hall Institute Kew Animal Facility (Kew, VIC, Australia), whereas CD45.1+ P14 transgenic73 mice were bred in-house with constant backcrossing to the Kew Animal Facility C57BL/6 background. Both male and female mice were used at 6–14 weeks of age, and all mice were maintained in specific pathogen free conditions. Both male and female mice were used in the study and sex was assigned according to mouse availability and donor mouse sex match. We do not see sex-dependent differences in exhausted T cell differentiation after chronic LCMV infection, although mice within each experiment were still sex matched across groups. Mice were euthanised by cervical dislocation or CO2 asphyxiation. All animal work was in accordance with protocols approved by the Peter MacCallum Cancer Centre Animal Experimentation Ethics Committee (Protocols E669 and 2024-25) and current guidelines from the Australian Code of Practice for the Care and Use of Animals for Scientific Purposes.

Cell lines
HEK293T cells for retrovirus production were obtained from the American Type Culture Collection (ATCC). Cells were maintained in DMEM medium (Gibco Life Technologies) supplemented with 10% heat-inactivated fetal bovine serum, 2mM glutamine and 100U ml−1 penicillin and 100 μg ml−1 streptomycin. These cells were cultured at 37°C in a humidified incubator with 5% CO2.

CD8+ T cell isolation and activation
Naïve CD45.1+ P14 cells were enriched using the EasySep Mouse CD8+ T Cell Isolation Kit (STEMCELL Technologies) from spleens and lymph nodes isolated from the CD45.1+ P14 TCR transgenic mouse strain. Spleens and lymph nodes were homogenized through a 70 μm strainer to obtain a single cell suspension prior to red blood cell lysis in 0.83% NH4Cl. Cells were then resuspended in PBS containing 0.5% Fetal Bovine Serum and 2mM EDTA for CD8+ T cell enrichment. Isolated P14 cells were subsequently activated at a concentration of 2 × 106 cells/mL in a cell culture plate coated with murine anti-CD3 (0.5 μg/mL) and murine anti-CD28 (0.5 μg/mL) antibodies, in the presence of complete RPMI (Gibco) containing 50 μM β-mercaptoethanol (Gibco) supplemented with hIL-2 (100 IU/mL) and mIL-7 (5 ng/mL) for 24 hours. For IL-7 culture, cells were cultured in complete RPMI containing β-mercaptoethanol (50 μM) supplemented with mIL-7 (5 ng/mL) for 24 hours.

CRISPR/Cas9 gene editing and AAV transduction
To perform CRISPR/Cas9 editing, 26.6 pmoles recombinant Cas9 (IDT) and 270 pmoles sgRNA (IDT) were combined and incubated at RT for 10 minutes to form Cas9/sgRNA ribonucleoprotein (RNP) complexes. P14 cells were harvested from culture plates and washed twice with PBS. Up to 20 × 106 T cells were resuspended in 20 μL of warm P3 electroporation buffer (containing 82% P3 buffer and 18% Supplement 1; Lonza), combined with RNP and electroporated using a 4D-Nucleofector X Unit (Lonza) using pulse code CM137 for activated murine T cells and DN100 for naïve and IL-7 cultured murine T cells. After electroporation, T cells were resuspended in warm complete RPMI at a concentration of 50 × 106 cells/mL and cells were then immediately added to AAV6 at a multiplicity of infection (MOI) of 100K for activated cells and 400K for naïve cells in the presence of 2 uM of M38142 NHEJ inhibitor (MedChemExpress), not exceeding 30 μL of volume per well in a 96-U bottom well plate. Cells were incubated at 37°C for 4 hours before AAV6 was washed off and cells were either kept in vitro or transferred into mice as specified in the text.

Adoptive transfer and LCMV infection
5 × 103 CD45.1+ P14 cells were adoptively transferred into B6 recipient mice intravenously (i.v.). Mice were then i.v. infected with 2 × 106 p.f.u. Cl.13 on day 5 post transfer of edited P14 cells, unless stated otherwise as in Fig. 1.

HDR templates and AAV vectors
HDR templates and sgRNA were designed using the Benchling online tool (https://benchling.com 2024, USA). The Thy1-GFP HDR template was designed by targeting the start of the second exon of Thy1 genomic locus (sgRNA: GGAGAGCGACGCTGATGGCT) and homology arms were designed to exclude the Thy1 start codon, resulting in deletion of the endogenous Thy1 gene upon HDR knock-in. The GFP sequence was added between the homology arms followed by a polyA tail. Thy1-GFP [end] construct was designed to target Thy1 exon 4 using a different sgRNA (TCTGTGACTGGTTGGGCCCA). Homology arms were designed to exclude the stop codon, a P2A linker was added before the GFP sequence and no polyA tail was added to the construct. Rosa26-GFP HDR template was designed based on previous work74, using the same sgRNA, but homology arms were shortened to 600bp of length. CX3CR1-GFP template was designed targeting the end of exon 2 (sgRNA: GGGTCTCTCCTGCTCTGAAG), homology arms were designed to exclude the stop codon and a P2A linker was added before the GFP sequence. The TCF1 over-expression templates were designed from Thy1-GFP and CX3CR1-GFP by replacing the GFP sequence with the full-length TCF1 cDNA sequence.

Plasmids containing HDR templates were cloned into a pAAV6 backbone and synthesized by Genscript (USA). AAV6 production was performed by Packgene (USA).

Retroviral over-expression
TCF1 p33, TCF1 p45 and β-catenin sequences were cloned into the MSCV-IRES-mCherry plasmid (Addgene #52114) by Genscript. Retroviruses were produced in HEK293T cells with MSCV and pCL-Eco (AddGene #12371) plasmids using FuGENE HD (Promega). P14 cells were activated for 24 hours as per the CRISPR-HDR protocol and then transduced with retroviral supernatant containing Polybrene (2 μg/mL) and hIL-2 (100 IU/mL) by spinfection (1,260g for 90 minutes at 25°C), followed by incubation at 37°C for 4 hours. Retroviral supernatant was then replaced with complete media containing hIL-2 (100 IU/mL) and mIL-7 (5 ng/mL) and cells were kept in vitro for 24 hours before transfer into mice.

Multi-ome analysis
For combined whole cell scRNA-seq and scATAC-seq multi-ome analysis, B6 mice were injected with 5 × 103 CRISPR-edited P14 CD45.1 cells and infected with Cl.13 after 5 days. 20 days post infection, CD45.1+ CD8+ cells (i.e. P14 cells) were sorted from splenocytes of recipient B6 mice. Sorted cells were incubated with Biolegend TotalSeq A anti-mouse antibody hashtags at a concentration of 0.82 µg/1 × 105 cells/50 µl (#1-3, Cat# 155801, #155803 and #155805) to enable sample multiplexing and super-loading on the 10X Genomics single cell sequencing platform. Whole cell scATAC-seq and scRNA-seq multi-ome analysis was performed using a previously reported protocol75 (using the OMNI permeabilization method) by the Molecular Genomics Core at the PMCC. Cells were processed using the Chromium Next GEM Single Cell Multiome ATAC + Gene Expression Reagent Kit (10X Genomics) and 1.6 × 104 cells were loaded onto the 10X Genomics Platform for downstream sequencing and analysis. HashTag sequences were recovered through sequencing on an Illumina NextSeq machine, while gene expression and ATAC seq data were recovered through sequencing on an Illumina NovaSeq machine.

Data matrices from multi-omic data were generated using the 10× CellRanger ARC pipeline (version 2.0.0) and mm10 genome. Specifically, cellranger-arc count was used to generate raw feature barcode matrices from FASTQ files. The resulting reads were used to generate antibody capture data matrices with CITE-seq-Count (version 1.4.5). Downstream analysis was performed in R (version 4.4.2) and Seurat (version 5.0.2).

Count matrices were processed using decontX to remove ambient RNA contamination using both raw and filtered feature-barcode matrices as input. The decontaminated counts were then passed on to quality control using the scater and scuttle packages. Per-cell QC metrics were computed using addPerCellQCMetrics. Outlier cells were detected using median absolute deviation (MAD)-based thresholds for total counts (library size), number of detected features, and mitochondrial transcript percentage (nmads=c(low=2, high=3)). Cells failing any threshold were discarded as low-quality cells along with mitochondrial genes. Filtered data was then log-normalised using logNormCounts, and doublet detection was performed using scDblFinder (v1.16.0). This filtered data was then converted into a Seurat object, and the doublets were retained until the later HTO demultiplexing step.

Normalised expression matrices were processed and scaled with Seurat’s NormalizeData, FindVariableFeatures and ScaleData workflow, with regression on mitochondrial percentage. Dimensionality reduction with PCA and UMAP was performed using the first 30 PCs. Standard Seurat processing and normalisation steps for a gene expression assay were performed: NormalizeData, FindVariableFeatures, ScaleData, RunPCA, RunUMAP, FindNeighbors and FindClusters.

Antibody capture data matrices from hashtag oligonucleotides (HTO) were demultiplexed using HTODemux (Seurat). Doublets were detected using DoubletFinder (version 2.0.4). Cells that were not HTO singlets and/or droplet singlets were filtered out.

Chromatin accessibility data was analysed using Signac (version 1.12.0) and ArchR (version 1.0.2) using default parameters unless otherwise noted. The union peak list was generated using a hybrid approach utilising MACS3 and ArchR. A first set of peaks was called with ArchR with default parameters based on clusters generated from the latent semantic indexing dimension reduction of the tile matrix, which allowed peaks to be called on unbiased cell clusters. In addition, we called a second set of peaks on this assay using the CallPeaks function which uses the MACS3 algorithm and its default parameters. A union of both two peak lists was used to generate a custom peak list, which was added to the Seurat object using FeatureMatrix and CreateChromatinAssay. Peak annotation was performed using the ClosestFeature function and using EnsDb.Mmusculus version 79 annotations. Standard Signac processing and normalisation steps for a chromatin assay were performed as follows: FindTopFeatures, RunTFIDF, RunSVD and RunUMAP. Signac LinkPeaks was applied to calculate the correlation gene expression and peak accessibility, taking into account its GC-content, accessibility, and length of the peak from the gene transcription start site (TSS).

To perform integrative analysis of both these modalities, the FindMultiModalNeighbors function was applied to calculate the nearest neighbours in the dataset based on a weighted combination of gene expression and chromatin accessibility similarities per cell, based on its respective dimensionalities. Dimensional reduction using RunUMAP was then performed to visualise cell-specific weighted nearest neighbour (WNN) information with graph-based clustering using Seurat DimPlot. Clustering analysis of the weighted shared nearest neighbour (WSNN) information in these assays was performed with FindClusters. Clusters with low-quality metrics were removed, and the final cluster resolution of 1.1 was determined using results from the clustree package (version 0.5.0).

Cell clusters were annotated using a combination of manual annotation of known cell type markers and ProjecTILs (version 3.0.3). A ProjecTILs reference object was generated with the scRNA-seq data from Giles et al.56 with the make.reference function. The cell labels were projected onto our query dataset using the Run.ProjecTILs function. Multi-omic data was manually split into CX3CR1+ or Tpex subsets based on cell type (CX3CR1+ = Cells annotated as ExhInt, TransCTL, Efflike in the multi-ome dataset; Tpex = Cells annotated as Tpex in the multi-ome dataset). Each subset object was subject to re-normalisation per the steps mentioned above. Motif activities for each object were calculated with RunChromVar using BSgenome.Mmusculus.UCSC.mm10 (version 1.4.3) and JASPAR2020 (species 9606, version 0.99.10) as references. The resulting motif information was added to each respective Seurat object as a “chromvar” assay.

Cell cluster proportion bar plots were plotted with dittoSeq (version 1.8.1). Differentially expressed genes (DEGs) and differently accessible chromatin regions (DACRs) were detected with Seurat FindMarkers, supplying gene counts and peak counts as latent variables, respectively. Where applicable, Signac ClosestFeatures was used to define the closest gene to each peak set. DEGs and DACRs were deemed significantly differential if the adjusted p-value was <0.05. TCF1+ cluster gene expression heatmap and the DACR heatmap were generated with ComplexHeatmap (version 2.14.0).

Flow cytometric analysis
Spleens were harvested and processed as described above. For cell surface staining, cells were stained on ice in FACS buffer (PBS containing 2.5% FCS and 0.1% sodium azide) using the following anti-mouse Abs and reagents: Zombie Aqua (BioLegend, Cat. no. 423102, 1:1000), anti-CD8-BUV395 (Clone 53-6.7, BD Biosciences, Cat. no. 563786, 1:200), anti-CD45.1-Pacific Blue (Cat. no. 110722, 1:200), -FITC (Cat. no. 110706, 1:200) or -APC (Cat. no. 110714, 1:200) (Clone A20, BioLegend), anti-Thy1.2-PE (Clone 53-2.1, BD Biosciences, Cat. no. 553005, 1:1000), anti-CD44-Pacific Blue (Cat. no. 103020, 1:200) or -APC (Cat. no. 103012, 1:200) (Clone IM7, BioLegend), anti-CXCR5-PECy7 (clone 2G8, BD Biosciences, Cat. no. 560617, 1:25), anti-PD-1-BV785 (clone 29F.1A12, BioLegend, Cat. no. 135225, 1:200). For intracellular staining, cells were fixed and permeabilized using eBioscience Foxp3/Transcription factor staining buffer set (ThermoFisher Scientific) and stained with rabbit anti-mouse TCF1 mAb (clone C63D9, Cell Signaling Technology, Cat. no. 2203S, 1:400) detected with a polyclonal secondary anti-rabbit IgG AlexaFluor594 Ab (ThermoFisher Scientific, Cat. no. A-11012, 1:1000), anti-Granzyme-B-APC (Clone GB11, Invitrogen, Cat. no. GRB05, 1:200) and anti-TOX-PE (clone TXRX10, Invitrogen, Cat. no. 12-6502-82, 1:10). For intracellular cytokine staining, splenocytes were restimulated with 0.1 μg/mL of the GP33-41 LCMV peptide (Biomolecular Resource Facility, ANU) in media containing 3 μg/mL Brefeldin A (eBioscience). Cell suspensions were incubated at 37°C, 5% CO2 for 5 hours, prior to surface staining then fixation with Biolegend Fixation Buffer, and intracellular cytokine staining in eBioscience Permeabilization Buffer containing the following antibodies: anti-IFNγ-PECy7 (clone XMG1.2, eBioscience, Cat. no. 25-7311-82, 1:2000) and anti-TNFα-PE (clone MP6-XT22, BioLegend, Cat. no. 506306, 1:2000). Samples were acquired on a BD LSRFortessa X-20 (BD Biosciences) and analyzed using FlowJo software (BD Biosciences) and GraphPad Prism (GraphPad Software). Gating strategies are illustrated in Supplementary Fig. 5.

Statistics
Microsoft Excel (v16.71) was used for data calculations and statistical analysis was performed using Prism Software (GraphPad, v9.0.2). P values were calculated using an unpaired t-test, one-way ANOVA with a Tukey’s Multiple Comparisons test or two-way ANOVA as specified in each figure legend.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In the study, “High efficiency CRISPR knock-in demonstrates that TCF1 is insufficient to reverse T cell exhaustion,” de Menezes et al. report optimization of a highly efficient CRISPR knock-in editing protocol in murine CD8 T cells and apply this technique to interrogate T cell exhaustion. They specifically ask the question as to whether TCF1 can actively de-differentiate more terminally exhausted T cell subsets back into a stem-like state. The authors highlight that high efficiency homology-directed repair editing in TCR-transgenic CD8 T (P14) cells is achieved with T cell activation and lower template insert size. Additionally, utilizing their protocol and Thy1 gene stop codon, they demonstrate that knock-in editing of GFP in endogenous regulatory elements does not disrupt the endogenous gene expression. The authors applied this technique to specifically target CX3CR1 Tex-int CD8 T cell populations and conditionally over-express TCF1. Interestingly, constitutive but not conditional over-expression of TCF1 lead to an increase in Tpex numbers. In summary, the authors provide an exciting tool that is certain to be a valuable resource. However, the majority of this work is about optimization of a technique using one biological observation.\nMajor Comments\n1. Most of the main figures (Fig. 1-3) describe optimization of conditions to yield high editing efficiency, but these figures could be moved to supplemental to allow for additional characterization on the TCF1 knock-in studies.\n2. The authors suggest that conditional overexpression of TCF1 in CXC3CR1 expressing Tex-int cells is insufficient to reprogram Tex-int into Tpex by phenotype. What is the transcriptional and epigenomic landscape of these conditional knock-in cells?", "reviewer_2": "De Menezes et al. aim to explore the role of the T cell-specific nuclear factor TCF1 in murine models of T cell exhaustion. They utilize the well-established LCMV infection model to investigate the functional importance of TCF1 in precursors of exhausted T cells, known as Tpex. TCF1 expression is lost upon Tpex differentiation. The authors use CRISPR-Cas9 to engineer transgenic CD8+ T cells in this model. However, much of the manuscript focuses on optimizing efficient CRISPR editing, which is not novel.\nBy targeting the CX3CR1 locus—shown by the authors to be more highly expressed in differentiated Tpex cells—they re-express a form of TCF1 but fail to rescue Tpex development. This result contrasts with previous reports that demonstrated increased Tpex numbers upon ectopic TCF1 expression. The authors conclude that TCF1 cannot revert T cell exhaustion via de-differentiation into Tpex.\n1. While the conclusions appear valid based on the reported experiments, it is unclear whether this general conclusion is correct. Importantly, TCF1 expression from the CX3CR1 locus is at least fivefold lower than in native Tpex cells (see Fig 5d of the manuscript), suggesting that TCF1 levels may be insufficient to drive de-differentiation. A more precise quantification of TCF1 protein, such as by Western blot, would be helpful—this also relates to my second point.\n2. TCF1 is a highly complex gene/protein with at least eight alternative splice variants and multiple promoters, which have been shown to exert distinct functional effects in certain contexts, most notably in the thymus. Which TCF1 cDNA variant is being used—p33, p45, or p56? This could significantly influence the results, although re-expression experiments in TCF1-/- CD8 T cells have suggested some splice variant-independent functions.\n3. TCF1-mediated stemness is associated with canonical Wnt signaling, and reporter mice indicate active Wnt signaling in these cells. Have the authors considered expressing Wnt pathway components or a TCF1-β-catenin fusion protein to potentially compensate for possibly reduced Wnt signaling under exhaustion conditions?\n4. Are there any negative side effects of CRISPR editing at these loci, such as translocations or large deletions? Have the authors considered using a non-integrating lentivirus instead of AAV6, which would allow for the introduction of a much larger DNA template?", "reviewer_3": "This study explores an intriguing topic: reversing T-cell exhaustion and demonstrates proof of concept novel “knock-in” CRISPR editing of T cells. This technology will be incredibly valuable for the field and thus we are very excited by this study. Moreover, the authors use this technology to reverse engineer T cells to study ways of modifying the differentiation of T cells, particularly hyporesponsive, exhausted T cells. Extensive research has examined the causes of T-cell exhaustion, with ongoing efforts to develop therapeutic strategies to counteract this process. Gaining a deeper understanding of the mechanisms that regulate exhaustion could prolong the functional lifespan of T cells, thereby enhancing immune responses against chronic diseases. After a series of experiments demonstrating the abilities and limitations of performing homologous directed repair (HDR), this research focuses on the transcription factor TCF1, the key regulator of Tpex cells, to determine whether its overexpression can reverse T-cell exhaustion by reprogramming cells that are already progressing toward exhaustion.\nPrevious studies have shown that high TCF1 expression correlates with longer overall survival (OS) in human patients. This study aims to investigate whether TCF1 alone is responsible for this effect or if other factors contribute to its role in promoting longer OS. To investigate this question, the authors employed an innovative CRISPR-based approach to efficiently engineer T-cells with either constitutive or conditional TCF1 over-expression. The study demonstrates that while constitutive TCF1 over-expression increases the Tpex population, conditional TCF1 over-expression in Tex-int cells fails to revert them back to a Tpex-like state. This is a simple, but interesting point, and unexpected since since TCF1 is touted to be a pioneer factor. It would be great to see how FoxoAAA could differ from TCF1 in this regard…or late stage deletion of ID2 or Blimp1.\nWhile CRISPR-Cas9 knock-in strategies have been widely used, the efficiency of the homology-directed repair (HDR) technique remains a challenge. The authors introduce an innovative approach for achieving high-efficiency CRISPR HDR-based gene knock-in in primary murine CD8+ T cells, making it applicable for both in vitro and in vivo studies. The high-efficiency CRISPR knock-in technique described in this study represents a key methodological advancement, enabling precise genetic modifications within murine T-cells for in vivo experimentation. Thus, this study will also be an important proof of technique for the field and it makes a simple, but interesting observation related to CD8 T cell differentiation.\nThis reviewer only has a few minor concerns, mostly related to the techniques.\n1) It is a bit confusing in Fig. 1 why the non-infected T cells (GFP-) do not have higher levels of Thy1.2. The whole point of showing HDR is that the GFP insertion will disrupt Thy1 open reading frame, so it is expected that the GFP+ infected cells will have low levels of Thy1.2 staining, but what I can’t understand is why the non-infected cells have the same low levels of Thy1.2. Am I misunderstanding the experimental design, or is there an issue with shedding of CD90 since it is a GPI-linked protein in the uninfected cells? This needs to be clarified and perhaps a different locus should be used altogether if they are getting CD90 shedding.\n2) Do they authors know the % of homozygous vs heterozygous HDR in the cells? This should be another parameter they discuss.\n3) In Figure 5e and h-k, I am very confused how they are gating on CX3CR1+ cells when they are targeting this locus for HDR. In fig 5B they should show CX3Cr1 by TCF1 to show overall expression of teh two proteins and they could also use KLRG1 in addition to CX3CR1 to identify the CX3Cr1+ cells. And for teh TCF1 they should also analyze that with slamf6 and cxcr5. They need to do more diligence in the analysis of these cells. Also, it would be great if indeed KLRG1 is not affected by TCF1 expression, then they could sort out KLRG1+ TCF1+ and KLRG1+ TCF- cells and compare the two transcriptionally to better examine how TCF1 overexpression affects the T cells. Lastly, they should look at IL-2 and see if this is increased at all by TCF1 OE.\nIn summary, these findings challenge the notion that TCF1 alone is sufficient to drive de-differentiation and suggest that additional factors may be required for reprogramming exhausted T-cells. The research carries significant clinical implications, particularly in the context of immune checkpoint blockade therapies, where restoring stem-like T-cell populations could improve therapeutic outcomes for patients with chronic infections or cancer."}, "author_response": "Point-by-point response to Reviewer’s comments\nWe thank the reviewers for their constructive feedback on our manuscript. Following\nthe reviewers’ recommendations, we have added additional data to address the\nreviewers’ concerns which we believe has improved the quality and novelty of the\nstudy. The major changes are:\n- We have included multi-ome analysis providing evidence that TCF1 over-\nexpression only causes mild transcriptional and epigenetic changes to the\nCX3CR1+ cell population. These new results provide mechanistic insight into\nour phenotype.\n- We provide evidence that our CRISPR HDR optimized protocol works as\nefficiently as conventional over-expression systems, such as retroviral over-\nexpression.\n- We demonstrate that TCF1 isoforms work similarly to increase Tpex when over-\nexpressed and that b-catenin is not a limiting factor in this context.\n- We present an estimated quantification of homozygous vs heterozygous\nintegration of transgenes using our CRISPR HDR protocol.\nWe have updated the manuscript text to reflect these changes and to accommodate\nadditional reviewer requests (revised text is marked in red). Overall, we strongly feel\nthat these revisions have significantly strengthened the manuscript. We provide a\npoint-by-point response to the reviewer comments below.\nReviewer #1\nIn the study, “High efficiency CRISPR knock-in demonstrates that TCF1 is insufficient\nto reverse T cell exhaustion,” de Menezes et al. report optimization of a highly efficient\nCRISPR knock-in editing protocol in murine CD8 T cells and apply this technique to\ninterrogate T cell exhaustion. They specifically ask the question as to whether TCF1\ncan actively de-differentiate more terminally exhausted T cell subsets back into a\nstem-like state. The authors highlight that high efficiency homology-directed repair\nediting in TCR-transgenic CD8 T (P14) cells is achieved with T cell activation and\nlower template insert size. Additionally, utilizing their protocol and Thy1 gene stop\ncodon, they demonstrate that knock-in editing of GFP in endogenous regulatory\nelements does not disrupt the endogenous gene expression. The authors applied this\ntechnique to specifically target CX3CR1 Tex-int CD8 T cell populations and\nconditionally over-express TCF1. Interestingly, constitutive but not conditional over-\nexpression of TCF1 lead to an increase in Tpex numbers. In summary, the authors\nprovide an exciting tool that is certain to be a valuable resource. However, the majority\nof this work is about optimization of a technique using one biological observation.\nMajor Comments\n1. Most of the main figures (Fig. 1-3) describe optimization of conditions to yield high\nediting efficiency, but these figures could be moved to supplemental to allow for\nadditional characterization on the TCF1 knock-in studies.\nWe thank the reviewer for the suggestion, but given that protocol development for in\nvivo mouse studies is a major component of the study that we feel is novel and would\nbe of interest to the field, we would respectfully prefer to keep Figures 1-3 as main\nfigures. In particular, we show that we can achieve high efficiency CRISPR KI with only\nminimal in vitro culture that does not affect differentiation in vivo. This has not been\nshown previously and would be of interest to those who wish to apply this approach to\ntheir in vivo T cell studies.\n2. The authors suggest that conditional overexpression of TCF1 in CXC3CR1\nexpressing Tex-int cells is insufficient to reprogram Tex-int into Tpex by phenotype.\nWhat is the transcriptional and epigenomic landscape of these conditional knock-in\ncells?\nWe agree with the reviewer that this is an important point to resolve. To address this\npoint, we performed whole cell multi-ome analysis (combined scRNA-seq + scATAC-\nseq) on CRISPR-edited P14 cells at day 20 p.i. and the results are now shown in the\nnew Figure 6 and Supplementary Figure 4. Consistent with our flow cytometry\nphenotyping in the initial submission, we find that TCF1 over-expression only causes\nmild epigenetic and transcriptional changes within the CX3CR1+ subset (Figure 6d-g).\nNevertheless, there was still a significant enrichment of the Tpex transcriptional\nsignature, and depletion of the CX3CR1+ cell transcriptional signature, within TCF1\nover-expressing CX3CR1+ cells (Figure 6f) alongside a slight increase in TCF1 motif\naccessibility (Figure 6g). Collectively, these data support a model whereby TCF1 over-\nexpression within CX3CR1+ cells can cause mild transcriptional and epigenetic\nreprogramming of cells towards a Tpex phenotype, but overall these changes are not\nsufficient to push cells into a bona fide Tpex state. These new data are discussed in\nlines 294-322, 368-369 and 379-381 of the revised manuscript.\nReviewer #2\nDe Menezes et al. aim to explore the role of the T cell-specific nuclear factor TCF1 in\nmurine models of T cell exhaustion. They utilize the well-established LCMV infection\nmodel to investigate the functional importance of TCF1 in precursors of exhausted T\ncells, known as Tpex. TCF1 expression is lost upon Tpex differentiation. The authors\nuse CRISPR-Cas9 to engineer transgenic CD8+ T cells in this model. However, much\nof the manuscript focuses on optimizing efficient CRISPR editing, which is not novel.\nBy targeting the CX3CR1 locus—shown by the authors to be more highly expressed\nin differentiated Tpex cells—they re-express a form of TCF1 but fail to rescue Tpex\ndevelopment. This result contrasts with previous reports that demonstrated increased\nTpex numbers upon ectopic TCF1 expression. The authors conclude that TCF1\ncannot revert T cell exhaustion via de-differentiation into Tpex.\nWe thank the reviewer for their summary and comments about the manuscript. Whilst\nCRISPR-mediated knockout is now routinely used in the field, there have been very\nfew studies that have reported high efficiency CRISPR-mediated knock-in as used in\nour study. We would therefore respectfully disagree that the optimization of CRISPR\nknock-in is not novel, since there is no report to date of high efficiency CRISPR HDR\nediting in primary murine CD8+ T cells for in vivo studies of T cell differentiation. Prior\nstudies were predominantly in vitro, and the little in vivo work involved extensive cell\nculture prior to in vivo transfer (e.g. using CAR T cells), which is problematic as long-\nterm in vitro culture is known to disrupt in vivo T cell differentiation. It was thus unknown\nwhether the short-term culture conditions required for the study of in vivo T cell\ndifferentiation would support CRISPR HDR. Our optimized protocol demonstrates that\nthis is achievable and represents the only protocol to date suitable for in vivo T cell\nbiology and differentiation studies. As noted by Reviewer 3 “The high-efficiency\nCRISPR knock-in technique described in this study represents a key methodological\nadvancement, enabling precise genetic modifications within murine T-cells for in vivo\nexperimentation.”\nAdditionally, it’s important to note that our results are consistent with previous studies\ninvolving constitutive ectopic TCF1 expression. We find that constitutive TCF1 over-\nexpression by knock-in to the Thy1 locus does increase Tpex numbers, in accordance\nwith previous studies employing constitutive TCF1 expression (eg. Shan et al, Cell Mol\nImmunol 2020). It was only after conditional TCF1 over-expression in CX3CR1+ cells\nthat we failed to observe an increase in Tpex. Neither the cited Shan et al study nor\nany previous study has conditionally over-expressed TCF1 after Tpex differentiation.\n1. While the conclusions appear valid based on the reported experiments, it is unclear\nwhether this general conclusion is correct. Importantly, TCF1 expression from the\nCX3CR1 locus is at least fivefold lower than in native Tpex cells (see Fig 5d of the\nmanuscript), suggesting that TCF1 levels may be insufficient to drive de-differentiation.\nA more precise quantification of TCF1 protein, such as by Western blot, would be\nhelpful—this also relates to my second point.\nThank you for the comment, and we apologize for the confusion regarding the levels\nof TCF1 expressed by each population. We have now updated Figure 5d to include\nTCF1 expression levels in native Tpex cells to enable a direct comparison between\nTpex and the CRISPR-edited cells. Notably, there is no significant difference in TCF1\nexpression between the edited cells in the CX3CR1-TCF1 group and native Tpex cells.\nMoreover, TCF1 levels in edited cells were higher in the CX3CR1-TCF1 group than\nthe Thy1-TCF1 group. Given that TCF1 expression levels in the Thy1-TCF1 group\nwere sufficient to increase Tpex numbers, these results suggest that insufficient TCF1\nexpression is not the reason why CX3CR1-TCF1 edited cells fail to de-differentiate.\nThese new data are discussed and better clarified in lines 250 to 253 of the revised\nmanuscript. We have also added a label to Figure 5d to make it clearer that the TCF1\nMFI within the graph was measured within edited (ie. TCF1+) cells in the CX3CR1-\nTCF1 and Thy1-TCF1 groups.\nWe thank the reviewer for the suggestion to quantify the levels of TCF1 by Western\nblotting. This experiment requires that we first sort on the edited TCF1+ cells otherwise\nthe contaminating unedited cells will prevent accurate assessment of TCF1 over-\nexpression after CRISPR HDR knock-in. We attempted to sort TCF1 expressing cells\nfrom the different groups and perform Western blotting to quantify TCF1 protein levels\nin each condition, but the cell fixation, permeabilization and staining required for\nsorting disrupted downstream TCF1 detection by Western blotting. This meant that\nTCF1 quantification by Western blotting was unfortunately not feasible.\n2. TCF1 is a highly complex gene/protein with at least eight alternative splice variants\nand multiple promoters, which have been shown to exert distinct functional effects in\ncertain contexts, most notably in the thymus. Which TCF1 cDNA variant is being\nused—p33, p45, or p56? This could significantly influence the results, although re-\nexpression experiments in TCF1-/- CD8 T cells have suggested some splice variant-independent functions.\nThank you for raising this important point. The isoform used in our over-expression\nsystem is the p45 (full-length) isoform and we have added this information to the main\ntext on line 240.\nPrior work has demonstrated that the functional domains of TCF1 for T cells reside within\nthe N-terminal domain (Xing et al, Nat Immunol 2016) consistent with prior work showing\nthat p56 and p45 are functionally equivalent in T cells (Van de Wetering et al, Mol Cell\nBiol 1996). For this reason, the CD8+ T cell field has focused solely on p33 and p45 when\nstudying TCF1 function (Gullicksrud et al, J Immunol 2017; Chen et al, Immunity 2019;\nShan et al, Cell Mol Immunol 2020), and we have thus also focused on these two\nisoforms to address this question. To examine the effect of different TCF1 isoforms on\nTpex differentiation, we measured the impact of retroviral over-expression of the p33\nor p45 isoforms on Tpex frequency (Supplementary Figure 3f,g in the revised\nmanuscript). Consistent with previous studies (Chen et al, Immunity 2019), both\nisoforms similarly increased Tpex frequency in comparison to the empty vector control.\nThus, at least in the context of Tpex differentiation, p33 and p45 are functionally\nequivalent. Importantly, the increase in Tpex after retroviral expression was similar to\nthat seen after constitutive TCF1 over-expression by our CRISPR KI strategy (ie. TCF1\nKI to the Thy1 locus) (Supplementary Figure 3f,g). These new data are discussed in\nlines 277 to 282 of the revised manuscript.\n3. TCF1-mediated stemness is associated with canonical Wnt signaling, and reporter\nmice indicate active Wnt signaling in these cells. Have the authors considered\nexpressing Wnt pathway components or a TCF1-β-catenin fusion protein to potentially\ncompensate for possibly reduced Wnt signaling under exhaustion conditions?\nWe thank the reviewer for the suggestion. Since the p33 isoform does not contain the\nb-catenin binding domain, the results in Supplementary Figure 3f,g suggest that b-\ncatenin does not play a role in the Tpex increase seen after TCF1 over-expression.\nThe b-catenin (Ctnnb1) coding sequence is 2.4 kb long, meaning that the transgene\nsize required for a TCF1-b-catenin fusion protein (or even b-catenin alone) is too large\nfor CRISPR HDR (see Figure 2 of the manuscript). Thus, to address if b-catenin was\na limiting factor preventing reprogramming, we instead retrovirally over-expressed the\nstable form of b-catenin (Harada et al, EMBO J 1999) together with conditional TCF1\nknock-in (CX3CR1-TCF1). While b-catenin over-expression alone increased Tpex\nproportions, Tpex were not further increased in b-catenin over-expressing CX3CR1-\nTCF1 cells (Supplementary Figure 3h,i in the revised manuscript). These results\nsuggest that insufficient b-catenin does not explain why conditional TCF1 over-\nexpression fails to de-differentiate CX3CR1+ cells into Tpex. The increase in Tpex\nseen after b-catenin over-expression alone could be due to the actions of b-catenin on\nendogenous TCF1 or LEF1, but it should be noted that b-catenin has TCF1-independent functions (Valenta et al, EMBO J 2012). Note that the inability of b-catenin\nover-expression to synergise with TCF-1 over-expression to increase Tpex is\nconsistent with prior findings (Shan et al, Cell Mol Immunol 2020). These new data are\ndiscussed in lines 282 to 289 of the revised manuscript.\n4. Are there any negative side effects of CRISPR editing at these loci, such as\ntranslocations or large deletions? Have the authors considered using a non-integrating\nlentivirus instead of AAV6, which would allow for the introduction of a much larger DNA\ntemplate?\nWe thank the reviewer for the question. We wish to clarify that the main purpose of the\nAAV6 and CRISPR-HDR approach was to enable us to insert transgenes under the\ncontrol of promoters such as CX3CR1 that only become active once the CD8+ T cells\nbegin to differentiate. This enables us to assess the ability of TCF1 overexpression to\nreverse exhaustion in cells that have already undergone differentiation. Other\nstrategies such as non-integrating lentivirus are only suitable for constitutive\nexpression and so would not enable us to address this research question. Although\nchromosomal translocations/large deletions have been reported in CRISPR/Cas9\nedited T cells, these are rare and in the majority of cases these cells are not viable\nand so do not have a functional impact. Importantly, CRISPR/Cas9 edited cells have\nsuccessfully and safely been used in patients (Stadtmauer et al, Science 2020). We\nagree that a careful examination of such events is needed for any CRISPR-edited T\ncell product that is designed for clinical use. However, given their low frequency, such\noff-target effects are unlikely to impact our current research question.\nWe nevertheless acknowledge that one concern with our editing approach may be that\nit is unable to drive as much transgene expression as retroviral approaches. To further\ncompare our CRISPR KI strategy to more conventional over-expression systems used\nfor mouse T cells, we constitutively over-expressed TCF1 using CRISPR HDR or\nretrovirus and observed higher TCF1 expression levels in edited cells by CRISPR\nknock-in. We have now included this data in the manuscript as Figure 5h.\nFurthermore, as noted in point 2 above, in Supplementary Figure 3f,g we show that\nTpex expansion is comparable between CRISPR edited and retrovirally over-\nexpressing cells. These results suggest that our optimized CRISPR KI protocol works\nas efficiently as retroviral over-expression systems. These new data are discussed in\nlines 265 to 276 of the revised manuscript.\nReviewer #3\nThis study explores an intriguing topic: reversing T-cell exhaustion and demonstrates\nproof of concept novel “knock-in” CRISPR editing of T cells. This technology will be\nincredibly valuable for the field and thus we are very excited by this study. Moreover,\nthe authors use this technology to reverse engineer T cells to study ways of modifying\nthe differentiation of T cells, particularly hyporesponsive, exhausted T cells. Extensive\nresearch has examined the causes of T-cell exhaustion, with ongoing efforts to\ndevelop therapeutic strategies to counteract this process. Gaining a deeper\nunderstanding of the mechanisms that regulate exhaustion could prolong the\nfunctional lifespan of T cells, thereby enhancing immune responses against chronic\ndiseases. After a series of experiments demonstrating the abilities and limitations of\nperforming homologous directed repair (HDR), this research focuses on the\ntranscription factor TCF1, the key regulator of Tpex cells, to determine whether its\noverexpression can reverse T-cell exhaustion by reprogramming cells that are already\nprogressing toward exhaustion.\nPrevious studies have shown that high TCF1 expression correlates with longer overall\nsurvival (OS) in human patients. This study aims to investigate whether TCF1 alone\nis responsible for this effect or if other factors contribute to its role in promoting longer\nOS. To investigate this question, the authors employed an innovative CRISPR-based\napproach to efficiently engineer T-cells with either constitutive or conditional TCF1\nover-expression. The study demonstrates that while constitutive TCF1 over-\nexpression increases the Tpex population, conditional TCF1 over-expression in Tex-\nint cells fails to revert them back to a Tpex-like state. This is a simple, but interesting\npoint, and unexpected since since TCF1 is touted to be a pioneer factor. It would be\ngreat to see how FoxoAAA could differ from TCF1 in this regard…or late stage deletion\nof ID2 or Blimp1.\nWhile CRISPR-Cas9 knock-in strategies have been widely used, the efficiency of the\nhomology-directed repair (HDR) technique remains a challenge. The authors\nintroduce an innovative approach for achieving high-efficiency CRISPR HDR-based\ngene knock-in in primary murine CD8+ T cells, making it applicable for both in vitro\nand in vivo studies. The high-efficiency CRISPR knock-in technique described in this\nstudy represents a key methodological advancement, enabling precise genetic\nmodifications within murine T-cells for in vivo experimentation. Thus, this study will\nalso be an important proof of technique for the field and it makes a simple, but\ninteresting observation related to CD8 T cell differentiation.\nThis reviewer only has a few minor concerns, mostly related to the techniques.\nWe thank the reviewer for the kind words and comprehensive summary. We agree that\nFoxoAAA is an interesting target, and in line with this idea we are currently conducting\nlarge-scale screening for factors that can reprogram cells into Tpex as part of a larger\nfollow-up study. Late-stage deletion of effector-associated transcription factors is also\nan interesting idea to investigate, although not something that would be achievable\nusing our current CRISPR HDR approach.\n1) It is a bit confusing in Fig. 1 why the non-infected T cells (GFP-) do not have higher\nlevels of Thy1.2. The whole point of showing HDR is that the GFP insertion will disrupt\nThy1 open reading frame, so it is expected that the GFP+ infected cells will have low\nlevels of Thy1.2 staining, but what I can’t understand is why the non-infected cells\nhave the same low levels of Thy1.2. Am I misunderstanding the experimental design,\nor is there an issue with shedding of CD90 since it is a GPI-linked protein in the\nuninfected cells? This needs to be clarified and perhaps a different locus should be\nused altogether if they are getting CD90 shedding.\nWe apologise for the confusion and thank the reviewer for the comment. While the\nHDR-mediated insertion of GFP will indeed disrupt Thy1 expression, this is not 100%\nefficient and a certain percentage of the cells will be edited through the NHEJ pathway,\nresulting in disruption of the Thy1 open reading frame by indels without insertion of\nGFP. In other words, the Thy1.2-GFP- population represents cells where Thy1.2 was\nsuccessfully knocked out but where GFP was not successfully knocked-in. This is now\nclarified in lines 130 to 132 of the manuscript.\n2) Do they authors know the % of homozygous vs heterozygous HDR in the cells?\nThis should be another parameter they discuss.\nWe thank the reviewer for raising this important question. To address this question, we\nperformed a CRISPR KI experiment where we added in two different DNA templates\nencoding different transgenes (GFP or mCherry) but targeting the same locus (Cx3cr1)\n(Figure 4i,j in the revised manuscript). In this experimental setup, cells that are double\npositive for both GFP and mCherry after CRISPR KI must have undergone\nheterozygous editing of the Cx3cr1 locus. The percentage of double-positive cells thus\nenables an estimate of heterozygous integration frequencies, as previously shown by\nothers (Roth et al, Nature 2018). On average we detected ~20% double positive cells,\nalthough the frequencies were highly variable, reaching as high as 50%. These data\ndemonstrate that heterozygous editing does variably occur using our protocol. These\nnew data are discussed in lines 225 to 232 of the revised manuscript.\n3) In Figure 5e and h-k, I am very confused how they are gating on CX3CR1+ cells\nwhen they are targeting this locus for HDR.\nWe thank the reviewer for the comment and apologize for the confusion. As shown in\nFigure 4, we engineer the CX3CR1-targeting HDR templates (CX3CR1-GFP and\nCX3CR1-TCF1) to be inserted immediately after the Cx3cr1 coding sequence\nseparated by a P2A linker, which results in maintenance of CX3CR1 protein\nexpression alongside GFP expression after CRISPR KI. We have added further text\nto clarify this point in line 222.\nIn fig 5B they should show CX3Cr1 by TCF1 to show overall expression of teh two\nproteins and they could also use KLRG1 in addition to CX3CR1 to identify the\nCX3Cr1+ cells. And for teh TCF1 they should also analyze that with slamf6 and cxcr5.\nThey need to do more diligence in the analysis of these cells. Also, it would be great if\nindeed KLRG1 is not affected by TCF1 expression, then they could sort out KLRG1+\nTCF1+ and KLRG1+ TCF- cells and compare the two transcriptionally to better\nexamine how TCF1 overexpression affects the T cells. Lastly, they should look at IL-2 and see if this is increased at all by TCF1 OE.\nWe thank the reviewer for the suggestions and agree that it is important to ensure that\nwe are correctly gating on the CX3CR1+ population. Unfortunately, though, unlike in\nacute infection, KLRG1 is largely not expressed within the exhausted CX3CR1+ cell\npopulation at day 20 p.i. during chronic LCMV as demonstrated in multiple previous\nstudies (Chen et al, Immunity 2019; Beltra et al, Immunity 2020; Daniel et al, Nat\nImmunol 2022). For this reason, KLRG1 is not a viable alternative marker for\nidentifying effector exhausted cell populations.\nWe agree that transcriptional analysis is needed to better define the effect of TCF1\nover-expression on CX3CR1+ cells. To address this concern, we have now included\nsingle-cell multi-ome analysis of control, Thy1-TCF1 and CX3CR1-TCF1 P14 cells\n(Figure 6 and Supplementary Figure 4). This single cell approach allowed us to more\nprecisely isolate the effector exhausted cell cluster for further analysis, and we find\nthat TCF1 expression only minimally impacts the CX3CR1+ cell phenotype in\nagreement with our flow cytometry findings in the original submission. Please see our\nresponse to Reviewer 1’s similar query for a more detailed description of the data.\nLastly, they should look at IL-2 and see if this is increased at all by TCF1 OE.\nWe agree that investigating if TCF1 over-expression increases IL-2 production is a\nvaluable point. IL-2 production is typically barely detectable in exhausted T cells\n(Wherry et al, J Virol 2003; Wherry et al, Immunity 2007) and in Reviewer Figure 1\nwe show that TCF1 over-expression was not able to rescue IL-2 production in\nexhausted P14 cells at day 20 p.i.\nIn summary, these findings challenge the notion that TCF1 alone is sufficient to drive\nde-differentiation and suggest that additional factors may be required for\nreprogramming exhausted T cells. The research carries significant clinical\nimplications, particularly in the context of immune checkpoint blockade therapies,\nwhere restoring stem-like T cell populations could improve therapeutic outcomes for\npatients with chronic infections or cancer."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The reviewers have adequately editorially and experimentally responded to this reviewer's the prior comments. The readership at Nature Communications should find this work valuable.", "reviewer_2": "The authors have addressed most of the comments raised by me and the other reviewers. However, it is surprising that a Western blot could not be performed, given that SDS–PAGE buffers, loading dyes, and protein extraction buffers are generally harsher in terms of experimental conditions than intracellular flow cytometry and threfore easier to do. If the authors could specifically clarify why a Western blot was not feasible in this case, this would help to resolve the issue. Alternatively, inclusion of actual results from such an experiment would substantially strengthen the manuscript.", "reviewer_3": "I strongly recommend publication of this revised manuscript. The authors have fully addressed my concerns, particularly the efficiency of HET vs HOMO recombination. This study represents a technical advance in the field that is highly deserving of publication. The work is rigorously done."}, "author_response": "Point-by-point response to Reviewer’s comments\nWe thank the reviewers for their positive feedback on our revised manuscript. We\nprovide a point-by-point response to the reviewer comments below to address the\nremaining point raised by Reviewer #2.\nReviewer #1\nThe reviewers have adequately editorially and experimentally responded to this\nreviewer's the prior comments. The readership at Nature Communications should find\nthis work valuable.\nWe thank the reviewer for the kind words.\nReviewer #2\nThe authors have addressed most of the comments raised by me and the other\nreviewers. However, it is surprising that a Western blot could not be performed, given\nthat SDS–PAGE buffers, loading dyes, and protein extraction buffers are generally\nharsher in terms of experimental conditions than intracellular flow cytometry and\nthrefore easier to do. If the authors could specifically clarify why a Western blot was\nnot feasible in this case, this would help to resolve the issue. Alternatively, inclusion of\nactual results from such an experiment would substantially strengthen the manuscript.\nWe apologise for the confusion as the reviewer appears to have misunderstood our\nresponse to this query. The reviewer is correct that Western blotting is normally simple\nto perform on primary mouse T cells. However, in order to assess levels of TCF1\nexpression within CRISPR HDR edited T cells, we must first isolate the edited cells\nfrom the bulk T cell population as only ~40% of cells were productively edited in our\nTCF1 knock-in experiments. If we were to conduct Western blotting on the edited cells\nwithout first enriching for the cells that had been edited to knock-in TCF1, then the\nlarge number of contaminating TCF1- unedited cells would make it impossible to\naccurately assess TCF1 over-expression levels within the edited population.\nAs there was no surface marker to isolate edited cells, the only way that we could\nisolate these cells is by intracellular flow cytometric staining for TCF1 followed by\nFACS sorting of TCF1+ cells. We discovered that the fixation and permeabilization\nsteps required for this sorting procedure disrupted protein migration on an SDS gel\neven after boiling in loading buffer, likely because of the formaldehyde cross-links\nintroduced by the flow cytometry fixation step. For this reason, we could not accurately\nidentify TCF1 by Western blotting, and thus could not conduct the requested\nexperiment.\nGiven that this query was due to a misunderstanding of our rebuttal response, we have\nnot altered the manuscript text or added more data. We would respectively argue that\nthe original query around TCF1 expression levels in edited cells was already\ncomprehensively quantitatively addressed by flow cytometry in Figure 5d of the\nrevised manuscript in a manner that is arguably more quantitative/accurate than can\nbe achieved by Western blotting.\nReviewer #3\nI strongly recommend publication of this revised manuscript. The authors have fully\naddressed my concerns, particularly the efficiency of HET vs HOMO recombination.\nThis study represents a technical advance in the field that is highly deserving of\npublication. The work is rigorously done.\nWe thank the reviewer for the strong endorsement of our manuscript."}]

Decision letter