Evidence map›Paper›PMID 40453079›Full record

ReviewFrontiers in immunology2025

Strategies for CRISPR-based knock-ins in primary human B cells and lymphoma cell lines.

Sophie Lund, Chun Gong, Xin Yu, Louis M Staudt, Daniel J Hodson, Sebastian Scheich

Abstract readReview
In one paragraph

Review in Frontiers in immunology, 2025. 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

6 authors.

Sophie Lund *Goethe University Frankfurt, University Hospital, Frankfurt am Main, Germany.
Chun Gong *Cambridge Stem Cell Institute, Cambridge Biomedical Campus, Cambridge, United Kingdom.
Xin YuLymphoid Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States.
Louis M StaudtLymphoid Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States.
Daniel J HodsonCambridge Stem Cell Institute, Cambridge Biomedical Campus, Cambridge, United Kingdom.
Sebastian ScheichGoethe University Frankfurt, University Hospital, Frankfurt am Main, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Since its advent about ten years ago, the CRISPR-Cas9 system has been frequently used in biomedical applications. It has advanced various fields, and CRISPR-Cas9-based therapeutics have shown promising results in the treatment of specific hematological diseases. Furthermore, CRISPR gene editing technologies have revolutionized cancer research by enabling a broad range of genetic perturbations, including genetic knockouts and precise single nucleotide changes. This perspective focuses on the state-of-the-art methodology of CRISPR knock-ins to engineer immune cells. Since this technique relies on homology-directed repair (HDR) of double-strand breaks (DSBs) induced by the Cas9 enzyme, it can be used to introduce specific mutations into the target genome. Therefore, this methodology offers a valuable opportunity to functionally study specific mutations and to uncover their impacts not only on overall cell functions but also on the mechanisms behind cancer-related alterations in common signaling pathways. This article highlights CRISPR knock-in strategies, protocols, and applications in cancer and immune research, with a focus on diffuse large B cell lymphoma.

Indexed as

B-LymphocytesCRISPR-Cas SystemsGene EditingGene Knock-In TechniquesLymphoma, Large B-Cell, DiffuseCell Line, TumorHumansDLBCLgene editing (CRISPR/Cas9)knock-inlymphomaNF-kappa B (NF-KB)

Identifiers

PMID40453079
PMCPMC12122508

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Registered trials

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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.