<ol><li>Fu, Q., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Highly+Reproducible+Automated+Proteomics+Sample+Preparation+Workflow+for+Quantitative+Mass+Spectrometry%5BTitle%5D)+AND+%22Journal+of+Proteome+Research%22%5BJournal%5D)">Highly Reproducible Automated Proteomics Sample Preparation Workflow for Quantitative Mass Spectrometry.</a> Journal of Proteome Research. 17 (1), 420-428 (2018).</li>
<li>Lehmann, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Clinical+mass+spectrometry+proteomics+%28cMSP%29+for+medical+laboratory%3A+What+does+the+future+hold%3F%5BTitle%5D)+AND+%22Clinica+Chimica+Acta%22%5BJournal%5D)">Clinical mass spectrometry proteomics (cMSP) for medical laboratory: What does the future hold?</a> Clinica Chimica Acta. 467, 51-58 (2017).</li>
<li>Abbatiello, S. E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Large-Scale+Interlaboratory+Study+to+Develop%2C+Analytically+Validate+and+Apply+Highly+Multiplexed%2C+Quantitative+Peptide+Assays+to+Measure+Cancer-Relevant+Proteins+in+Plasma%5BTitle%5D)+AND+%22Molecular+%26+Cellular+Proteomics%22%5BJournal%5D)">Large-Scale Interlaboratory Study to Develop, Analytically Validate and Apply Highly Multiplexed, Quantitative Peptide Assays to Measure Cancer-Relevant Proteins in Plasma.</a> Molecular & Cellular Proteomics. 14 (9), 2357-2374 (2015).</li>
<li>Kuster, B., Schirle, M., Mallick, P., Aebersold, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Scoring+proteomes+with+proteotypic+peptide+probes%5BTitle%5D)+AND+%22Nature+Reviews+Molecular+Cell+Biology%22%5BJournal%5D)">Scoring proteomes with proteotypic peptide probes.</a> Nature Reviews Molecular Cell Biology. 6 (7), 577-583 (2005).</li>
<li>Carr, S. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Targeted+peptide+measurements+in+biology+and+medicine%3A+best+practices+for+mass+spectrometry-based+assay+development+using+a+fit-for-purpose+approach%5BTitle%5D)+AND+%22Molecular+%26+Cellular+Proteomics%22%5BJournal%5D)">Targeted peptide measurements in biology and medicine: best practices for mass spectrometry-based assay development using a fit-for-purpose approach.</a> Molecular & Cellular Proteomics. 13 (3), 907-917 (2014).</li>
<li>Yates, J. R. 3rd <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Pivotal+role+of+computers+and+software+in+mass+spectrometry+-+SEQUEST+and+20+years+of+tandem+MS+database+searching%5BTitle%5D)+AND+%22Journal+of+the+American+Society+for+Mass+Spectrometry%22%5BJournal%5D)">Pivotal role of computers and software in mass spectrometry - SEQUEST and 20 years of tandem MS database searching.</a> Journal of the American Society for Mass Spectrometry. 26 (11), 1804-1813 (2015).</li>
<li>Nilsson, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Mass+spectrometry+in+high-throughput+proteomics%3A+ready+for+the+big+time%5BTitle%5D)+AND+%22Nature+Methods%22%5BJournal%5D)">Mass spectrometry in high-throughput proteomics: ready for the big time.</a> Nature Methods. 7 (9), 681-685 (2010).</li>
<li>Van Eyk, J. E., Sobhani, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Precision+Medicine%5BTitle%5D)+AND+%22Circulation%22%5BJournal%5D)">Precision Medicine.</a> Circulation. 138 (20), 2172-2174 (2018).</li>
<li>Hoofnagle, A. N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Recommendations+for+the+Generation%2C+Quantification%2C+Storage%2C+and+Handling+of+Peptides+Used+for+Mass+Spectrometry-Based+Assays%5BTitle%5D)+AND+%22Clinical+Chemistry%22%5BJournal%5D)">Recommendations for the Generation, Quantification, Storage, and Handling of Peptides Used for Mass Spectrometry-Based Assays.</a> Clinical Chemistry. 62 (1), 48-69 (2016).</li>
<li>Wijayawardena, B., Fu, Q., Johnson, C., Van Eyk, J. E., Kowalski, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Highly+Reproducible+Automated+Proteomics+Sample+Preparation+on+Biomek+i-Series%5BTitle%5D)+AND+%22Technical+note%2C+Beckman+Coulter+Life+Science.+Document+%23+AAG-4890APP02%22%5BJournal%5D)">Highly Reproducible Automated Proteomics Sample Preparation on Biomek i-Series.</a> Technical note, Beckman Coulter Life Science. Document # AAG-4890APP02. 19, (2019).</li>
</ol>

Sample processing for mass spectrometry requires protein denaturation, reduction and alkylation to block cysteines, and trypsin digestion to cleave proteins into peptides. Each chemical or enzymatic reaction needs to be initiated at a designated time and performed at a controlled temperature, and every step in the process involves multiple liquid transfer steps where experimental variation can be introduced. Automated sample processing provides a solution to this dilemma. Presently available liquid handling systems have the capability to transfer reagents into 96-well plates with an accuracy and precision of less than 5%, depending on the head and tip type used, and to incubate samples, with shaking if desired, under controlled temperatures ranging from 14 &#176;C to 70 &#176;C. We used an automated liquid handler to process plasma for SRM assays in a 96-well format.
There are many thousands of protease cleavage sites within the complex mixtures of proteins in serum, plasma, and other biological samples; and each of these proteins has unique properties affecting cleavage site accessibility and the stability of the resulting peptides. It is, therefore, impossible to design a sample processing procedure that is optimal for every protein. The best alternative is to be as consistent as possible.
To achieve consistency, we optimized each pipetting step performed by the automated liquid handler. We first considered the volume required and constraints imposed by the liquid&#8217;s type (plasma, aqueous, or organic) and corresponding properties (viscosity, cohesion, and volatility), hardware (workstation pipetting-head and plate-grabbing arms), and labware. We then varied the speed and trailing air gap for aspiration, the speed and blowout volume for dispensing, and the force and duration of mixing, while incorporating a tip touch after aspiration and/or dispensing, if needed, to eliminate liquid adhering to the outside of the tips (Figure 13, Supplemental Table 1). A unique SIL peptide, pre-screened for stability, was spiked into each reagent to make it possible to monitor the precision of each liquid handling step. After optimization, the process CVs for the majority of the transitions were less than 10%, demonstrating good reproducibility with the automated workstation (Figure 9, Figure 10, Figure 11 and Figure 12).
The automated workflow presented here provides for consistent enzymatic digestion with improved reproducibility and throughput compared to manual methods (Figure 9, Figure 10). This approach promises to improve the accuracy and reliability of biomarker discovery and validation by mass spectrometry.

Mass spectrometry (MS) -based protein and peptide quantification is increasingly being applied as a bioanalytical tool for plasma analysis in basic research and clinical laboratories2,3. The requisite instrumentation and informatics have advanced rapidly as MS has become the method of choice for quantifying proteins or modified proteins by targeting specific peptide sequences due to its ability to quantify hundreds of peptides in a single MS run4. Sample preparation serves as the foundation for any proteomic analysis. Prior to MS analysis, the proteins in a biological sample are typically denatured, reduced, alkylated, digested into tryptic peptides, and desalted5. Alkylation blocks cysteines to prevent uncontrolled modifications and ensure all cysteines have the same mass. Next, trypsin is added to digest proteins into peptides. Each of these steps requires optimization, and the entire process is traditionally performed manually, allowing for the introduction of analytical errors.
The traditional biomarker development pipeline consists of two main processes: shotgun proteomics for global protein discovery to create an in-depth protein inventory6 and targeted proteomics for verification and validation aimed at high-precision and high-throughput protein quantitation7. Regardless of the MS approach, sample preparation is the same and centers on enzymatic cleavage of proteins into a peptide mixture. As proposed by Van Eyk and Sobhani8, having a method that allows for accurate, precise, and reproducible analysis for both discovery and targeted assays is desired to effectively move discovery biomarkers to clinical implemented assays. To do this, automation of sample preparation will be helpful and provide the ability to increase the efficiency to high-throughput analysis. Manual methods typically introduce analytical errors beyond acceptable limits specified by the Food and Drug Administration (FDA) in the revised Bioanalytical Method Validation Guidance, which includes biomarkers and diagnostics2,9. The development of a fast, highly accurate, and hands-free MS protein sample preparation workflow will be necessary to facilitate biomarker research, which requires preparing and analyzing thousands of biological samples. MS sample preparation has many steps where errors can be introduced, and the process is tedious and time-consuming.
In our improved method, an automated workstation was programed to perform all necessary plasma sample preparation procedures in a total of 6 steps (Figure 1) to ensure: 1) accurate liquid transfers; 2) the reaction is initiated and stopped at a consistent time; 3) the reaction is performed at a controlled temperature (i.e., incubator); and 4) the reaction has uniform mixing for all reactions. We also implemented adding exogenous quality control proteins and internal standards (stable isotope-labeled peptide standards) to ensure a quality and reproducible throughput of LC-MS-based protein and peptide quantification in a 96-well format. Including reagent preparation, hours of work time and a total of 1,000,000 pipetting steps would be required to process 96 samples in individual tubes. Automation reduces the hands-on time and the number of human interactions involved.

<table><tbody><tr><td>A Pooled healthy human plasma</td><td>Bioreclamation Inc.</td><td></td><td>human plasma tested in the manuscript</td></tr><tr><td>Acetonitrile, HPLC Grade</td><td>Thermo Fisher Scientific</td><td>A998SK1</td><td>LC MS/MS solvent</td></tr><tr><td>B-Galactosidase Recombinant from E.Coli</td><td>Sigma-Aldrich</td><td>G3153-5MG</td><td>exogenous control proteins.</td></tr><tr><td>Biomek i7 Automated Workstation</td><td>Beckman Coulter, Inc.</td><td></td><td>The proteomic sample preparation workstation: Biomek automated workstations are not intended or validated for use in the diagnosis of disease or other conditions</td></tr><tr><td>Biomek i-Series Tips 230&amp;micro;L Non-Sterile</td><td>Beckman Coulter, Inc.</td><td>B85903&amp;nbsp;</td><td>Tips, i7 consumable</td></tr><tr><td>Biomek i-Series Tips 90&amp;micro;L Non-Sterile</td><td>Beckman Coulter, Inc.</td><td>B85881&amp;nbsp;</td><td>Tips, i7 consumable</td></tr><tr><td>FG, Kit Trypsin TPCK</td><td>Sciex</td><td>4445250</td><td>Trypsin used in digestion</td></tr><tr><td>Hard-Shell 96-Well PCR Plates, low profile, thin wall, skirted, green/clear&amp;nbsp;</td><td>Bio-Rad</td><td>#hsp9641</td><td>Autosampler plate</td></tr><tr><td>Octyl B-D-Glucopyranoside</td><td>Sigma-Aldrich</td><td>O9882-5G</td><td>detergent used in digestion</td></tr><tr><td>Polypropylene, 96-Round Deep Well Plates Sterile</td><td>Beckman Coulter, Inc.</td><td>267007</td><td>Reagent and digestion plate</td></tr><tr><td>Prominence UFLCXR HPLC system</td><td>Shimadzu, Japan</td><td></td><td>High flow LC sytem</td></tr><tr><td>QTRAP 6500</td><td>SCIEX</td><td></td><td>Mass spectrometer</td></tr><tr><td>Water, HPLC Grade</td><td>Thermo Fisher Scientific</td><td>W54</td><td>LC MS/MS solvent</td></tr><tr><td>Xbridge BEH30 C18 2.1mm x 100mm</td><td>Waters</td><td>186003564</td><td>LC MSMS column</td></tr></tbody></table>

a plasma sample preparation for mass spectrometry using an automated workstation

Sample preparation for mass spectrometry analysis in proteomics requires enzymatic cleavage of proteins into a peptide mixture. This process involves numerous incubation and liquid transfer steps in order to achieve denaturation, reduction, alkylation, and cleavage. Adapting this workflow onto an automated workstation can increase efficiency and reduce coefficients of variance, thereby providing more reliable data for statistical comparisons between sample types. We previously described an automated proteomic sample preparation workflow1. Here, we report the development of a more efficient and better controlled workflow with the following advantages: 1) The number of liquid transfer steps is reduced from nine to six by combining reagents; 2) Pipetting time is reduced by selective tip pipetting using a 96-position pipetting head with multiple channels; 3) Potential throughput is increased by the availability of up to 45 deck positions; 4) Complete enclosure of the system provides improved temperature and environmental control and reduces the potential for contamination of samples or reagents; and 5) The addition of stable isotope labeled peptides, as well as &#946;-galactosidase protein, to each sample makes monitoring and quality control possible throughout the entire process. These hardware and process improvements provide good reproducibility and improve intra-assay and inter-assay precision (CV of less than 20%) for LC-MS based protein and peptide quantification. The entire workflow for digesting 96 samples in a 96-well plate can be completed in approximately 5 hours.

Watch this Scientific Journal Video about A Plasma Sample Preparation for Mass Spectrometry using an Automated Workstation at JoVE.com

A Plasma Sample Preparation for Mass Spectrometry using an Automated Workstation

Here, we present an automated plasma protein digestion method for mass spectrometry-based quantitative proteomic analysis. In this protocol, the liquid transferring and incubation steps for protein denaturation, reduction, alkylation, and trypsin digestion reactions are streamlined and automated. It takes approximately five hours to prepare a 96-well plate with desired precision.

Sample preparation for mass spectrometry analysis in proteomics requires enzymatic cleavage of proteins into a peptide mixture. This process involves ...

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JoVE Journal

Biochemistry

10.0K Views.  Cedars Sinai Medical Center. Here, we present an automated plasma protein digestion method for mass spectrometry-based quantitative proteomic analysis. In this protocol, the liquid transferring and incubation steps for protein denaturation, reduction, alkylation, and trypsin digestion reactions are streamlined and automated. It takes approximately five hours to prepare a 96-well plate with desired precision.

Video: A Plasma Sample Preparation for Mass Spectrometry using an Automated Workstation

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS)

Non-targeted metabolite profiling by ultra performance liquid chromatography coupled with mass spectrometry (UPLC-MS) is a powerful technique to investigate metabolism. The approach offers an unbiased and in-depth analysis that can enable the development of diagnostic tests, novel therapies, and further our understanding of disease processes. The inherent chemical diversity of the metabolome creates significant analytical challenges and there is no single experimental approach that can detect all metabolites. Additionally, the biological variation in individual metabolism and the dependence of metabolism on environmental factors necessitates large sample numbers to achieve the appropriate statistical power required for meaningful biological interpretation. To address these challenges, this tutorial outlines an analytical workflow for large scale non-targeted metabolite profiling of serum by UPLC-MS. The procedure includes guidelines for sample organization and preparation, data acquisition, quality control, and metabolite identification and will enable reliable acquisition of data for large experiments and provide a starting point for laboratories new to non-targeted metabolite profiling by UPLC-MS.

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS

Non-targeted metabolite profiling by ultra performance liquid chromatography coupled with mass spectrometry (UPLC-MS) is a powerful technique to investigate metabolism. This article outlines a typical workflow utilized for non-targeted metabolite profiling of serum including sample organization and preparation, data acquisition, data analysis, quality control, and metabolite identification.

Non-targeted metabolite profiling by ultra performance liquid chromatography coupled with mass spectrometry (UPLC-MS) is a powerful technique to ...

Chemistry

Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry (UPLC-HRMS)

Here we present a workflow to analyze the metabolic profiles for biological samples of interest including; cells, serum, or tissue. The sample is first separated into polar and non-polar fractions by a liquid-liquid phase extraction, and partially purified to facilitate downstream analysis. Both aqueous (polar metabolites) and organic (non-polar metabolites) phases of the initial extraction are processed to survey a broad range of metabolites. Metabolites are separated by different liquid chromatography methods based upon their partition properties. In this method, we present microflow ultra-performance (UP)LC methods, but the protocol is scalable to higher flows and lower pressures. Introduction into the mass spectrometer can be through either general or compound optimized source conditions. Detection of a broad range of ions is carried out in full scan mode in both positive and negative mode over a broad m/z range using high resolution on a recently calibrated instrument. Label-free differential analysis is carried out on bioinformatics platforms. Applications of this approach include metabolic pathway screening, biomarker discovery, and drug development.

Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry UPLC-HRMS

Untargeted metabolomics provides a hypothesis generating snapshot of a metabolic profile. This protocol will demonstrate the extraction and analysis of metabolites from cells, serum, or tissue. A range of metabolites are surveyed using liquid-liquid phase extraction, microflow ultraperformance liquid chromatography/high-resolution mass spectrometry (UPLC-HRMS) coupled to differential analysis software.

Here we present a workflow to analyze the metabolic profiles for biological samples of interest including; cells, serum, or tissue. The sample is first ...

Optimal Preparation of Formalin Fixed Samples for Peptide Based Matrix Assisted Laser Desorption/Ionization Mass Spectrometry Imaging Workflows

The use of matrix-assisted laser desorption/ionization, mass spectrometry imaging (MALDI MSI) has rapidly expanded, since this technique analyzes a host of biomolecules from drugs and lipids to N-glycans. Although various sample preparation techniques exist, detecting peptides from formaldehyde preserved tissues remains one of the most difficult challenges for this type of mass spectrometric analysis. For this reason, we have created and optimized a robust methodology that preserves the spatial information contained within the sample, while eliciting the greatest number of ionizable peptides. We have also aimed to achieve this in a cost effective and simple way, thereby eliminating potential bias or preparation error, which can occur when using automated instrumentation. The end result is a reproducible and inexpensive protocol.

This protocol describes a reproducible and reliable method for the sublimation-based preparation of formalin fixed tissue destined for imaging mass spectrometry.

The use of matrix-assisted laser desorption/ionization, mass spectrometry imaging (MALDI MSI) has rapidly expanded, since this technique analyzes a host ...

Sample Preparation for Probe Electrospray Ionization Mass Spectrometry

Mass spectrometry (MS) is a powerful tool in analytical chemistry because it provides very accurate information about molecules, such as mass-to-charge ratios (m/z), which are useful to deduce molecular weights and structures. While it is essentially a destructive analytical method, recent advancements in the ambient ionization technique have enabled us to acquire data while leaving tissue in a relatively intact state in terms of integrity. Probe electrospray ionization (PESI) is a so-called direct method because it does not require complex and time-consuming pretreatment of samples. A fine needle serves as a sample picker, as well as an ionization emitter. Based on the very sharp and fine property of the probe tip, destruction of the samples is minimal, allowing us to acquire the real-time molecular information from living things in situ. Herein, we introduce three applications of PESI-MS technique that will be useful for biomedical research and development. One involves the application to solid tissue, which is the basic application of this technique for the medical diagnosis. As this technique requires only 10 mg of the sample, it may be very useful in the routine clinical settings. The second application is for in vitro medical diagnostics where human blood serum is measured. The ability to measure fluid samples is also valuable in various biological experiments where a sufficient volume of sample for conventional analytical techniques cannot be provided. The third application leans toward the direct application of probe needles in living animals, where we can obtain real-time dynamics of metabolites or drugs in specific organs. In each application, we can infer the molecules that have been detected by MS or use artificial intelligence to obtain a medical diagnosis.

This article introduces sample preparation methods for a unique real-time analytical method based on the ambient mass spectrometry. This method lets us perform real-time analysis of the biological molecules in vivo without any special pretreatments.

Mass spectrometry (MS) is a powerful tool in analytical chemistry because it provides very accurate information about molecules, such as mass-to-charge ...

Preparation of Human Tissues Embedded in Optimal Cutting Temperature Compound for Mass Spectrometry Analysis

Sphingolipids are cellular components that have well-established roles in human metabolism and disease. Mass spectrometry can be used to determine whether sphingolipids are altered in a disease and investigate whether sphingolipids can be targeted clinically. However, properly powered prospective studies that acquire tissues directly from the surgical suite can be time consuming, and technically, logistically, and administratively challenging. In contrast, retrospective studies can take advantage of cryopreserved human specimens already available, usually in large numbers, at tissue biorepositories. Other advantages of procuring tissues from biorepositories include access to information associated with the tissue specimens including histology, pathology, and in some instances clinicopathological variables, all of which can be used to examine correlations with lipidomics data. However, technical limitations related to the incompatibility of optimal cutting temperature compound (OCT) used in the cryopreservation and mass spectrometry is a technical barrier for the analysis of lipids. However, we have previously shown that OCT can be easily removed from human biorepository specimens through cycles of washes and centrifugation without altering their sphingolipid content. We have also previously established that sphingolipids in human tissues cryopreserved in OCT are stable for up to 16 years. In this report, we outline the steps and workflow to analyze sphingolipids in human tissue specimens that are embedded in OCT, including washing tissues, weighing tissues for data normalization, the extraction of lipids, preparation of samples for analysis by liquid chromatography electrospray ionization tandem mass spectrometry (LC-ESI-MS/MS), mass spectrometry data integration, data normalization, and data analysis.

Sphingolipids are bioactive metabolites with well-established roles in human disease. Characterizing alterations in tissues with mass-spectrometry can reveal roles in disease etiology or identify therapeutic targets. However, the OCT-compound used for cryopreservation in biorepositories interferes with mass-spectrometry. We outline methods to analyze sphingolipids in human tissues embedded in OCT with LC-ESI-MS/MS.

Sphingolipids are cellular components that have well-established roles in human metabolism and disease. Mass spectrometry can be used to determine whether ...

Cancer Research

Digital Microfluidics for Automated Proteomic Processing

Clinical proteomics has emerged as an important new discipline, promising the discovery of biomarkers that will be useful for early diagnosis and prognosis of disease. While clinical proteomic methods vary widely, a common characteristic is the need for (i) extraction of proteins from extremely heterogeneous fluids (i.e. serum, whole blood, etc.) and (ii) extensive biochemical processing prior to analysis. Here, we report a new digital microfluidics (DMF) based method integrating several processing steps used in clinical proteomics. This includes protein extraction, resolubilization, reduction, alkylation and enzymatic digestion. Digital microfluidics is a microscale fluid-handling technique in which nanoliter-microliter sized droplets are manipulated on an open surface. Droplets are positioned on top of an array of electrodes that are coated by a dielectric layer - when an electrical potential is applied to the droplet, charges accumulate on either side of the dielectric. The charges serve as electrostatic handles that can be used to control droplet position, and by biasing a sequence of electrodes in series, droplets can be made to dispense, move, merge, mix, and split on the surface. Therefore, DMF is a natural fit for carrying rapid, sequential, multistep, miniaturized automated biochemical assays. This represents a significant advance over conventional methods (relying on manual pipetting or robots), and has the potential to be a useful new tool in clinical proteomics.
Mais J. Jebrail, Vivienne N. Luk, and Steve C. C. Shih contributed equally to this work.
Sergio L. S. Freire's current address is at the University of the Sciences in Philadelphia located at 600 South 43rd Street, Philadelphia, PA 19104.

Digital Microfluidics is a technique characterized by the manipulation of discrete droplets (~nL - mL) on an array of electrodes by the application of electrical fields. It is well-suited for carrying out rapid, sequential, miniaturized automated biochemical assays. Here, we report a platform capable of automating several proteomic processing steps.

Clinical proteomics has emerged as an important new discipline, promising the discovery of biomarkers that will be useful for early diagnosis and ...

Biology

Semi-Automated Phenotypic Analysis of Functional 3D Spheroid Cell Cultures

We present a protocol that describes the properties and advantages of using a standalone clinostat incubator for growing, treating, and monitoring 3D cell cultures. The clinostat mimics an environment where cells can assemble as highly reproducible spheroids with low shear forces and active nutrient diffusion. We demonstrate that both cancer and non-cancer hepatocytes (HepG2/C3A and THLE-3 cell lines) require 3 weeks of growth prior to achieving functionalities comparable to liver cells. This protocol highlights the convenience of utilizing incubators for 3D cells with cameras monitoring the cell growth, as snapshots can be taken to count and measure spheroids upon treatment. We describe the comparison of THLE-3 and HepG2/C3A cell lines, showing how non-cancerous cell lines can be grown as well as immortalized cancer cells. We demonstrate and illustrate how proteomics experiments can be conducted from a few spheroids, which can be collected without perturbing cell signaling, i.e., no trypsinization required. We show that proteomics analysis can be used to monitor the typical liver phenotype of respiratory chain metabolism and the production of proteins involved in metal detoxification and describe a semi-automated system to count and measure the spheroid's area. Altogether, the protocol presents a toolbox that comprises a phenotypic characterization via image capture and a proteomics pipeline to experiment on 3D cell culture models.

We present a protocol for growing high-reproducible spheroids and their phenotypic characterization using image capture and proteomics.

We present a protocol that describes the properties and advantages of using a standalone clinostat incubator for growing, treating, and monitoring 3D cell ...

A Streamlined Approach for Mass Spectrometry-Based Proteomics Using Selected Tissue Regions

Mass spectrometry (MS)-based proteomics enables comprehensive proteome analysis across a wide range of biological samples, including cells, tissues, and body fluids. Formalin-fixed, paraffin-embedded (FFPE) tissue sections, commonly used for long-term archiving, have emerged as valuable resources for proteomic studies. Beyond their storage benefits, researchers can isolate regions of interest (ROIs) from normal tissue regions through collaborative efforts with pathologists. Despite this potential, a streamlined approach for proteomic experiments encompassing ROI isolation, proteomic sample preparation, and MS analysis remains lacking. In this protocol, an integrated workflow that combines macrodissection of ROIs, suspension trapping-based sample preparation, and high-throughput MS analysis is presented. Through this approach, the ROIs of patients' FFPE tissues, consisting of benign serous cystic neoplasms (SCN) and precancerous intraductal papillary mucinous neoplasms (IPMN) diagnosed by pathologists, were macrodissected, collected, and analyzed, resulting in high proteome coverage. Furthermore, molecular differences between the two distinct pancreatic cystic neoplasms were successfully identified, thus demonstrating the applicability of this approach for advancing proteomic research with FFPE tissues.

Here, we establish a mass spectrometry-based proteomic method using isolated regions of interest in formalin-fixed, paraffin-embedded tissue sections. This protocol is used to analyze proteome from specific tissue areas in archived formalin-fixed, paraffin-embedded tissue sections.

Mass spectrometry (MS)-based proteomics enables comprehensive proteome analysis across a wide range of biological samples, including cells, tissues, and ...

Sample Preparation for Mass-spectrometry-based Proteomics Analysis of Ocular Microvessels

The use of isolated ocular blood vessels in vitro to decipher the pathophysiological state of the eye using advanced technological approaches has greatly expanded our understanding of certain diseases. Mass spectrometry (MS)-based proteomics has emerged as a powerful tool to unravel alterations in the molecular mechanisms and protein signaling pathways in the vascular beds in health and disease. However, sample preparation steps prior to MS analyses are crucial to obtain reproducible results and in-depth elucidation of the complex proteome. This is particularly important for preparation of ocular microvessels, where the amount of sample available for analyses is often limited and thus, poses a challenge for optimum protein extraction. This article endeavors to provide an efficient, rapid and robust protocol for sample preparation from an exemplary retrobulbar ocular vascular bed employing the porcine short posterior ciliary arteries. The present method focuses on protein extraction procedures from both the supernatant and pellet of the sample following homogenization, sample cleaning with centrifugal filter devices prior to one-dimensional gel electrophoresis and peptide purification steps for label-free quantification in a liquid chromatography-electrospray ionization-linear ion trap-Orbitrap MS system. Although this method has been developed specifically for proteomics analyses of ocular microvessels, we have also provided convincing evidence that it can also be readily employed for other tissue-based samples.

Proteome characterization of ocular microvascular beds is pivotal for in-depth understanding of many ocular pathologies in humans. This study demonstrates an effective, rapid, and robust method for protein extraction and sample preparation from small blood vessels employing the porcine short posterior ciliary arteries as model vessels for mass-spectrometry-based proteomics analyses.

The use of isolated ocular blood vessels in vitro to decipher the pathophysiological state of the eye using advanced technological approaches has greatly ...

Comprehensive Workflow of Mass Spectrometry-based Shotgun Proteomics of Tissue Samples

Recent advances in mass spectrometry have resulted in deep proteomic&#160;analysis along with the generation of&#160;robust and&#160;reproducible datasets. However, despite the considerable technical advancements, sample preparation from biospecimens such as patient blood, CSF, and tissue still poses considerable challenges. For identifying biomarkers, tissue proteomics often provides an attractive sample source to translate the research findings from the bench to the clinic. It can reveal potential candidate biomarkers for early diagnosis of cancer and neurodegenerative diseases such as Alzheimer&#39;s disease, Parkinson&#39;s disease, etc. Tissue proteomics also yields a wealth of systemic information based on the abundance of proteins and helps to address interesting biological questions.
Quantitative proteomics analysis can be grouped into two broad categories: a label-based and a label-free approach. In the label-based approach, proteins or peptides are labeled using stable isotopes such as SILAC (stable isotope labeling with amino acids in cell culture) or by chemical tags such as ICAT (isotope-coded affinity tags), TMT (tandem mass tag) or iTRAQ (isobaric tag for relative and absolute quantitation). Label-based approaches have the advantage of more accurate quantitation of proteins and using isobaric labels, multiple samples can be analyzed in a single experiment. The label-free approach provides a cost-effective alternative to label-based approaches. Hundreds of patient samples belonging to a particular cohort can be analyzed and compared with other cohorts based on clinical features. Here, we have described an optimized quantitative proteomics workflow for tissue samples using label-free and label-based proteome profiling methods, which is crucial for applications in life sciences, especially biomarker discovery-based projects.

The described protocol provides an optimized quantitative proteomics analysis of tissue samples using two approaches: label-based and label free quantitation. Label-based approaches have the advantage of more accurate quantitation of proteins, while a label-free approach is more cost-effective and used to analyze hundreds of samples of a cohort.