Back

SEEKER: A genome-scale library-on-library screening platform for deciphering T cell recognition of antigen

Li, S.; Lin, L.; Wu, W.; Liu, B.; Ji, H.; Guo, Z.; Liu, Y.; Wu, J.; Liu, B.; Zhong, L.; Wang, X.; Xu, H.; Qi, H.

2026-10-01 immunology
10.64898/2026.09.25.753432 bioRxiv
Show abstract

Decoding which antigens activate a given T cell receptor (TCR) is a central challenge in immunology. We present SEEKER, a functional genome-scale library-on-library screening platform built on three elements: a Jurkat-derived T-APC cell (TAPCell) that combinatorially expresses one TCR and one peptide-MHC drawn from separate libraries together with an NFAT activation reporter; encapsulation of single TAPCells in a thermos-reversible hydrogel for isolated clonal expansion, so recognition is read out as intraclonal activation; and dual-asymmetric PCR that links TCR- and pMHC-encoding sequences into a heritable unit, enabling progressive hit enrichment over screening rounds. SEEKER interrogates ~10^8 TCR-pMHC combinations per run without prior knowledge. Screening joint-infiltrating CD8+ T cells from HLA-B*27+ ankylosing spondylitis patients against proteome-wide libraries validated 53 TCR-peptide pairs, uncovered extensive cross-recognition of self and common viral epitopes, and revealed highly polyreactive T cells recognizing up to 80 autoantigens, implicating virus-triggered poly-autoreactivity in multi-organ autoimmune diseases.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

1
Cell
○ 431 papers in training set
Top 0.2%
12.4%
Similar papers in this journal
2
Nature
○ 645 papers in training set
Top 1%
10.4%
Similar papers in this journal
3
Nature Immunology
○ 79 papers in training set
Top 0.2%
10.4%
Similar papers in this journal
4
Nature Communications
○ 5641 papers in training set
Top 20%
8.7%
Similar papers in this journal
5
Nature Methods
○ 385 papers in training set
Top 1%
7.7%
Similar papers in this journal
6
Nature Biotechnology
○ 172 papers in training set
Top 0.3%
7.7%
50% of probability mass above
7
Science
● 477 papers in training set
Top 2%
5.3%
8
Immunity
○ 67 papers in training set
Top 0.3%
4.7%
9
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 20%
2.7%
10
Science Advances
● 1243 papers in training set
Top 13%
2.7%
11
Cell Reports
○ 1498 papers in training set
Top 17%
2.1%
12
Science Immunology
● 88 papers in training set
Top 1%
2.1%
13
Nature Chemical Biology
○ 119 papers in training set
Top 1%
1.9%
14
Cell Systems
○ 201 papers in training set
Top 3%
1.7%
15
Advanced Science
○ 286 papers in training set
Top 6%
1.3%
16
Cell Reports Methods
○ 165 papers in training set
Top 3%
1.1%
17
Journal of Experimental Medicine
● 119 papers in training set
Top 3%
1.0%
18
eLife
● 5828 papers in training set
Top 63%
1.0%
19
Nucleic Acids Research
◐ 1281 papers in training set
Top 13%
1.0%
20
Nature Microbiology
○ 155 papers in training set
Top 3%
0.8%
21
Molecular Cell
○ 350 papers in training set
Top 5%
0.8%
22
Nature Medicine
○ 125 papers in training set
Top 4%
0.8%
23
iScience
○ 1154 papers in training set
Top 42%
0.6%
24
Nature Biomedical Engineering
○ 47 papers in training set
Top 2%
0.6%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.