Biology subjects

Hatzakis, N. S.

Publications and source records attributed to Hatzakis, N. S..

At least 19 recordsLinked to original sources

bioRxiv2026

Lipid diffusion enhanced by molecular motors

An experimental study of myosin I motors on a supported lipid membrane with a nematic actin filament layer is presented. The activities of myosin I motors re-organize the actin layer, in particular the morphology of +1/2 nematic defects. Data from single-particle tracking (SPT) of lipid molecules reveal two interesting features. First, the distribution of average lipid speed over a track shows two groups of lipids (high-speed and low-speed), and the proportion of lipids to be high-speed is higher in regions rich in actin. Second, by classifying short-time movement of the lipids into normal diffusion, directed motion, confined motion, and subdiffusion states, we found that lipids in regions rich in actin have a lower probability to stay in the confined and subdiffusion states and more likely to perform normal diffusion. Furthermore, fluorescence recovery after photobleaching (FRAP) measurements show that the diffusion of lipids in the actin-rich regions is enhanced. The SPT and FRAP results suggest that myosin I motor activities turn low-speed lipids to high-speed ones, and affect the lipid moving state by reducing confined and subdiffusive motion. As a result, lipid diffusion is enhanced by motor activities.

biophysics↗

bioRxiv2026

BioBrain: A Multi-Agent Framework for Natural Language Driven Quantitative Microscopy Data Analysis

Advances in fluorescence microscopy have dramatically expanded the range of biological questions that can be addressed, enabling quantitative observations of molecular interactions and cellular dynamics with unprecedented spatial and temporal resolution. However, the growing complexity of imaging data has outpaced our ability to analyze them. Despite numerous computational methods exist, they often rely on specialized software environments, heterogeneous data formats, and technical expertise, limiting adoption and widening the gap between data acquisition and quantitative biological interpretation. Here we introduce BioBrain, a multi-agent framework that translates natural-language analytical goals into executable and reproducible microscopy analysis pipelines. Instead of generating analysis code, BioBrain assembles validated analytical methods and can expands its analytical capabilities by integrating existing laboratory scripts into a unified conversational framework. Every selected method and inferred parameter is transparently reported, ensuring traceable and reproducible analyses. On two-channel total internal reflection fluorescence and three-dimensional lattice light-sheet benchmarks, BioBrain exactly reproduces expert-derived results when parameters are specified and degrades predictably and traceably when they are not, while frontier language models generated large, model-dependent quantitative errors despite completing without warning. BioBrain offers a practical path for closing the widening gap between data acquisition and biological discovery, enabling experimental scientists to communicate with computational analysis in the language of biology rather than the language of software.

biophysics↗

bioRxiv2026

APOSM: Pairwise preference learning improves generative small-molecule design

Small-molecule lead refinement is constrained by the cost of synthesizing and assaying candidates, making the surrogate models that prioritize compounds for experimental testing central to the design process. The reliability of such surrogates is limited by the noise and sparsity of screening measurements. We show that training the surrogate on pairwise comparisons between candidate molecules, rather than on absolute predicted scores, yields a substantially more reliable signal for active candidate selection in this regime. We develop APOSM, an active-learning algorithm that combines a fragment-based generator, a pairwise message-passing graph neural network surrogate, and probabilistic ranking inside a batched acquisition loop. On the Practical Molecular Optimization benchmark and a GPCR ligand rediscovery task, APOSM improves target attainment and sampling efficiency over unguided fragment-based optimization, the Graph-GA genetic algorithm, and a pointwise-regression ablation, with the largest gains on tasks where absolute scores are hardest to calibrate.

bioinformatics↗

bioRxiv2026

Rapid and reliable quantification of cytosolic mRNA escape (RNASCAPE)

Endosomal escape of mRNA remains a critical bottleneck in oligonucleotide therapeutics, with typically less than 5% of delivered mRNA reaching the cytosol. Precise quantification of this escape is hindered by stochastic variability in uptake, release, and expression, while existing methods lack scalability and accuracy. Here we introduce RNASCAPE, a deep learning framework trained on biologically realistic simulations that estimates unlabelled mRNA cytosolic escape efficiency using only three timepoints of EGFP reporter expression and four lipid nanoparticle (LNP) meta-parameters. Benchmarking on synthetic data shows RNASCAPE achieves a mean absolute percentage accuracy of 78%. RNASCAPE accurately predicts escape efficiencies around 5-9% across diverse cell types and revealed that exchanging cholesterol to {beta}-sitosterol drastically reduces mRNA loading and enhances twofold its functional release. By enabling robust, microscopy-agnostic quantification without requiring specialized cell lines or labeled cargo, RNASCAPE provides a scalable framework for benchmarking and rational design of LNP formulations, advancing nucleic acid therapeutic delivery.

biophysics↗

bioRxiv2026

MORPHIS (MORPHological Interpretable Signature) captures heterogeneous treatment- and aging-related responses of single cells

Cell morphology encodes changes in cytoskeletal and organelle organization during disease, treatment, and aging, yet is often assessed qualitatively or through poorly interpretable feature sets extracted from microscopy images. Here we introduce MORPHIS (MORPHological Interpretable Signature), a machine learning framework on explainable, analytically rich features paired with statistical methodologies for robust and interpretable quantification of single-cell morphological signature. MORPHIS extracts compact, interpretable feature signatures that capture both perturbation-specific response magnitude and heterogeneous cellular responses. It accurately distinguishes treatment-specific morphological signatures of eight mechanistically distinct membrane-active and intracellular-targeting compounds in Caco-2 and HeLa cells elucidating conserved and divergent phenotypic responses among compound-classes, as well as ultrastructural nuclear alterations upon aging of C. elegans and quantifies heterogeneous single-cell fractional responses. By remaining cell-type and perturbation agnostic, MORPHIS provides a generalizable framework for quantifying morphological signatures across diverse biological contexts including pharmacological treatment or aging.

biophysics↗

bioRxiv2026

A Modular Framework for Automated Segmentation and Analysis of AFM Imaging of Chromatin Organization

Chromatin organization underlies essential genome functions, but its nanoscale organization remains challenging to capture and quantify with precision. Atomic force microscopy (AFM) offers direct structural readouts of DNA and chromatin, yet translating these rich images into reproducible biological metrics has been limited by the lack of standardized, scalable analysis tools. Here we present DNAsight, an automated analysis framework that integrates machine learning (ML)-based segmentation with modular, base-pair-calibrated quantification of DNA spatial organization, looping, nucleosome spacing, and protein clustering. Applied across diverse chromatin-associated proteins, DNAsight reveals protein-specific organizational signatures, including topology-dependent compaction by integration host factor (IHF), condition-dependent changes in loop-like DNA structures in cohesin-CTCF-precocious dissociation of sisters 5A (PDS5A) reactions, and promoter-driven multimerization of GAGA factor (GAF) clusters. The framework further enables direct extraction of nucleosome spacing distributions from raw AFM images, providing a label-free route to investigate chromatin fiber architecture. Together, these advances establish DNAsight as a generalizable and scalable approach for converting AFM measurements into quantitative insights into the physical principles of chromatin organization. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=79 SRC="FIGDIR/small/708946v2_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@1225883org.highwire.dtl.DTLVardef@1e1041dorg.highwire.dtl.DTLVardef@1d53d18org.highwire.dtl.DTLVardef@9e0a2a_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2026

Tuning siRNA packing order in lipid nanoparticles modulates oligonucleotide functional delivery

Efficient siRNA delivery by lipid nanoparticles (LNPs) is widely attributed to carrier composition, yet how intraparticle packing governs function remains unclear. Here, we developed a single-particle fluorescence microscopy assay that simultaneously quantifies size and siRNA loading of individual, chromophore-labeled LNPs. Imaging ~0.5M particles per condition per hour uncovered two major packing modes: a high and a low order corroborated by cryo-EM. Quantitative live cell imaging combined with systematic variation of LNPs lipid composition and N/P ratio allowed deconvolution of siRNA packing, internalization, and silencing and its dependance on lipid composition and electrostatics. Surprisingly, low-order particles while encapsulating modest RNA mediate more efficient knockdown of a fluorescent reporter than their high-order counterparts. Guided by these findings we predicted and experimentally validated that tuning composition and N/P ratio to favor less compact siRNA packing enhances silencing potency. This framework offers actionable guidance for the rational optimization of LNP formulations for RNA therapeutics.

biophysics↗

bioRxiv2025

Single-Particle Tracking and Positional Phenotyping Reveals Variant-Specific Early Checkpoints in SARS-CoV-2 Cell Entry

SARS-CoV-2 entry is governed by Spike (S) protein-mediated engagement of ACE2 and subsequent activation of either plasma membrane fusion mediated by TMPRSS2 or endocytic uptake. Currently, most insights into these pathways come from bulk assays that obscure the fate of individual virions, thereby concealing intricate mechanistic details that can inform on therapeutic intervention strategies. Here, we applied single-particle fluorescence imaging to directly observe the early checkpoints of SARS-CoV-2 cell entry pathways and separate binding from internalization. Fluorescent virus-like particles (VLPs) pseudotyped with either G614 or Omicron BA.5 S protein variants were imaged on HEK293T-ACE2 (TMPRSS2-negative) and classified at the single-particle level as surface-interacting, crossing, or internal. At baseline, G614 VLPs show higher binding and a larger internalized share than BA.5 VLPs, revealing general divergence in early entry behavior between variants. A trivalent anti-S receptor-binding domain aptamer reduces G614 binding and lowers its internalization. Conversely, the aptamer does not block BA.5 VLP cell binding but increases its internalization efficiency. Pitstop 2, an inhibitor of clathrin-mediated endocytosis, causes no significant change in this observation window, consistent with early clathrin-sensitive events having already progressed. Quantification of trajectories reveals variant-specific mobility: BA.5 displays higher step length than G614, consistent with greater lateral scanning and surface retention. Together, these compact single-particle readouts expose variant-resolved early checkpoints in entry and provide a simple platform to test how ligands and pathway probes shift binding and internalization.

biophysics↗

bioRxiv2025

Ultrafast CTCF dynamics control cohesin barrier function

Genomes are organized into chromatin loops through cohesin-mediated extrusion, with CTCF acting as a polar boundary element. As cohesin approaches CTCF at kilobase-per-second speeds, it must rapidly choose whether to stall or bypass. How CTCF encodes this probabilistic decision within a brief encounter window has remained unclear. Here we show that CTCF governs this probabilistic outcome by rapidly sampling a dynamic ensemble of conformations generated by spontaneous rearrangements of its DNA-binding zinc fingers. This ensemble is tuned by DNA sequence, CpG methylation, nearby nucleosomes, and the cohesin regulator PDS5A before cohesin engagement. Upon cohesin binding, PDS5A enhances loop-anchor mechanical stability, reinforcing orientation-dependent boundaries. These findings establish conformational ensemble tuning, rather than static occupancy, as a regulatory principle linking base pair-scale motions to megabase-scale genome organization. One sentence summaryChromatin boundary function is governed not by CTCF occupancy alone, but by a tunable ensemble of DNA-bound conformations that probabilistically gates cohesin capture.

biophysics↗

bioRxiv2025

Polymer functionalized liposomes as universal nanocarriers for drug delivery: Single particle insights on size-dependent performance and intracellular behavior

Nanomedicine requires smart delivery systems that are precise, robust, and universal. While liposomes are established vehicles in drug delivery, their full potential is challenged by limited stability, leakage, insufficient response and limited insight into particle size dependent performance. Here, we provide polymer-modified liposomes (PMLs), engineered for high structural integrity, broad cargo compatibility, and stimuli-responsive cargo release. We thoroughly characterize PMLs at the single particle level shedding light on key structure-function relationships within polydisperse formulations revealing that small vesicles (<100 nm) displayed significantly higher cargo packing densities, while release was independent of vesicle size. PMLs display high versatility effectively encapsulating cargo types ranging from positively or negatively charged small molecules, to oligonucleotides, and proteins. Studies on PMLs interaction with cell membrane show that PMLs maintain high internalization rate in HeLa and hCMEC/D3 brain cells and achieve [~]50% reduction in cell viability within 24 hours when loaded with the anticancer drug 5-fluorouracil. Finaly, PMLs successfully deliver siRNA targeting eGFP in HEK293-d2eGFP cells, achieving a 10-12% knockdown of eGFP expression, as resolved by machine learning-driven single-cell analysis. This work establishes a framework for PMLs high-resolution functional profiling and opens the way for the next generation rational design of tunable PMLs for drug delivery.

biophysics↗

bioRxiv2024

Direct observation of assembly and function of trigger responsive lipase biohybrids

Protein-polymer biohybrids mark a cutting edge in the creation of advanced materials designed for high performance under diverse conditions by combining the intrinsic stability and adaptability of synthetic polymers with the remarkable functionality and specificity of proteins. Despite the existence of stimuli responsive polymers, their potential for achieving precise, trigger responsive control over both the function and the assembly of protein-polymer biohybrids has yet to be fully attained. Here, we report the synthesis and thorough characterization of pH and temperature responsive lipase-polymer conjugates at both the supramolecular and the single molecule level. In contrast to conventional ensemble measurements performed in solution, we extensively characterized the stimuli triggered reversibility of the self-assembly of these lipase-based biohybrids and recorded the enzymatic activity at the fundamental level of individual nanoparticles. Our findings unexpectedly demonstrated that the stimuli responsive biohybrids exhibited not only a twofold increased enzymatic activity as compared to native lipases in certain cases but also maintained the trigger responsive control over enzymatic activity by temperature. Direct single particle recordings of biohybrid activity showed a correlation of the biohybrid size to catalytic activity, revealing individual enzymes to display higher activity in smaller nanoparticles. The biohybrids exhibit impressive long-term stability, in some cases almost 95% of the enzymatic activity as compared to [~]40-60% for native lipases after 1 year of storage. The improved stability and activity as compared to native enzymes accompanied with their triggered responsiveness highlight their significant potential for tightly controlled biocatalysis and sustainable industrial applications.

biophysics↗

bioRxiv2024

Diverse Intracellular Trafficking of Insulin Analogs byMachine Learning-based Colocalization and DiffusionAnalysis

Insulin signaling is crucial for maintaining cellular function and systemic homeostasis, with its dysregulation leading to metabolic disorders in particular diabetes. While insulin analogs are essential in type 1 diabetes treatment, their intracellular trafficking and sorting compared to endogenous insulin remains largely unresolved at the molecular level. Understanding these are important in improving therapeutics and guiding future drug development. However, current methods rely on static imaging and bulk receptor assays conducted in non-physiological conditions, which often disrupt native insulin signaling and provide only snapshots that fail to capture the temporal dynamics of insulin trafficking and characteristic sorting pathways. Here, we directly observe insulin trafficking and intracellular sorting and compare the characteristics of Atto655 labeled recombinant human insulin (HI655) with insulin aspart (IAsp655), a clinically approved, rapid-acting insulin analog. We developed a combined approach integrating Colocalization Fingerprinting, a machine learning framework for reliable, time-resolved colocalization analysis, together with our recently developed deep learning-assisted single-particle diffusional analysis (DeepSPT). Our analysis revealed subtle, yet significant differences in intracellular behavior between IAsp655 and HI655, particularly in diffusional behavior and lysosomal colocalization, highlighting the potential of our approach to decipher subtle differences in intracellular trafficking and sorting characteristics. In addition to contributing to a more detailed understanding of biology of insulin analogs and intracellular sorting, we provide a reliable machine-learning methodology to study intricate cellular processes.

biophysics↗

bioRxiv2024

Organic opto-nanobiointerface enables multiscale biomodulation

Virtually all organic material on Earth has been produced converting solar energy through photosynthesis in chloroplasts, a sack-like, double membrane organelle in plants and algae, where transmembrane electron transfer occurs from lumen to stroma. Although animals hardly harness the power of photosynthesis, their bioelectrical signals extensively regulate complex electrophysiological behaviors, rendering it a superior target for biomedical innovation. Here, a crude structural mimicry of chloroplast has led us to discover that hollow sphere graphitic carbon nitride nanoparticles (hg-C3N4 NPs) endowed non-genetic, subcellular and intercellular photo-modulation of various excitable and non-excitable cells, accumulatively achieving modulation at tissue/organ function level. The homogeneous hg-C3N4 NPs showed responsiveness to light via both photoelectrochemical and photothermal mechanisms. The hg-C3N4 NPs can be spontaneously internalized with excellent cytocompatibility. Using a focusing laser, the hg-C3N4 NPs enable intracellular optical stimulation with subcellular resolution, inducing calcium transient release in multiple cells and propagation in primary cardiomyocytes and cardiac fibroblasts. At multicellular scale, optical pacing and synchronization of cardiomyocyte beating is readily achieved by LED. Further, we demonstrate that hg-C3N4 nanoparticles can be safely delivered into the mouse eye and elicit measurable cortical and behavioral light responses in a subset of animals in a model of advanced retinal degeneration. Finally, application of hg-C3N4 NPs to porcine retinal tissue ex vivo confirmed their modulation capability to directly activate RGCs activity under LED photostimulation. Taken together, these nanostructured biomimetic photocatalytic NPs offer high resolution, leadless optical probing, non-invasive delivery and great biocompatibility, serving as a versatile tool for addressing a range of complex biomedical challenges through subcellular, intercellular and tissue-level photo-modulation across a broad spectrum of scales.

bioengineering↗

bioRxiv2024

Single-molecule analysis of protein targeting from the endoplasmic reticulum to lipid droplets

Numerous metabolic enzymes translocate from the ER membrane bilayer to the lipid droplet (LD) monolayer, where they perform essential functions. Mislocalization of certain LD-targeted membrane proteins, including HSD17B13 and PNPLA3, is implicated in metabolic dysfunction-associated steatotic liver disease (MASLD). However, the mechanisms governing the trafficking and accumulation of ER proteins on LDs remain poorly understood. Here, using MINFLUX and HILO single-molecule tracking combined with machine learning, we show that HSD17B13, GPAT4, and the model cargo LiveDrop diffuse at comparable speeds in the ER and on LDs, but become nano-confined upon reaching the LD surface. Mechanistic dissection of LiveDrop targeting revealed that this confinement, along with protein accumulation on LDs, depends on specific residues within its targeting motif. These residues mediate preferential and repeated interactions with nanoscale membrane domains, suggesting that LD-targeted proteins selectively partition into distinct lipid-protein environments that transiently retain and concentrate them at the LD surface. Single-molecule trajectories further revealed bidirectional trafficking of LiveDrop across seipin-containing ER-LD bridges, providing direct evidence for lateral protein transfer across membrane contact sites. These findings establish nanodomain-based confinement as a key mechanism driving selective protein accumulation on LDs and reveal how membrane bridges between organelles facilitate protein sorting.

cell biology↗

bioRxiv2024

Proteolytic Performance is Dependent on Binding Efficiency, Processivity and Turnover: Single Protease Insights

Proteases are essential enzymes for a plethora of biological processes and biotechnological applications, e.g., within the dairy, pharmaceutical, and detergent industries. Decoding the molecular level mechanisms that drive protease performance is key to designing improved biosolutions. However, direct dynamic assessment of the fundamental partial reactions of substrate binding and activity has proven a challenge with conventional ensemble approaches. We developed a single-molecule (SM) assay for the direct and parallel recording of the stochastic binding interaction of Savinase, a serine-type protease broadly employed in biotechnology, with casein synchronously with monitoring proteolytic degradation of the substrate. SM recordings enabled us to determine how the overall activity of Savinase and two mutants relies on binding efficiency, enzymatic turnover and activity per binding event. Analysis of residence times revealed three characteristic binding states. Mutations were found to dominantly alter the likelihood of sampling the long lived state, with lifetimes longer than 30 seconds, indicating this state contributes to overall activity and supporting a level of processivity for Savinase. This observation challenges conventional expectations, as the protease has no characterized substrate binding site, or binding domain, aside from the active site. These insights, inaccessible through conventional assays, offer new perspectives for engineering proteases with improved hydrolytic performance.

biophysics↗

bioRxiv2024

Defect-Engineered Metal-Organic Frameworks as Nanocarriers for Pharmacotherapy: Insights into Intracellular Dynamics at The Single Particle Level

NanoMOFs are widely implemented in a host of assays involving drug delivery, biosensing catalysis, and bioimaging. Despite their wide use, the cell entry pathways and cell fate remain poorly understood. Here we have synthesized a new fluorescent nanoMOF integrating ATTO 655 into surface defects of colloidal nano UiO-66 that allowed us to track the spatiotemporal localization of Single nanoMOF in live cells. Density Functional Theory(DFT) reveals the stronger binding of ATTO 655 to the uncoordinated saturated Zr6 cluster nodes compared with phosphate and Alendronate Sodium (AL). Parallelized tracking of the spatiotemporal localization of tens of thousands of nanoMOFs and analysis using machine learning platforms revealed whether nanoMOFs remain outside as well as their cellular internalization pathways. To quantitatively assess their colocalization with endo/lysosomal compartments, we developed a colocalization proxy approach relying on the nanoMOF detection of particles in one channel to the signal in the corresponding endo/lysosomal compartments channel, considering signal vs local background intensity ratio (S/B) and signal-to-noise ratio (SNR). This strategy effectively mitigates the potential inflation of colocalization values arising from the heightened expression of signals originating from endo/lysosomal compartments, it also overcomes limitations of low SNRs in the endo/lysosomal compartments marker channel, which incapacitates any trajectory-trajectory colocalization assessment. The results accurately measure the amount of nanoMOFs colocalization in real-time from early (EE) to late endosomes(LE) and lysosomes(LY) and emphasize the importance of understanding their intracellular dynamics based on single-particle tracking (SPT) for optimal and safe drug delivery.

biophysics↗

bioRxiv2024

Constitutive Endolysosomal Perforation in Neurons allows Induction of alpha-Synuclein Aggregation by Internalized Pre-Formed Fibrils

The endocytic pathway is both an essential route of molecular uptake in cells and a potential entry point for pathology-inducing cargo. The cell-to-cell spread of cytotoxic aggregates, such as those of -synuclein (-syn) in Parkinsons Disease (PD), exemplifies this duality. Here we used a human iPSC-derived induced neuronal model (iNs) prone to death mediated by aggregation in late endosomes and lysosomes of endogenous -syn, seeded by internalized pre-formed fibrils of -syn (PFFs). This PFF-mediated death was not observed with parental iPSCs or other non-neuronal cells. Using live-cell optical microscopy to visualize the read out of biosensors reporting endo-lysosome wounding, we discovered that up to about 10% of late endosomes and lysosomes in iNs exhibited spontaneous constitutive perforations, regardless of the presence of internalized PFFs. This wounding, absent in parental iPSCs and non-neuronal cells, corresponded to partial damage by nanopores in the limiting membranes of a subset of endolysosomes directly observed by volumetric focused ion beam scanning electron microscopy (FIB-SEM) in iNs and in CA1 pyramidal neurons from mouse brain, and not found in iPSCs or in other non-neuronal cells in culture or in mouse liver and skin. We suggest that the compromised limiting membranes in iNs and neurons in general are the primary conduit for cytosolic -syn to access PFFs entrapped within endo-lysosomal lumens, initiating PFF-mediated -syn aggregation. Significantly, eradicating the intrinsic endolysosomal perforations in iNs by inhibiting the endosomal Phosphatidylinositol-3-Phosphate/Phosphatidylinositol 5-Kinase (PIKfyve kinase) using Apilimod or Vacuolin-1 markedly reduced PFF-induced -syn aggregation, despite PFFs continuing to enter the endolysosomal compartment. Crucially, this intervention also diminished iN death associated with PFF incubation. Our results reveal the surprising presence of intrinsically perforated endo-lysosomes in neurons, underscoring their crucial early involvement in the genesis of toxic -syn aggregates induced by internalized PFFs. This discovery offers a basis for employing PIKfyve kinase inhibition as a potential therapeutic strategy to counteract synucleinopathies.

cell biology↗

bioRxiv2023

Deep learning assisted single particle tracking for automated correlation between diffusion and function

Sub-cellular diffusion in living systems reflects cellular processes and interactions. Recent advances in optical microscopy allow the tracking of this nanoscale diffusion of individual objects with an unprecedented level of precision. However, the agnostic and automated extraction of functional information from the diffusion of molecules and organelles within the sub-cellular environment, is labor-intensive and poses a significant challenge. Here we introduce DeepSPT, a deep learning framework to interpret the diffusional 2D or 3D temporal behavior of objects in a rapid and efficient manner, agnostically. Demonstrating its versatility, we have applied DeepSPT to automated mapping of the early events of viral infections, identifying distinct types of endosomal organelles, and clathrin-coated pits and vesicles with up to 95% accuracy and within seconds instead of weeks. The fact that DeepSPT effectively extracts biological information from diffusion alone indicates that besides structure, motion encodes function at the molecular and subcellular level.

biophysics↗