The iterative shrinkage thresholding algorithm reveals dynamic aging trajectories of human T lymphocytes via multidimensional spectral flow cytometry analysis.

A progressive age-associated decline in the frequencies of CD8+, γδ+, and Vδ2+ T cells was identified alongside an increase in CD4+ T cells.

• 462 healthy individuals were enrolled and stratified into six groups spanning 20 to over 70 years.
• Multiparametric spectral flow cytometry was used to characterize T lymphocyte subsets.
• The decline was observed for CD8+, γδ+, and Vδ2+ T cell populations.
• CD4+ T cell frequencies increased with age across the study population.

Aging was associated with a reduction of naive T cells and an expansion of terminally differentiated effector memory populations.

• Both naive T cell decline and terminally differentiated effector memory expansion were characterized across six age groups from 20 to over 70 years.
• These changes were identified through immunophenotypic diversity and functional status characterization using spectral flow cytometry.
• The findings reflect progressive remodeling of T lymphocyte subsets across the human lifespan.

CD8+ T cells exhibited the greatest sensitivity to age-associated immunophenotypic remodeling among all T cell subsets examined.

• CD8+ T cell remodeling was characterized by reduced expression of co-stimulatory markers CD27 and CD28.
• Increased expression of senescence-associated surface markers including CD57 and KLRG1 was observed in CD8+ T cells with aging.
• Elevated cytotoxic effector molecules including IFN-γ and granzyme B were also observed in CD8+ T cells with aging.
• CD8+ T cells showed greater age sensitivity compared to other T cell subsets in the panel.

Ten immune biomarkers identified through machine learning accurately predicted chronological age with R2=0.81.

• Machine learning approaches were used to define the 10 immune biomarkers from the multiparametric spectral flow cytometry data.
• The model achieved R2=0.81, P<0.001 for prediction of chronological age.
• The biomarkers were derived from the 462 healthy individuals spanning ages 20 to over 70 years.
• These parameters provide a foundation for immune age modeling and risk stratification for unhealthy aging.

ISTA-based sparse coding successfully reconstructed T cell aging trajectories capturing aging-related immunophenotypic patterns in over 80% of individuals across age groups.

• The iterative shrinkage thresholding algorithm (ISTA) was applied to reconstruct aging trajectories from the spectral flow cytometry data.
• The ISTA-based sparse coding approach captured aging-related immunophenotypic patterns in over 80% of individuals across all age groups.
• This method was applied to the multiparametric dataset from 462 healthy individuals stratified into six age groups.
• The approach represents a novel application of sparse coding methodology to immune aging trajectory analysis.

The study enrolled 462 healthy individuals stratified into six age groups spanning 20 to over 70 years for cross-sectional immune aging analysis.

• Six age groups were created spanning from 20 years of age to over 70 years.
• All participants were characterized as healthy individuals.
• Multiparametric spectral flow cytometry was used to capture both immunophenotypic diversity and functional status of T lymphocyte subsets.
• The study systematically characterized T lymphocyte subsets across this broad age range.