Shieh, J. | Search bioRxiv

bioRxiv2023

Cell type specificity of mosaic chromosome 1q gain resolved by snRNA-seq in a case of epilepsy with hyaline protoplasmic astrocytopathy

STRUCTURED ABSTRACTO_ST_ABSIntroductionC_ST_ABSMosaic gain of chromosome 1q (chr1q) has been associated with malformation of cortical development (MCD) and epilepsy. Hyaline protoplasmic astrocytopathy (HPA) is a rare neuropathological finding seen in cases of epilepsy with MCD. The cell-type specificity of mosaic chr1q gain in the brain and the molecular signatures of HPA are unknown. MethodsWe present a child with pharmacoresistant epilepsy who underwent epileptic focus resections at age 3 and 5 years and was found to have mosaic chr1q gain and HPA. We performed single-nuclei RNA-sequencing (snRNA-seq) of brain tissue from the second resection. ResultssnRNA-seq showed increased expression of chr1q genes specifically in subsets of neurons and astrocytes. Differentially expressed genes associated with inferred chr1q gain included AKT3 and genes associated with cell adhesion or migration. A subpopulation of astrocytes demonstrated marked enrichment for synapse-associated transcripts, possibly linked to the astrocytic inclusions observed in HPA. DiscussionsnRNA-seq may be used to infer the cell type-specificity of mosaic chromosomal copy number changes and identify associated gene expression alterations, which in the case of chr1q gain may involve aberrations in cell migration. Future studies using spatial profiling could yield further insights on the molecular signatures of HPA.

neuroscience↗

bioRxiv2022

Deep learning-based approach for the characterization and quantification of histopathology in mouse models of colitis

Inflammatory bowel disease (IBD) is a chronic immune-mediated disease of the gastrointestinal tract. While therapies exist, response can be limited within the patient population. As such, researchers have studied mouse models of colitis to further understand its pathogenesis and identify new treatment targets. Although bench methods like flow cytometry and RNA-sequencing can characterize immune responses with single-cell resolution, whole murine colon specimens are processed at once. Given the simultaneous presence of colonic regions that are involved or uninvolved with abnormal histology, processing whole colons may lead to a loss of spatial context. Detecting these regions in hematoxylin and eosin (H&E)-stained colonic tissues offers the downstream potential of quantifying immune populations in areas with and without disease involvement by immunohistochemistry on serially sectioned slides. This could provide a complementary, spatially-aware approach to further characterize populations identified by other methods. However, detection of such regions requires expert interpretation by pathologists and is a tedious process that may be difficult to perform consistently across experiments. To this end, we have trained a deep learning model to detect Involved and Uninvolved regions from H&E-stained colonic slides across controls and three mouse models of colitis - the dextran sodium sulfate (DSS) chemical induction model, the recently established intestinal epithelium-specific, inducible Klf5{Delta}IND (Villin-CreERT2;Klf5fl/fl) genetic model, and one that combines both induction methods. The trained classifier allows for extraction of Involved colonic regions across mice to cluster and identify histological classes. Here, we show that quantification of Involved and Uninvolved image patch classes in swiss rolls of colonic specimens can be utilized to train a linear determinant analysis classifier to distinguish between mouse models. Such an approach has the potential for revealing histological links and improving synergy between various colitis mouse model studies to identify new therapeutic targets and pathophysiological mechanisms.

immunology↗