Set of Novel Automated Quantitative Microproteomics Protocols for Small Sample Amounts and Its Application to Kidney Tissue Substructures

Here we assessed the ability of an automated sample preparation device equipped with disposable microcolumns to prepare mass-limited samples for high-sensitivity quantitative proteomics, using both label-free and isobaric labeling approaches. First, we compared peptide label-free quantification reproducibility for 1.5–150 μg of cell lysates and found that labware preconditioning was essential for reproducible quantification of <7.5 μg digest. Second, in-solution and on-column tandem mass tag (TMT) labeling protocols were compared and optimized for 1 μg of sample. Surprisingly, standard methods for in-solution and on-column labeling showed poor TMT labeling (50–85%); however, novel optimized and automated protocols restored efficient labeling to >98%. Third, compared with a single long gradient experiment, a simple robotized high-pH fractionation protocol using only 6 μg of starting material doubled the number of unique peptides and increased proteome coverage 1.43-fold. To facilitate the analysis of heterogeneous tissue samples, such as those obtained from laser capture microdissection, a modified BCA protein assay was developed that consumes and detects down to 15 ng of protein. As a proof-of-principle, the modular automated workflow was applied to 0.5 and 1 mm² mouse kidney cortex and medulla microdissections to show the method’s potential for real-life small sample sources and to create kidney substructure-specific proteomes

Set of Novel Automated Quantitative Microproteomics Protocols for Small Sample Amounts and Its Application to Kidney Tissue Substructures

Here we assessed the ability of an automated sample preparation device equipped with disposable microcolumns to prepare mass-limited samples for high-sensitivity quantitative proteomics, using both label-free and isobaric labeling approaches. First, we compared peptide label-free quantification reproducibility for 1.5–150 μg of cell lysates and found that labware preconditioning was essential for reproducible quantification of <7.5 μg digest. Second, in-solution and on-column tandem mass tag (TMT) labeling protocols were compared and optimized for 1 μg of sample. Surprisingly, standard methods for in-solution and on-column labeling showed poor TMT labeling (50–85%); however, novel optimized and automated protocols restored efficient labeling to >98%. Third, compared with a single long gradient experiment, a simple robotized high-pH fractionation protocol using only 6 μg of starting material doubled the number of unique peptides and increased proteome coverage 1.43-fold. To facilitate the analysis of heterogeneous tissue samples, such as those obtained from laser capture microdissection, a modified BCA protein assay was developed that consumes and detects down to 15 ng of protein. As a proof-of-principle, the modular automated workflow was applied to 0.5 and 1 mm² mouse kidney cortex and medulla microdissections to show the method’s potential for real-life small sample sources and to create kidney substructure-specific proteomes

Characterization of Degraded Proteins in Paintings Using Bottom-Up Proteomic Approaches: New Strategies for Protein Digestion and Analysis of Data

Chemical hydrolysis assisted by microwave irradiation has been proposed as an alternative method for the analysis of proteins in highly insoluble matrices. In this work, chemical hydrolysis was applied for the first time to detect degraded proteins from paintings and polychromies. To evaluate the performance of this approach, the number of identified peptides, protein sequence coverage (%), and PSMs were compared with those obtained using two trypsin-based proteomics procedures used for the analysis of samples from cultural heritage objects. It was found that chemical hydrolysis allowed the successful identification of all proteinaceous materials in all paint samples analyzed except for egg proteins in one extremely degraded sample. Moreover, in one sample, casein was only identified by chemical digestion. In general, chemical hydrolysis identified more peptides, more PSM’s, and greater sequence coverage in the samples containing caseins, and often also in animal glue, highlighting the great potential of this approach for the rapid digestion and identification of insoluble and degraded proteins from the field of the cultural heritage

Mass Spectrometry Imaging, Laser Capture Microdissection, and LC-MS/MS of the Same Tissue Section

Mass spectrometry imaging (MSI) is able to simultaneously record the distributions of hundreds of molecules directly from tissue. Rapid direct tissue analysis is essential for MSI in order to maintain spatial localization and acceptable measurement times. The absence of an explicit analyte separation/purification step means MSI lacks the depth of coverage of LC-MS/MS. In this work, we demonstrate how atmospheric pressure MALDI-MSI enables the same tissue section to be first analyzed by MSI, to identify regions of interest that exhibit distinct molecular signatures, followed by localized proteomics analysis using laser capture microdissection isolation and LC-MS/MS

Brain Region-Specific Dynamics of On-Tissue Protein Digestion Using MALDI Mass Spectrometry Imaging

In mass spectrometry imaging (MSI), on-tissue proteolytic digestion is performed to access larger protein species and to assign protein identities through matching the detected peaks with those obtained by LC–MS/MS analyses of tissue extracts. The on-tissue proteolytic digestion also allows the analysis of proteins from formalin-fixed, paraffin-embedded tissues. For these reasons, on-tissue digestion-based MSI is frequently used in clinical investigations, for example, to determine changes in protein content and distribution associated with a disease. In this work, we sought to investigate the completeness and uniformity of the digestion in on-tissue digestion MSI. On the basis of an extensive experiment investigating three groups with varying incubation times: (i) 1.5 h, (ii) 3 h, and (iii) 18 h, we have found that longer incubation times improve the repeatability of the analyses. Furthermore, we discovered morphology-associated differences in the completeness of the proteolysis for short incubation times. These results support the notion that a more complete proteolysis allows better quantitation

Histology-Guided High-Resolution Matrix-Assisted Laser Desorption Ionization Mass Spectrometry Imaging

Mass spectrometry imaging (MSI) is widely used for clinical research because when combined with histopathological analysis the molecular signatures of specific cells/regions can be extracted from the often-complex histologies of pathological tissues. The ability of MSI to stratify patients according to disease, prognosis, and response is directly attributable to this cellular specificity. MSI developments are increasingly focused on further improving specificity, through higher spatial resolution to better localize the signals or higher mass resolution to better resolve molecular ions. Higher spatial/mass resolution leads to increased data size and longer data acquisition times. For clinical applications, which analyze large series of patient tissues, this poses a challenge to keep data load and acquisition time manageable. Here we report a new tool to perform histology guided MSI; instead of analyzing large parts of each tissue section the histology from adjacent tissue sections is used to focus the analysis on the areas of interest, e.g., comparable cell types in different patient tissues, thereby minimizing data acquisition time and data load. The histology tissue section is annotated and then automatically registered to the MSI-prepared tissue section; the registration transformation is then applied to the annotations, enabling them to be used to define the MSI measurement regions. Using a series of formalin-fixed, paraffin-embedded human myxoid liposarcoma tissues, we demonstrate an 80% reduction of data load and acquisition time, thereby enabling high resolution (mass or spatial) to be more readily applied to clinical research. The software is freely available for download

Comprehensive Analysis of the Mouse Brain Proteome Sampled in Mass Spectrometry Imaging

On-tissue enzymatic digestion is performed in mass spectrometry imaging (MSI) experiments to access larger proteins and to assign protein identities. Most on-tissue digestion MSI studies have focused on method development rather than identifying the molecular features observed. Herein, we report a comprehensive study of the mouse brain proteome sampled by MSI. Using complementary proteases, we were able to identify 5337 peptides in the matrix-assisted laser desorption/ionization (MALDI) matrix, corresponding to 1198 proteins. 630 of these peptides, corresponding to 280 proteins, could be assigned to peaks in MSI data sets. Gene ontology and pathway analyses revealed that many of the proteins are involved in neurodegenerative disorders, such as Alzheimer’s, Parkinson’s, and Huntington’s disease

Design and Performance of a Novel Interface for Combined Matrix-Assisted Laser Desorption Ionization at Elevated Pressure and Electrospray Ionization with Orbitrap Mass Spectrometry

Matrix-Assisted Laser Desorption Ionization, MALDI, has been increasingly used in a variety of biomedical applications, including tissue imaging of clinical tissue samples, and in drug discovery and development. These studies strongly depend on the performance of the analytical instrumentation and would drastically benefit from improved sensitivity, reproducibility, and mass/spatial resolution. In this work, we report on a novel combined MALDI/ESI interface, which was coupled to different Orbitrap mass spectrometers (Elite and Q Exactive Plus) and extensively characterized with peptide and protein standards, and in tissue imaging experiments. In our approach, MALDI is performed in the elevated pressure regime (5–8 Torr) at a spatial resolution of 15–30 μm, while ESI-generated ions are injected orthogonally to the interface axis. We have found that introduction of the MALDI-generated ions into an electrodynamic dual-funnel interface results in increased sensitivity characterized by a limit of detection of ∼400 zmol, while providing a mass measurement accuracy of 1 ppm and a mass resolving power of 120 000 in analysis of protein digests. In tissue imaging experiments, the MALDI/ESI interface has been employed in experiments with rat brain sections and was shown to be capable of visualizing and spatially characterizing very low abundance analytes separated only by 20 mDa. Comparison of imaging data has revealed excellent agreement between the MALDI and histological images

Automatic Registration of Mass Spectrometry Imaging Data Sets to the Allen Brain Atlas

Mass spectrometry imaging holds great potential for understanding the molecular basis of neurological disease. Several key studies have demonstrated its ability to uncover disease-related biomolecular changes in rodent models of disease, even if highly localized or invisible to established histological methods. The high analytical reproducibility necessary for the biomedical application of mass spectrometry imaging means it is widely developed in mass spectrometry laboratories. However, many lack the expertise to correctly annotate the complex anatomy of brain tissue, or have the capacity to analyze the number of animals required in preclinical studies, especially considering the significant variability in sizes of brain regions. To address this issue, we have developed a pipeline to automatically map mass spectrometry imaging data sets of mouse brains to the Allen Brain Reference Atlas, which contains publically available data combining gene expression with brain anatomical locations. Our pipeline enables facile and rapid interanimal comparisons by first testing if each animal’s tissue section was sampled at a similar location and enabling the extraction of the biomolecular signatures from specific brain regions

Automatic Generic Registration of Mass Spectrometry Imaging Data to Histology Using Nonlinear Stochastic Embedding

The combination of mass spectrometry imaging and histology has proven a powerful approach for obtaining molecular signatures from specific cells/tissues of interest, whether to identify biomolecular changes associated with specific histopathological entities or to determine the amount of a drug in specific organs/compartments. Currently there is no software that is able to explicitly register mass spectrometry imaging data spanning different ionization techniques or mass analyzers. Accordingly, the full capabilities of mass spectrometry imaging are at present underexploited. Here we present a fully automated generic approach for registering mass spectrometry imaging data to histology and demonstrate its capabilities for multiple mass analyzers, multiple ionization sources, and multiple tissue types