Biology subjects

Dreisler, M. W.

Publications and source records attributed to Dreisler, M. W..

7 recordsLinked to original sources

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

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↗

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↗

bioRxiv2023

SEMORE: SEgmentation and MORphological fingErprinting by machine learning automates super-resolution data analysis.

The morphology of protein assemblies impacts their behavior and contributes to beneficial and aberrant cellular responses. While single-molecule localization microscopy provides the required spatial resolution to investigate these assemblies, the lack of universal robust analytical tools to extract and quantify underlying structures limits this powerful technique. Here we present SEMORE, a semi-automatic machine learning framework for universal, system and input-dependent, analysis of super-resolution data. SEMORE implements a multi-layered density-based clustering module to dissect biological assemblies and a morphology fingerprinting module for quantification by multiple geometric and kinetics-based descriptors. We demonstrate SEMORE on simulations and diverse raw super-resolution data; time-resolved insulin aggregates and imaging of nuclear pore complexes. SEMORE extracts and quantifies all protein assemblies enabling classification of heterogeneous insulin aggregation pathways and NPC geometry in minutes. SEMORE is a general analysis platform for super-resolution data, and being the first time-awar e framework can also support the rise of 4D super-resolution data.

biophysics↗