Biology subjects

Schmidt, H.

Publications and source records attributed to Schmidt, H..

At least 19 recordsLinked to original sources

bioRxiv2026

Sex-stratified fixel-based analysis reveals reduced frontal fibre density in males with first-episode of psychosis

Background: Schizophrenia has been conceptualised as a disorder of brain disconnectivity, with white matter abnormalities contributing to altered neural communication. However, fibre-specific white matter alterations in early illness and their potential sex-related differences remain insufficiently characterised. We applied fixel-based analysis to investigate sex-stratified white matter microstructural alterations in first-episode schizophrenia (FES). Methods: Diffusion MRI data from individuals with FES and healthy controls were stratified by biological sex. The male cohort included 79 patients and 29 controls, and the female cohort included 48 patients and 46 controls. Fixel-based analysis was performed separately in males and females to assess fibre density, fibre-bundle cross-section, and their combined measure. Head motion was included as a covariate in the fixel-based analyses. A descriptive follow-up voxel-scale analysis extracted mean fibre density from a binary mask derived from significant male fixels and applied the same region to the female cohort. Results: Males with first-episode schizophrenia showed significantly reduced fibre density compared with male controls, primarily in bilateral frontal white matter and a thin segment of the anterior corpus callosum. The voxel-scale follow-up confirmed lower mean fibre density in males within the significant fixel-derived mask. In contrast, females showed no significant group differences in any fixel-based metric or within the male-derived region. Conclusions: Early white matter microstructural alterations were detected in males with FES but not in females. These findings highlight a differential pattern emerging from sex-stratified analyses and support further investigation of sex-related differences in white matter alterations during the early stages of schizophrenia.

neuroscience↗

bioRxiv2026

Spinal Recurrent Inhibition Shapes the Dynamics of TMS-induced Motor-Evoked Potentials: A Computational Modeling Study

Motor-evoked potentials (MEPs) recorded via surface electromyography (EMG) from peripheral muscles following transcranial magnetic stimulation (TMS) of the motor cortex reflect the integrity of the entire corticospinal pathway and are widely used in both basic neuroscience and clinical practice. However, the relative contributions of spinal and peripheral mechanisms to the observed MEP waveform remain poorly understood, partly because computational models that capture individual MEP characteristics are lacking. Here, we present a biologically plausible and computationally efficient model of the descending motor pathway, spanning the spinal cord and hand muscles, that can be fitted to individual MEP waveforms across a range of TMS intensities. The model successfully reproduces individual MEP waveforms, accounting for approximately 90% of the observed variance in waveforms across 10 healthy participants. Crucially, we demonstrate that recurrent inhibition of Renshaw cells in the spinal cord is indispensable for reproducing the fine temporal structure of MEP waveforms, even when input-output curve fitting appears adequate without it. Beyond waveform reproduction, the fitted model provides interpretable estimates of latent neural dynamics and subject-specific pathway parameters, including motor neuron size distribution, synaptic receptor balance, axonal conduction delay, and hand muscle refractoriness, that are consistent with known biological ranges. These results suggest that individual MEP waveforms, when analyzed using a biologically grounded model, carry substantially more information about spinal and peripheral motor pathway integrity than conventional amplitude-based measures alone.

neuroscience↗

bioRxiv2026

Pyramidal neuron synapses in M2 exhibit properties intermediate between prefrontal cortex and M1 synapses

Motor planning and control is executed by different motor areas within the neocortex. Despite their distinct functions these areas are built by the same archetypes of neurons as the rest of the cortex, with the pyramidal neurons (PNs) as their principal building blocks. Recent results suggest that the synapses of the PNs are modeled and adapted to their required functions in an area specific manner. PN synapses in a cortical area engaged in higher order functions, the prefrontal cortex (PFC), were found to operate with loose microdomain calcium-influx-to-release coupling and showed short-term facilitation, whereas synapses processing sensory information in a lower order cortical area, the primary somatosensory cortex (S1), featured tight nanodomain coupling and showed short-term depression. In the present study, we asked for the functional coupling configuration of an intermediate processing area. We focused on PN synapses in the premotor cortex M2 and compared their properties to those of PN synapses in the primary motor cortex M1. In both areas we found tight nanodomain coupling and high release probability, but a significant difference in short-term plasticity. Synapses in M1 showed paired-pulse depression similar to S1. In contrast, synapses in M2 exhibited paired-pulse facilitation. Our data suggest that this facilitation results from an accelerated recruitment of synaptic vesicles to the readily releasable pool from an enlarged replenishment pool. Thus, PN synapses in M2 appear to have properties intermediate between those in PFC and M1. Significance StatementNeocortical areas perform diverse computations despite being composed of the same principal neuronal cell types. How synaptic properties are adapted to these functional demands remains poorly understood. We show that L2/3-L5 pyramidal neuron synapses in the secondary motor cortex (M2) combine features of the primary motor cortex (M1), including tight nanodomain Ca{superscript 2}-influx-to-release coupling and high release probability, with paired-pulse facilitation, previously associated with higher-order cortical areas such as the prefrontal cortex (PFC). Our data suggest that this facilitation is mediated by an enlarged replenishment pool and accelerated vesicle recruitment rather than differences in coupling architecture, highlighting vesicle pool organization as an additional mechanism of area-specific synaptic specialization. Thus, L2/3-L5 pyramidal neuron synapses in M2 exhibit intermediate properties between M1 and PFC.

neuroscience↗

bioRxiv2026

Resting-state EEG alpha-BOLD coupling spatially follows cortical cell-type and receptor gradients

The coupling between electroencephalography (EEG) and blood-oxygen-level-dependent (BOLD) signals has been investigated across numerous studies, but its neurobiological underpinnings remain poorly understood. Resting-state EEG alpha-BOLD coupling follows a characteristic spatial pattern, shifting from negative correlations in sensory regions to positive correlations in association cortices. In this study, we examined neurobiological correlates of resting-state alpha-BOLD coupling. We compared the spatial pattern of the alpha-BOLD coupling map to 82 cortical feature maps, including gene expression profiles of different cell types and receptor subunits as well as structural MRI measures. We identified three statistically significant (q < 0.05 FDR-corrected) maps: the layer 6 VIP interneuron marker, excitatory layer-5 marker, and NMDA receptor subunit GRIN2C. The three significant gene maps, combined in a multiple linear regression model, explained R2 = 0.312 of the spatial variance in alpha-BOLD coupling. Analysis of the spatial mismatch between cortical maps and the alpha-BOLD coupling map revealed that the early auditory cortex is the region that consistently diverges from predictions across gene expression and T1/T2 maps. The spatial correspondence between alpha-BOLD coupling and gene expression profiles of specific receptor subunits, neuronal types, and layer-specific populations identifies these as concrete candidates for future computational and experimental studies of alpha-BOLD coupling. Author SummaryThe brains electrical rhythms and metabolic activity are coupled, yet why this coupling differs across brain regions remains poorly understood. This study shows that resting-state alpha-BOLD coupling, a well-established link between EEG alpha oscillations and fMRI signals, maps onto the brains cellular landscape: regions enriched in specific inhibitory interneurons and NMDA receptor subunits show systematically different coupling strengths. These findings suggest that regional differences in cell-type composition and receptor expression, rather than purely anatomical features, could shape the spatial organization of alpha-BOLD coupling. By identifying candidate cortical features, this work can guide future experimental and computational studies, ultimately helping to establish alpha-BOLD coupling as a relevant biomarker for psychiatric and neurological disorders.

neuroscience↗

bioRxiv2026

LAML-Pro: Maximum Likelihood Inference of Cell Genotypes and Cell Lineage Trees

MotivationRecent dynamic lineage tracing technologies use genome editing to induce heritable mutations, or edits, that accumulate across successive cell divisions. These edits are measured using single-cell sequencing or imaging, providing data to reconstruct cell lineages at single-cell resolution. Current computational approaches to infer cell lineage trees, or phylogenies, from these data perform two separate steps: (1) Identify each cells edits (genotype) from the raw sequencing or imaging data; (2) Infer a cell lineage tree from the cell genotypes. However, genotyping cells is an inexact process and genotype errors can yield an inaccurate lineage tree. For example, using fluorescence based-imaging to measure edits results in a high fraction ({approx} 25-50%) of uncertain or erroneous genotypes. ResultsWe introduce Lineage Analysis via Maximum Likelihood with PRobabilistic Observations (LAML-Pro), an algorithm that jointly infers cell genotypes and a cell lineage tree. LAML-Pro is based on the Probabilistic Mixed-type Missing Observation (PMMO) model, which we derive to describe both the genome editing and genotype observation processes. LAML-Pro constructs lineage trees from thousands of cells in under an hour by leveraging the sparsity of transitions under the PMMO model. On simulated data, we demonstrate that LAML-Pro corrects genotype errors and infers substantially more accurate trees than existing methods which are vulnerable to genotype errors. Applied to data from two recent imaging-based lineage tracing systems, LAML-Pro reduces genotype errors by 5-fold and produces more spatially coherent lineage trees compared to existing methods. Availability and ImplementationLAML-Pro is freely available at: github.com/raphael-group/LAML-Pro.

bioinformatics↗

bioRxiv2026

POTTR: Identifying Recurrent Trajectories in Evolutionary and Developmental Processes using Posets

Multiple biological processes, including cancer evolution and organismal development, are described as a sequence of events with a temporal ordering. While cancer evolves independently in each patient, DNA sequencing studies have shown that in some cancers different patients share specific orders of mutations and these correlate with distinct morphology, drug response, and treatment outcomes. Several methods have been developed to identify such recurrent trajectories of genetic events from phylogenetic trees, but this is complicated by high intra- and inter-tumor heterogeneity as well as uncertainty in the inferred tumor phylogenies including the ambiguous orders between some mutations. We formalize the problem of finding recurrent mutation trajectories using a novel framework of incomplete partially ordered sets (posets), which generalize representations used in previous works and explicitly account for the uncertainty in tumor phylogenies. We define the problem of identifying the largest recurrent trajectories shared in at least k input phylogenies as the maximum k-common induced incomplete subposet (MkCIIS) problem, which we show is NP-hard. We present a combinatorial algorithm, POsets for Temporal Trajectory Resolution (POTTR), to solve the MkCIIS problem using a conflict graph that models recurrent trajectories as independent sets. Thereby we identify maximum recurrent trajectories while resolving multiple sources of uncertainty, like mutation clusters, in the phylogenetic data. We apply POTTR to TRACERx non-small cell lung cancer bulk sequencing and acute myeloid leukemia single-cell sequencing data and through resolution of mutation clusters discover previously unreported trajectories of high statistical significance. On lineage tracing data of an in vitro embryoid model, POTTR identifies conserved differentiation routes across biological replicates and how these routes change in response to chemical perturbations.

bioinformatics↗

bioRxiv2026

Fully T2T pedigree assemblies reveal genetic stability and epigenetic plasticity of human centromeres across inheritance and cell-fate transitions

Centromeres are essential chromosome components yet remain poorly understood due to their highly repetitive sequence architecture. Using fully-phased telomere-to-telomere diploid assemblies from a three-generation pedigree integrated with long-read epigenomes from matched peripheral blood mononuclear cells, induced pluripotent stem cells, and neural progenitor cells, we generate allele-resolved single basepair resolution maps of centromere genetic and epigenetic dynamics across inheritance, reprogramming, and differentiation. We show that centromeric dip regions (CDRs), which define the functional core of centromeres, are positionally stable across generations and cell-fate transitions. In contrast, CDR epigenetic architecture is highly dynamic. Reprogramming markedly attenuates CDR hypomethylation, which is partially restored during differentiation in parallel with global hypomethylation of active alpha-satellite arrays and coordinated changes in nucleosome organization and protein occupancy. Centromeric remodeling is insulated from X-chromosome status, including Xa, Xi, and erosion. Finally, de novo mutations arising during reprogramming are enriched in centromeric regions but depleted within functional centromeric cores.

genomics↗

bioRxiv2025

Loose coupling between Ca2+ channels and release sensors as a synaptic correlate of higher order brain function

In the mature neocortex, functionally distinct areas are built by the same archetypes of neurons, but depending on the area, these neurons and their synapses are engaged in very different functions, ranging from lower order processing of sensory information to higher order associations and cognitive functions. We found significant differences in the functional presynaptic nanoarchitectures of the same types of pyramidal neuron synapses, depending on whether they are located in the prefrontal cortex (PFC) or in the primary somatosensory cortex (S1). Synapses in PFC operated with loose microdomain coupling as opposed to tight nanodomain coupling in S1. These differences were associated with significant differences in synaptic timing, efficacy and plasticity between areas. Our data suggest that the mature neocortex uses tuning of synaptic topographies to specialize seemingly identical types of neurons for their required function. They suggest that microdomain coupling in pyramidal neuron synapses could be a presynaptic structure-function correlate of higher order neocortical functions.

neuroscience↗

bioRxiv2025

Inferring Cell Differentiation Dynamics with Unobserved Progenitors

A cell differentiation map describes the transitions between cell types during a developmental process, and determining this map is a key challenge in developmental biology. Recent single-cell lineage tracing technologies generate lineage trees that describe the history of cell divisions during a developmental process but do not directly measure differentiation events between cell types. Current approaches to infer cell differentiation maps from cell lineage trees make unrealistic assumptions about the developmental process, do not allow for unobserved progenitor cell types, or do not infer cell-type specific rates of growth and transitions. To address these issues, we introduce TROUPE, a likelihood-based framework that infers differentiation and growth dynamics directly from leaf-labeled cell lineage trees while allowing for unobserved progenitor types via biologically motivated potency constraints. We provide an efficient algorithm for computing the maximum likelihood transition and growth rates, as well as a simple model-selection scheme to choose the number of unobserved types. On simulations, TROUPE recovers transition and growth rates better than previous approaches even when there are unobserved types. We apply TROUPE to a model of mammalian development, Trunk-Like Structures (TLS), and show that TROUPE computes reasonable rates of self-renewal and differentiation in both standard and perturbed conditions.

developmental biology↗

bioRxiv2025

Revised 16S rRNA V4 hypervariable region targeting primers enhance detection of Patescibacteria and other lineages across diverse environments

Primer bias in 16S rRNA gene amplicon sequencing can distort microbial diversity estimates by underrepresenting key taxa. We introduce a modified primer pair (V4-EXT) targeting the hypervariable V4 region of bacterial and archaeal 16S rRNA genes, with improved in silico taxonomic inclusivity. To benchmark performance, we analyzed 938 samples from terrestrial, aquatic, and host-associated habitats, comparing microbial community profiles derived with V4-EXT and the currently most widely used V4-targeted primers. V4-EXT substantially improved the detection of Patescibacteria and other underrepresented lineages, such as Chloroflexota and Iainarchaeota, while enhancing recovery of novel amplicon sequence variants across sample types. Overall, V4-EXT provides broader taxonomic coverage and more inclusive microbial community profiles, particularly in high-diversity ecosystems such as groundwater and soils. We propose V4-EXT as a robust successor for comprehensive microbial community analysis across diverse habitats.

microbiology↗

bioRxiv2025

Genetic mechanisms of resistance to targeted KRAS inhibition

KRAS mutations are among the most prevalent oncogenic drivers in non-small cell lung cancer (NSCLC), yet the mechanisms of therapeutic resistance to KRAS inhibitors in these cancers remains poorly understood. Here, we deploy high-throughput CRISPR base editing screens to systematically map resistance mutations to three mechanistically distinct KRAS-targeted therapies, including KRAS-G12C(OFF) inhibitor (adagrasib), RAS(ON) G12C-selective tri-complex inhibitor (RMC-4998), and RAS(ON) multi-selective tri-complex inhibitor (RMC-7977). Using both a saturation Kras tiling approach and cancer-associated mutation library, we identify common and compound-selective second-site resistance mutations in Kras, as well as gain-of-function and loss-of-function variants across cancer-associated genes that rewire signaling networks in a context-dependent manner. Notably, we identify a recurrent missense mutation in capicua (Cic), that promotes resistance to RMC-7977 in vitro and in vivo. Moreover, we show that targeting NF{kappa}B signaling in CIC-mutant cells can resensitize them to RAS pathway inhibition and overcome resistance.

cancer biology↗

bioRxiv2025

Long-Range Input to Cortical Microcircuits Shapes EEG-BOLD Correlation

Electroencephalography (EEG) rhythms and blood-oxygen-level-dependent (BOLD) activity, though generated by different mechanisms, exhibit correlations. The level of correlation varies between EEG frequency bands, brain regions, and experimental paradigms, but the underpinning mechanisms of this correlation remain poorly understood. Here we create a mathematical, data-informed model of a cortical microcircuit that encompasses all major neuron types across cortical layers, and use it to generate EEG and BOLD under various external input conditions. The model exhibits noise-driven fluctuations giving rise to distinct EEG rhythms, with external inputs modulating EEG spectral characteristics. In line with experimental findings, we observe negative alpha-BOLD correlations and positive gamma-BOLD correlations across different input configurations. Temporal variability of the input is found to increase EEG-BOLD correlation and to improve the correspondence with experimental results. This study provides a mathematical framework to theoretically study the correlation of EEG and BOLD features in a comprehensive way.

neuroscience↗

bioRxiv2025

Characterising Structural Brain Connectivity of Patients with First Episode of Psychosis

BACKGROUND AND HYPOTHESISSchizophrenia is associated with widespread neuroanatomical abnormalities affecting both grey matter and white matter (WM). Early symptoms are often linked to dysfunctions in the frontal cortex and the temporal lobe. This study investigates WM disruptions and explores how structural connectivity (SC) may contribute to the underlying mechanisms of the disorder. STUDY DESIGNWe analysed SC derived from diffusion MRI in 127 patients experiencing their first episode of schizophrenia (FES group), compared with healthy controls. Focusing on the fronto-parietal-temporal network, we examined SC across three hierarchical levels: network, node, and connection. SC metrics were compared between groups using 3-factor ANCOVA, accounting for relevant covariates. We also investigated associations between SC metrics and core positive symptoms using non-parametric correlations. RESULTSThe FES group showed significantly reduced average SC strength and global efficiency within the fronto-parietal-temporal network. At the nodal level, SC strength was significantly lower in the left inferior and middle temporal gyri (L.ITG, L.MTG), and in the right inferior parietal gyrus (R.IPG) and temporal pole (R.TP). No significant group differences emerged at the connection level. Notably, SC strength in the R.IPG was negatively correlated with Conceptual Disorganisation scores. CONCLUSIONSOur findings reveal global and regional SC disruptions in early psychosis, particularly in areas supporting cognitive, language, and executive functions. The observed association between R.IPG connectivity and Conceptual Disorganisation supports the link between disrupted SC and formal thought disorder, reinforcing the role of impaired structural integration in early psychosis.

neuroscience↗

bioRxiv2025

Connectomic traces of Hebbian plasticity in the entorhinal-hippocampal system

The key model of how we learn and memorize is Hebbian learning in the hippocampus, via long-term potentiation of synapses, allowing the storage of associations, linkage to places, and their consolidation into imprinted episodes. Learning is therefore expected to change synaptic weights. With the notion of hippocampal circuits being the primary site of learning in the mammalian brain, it has been assumed that all synapses in these circuits are constantly exposed to synaptic plasticity, and possibly in a learned state. However, a testing of these hypotheses is so far missing. In particular, the systematic mapping of synaptic weight distributions, and their relation to Hebbian preconditions has not been achieved yet in the hippocampal-entorhinal system. Here, we report such a systematic connectomic mapping of synaptic weight distributions and their relation to same-axon same-dendrite paired synaptic configurations across the hippocampal-entorhinal system. By analyzing millions of synapses and tens of thousands of paired synaptic configurations from 3D EM-based automated circuit reconstructions in hippocampal areas CA3, CA1, and layers 2 and 3 of the medial entorhinal cortex (MEC), we found systematic and unique synaptic weight distributions, with almost 50% (but not 100%) of synaptic weights in CA1 being in a Hebbian-consistent state, CA3 uniquely exhibiting small synaptic weights with indications of learned states, and MEC resembling previous data from other isocortices, with only up to 20% learned synaptic configurations. We further analyzed the sublayer-specificity of these weight distributions, finding the molecular layer of CA1 and the lower layers of CA3 being the most unique site of potential learning. Together, this data provides a first systematic synaptic weight analysis of the key neuronal system involved in memory formation in mammalian brains.

neuroscience↗

bioRxiv2025

Large-scale 3D EM connectomics dataset of mouse hippocampal area CA1

The hippocampal formation is thought to be crucial for memory and learning, with subarea cornu ammonis 1 (CA1) considered to play a major role in spatial and episodic memory formation, and for evaluating the match between retrieved memories and current sensory information. While enormous progress has been made in classifying CA1 neurons based on molecular, morphological, and functional properties, and in identifying their role in behavioral tasks, a clear understanding of the underlying circuits is still missing. Here, we present the first large-scale three dimensional (3D) electron microscopy dataset of mouse CA1 at nanometer-scale resolution. The dataset is available online and can be readily used for circuit reconstructions, as demonstrated for inputs to CA1 superficial layers. Example volume segmentations show that automated reconstruction detection is feasible. Using these data, we find evidence against the long-held assumption of a homogeneous pyramidal cell population. Furthermore we find substantial possibly long-range axonal innervation of stratum-lacunosum interneurons, suggested previously to originate in L2 of MEC. These first analyses illustrate the usability of this dataset for finally clarifying the connectomic properties of mouse CA1, a key structure in mammalian brains.

neuroscience↗

bioRxiv2025

Ion Dynamics Underlying the Seizure Delay Effect of Low-Frequency Electrical Stimulation

The biological mechanisms underlying the spontaneous and recurrent transition to seizures in the epileptic brain are still poorly understood. As a result, seizures remain uncontrolled in a substantial proportion of patients. Brain stimulation is an emerging and promising method to treat various brain disorders, including drug-refractory epilepsy. Selected stimulation protocols previously demonstrated therapeutic efficacy in reducing the seizure rate. The stimulation efficacy critically depends on chosen stimulation parameters, such as the time point, amplitude, and frequency of stimulation. This study aims to explore the neurobiological impact of 1Hz stimulation and provide the mechanistic explanation behind its seizure-delaying effects. We study this effect using a computational model, a modified version of the Epileptor-2 model, in close comparison with such stimulation effects on spontaneous seizures recorded in vitro in a high-potassium model of ictogenesis in rat hippocampal slices. In particular, we investigate the mechanisms and dynamics of spontaneous seizure emergence, the seizure-delaying effect of the stimulation, and the optimal stimulation parameters to achieve the maximal anti-seizure effect. We show that the modified Epileptor-2 model replicates key experimental observations, and captures seizure dynamics and the anti-seizure effects of low-frequency electrical stimulation (LFES) observed in hippocampal slices. We identify the critical thresholds in the model for seizure onset and determine the optimal stimulation parameters - timing, amplitude, and duration - that exceed specific thresholds to delay seizures without triggering premature seizures. Our study highlights the central role of sodium-potassium pump dynamics in terminating seizures and mediating the LFES effect. Author SummaryThis study investigates the mechanisms by which low-frequency electrical stimulation can suppress epileptic seizures. Epilepsy patients often do not respond to pharmacological treatment, necessitating alternative approaches, such as brain stimulation. Using a combination of computational modeling and in vitro experiments on rat hippocampal slices, we examine how periodic stimulation at 1 Hz influences seizure occurrence. Our results show that carefully timed low-frequency stimulation can delay seizure onset by modulating neuronal excitability, largely through the action of the Na-K-pump that maintains ion homeostasis. We employ a modified version of the Epileptor 2 model to reproduce the protective effects seen experimentally. By systematically varying stimulation parameters, we identify conditions that effectively delay seizures, helping to explain the antagonistic effects of stimulation observed by previous studies. Overall, this work advances our understanding of how low-frequency electrical stimulation interacts with intrinsic neuronal mechanisms to prevent seizures, thus offering a potential target for more effective neuromodulation strategies in drug-resistant epilepsy.

neuroscience↗

bioRxiv2025

Neural signatures of recollection are sensitive to memory quality and specific event features

Episodic memories reflect a bound representation of multimodal features that can be recollected with varying levels of precision. Recent fMRI investigations have demonstrated that the precision and content of information retrieved from memory engage a network of posterior medial temporal and parietal regions co-activated with the hippocampus. Yet, comparatively little is known about how memory content and precision affect common neural signatures of memory captured by electroencephalography (EEG), where recollection has been associated with changes in event-related potential (ERP) and oscillatory measures of neural activity. Here, we used a multi-feature paradigm previously reported in Cooper & Ritchey (2019) with continuous measures of memory, in conjunction with scalp EEG, to characterize the content and quality of information that drives ERP and oscillatory markers of episodic memory. A common signature of memory retrieval in left posterior regions, called the late positive component (LPC), was sensitive to overall memory quality and also to precision of recollection for spatial features. Analysis of oscillatory markers during recollection revealed that alpha/beta desynchronization was modulated by overall memory quality and also by individual features in memory. Importantly, we found evidence of a relationship between these two neural markers of memory retrieval, suggesting that they may represent complementary aspects of the recollection experience. These findings demonstrate how time-sensitive and dynamic processes identified with EEG correspond to overall episodic recollection, and also to the retrieval of precise features in memory.

neuroscience↗

bioRxiv2025

LRP2 expression in melanoma is associated with a transitory cell state, increased T cell infiltration, and is upregulated by IFNγ signaling

Low density lipoprotein receptor-related protein 2 (LRP2) is a 600 kilodalton multi-ligand endocytic membrane receptor expressed in several cell types during fetal development, including neuroepithelial cells, and in select absorptive epithelial cells in the adult. In epithelial cancers, LRP2 expression is associated with a differentiated tumor cell state and better prognosis. In previous work, we found that while LRP2 is not expressed in benign naevi, it is frequently acquired in melanoma. However, the molecular drivers of LRP2 expression in melanoma and characteristics of LRP2-expressing melanoma have yet to be described. Here, we show that LRP2 expression is related to a transitory melanoma cell state defined by co-expression of melanocyte lineage and neural crest transcriptional programs. Further, we reveal that melanoma LRP2 expression is increased in T cell-inflamed tumors, and is directly upregulated through interferon-gamma signaling. Correlation of melanoma LRP2 expression with clinicopathological variables demonstrates that LRP2 expression is associated with low Breslow thickness and low clinical stage in primary melanomas. Taken together, the present study describes the characteristics of LRP2-expressing melanoma and reveals interferon gamma signaling as a novel strong positive regulator of LRP2 expression in melanoma. SignificanceMelanoma cells often acquire LRP2 expression but the drivers of LRP2 expression in this setting and characteristics of LRP2-expressing melanoma remain unclear. Here, we show that LRP2 expression is related to a transitory melanoma differentiation cell state. Further, LRP2 expression in melanoma correlates with a T cell-inflamed tumor microenvironment and LRP2 expression in melanoma cells can be directly increased by interferon-gamma. In addition, LRP2 expression is associated with less advanced histopathological characteristics of melanoma. These findings encourage future studies on LRP2 in settings with increased interferon signaling, in particular in melanoma metastases following immunotherapy.

cancer biology↗