Biology subjects

Rasmussen, R.

Publications and source records attributed to Rasmussen, R..

4 recordsLinked to original sources

bioRxiv2026

Predicting Neoantigen Immunogenicity from In Vivo Immune Editing

Neoantigen immunogenicity prediction is fundamental to personalized cancer vaccines, tumor-infiltrating lymphocyte (TIL) therapy, and TCR-T cell engineering. Existing computational predictors rely primarily on in-vitro correlates of peptide presentation or models trained against assay-based reactivity, and they are typically validated within a single therapeutic setting. We reasoned that the most direct evidence of neoantigen immunogenicity is longitudinal in-vivo elimination: under immune checkpoint blockade (ICB), subclones bearing recognized neoantigens are selectively depleted over time. Here, we present the Neoantigen Elimination Model (NEMo), a two-compartment (CD8 and CD4) machine learning classifier trained on the in-vivo editing (IVE) of neoantigens across serially sequenced, ICB-treated tumors. By using mechanistically inspired NeoPrecis features designed to capture determinants of immunogenicity beyond MHC binding affinity, NEMo recovered assay-confirmed immunogenic neoantigens across four independent, unseen clinical settings -- pre-existing immunogenicity screening, personalized cancer vaccines, TIL therapy, and a radiotherapy +/- ICB ctDNA cohort -- and stratified progression-free survival more strongly than ELISPOT-confirmed reactivity. The editing signal further revealed an immune-evasion architecture in which oncogenic drivers and neoantigens restricted to lost or silenced HLA alleles are systematically spared from editing.

cancer biology↗

bioRxiv2026

Transcription-induced coacervation accelerates and sensitizes cell-free biosensing

Cell-free biosensors leverage in vitro gene expression reactions to detect chemicals. While inexpensive, modular, and distributable, these platforms are constrained by slow readouts at ambient temperatures, precluding practical field operation. In cells, phase separation accelerates biochemical reactions; however, recapitulating these gains in vitro has remained challenging for complex biochemistries. Here, we report the first self-assembling coacervate system that accelerates in vitro transcription. Prepared by simple mixing, coacervation with spermine and polyacrylic acid occurs dynamically in response to NTP consumption and co-localizes DNA templates and RNA polymerase to accelerate transcription, mimicking intracellular phenomena. We exploit this discovery to accelerate the cell-free biosensing of six ligands, demonstrating that coacervation can preserve platform modularity, improve sensitivity, retain lyophilization compatibility, function in field matrices, and reduce ambient-temperature time-to-signal by hours. This work contributes to a growing understanding of phase separation in biology and advances the use of membrane-less organization for real-world applications.

synthetic biology↗

bioRxiv2020

Theoretical analysis predicts an optimal therapeutic strategy in distinct parkinsonian landscapes of the striatum

Parkinsons disease (PD) results from a loss of dopaminergic neurons. The age of disease onset, its progression and symptoms vary significantly between patients, pointing to a complex relationship between neuron loss and PD etiology. Yet, our understanding of the clinical variability remains incomplete. Here, we use biophysical modelling to characterize the dopaminergic landscape in the healthy and denervated striatum. Based on currently proposed mechanisms causing PD, we model three distinct denervation patterns, and show notable differences in the dopaminergic network as denervation progresses. We find local and global differences in the activity of two types of striatal neurons depending on the denervation pattern. Finally, we identify an optimal cellular strategy for maintaining normal dopamine signaling when neurons degenerate stochastically within our model. Our results derive a conceptual framework in which the clinical variability of PD is rooted in distinct denervation patterns and forms testable predictions for future PD research.

biophysics↗

bioRxiv2020

EyeLoop: An open-source, high-speed eye-tracker designed for dynamic experiments

Eye-tracking is a method for tracking the position of the eye and size of the pupil, often employed in neuroscience laboratories and clinics. Eye-trackers are widely used, from studying brain dynamics to investigating neuropathology and disease models. Despite this broad utility, eye-trackers are expensive, hardware-intensive, and proprietary, which have limited this approach to high-resource facilities. Besides, experiments have largely been confined to static open-loop designs and post hoc analysis due to the inflexibility of current systems. Here, we developed an open-source eye-tracking system, named EyeLoop, tailored to dynamic experiments. This Python-based software easily integrates custom functions via a modular logic, tracks a multitude of eyes, including rodent, human, and non-human primate eyes, and it operates well on inexpensive consumer-grade hardware. One of the most appealing applications of EyeLoop is closed-loop experiments, in which the eyes evoke stimulus feedback, such as rapid neuronal optogenetic stimulation. By using EyeLoop, we demonstrate its utility in an open-loop, a closed-loop, and a biomedical experiment. With a remarkably low minimal hardware cost amounting to 29 USD, EyeLoop makes dynamic eye-tracking accessible to low-resource facilities, such as high schools, small laboratories, and small clinics.

neuroscience↗