The Carrier Proteome Should Be Reassessed for Each Mass Analyzer Architecture.

A clever utilization of classic proteomics reagents now allows the effective amplification of the peptide sequencing potential in shotgun proteomics. The application of this method has helped usher in the exciting new field of single cell proteomics. While it was easy to first think that this approach was finally the answer for the polymerase chain reaction in protein chemistry, limitations were carefully described by the authors and others. A study by Cheung et al. systematically identified the consequences of higher concentration carrier proteomes and defined the "carrier proteome limit" [Cheung et al. Nat. Methods 2021, 18, 76]. While this work has been replicated by others, every analysis published to date has used a variation of the same mass analyzer. When the same analysis is performed on alternative instruments, these limits appear to be very different and can be attributed to defined characteristics of each mass analyzer. Specifically, in mass analyzers with a higher relative intrascan linear dynamic range, increased carrier channels appear less detrimental to quantitative accuracy. As such, we may be limiting the power of isobaric peptide signal "amplification" by restricting ourselves to traditional mass analyzer options for shotgun proteomics.

Cocaine regulates antiretroviral therapy CNS access through pregnane-x receptor-mediated drug transporter and metabolizing enzyme modulation at the blood brain barrier.

BACKGROUND: Appropriate interactions between antiretroviral therapies (ART) and drug transporters and metabolizing enzymes at the blood brain barrier (BBB) are critical to ensure adequate dosing of the brain to achieve HIV suppression. These proteins are modulated by demographic and lifestyle factors, including substance use. While understudied, illicit substances share drug transport and metabolism pathways with ART, increasing the potential for adverse drug:drug interactions. This is particularly important when considering the brain as it is relatively undertreated compared to peripheral organs and is vulnerable to substance use-mediated damage. METHODS: We used an in vitro model of the human BBB to determine the extravasation of three first-line ART drugs, emtricitabine (FTC), tenofovir (TFV), and dolutegravir (DTG), in the presence and absence of cocaine, which served as our illicit substance model. The impact of cocaine on BBB integrity and permeability, drug transporters, metabolizing enzymes, and their master transcriptional regulators were evaluated to determine the mechanisms by which substance use impacted ART central nervous system (CNS) availability. RESULTS: We determined that cocaine had a selective impact on ART extravasation, where it increased FTC's ability to cross the BBB while decreasing TFV. DTG concentrations that passed the BBB were below quantifiable limits. Interestingly, the potent neuroinflammatory modulator, lipopolysaccharide, had no effect on ART transport, suggesting a specificity for cocaine. Unexpectedly, cocaine did not breach the BBB, as permeability to albumin and 4 kDa FITC-dextran, as well as tight junction proteins and adhesion molecules remained unchanged. Rather, cocaine selectively decreased the pregnane-x receptor (PXR), but not constitutive androstane receptor (CAR). Consequently, drug transporter expression and activity decreased in endothelial cells of the BBB, including p-glycoprotein (P-gp), breast cancer resistance protein (BCRP), and multidrug resistance-associated protein 4 (MRP4). Further, cytochrome P450 3A4 (CYP3A4) enzymatic activity increased following cocaine treatment that coincided with decreased expression. Finally, cocaine modulated adenylate kinases that are required to facilitate biotransformation of ART prodrugs to their phosphorylated, pharmacologically active counterparts. CONCLUSION: Our findings indicate that additional considerations are needed in CNS HIV treatment strategies for people who use cocaine, as it may limit ART efficacy through regulation of drug transport and metabolizing pathways at the BBB.

Single cell proteomics by mass spectrometry reveals deep epigenetic insight into the actions of an orphan histone deacetylase inhibitor.

Epigenetic programming has been shown to play a role in nearly every human system and disease where anyone has thought to look. However, the levels of heterogeneity at which epigenetic or epiproteomic modifications occur at single cell resolution across a population remains elusive. While recent advances in sequencing technology have allowed between 1 and 3 histone post-translational modifications to be analyzed in each single cell, over twenty separate chemical PTMs are known to exist, allowing thousands of possible combinations. Single cell proteomics by mass spectrometry (SCP) is an emerging technology in which hundreds or thousands of proteins can be directly quantified in typical human cells. As the proteins detected and quantified by SCP are heavily biased toward proteins of highest abundance, chromatin proteins are an attractive target for analysis. To this end, I applied SCP to the analysis of cancer cells treated with mocetinostat, a class specific histone deacetylase inhibitor. I find that 16 PTMs can be confidently identified and localized with high site specificity in single cells. In addition, the high abundance of histone proteins allows higher throughput methods to be utilized for SCP than previously described. While quantitative accuracy suffers when analyzing more than 700 cells per day, 9 histone proteins can be measured in single cells analyzed at even 3,500 cells per day, a throughput 10-fold greater than any previous report. In addition, the unbiased global approach utilized herein identifies a previously uncharacterized response to this drug through the S100-A8/S100-A9 protein complex partners. This response is observed in nearly every cell of the over 1,000 analyzed in this study, regardless of the relative throughput of the method utilized. While limitations exist in the methods described herein, current technologies can easily improve upon the results presented here to allow comprehensive analysis of histone PTMs to be performed in any mass spectrometry lab. All raw and processed data described in this study has been made publicly available through the ProteomeXchange/MASSIVE repository system as MSV000093434.

Metabolomic, Proteomic, and Single-Cell Proteomic Analysis of Cancer Cells Treated with the KRASG12D Inhibitor MRTX1133.

Mutations in KRAS are common drivers of human cancers and are often those with the poorest overall prognosis for patients. A recently developed compound, MRTX1133, has shown promise in inhibiting the activity of KRASG12D mutant proteins, which is one of the main drivers of pancreatic cancer. To better understand the mechanism of action of this compound, I performed both proteomics and metabolomics on four KRASG12D mutant pancreatic cancer cell lines. To obtain increased granularity in the proteomic observations, single-cell proteomics was successfully performed on two of these lines. Following quality filtering, a total of 1498 single cells were analyzed. From these cells, 3140 total proteins were identified with approximately 953 proteins quantified per cell. At 48 h of treatment, two distinct populations of cells can be observed based on the level of effectiveness of the drug in decreasing the total abundance of the KRAS protein in each respective cell, with results that are effectively masked in the bulk cell analysis. All mass spectrometry data and processed results are publicly available at www.massive.ucsd.edu at accessions PXD039597, PXD039601, and PXD039600.

Acetic acid is a superior acidifier for sub-nanogram and single cell proteomic studies.

A recent study demonstrated a substantial signal increase when employing a 0.5% acetic acid buffer additive instead of the traditional 0.1% formic acid used in shotgun proteomics. In this study I compare these two buffers for a dilution series of tryptic digests down to 20 picograms peptide on column on a TIMSTOF single cell proteome (SCP) system. I observe a comparable relative level of signal increase as previously reported, which translates to improvements in proteome coverage at every peptide load assessed. The relative increase in peptide identifications is more apparent at lower concentrations with a striking 1.8-fold more peptides identified at 20 pg peptide load, resulting in over 2,000 protein groups identified in 30 minutes on this system. These results translate well to isolated single human cancer cells allowing over 1,000 protein groups to be identified in single human cells processed using a simple one step method in standard 96-well plates. All vendor raw and processed data has been made publicly available at www.massive.ucsd.edu and can be accessed as MSV000092563.

Cocaine Regulates Antiretroviral Therapy CNS Access Through Pregnane-X Receptor-Mediated Drug Transporter and Metabolizing Enzyme Modulation at the Blood Brain Barrier.

Background: Appropriate interactions between antiretroviral therapies (ART) and drug transporters and metabolizing enzymes at the blood brain barrier (BBB) are critical to ensure adequate dosing of the brain to achieve HIV suppression. These proteins are modulated by demographic and lifestyle factors, including substance use. While understudied, illicit substances share drug transport and metabolism pathways with ART, increasing the potential for adverse drug:drug interactions. This is particularly important when considering the brain as it is relatively undertreated compared to peripheral organs and is vulnerable to substance use-mediated damage. Methods: We used an in vitro model of the human BBB to determine the extravasation of three first-line ART drugs, emtricitabine (FTC), tenofovir (TFV), and dolutegravir (DTG), in the presence and absence of cocaine, which served as our illicit substance model. The impact of cocaine on BBB integrity and permeability, drug transporters, metabolizing enzymes, and their master transcriptional regulators were evaluated to determine the mechanisms by which substance use impacted ART central nervous system (CNS) availability. Results: We determined that cocaine had a selective impact on ART extravasation, where it increased FTC's ability to cross the BBB while decreasing TFV. DTG concentrations that passed the BBB were below quantifiable limits. Interestingly, the potent neuroinflammatory modulator, lipopolysaccharide, had no effect on ART transport, suggesting a specificity for cocaine. Unexpectedly, cocaine did not breach the BBB, as permeability to albumin and tight junction proteins and adhesion molecules remained unchanged. Rather, cocaine selectively decreased the pregnane-x receptor (PXR), but not constitutive androstane receptor (CAR). Consequently, drug transporter expression and activity decreased in endothelial cells of the BBB, including p-glycoprotein (P-gp), breast cancer resistance protein (BCRP), and multidrug resistance-associated protein 4 (MRP4). Further, cytochrome P450 3A4 (CYP3A4) enzymatic activity increased following cocaine treatment that coincided with decreased expression. Finally, cocaine modulated adenylate kinases are required to facilitate biotransformation of ART prodrugs to their phosphorylated, pharmacologically active counterparts. Conclusion: Our findings indicate that additional considerations are needed in CNS HIV treatment strategies for people who use cocaine, as it may limit ART efficacy through regulation of drug transport and metabolizing pathways at the BBB.

Metabolomic, proteomic and single cell proteomic analysis of cancer cells treated with the KRASG12D inhibitor MRTX1133.

Mutations in KRAS are common drivers of human cancers and are often those with the poorest overall prognosis for patients. A recently developed compound, MRTX1133, has shown promise in inhibiting the activity of KRASG12D mutant proteins, one of the main drivers in pancreatic cancer. To better understand the mechanism of action of this compound I performed both proteomics and metabolomics on four KRASG12D mutant pancreatic cancer cell lines. To obtain increased granularity in the proteomic observations, single cell proteomics was successfully performed on two of these lines. Following quality filtering, a total of 1,498 single cells were analyzed. From these cells 3,140 total proteins were identified with approximately 953 proteins quantified per cell. At 48 hours of treatment, two distinct populations of cells can be observed based on the level of effectiveness of the drug in decreasing total abundance of the KRAS protein in each respective cell, results that are effectively masked in the bulk cell analysis. All mass spectrometry data and processed results are publicly available at the www.massive.ucsd.edu at accessions PXD039597, PXD039601 and PXD039600.

Time-of-Flight Fragmentation Spectra Generated by the Proteomic Analysis of Single Human Cells Do Not Exhibit Atypical Fragmentation Patterns.

Recent work detailed the unique characteristics of fragmentation spectra derived from peptides from single human cells. This valuable report utilized an ultrahigh-field Orbitrap and directly compared the spectra obtained from high-concentration bulk cell HeLa lysates to those obtained from nanogram dilutions of the same and from nanowell-processed single HeLa cells. The analysis demonstrated marked differences between the fragmentation spectra generated at high and single-cell loads, most strikingly, the loss of high-mass y-series fragment ions. As significant differences exist in the physics of Orbitrap and time-of-flight mass analyzers, a comparison appeared warranted. A similar analysis was performed using isolated single pancreatic cancer cells compared to pools consisting of 100 cells. While a reanalysis of the prior Orbitrap data supports the author's original findings, the same trends are not observed in time-of-flight mass spectra of peptides from single human cells. The results are particularly striking when directly comparing the matched intensity fragment values between bulk and single-cell data generated on the same mass analyzers. Instrument acquisition files, processed data, and spectrum libraries are publicly available on MASSIVE via accession MSV000090635.

Achieving a Deeper Understanding of Drug Metabolism and Responses Using Single-Cell Technologies.

Recent advancements in single-cell technologies have enabled detection of RNA, proteins, metabolites, and xenobiotics in individual cells, and the application of these technologies has the potential to transform pharmacological research. Single-cell data has already resulted in the development of human and model species cell atlases, identifying different cell types within a tissue, further facilitating the characterization of tumor heterogeneity, and providing insight into treatment resistance. Research discussed in this review demonstrates that distinct cell populations express drug metabolizing enzymes to different extents, indicating there may be variability in drug metabolism not only between organs, but within tissue types. Additionally, we put forth the concept that single-cell analyses can be used to expose underlying variability in cellular response to drugs, providing a unique examination of drug efficacy, toxicity, and metabolism. We will outline several of these techniques: single-cell RNA-sequencing and mass cytometry to characterize and distinguish different cell types, single-cell proteomics to quantify drug metabolizing enzymes and characterize cellular responses to drug, capillary electrophoresis-ultrasensitive laser-induced fluorescence detection and single-probe single-cell mass spectrometry for detection of drugs, and others. Emerging single-cell technologies such as these can comprehensively characterize heterogeneity in both cell-type-specific drug metabolism and response to treatment, enhancing progress toward personalized and precision medicine. SIGNIFICANCE STATEMENT: Recent technological advances have enabled the analysis of gene expression and protein levels in single cells. These types of analyses are important to investigating mechanisms that cannot be elucidated on a bulk level, primarily due to the variability of cell populations within biological systems. Here, we summarize cell-type-specific drug metabolism and how pharmacologists can utilize single-cell approaches to obtain a comprehensive understanding of drug metabolism and cellular heterogeneity in response to drugs.

Insights into protein post-translational modification landscapes of individual human cells by trapped ion mobility time-of-flight mass spectrometry.

Single cell proteomics is a powerful tool with potential for markedly enhancing understanding of cellular processes. Here we report the development and application of multiplexed single cell proteomics using trapped ion mobility time-of-flight mass spectrometry. When employing a carrier channel to improve peptide signal, this method allows over 40,000 tandem mass spectra to be acquired in 30 min. Using a KRASG12C model human-derived cell line, we demonstrate the quantification of over 1200 proteins per cell with high relative sequence coverage permitting the detection of multiple classes of post-translational modifications in single cells. When cells were treated with a KRASG12C covalent inhibitor, this approach revealed cell-to-cell variability in the impact of the drug, providing insight missed by traditional proteomics. We provide multiple resources necessary for the application of single cell proteomics to drug treatment studies including tools to reduce cell cycle linked proteomic effects from masking pharmacological phenotypes.

Reinspection of a Clinical Proteomics Tumor Analysis Consortium (CPTAC) Dataset with Cloud Computing Reveals Abundant Post-Translational Modifications and Protein Sequence Variants.

The Clinical Proteomic Tumor Analysis Consortium (CPTAC) has provided some of the most in-depth analyses of the phenotypes of human tumors ever constructed. Today, the majority of proteomic data analysis is still performed using software housed on desktop computers which limits the number of sequence variants and post-translational modifications that can be considered. The original CPTAC studies limited the search for PTMs to only samples that were chemically enriched for those modified peptides. Similarly, the only sequence variants considered were those with strong evidence at the exon or transcript level. In this multi-institutional collaborative reanalysis, we utilized unbiased protein databases containing millions of human sequence variants in conjunction with hundreds of common post-translational modifications. Using these tools, we identified tens of thousands of high-confidence PTMs and sequence variants. We identified 4132 phosphorylated peptides in nonenriched samples, 93% of which were confirmed in the samples which were chemically enriched for phosphopeptides. In addition, our results also cover 90% of the high-confidence variants reported by the original proteogenomics study, without the need for sample specific next-generation sequencing. Finally, we report fivefold more somatic and germline variants that have an independent evidence at the peptide level, including mutations in ERRB2 and BCAS1. In this reanalysis of CPTAC proteomic data with cloud computing, we present an openly available and searchable web resource of the highest-coverage proteomic profiling of human tumors described to date.

Cloud Computing Based Immunopeptidomics Utilizing Community Curated Variant Libraries Simplifies and Improves Neo-Antigen Discovery in Metastatic Melanoma.

Unique peptide neo-antigens presented on the cell surface are attractive targets for researchers in nearly all areas of personalized medicine. Cells presenting peptides with mutated or other non-canonical sequences can be utilized for both targeted therapies and diagnostics. Today's state-of-the-art pipelines utilize complementary proteogenomic approaches where RNA or ribosomal sequencing data helps to create libraries from which tandem mass spectrometry data can be compared. In this study, we present an alternative approach whereby cloud computing is utilized to power neo-antigen searches against community curated databases containing more than 7 million human sequence variants. Using these expansive databases of high-quality sequences as a reference, we reanalyze the original data from two previously reported studies to identify neo-antigen targets in metastatic melanoma. Using our approach, we identify 79 percent of the non-canonical peptides reported by previous genomic analyses of these files. Furthermore, we report 18-fold more non-canonical peptides than previously reported. The novel neo-antigens we report herein can be corroborated by secondary analyses such as high predicted binding affinity, when analyzed by well-established tools such as NetMHC. Finally, we report 738 non-canonical peptides shared by at least five patient samples, and 3258 shared across the two studies. This illustrates the depth of data that is present, but typically missed by lower statistical power proteogenomic approaches. This large list of shared peptides across the two studies, their annotation, non-canonical origin, as well as MS/MS spectra from the two studies are made available on a web portal for community analysis.

Evaluation of the Sensitivity of Proteomics Methods Using the Absolute Copy Number of Proteins in a Single Cell as a Metric.

Proteomic technology has improved at a staggering pace in recent years, with even practitioners challenged to keep up with new methods and hardware. The most common metric used for method performance is the number of peptides and proteins identified. While this metric may be helpful for proteomics researchers shopping for new hardware, this is often not the most biologically relevant metric. Biologists often utilize proteomics in the search for protein regulators that are of a lower relative copy number in the cell. In this review, I re-evaluate untargeted proteomics data using a simple graphical representation of the absolute copy number of proteins present in a single cancer cell as a metric. By comparing single-shot proteomics data to the coverage of the most in-depth proteomic analysis of that cell line acquired to date, we can obtain a rapid metric of method performance. Using a simple copy number metric allows visualization of how proteomics has developed in both sensitivity and overall dynamic range when using both relatively long and short acquisition times. To enable reanalysis beyond what is presented here, two available web applications have been developed for single- and multi-experiment comparisons with reference protein copy number data for multiple cell lines and organisms.

Host Plants Indirectly Influence Plant Virus Transmission by Altering Gut Cysteine Protease Activity of Aphid Vectors.

The green peach aphid, Myzus persicae, is a vector of the Potato leafroll virus (PLRV, Luteoviridae), transmitted exclusively by aphids in a circulative manner. PLRV transmission efficiency was significantly reduced when a clonal lineage of M. persicae was reared on turnip as compared with the weed physalis, and this was a transient effect caused by a host-switch response. A trend of higher PLRV titer in physalis-reared aphids as compared with turnip-reared aphids was observed at 24 h and 72 h after virus acquisition. The major difference in the proteomes of these aphids was the up-regulation of predicted lysosomal enzymes, in particular the cysteine protease cathepsin B (cathB), in aphids reared on turnip. The aphid midgut is the site of PLRV acquisition, and cathB and PLRV localization were starkly different in midguts of the aphids reared on the two host plants. In viruliferous aphids that were reared on turnip, there was near complete colocalization of cathB and PLRV at the cell membranes, which was not observed in physalis-reared aphids. Chemical inhibition of cathB restored the ability of aphids reared on turnip to transmit PLRV in a dose-dependent manner, showing that the increased activity of cathB and other cysteine proteases at the cell membrane indirectly decreased virus transmission by aphids. Understanding how the host plant influences virus transmission by aphids is critical for growers to manage the spread of virus among field crops.

Comparative proteomics of a model MCF10A-KRasG12V cell line reveals a distinct molecular signature of the KRasG12V cell surface.

Oncogenic Ras mutants play a major role in the etiology of most aggressive and deadly carcinomas in humans. In spite of continuous efforts, effective pharmacological treatments targeting oncogenic Ras isoforms have not been developed. Cell-surface proteins represent top therapeutic targets primarily due to their accessibility and susceptibility to different modes of cancer therapy. To expand the treatment options of cancers driven by oncogenic Ras, new targets need to be identified and characterized at the surface of cancer cells expressing oncogenic Ras mutants. Here, we describe a mass spectrometry-based method for molecular profiling of the cell surface using KRasG12V transfected MCF10A (MCF10A-KRasG12V) as a model cell line of constitutively activated KRas and native MCF10A cells transduced with an empty vector (EV) as control. An extensive molecular map of the KRas surface was achieved by applying, in parallel, targeted hydrazide-based cell-surface capturing technology and global shotgun membrane proteomics to identify the proteins on the KRasG12V surface. This method allowed for integrated proteomic analysis that identified more than 500 cell-surface proteins found unique or upregulated on the surface of MCF10A-KRasG12V cells. Multistep bioinformatic processing was employed to elucidate and prioritize targets for cross-validation. Scanning electron microscopy and phenotypic cancer cell assays revealed changes at the cell surface consistent with malignant epithelial-to-mesenchymal transformation secondary to KRasG12V activation. Taken together, this dataset significantly expands the map of the KRasG12V surface and uncovers potential targets involved primarily in cell motility, cellular protrusion formation, and metastasis.

Gene expression profiling of 49 human tumor xenografts from in vitro culture through multiple in vivo passages--strategies for data mining in support of therapeutic studies.

BACKGROUND: Development of cancer therapeutics partially depends upon selection of appropriate animal models. Therefore, improvements to model selection are beneficial. RESULTS: Forty-nine human tumor xenografts at in vivo passages 1, 4 and 10 were subjected to cDNA microarray analysis yielding a dataset of 823 Affymetrix HG-U133 Plus 2.0 arrays. To illustrate mining strategies supporting therapeutic studies, transcript expression was determined: 1) relative to other models, 2) with successive in vivo passage, and 3) during the in vitro to in vivo transition. Ranking models according to relative transcript expression in vivo has the potential to improve initial model selection. For example, combining p53 tumor expression data with mutational status could guide selection of tumors for therapeutic studies of agents where p53 status purportedly affects efficacy (e.g., MK-1775). The utility of monitoring changes in gene expression with extended in vivo tumor passages was illustrated by focused studies of drug resistance mediators and receptor tyrosine kinases. Noteworthy observations included a significant decline in HCT-15 colon xenograft ABCB1 transporter expression and increased expression of the kinase KIT in A549 with serial passage. These trends predict sensitivity to agents such as paclitaxel (ABCB1 substrate) and imatinib (c-KIT inhibitor) would be altered with extended passage. Given that gene expression results indicated some models undergo profound changes with in vivo passage, a general metric of stability was generated so models could be ranked accordingly. Lastly, changes occurring during transition from in vitro to in vivo growth may have important consequences for therapeutic studies since targets identified in vitro could be over- or under-represented when tumor cells adapt to in vivo growth. A comprehensive list of mouse transcripts capable of cross-hybridizing with human probe sets on the HG-U133 Plus 2.0 array was generated. Removal of the murine artifacts followed by pairwise analysis of in vitro cells with respective passage 1 xenografts and GO analysis illustrates the complex interplay that each model has with the host microenvironment. CONCLUSIONS: This study provides strategies to aid selection of xenograft models for therapeutic studies. These data highlight the dynamic nature of xenograft models and emphasize the importance of maintaining passage consistency throughout experiments.