2-Deoxy-2-[18F]fluoro-D-glucose (2-FDG) with positron emission tomography (2-FDG-PET) is undeniably useful in the clinic, among other uses, to monitor change over time using the 2-FDG standardized uptake values (SUV) metric. This report suggests some potentially serious caveats for this and related roles for 2-FDG PET. Most critical is the assumption that there is an exact proportionality between glucose metabolism and 2-FDG metabolism, called the lumped constant, LC. This report describes that LC is not constant for a specific tissue and may be variable before and after disease treatment. The purpose of this work is not to deny the clinical value of 2-FDG PET; it is a reminder that when one extends the use of an appropriately qualified imaging method, new observations may arise and further validation would be necessary. Current understanding of glucose-based energetics in vivo is based on the quantification of glucose metabolic rates with 2-FDG PET, a method that permits the non-invasive assessment in various human disorders. However, 2-FDG is only a good substrate for facilitated-glucose transporters (GLUTs) but not for sodium-dependent glucose co-transporters (SGLTs), which have recently been shown to be distributed in multiple human tissues. Thus, the GLUT-mediated in vivo glucose utilization measured by 2-FDG PET would be blinded to the potentially substantial role of functional SGLTs in glucose transport and utilization. Therefore, in these circumstances the 2-FDG LC used to quantify in vivo glucose utilization should not be expected to remain constant. 2-FDG LC variations have been especially significant in tumors, particularly at different stages of cancer development, affecting the accuracy of quantitative glucose measures and potentially limiting the prognostic value of 2-FDG, as well as its accuracy in monitoring treatments. SGLT-mediated glucose transport can be estimated using α-methyl-4-deoxy-4-[18F]fluoro-D-glucopyranoside (Me-4FDG). Utilizing both 2-FDG and Me-4FDG should provide a more complete picture of glucose utilization via both GLUT and SGLT transporters in health and disease stages. Given the widespread use of 2-FDG PET to infer glucose metabolism, appreciating the potential limitations of 2-FDG as a surrogate for glucose metabolic rate and the potential reasons for variability in LC is relevant. Even when the readout for the 2-FDG PET study is only an SUV parameter, variability in LC is important, particularly if it changes over the course of disease progression (e.g., an evolving tumor).

⁶⁸Ga-DOTATOC-PET/MRI (⁶⁸Gallium-DOTATOC-positron emission tomography/magnetic resonance imaging) combines the advantages of PET in the acquisition of metabolic-functional information with the high soft tissue contrast of MRI. Standardized uptake values (SUV) in tumors were suggested as a measure of somatostatin receptor expression. A challenge with receptor ligands is, that the distribution volume is confined to tissues with tracer-uptake, potentially limiting SUV quantification. In this study, different functional, three-dimensional (3D) SUV, apparent diffusion coefficient (ADC) parameters and arterial tumor enhancement were tested for the characterization of gastroendopancreatic neuroendocrine tumors (GEP-NET). Methods: For this single-center, cross-sectional study, 22 patients with 24 histologically confirmed GEP-NET lesions (15 men/7 women; median, 61 years, range, 43-81 years), who received hybrid ⁶⁸Ga-DOTA-PET/MRI examinations at 3T between January 2017 and July 2019 met eligibility criteria. SUVs, tumor-to-background ratios (TBR), the total functional tumor volume (TFTV), ADCmean and ADCmin were measured based on volumes of interest (VOI) and examined with receiver operating characteristic analysis to determine cut-off values for differentiation between low and intermediate grade GEP-NET. Spearman’s rank correlation coefficients were used to assess correlations between functional imaging parameters. Results: The ratio of PET-derived SUVmean and diffusion-weighted imaging (DWI)-derived ADCmin was introduced as a combined variable to predict tumor grade, outperforming single predictors. Based on a threshold ratio of 0.03 to be exceeded, tumors could be classified as grade 2 with a sensitivity of 86% and specificity of 100%. SUV and functional ADC values as well as arterial contrast enhancement parameters showed non-significant and mostly negligible correlations. Conclusion: As receptor density and tumor cellularity appear to be independent, potentially complementary phenomena, the combined PET/MRI ratio SUVmean/ADCmin may be used as a novel biomarker, allowing to differentiate between grade 1 and 2 GEP-NET.

Prediction of post-operative pulmonary function in lung cancer patients before tumor resection is essential for patient selection for surgery and is conventionally done with a non-imaging segment counting method (SC) or a two-dimensional planar lung perfusion scintigraphy (PS). The purpose of this study was to compare quantitative analysis of PS to single photon emission computed tomography/computed tomography (SPECT/CT) and to estimate the accuracy of SC, PS and SPECT/CT in predicting post-operative pulmonary function in patients undergoing lobectomy. Methods: Seventy-five non-small cell lung cancer (NSCLC) patients planned for lobectomy were prospectively enrolled (68% males, average age 68.1±8 years ). All patients completed pre-operative forced expiratory volume capacity (FEV1), diffusing capacity of the lung for carbon monoxide (DLCO), Tc99m-MAA lung perfusion scintigraphy with PS and SPECT/CT quantification. A subgroup of 60 patients underwent video-assisted thoracoscopic (VATS) lobectomy and measurement of post-operative FEV1 and DLCO. Relative uptake of the lung lobes estimated by PS and SPECT/CT were compared. Predicted post-operative FEV1 and DLCO were derived from SC, PS and SPECT/CT. Prediction results were compared between the different methods and the true post-operative measurements in patients who underwent lobectomy. Results: Relative uptake measurements differed significantly between PS and SPECT/CT in right lung lobes, with a mean difference of -8.2±3.8, 18.0±5.0 and -11.5±6.1 for right upper, middle and lower lobes respectively (p<0.001). The differences between the methods in the left lung lobes were minor with a mean difference of -0.4±4.4 (p>0.05) and -2.0±4.0 (p<0.001) for left upper and lower lobes respectively. No significant difference and strong correlation (R=0.6-0.76, p<0.001) were found between predicted post-operative lung function values according to SC, PS, SPECT/CT and the actual post-operative FEV1 and DLCO. Conclusion: Although lobar quantification parameters differed significantly between PS and SPECT/CT, no significant differences were found between the predicted post-operative lung function results derived from these methods and the actual post-operative results. The additional time and effort of SPECT/CT quantification may not have an added value in patient selection for surgery. SPECT/CT may be advantageous in patients planned for right lobectomies but further research is warranted.

Quantitative evaluation of radiolabeled Prostate-Specific Membrane Antigen (PSMA) PET scans may be used to monitor treatment response in patients with prostate cancer (PCa). To interpret longitudinal differences in PSMA uptake, the intrinsic variability of tracer uptake in PCa lesions needs to be defined. The aim of this study was to investigate the repeatability of quantitative ¹⁸F-DCFPyL (a second generation ¹⁸F-PSMA-ligand) PET/CT measurements in patients with PCa. Methods: Twelve patients with metastatic PCa were prospectively included, of which 2 were excluded from final analyses. Patients received two whole-body ¹⁸F-DCFPyL PET/CT scans (median dose 317 MBq; uptake time 120 min), within median 4 days (range 1-11 days). After semi-automatic (isocontour-based) tumor delineation, the following lesion-based metrics were derived: Tumor-to-Blood ratio (TBRmean, TBRpeak, and TBRmax), Standardized Uptake Value (SUVmean, SUVpeak, SUV_max, normalized to bodyweight), tumor volume, and total lesion tracer uptake (TLU). Additionally, patient-based Total Tumor Volume (sum of PSMA-positive tumor volumes; TTV) and Total Tumor Burden (sum of all lesion TLUs; TTB) were derived. Repeatability was analyzed using repeatability coefficients (RC) and intra-class correlations (ICC). Additionally, the effect of point spread function (PSF) image reconstruction on the repeatability of uptake metrics was evaluated. Results: In total, 36 ¹⁸F-DCFPyL PET positive lesions were analyzed (up to 5 lesions per patient). RCs of TBRmean, TBRpeak, and TBRmax were 31.8%, 31.7%, and 37.3%, respectively. For SUVmean, SUVpeak, SUV_max the RCs were 24.4%, 25.3% and 31.0%, respectively. All ICC were ≥0.97. Tumor volume delineations were well repeatable, with RC 28.1% for individual lesion volumes and RC 17.0% for TTV. TTB had a RC of 23.2% and 33.4%, when based on SUVmean and TBRmean, respectively. Small lesions (<4.2mL) had worse repeatability for volume measurements. The repeatability of SUVpeak, TLU, and all patient-level metrics were not affected by PSF-reconstruction. Conclusion: ¹⁸F-DCFPyL uptake measurements are well repeatable and can be used for clinical validation in future treatment response assessment studies. Patient-based TTV may be preferred for multicenter studies since its repeatability was both high and robust to different image reconstructions.

The aim of this work was to quantify the uptake of [¹⁸F]BMS-986192, a PD-L1 adnectin PET tracer, in patients with non-small-cell lung cancer (NSCLC). To this end, plasma input kinetic modeling of dynamic tumor uptake data with online arterial blood sampling was performed. In addition, the accuracy of simplified uptake metrics such as standardized uptake value (SUV) was investigated. Methods: Data from a study with [¹⁸F]BMS-986192 in patients with advanced stage NSCLC eligible for nivolumab treatment were used if a dynamic scan was available and lesions were present in the field of view of the dynamic scan. After injection of [¹⁸F]BMS-986192, a 60-minutes dynamic PET-CT scan was started, followed by a 30-min whole body PET-CT scan. Continuous arterial and discrete arterial and venous blood sampling were performed to determine a plasma input function. Tumor time activity curves were fitted by several plasma input kinetic models. Simplified uptake parameters included tumor to blood ratio as well as several SUV measures. Results: Twenty two tumors in nine patients were analyzed. The arterial plasma input single-tissue reversible compartment model with fitted blood volume fraction seems to be the most preferred model as it best fitted 11 out of 18 tumor time activity curves. The distribution volume V_T ranged from 0.4 to 4.8 mL·cm-3. Similar values were obtained with an image derived input function. From the simplified measures, SUV normalized for body weight (SUV_BW) at 50 and 67 minutes post injection correlated best with V_T, with an R² > 0.9. Conclusion: A single tissue reversible model can be used for the quantification of tumor uptake of the PD-L1 PET tracer [¹⁸F]BMS-986192. SUV_BW at 60 minutes post injection, normalized for body weight, is an accurate simplified parameter for uptake assessment of baseline studies. In order to assess its predictive value for response evaluation during PD-(L)1 immune checkpoint inhibition further validation of SUV against V_T based on an image derived input function is recommended.

The accuracy of lutetium-177 (¹⁷⁷Lu) radiotracer concentration measurements using quantitative clinical software was determined by comparing in vivo results for a digital solid-state cadmium-zinc-telluride SPECT/CT (single photon emission computed tomography / x-ray computed tomography) system to in vitro sampling. First, image acquisition parameters were assessed for an International Electrotechnical Commission (IEC) body phantom emulating clinical count rates loaded with a "lung" insert and 6 hot spheres with a 12:1 target-to-background ratio of ¹⁷⁷Lu solution. Then, the data of 28 whole-body SPECT/CT scans of 7 patients who underwent ¹⁷⁷Lu prostate membrane antigen (¹⁷⁷Lu-PSMA) radioligand therapy was retrospectively analyzed. Three users analyzed SPECT/CT images for in vivo urinary bladder radiotracer uptake using quantitative software (Q.Metrix, GE Healthcare). In vitro radiopharmaceutical concentrations were calculated using urine sampling obtained immediately after each scan, scaled to standardized uptake values (SUVs). Any in vivo/in vitro identity relations were determined by linear regression (ideally slope=1, intercept=0), within a 95 % confidence interval (CI). Phantom results demonstrated lower quantitative error for acquisitions using the 113 keV ¹⁷⁷Lu energy peak rather than including the 208 keV peak, given that only low-energy collimation was available in this camera configuration. In the clinical study, 24 in vivo/in vitro pairs were eligible for further analysis, having rejected 4 as outliers (via Cook’s distance calculations). All linear regressions (R2 ≥ 0.92, P<0.0001) provided identity in vivo/in vitro relations (95 % CI), with SUV averages from all users giving a slope of 1.03±0.09, an intercept of –0.25±0.64 g/mL, and an average residual difference of 20.4 %. Acquiring with the lower energy ¹⁷⁷Lu energy peak, solid-state SPECT/CT imaging provides an accuracy to within ~20 % for in vivo urinary bladder radiotracer concentrations. This non-invasive in vivo quantitation method can potentially improve diagnosis, improve patient management and treatment response assessment, and provide data essential to ¹⁷⁷Lu dosimetry.

Prostate specific membrane antigen (PSMA) targeting Positron Emission Tomography (PET) imaging is becoming the reference standard for prostate cancer (PC) staging, especially in advanced disease. Yet, the implications of PSMA-PET derived whole-body tumor volume for overall survival are poorly elucidated to date. This might be due to the fact that (semi-) automated quantification of whole-body tumor volume as PSMA-PET biomarker is an unmet clinical challenge. Therefore, a novel semi-automated software is proposed and evaluated by the present study, which enables the semi-automated quantification of PSMA-PET biomarkers such as whole-body tumor volume. Methods: The proposed quantification is implemented as a research prototype (MI Whole Body Analysis Suite, v1.0, Siemens Medical Solutions USA, Inc., Knoxville, TN). PSMA accumulating foci were automatically segmented by a percental threshold (50% of local SUV_max). Neural networks were trained to segment organs in PET-CT acquisitions (training CTs: 8,632, validation CTs: 53). Thereby, PSMA foci within organs of physiologic PSMA uptake were semi-automatically excluded from the analysis. Pretherapeutic PSMA-PET-CTs of 40 consecutive patients treated with ¹⁷⁷Lu-PSMA-617 therapy were evaluated in this analysis. The volumetric whole-body tumor volume (PSMATV50), SUV_max, SUVmean and other whole-body imaging biomarkers were calculated for each patient. Semi-automatically derived results were compared with manual readings in a sub-cohort (by one nuclear medicine physician using syngo.MM Oncology software, Siemens Healthineers, Knoxville, TN). Additionally, an inter-observer evaluation of the semi-automated approach was performed in a sub-cohort (by two nuclear medicine physicians). Results: Manually and semi automatically derived PSMA metrics were highly correlated (PSMATV50: R2=1.000; p<0.001; SUV_max: R2=0.988; p<0.001). The inter-observer agreement of the semi-automated workflow was also high (PSMATV50: R2=1.000; p<0.001; ICC=1.000; SUV_max: R2=0.988; p<0.001; ICC=0.997). PSMATV50 [ml] was a significant predictor of overall survival (HR: 1.004; 95%CI: 1.001-1.006, P = 0.002) and remained so in a multivariate regression including other biomarkers (HR: 1.004; 95%CI: 1.001-1.006 P = 0.004). Conclusion: PSMATV50 is a promising PSMA-PET biomarker that is reproducible and easily quantified by the proposed semi-automated software. Moreover, PSMATV50 is a significant predictor of overall survival in patients with advanced prostate cancer that receive ¹⁷⁷Lu-PSMA-617 therapy.

At diagnosis 22% of colorectal cancer (CRC) patients have metastases and 50% later develop metastasis. Peptide receptor radionuclide therapy (PRRT) with lutetium-177 (¹⁷⁷Lu)-PSMA-617 is employed to treat metastatic prostate cancer (PC). ¹⁷⁷Lu-PSMA-617 targets Prostate Specific Membrane Antigen (PSMA) a cell surface protein enriched in PC and the neovasculature of other solid tumors including CRC. We performed gallium-68 (⁶⁸Ga)-PSMA-11 PET-CT imaging of ten metastatic CRC patients to assess metastasis avidity. Eight patients had lesions lacking avidity and two had solitary metastases exhibiting very low avidity. Despite expression of PSMA in CRC neovasculature, none of the patients exhibited tumor avidity sufficient to be considered for ¹⁷⁷Lu-PSMA-617 PRRT.

Rationale: To conduct a retrospective study comparing three ¹²³I-FP-CIT-SPECT quantitative methods in patients with neurodegenerative syndromes as referenced to neuropathological findings. Methods: ¹²³I-FP-CIT-SPECT and neuropathological findings among patients with neurodegenerative syndromes from the Mayo Alzheimer's Disease Research Center and Mayo Clinic Study of Aging were examined. Three ¹²³I-FP-CIT-SPECT quantitative assessment Methods: MIMneuro (MIM Software Inc.), DaTQUANT (GE Healthcare), and manual region of interest (ROI) creation on an Advantage Workstation (GE Healthcare) were compared to neuropathological findings describing the presence or absence of Lewy body disease (LBD). Striatum to background ratios (SBRs) generated by DaTQUANT were compared to the calculated SBRs of the manual method and MIMneuro. The left and right SBRs for caudate, putamen and striatum were evaluated with the manual method. For DaTQUANT and MIMneuro the left, right, total and average SBRs and z-scores for whole striatum, caudate, putamen, anterior putamen, and posterior putamen were calculated. Results: The cohort included 24 patients [20 (83%) male, aged 75.4 +/- 10.0 at death]. The antemortem clinical diagnoses were Alzheimer’s disease dementia (ADem, N = 6), probable dementia with Lewy bodies (pDLB, N = 12), mixed ADem/pDLB (N = 1), Parkinson’s disease with mild cognitive impairment (N = 2), corticobasal syndrome (N = 1), idiopathic rapid eye movement sleep behavior disorder (iRBD) (N = 1) and behavioral variant frontotemporal dementia (N = 1). Seventeen (71%) had LBD pathology. All three ¹²³I-FP-CIT-SPECT quantitative methods had area under the receiver operating characteristics (AUROC) values above 0.93 and up to 1.000 (p<0.001) and showed excellent discrimination between LBD and non-LBD patients in each region assessed, p<.001. There was no significant difference between the accuracy of the regions in discriminating the two groups, with good discrimination for both caudate and putamen. Conclusion: All three ¹²³I-FP-CIT-SPECT quantitative methods showed excellent discrimination between LBD and non-LBD patients in each region assessed, using both SBRs and z-scores.

Research Event

Event participants

Tutu Alicante, Executive Director, EG Justice
Chair: Dr Alex Vines OBE, Managing Director, Ethics, Risk & Resilience; Director, Africa Programme, Chatham House

Research Event

7 May 2020

Dynamic tyrosine phosphorylation is fundamental to a myriad of cellular processes. However, the inherently low abundance of tyrosine phosphorylation in the proteome and the inefficient enrichment of phosphotyrosine(pTyr)-containing peptides has led to poor pTyr peptide identification and quantitation, critically hindering researchers' ability to elucidate signaling pathways regulated by tyrosine phosphorylation in systems where cellular material is limited. The most popular approaches to wide-scale characterization of the tyrosine phosphoproteome use pTyr enrichment with pan-specific, anti-pTyr antibodies from a large amount of starting material. Methods that decrease the amount of starting material and increase the characterization depth of the tyrosine phosphoproteome while maintaining quantitative accuracy and precision would enable the discovery of tyrosine phosphorylation networks in rarer cell populations. To achieve these goals, the BOOST (Broad-spectrum Optimization Of Selective Triggering) method leveraging the multiplexing capability of tandem mass tags (TMT) and the use of pervanadate (PV) boost channels (cells treated with the broad-spectrum tyrosine phosphatase inhibitor PV) selectively increased the relative abundance of pTyr-containing peptides. After PV boost channels facilitated selective fragmentation of pTyr-containing peptides, TMT reporter ions delivered accurate quantitation of each peptide for the experimental samples while the quantitation from PV boost channels was ignored. This method yielded up to 6.3-fold boost in pTyr quantification depth of statistically significant data derived from contrived ratios, compared with TMT without PV boost channels or intensity-based label-free (LF) quantitation while maintaining quantitative accuracy and precision, allowing quantitation of over 2300 unique pTyr peptides from only 1 mg of T cell receptor-stimulated Jurkat T cells. The BOOST strategy can potentially be applied in analyses of other post-translational modifications where treatments that broadly elevate the levels of those modifications across the proteome are available.

State-of-the-art proteomics-grade mass spectrometers can measure peptide precursors and their fragments with ppm mass accuracy at sequencing speeds of tens of peptides per second with attomolar sensitivity. Here we describe a compact and robust quadrupole-orbitrap mass spectrometer equipped with a front-end High Field Asymmetric Waveform Ion Mobility Spectrometry (FAIMS) Interface. The performance of the Orbitrap Exploris 480 mass spectrometer is evaluated in data-dependent acquisition (DDA) and data-independent acquisition (DIA) modes in combination with FAIMS. We demonstrate that different compensation voltages (CVs) for FAIMS are optimal for DDA and DIA, respectively. Combining DIA with FAIMS using single CVs, the instrument surpasses 2500 peptides identified per minute. This enables quantification of >5000 proteins with short online LC gradients delivered by the Evosep One LC system allowing acquisition of 60 samples per day. The raw sensitivity of the instrument is evaluated by analyzing 5 ng of a HeLa digest from which >1000 proteins were reproducibly identified with 5 min LC gradients using DIA-FAIMS. To demonstrate the versatility of the instrument, we recorded an organ-wide map of proteome expression across 12 rat tissues quantified by tandem mass tags and label-free quantification using DIA with FAIMS to a depth of >10,000 proteins.

We present a technological advancement for the estimation of the affinities of Protein-Protein Interactions (PPIs) in living cells. A novel set of vectors is introduced that enables a quantitative yeast two-hybrid system based on fluorescent fusion proteins. The vectors allow simultaneous quantification of the reaction partners (Bait and Prey) and the reporter at the single-cell level by flow cytometry. We validate the applicability of this system on a small but diverse set of PPIs (eleven protein families from six organisms) with different affinities; the dissociation constants range from 117 pm to 17 μm. After only two hours of reaction, expression of the reporter can be detected even for the weakest PPI. Through a simple gating analysis, it is possible to select only cells with identical expression levels of the reaction partners. As a result of this standardization of expression levels, the mean reporter levels directly reflect the affinities of the studied PPIs. With a set of PPIs with known affinities, it is straightforward to construct an affinity ladder that permits rapid classification of PPIs with thus far unknown affinities. Conventional software can be used for this analysis. To permit automated analysis, we provide a graphical user interface for the Python-based FlowCytometryTools package.

Anal squamous cell carcinoma is a rare tumor. Chemo-radiotherapy yields a 50% 3-year relapse-free survival rate in advanced anal cancer, so improved predictive markers and therapeutic options are needed. High-throughput proteomics and whole-exome sequencing were performed in 46 paraffin samples from anal squamous cell carcinoma patients. Hierarchical clustering was used to establish groups de novo. Then, probabilistic graphical models were used to study the differences between groups of patients at the biological process level. A molecular classification into two groups of patients was established, one group with increased expression of proteins related to adhesion, T lymphocytes and glycolysis; and the other group with increased expression of proteins related to translation and ribosomes. The functional analysis by the probabilistic graphical model showed that these two groups presented differences in metabolism, mitochondria, translation, splicing and adhesion processes. Additionally, these groups showed different frequencies of genetic variants in some genes, such as ATM, SLFN11 and DST. Finally, genetic and proteomic characteristics of these groups suggested the use of some possible targeted therapies, such as PARP inhibitors or immunotherapy.

Laser-capture microdissection (LCM) allows the visualization and isolation of morphologically distinct subpopulations of cells from heterogeneous tissue specimens. In combination with formalin-fixed and paraffin-embedded (FFPE) tissue it provides a powerful tool for retrospective and clinically relevant studies of tissue proteins in a healthy and diseased context. We first optimized the protocol for efficient LCM analysis of FFPE tissue specimens. The use of SDS containing extraction buffer in combination with the single-pot solid-phase-enhanced sample preparation (SP3) digest method gave the best results regarding protein yield and protein/peptide identifications. Microdissected FFPE human substantia nigra tissue samples (~3,000 cells) were then analyzed, using tandem mass tag (TMT) labeling and LC-MS/MS, resulting in the quantification of >5,600 protein groups. Nigral proteins were classified and analyzed by abundance, showing an enrichment of extracellular exosome and neuron-specific gene ontology (GO) terms among the higher abundance proteins. Comparison of microdissected samples with intact tissue sections, using a label-free shotgun approach, revealed an enrichment of neuronal cell type markers, such as tyrosine hydroxylase and alpha-synuclein, as well as proteins annotated with neuron-specific GO terms. Overall, this study provides a detailed protocol for laser-capture proteomics using FFPE tissue and demonstrates the efficiency of LCM analysis of distinct cell subpopulations for proteomic analysis using low sample amounts.

Mass spectrometry (MS)-based proteomics has great potential for overcoming the limitations of antibody-based immunoassays for antibody-independent, comprehensive, and quantitative proteomic analysis of single cells. Indeed, recent advances in nanoscale sample preparation have enabled effective processing of single cells. In particular, the concept of using boosting/carrier channels in isobaric labeling to increase the sensitivity in MS detection has also been increasingly used for quantitative proteomic analysis of small-sized samples including single cells. However, the full potential of such boosting/carrier approaches has not been significantly explored, nor has the resulting quantitation quality been carefully evaluated. Herein, we have further evaluated and optimized our recent boosting to amplify signal with isobaric labeling (BASIL) approach, originally developed for quantifying phosphorylation in small number of cells, for highly effective analysis of proteins in single cells. This improved BASIL (iBASIL) approach enables reliable quantitative single-cell proteomics analysis with greater proteome coverage by carefully controlling the boosting-to-sample ratio (e.g. in general <100x) and optimizing MS automatic gain control (AGC) and ion injection time settings in MS/MS analysis (e.g. 5E5 and 300 ms, respectively, which is significantly higher than that used in typical bulk analysis). By coupling with a nanodroplet-based single cell preparation (nanoPOTS) platform, iBASIL enabled identification of ~2500 proteins and precise quantification of ~1500 proteins in the analysis of 104 FACS-isolated single cells, with the resulting protein profiles robustly clustering the cells from three different acute myeloid leukemia cell lines. This study highlights the importance of carefully evaluating and optimizing the boosting ratios and MS data acquisition conditions for achieving robust, comprehensive proteomic analysis of single cells.

Long Xu
Apr 1, 2020; 61:560-569
Methods

Momoko Kawana
Apr 7, 2020; 0:jlr.RA120000671v1-jlr.RA120000671
Research Articles

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Ceramides are the predominant lipids in the stratum corneum (SC) and are crucial components for normal skin barrier function. Although the composition of various ceramide classes in the human SC has been reported, that in mice is still unknown, despite mice being widely used as animal models of skin barrier function. Here, we performed LC–MS/MS analyses using recently available ceramide class standards to measure 25 classes of free ceramides and 5 classes of protein-bound ceramides from the human and mouse SC. Phytosphingosine-type ceramides (P-ceramides) and 6-hydroxy sphingosine-type ceramides (H-ceramides), which both contain an additional hydroxyl group, were abundant in human SC (35% and 45% of total ceramides, respectively). In contrast, in mice, P-ceramides and H-ceramides were present at ~1% and undetectable levels, respectively, and sphingosine-type ceramides accounted for ~90%. In humans, ceramides containing α-hydroxy FA were abundant, whereas ceramides containing β-hydroxy FA (B-ceramides) or -hydroxy FA were abundant in mice. The hydroxylated β-carbon in B-ceramides was in the (R)-configuration. Genetic knockout of β-hydroxy acyl-CoA dehydratases in HAP1 cells increased B-ceramide levels, suggesting that β-hydroxy acyl-CoA, an FA-elongation cycle intermediate in the endoplasmic reticulum, is a substrate for B-ceramide synthesis. We anticipate that our methods and findings will help to elucidate the role of each ceramide class in skin barrier formation and in the pathogenesis of skin disorders.

7 May 2020

As the capability of mass spectrometry-based proteomics has matured, tens of thousands of peptides can be measured simultaneously, which has the benefit of offering a systems view of protein expression. However, a major challenge is that with an increase in throughput, protein quantification estimation from the native measured peptides has become a computational task. A limitation to existing computationally-driven protein quantification methods is that most ignore protein variation, such as alternate splicing of the RNA transcript and post-translational modifications or other possible proteoforms, which will affect a significant fraction of the proteome. The consequence of this assumption is that statistical inference at the protein level, and consequently downstream analyses, such as network and pathway modeling, have only limited power for biomarker discovery. Here, we describe a Bayesian model (BP-Quant) that uses statistically derived peptides signatures to identify peptides that are outside the dominant pattern, or the existence of multiple over-expressed patterns to improve relative protein abundance estimates. It is a research-driven approach that utilizes the objectives of the experiment, defined in the context of a standard statistical hypothesis, to identify a set of peptides exhibiting similar statistical behavior relating to a protein. This approach infers that changes in relative protein abundance can be used as a surrogate for changes in function, without necessarily taking into account the effect of differential post-translational modifications, processing, or splicing in altering protein function. We verify the approach using a dilution study from mouse plasma samples and demonstrate that BP-Quant achieves similar accuracy as the current state-of-the-art methods at proteoform identification with significantly better specificity. BP-Quant is available as a MatLab ® and R packages at https://github.com/PNNL-Comp-Mass-Spec/BP-Quant.

Protein tyrosine kinases (PTKs) play key roles in cellular signal transduction, cell cycle regulation, cell division, and cell differentiation. Dysregulation of PTK-activated pathways, often by receptor overexpression, gene amplification, or genetic mutation, is a causal factor underlying numerous cancers. In this study, we have developed a parallel reaction monitoring (PRM)-based assay for quantitative profiling of 83 PTKs. The assay detects 308 proteotypic peptides from 54 receptor tyrosine kinases and 29 nonreceptor tyrosine kinases in a single run. Quantitative comparisons were based on the labeled reference peptide method. We implemented the assay in four cell models: 1) a comparison of proliferating versus epidermal growth factor (EGF)-stimulated A431 cells, 2) a comparison of SW480Null (mutant APC) and SW480APC (APC restored) colon tumor cell lines, and 3) a comparison of 10 colorectal cancer cell lines with different genomic abnormalities, and 4) lung cancer cell lines with either susceptibility (11-18) or acquired resistance (11-18R) to the epidermal growth factor receptor tyrosine kinase inhibitor erlotinib. We observed distinct PTK expression changes that were induced by stimuli, genomic features or drug resistance, which were consistent with previous reports. However, most of the measured expression differences were novel observations. For example, acquired resistance to erlotinib in the 11-18 cell model was associated not only with previously reported upregulation of MET, but also with upregulation of FLK2 and downregulation of LYN and PTK7. Immunoblot analyses and shotgun proteomics data were highly consistent with PRM data. Multiplexed PRM assays provide a targeted, systems-level profiling approach to evaluate cancer-related proteotypes and adaptations. Data are available through Proteome eXchange Accession PXD002706.

This article has been withdrawn by the authors. We discovered an error after this manuscript was published as a Paper in Press. Specifically, we learned that the structures of glycans presented for the PD-L1 peptide were drawn and labeled incorrectly. We wish to withdraw this article and submit a corrected version for review.

Ion mobility can add a dimension to LC-MS based shotgun proteomics which has the potential to boost proteome coverage, quantification accuracy and dynamic range.  Required for this is suitable software that extracts the information contained in the four-dimensional (4D) data space spanned by m/z, retention time, ion mobility and signal intensity. Here we describe the ion mobility enhanced MaxQuant software, which utilizes the added data dimension. It offers an end to end computational workflow for the identification and quantification of peptides and proteins in LC-IMS-MS/MS shotgun proteomics data. We apply it to trapped ion mobility spectrometry (TIMS) coupled to a quadrupole time-of-flight (QTOF) analyzer. A highly parallelizable 4D feature detection algorithm extracts peaks which are assembled to isotope patterns. Masses are recalibrated with a non-linear m/z, retention time, ion mobility and signal intensity dependent model, based on peptides from the sample. A new matching between runs (MBR) algorithm that utilizes collisional cross section (CCS) values of MS1 features in the matching process significantly gains specificity from the extra dimension. Prerequisite for using CCS values in MBR is a relative alignment of the ion mobility values between the runs. The missing value problem in protein quantification over many samples is greatly reduced by CCS aware MBR.MS1 level label-free quantification is also implemented which proves to be highly precise and accurate on a benchmark dataset with known ground truth. MaxQuant for LC-IMS-MS/MS is part of the basic MaxQuant release and can be downloaded from http://maxquant.org.

In bottom-up mass spectrometry-based proteomics, relative protein quantification is often achieved with data-dependent acquisition (DDA), data-independent acquisition (DIA), or selected reaction monitoring (SRM). These workflows quantify proteins by summarizing the abundances of all the spectral features of the protein (e.g., precursor ions, transitions or fragments) in a single value per protein per run. When abundances of some features are inconsistent with the overall protein profile (for technological reasons such as interferences, or for biological reasons such as post-translational modifications), the protein-level summaries and the downstream conclusions are undermined. We propose a statistical approach that automatically detects spectral features with such inconsistent patterns. The detected features can be separately investigated, and if necessary removed from the dataset. We evaluated the proposed approach on a series of benchmark controlled mixtures and biological investigations with DDA, DIA and SRM data acquisitions. The results demonstrated that it can facilitate and complement manual curation of the data. Moreover, it can improve the estimation accuracy, sensitivity and specificity of detecting differentially abundant proteins, and reproducibility of conclusions across different data processing tools. The approach is implemented as an option in the open-source R-based software MSstats.

Genetic and genomic research has greatly advanced our understanding of heart disease. Yet, comprehensive, in-depth, quantitative maps of protein expression in hearts of living humans are still lacking. Using samples obtained during valve replacement surgery in patients with mitral valve prolapse (MVP), we set out to define inter-chamber differences, the intersect of proteomic data with genetic or genomic datasets, and the impact of left atrial dilation on the proteome of patients with no history of atrial fibrillation (AF).  We collected biopsies from right atria (RA), left atria (LA) and left ventricle (LV) of seven male patients with mitral valve regurgitation with dilated LA but no history of AF. Biopsy samples were analyzed by high-resolution mass spectrometry (MS), where peptides were pre-fractionated by reverse phase high-pressure liquid chromatography prior to MS measurement on a Q-Exactive-HF Orbitrap instrument. We identified 7,314 proteins based on 130,728 peptides. Results were confirmed in an independent set of biopsies collected from three additional individuals. Comparative analysis against data from post-mortem samples showed enhanced quantitative power and confidence level in samples collected from living hearts. Our analysis, combined with data from genome wide association studies suggested candidate gene associations to MVP, identified higher abundance in ventricle for proteins associated with cardiomyopathies and revealed the dilated LA proteome, demonstrating differential representation of molecules previously associated with AF, in non-AF hearts. This is the largest dataset of cardiac protein expression from human samples collected in vivo. It provides a comprehensive resource that allows insight into molecular fingerprints of MVP and facilitates novel inferences between genomic data and disease mechanisms. We propose that over-representation of proteins in ventricle is consequent not to redundancy but to functional need, and conclude that changes in abundance of proteins known to associate with AF are not sufficient for arrhythmogenesis.

Label-Free Quantitative mass spectrometry based workflows for differential expression (DE) analysis of proteins impose important challenges on the data analysis due to peptide-specific effects and context dependent missingness of peptide intensities. Peptide-based workflows, like MSqRob, test for DE directly from peptide intensities and outperform summarization methods which first aggregate MS1 peptide intensities to protein intensities before DE analysis. However, these methods are computationally expensive, often hard to understand for the non-specialised end-user, and do not provide protein summaries, which are important for visualisation or downstream processing. In this work, we therefore evaluate state-of-the-art summarization strategies using a benchmark spike-in dataset and discuss why and when these fail compared to the state-of-the-art peptide based model, MSqRob. Based on this evaluation, we propose a novel summarization strategy, MSqRobSum, which estimates MSqRob’s model parameters in a two-stage procedure circumventing the drawbacks of peptide-based workflows. MSqRobSum maintains MSqRob’s superior performance, while providing useful protein expression summaries for plotting and downstream analysis. Summarising peptide to protein intensities considerably reduces the computational complexity, the memory footprint and the model complexity, and makes it easier to disseminate DE inferred on protein summaries. Moreover, MSqRobSum provides a highly modular analysis framework, which provides researchers with full flexibility to develop data analysis workflows tailored towards their specific applications.

Bile acids (BAs) serve multiple biological functions, ranging from the absorption of lipids and fat-soluble vitamins to serving as signaling molecules through the direct activation of dedicated cellular receptors. Synthesized by both host and microbial pathways, BAs are increasingly understood as participating in the regulation of numerous pathways relevant to metabolic diseases, including lipid and glucose metabolism, energy expenditure, and inflammation. Quantitative analyses of BAs in biological matrices can be problematic due to their unusual and diverse physicochemical properties, making optimization of a method that shows good accuracy, precision, efficiency of extraction, and minimized matrix effects across structurally distinct human and murine BAs challenging. Herein we develop and clinically validate a stable-isotope-dilution LC/MS/MS method for the quantitative analysis of numerous primary and secondary BAs in both human and mouse biological matrices. We also utilize this tool to investigate gut microbiota participation in the generation of structurally specific BAs in both humans and mice. We examine circulating levels of specific BAs and in a clinical case-control study of age- and gender-matched type 2 diabetes mellitus (T2DM) versus nondiabetics. BAs whose circulating levels are associated with T2DM include numerous 12α-hydroxyl BAs (taurocholic acid, taurodeoxycholic acid, glycodeoxycholic acid, deoxycholic acid, and 3-ketodeoxycholic acid), while taurohyodeoxycholic acid was negatively associated with diabetes. The LC/MS/MS-based platform described should serve as a robust, high-throughput investigative tool for studying the potential involvement of structurally specific BAs and the gut microbiome on both physiological and disease processes.

This article focuses on the establishment of an accurate and sensitive quantitation method for the analysis of furan fatty acids. In particular, the sensitivity of GC/MS and UPLC/ESI/MS/MS was compared for the identification and quantification of furan fatty acids. Different methylation methods were tested with respect to GC/MS analysis. Special attention needs to be paid to the methylation of furan fatty acids, as acidic catalysts might lead to the degradation of the furan ring. GC/MS analysis in full-scan mode demonstrated that the limit of quantitation was 10 μM. UPLC/ESI/MS/MS in multiple reaction monitoring mode displayed a higher detection sensitivity than GC/MS. Moreover, the identification of furan fatty acids with charge-reversal derivatization was tested in the positive mode with two widely used pyridinium salts. Significant oxidation was unexpectedly observed using N-(4-aminomethylphenyl) pyridinium as a derivatization agent. The formed 3-acyl-oxymethyl-1-methylpyridinium iodide derivatized by 2-bromo-1-methylpyridinium iodide and 3-carbinol-1-methylpyridinium iodide improved the sensitivity more than 2,000-fold compared with nonderivatization in the negative mode by UPLC/ESI/MS/MS. This charge-reversal derivatization enabled the targeted quantitation of furan fatty acids in human plasma. Thus, it is anticipated that this protocol could greatly contribute to the clarification of pathological mechanisms related to furan fatty acids and their metabolites.

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Members Event

Event participants

Michelle Bachelet, United Nations High Commissioner for Human Rights

Chair: Ruma Mandal, Head, International Law Programme, Chatham House

18 December 2019

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Steven E Kahn
Nov 1, 1993; 42:1663-1672
Original Article

Days after The Gleaner reported a clarion call from Port Maria Mayor Richard Creary for the quarantine of St Mary communities owing to growing concerns about the spread of COVID-19 in the parish, the Government responded with the lockdown of three...