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alexcwsmith/README.md

Hi there, I'm Alex 👋

Click here to skip this introduction and view all of my repositories

I am a neuroscientist & professor at the Medical University of South Carolina, in Charleston. My research uses a broad array of behavioral, cellular, and molecular techniques to study synaptic plasticity that results from chronic drug exposure, and how this maladaptive plasticity contributes to heightened vulnerability to relapse during periods of attempted abstinence.

I have been coding in Python since 2018. I began on this journey when I became very interested in computational techniques for analyzing large data sets, primarily high-volume imaging results from tissue clearing and light-sheet microscopy. I use the iDISCO+ tissue clearing method to examine neuroadaptations that occur in rodent models of drug addiction, and Python to automatically detect and count cells, and to segment cell counts into regions by aligning brains to the Allen Brain Atlas.
You can view my image processing scripts at: Image Processing

I conduct high-throughput mapping of immediate early gene expression following exposure to drugs of abuse, and/or following cue-induced drug seeking. This type of mapping allows unbiased discovery of novel regions that are affected by drugs of abuse. I further use tissue clearing and light-sheet imaging to examine inputs/outputs (retrograde- and antero-grade tracing) from these regions to identify novel circuits regulated by drugs of abuse. In order to elucidate the behavioral function of these circuits, I use optogenetic and chemogenetic manipulation of neurocircuitry of transgenic mice, using Cre-dependent techniques as well as Cre/Flp intersectional genetics. Recently I have been highly interested in using the FosTRAP2 mice to perform targetted recombination in active populations, and using this technology to "tag" neuronal enssembles that are activated by a specific stimulus.. My repository imageProcessing contains code for analyzing both c-Fos expression as well as axons segmented by Ilastik or TrailMap.

The overarching mission for my research career is to use this approach to identify novel circuitry that contributes to substance use disorders (SUDs). The vast majority of research on the neurobiology of addiction for the past three decades has focused on a small number of brain structures, most notably the mesocorticolimbic circuitry and the extended amygdala. Thus, I hypothesize that using unbiased, high-throughput techniques to detect drug-induced maladaptive neuroadaptations throughout the entire brain will be key to development of efficacious pharmacotherapeutics to treat SUDs.



I am currently funded by a K99 from NIDA to identify novel circuitry activated by cue-induced reinstatement of oxycodone seeking (CROS). Following identification novel structures that are necessary for reinstatement, I will use single-cell sequencing to identify genes that are regulated by CROS. In the R00 phase of this project, I will prioritize differentially expressed genes, and identify novel pharmacotherapeutic targets for prevention of relapse.


You can view my single-cell sequencing analysis scripts at:
Single Cell Tools



I have also developed a Generative Adversarial Network (GAN) for image enhancment which can be found here.

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  1. imageProcessing imageProcessing Public

    Image Processing Scripts

    Python 1

  2. singleCellTools singleCellTools Public

    Tools for analysis of single-cell sequencing data, by Alex Smith in the lab of Paul Kenny @ Mount Sinai.

    Python 1 2

  3. VAME VAME Public

    Forked from LINCellularNeuroscience/VAME

    Variational Animal Motion Embedding

    Python 1 3

  4. scNLP scNLP Public

    Mining of PubMed and Natural Language Processing literature on results from single-cell sequencing data

    Python 1