Bystrom, P. | Search bioRxiv

bioRxiv2026

Pubertal maturation and chemotherapy-associated disruption of the pediatric ovary revealed by multimodal single-cell profiling

Ovarian tissue cryopreservation enables fertility preservation in females undergoing gonadotoxic therapies, restoring fertility in adults. Although offered even before puberty, the childhood ovary and its vulnerability to therapy remain poorly characterized. Here, ovarian tissue from 16 patients undergoing fertility preservation (aged 1-16 years) and 11 adult controls (aged 22-32 years) was analyzed using single-cell RNA sequencing, spatial transcriptomics, and multiplex immunostaining. In chemotherapy-naive samples, 13 somatic cell populations underwent extracellular matrix remodeling, vascular, neural, and stromal maturation during puberty, whereas changes in germline related to chromatin remodeling. Spatial transcriptomics resolved 23 clusters across, revealing distinct tissue organization and follicular niche composition between children and adults. Chemotherapy exposure depleted perifollicular and vascular cells, suppressed intercellular signaling, and dysregulated over half of puberty-associated genes, converging on stress responses and extracellular matrix remodeling, with SEPTIN7 as a potential biomarker. These findings uncover critical developmental vulnerabilities of the pediatric ovary relevant to fertility preservation.

developmental biology↗

bioRxiv2025

Single-cell morphological profiling reveals insights into cell death

Analysis at the single-cell level is a powerful approach to study biological processes and responses to perturbations. However, its application in morphological profiling with phenomics remains underexplored. Here, we use the Cell Painting assay to investigate morphological effects of 53 small molecule compounds, associated with six distinct programmed cell death mechanisms, across six concentrations in MCF7 cells. To compare single-cell and aggregated analysis strategies, we conduct both supervised and unsupervised evaluations aimed at identifying features linked to programmed cell death. We apply an energy distance as a metric to quantify morphological perturbation strength, enabling efficient filtering. Among three tested feature extraction methods, self-supervised DINO embeddings applied to single-cell data captured high-resolution morphological patterns. Focused analyses of apoptosis-inducing compounds revealed biological heterogeneity attributable to specific molecular targets and concentration-dependent effects, which were not apparent in aggregated profiles. In contrast, multi-class classification models for the six programmed cell death mechanisms trained on single-cell features achieved F1 scores of 79.86%, while models trained on aggregated features reached F1 scores of up to 89.97%. Our results highlight the advantages of single-cell data for unsupervised exploration and show that aggregated representations yield more robust and accurate performance in supervised models.

bioinformatics↗