Source 1primary paper2025MED
Toolkit/Tope-seq
Tope-seq
Taxonomy: Technique Branch / Method. Workflows sit above the mechanism and technique branches rather than replacing them.
Summary
We then use Tope-seq to screen epitope libraries against a model therapeutic candidate TCR.
Usefulness & Problems
Why this is useful
Tope-seq is used here to functionally screen peptide-coding epitope libraries against a model therapeutic TCR. The assay is presented as a high-throughput live-cell method for detecting cross-reactive epitopes.; screening epitope libraries against candidate therapeutic TCRs; detecting cross-reactive epitopes in high-throughput live-cell functional assays; unbiased TCR epitope discovery
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Tope-seq is used here to functionally screen peptide-coding epitope libraries against a model therapeutic TCR. The assay is presented as a high-throughput live-cell method for detecting cross-reactive epitopes.
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screening epitope libraries against candidate therapeutic TCRs
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detecting cross-reactive epitopes in high-throughput live-cell functional assays
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unbiased TCR epitope discovery
Problem solved
It addresses the need to assess off-target autoreactivity while engineering TCR therapeutics for efficacy. The method supports broad, potentially unbiased discovery of peptides that trigger a candidate TCR.; profiles large peptide libraries for TCR-triggering activity; supports assessment of off-target cross-reactivity risk
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It addresses the need to assess off-target autoreactivity while engineering TCR therapeutics for efficacy. The method supports broad, potentially unbiased discovery of peptides that trigger a candidate TCR.
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profiles large peptide libraries for TCR-triggering activity
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supports assessment of off-target cross-reactivity risk
Problem links
profiles large peptide libraries for TCR-triggering activity
LiteratureIt addresses the need to assess off-target autoreactivity while engineering TCR therapeutics for efficacy. The method supports broad, potentially unbiased discovery of peptides that trigger a candidate TCR.
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It addresses the need to assess off-target autoreactivity while engineering TCR therapeutics for efficacy. The method supports broad, potentially unbiased discovery of peptides that trigger a candidate TCR.
supports assessment of off-target cross-reactivity risk
LiteratureIt addresses the need to assess off-target autoreactivity while engineering TCR therapeutics for efficacy. The method supports broad, potentially unbiased discovery of peptides that trigger a candidate TCR.
Source:
It addresses the need to assess off-target autoreactivity while engineering TCR therapeutics for efficacy. The method supports broad, potentially unbiased discovery of peptides that trigger a candidate TCR.
Published Workflows
Comprehensive self-antigen screening to assess cross-reactivity in promiscuous T-cell receptors.
2025Objective: Assess cross-reactivity and off-target toxicity risk of an engineered candidate therapeutic TCR by functionally screening comprehensive self-antigen peptide libraries in a live-cell context.
Why it works: The workflow is presented as combining comprehensive peptide library coverage with functional live-cell TCR profiling, then adding iterative enrichment and computational refinement to improve sensitivity and accuracy.
TCR triggering by genome-encoded peptidesfunctional detection of cross-reactive epitopescomprehensive self-antigen cell library constructionTope-seq screeningiterative biopanningbioinformatic refinement
Stages
- 1.comprehensive self-antigen cell library construction(library_build)
This stage creates the comprehensive peptide-coding library substrate needed for broad functional screening of TCR cross-reactivity.
Selection: construct comprehensive self-antigen libraries covering genome-encoded peptides for downstream functional screening
- 2.first-pass bulk Tope-seq screening(broad_screen)
This stage performs the initial high-throughput functional scan across large peptide libraries to identify candidate cross-reactive epitopes.
Selection: screen epitope libraries against a model therapeutic candidate TCR to detect cross-reactive epitopes
- 3.iterative biopanning enrichment(selection)
This stage is added to improve sensitivity and accuracy beyond first-pass bulk screening.
Selection: iterative biopanning to improve sensitivity and accuracy after first-pass screening
- 4.bioinformatic refinement(secondary_characterization)
This stage computationally refines screening results to improve performance after experimental screening and enrichment.
Selection: refine screening outputs computationally to improve sensitivity and accuracy
Steps
- 1.construct comprehensive self-antigen cell libraries
Generate broad self-antigen peptide-coding libraries for downstream functional TCR cross-reactivity screening.
Library construction must occur before functional screening because the screen requires comprehensive peptide-coding inputs.
- 2.screen epitope libraries against the model therapeutic TCR using Tope-seqassay method
Identify peptides that functionally trigger the candidate therapeutic TCR and detect known cross-reactive epitopes.
This is the first-pass high-throughput functional readout applied after library generation to broadly identify candidate cross-reactive peptides.
- 3.apply iterative biopanning to improve screening sensitivity and accuracyenrichment method
Enrich or recover relevant candidates beyond first-pass screening.
It follows first-pass screening because it is explicitly incorporated to improve sensitivity and accuracy after the initial broad screen.
- 4.refine screening outputs bioinformaticallycomputational refinement method
Improve sensitivity and accuracy of candidate cross-reactive epitope calls.
This analysis step follows experimental screening and enrichment because it refines the resulting candidate set.
Taxonomy & Function
Primary hierarchy
Technique Branch
Method: A concrete measurement method used to characterize an engineered system.
Target processes
recombinationselectionImplementation Constraints
cofactor dependency: cofactor requirement unknownencoding mode: genetically encodedimplementation constraint: context specific validationoperating role: sensor
The abstract indicates that Tope-seq requires peptide-coding epitope or self-antigen libraries and a live-cell TCR profiling setup. It is applied to engineered candidate therapeutic TCRs.; requires epitope or self-antigen peptide-coding libraries; requires a live-cell TCR profiling context
The abstract does not show that Tope-seq alone is sufficient for maximal sensitivity or accuracy, because iterative biopanning and bioinformatic refinement were added to improve both. It also does not establish clinical toxicity outcomes directly from the abstract.; sensitivity and accuracy required improvement through iterative biopanning and bioinformatic refinement
Validation
Cell-freeBacteriaMammalianMouseHumanTherapeuticIndep. Replication
Supporting Sources
Ranked Claims
Iterative biopanning and bioinformatic refinement were incorporated to improve sensitivity and accuracy of the screening strategy.
We also incorporate strategies for iterative biopanning and bioinformatic refinement to improve sensitivity and accuracy
Tope-seq detected known cross-reactive epitopes from libraries of more than 5 × 10^5 unique peptide-coding sequences in first-pass bulk screening at a significance threshold of p < 0.01.
show that this strategy can be used to detect known cross-reactive epitopes from libraries of >5 × 105 unique peptide-coding sequences at a significance threshold of p < 0.01 in first-pass bulk screening
library size 500000 unique peptide-coding sequencessignificance threshold 0.01
The study demonstrates proof-of-principle functional TCR screening on a library of more than 2 × 10^7 peptide-coding DNA fragments.
demonstrate here the first proof-of-principle for functional TCR screening on a library of >2 × 107 peptide-coding DNA fragments
library size 20000000 peptide-coding DNA fragments
Approval Evidence
1 source2 linked approval claimsfirst-pass slug tope-seq
We then use Tope-seq to screen epitope libraries against a model therapeutic candidate TCR.
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performancesupports
Tope-seq detected known cross-reactive epitopes from libraries of more than 5 × 10^5 unique peptide-coding sequences in first-pass bulk screening at a significance threshold of p < 0.01.
show that this strategy can be used to detect known cross-reactive epitopes from libraries of >5 × 105 unique peptide-coding sequences at a significance threshold of p < 0.01 in first-pass bulk screening
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proof of principlesupports
The study demonstrates proof-of-principle functional TCR screening on a library of more than 2 × 10^7 peptide-coding DNA fragments.
demonstrate here the first proof-of-principle for functional TCR screening on a library of >2 × 107 peptide-coding DNA fragments
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Comparisons
Source-stated alternatives
The paper mentions multiple approaches for constructing comprehensive self-antigen cell libraries and also adds iterative biopanning plus bioinformatic refinement around the core screen. No direct competing assay is named in the abstract.
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The paper mentions multiple approaches for constructing comprehensive self-antigen cell libraries and also adds iterative biopanning plus bioinformatic refinement around the core screen. No direct competing assay is named in the abstract.
Source-backed strengths
detected known cross-reactive epitopes in first-pass bulk screening; scales to libraries larger than 5 × 10^5 unique peptide-coding sequences and proof-of-principle at larger than 2 × 10^7 peptide-coding DNA fragments; operates in a live-cell functional context
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detected known cross-reactive epitopes in first-pass bulk screening
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scales to libraries larger than 5 × 10^5 unique peptide-coding sequences and proof-of-principle at larger than 2 × 10^7 peptide-coding DNA fragments
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operates in a live-cell functional context
Compared with haematoxylin-eosin stained histological sections
Tope-seq and haematoxylin-eosin stained histological sections address a similar problem space because they share recombination, selection.
Shared frame: same top-level item type; shared target processes: recombination, selection
Compared with open-source microplate reader
Tope-seq and open-source microplate reader address a similar problem space because they share recombination, selection.
Shared frame: same top-level item type; shared target processes: recombination, selection
Strengths here: looks easier to implement in practice.
Compared with touchscreen-equipped operant conditioning chambers
Tope-seq and touchscreen-equipped operant conditioning chambers address a similar problem space because they share recombination, selection.
Shared frame: same top-level item type; shared target processes: recombination, selection
Ranked Citations
- 1.