Biology subjects

Olsen, S.

Publications and source records attributed to Olsen, S..

At least 19 recordsLinked to original sources

bioRxiv2026

A Compact, Standalone & Battery-Powered 3D Organoid-on-Chip System with Programmable Flow Control

Organoids-on-chip combine the 3D complex microenvironment and cellular composition of organoids with microfluidic flow, increasing nutrient-waste exchange and mimicking the contributions of in vivo interstitial and vascular flow. However, the widespread adoption of organoid-on-chip platforms is limited by the lack of incubator-friendly flow control systems. Existing approaches often rely on commercially available syringe or peristaltic pumps, but these are bulky, lack scalability, and present a significant barrier for clinical translation. To circumvent these issues, we present the Compact Active Perfusion Standalone Organoid-on-Chip (CAPS-OC) platform, a fully integrated and battery-powered microfluidic system capable of culturing organoids in active media flow. To achieve this, we introduce a novel low-power mechanism of pressure pulse generation using an off-the-shelf compact rotary actuator (CRA), and package it into a compact electromechanical assembly. This assembly provides timed pneumatic inputs to achieve programmable control of membrane-based peristaltic pumps, with [~]100 {micro}L/hr dynamic range achieved on a custom microfluidic organoid chip. We biologically validated this system by culturing pancreatic cancer organoids derived from a KrasLSL-G12D/+; Trp53LSL-R172H/WT; Pdx1-Cre (KPC) genetically engineered mouse model. We found that our chip enhances proliferation and helps sustain a population of larger (>150 {micro}m) organoids compared to standard dome based static culture. Additionally, through immunostaining, we observe that KPC organoids cultured in the chip show more aggressive PDAC phenotype with reduced nuclear expression of GATA6, whereas organoids in static culture retain less aggressive classical- like subtype. Finally, testing of a RAS inhibitor drug, daraxonrasib, on the KPC organoids on chip showed size-based sensitivity elucidating the impact of active perfusion on the drug diffusion kinetics. Altogether, we establish CAPS-OC as a valuable tool for the bioengineering community and for clinically translating organoid model systems.

bioengineering↗

bioRxiv2025

A multimodal approach for visualization and identification of electrophysiological cell types in vivo

Neurons of different types perform diverse computations and coordinate their activity during sensation, perception, and action. While electrophysiological recordings can measure the activity of many neurons simultaneously, identifying cell types during these experiments remains difficult. To identify cell types, we developed PhysMAP, a framework that weighs multiple electrophysiological modalities simultaneously to obtain interpretable multimodal representations. We apply PhysMAP to seven datasets and demonstrate that these multimodal representations are better aligned with known transcriptomically-defined cell types than any single modality alone. We then show that such alignment allows PhysMAP to better identify putative cell types in the absence of ground truth. We also demonstrate how annotated datasets can be used to infer multiple cell types simultaneously in unannotated datasets and show that the properties of inferred types are consistent with the known properties of these cell types. Finally, we provide a first-of-its-kind demonstration of how PhysMAP can help understand how multiple cell types interact to drive circuit dynamics. Collectively, these results demonstrate that multimodal representations from PhysMAP enable the study of multiple cell types simultaneously, thus providing insight into neural circuit dynamics.

neuroscience↗

bioRxiv2024

In vivo cell-type and brain region classification via multimodal contrastive learning

AO_SCPLOWBSTRACTC_SCPLOWCurrent electrophysiological approaches can track the activity of many neurons, yet it is usually unknown which cell-types or brain areas are being recorded without further molecular or histological analysis. Developing accurate and scalable algorithms for identifying the cell-type and brain region of recorded neurons is thus crucial for improving our understanding of neural computation. In this work, we develop a multimodal contrastive learning approach for neural data that can be fine-tuned for different downstream tasks, including inference of cell-type and brain location. We utilize this approach to jointly embed the activity autocorrelations and extracellular waveforms of individual neurons. We demonstrate that our embedding approach, Neuronal Embeddings via MultimOdal contrastive learning (NEMO), paired with supervised fine-tuning, achieves state-of-the-art cell-type classification for two opto-tagged datasets and brain region classification for the public International Brain Laboratory Brain-wide Map dataset. Our method represents a promising step towards accurate cell-type and brain region classification from electrophysiological recordings. The project page and code are available at https://ibl-nemo.github.io/.

neuroscience↗

bioRxiv2024

Distinct roles of the two BRCA2 DNA binding domains in DNA damage repair and replication fork preservation

Homologous recombination (HR) is a highly conserved tool for the removal of DNA double-strand breaks (DSBs) and the preservation of stalled and damaged DNA replication forks. Successful completion of HR requires the tumor suppressor BRCA2. Germline mutations in BRCA2 lead to familial breast, ovarian, and other cancers, underscoring the importance of this protein for maintaining genome stability. BRCA2 harbors two distinct DNA binding domains, one that possesses three oligonucleotide/oligosaccharide binding (OB) folds (known as the OB-DBD), and with the other residing in the C-terminal recombinase binding domain (termed the CTRB-DBD) encoded by the last gene exon. Here, we employ a combination of genetic, biochemical, and cellular approaches to delineate contributions of these two DNA binding domains toward HR and the maintenance of stressed DNA replication forks. We show that OB-DBD and CTRB-DBD confer ssDNA and dsDNA binding capabilities to BRCA2, respectively, and that BRCA2 variants mutated in either DNA binding domain are impaired in the ability to load the recombinase RAD51 onto ssDNA pre-occupied by RPA. While the CTRB-DBD mutant is modestly affected for HR, it exhibits a strong defect in the protection of stressed replication forks. In contrast, the OB-DBD is indispensable for both BRCA2 functions. Our study thus defines the unique contributions of the two BRCA2 DNA binding domains in genome maintenance.

biochemistry↗

bioRxiv2023

SHIELD: Skull-shaped hemispheric implants enabling large-scale-electrophysiology datasets in the mouse brain

To understand the neural basis of behavior, it is essential to measure spiking dynamics across many interacting brain regions. While new technologies, such as Neuropixels probes, facilitate multi-regional recordings, significant surgical and procedural hurdles remain for these experiments to achieve their full potential. Here, we describe a novel 3D-printed cranial-replacement implant (SHIELD) enabling electrophysiological recordings from distributed areas of the mouse brain. This skull-shaped implant is designed with customizable insertion holes, allowing dozens of cortical and subcortical structures to be recorded in a single mouse using repeated multi-probe insertions over many days. We demonstrate the procedures high success rate, biocompatibility, lack of adverse effects on behavior, and compatibility with imaging and optogenetics. To showcase the scientific utility of the SHIELD implant, we use multi-probe recordings to reveal novel insights into how alpha rhythms organize spiking activity across visual and sensorimotor networks. Overall, this method enables powerful large-scale electrophysiological measurements for the study of distributed brain computation.

neuroscience↗

bioRxiv2023

Familiarity-modulated synapses model visual cortical circuit novelty responses

Since environments are constantly in flux, the brains ability to identify novel stimuli that fall outside its own internal representation of the world is crucial for an organisms survival. Within the mammalian neocortex, inhibitory microcircuits are proposed to regulate activity in an experience-dependent manner and different inhibitory neuron subtypes exhibit distinct novelty responses. Discerning the function of diverse neural circuits and their modulation by experience can be daunting unless one has a biologically plausible mechanism to detect and learn from novel experiences that is both understandable and flexible. Here we introduce a learning mechanism, familiarity modulated synapses (FMSs), through which a network response that encodes novelty emerges from unsupervised multiplicative synaptic modifications depending only on the presynaptic or both the pre- and postsynaptic activity. FMSs stand apart from other familiarity mechanisms in their simplicity: they operate under continual learning, do not require specialized architecture, and can distinguish novelty rapidly without requiring feedback. Implementing FMSs within an experimentally-constrained model of a visual cortical circuit, we demonstrate the generalizability of FMSs by reproducing three distinct novelty effects recently observed in experiments: absolute, contextual (or oddball), and omission novelty. Additionally, our model reproduces functional diversity within cell subpopulations, leading to experimentally testable predictions about connectivity and synaptic dynamics that can produce both population-level novelty responses and heterogeneous individual neuron signals. Altogether, our findings demonstrate how simple plasticity mechanisms within the cortical circuit structure can give rise to qualitatively distinct novelty responses. The flexibility of FMSs opens the door to computationally and theoretically investigating how distinct synapse modulations can lead to a variety of experience-dependent responses in a simple, understandable, and biologically plausible setup.

neuroscience↗

bioRxiv2023

Recurrent pattern completion drives the neocortical representation of sensory inference

When sensory information is incomplete or ambiguous, the brain relies on prior expectations to infer perceptual objects. Despite the centrality of this process to perception, the neural mechanism of sensory inference is not known. Illusory contours (ICs) are key tools to study sensory inference because they contain edges or objects that are implied only by their spatial context. Using cellular resolution, mesoscale two-photon calcium imaging and multi-Neuropixels recordings in the mouse visual cortex, we identified a sparse subset of neurons in the primary visual cortex (V1) and higher visual areas that respond emergently to ICs. We found that these highly selective IC-encoders mediate the neural representation of IC inference. Strikingly, selective activation of these neurons using two-photon holographic optogenetics was sufficient to recreate IC representation in the rest of the V1 network, in the absence of any visual stimulus. This outlines a model in which primary sensory cortex facilitates sensory inference by selectively strengthening input patterns that match prior expectations through local, recurrent circuitry. Our data thus suggest a clear computational purpose for recurrence in the generation of holistic percepts under sensory ambiguity. More generally, selective reinforcement of top-down predictions by pattern-completing recurrent circuits in lower sensory cortices may constitute a key step in sensory inference.

neuroscience↗

bioRxiv2023

Fight not flight: parasites drive the bacterial evolution of resistance, not avoidance

In the face of ubiquitous threats from parasites, hosts can evolve strategies to resist infection or to altogether avoid parasitism, for instance by avoiding behavior that could expose them to parasites or by dispersing away from local parasite threats. At the microbial scale, bacteria frequently encounter viral parasites, bacteriophages. While bacteria are known to utilize a number of strategies to resist infection by phages, and can have the capacity to avoid moving towards phage-infected cells, it is unknown whether bacteria can evolve dispersal to escape from phages. In order to answer this question, we combined experimental evolution and mathematical modeling. Experimental evolution of the bacterium Pseudomonas fluorescens in environments with differing spatial distributions of the phage Phi2 revealed that the host bacteria evolved resistance depending on parasite distribution, but did not evolve dispersal to escape parasite infection. Simulations using parameterized mathematical models of bacterial growth and swimming motility showed that this is a general finding: while increased dispersal is adaptive in the absence of parasites, in the presence of parasites that fitness benefit disappears and resistance becomes adaptive, regardless of the spatial distribution of parasites. Together, these experiments suggest that parasites should rarely, if ever, drive the evolution of bacterial escape via dispersal.

evolutionary biology↗

bioRxiv2023

Behavioral strategy shapes activation of the Vip-Sst disinhibitory circuit in visual cortex

In complex environments, animals can adopt diverse strategies to find rewards. How distinct strategies differentially engage brain circuits is not well understood. Here we investigate this question, focusing on the cortical Vip-Sst disinhibitory circuit. We characterize the behavioral strategies used by mice during a visual change detection task. Using a dynamic logistic regression model we find individual mice use mixtures of a visual comparison strategy and a statistical timing strategy. Separately, mice also have periods of task engagement and disengagement. Two-photon calcium imaging shows large strategy dependent differences in neural activity in excitatory, Sst inhibitory, and Vip inhibitory cells in response to both image changes and image omissions. In contrast, task engagement has limited effects on neural population activity. We find the diversity of neural correlates of strategy can be understood parsimoniously as increased activation of the Vip-Sst disinhibitory circuit during the visual comparison strategy which facilitates task appropriate responses.

neuroscience↗

bioRxiv2023

Associations between in vitro, in vivo and in silico cell classes in mouse primary visual cortex

The brain consists of many cell classes yet in vivo electrophysiology recordings are typically unable to identify and monitor their activity in the behaving animal. Here, we employed a systematic approach to link cellular, multi-modal in vitro properties from experiments with in vivo recorded units via computational modeling and optotagging experiments. We found two one-channel and six multi-channel clusters in mouse visual cortex with distinct in vivo properties in terms of activity, cortical depth, and behavior. We used biophysical models to map the two one- and the six multi-channel clusters to specific in vitro classes with unique morphology, excitability and conductance properties that explain their distinct extracellular signatures and functional characteristics. These concepts were tested in ground-truth optotagging experiments with two inhibitory classes unveiling distinct in vivo properties. This multi-modal approach presents a powerful way to separate in vivo clusters and infer their cellular properties from first principles.

neuroscience↗

bioRxiv2021

The Geometry of Representational Drift in Natural and Artificial Neural Networks

Neurons in sensory areas encode/represent stimuli. Surprisingly, recent studies have suggested that, even during persistent performance, these representations are not stable and change over the course of days and weeks. We examine stimulus representations from fluorescence recordings across hundreds of neurons in the visual cortex using in vivo two-photon calcium imaging and we corroborate previous studies finding that such representations change as experimental trials are repeated across days. This phenomenon has been termed "representational drift". In this study we geometrically characterize the properties of representational drift in the primary visual cortex of mice in two open datasets from the Allen Institute and propose a potential mechanism behind such drift. We observe representational drift both for passively presented stimuli, as well as for stimuli which are behaviorally relevant. Across experiments, the drift most often occurs along directions that have the most variance, leading to a significant turnover in the neurons used for a given representation. Interestingly, despite this significant change due to drift, linear classifiers trained to distinguish neuronal representations show little to no degradation in performance across days. The features we observe in the neural data are similar to properties of artificial neural networks where representations are updated by continual learning in the presence of dropout, i.e. a random masking of nodes/weights, but not other types of noise. Therefore, we conclude that a potential reason for the representational drift in biological networks is driven by an underlying dropout-like noise while continuously learning and that such a mechanism may be computational advantageous for the brain in the same way it is for artificial neural networks, e.g. preventing overfitting.

neuroscience↗

bioRxiv2021

Geometry of inter-areal interactions in mouse visual cortex

The response of a set of neurons in an area is the result of the sensory input, the interaction of the neurons within the area as well as the long range interactions between areas. We aimed to study the relation between interactions among multiple areas, and if they are fixed or dynamic. The structural connectivity provides a substrate for these interactions, but anatomical connectivity is not known in sufficient detail and it only gives us a static picture. Using the Allen Brain Observatory Visual Coding Neuropixels dataset, which includes simultaneous recordings of spiking activity from up to 6 hierarchically organized mouse cortical visual areas, we estimate the functional connectivity between neurons using a linear model of responses to flashed static grating stimuli. We characterize functional connectivity between populations via interaction subspaces. We find that distinct subspaces of a source area mediate interactions with distinct target areas, supporting the notion that cortical areas use distinct channels to communicate. Most importantly, using a piecewise linear model for activity within each trial, we find that these interactions evolve dynamically over tens of milliseconds following a stimulus presentation. Inter-areal subspaces become more aligned with the intra-areal subspaces during epochs in which a feedforward wave of activity propagates through visual cortical areas. When the short-term dynamics are averaged over, we find that the interaction subspaces are stable over multiple stimulus blocks. These findings have important implications for understanding how information flows through biological neural networks composed of interconnected modules, each of which may have a distinct functional specialization.

neuroscience↗

bioRxiv2020

A pseudomolecule assembly of the Rocky Mountain elk genome reveals putative immune system gene loss near chromosomal fissions

Rocky Mountain elk (Cervus canadensis) is a major reservoir for Brucella abortus in the Greater Yellowstone area, which has significant economic implications to the cattle industry. Vaccination attempts against intracellular bacterial diseases in elk populations have not been successful due to a negligible adaptive cellular immune response. A lack of genomic resources has impeded attempts to better understand why vaccination does not induce protective immunity. To overcome this limitation, PacBio, Illumina, and HiC sequencing with a total of 686-fold coverage was used to assemble the elk genome into 35 pseudomolecules. A robust gene annotation was generated resulting in 18,013 gene models and 33,422 mRNAs. The accuracy of the assembly was assessed using synteny to the red deer and cattle genomes identifying several chromosomal rearrangements, fusions and fissions. Because this genome assembly and annotation provide a foundation for genome-enabled exploration of Cervus species, we demonstrate its utility by exploring the conservation of immune system-related genes. We conclude by comparing cattle immune system-related genes to the elk genome, revealing nine putative gene losses in elk. Author SummaryBrucellosis, also known as contagious abortion, is a bacterial disease that commonly affects livestock and remains prevalent in Rocky Mountain elk (Cervus canadensis). Since the 1920s the USDA has led a program to eradicate Brucellosis from cattle, yet wild Rocky Mountain elk continue to be a source of transmission. Attempts to vaccinate wild elk herds have been unsuccessful, due to a poor and short-lived immune response. To investigate the genetic basis for this inherent difference, we created the first genome and annotation for the Rocky Mountain elk. This genome assembly is of the highest quality and contains single linear sequences for all 35 chromosomes. In order to generate gene models, an array of RNA-Seq data and proteins from many different organ tissues and cells were used in gene prediction software. Specifically, we compare cattle immune system genes with the Rocky Mountain elk, revealing the putative loss of nine immune-system related genes in elk.

genomics↗

bioRxiv2020

Ebselen derivatives are very potent dual inhibitors of SARS-CoV-2 proteases - PLpro and Mpro in in vitro studies

Proteases encoded by SARS-CoV-2 constitute a promising target for new therapies against COVID-19. SARS-CoV-2 main protease (Mpro, 3CLpro) and papain-like protease (PLpro) are responsible for viral polyprotein cleavage - a process crucial for viral survival and replication. Recently it was shown that 2-phenylbenzisoselenazol-3(2H)-one (ebselen), an organoselenium anti-inflammatory small-molecule drug, is a potent, covalent inhibitor of both the proteases and its potency was evaluated in enzymatic and anti-viral assays. In this study, we screened a collection of 23 ebselen derivatives for SARS-CoV-2 PLpro and Mpro inhibitors. Our studies revealed that ebselen derivatives are potent inhibitors of both the proteases. We identified three PLpro and four Mpro inhibitors superior to ebselen. Our work shows that ebselen constitutes a promising platform for development of new antiviral agents targeting both SARS-CoV-2 PLpro and Mpro.

biochemistry↗

bioRxiv2019

Quantifying and Modelling Transfer Learning in Mice Between Consecutive Training Stages of a Change Detection Task

Animals are known to be able to rapidly transfer knowledge between tasks with similar structure. We trained a set of mice on a visual change detection task with multiple stages, starting with direct transitions between gratings, adding an intervening gray screen, and subsequently moving to multiple sets of natural images. We observe that, when progressing to new stages, the performance increases very fast. However, when transitioning to a task of higher complexity, the peak performance decreases. This setup facilitates for the first time an experimental platform to study the transfer learning phenomena in mice using visual stimuli. Based on these results and additional neuroscience insight, we propose a cognitive model to explain the quick adaptation observed in mice. It extends a deep Q learning agent with a multi-tiered architecture and the possibility of performing a representation remapping at every level of the hierarchy to prevent the downstream propagation of representation anomalies. This architecture provides the substrate of an adaptation algorithm based on ideas of optimal transport of probability distributions. It matches well key behavioral aspects observed in mice and the experimental constraint that the mice are initially trained using a single task variant before it transitions to the new training phase. The modelling process helped us to gain biological insights: first, the optimal transport mechanism of the representation remapping indicated that a possible reason for the reduced performance when mice move to a new more complex task could be due to limited representation resources which were optimized for the previous task. Second, the multi-tiered architecture was first an engineering constraint and later a biologic insight confirmed by the literature. A final insight came from the computations required to perform the representation remapping. These computations are interesting because they could help to confirm this transfer learning theory by looking for similar neural correlates during the adaptation process.

animal behavior and cognition↗

bioRxiv2019

Class A PBPs have a distinct and unique role in the construction of the pneumococcal cell wall.

In oval shaped Streptococcus pneumoniae, septal and longitudinal peptidoglycan synthesis is performed by independent functional complexes; the divisome and the elongasome. Penicillin binding proteins (PBPs) were long considered as the key peptidoglycan synthesizing enzymes in these complexes. Among these were the bifunctional class A PBPs, which are both glycosyltransferases and transpeptidases, and monofunctional class B PBPs with only transpeptidase activity. Recently, however, it was established that the monofunctional class B PBPs work together with non-PBP glycosyltransferases (FtsW and RodA) to make up the core peptidoglycan synthesizing machineries within the pneumococcal divisome (FtsW/PBP2x) and elongasome (RodA/PBP2b). The function of class A PBPs is therefore now an open question. Here we utilize the peptidoglycan hydrolase CbpD to show that class A PBPs have an autonomous role during cell wall synthesis in S. pneumoniae. Purified CbpD was shown to target the septum of S. pneumoniae cells. Using assays to specifically inhibit PBP2x, we demonstrate that CbpD specifically target nascent peptidoglycan synthesized by the divisome. Notably, class A PBPs could process this nascent peptidoglycan from a CbpD-sensitive to a CbpD-resistant form. The class A PBP mediated processing was independent of divisome and elongasome activities. Class A PBPs thus constitute an autonomous functional entity which processes or repairs nascent peptidoglycan synthesized by FtsW/PBP2x. Our results support a model in which pneumococcal peptidoglycan is made by three functional entities, the divisome, the elongasome and a peptidoglycan-repairing or -remodelling complex consisting of bifunctional PBPs. To our knowledge this is the first time a specific function has been identified for class A PBPs in bacterial cell wall synthesis.

microbiology↗

bioRxiv2018

High-density extracellular probes reveal dendritic backpropagation and facilitate neuron classification

Different neuron types serve distinct roles in neural processing. Extracellular electrical recordings are extensively used to study brain function but are typically blind to cell identity. Morpho-electric properties of neurons measured on spatially dense electrode arrays might be useful for distinguishing neuron types. Here we used Neuropixels probes to record from cortical and subcortical regions of the mouse brain. Extracellular waveforms of each neuron were detected across many channels and showed distinct spatiotemporal profiles among brain regions. Classification of neurons by brain region was improved with multi-channel compared to single-channel waveforms. In visual cortex, waveform clustering identified the canonical regular spiking (RS) and fast spiking (FS) classes, but also uncovered a subclass of RS units with unidirectional backpropagating action potentials (BAPs). Moreover, BAPs were observed in many hippocampal RS cells. Overall, waveform analysis of spikes from high-density probes aids neuron identification and can reveal dendritic backpropagation.

neuroscience↗

bioRxiv2018

A large-scale, standardized physiological survey reveals higher order coding throughout the mouse visual cortex

To understand how the brain processes sensory information to guide behavior, we must know how stimulus representations are transformed throughout the visual cortex. Here we report an open, large-scale physiological survey of neural activity in the awake mouse visual cortex: the Allen Brain Observatory Visual Coding dataset. This publicly available dataset includes cortical activity from nearly 60,000 neurons collected from 6 visual areas, 4 layers, and 12 transgenic mouse lines from 221 adult mice, in response to a systematic set of visual stimuli. Using this dataset, we reveal functional differences across these dimensions and show that visual cortical responses are sparse but correlated. Surprisingly, responses to different stimuli are largely independent, e.g. whether a neuron responds to natural scenes provides no information about whether it responds to natural movies or to gratings. We show that these phenomena cannot be explained by standard local filter-based models, but are consistent with multi-layer hierarchical computation, as found in deeper layers of standard convolutional neural networks.

neuroscience↗