Back

Development of immunological reagents for the identification of biomarkers of inflammation in the common marmoset

Lambrou, E.; Hallengard, D.; Turbow, B.; Callery, J.; Hodara, V. L.; King, J.; Ross, C. N.; Ahlborg, N.; Makower, B.; Giavedoni, L. D.

2026-09-23 immunology
10.64898/2026.09.17.752376 bioRxiv
Show abstract

Marmosets (MAR, Callithrix jacchus) are nonhuman primates extensively studied in a broad array of preclinical biomedical research areas. However, the limited number of available immunological reagents remains a critical shortcoming of these models. We successfully developed monoclonal antibodies (mAbs) pairs that can be used in immunoassays for the identification and quantification of the biomarkers of inflammation C-reactive protein (CRP), CXCL-10 (IP-10), interleukin (IL)-4, IL-6, and IL-10. Recombinant MAR protein variants were produced in mammalian cells, purified and used for mice immunization. Hybridoma clones were selected first for their capacity to bind the recombinant proteins. All possible pair combinations of these mAbs were then tested for their capacity to identify their targets in MAR-derived samples. These MAR samples consisted of supernatant of PBMCs exposed ex vivo to different stimulants, and plasma samples from animals stimulated in vivo with a low-dose intravenous LPS inoculation. The majority of the mAbs that recognized the recombinant proteins failed to bind to the MAR-derived homologs. However, we found MAR-derived reactive mAbs and selected pairs with optimal sensitivity and signal-to-noise ratio for immunoassays that included Luminex multiplexing assays, ELISA, and ELISpot assays. These immunoreagents and assays will improve the translational value of different MAR biomedical models.

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"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.