Yutong Sha

The retina is uniquely enriched in polyunsaturated fatty acids (PUFAs), primarily localized in cell membranes, where they govern membrane biophysical properties. During aging, alterations in lipid metabolism lead to reduced content of very long–chain PUFAs (VLC-PUFAs) in the retina, which is associated with normal age-related reductions in contrast sensitivity, diminished photoreceptor function and delayed rod-mediated dark adaptation recovery, and pathological age-related macular degeneration (AMD). ELOVL2 ( elongation of very long chain fatty acids-like 2 ) encodes a transmembrane protein that produces precursors to docosahexaenoic acid (DHA) and VLC-PUFAs. The methylation status of the ELOVL2 promoter is currently one of the best predictors of chronological age. Here, we show that lower VLC-PUFA abundance in the aged mouse retina is accompanied by a reduction in visual function. Similarly, mice lacking ELOVL2-specific enzymatic activity ( Elovl2 ^C234W ) demonstrate reduced contrast sensitivity and slower rod-mediated dark adaptation. Intravitreal supplementation with the direct product of ELOVL2, 24:5n-3, in aged animals improved visual function for up to 4 weeks and reduced accumulation of APOE- and C3d-positive sub-RPE deposits. The gene expression pattern observed in supplemented retinas exhibited a partial rejuvenation profile, including decreased expression of aging-related genes and a transcriptomic signature resembling younger retinas. Last, human genetic data from the IAMDGC and UK Biobank linked two variants in the ELOVL2 locus with the onset of intermediate AMD, underlining the translational importance of our findings. Our work highlights VLC-PUFA supplementation as a potential therapeutic opportunity and defines ELOVL2 as a promising target for interventions to prevent age-related vision loss.

Despite the major roles of choroid plexus epithelial cells (CPECs) in brain homeostasis and repair, their developmental lineage and diversity remain undefined. In simplified differentiations from human pluripotent stem cells, derived CPECs (dCPECs) display canonical properties and dynamic motile multiciliated phenotypes that interact with Aβ uptake. Single dCPEC transcriptomes over time correlate well with human organoid and fetal CPECs, while pseudotemporal and cell cycle analyses highlight the direct CPEC origin from neuroepithelial cells. In addition, time series analyses define metabolic (type 1) and ciliogenic dCPECs (type 2) at early timepoints, followed by type 1 diversification into anabolic-secretory (type 1a) and catabolic-absorptive subtypes (type 1b) as type 2 cells contract. These temporal patterns are then confirmed in independent derivations and mapped to prenatal stages using human tissues. In addition to defining the prenatal lineage of human CPECs, these findings suggest dynamic models of ChP support for the developing human brain.

Time-series single-cell RNA sequencing (scRNA-seq) datasets provide unprecedented opportunities to learn dynamic processes of cellular systems. Due to the destructive nature of sequencing, it remains challenging to link the scRNA-seq snapshots sampled at different time points. Here we present TIGON, a dynamic, unbalanced optimal transport algorithm that reconstructs dynamic trajectories and population growth simultaneously as well as the underlying gene regulatory network from multiple snapshots. To tackle the high-dimensional optimal transport problem, we introduce a deep learning method using a dimensionless formulation based on the Wasserstein–Fisher–Rao (WFR) distance. TIGON is evaluated on simulated data and compared with existing methods for its robustness and accuracy in predicting cell state transition and cell population growth. Using three scRNA-seq datasets, we show the importance of growth in the temporal inference, TIGON’s capability in reconstructing gene expression at unmeasured time points and its applications to temporal gene regulatory networks and cell–cell communication inference.

How basal cell carcinoma (BCC) interacts with its tumor microenvironment to promote growth is unclear. We use singe-cell RNA sequencing to define the human BCC ecosystem and discriminate between normal and malignant epithelial cells. We identify spatial biomarkers of tumors and their surrounding stroma that reinforce the heterogeneity of each tissue type. Combining pseudotime, RNA velocity–PAGA, cellular entropy, and regulon analysis in stromal cells reveals a cancer-specific rewiring of fibroblasts, where STAT1, TGF-β, and inflammatory signals induce a noncanonical WNT5A program that maintains the stromal inflammatory state. Cell-cell communication modeling suggests that tumors respond to the sudden burst of fibroblast-specific inflammatory signaling pathways by producing heat shock proteins, whose expression we validated in situ. Last, dose-dependent treatment with an HSP70 inhibitor suppresses in vitro vismodegib-resistant BCC cell growth, Hedgehog signaling, and in vivo tumor growth in a BCC mouse model, validating HSP70’s essential role in tumor growth and reinforcing the critical nature of tumor microenvironment cross-talk in BCC progression.