Single-Cell Transcriptome Analysis of Neural Stem Cells

Genetic Tracing of Progenitors from Embryo to Postnatal Brain

Brain development is an extraordinarily intricate process originating from neural progenitor cells (NPCs). Ongoing research emphasizes the vast diversity within NPC populations. To contribute to unraveling this complexity, the cutting-edge approach known as StarTrack enables the tracing of NPCs from their embryonic or postnatal origins to adulthood, offering profound insight into NPC diversity. By tagging NPCs and their progeny with heritable "color codes," StarTrack provides researchers with invaluable tools to unravel the complexities of brain development. This technique comprises two key components: a transposase and integrable StarTrack plasmids that can be customized to specific research goals, facilitating the targeting of particular progenitor types or the exploration of distinct lineages in their progeny. Additionally, this genetic tool can be utilized both in vitro and in vivo. In vivo labeling involves in utero electroporation of StarTrack plasmids, enabling the exploration of temporal and spatial diversity of progenitors. By integrating StarTrack with other methodologies, such as transcriptomics, cell cultures, electrophysiology, and immunostaining, among others, researchers can decipher essential aspects of progenitors, including their cell progeny, potential, dynamics, and molecular profiles. Overall, StarTrack revolutionizes our understanding of neurodevelopment by unveiling the NPCs' heterogeneity and the rich diversity of their progeny.

ImmuneMirror: A machine learning-based integrative pipeline and web server for neoantigen prediction

Neoantigens are derived from somatic mutations in the tumors but are absent in normal tissues. Emerging evidence suggests that neoantigens can stimulate tumor-specific T-cell-mediated antitumor immune responses, and therefore are potential immunotherapeutic targets. We developed ImmuneMirror as a stand-alone open-source pipeline and a web server incorporating a balanced random forest model for neoantigen prediction and prioritization. The prediction model was trained and tested using known immunogenic neopeptides collected from 19 published studies. The area under the curve of our trained model was 0.87 based on the testing data. We applied ImmuneMirror to the whole-exome sequencing and RNA sequencing data obtained from gastrointestinal tract cancers including 805 tumors from colorectal cancer (CRC), esophageal squamous cell carcinoma (ESCC) and hepatocellular carcinoma patients. We discovered a subgroup of microsatellite instability-high (MSI-H) CRC patients with a low neoantigen load but a high tumor mutation burden (> 10 mutations per Mbp). Although the efficacy of PD-1 blockade has been demonstrated in advanced MSI-H patients, almost half of such patients do not respond well. Our study identified a subset of MSI-H patients who may not benefit from this treatment with lower neoantigen load for major histocompatibility complex I (P < 0.0001) and II (P = 0.0008) molecules, respectively. Additionally, the neopeptide YMCNSSCMGV-TP53G245V, derived from a hotspot mutation restricted by HLA-A02, was identified as a potential actionable target in ESCC. This is so far the largest study to comprehensively evaluate neoantigen prediction models using experimentally validated neopeptides. Our results demonstrate the reliability and effectiveness of ImmuneMirror for neoantigen prediction.

Gliogenic Potential of Single Pallial Radial Glial Cells in Lower Cortical Layers

During embryonic development, progenitor cells are progressively restricted in their potential to generate different neural cells. A specific progenitor cell type, the radial glial cells, divides symmetrically and then asymmetrically to produce neurons, astrocytes, oligodendrocytes, and NG2-glia in the cerebral cortex. However, the potential of individual progenitors to form glial lineages remains poorly understood. To further investigate the cell progeny of single pallial GFAP-expressing progenitors, we used the in vivo genetic lineage-tracing method, the UbC-(GFAP-PB)-StarTrack. After targeting those progenitors in embryonic mice brains, we tracked their adult glial progeny in lower cortical layers. Clonal analyses revealed the presence of clones containing sibling cells of either a glial cell type (uniform clones) or two different glial cell types (mixed clones). Further, the clonal size and rostro-caudal cell dispersion of sibling cells differed depending on the cell type. We concluded that pallial E14 neural progenitors are a heterogeneous cell population with respect to which glial cell type they produce, as well as the clonal size of their cell progeny.