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Results for “cell biology”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2026

Magnesium induces iron starvation and metabolic rewiring to support the viability of cell envelope mutants and antibiotic-stressed cells

Magnesium supplementation permits deletion of otherwise essential genes involved in cell envelope biogenesis in the Gram-positive model bacterium Bacillus subtilis. Yet, the specific underlying mechanism has remained elusive. To address this key knowledge gap, we made use of a mutant lacking ezrA and gpsB. Deletion of both of these genes involved in cell wall synthesis leads to severe growth inhibition which is ameliorated by magnesium addition. Our results indicate that, in the absence of magnesium, this mutant contains elevated levels of labile iron, is impaired in activating the oxidative stress response, and displays extreme sensitivity to iron and manganese intoxication. Intriguingly, we find that an ezrA single deletion, but not gpsB, exhibits heightened susceptibility to excess iron and manganese. This observation allowed us to investigate the source of toxicity and how EzrA may support metal homeostasis. Our data suggests that the major contributor of ROS is the electron transport system involved in cellular respiration. Both genetic and chemical means to reprogram the cells in favor of fermentation alleviate the metal toxicity in cells lacking ezrA. Collectively, our data shows that magnesium limits iron availability and redirects metabolism towards pathways that are preferred during iron scarcity. Consequently, these mechanisms result in reduced ROS production and oxidative stress mitigation. This explains why magnesium supplementation may render essential genes dispensable. In support of this model, we find that addition of magnesium helps cells to circumvent lysis typically caused by the treatment of an antibiotic that disrupts cell wall synthesis. Taken together, our results suggest that unmitigated oxidative stress fueled by labile iron is likely responsible for the detrimental effects of specific gene disruptions and certain antibiotic treatments. By reducing the pool of free iron and reprogramming cellular metabolism, magnesium mitigates oxidative damage and protects cells from ROS-mediated death.

microbiology↗

bioRxiv2026

RECON infers regions of interest from H&E images and reconstructs whole-slide molecular profiles at single-cell resolution

Spatial omics technologies resolve molecular expression and spatial architecture at single-cell resolution, but profiling whole slides remains costly. In practice, only a few regions of interest (ROIs) are profiled, leaving the rest of the tissue unmeasured. S2-omics was the first framework to unify ROI selection with out-of-ROI prediction, but it operates on superpixels rather than individual cells and predicts discrete cell types rather than continuous molecular profiles. Superpixel-based representations do not explicitly preserve cell boundaries, while categorical cell-type labels cannot quantify molecular expression within cells. Here we present RECON, a two-stage framework that performs ROI inference and whole-slide molecular reconstruction at single-cell resolution, predicting both continuous molecular profiles and discrete cell-type labels. In the first stage, RECON extracts morphological and microenvironmental features from individual cells to identify a representative ROI for spatially resolved single-cell molecular profiling. In the second stage, RECON trains deep learning models on molecular measurements acquired within the selected ROI and reconstructs transcriptomic or proteomic profiles for all remaining cells on the slide. Benchmarked against pathologist annotations, RECONs ROI selection outperforms the superpixel-based S2-omics approaches (IoU: 0.75 versus 0.64). For transcriptomics, refining the modeling unit from superpixels to single cells improves per-gene Pearson correlation by 22%. For proteomics, RECON surpasses the current state-of-the-art method, ROSIE, across all 16 markers, with a median per-cell Pearson correlation of 0.91 versus 0.84. Moreover, RECON delineates tumour boundaries and regions with distinct immune-cell densities, and highlights candidate tertiary lymphoid structures. Together, these results demonstrate that RECON enables informative ROI selection and whole-slide molecular reconstruction at single-cell resolution for both spatial transcriptomics and spatial proteomics.

bioinformatics↗

bioRxiv2026

Tumor γδ T-cell abundance is associated with favorable cancer treatment outcomes

Purpose: Clinical response to immune checkpoint blockade (ICB) remains variable. We asked whether immune-cell populations in the tumor microenvironment (TME) are associated with benefit across treatments and tumor types. Experimental Design: We analyzed pretreatment bulk tumor RNA-seq from ICB cohorts and TCGA. Gene-level effects associated with ICB response or TCGA survival were projected onto Human Primary Cell Atlas profiles of 157 cell types. Cox and mixed-effects models accounted for cancer type, cohort, and therapy, as appropriate. After {gamma}{delta} T cells emerged as a leading population, we adjusted their associations for eight CD8 estimators and evaluated them using TRUST4-based TRG/TRD reconstruction and single-cell RNA-seq. Results: {gamma}{delta} T-cell programs were among the signatures consistently associated with ICB response and favorable TCGA survival. Across ICB cohorts, {gamma}{delta} T-cell abundance was associated with response (n=1,356; OR, 1.38; 95% CI, 1.23-1.56) and overall survival (n=1,074; HR, 0.82; 95% CI, 0.76-0.88), with associations persisting after CD8 adjustment. ICB-response-associated cell-type profiles were strongly concordant with chemotherapy response (r=0.92) and moderately concordant with radiation response (r=0.58); targeted and hormone therapy analyses were underpowered. TRUST4 reconstruction and single-cell RNA-seq provided orthogonal support for the {gamma}{delta} signal. Conclusions: Pretreatment {gamma}{delta} T-cell abundance was associated with favorable ICB outcomes and survival across cancers, while related cell-type programs extended to selected non-immunotherapy response settings. Although associative and context dependent, these findings support prospective evaluation of {gamma}{delta} T-cell abundance as a candidate tumor-immune biomarker.

immunology↗

bioRxiv2026

Genetic Disruption at the CIP2A Locus Modulates T Cell Responses and Attenuates Experimental Autoimmune Encephalomyelitis

Multiple sclerosis (MS) is a chronic autoimmune disease of the central nervous system (CNS) driven by pathogenic T cell-mediated inflammation. Fingolimod (FTY720), an approved therapy for MS, is an established activator of protein phosphatase 2A (PP2A). However the contribution of PP2A in autoimmune neuroinflammation remains incompletely understood. Here, we addressed this question using experimental autoimmune encephalomyelitis (EAE), a murine model of MS, in mice carrying a genetic disruption of the locus encoding cancerous inhibitor of protein phosphatase 2A (CIP2A), an endogenous inhibitor of PP2A. Mice with disruption of the CIP2A locus, the knock out (KO) mice, exhibited attenuated EAE severity compared with wild-type (WT) controls. Histological and flow-cytometric analyses revealed markedly reduced infiltration of mononuclear cells, including CD4 and CD4CXCR6 encephalitogenic T cells, in the CNS of diseased KO mice. Reduced numbers of these T cell populations were also observed in peripheral lymphoid organs of the Cip2a-deficient mice during EAE, while T cell abundance was comparable under steady-state conditions, suggesting impaired activation-induced expansion rather than altered homeostasis or migration. Single-cell RNA sequencing of CNS and lymph node immune cells revealed changes in cell-type abundance and gene expression. Notably, Il17a expression was reduced in CNS CD8+ T cells and showed a similar trend in {gamma}{delta} T cells. Together, our findings reveal that genetic disruption at the CIP2A locus attenuates EAE, possibly by limiting the expansion and accumulation of encephalitogenic T cell populations in CNS. These results identify the CIP2A locus as a previously unrecognized regulator of T cell-driven autoimmune neuroinflammation and provide new insights into mechanisms that restrain pathogenic T cell responses during EAE.

immunology↗

bioRxiv2026

Comprehensive characterization of genomic, transcriptomic and epigenomic artifacts introduced in formalin-fixed, paraffin-embedded tissues.

Genomic, transcriptomic and epigenomic characterization has accelerated the discovery of clinically-relevant alterations in cancer, predominantly using fresh frozen (FF) specimens. However, clinical molecular pathology laboratories prefer formalin-fixed paraffin-embedded (FFPE) methods, known to introduce artifacts at the nucleic acid level, over fresh frozen methods. Extending the multi-platform analysis to FFPE specimens for comprehensive clinical molecular diagnosis requires a thorough understanding of the consequence of formalin-fixation. We present a detailed multi-platform characterization of FFPE preservation using paired FF specimens as the 'gold standard'. DNA and RNA were obtained from 38 patients across 6 cancer types using a FFPE optimized co-isolation. The impact of FFPE on exome sequencing was dependent on filtering, where a minimum coverage or supporting read filter can mitigate FFPE-specific false positives. Copy number alterations, MSI assessment, mutational signatures, and DNA methylation were comparable between FFPE and FF. FFPE biases in RNA expression can be overcome when using biology-relevant genes and we describe a novel consequence of FFPE on miRNA species diversity. Collectively, this data provides a broad view of FFPE artifact and offers best practices for overcome these biases.

bioinformatics↗

bioRxiv2026

Local mechanical heterogeneity drives epidermal cell delamination

Delamination within stratified epithelia like the skin epidermis describes the detachment and upward motion of cells originating from the basal layer. Despite its fundamental importance for tissue development, homeostatic regeneration and repair, the mechanisms that drive delamination remain a longstanding open question. Upward motion follows cell shape changes, which are inherently driven by physical forces, but their role is elusive. Here, we investigate delamination in stratifying keratinocytes by combining imaging, force measurements and theoretical modeling. We identify a local change in force balance between differentiating cells and their environment as the key step initiating delamination. Within a homogeneous cell layer with apically polarized contractility, differentiation leads to actomyosin remodeling, redistributing cellular force exertion to the basal side. Such mechanical heterogeneity then results in differentiating cells experiencing and inward basal and outward apical forces that manifest in the formation of a +1 force defect and promote shape changes culminating in upward motion. Simultaneously, delaminating cells actively pull on their underlying neighbors, generating convergent tissue flows which close the basal layer below. Together, we propose a general physical description of delamination initiation, which may act across various multilayered epithelia.

biophysics↗

bioRxiv2026

The Unreasonable Effectiveness of Cell Types in Describing Neuronal Physiological Features

Single-cell RNA sequencing (scRNA-seq) captures detailed gene expression profiles at scale, while patch-clamp recordings measure intrinsic neuronal electrophysiological properties. Modeling the relations between these two modalities remains a challenge. Here, we compare how well electrophysiological features can be predicted by traditional transcriptomic cell type classification, representations derived from a foundational model (scGPT) pretrained on large-scale scRNA-seq datasets, ion channel-coding genes, and highly variable genes. Using paired transcriptomic and electrophysiological patch-sequencing data from 495 human neurons from neurosurgical tissue, we find that cluster-level cell type representations consistently outperform highly variable gene selection, ion channel gene selection, and context-enriched scGPT embeddings. Notably, performance varies across model architectures and initializations, and the best results are obtained by combining the outputs of separate cell type and scGPT-based models. Together, these findings suggest that traditional discrete cellular classification is highly effective in predicting physiological features. For maximum performance it can be complemented by pretrained transformer models.

neuroscience↗

bioRxiv2026

Human Osteocytes Express MHC ClassII and Act as Non-classical Antigen-Presenting Cells During Bacterial Infection

Osteocytes are the most abundant cells in bone and are increasingly recognised not only for their role in skeletal remodelling and inflammatory signalling but also for their potential involvement in immune responses. In this study, we searched available gene expression datasets of human primary osteocyte-like cells exposed acutely to Staphylococcus aureus and identified significantly induced expression of key genes related to antigen processing and presentation. We then confirmed that human bone explant-derived osteoblastic cells, representative of a mature osteoblast-pre-osteocyte stage, expressed, as expected, high cell surface levels of major histocompatibility complex (MHC) Class I but also, low basal levels of the MHC Class II family member, HLA-DR. However, confocal imaging revealed high expression of MHC Class II molecules and the peptide-loading chaperone HLA-DM within the lysosomal compartments, consistent with canonical antigen-processing machinery. Differentiation towards a mature osteocyte phenotype increased MHC Class II protein levels and maintained expression of intracellular HLA-DM. Exposure of mature osteocyte-like cells to S. aureus further up-regulated both intracellular and cell surface MHC Class II expression. Demonstrative of antigen presenting cell functionality, S. aureus-exposed osteocytes induced autologous CD4+ T cell proliferation. Furthermore, MHC Class II expression in osteocytes was detected in bone sampled from patients with periprosthetic joint infections, providing evidence that these mechanisms operate in vivo. Together, our findings reveal that human osteocytes are capable of inducible MHC Class II-associated antigen presentation in response to bacterial challenge, pointing to a novel role for osteocytes in adaptive immune surveillance within bone.

immunology↗

bioRxiv2026

AnnFlux: object-conditioned neural stochastic differential equations for single-cell perturbation dynamics

Single-cell perturbation profiling measures responses to genetic and chemical interventions, yet most models learn a static map, ignoring how populations move over time and how perturbations combine. AnnFlux, an object-conditioned stochastic differential equation, learns a drift field in latent cell-state space. Conditioning on the perturbing object makes the field queryable one object at a time, yielding per-object drifts comparable across genes and drugs. By learning a drift field tailored to each perturbation context, it interpolates a held-out timepoint in an epithelial-mesenchymal transition time course and predicts unseen perturbations. Beyond point estimates, AnnFlux improves distributional fidelity and predicts responses to held-out perturbation combinations. An IFN-response signature predicted by AnnFlux was associated with TLS proximity in an independent pan-cancer spatial atlas. This framework maps perturbation-driven cell-state evolution as continuous trajectories and represents unseen perturbations using prior-knowledge embeddings.

bioinformatics↗

bioRxiv2026

A mechanistic basis for CD8+ T cell expansion sensitivity as a predictor of HIV post-treatment control

A key goal in HIV-1 cure research is to understand why some individuals control viral rebound after stopping antiretroviral therapy (ART). Recent human studies have identified responding CD8+ T cells expressing Ki-67 and the transcription factor TCF-1 as correlates of post-treatment control, but the mechanistic basis of this association remains unclear. Using the theoretical framework of Conway and Perelson, we fit mechanistic within-host models to viral load and CD8+ T cell data from 9 individuals in a combination immunotherapy trial following ART interruption. Although Ki-67 and TCF-1 measurements were not used for fitting, the inferred effector cell expansion sensitivity, i.e., the responsiveness of effector expansion to low antigen levels, shows a strong linear relationship with Ki-67 and TCF-1 levels at rebound (Pearsons r {approx} 0.8). Building on this, we show analytically that the post-rebound viral load set point is inversely proportional to the effector cell expansion sensitivity, and thus strongly correlates with cycling (Ki-67+) CD8+ T cells (r {approx} -0.8) at rebound, and a subset that expresses TCF-1 (r {approx} -0.9). In effect, individuals with a larger proportion of CD8+ T cells responding to viral rebound, and a greater representation of TCF-1 expressing cells within the responding subset, achieve markedly lower viral set points through a higher effector cell expansion sensitivity. This mechanism is consistent with prior modeling in a non-intervention ATI setting, suggesting it may generalize across more rebound contexts. Our results provide a mechanistic explanation why both Ki-67+ responding CD8+ T cells and their TCF-1-expressing subset predict post-treatment control, linking clinical correlation to its underlying cause and highlighting Ki-67 and TCF-1 as potential early biomarkers of HIV immunotherapy success.

immunology↗

bioRxiv2026

Hindbrain explants enable multimodal and longitudinal analysis of the developing olivo-cerebellar circuit at single-cell resolution

Experimental models that preserve native mammalian CNS circuitry while enabling longitudinal analysis of circuit assembly at single-cell resolution remain scarce, limiting mechanistic studies and therapeutic discovery. Here, we establish embryonic mouse hindbrain explants as a scalable in vitro model that maintains the long-range olivo-cerebellar circuit while providing direct experimental access to both pre- and postsynaptic neurons. The preparation supports repeated live imaging, targeted single-cell manipulation and labelling, electrophysiology, ultrastructural analysis, and single-cell RNA sequencing during circuit assembly. Hindbrain explants faithfully recapitulate key features of olivo-cerebellar organization and development, including cytoarchitecture, synaptic organization and maturation, neuronal differentiation, and spontaneous network activity while preserving developmental glial features. By combining developmental and physiological fidelity with longitudinal multimodal accessibility, this resource bridges the gap between reductionist cultures and technically demanding in vivo approaches, providing a versatile and ethical model for investigating the molecular and cellular mechanisms of cerebellar circuit assembly and disease.

neuroscience↗

bioRxiv2026

Interactive downstream proteomics analysis with MiraProt using Mueller cell proteomes from equine recurrent uveitis

Mass spectrometry-based proteomics requires downstream analysis of processed protein abundance data, including data inspection, filtering, statistical testing, functional enrichment, protein set comparison, network analysis, and visualization. MiraProt was developed as a modular, metadata-aware R Shiny platform that integrates these steps in a single interactive workflow for processed protein-level proteomics data. Its metadata-aware design enables identifiers, sample information, experimental conditions, transformations, and derived data columns to be defined during data preparation and reused consistently across downstream analyses. To demonstrate its use, we reanalyzed a previously published label-free proteomic dataset of primary retinal Mueller cells from healthy horses and horses with equine recurrent uveitis (ERU). ERU is a naturally occurring autoimmune eye disease of horses characterized by recurrent intraocular inflammation triggered by autoreactive T-cells. Mueller cells are specialized retinal macroglia with various functions such as maintaining retinal ion homeostasis and supporting retinal neuron metabolism. Of 193 proteins with an adjusted p-value [≤] 0.05, 187 also showed at least a twofold abundance difference between ERU-derived and control Mueller cells. Functional enrichment highlighted nuclear RNA processing, chromatin-associated structures, DNA and RNA binding, interferon responses, and cell-cycle-associated programs. Gene set enrichment analysis identified positive enrichment of Interferon Alpha Response, Interferon Gamma Response, and MYC-, E2F-, and G2M-associated gene sets. Network analysis of shared proteins further linked this signature to DNA replication, mitotic checkpoint control, and RNA processing. ERU-derived Mueller cells also showed increased abundance of MHC class II-associated proteins. Together, these findings identified an interferon-responsive, cell-cycle-associated, and MHC class II-associated Mueller cell protein signature in ERU and generated experimentally testable hypotheses for further mechanistic studies. MiraProt provides an accessible, metadata-aware framework for reproducible downstream exploration of processed proteomic datasets and prioritization of candidate proteins and pathways for experimental follow-up.

bioinformatics↗

bioRxiv2026

Modelling and measuring effects of shear stress in extrusion bioprinting of endothelial- epithelial cell co-cultures

Extrusion-based bioprinting enables the development of tissue-like constructs; however, the impact of printing-associated shear stress on cell viability and function remains a critical consideration. To address this, we developed a comprehensive workflow combining rheological characterization, computational fluid dynamics (CFD) modelling, and experimental validation to predict and assess shear stress effects during bioprinting. The rheological properties of gelatin methacryloyl (GelMA) at 5 % (w/v, 20 {degrees}C) and 10 % (30 {degrees}C) concentrations were modelled, comparing various non-Newtonian regression models. CFD simulations were validated using micro-particle image velocimetry, showing agreement between predicted and measured velocities. The impact of bioprinting-associated shear stress on cell viability was assessed using a co-culture of human umbilical vein endothelial cells and breast epithelial cells. Immediate post-printing analysis revealed increased apoptosis in GelMA 5 % (w/v, 20 {degrees}C), although 10 % (w/v) GelMA demonstrated higher shear stress levels compared to 5 % GelMA. After 1 day of culture in crosslinked hydrogels, apoptosis increased in extrusion pressure, demonstrating the impact of low levels of acute shear stress. This workflow provides a robust methodology for predicting acute shear stress impacts during bioprinting, laying the foundation for future optimization studies.

bioengineering↗

bioRxiv2026

Cell-type separability predicts annotation accuracy and outweighs algorithm choice: a factorial benchmark across seven scRNA paradigms

Automated cell-type annotation is a prerequisite for most single-cell RNA-sequencing (scRNA-seq) analyses, but the rapid proliferation of methods spanning marker-based, correlation-based, classical machine-learning, deep-learning, semi-supervised, large-language-model (LLM), and transformer foundation-model paradigms has outpaced head-to-head evaluation. Existing benchmarks rely on convenience samples of real datasets in which cell count, class imbalance, cell-type number, and differential-expression strength co-vary uncontrollably, precluding causal attribution of performance to any dataset property. To resolve this, we benchmarked 63 tools across seven paradigms using a Taguchi L9(34) orthogonal array that varies four dataset properties independently, progressively reconfiguring experimental control across five phases: fully controlled simulation, within-platform and cross-platform real-data validation, database-connected and LLM-based annotation under ontology-aware scoring, and fine-tuned foundation models. Using standardized oracle inputs and Cohen's {kappa}, we found that, within the ranges tested, the major paradigms achieved comparable accuracy. Accuracy was predicted near-linearly by the separability of cell types in a shared expression embedding, measured as k-nearest-neighbor (kNN) purity, a relationship that held across sequencing platforms and in fine-tuned foundation models. We attributed the vast majority of {kappa} variance to dataset structure and only a small share to tool identity. Computational cost traded against workflow accessibility rather than accuracy: accessible correlation-based and LLM-based approaches performed competitively, while foundation models matched them only after fine-tuning. Because our oracle design isolates algorithmic capability from upstream noise, these results reframe how methods should be selected: the field's near-term gains lie in strengthening infrastructure--prioritizing tool accessibility, standardized evaluation, and robustness to pipeline variation.

bioinformatics↗

bioRxiv2026

Dehydration triggers anomalous subdiffusion in biomimetic cell membranes

Lipid diffusion plays a central role in shaping the structural organization of cell membranes, maintaining lipid homeostasis, and facilitating cellular transport and signaling. The lateral mobility of phospholipids in membranes depends heavily on their hydration state. Furthermore, the activation energy of diffusion increases in conditions of reduced membrane hydration, suggesting that the underlying diffusion mechanism changes upon dehydration. Using two variants of fluorescence correlation spectroscopy (point FCS and scanning FCS) and two membrane reporters, we demonstrate that mild dehydration of phase-separated biomimetic cell membranes alters the lipid diffusion mechanism, resulting in anomalous subdiffusion rather than free Brownian motion. Importantly, the anomalous diffusion parameter, , decreases significantly upon the initial reduction of the membrane hydration layer, and the effect is fully reversible upon rehydration. These observations strongly indicate the reversible shift in lipid diffusion mode rather than irreversible membrane damage. We propose that this anomalous subdiffusion is caused by the formation of temporarily immobile lipid pockets in the membrane upon dehydration. These results therefore provide important insights into the mechanism of lipid diffusion in membranes undergoing local and transient dehydration, which is an important intermediate step in various biological processes associated with membrane fusion, such as neurotransmission, fertilization, and viral entry.

biophysics↗

bioRxiv2026

Scaling recipes for single-cell RNA sequencing foundation models: when do scaling laws hold?

Deep learning models exhibit empirical scaling laws whereby performance changes predictably with model size, dataset size, and training compute. Although these relationships are well established in domains such as language and image modelling, their applicability to biological data remains unclear. Here, we investigate scaling behaviour in foundation models trained on large collec tions of single-cell transcriptomes. We show that pre-training loss decreases systematically with model capacity and training compute, exhibiting a power law dependence on model size. The strength and regularity of these trends differ between model formulations. We identify and quantify empirical relationships linking the optimal learning rate and depth-to-width ratio to model size and depth or compute. These results demonstrate that scaling principles extend to transcriptomic modelling. More broadly, they provide a quantitative framework for estimating the expected returns from additional resources and selecting suit able hyperparameters and architectures, thereby supporting the development of increasingly capable foundation models for omics data.

bioinformatics↗

bioRxiv2026

Proteomic signatures of APOE ε4 across human tissues and cell types in Alzheimers disease

The apolipoprotein E {varepsilon}4 (APOE {varepsilon}4) allele is the strongest genetic risk factor for late-onset Alzheimers disease (AD). However, the underlying molecular mechanisms remain unclear. This study included 1691 participants from the Religious Orders Study and Rush Memory and Aging Project (ROSMAP), 1226 participants from the Accelerating Medicines Partnership - Alzheimers Disease (AMP-AD) Diverse Cohorts Study, and 735 participants from the Alzheimers Disease Neuroimaging Initiative (ADNI). To characterise APOE {varepsilon}4 molecular effects, we analysed proteomic data from plasma, cerebrospinal fluid (CSF), and induced pluripotent stem cell (iPSC)-derived astrocytes and neurons, as well as transcriptomic and proteomic data from multiple brain regions. The association of APOE {varepsilon}4 with AD neuropathology was also examined. APOE {varepsilon}4 carriers shared a plasma proteomic signature enriched for immune processes, irrespective of AD diagnosis. A machine learning classifier trained on this signature discriminated APOE {varepsilon}4 carriers from non-carriers in an independent cohort using CSF proteomics. APOE {varepsilon}4 carriage was associated with higher Braak stages and Consortium to Establish a Registry for Alzheimers Disease (CERAD) score. However, only limited APOE {varepsilon}4-associated transcriptomic and proteomic changes were observed in bulk brain tissue, with poor cross-layer concordance. Proteomic analyses of iPSC-derived astrocytes and neurons further revealed cell-type-specific APOE {varepsilon}4-associated changes. APOE {varepsilon}4 is associated with a consistent proteomic signature across plasma and CSF. Its molecular effects in the brain differ across cell types, brain regions and molecular layers. These findings support the need for cell-type-resolved multi-omic studies to elucidate how APOE {varepsilon}4 confers AD risk.

neuroscience↗

bioRxiv2026

Single-Cell Analytics for Dose Response (SCADR) discriminates PTEN missense variants by lipid and protein phosphatase dysfunction

The proliferation of sequencing efforts has revealed a vast and expanding catalog of single nucleotide gene variants, many associated to, but with unclear roles in disease. Fully charactering variant impacts and linking specific protein dysfunctions to disease are challenging due to the multi-functional nature of many proteins and varying degree of variant effects on these functions. Lagging are sensitive approaches to empirically assess the impact of missense variant-induced single amino acid changes on a wide range of protein functions. To address these issues, we have developed an open-source computational analysis tool called SCADR (Single-Cell Analytics for Dose Response) for simultaneously measuring and comparing impacts of exogenously-expressed variants on multiple signaling pathways using multiplex phospho-antibody spectral flow cytometry in human cell lines. SCADR retains and correlates single-cell measures of signal protein activity states along with expression levels of exogenously-expressed variants, providing rich characterization of multiple protein functions, signaling protein interactions, and enhanced discrimination of variant impacts on different signaling pathways, highlighting each variants unique dysfunction profile. Here, we apply SCADR for analyses of the impact of 6 variants of the tumor-suppressor protein PTEN (P38H, C124S, G129E, Y138L, D268E, 4A) expressed in HEK293 cells on the phosphorylation states of the canonical and noncanonical downstream signaling proteins Akt, S6, CREB, ERK, and p38 detected with fluorophore-conjugated phospho-antibodies, along with an antibody detecting an N-terminal HA tag on PTEN variants allowing measures of dose-response effects of each variants expression on signaling cascades. Results identify variant-specific impacts on downstream signaling cascades.

genomics↗