Spatially Resolved Top-Down Proteomics of Tissue Sections Based on a Microfluidic Nanodroplet Sample Preparation Platform.

Conventional proteomic approaches measure the averaged signal from mixed cell populations or bulk tissues, leading to the dilution of signals arising from subpopulations of cells that might serve as important biomarkers. Recent developments in bottom-up proteomics have enabled spatial mapping of cellular heterogeneity in tissue microenvironments. However, bottom-up proteomics cannot unambiguously define and quantify proteoforms, which are intact (i.e., functional) forms of proteins capturing genetic variations, alternatively spliced transcripts and posttranslational modifications. Herein, we described a spatially resolved top-down proteomics (TDP) platform for proteoform identification and quantitation directly from tissue sections. The spatial TDP platform consisted of a nanodroplet processing in one pot for trace samples-based sample preparation system and an laser capture microdissection-based cell isolation system. We improved the nanodroplet processing in one pot for trace samples sample preparation by adding benzonase in the extraction buffer to enhance the coverage of nucleus proteins. Using ∼200 cultured cells as test samples, this approach increased total proteoform identifications from 493 to 700; with newly identified proteoforms primarily corresponding to nuclear proteins. To demonstrate the spatial TDP platform in tissue samples, we analyzed laser capture microdissection-isolated tissue voxels from rat brain cortex and hypothalamus regions. We quantified 509 proteoforms within the union of top-down mass spectrometry-based proteoform identification and characterization and TDPortal identifications to match with features from protein mass extractor. Several proteoforms corresponding to the same gene exhibited mixed abundance profiles between two tissue regions, suggesting potential posttranslational modification-specific spatial distributions. The spatial TDP workflow has prospects for biomarker discovery at proteoform level from small tissue sections.

Proteomic Profiling of Intra-Islet Features Reveals Substructure-Specific Protein Signatures.

Despite their diminutive size, islets of Langerhans play a large role in maintaining systemic energy balance in the body. New technologies have enabled us to go from studying the whole pancreas to isolated whole islets, to partial islet sections, and now to islet substructures isolated from within the islet. Using a microfluidic nanodroplet-based proteomics platform coupled with laser capture microdissection and field asymmetric waveform ion mobility spectrometry, we present an in-depth investigation of protein profiles specific to features within the islet. These features include the islet-acinar interface vascular tissue, inner islet vasculature, isolated endocrine cells, whole islet with vasculature, and acinar tissue from around the islet. Compared to interface vasculature, unique protein signatures observed in the inner vasculature indicate increased innervation and intra-islet neuron-like crosstalk. We also demonstrate the utility of these data for identifying localized structure-specific drug-target interactions using existing protein/drug binding databases.

Data-Dependent Acquisition with Precursor Coisolation Improves Proteome Coverage and Measurement Throughput for Label-Free Single-Cell Proteomics.

We combined efficient sample preparation and ultra-low-flow liquid chromatography with a newly developed data acquisition and analysis scheme termed wide window acquisition (WWA) to quantify >3,000 proteins from single cells in rapid label-free analyses. WWA employs large isolation windows to intentionally co-isolate and co-fragment adjacent precursors along with the selected precursor. Optimized WWA increased the number of MS2-identified proteins by ≈40 % relative to standard data-dependent acquisition. For a 40-min LC gradient operated at ≈15 nL/min, we identified an average of 3,524 proteins per single-cell-sized aliquot of protein digest. Reducing the active gradient to 20 min resulted in a modest 10 % decrease in proteome coverage. Using this platform, we compared protein expression between single HeLa cells having an essential autophagy gene, atg9a, knocked out, with their isogenic WT parental line. Similar proteome coverage was observed, and 268 proteins were significantly up- or downregulated. Protein upregulation primarily related to innate immunity, vesicle trafficking and protein degradation.

Multimodal Mass Spectrometry Imaging of Rat Brain Using IR-MALDESI and NanoPOTS-LC-MS/MS.

Multimodal mass spectrometry imaging (MSI) is a critical technique used for deeply investigating biological systems by combining multiple MSI platforms in order to gain the maximum molecular information about a sample that would otherwise be limited by a single analytical technique. The aim of this work was to create a multimodal MSI approach that measures metabolomic and proteomic data from a single biological organ by combining infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) for metabolomic MSI and nanodroplet processing in one pot for trace samples (nanoPOTS) LC-MS/MS for spatially resolved proteome profiling. Adjacent tissue sections of rat brain were analyzed by each platform, and each data set was individually analyzed using previously optimized workflows. IR-MALDESI data sets were annotated by accurate mass and spectral accuracy using HMDB, METLIN, and LipidMaps databases, while nanoPOTS-LC-MS/MS data sets were searched against the rat proteome using the Sequest HT algorithm and filtered with a 1% FDR. The combined data revealed complementary molecular profiles distinguishing the corpus callosum against other sampled regions of the brain. A multiomic pathway integration showed a strong correlation between the two data sets when comparing average abundances of metabolites and corresponding enzymes in each brain region. This work demonstrates the first steps in the creation of a multimodal MSI technique that combines two highly sensitive and complementary imaging platforms. Raw data files are available in METASPACE (https://metaspace2020.eu/project/pace-2021) and MassIVE (identifier: MSV000088211).

Single-cell Proteomics: Progress and Prospects.

MS-based proteome profiling has become increasingly comprehensive and quantitative, yet a persistent shortcoming has been the relatively large samples required to achieve an in-depth measurement. Such bulk samples, typically comprising thousands of cells or more, provide a population average and obscure important cellular heterogeneity. Single-cell proteomics capabilities have the potential to transform biomedical research and enable understanding of biological systems with a new level of granularity. Recent advances in sample processing, separations and MS instrumentation now make it possible to quantify >1000 proteins from individual mammalian cells, a level of coverage that required an input of thousands of cells just a few years ago. This review discusses important factors and parameters that should be optimized across the workflow for single-cell and other low-input measurements. It also highlights recent developments that have advanced the field and opportunities for further development.

Nanowell-mediated multidimensional separations combining nanoLC with SLIM IM-MS for rapid, high-peak-capacity proteomic analyses.

Mass spectrometry (MS)-based analysis of complex biological samples is essential for biomedical research and clinical diagnostics. The separation prior to MS plays a key role in the overall analysis, with separations having larger peak capacities often leading to more identified species and improved confidence in those identifications. High-resolution ion mobility (IM) separations enabled by Structures for Lossless Ion Manipulation (SLIM) can provide extremely rapid, high-resolution separations and are well suited as a second dimension of separation following nanoscale liquid chromatography (nanoLC). However, existing sample handling approaches for offline coupling of separation modes require microliter-fraction volumes and are thus not well suited for analysis of trace biological samples. We have developed a novel nanowell-mediated fractionation system that enables nanoLC-separated samples to be efficiently preconcentrated and directly infused at nanoelectrospray flow rates for downstream analysis. When coupled with SLIM IM-MS, the platform enables rapid and high-peak-capacity multidimensional separations of small biological samples. In this study, peptides eluting from a 100 nL/min nanoLC separation were fractionated into ~ 60 nanowells on a microfluidic glass chip using an in-house-developed robotic system. The dried samples on the chip were individually reconstituted and ionized by nanoelectrospray for SLIM IM-MS analysis. Using model peptides for characterization of the nanowell platform, we found that at least 80% of the peptide components of the fractionated samples were recovered from the nanowells, providing up to ~tenfold preconcentration for SLIM IM-MS analysis. The combined LC-SLIM IM separation peak capacities exceeded 3600 with a measurement throughput that is similar to current one-dimensional (1D) LC-MS proteomic analyses. Graphical abstract A nanowell-mediated multidimensional separation platform that combines nanoLC with SLIM IM-MS enables rapid, high-peak-capacity proteomic analyses.

Nanoproteomics comes of age.

INTRODUCTION: Nanoproteomics, which is defined as quantitative proteome profiling of small populations of cells (<5000 cells), can reveal critical information related to rare cell populations, hard-to-obtain clinical specimens, and the cellular heterogeneity of pathological tissues. Areas covered: We present a brief review of the recent technological advances in nanoproteomics. These advances include new technologies or approaches covering major areas of proteomics workflow ranging from sample isolation, sample processing, high-resolution separations, to MS instrumentation. Expert commentary: We comment on the current state of nanoproteomics and discuss perspectives on both future technological directions and potential enabling applications.

Microfluidic Sample Preparation for Multiplexed Single-Cell Proteomics Using a Nested Nanowell Chip.

Mass spectrometry-based single-cell proteomics has undergone rapid progress and has become an active research area. However, because of the ultralow amount of proteins in single cells, it is still highly challenging to achieve efficient sample preparation and sensitive LC-MS detection. Here, we provide a detailed protocol for isobaric labeling-based single-cell proteomics relying on a microfluidic droplet-based sample processing technology. The protocol allows for processing both single cells and carrier samples in separate microchips using a commercially available platform (cellenONE) with high sample recovery and high throughput. We also provide an optimized LC-MS method for sensitive and robust data collection.

TDP-43-stratified single-cell proteomics of postmortem human spinal motor neurons reveals protein dynamics in amyotrophic lateral sclerosis.

A limitation of conventional bulk-tissue proteome studies in amyotrophic lateral sclerosis (ALS) is the confounding of motor neuron (MN) signals by admixed non-MN proteins. Here, we leverage laser capture microdissection and nanoPOTS single-cell mass spectrometry-based proteomics to query changes in protein expression in single MNs from postmortem ALS and control tissues. In a follow-up analysis, we examine the impact of stratification of MNs based on cytoplasmic transactive response DNA-binding protein 43 (TDP-43)+ inclusion pathology on the profiles of 2,238 proteins. We report extensive overlap in differentially abundant proteins identified in ALS MNs with or without overt TDP-43 pathology, suggesting early and sustained dysregulation of cellular respiration, mRNA splicing, translation, and vesicular transport in ALS. Together, these data provide insights into proteome-level changes associated with TDP-43 proteinopathy and begin to demonstrate the utility of pathology-stratified trace sample proteomics for understanding single-cell protein dynamics in human neurologic diseases.

TDP-43-stratified single-cell proteomic profiling of postmortem human spinal motor neurons reveals protein dynamics in amyotrophic lateral sclerosis.

Unbiased proteomics has been employed to interrogate central nervous system (CNS) tissues (brain, spinal cord) and fluid matrices (CSF, plasma) from amyotrophic lateral sclerosis (ALS) patients; yet, a limitation of conventional bulk tissue studies is that motor neuron (MN) proteome signals may be confounded by admixed non-MN proteins. Recent advances in trace sample proteomics have enabled quantitative protein abundance datasets from single human MNs (Cong et al., 2020b). In this study, we leveraged laser capture microdissection (LCM) and nanoPOTS (Zhu et al., 2018c) single-cell mass spectrometry (MS)-based proteomics to query changes in protein expression in single MNs from postmortem ALS and control donor spinal cord tissues, leading to the identification of 2515 proteins across MNs samples (>900 per single MN) and quantitative comparison of 1870 proteins between disease groups. Furthermore, we studied the impact of enriching/stratifying MN proteome samples based on the presence and extent of immunoreactive, cytoplasmic TDP-43 inclusions, allowing identification of 3368 proteins across MNs samples and profiling of 2238 proteins across TDP-43 strata. We found extensive overlap in differential protein abundance profiles between MNs with or without obvious TDP-43 cytoplasmic inclusions that together point to early and sustained dysregulation of oxidative phosphorylation, mRNA splicing and translation, and retromer-mediated vesicular transport in ALS. Our data are the first unbiased quantification of single MN protein abundance changes associated with TDP-43 proteinopathy and begin to demonstrate the utility of pathology-stratified trace sample proteomics for understanding single-cell protein abundance changes in human neurologic diseases.

Three-dimensional feature matching improves coverage for single-cell proteomics based on ion mobility filtering.

Single-cell proteomics (scProteomics) promises to advance our understanding of cell functions within complex biological systems. However, a major challenge of current methods is their inability to identify and provide accurate quantitative information for low-abundance proteins. Herein, we describe an ion-mobility-enhanced mass spectrometry acquisition and peptide identification method, transferring identification based on FAIMS filtering (TIFF), to improve the sensitivity and accuracy of label-free scProteomics. TIFF extends the ion accumulation times for peptide ions by filtering out singly charged ions. The peptide identities are assigned by a three-dimensional MS1 feature matching approach (retention time, accurate mass, and FAIMS compensation voltage). The TIFF method enabled unbiased proteome analysis to a depth of >1,700 proteins in single HeLa cells, with >1,100 proteins consistently identified. As a demonstration, we applied the TIFF method to obtain temporal proteome profiles of >150 single murine macrophage cells during lipopolysaccharide stimulation and identified time-dependent proteome changes. A record of this paper's transparent peer review process is included in the supplemental information.

Cell-Type-Specific Proteomics Analysis of a Small Number of Plant Cells by Integrating Laser Capture Microdissection with a Nanodroplet Sample Processing Platform.

Plant organs and tissues contain multiple cell types, which are well organized in 3-dimensional structure to efficiently perform physiological functions such as homeostasis and response to environmental perturbation and pathogen infection. It is critically important to perform molecular measurements at the cell-type-specific level to discover mechanisms and unique features of cell populations that govern differentiation and respond to external perturbations. Although mass spectrometry-based proteomics has been demonstrated as an enabling discovery tool for studying plant physiology, conventional approaches require millions of cells to generate robust biological conclusions. Such requirements mask the cell-to-cell heterogeneities and limit the comprehensive profiling of plant proteins at spatially resolved and cell-type-specific resolutions. This article describes a recently developed proteomics workflow for studying a small number of plant cells by integrating laser capture microdissection, microfluidic nanodroplet-based sample preparation, and ultrasensitive liquid chromatography-mass spectrometry. Using poplar as a model tree species, we provide detailed protocols, including plant leaf and root tissue harvest, sample preparation, cryosectioning, laser microdissection, protein digestion, mass spectrometry measurement, and data analysis. We show that the workflow enables the precise identification and quantification of thousands of proteins from hundreds of isolated plant root and leaf cells. © 2021 Wiley Periodicals LLC. Basic Protocol 1: Plant tissue fixation and embedding Support Protocol 1: Preparation of 2.5% CMC solution Support Protocol 2: Slow freezing of CMC blocks to avoid crack development in the block Basic Protocol 2: Preparation of cryosections Alternate Protocol: Using a vacuum manifold to dehydrate the cryosection slides (primarily for root tissues) Basic Protocol 3: Laser capture microdissection of specific types of plant cells Basic Protocol 4: Nanodroplet-based sample preparation for ultrasensitive proteomic analysis Support Protocol 3: Fabrication of nanowell chips Basic Protocol 5: Liquid chromatography and mass spectrometry.

Adapting a Low-Cost and Open-Source Commercial Pipetting Robot for Nanoliter Liquid Handling.

Low-volume liquid handling capabilities in bioanalytical workflows can dramatically improve sample processing efficiency and reduce reagent costs, yet many commercial nanoliter liquid handlers cost tens of thousands of dollars or more. We have successfully adapted a low-cost and open-source commercial pipetting robot, the Opentrons OT-1, to accurately aspirate and dispense nanoliter volumes. Based on fluorescence measurements, the modified OT-1 was able to reproducibly transfer 50 nL of water with less than 3% measurement error and 5% coefficient of variation (CV). For 15 nL transfers, the volume measurements indicated less than 4% error and 4% CV. We applied this platform to the preparation of low-nanogram proteomic samples for liquid chromatography-mass spectrometry analysis, demonstrating that the modified OT-1 is an effective platform for nanoliter liquid handling. At a total materials cost of less than $6000, including the commercial liquid handler and all modifications, this system is also far less expensive than other platforms with similar capabilities, placing automated nanoliter handling within reach of a far broader scientific community.

In-Depth Mass Spectrometry-Based Single-Cell and Nanoscale Proteomics.

Leukemic stem cells are highly dynamic and heterogeneous. Analysis of leukemic stem cells at the single-cell level should provide a wealth of insights that would not be possible using bulk measurements. Mass spectrometry (MS)-based proteomic workflows can quantify hundreds or thousands of proteins from a biological sample and has proven invaluable for biomedical research, but samples comprising large numbers of cells are typically required due to limited sensitivity. Recent developments in sample processing, chromatographic separations, and MS instrumentation are now extending in-depth proteome profiling to single mammalian cells. Here, we describe specific techniques that increase the sensitivity of single-cell proteomics by orders of magnitude, enabling the promise of single-cell proteomics to become a reality. We anticipate such techniques can significantly advance the understanding of leukemic stem cells.