Evidence map›Paper›PMID 42717414›Full record

ReviewImmunological reviews2026

Defining Functional T Cell Receptor Repertoires With Nanovial-Based Screening.

Dino Di Carlo

Abstract readReview
In one paragraph

Review in Immunological reviews, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Dino Di CarloDepartment of Bioengineering, California NanoSystems Institute, Jonsson Comprehensive Cancer Center, University of California, Los Angeles, California, USA.ORCID https://orcid.org/0000-0003-3942-4284

Funding

Japan Science and Technology Agency EXPERT-JSilicon Valley Community Foundation 2023-332384
6 · The paper itself

Abstract

T-cell receptor repertoires are commonly characterized through sequence diversity and antigen binding, while the cellular functions associated with individual receptors are often measured separately. In this review, I consider how functional information can be incorporated more directly into repertoire analysis by linking receptor identity with measurable effector outputs at single-cell resolution. I focus on Nanovial-based technologies that combine antigen-specific capture, stimulation, secretion measurement, viable-cell recovery, and downstream sequencing in workflows compatible with flow cytometry and single-cell sequencing platforms. Studies using conventional peptide-major histocompatibility complex ligands, the non-classical antigen-presenting molecules MR1 and CD1d, and secretion-encoded single-cell sequencing illustrate how these approaches can complement binding-based enrichment by adding information about secretory effector responses. I also discuss the extension of this framework from ligand-defined screening to dyad-resolved assays involving target cells, which can reveal how receptor identity relates to transcriptional and functional programs arising during direct cell-cell interactions. Together, these developments provide tools for studying relationships among TCR sequence, antigen recognition, and cellular response, with potential applications in therapeutic receptor discovery and in generating datasets for predictive models of T-cell function.

Indexed as

NanotechnologyReceptors, Antigen, T-CellT-LymphocytesAnimalsHumansSingle-Cell AnalysisReceptors, Antigen, T-Cellcell–cell interactionscell therapiesfunction‐first screeningnanovialssecretion‐encoded single‐cell sequencingT‐cell receptor (TCR)therapeutic receptor discovery

Identifiers

PMID42717414
PMCPMC13558779

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.