Biology subjects

Tan, S. Z. K.

Publications and source records attributed to Tan, S. Z. K..

5 recordsLinked to original sources

bioRxiv2022

The BRAIN Initiative Cell Census Data Ecosystem: A User's Guide

Characterizing cellular diversity at different levels of biological organization across data modalities is a prerequisite to understanding the function of cell types in the brain. Classification of neurons is also required to manipulate cell types in controlled ways, and to understand their variation and vulnerability in brain disorders. The BRAIN Initiative Cell Census Network (BICCN) is an integrated network of data generating centers, data archives and data standards developers, with the goal of systematic multimodal brain cell type profiling and characterization. Emphasis of the BICCN is on the whole mouse brain and demonstration of prototypes for human and non-human primate (NHP) brains. Here, we provide a guide to the cellular and spatial approaches employed, and to accessing and using the BICCN data and its extensive resources, including the BRAIN Cell Data Center (BCDC) which serves to manage and integrate data across the ecosystem. We illustrate the power of the BICCN data ecosystem through vignettes highlighting several BICCN analysis and visualization tools. Finally, we present emerging standards that have been developed or adopted by the BICCN toward FAIR (Wilkinson et al. 2016a) neuroscience. The combined BICCN ecosystem provides a comprehensive resource for the exploration and analysis of cell types in the brain.

bioinformatics↗

bioRxiv2021

Brain Data Standards Ontology: A data-driven ontology of transcriptomically defined cell types in the primary motor cortex

Large-scale single-cell omics profiling is revolutionising our understanding of cell types in complex organs like the brain, where it is being used to define a complete catalogue of cell types, something that traditional methods struggle with due to the diversity and complexity of the brain. But this poses a problem. How do we organise such a catalogue - providing a standard way to refer to the cell types discovered, linking their classification and properties to supporting data? Cell ontologies provide a solution to recording definitions, classifications, and properties of cell types and provide standard identifiers for annotation, but they currently do not support the data driven cell type definitions and classifications needed for multi-modal single cell omics profiling. Here we describe the construction and application of a semi-automated, data-linked extension to the Cell Ontology that represents cell types in the Primary Motor Cortex of humans, mice and marmosets. The methods and resulting ontology are designed to be scalable and applicable to similar whole brain atlases currently in preparation.

neuroscience↗

bioRxiv2020

Dropout in Neural Networks Simulates theParadoxical Effects of Deep Brain Stimulation onMemory

Neuromodulation techniques such as Deep Brain Stimulation (DBS) are a promising treatment for memory-related disorders including anxiety, addiction, and dementia. However, the outcome of these treatments appears to be paradoxical, as the use of these techniques can both disrupt and enhance memory even when applied to the same brain target. In this paper, we hypothesize that disruption and enhancement of memory through neuromodulation can be explained by the dropout of engram nodes. We used a convolutional neural network to classify handwritten digits and letters, applying dropout at different stages to simulate DBS effects on engrams. We showed that dropout applied during training improves the accuracy of prediction, whereas dropout applied during testing dramatically decreases accuracy of prediction, which mimics enhancement and disruption of memory, respectively. We further showed that transfer learning of neural networks with dropout had increased accuracy and rate of learning. Dropout during training provided a more robust "skeleton" network where transfer learning can be applied, mimicking the effects of chronic DBS on memory. Overall, we show that dropout of nodes can be a potential mechanism by which neuromodulation techniques such as DBS can both disrupt and enhance memory and provides a unique perspective on this paradox.

neuroscience↗

bioRxiv2019

Serotonergic treatment normalizes midbrain dopaminergic neuron increase after periaqueductal gray stimulation-induced anticipatory fear in a rat model

BackgroundElectrical stimulation of the dorsolateral periaqueductal gray (dlPAG) in rats has been shown to elicit panic-like behaviour and can be a useful tool for modelling anticipatory fear and agoraphobia.\n\nMethodsIn this study, we further analysed our previous data on the effects of escitalopram (a selective serotonin reuptake inhibitor, SSRI) and buspirone (a 5-HT1A receptor partial agonist) on dlPAG-induced anticipatory fear behaviour in a rat model using freezing as a measure. We then used tyrosine hydroxylase (TH) immunohistochemistry to probe the effects on dopaminergic neurons.\n\nResultsAlthough acute treatment of escitalopram, but not buspirone, was effective in reducing anticipatory freezing behaviour, chronic administrations of both drugs were comparably effective. We found that the number of dopaminergic neurons in the ventral tegmental area (VTA) was lowered in both chronic buspirone and escitalopram groups. We showed a strong correlation between the number of dopaminergic neurons and freezing in the VTA. We further showed positive correlations between dopaminergic neurons in the VTA and substantia nigra pars compacta in escitalopram and buspirone groups, respectively.\n\nLimitationsAlthough our data strongly hint to a role of dopaminergic mechanisms in the dlPAG induced fear response, more in-depth studies with larger sample sizes are needed to understand the neuronal mechanisms underlying the interactions between serotonergic drugs and dopaminergic cell number and fear behavior.\n\nConclusionChronic treatment with an SSRI and a 5-HT1A agonist decrease the number of dopaminergic neurons in the VTA. These effects seem to be associated with reduced dlPAG-induced anticipatory freezing behaviour.\n\nKey PointsO_LIChronic treatment of escitalopram and buspirone was effective in reducing dlPAG induced anticipatory freezing behaviour.\nC_LIO_LIThe number of dopaminergic neurons in the ventral tegmental area (VTA) was lowered in both chronic buspirone and escitalopram groups and was correlated to freezing.\nC_LIO_LIWe found positive correlations between dopaminergic neurons in the VTA and substantia nigra pars compacta in escitalopram and buspirone groups, respectively.\nC_LI

neuroscience↗

bioRxiv2019

Deep Brain Stimulation of the Ventromedial Prefrontal Cortex Disrupts Consolidation of Fear Memories

Anxiety disorders pose one of the biggest threats to mental health worldwide, yet current therapeutics have been mostly ineffective due to issues with relapse, efficacy, and toxicit. Deep brain stimulation (DBS) is a promising therapy for treatment-resistant psychiatric disorders including anxiety, but very little is known about the effects of DBS on fear memories. In this study, we used a modified plus-maze discriminative and showed that DBS of the ventromedial prefrontal cortex (vmPFC) was able to disrupt consolidation, but not acquisition or retrieval of fear memories. We validated these results using a standard tone-footshock fear conditioning paradigm. We further demonstrated short-term changes in dopaminergic receptor and c-Fos expression in the ventral hippocampus (vHPC) and established a partial casual role of dopamine 2 receptors in this effect. Lastly, we showed changes in neurotransmitter levels in the vHPC. This study highlights the potential therapeutic effect of vmPFC DBS to treat anxiety disorders.

neuroscience↗