Speaker
Description
"In the last decade, the widespread adoption of hiPSC technologies and their differentiation into brain organoids (BOs), together with the massive use of single-cell (SC) transcriptional profiling, has provided a significant boost to the study of human brain pathophysiology. This progress has enabled time-resolved characterization of cellular players and their interactions, which is particularly relevant to developmental neurobiology - a major field of application for BOs. Despite the expansion of protocols leading to increasingly accurate and multifaceted BO models, the dissemination of streamlined methods to assess their fidelity to primary tissues at SC level and with temporal attention is still lagging behind, ultimately slowing BOs optimization and translational potential.
Although recent efforts significantly contributed to the field (Fleck et al. 2021, He et al. 2024), releasing pre-trained models and methods focused on out-of-sample label transfer, the landscape remains sparse compared to the pace of experimental advances. Furthermore, these models do not guarantee sub-regional and temporal mapping capabilities directly onto fetal brain transcriptomes, leaving space for additional complementary tools.
Here, we present a lightweight resource designed to enable assessment of BO cultures profiled at SC resolution, whether generated using novel or established protocols. The data foundation of this project is based on primary samples collected from 14-previous studies and profiled at SC-level, from four major brain areas: cortex, cerebellum, thalamus and subpallium, six cortical regions: prefrontal, temporal, somatosensory, motor, parietal and visual, and 26 timepoints (spanning from post-conceptional week 5 to 36). Alongside the data core, the framework we propose encompasses a substantial set of functions and methods to carry-out in vivo – in vitro comparative analysis with tunable resolution focus, ranging from cell type to sub-region (cortical areas), region and developmental stage. The adoption of metacell aggregation and objective-aware reductionism in both building and querying methods allowed us to distill information to score similarities between BOs and primary samples, while minimizing computational burden, limiting confounding effects and keeping a low entry barrier for its utilization."
| Author(s) | Davide Castaldi, Emanuele Villa, Nicolo' Caporale, Giuseppe Testa |
|---|---|
| Affiliation(s) | Human Technopole |
