BackgroundRe-operation for positive resection margins following breast-conserving surgery occurs frequently (average = 20–25%), is cost-inefficient, and leads to physical and psychological morbidity. Current margin assessment techniques are slow and labour intensive. Rapid evaporative ionisation mass spectrometry (REIMS) rapidly identifies dissected tissues by determination of tissue structural lipid profiles through on-line chemical analysis of electrosurgical aerosol toward real-time margin assessment.MethodsElectrosurgical aerosol produced from ex-vivo and in-vivo breast samples was aspirated into a mass spectrometer (MS) using a monopolar hand-piece. Tissue identification results obtained by multivariate statistical analysis of MS data were validated by histopathology. Ex-vivo classification models were constructed from a mass spectral database of normal and tumour breast samples. Univariate and tandem MS analysis of significant peaks was conducted to identify biochemical differences between normal and cancerous tissues. An ex-vivo classification model was used in combination with bespoke recognition software, as an intelligent knife (iKnife), to predict the diagnosis for an ex-vivo validation set. Intraoperative REIMS data were acquired during breast surgery and time-synchronized to operative videos.ResultsA classification model using histologically validated spectral data acquired from 932 sampling points in normal tissue and 226 in tumour tissue provided 93.4% sensitivity and 94.9% specificity. Tandem MS identified 63 phospholipids and 6 triglyceride species responsible for 24 spectral differences between tissue types. iKnife recognition accuracy with 260 newly acquired fresh and frozen breast tissue specimens (normal n = 161, tumour n = 99) provided sensitivity of 90.9% and specificity of 98.8%. The ex-vivo and intra-operative method produced visually comparable high intensity spectra. iKnife interpretation of intra-operative electrosurgical vapours, including data acquisition and analysis was possible within a mean of 1.80 seconds (SD ±0.40).ConclusionsThe REIMS method has been optimised for real-time iKnife analysis of heterogeneous breast tissues based on subtle changes in lipid metabolism, and the results suggest spectral analysis is both accurate and rapid. Proof-of-concept data demonstrate the iKnife method is capable of online intraoperative data collection and analysis. Further validation studies are required to determine the accuracy of intra-operative REIMS for oncological margin assessment.Electronic supplementary materialThe online version of this article (doi:10.1186/s13058-017-0845-2) contains supplementary material, which is available to authorized users.

SummaryPKCβ-null (Prkcb−/−) mice are severely immunodeficient. Here we show that mice whose B cells lack PKCβ failed to form germinal centers and plasma cells, which undermined affinity maturation and antibody production in response to immunization. Moreover, these mice failed to develop plasma cells in response to viral infection. At the cellular level, we have shown that Prkcb−/− B cells exhibited defective antigen polarization and mTORC1 signaling. While altered antigen polarization impaired antigen presentation and likely restricted the potential of GC development, defective mTORC1 signaling impaired metabolic reprogramming, mitochondrial remodeling, and heme biosynthesis in these cells, which altogether overwhelmingly opposed plasma cell differentiation. Taken together, our study reveals mechanistic insights into the function of PKCβ as a key regulator of B cell polarity and metabolic reprogramming that instructs B cell fate.

Mass spectrometric (MS) approaches developed for tissue identification in surgical environments are reviewed. MS Imaging (MSI) techniques enable the direct analysis of human tissue and can be used as an alternative means for margin assessment. While MSI-based approaches were demonstrated to improve the examiner-related variance of the data, the time demand and the cost of these analyses remained high. Furthermore, the necessity of MS expertise for the clinical deployment of these techniques has hindered large-scale clinical testing. The advent of ‘ambient’ MS methods contributed to the application of MSI techniques in this field, however alternative methods have been developed for the direct analysis of tissue samples without sample preparation. One group of methods employs surgical tissue manipulation for ionization while the other one uses minimally invasive probes for sampling prior to ionization. The methods are summarised and compared with regard to the information delivered, turnaround time and tissue identification performance

Stable isotope labelling experiments are used routinely in metabolic flux analysis (MFA) to determine the metabolic phenotype of cells and tissues. A complication arises in multicellular systems because single cell measurements of transcriptomes, proteomes and metabolomes in multicellular organisms suggest that the metabolic phenotype will differ between cell types. In silico analysis of simulated metabolite isotopomer datasets shows that cellular heterogeneity confounds conventional MFA because labelling data averaged over multiple cell types does not necessarily yield averaged flux values. A potential solution to this problem—the use of cell-type specific reporter proteins as a source of cell-type specific labelling data—is proposed and the practicality of implementing this strategy in the roots of Arabidopsis thaliana seedlings is explored. A protocol for the immunopurification of ectopically expressed green fluorescent protein (GFP) from Arabidopsis thaliana seedlings using a GFP-binding nanobody is developed, and through GC-MS analysis of protein hydrolysates it is established that constitutively expressed GFP reports accurately on the labelling of total protein in root tissues. It is also demonstrated that the constitutive expression of GFP does not perturb metabolism. The principal obstacle to the implementation of the method in tissues with cell-type specific GFP expression is the sensitivity of the GC-MS system.

Glucosinolates are a group of plant secondary metabolites that can be hydrolyzed into a variety of breakdown products such as isothiocyanates, thiocyanates, and nitriles. These breakdown products can facilitate plant defense and function as attractants to natural enemies of insect pests. As part of the diet, some of these compounds have shown cancer-preventing activities, and the levels of these metabolites in the edible parts of the plants are of interest. In this study, we systematically examined variations in glucosinolates, their precursors, and their breakdown products in 12 commonly consumed vegetables of the Brassicaceae family with gas chromatography—quadrupole time-of-flight mass spectrometer (GC-Q-TOF/MS), liquid chromatography–quadrupole time-of-flight mass spectrometer (LC-Q-TOF/MS), and liquid chromatography—triple quadrupole mass spectrometer (LC-QQQ/MS), using both untargeted and targeted approaches. The findings were integrated with data from literature to provide a comprehensive map of pathways for biosynthesis of glucosinolates and isothiocyanates. The levels of precursor glucosinolates are found to correlate well with their downstream breakdown products. Further, the types and abundances of glucosinolates among different genera are significantly different, and these data allow the classification of plants based on morphological taxonomy. Further validation on three genera, which are grown underground, in damp soil, and above ground, suggests that each genus has its specific biosynthetic pathways and that there are variations in some common glucosinolate biosynthesis pathways. Our methods and results provide a good starting point for further investigations into specific aspects of glucosinolate metabolism in the Brassica vegetables.