DOI: 10.5061/dryad.dv41ns27h
title: KLF2 maintains lineage fidelity and suppresses CD8 T cell exhaustion during acute LCMV infection (LCMV DSM scRNA data and ATAC-seq)

description: Naïve CD8 T cells have the potential to differentiate into a variety of
 functional states during an immune response. How these developmental
 decisions are made and what mechanisms exist to suppress differentiation
 towards alternative fates remains unclear. We employed in vivo
 CRISPR/Cas9-based perturbation sequencing to assess the role of ~40
 transcription factors (TFs) and epigenetic modulators (EMs) in T cell fate
 decisions. Unexpectedly, we found that knockout of TF Klf2 resulted in
 aberrant differentiation to exhausted-like CD8 T cells during acute
 infection. KLF2 was required to suppress the exhaustion-promoting TF TOX
 and to enable TBET to drive effector differentiation. KLF2 was also
 necessary to maintain tumor-specific progenitor-exhausted T cells. Thus,
 KLF2 provides effector CD8 T cell lineage fidelity and suppresses the
 exhaustion program.
description: This dataset contains scRNA-sequencing of endogenous H2Db-gp33
 specific CD8 T cells in acute and chronic LCMV infection at days 4,8,28,
 and 40 post-infection (this Dryad submission). There is also scRNA
 sequencing with CRISPR features (perturbSeq) of P14 Cas9 T cells in acute
 infection with a sgRNA library targeting ~40 transcription factors and
 epigenetic modulators (linked Dryad submission). In addition, there is
 bulk atac-seq data of WT and KLF2 KO P14 T cells from day 8 of acute and
 chronic infection (linked Dryad submission). Finally data from Connolly et
 al, Science Immunology 2021 was reanalyzed and an rds object is uploaded
 at the linked dryad submission. This data is scRNA of H2Db-gp33 specific
 CD8s from KP-NINJA autochthonous lung tumor and tdLN. 
 Raw sequencing files are included of all data in addition to
 bigwig files of atac data (linked dryad submission) and rds objects of
 single cell data. Dryad_data_key.xls contains details on all samples for
 this and linked upload.
description: # KLF2 maintains lineage fidelity and suppresses CD8 T cell exhaustion
 during acute LCMV infection (LCMV DSM scRNA data)
 [https://doi.org/10.5061/dryad.dv41ns27h](https://doi.org/10.5061/dryad.dv41ns27h) ## Description of the data and file structure scRNA sequencing was performed on H2Db-gp33 specific CD8 T cells responding to acute or chronic LCMV infection at days 4, 8, 28, and 40 (this Dryad submission). Paired scRNA + CRISPR sequencing was performed on P14 Cas9 transgenic CD8 T cells transduced with a sgRNA library target 39 genes, many of which are transcription factors and epigenetic modulators (linked Dryad submission). KLF2 came out as a gene of interest and bulk ATAC-seq was performed on sgNTC (CTRL/WT) P14s from day 8 acute and chronic infection as well as sgKLF2 (KO) P14s from day 8 acute infection (this Dryad submission). **Individual sample file info can be found in the provided Dryad_data_key.xlsx file.** Additional data files in linked dryad upload. ### Files and variables #### File: ChronicD8\_MMT\_S4\_L002\_I1\_001.fastq.gz **Description:** scRNA seq of H2Db-gp33 CD8s at D8 of LCMV Clone13 #### File: ChronicD8\_MMT\_S4\_L002\_R2\_001.fastq.gz **Description:** scRNA seq of H2Db-gp33 CD8s at D8 of LCMV Clone13 #### File: ChronicD8\_MMT\_S4\_L002\_R1\_001.fastq.gz **Description:** scRNA seq of H2Db-gp33 CD8s at D8 of LCMV Clone13 #### File: ChronicD4\_MMT\_S2\_L002\_I1\_001.fastq.gz **Description:** scRNA seq of H2Db-gp33 CD8s at D4 of LCMV Clone13 #### File: ChronicD4\_MMT\_S2\_L002\_R2\_001.fastq.gz **Description:** scRNA seq of H2Db-gp33 CD8s at D4 of LCMV Clone13 #### File: ChronicD4\_MMT\_S2\_L002\_R1\_001.fastq.gz **Description:** scRNA seq of H2Db-gp33 CD8s at D4 of LCMV Clone13 #### File: ChronicD4\_MMT\_S2\_L002\_R3\_001.fastq.gz **Description:** scRNA seq of H2Db-gp33 CD8s at D4 of LCMV Clone13 #### File: ChronicD8\_MMT\_S4\_L002\_R3\_001.fastq.gz **Description:** scRNA seq of H2Db-gp33 CD8s at D8 of LCMV Clone13 #### File: Chronic40\_MMT\_S6\_L002\_I1\_001.fastq.gz **Description:** scRNA seq of H2Db-gp33 CD8s at D40 of LCMV Clone13 #### File: Chronic40\_MMT\_S6\_L002\_R2\_001.fastq.gz **Description:** scRNA seq of H2Db-gp33 CD8s at D40 of LCMV Clone13 #### File: Chronic40\_MMT\_S6\_L002\_R1\_001.fastq.gz **Description:** scRNA seq of H2Db-gp33 CD8s at D40 of LCMV Clone13 #### File: Chronic40\_MMT\_S6\_L002\_R3\_001.fastq.gz **Description:** scRNA seq of H2Db-gp33 CD8s at D40 of LCMV Clone13 #### File: AcuteD8\_MMT\_S3\_L002\_I1\_001.fastq.gz **Description:** scRNA seq of H2Db-gp33 CD8s at D8 of LCMV Armstrong #### File: AcuteD8\_MMT\_S3\_L002\_R2\_001.fastq.gz **Description:** scRNA seq of H2Db-gp33 CD8s at D8 of LCMV Armstrong #### File: AcuteD8\_MMT\_S3\_L002\_R1\_001.fastq.gz **Description:** scRNA seq of H2Db-gp33 CD8s at D8 of LCMV Armstrong #### File: AcuteD8\_MMT\_S3\_L002\_R3\_001.fastq.gz **Description:** scRNA seq of H2Db-gp33 CD8s at D8 of LCMV Armstrong #### File: AcuteD4\_MMT\_S1\_L002\_I1\_001.fastq.gz **Description:** scRNA seq of H2Db-gp33 CD8s at D4 of LCMV Armstrong #### File: AcuteD4\_MMT\_S1\_L002\_R2\_001.fastq.gz **Description:** scRNA seq of H2Db-gp33 CD8s at D4 of LCMV Armstrong #### File: AcuteD4\_MMT\_S1\_L002\_R1\_001.fastq.gz **Description:** scRNA seq of H2Db-gp33 CD8s at D4 of LCMV Armstrong #### File: AcuteD4\_MMT\_S1\_L002\_R3\_001.fastq.gz **Description:** scRNA seq of H2Db-gp33 CD8s at D4 of LCMV Armstrong #### File: Acute40\_MMT\_S5\_L002\_I1\_001.fastq.gz **Description:** scRNA seq of H2Db-gp33 CD8s at D40 of LCMV Armstrong #### File: Acute40\_MMT\_S5\_L002\_R2\_001.fastq.gz **Description:** scRNA seq of H2Db-gp33 CD8s at D40 of LCMV Armstrong #### File: Acute40\_MMT\_S5\_L002\_R1\_001.fastq.gz **Description:** scRNA seq of H2Db-gp33 CD8s at D40 of LCMV Armstrong #### File: Acute40\_MMT\_S5\_L002\_R3\_001.fastq.gz **Description:** scRNA seq of H2Db-gp33 CD8s at D40 of LCMV Armstrong #### File: 20199\_MMT\_S2\_L002\_I1\_001.fastq.gz **Description:** scRNA seq of H2Db-gp33 CD8s at D28 of LCMV Clone13 #### File: 20199\_MMT\_S2\_L002\_R1\_001.fastq.gz **Description:** scRNA seq of H2Db-gp33 CD8s at D28 of LCMV Clone13 #### File: 20199\_MMT\_S2\_L002\_R2\_001.fastq.gz **Description:** scRNA seq of H2Db-gp33 CD8s at D28 of LCMV Clone13 #### File: 19710\_MMT\_S1\_L001\_I1\_001.fastq.gz **Description:** scRNA seq of H2Db-gp33 CD8s at D28 of LCMV Armstrong #### File: 19710\_MMT\_S1\_L001\_R1\_001.fastq.gz **Description:** scRNA seq of H2Db-gp33 CD8s at D28 of LCMV Armstrong #### File: 19710\_MMT\_S1\_L001\_R2\_001.fastq.gz **Description:** scRNA seq of H2Db-gp33 CD8s at D28 of LCMV Armstrong #### File: KLF\_A8\_1\_S13\_L006\_R1\_001.fastq.gz **Description:** Bulk ATAC-seq of KLF2 KO P14s at D8 of LCMV Armstrong #### File: KLF\_A8\_1\_S13\_L006\_R2\_001.fastq.gz **Description:** Bulk ATAC-seq of KLF2 KO P14s at D8 of LCMV Armstrong #### File: KLF\_A8\_2\_S14\_L006\_R1\_001.fastq.gz **Description:** Bulk ATAC-seq of KLF2 KO P14s at D8 of LCMV Armstrong #### File: KLF\_A8\_2\_S14\_L006\_R2\_001.fastq.gz **Description:** Bulk ATAC-seq of KLF2 KO P14s at D8 of LCMV Armstrong #### File: NT\_A8\_1\_S11\_L006\_R1\_001.fastq.gz **Description:** Bulk ATAC-seq of CTRL P14s at D8 of LCMV Armstrong #### File: NT\_A8\_1\_S11\_L006\_R2\_001.fastq.gz **Description:** Bulk ATAC-seq of CTRL P14s at D8 of LCMV Armstrong #### File: NT\_A8\_2\_S12\_L006\_R2\_001.fastq.gz **Description:** Bulk ATAC-seq of CTRL P14s at D8 of LCMV Armstrong #### File: NT\_A8\_2\_S12\_L006\_R1\_001.fastq.gz **Description:** Bulk ATAC-seq of CTRL P14s at D8 of LCMV Armstrong #### File: NT\_C8\_1\_S15\_L006\_R1\_001.fastq.gz **Description:** Bulk ATAC-seq of CTRL P14s at D8 of LCMV Clone13 #### File: NT\_C8\_1\_S15\_L006\_R2\_001.fastq.gz **Description:** Bulk ATAC-seq of CTRL P14s at D8 of LCMV Clone13 #### File: NT\_C8\_2\_S16\_L006\_R1\_001.fastq.gz **Description:** Bulk ATAC-seq of CTRL P14s at D8 of LCMV Clone13 #### File: NT\_C8\_2\_S16\_L006\_R2\_001.fastq.gz **Description:** Bulk ATAC-seq of CTRL P14s at D8 of LCMV Clone13 #### File: KLF\_A8\_1\_S13\_L006.bw **Description:** Bigwig file of bulk ATAC-seq of KLF2 KO P14s at D8 of LCMV Armstrong #### File: KLF\_A8\_2\_S14\_L006.bw **Description:** Bigwig file of bulk ATAC-seq of KLF2 KO P14s at D8 of LCMV Armstrong #### File: NT\_A8\_1\_S11\_L006.bw **Description:** Bigwig file of bulk ATAC-seq of CTRL P14s at D8 of LCMV Armstrong #### File: NT\_A8\_2\_S12\_L006.bw **Description:** Bigwig file of bulk ATAC-seq of CTRL P14s at D8 of LCMV Armstrong #### File: NT\_C8\_1\_S15\_L006.bw **Description:** Bigwig file of bulk ATAC-seq of CTRL P14s at D8 of LCMV Clone13 #### File: NT\_C8\_2\_S16\_L006.bw **Description:** Bigwig file of bulk ATAC-seq of CTRL P14s at D8 of LCMV Clone13 #### File: dsm\_pub.rds **Description:** Integrated Seurat object of LCMV timepoint data ## Code/software **Single-cell RNA sequencing** **Data for LCMV map is in this Dryad submission:** To generate the LCMV map data, H2Db-gp33 specific CD8 T cells were sorted from the spleens of mice infected with acute or chronic LCMV at days 4, 8, 28, and 40 post-infection. Days 4, 8, and 40 from both infections were sorted and submitted for 10X library prep together, while day 28 samples from both infections were performed separately. This denoted the “batch” assigned for downstream analysis (see analysis section). Each sample was loaded into its own Chromium instrument lane for 5’ single-cell RNA library prep. Resulting library preps were sequenced on an Illumina Novaseq at a depth of 20,000 reads per cell. Library preparation and sequencing was performed according to manufacturer’s instructions by the Yale Center for Genome Analysis (5’ v2 single-cell RNA sequencing, 10x Genomics) (YCGA) the same day as sample collection. Data was processed with cellranger-7.1.0 using the mm10 reference genome. **Data for PerturbSeq is in linked Dryad submission**To generate the perturbSEQ data, transduced P14 DsRED Cas9 T cells and endogenous H2Db-gp33 specific endogenous CD8 T cells were sorted out of the spleens of acute LCMV infected mice at day 8. They were then mixed at a 1:10 ratio and submitted for 5’ 10X Genomics single cell RNA and CRISPR library preparation. In total 240,000 cells were loaded onto 6 lanes of a ChromiumX instrument. Resulting library preps were sequenced on an Illumina NovaSeq at a depth of 20,000 reads per cell for the RNA library and 5,000 reads per cells for the CRISPR library. Library preparation and sequencing was performed according to manufacturer’s instructions by the Yale Center for Genome Analysis (5’ v2 single-cell RNA sequencing with feature barcode technology for CRISPR screening, 10x Genomics) was performed by the Yale Center for Genome Analysis (YCGA) the same day as sample collection. Data was processed with cellranger-7.1.0 using a custom reference transcriptome that added the DsRED RNA sequences to the mm10 reference genome to distinguish P14 from endogenous T cells. scRNA-seq data of endogenous H2Db-gp33 specific CD8 T cells from KP-NINJA tumor bearing mice was generated as described in the original paper Connolly et al. Science Immunology 2021. **Single-cell RNA analysis** 5’ single-cell RNA sequencing from sorted H2Db-gp33 specific CD8 T cells at days 4, 8, 28, and 40 post-acute or chronic LCMV infection was processed with Cell Ranger 7.1.0 using the mm10 mouse genome indices from 10X Genomics. R (v4.2.1) was used for analysis and the packages Seurat (v4.3.0), DoubletFinder (v2.0.3), and SingleR (v1.10.0) were used to preprocess the data. Briefly, only cells with greater than 200 features and less than 5% mitochondrial reads were included. Further, the data was subset on predicted singlets as assigned by DoubletFinder and predicted T cells or NK cells as assigned by SingleR using the mouse bulk RNA-seq reference data (MouseRNAseqData) from celldex (v1.6.0). Cell cycle scoring was performed using Seurat’s CellCycleScoring function. After these filtering steps we proceeded to analyze a total of 50,577 cells (6466 acute d4, 7163 acute d8, 9840 acute d28, 6630 acute d40, 6525 chronic d4, 7736 chronic d8, 1178 chronic d28, 5039 chronic d40). Cells were split into batch and cycling vs non-cycling status prior to data integration through the IntegrateData function using the non-cycling cells as the anchorset. Variable TCR, variable BCR, mitochondrial, and cell cycle genes were excluded from integration features. This integration assay was then scaled and pca was run. FindNeighbors was then run with pca dimensions 1 through 8. FindClusters was run with a resolution of 0.35. RunUMAP was then performed using pca dimensions 1 through 8, min.dist of 0.08 and n.components of 3L to generate 3D UMAP reduction. Clusters were assigned names based on gene module enrichment for previously defined CD8 T cell clusters. Gene module enrichment was performed using the function AddModuleScore. Differentially expressed genes were calculated using the functions FindAllMarkers or FindMarkers for pairwise comparisons. The package dittoSeq (v1.8.1) was also used for some visualizations. Monocle3 (v1.2.9) was used to infer pseudotime on cells from either acute or chronic infection separately using SeuratWrappers (v0.3.0) and setting those cells with highest *Ccr7* expression as the root node.   5’ single-cell RNA sequencing with CRISPR from sorted transduced P14 DsRED Cas9 and endogenous H2Db-gp33 T cells was processed with Cell Ranger 7.1.0 using the mm10 mouse genome indices from 10X Genomics with DsRED added as an additional feature. Cells were preprocessed and filtered as above, integrating across lanes and cell cycle status. We added an additional step to only analyze cells that contained a sgRNA feature. DsRED was also removed from integration features on top of variable TCR, variable BCR, mitochondrial, and cell cycle genes. A total of 59,815 cells were analyzed. The integrated assay was scaled and pca was performed. FindNeighbors was performed using pca dimensions 1 through 10. FindClusters was run with a resolution of 0.2. RunUMAP was then performed using pca dimensions 1 through 10, a min.dist of 0.1, and n.components of 3L. PHATE reduction was performed using phateR and the integrated assay. The phate function was run using 50 pca dimensions, knn of 10, and ndim of 3. Cells were assigned to a perturbation by first filtering out any cells that only had 1 sgRNA UMI captured to help correct for any ambient contamination. We then identified if any sgRNA was captured as~~t~~ a total of 80% or more of all sgRNA UMIs in the cell. Cells that did not meet this criterion were labeled as “inconclusive”. Cells that were assigned a prominent sgRNA with this method were then assigned a perturbation label based on the target gene of that prominent sgRNA. For perturbation specific analysis, cells from all 3 sgRNA or all 6 sgRNAs (NTC) were pooled together. Gene modules derived from perturbSeq clusters were based on DEGs for each cluster using FindAllMarkers. KLF2 KO gene module was derived from DEGs between sgKLF2 and sgNTC cells using FindMarkers. Prediction of clusters in the LCMV map data was performed using the functions FindTransferAnchors, TransferData, and IntegrateEmbeddings using the integrated assay for each dataset. The packages EnhancedVolcano (v1.14.0) and pheatmap (v1.0.12) were used for additional visualizations. 5’ single-cell RNA sequencing of endogenous H2Db-gp33 T cells in KP-NINJA tumor mice was initially processed through Cell Ranger as in the original paper. The four samples (dLN, tumor, week8, week17) were merged and were preprocessed and filtered to remove any contaminating B or myeloid cells based on canonical gene expression. Data was normalized and scaled (regressing out mitochondrial reads and number of RNA reads) and variable TCR sequences were removed from variable features used for pca analysis. FindNeighbors was performed with pca dimensions 1 through 20, FindClusters was performed with a resolution of 0.5, and RunUMAP was performed with pca dimensions 1 through 20. Gene module enrichment was performed used AddModuleScore and heatmap visualization was performed with pheatmap (v1.0.12) **Bulk ATAC-seq Preparation and Analysis** **Data for ATAC-seq is in this Dryad submission:** Control and KLF2 KO P14s were generated as described above and transferred into mice subsequently infected with LCMV ARM (CTRL, KO) or LCMV CL13 (CTRL). Spleens were pooled from respective infection cohorts and P14s were sorted out in replicate at day 8 post-infection and cryopreserved in 2 technical replicates in 90:10 FBS:DMSO prior to ATAC-seq library preparation. Samples were sequenced at a depth of 50M reads per sample on an Illumina NovaSeq instrument. *BULK ATAC-SEQ PROCESSING* The study is comprised of three sample groups, each with two replicates (n=6 total). Quality control (QC) for each sample was assessed using the MultiQC report generated by the nf-core atacseq pipeline (v2.1.2, [https://nf-co.re/atacseq/2.1.2](https://nf-co.re/atacseq/2.1.2)). Bulk ATAC-seq data was processed according to a published ATAC-seq pipeline published by Reske et al., with scripts available at [https://github.com/reskejak/ATAC-seq](https://github.com/reskejak/ATAC-seq). This pipeline included alignment of FASTQ files to the mm10 genome (Bowtie2), sorting and indexing of reads (samtools), removal of mitochondrial DNA reads, filtering for properly paired reads, estimation of library complexity (ATACseqQC), and subsampling to maintain comparable complexity across samples, PCR duplicate removal (Picard) and read mate fixing (samtools). BAM files were converted to BEDPE format (bedtools), and coordinates were shifted +4 and -5 bp for positive and negative strands, respectively, to compensate for Tn5 insertion. Broad peaks were called using MACS2 followed by removal of ENCODE blacklist regions ([https://github.com/Boyle-Lab/Blacklist/blob/master/lists/mm10-blacklist.v2.bed.gz](https://github.com/Boyle-Lab/Blacklist/blob/master/lists/mm10-blacklist.v2.bed.gz)) and unplaced contigs (bedtools).  ## Access information Other publicly accessible locations of the data: NA Data was derived from the following sources: Mouse primary T cells