DOI: 10.5061/dryad.vmcvdnd53
title: Human memory CD4+ T cells recognize <em>Mycobacterium tuberculosis</em>-infected macrophages amid broader pathogen-specific responses

description: CD4+ T cell-mediated control of tuberculosis (TB) requires recognition of
 macrophages infected with Mycobacterium tuberculosis (Mtb). Yet not all
 Mtb-specific T cells recognize infected macrophages. Using infected
 monocyte-derived macrophages (MDMs) and autologous memory CD4+ T cells
 from individuals with latent Mycobacterium tuberculosis (Mtb) infection
 (LTBI), we isolate and quantify CD4+ T cells activated in response to
 infected macrophages. We use T cell antigen receptor (TCR) sequencing to
 determine the total and unique Mtb-specific TCR clonotypes linked to
 recognition of infected macrophages, and find that a subset required
 exogenous antigen exposure, suggesting incomplete recognition. Clonotypes
 specific for multiple Mtb antigens and other pathogens were also
 identified. We use bulk TCRb deep sequencing from total CD4+ T cells
 isolated from 10x106 PBMCs from each participant to determine the natural
 circulating frequencies of relevant TCR clonotypes. We used single-cell
 RNA sequencing (scRNAseq) to examine the effector functions of CD4+ T
 cells in response to infected macrophages. Mtb-specific clonotypes
 expressed signature effector functions dominated by IFNg, TNF, IL-2, and
 GM-CSF or chemokine production and signaling. We propose TB vaccines that
 elicit T cells specific for T7SS substrates, recognize infected
 macrophages, and express canonical effector functions will offer
 protection against TB.
description: <strong>Bulk TCRb deep sequencing</strong>
 Unstimulated CD4+ T cells were isolated by immunomagnetic
 positive selection from 10x10<sup>6</sup> PBMCs from each
 participant using human anti-CD4 microbeads (Miltenyi Biotec). Genomic DNA
 was isolated using the QiaAMP DNA Mini Kit (Qiagen, Hilden, Germany)
 according to the manufacturer’s instructions. Ultra-deep level TCRβ
 sequencing was performed by Adaptive Biotechnologies (Seattle, WA, USA).
 Data was downloaded from the ImmunoSeq Analyzer (Adaptive) and used to
 estimate the natural circulating frequencies of TCRs. TCRb sequences were
 identified in the scTCRseq analysis of CD4+ T cell responses to
 Mtb-infected macrophages, cross-referenced to CDR3b sequences in bulk TCRb
 deep sequencing data from PBMCs, which were enumerated using Microsoft
 Excel and graphed in GraphPad Prism. <strong>Flow
 sorting of activated T cells for scRNAseq</strong>
 Non-adherent cells were harvested from the T cell-infected MDM
 co-culture, and immunostaining was performed with monoclonal antibodies
 used to identify CD4+ T cells that co-expressed activation markers. After
 20 min at 4 <sup>°C,</sup> cells were passed through a 40μm
 filter top tubes (Corning). Approximately 2 min before sorting each
 sample, 7-AAD viability dye (Invitrogen) was added to each sample. Samples
 were sorted using the “purity” setting, gating on 7-AADlo CD4+ singles
 cells, and co-expression of CD69 and either CD40L or CD25. Sorting was
 performed on a Sony MA900 Multi-Application Cell Sorter (Sony
 Biotechnology, San Jose, CA, USA). Fluorescent antibodies used in flow
 sorting experiments include FITC anti-human CD4 (clone RPA-T4), PE
 anti-CD25 (clone BC96), PE-Dazzle-594 anti-CD40L (clone 24-31), BV605
 anti-CD8a (clone RPA-T6), and APC anti-CD69 (clone FN50) from
 Biolegend. <strong>Exogenous antigen stimulation
 of CD4+T cells</strong> Exogenous antigens were
 added to some samples of Mtb-infected MDMs for co-culture with autologous
 CD4+T cells. H37Rv whole cell lysate (BEI Resources, NR-14822) was added
 to Mtb-infected MDMs at a final concentration of 10 mg/mL for 24h. Lysate
 was washed out with cRPMI before the addition of CD4+ T cells. For
 evaluation of T cell responses to MTB300 peptides, MTB300 was diluted to 1
 μg/mL in cRPMI, then added to Mtb-infected MDMs ~ 10 minutes before adding
 CD4+ T cells in co-culture. For each participant, PBMCs were also
 separately stimulated with either MTB300 or viral and vaccine “control”
 peptide megapools to identify TCR clonotypes linked to antigen-specific T
 cell responses. For Mtb peptide stims, 1μg/mL of the MTB300 peptide
 megapool was added to 10x10<sup>6</sup> PBMCs in cRPMI. For
 “control” peptide stimulations, another 10x10<sup>6</sup>
 PBMCs were exposed to a final concentration of 1μg/mL each of
 Cytomegalovirus (CMV), Epstein-Barr virus (EBV), Pertussis (P), and
 Tetanus toxoid (TT) peptide megapools, and 1ug/mL of the SARS-CoV-2
 USA-WA1/2020 Coronavirus Spike (S) protein overlapping peptides (BEI
 resources NR-52402). Peptides were added to PBMCs in cRPMI together with
 anti-CD28/CD49d costimulatory mAbs (BD Biosciences) and anti-CD40 blocking
 mAb (BioLegend) for 16- 18 hours. Following stimulation, CD4-positive
 immunomagnetic selection was performed according to the manufacturer’s
 instructions using human anti-CD4 microbeads (Miltenyi Biotec), followed
 by flow sorting of the activated CD4+ T cells for downstream
 scTCRseq. <strong>Sample processing for
 scRNAseq</strong> Sequencing of mRNA from single
 T cells was performed using the 10x Genomics Chromium Next GEM Single Cell
 5’ V2 platform (10X Genomics, Pleasanton, CA, USA). Activated CD4+ were
 resuspended in 0.04% BSA in PBS and loaded onto the 10X Chromium
 Controller. RNA was reverse-transcribed, and cDNA was isolated and
 PCR-amplified according to the 10X Genomics NextGEM 5’ V2 scRNAseq User
 Guide (Rev D). Quality control and quantification of cDNA and final
 libraries were performed on either the Agilent fragment analyzer or
 Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA).
 V(D)J amplification and library preparation, and gene expression library
 construction were performed using 10X Genomics kits according to the User
 Guide. Paired-end sequencing was performed by the Cleveland Clinic Lerner
 Research Institute Genomics Core (Cleveland, OH, USA) using an Illumina
 NovaSeq 6000 sequencer (Illumina, San Diego, CA, USA) according to the 10X
 Genomics User Guide. Demultiplexed FASTQ files were mapped to the human
 genome and TCR sequences simultaneously, using Cell Ranger Multi V7.1 (10X
 Genomics) on the 10X Cloud server and the GRCh38.p14 reference
 genome. <strong>Single-cell data
 analysis</strong> Filtered_contig_annotations.csv
 and clonotypes.csv files from Cell Ranger containing productive,
 full-length TCR sequences for each sample were used for TCR clonotype
 analysis. Filtered feature barcode matrices (mRNA data) and contig
 annotations (CDR3a and b sequences) were linked to individual cell
 barcodes. Gene expression and TCR annotations were generated for each cell
 for simultaneous TCR and transcriptomics analysis after quality control
 and integration were performed using the R-based Seurat package (v 5.1.0)
 with seed 123. High-quality cells and features were selected based on the
 following parameters: transcript detection in at least 3 cells,
 200&lt;nFeature&lt;4,000, and percentage of mitochondrial RNA is
 less than 20% per cell. TCR information was added to Seurat objects using
 the scRepertoire package. TCR clonotypes with identical CDR3α/β sequences,
 present in more than 2 cells (based on unique barcodes), were considered
 “expanded”. Clonotypes with only an individual CDR3α or CDR3β chain were
 classified as “non-expanded”. After merging the samples, SCTransform was
 used for normalization, the percent mitochondrial and ribosomal counts,
 and cell-cycle gene scores were regressed out, and TCR genes were removed
 from the variable feature list using the quietTCRgenes function from the
 Trex package. Principal component analysis (RunPCA function) was followed
 by data integration and batch correction with the Harmony algorithm. A
 UMAP (via RunUMAP) was calculated using the first 30 dimensions of the
 Harmony embedding with default parameters, except that the number of
 neighbors was set to 50 in addition to the FindNeighbors default
 parameters. Clustering was done on this shared-nearest neighbor graph
 using the FindClusters default parameters, except the resolution was set
 to 0.3 using the Louvain algorithm with multilevel refinement. From 19
 clusters obtained (from 0 to 18), clusters 17 (27 cells) and 18 (25 cells)
 were removed due to low cell numbers, and cluster 16 (830 cells) was
 removed due to monocytic contamination. A total of 157,462 cells, split
 into 16 clusters, were left for the downstream analysis.
description: # Human memory CD4+ T cells recognize *Mycobacterium
 tuberculosis*-infected macrophages amid broader pathogen-specific
 responses Dataset DOI: [10.5061/dryad.vmcvdnd53](10.5061/dryad.vmcvdnd53)
 ## Description of the data and file structure Sequencing of mRNA from
 single T cells was performed using the 10x Genomics Chromium Next GEM
 Single Cell 5’ V2 platform (10X Genomics, Pleasanton, CA, USA). Activated
 CD4+ were resuspended in 0.04% BSA in PBS and loaded onto the 10X Chromium
 Controller. RNA was reverse-transcribed, and cDNA was isolated and
 PCR-amplified according to the 10X Genomics NextGEM 5’ V2 scRNAseq User
 Guide (Rev D). Quality control and quantification of cDNA and final
 libraries was performed on either the Agilent fragment analyzer or Agilent
 2100 Bioanalyzer (Agilent technologies, Santa Clara, CA, USA). V(D)J
 amplification and library preparation, and gene expression library
 construction were performed using 10X Genomics kits according to the User
 Guide. Paired-end sequencing was performed by the Cleveland Clinic Lerner
 Research Institute Genomics Core (Cleveland, OH, USA) using an Illumina
 NovaSeq 6000 sequencer (Illumina, San Diego, CA, USA) according to the 10X
 Genomics User Guide. Demultiplexed FASTQ files were mapped to the human
 genome and TCR sequences, simultaneously, using Cell Ranger Multi v7.1
 (10X Genomics) on the 10X Cloud server and the GRCh38.p14 reference
 genome. ### Files and variables | Condition | Description | |
 -------------: | :--------------------------------------------------- | |
 Infected | Infection with Mtb | | Lysate | Infection with Mtb and treated
 with Mtb Lysate | | MTB300 | Infection with Mtb and treated with 300 Mtb
 peptides | | ControlPeptide | Infection with Mtb and treated with viral
 peptides | #### Cell Ranger Outputs FastQ files above have been processed
 with [Cell
 Ranger](https://www.10xgenomics.com/support/software/cell-ranger/latest).
 The MarketMatrix directories correspond with the filtered feature-barcode
 matrix output of Cell Ranger (ending in
 `-filtered_feature_bc_matrix.tar.gz`). The CSV files correspond to the TCR
 sequencing `filtered_contig_annotations.csv` output from Cell Ranger. The
 TSV files are the AIRR formatted (`-airr_rearrangement.tsv`) TCR
 sequencing outputs. More information on the AIRR format
 [here](https://docs.airr-community.org/en/stable/datarep/rearrangements.html#fields). Each filename has the structure `(Donor ID)-(Condition)-(file type)`. These can be found in `gene_expression.tar.gz` (54 files), `t_cell_stimulations.tar.gz` (30 files) and `pbmc_stimulation.tar.gz` (32 files). Cells containing "-" indicate no reads were sequenced. ##### AIRR formatted files * **cell_id**: Cell barcode defining the cell for the query sequence. * **clone_id**: Clonotype ID/clonotype assignment. * **sequence_id**: The name of the contig associated with the rearrangement. * **sequence**: The nucleotide sequence of the rearrangement. * **sequence_aa**: The amino acid sequence of the rearrangement. * **productive**: Whether or not the rearrangement is productive. * **rev_comp**: Set to `false` by default (10x Genomics V(D)J sequences are not reverse complemented). * **v_call**: The name of the aligned V gene for the rearrangement. * **v_cigar**: The CIGAR string of the V gene alignment. * **d_call**: The name of the aligned D gene for the rearrangement. * **d_cigar**: The CIGAR string of the D gene alignment. * **j_call**: The name of the aligned J gene for the rearrangement. * **j_cigar**: The CIGAR string of the J gene alignment. * **c_call**: The name of the aligned C gene for the rearrangement. * **c_cigar**: The CIGAR string of the C gene alignment. * **sequence_alignment**: The aligned sequence of the VDJ rearrangement. * **germline_alignment**: The assembled, aligned, full-length inferred germline sequence of the aligned sequence. * **junction**: The nucleotide sequence of the rearrangement's junction (CDR3). * **junction_aa**: The amino acid sequence of the rearrangement's junction (CDR3). * **junction_length**: The length of the rearrangement's junction nucleotide sequence. * **junction_aa_length**: The length of the rearrangement's junction amino acid sequence. * **v_sequence_start**: 1-based index on the contig of the V region start position. * **v_sequence_end**: 1-based index on the contig of the V region end position. * **d_sequence_start**: 1-based index on the contig of the D region start position. * **d_sequence_end**: 1-based index on the contig of the D region end position. * **j_sequence_start**: 1-based index on the contig of the J region start position. * **j_sequence_end**: 1-based index on the contig of the J region end position. * **c_sequence_start**: 1-based index on the contig of the C region start position. * **c_sequence_end**: 1-based index on the contig of the C region end position. * **consensus_count**: The number of reads associated with this rearrangement. * **duplicate_count**: The number of unique molecular identifiers associated with this rearrangement. * **is_cell**: Is this rearrangement cell-associated? ##### Cell Ranger VDJ formatted files * **barcode**: Cell barcode for this contig. * **is_cell**: True or False value indicating whether the barcode was called as a cell. * **contig_id**: Unique identifier for this contig. * **high_confidence**: True or False value indicating whether the contig was called as high-confidence (unlikely to be a chimeric sequence or other artifact). * **length**: The contig sequence length in nucleotides. * **chain**: The chain associated with this contig: TRA, TRB, IGK, IGL, or IGH. * **v_gene**: The highest-scoring V segment, e.g., TRAV1-1. * **d_gene**: The highest-scoring D segment, e.g., TRBD1. * **j_gene**: The highest-scoring J segment, e.g., TRAJ1-1. * **c_gene**: The highest-scoring C segment, e.g., TRBC2. * **full_length**: True or False value indicating if the contig was declared as full-length. * **productive**: True or False value indicating if the contig was declared as productive. * **fwr1**: The predicted FWR1 amino acid sequence. * **fwr1_nt**: The predicted FWR1 nucleotide sequence. * **cdr1**: The predicted CDR1 amino acid sequence. * **cdr1_nt**: The predicted CDR1 nucleotide sequence. * **fwr2**: The predicted FWR2 amino acid sequence. * **fwr2_nt**: The predicted FWR2 nucleotide sequence. * **cdr2**: The predicted CDR2 amino acid sequence. * **cdr2_nt**: The predicted CDR2 nucleotide sequence. * **fwr3**: The predicted FWR3 amino acid sequence. * **fwr3_nt**: The predicted FWR3 nucleotide sequence. * **cdr3**: The predicted CDR3 amino acid sequence. * **cdr3_nt**: The predicted CDR3 nucleotide sequence. * **fwr4**: The predicted FWR4 amino acid sequence. * **fwr4_nt**: The predicted FWR4 nucleotide sequence. * **reads**: The number of reads aligned to this contig. * **umis**: The number of distinct UMIs aligned to this contig. * **raw_clonotype_id**: The clonotype ID assigned to this cell barcode. * **raw_consensus_id**: The ID of the consensus sequence to which this contig was assigned. * **exact_subclonotype_id**: The ID of the exact subclontype to which this cell barcode was assigned. ##### Cell Ranger Market Matrix direcectories * **barcodes.tsv.gz**: Column indicies. * **features.tsv.gz**: Row indicies. Column 1: Feature Name, Column 2: Feature ID, Column 3: Feature Type (Gene Expression) * **matrix.mtx.gz**: A sparse matrix representation of the count matrix. #### Bulk TCR Sequencing `bulk_tcr.tar.gz` contains the background TCR sequencing data for 9 samples (9 files). Each filename has the structure `(Donor ID)_CD4-Bulk_TCRB.tsv`. The sequencing and analysis was done by [Adaptive Biotechnologies](https://www.adaptivebiotech.com). Blank cells indicate there was no available information or sequencing did not capture sufficient reads to generate results. Each file contains the following columns: * **nucleotide**: The nucleotide sequence of the rearrangement. * **aminoAcid**: The amino acid sequence of the rearrangement. * **count (templates/reads)**: Number of reads or templates in the sequencing data * **frequencyCount (%)**: The sequence frequency * **cdr3Length**: CDR3β sequence length * **vMaxResolved**: Vβ gene and allele used * **vFamilyName**: Vβ family used * **vGeneName**: Vβ gene used * **vGeneAllele**: Vβ allele used * **vFamilyTies**: Equivalently-scored V families identified during annotation * **vGeneNameTies**: Equivalently-scored V genes identified during annotation * **vGeneAlleleTies**: Equivalently-scored V alleles identified during annotation * **dMaxResolved**: D gene and allele used * **dFamilyName**: D family used * **dGeneName**: D gene used * **dGeneAllele**: D allele used * **dFamilyTies**: Equivalently-scored D families identified during annotation * **dGeneNameTies**: Equivalently-scored D genes identified during annotation * **dGeneAlleleTies**: Equivalently-scored D alleles identified during annotation * **jMaxResolved**: Jβ gene and allele used * **jFamilyName**: Jβ family used * **jGeneName**: Jβ gene used * **jGeneAllele**: Jβ allele used * **jFamilyTies**: Equivalently-scored J families identified during annotation * **jGeneNameTies**: Equivalently-scored J genes identified during annotation * **jGeneAlleleTies**: Equivalently-scored J alleles identified during annotation * **vDeletion**: Number of nucleotides deleted from the V gene during recombination * **n1Insertion**: Number of nucleotides inserted in the N1 (VD) junction during recombination. * **d5Deletion**: Number of nucleotides deleted from the 5' end of the D gene during recombination * **d3Deletion**: Number of nucleotides deleted from the 3' end of the D gene during recombination * **n2Insertion**: Number of nucleotides inserted in the N2 (DJ) junction during recombination. * **jDeletion**: Number of nucleotides deleted from the J gene during recombination * **vIndex**: The index within the full nucleotide sequence that denotes the Cysteine beginning the CDR3 * **n1Index**: The index within the full nucleotide sequence that denotes the start of the N1 (VD) region * **dIndex**: The index within the full nucleotide sequence that denotes the start of the D region * **n2Index**: The index within the full nucleotide sequence that denotes the start of the N2 (DJ) region * **jIndex**: index within the full nucleotide sequence that denotes the start of the J region * **estimatedNumberGenomes**: Estimated number of T/B cell genomes present in the sample * **sequenceStatus**: The functional state of a rearrangement: in-frame (productive), out-of-frame, or containing a stop codon. * **cloneResolved**: Which portions of the clone are resolved #### MD5sum `files.md5` contain MD5sums for each file included. These can be validated with `md5sum -c files.md5`. #### GLIPH2 results `GLIPH2_output_cleveland_cohort.csv` contains all the results from only the Cleveland cohort. `GLIPH2_output_combined_cohorts.csv` contains all the results from both the Cleveland and [South African](https://doi.org/10.1038/s41587-020-0505-4) cohorts. Cells containing "-" indicate no available HLA typing information for the indicated allele. Both contain the following columns: * **index**: numeric index for cluster * **pattern**: TCR motif for cluster * **Fisher_score**: Fisher score comparing the pattern to a background * **number_subject**: The number of subjects in the cluster * **number_unique_cdr3**: The number of unique CDR3 sequences in the cluster * **final_score**: A combination of HLA, Vβ, expansion, length, and cluster size scores as a probability conflation * **hla_score**: The score quantifying the total HLA associations for the cluster. * **vb_score**: Probability a random selection of TCR sequences of the same size has a higher Simpson diversity index for Vβ genes * **expansion_score**: Probability that a random set of equal size from the same data set would have at least this same number of expanded members * **length_score**: Probability a random selection of TCR sequences of the same size has a higher Simpson diversity index for CDR3β lengths * **cluster_size_score**: Probability of a cluster this size appearing in a random selection. * **type**: global pattern contains '%', which indicates position allowing variants; local pattern starts with 'motif-'; and singlet likes global pattern without '%' symbol * **ulTcRb**: amino acids encoded by codon overlapping with N-addition nucleotides are labeled as lower case, otherwise, it is labeled as upper cases * **TcRb**: The TCR CDR3β sequence * **V**: The Vβ gene used * **J**: The Jβ gene used * **TcRa**: The TCR CDR3α sequence * **Sample**: The `(Donor ID):(condition)` * **Freq**: The number of cells (single-cell) or reads (bulk) this TCR appears in. * **HLA-(X)**: HLA alleles found to associate with the cluster. Significant associates are denoted with a "!" ## Code/software Cell Ranger is needed to process the FastQ files into single-cell count matrices. To view the MarketMatrix outputs, using various R packages (Seurat, SingleCellExperiment) or Python modules (scanpy) are appropriate. The CSV outputs can be used in R (via scRepertoire) or Python (via scirpy).  The original code reported in this paper is available through the GitHub: [carpenter-lab/StetsenkoGail2025](https://github.com/carpenter-lab/StetsenkoGail2025). ### Single-cell data analysis Filtered_contig_annotations.csv and clonotypes.csv files from Cell Ranger containing productive, full-length TCR sequences for each sample were used for TCR clonotype analysis. Filtered feature barcode matrices (mRNA data) and contig annotations (CDR3a and b sequences) were linked to individual cell barcodes. Gene expression and TCR annotations were generated for each cell for simultaneous TCR and transcriptomics analysis after quality control and integration were performed using the R-based Seurat package (v 5.1.0) (Satija et al., 2015) with seed 123. High-quality cells and features were selected based on the following parameters: transcript detection in at least 3 cells, 200