Biology subjects

Crampsie, S.

Publications and source records attributed to Crampsie, S..

5 recordsLinked to original sources

bioRxiv2026

Epithelial Stem Cell Fate Determines Chemoradiotherapy Response in Rectal Cancer

Rectal cancers are often treated with neoadjuvant chemoradiotherapy (CRT), yet 85% of patients do not achieve a pathological complete response. To identify the molecular determinants of CRT response, we profiled the single-cell signalling, DNA-damage, cell-cycle, apoptotic, and cell-fate responses of 2,769 patient-derived organoid cultures treated with CRT, cancer-associated fibroblasts (CAFs), and signal-rewiring agents. We find that CRT response is determined by stem cell-fate. CRT triggers comparable DNA-damage in isogenic proliferative (proCSC) and revival (revCSC) colonic stem cells, but proCSC retain damage and die whereas revCSC resolve damage and persist. Both CRT and CAFs drive proCSC to a common treatment-resistant revCSC fate and high revCSC predicts worse survival in patients. Pharmacologically constraining stem-cell plasticity increases CRT sensitivity, and Spatial Perturbation of ARrayed Tumour Assembloids (SPARTA) confirms YAP/TEAD inhibition improves chemotherapy responses in human stromal-tumour models. These results suggest that cancer cell-fate, not genotoxic damage itself, ultimately governs response to standard-of-care chemoradiotherapy. HIGHLIGHTSO_LIRectal cancer stem cell-fate determines chemoradiotherapy-induced apoptosis C_LIO_LIproCSCs retain DNA-damage and die, whereas revCSCs repair damage and persist C_LIO_LICAFs and chemoradiotherapy converge on a common chemo-radioresistant revCSC state C_LIO_LISPARTA reveals TEAD inhibition blocks DNA-repair persisters in stromal assembloids C_LI

cancer biology↗

bioRxiv2026

Stromal Prostaglandin is a Dominant Spatial Regulator of Cell-fate Plasticity in Colorectal Cancer

Colorectal cancer (CRC) tumours with high stromal content have a worse outcome, but the mechanisms governing this are unclear. Using high-throughput single-cell perturbation analysis of CRC patient-derived organoids (PDOs) and cancer-associated fibroblasts (CAFs) we find that epithelial cells with high stromal-communication potential are marked by the transcriptional co-repressor DACH1. To define the causal regulators of stromal-epithelial signalling, we developed a novel CRISPR screening platform to perturb the CAF secretome and measure epithelial stem cell responses at single-cell resolution. Intercellular CRISPR screening and full factorial ligand analysis revealed that stromal Prostaglandin E2 (PGE2) is a dominant regulator of CRC cell-fate plasticity. Stromal PGE2 converts DACH1+ epithelia from a chemosensitive proliferative colonic stem cell (proCSC) fate into a chemorefractory and prometastatic revival colonic stem cell (revCSC) fate. PGE2-driven epithelial transdetermination is rapid and reversible, providing an acute mechanism for stromal-driven plasticity in CRC tumours. Genetic and pharmacological inhibition of stromal COX2 inhibits epithelial plasticity, trapping CRC epithelia in an anti-metastatic and chemosensitive proCSC fate. PTGS2+ CAFs support a spatially resolved revCSC to proCSC plasticity gradient in human CRC tumours marked by increasing DACH1 expression. These results reveal that stromal prostaglandin is a dominant spatial regulator of poor-prognosis cell-fates and may explain the benefit of anti-COX therapies in both preventing and treating CRC.

cancer biology↗

bioRxiv2025

Fast segmentation with the NextBrain histological atlas

Structural brain analysis at the subregion level offers critical insights into healthy aging and neurodegenerative diseases. The NextBrain histological atlas was recently introduced to support such fine-grained investigations, but its existing Bayesian segmentation framework remains computationally prohibitive, particularly for large-scale studies. We present a new, open-source tool that dramatically accelerates segmentation using a hybrid approach combining: machine learning, contrast-adaptive segmentation; target-specific image synthesis; and fast diffeomorphic registration (all three with GPU support). Our method enables highly granular segmentation of brain MRI scans of any resolution and contrast (in vivo or ex vivo) at a fraction of the computational cost of the original method (<5 minutes on a GPU). We validate our tool on four different modalities (in vivo MRI, ex vivo MRI, HiP-CT, and photography) across a total of approximately 4,000 brain scans. Our results demonstrate that the accelerated approach achieves comparable accuracy to the original method in terms of Dice scores, while reducing runtime by over an order of magnitude. This work enables high-resolution anatomical analysis at unprecedented scale and flexibility, providing a practical solution for large neuroimaging studies. Our tool is publicly available in FreeSurfer (https://surfer.nmr.mgh.harvard.edu/fswiki/HistoAtlasSegmentation).

neuroscience↗

bioRxiv2024

A next-generation, histological atlas of the human brain and its application to automated brain MRI segmentation

Magnetic resonance imaging (MRI) is the standard tool to image the human brain in vivo. In this domain, digital brain atlases are essential for subject-specific segmentation of anatomical regions of interest (ROIs) and spatial comparison of neuroanatomy from different subjects in a common coordinate frame. High-resolution, digital atlases derived from histology (e.g., Allen atlas [7], BigBrain [13], Julich [15]), are currently the state of the art and provide exquisite 3D cytoarchitectural maps, but lack probabilistic labels throughout the whole brain. Here we present NextBrain, a next - generation probabilistic atlas of human brain anatomy built from serial 3D histology and corresponding highly granular delineations of five whole brain hemispheres. We developed AI techniques to align and reconstruct [~]10,000 histological sections into coherent 3D volumes with joint geometric constraints (no overlap or gaps between sections), as well as to semi-automatically trace the boundaries of 333 distinct anatomical ROIs on all these sections. Comprehensive delineation on multiple cases enabled us to build the first probabilistic histological atlas of the whole human brain. Further, we created a companion Bayesian tool for automated segmentation of the 333 ROIs in any in vivo or ex vivo brain MRI scan using the NextBrain atlas. We showcase two applications of the atlas: automated segmentation of ultra-high-resolution ex vivo MRI and volumetric analysis of Alzheimers disease and healthy brain ageing based on [~]4,000 publicly available in vivo MRI scans. We publicly release: the raw and aligned data (including an online visualisation tool); the probabilistic atlas; the segmentation tool; and ground truth delineations for a 100 m isotropic ex vivo hemisphere (that we use for quantitative evaluation of our segmentation method in this paper). By enabling researchers worldwide to analyse brain MRI scans at a superior level of granularity without manual effort or highly specific neuroanatomical knowledge, NextBrain holds promise to increase the specificity of MRI findings and ultimately accelerate our quest to understand the human brain in health and disease.

neuroscience↗

bioRxiv2020

A multimodal computational pipeline for 3D histology of the human brain

Ex vivo imaging enables analysis of the human brain at a level of detail that is not possible in vivo with MRI. In particular, histology can be used to study brain tissue at the microscopic level, using a wide array of different stains that highlight different microanatomical features. Complementing MRI with histology has important applications in ex vivo atlas building and in modeling the link between microstructure and macroscopic MR signal. However, histology requires sectioning tissue, hence distorting its 3D structure, particularly in larger human samples. Here, we present an open-source computational pipeline to produce 3D consistent histology reconstructions of the human brain. The pipeline relies on a volumetric MRI scan that serves as undistorted reference, and on an intermediate imaging modality (blockface photography) that bridges the gap between MRI and histology. We present results on 3D histology reconstruction of a whole human hemisphere.

neuroscience↗