Evidence map›Paper›PMID 42345851›Full record

ReviewAntibodies (Basel, Switzerland)2026

From Single Cells to Silicon: Emerging Technologies Transforming Monoclonal Antibody Discovery.

Victoria Sherwood, Denise Harold, Richard O'Kennedy, Christine Loscher, Paul Leonard

Abstract readReview
In one paragraph

Review in Antibodies (Basel, Switzerland), 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

5 authors.

Victoria SherwoodSchool of Biotechnology, The DCU Life Sciences Institute, Dublin City University, Glasnevin, D09 V209 Dublin, Ireland.ORCID 0009-0000-6532-4661
Denise HaroldSchool of Biotechnology, The DCU Life Sciences Institute, Dublin City University, Glasnevin, D09 V209 Dublin, Ireland.
Richard O'KennedySchool of Biotechnology, The DCU Life Sciences Institute, Dublin City University, Glasnevin, D09 V209 Dublin, Ireland.
Christine LoscherSchool of Biotechnology, The DCU Life Sciences Institute, Dublin City University, Glasnevin, D09 V209 Dublin, Ireland.
Paul LeonardSchool of Biotechnology, The DCU Life Sciences Institute, Dublin City University, Glasnevin, D09 V209 Dublin, Ireland.ORCID 0000-0003-4688-1045

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Monoclonal antibody (mAb) discovery has been transformed by advances in single-cell technologies, microfluidics, high-throughput sequencing, and computational design. Modern platforms enable the interrogation of large numbers of individual B cells, directly linking antibody sequence with antigen specificity and functional activity. Microfluidic and optofluidic systems now support high-throughput compartmentalisation and functional screening of antibody-secreting cells, while sequencing-based approaches allow parallel recovery of paired heavy- and light-chain sequences. These developments have shifted antibody discovery from binding-based selection toward function-first paradigms, enabling the rapid identification of diagnostic and therapeutically relevant antibodies. Integration with computational tools, including machine learning and structure-based modelling, has further enabled the emergence of closed-loop discovery pipelines, in which experimental and in silico methods iteratively refine candidates. This review summarises key advances in single-cell microtools over the last decade and highlights how the convergence of experimental and computational technologies is reshaping antibody discovery toward scalable, data-driven, and increasingly automated platforms.

Indexed as

artificial intelligencein silico discoverymachine learningmicrofluidicsmicrotoolsmonoclonal antibody discoverysingle cell analysis

Identifiers

PMID42345851
PMCPMC13296331

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.