Biology subjects

Hanson, S. M.

Publications and source records attributed to Hanson, S. M..

At least 19 recordsLinked to original sources

bioRxiv2026

cryoWEight: Conformational ensembles from cryo-EM particle images with weighted ensemble simulations

Atomistic simulations often struggle to capture the full conformational landscape of biomolecular complexes on experimentally relevant timescales, leaving gaps in our understanding of functionally relevant thermodynamics and kinetics. Cryo-electron microscopy (cryo-EM) provides high-resolution structural snapshots spanning multiple conformational states, but translating these static images into dynamic mechanisms remains challenging. Existing approaches for integrating cryo-EM data with molecular simulations either perturb the underlying dynamics through artificial biasing potentials or remain confined to conformations already present in the prior ensemble. We introduce cryoWEight, an automated and extensible Python framework that couples iterative weighted ensemble (WE) simulations with ensemble reweighting against cryo-EM particle images to steer simulation trajectories towards conformational states consistent with cryo-EM populations. We have found that reweighting in the image space introduces a directional bias towards the target distribution even when the prior ensemble has no overlap with the target distribution in the collective variable space. The reweighting requires only the state likelihoods that set the ensemble weights, and any observable that defines these likelihoods can serve as the target distribution, therefore generalizing beyond the application of cryo-EM. cryoWEight is validated on three proteins: chignolin, the N-terminal domain of ribosomal protein L9, and adenylate kinase. The framework converges towards the target conformational states across different sampling strategies and noise levels, providing a scalable route to integrate cryo-EM data with enhanced sampling approaches.

biophysics↗

bioRxiv2026

PySTARC: GPU-accelerated Brownian dynamics for bimolecular association rate constants

Drug-target association and dissociation rates often determine in vivo efficacy more than affinity alone. The association rate constant, however, is computationally challenging to predict since the productive encounter is a rare event in a large translational and orientational search space, which lies beyond the reach of conventional atomistic simulations. We present PySTARC (Python Simulation Toolkit for Association Rate Constants), a GPU-accelerated Brownian dynamics engine for estimating bimolecular association rate constants. PySTARC is a Python reimplementation of the BrownDye engine that converges even small reaction probabilities on a single GPU, at a throughput that would otherwise require a large CPU cluster. The current framework resolves reactions between integration steps with a closed-form Brownian bridge, models the internal flexibility of the solute through a coarse-grained bead chain, distributes trajectories across multiple GPUs, checkpoints long runs, monitors convergence, and automates the entire workflow from input structures to rate estimates. PySTARC is validated against protein-ligand and protein-protein complexes spanning five orders of magnitude in the association rate constant, with most estimates within one order of magnitude of experiment.

biophysics↗

bioRxiv2026

The CAHRA Challenge: A Community-Wide Assessment of Cryo-EM Heterogeneous Reconstruction Algorithms

The ability of cryo-electron microscopy (cryo-EM) to interrogate the atomic structure and motion of biomolecules has motivated the development of a wide range of algorithms for heterogeneity analysis. However, evaluating and comparing these methods remains challenging because ground-truth structures are generally unknown for experimental samples. Here, we introduce the 2026 Community-Wide Assessment of Heterogeneous Reconstruction Algorithms (CAHRA), a community-wide challenge centered on three benchmark datasets that probe distinct tasks in heterogeneity analysis. These include (1) compositional heterogeneity arising from mixtures of distinct protein complexes, (2) continuous conformational variability and atomic modeling, and (3) entanglement between molecular conformation and particle pose. We describe the design, construction, and validation of these datasets, as well as their use in the recently completed CAHRA Challenge.

biophysics↗

bioRxiv2026

Deep Learning-based Modeling Enhances Efficacy of Natural Ligand CAR Binders Targeting CD70

CD70 is well-recognized as a promising "pan-cancer: chimeric antigen receptor (CAR) T-cell target. Prior work has shown that a "natural ligand" (NL)-based CAR targeting CD70, employing its physiological interaction partner CD27, may have therapeutic advantages over antibody-based CARs. Yet while antibody-based CARs are routinely optimized by affinity maturation of their scFv, whether the binding sequence of an NL CAR can be engineered to improve its function remains unexplored. Here, we combined deep learning with physics-based modeling to redesign residues at the CD27:CD70 interface, identifying a CD27 variant, "N88A", which enhances the efficacy of CD70-targeting CAR T-cells across models of acute myeloid leukemia, multiple myeloma, and renal cell carcinoma. Biophysical approaches, including molecular dynamics simulations, support a mechanism of increased binder conformational freedom underlying potency enhancement. Our work presents CD27N88A CAR T-cells as a promising new therapeutic option and proposes that computational modeling could be applied to enhance efficacy of other NL-based immunotherapies.

cancer biology↗

bioRxiv2026

Cooling fast and slow: Characterising the effects of vitrification in cryo-EM and the subsequent recovery of equilibrium populations

Single-particle cryogenic electron microscopy (cryo-EM) has enabled near-atomic resolution structure determination of diverse biomolecules. Because the high vacuum required for electron microscopy prevents the imaging of liquid-phase samples, cryo-EM samples are prepared by plunging the sample into a cryogen, rapidly cooling the sample and suspending the ensemble of biomolecules in a matrix of water glass. However, the effects of this vitrification on the biomolecular ensemble are unknown, complicating efforts to use cryo-EM to derive conformational ensembles of biomolecules. To study these effects, we carried out extensive molecular dynamics simulations (over 50 milliseconds) of the Trp-cage miniprotein at equilibrium and undergoing rapid cooling. We simulated seven cooling rates spanning three orders of magnitude, with the slowest coolings matching experimental rates. By inspecting molecular mobility and density-temperature equations of state for water with and without protein, we found that water vitrification is unaltered by the protein. To track protein conformation changes, and to relate them to conformational kinetics, we made a Markov State Model (MSM) of Trp-cage from 5.4 milliseconds of equilibrium sampling at 277 K. We observed that MSM states with a characteristic time longer than the duration of the non-equilibrium cooling, tend to be more robust to artefacts induced by such cooling. Critically, although we observe that some states vanish in the equilibrium ensemble at 230 K, none do in our nonequilibrium cooled ensembles. However, to account for perturbations induced by nonequilibrium cooling for more labile states, we developed a thermodynamic inference framework for recovering equilibrium populations from the measured vitrified ensembles. These results indicate that cryo-EM has the capacity to be a reliable and accurate biophysical technique for the study of biomolecular ensembles. SignificanceCryogenic electron microscopy images biomolecules trapped in vitreous ice. To vitrify the sample, it must be cooled over the course of 22 microseconds. However, the degree to which this cooling causes the ensemble of the molecules to be perturbed from equilibrium is unknown. Here we present extensive molecular dynamics simulations to quantify the equilibrium dynamics of the Trp-cage miniprotein and the effects of cooling on its conformational ensemble. By simulating cooling at seven different rates, including the slowest experimental rates that still result in vitrification, we connect the kinetic properties of a proteins conformational state to the change in state population from cooling. We show that cooling-induced population shifts are small but observable. We further introduce a thermodynamic-inference method to recover equilibrium populations from the cooled ensembles.

biophysics↗

bioRxiv2025

CryoJAX - A Cryo-Electron Microscopy Image Simulation Library in JAX

While cryo-electron microscopy (cryo-EM) has come to prominence in the last decade due to its ability to resolve biomolecular complexes at atomic resolution, advancements in experimental and computational methods have made cryo-EM promising for investigating intracellular organization and heterogeneous molecular states. A primary challenge for these alternative applications is the development of techniques for cryo-EM data analysis, which are very computationally demanding. To this end, it is advantageous to leverage advanced scientific computing frameworks for statistical analysis. One such framework is JAX, an emerging array-oriented Python numerical computing package for automatic differentiation and vectorization with a growing ecosystem for statistical inference and machine learning. We have developed cryoJAX, a cryo-EM image simulation library for building computational data analysis applications in JAX. CryoJAX is a flexible modeling language for cryo-EM image formation and therefore can support a wide range of data analysis downstream. By integrating with the JAX ecosystem, cryoJAX enables the development and deployment of algorithms for the growing breadth of scientific applications for cryo-EM. SynopsisThe authors have developed cryoJAX, a cryo-EM image simulation library for developing data analysis techniques across cryo-EM modalities. CryoJAX is built on JAX, an emerging scientific computing framework in Python well suited for cryo-EM data analysis.

biophysics↗

bioRxiv2025

seekrflow: Towards end-to-end automated simulation pipeline with machine-learned force fields for accelerated drug-target kinetic and ther-modynamic predictions

Accurate prediction of drug-target binding and unbinding kinetics and thermodynamics is essential for guiding drug discovery and lead optimization. However, traditional atomistic simulations are often too computationally expensive to capture rare events that govern ligand (un)binding. Several enhanced sampling methods exist to overcome these limitations, but they require extensive manual intervention and introduce variability and artifacts in free energy and kinetic estimates that limit high-throughput scalability. The present work introduces seekrflow, an automated multiscale milestoning simulation pipeline that streamlines the entire workflow from a single receptor-ligand input structure to kinetic and thermodynamic predictions in a single step. This integrated approach minimizes manual intervention, reduces computational overhead, and enhances the reproducibility and accuracy of kinetic and thermodynamic predictions. The accuracy and efficiency of the pipeline is demonstrated on multiple receptor-ligand complexes, including inhibitors of heat shock protein 90, threonine-tyrosine kinase, and the trypsin protein, with predicted kinetic parameters closely matching experimental estimates. seekrflow establishes a new benchmark for automated and high-throughput physics-based predictions of kinetics and thermodynamics. O_FIG O_LINKSMALLFIG WIDTH=187 HEIGHT=200 SRC="FIGDIR/small/669965v2_ufig1.gif" ALT="Figure 1"> View larger version (74K): org.highwire.dtl.DTLVardef@c4da23org.highwire.dtl.DTLVardef@1cd386eorg.highwire.dtl.DTLVardef@33f50corg.highwire.dtl.DTLVardef@11d1f5a_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2025

Study of Protein-Protein Interactions in Septin Assembly: Multiple amphipathic helix domains cooperate in binding to the lipid membrane

Septins are a conserved family of cytoskeletal proteins known for sensing micron-scale membrane curvature via amphipathic helix (AH) domains. While cooperative interactions in septin assembly have been suggested, the molecular mechanisms governing membrane binding and assembly remain unclear. Building on prior findings, we use all-atom molecular dynamics simulations to examine how single and paired extended AH domains, derived from Cdc12, interact with lipid bilayers. A single membrane-bound AH adopts a curved conformation. In solution, a second AH peptide preferentially interacts with the bound peptide through conserved salt bridges, favoring an antiparallel arrangement. Simulations of covalently linked AH tandems confirm this configuration. Dual membrane-bound peptides induce lipid packing defects, reduce tail order, and exhibit slight membrane displacement, suggesting curved membranes may better accommodate multiple AH domains. Our findings advance the mechanistic understanding of septin-membrane interactions and highlight the role of cooperative AH domain binding in stabilizing higher-order structures.

biophysics↗

bioRxiv2025

The Inaugural Flatiron Institute Cryo-EM Conformational Heterogeneity Challenge

Despite the rise of single particle cryo-electron microscopy (cryo-EM) as a premier method for resolving macromolecular structures at atomic resolution, methods to address molecular heterogeneity in vitrified samples have yet to reach maturity. With an increasing number of new methods to analyze the multitude of heterogeneous states captured in single particle images, a systematic approach to validation in this field is needed. With this motivation, we issued a challenge to the community to analyze two cryo-EM particle image sets of thyroglobulin that exhibit continuous conformational heterogeneity. The first dataset was experimental and the second was generated with a simulator, allowing control over the distribution of molecular structures and enabled direct comparison between participants submissions and the ground truth molecular structures and distributions. Participants were asked to submit 80 volumes representing the heterogeneous ensemble and estimate their respective populations in the image sets provided. Participation of the research community in the challenge was strong, with submissions from nearly all developers of heterogeneity methods, resulting in 41 submissions across both datasets. Submissions qualitatively exceeded expectations, with the molecular motions identified by methods resembling both each other and the ground truth motion. However, quantitatively assessing these similarities was a challenge in and of itself. In the process of assessing the submissions, we developed several validation metrics, most of which require reference to the underlying ground truth volumes. However, we have also explored the use of metrics that do not necessarily reference ground truth. This is particularly apt for experimental datasets where ground truth is inaccessible. These approaches allowed us to assess the similarity and accuracy in volume quality, molecular motions, and conformational distribution of di!erent submissions. These metrics and the e!orts of all participants help chart a path forward for the improvements of heterogeneity methods for cryo-EM and for future challenges to validate these new methods as they continue to be developed by the community.

biophysics↗

bioRxiv2025

RocketSHP: Ultra-fast Proteome-scale Prediction of Protein Dynamics

Proteins are dynamic molecules that depend on conformational flexibility to carry out functions in the cell, yet despite significant advances in the modeling of static protein structure, prediction of these dynamics remains challenging. We introduce PLANET-MD, a machine learning model that predicts dynamic protein properties from sequence or static structure with unprecedented speed and accuracy. Trained on thousands of molecular dynamics trajectories spanning diverse protein families, PLANET-MD simultaneously models multiple dynamics features: root-mean-square fluctuations (RMSF), generalized correlation coefficients (GCC-LMI), and a novel structural heterogeneity profile (SHP) based on recent structure quantization methods. PLANET-MD significantly outperforms existing methods in predicting simulation-derived dynamics. We reduce RMSF prediction error by 57% compared to BioEmu and calibrated Dyna-1 predictions, including an up to 73% error reduction for long proteins. We validate these predictions with experimental hetNOE data, and we demonstrate the ability to adapt predictions to different physical temperatures. We highlight PLANET-MDs utility in constructing allosteric networks in the oncogene KRAS and identify structural sub-modules with correlated motions, and we validate PLANET-MD by showing that changes in node centrality within predicted KRAS allosteric networks correlate with changes of folding free energy in experimental DMS data. Our approach makes predictions in seconds rather than hours or days, enabling us to perform the first comprehensive dynamics analysis of the entire human proteome. PLANET-MD bridges the gap between static structural biology and dynamic functional understanding, enabling dynamics-aware structural analysis and variant effect prediction at scales previously unavailable. PLANET-MD is available as free and open-source software at https://github.com/flatironinstitute/PLANET-MD.

bioinformatics↗

bioRxiv2024

Heterogeneity in ligand-bound TRPV1: A comparison of methods in cryo-EM and molecular dynamics simulation

Cryogenic electron microscopy (cryo-EM) has emerged as a powerful method for resolving the atomistic details of cellular components. In recent years, several computational methods have been developed to study the heterogeneity of molecules in single-particle cryo-EM. In this study, we analyzed a publicly available single-particle dataset of TRPV1 using five of these methods: 3D Flexible Refinement, 3D Variability Analysis, cryoDRGN, ManifoldEM, and Bayesian ensemble reweighting. Beyond what we initially expected, we have found that this dataset contains significant heterogeneity-- indicating that single particle datasets likely contain a rich spectrum of biologically relevant states. Further, we have found that different methods are best suited to studying different kinds of heterogeneity, with some methods being more sensitive to either compositional or conformational heterogeneity. We also apply a combination of Bayesian ensemble reweighting and molecular dynamics as supporting evidence for the presence of these rarer states within the sample. Finally, we developed a quantitative metric based on the analysis of the singular value decomposition and power spectra to compare the resulting volumes from each method. This work represents a detailed view of the variable outcomes of different heterogeneity methods used to analyze a single real dataset and presents a pathway to a deeper understanding of the biology of complex macromolecules like the TRPV1 ion channel.

biophysics↗

bioRxiv2024

EGDiff: An Experiment-Guided Diffusion model for protein conformational ensemble generation

Understanding protein conformation is key to understanding their function. Importantly, most proteins adopt multiple conformations with non-trivial ensemble distributions that change depending on their environment to perform functions like catalysis, signaling, and transport. Recently, machine learning techniques, especially deep generative models, have been employed to develop protein conformation generators. These models, known as unified protein ensemble samplers, are trained on the PDB dataset and can generate diverse protein conformation ensembles given a protein sequence. However, their reliance solely on structural data from the PDB, which primarily captures folded protein states, restricts the diversity of the generated ensembles and can result in physically unrealistic conformations. In this paper, we overcome these challenges by introducing ExEnDiff, an experiment-guided diffusion model for protein conformation generation. ExEnDiff integrates experimental measurements as a physical prior, enabling the generation of protein conformations with desired properties. Our experiments on a variety of fast-folding and intrinsically disordered proteins demonstrate that ExEnDiff significantly advances the capabilities of current unified protein ensemble samplers. With little computational cost, ExEnDiff can capture important proteins configuration properties and the underlying Boltzmann distribution, paving the way for a next-generation molecular dynamics engine. We further demonstrate the effectiveness of ExEnDiff to capture conformational changes in the presence of mutations and as an efficient tool for determining a reasonable CV space for protein ensembles. With these results, ExEnDiff is well-poised to push the study of protein ensembles into a data-rich regime currently available to few problems in biology.

biophysics↗

bioRxiv2024

Molecular dynamics simulations illuminate the role of sequence context in the ELF3-PrD-based temperature sensing mechanism in plants

Withdrawal StatementThe authors have withdrawn this manuscript due to a duplicate posting of manuscript number BIORXIV/2023/532793. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author. The correct preprint can be found at doi: 10.1101/2023.03.15.532793

biophysics↗

bioRxiv2024

Charge distribution and helical content tune the binding of septins amphipathic helix domain to lipid membranes

Septins are a class of cytoskeletal proteins that preferentially bind to domains of micron-scale curvature on the cell membrane. Studies have shown that amphipathic helix (AH) domains in septin oligomers are essential for septin curvature sensing. Yet, the underlying mechanochemical interactions that modulate this curvature sensing remain ambiguous. Here we use all-atom molecular dynamics alongside a metadynamics enhanced sampling approach to bridge the gap between time and length scales required to optimize and validate experimental design of amphipathic helices. Simulations revealed that the local charge on the termini of an 18-amino-acid AH peptide impacts its helical content and positioning within lipid membranes. These computational observations are confirmed with experiments measuring the binding of synthetic AH constructs with variable helical content and charged termini to lipid vesicles. Taken together, these results identify the helical content of amphipathic helices as a regulator of septin binding affinity to lipid membranes. Additionally, we examined an extended AH sequence including 8 amino acids upstream and downstream of the minimal 18-amino-acid-long AH domain to more closely mimic the native protein in simulations and experiments. Simulations and experiments show that the extended peptide sequence adopts a strong alpha-helical conformation when free in solution, giving rise to a higher affinity to lipid membranes than that of the shorter AH sequence. Together, these results provide insight into how the native septin proteins interact with membranes, and establish general design principles that can guide the interaction of future synthetic materials with lipid membranes in a programmable manner. STATEMENT OF SIGNIFICANCEUnderstanding how cells sense and react to their shape is necessary for numerous biological processes. Here we explore the interactions between amphipathic helices, a curvature sensing protein motif, and lipid membranes. Using molecular dynamics simulations, enhanced simulation sampling techniques, and experiments, we find that increasing the helical content of the amphipathic helix or adding charged capping sequences yields higher membrane binding affinity. Understanding these parameters for membrane-binding could enable us to interface and regulate native protein functions, as well as guide the design of synthetic curvature-sensing materials that can interact with and deform lipid membranes.

biophysics↗

bioRxiv2024

Martini without the twist: Unveiling a mechanically correct microtubule through bottom-up coarse-graining in Martini 3

Microtubules are essential cytoskeletal filaments involved in cell motility, division, and intracellular transport. These biomolecular assemblies can exhibit complex structural be-haviors influenced by various biophysical factors. However, simulating microtubule systems at the atomistic scale is challenging due to their large spatial scales. Here, we present an approach utilizing the Martini 3 Coarse-Grained (CG) model coupled with an appropriate elastic network to simulate microtubule-based systems accurately. By iteratively optimiz-ing the elastic network parameters, we matched the structural fluctuations of CG hetero-dimer building blocks to their atomistic counterparts. Our efforts culminated in a [~] 200nm microtubule built with [~] 6 million interaction-centers that could reproduce experimentally observed mechanical properties. Our aim is to employ these CG simulations to investigate specific biophysical phenomena at a microscopic level. These microscopic perspectives can provide valuable insights into the underlying mechanisms and contribute to our knowledge of microtubule-associated processes in cellular biology. With MARTINI 3 CG simulations, we can bridge the gap between computational efficiency and molecular detail, enabling in-vestigations into these biophysical processes over longer spatio-temporal scales with amino acid-level insights.

biophysics↗

bioRxiv2024

Multiscale simulations of molecular recognition by phase separated MUT-16: A scaffolding protein of Mutator foci

Phase separation of proteins plays a critical role in cellular organisation. How phase separated protein condensates underpin biological function and how condensates achieve specificity remain elusive. We investigated the phase separation of MUT-16, a scaffold protein in Mutator foci, and its role in recruiting the client protein MUT-8, a key component in RNA silencing in C. elegans. We employed a multiscale approach that combined coarse-grained (residue-level CALVADOS2 and near-atomistic Martini3) and atomistic simulations. Simulations across different resolutions provide a consistent perspective on how MUT-16 condensates recruit MUT-8, enabling the fine-tuning of chemical details while balancing the computational cost. Both coarse-grained models (CALVADOS2 and Martini3) predicted the relative phase separation propensities of MUT-16s disordered regions, which we confirmed through in vitro experiments. Simulations also identified key sequence features and residues driving phase separation while revealing differences in residue interaction propensities between CALVADOS2 and Martini3. Furthermore, Martini3 and 350 {micro}s atomistic simulations on Folding@Home of MUT-8s N-terminal prion-like domain with MUT-16 M8BR cluster highlighted the importance of cation-{pi} interactions between Tyr residues of MUT-8 and Arg residues of MUT-16 M8BR. Lys residues were observed to be more prone to interact in Martini3. Atomistic simulations revealed that the guanidinium group of Arg also engages in sp2-{pi} interactions and hydrogen bonds with the backbone of Tyr, making Arg-Tyr interactions stronger than Lys-Tyr, where these additional favourable contacts are absent. In agreement with our simulations, in vitro co-expression pulldown experiments demonstrated a progressive loss of MUT-8 recruitment following the mutation of Arg in MUT-16 M8BR to Lys or Ala, confirming the critical role of Arg in this interaction. These findings advance our understanding of MUT-16 phase separation and subsequent MUT-8 recruitment, key processes in assembling Mutator foci that drive RNA silencing in C. elegans. Statement of SignificanceIn cells proteins phase separate and form condensates. These protein condensates can play important role in bringing molecules together and facilitate biochemical processes. In this work, we used molecular dynamics simulations to understand how MUT-16 phase separates and forms the scaffold of the so-called Mutator focus. Mutator foci produce small RNA which help to regulates genes. As the scaffold of the Mutator focus, MUT-16 recruit multiple proteins which are important for the production of such small RNAs.

bioinformatics↗

bioRxiv2024

Conditions for the co-existence of promoter and gene-body condensates

In cells, transcription is tightly regulated on multiple layers. The condensation of the transcription machinery into distinct phases is hypothesised to spatio-temporally fine tune RNA polymerase II behaviour during two key stages, transcription initiation and the elongation of the nascent RNA transcripts. However, it has remained unclear whether these phases would mix when present at the same time or remain distinct chemical environments; either as multi-phase condensates or by forming entirely separate condensates. Here we combine particle-based multi-scale simulations and experiments in the model organism C. elegans to characterise the biophysical properties of RNA polymerase II condensates. Both simulations and the in vivo work describe a lower critical solution temperature (LCST) behaviour of RNA Polymerase II, with condensates dissolving at lower temperatures whereas higher temperatures promote condensate stability. Importantly this gradual change in temperature correlates with an incremental transcriptional response to temperature, but is largely uncoupled from the classical stress response. The LCST behaviour of CTD also highlights that these condensates are physio-chemically distinct from heterochromatin condensates. Expanding the simulations we model how the degree of phosphorylation of the disordered C-terminal domain of RNA polymerase II (CTD), which is characteristic for each step of transcription, controls demixing of CTD and pCTD in line with phase separation experiments. We show that the two phases putatively underpinning the initiation of transcription and transcription elongation constitute distinct chemical environments and are in agreement with RNA polymerase II condensates observed in C. elegans embryos by super resolution microscopy. Our analysis reveals how depending on its post-translational modifications and its interaction partners a single protein can adopt multiple morphologies and how partially engulfed condensates promote the selective recruitment of additional factors to the different phases.

biophysics↗

bioRxiv2023

Developmental origin and local signals cooperate to determine septal astrocyte identity

Astrocyte specification during development is influenced by both intrinsic and extrinsic factors, but the precise contribution of each remains poorly understood. Here we show that septal astrocytes from Nkx2.1 and Zic4 expressing progenitor zones are allocated into non-overlapping domains of the medial (MS) and lateral septal nuclei (LS) respectively. Astrocytes in these areas exhibit distinctive molecular and morphological features tailored to the unique cellular and synaptic circuit environment of each nucleus. Using single-nucleus (sn) RNA sequencing, we trace the developmental trajectories of cells in the septum and find that neurons and astrocytes undergo region and developmental stage-specific local cell-cell interactions. We show that expression of the classic morphogens Sonic hedgehog (Shh) and Fibroblast growth factors (Fgfs) by MS and LS neurons respectively, functions to promote the molecular specification of local astrocytes in each region. Finally, using heterotopic cell transplantation, we show that both morphological and molecular specifications of septal astrocytes are highly dependent on the local microenvironment, regardless of developmental origins. Our data highlights the complex interplay between intrinsic and extrinsic factors shaping astrocyte identities and illustrates the importance of the local environment in determining astrocyte functional specialization.

neuroscience↗