Biology subjects

Langley, J.

Publications and source records attributed to Langley, J..

5 recordsLinked to original sources

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

Compound Delivery of eVLPs Enhances Prime Editing for Targeted Genome Engineering and High-Throughput Screening

Engineered virus-like particles (eVLPs) enable transgene-free ribonucleoprotein delivery for genome editing applications, yet optimized delivery strategies for high-throughput applications remain unexplored. Prime editing enables precise genomic modifications but suffers from limited efficiency that constrains its widespread adoption. Here, we present PRIME-VLP (Progressive Repeated Infections for Maximized Editing via Virus-Like Particles), a delivery strategy that enhances prime editing efficiency for both targeted genome engineering and high-throughput prime editing screening. PRIME-VLP leverages the temporal dynamics of eVLP-mediated editing through multiple sequential transductions with sub-saturating eVLP doses delivered at optimal intervals. This approach achieves 1.5 to 2.8-fold improvements in editing efficiency across diverse genomic targets and cell types. PRIME-VLP maintains high specificity without increasing off-target effects, compromising cellular viability or causing transcriptional perturbations. By decoupling pegRNA and editor delivery through pegRNA-free eVLPs, PRIME-VLP enables pooled prime editing screens, circumventing transgene silencing limitations of conventional lentiviral-based screens. Using a 6,000-pegRNA library targeting TP53, PRIME-VLP achieved 2.8-fold higher editing efficiency and improved reproducibility compared to conventional lentiviral delivery. An eVLP-based screen identified functional TP53 loss-of-function variants that confer resistance to MDM2 inhibition by Nutlin-3. This work expands the versatility of eVLPs beyond their current in vivo therapeutic applications, demonstrating their promise for high-throughput functional genomics by overcoming the delivery limitations of lentiviral systems.

genomics↗

bioRxiv2025

Dynamic fingerprinting of the human functional connectome

Resting-state functional connectivity (FC) have distinct, personalized patterns that could serve as a unique fingerprint of each individuals brain. While previous brain fingerprinting methods have used functional connectivity maps over a scanning session (static method), it has been shown that the brain is a dynamic system that switches between several metastable states, each of which having a different FC map. Taking the dynamic nature of brain connectivity into account will likely lead to more subject-specific information and better individual identification. In this paper, we derived the state-specific FCs using sliding window correlation and clustering and evaluated their performance in individual identification and cognitive score prediction. The resultant dynamic fingerprints outperformed the static fingerprints in identification accuracy. Furthermore, some of the brain states were more accurate in predicting cognitive scores, indicating that connectivity in some brain states is informative of cognition abilities, possibly useful as biomarkers for brain disorders. Impact StatementOur findings suggest that state-specific functional connectivity patterns of the brain are unique for each individual. These brain states predicted participants cognitive performance, suggesting that they have the potential to be used as biomarkers for cognitive function or neurological disorders. Integrating state-based functional connectivity into clinical frameworks could potentially enhance early diagnosis and patient stratification, and lead to targeted interventions for neuropsychiatric and neurodegenerative conditions.

neuroscience↗

bioRxiv2024

Locus Coeruleus Contrast and Diffusivity: Effects of Age and Relations to Memory

Neurocognitive aging researchers are increasingly focused on the locus coeruleus, a neuromodulatory brainstem structure that degrades with age. With this rapid growth, the field will benefit from consensus regarding which magnetic resonance imaging (MRI) metrics of locus coeruleus structure are most sensitive to age and cognition. To address this need, the current study acquired magnetization transfer- and diffusion-weighted MRI images in younger and older adults who also completed a free recall memory task. Results revealed significantly larger differences between younger and older adults for maximum than average magnetization transfer-weighted contrast (MTC), axial than mean or radial single-tensor diffusivity (DTI), and free than restricted multi-compartment diffusion (NODDI) metrics in the locus coeruleus; with maximum MTC being the best predictor of age group. Age effects for the MTC and NODDI metrics interacted with sex such that larger age group differences were seen in males than females. Age group differences were also larger for DTI metrics in the rostral, and NODDI metrics in the caudal, locus coeruleus subdivision. Within older adults, however, there were no significant effects of age on any measure of locus coeruleus structure. Finally, independent of age and sex, higher restricted diffusion in the locus coeruleus was significantly related to better (lower) recall variability, but not mean recall. Whereas MTC has been widely used in the literature, our comparison between the average and maximum MTC metrics, and inclusion of DTI and NODDI metrics, make important and novel contributions to our understanding of the aging of locus coeruleus structure.

neuroscience↗

bioRxiv2022

A novel hidden Markov approach to studying dynamic functional connectivity states in human neuroimaging

IntroductionHidden Markov models are a popular choice to extract and examine recurring patterns of activity or functional connectivity in neuroimaging data, both in terms of spatial patterns and their temporal progression. Although many diverse hidden Markov models have been applied to neuroimaging data, most have defined states based on activity levels (intensity-based states) rather than patterns of functional connectivity between brain areas (connectivity-based states), which is problematic if we want to understand connectivity dynamics: intensity-based states are unlikely to provide comprehensive information about dynamic connectivity patterns. MethodsWe addressed this problem by introducing a new hidden Markov model that defines states based on full functional connectivity profiles among brain regions. We empirically explored the behavior of this new model in comparison to existing approaches based on intensity-based or summed functional connectivity states using the HCP unrelated 100 functional magnetic resonance imaging "resting state" dataset. ResultsOur full functional connectivity model discovered connectivity states with more distinguishable (i.e., unique and separable from each other) patterns than previous approaches, and recovered simulated connectivity-based states more faithfully than the other models tested. DiscussionThus, if our goal is to extract and interpret connectivity states in neuroimaging data, our new model outperforms previous methods which miss crucial information about the evolution of functional connectivity in the brain. Impact statementHidden Markov models can be used to investigate brain states noninvasively. Previous models "recover" connectivity from intensity-based hidden states, or from connectivity summed across nodes. Here we introduce a novel connectivity-based hidden Markov model and show how it can reveal true connectivity hidden states under minimal assumptions.

neuroscience↗