Multimode chromatography-based techniques for high purity isolation of extracellular vesicles from human blood plasma.

Extracellular vesicles (EVs) play a pivotal role in various biological pathways, such as immune responses and the progression of diseases, including cancer. However, it is challenging to isolate EVs at high purity from blood plasma and other biofluids due to their low abundance compared to more predominant biomolecular species such as lipoprotein particles and free protein complexes. Ultracentrifugation-based EV isolation, the current gold standard technique, cannot overcome this challenge due to the similar biophysical characteristics of such species. We developed several novel approaches to enrich EVs from plasma while depleting contaminating molecular species using multimode chromatography-based strategies. On average, we identified 716 ± 68 and 1054 ± 35 protein groups in EV isolates from 100 µL of plasma using multimode chromatography- and ultracentrifugation-based techniques, respectively. The developed methods resulted in similar EV isolates purity, providing significant advantages in simplicity, throughput, scalability, and applicability for various downstream analytical and potential clinical applications.

Improved Data Acquisition Settings on Q Exactive HF-X and Fusion Lumos Tribrid Orbitrap-Based Mass Spectrometers for Proteomic Analysis of Limited Samples.

Deep proteomic profiling of complex biological and medical samples available at low nanogram and subnanogram levels is still challenging. Thorough optimization of settings, parameters, and conditions in nanoflow liquid chromatography-tandem mass spectrometry (MS)-based proteomic profiling is crucial for generating informative data using amount-limited samples. This study demonstrates that by adjusting selected instrument parameters, e.g., ion injection time, automated gain control, and minimally altering the conditions for resuspending or storing the sample in solvents of different compositions, up to 15-fold more thorough proteomic profiling can be achieved compared to conventionally used settings. More specifically, the analysis of 1 ng of the HeLa protein digest standard by Q Exactive HF-X Hybrid Quadrupole-Orbitrap and Orbitrap Fusion Lumos Tribrid mass spectrometers yielded an increase from 1758 to 5477 (3-fold) and 281 to 4276 (15-fold) peptides, respectively, demonstrating that higher protein identification results can be obtained using the optimized methods. While the instruments applied in this study do not belong to the latest generation of mass spectrometers, they are broadly used worldwide, which makes the guidelines for improving performance desirable to a wide range of proteomics practitioners.

Ultralow flow liquid chromatography and related approaches: A focus on recent bioanalytical applications.

Ultralow flow LC employs ultra-narrow bore columns and mid-range pL/min to low nL/min flow rates (i.e., ≤20 nL/min). The separation columns that are used under these conditions are typically 2-30 µm in inner diameter. Ultralow flow LC systems allow for exceptionally high sensitivity and frequently high resolution. There has been an increasing interest in the analysis of scarce biological samples, for example, circulating tumor cells, extracellular vesicles, organelles, and single cells, and ultralow flow LC was efficiently applied to such samples. Hence, advances towards dedicated ultralow flow LC instrumentation, technical approaches, and higher throughput (e.g., tens-to-hundreds of single cells analyzed per day) were recently made. Here, we review the types of ultralow flow LC technology, followed by a discussion of selected representative ultralow flow LC applications, focusing on the progress made in bioanalysis of amount-limited samples during the last 10 years. We also discuss several recently reported high-sensitivity applications utilizing flow rates up to 100 nL/min, which are below commonly used nanoLC flow rates. Finally, we discuss the path forward for future developments of ultralow flow LC.

Characterization of bispecific antigen-binding biotherapeutic fragmentation sites using microfluidic capillary electrophoresis coupled to mass spectrometry (mCZE-MS).

Fragmentation of therapeutic proteins is a potential critical quality attribute (CQA) that can occur in vivo or during manufacturing or storage due to enzymatic and non-enzymatic degradation pathways, such as hydrolysis, peroxide mediation, and acid/metal catalysis. Characterization of the fragmentation pattern of a therapeutic protein is traditionally accomplished using capillary gel electrophoresis with UV detection under both non-reducing and reducing conditions (nrCGE and rCGE). However, such methods are incompatible with direct coupling to mass spectrometry (MS) due to the use of anionic surfactants, e.g., sodium dodecyl sulfate (SDS). Here, we present a novel method to characterize size-based fragmentation variants of a new biotherapeutic kind using microfluidic ZipChip® capillary zone electrophoresis (mCZE) system interfaced with mass spectrometry (MS) to determine the molecular masses of fragments. A new modality of immuno-oncology therapy, bispecific antigen-binding biotherapeutic, was chosen to investigate its fragmentation pattern using mCZE-MS for the first time, according to our knowledge. Bispecific antigen-binding biotherapeutic samples from different stages of downstream column purification and forced degradation conditions were analyzed. The results were cross-validated with denaturing size-exclusion chromatography-mass spectrometry and conventional rSDS-CGE. In this study, we demonstrated that mCZE-MS could separate and characterize 12-40 kDa bispecific antigen-binding biotherapeutic fragments rapidly (within ≤12 minutes), with higher resolution and better sensitivity than traditional LC-MS methods.

On-capillary Cell Lysis Enables Top-down Proteomic Analysis of Single Mammalian Cells by CE-MS/MS.

Proteomic analysis of limited samples and single cells requires specialized methods that prioritize high sensitivity and minimize sample loss. Consequently, sample preparation is one of the most important steps in limited sample analysis workflows to prevent sample loss. In this work, we have eliminated sample handling and transfer steps by processing intact cells directly in the separation capillary, online with capillary electrophoresis coupled to tandem mass spectrometry (CE-MS/MS) for top-down proteomic (TDP) analysis of low numbers of mammalian cancer cells (<10) and single cells. We assessed spray voltage injection of intact cells from a droplet of cell suspension (∼1000 cells) and demonstrated 0-9 intact cells injected with a dependency on the duration of spray voltage application. Spray voltage applied for 2 min injected an average of 7 ± 2 cells and resulted in 33-57 protein and 40-88 proteoform identifications (N = 4). To analyze single cells, manual cell loading by hydrodynamic pressure was used. Replicates of single HeLa cells (N = 4) lysed on the capillary and analyzed by CE-MS/MS demonstrated a range of 17-40 proteins and 23-50 proteoforms identified. An additional cell line, THP-1, was analyzed at the single-cell level, and proteoform abundances were compared to show the capabilities of single-cell TDP (SC-TDP) for assessing cellular heterogeneity. This study demonstrates the initial application of TDP in single-cell proteome-level profiling. These results represent the highest reported identifications from TDP analysis of a single HeLa cell and prove the tremendous potential for CE-MS/MS on-capillary sample processing for high sensitivity analysis of single cells and limited samples.

MPAPASS software enables stitched multiplex, multidimensional EV repertoire analysis and a standard framework for reporting bead-based assays.

Extracellular vesicles (EVs) of various types are released or shed from all cells. EVs carry proteins and contain additional protein and nucleic acid cargo that relates to their biogenesis and cell of origin. EV cargo in liquid biopsies is of widespread interest owing to its ability to provide a retrospective snapshot of cell state at the time of EV release. For the purposes of EV cargo analysis and repertoire profiling, multiplex assays are an essential tool in multiparametric analyte studies but are still being developed for high-parameter EV protein detection. Although bead-based EV multiplex analyses offer EV profiling capabilities with conventional flow cytometers, the utilization of EV multiplex assays has been limited by the lack of software analysis tools for such assays. To facilitate robust EV repertoire studies, we developed multiplex analysis post-acquisition analysis (MPAPASS) open-source software for stitched multiplex analysis, EV database-compatible reporting, and visualization of EV repertoires.

Channeling macrophage polarization by rocaglates increases macrophage resistance to Mycobacterium tuberculosis.

Macrophages contribute to host immunity and tissue homeostasis via alternative activation programs. M1-like macrophages control intracellular bacterial pathogens and tumor progression. In contrast, M2-like macrophages shape reparative microenvironments that can be conducive for pathogen survival or tumor growth. An imbalance of these macrophages phenotypes may perpetuate sites of chronic unresolved inflammation, such as infectious granulomas and solid tumors. We have found that plant-derived and synthetic rocaglates sensitize macrophages to low concentrations of the M1-inducing cytokine IFN-gamma and inhibit their responsiveness to IL-4, a prototypical activator of the M2-like phenotype. Treatment of primary macrophages with rocaglates enhanced phagosome-lysosome fusion and control of intracellular mycobacteria. Thus, rocaglates represent a novel class of immunomodulators that can direct macrophage polarization toward the M1-like phenotype in complex microenvironments associated with hypofunction of type 1 and/or hyperactivation of type 2 immunity, e.g., chronic bacterial infections, allergies, and, possibly, certain tumors.

Rapid Highly-Efficient Digestion and Peptide Mapping of Adeno-Associated Viruses.

Adeno-associated viruses (AAVs) comprise an area of rapidly growing interest due to their ability to act as a gene delivery vehicle in novel gene therapy strategies and vaccine development. Peptide mapping is a common technique in the biopharmaceutical industry to confirm the correct sequence, product purity, post-translational modifications (PTMs), and stability. However, conventional peptide mapping is time-consuming and has proven difficult to reproduce with viral capsids because of their high structural stability and the suboptimal localization of trypsin cleavage sites in the AAV protein sequences. In this study, we present an optimized peptide mapping-based workflow that provides thorough characterization within 1 day. This workflow is also highly reproducible due to its simplicity having very few steps and is easy to perform proteolytic digestion utilizing thermally stable pepsin, which is active at 70 °C in acidic conditions. The acidic conditions of the peptic digestions drive viral capsid denaturation and improve cleavage site accessibility. We characterized the efficiency and ease of digestion through peptide mapping of the AAV2 viral capsid protein. Using nanoflow liquid chromatography coupled with tandem mass spectrometry, we achieved 100% sequence coverage of the low-abundance VP1 capsid protein with a digestion process taking only 10 min to prepare and 45 min to complete the digestion.

High-throughput microfluidic 3D biomimetic model enabling quantitative description of the human breast tumor microenvironment.

Cancer is driven by both genetic aberrations in the tumor cells and fundamental changes in the tumor microenvironment (TME). These changes offer potential targets for novel therapeutics, yet lack of in vitro 3D models recapitulating this complex microenvironment impedes such progress. Here, we generated several tumor-stroma scaffolds reflecting the dynamic in vivo breast TME, using a high throughput microfluidic system. Alginate (Alg) or alginate-alginate sulfate (Alg/Alg-S) hydrogels were used as ECM-mimics, enabling the encapsulation and culture of tumor cells, fibroblasts and immune cells (macrophages and T cells, of the innate and adaptive immune systems, respectively). Specifically, Alg/Alg-S was shown capable of capturing and presenting growth factors and cytokines with binding affinity that is comparable to heparin. Viability and cytotoxicity were shown to strongly correlate with the dynamics of cellular milieu, as well as hydrogel type. Using on-chip immunofluorescence, production of reactive oxygen species and apoptosis were imaged and quantitatively analyzed. We then show how macrophages in our microfluidic system were shifted from a proinflammatory to an immunosuppressive phenotype when encapsulated in Alg/Alg-S, reflecting in vivo TME dynamics. LC-MS proteomic profiling of tumor cells sorted from the TME scaffolds revealed upregulation of proteins involved in cell-cell interactions and immunomodulation in Alg/Alg-S scaffolds, correlating with in vivo findings and demonstrating the appropriateness of Alg/Alg-S as an ECM biomimetic. Finally, we show the formation of large tumor-derived vesicles, formed exclusively in Alg/Alg-S scaffolds. Altogether, our system offers a robust platform for quantitative description of the breast TME that successfully recapitulates in vivo patterns. STATEMENT OF SIGNIFICANCE: Cancer progression is driven by profound changes in both tumor cells and surrounding stroma. Here, we present a high throughput microfluidic system for the generation and analysis of dynamic tumor-stroma scaffolds, that mimic the complex in vivo TME cell proportions and compositions, constructing robust in vitro models for the study of the TME. Utilizing Alg/Alg-S as a bioinspired ECM, mimicking heparin's in vivo capabilities of capturing and presenting signaling molecules, we show how Alg/Alg-S induces complex in vivo-like responses in our models. Alg/Alg-S is shown here to promote dynamic protein expression patterns, that can serve as potential therapeutic targets for breast cancer treatment. Formation of large tumor-derived vesicles, observed exclusively in the Alg/Alg-S scaffolds suggests a mechanism for tumor survival.

Lipidome-based Targeting of STAT3-driven Breast Cancer Cells Using Poly-l-glutamic Acid-coated Layer-by-Layer Nanoparticles.

The oncogenic transcription factor STAT3 is aberrantly activated in 70% of breast cancers, including nearly all triple-negative breast cancers (TNBCs). Because STAT3 is difficult to target directly, we considered whether metabolic changes driven by activated STAT3 could provide a therapeutic opportunity. We found that STAT3 prominently modulated several lipid classes, with most profound effects on N-acyl taurine and arachidonic acid, both of which are involved in plasma membrane remodeling. To exploit these metabolic changes therapeutically, we screened a library of layer-by-layer (LbL) nanoparticles (NPs) differing in the surface layer that modulates interactivity with the cell membrane. We found that poly-l-glutamic acid (PLE)-coated NPs bind to STAT3-transformed breast cancer cells with 50% greater efficiency than to nontransformed cells, and the heightened PLE-NP binding to TNBC cells was attenuated by STAT3 inhibition. This effect was also observed in densely packed three-dimensional breast cancer organoids. As STAT3-transformed cells show greater resistance to cytotoxic agents, we evaluated whether enhanced targeted delivery via PLE-NPs would provide a therapeutic advantage. We found that cisplatin-loaded PLE-NPs induced apoptosis of STAT3-driven cells at lower doses compared with both unencapsulated cisplatin and cisplatin-loaded nontargeted NPs. In addition, because radiation is commonly used in breast cancer treatment, and may alter cellular lipid distribution, we analyzed its effect on PLE-NP-cell binding. Irradiation of cells enhanced the STAT3-targeting properties of PLE-NPs in a dose-dependent manner, suggesting potential synergies between these therapeutic modalities. These findings suggest that cellular lipid changes driven by activated STAT3 may be exploited therapeutically using unique LbL NPs.

The integrated stress response mediates necrosis in murine Mycobacterium tuberculosis granulomas.

The mechanism by which only some individuals infected with Mycobacterium tuberculosis develop necrotic granulomas with progressive disease while others form controlled granulomas that contain the infection remains poorly defined. Mice carrying the sst1-suscepible (sst1S) genotype develop necrotic inflammatory lung lesions, similar to human tuberculosis (TB) granulomas, which are linked to macrophage dysfunction, while their congenic counterpart (B6) mice do not. In this study we report that (a) sst1S macrophages developed aberrant, biphasic responses to TNF characterized by superinduction of stress and type I interferon pathways after prolonged TNF stimulation; (b) the late-stage TNF response was driven via a JNK/IFN-ß/protein kinase R (PKR) circuit; and (c) induced the integrated stress response (ISR) via PKR-mediated eIF2α phosphorylation and the subsequent hyperinduction of ATF3 and ISR-target genes Chac1, Trib3, and Ddit4. The administration of ISRIB, a small-molecule inhibitor of the ISR, blocked the development of necrosis in lung granulomas of M. tuberculosis-infected sst1S mice and concomitantly reduced the bacterial burden. Hence, induction of the ISR and the locked-in state of escalating stress driven by the type I IFN pathway in sst1S macrophages play a causal role in the development of necrosis in TB granulomas. Interruption of the aberrant stress response with inhibitors such as ISRIB may offer novel host-directed therapy strategies.

New mixture models for decoy-free false discovery rate estimation in mass spectrometry proteomics.

MOTIVATION: Accurate estimation of false discovery rate (FDR) of spectral identification is a central problem in mass spectrometry-based proteomics. Over the past two decades, target-decoy approaches (TDAs) and decoy-free approaches (DFAs) have been widely used to estimate FDR. TDAs use a database of decoy species to faithfully model score distributions of incorrect peptide-spectrum matches (PSMs). DFAs, on the other hand, fit two-component mixture models to learn the parameters of correct and incorrect PSM score distributions. While conceptually straightforward, both approaches lead to problems in practice, particularly in experiments that push instrumentation to the limit and generate low fragmentation-efficiency and low signal-to-noise-ratio spectra. RESULTS: We introduce a new decoy-free framework for FDR estimation that generalizes present DFAs while exploiting more search data in a manner similar to TDAs. Our approach relies on multi-component mixtures, in which score distributions corresponding to the correct PSMs, best incorrect PSMs and second-best incorrect PSMs are modeled by the skew normal family. We derive EM algorithms to estimate parameters of these distributions from the scores of best and second-best PSMs associated with each experimental spectrum. We evaluate our models on multiple proteomics datasets and a HeLa cell digest case study consisting of more than a million spectra in total. We provide evidence of improved performance over existing DFAs and improved stability and speed over TDAs without any performance degradation. We propose that the new strategy has the potential to extend beyond peptide identification and reduce the need for TDA on all analytical platforms. AVAILABILITYAND IMPLEMENTATION: https://github.com/shawn-peng/FDR-estimation. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Improved Sensitivity of Ultralow Flow LC-MS-Based Proteomic Profiling of Limited Samples Using Monolithic Capillary Columns and FAIMS Technology.

In this work, we pioneered a combination of ultralow flow (ULF) high-efficiency ultranarrow bore monolithic LC columns coupled to MS via a high-field asymmetric waveform ion mobility spectrometry (FAIMS) interface to evaluate the potential applicability for high sensitivity, robust, and reproducible proteomic profiling of low nanogram-level complex biological samples. As a result, ULF LC-FAIMS-MS brought unprecedented sensitivity levels and high reproducibility in bottom-up proteomic profiling. In addition, FAIMS improved the dynamic range, signal-to-noise ratios, and detection limits in ULF LC-MS-based measurements by significantly reducing chemical noise in comparison to the conventional nanoESI interface used with the same ULF LC-MS setup. Two, three, or four compensation voltages separated by at least 15 V were tested within a single LC-MS run using the FAIMS interface. The optimized ULF LC-ESI-FAIMS-MS/MS conditions resulted in identification of 2,348 ± 42 protein groups, 10,062 ± 285 peptide groups, and 15,734 ± 350 peptide-spectrum matches for 1 ng of a HeLa digest, using a 1 h gradient at the flow rate of 12 nL/min, which represents an increase by 38%, 91%, and 131% in respective identifications, as compared to the control experiment (without FAIMS). To evaluate the practical utility of the ULF LC-ESI-FAIMS-MS platform in proteomic profiling of limited samples, approximately 100, 1,000, and 10,000 U937 myeloid leukemia cells were processed, and a one-tenth of each sample was analyzed. Using the optimized conditions, we were able to reliably identify 251 ± 54, 1,135 ± 80, and 2,234 ± 25 protein groups from injected aliquots corresponding to ∼10, 100, and 1,000 processed cells.

Machine learning-aided quantification of antibody-based cancer immunotherapy by natural killer cells in microfluidic droplets.

Natural killer (NK) cells have emerged as an effective alternative option to T cell-based immunotherapies, particularly against liquid (hematologic) tumors. However, the effectiveness of NK cell therapy has been less than optimal for solid tumors, partly due to the heterogeneity in target interaction leading to variable anti-tumor cytotoxicity. This paper describes a microfluidic droplet-based cytotoxicity assay for quantitative comparison of immunotherapeutic NK-92 cell interaction with various types of target cells. Machine learning algorithms were developed to assess the dynamics of individual effector-target cell pair conjugation and target death in droplets in a semi-automated manner. Our results showed that while short contacts were sufficient to induce potent killing of hematological cancer cells, long-lasting stable conjugation with NK-92 cells was unable to kill HER2+ solid tumor cells (SKOV3, SKBR3) significantly. NK-92 cells that were engineered to express FcγRIII (CD16) mediated antibody-dependent cellular cytotoxicity (ADCC) selectively against HER2+ cells upon addition of Herceptin (trastuzumab). The requirement of CD16, Herceptin and specific pre-incubation temperature served as three inputs to generate a molecular logic function with HER2+ cell death as the output. Mass proteomic analysis of the two effector cell lines suggested differential changes in adhesion, exocytosis, metabolism, transport and activation of upstream regulators and cytotoxicity mediators, which can be utilized to regulate specific functionalities of NK-92 cells in future. These results suggest that this semi-automated single cell assay can reveal the variability and functional potency of NK cells and may be used to optimize immunotherapeutic efficacy for preclinical analyses.

Multi-Omics Reveals Impact of Cysteine Feed Concentration and Resulting Redox Imbalance on Cellular Energy Metabolism and Specific Productivity in CHO Cell Bioprocessing.

Chinese hamster ovary (CHO) cells are currently the primary host cell lines used in biotherapeutic manufacturing of monoclonal antibodies (mAbs) and other biopharmaceuticals. Cellular energy metabolism and endoplasmic reticulum (ER) stress are known to greatly impact cell growth, viability, and specific productivity of a biotherapeutic; but the molecular mechanisms are not fully understood. The authors previously employed multi-omics profiling to investigate the impact of a reduction in cysteine (Cys) feed concentration in a fed-batch process and found that disruption of the redox balance led to a substantial decline in cell viability and titer. Here, the multi-omics findings are expanded, and the impact redox imbalance has on ER stress, mitochondrial homeostasis, and lipid metabolism is explored. The reduced Cys feed activates the amino acid response (AAR), increases mitochondrial stress, and initiates gluconeogenesis. Multi-omics analysis reveals that together, ER stress and AAR signaling shift the cellular energy metabolism to rely primarily on anaplerotic reactions, consuming amino acids and producing lactate, to maintain energy generation. Furthermore, the pathways are demonstrated in which this shift in metabolism leads to a substantial decline in specific productivity and altered mAb glycosylation. Through this work, meaningful bioprocess markers and targets for genetic engineering are identified.

A nanoscale, multi-parametric flow cytometry-based platform to study mitochondrial heterogeneity and mitochondrial DNA dynamics.

Mitochondria are well-characterized regarding their function in both energy production and regulation of cell death; however, the heterogeneity that exists within mitochondrial populations is poorly understood. Typically analyzed as pooled samples comprised of millions of individual mitochondria, there is little information regarding potentially different functionality across subpopulations of mitochondria. Herein we present a new methodology to analyze mitochondria as individual components of a complex and heterogeneous network, using a nanoscale and multi-parametric flow cytometry-based platform. We validate the platform using multiple downstream assays, including electron microscopy, ATP generation, quantitative mass-spectrometry proteomic profiling, and mtDNA analysis at the level of single organelles. These strategies allow robust analysis and isolation of mitochondrial subpopulations to more broadly elucidate the underlying complexities of mitochondria as these organelles function collectively within a cell.

Multi-Omics Study on the Impact of Cysteine Feed Level on Cell Viability and mAb Production in a CHO Bioprocess.

There is continual demand to maximize CHO cell culture productivity of a biotherapeutic while maintaining product quality. In this study, a comprehensive multi-omics analysis is performed to investigate the cellular response to the level of dosing of the amino acid cysteine (Cys) in the production of a monoclonal antibody (mAb). When Cys feed levels are insufficient, there is a significant decrease in protein titer. Multi-omics (metabolomics and proteomics, with support from RNAseq) is performed over the time course of the CHO bioprocess producing an IgG1 mAb in 5 L bioreactors. Pathway analysis reveals that insufficient levels of Cys in the feed lead to Cys depletion in the cell. This depletion negatively impacts antioxidant molecules, such as glutathione (GSH) and taurine, leading to oxidative stress with multiple deleterious cellular effects. In this paper, the resultant ER stress and subsequent apoptosis that affects cell viability and viable cell density has been considered. Key metabolic enzymes and metabolites are identified that can be potentially monitored as the process progresses and/or increased in the cell either by nutrient feeding or genetic engineering. This work reinforces the centrality of redox balance to cellular health and success of the bioprocess as well as the power of multi-omics to provide an in-depth understanding of the CHO cell biology during biopharmaceutical production.

Host Cell Protein Profiling by Targeted and Untargeted Analysis of Data Independent Acquisition Mass Spectrometry Data with Parallel Reaction Monitoring Verification.

Host cell proteins (HCPs) are process-related impurities of biopharmaceuticals that remain at trace levels despite multiple stages of downstream purification. Currently, there is interest in implementing LC-MS in biopharmaceutical HCP profiling alongside conventional ELISA, because individual species can be identified and quantitated. Conventional data dependent LC-MS is hampered by the low concentration of HCP-derived peptides, which are 5-6 orders of magnitude less abundant than the biopharmaceutical-derived peptides. In this paper, we present a novel data independent acquisition (DIA)-MS workflow to identify HCP peptides using automatically combined targeted and untargeted data processing, followed by verification and quantitation using parallel reaction monitoring (PRM). Untargeted data processing with DIA-Umpire provided a means of identifying HCPs not represented in the assay library used for targeted, peptide-centric, data analysis. An IgG1 monoclonal antibody (mAb) purified by Protein A column elution, cation exchange chromatography, and ultrafiltration was analyzed using the workflow with 1D-LC. Five protein standards added at 0.5 to 100 ppm concentrations were detected in the background of the purified mAb, demonstrating sensitivity to low ppm levels. A calibration curve was constructed on the basis of the summed peak areas of the three highest intensity fragment ions from the highest intensity peptide of each protein standard. Sixteen HCPs were identified and quantitated on the basis of the calibration curve over the range of low ppm to over 100 ppm in the purified mAb sample. The developed approach achieves rapid HCP profiling using 1D-LC and specific identification exploiting the high mass accuracy and resolution of the mass spectrometer.

Binding Site Characterization of AM1336, a Novel Covalent Inverse Agonist at Human Cannabinoid 2 Receptor, Using Mass Spectrometric Analysis.

Cannabinoid 2 receptor (CB2R), a Class-A G-protein coupled receptor (GPCR), is a promising drug target under a wide array of pathological conditions. Rational drug design has been hindered due to our poor understanding of the structural features involved in ligand binding. Binding of a high-affinity biarylpyrazole inverse agonist AM1336 to a library of the human CB2 receptor (hCB2R) cysteine-substituted mutants provided indirect evidence that two cysteines in transmembrane helix-7 (H7) were critical for the covalent attachment. We used proteomics analysis of the hCB2R with bound AM1336 to directly identify peptides with covalently attached ligand and applied in silico modeling for visualization of the ligand-receptor interactions. The hCB2R, with affinity tags (FlaghCB2His6), was produced in a baculovirus-insect cell expression system and purified as a functional receptor using immunoaffinity chromatography. Using mass spectrometry-based bottom-up proteomic analysis of the hCB2R-AM1336, we identified a peptide with AM1336 attached to the cysteine C284(7.38) in H7. The hCB2R homology model in lipid bilayer accommodated covalent attachment of AM1336 to C284(7.38), supporting both biochemical and mass spectrometric data. This work consolidates proteomics data and in silico modeling and integrates with our ligand-assisted protein structure (LAPS) experimental paradigm to assist in structure-based design of cannabinoid antagonist/inverse agonists.