Biology subjects

Dunn, B.

Publications and source records attributed to Dunn, B..

11 recordsLinked to original sources

bioRxiv2026

Neural spiketrains and population vectors entangle neural representations

Neural recordings are usually analyzed by comparing neural spiketrains or comparing time bins (population vectors). If multiple variables drive the neural activity these comparisons will be affected by all of them. Our aim is to disentangle these different latent variables or covariates that drive neural activity and reveal their structure and geometry. The central idea of the paper is that a matrix is disentangled when its rows and columns are local on each other, a condition we call bidirectional locality. In such a matrix, rows and columns encode the same geometry and they respond to only one localized part of it. This suggests finding bidirectional local matrices in a given data matrix, from which we can recover the geometry of the covariates driving it in a straightforward way. We present two ways of doing just this. The first method, coherent projections, works by finding non-negative projections of the neural data matrix (neurons by time bins) that are bidirectionally local. The second method, clumps, works by finding dense submatrices of the neural data matrix, that each identify a local region of one covariate. We apply these methods to two neural datasets, showing that they can separate grid cell modules and reveal a movement-driven low-dimensional structure in the motor cortex.

neuroscience↗

bioRxiv2026

DNA-Based Nanoprobes for Fluorescence K+ Sensing in Neural Systems

SignificanceAbnormalities in potassium ion concentrations across subregions of the hippocampus have been implicated in seizures and other pathologies. Direct measurements of potassium ion concentrations are largely made using invasive electrodes, which do not allow for wide spatial coverage. This fluorescent nanoparticle potassium sensor enables direct visualization of potassium dynamics and represents a minimally invasive alternative to electrode-based methods. AimHere, we present a DNA-based fluorescence nanoprobe capable of sensing relative concentrations of potassium ions within populations of neurons. We present its effectiveness in monitoring neuronal K+ dynamics in response to electrical stimulation ex vivo. ApproachWe used widefield fluorescence microscopy to monitor changes in fluorescence intensity in labeled brain tissue in response to electrical stimulation ex vivo. ResultsWe found that our nanoprobe could be retained within the intracellular compartment and modulate in fluorescence intensity linearly in response to induced electrical current. Our K+ Sensor showed a fractional fluorescence change of approximately 1% per 10 mA of applied stimulation current in brain tissue. Optical spectroscopy confirmed the selectivity of the nanoprobes to potassium ions over other endogenous ions. ConclusionsOur findings indicate that this nanoprobe can be used to detect more complex potassium dynamics implicated in various pathologies of the nervous system, such as migraines, seizures, and trauma.

bioengineering↗

bioRxiv2025

Using Experimental Evolution to Correct Mother-Daughter Separation Defects in Brewing Yeast

The budding yeast, Saccharomyces cerevisiae, is the workhorse of the brewing industry. Brewers have domesticated a vast array of different strains with traits that complement the beers they wish to brew. Yet, some domesticated strains also harbor traits that are undesirable. One example of an undesirable trait in brewing strains is the mother-daughter separation defect (MDSD). MDSDs are a reproductive flaw present in a widely used brewing strain, London Ale III. MDSDs cause cells to form large clusters, possibly leading to the known requirement for more headspace during London Ale III fermentations that result in a lower fermentative yield. Because MDSDs can be caused by mutations to a number of genes, targeted genetic approaches to reduce MDSDs are experimentally challenging, especially for a tetraploid strain like London Ale III. To improve MDSDs in this strain, we employed experimental evolution and passaged populations from three biological replicates for over 200 generations to generate three independent evolved strains that form fewer clusters than the ancestral strain, as seen by microscopy. To confirm these results, we used flow cytometry to measure the average size of the clusters in clones of our evolved replicates and found them to be smaller on average than the ancestor. We also qualitatively assessed the aggregation phenotype using a settling assay and found that our evolved replicates settle slower than the ancestor. Finally, we characterized the mutations in our evolved replicates using whole genome sequencing and identified increased copy numbers of chromosome 1 and chromosome 14 in all three evolved clones. The best-performing strain generated by this project is now available commercially. This project demonstrates how experimental evolution can be used to select against less desirable traits in industrial yeast strains when targeted genetic approaches present considerable challenges. Future research could implement a similar approach to improve other traits in widely used brewing and baking strains.

genetics↗

bioRxiv2023

Insights in neuronal tuning: Navigating the statistical challenges of autocorrelation and missing variables

Recent advances in neuroscience have improved our ability to investigate neural activity by making it possible to measure vast amounts of neurons and behavioral variables, and explore the underlying mechanisms that connect them. However, comprehensively understanding neuronal tuning poses challenges due to statistical issues such as temporal autocorrelation and missing variables, as neurons are likely driven in part by unknown factors. The field consequently needs a systematic approach to address these challenges. This study compares various methods for covariate selection using both simulated data and calcium data from the medial entorhinal cortex. We conclude that a combination of cross-validation and a cyclical shift permutation test yields higher test power than other evaluated methods while maintaining proper error rate control, albeit at a higher computational cost. This research sheds light on the quest for a systematic understanding of neuronal tuning and provides insight into covariate selection in the presence of statistical complexities.

neuroscience↗

bioRxiv2023

A topological perspective on the dual nature of the neural state space and the correlation structure

When analysing neural activity, one often studies either the neural correlations or the state space of population vectors. However, the reason for choosing one over the other is seldom discussed. Here, with methods from the mathematical field of topology, we compare these approaches and propose using both for unsupervised inference of neural representations. If the recorded neurons have convex receptive fields on a single covariate space, there is a duality between the topological signatures derived from correlations on the one hand and population vectors on the other hand. However, in the presence of multiple neural modules with non convex receptive fields, this duality breaks down. We explain how to leverage complementary information derived from both approaches to sucessfully characterize the represented covariate spaces directly from the data also under these challenging circumstances. Furthermore, we prove appropriate reconstruction results and showcase applications to multiple neural datasets from various brain regions and diverse neural modules.

neuroscience↗

bioRxiv2023

Systematic Profiling of Ale Yeast Protein Dynamics across Fermentation and Repitching

Studying the genetic and molecular characteristics of brewing yeast strains is crucial for understanding their domestication history and adaptations accumulated over time in fermentation environments, and for guiding optimizations to the brewing process itself. Saccharomyces cerevisiae (brewing yeast) is amongst the most profiled organisms on the planet, yet the temporal molecular changes that underlie industrial fermentation and beer brewing remain understudied. Here, we characterized the genomic makeup of a Saccharomyces cerevisiae ale yeast widely used in the production of Hefeweizen beers, and applied shotgun mass spectrometry to systematically measure the proteomic changes throughout two fermentation cycles which were separated by 14 rounds of serial repitching. The resulting brewing yeast proteomics resource includes 64,740 protein abundance measurements. We found that this strain possesses typical genetic characteristics of Saccharomyces cerevisiae ale strains and displayed progressive shifts in molecular processes during fermentation based on protein abundance changes. We observed protein abundance differences between early fermentation batches compared to those separated by 14 rounds of serial repitching. The observed abundance differences occurred mainly in proteins involved in the metabolism of ergosterol and isobutyraldehyde. Our systematic profiling serves as a starting point for deeper characterization of how the yeast proteome changes during commercial fermentations and additionally serves as a resource to guide fermentation protocols, strain handling, and engineering practices in commercial brewing and fermentation environments. Finally, we created a web interface (https://brewing-yeast-proteomics.ccbb.utexas.edu/) to serve as a valuable resource for yeast geneticists, brewers, and biochemists to provide insights into the global trends underlying commercial beer production.

systems biology↗

bioRxiv2022

Ventral subiculum inputs to nucleus accumbens medial shell preferentially innervate D2R medium spiny neurons and contain calcium permeable AMPARs

Ventral subiculum (vSUB) is the major output region of ventral hippocampus (vHIPP) and sends major projections to nucleus accumbens medial shell (NAcMS). Hyperactivity of the vSUB-NAcMS circuit is associated with substance use disorders (SUDs) and the modulation of vSUB activity alters drug seeking and drug reinstatement behavior in rodents. However, to the best of our knowledge, the cell-type specific connectivity and synaptic transmission properties of the vSUB-NAcMS circuit have never been directly examined. Instead, previous functional studies have focused on total ventral hippocampal (vHIPP) output to NAcMS without distinguishing vSUB from other subregions of vHIPP, including ventral CA1 (vCA1). Using ex vivo electrophysiology, we systematically characterized the vSUB-NAcMS circuit with cell-type and synapse specific resolution in male and female mice and found that vSUB output to dopamine receptor type-1 (D1R) and type-2 (D2R) expressing medium spiny neurons (MSNs) displays a functional connectivity bias for D2R MSNs. Furthermore, we found that vSUB-D1R and -D2R MSN synapses contain calcium-permeable AMPA receptors in drug-naive mice. Finally, we find that, distinct from other glutamatergic inputs, cocaine exposure selectively induces plasticity at vSUB-D2R synapses. Importantly, we directly compared vSUB and vCA1 output to NAcMS and found that vSUB synapses are functionally distinct and that vCA1 output recapitulated the synaptic properties previously ascribed to vHIPP. Our work highlights the need to consider the contributions of individual subregions of vHIPP to SUDs and represents an important first step toward understanding how the vSUB-NAcMS circuit contributes to the etiologies that underlie SUDs.

neuroscience↗

bioRxiv2022

S. cerevisiae cells can grow without the Pds5 cohesin subunit

During DNA replication, the newly created sister chromatids are held together until their separation at anaphase. The cohesin complex is in charge of creating and maintaining sister-chromatid cohesion (SCC) in all eukaryotes. In S. cerevisiae cells, cohesin is composed of two elongated proteins, Smc1 and Smc3, bridged by the kleisin Mcd1/Scc1. The latter also acts as a scaffold for three additional proteins, Scc3/Irr1, Wpl1/Rad61, and Pds5. Although the HEAT-repeat protein Pds5 is essential for cohesion, its precise function is still debated. Deletion of the ELG1 gene, encoding a PCNA unloader, can partially suppress the temperature-sensitive pds5-1 allele, but not a complete deletion of PDS5. We carried out a genetic screen for high copy number suppressors and another for spontaneously arising mutants, allowing the survival of a pds5{Delta} elg1{Delta} strain. Our results show that cells remain viable in the absence of Pds5 provided that there is both an elevation in the level of Mcd1 (which can be due to mutations in the CLN2 gene, encoding a G1 cyclin), and an increase in the level of SUMO-modified PCNA on chromatin (caused by lack of PCNA unloading in elg1{Delta} mutants). The elevated SUMO-PCNA levels increase the recruitment of the Srs2 helicase, which evicts Rad51 molecules from the moving fork, creating ssDNA regions that serve as sites for increased cohesin loading and SCC establishment. Thus, our results delineate a double role for Pds5 in protecting the cohesin ring and interacting with the DNA replication machinery. IMPORTANCESister chromatid cohesion is vital for faithful chromosome segregation, chromosome folding into loops, and gene expression. A multisubunit protein complex known as cohesin holds the sister chromatids from S-phase until the anaphase stage. In this study, we explore the function of the essential cohesin subunit Pds5 in the regulation of sister chromatid cohesion. We performed two independent genetic screens to bypass the function of the Pds5 protein. We observe that Pds5 protein is a cohesin stabilizer, and elevating the levels of Mcd1 protein along with SUMO-PCNA accumulation on chromatin can compensate for the loss of the PDS5 gene. In addition, Pds5 plays a role in coordinating the DNA replication and sister chromatid cohesion establishment. This work elucidates the function of cohesin subunit Pds5, the G1 cyclin Cln2, and replication factors PCNA, Elg1 and Srs2 in the proper regulation of sister chromatid cohesion.

genetics↗

bioRxiv2021

Removing Inter-Experimental Variability from Functional Data in Systems Neuroscience

Integrating data from multiple experiments is common practice in systems neuroscience but it requires inter-experimental variability to be negligible compared to the biological signal of interest. This requirement is rarely fulfilled; systematic changes between experiments can drastically affect the outcome of complex analysis pipelines. Modern machine learning approaches designed to adapt models across multiple data domains offer flexible ways of removing inter-experimental variability where classical statistical methods often fail. While applications of these methods have been mostly limited to single-cell genomics, in this work, we develop a theoretical framework for domain adaptation in systems neuroscience. We implement this in an adversarial optimization scheme that removes inter-experimental variability while preserving the biological signal. We compare our method to previous approaches on a large-scale dataset of two-photon imaging recordings of retinal bipolar cell responses to visual stimuli. This dataset provides a unique benchmark as it contains biological signal from well-defined cell types that is obscured by large inter-experimental variability. In a supervised setting, we compare the generalization performance of cell type classifiers across experiments, which we validate with anatomical cell type distributions from electron microscopy data. In an unsupervised setting, we remove inter-experimental variability from data which can then be fed into arbitrary downstream analyses. In both settings, we find that our method achieves the best trade-off between removing inter-experimental variability and preserving biological signal. Thus, we offer a flexible approach to remove inter-experimental variability and integrate datasets across experiments in systems neuroscience. Code available at https://github.com/eulerlab/rave.

neuroscience↗

bioRxiv2017

Decoding of neural data using cohomological learning

We introduce a novel data-driven approach to discover and decode features in the neural code coming from large population neural recordings with minimal assumptions, using cohomological learning. We apply our approach to neural recordings of mice moving freely in a box, where we find a circular feature. We then observe that the decoded value corresponds well to the head direction of the mouse. Thus we capture head direction cells and decode the head direction from the neural population activity without having to process the behaviour of the mouse. Interestingly, the decoded values convey more information about the neural activity than the tracked head direction does, with differences that have some spatial organization. Finally, we note that the residual population activity, after the head direction has been accounted for, retains some low-dimensional structure but with no discernible shape.

neuroscience↗

bioRxiv2017

Grid cells show field-to-field variability and this explains the aperiodic response of inhibitory interneurons

Research on network mechanisms and coding properties of grid cells assume that the firing rate of a grid cell in each of its fields is the same. Furthermore, proposed network models predict spatial regularities in the firing of inhibitory interneurons that are inconsistent with experimental data. In this paper, by analyzing the response of grid cells recorded from rats during free navigation, we first show that there are strong variations in the mean firing rate of the fields of individual grid cells and thus show that the data is inconsistent with the theoretical models that predict similar peak magnitudes. We then build a two population excitatory-inhibitory network model in which sparse spatially selective input to the excitatory cells, presumed to arise from e.g. salient external stimuli, hippocampus or a combination of both, leads to the variability in the firing field amplitudes of grid cells. We show that, when combined with appropriate connectivity between the excitatory and inhibitory neurons, the variability in the firing field amplitudes of grid cells results in inhibitory neurons that do not exhibit regular spatial firing, consistent with experimental data. Finally, we show that even if the spatial positions of the fields are maintained, variations in the firing rates of the fields of grid cells are enough to cause remapping of hippocampal cells.

neuroscience↗