Biology subjects

Dyer, E.

Publications and source records attributed to Dyer, E..

6 recordsLinked to original sources

bioRxiv2025

Projection-specific Routing of Odor Information in the Olfactory Cortex

Sensory processing in the mammalian cortex relies on extensive feedforward and feedback connections, yet how information is routed along these pathways remains poorly understood. Here, we examined the functional properties of feedback and feedforward neurons in the mouse olfactory (piriform) cortex. We selectively labeled neurons projecting to the olfactory bulb (OB, feedback) or medial prefrontal cortex (mPFC, feedforward) and recorded their activity during passive odor exposure and learning of an odor discrimination task. We found that odor identity and reward associations were encoded by OB-projecting ensembles early during odor exposure, whereas mPFC-projecting neurons encoded this information later, aligned with behavioral responses. Moreover, mPFC-projecting neurons maintained a stable representation of valence across days, while OB-projecting neurons exhibited pronounced plasticity. Together, these findings reveal that odor information is selectively routed through feedforward and feedback pathways and suggest that the functional properties of piriform neurons mirror the computational demands of their downstream targets.

neuroscience↗

bioRxiv2023

Validating a low-cost, open-source, locally manufactured workstation and computational pipeline for automated histopathology evaluation using deep learning

Deployment and access to state-of-the-art diagnostic technologies remains a fundamental challenge in providing equitable global cancer care to low-resource settings. The expansion of digital pathology in recent years and its interface with computational biomarkers provides an opportunity to democratize access to personalized medicine. Here we describe a low-cost platform for digital side capture and computational analysis composed of open-source components. The platform provides low-cost ($200) digital image capture from glass slides and is capable of real-time computational image analysis using an open-source deep learning (DL) algorithm and Raspberry Pi ($35) computer. We validate the performance of deep learning models performance using images captured from the open-source workstation and show similar model performance when compared against significantly more expensive standard institutional hardware.

pathology↗

bioRxiv2022

Carcinoma associated mesenchymal stem cells promote ovarian cancer metastasis by increasing tumor heterogeneity through direct mitochondrial transfer

Ovarian cancer is characterized by early, diffuse metastatic spread with most women presenting with extensive abdominal metastasis at the time of diagnosis. Prior work demonstrated carcinoma-associated mesenchymal stem cells (CA-MSCs) enhance ovarian cancer metastasis through a process of direct cellular interaction and formation of heterocellular CA-MSC and tumor cell complexes. In this study, we demonstrated that CA-MSCs enhance metastasis by increasing tumor cell heterogeneity through mitochondrial donation. We showed that CA-MSCs directly interacted with ovarian cancer cells via tunneling nanotubules (TNTs), and CA-MSCs used these TNTs to transfer live mitochondria to adjacent ovarian cancer cells. This mitochondrial donation preferentially occurred with ovarian cancer cells that had the lowest mitochondrial mass, as quantified using live, actively respiring mitochondrial labeling. These mito poor cancer cells demonstrated decreased proliferation, increased sensitivity to chemotherapy, and decreased oxidative phosphorylation compared to mito rich cancer cells. CA-MSCs rescued the phenotypes of mito poor cancer cells, restoring their proliferative capacity, increasing chemotherapy resistance, and increasing oxidative phosphorylation. We validated these findings in a fully autologous system using CA-MSCs and cancer cells derived from the same patient to prevent confounding effects of cellular response to foreign organelle/DNA. Using a knockdown of the mitochondrial motor protein, MIRO1, in CA-MSCs, we demonstrated that mitochondrial transfer is necessary for the CA-MSC-mediated rescue of mito poor cancer cells. Mitochondria of CA-MSC origin persisted in tumor cells over multiple passages. Importantly, CA-MSC mitochondrial donation occurred in vivo, significantly enhanced tumor cell heterogeneity and decreased survival in an orthotopic ovarian cancer mouse model. Collectively, this work identified CA-MSC mitochondrial transfer as a critical mediator of ovarian cancer cell survival, heterogeneity, and metastasis, and blocking CA-MSC mitochondrial transfer represents a unique therapeutic target in ovarian cancer.

cancer biology↗

bioRxiv2020

A generative modeling approach for interpreting population-level variability in brain structure

Understanding how neural structure varies across individuals is critical for characterizing the effects of disease, learning, and aging on the brain. However, disentangling the different factors that give rise to individual variability is still an outstanding challenge. In this paper, we introduce a deep generative modeling approach to find different modes of variation across many individuals. To do this, we start by training a variational autoencoder on a collection of auto-fluorescence images from a little over 1,700 mouse brains at 25 micron resolution. To then tap into the learned factors and validate the models expressiveness, we developed a novel bi-directional technique to interpret the latent space-by making structured perturbations to both, the high-dimensional inputs of the network, as well as the low-dimensional latent variables in its bottleneck. Our results demonstrate that through coupling generative modeling frameworks with structured perturbations, it is possible to probe the latent space to provide insights into the representations of brain structure formed in deep neural networks.

neuroscience↗

bioRxiv2019

Multi-sensory integration in the mouse cortical connectome using a network diffusion model

Having a structural network representation of connectivity in the brain is instrumental in analyzing communication dynamics and information processing in the brain. In this work, we make steps towards understanding multi-sensory information flow and integration using a network diffusion approach. In particular, we model the flow of evoked activity, initiated by stimuli at primary sensory regions, using the Asynchronous Linear Threshold (ALT) diffusion model. The ALT model captures how evoked activity that originates at a given region of the cortex "ripples through" other brain regions (referred to as an activation cascade). By comparing the model results to functional datasets based on Voltage Sensitive Dye (VSD) imaging, we find that in most cases the ALT model predicts the temporal ordering of an activation cascade correctly. Our results on the Mouse Connectivity Atlas from the Allen Institute for Brain Science show that a small number of brain regions are involved in many primary sensory streams - the claustrum and the parietal temporal cortex being at the top of the list. This suggests that the cortex relies on an hourglass architecture to first integrate and compress multi-sensory information from multiple sensory regions, before utilizing that lower-dimensionality representation in higher-level association regions and more complex cognitive tasks.

neuroscience↗

bioRxiv2019

Toward A Reproducible, Scalable Framework for Processing Large Neuroimaging Datasets

Emerging neuroimaging datasets (collected through modalities such as Electron Microscopy, Calcium Imaging, or X-ray Microtomography) describe the location and properties of neurons and their connections at unprecedented scale, promising new ways of understanding the brain. These modern imaging techniques used to interrogate the brain can quickly accumulate gigabytes to petabytes of structural brain imaging data. Unfortunately, many neuroscience laboratories lack the computational expertise or resources to work with datasets of this size: computer vision tools are often not portable or scalable, and there is considerable difficulty in reproducing results or extending methods. We developed an ecosystem of neuroimaging data analysis pipelines that utilize open source algorithms to create standardized modules and end-to-end optimized approaches. As exemplars we apply our tools to estimate synapse-level connectomes from electron microscopy data and cell distributions from X-ray microtomography data. To facilitate scientific discovery, we propose a generalized processing framework, that connects and extends existing open-source projects to provide large-scale data storage, reproducible algorithms, and workflow execution engines. Our accessible methods and pipelines demonstrate that approaches across multiple neuroimaging experiments can be standardized and applied to diverse datasets. The techniques developed are demonstrated on neuroimaging datasets, but may be applied to similar problems in other domains.

neuroscience↗