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Record W4417018394 · doi:10.1182/blood-2025-5918

Lipid nanoparticle-mediated mRNA transfection enables efficient and safe generation of CAR macrophages.

2025· article· en· W4417018394 on OpenAlexaff
Felix Chiu, Angela Hamie, Bee Shin Tan, Carina Debes-Marun, Justine Lai, Charles Yin, Pankaj Tailor, Michael P. Chu

Bibliographic record

VenueBlood · 2025
Typearticle
Languageen
FieldMedicine
TopicCAR-T cell therapy research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsTransfectionChimeric antigen receptorGenetic enhancementCarcinogenesisCytokineImmune systemCancer cellCancerImmunotherapy

Abstract

fetched live from OpenAlex

Abstract With the development of Chimeric Antigen Receptor (CAR) T-cells and its subsequent clinical trials and FDA approval, CAR T-cells have since provided an additional therapeutic modality for hematological malignancies. Despite this success, CAR T cells have shown to be less effective against solid tumors, which account for the majority of cancers. Failure of CAR-T cells can often be attributed to the characteristics typically found within the tumor microenvironment such as its immunosuppressive milieu and dense extracellular matrices (Du et al, Cancer Cell 2025). In face of these challenges, recent studies have investigated the therapeutic value of transfecting macrophages with a CAR construct. As of now, preclinical studies on CAR macrophages (CAR-M) have shown some promising results on CAR-M's capacity to target solid tumors (Klichinsky et al., Nat. Biotechnol 2020). In addition, an initial CAR-M phase 1 clinical trial reported that CAR-Ms were well tolerated in comparison to the potential adverse effects of CAR-T therapy such as cytokine release syndrome (Reiss et al., Nat Med 2025). Nevertheless, current CAR therapies and studies continue to use lentiviral transduction methods to introduce the CAR constructs. Yet, recent case reports have suggested the possibility of potential secondary malignancies due to lentiviral-related insertional oncogenesis (Harrison et al., N. Engl. J. Med. 2025). To mitigate this risk while leveraging macrophages as CAR-Ms, our study utilizes a non-viral method of transfection of CAR-Ms through lipid nanoparticle (LNP) delivery of messenger RNA. We have conducted transfection experiments with an eGFP mRNA construct on THP-1-derived macrophages which has a transfection efficiency of 80-90% and viability of 60-70%. Afterwards, we utilized a previously characterized anti-B-cell maturation antigen (BCMA) CAR construct on THP-1-derived macrophages. Similar to eGFP, our data showed around 75-80% transfection of THP-1-derived macrophages with a viability of 60-70%. In addition, the kinetics of the anti-BCMA CAR expression demonstrates stable expression up to 4 days in vitro. Our current preliminary results suggest that relative to other models of inducing CAR expression such as lentiviral transduction, LNP-mediated transfection of macrophages may be an adequate and safer alternative form of gene transfer towards developing new CAR-M therapies. Our future studies aim to design and assess the functionality and efficacy of macrophage-specific CAR constructs.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.018
GPT teacher head0.272
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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