Biology subjects

Guiot, M.-C.

Publications and source records attributed to Guiot, M.-C..

5 recordsLinked to original sources

bioRxiv2026

Pathology in resected areas of FDG PET hypometabolism in pediatric epilepsy patients with focal cortical dysplasia

Introduction[18F]fluorodeoxyglucose positron emission tomography (FDG-PET) frequently reveals hypometabolism extending beyond the epileptogenic zone in focal cortical dysplasia (FCD). However, it is unclear whether these peripheral hypometabolic areas harbour pathological cells potentially contributing to seizure generation. This study characterized histopathology in the lesion epicentre vs borders of the FDG-PET hypometabolism-informed resections in pediatric patients undergoing epilepsy surgery. MethodsFourteen children with intractable, extra-temporal focal epilepsy (mean age 9.0{+/-}5.0 years; 9 female) were retrospectively reviewed. FDG-PET contributed significantly to surgical planning in all cases, with the resection encompassing the visually-apparent MRI signal abnormalities as well as areas of surrounding hypometabolism when safely feasible. Multiple pathological specimens were obtained from the epicentre and surrounding hypometabolic areas. Overall, 136 specimens were analyzed: 64 epicentre (mean 4.6{+/-}3.2/patient) and 72 border (mean 5.1{+/-}3.5/patient). ResultsPathology was identified in 75% of epicentre specimens (59% with frank FCD (fFCD) IIa/b, 16% with dysmorphic neurons only (DNO)). Border specimens showed pathology in 62% (31% fFCD IIa/b, 31% DNO). We fitted a Bayesian logistic mixed model with pathology as outcome variable, location as predictor, and subject as a random effect. Compared to negative pathology, the log-odds of fFCD in the epicentre was 1.00 (confidence interval (CI) 0.32, 1.77) and -1.25 in the border (CI -2.17, -0.40). The log-odds of DNO vs negative pathology was non-significant in both locations. All patients achieved Engel Ia status at one-year follow-up with no long-term neurological deficits. ConclusionThese findings suggest a gradient of histopathology, with fFCD concentrated in the epicentre and DNO present in both the epicentre and hypometabolic borders. Thus, FDG-PET may be used to better detect the histopathological borders of FCD type II, and the high seizure-freedom rate presented here supports the inclusion of these surrounding hypometabolic regions in the surgical resection (when safe to do so), potentially improving the likelihood of removing epileptogenic cells. Key PointsO_LIPathological cells are present not only in the MRI signal abnormality in FCD but also in the periphery of the FDG-PET hypometabolism. C_LIO_LIWe observe a gradient of histopathology, with frank FCD concentrated in the epicentre and dysmorphic neurons spread throughout the area of hypometabolism. C_LIO_LIMaximal safe resection of the area of hypometabolism may increase likelihood of removing epileptogenic cells, thus improving surgical outcome. C_LI

neuroscience↗

bioRxiv2023

Cryo-EM structure of Alzheimer disease tau filaments with PET ligand MK-6240

Positron Emission Tomography (PET) ligands have advanced Alzheimers disease (AD) diagnosis and treatment. Using autoradiography and cryo-EM, we identified AD brain tissue with elevated tau burden, purified filaments, and determined the structure of second-generation high avidity PET ligand MK-6240 at 2.31 [A] resolution, which bound at a 1:1 ratio within the cleft of tau paired-helical filament (PHF), engaging with glutamine 351, lysine K353, and isoleucine 360. This information elucidates the basis of MK-6240 PET in quantifying PHF deposits in AD and may facilitate the structure-based design of superior ligands against tau amyloids.

neuroscience↗

bioRxiv2022

Machine learning meets classical computer vision for accurate cell identification

High-parameter multiplex immunostaining techniques have revolutionized our ability to image healthy and diseased tissues with unprecedented depth; however, accurate cell identification and segmentation remain significant downstream challenges. Identifying individual cells with high precision is a requisite to reliably and reproducibly interpret acquired data. Here we introduce CIRCLE, a cell identification pipeline that combines classical and modern machine learning-based computer vision algorithms to address the shortcomings of current cell segmentation tools for 2D images. CIRCLE is a fully automated hybrid cell detection model, eliminating subjective investigator bias and enabling high-throughput image analysis. CIRCLE accurately distinguishes cells across diverse tissues microenvironments, resolves low-resolution structures, and can be applied to any 2D image that contains nuclei. Importantly, we quantitatively demonstrate that CIRCLE outperforms current state-of-the-art image segmentation tools using multiple accuracy measures. As high-throughput multiplex imaging grows closer toward standard practice for histology, integration of CIRCLE into analysis protocols will deliver unparalleled segmentation quality.

cell biology↗

bioRxiv2018

Single-cell RNA-seq reveals that glioblastoma recapitulates normal brain development

SummaryCancer stem cells are critical for cancer initiation, development, and resistance to treatments. Our understanding of these processes, and how they relate to glioblastoma heterogeneity, is limited. To overcome these limitations, we performed single-cell RNA-sequencing on 38 296 glioblastoma cells and 22 637 normal human fetal brain cells. Using an unbiased approach, we mapped the lineage hierarchy of the developing human brain and compared the transcriptome of each cancer cell to this roadmap. We discovered a conserved neural trilineage cancer hierarchy with glial progenitor-like cells at the apex. We also found that this progenitor population contains the majority of cancers cycling cells and is the origin of heterogeneity. Finally, we show that this hierarchal map can be used to identify therapeutic targets specific to progenitor cancer stem cells. Our analyses show that normal brain development reconciles glioblastoma development, unravels the origin of glioblastoma heterogeneity, and helps to identify cancer stem cell-specific targets.

cancer biology↗

bioRxiv2018

Infiltration of CD8+ T cells into tumor-cell clusters in Triple Negative Breast Cancer

Infiltration of CD8+ T lymphocytes into solid tumors is associated with good prognosis in various types of cancer, including Triple Negative Breast Cancers (TNBC). However, the mechanisms underlying different infiltration-levels are largely unknown. Here, we have characterized the spatial profile of CD8+ T cells around tumorcell clusters in the core and margin regions in TNBC. Combining mathematical modeling and data analysis, we propose that there exists a possible chemo-repellent inside tumor-cell clusters, which prevents CD8+ T cells from infiltrating into tumor-cell clusters. Furthermore, investigation into the properties of collagen fibers suggests that variations in desmoplastic elements does not limit infiltration of CD8+ T lymphocytes into tumor-cell clusters. This is consistent with the prediction of our mathematical modeling analysis whereby CD8+ T cells are predicted to infiltrate the fibrotic barrier and their infiltration into tumor clusters is governed by other mechanisms involving a local repellent.

cancer biology↗