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. 2023 May;22(5):e13802.
doi: 10.1111/acel.13802. Epub 2023 Mar 2.

Intestinal stem cell aging at single-cell resolution: Transcriptional perturbations alter cell developmental trajectory reversed by gerotherapeutics

Jiahn Choi  1 , Michele Houston  1 , Ruixuan Wang  2 , Kenny Ye  3 , Wenge Li  1 , Xusheng Zhang  4 , Derek M Huffman  2   5 , Leonard H Augenlicht  1   5
Affiliations

Intestinal stem cell aging at single-cell resolution: Transcriptional perturbations alter cell developmental trajectory reversed by gerotherapeutics

Jiahn Choi et al. Aging Cell. 2023 May.

Abstract

The intestinal epithelium consists of cells derived from continuously cycling Lgr5hi intestinal stem cells (Lgr5hi ISCs) that mature developmentally in an ordered fashion as the cells progress along the crypt-luminal axis. Perturbed function of Lgr5hi ISCs with aging is documented, but the consequent impact on overall mucosal homeostasis has not been defined. Using single-cell RNA sequencing, the progressive maturation of progeny was dissected in the mouse intestine, which revealed that transcriptional reprogramming with aging in Lgr5hi ISCs retarded the maturation of cells in their progression along the crypt-luminal axis. Importantly, treatment with metformin or rapamycin at a late stage of mouse lifespan reversed the effects of aging on the function of Lgr5hi ISCs and subsequent maturation of progenitors. The effects of metformin and rapamycin overlapped in reversing changes of transcriptional profiles but were also complementary, with metformin more efficient than rapamycin in correcting the developmental trajectory. Therefore, our data identify novel effects of aging on stem cells and the maturation of their daughter cells contributing to the decline of epithelial regeneration and the correction by geroprotectors.

Keywords: gerotherapeutics; intestinal stem cell aging; intestinal stem cells; metformin; rapamycin; single-cell RNA sequencing.

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Conflict of interest statement

All authors state that there is no conflict of interest.

Figures

FIGURE 1
FIGURE 1
scRNAseq analysis of small intestinal epithelial cells: (a) Schematic representation of experimental designs; drug treatment was started at age of 21‐month‐old for 3 months. 3 replicates were used in each group. (b) Cluster map and cell lineages from 12 combined scRNAseq libraries. Abbreviates; R—replicating; R‐Div—replicating to dividing; Div—dividing; EC—enterocytes; Sec—secretory progenitor cells; EE—enteroendocrine cells; (c) number of differentially expressed genes (fold change >1.5 & P adj <0.01) in each cluster from the comparison between old vs young (red bar), metformin‐treated vs old (green bar), and rapamycin‐treated vs old (blue bar)
FIGURE 2
FIGURE 2
Impact of aging on function of intestinal stem cells: (a) average mRNA expression of the Lgr5 genes in the Stem cluster. (b) Expression ratio of old vs young of 467 genes of an Lgr5hi ISC signature (Muñoz et al., 2012), and corresponding ratio of old‐met vs young or old‐rap vs young (lower panel). (c) Distribution of genes as a function of the ratio of expression; corresponding ratios in Table 1. (d) Cumulative density graph of ratio distribution. Statistical analysis was performed using the Kolmogorov–Smirnov test between two groups (***: p < 0.001). (e) Differentially‐regulated pathways (P adj <0.05) in the stem cluster using GSEA KEGG pathways; NES—normalized enrichment score. (f) Scatter plot for two variables with mean module score. Mean module scores from each mouse (filled) or each condition (empty, average value) were plotted for Wnt and cell cycle pathways (top) or ribosome and cell cycle pathways (bottom). p value was calculated using MANOVA, assuming each mouse as independent.
FIGURE 3
FIGURE 3
Impact of aging on stem cells differs depending on the position along the trajectory: (a) trajectory graph for each condition. Based on the position of each cell along the trajectory, cells on main track color‐coded green, and cells on side branches color‐coded pink. Cells not included in the analysis are color‐coded gray. (b–i) Module scores of four different pathways plotted separately by position (main: b–e, side: f–i) on the track and condition. Statistical significance was determined by the ANOVA test and applied for each position separately.
FIGURE 4
FIGURE 4
Aging delays proper cell maturation along the developmental trajectory: (a–d) assignment of disease pathway based on differentially expressed genes (P adj <0.05) in main cells of Stem and subsequent progeny clusters were analyzed using QIAGEN IPA (QIAGEN Inc., https://digitalinsights.qiagen.com/IPA). Dotted green box indicates cell types assigned to clusters early in the trajectory (Stem and R); dotted blue box indicates cell types later in the trajectory (R‐Div, Div1, and Div2). (e) Cell‐type transition along the branch points of the trajectory plot. Dashed line indicates compartments that divide the trajectory into three segments (early(i), mid(ii), and late(iii)). This compartmentalization was utilized for statistical analysis of the alteration of cell identity along the developmental trajectory under the different conditions using the Pearson's chi‐squared test. The analysis confirmed that cell‐type distribution in each compartment is significantly different (compartment i: p = 0.001; compartment ii: p = 0.04; compartment iii: p = 0.015). (f) Module score of OXPHOS pathway at each branch point analyzed for cell‐type transition in e. (g) Module score of cell cycle pathway at each branch point analyzed for cell‐type transition in e
FIGURE 5
FIGURE 5
Retardation of developmental trajectory is reflected in cell maturation process: (a) mean mRNA expression of Uhrf1 and Ccnb1, which expression level changes along with developmental trajectory. (b) Representative images of IF for proteins encoded by Uhrf1 or Ccnb1 in FFPE tissues. Images show cells stained with either protein alone (yellow arrows) or cells co‐stained for both proteins (white arrows). Scale bar: 10 μm. (c) Quantitation of co‐stained cells in each condition. 15 crypts were analyzed from 3 mice of each condition. Statistical analysis was performed using one‐way ANOVA followed by Tukey's multiple comparison.
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