Biology subjects

Petersen, C. L.

Publications and source records attributed to Petersen, C. L..

4 recordsLinked to original sources

bioRxiv2023

CRF release from a unique subpopulation of accumbal neurons constrains action-outcome acquisition in reward learning

BackgroundThe nucleus accumbens (NAc) mediates reward learning and motivation. Despite an abundance of neuropeptides, peptidergic neurotransmission from the NAc has not been integrated into current models of reward learning. The existence of a sparse population of neurons containing corticotropin releasing factor (CRF) has been previously documented. Here we provide a comprehensive analysis of their identity and functional role in shaping reward learning. MethodsTo do this, we took a multidisciplinary approach that included florescent in situ hybridization (Nmice [≥] 3), tract tracing (Nmice = 5), ex vivo electrophysiology (Ncells [≥] 30), in vivo calcium imaging with fiber photometry (Nmice [≥] 4) and use of viral strategies in transgenic lines to selectively delete CRF peptide from NAc neurons (Nmice [≥] 4). Behaviors used were instrumental learning, sucrose preference and spontaneous exploration in an open field. ResultsHere we show that the vast majority of NAc CRF-containing (NAcCRF) neurons are spiny projection neurons (SPNs) comprised of dopamine D1-, D2- or D1/D2-containing SPNs that primarily project and connect to the ventral pallidum and to a lesser extent the ventral midbrain. As a population, they display mature and immature SPN firing properties. We demonstrate that NAcCRF neurons track reward outcomes during operant reward learning and that CRF release from these neurons acts to constrain initial acquisition of action-outcome learning, and at the same time facilitates flexibility in the face of changing contingencies. ConclusionWe conclude that CRF release from this sparse population of SPNs is critical for reward learning under normal conditions.

neuroscience↗

bioRxiv2023

A single-cell map of antisense oligonucleotide activity in the brain

Antisense oligonucleotides (ASOs) dosed into cerebrospinal fluid (CSF) distribute broadly throughout the brain and hold the promise of treating myriad brain diseases by modulating RNA. CNS tissue is not routinely biopsied in living individuals, leading to reliance on CSF biomarkers to inform on drug target engagement. Animal models can link CSF biomarkers to brain parenchyma, but our understanding of how individual cells contribute to bulk tissue signal is limited. Here we employed single nucleus transcriptomics on tissue from mice treated with RNase H1 ASOs against Prnp and Malat1 and macaques treated with an ASO against PRNP. Activity was observed in every cell type, though sometimes with substantial differences in magnitude. Single cell RNA count distributions implied target suppression in every single sequenced cell, rather than intense knockdown in only some cells. Duration of action up to 12 weeks post-dose differed across cell types, being shorter in microglia than in neurons. Suppression in neurons was generally similar to, or more robust than, the bulk tissue. In macaques, PrP in CSF was lowered 40% in conjunction with PRNP knockdown across all cell types including neurons, arguing that a CSF biomarker readout is likely to reflect disease-relevant cells in a neuronal disorder.

neuroscience↗

bioRxiv2020

Investigation of Capsule-Inspired Neural Network Approaches for DNA Methylation

DNA methylation (DNAm) alterations have been heavily implicated in carcinogenesis and the pathophysiology of diseases through upstream regulation of gene expression. DNAm deep-learning approaches are able to capture features associated with aging, cell type, and disease progression, but lack incorporation of prior biological knowledge. Here, we present modular, user-friendly deep learning methodology and software, MethylCapsNet and MethylSPWNet, that group CpGs into biologically relevant capsules - such as gene promoter context, CpG island relationship, or user-defined groupings - and relate them to diagnostic and prognostic outcomes. We demonstrate these models utility on 3,897 individuals in the classification of central nervous system (CNS) tumors. MethylCapsNet and MethylSPWNet provide an opportunity to increase DNAm deep learning analyses interpretability by enabling a flexible organization of DNAm data into biologically relevant capsules.

bioinformatics↗

bioRxiv2019

MethylNet: A Modular Deep Learning Approach to Methylation Prediction

BackgroundDNA methylation (DNAm) is an epigenetic regulator of gene expression programs that can be altered by environmental exposures, aging, and in pathogenesis. Traditional analyses that associate DNAm alterations with phenotypes suffer from multiple hypothesis testing and multi-collinearity due to the high-dimensional, continuous, interacting and non-linear nature of the data. Deep learning analyses have shown much promise to study disease heterogeneity. DNAm deep learning approaches have not yet been formalized into user-friendly frameworks for execution, training, and interpreting models. Here, we describe MethylNet, a DNAm deep learning method that can construct embeddings, make predictions, generate new data, and uncover unknown heterogeneity with minimal user supervision. ResultsThe results of our experiments indicate that MethylNet can study cellular differences, grasp higher order information of cancer sub-types, estimate age and capture factors associated with smoking in concordance with known differences. ConclusionThe ability of MethylNet to capture nonlinear interactions presents an opportunity for further study of unknown disease, cellular heterogeneity and aging processes.

bioinformatics↗