Highly multiplexed, label-free proteoform imaging of tissues by individual ion mass spectrometry

Su, Pei; McGee, John P.; Durbin, Kenneth R.; Hollas, Michael A.; Yang, Manxi; Neumann, Elizabeth; Allen, James L.; Drown, Bryon; Butun, Fatma Ayaloglu; Greer, Joseph B.; Early, Bryan P.; Fellers, Ryan T.; Spraggins, Jeffrey M.; Laskin, Julia; Camarillo, Jeannie; Kafader, Jared O.; Kelleher, Neil L.

doi:10.1126/sciadv.abp9929

Cited by 35 publications

(49 citation statements)

References 64 publications

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“…Direct fragmentation of proteoforms in situ during MSI have been demonstrated recently as the TDP methods and instruments improved. (Garza et al, 2018;Griffiths et al, 2020;Su et al, 2022;Yang et al, 2022) Herein, we have shown the assignment of several high abundance proteins and a few cysteine-rich peptides. Fully automated peak assignment for proteoform analysis is being actively developed.…”

Section: Discussionmentioning

confidence: 62%

“…(Hollas et al, 2021;Smith et al, 2021;Melani et al, 2022) The limitations of intact protein sample complexity, low sensitivity, and sequence coverage against large proteins remain, but are being addressed with new experimental technologies including solution-phase separation and enrichment (Melby et al, 2021), ion mobility (Fulcher et al, 2021;Gerbasi et al, 2021), proton transfer reaction (Huguet et al, 2019;Ugrin et al, 2019) and charge detection mass spectrometry (Harper et al, 2019;Kafader et al, 2020;Todd et al, 2020;Worner et al, 2020;Su et al, 2022). Notably, charge detection-based method expanded the measurable proteoforms into 70 kDa range in a recent TDP tissue imaging study by nanospray desorption electrospray ionization (nanoDESI), (Su et al, 2022), highlighting the potential for easier access of high mass proteoforms. Our TDP results on soybean root nodule demonstrate the ways in which TDP can already make significant contributions in studying the proteome <25 kDa despite technologies not yet being mature enough for accessing the full proteome.…”

Section: Discussionmentioning

confidence: 99%

“…Currently, the standard TDP methods used here are limited to small proteins up to 20-30 kDa. (Huguet et al, 2019;Su et al, 2022;Zemaitis et al, 2022). We also discussed new developments in TDP instrumentation that will gradually bridge the gap and allow full integration with BUP data.…”

Section: Introductionmentioning

confidence: 99%

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Discovery top-down proteomics in symbiotic soybean root nodules

Zhou

Fulcher²,

Zemaitis³

et al. 2022

Front. Anal. Sci.

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Proteomic methods have been widely used to study proteins in complex biological samples to understand biological molecular mechanisms. Most well-established methods (known as bottom-up proteomics, BUP) employ an enzymatic digestion step to cleave intact proteins into smaller peptides for liquid chromatography (LC) mass spectrometry (MS) detection. In contrast, top-down proteomics (TDP) directly characterizes intact proteins including all possible post-translational modifications (PTMs), thus offering unique insights into proteoform biology where combinations of individual PTMs may play important roles. We performed TDP on soybean root nodules infected by the symbiotic Bradyrhizobium japonicum in both the wildtype bacterium and a nifH- mutant, which lacks the ability to fix nitrogen in the soybean root nodule. TDP captured 1648 proteoforms derived from 313 bacterial genes and 178 soybean genes. Leghemoglobin, the most abundant protein in the sample, existed in many truncated proteoforms. Interestingly, these truncated proteoforms were considerably more abundant in the wildtype relative to the nifH- mutant, implicating protease activity as an important factor in nitrogen fixation. Proteoforms with various PTMs and combinations thereof were identified using an unrestricted open modification search. This included less common PTMs such as myristoylation, palmitoylation, cyanylation, and sulfation. In parallel, we collected high resolution MS imaging (MSI) data of intact proteins and biopolymers (<20 kDa due to current technical limitations) from sections of the soybean root nodules using matrix-assisted laser desorption/ionization (MALDI) coupled to high resolution Orbitrap. Several detected proteoforms exhibited unique spatial distributions inside the infection zone and cortex, suggesting functional compartmentalization in these regions. A subset of peaks from the MALDI-MSI were assigned to proteoforms detected in TDP LCMS data based on matching accurate masses. Many of the proteins detected in both LCMS and MALDI-MSI are currently uncharacterized in UniProt: the PTM and spatial information presented here will be valuable in understanding their biological functions. Taken together, our study demonstrates how untargeted TDP approach can provide unique insights into plant proteoform biology. On-going technology developments are expected to further improve TDP coverage for more comprehensive high-throughput analysis of proteoforms.

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Section: Discussionmentioning

confidence: 62%

Section: Discussionmentioning

confidence: 99%

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Discovery top-down proteomics in symbiotic soybean root nodules

Zhou

Fulcher²,

Zemaitis³

et al. 2022

Front. Anal. Sci.

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“…Su et al. [ 260 ] developed a proteoform imaging mass spectrometry method by coupling nano‐DESI MSI with individual ion mass spectrometry (I 2 MS). [ 261 ] Using Orbitrap‐based charge detection I 2 MS and computational signal transformation, 169 proteoforms have been identified in every pixel and proteoform‐selective imaging data were obtained with a spatial resolution of 80 µm.…”

Section: Computational Methods For Msi Experimentsmentioning

confidence: 99%

Emerging Computational Methods in Mass Spectrometry Imaging

Laskin

2022

Advanced Science

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Mass spectrometry imaging (MSI) is a powerful analytical technique that generates maps of hundreds of molecules in biological samples with high sensitivity and molecular specificity. Advanced MSI platforms with capability of high-spatial resolution and high-throughput acquisition generate vast amount of data, which necessitates the development of computational tools for MSI data analysis. In addition, computation-driven MSI experiments have recently emerged as enabling technologies for further improving the MSI capabilities with little or no hardware modification. This review provides a critical summary of computational methods and resources developed for MSI data analysis and interpretation along with computational approaches for improving throughput and molecular coverage in MSI experiments. This review is focused on the recently developed artificial intelligence methods and provides an outlook for a future paradigm shift in MSI with transformative computational methods.

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“…While these technologies are highly effective and can provide single-cell level or better spatial resolution, protein coverage is limited by the availability of reliable antibodies and the multiplexing limit of the labels. Imaging mass spectrometry (MALDI, Laser Ablation) is also a powerful tool for protein mapping that does not depend on antibody recognition; but due to the direct coupling to the mass spectrometer, these techniques are limited in their dynamic range and accuracy of quantitation [20][21][22][23][24] .…”

Section: Introductionmentioning

confidence: 99%

Proteome mapping of the human pancreatic islet microenvironment reveals endocrine-exocrine signaling sphere of influence

Gosline¹,

Veličković²,

Pino³

et al. 2022

Preprint

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The need for a clinically accessible method with the ability to match protein activity within heterogeneous tissues is currently unmet by existing technologies. Our proteomics sample preparation platform, named microPOTS (Microdroplet Processing in One pot for Trace Samples), can be used to measure relative protein abundance in micron-scale samples alongside the spatial location of each measurement, thereby tying biologically interesting proteins and pathways to distinct regions. However, given the smaller sample number and amount of tissue measu red, standard mass spectrometric analysis pipelines have proven inadequate. Here we describe how existing computational approaches can be adapted to focus on the specific biological questions asked in spatial proteomics experiments. We apply this approach to present an unbiased characterization of the human islet microenvironment comprising the entire complex array of tissues involved while maintaining spatial information and the degree of the islet's sphere of influence. We identify specific functional activity unique to the pancreatic islet cells and demonstrate how far their signature can be measured. Our results show that we can distinguish pancreatic islet cells from the neighboring exocrine tissue environment, recapitulate known biological functions of islet cells, and identify a spatial gradient in the expression of RNA processing proteins within the islet microenvironment.

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Highly multiplexed, label-free proteoform imaging of tissues by individual ion mass spectrometry

Cited by 35 publications

References 64 publications

Discovery top-down proteomics in symbiotic soybean root nodules

Discovery top-down proteomics in symbiotic soybean root nodules

Emerging Computational Methods in Mass Spectrometry Imaging

Proteome mapping of the human pancreatic islet microenvironment reveals endocrine-exocrine signaling sphere of influence

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