SEC-seq reveals translation-focused metabolic strategies for high IgG productivity in clonal CHO cells.

Authors

Jasmine Tat, Fides D Lay, Jennitte Stevens, Nathan E Lewis

Published in

Metabolic engineering. Pages 102554. Sep 16, 2026. Epub Sep 16, 2026.

Abstract

Chinese hamster ovary (CHO) cells are the dominant host for therapeutic protein production, yet intra- and inter-clonal heterogeneity in manufacturing phenotypes, and the underlying metabolic and secretory circuitry, remain poorly defined at single-cell resolution. Using secretion encoded single-cell sequencing (SEC-seq), we simultaneously measure transcriptomes and secreted IgG in single cells from a parental production cell line and five CHO clones, each varying in cell-specific productivity. Endpoint IgG mRNA abundance and accumulated IgG secretion are only moderately coupled across single cells, indicating that recombinant transcript abundance, sampled at a single time point, alone does not explain intra-clonal secretion heterogeneity. By integrating SEC-seq with single-cell metabolic and secretory task scoring, we find that CHO cells accommodating recombinant protein expression burden have more active translation-associated pathways and suppressed energy-intensive endogenous secreted protein processing. Three high-secreting clones converge on this translation-focused state but differ in their subpopulation composition and energy/redox transcriptomic programs coupled to IgG output: one highly productive clone shows a low-growth, glycolytic, NAD/one-carbon-associated and UPR-activated program; a second shows increased oxidative phosphorylation and fatty-acid β-oxidation, and a third shows more modest central-carbon and mitochondrial lipid-metabolism. Genes such as Aldoa, Ndufab1, Acsl5, and Mthfd2 showed clone-specific correlations with IgG, linking glycolysis, mitochondrial respiration, fatty-acid metabolism, and redox to secretion. Together, these results demonstrate that SEC-seq can resolve IgG-coupled metabolic-secretory signatures within and between CHO clones, providing a framework to identify subpopulation and circuit features to engineer or select for improved recombinant protein production.

PMID:
42749212
Bibliographic data and abstract were imported from PubMed on 17 Sep 2026.

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