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 (microCE-ES...

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Bibliographic Details
Published inbioRxiv
Main Authors Choi, Sam B, Munoz-Llancao, Pablo, Manzini, Maria Chiara, Nemes, Peter
Format Paper
LanguageEnglish
Published Cold Spring Harbor Cold Spring Harbor Laboratory Press 04.08.2021
Subjects
Capillary electrophoresis
Data acquisition
Hippocampus
Ionization
Ions
Mass spectroscopy
Nervous system
Proteins
Proteomes
Proteomics
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Summary: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 (microCE-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 microCE-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. Competing Interest Statement The authors have declared no competing interest.
DOI:10.1101/2021.08.03.454943