MicroPOTS Analysis of Barrett's Esophageal Cell Line Models Identifies Proteomic Changes after Physiologic and Radiation Stress

Moving from macroscale preparative systems in proteomics to micro- and nanotechnologies offers researchers the ability to deeply profile smaller numbers of cells that are more likely to be encountered in clinical settings. Herein a recently developed microscale proteomic method, microdroplet processing in one pot for trace samples (microPOTS), was employed to identify proteomic changes in ∼200 Barrett’s esophageal cells following physiologic and radiation stress exposure. From this small population of cells, microPOTS confidently identified >1500 protein groups, and achieved a high reproducibility with a Pearson’s correlation coefficient value of R > 0.9 and over 50% protein overlap from replicates. A Barrett’s cell line model treated with either lithocholic acid (LCA) or X-ray had 21 (e.g., ASNS, RALY, FAM120A, UBE2M, IDH1, ESD) and 32 (e.g., GLUL, CALU, SH3BGRL3, S100A9, FKBP3, AGR2) overexpressed proteins, respectively, compared to the untreated set. These results demonstrate the ability of microPOTS to routinely identify and quantify differentially expressed proteins from limited numbers of cells.

Sensitive Top-Down Proteomics Analysis of a Low Number of Mammalian Cells Using a Nanodroplet Sample Processing Platform

Top-down proteomics is a powerful tool for characterizing genetic variations and post-translational modifications at intact protein level. However, one significant technical gap of top-down proteomics is the inability to analyze a low amount of biological samples, which limits its access to isolated rare cells, fine needle aspiration biopsies, and tissue substructures. Herein, we developed an ultrasensitive top-down platform by incorporating a microfluidic sample preparation system, termed nanoPOTS (nanodroplet processing in one pot for trace samples), into a top-down proteomic workflow. A unique combination of a nonionic detergent dodecyl-β-d-maltopyranoside (DDM) with urea as protein extraction buffer significantly improved both protein extraction efficiency and sample recovery. We hypothesize that the DDM detergent improves protein recovery by efficiently reducing nonspecific adsorption of intact proteins on container surfaces, while urea serves as a strong denaturant to disrupt noncovalent complexes and release intact proteins for downstream analysis. The nanoPOTS-based top-down platform reproducibly and quantitatively identified ∼170 to ∼620 proteoforms from ∼70 to ∼770 HeLa cells containing ∼10 to ∼115 ng of total protein. A variety of post-translational modifications including acetylation, myristoylation, and iron binding were identified using only less than 800 cells. We anticipate the nanoPOTS top-down proteomics platform will be broadly applicable in biomedical research, particularly where clinical specimens are not available in amounts amenable to standard workflows.

Proteome analysis of tissues by mass spectrometry

Tissues and biofluids are important sources of information used for the detection of diseases and decisions on patient therapies. There are several accepted methods for preservation of tissues, among which the most popular are fresh-frozen and formalin-fixed paraffin embedded methods. Depending on the preservation method and the amount of sample available, various specific protocols are available for tissue processing for subsequent proteomic analysis. Protocols are tailored to answer various biological questions, and as such vary in lysis and digestion conditions, as well as duration. The existence of diverse tissue-sample protocols has led to confusion in how to choose the best protocol for a given tissue and made it difficult to compare results across sample types. Here, we summarize procedures used for tissue processing for subsequent bottom-up proteomic analysis. Furthermore, we compare protocols for their variations in the composition of lysis buffers, digestion procedures, and purification steps. For example, reports have shown that lysis buffer composition plays an important role in the profile of extracted proteins: the most common are tris(hydroxymethyl)aminomethane, radioimmunoprecipitation assay, and ammonium bicarbonate buffers. Although, trypsin is the most commonly used enzyme for proteolysis, in some protocols it is supplemented with Lys-C and/or chymotrypsin, which will often lead to an increase in proteome coverage. Data show that the selection of the lysis procedure might need to be tissue-specific to produce distinct protocols for individual tissue types. Finally, selection of the procedures is also influenced by the amount of sample available, which range from biopsies or the size of a few dozen of mm² obtained with laser capture microdissection to much larger amounts that weight several milligrams.

Membrane Solubilization by Styrene-Maleic Acid Copolymers: Delineating the Role of Polymer Length

Styrene-maleic acid (SMA) copolymers have attracted interest in membrane research because they allow the solubilization and purification of membrane-spanning proteins from biological membranes in the form of native-like nanodisks. However, our understanding of the underlying SMA-lipid interactions is hampered by the fact that SMA preparations are very polydisperse. Here, we obtained fractions of the two most commonly used SMA preparations: SMA 2:1 and SMA 3:1 (both with specified Mw ∼10 kD), with different number-average molecular weight (Mn) and styrene content. The fractionation is based on the differential solubility of styrene-maleic anhydride (SMAnh) in hexane and acetone mixtures. SMAnh fractions were hydrolyzed to SMA and added to lipid self-assemblies. It was found that SMA fractions inserted in monolayers and solubilized vesicles to a different extent, with the highest efficiency being observed for low-Mn SMA polymers. Electron microscopy and dynamic light scattering size analyses confirmed the presence of nanodisks independent of the Mn of the SMA polymers forming the belt, and it was shown that the nanodisks all have approximately the same size. However, nanodisks bounded by high-Mn SMA polymers were more stable than those bounded by low-Mn polymers, as indicated by a better retention of the native lipid thermotropic properties and by slower exchange rates of lipids between nanodisks. In conclusion, we here present a simple method to separate SMAnh molecules based on their Mn from commercial SMAnh blends, which allowed us to obtain insights into the importance of SMA length for polymer-lipid interactions.

Fast and Simple Protocols for Mass Spectrometry-Based Proteomics of Small Fresh Frozen Uterine Tissue Sections

Human tissues are an important link between organ-specific spatial molecular information, patient pathology, and patient treatment options. However, patient tissues are uniquely obtained by time and location, and limited in their availability and size. Currently, little knowledge exists about appropriate and simplified protocols for routine MS-based analysis of the various types and sizes of tissues. Following standard procedures used in pathology, we selected small fresh frozen uterine tissue samples to investigate how the tissue preparation protocol affected the subsequent proteomics analysis. First, we observed that protein extraction with 0.1% SDS followed by extraction with a 30% ACN/urea resulted in a decrease in the number of identified proteins, when compared to extraction with 30% ACN/urea only. The decrease in the number of proteins was approximately 55% and 20%, for 10 and 16 μm thick tissue, respectively. Interestingly, the relative abundance of the proteins shared between the two methods was higher when SDS/ACN/urea was used, compared to the 30% ACN/urea extraction, indicating the role of SDS to be beneficial for protein solubility. Second, the influence of tissue thickness was investigated by comparing the results obtained for 10, 16, and 20 μm thick (1 mm²) tissue using urea/30% ACN. We observed an increase in the number of identified proteins and corresponding quantity with an increase in the tissue thickness. Finally, by analyzing very small amounts of tissues (∼0.2 mm²) of 10, 16, and 20 μm thickness, we observed that the increase in tissue thickness resulted in a higher number of protein identifications and corresponding quantitative values.