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<\/a>Schematic Figure made with the help of ChatGPT<\/p>\nShare Ground Truths<\/a><\/p>\n Some very impressive stats for who was studied and what was done:<\/p>\n \u20148 international cohorts, 2,609 individuals, 12,394,834 white blood cells <\/p>\n \u2014age 18 to 98 years<\/p>\n \u201459 immune cell types from sc-RNA seq harmonization<\/p>\n The story starts with the thymus gland, which starts to involute in early life and accelerates after puberty, after which there is progressive loss of naive T cells and recent thymic emigrants (RTEs) with ageThis decline was the strongest predictor of chronological age in the study. The problem is not so much that the naive T cells, the \u201crookies,\u201d are declining but what is coming in, as the immune system adapts, to replace them\u2014the \u201cveterans.\u201d<\/p>\n Until this study, prior cell sorting (flow cytometry) or pooled sequencing methods could not differentiate CD8+ effector memory T cells. That was only enabled by obtaining single-cell transcriptomes at scale.<\/p>\n The 2 types of veterans are effector memory T cells (Tem) that are either mainly granzyme K or B. You can think of the B is \u201cbad,\u201d since these cells are leaky, exhausted, and spilling of their granules leads to dissolution of matrix, disruption of tissue, activation of macrophages, and inflammation. That profile of the terminally differentiated GZMB+ cells was characterized from experiments in mice. The bifurcation (Figure below) is influenced towards GZMB+ by cytomegalovirus (CMV) exposure and chronic diseases, such as cardiovascular (CVD) or metabolic (such as Type 2 diabetes). <\/p>\n Doing single-cell RNA seq at scale to correlate with clinical outcomes or for development of a clinically useful blood test is not practical, not to mention expense. Instead, using the Olink assay of thousands of plasma proteins in 4 independent cohorts (of the 8 used in Phase 1) per cent TemB+ (Tem stands for T effector memory cell) correlated the best with the single cell GZMB+ data (using a LASSO computational model). That protein signature was tightly correlated with the single-cell RNA-seq work, as seen below in each of the 4 cohorts.<\/p>\n Note: Throughout this post please recognize the interchangeability of terms established between GZMB+ and TemB+, or GZMK\u201d and TemK+.<\/p>\n Then TemB+ was validated for clinical outcomes in the UK Biobank in 48,620 participants with 17-year follow-up, using both the cell protein data. As you can see below, individuals with a high per cent TemB+ had a much higher all-cause mortality than those with low % Tem B+ or the average group, as categorized in tertiles. The x-axis is years of follow-up.<\/p>\n The TemB+ % was predictive of multiple specific health outcome as shown below.<\/p>\n In contrast, however, note the lack of any predictive correlation with neurodegenerative diseases and macular degeneration : Alzheimer\u2019s disease, Parkinson\u2019s disease, dementia, and age-related macular degeneration. <\/p>\n To emphasize the contribution of viral exposure and inflammation, the people who were seropositive for CMV had higher GZMB+ and those with higher C-reactive protein had higher % TemB+<\/p>\n<\/a>The 2 paths that the naive T cells can differentiate to as the rookies become veterans. The path to GZMB+ is influenced by CMV and chronic diseases. Figure made with combination of Google Gemini and ChatGPT <\/p>\n<\/a><\/p>\n<\/a>Figure made with help of Google Gemini<\/p>\n<\/a><\/p>\n