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WO2024175926A1 - Procédé d'identification de récepteurs de lymphocytes t d'intérêt - Google Patents

Procédé d'identification de récepteurs de lymphocytes t d'intérêt Download PDF

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WO2024175926A1
WO2024175926A1 PCT/GB2024/050499 GB2024050499W WO2024175926A1 WO 2024175926 A1 WO2024175926 A1 WO 2024175926A1 GB 2024050499 W GB2024050499 W GB 2024050499W WO 2024175926 A1 WO2024175926 A1 WO 2024175926A1
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cell
cells
activated
cxcr3
tcr
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Rasa ELMENTAITE
Sarah Amalia POLONIUS-TEICHMANN
Lisa Marietta DRATVA
Rik Gerrit Hendrik LINDEBOOM
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Genome Research Ltd
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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6881—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for tissue or cell typing, e.g. human leukocyte antigen [HLA] probes
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61K—PREPARATIONS FOR MEDICAL, DENTAL OR TOILETRY PURPOSES
    • A61K40/00—Cellular immunotherapy
    • A61K40/10—Cellular immunotherapy characterised by the cell type used
    • A61K40/11—T-cells, e.g. tumour infiltrating lymphocytes [TIL] or regulatory T [Treg] cells; Lymphokine-activated killer [LAK] cells
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61K—PREPARATIONS FOR MEDICAL, DENTAL OR TOILETRY PURPOSES
    • A61K40/00—Cellular immunotherapy
    • A61K40/30—Cellular immunotherapy characterised by the recombinant expression of specific molecules in the cells of the immune system
    • A61K40/32—T-cell receptors [TCR]
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61K—PREPARATIONS FOR MEDICAL, DENTAL OR TOILETRY PURPOSES
    • A61K40/00—Cellular immunotherapy
    • A61K40/40—Cellular immunotherapy characterised by antigens that are targeted or presented by cells of the immune system
    • A61K40/46—Viral antigens
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
    • A61P31/00—Antiinfectives, i.e. antibiotics, antiseptics, chemotherapeutics
    • A61P31/12—Antivirals
    • A61P31/14—Antivirals for RNA viruses
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12N—MICROORGANISMS OR ENZYMES; COMPOSITIONS THEREOF; PROPAGATING, PRESERVING, OR MAINTAINING MICROORGANISMS; MUTATION OR GENETIC ENGINEERING; CULTURE MEDIA
    • C12N5/00—Undifferentiated human, animal or plant cells, e.g. cell lines; Tissues; Cultivation or maintenance thereof; Culture media therefor
    • C12N5/06—Animal cells or tissues; Human cells or tissues
    • C12N5/0602—Vertebrate cells
    • C12N5/0634—Cells from the blood or the immune system
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/158—Expression markers

Definitions

  • the invention relates to methods of analysing and identifying activated T cells and antigen specific TCR motifs using marker gene or protein expression.
  • the invention also relates to isolated activated T cells and antigen-specific TCRs identified using the methods.
  • T cells are major effectors of the adaptive immune response. T cell responses can target infected and cancerous cells, as well as supporting B cell and antibody responses. Dysregulation of T cell responses can result in immune pathologies such as autoimmune disease.
  • T cell responses function through an interaction between a high-affinity T cell receptor (TCR) and an antigen that is presented by a diseased cell or an antigen-presenting cell, eventually leading to cytotoxicity against the diseased cell, clonal expansion of the T cell, and the creation of long-term immune memory.
  • TCR high-affinity T cell receptor
  • Variation in the amino acid sequence of the TCR determines which antigen and HLA it can bind. Being able to match TCR sequences to antigens and diseases is therefore extremely valuable in translational research.
  • This knowledge can be used to engineer cells expressing an isolated TCR to bind specific targets, including engineering T cells, e.g. T cells with engineered T cell receptors (TCR T cells), and in the development of TCR-like antibodies and TCR mimetics.
  • Antigen-specific TCR-expressing engineered cells, TCR mimics, or TCR-like antibodies are used in the treatment of disease, including cancer, autoimmune or inflammatory disease.
  • the information can also be used to inform vaccine development, and to determine the antigens responsible for invoking a T cell response.
  • the presence and abundance of activated T cells, as well as specific TCRs can be used as a biomarker to diagnose and monitor disease such as cancer, infections, autoimmune disease, graft-versus-host disease, immunopathologies and diseases that require immunomodulatory treatments.
  • both approaches require pre-existing knowledge about the antigens that trigger the T cell response. While in infections the pathogen is often known, one cannot be certain which of the 1000s of possible antigens will be immunogenic and targeted by T cells in vivo. The requirement for known antigens is even more problematic in diseases such as cancer and autoimmune disease, where the relevant antigens are often completely unknown or heterogeneous across patients.
  • Another major drawback of tetramers and similar assays is scalability, where reagents for each antigen in a specific MHC context need to be created individually.
  • the AIM assay and similar approaches rely on isolating activated T cells to identify antigen specific T cells.
  • these assays rely on acute and in vitro stimulation to induce the presentation of activation markers instead of analysing naturally occurring markers.
  • These signals include the production of interferon gamma, IL-12, IL-2, or the presentation of markers such as CD69, CD137 and CD134.
  • these markers can also be activated in immune cells that are in proximity of antigen-specific T cells through cytokine secretion, to activate so-called bystanders, leading to the inclusion by-stander T cells after isolation which complicates antigen specificity inference.
  • AIM-assays are in vitro assays where known-antigens are presented in non-physiological conditions, at high concentrations and through unknown antigen-presentation mechanisms, which often limits the physiological relevance of the identified T cells and TCR sequences.
  • existing methods to detect antigen specific T cells are restricted to known antigens and their physiological relevance can be restricted due to the in vitro nature of these experiments.
  • a method of identifying an antigenspecific T Cell Receptor comprising: (i) identifying an activated T cell that expresses one or more markers selected from the group consisting of: CD38, CD28, CD27, SELL, GZMK, CTLA4, GIMAP4, ICOS, GBP2, CXCR3, S1 PR4, SIT1 and CCR5;
  • an isolated antigen-specific TCR identified using the method as defined herein.
  • a method of detecting an activated T cell comprising detecting the expression of one or more markers selected from the group consisting of: CD38, CD28, CD27, SELL, GZMK, CTLA4, GIMAP4, ICOS, GBP2, CXCR3, S1 PR4, SIT1 and CCR5.
  • an isolated activated T cell which expresses one or more markers selected from the group consisting of: CD38, CD28, CD27, SELL, GZMK, CTLA4, GIMAP4, ICOS, GBP2, CXCR3, S1 PR4, SIT1 and CCR5.
  • an isolated antigen-specific TCR or activated T cell as defined herein for use in therapy, such as immunotherapy or adoptive immunotherapy.
  • a method of diagnosing a disease condition in a patient comprising detecting the presence of an antigen-specific TCR identified according to the method as defined herein, or the activated T cells according to the method as defined herein, in a sample obtained from the patient.
  • a method of screening an agent capable of activating T cells comprising:
  • FIG. 1 Illustration of human SARS-CoV-2 challenge study design and cohort composition. Sampling time points indicated on the right, which were different for nasal swabs and blood data. 6/16 study participants (37.5%) were successfully infected (sustained infection), 3/16 (18.75%) resisted the infection and had a single positive PCR test (transient infection), and 7/16 (43.75%) prevented an infection altogether (abortive infection).
  • a higher score indicates target genes were upregulated compared to a set of randomly drawn genes (Scanpy function scanpy.tl.score_genes).
  • B Box plots of the score for different cell types, separated into non-activated and activated T cell types. P value after Mann-Whitney test is shown and significant, confirming that activated T cells score significantly higher for the activation gene signature than other cell types.
  • CD8A, CD4, PRF1 , FOXP3, MKI67 activation gene signature
  • activation gene signature CD38, CD28, CD27, SELL, GZMK, CTLA4, GIMAP4, ICOS, GBP2, CXCR3, S1 PR4, SIT1 , CCR5
  • Figure 5 Dot plot visualizing the mean expression of marker genes used in AIM assay across T cell types in participants with sustained infections for PBMCs (top left) and nasal swab data (bottom left), and surface protein expression for PBMCs (top right).
  • Figure 6 Mean accuracy in identifying activated T cells of each gene shown in 2 or 3 gene combinations across all T cells from the test set.
  • FIG. 7 Dot plot showing fold changes in abundance of nasopharynx resident (left) and blood circulating (right) activated T cells.
  • nasopharyngeal T cells cell abundances were scaled to the total amount of detected epithelial cells, while blood cells were scaled to all detected cells in every sample. Fold changes over days since inoculation compared to preinfection (day -1) were calculated by fitting a GLMM on scaled abundances. The mean cell type proportions over all cells and samples is shown in the squares right of the dot plot to aid the interpretation of changes in cell type abundances.
  • B Percentage of nasopharyngeal T cells (left) and circulating T cells (right) that were annotated as activated T cells, split over days since inoculation and colour coded by infection group.
  • FIG. 8 Abundance of TCR clonotype clusters relative to all TCRs are shown over time since inoculation. Activated TCR clonotype clusters are shown in the top panel, while all other TCR clonotype clusters are shown in the bottom panel. All activated TCR clonotype cluster motifs are shown at the right. Motifs are presented as TCR alpha motifs and TCR beta motifs separated by a space, sequences are SEQ ID NOs: 35-74 as indicated.
  • SARS-CoV-2 antigens The total amount of bound cells to each Dextramer is shown, colour- coded by predicted cell state. If barcodes from several Dextramers were detected to be bound to the same cell, we only selected the Dextramer with the highest signal as bound. As a control to separate background and real binding, cells are separated based on the HLA haplotype compatibility with the tested Dextramer. Only Dextramers with at least 10 HLA matched bound cells are shown. FDR corrected p values were determined by a Fisher-exact test comparing the proportion of HLA matched activated T cells in the Dextramer bound cells to the proportion of unbound HLA matched activated T cells, 'n’ represents the amount of cells in each shown bar.
  • the right-most bar represents the overall distribution of cell types across all Dextramer experiments.
  • B Cell types of exactly matching clonotypes (both TCR alpha and beta chains) between dextramer data and data from previous samples. Only matches within the same patient were considered. Matches are found mostly within the same compartments for both sources of data, confirming that the considered cells are comparable and can be used for downstream analyses. Cell types with a matching percentage of less than 5% were omitted.
  • C Overlap of T cells in TCR motifs identified from the 5 COVID-19 data sets (public motif) with CD8 T cells in TCR motifs identified from the 5 COVID-19 data sets plus the dextramer data (public+dextramer motif). TCR motifs were identified as described earlier. Public motifs with overlap in public+dextramer motifs are thus validated to cluster with antigen-specific T cells, and can be themselves considered antigen-specific.
  • FIG. 12 (A) Fraction of all T cells that are activated T cells in various cancer and control samples. Data was obtained from https://github.com/ncborcherding/utility, and only studies with matched healthy samples were considered. Samples were split by tissue type. Tumour and metastasis tissues have consistently more activated T cells compared to matched normal tissue. (B) Total number of activated T cells identified for each indicated cancer type across a pan-cancer dataset. Activated T cells were identified using a logistic regression classifier (Celltypist) trained on PBMC cell annotations from the study discussed in Example 1.
  • Celltypist logistic regression classifier
  • FIG. 13 T otal number of activated T cell types identified, shaded by dengue severity. Eight donors and equal number of samples were available for each dengue severity. Activated T cells were identified using a logistic regression classifier (Celltypist) trained on PBMC cell annotations from the study discussed in Example 1. (B) Fraction of activated T cells over all T cells, calculated for each dengue severity. Eight donors and equal number of samples were available for each dengue severity. Activated T cells were identified using a logistic regression classifier (Celltypist) trained on PBMC cell annotations from the study discussed in Example 1.
  • FIG. 14 Total number of activated T cell types identified, shaded by inflammatory bowel disease (IBD) status. Eight individuals were profiled for the healthy and colitis conditions each, six for the ‘no colitis’ condition. Activated T cells were identified using a logistic regression classifier (Celltypist) trained on PBMC cell annotations from the study discussed in Example 1. DETAILED DESCRIPTION
  • the present invention relates to isolation of T cells that have recently responded to a pathogen or antigen.
  • the invention does not require prior knowledge about the antigens that trigger the immune response.
  • it enables capturing T cells that are naturally, strongly responsive to an antigen, without requiring in vitro re-stimulation, making the identified T cells more likely to be physiologically relevant and high-affinity binders.
  • the present inventors have identified genes and proteins that characterise several molecularly distinct subsets of T cells that have been activated through TCR signalling. The presence, abundance, and TCR repertoire of these activated T cells can then be used in therapeutic applications of antigenspecific T cells. In particular embodiments, no in vitro re-stimulation is performed. Thus, in certain embodiments the methods described herein do not comprise in vitro re-stimulation.
  • the invention quantifies the abundance of RNA or proteins to select T cells that express CD38, CD28, CD27, SELL, GZMK, CTLA4, GIMAP4, ICOS, GBP2, CXCR3, S1 PR4, SIT1 and CCR5 or a selection of the mentioned genes, to identify T cells that have been activated through TCR signalling in response to a recent or ongoing disease, pathogen, or foreign antigen.
  • T cells that co-express CD38, CD28, CD27, SELL, GZMK, CTLA4, GIMAP4, ICOS, GBP2, CXCR3, S1PR4, SIT1 and CCR5 form a group of T cells that is molecularly distinct from classical memory or naive T cells. This has allowed the production of a predictive logistic regression model on the gene expression profiles of activated T cells, to identify activated T cells in other single cell RNA-seq and CITE-seq datasets.
  • T cells specifically appear in the blood and in the diseased tissue around a week after exposure to SARS-CoV-2 and remain present up to a month after onset of symptoms.
  • the TCR repertoires of these T cells are highly clonal, show detectable expansion evidenced by cell cycle markers, and multiple form independent TCR clones with highly similar amino acid sequences.
  • these cells are absent in exposed individuals who did not get a sustained SARS-CoV-2 infection and subsequent COVID-19.
  • the exclusive presence in infected individuals, only after a week post-exposure, both in the blood and at the site of infection, together with the clonal character of these T cells provides strong evidence that these T cells have been specifically activated by SARS-CoV-2 antigens.
  • analysis showed an enrichment of proven antigen specific TCRs in activated T cells that have been validated against public COVID-19 data.
  • a T cell is a type of lymphocyte and therefore may also be referred to as “T lymphocytes”. They are formed in the bone marrow, migrate to and mature in the thymus and then enter the peripheral blood and lymphatic circulation.
  • a “T cell” may be defined by its cellular markers and/or function. Therefore references to “T cell” include CD8+ T cells, and/or T helper (Th) cell subsets marked by CD4+ (e.g. Th1, Th2 and Th17 cells), gamma/delta-TCR+ T cells, and T regulatory (Treg), and mucosa-associated invariant T (MAIT) cells.
  • CD3 is a phenotypic marker expressed at all stages of T-cell development. Therefore, the T cell may be referred to as a CD3+ lymphoid cell.
  • T cells unlike B cells, do not produce antibody molecules, but express a cell surface receptor (a “T-cell receptor”) that recognizes peptide fragments of antigenic proteins that are attached to proteins of the major histocompatibility complex (MHC) and expressed on the surfaces of target cells.
  • T cell receptor or “TCR” as used herein refers to a heterodimeric molecule comprising an alpha polypeptide chain (alpha chain) and a beta polypeptide chain (beta chain), which is capable of binding to an antigen.
  • alpha and beta chains of alpha/beta TCRs are generally regarded as each having two “domains”, namely variable and constant domains.
  • variable domain consists of a concatenation of a variable (V) region and a joining (J) region.
  • Beta chains may also include a diversity (D) region.
  • alpha and beta constant domains may also include C-terminal transmembrane (TM) domains that anchor the alpha and beta chains to the cell membrane.
  • TM transmembrane
  • a TCR can also refer to a heterodimeric molecule comprising a delta and gamma polypeptide TCR chain.
  • an “activated T cell” is a T cell that has experienced an antigen (or a fragment thereof) and is capable of mounting an antigen-specific response to that antigen.
  • the activated T cell will therefore express a TCR that specifically binds to the antigen (“an antigen-specific TCR”).
  • the activated T cell may comprise a TCR that is signalling.
  • an “antigen-specific TCR” is a TCR that specifically binds to an antigen. This term includes TCR motifs, i.e. the entire TCR sequence does not need to have been identified and the antigen binding portion, e.g. CDR3 motif, may be sufficient to provide useful information for identifying the antigen-specific TCR.
  • a TCR that “specifically binds” to an antigen is a term well understood in the art. A molecule is said to exhibit “specific binding” if it reacts more frequently, more rapidly, with greater duration and/or with greater affinity with a particular target antigen or epitope, than it does with alternative targets.
  • a TCR “specifically binds” to a target antigen if it binds with greater affinity, avidity, more readily, and/or with greater duration than it binds to other antigens.
  • antigen refers to a molecule that can provoke an immune response, e.g. involving activation of immune cells and/or antibody generation. Any macromolecule, including almost all proteins or peptides, can be an antigen. Antigens can also be derived from genomic recombinant or DNA. For example, any DNA comprising a nucleotide sequence or a partial nucleotide sequence that encodes a protein capable of eliciting an immune response encodes an “antigen”. An antigen can be synthesized or can be derived from a biological sample, e.g. a tissue sample, a tumour sample, a cell, or a fluid with other biological components.
  • a biological sample e.g. a tissue sample, a tumour sample, a cell, or a fluid with other biological components.
  • marker refers to a DNA, RNA, protein, carbohydrate, glycolipid, or cell-based molecular marker, the expression or presence of which in a sample can be detected by standard methods (or methods disclosed herein).
  • a T cell that “expresses” a marker of interest is one in which mRNA encoding the protein, or the protein itself, including fragments thereof, is determined to be present in the T cell. Expression of a marker can be detected by various means as described herein.
  • transcriptome refers to the set of all transcript molecules (i.e. messenger RNA, or mRNA) expressed by a cell. Analysis of the transcriptome can provide a snapshot of actively expressed genes and transcripts in a cell under various conditions. In one embodiment, a transcriptome refers to a set of mRNA molecules.
  • the activated T cells identified by methods of the invention may be isolated.
  • An “isolated” activated T cell is one that is removed from its original environment.
  • the term “isolated” may be used to refer to an activated T cell that is substantially free of other T cells (e.g. an isolated activated T cell is substantially free of T cells that are not activated).
  • the term “isolated” may also be used to refer to preparations where the isolated activated T cell is sufficiently pure to be administered therapeutically when formulated as an active ingredient of a pharmaceutical composition.
  • References to a “variant” when referring to a polypeptide could be, for example, an amino acid sequence at least 50%, 55%.
  • the variant is at least 80%, such as at least 90%, 95%, 98%, or 99% identical to the full-length polypeptide.
  • the variant could be a fragment of full-length polypeptide, in particular a functional fragment of the polypeptide.
  • the fragment may be at least 50%, 60%, 70%, 80%, 85%, 90%, 95%, 98%, or 99% as long as the full-length wild type polypeptide or a domain thereof having an activity of interest. Variations known in the art to eliminate or substantially reduce the activity of the protein are preferably avoided.
  • the variant lacks an N- and/or C-terminal portion of the full-length polypeptide, e.g., up to 10, 20, or 50 amino acids from either terminus is lacking.
  • a functional variant or fragment has at least 50%, 60%, 70%, 80%, 90%, 95% or more of the activity of the full-length wild type polypeptide.
  • Embodiments which refer herein to “at least” or “greater” will be understood to include values equal to or greater than the specified amount. For example, “at least 80%” or “80% or greater”, will be understood to include all values equal to or greater than 80%, such as 85%, 90%, 95%, 97%, 98%, 99% or 100% sequence identity.
  • the polypeptides described herein comprise at least 85%, such as at least 90%, at least 95%, at least 97%, at least 98% or at least 99% sequence identity to the specified sequence.
  • the “% sequence identity” between a first polypeptide sequence and a second polypeptide sequence may be calculated using the tcrdistS algorithm (Mayer-Blackwell etal. (2020)) or using the Levenshtein distance. Alternatively, one could use NCBI BLAST v2.0, using standard settings for polypeptide sequences (BLASTP). For the purposes of comparing two closely-related polynucleotide sequences, the “% sequence identity” between a first nucleotide sequence and a second nucleotide sequence may be calculated using NCBI BLAST v2.0, using standard settings for nucleotide sequences (BL STN).
  • Polypeptide or polynucleotide sequences are said to be the same as or “identical” to other polypeptide or polynucleotide sequences, if they share 100% sequence identity over their entire length. Residues in sequences are numbered from left to right, i.e. from N- to C- terminus for polypeptides; from 5’ to 3’ terminus for polynucleotides. A “difference” between sequences refers to an insertion, deletion or substitution of a single amino acid residue in a position of the second sequence, compared to the first sequence. Two polypeptide sequences can contain one, two or more such amino acid differences.
  • the number of additions, substitutions and/or deletions made to the first sequence to produce the second sequence may be ascertained.
  • An “addition” is the addition of one amino acid residue into the sequence of the first polypeptide (including addition at either terminus of the first polypeptide).
  • a “substitution” is the substitution of one amino acid residue in the sequence of the first polypeptide with one different amino acid residue. Said substitution may be conservative or non-conservative.
  • a “deletion” is the deletion of one amino acid residue from the sequence of the first polypeptide (including deletion at either terminus of the first polypeptide).
  • a “conservative” amino acid substitution is an amino acid substitution in which an amino acid residue is replaced with another amino acid residue of similar chemical nature and which is expected to have little influence on the function, activity or other biological properties of the polypeptide.
  • a conservative substitution may involve exchanging a hydrophobic, hydrophilic, acidic, basic, aromatic, aliphatic, charged or uncharged amino acid for another amino acid of the same group.
  • references to “subject”, “patient”, “donor”, “participant”, or “individual” refer to a subject, in particular a mammalian subject, to be treated.
  • Mammalian subjects include humans, nonhuman primates, farm animals (such as cows), sports animals, or pet animals, such as dogs, cats, guinea pigs, rabbits, rats or mice.
  • the subject is a human.
  • the subject is a non-human mammal, such as a mouse.
  • Cancer refers to the abnormal growth or division of cells. Generally, the growth and/or life span of a cancer cell exceeds, and is not coordinated with, that of the normal cells and tissues around it. Cancers may be benign, pre-malignant or malignant.
  • Cancer occurs in a variety of cells and tissues, including the oral cavity (e g., mouth, tongue, pharynx, etc.), digestive system (e.g., oesophagus, stomach, small intestine, colon, rectum, liver, bile duct, gall bladder, pancreas, etc.), respiratory system (e.g., larynx, lung, bronchus, etc.), bones, joints, skin (e.g., basal cell, squamous cell, meningioma, etc.), breast, genital system, (e.g., uterus, ovary, fallopian tube, endometrial, prostate, testis, etc.), urinary system (e.g., bladder, kidney, ureter, etc.), eye, nervous system (e.g., brain, etc.), endocrine system (e.g., thyroid, etc.), and hematopoietic system (e.g., lymphoma, myeloma,
  • inflammatory disease refers to a disease characterised by inflammation, i.e. the biological response to harmful stimuli, including but not limited to stimuli such as pathogens, damaged cells, irritants, antigens and, in the case of autoimmune disease, substances and tissues normally present in the body.
  • inflammatory diseases include rheumatoid arthritis (RA), atherosclerosis, asthma, autoimmune diseases, chronic inflammation, chronic prostatitis, glomerulonephritis, hypersensitivities, inflammatory bowel diseases (such as ulcerative colitis), pelvic inflammatory disease, reperfusion injury, transplant rejection, and vasculitis.
  • Inflammatory diseases can characterized by periods of intense symptoms (flares) and periods of remission. References to “flares” as used herein refers to a period during which an inflammatory disease enters into an active phase or exacerbates, and thus leads to an increase in severity of the disease.
  • infection refers to a condition in which pathogens that cause disease have entered the body. Infections can be caused by different pathogens and environmental factors.
  • the infection can be a viral infection, bacterial infection, fungal infection or a parasitic infection. Infections can begin anywhere in the body and may spread to different cells and/or tissues during the progression of the infection.
  • the term “about” when used herein includes up to and including 10% greater and up to and including 10% lower than the value specified, suitably up to and including 5% greater and up to and including 5% lower than the value specified, especially the value specified.
  • the term “between” includes the values of the specified boundaries.
  • any method as described herein may have one or more, or all, steps performed in vitro, ex vivo, in silico or in vivo.
  • TCR antigenspecific T Cell Receptor
  • methods of the invention utilise a selection of markers (i.e. an “activated T cell signature”), whereby expression of the signature indicates that the immune cell has an activated immune cell phenotype.
  • CD38 is a protein found on the surface of many immune cells.
  • the CD38 gene sequence is known in the art, for example see Ensembl ID: ENSG00000004468.
  • the CD38 gene encodes the CD38 protein (also known as cyclic ADP ribose hydrolase).
  • the CD38 protein sequence is known in the art, for example see UniProt ID: P28907 (Last updated: v2 2004-11-23; Checksum: 47BBE38C3DE3E6AA).
  • CD28 is a protein involved in T cell signalling.
  • the CD28 gene sequence is known in the art, for example see Ensembl ID: ENSG00000178562.
  • the CD28 gene encodes the CD28 protein (also known as T-cell-specific surface glycoprotein CD28).
  • the CD28 protein sequence is known in the art, for example see UniProt ID: P10747 (Last updated: v1 1989-07-01 ; Checksum: 1 D9B6552A5878D0F).
  • CD27 is a member of the TNF-receptor superfamily.
  • the CD27 gene sequence is known in the art, for example see Ensembl ID: ENSG00000139193.
  • the CD27 gene encodes the CD27 protein.
  • the CD27 protein sequence is known in the art, for example see UniProt ID: P26842 (Last updated: v2 2009-11-24; Checksum: 43C38B895B2AD4E2).
  • SELL also known as L-selectin or CD62L
  • L-selectin or CD62L is a cell adhesion molecule found on the surface of leukocytes.
  • the SELL gene sequence is known in the art, for example see Ensembl ID: ENSG00000188404.
  • the SELL gene encodes the L-selectin protein.
  • the L-selectin protein sequence is known in the art, for example see UniProt ID: P14151 (Last updated: v2 1991-02- 01; Checksum: 6EA9918ECA2D3643).
  • GZMK encodes a serine protease found in granules of natural killer (NK) cells and cytotoxic T lymphocytes.
  • the GZMK gene sequence is known in the art, for example see Ensembl ID: ENSG00000113088.
  • the GZMK gene encodes the Granzyme K protein.
  • the Granzyme K protein sequence is known in the art, for example see UniProt ID: P49863 (Last updated: v1 1996-10-01 ; Checksum: 2A93FDBAF9286CC5).
  • CTLA4 is a protein receptor that functions as an immune checkpoint.
  • the CTLA4 gene sequence is known in the art, for example see Ensembl ID: ENSG00000163599.
  • the CTLA4 gene encodes the CTLA4 protein (Cytotoxic T-lymphocyte protein 4, also known as CD152).
  • the CTLA4 protein sequence is known in the art, for example see UniProt ID: P16410 (Last updated: v32003-01-10; Checksum: 6F9466FB2E139A5A).
  • GIMAP4 is an enzyme belonging to the GTP-binding superfamily.
  • the GIMAP4 gene sequence is known in the art, for example see Ensembl ID: ENSG00000133574.
  • the GIMAP4 gene encodes the GIMAP4 protein (GTPase IMAP family member 4).
  • the GIMAP4 protein sequence is known in the art, for example see UniProt ID: Q9NUV9 (Last updated: v1 2000- 10-01 ; Checksum: 9D64BA4FB1C5DF72).
  • ICOS is a CD28-superfamily costimulatory molecule.
  • the ICOS gene sequence is known in the art, for example see Ensembl ID: ENSG00000163600.
  • the ICOS gene encodes the ICOS protein (Inducible T-cell costimulatory, also known as CD278).
  • the ICOS protein sequence is known in the art, for example see UniProt ID: Q9Y6W8 (Last updated: v1 1999-11-01 ; Checksum: 214EC741C9BDC9FC).
  • GBP2 belongs to the superfamily of large GTPases which can be induced mainly by interferon gamma.
  • the GBP2 gene sequence is known in the art, for example see Ensembl ID: ENSG00000162645.
  • the GBP2 gene encodes the GBP2 protein (Guanylate Binding Protein 2).
  • the GBP2 protein sequence is known in the art, for example see UniProt ID: P32456 (Last updated: v32009-04-14; Checksum: B09B3C2F3C3E1 EA2).
  • CXCR3 is a G protein-coupled receptor in the CXC chemokine receptor family.
  • the CXCR3 gene sequence is known in the art, for example see Ensembl ID: ENSG00000186810.
  • the CXCR3 gene encodes the CXCR3 protein (C-X-C chemokine receptor type 3, also known as CD183).
  • the CXCR3 protein sequence is known in the art, for example see UniProt ID: P49682 (Last updated: v2 1997-11-01 ; Checksum: F08A3B44B2BBAD04).
  • S1 PR4 is a G protein-coupled receptor which binds the lipid signalling molecule sphingosine 1-phosphate (S1 P).
  • the S1 PR4 gene sequence is known in the art, for example see Ensembl ID: ENSG00000125910.
  • the S1 PR4 gene encodes the S1PR4 protein (C sphingosine-1- phosphate receptor 4).
  • the S1 PR4 protein sequence is known in the art, for example see UniProt ID: 095977 (Last updated: v1 1999-05-01 ; Checksum: 369A7BC56AB46A47).
  • SIT1 regulates TCR-mediated signalling in T-cells.
  • the SIT1 gene sequence is known in the art, for example see Ensembl ID: ENSG00000137078.
  • the SIT1 gene encodes the SIT1 protein (Signaling threshold-regulating transmembrane adapter 1).
  • the SIT 1 protein sequence is known in the art, for example see UniProt ID: Q9Y3P8 (Last updated: v1 1999-11-01 ; Checksum: 2A0C48C9466F7F07).
  • CCR5 is a receptor for chemokines found on the surface of leukocytes.
  • the CCR5 gene sequence is known in the art, for example see Ensembl ID: ENSG00000160791.
  • the CCR5 gene encodes the CCR5 protein (C-C motif chemokine receptor 5).
  • the CCR5 protein sequence is known in the art, for example see UniProt ID: P51681 (Last updated: v1 1996-10- 01; Checksum: 88ECE1F38E6D45A7).
  • Expression of the one or more markers may be determined by any method known in the art.
  • the method comprises determining the expression of the one or more markers to produce expression data.
  • the expression data is RNA expression data, DNA expression data, or protein expression data.
  • Gene expression can be determined by sequencing, preferably RNA-seq (e.g. single cell RNA-seq or bulk RNA-seq), PCR (e.g.
  • the expression may be determined by a method allowing for single-cell resolution of the measurement, i.e., on a single-cell level, such as by single cell RNA sequencing and/or cellular indexing of transcriptomes and epitopes (CITE- seq).
  • determining whether or not an immune cell expresses a marker may comprise cell sorting.
  • expression of a marker refers to a surface density of the marker on a cell.
  • Mean fluorescence intensity is representative of the density of a marker on a population of cells.
  • a population of cells can be said to express a marker if the MFI of an antibody to that marker is significantly higher than the MFI of an appropriate isotype control antibody on the same population of cells, stained under equivalent conditions. If step (a) involves physically separating the activated T cell, then this step may be referenced as isolating an activated T cell that expresses one or more of the listed markers.
  • identifying an activated T cell that expresses one or more markers as required by the method can include computational methods of prediction and identification.
  • any logistic regression or predictive model that is trained using the markers discussed herein can be used to predict the presence of activated T cells in silico. Therefore, in one embodiment, identifying an activated T cell comprises using the expression levels of the one or more markers and a mathematical model (e.g. a logistic regression model) to determine whether a T cell is an activated T cell.
  • identifying the activated T cell comprises:
  • the transcriptome data is used to identify activated T cells, for example through computational methods, i.e. by application of one or more algorithms.
  • a machine learning algorithm is used to analyse the transcriptome data. Suitable machine learning algorithms, such as those discussed in the Examples herein, will be understood by a person skilled in the art.
  • the machine learning algorithm may involve any suitable logistic regression or linear regression model.
  • TCR antigen-specific T Cell Receptor
  • methods are known in the art for obtaining single cell transcriptome data. Such methods may comprise isolating target cells by label-free imaging flow cytometry and quantifying gene expression in the target cells by single cell sequencing. According to this aspect of the invention, one or more of the markers described herein is used to identify an activated T cell. Therefore, it will be understood that any of the embodiments discussed herein in relation to the markers, may be applied to this aspect of the invention.
  • T cells can be obtained from a number of sources, including any fluid or solid tissue sample such as: blood, peripheral blood mononuclear cells (PBMCs), lymph fluids, airway scrapings/swabs, gastrointestinal scrapings/swabs, and any solid tissue such as bone marrow, lymph node tissue, spleen tissue, tumours, organs, and epithelial tissues.
  • PBMCs peripheral blood mononuclear cells
  • T cells can be obtained from a unit of blood collected from a subject using any number of techniques known in the art, such as Ficoll separation.
  • cells from the circulating blood of an individual are obtained by apheresis or leukapheresis.
  • the apheresis product typically contains lymphocytes, including T cells, monocytes, granulocytes, B cells, other nucleated white blood cells, red blood cells, and platelets.
  • lymphocytes including T cells, monocytes, granulocytes, B cells, other nucleated white blood cells, red blood cells, and platelets.
  • the cells collected by apheresis may be washed to remove the plasma fraction and to place the cells in an appropriate buffer or media for subsequent processing steps.
  • the T cells may be obtained from any mammal.
  • mammal refers to any mammal including, but not limited to, humans, non-human primates, mice, rabbits, cats, dogs, cows, pigs or horses.
  • the mammal is human. Therefore, in one embodiment, the activated T cell is a human activated T cell.
  • the activated T cells may express one or more of the following markers: CD38, CD28, CD27, SELL, GZMK, CTLA4, GIMAP4, ICOS, GBP2, CXCR3, S1 PR4, SIT1 and CCR5.
  • the method may comprise detecting one, multiple or all markers listed herein. In one embodiment, the method comprises detecting the expression of at least two or more of the markers, in particular three, four or five or more of the markers. In a further embodiment, the method comprises detecting the expression of at least three of the markers.
  • the method comprises detecting the expression of between two and ten markers, such as between two and nine, two and eight, two and seven, two and six, two and five, three and nine, three and eight, three and seven, three and six or three and five markers.
  • the method comprises detecting the expression of between two and six markers, in particular between three and five markers.
  • the method comprises detecting three markers, four markers or five markers.
  • the markers are selected from the group consisting of:
  • the markers are selected from the group consisting of: CD28, SELL, GZMK, CTLA4, GIMAP4, GBP2, CXCR3, S1 PR4, SIT1 and CCR5. In a further embodiment, the markers are selected from the group consisting of: GIMAP4, GBP2, CXCR3, S1 PR4, SIT1 and CCR5.
  • the marker is CD38.
  • CD38 expression may be detected in combination with one or more, such as one, two, three, four, or five markers selected from the group consisting of: CD28, CD27, SELL, GZMK, CTLA4, GIMAP4, ICOS, GBP2, CXCR3, S1PR4, SIT1 and CCR5.
  • the method comprises detecting the expression of between two and six markers selected from CD38 in combination with CD28, CD27, SELL, GZMK, CTLA4, GIMAP4, ICOS, GBP2, CXCR3, S1 PR4, SIT1 and CCR5.
  • the marker is CD27.
  • CD27 expression may be detected in combination with one or more, such as one, two, three, four, or five markers selected from the group consisting of: CD38, CD28, SELL, GZMK, CTLA4, GIMAP4, ICOS, GBP2, CXCR3, S1PR4, SIT1 and CCR5.
  • the method comprises detecting the expression of between two and six markers selected from CD27 in combination with CD38, CD28, SELL, GZMK, CTLA4, GIMAP4, ICOS, GBP2, CXCR3, S1 PR4, SIT1 and CCR5.
  • the marker is ICOS.
  • ICOS expression may be detected in combination with one or more, such as one, two, three, four, or five markers selected from the group consisting of: CD38, CD28, CD27, SELL, GZMK, CTLA4, GIMAP4, GBP2, CXCR3, S1PR4, SIT1 and CCR5.
  • the method comprises detecting the expression of between two and six markers selected from ICOS in combination with CD38, CD28, CD27, SELL, GZMK, CTLA4, GIMAP4, GBP2, CXCR3, S1PR4, SIT1 and CCR5.
  • the marker is CXCR3.
  • CXCR3 expression may be detected in combination with one or more, such as one, two, three, four, or five markers selected from the group consisting of: CD38, CD28, CD27, SELL, GZMK, CTLA4, GIMAP4, ICOS, GBP2, S1PR4, SIT1 and CCR5.
  • the method comprises detecting the expression of between two and six markers selected from CXCR3 in combination with CD38, CD28, CD27, SELL, GZMK, CTLA4, GIMAP4, ICOS, GBP2, S1PR4, SIT1 and CCR5.
  • the method comprises detecting the expression of CXCR3 in combination with any one of: CD38, CD28, CD27, SELL, GZMK, CTLA4, GIMAP4, ICOS, GBP2, S1 PR4, SIT1 and CCR5.
  • the method comprises detecting the expression of CXCR3 in combination with any two of: CD38, CD28, CD27, SELL, GZMK, CTLA4, GIMAP4, ICOS, GBP2, S1 PR4, SIT 1 and CCR5.
  • the method comprises detecting the expression of CXCR3 in combination with any one, two or more of: CD38, CD28, CD27, SELL, GZMK, CTLA4, GIMAP4, ICOS, GBP2, S1PR4, SIT1 and CCR5.
  • the method may comprise detecting one or more additional markers.
  • the activated T cell expresses one or more additional markers selected from the group consisting of:
  • SELP SELP, ITGA2B, CD36, CD86, FAS, ITGB1 and ENTPD1.
  • the markers are selected from the group consisting of:
  • SELP CD38, ITGA2B, ICOS, CD36, CCR5, CD86, FAS, ITGB1 and ENTPD1 ;
  • SELP CD38, ITGA2B, ICOS and CD36;
  • GBP2 CD27, CD38, COTL1, UCP2, UBE2L6, CXCR3, IQGAP2, ARID5B and TPM4; or GBP2, CD27, CD38, COTL1 and UCP2.
  • the markers are selected from one or more of the markers listed in Table 1.
  • Table 1 Top 50 ranking genes associated with activated T cells identified using methods described in the Examples.
  • the markers are selected from one or more of those listed in Table 3.
  • the markers may be selected from any combination of two or three markers listed in Table 3, such as any combination of two or three markers listed in Table 3 to provide an accuracy equal to or greater than 0.7, in particular equal to or greater than 0.8.
  • the method comprises detecting the expression of at least two or three markers in any combination provided in Table 3.
  • the method comprises detecting the expression of at least two or three markers in any combination listed in Table 3 to provide an accuracy equal to or greater than 0.8.
  • the method comprises detecting the expression of at least CD27 and CXCR3. In a further embodiment, the method comprises detecting the expression of at least CD38 and GZMK. In a further embodiment, the method comprises detecting the expression of at least CTLA4 and CXCR3. In a further embodiment, the method comprises detecting the expression of at least CD38 and CXCR3. In a further embodiment, the method comprises detecting the expression of at least CXCR3 and CCR5. In a further embodiment, the method comprises detecting the expression of at least CD38 and SIT 1. In a further embodiment, the method comprises detecting the expression of at least CD38 and CTLA4. In a further embodiment, the method comprises detecting the expression of at least SELL and CXCR3.
  • the method comprises detecting the expression of at least CD28 and CXCR3. In a further embodiment, the method comprises detecting the expression of at least CXCR3 and S1PR4. In a further embodiment, the method comprises detecting the expression of at least CXCR3 and SIT1. In a further embodiment, the method comprises detecting the expression of at least GZMK and CTLA4. In a further embodiment, the method comprises detecting the expression of at least GZMK and CXCR3.
  • the method comprises detecting the expression of: at least CTLA4, GBP2 and CXCR3; at least GZMK, GIMAP4 and CXCR3; at least CTLA4, CXCR3 and CCR5; at least GZMK, CTLA4 and CXCR3; at least CD38, CXCR3 and CCR5; at least CTLA4, CXCR3 and S1 PR4; at least CD38, ICOS and CXCR3; at least IGOS, CXCR3 and S1 PR4; at least ICOS, GBP2 and CXCR3; at least GBP2, CXCR3 and S1PR4; at least GBP2, CXCR3 and SIT 1 ; at least CD38, CD28 and CXCR3; at least CD38, CTLA4 and CXCR3; at least CD38, CXCR3 and SIT1 ; at least GZMK, CTLA4 and S1PR4; at least CTLA4, GIMAP4 and CXCR3;
  • the method comprises detecting the expression of two or three markers in any combination provided in Table 3 to provide an accuracy equal to or greater than 0.7.
  • activated T cells markers determined herein are different from activation induced T cell markers, such as those identified in the AIM assay (see Dan et al. (2016), which is herein incorporated by reference).
  • the activated T cell does not express one or more markers selected from the group consisting of: CD40LG, CD69, LAMP1, TNFRSF9, TNFRSF4, IL2RA and CD274.
  • the activated T cells may express one or more markers that are highly correlated with the expression of the markers described hereinbefore.
  • a mathematical model may be trained to identify markers with a similar expression pattern (i.e. are computationally related) to the markers described herein. Therefore, in one aspect or embodiment, the method comprises preparing a mathematical model (e.g. a predictive logistic regression model) using the gene expression profiles of activated T cells.
  • the activated T cell is identified in (optionally, isolated from) a sample obtained from a patient exposed to the antigen. In this embodiment, there may be prior knowledge of the target antigen. Therefore, in one embodiment, the method comprises initially exposing a population of T cells to the antigen.
  • the method comprises obtaining the sample from a patient already exposed to the antigen, i.e. a patient with a disease.
  • the activated T cells will naturally emerge during the course of disease and can be identified using methods of the invention.
  • the method may additionally comprise determining the antigen bound by the antigen-specific TCR (“antigen inference”). Also according to this embodiment, no in vitro re-stimulation is required, such that the methods described herein may not comprise in vitro re-stimulation.
  • PBMCs Peripheral blood mononuclear cells
  • TILs tumour-infiltrating lymphocytes
  • tumour biopsies are used to provide the population of T cells.
  • the antigen is presented on an antigen presenting cell to the population of T cells.
  • the antigen is presented on an antigen presenting cell (APC).
  • APCs present epitopes of an antigen through Major Histocompatibility Complexes (MHCs), potentially after endogenous processing of the antigen.
  • MHCs Major Histocompatibility Complexes
  • the antigen may be provided to the APCs as RNA, DNA, protein or polypeptide to enable endogenous processing and presentation by the APCs. Therefore, prior knowledge of the epitope presented by the APC is not required. For example, stable or transient expression after transfection is possible.
  • the APCs are preferably autologous APCs, i.e. they are isolated from the same donor as the population of T cells. Alternatively, they may be heterologous APCs, as long as they share at least one MHC allele, preferably, all MHC I or II (preferably MHC I) alleles with the donor.
  • the APCs are dendritic cells, in particular, mature dendritic cells. Exposure of the T cells to the antigen may be carried out for 7-42 days, most preferably for 14- 28 days. The ratio of PBMCs to APCs may be about 5:1 to 20:1 , preferably about 10:1. T cells may be stimulated in medium containing cytokines favouring T cell proliferation, for example IL-2, IL-7 and/or IL-15 to prevent antigen-unspecific T cell proliferation.
  • the antigen may be derived from a disease of interest. It will be understood that the method described herein will be applicable to a range of diseases, such as infections, inflammatory diseases and cancer. Therefore, in one embodiment, the antigen is derived from an infection (including the microbiome), an inflammatory disease or cancer.
  • the antigen may be derived from therapy, i.e. the T cell is activated in response to a therapy.
  • the therapy may include immune checkpoint inhibitors or radiotherapy.
  • the therapy may include vaccination.
  • the TCR associated with the activated cell may be identified.
  • Analysing a TCR may comprise isolating a nucleotide or amino acid sequence that encodes a TCR, or an antigen-binding portion thereof. Therefore, in one embodiment, analysing the TCR comprises sequencing, in particular sequencing one or more of the Complementarity Determining Regions (CDRs) of the TCR.
  • CDRs Complementarity Determining Regions
  • analysing the TCR comprises sequencing the nucleic acid sequences encoding the TCR alpha and/or TCR beta chains of the TCR.
  • analysing the TCR comprises sequencing the amino acid sequences encoding the TCR alpha and/or TCR beta chains of the TCR.
  • the nucleic acid sequences encoding the TCR may be directly isolated.
  • RNA may be isolated to generate cDNA via 5' rapid amplification of cDNA ends (RACE) of TCR genes followed by PCR amplification.
  • PCR products may be cloned into expression plasmids to transform bacteria.
  • Each bacterial colony may be regarded as containing one sequencing vector with one PCR TCR gene fragment.
  • Vector DNA of numerous bacterial colonies may be prepared, followed by sequencing of vector inserts (i.e. TCR gene fragments). Sequencing results of each bacterial colony may be analysed, e.g. by using IMGTN-Quest.
  • Frequencies of identical TCR alpha or TCR beta chains reflect the proportion of identical T cell clonotypes within the sorted T cell sample.
  • Another strategy to analyse TCR alpha and TCR beta gene sequences of sorted T cells is the use of next-generation sequencing approaches. For example, random primers or primers that specifically target TCR genes are amplified using PCR and ligated to sequencing adapters. These PCR products are then sequenced using next generation-sequencing using for example Illumina sequencing. Frequency of identical TCR alpha or TCR beta genes are regarded to be representative for the initial amount of T cells of the same clonotype within the sample of sorted T cells.
  • Frequency matching of TCR alpha and TCR beta chains within a T cell sample can be used for analysing pairing of functional TCRs (Linnemann et al. (2013)). Sensitivity of these methods enables detailed analysis of TCR repertoires within T cell samples. Matching the frequencies of TCR alpha and TCR beta chains additionally enables the reconstitution of abundant TCR chain pairs from T cell samples. When single cell RNA- sequencing is used to characterise TCR sequences, one can count the T cells that express the same or similar TCR sequence to quantify the abundance of a clonotype.
  • Analysing the TCR may use an appropriate method, in particular a method allowing for singlecell resolution.
  • Methods of processing single cell data include, for example, single cell RNA sequencing, single cell immune profiling, cellular indexing of transcriptomes and epitopes (CITE- seq), nuclei seq and multiomics data (i.e. data generated from multiple sources, such as the genome, proteome, transcriptome, metabolome, and epigenome).
  • analysing the TCR comprises single cell immune profiling.
  • single cell immune profiling may be provided by 10X GENOMICS, e.g. V(D)J T Cell and B Cell Analysis using their software, cellranger vdj.
  • TCR analysis may comprise isolating TCR sequences from one cell or a pool of cells.
  • the activated T cell transcriptome may be analysed by lysing and pooling the isolated activated T cells obtained from the sample.
  • TCR alpha and TCR beta chain of the selected population of T cells If more than one TCR alpha and TCR beta chain of the selected population of T cells is identified after analysis, the frequency of the TCR alpha and TCR beta chains in the population may be analysed and TCR alpha and TCR beta chains constituting a TCR capable of recognising the epitope are matched based on their frequency. Further analysis may also be carried out, e.g. generation of T cells carrying combinations of different predominant TCR alpha and TCR beta chains, and analysis of their activation by the APCs. Activated T cell phenotype
  • the activated T cell is selected from a CD4+ T cell, CD8+ T cell, regulatory T cell or mucosal-associated invariant T (MAIT) cell.
  • CD4+ and CD8+ T cells may be cytotoxic, i.e. cause cell lysis of the cell bearing the target antigen. Therefore, in one embodiment the activated T cell is a CD4+ activated cytotoxic T lymphocyte (CTL) or a CD8+ activated CTL.
  • CTL cytotoxic T lymphocyte
  • CD4+ T cells may be helper cells, i.e. indirectly stimulate an immune response by releasing cytokines and growth factors that regulate other immune cells. Therefore, in one embodiment the activated T cell is a CD4+ activated helper T cell.
  • Regulatory T cells may be identified by the markers they express, e.g. FOXP3 and/or TIG IT.
  • activated Tregs identified by the inventors downregulate FOXP3. Therefore, in one embodiment, the activated regulatory T cell expresses TIGIT.
  • MAIT cells are a subset of nonconventional T cells typically found in the blood, liver and mucosae of humans.
  • MAIT cells defend against microbial activity and infection, and are characterized by a semi-invariant T cell receptor alpha (TCRa) chain.
  • TCRa T cell receptor alpha
  • the semi-invariant TCRa chain can combine with a restricted number of possible TCR chains.
  • MAIT cells are generally CD8+ or CD4-/CD8- (DN), and are restricted by the non-classical MHC class I molecule, MR1.
  • the activated T cell additionally expresses MKI67.
  • MKI67 is a marker for cycling cells (i.e. cells which are in a state of active proliferation or division). It has been shown that activation of T cells in patients during an immune response leads to cell proliferation.
  • a method of detecting an activated T cell comprising detecting the expression of one or more markers selected from the group consisting of: CD38, CD28, CD27, SELL, GZMK, CTLA4, GIMAP4, ICOS, GBP2, CXCR3, S1 PR4, SIT1 and CCR5.
  • an isolated activated T cell which expresses one or more markers selected from the group consisting of: CD38, CD28, CD27, SELL, GZMK, CTLA4, GIMAP4, ICOS, GBP2, CXCR3, S1 PR4, SIT1 and CCR5.
  • the activated T cell is selected from a CD4+ T cell, CD8+ T cell or regulatory T cell.
  • an isolated antigen-specific TCR identified using a method of the invention as described herein.
  • the identified antigen-specific TCRs or isolated activated T cells obtained according to any of the methods of the invention have applications in basic and medical research, diagnostic and therapeutic methods.
  • the cells may be used in vitro to study cellular development, provide test systems for new drugs, enable screening methods to be developed, scrutinise therapeutic regimens, provide diagnostic tests and the like. These uses form part of the present invention.
  • the cells may be transplanted into a human or animal patient for diagnostic or therapeutic purposes.
  • T cells with completely novel, not naturally occurring TCRs based on activated T cells may also be engineered for use in therapy. The use of the cells in therapy is also included in the present invention.
  • an isolated activated T cell or antigen-specific TCR as described herein, for use in therapy for use in therapy.
  • Example therapies include immunotherapy or adoptive immunotherapy.
  • the therapy may be autologous, i.e. the activated T cells may be transferred back into the same patient from which they were obtained, or the therapy may be allogeneic, i.e. the activated T cells from one person may be transferred into a different patient.
  • TCR-like antibodies can also be used to target cell types through several distinct molecular mechanisms (see He et al. (2019)).
  • a method of diagnosing a disease condition in a patient comprising detecting the presence of activated T cells or antigenspecific TCRs according to the method as described herein, in a sample obtained from the patient.
  • the methods described herein for identifying an antigenspecific T Cell Receptor (TCR) and/or activated T cells may be used in diagnosis.
  • the antigen-specific TCR can be used to identify those T cells responding to disease and/or infection, such that the disease and/or infection may be diagnosed using the identity of the responding cells and/or the identity of the antigen-specific TCR.
  • the identity of the antigen-specific TCR is used for diagnosis.
  • the identity of the activated T cells is used for diagnosis.
  • the identity of the activated T cells responding to a disease condition or infection are used for diagnosis of said disease condition or infection.
  • the identity of the antigen-specific TCR is used for diagnosis of a disease condition or infection, wherein the identified TCR recognises an antigen associated with said disease condition or infection.
  • the method is repeated on one or more occasions and any changes in the level of activated T cells or antigen-specific TCRs is used to monitor the progression of the disease condition in the patient.
  • a method of monitoring an adaptive immune response in a patient comprising detecting the presence of activated T cells or antigen-specific TCRs according to the method as described herein, in a sample obtained from a patient who has experienced an antigen.
  • the method is performed on a patient following vaccination.
  • the methods may be used in the diagnosis or treatment of any disease in which an adaptive immune response can be mounted.
  • the disease condition is selected from an infection, an inflammatory disease or cancer.
  • the method comprises contacting a population of T cells with a test substance and observing a change (e.g., an effect) in the T cells induced by the test substance.
  • the change or effect may be observed using methods known in the art, for example using pharmacological or toxicological assays.
  • the method of assessing a test substance e.g., a drug, such as a compound
  • the method may comprise: a) contacting a T cell with the test substance; and b) assaying an effect of the test substance on the cell.
  • Assessment of the activity of a candidate molecule may involve combining the T cells with the candidate molecule, determining any change in the activation status of the T cells that is attributable to the molecule (i.e., compared with a control, such as untreated cells or cells treated with an inert compound), and then correlating the effect of the molecule with the observed change.
  • the screening may be done either because the candidate molecule is designed to have an effect on the activation status of T cells, or because the molecule is designed to have effects elsewhere but there is a need to determine if it has and unintended side effects.
  • a method of screening an agent capable of activating T cells comprising:
  • polypeptide comprising an antigen binding variable region comprising a CDR3 comprising a sequence having at least 80% sequence identity with any one of SEQ ID NOs: 1-16.
  • the antigen binding variable region comprises a CDR3 comprising a sequence having at least 85%, 90%, 95%, 97%, 98% or 99% sequence identity with any one of SEQ ID NOs: 1-16. In one embodiment, the antigen binding variable region comprises a CDR3 consisting of a sequence having at least 85%, 90%, 95%, 97%, 98% or 99% sequence identity with any one of SEQ ID NOs: 1-16.
  • any residues of the CDR3 differing from their corresponding residues in SEQ ID NOs: 1-16 may be conservative substitutions with respect to their corresponding residues.
  • any residues of CDR3 differing from their corresponding residues in SEQ ID NOs: 1- 16 may be conservative substitutions with respect to their corresponding residues.
  • the polypeptide comprises a T cell receptor alpha (TCR-a) variable region. In a further embodiment, the polypeptide comprises a TCR-a variable and constant region (i.e. a TCR-alpha chain).
  • the polypeptide comprises a T cell receptor beta (TCR-p) variable region. In a further embodiment, the polypeptide comprises a TCR-p variable and constant region (i.e. a TCR-beta chain).
  • TCR-p T cell receptor beta
  • the polypeptide comprises a TCR-p variable and constant region (i.e. a TCR-beta chain).
  • the polypeptide comprises a TCR-alpha chain and a TCR-beta chain.
  • the TCR-alpha chain and the TCR-beta chain are operably linked, optionally through a peptide bond.
  • the polypeptide may comprise a linker between the TCR-alpha and TCR-beta chain. Said linker may preferably comprise glycine and serine residues.
  • the polypeptide is a TCR.
  • the TCR may comprise a hinge domain, a transmembrane domain, an intracellular domain, a co-stimulatory domain, and/or a CD3zeta signalling domain.
  • the co-stimulatory domain comprises CD28, 4-1 BB or fragments thereof.
  • the polypeptide further comprises a signal peptide.
  • polypeptides may be designed and made inclusive of N- terminal and C-terminal modifications to aid with translation, purification and detection.
  • N-terminus an additional methionine and/or alanine amino acid residue may be included.
  • additional sequences may be included such as (i) a partial sequence of the constant region and/or (ii) additional synthetic sequences inclusive of tags, such as His-tags and Flag-tags, to aid with purification and detection.
  • tags such as His-tags and Flag-tags
  • the polypeptide comprises an antigen binding variable region comprising a CDR3 comprising the amino acid sequence of SEQ ID NO: 1 and/or SEQ ID NO: 3. These motifs were found in 27 unique clones from 15 individuals.
  • the CDR3 may comprise an amino acid sequence of: CALGGSQGNLIF (SEQ ID NO: 2).
  • SEQ ID NO: 1 and 2 are present in a TCR alpha variable region.
  • Xi is S or T;
  • X 2 is S or A;
  • X 3 is L, S, D, P or F;
  • X 4 is R, S, A or G;
  • X 5 is S, A or D;
  • X 6 is T, W, F, R, N, G, V, S, Q or H;
  • X 7 is D, E or N;
  • X 8 is T, E or K;
  • X 9 is Q or L;
  • X is Y or F.
  • the CDR3 may comprise an amino acid sequence of: CASSLRSGTDTQYF (SEQ ID NO: 4).
  • SEQ ID NO: 3 and 4 are present in a TCR beta variable region.
  • the polypeptide comprises an antigen binding variable region comprising a CDR3 comprising the amino acid sequence of SEQ ID NO: 5 and/or SEQ ID NO: 7. These motifs were found in 12 unique clones from 10 individuals.
  • the CDR3 may comprise an amino acid sequence of: CALSGTGNQFYF (SEQ ID NO: 6).
  • SEQ ID NO: 5 and 6 are present in a TCR alpha variable region.
  • the CDR3 may comprise an amino acid sequence of: CASSDRGPNQPQHF (SEQ ID NO: 8).
  • SEQ ID NO: 7 and 8 are present in a TCR beta variable region.
  • the polypeptide comprises an antigen binding variable region comprising a CDR3 comprising the amino acid sequence of SEQ ID NO: 9 and/or SEQ ID NO: 11. These motifs were found in 16 unique clones from 13 individuals.
  • the CDR3 may comprise an amino acid sequence of: CAVHNAGNMLTF (SEQ ID NO: 10).
  • SEQ ID NO: 9 and 10 are present in a TCR alpha variable region.
  • the CDR3 may comprise an amino acid sequence of: CASSLAGGSTDTQYF (SEQ ID NO: 12).
  • SEQ ID NO: 11 and 12 are present in a TCR beta variable region.
  • the polypeptide comprises an antigen binding variable region comprising a CDR3 comprising the amino acid sequence of SEQ ID NO: 13 and/or SEQ ID NO: 15. These motifs were found in 17 unique clones from 11 individuals and are directed to the orflab polyprotein as antigen. SEQ ID NO: 13
  • CAX1X2GSQGNLI F wherein Xi is L or P; X 2 is G, S or A. In a further embodiment, Xi is L and X 2 is G. Therefore, the CDR3 may comprise an amino acid sequence of: CALGGSQGNLIF (SEQ ID NO: 14). In one embodiment, SEQ ID NO: 13 and 14 are present in a TCR alpha variable region.
  • the CDR3 may comprise an amino acid sequence of: CASSLRSGWDEQFF (SEQ ID NO: 16).
  • SEQ ID NO: 15 and 16 are present in a TCR beta variable region.
  • TCRs comprising SEQ ID NOs: 1-16 and the motifs in Table 2 have been identified using methods of the invention in patients with COVID-19. Therefore, T cells may be engineered to express TCRs comprising SEQ ID NOs: 1-16 and/or the motifs in Table 2 in order to provide T cell specific for COVID-19 antigens.
  • the invention therefore provides an engineered T cell containing or expressing a TCR comprising any one of SEQ ID NOs: 1-16 and/or the motifs in Table 2, for use in the treatment of COVID-19.
  • nucleic acid molecule encoding a TCR as described herein.
  • a vector comprising a nucleic acid molecule as described herein.
  • a host cell containing or expressing a vector as described herein.
  • an engineered T cell containing or expressing a TCR as described herein.
  • the TCR sequences, or a part thereof may be used to prepare engineered T cells.
  • the TCRs identified according to methods of the invention may be used in whole or in part to engineer a T cell. Novel, synthetic TCRs may be created, for instance, by combining TCR alpha chains from one TCR with a TCR beta chain from another TCR identified by methods of the invention.
  • the T cell is a human T cell.
  • composition comprising the engineered T cells as described herein.
  • Participants were inoculated intranasally with an wild-type pre-alpha SARS-CoV-2 challenge virus (full formal name: SARS-CoV-2/human/GBR/484861/2020) at dose 10 TCID50 at day 0. 100 pl per naris was pipetted between both nostrils and the participant was asked to remain supine (face and torso facing up) for 10 minutes, followed by 20 minutes in a sitting position wearing a nose clip after inoculation to ensure maximum contact time with the nasal and pharyngeal mucosa.
  • SARS-CoV-2 challenge virus full formal name: SARS-CoV-2/human/GBR/484861/2020
  • Mid-turbinate nose and throat samples were collected twice daily using flocked swabs and placed in 3 ml of viral transport medium (BSV-VTM-001 , Bio-Serv) that was aliquoted and stored at -80 °C in order to evaluate viral kinetics (infection status). Participants remained in quarantine for a minimum of 14 days post-inoculation.
  • BSV-VTM-001 viral transport medium
  • Samples were collected at the Royal Free Hospital by trained healthcare providers at 7 timepoints; day-1 (pre-inoculation) and day 1, 3, 5, 7, 10 and 14 post-inoculation. The patients were asked to clear any mucus from their nasal cavities and nasopharyngeal samples were collected using FLOQSwabs (Copan flocked swabs, Ref 501CS01) inserted along the nasal septum, above the floor of the nasal passage to the nasopharynx until a slight resistance was felt.
  • FLOQSwabs Copan flocked swabs, Ref 501CS01
  • the swab was then rotated in this potion in both directions for 10 seconds and slowly removed whilst still rotating and immediately stored in a pre-cooled cryovial on wet ice containing freeze media (90% heat inactivated fetal bovine serum (FBS) and 10% dimethyl sulfoxide (DMSO)).
  • FBS heat inactivated fetal bovine serum
  • DMSO dimethyl sulfoxide
  • the cryovials vials were the transferred to the hospital chutes where they were sent down to the laboratory ( ⁇ 2 mins at RT) and placed in a slow-cooling device (Mr. Frosty Freezing Container, Thermo Fisher Scientific) and stored at -20 °C until all samples were collected, at which point they were moved to -80 °C freezers for at least 48 hours for optimum freezing. Samples were moved, stored and in liquid nitrogen for later processing.
  • Peripheral blood were collected at the Royal Free Hospital in EDTA tubes at 5 timepoints; day-1 (pre-inoculation) and day 3, 5, 10, 14 and 28 post-inoculation. Each day the blood was transferred at room temperature to Imperial College London for PBMC isolation by means of Histopaque Ficoll separation. The PBMC pellet was collected, and then both the cell number and viability were assessed using Trypan Blue. Cell freezing medium (90% FBS, 10% DMSO) was added dropwise to PBMCs slowly on ice and the mixture was then cryopreserved at -80 °C. Blood and nasopharyngeal samples to be collected within 2 hours of each other.
  • the Chromium Next GEM Single Cell 5' V(D)J Reagent Kit (V1.1 chemistry) was used for single-cell RNA-seq library construction for all nasopharyngeal swab samples, and the Chromium Next GEM Single Cell V(D)J Reagent Kit v1.1 with Feature Barcoding technology for cell surface proteins was used for PBMCs.
  • GEX and V(D)J libraries were prepared according to the manufacturer’s protocol (10x Genomics) using individual Chromium i7 Sample Indices.
  • the cell surface protein libraries were created according to the manufacturer’s protocol with slight modifications that included doubling the SI primer amount per reaction and reducing the number of amplification cycles to 7 during the index PCR to avoid the daisy chains effect.
  • GEX, V(D)J and cell surface protein indexed libraries were pooled at a ratio of 1 :0.1 :0.4 and sequenced on a NovaSeq 6000 S4 Flowcell (paired-end, 150 bp reads) aiming for a minimum of 50,000 paired-end reads per cell for GEX libraries and 5,000 paired-end reads per cell for V(D)J and cell surface protein libraries.
  • RNA-seq and CITE-seq data from PBMCs was jointly aligned against the GRCh38 reference that 10X Genomics provided with CellRanger 3.0.0, and alignment was performed using CellRanger 4.0.0.
  • CITE-seq antibody-derived tag (ADT) barcodes were aligned against a barcode reference provided by the supplier, which we annotated to add informative protein names and made available in our GitHub repository.
  • Single-cell RNA-seq data from nasopharyngeal swab samples were aligned against the same reference using STARSolo 2.7.3a, and post-processed with an implementation of emptydrops extracted from CellRanger 3.0.2.
  • RNA-seq alignment To detect viral RNA in infected cells, we added 21 viral genomes including pre-Alpha SARS-CoV-2 (NC_045512.2) to the above mentioned reference genomes for RNA-seq alignment, as described in Yoshida et al. (2022). Single cell alpha/beta TCR data was aligned using CellRanger 4.0.0 with the accompanying GRCh38 VDJ reference that 10X Genomics provided. Single cell gamma/delta TCR data was aligned against the GRCh38 reference that 10X Genomics provided with CellRanger 5.0.0, using CellRanger 6.1.2.
  • Aligned alpha/beta TCR sequencing data was imported in scirpy to obtain a cell by TCR formatted table, which was then added to Seurat objects containing gene expression data. Aligned single cell gamma/delta TCR data was reannotated using Dandelion VO.2.4.
  • TCR clonotype groups To identify TCR clonotype groups, we used tcrdistS (Mayer-Blackwell, K. et al. (2021)) with the provided human references to compute a sparse representation of the distance matrices for all identified TRA and TRB CDR3 sequences, with the radius parameter set to 150. We then summed the distances for TRA and TRB to obtain a combined distance matrix. Next, we iterated over possible TCR distance thresholds between 5 and 150 with increments of 5, to compute TCR clonotype groups at each thresholds.
  • T cells that were annotated as I should not participate in an expanded clonotype group, and quantified the proportion of I T cells in each clonotype group to determine the largest threshold at which we observed minimal participation of I T cells.
  • CD4+ T cells and CD8+ T cells should never be part of the same TCR clonotype group, so we set out to quantify the proportion of CD4+/CD8+ mixing in each clonotype group to find the largest threshold where mixing is minimal. Both approaches revealed the same optimal threshold of 35 at which both I T cell participation and CD4+/CD8+ mixing is minimal, which we then used for downstream analyses.
  • activated TOR clonotype groups To identify activated TOR clonotype groups we assumed that these groups should include activated T cells and that we should at least detect multiple independent TOR clonotypes that appear to be raised against the same antigen at the same time; we therefore selected clonotype groups that contained at least one participating activated T cell and that contained at least two unique CDR3 nucleotide sequences.
  • TCR logos in Table 2 were generated by providing the CDR3 amino acid sequences of each clonotype group to the ggseqlogo R package (Wagih (2017)) or the logomaker Python package (Tareen and Kinney (2020)).
  • clonotype groups contained CDR3 amino acid sequences of variable lengths, we selected the sequences with the most frequently occurring length within each group for visualization purposes only.
  • EXAMPLE 2 Immune response dynamics in Human SARS-CoV-2 challenge
  • the SARS-CoV-2 human challenge study has been shown to be safe for 36 young volunteers aged 18 to 29 years, all of whom were seronegative at the time of nasal inoculation with 10 TCID50 (Median Tissue Culture Infectious Dose) of the ancestral SARS-CoV-2 (pre-Alpha I Wuhan strain) virus (Killingley et al. (2022)).
  • 10 TCID50 Median Tissue Culture Infectious Dose
  • pre-Alpha I Wuhan strain pre-Alpha I Wuhan strain
  • Six participants from our cohort developed sustained infections that included multiple consecutive PCR positive tests and symptoms (Figure 1). In contrast, three individuals produced multiple sporadic and borderlinepositive PCR tests between day 1.5 and 7 post-inoculation.
  • an activation signature to identify activated T cells.
  • We determined a ranked list of marker genes for activated T cells by performing two types of statistical tests and ranking genes by the average performance.
  • the TCR sequences that these activated subset express are highly overlapping with subsets that are found in both the blood and nasopharynx, validating that they are indeed the same cell types that are found both in circulation and at the site of infection.
  • the immune repertoires of activated T cells are significantly more restricted and clonal than other mature T cell types, indicating that they were activated and expanded in a TCR and antigen specific manner.
  • we detect high amounts of cycling T cells within the activated T cell compartment we also detected many activated T cells that are not cycling and cycling T cells that do not appear to be activated, underscoring that our activation signature is independent from the cell cycle gene signature.
  • T cells with TCR were taken from the challenge study (152,918 T cells, from 16 donors, of which 2,399 were activated T cells) and only log-normalized counts of our 13 signature genes (CCR5, CD27, CD28, CD38, CTLA4, CXCR3, GBP2, GIMAP4, GZMK, ICOS, S1 PR4, SELL, SIT1) were considered for every cell.
  • CD4+ and CD8+ activated T cells In contrast to CD4+ and CD8+ activated T cells whose infiltration peaks at day ten, the amount of activated regulatory T cells is highest at day 14 at the site of infection, where they produce large amounts of anti-inflammatory cytokine IL-10. This peak of activated regulatory T cells coincides with a clearance of the observed global immune infiltration and nearly absent IFN stimulation levels, suggesting that these regulatory T cells are suppressing further inflammation after the infection has been cleared.
  • CD4+ and CD8+ T cells that lack both activation and tissue-residency markers which infiltrates the nasopharynx which we annotated as infiltrating memory T cells. Their timing and lack of activation markers suggest that these are aspecific bystander T cells that infiltrate alongside the observed global immune infiltration from day five post-inoculation onwards.
  • Activated CD4+ T cells express cytolytic proteins
  • Activated T cells appear to express markers for both early (e.g. SELL and GZMK) and mature (e.g. PRF1) effector T cells, suggesting that these cells are a transitional cell state to mature T cells.
  • activated CD4+ T cells also express high amounts of cytotoxicity genes (e.g. PRF1) that are normally expressed in Natural Killer (NK) and CD8+ T cells.
  • PRF1 cytotoxicity genes
  • NK Natural Killer
  • CD4+ T cells can only recognise antigens in MHC class II context that is normally exclusive to professional antigen-presenting immune cells, the function and relevance of cytotoxic CD4 T cells remains poorly understood.
  • several studies have reported their emergence during the adaptive immune response against SARS-CoV-2 (Kaneko et al. (2022)).
  • TCR clonotypes that can specifically recognise SARS-CoV-2.
  • We next selected activated clonotype groups that appear to expand in an antigen-specific manner i.e.
  • Integrating COVID-19 patient data uncovers public SARS-CoV-2 TCR motifs and validates SARS-CoV-2 specificity of activated T cells
  • Most of the activated T cell clonotype groups recognise viral proteins encoded by ORFIab, but we also find Membrane and Spike specific TCR clonotype groups.
  • our cell state aware clonotype selection method is capable of identifying SARS-CoV-2 specific TCRs without any prior antigen information, it is possible that our results also includes TCRs that recognise SARS-CoV-2 antigens that have not been tested. Together, these results validate the specificity of the adaptive immune response that we observed at day ten, and highlight the power of defining activated T cells for detecting diseasespecific antigens in an unbiased manner.
  • activated T cells are antigen-specific and can recognize SARS- CoV-2 peptides
  • peptide-MHC-l stainings on PBMCs using DNA-barcoded Dextramers loaded with SARS-CoV-2 antigens to detect peptide-MHC-l binding in parallel with scRNA-seq and scTCR-seq.
  • the activation signature was used to identify activated T cells in various cancer and control samples from public data obtained from https://github.com/ncborcherding/utility. Only studies with matched healthy samples were considered.
  • Figure 12A shows the results with samples split by tissue type. It was found that tumour and metastasis tissues have consistently more activated T cells compared to matched normal tissue.
  • Figure 12B shows the total number of activated T cells identified for each indicated cancer type and Figure 12C shows the fraction of activated T cells over all T cells, calculated for each cancer type across a pan-cancer dataset (see description of the figure for the number of samples profiled per cancer type).
  • Activated T cells were identified using a logistic regression classifier (Celltypist) trained on PBMC cell annotations from the study discussed in Example 1.
  • EXAMPLE 4 Activated T cells in Dengue fever
  • Figure 13A shows the total number of activated T cell types identified, shaded by dengue severity. Eight donors and equal number of samples were available for each dengue severity. Activated T cells were identified using a logistic regression classifier (Celltypist) trained on PBMC cell annotations from the study discussed in Example 1.
  • Figure 13B shows Fraction of activated T cells over all T cells, calculated for each dengue severity. Eight donors and equal number of samples were available for each dengue severity. Activated T cells were identified using a logistic regression classifier (Celltypist) trained on PBMC cell annotations from the human SARS-CoV-2 challenge study.
  • Celltypist logistic regression classifier
  • EXAMPLE 5 Activated T cells in Ulcerative Colitis
  • Figure 14 shows the total number of activated T cell types identified, shaded by IBD status. Eight individuals were profiled for the healthy and colitis conditions each, six for the ‘no colitis’ condition. Activated T cells were identified using a logistic regression classifier (Celltypist) trained on PBMC cell annotations from the study discussed in Example 1 .
  • Celltypist logistic regression classifier

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Abstract

L'invention concerne des procédés d'analyse et d'identification de lymphocytes T activés et de motifs TCR spécifiques d'un antigène à l'aide d'un gène marqueur ou d'une expression de protéine. L'invention concerne également des lymphocytes T activés isolés et des TCR spécifiques d'un antigène identifiés à l'aide des procédés.
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