Biology subjects

Choi, S. B.

Publications and source records attributed to Choi, S. B..

4 recordsLinked to original sources

bioRxiv2026

Proteome-Driven Phenotyping of Identified Single Neurons in Intact Brain Tissue by Aspiration Patch Proteomics

Single-cell proteomics has advanced rapidly, but direct proteome measurements from identified neurons in intact brain tissue remain difficult because most workflows require cell isolation and recent patch-based studies have emphasized whole-soma retrieval. Here we show that aspiration-based patch proteomics enables deep proteome profiling of identified single neurons directly in acute mouse brain slices. We combined fluorescence-guided patch-clamp microsampling, minimal-loss bottom-up proteomics, and high-sensitivity capillary electrophoresis-timsTOF mass spectrometry to analyze partial somal aspirates from dopaminergic, parvalbumin, and serotonergic neurons in situ. The workflow identified more than 1,000 proteins from single-neuron samples under optimized conditions while consuming only about 0.25% of the processed digest per analysis. These proteomes were sufficient to separate biological replicates by neuronal phenotype, distinguish neuronal subtypes on the basis of protein expression alone, and define a conserved somal proteome shared across neuronal classes. Our results establish that controlled aspiration of partial somal material can support proteome-driven phenotyping without whole-soma retrieval, cell dissociation, or loss of native tissue context. Aspiration patch proteomics therefore provides an accessible route for subtype-level proteome phenotyping in intact brain tissue.

neuroscience↗

bioRxiv2021

Patch-Clamp Proteomics of Single Neuronal Somas in Tissue Using Electrophysiology and Subcellular Capillary Electrophoresis Mass Spectrometry

Understanding of the relationship between cellular function and molecular composition holds a key to next-generational therapeutics but requires measurement of all types of molecules in cells. Developments in sequencing enabled semi-routine measurement of single-cell genomes and transcriptomes, but analytical tools are scarce for detecting diverse proteins in tissue-embedded cells. To bridge this gap for neuroscience research, we report the integration of patch-clamp electrophysiology with subcellular shot-gun proteomics by high-resolution mass spectrometry (HRMS). Recording of electrical activity permitted identification of dopaminergic neurons in the substantia nigra pars compacta. Ca. 20-50% of the neuronal soma content, containing an estimated 100 pg of total protein, was aspirated into the patch pipette filled with ammonium bicarbonate. About ~1 pg of somal protein, or ~0.25% of the total cellular proteome, was analyzed on a custom-built capillary electrophoresis (CE) high-resolution mass spectrometer (HRMS). A series of experiments were conducted to systematically enhance detection sensitivity through refinements in sample processing and detection, allowing us to quantify ~275 different proteins from somal aspirate-equivalent protein digests. From single neurons, patch-clamp proteomics of the soma quantified 91, 80, and 95 different proteins from 3 different dopaminergic neurons, or 157 proteins in total. Quantification revealed detectable proteomic differences between the somal protein samples. Analysis of canonical knowledge predicted rich interaction networks between the proteins. The integration of patch-clamp electrophysiology with subcellular CE-HRMS proteomics expands the analytical toolbox of neuroscience.

neuroscience↗

bioRxiv2021

A Data-Dependent Acquisition Ladder for Ultrasensitive (Neuro)Proteomics

Measurement of broad types of proteins from a small number of cells to single cells would help to better understand the nervous system but requires significant leaps in high-resolution mass spectrometry (HRMS) sensitivity. Microanalytical capillary electrophoresis electrospray ionization (CE-ESI) offers a path to ultrasensitive proteomics by integrating scalability with sensitivity. We report here a data acquisition strategy that expands the detectable and quantifiable proteome in trace amounts of digests using CE-ESI-HRMS. Data-dependent acquisition (DDA) was programmed to progressively exclude high-intensity peptide signals during repeated measurements. These nested experiments formed rungs of our "DDA ladder." The method was tested for replicates analyzing ~500 pg of protein digest from cultured hippocampal (primary) neurons (mouse), which estimates to the total amount of protein from a single neuron. Analysis of net amounts approximating to ~10 neurons identified 428 nonredundant proteins (415 quantified), an ~35% increase over traditional DDA. The identified proteins were enriched in neuronal marker genes and molecular pathways of neurobiological importance. The DDA ladder deepens the detectable proteome from trace amounts of proteins, expanding the analytical toolbox of neuroscience.

neuroscience↗

bioRxiv2019

Forecasting type-specific seasonal influenza after 26 weeks in the United States using influenza activities in other countries

To identify countries that have seasonal patterns similar to the time series of influenza surveillance data in the United States and other countries, and to forecast the 2018-2019 seasonal influenza outbreak in the U.S. using linear regression, auto regressive integrated moving average, and deep learning. We collected the surveillance data of 164 countries from 2010 to 2018 using the FluNet database. Data for influenza-like illness (ILI) in the U.S. were collected from the Fluview database. This cross-correlation study identified the time lag between the two time-series. Deep learning was performed to forecast ILI, total influenza, A, and B viruses after 26 weeks in the U.S. The seasonal influenza patterns in Australia and Chile showed a high correlation with those of the U.S. 22 weeks and 28 weeks earlier, respectively. The R2 score of DNN models for ILI for validation set in 2015-2019 was 0.722 despite how hard it is to forecast 26 weeks ahead. Our prediction models forecast that the ILI for the U.S. in 2018-2019 may be later and less severe than those in 2017-2018, judging from the influenza activity for Australia and Chile in 2018. It allows to estimate peak timing, peak intensity, and type-specific influenza activities for next season at 40th week. The correlation for seasonal influenza among Australia, Chile, and the U.S. could be used to decide on influenza vaccine strategy six months ahead in the U.S.

bioinformatics↗