Systems and methods for identifying cells that are antigen-specific for an immunogenic feature
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Publication Number
US-12580044-B1
Publication Date
2026-03-17
Expiration Date
Abstract
Systems and methods of identifying antigen-specific cells include obtaining a representation, for each respective immunogenic feature in a plurality of features, of a corresponding count of the respective feature bound to each immune cell in a population of cells. The immune cells are clustered using the representation to identify a plurality of clusters, each including a different subset of the plurality of cells. The total number of unique clonotypes and the total number of clonotypes detected within the subset of cells represented by a particular cluster is determined. This total number of unique clonotypes and total number of clonotypes are used, together with a distribution of a corresponding count of a first immunogenic feature bound to each cell within the subset of cells represented by a particular cluster, to determine whether the subset of cells represented by the respective cluster is antigen-specific for the first immunogenic feature.
Core Innovation
The invention relates to an antigen-specificity identification framework that computes an immunogenic-feature-by-cell count representation for a cell population. The representation includes corresponding counts for an immunogenic feature across at least 20 immunogenic features, such that each cell is represented by a count vector reflecting binding or presentation. Cells are then clustered based on similarity of their immunogenic-feature-by-cell count vectors to form one or more clusters.
For each cluster, the framework computes immune-receptor clonotype metrics to quantify clonal expansion and clonotype characteristics. The metrics include unique clonotypes and total clonotypes, and may further include a clonotypic enrichment ratio. The resulting clonotype metrics are combined with the per-cell count distribution of a first immunogenic feature to determine whether a cluster’s cell subset is antigen-specific.
The framework uses distribution transformations and embodiments to support the antigen-specificity determination, including log10, log2, or square root transformations of counts. It also supports embodiments with within-cluster refinement that remove antigen-non-specific cells using the first immunogenic feature count distribution. The document further describes feature types such as cell-surface markers, receptors, antigen-presenting complexes, and MHC/pMHC dextramers, and includes embodiments for B cells and T cells including cytotoxic T lymphocytes.
The document also describes a cancer-related application in which a subject is exposed using selected or cloned cells, based on antigen-specificity identification. It includes computing-system components and data structures, including an immunogenic-feature-by-cell representation, a VDJ clonotype dataset with barcodes/UMIs/contigs, clustering outputs, and a VDJ browser module, together with visualizations supporting distinguishing antigen-specific versus non-specific or noisy cluster behavior.
Claims Coverage
Not explicitly described in patent.
Not explicitly described in patent.
Stated Advantages
Enables determination of whether a cluster’s cell subset is antigen-specific by combining immune-receptor clonotype metrics with the per-cell count distribution of a first immunogenic feature.
Supports clustering of cells using similarity of immunogenic-feature-by-cell count vectors derived from binding or presentation counts.
Provides clonotype-based quantification of clonal expansion using unique clonotypes and total clonotypes, optionally including a clonotypic enrichment ratio.
Supports handling of different immunogenic feature types, including cell-surface features, receptors, antigen-presenting complexes, and MHC/pMHC dextramers, and different immune cell contexts, including B cells and T cells, including CTLs.
Documented Applications
Cancer-related use: exposing a subject, including a cancer afflicted subject, with selected or cloned cells based on antigen-specificity identification.
Visualization and analysis use: generating examples and figures, such as dextramer count distributions in clusters, to support distinguishing antigen-specific versus non-specific or noisy cluster behavior.
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