Biology subjects

Salehi, S.

Publications and source records attributed to Salehi, S..

At least 19 recordsLinked to original sources

bioRxiv2026

Engineering in vitro models of skeletal muscle with neuromuscular junctions using hierarchical micro-nano biomaterials: Cooperative effect of adhesion ligand nanoclustering and surface anisotropy

Robust development of in vitro mature skeletal muscle with functional neuromuscular junctions is an unmet challenge that must be addressed for advances in skeletal muscle tissue engineering and for the development of skeletal muscle tissue models for disease modelling and drug discovery. Herein, we developed hierarchical, anisotropic biomaterials that induced early maturation of more mature myotubes and the development of neuromuscular junctions (NMJs) during co-culture with motor neurons. We accomplished this by creating micro-nano biomaterial interfaces that presented nanoclusters of integrin-binding ligands to promote mechanotransduction on the surface of aligned electrospun microfibers. Controlling surface topography and nanoscale ligand clustering led to 1.5 to 2.5-fold increases in myoblast proliferation, myotube formation, elongation, and alignment; resulting in spontaneous twitching; and enhanced myotube-neuron connections, including increasing acetylcholine receptor clustering, neurite branching, and myotube contraction compared to control surfaces. These findings highlight the importance of tailoring adhesive peptide distribution and presentation for the in vitro development of NMJs and synaptic organization. This approach offers a valuable platform for fundamental research on muscle development and neuromuscular diseases, paving the way for improved skeletal muscle tissue engineering and drug screening strategies.

bioengineering↗

bioRxiv2026

A Data-Driven Image Extraction and Analysis Pipeline for Plant Phenotyping in Controlled Environments

Advances in automation, imaging, and artificial intelligence have enabled large-scale plant phenotyping, but image analysis remains a critical bottleneck for crop improvement and biological discovery. We developed an integrated multispectral phenotyping framework using imagery from the Texas A&M AgriLife Precision Automated Phenotyping Greenhouse and expanded Plant Growth and Phenotyping (PGP v2) data across maize, cotton, rice, and sorghum. The pipeline integrates pseudo-RGB generation, plant detection and segmentation, image stitching, vegetation-index analysis, texture analysis, morphological trait extraction, and temporal comparison of image-derived features to quantify changes in plant structure, spectral reflectance, and texture over time. Among the evaluated segmentation approaches, SAM v3 provided the highest and most consistent accuracy across diverse crop structures, although it required greater computational time than classical methods. SAM2Long maintained plant-instance associations across vertically stacked frames, while Scale-Invariant Feature Transform (SIFT)-based stitching reconstructed plant mosaics when individual plants extended beyond a single field of view. For each plant and imaging date, the pipeline generated an 863-dimensional feature vector spanning vegetation indices, spectral statistics, texture descriptors, and morphological traits. The framework was evaluated through two case studies: treatment-level temporal analysis of mutagenized sorghum lines and cold-stress phenotyping of maize using a separate imaging system. In both studies, the extracted features supported statistical and multivariate analyses of phenotypic variation and enabled separation of plants based on treatmentor stress-related responses. The combined dataset and workflow provide structured, automated, and well-documented phenotypic analysis across multiple crops, experimental settings, and imaging systems for controlledenvironment plant science and crop improvement. Plain Language SummaryTemporal imaging of plants in controlled environments helps scientists better understand growth and biological processes. However, analyzing large volumes of images has been limited by a lack of automated tools. Multispectral imagery captures additional information about plant pigments, structure, and stress beyond standard color images. We developed an automated analysis pipeline that identifies individual plants, tracks their growth over time, and measures traits such as height, area, shape, texture, and vegetation indices. Using artificial intelligence, the system efficiently processes thousands of images to provide consistent and repeatable measurements. By integrating engineering and plant biology, this work supports data-driven decisions for crop improvement and agricultural research.

plant biology↗

bioRxiv2026

Neuropathological and Functional Impact of Astrocyte-Derived Extracellular Vesicles in an Aged Model of Alzheimer's Disease

Extracellular vesicles (EVs) are lipid-bound particles that transfer cargos between cells. While plasma neuronal-derived EVs (NEVs) from individuals with mild cognitive impairment (MCI) and Alzheimers disease (AD) have been reported to exhibit high pathogenic potential, this study examined the impact of astrocyte-derived EVs (AEVs) in an aged AD mouse model. Plasma AEVs were isolated from cognitively normal control (CNC), MCI, and AD individuals using GLAST-based immunocapture and AEV size, purity, and tetraspanin were validated by flow cytometry, nanoparticle tracking, and super-resolution microscopy. AEVs pooled by clinical cohort were injected into the hippocampus of 6-month-old female PSAPP mice. Behavioral, biochemical, and neuropathological outcomes were assessed 6 months later. Rotarod assessment revealed significant impairment in motor coordination (p<0.0001) in mice receiving MCI- and AD-AEVs compared with those receiving CNC-AEVs. Morris water maze (MWM) also demonstrated cognitive deficits in MCI- and AD-AEV injected mice; however, no overt changes were observed in the staining of amyloid plaque burden (6E10), and neuroinflammation (GFAP). Immunoblotting of 82E1 and 22C11 confirmed A{beta}/AAP levels remained similar across all injected mice, whereas increased cortical tau accumulation was observed in MCI- and AD-AEV injected mice. Cerebellar synaptic density (SY-38) remained unchanged. While plasma AEVs from healthy individuals may confer neuroprotective benefits, AEVs from cognitively impaired individuals promoted tau accumulation in an amyloidogenic mouse model. These findings suggest that AEVs may play a dual role in AD as both potential biomarkers and mechanistic drivers of neurodegeneration, highlighting their relevance as targets for future therapeutic strategies.

neuroscience↗

bioRxiv2026

Evolution of oncogene amplification across 86,000 cancer cell genomes

High-level copy-number (CN) amplification (HLAMP) is a major mechanism of oncogene activation in human cancer. Despite progress in therapeutically targeting amplified oncogenes, the processes underlying amplicon evolution remain incompletely understood, leaving critical knowledge gaps in their etiology and mechanisms of therapeutic response. To address this, we analyzed the evolutionary trajectories of HLAMPs using single-cell whole-genome sequencing data from 86,239 cancer cells across 93 patients and 9 experimental systems. We found that cell-to-cell CN variability provides a quantifiable readout of HLAMP mechanism, clearly distinguishing extrachromosomal circular DNA (ecDNA) from intrachromosomal amplification (ICamp) through characteristic CN distributions that reflect distinct modes of segregation and correspond to clonal architecture. Notably, ICamp events frequently showed multiple amplitude peaks specific to subclones, indicating punctuated shifts in oncogene dosage through numeric or structural modulatory mechanisms with transcriptional impact. In contrast, ecDNAs exhibited broad, continuous CN distribution with extreme high-copy outliers, consistent with asymmetric segregation. The CN and structural diversity of ecDNA regions enabled systematic deconvolution of ecDNA subspecies and estimation of their per-cell abundance, revealing the history of ecDNA-mediated oncogenesis at single-nucleotide resolution. We observed ecDNA diversification through internal rearrangements across cases and, notably, convergent evolution in glioblastoma cases marked by multiple, recurrent acquisition of EGFR-targeting ecDNAs. Finally, single-cell genome-based identification of ecDNAs showed substantial discrepancy with bulk genome graph-based predictions and reliably distinguished actively maintained ecDNAs from historical genomic footprints after chromosomal re-integration. These findings reveal marked tissue-type specificity of ecDNAs, suggesting that ecDNA-mediated oncogenesis may depend on a permissive tissue context.

genomics↗

bioRxiv2025

Recognition of Ovarian Tumor-Derived Non-canonical Peptide Induces Memory-Like Features in Natural Killer Cells

Immune cell-based immunotherapy has emerged as a promising strategy for both hematologic malignancies and solid tumors. The adaptive properties of T and B cells, namely, antigen specificity and long-term immune memory, form the foundation of approaches such as chimeric antigen receptor (CAR) T-cell therapy and tumor vaccines. In contrast, natural killer (NK) cells, traditionally classified as innate lymphocytes, have been appreciated primarily for their immediate cytotoxicity against tumor cells, but not for long-term memory-like responses. Recent evidence has revealed a subset of NK cells defined as adaptive NK cells (aNK) capable of developing adaptive features, particularly in response to cytomegalovirus (CMV) primarily, and more recently, to ovarian tumor-derived antigens. However, whether tumor-derived peptides can specifically induce NK cell memory, and the corresponding interaction patterns, remains largely unexplored. In this study, we employed a proteogenomic approach combining RNA sequencing (RNA-seq) with HLA-E immunoprecipitation to identify both canonical and non-canonical peptides presented by primary ovarian tumor cells. Among four tumor-derived neo-antigenic peptides, the 9-mer peptide APAPAPAPL demonstrated the strongest binding affinity to HLA-E and engagement with the NK receptors NKG2C/A. Functional in vitro assays confirmed that this peptide could induce memory-like NK cell responses, including antigen-specific recall activity and enhanced tumor cytotoxicity. Furthermore, structural modeling using AutoDock Vina and Rosetta Dock illustrated that peptides with similar binding capacity shared conserved interaction patterns and docking orientations. Together, this systemic study highlights a novel mechanism for inducing NK cell memory through tumor-derived neoantigens. It also paves the way for the development of NK cell-targeted cancer vaccines, representing a new direction in tumor immunotherapy beyond conventional T cell-centered strategies.

immunology↗

bioRxiv2025

Single-Cell Integration of Chromatin Accessibility and Transcriptomics Reveals Regulatory Networks in Ovarian Tumor-Infiltrating Adaptive NK Cells

Natural killer (NK) cells are traditionally recognized for their rapid, non-specific responses against virus-infected or malignantly transformed cells, functioning as key effectors of innate immunity. However, a distinct subset known as adaptive NK (aNK) cells has been shown to acquire memory-like properties following viral infections, indicating their capacity for antigen-specific immune recall. Intriguingly, aNK cells have also been identified within the tumor microenvironment, where they can mediate tumor-specific recall responses. Yet, the regulatory mechanisms governing their function in tumor-infiltrating aNK cells remain largely undefined. In this study, we integrated publicly available multiomics datasets from ovarian cancer, including single-cell chromatin accessibility (scATAC-seq) and single-cell RNA sequencing (scRNA-seq), to identify chromatin-accessible regions and construct transcription factors (TF)-gene regulatory networks. To validate and extend these findings, we performed Smart-seq3 on NK cells isolated from ovarian tumors and applied SCENIC analysis to identify TF-driven gene regulation. By integrating results from both analyses, we identified PRDM1 and STAT2 as key TFs, along with their downstream targets CRCP and MTFP1, specifically enriched in tumor-infiltrating aNK cells. The expression levels of CRCP and MTFP1 positively correlated with NK cell infiltration in ovarian cancer tissues, suggesting their potential functions in supporting tumor-specific NK cell memory responses. In addition, external validation using data from the PROMIX clinical trial demonstrated that PRDM1 and STAT2 expression levels are positively associated with both overall survival and aNK cell-associated transcriptional features. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/676040v1_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@6df026org.highwire.dtl.DTLVardef@1b16dc8org.highwire.dtl.DTLVardef@19e916borg.highwire.dtl.DTLVardef@15d431a_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2025

Complementary modes of resistance to EGFR TKI in lung adenocarcinoma through MAPK activation and cellular plasticity

EGFR-mutant lung adenocarcinoma (LUAD) represents 20% of all non-small cell lung carcinomas, with most patients presenting with incurable metastatic disease. Treatment with mutant-selective EGFR tyrosine kinase inhibitors (TKIs) results in initial tumor reduction, yet nearly all patients eventually relapse. The mechanisms driving drug resistance are incompletely understood, creating significant barriers to curing metastatic disease. We integrated clinical genomic and single-nuclei RNA (snRNA) sequencing from a cohort of 62 EGFR-mutant LUAD patients treated with the third generation EGFR TKI, osimertinib, and compared treatment-naive (TN), minimal residual disease (MRD), and progressive disease (PD) tumors. We found that disease progression is associated with a marked decrease in alveolar lineage fidelity, coincident with reduced MAPK signaling and adenocarcinoma identity. PD tumors with sustained MAPK pathway activity, such as those with EGFR or MET amplifications, tended to retain adenocarcinoma identity. In contrast, MAPK-low tumors were more likely to undergo histological transformation to squamous or neuroendocrine lineages. Remarkably, we observed rare tumor cell populations prior to treatment that were poorly differentiated, in some cases with neuroendocrine or squamous features. At progression, these histologically divergent tumor cells increased in prevalence, both in cases with overt histological transformation, and in others with sub-clinical histological plasticity. These findings suggest that pre-existing capacity for histologic plasticity may be a substrate for therapy induced selection. Taken together, our results illuminate genomically encoded MAPK signaling and lineage plasticity as complementary mechanisms of acquired resistance to EGFR TKI in lung adenocarcinoma.

cancer biology↗

bioRxiv2025

Anti-Sense Oligonucleotide as a Therapeutic for Synucleinopathies: Pharmacokinetic, Safety and Efficacy Evaluation

Effective blood-brain barrier (BBB) penetration is a significant challenge for antisense oligonucleotide (ASO) therapies targeting neurodegenerative diseases. We utilized a peptide (ApoB11) mediated transport delivery of an ASO to the CNS following systemic delivery to reduce expression of targeted transcripts for neurodegenerative diseases. This study evaluates the pharmacokinetics, CNS penetration, and therapeutic efficacy of ApoB11:2-OMe ASO--Syn, an ASO for -synuclein (-Syn) suppression in synucleinopathies. After a single intraperitoneal (IP) injection (2 mg/kg) in C57BL/6 mice, ApoB11:ASO--Syn showed robust brain penetration, reaching peak concentrations (Cmax = 0.14 nMol/mg) at 1.5 hours and an extended brain half-life (t1/2 = 646.2 hours), indicating prolonged CNS retention. Immunofluorescence confirmed widespread uptake in neurons and endothelial cells. The ASO also accumulated in the liver (Cmax = 419.5 nMol/mg, t1/2 = 104.9 hours), consistent with receptor-mediated uptake. Acute and subacute toxicity studies revealed no systemic toxicity at the highest non-lethal dose (32 mg/kg). In a mouse model of dementia with lewy body (DLB) mice overexpressing human -Syn, ApoB11:ASO--Syn reduced -Syn mRNA and protein levels in the hippocampus and cortex by [~]50% at 16 mg/kg. These results demonstrate that ApoB11 is an effective ASO carrier for CNS delivery, supporting its potential as a therapeutic strategy for synucleinopathies. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=154 SRC="FIGDIR/small/651722v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@f73353org.highwire.dtl.DTLVardef@14ababforg.highwire.dtl.DTLVardef@12a92e3org.highwire.dtl.DTLVardef@1176b4_HPS_FORMAT_FIGEXP M_FIG C_FIG

pharmacology and toxicology↗

bioRxiv2025

Disrupted priming within draining lymph nodes drives immune quiescence in gastric cancer

The gastric mucosa is characterized by continuous innate immune surveillance and inflammatory signaling, yet a high proportion of gastric carcinomas (GCs) are recalcitrant to immune-directed therapies. The mechanisms by which GCs evade adaptive immune surveillance within the highly antigenic microenvironment of the gastric mucosa remains unknown. To address this, we collected patient-matched tumor tissue, distant normal tissue, metastasis, and draining lymph nodes to generate a large-scale single-cell immune profiling dataset from 64 patients (n=179 samples, >150,000 cells). From single cell analysis, we identified two distinct sources of impaired tumor surveillance within tumor draining lymph nodes. First, we observed that a significant fraction of tumor draining lymph nodes had undergone cytokine-driven reprogramming, leading to reduced dendritic cell homing and limited T cell priming. Second, T cells undergoing successful activation exhibited limited expansion and constrained differentiation, marked by expression of the quiescence-associated transcription factor Kruppel-like Factor 2 (KLF2). Overexpression of KLF2 in primary T cells limited both their differentiation and cytotoxic capacity. These findings implicate both impaired T cell priming and KLF2-dependent T cell quiescence in limiting T cell immunity in gastric adenocarcinoma. We suggest these findings represent an emerging model for immune silencing in tumors developing from tissues with chronic inflammation.

immunology↗

bioRxiv2025

Investigating Alzheimers Disease Biomarkers by Applying Machine Learning Models

ObjectiveAlzheimers Disease (AD) is a debilitating neurodegenerative disorder characterized by memory loss, cognitive decline, and the accumulation of amyloid plaques and neurofibrillary tangles. This study investigates the interplay of various biomarkers and clinical features in diagnosing AD using machine learning (ML) techniques. MethodsWe analyzed data from 191 AD patients and 59 non-AD subjects, employing classifiers including Naive Bayes (NB), Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN). ResultsOur findings indicate that KNN, SVM, RF, and DT achieved high sensitivity (94%) and accuracy (92%), demonstrating their potential as effective diagnostic tools. Notably, significant differences in feature values between AD patients and non-AD subjects suggest that biomarker-driven approaches can enhance diagnostic precision. Key biomarkers such as neprilysin, alpha-secretase, beta-secretase, amyloid plaques and urinary formic acid emerged as critical elements. ConclusionOur results underscore the importance of selecting a targeted subset of features to streamline the diagnostic process, allowing for more efficient and cost-effective screening. While our study reveals valuable insights into AD pathology and diagnosis, future research with larger, longitudinal cohorts is essential to further elucidate these relationships and enhance our understanding of Alzheimers mechanisms, ultimately aiming for innovative therapeutic strategies.

bioinformatics↗

bioRxiv2024

The mechanical anisotropy of adipose tissues regulates ovarian cancer invasion

High-grade serous ovarian cancer, the most common and aggressive form of ovarian cancer, generally metastasises to visceral adipose tissues. In these tissues, the extracellular matrix through which ovarian cancer cells adhere and migrate is confined by the presence and preponderance of adipocytes. How cells migrate in this unique environment is not known, yet critical to understanding metastatic progression. To study these processes, we develop biomimetic organo-hydrogels that recreate structural and mechanical properties of human visceral adipose tissues. We show that ovarian cancer cells present invasive tropism towards organo-hydrogels, replicating the behaviour observed in native adipose tissues. This migration is facilitated by the mechanical anisotropy and microstructure of organo-hydrogels and adipose tissues, allowing the formation of cell force-induced migratory tracks, a process regulated by TGF{beta} in an MMP degradation-independent manner. These results highlight the contribution of adipocytes to tissue biophysical features as a key regulatory factor of ovarian cancer cell migration.

bioengineering↗

bioRxiv2024

Machine Learning Approaches for Predicting Virus-Human Protein-Protein Interactions: An Evaluation of Retroviral Interaction Networks

Virus-human protein-protein interactions (VHPPI) are key to understanding how viruses manipulate host cellular functions. This study constructed a retroviral-human PPI network by integrating multiple public databases, resulting in 1,387 interactions between 29 retroviral and 1,026 human genes. Using minimal sequence similarity, we generated a pseudo-negative dataset for model reliability. Five machine learning models--Logistic Regression (LR), Support Vector Machine (SVM), Naive Bayes (NB), Decision Tree (DT), and Random Forest (RF)--were evaluated using accuracy, sensitivity, specificity, PPV, and NPV. LR and KNN models demonstrated the strongest predictive performance, with sensitivities up to 77% and specificities of 52%. Feature importance analysis identified GC content and semantic similarity as influential predictors. Models trained on selected features showed enhanced accuracy with reduced complexity. Our approach highlights the potential of computational models for VHPPI predictions, offering valuable insights into viral-host interaction networks and guiding therapeutic target identification. SignificanceThis study addresses a crucial gap in antiviral research by focusing on the prediction of virus-host protein-protein interactions (VHPPI) for retroviruses, which are linked to serious diseases, including certain cancers and autoimmune disorders. By leveraging machine learning models, we identified essential host-pathogen interactions that underlie retroviral survival and pathogenesis. These models were optimized to predict interactions accurately, offering valuable insights into the complex mechanisms that retroviruses use to manipulate host cellular processes. Our approach highlights key host and viral proteins, such as ENV_HV1H2 and CD4, that play pivotal roles in retroviral infection and persistence. Targeting these specific interactions can potentially disrupt the viral lifecycle while minimizing toxicity to human cells. This study thus opens avenues for the development of selective therapeutic strategies, contributing to more effective and targeted antiviral interventions with fewer side effects, marking a significant step forward in computational virology and drug discovery.

bioinformatics↗

bioRxiv2024

Enhanced NK cell proficiency in solid tumors through antigen-specific memory via NKG2C/A-HLA-E/ABC recruited to tumors by CXCR2

Natural killer (NK) cells have emerged as promising effectors in cancer immunotherapy due to their ability to recognize and eliminate tumor cells. To investigate the immunological memory and tumor reactivity of adaptive (a)NK cells in the context of desmoplastic tumors, we used human ovarian cancer as a model. Through in vitro culture systems resembling dendritic cell (DC)-mediated T cell activation, we demonstrated that aNK cells exhibit antigen-specific cytotoxic responses and memory generation towards ovarian tumor antigens. Furthermore, mature DCs presenting tumor-associated antigens induced the expansion of aNK cells, suggesting antigen-specific proliferation. Single-cell transcriptomics revealed a distinct genetic signature of aNK cells in tumor samples, characterized by a cytotoxic phenotype and interactions with myeloid cells, particularly DCs. The spatial analysis confirmed the intratumoral presence of aNK cells, with higher abundance in the tumor nest compared to conventional (c)NK cells. Functional assays demonstrated the cytotoxicity of expanded aNK cells against autologous ovarian tumors, accompanied by an activated receptor profile. Importantly, aNK cells displayed antigen-specific memory responses towards primary tumors, maintaining specificity over time. Blockade of NKG2C and HLA-E influenced aNK cell recall responses, indicating their roles in the adaptive NK cell immune memory. Additionally, CXCR2 was essential for efficient aNK cell migration toward tumors. These findings shed light on the therapeutic potential of aNK cells in ovarian cancer immunotherapy, highlighting their ability to develop immunological memory and effectively eradicate tumor cells.

immunology↗

bioRxiv2023

Single cell decoding of drug induced transcriptomic reprogramming in triple negative breast cancers

BackgroundThe encoding of cell intrinsic resistance states in breast cancer reflects the contributions of genomic and non-genomic variation. However, identifying the potential contributions of each requires accurate measurement and subtraction of the contribution of clonal fitness from co-measurement of transcriptional states. Somatic genomic variation in gene dosage, copy number variation, is the dominant mutational mechanism in breast cancer contributing to transcriptional variation and has recently been shown to contribute to platinum chemotherapy resistance states. Here we deploy time series measurements of triple negative breast cancer single cell transcriptomes in conjunction with co-measured single cell copy number associated clonal fitness to identify the contributions of genomic and non-genomic mechanisms to drug associated transcription states. ResultsWe generated serial scRNA-seq data (126,556 cells) from triple negative breast cancer (TNBC) patient-derived xenograft (PDX) experiments over 2.5 years in duration, and matched it against genomic copy number single cell data from the same biological samples. We show that the cell memory of transcriptional states of TNBC tumors serially exposed to platinum identifies distinct clonal responses within individual tumours. Copy-number clones with high drug fitness leading to clonal sweeps exhibit less transcriptional reversion, whereas clones with weak drug fitness exhibit highly dynamic transcription on drug withdrawal. Pathway analysis shows that copy number associated and copy number independent transcripts converge on epithelial-mesenchymal transition (EMT) and cytokine signaling states associated with resistance. We show from trajectory analysis that transcriptional reversion exhibits hysteresis, indicating that new intermediate transcriptional states are generated by platinum exposure. ConclusionsWe discovered that copy number clones with strong genotype associated fitness under platinum became fixed in their states, resulting in minimal transcriptional reversion on drug withdrawal. In contrast clones with weaker fitness undergo non-genomic transcriptional plasticity and these distinct responses co-exist within single tumours. Together the data suggest that copy number associated and copy number independent transcriptional states may contribute to platinum drug resistance within individual tumours. The dominance of genomic or non-genomic mechanisms within individual polyclonal tumours has implications for approaches to restoration of drug sensitivity and re-treatment strategies. Data availabilityUploaded Data URL: https://ega-archive.org/studies/EGAS00001007242 Github manuscript: https://github.com/molonc/drug_resistant_material/

genomics↗

bioRxiv2023

Prevention of tau accumulation through inhibition of hnRNP R-dependent axonal Mapt mRNA localization

Deposition of neurofibrillary tangles composed of hyperphosphorylated tau in the brain is a pathological hallmark and closely correlates with onset and course of Alzheime[r]s disease. While tau reduction is being pursued as therapeutic strategy, prolonged lowering of total tau might lead to adverse effects, necessitating the development of more targeted approaches. We report that the RNA-binding protein hnRNP R facilitates the axonal localization of the Mapt mRNA encoding tau. Depletion of hnRNP R reduces tau in axons but not neuronal cell bodies. Brains of Alzheimers disease mice deficient for hnRNP R contain less tau tangles and amyloid-{beta} plaques. Neurons treated with blocking antisense oligonucleotides to mask hnRNP R binding sites of Mapt mRNA show reduced axonal tau levels, similar to hnRNP R-deficient neurons. Lowering of tau levels selectively in axons, a major subcellular site of tangle formation and spreading, thus represents a novel therapeutic perspective for treatment of Alzheimers disease.

neuroscience↗

bioRxiv2023

Rational positioning of 3D printed micro-bricks to realize high-fidelity, multi-functional soft-hard interfaces

AO_SCPLOWBSTRACTC_SCPLOWLiving organisms have developed design principles, such as functional gradients (FGs), to interface hard materials with soft ones (e.g., bone and tendon). Mimicking such design principles can address the challenges faced when developing engineered constructs with soft-hard interfaces. To date, implementing these FG design principles has been primarily performed by varying the ratio of the hard phase to that of the soft phase. Such design approaches, however, lead to inaccurate mechanical properties within the transition zone. That is due to the highly nonlinear relationship between the material distribution at the microscale and the macroscale mechanical properties. Here, we 3D print micro-bricks from either a soft or a hard phase and study the nonlinear relationship between their arrangements within the transition zone and the resulting macroscale properties. We carry out experiments at the micro- and macroscales as well as finite element simulations at both scales. Based on the obtained results, we develop a co-continuous power-law model relating the arrangement of the micro-bricks to the local mechanical properties of the micro-brick composites. We then use this model to rationally design FGs at the individual micro-brick level and create two types of biomimetic soft-hard constructs, including a specimen modeling bone-ligament junctions in the knee and another modeling the nucleus pulposus-annulus fibrosus interface in intervertebral discs. We show that the implemented FGs drastically enhance the stiffness, strength, and toughness of both types of specimens as compared to non-graded designs. Furthermore, we hypothesize that our soft-hard FGs regulate the behavior of murine preosteoblasts and primary human bone marrow-derived mesenchymal stromal cells (hBMSCc). We culture those cells to confirm the effects of soft-hard interfaces on cell morphology as well as on regulating the expression of focal adhesion kinase, subcellular localization, and YAP nuclear translocation of hBMSCs. Taken together, our results pave the way for the rational design of soft-hard interfaces at the micro-brick level and (biomedical) applications of such designs.

bioengineering↗

bioRxiv2022

Efficacy and safety of glycosphingolipid SSEA-4 targeting CAR-T cells in solid tumors

Chimeric antigen receptor (CAR) T-cell immunotherapies for solid tumors face critical challenges such as heterogeneous antigen expression. We characterized SSEA-4 cell-surface glycolipid as a target for CAR-T cell therapy. SSEA-4 is mainly expressed during embryogenesis but is also found in several cancer types making it an attractive tumor-associated antigen. Anti-SSEA-4 CAR-T cells were generated and assessed pre-clinically in vitro and in vivo for anti-tumor response and safety. SSEA-4 CAR-T cells effectively eliminated SSEA-4 positive cells in all tested cancer cell lines whereas SSEA-4 negative cells lines were not targeted. In vivo efficacy and safety studies using NSG mice and the high-grade serous ovarian cancer cell line OVCAR4 demonstrated a remarkable and specific anti-tumor response at all CAR-T cell doses used. At high T cell doses, CAR-T cell-treated mice showed signs of health deterioration after a follow-up period. However, severity of toxicity was reduced with delayed onset when lower CAR-T cell doses were used. Our data demonstrate the efficacy of anti-SSEA-4 CAR-T therapy; however, safety strategies, such as dose-limiting and/or equipping CAR-T cells with combinatorial antigen recognition should be implemented for its potential clinical translation.

cancer biology↗

bioRxiv2022

GeneVector: Identification of transcriptional programs using dense vector representations defined by mutual information.

Deciphering individual cell phenotypes from cell-specific transcriptional processes requires high dimensional single cell RNA sequencing. However, current dimensionality reduction methods aggregate sparse gene information across cells, without directly measuring the relationships that exist between genes. By performing dimensionality reduction with respect to gene co-expression, low-dimensional features can model these gene-specific relationships and leverage shared signal to overcome sparsity. We describe GeneVector, a scalable framework for dimensionality reduction implemented as a vector space model using mutual information between gene expression. Unlike other methods, including principal component analysis and variational autoencoders, GeneVector uses latent space arithmetic in a lower dimensional gene embedding to identify transcriptional programs and classify cell types. In this work, we show in four single cell RNA-seq datasets that GeneVector was able to capture phenotypespecific pathways, perform batch effect correction, interactively annotate cell types, and identify pathway variation with treatment over time.

bioinformatics↗