Journal: Nature aging

Article Title: An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging

doi: 10.1038/s43587-021-00082-y

Figure Lengend Snippet: The Stanford 1000 Immunomes Project consist of 1001 ambulatory subjects age 8 to 96 (34% males, 66% females) recruited during the years 2007 to 2016 for a longitudinal study of aging and vaccination, and for an independent study of chronic fatigue syndrome from which only healthy controls were included. For all samples of the Stanford 1KIP, deep immune phenotyping was conducted at the Stanford Human Immune Monitoring Center, where peripheral blood specimens were isolated and analyzed using standard procedures. Peripheral blood samples were obtained by venipuncture and peripheral blood mononuclear cells or whole blood samples were used for determination of cellular phenotypes and frequencies (N = 935) and for investigation of in vitro cellular responses to a variety of cytokine stimulations (N = 818); serum samples were obtained and used for protein content determination (including a total of 50 cytokines, chemokines and growth factors) (N = 1001). Clinical characterization was assessed via clinical questionnaire in a total of 902 subjects who completed the full set of 53 clinical items. From a total of 97 healthy young and older adults, comprehensive cardiovascular phenotyping was also conducted

Article Snippet: Cellular senescence assay was performed to detect SA-β-gal activity using a fluorometric format (Enzo, catalog no. ENZ-KIT129).

Techniques: Isolation, In Vitro

Journal: Nature aging

Article Title: An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging

doi: 10.1038/s43587-021-00082-y

Figure Lengend Snippet: Immune protein data from serum samples were subjected to normalization and batch correction procedures (See Methods) to ensure data from different sources can be combined and used as a whole. a, Spearman correlation between immune protein features and batch ID shows a strong dependency of data source on top 4 components (raw data, green line), which reaches a steady state after component 5. Data normalization and batch correction removes batch effect as indicated by lower mean absolute Spearman correlation between all features and batch id (blue line), which indicates impossibility to distinguish sample source from corrected data. b, Upper panel: immune protein expression heatmap of uncorrected data, Lower panel: immune protein expression heatmap of corrected data. The two batches come from two study cohorts, the Chronic Fatigue Syndrome Study (CFS) and Aging and vaccination study cohort (Flu).

Article Snippet: Cellular senescence assay was performed to detect SA-β-gal activity using a fluorometric format (Enzo, catalog no. ENZ-KIT129).

Techniques: Expressing

Journal: Nature aging

Article Title: An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging

doi: 10.1038/s43587-021-00082-y

Figure Lengend Snippet: a, Pathway enrichment analysis and tube network formation of Scramble versus CXCL9-KD were analyzed. hiPSCs infected with lentivirus carrying nonsense-sequence shRNA (Scramble) and hiPSCs infected with lentivirus carrying sequence-specific shRNA to knockdown expression of CXCL9 (CXCL9-KD) were both induced to ECs (Methods). RNA-seq analysis was conducted on cells at passage 0, 2, 4, 6 and 8 for both conditions. CXCL9 messenger RNA in Scramble was highly upregulated as early as passage 4, whereas CXCL9 mRNA expression in CXCL9-KD did not significantly change with in vitro cellular aging. b, Pathway enrichment comparing Scramble at passage 0 and passage 8. Upregulated inflammatory pathways and downregulated proliferation pathways are depicted (P8 versus P0). c, Comparing Scramble at P8 with CXCL9-KD at P8 shows that silencing of CXCL9 leads to a complete reversal of the early EC senescence phenotype. An example of inflammatory pathway (IFN-γ) and an example of proliferation pathway (E2F targets) is shown in d. d, Relative expression of genes in the hallmark pathways for Scramble at passage 0, 2, 4, 6 and 8 (S0, S2, S4, S6 and S8) are shown. e, Example of inflammatory pathway (IFN-γ) and an example of proliferation pathway (E2F targets) for CXCL9-KD at passage 0, 2, 4, 6 and 8 (KD0, KD2, KD4, KD6 and KD8) are shown. ***P < 0.001; **P < 0.01; *P < 0.05.

Article Snippet: Cellular senescence assay was performed to detect SA-β-gal activity using a fluorometric format (Enzo, catalog no. ENZ-KIT129).

Techniques: Infection, Sequencing, shRNA, Knockdown, Expressing, RNA Sequencing, In Vitro

Journal: Nature aging

Article Title: An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging

doi: 10.1038/s43587-021-00082-y

Figure Lengend Snippet: a, Decomposition of the inflammatory score was conducted by estimating the most variable Jacobians (first-order partial derivative of the inflammatory clock). Boxes represent 25th and 75th percentiles around the median (line); whiskers represent 1.5× interquartile range. Both positive and negative contributors to the inflammatory clock are observed. b, The top 15 most variable Jacobians were CXCL9, EOTAXIN, Mip-1α, LEPTIN, IL-1β, IL-5, IFN-α and IL-4 (positive contributors), and TRAIL, IFN-γ, CXCL1, IL-2, TGF-α, PAI-1 and LIF (negative contributors). Significant differences in the levels of CXCL9 were observed between age groups (P < 0.001, by one-way ANOVA). The pairwise differences between groups were evaluated with the Tukey’s honest significant differences test. Significant differences were shown for older age groups (60–80 years and >80 years) and younger age groups (<20 years, 20–40 years, 40–60 years). ***P < 0.001; **P < 0.01; *P < 0.05; #P < 0.1. Exact P values for each pairwise comparisons are as follows: <20 versus 20–40, 0.72; <20 versus 40–60, 0.99; <20 versus 60–80, 0.09; <20 versus >80, 0; 20–40 versus 40–60, 0.13; 20–40 versus 60–80, 3.5 × 10−6; 20–40 versus >80, 0; 40–60 versus 60–80, 0.023; 40–60 versus >80, 0; 60–80 versus >80, 7.7 × 10−6. Boxes represent 25th and 75th percentiles around the median (line); whiskers denote 1.5× interquartile range. c,d, In a validation study, 97 healthy adults (aged 25–90 years) well matched for cardiovascular risk factors were selected from a total of 151 recruited participants. Cardiovascular age was estimated using aortic PWV and RWT. Using multiple linear regression analysis after adjusting for age, sex, BMI, heart rate, systolic blood pressure, fasting glucose and total cholesterol to HDL ratio, positive correlations were obtained between CXCL9 and PWV (R = 0.22) and RWT (R = 0.3) (P < 0.01), and negative correlations were observed between LIF and PWV (R = −0.27) (c) and RWT (R = −0.22) (d). P values are derived from hypothesis testing, where the null hypothesis is that the variable has no correlation with the dependent variable. e,f, Direct comparisons between CXCL9 and the two cardiovascular aging phenotypes (PWV (e) and RWT (f)) are depicted. No other variable included in the models had high co-linearity as suggested by variance inflation factors (VIF) <3 for each factor.

Article Snippet: Cellular senescence assay was performed to detect SA-β-gal activity using a fluorometric format (Enzo, catalog no. ENZ-KIT129).

Techniques: Biomarker Discovery, Derivative Assay

Journal: Nature aging

Article Title: An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging

doi: 10.1038/s43587-021-00082-y

Figure Lengend Snippet: a, Representative images of human blood progenitor endothelial cells from young (left) and old (right) individuals. b, Representative images of capillary-like networks show impaired tube formation by human BECs of old individuals compared to young. To further confirm the potential contribution of CXCL9 in cardiovascular aging, we assessed its expression in young (3–4 month) and old mice (2 yr.) endothelial cells (c). ECs isolated from old mice showed higher levels of CXCL9 (P value = 0.023) (d), while at the same time showed impaired EC function as evident by decreased tube formation (P value = 0.042) (a, f). Figure S8: All data represented as mean ± SEM, n = 3, *P < 0.05. Statistical analyses were performed using Student’s t-test (paired). Scale bar: 50 μm.

Article Snippet: Cellular senescence assay was performed to detect SA-β-gal activity using a fluorometric format (Enzo, catalog no. ENZ-KIT129).

Techniques: Expressing, Isolation

Journal: Nature aging

Article Title: An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging

doi: 10.1038/s43587-021-00082-y

Figure Lengend Snippet: a, Quantitative PCR data show increased expression of CXCL9 in BECs of older individuals compared to younger individuals (P = 0.0075). b, Significant differences in tube formation capacity are observed in BECs from older and younger individuals (P = 0.0323). c, Quantification of NO production shows impaired capacity of BECs from older individuals to produce NO when compared to younger individuals in response to acetylcholine (Ach) (adjusted P value (Padj) of BECs (young) versus BECs (old), P <0.0001; Padj value of BECs (young) Ach versus BECs (old) Ach, 0.0002). d, Quantification of LDL uptake show impaired capacity of BECs from older individuals to uptake Ac-LDL when compared to younger individuals (Padj = 0.0002). e–g, Quantification of number of tubes, LDL uptake and NO production in response to Ach in Scramble and CXCL9-KD iPSC-ECs shows a significant improvement in aging phenotypes in ECs at passage 6 and 8 with silencing of the CXCL9 gene. Padj values for P6 (Scramble) versus P6 (CXCL9 shRNA) = 0.008 (e); P8 (Scramble) versus P8 (CXCL9 shRNA) = 0.0475. Padj values for P6 (Scramble) versus P6 (CXCL9 shRNA) = 0.044; P8 (Scramble) versus P8 (CXCL9 shRNA) = 0.001 (f). Padj values for P6 (Scramble) Ach versus P6 (CXCL9-KD) Ach = 0.0116; P8 (Scramble) Ach versus P8 (CXCL9-KD) Ach = 0.0001 (g). Scramble are hiPSCs infected with lentivirus carrying nonsense-sequence shRNA. CXCL9-KD are hiPSCs infected with lentivirus carrying sequence-specific shRNA to knockdown expression of CXCL9. All data are represented as mean ± s.e.m., n = 3, *P < 0.05, **P < 0.01, ***P < 0.001; ****P < 0.0001; NS, not significant. Statistical analyses were performed using Student’s t-test or one-way ANOVA corrected with the Bonferroni method.

Article Snippet: Cellular senescence assay was performed to detect SA-β-gal activity using a fluorometric format (Enzo, catalog no. ENZ-KIT129).

Techniques: Real-time Polymerase Chain Reaction, Expressing, shRNA, Infection, Sequencing, Knockdown

Journal: Nature aging

Article Title: An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging

doi: 10.1038/s43587-021-00082-y

Figure Lengend Snippet: The expression levels of hallmark vascular stiffness genes—CAMs, MMPs and COLs—were analyzed in Scramble and CXCL9-KD aging cells. a, CAMs, MMPs and COLs are highly expressed in Scramble passage 8 compared to passage 0. b, Knockdown of CXCL9 completely restores the expression of CAMs and MMPs, but not COLs. c, Line graph of percent relaxation of mouse thoracic aortic sections incubated with increasing concentrations of CXCL9 shows impaired vascular reactivity to acetylcholine, suggesting that CXCL9 dampens vascular function. d, A similar trend is observed when CXCL9 is given to either young or old mice. CXCL9 disrupts the relaxation supposedly induced by acetylcholine. All data are represented as mean ± s.e.m., n = 3, *Padj value of young mice (PBS) versus young mice (CXCL9) = 0.0237; #Padj value of young mice (PBS) versus old mice (PBS) = 0.0003, $Padj value of young mice (PBS) versus young mice (CXCL9) < 0.0001. Statistical analyses were performed using two-way ANOVA followed by a Bonferroni post hoc test; n = 3 (three separate segments of aortas).

Article Snippet: Cellular senescence assay was performed to detect SA-β-gal activity using a fluorometric format (Enzo, catalog no. ENZ-KIT129).

Techniques: Expressing, Knockdown, Incubation

Journal: Nature aging

Article Title: An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging

doi: 10.1038/s43587-021-00082-y

Figure Lengend Snippet: a, Growth curves over 4 d show recovery of cell proliferation in CXCL9-KD iPSC-ECs in later passages when compared to Scramble iPSC-ECs (Padj value of P8 Scramble (day 4) versus P8 CXCL9-KD (day 4) = 0.0232). b, Cellular senescence activity assay shows restoration of SA-β-gal activity in CXCL9-KD iPSC-ECs at later passages when compared to Scramble iPSC-ECs (Padj value of P6 (Scramble) versus P6 (CXCL9 shRNA) = 0.0406; Padj value of P8 (Scramble) versus P8 (CXCL9 shRNA) = 0.0278). c, Representative immunohistochemical images showing CD31+ human capillaries from serially passaged Scramble and CXCL9-KD iPSC-ECs. Arrows denote CD31 staining on iPSC-EC indicating capillary formation. d, Quantification of CD31+ capillaries show improved capacity of late passaged CXCL9-KD iPSC-ECs to form in vivo capillary networks (Padj value of P0 (Scramble) versus P8 (Scramble) <0.0001; Padj value of P8 (Scramble) versus P8 (CXCL9 shRNA) = 0.0487). All data are represented as mean ± s.e.m., n = 3, *P < 0.05, ****P < 0.001. Statistical analyses were performed using one-way ANOVA corrected with the Bonferroni method. Scale bars, 100 μm.

Article Snippet: Cellular senescence assay was performed to detect SA-β-gal activity using a fluorometric format (Enzo, catalog no. ENZ-KIT129).

Techniques: Activity Assay, shRNA, Immunohistochemical staining, Staining, In Vivo