Biology subjects

Saberi, A.

Publications and source records attributed to Saberi, A..

At least 19 recordsLinked to original sources

bioRxiv2026

EigenBrain maps cortical molecular architecture from functional, geometric, and structural eigenmodes

Neurotransmitter receptor and transporter distributions shape neural communication across the cortex and underpin mechanistic interpretations of brain function. Magnetic resonance imaging (MRI)-derived eigenmodes, low-dimensional spatial patterns that capture the principal axes of cortical organization, are increasingly used to reconstruct brain maps and offer a potential route to approximating molecular topographies in large cohorts where direct measurements are unavailable. Yet whether the type of MRI data used to derive these eigenmodes determines which receptor and transporter distributions can be approximated, and which molecular systems are accessible through this approach at all, remains unknown. Using a spatial reconstruction framework applied to 23 molecular systems spanning neurotransmitter, metabolic, and pathological markers, we show that eight receptor and transporter maps, spanning serotonin, dopamine, histamine, cholinergic, and opioid markers, can be selectively approximated from functional eigenmodes but not structural or geometric ones. This selectivity also reveals that functional brain organization encodes a specific subset of neuromodulatory topographies that structural and geometric organization does not. To illustrate translational potential in clinical cohorts where molecular imaging is unavailable, we applied individual-level eigenmode decompositions of MRI data projected onto the eight validated molecular systems and detected cross-sectional differences in autism and longitudinal changes in schizophrenia. Together, these findings reveal a difference in how geometric, structural, and functional brain organization relate to molecular architecture, offering a method for approximating validated molecular profiles in population neuroimaging cohorts that lack direct molecular imaging.

neuroscience↗

bioRxiv2026

Functional Performance of Electrospun Tissue-Engineered Heart Valve Scaffolds Across Geometries Under Aortic Conditions

Electrospun, bioresorbable polymer scaffolds represent a compelling platform for tissue-engineered heart valves (TEHVs) owing to their fibrous microstructure, tunable mechanics and inherent capacity to support host-cell infiltration and remodeling. While recent in-vitro parametric studies of bioprosthetic heart valve (BHV) geometry have used silicone cast valves and Doppler echocardiography to link design parameters to hemodynamic performance and leaflet strain, the characterization of the functional behavior of electrospun whole-valve TEHV scaffolds across clinically relevant aortic conditions and across distinct leaflet geometries remains limited. Here, we present an end-to-end workflow in which polycaprolactone (PCL) is electrospun directly onto parametrized, 3D-printed mandrels derived from two established analytical leaflet geometry models, yielding 21 whole-valve scaffolds across five geometries without cutting or suturing of flat fiber sheets. Fiber diameter, orientation and scaffold porosity were assessed by scanning electron microscopy and confirmed to be consistent across scaffolds and geometries. A subset of 8 scaffolds were tested in a pulse duplicator under hypotensive, normotensive and hypertensive aortic conditions, achieving a mean geometric orifice area of 44.51% of the total valve area and demonstrating physiological opening and closing behavior throughout. These results emphasize electrospun whole-valve TEHVs as hemodynamically competent constructs at clinically relevant aortic conditions and provide a reproducible, geometry-driven fabrication and evaluation framework to further support their translation toward regenerative heart valve replacement.

bioengineering↗

bioRxiv2026

Nucleus-level thalamic organization anchors multimodal signatures of thalamocortical maturation

The human thalamus is composed of multiple nuclei that differ in structure and function. From early development onwards, these nuclei form reciprocal, nucleus-specific connections with the cerebral cortex, contributing to sensory and cognitive processing. In childhood and adolescence, a key period of neurocognitive development, these connections undergo widespread refinement, yet how developmental trajectories of thalamocortical connections vary across nuclei remains unknown. Here, we leveraged the Human Connectome Project in Development dataset (HCP-D, N = 604, age range 8-21) and segmented 10 thalamic nuclei using a segmentation approach optimized for intrathalamic contrast. Applying probabilistic tractography, we reconstructed nucleus-specific thalamocortical connections and charted their maturational profiles based on changes in fractional anisotropy (FA) using generalized additive models. We found FA to increase in thalamocortical connections, with nucleus-specific variation in temporal profiles and magnitude of age effects. Connections of core-cell-rich, sensory-projecting nuclei, such as the lateral geniculate nucleus, showed earlier maturational plateaus, whereas matrix-cell-rich, association-projecting nuclei, such as ventral anterior nucleus, showed more sustained maturation. This links maturational heterochronicity to thalamic organization of cell distribution and connectivity embedding. In parallel, functional thalamocortical connectivity decreased with age, with FA and functional connectivity age effects coupled in nucleus-connections showing prolonged maturation. Finally, concordant age effects in connectivity and nucleus volumes suggest that intra-nucleus remodeling may support refinement of structural connections while reducing thalamocortical functional synchrony. Together, our work reveals that thalamocortical maturation is anchored in the developmental and organizational heterogeneity of thalamic nuclei, offering a framework for understanding how diverse thalamic nuclei contribute to neurocognitive development.

neuroscience↗

bioRxiv2026

A latent space thermodynamic model of cell differentiation

Inferring the governing dynamics of differentiation that capture cell state evolution remains a central challenge in single-cell biology. We present Latent Space Dynamics (LSD), a thermodynamics-inspired framework that models cell differentiation as evolution on a learned Waddington landscape in latent space. LSD jointly infers a low-dimensional cell state, a differentiable potential function governing developmental flow, and a local entropy term that quantifies cellular plasticity. Using a neural ordinary differential equation, LSD reconstructs continuous differentiation trajectories from time-ordered single-cell data. Across diverse developmental systems, LSD accurately recovers lineage hierarchies, predicts fate commitment for unseen cell types, and outperforms existing trajectory inference approaches in directional accuracy. Moreover, in silico gene perturbations reveal how individual regulators reshape the landscape, and entropy provides a quantitative measure of plasticity in development and cancer.

bioinformatics↗

bioRxiv2025

cuBNM: GPU-Accelerated Brain Network Modeling

Brain network modeling uses computer simulations to infer about latent neural properties at micro- and mesoscales by fitting brain dynamic models to empirical data of individual subjects or groups. However, computational costs of (individualized) model fitting is a major bottleneck, limiting the practical feasibility of this approach to larger cohorts and more complex models, and highlighting the need for scalable simulation implementations. Here, we introduce cuBNM, a Python package which leverages parallel processing of graphics processing units to massively accelerate simulations of brain network models. We show running simulations on graphics processing units is several hundred times faster compared to central processing units. We demonstrate the usage of cuBNM by running optimization of group-level and individualized low- and high-dimensional models. As examples of the utility of individualized models, we investigated test-retest reliability and heritability of simulated and empirical measures in the Human Connectome Project dataset. We found simulated features were fairly reliable and significantly heritable, suggesting their biological plausibility. Overall, cuBNM enables large-scale simulations of brain network models, opening new avenues for studying latent neural processes across diverse populations, dense networks, and high-dimensional models, which was previously impractical due to computational constraints.

neuroscience↗

bioRxiv2024

Bias-accounting meta-analyses overcome cerebellar neglect to refine the cerebellar behavioral topography

The cerebellum plays important roles in motor, cognitive, and emotional behaviors. Previous cerebellar coordinate-based meta-analyses (CBMAs) have complemented precision-mapping and parcellation approaches by finding generalizable cerebellar activations across the largest possible set of behaviors. However, cerebellar CBMAs face challenges due to inherent methodological limitations exacerbated by historical cerebellar neglect in neuroimaging. Here, we show overrepresentation of superior activations, rendering the null hypothesis of standard activation likelihood estimation (ALE) unsuitable. Our new method, cerebellum-specific ALE (C-SALE), finds behavioral convergence beyond baseline activation rates. It does this by testing experimental activations versus null models sampled from a data-driven probability distribution of finding activations at any cerebellar location. Task-specific mappings in the BrainMap meta-analytic database illustrated improved specificity of the new method. Multiple (sub)domains reached convergence in specific cerebellar subregions, supporting dual motor representations and placing cognition in posterior-lateral regions. We show our method and findings were replicable within NeuroSynth. Across both databases, 54/138 task domains or behavioral terms, including sustained attention, somesthesis, inference, anticipation and rhythm, reached convergence in specific cerebellar subgregions. Maps largely corresponded with cerebellar atlases but also showed many complementary mappings. Repeated subsampling showed that motor behaviors, and to a lesser extent language and working memory, mapped to especially consistent cerebellar subregions. Lastly, we found that cerebellar clusters were parts of brain-wide coactivation networks with cortical and subcortical regions implied in these behaviors. Together, our method further complements and expands understanding of cerebellar involvement in human behavior, highlighting regions for future investigation in both basic and clinical applications. Highlights[bullet] Biases in reported cerebellar activations strongly favors superior regions. [bullet]A new method of meta-analysis increases cerebellar mapping specificity and accuracy. [bullet]Large-scale meta-analyses support cerebellar roles in cognitive, affective, and motor behaviors. [bullet]54 task domains/ terms converged, including sustained attention, somesthesis, inference, anticipation and rhythm. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=100 SRC="FIGDIR/small/621398v3_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@716ea0org.highwire.dtl.DTLVardef@11601fforg.highwire.dtl.DTLVardef@1dd1208org.highwire.dtl.DTLVardef@caf535_HPS_FORMAT_FIGEXP M_FIG Graphical abstract C_FIG

neuroscience↗

bioRxiv2024

A Suite of Foundation Models Captures the Contextual Interplay Between Codons

In the canonical genetic code, many amino acids are assigned more than one codon. Work by us and others has shown that the choice of these synonymous codon is not random, and carries regulatory and functional consequences. Existing protein foundation models ignore this context-dependent role of coding sequence in shaping the protein landscape of the cell. To address this gap, we introduce cdsFM, a suite of codon-resolution large language models, including both EnCodon and DeCodon models, with up to 1B parameters. Pre-trained on 60 million protein-coding sequences from more than 5,000 species, our models effectively learn the relationship between codons and amino acids, recapitualing the overall structure of the genetic code. In addition to outperforming state-of-the-art genomic foundation models in a variety of zero-shot and few-shot learning tasks, the larger pre-trained models were superior in predicting the choice of synonymous codons. To systematically assess the impact of synonymous codon choices on protein expression and our models ability to capture these effects, we generated a large dataset measuring overall and surface expression levels of three proteins as a function of changes in their synonymous codons. We showed that our EnCodon models could be readily fine-tuned to predict the contextual consequences of synonymous codon choices. Armed with this knowledge, we applied EnCodon to existing clinical datasets of synonymous variants, and we identified a large number of synonymous codons that are likely pathogenic, several of which we experimentally confirmed in a cellbased model. Together, our findings establish the cdsFM suite as a powerful tool for decoding the complex functional grammar underlying the choice of synonymous codons.

genomics↗

bioRxiv2024

A long context RNA foundation model for predicting transcriptome architecture

Linking DNA sequence to genomic function remains one of the grand challenges in genetics and genomics. Here, we present a large-scale compendium of single-molecule transcriptome sequencing of diverse cancer cell lines, revealing their isoform diversity and specificity. We used this compendium to build Mach-1, an RNA foundation model that learns how the nucleotide sequence of unspliced pre-mRNA dictates transcriptome architecture--the relative abundances and molecular structures of mRNA isoforms. By using the Striped-Hyena architecture, Mach-1 handles extremely long sequence inputs at nucleotide resolution (64 kilobase pairs), allowing for quantitative, zero-shot prediction of all aspects of transcriptome architecture, spanning isoform abundance, structure, and variant-induced splicing changes. To test both the interpretive and generative capabilities of Mach-1, we experimentally validated its learned regulatory grammar and predictions through perturbation of RNA-binding proteins nominated by Mach-1 to impact targeted splicing, precise CRISPR editing of variants of uncertain significance that the model predicted to alter splicing, and de novo transcript synthesis and expression in human cells. Together, this release establishes a new foundation for sequence-to-transcript modeling. Mach-1s representations can be extended and fine-tuned across a spectrum of biological contexts, from variant interpretation to RNA engineering.

bioinformatics↗

bioRxiv2024

Accurate isoform quantification by joint short- and long-read RNA-sequencing

Accurate quantification of transcript isoforms is crucial for understanding gene regulation, functional diversity, and cellular behavior. Existing RNA sequencing methods have significant limitations: short-read (SR) sequencing provides high depth but struggles with isoform deconvolution, whereas long-read (LR) sequencing offers isoform resolution at the cost of lower depth, higher noise, and technical biases. Addressing this gap, we introduce Multi-Platform Aggregation and Quantification of Transcripts (MPAQT), a generative model that combines the complementary strengths of different sequencing platforms to achieve state-of-the-art isoform-resolved transcript quantification, as demonstrated by extensive simulations and experimental benchmarks. By applying MPAQT to an in vitro model of human embryonic stem cell differentiation into cortical neurons, followed by machine learning-based modeling of transcript abundances, we show that untranslated regions (UTRs) are major determinants of isoform proportion and exon usage; this effect is mediated through isoform-specific sequence features embedded in UTRs, which likely interact with RNA-binding proteins that modulate mRNA stability. These findings highlight MPAQTs potential to enhance our understanding of transcriptomic complexity and underline the role of splicing-independent post-transcriptional mechanisms in shaping the isoform and exon usage landscape of the cell.

bioinformatics↗

bioRxiv2024

Adolescent maturation of cortical excitation-inhibition balance based on individualized biophysical network modeling

The balance of excitation and inhibition is a key functional property of cortical microcircuits which changes through the lifespan. Adolescence is considered a crucial period for the maturation of excitation-inhibition balance. This has been primarily observed in animal studies, yet human in vivo evidence on adolescent maturation of the excitation-inhibition balance at the individual level is limited. Here, we developed an individualized in vivo marker of regional excitation-inhibition balance in human adolescents, estimated using large-scale simulations of biophysical network models fitted to resting-state functional magnetic resonance imaging data from two independent cross-sectional (N = 752) and longitudinal (N = 149) cohorts. We found a widespread relative increase of inhibition in association cortices paralleled by a relative age-related increase of excitation, or lack of change, in sensorimotor areas across both datasets. This developmental pattern co-aligned with multiscale markers of sensorimotor-association differentiation. The spatial pattern of excitation-inhibition development in adolescence was robust to inter-individual variability of structural connectomes and modeling configurations. Notably, we found that alternative simulation-based markers of excitation-inhibition balance show a variable sensitivity to maturational change. Taken together, our study highlights an increase of inhibition during adolescence in association areas using cross sectional and longitudinal data, and provides a robust computational framework to estimate microcircuit maturation in vivo at the individual level.

neuroscience↗

bioRxiv2024

Microstructural asymmetry in the human cortex

While macroscale brain asymmetry and its relevance for human cognitive function have been consistently shown, the underlying neurobiological signatures remain an open question. Here, we probe layer-specific microstructural asymmetry of the human cortex using intensity profiles from post-mortem cytoarchitecture. An anterior-posterior cortical pattern of left-right asymmetry was found, varying across cortical layers. A similar anterior-posterior pattern was observed using in vivo microstructural imaging, with in vivo asymmetry showing the strongest similarity with layer III. Microstructural asymmetry varied as a function of age and sex and was found to be heritable. Moreover, asymmetry in microstructure corresponded to asymmetry of intrinsic function, in particular in sensory areas. Last, probing the behavioral relevance, we found differential association of language and markers of mental health with asymmetry, illustrating a functional divergence between inferior-superior and anterior-posterior microstructural axes anchored in microstructural development. Our study highlights the layer-based patterning of microstructural asymmetry of the human cortex and its functional relevance.

neuroscience↗

bioRxiv2024

Perturbation-aware predictive modeling of RNA splicing using bidirectional transformers

Predicting molecular function directly from DNA sequence remains a grand challenge in computational and molecular biology. Here, we engineer and train bidirectional transformer models to predict the chemical grammar of alternative human mRNA splicing leveraging the largest perturbative full-length RNA dataset to date. By combining high-throughput single-molecule long-read "chemical transcriptomics" in human cells with transformer models, we train AllSplice - a nucleotide foundation model that achieves state-of-the-art prediction of canonical and noncanonical splice junctions across the human transcriptome. We demonstrate improved performance achieved through incorporation of diverse noncanonical splice sites in its training set that were identified through long-read RNA data. Leveraging chemical perturbations and multiple cell types in the data, we fine-tune AllSplice to train ChemSplice - the first predictive model of sequence-dependent and cell-type specific alternative splicing following programmed cellular perturbation. We anticipate the broad application of AllSplice, ChemSplice, and other models fine-tuned on this foundation to myriad areas of RNA therapeutics development.

genomics↗

bioRxiv2024

A Multimodal Characterization of Low-Dimensional Thalamocortical Structural Connectivity Patterns

The human thalamus is a bilateral and heterogeneous grey matter structure that plays a crucial role in coordinating whole-brain activity. Investigations of its complex structural and functional internal organization revealed to a certain degree overlapping parcellations, however, a consensus on thalamic subnuclei boundaries remains absent. Recent work suggests that thalamic organization might additionally reflect continuous axes transcending nuclear boundaries. In this study, we used a multimodal approach to uncover how low-dimensional axes that describe thalamic connectivity patterns to the cortex are related to internal thalamic microstructural features, functional connectivity, and structural covariance. We computed a thalamocortical structural connectome via probabilistic tractography on diffusion MRI and derived two main axes of thalamic organization. The principal thalamic gradient, extending from medial to lateral and differentiating between transmodal and unimodal nuclei, was related to intrathalamic myelin profiles, and patterns of functional connectivity, while the secondary axis showed correspondence to core-matrix cell type distributions. Lastly, exploring multimodal thalamocortical associations on a global scale, we observed that the medial-to- lateral gradient consistently differentiated limbic, frontoparietal, and default mode network nodes from dorsal and ventral attention networks across modalities. However, the link with sensory modalities varied. In sum, we show the coherence between lower dimensional patterns of thalamocortical structural connectivity and various modalities, shedding light on multiscale thalamic organization.

neuroscience↗

bioRxiv2023

Relating sex differences in cortical and hippocampal microstructure to sex hormones

Sex hormone receptors are expressed widely in both neurons and glial cells, which allows them to interact with the brains major cell groups via several molecular mechanisms. These mechanisms lead to sex differences in brain structure as well as hormone-induced plasticity in the female brain across the menstrual cycle. Adding to the literature on volumetric changes in cortical structure, here we set out to investigate sex differences in the microstructure of the human cortex in relation to sex hormones. We assessed regional variation in cortical microstructure as a function of sex, hormonal status and sex hormone receptor gene expression distribution based on quantitative intracortical profiling in vivo using the magnetic resonance imaging based T1w/T2w ratio in 992 healthy females and males of the Human Connectome Project young adult sample. We demonstrate that microstructure in isocortex and hippocampus differs regionally between males and females, that this effect varies with hormone levels of females and that implicated brain regions overlap with estrogen receptor and sex steroid synthesis gene expression. Lastly, we show that sex and sex hormone related brain structure variations are most pronounced in areas of low laminar cortical complexity (agranular cortex), which are also predicted to be most plastic based on their cytoarchitectural properties. Together, our data thus are suggestive of sex differences in cortical and hippocampal microstructure, as well as the modulatory function of sex hormones on these measures. Albeit correlative, this study underscores the importance of incorporating sex hormone variables into the investigation of brain structure and plasticity.

neuroscience↗

bioRxiv2023

Human cortex development is shaped by molecular and cellular brain systems

Human brain morphology undergoes complex changes over the lifespan. Despite recent progress in tracking brain development via normative models, current knowledge of underlying biological mechanisms is highly limited. We demonstrate that human cortical thickness development and aging trajectories unfold along patterns of molecular and cellular brain organization, traceable from population-level to individual developmental trajectories. During childhood and adolescence, cortex-wide spatial distributions of dopaminergic receptors, inhibitory neurons, glial cell populations, and brain-metabolic features explain up to 50% of variance associated with a lifespan model of regional cortical thickness trajectories. In contrast, modeled cortical thickness change patterns during adulthood are best explained by cholinergic and glutamatergic neurotransmitter receptor and transporter distributions. These relationships are supported by developmental gene expression trajectories and translate to individual longitudinal data from over 8,000 adolescents, explaining up to 59% of developmental change at cohort- and 18% at single-subject level. Integrating neurobiological brain atlases with normative modeling and population neuroimaging provides a biologically meaningful path to understand brain development and aging in living humans.

neuroscience↗

bioRxiv2023

Revealing the Grammar of Small RNA Secretion Using Interpretable Machine Learning

Small non-coding RNAs can be secreted through a variety of mechanisms, including exosomal sorting, in small extracellular vesicles, and within lipoprotein complexes 1,2. However, the mechanisms that govern their sorting and secretion are still not well understood. In this study, we present ExoGRU, a machine learning model that predicts small RNA secretion probabilities from primary RNA sequence. We experimentally validated the performance of this model through ExoGRU-guided mutagenesis and synthetic RNA sequence analysis, and confirmed that primary RNA sequence is a major determinant in small RNA secretion. Additionally, we used ExoGRU to reveal cis and trans factors that underlie small RNA secretion, including known and novel RNA-binding proteins, e.g., YBX1, HNRNPA2B1, and RBM24. We also developed a novel technique called exoCLIP, which reveals the RNA interactome of RBPs within the cell-free space. We used exoCLIP to reveal the RNA interactome of HNRNPA2B1 and RBM24 in extracellular vesicles. Together, our results demonstrate the power of machine learning in revealing novel biological mechanisms. In addition to providing deeper insight into complex processes such as small RNA secretion, this knowledge can be leveraged in therapeutic and synthetic biology applications.

systems biology↗

bioRxiv2023

The spatial arrangement of laminar thickness profiles in the human cortex scaffolds processing hierarchy

The human neocortex consists of tangentially organized layers with unique cytoarchitectural properties. These layers show spatial variations in thickness and cytoarchitecture across the neocortex, which is thought to support brain function through enabling targeted corticocortical connections. Here, leveraging maps of the six cortical layers in 3D human brain histology, we aimed to quantitatively characterize the systematic covariation of laminar structure in the cortex and its functional consequences. After correcting for the effect of cortical curvature, we identified a spatial pattern of changes in laminar thickness covariance from lateral frontal to posterior occipital regions, which differentiated the dominance of infra- versus supragranular layer thickness. Corresponding to the laminar regularities of cortical connections along cortical hierarchy, the infragranular-dominant pattern of laminar thickness was associated with higher hierarchical positions of regions, mapped based on resting-state effective connectivity in humans and tract-tracing of structural connections in macaques. Moreover, we show that regions with comparable laminar thickness patterns correspond to inter-regional structural covariance, maturational coupling, and transcriptomic patterning, indicating developmental relevance. In sum, here we characterize the association between organization of laminar thickness and processing hierarchy, anchored in ontogeny. As such, we illustrate how laminar organization may provide a foundational principle ultimately supporting human cognitive functioning.

neuroscience↗

bioRxiv2022

Topological supramolecular network enabled highly conductive and stretchable organic bioelectronics

Intrinsically stretchable bioelectronic devices based on soft and conducting organic materials have been regarded as the ideal interface for seamless and biocompatible integration with the human body. However, the grand challenge remains for the conducting polymer to possess both high mechanical ductility and good electrical conduction at cellular level feature sizes. This longstanding material limitation in organic bioelectronics has impeded the full exploitation of its unique benefits. Here, we introduce a new molecular engineering strategy based on rationally designed topological supramolecular networks, which allows effective decoupling of competing effects from multiple molecular building blocks to meet complex requirements. We achieve two orders of magnitude improvement in the conductivity under 100% strain in physiological environment, along with the capability for direct photopatterning down to 2 m. These unprecedented capabilities allow us to realize previously inaccessible bioelectronic applications including high-resolution monitoring of soft and malleable creatures, e.g., octopus, and localized neuromodulation down to single nucleus precision for controlling organ-specific activities through delicate tissues, e.g., brainstem.

bioengineering↗