Temporal lobe epilepsy is the most common form of focal epilepsy and can have various detrimental consequences within many neurologic domains. Recent evidence suggests that the piriform cortex may also be implicated in seizure physiology. The piriform cortex is a primary component of the olfactory network and is located at the junction of the frontal and temporal lobes, wrapping around the entorhinal sulcus. Similar to the hippocampus, it is a tri-layered allocortical structure, with connections to many adjacent regions including the orbitofrontal cortex, amygdala, peri- and entorhinal cortices, and insula. Both animal and human studies have implicated the piriform cortex as a critical node in the temporal lobe epilepsy network. It has additionally been shown that resection of greater than half of the piriform cortex may significantly increase the odds of achieving seizure freedom. Laser interstitial thermal therapy has also been shown to be an effective treatment strategy with recent evidence hinting that ablation of the piriform cortex may be important for seizure control as well. We propose that sampling piriform cortex in intracranial stereoelectroencephalography (sEEG) procedures with the use of a temporal pole or amygdalar electrode would be beneficial for further understanding the role of the piriform cortex in temporal lobe epilepsy.

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Adamantinomatous Craniopharyngioma (ACP) is a highly morbid, cellularly heterogeneous pediatric tumor arising in the sellar/suprasellar region of the brain. This cellular heterogeneity makes ACP an ideal candidate for study using single-cell RNA-sequencing (scRNA-seq). We collected a 10,000 cell scRNA-seq dataset on the 10X v3 platform, from 6 unique patients. Using the industry standard Seurat software package, we identified 34 unique cell clusters. By crossing the results of two separate expert curated cellular reference atlases (Azimuth and scHCL), we determined that 33 of these cell types were immune-related (e.g., T cells, monocytes, etc.) or histologically related (e.g., glial cells). The remaining 2,048 cells were inferred to be ACP driver cells. Rigorous statistical testing of third-generation graph topology-based network enrichment methods utilizing the Reactome database supported this conclusion. In order to identify effective antitumor therapies, it is critical to understand the temporal evolution of tumor cell behavior. Computational solutions that describe the potential lifecycle of tumor cells have been derived using scRNA-seq datasets. Using a well-established method, Monocle3, we generated a potential model of temporal evolution of the ACP driver cell population. To identify a specific transcriptional “point-of-no-return” for ACP driver cells, which may help define a rational target for intervention, we created a custom probabilistic Deep Learning framework in the form of a Convolutional Variational Autoencoder (CVAE). By applying this CVAE to our data, we identified 31 anomalous transcripts, each of which was aberrantly active at all times or demonstrated a temporal pattern of anomalous activity. Strikingly, this small list – representing roughly 0.15% of the protein coding genome – aligns closely with extant data describing the molecular behavior of ACP. This work provides a novel transcriptome benchmark for comparison of in vitromodels, a deeper understanding of ACP heterogeneity, as well as a generalizable approach for scRNA-seq analysis.