Imaging of proteoforms in human tissues is hindered by low molecular specificity and limited proteome coverage. Here, we introduce proteoform imaging mass spectrometry (PiMS), which increases the size limit for proteoform detection and identification by fourfold compared to reported methods and reveals tissue localization of proteoforms at <80-μm spatial resolution. PiMS advances proteoform imaging by combining ambient nanospray desorption electrospray ionization with ion detection using individual ion mass spectrometry. We demonstrate highly multiplexed proteoform imaging of human kidney, annotating 169 of 400 proteoforms of <70 kDa using top-down MS and a database lookup of ~1000 kidney candidate proteoforms, including dozens of key enzymes in primary metabolism. PiMS images reveal distinct spatial localizations of proteoforms to both anatomical structures and cellular neighborhoods in the vasculature, medulla, and cortex regions of the human kidney. The benefits of PiMS are poised to increase proteome coverage for label-free protein imaging of tissues.
Unraveling the complexity of biological systems relies on the development of new approaches for spatially resolved proteoform-specific analysis of the proteome. Herein, we employ nanospray desorption electrospray ionization mass spectrometry imaging (nano-DESI MSI) for the proteoform-selective imaging of biological tissues. Nano-DESI generates multiply charged protein ions, which is advantageous for their structural characterization using tandem mass spectrometry (MS/MS) directly on the tissue. Proof-of-concept experiments demonstrate that nano-DESI MSI combined with on-tissue top-down proteomics is ideally suited for the proteoform-selective imaging of tissue sections. Using rat brain tissue as a model system, we provide the first evidence of differential proteoform expression in different regions of the brain.
Mass spectrometry imaging (MSI) is a powerful tool for label-free mapping of the spatial distribution of proteins in biological tissues. We have previously demonstrated imaging of individual proteoforms in biological tissues using nanospray desorption electrospray ionization (nano-DESI), an ambient liquid extraction-based MSI technique. Nano-DESI MSI generates multiply charged protein ions, which is advantageous for their identification using top-down proteomics analysis. In this study, we demonstrate proteoform mapping in biological tissues with a spatial resolution down to 7 μm using nano-DESI MSI. A substantial decrease in protein signals observed in high-spatialresolution MSI makes these experiments challenging. We have enhanced the sensitivity of nano-DESI MSI experiments by optimizing the design of the capillary-based probe and the thickness of the tissue section. In addition, we demonstrate that oversampling may be used to further improve spatial resolution at little or no expense to sensitivity. These developments represent a new step in MSI-based spatial proteomics, which complements targeted imaging modalities widely used for studying biological systems.
Mass spectrometry imaging (MSI) enables label-free mapping
of hundreds
of molecules in biological samples with high sensitivity and unprecedented
specificity. Conventional MSI experiments are relatively slow, limiting
their utility for applications requiring rapid data acquisition, such
as intraoperative tissue analysis or 3D imaging. Recent advances in
MSI technology focus on improving the spatial resolution and molecular
coverage, further increasing the acquisition time. Herein, a deep
learning approach for dynamic sampling (DLADS) was employed to reduce
the number of required measurements, thereby improving the throughput
of MSI experiments in comparison with conventional methods. DLADS
trains a deep learning model to dynamically predict molecularly informative
tissue locations for active mass spectra sampling and reconstructs
high-fidelity molecular images using only the sparsely sampled information.
Experimental hardware and software integration of DLADS with nanospray
desorption electrospray ionization (nano-DESI) MSI is reported for
the first time, which demonstrates a 2.3-fold improvement in throughput
for a linewise acquisition mode. Meanwhile, simulations indicate that
a 5–10-fold throughput improvement may be achieved using the
pointwise acquisition mode.
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