Single-Cell Lipidomics Using Analytical Flow LC-MS Characterizes the Response to Chemotherapy in Cultured Pancreatic Cancer Cells.

In this work, we demonstrate the development and first application of nanocapillary sampling followed by analytical flow liquid chromatography-mass spectrometry for single-cell lipidomics. Around 260 lipids were tentatively identified in a single cell, demonstrating remarkable sensitivity. Human pancreatic ductal adenocarcinoma cells (PANC-1) treated with the chemotherapeutic drug gemcitabine can be distinguished from controls solely on the basis of their single-cell lipid profiles. Notably, the relative abundance of LPC(0:0/16:0) was significantly affected in gemcitabine-treated cells, in agreement with previous work in bulk. This work serves as a proof of concept that live cells can be sampled selectively and then characterized using automated and widely available analytical workflows, providing biologically relevant outputs.

Implications of missing data on reported breast cancer mortality.

BACKGROUND: National cancer registries are valuable tools to analyze patterns of care and clinical outcomes; yet, missing data may impact the accuracy and generalizability of these data. We sought to evaluate the association between missing data and overall survival (OS). METHODS: Using the NCDB (National Cancer Database) and SEER (Surveillance, Epidemiology, End Results Program), we assessed data missingness among patients diagnosed with invasive breast cancer from 2010 to 2014. Key variables included demographic (age, race, ethnicity, insurance, education, income), tumor (grade, ER, PR, HER2, TNM stages), and treatment (surgery in both databases; chemotherapy and radiation in NCDB). OS was compared between those with and without missing data using Cox proportional hazards models. RESULTS: Overall, 775,996 patients in the NCDB and 263,016 in SEER were identified; missing at least 1 key variable occurred for 29% and 13%, respectively. Of those, the overwhelming majority (NCDB 80%; SEER 88%) were missing tumor variables. When compared to patients with complete data, missingness was associated with a greater risk of death: NCDB HR 1.23 (99% CI 1.21-1.25) and SEER HR 2.11 (99% CI 2.05-2.18). Patients with complete tumor data had higher unadjusted OS estimates than that of the entire sample: NCDB 82.7% vs 81.8% and SEER 83.5% vs 81.7% for 5-year OS. CONCLUSIONS: Missingness of select variables is not uncommon within large national cancer registries and is associated with a worse OS. Exclusion of patients with missing variables may introduce unintended bias into analyses and result in findings that underestimate breast cancer mortality.

Nanocapillary sampling coupled to liquid chromatography mass spectrometry delivers single cell drug measurement and lipid fingerprints.

This work describes the development of a new approach to measure drug levels and lipid fingerprints in single living mammalian cells. Nanocapillary sampling is an approach that enables the selection and isolation of single living cells under microscope observation. Here, live single cell nanocapillary sampling is coupled to liquid chromatography for the first time. This allows molecular species to be separated prior to ionisation and improves measurement precision of drug analytes. The efficiency of transferring analytes from the sampling capillary into a vial was optimised in this work. The analysis was carried out using standard flow liquid chromatography coupled to widely available mass spectrometry instrumentation, highlighting opportunities for widespread adoption. The method was applied to 30 living cells, revealing cell-to-cell heterogeneity in the uptake of different drug molecules. Using this system, we detected 14-158 lipid features per single cell, revealing the association between bedaquiline uptake and lipid fingerprints.

A prospective, double-blinded cohort study using quantitative fetal fibronectin testing in symptomatic women for the prediction of spontaneous preterm delivery.

BACKGROUND: Spontaneous preterm birth (PTB) affects 6.5% of deliveries in Hong Kong. Quantitative fetal fibronectin (fFN) is under-utilised as a test for PTB prediction in Hong Kong. Our objective was to evaluate the effectiveness of quantitative fFN in predicting spontaneous PTB in women with symptoms of threatened preterm labour (TPTL) in our population. METHODS: A prospective, double-blinded cohort study of women with a singleton gestation and TPTL symptoms presenting to a tertiary hospital in Hong Kong between 24 + 0 to 33 + 6 weeks was performed from 1st October 2020 and 31st October 2021. Women with vaginal bleeding, ruptured membranes, and cervical dilation > 3 cm were excluded. The primary outcome was to test the characteristics of quantitative fFN in predicting spontaneous PTB < 37 weeks. Secondary outcome was to investigate the relationship between fFN value and time to PTB. Test characteristics of quantitative fFN at different thresholds were evaluated. RESULTS: 48 women with TPTL were recruited. All had fFN testing at admission with the results being concealed from the obstetrician managing the patient. 10 mothers had PTB (< 37 weeks' gestation). 7/48 (15%) had a subsequent PTB within 14 days from testing and 5 (10%) delivered within 48 h. The negative predictive value (NPV) of predicting delivery within 14 days was 97.3% and 100% when using a cut-off of < 50ng/ml and < 10ng/ml respectively. Using > 200 ng/ml as cut-off can also reliably predict delivery within 48 h - 7 days with positive predictive value PPV of 100%; as well as PTB before 37 weeks. CONCLUSIONS: Quantitative fFN has predictive value for spontaneous PTB prediction in symptomatic women in a Hong Kong population. fFN concentration could help clinicians rule out PTB and avoid unnecessary interventions and hospitalisation.

Meta-Analysis of COVID-19 Metabolomics Identifies Variations in Robustness of Biomarkers.

The global COVID-19 pandemic resulted in widespread harms but also rapid advances in vaccine development, diagnostic testing, and treatment. As the disease moves to endemic status, the need to identify characteristic biomarkers of the disease for diagnostics or therapeutics has lessened, but lessons can still be learned to inform biomarker research in dealing with future pathogens. In this work, we test five sets of research-derived biomarkers against an independent targeted and quantitative Liquid Chromatography-Mass Spectrometry metabolomics dataset to evaluate how robustly these proposed panels would distinguish between COVID-19-positive and negative patients in a hospital setting. We further evaluate a crowdsourced panel comprising the COVID-19 metabolomics biomarkers most commonly mentioned in the literature between 2020 and 2023. The best-performing panel in the independent dataset-measured by F1 score (0.76) and AUROC (0.77)-included nine biomarkers: lactic acid, glutamate, aspartate, phenylalanine, ß-alanine, ornithine, arachidonic acid, choline, and hypoxanthine. Panels comprising fewer metabolites performed less well, showing weaker statistical significance in the independent cohort than originally reported in their respective discovery studies. Whilst the studies reviewed here were small and may be subject to confounders, it is desirable that biomarker panels be resilient across cohorts if they are to find use in the clinic, highlighting the importance of assessing the robustness and reproducibility of metabolomics analyses in independent populations.

Colocation of Lipids, Drugs, and Metal Biomarkers Using Spatially Resolved Lipidomics with Elemental Mapping.

Elemental imaging is widely used for imaging cells and tissues but rarely in combination with organic mass spectrometry, which can be used to profile lipids and measure drug concentrations. Here, we demonstrate how elemental imaging and a new method for spatially resolved lipidomics (DAPNe-LC-MS, based on capillary microsampling and liquid chromatography mass spectrometry) can be used in combination to probe the relationship between metals, drugs, and lipids in discrete areas of tissues. This new method for spatial lipidomics, reported here for the first time, has been applied to rabbit lung tissues containing a lesion (caseous granuloma) caused by tuberculosis infection. We demonstrate how elemental imaging with spatially resolved lipidomics can be used to probe the association between ion accumulation and lipid profiles and verify local drug distribution.

Multi-Omics Reveals Mechanisms of Partial Modulation of COVID-19 Dysregulation by Glucocorticoid Treatment.

Treatments for COVID-19 infections have improved dramatically since the beginning of the pandemic, and glucocorticoids have been a key tool in improving mortality rates. The UK's National Institute for Health and Care Excellence guidance is for treatment to be targeted only at those requiring oxygen supplementation, however, and the interactions between glucocorticoids and COVID-19 are not completely understood. In this work, a multi-omic analysis of 98 inpatient-recruited participants was performed by quantitative metabolomics (using targeted liquid chromatography-mass spectrometry) and data-independent acquisition proteomics. Both 'omics datasets were analysed for statistically significant features and pathways differentiating participants whose treatment regimens did or did not include glucocorticoids. Metabolomic differences in glucocorticoid-treated patients included the modulation of cortisol and bile acid concentrations in serum, but no alleviation of serum dyslipidemia or increased amino acid concentrations (including tyrosine and arginine) in the glucocorticoid-treated cohort relative to the untreated cohort. Proteomic pathway analysis indicated neutrophil and platelet degranulation as influenced by glucocorticoid treatment. These results are in keeping with the key role of platelet-associated pathways and neutrophils in COVID-19 pathogenesis and provide opportunity for further understanding of glucocorticoid action. The findings also, however, highlight that glucocorticoids are not fully effective across the wide range of 'omics dysregulation caused by COVID-19 infections.

Vascularized composite allotransplantation in the United States: A retrospective analysis of the Organ Procurement and Transplantation Network data after 5 years of the Final Rule.

On July 3, 2014, the Organ Procurement and Transplantation Network (OPTN) began overseeing vascularized composite allotransplantation/allografts (VCA) in the United States. For the past 6 years, centers performing VCAs have been requested to submit data into a biometric repository, in parallel with systems used by solid organ transplant centers. Currently, 62 VCAs are reported in the entire OPTN database, with 36 of these transplants reported as performed after VCA was added to the OPTN Final Rule. Of these 36 recipients, 16 received uterus transplants, most of which (11) occurred from living donors. Ten patients received hand transplants and 6 received face transplants. Two patients received abdominal wall transplants, 1 patient received a scalp transplant, and 1 patient received a penile transplant. The present manuscript represents the query of a nationalized database for VCA type, immunosuppression treatment, and clinical outcomes for VCAs. This manuscript provides a report of the current VCA data reported to the OPTN after the Final Rule.

Nanoextraction Coupled to Liquid Chromatography Mass Spectrometry Delivers Improved Spatially Resolved Analysis.

Direct analyte-probed nanoextraction (DAPNe) is a technique that allows extraction of drug and endogenous compounds from a discrete location on a tissue sample using a nano capillary filled with solvent. Samples can be extracted from spot diameters as low as 6 µm. Studies previously undertaken by our group have shown that the technique can provide good precision (5%) for analyzing drug molecules in 150 µm diameter areas of homogenized tissue, provided an internal standard is sprayed on to the tissue prior to analysis. However, without an isotopically labeled standard, the repeatability is poor, even after normalization to the spot area or matrix compounds. By application to tissue homogenates spiked with drug compounds, we can demonstrate that it is possible to significantly improve the repeatability of the technique by incorporating a liquid chromatography separation step. Liquid chromatography is a technique for separating compounds prior to mass spectrometry (LC-MS) which enables separation of isomeric compounds that cannot be discriminated using mass spectrometry alone, as well as reducing matrix interferences. Conventionally, LC-MS is carried out on bulk or homogenized samples, which means analysis is essentially an average of the sample and does not take into account discrete areas. This work opens a new opportunity for spatially resolved liquid chromatography mass spectrometry with precision better than 20%.

True one cell chemical analysis: a review.

The constantly growing field of True One Cell (TOC) analysis has provided important information on the direct chemical composition of various cells and cellular components. Since the heterogeneity of individual cells has been established, more researchers are interested in the chemical differences between individual cells; TOC is the only form of analysis that can provide this information. This has resulted in the constant development of new technologies and methods. This review highlights the common techniques for micro- and nanomanipulation, Raman spectroscopy, microscopy, and mass spectrometric imaging as they pertain to TOC chemical analysis.

Assessment of microbiota:host interactions at the vaginal mucosa interface.

There is increasing appreciation of the role that vaginal microbiota play in health and disease throughout a woman's lifespan. This has been driven partly by molecular techniques that enable detailed identification and characterisation of microbial community structures. However, these methods do not enable assessment of the biochemical and immunological interactions between host and vaginal microbiota involved in pathophysiology. This review examines our current knowledge of the relationships that exist between vaginal microbiota and the host at the level of the vaginal mucosal interface. We also consider methodological approaches to microbiomic, immunologic and metabolic profiling that permit assessment of these interactions. Integration of information derived from these platforms brings the potential for biomarker discovery, disease risk stratification and improved understanding of the mechanisms regulating vaginal microbial community dynamics in health and disease.

Medical Swab Analysis Using Desorption Electrospray Ionization Mass Spectrometry: A Noninvasive Approach for Mucosal Diagnostics.

Medical swabs are routinely used worldwide to sample human mucosa for microbiological screening with culture methods. These are usually time-consuming and have a narrow focus on screening for particular microorganism species. As an alternative, direct mass spectrometric profiling of the mucosal metabolome provides a broader window into the mucosal ecosystem. We present for the first time a minimal effort/minimal-disruption technique for augmenting the information obtained from clinical swab analysis with mucosal metabolome profiling using desorption electrospray ionization mass spectrometry (DESI-MS) analysis. Ionization of mucosal biomass occurs directly from a standard rayon swab mounted on a rotating device and analyzed by DESI MS using an optimized protocol considering swab-inlet geometry, tip-sample angles and distances, rotation speeds, and reproducibility. Multivariate modeling of mass spectral fingerprints obtained in this way readily discriminate between different mucosal surfaces and display the ability to characterize biochemical alterations induced by pregnancy and bacterial vaginosis (BV). The method was also applied directly to bacterial biomass to confirm the ability to detect intact bacterial species from a swab. These results highlight the potential of direct swab analysis by DESI-MS for a wide range of clinical applications including rapid mucosal diagnostics for microbiology, immune responses, and biochemistry.

Sleep deprivation and false memories.

Many studies have investigated factors that affect susceptibility to false memories. However, few have investigated the role of sleep deprivation in the formation of false memories, despite overwhelming evidence that sleep deprivation impairs cognitive function. We examined the relationship between self-reported sleep duration and false memories and the effect of 24 hr of total sleep deprivation on susceptibility to false memories. We found that under certain conditions, sleep deprivation can increase the risk of developing false memories. Specifically, sleep deprivation increased false memories in a misinformation task when participants were sleep deprived during event encoding, but did not have a significant effect when the deprivation occurred after event encoding. These experiments are the first to investigate the effect of sleep deprivation on susceptibility to false memories, which can have dire consequences.

Delays in gender affirming healthcare due to COVID-19 are mitigated by expansion of telemedicine.

BACKGROUND: Gender-affirming healthcare is vital for transgender and gender diverse (TGD) patients, and during the pandemic, accessing healthcare became challenging. Hypothesizing that many had procedures postponed, we sought to characterize the impact of the pandemic on TGD patients. METHODS: A mixed-methods approach was employed, combining surveys and interviews; Duke patients were identified by ICD-10 codes, while non-Duke (national) patients were recruited through online social media. RESULTS: All specialties increased telemedicine usage during the pandemic. Duke surgical patients reported a nearly three-fold increase in telemedicine access. COVID-19 symptoms were reported by 24% of Duke and 20% of national patients; barriers to urgent care included the fear of discrimination (27%). CONCLUSION: Delays were experienced in all domains of care, mitigated in part by telemedicine. Nearly one-third of patients cite discrimination as a barrier to care. Though pandemic-related expansion of telemedicine may be a marker of success, significant barriers still complicate delivery of healthcare.

Spatial single cell metabolomics: Current challenges and future developments.

Single cell metabolomics is a rapidly advancing field of bio-analytical chemistry which aims to observe cellular biology with the greatest detail possible. Mass spectrometry imaging and selective cell sampling (e.g. using nanocapillaries) are two common approaches within the field. Recent achievements such as observation of cell-cell interactions, lipids determining cell states and rapid phenotypic identification demonstrate the efficacy of these approaches and the momentum of the field. However, single cell metabolomics can only continue with the same impetus if the universal challenges to the field are met, such as the lack of strategies for standardisation and quantification, and lack of specificity/sensitivity. Mass spectrometry imaging and selective cell sampling come with unique advantages and challenges which, in many cases are complementary to each other. We propose here that the challenges specific to each approach could be ameliorated with collaboration between the two communities driving these approaches.

Preoperative Body Image Factors Are Associated with Complications after Breast Reconstruction.

BACKGROUND: Psychological factors are broadly understood to contribute to overall health, but their contribution to wound healing is less well defined. Limited data exist on the association of preoperative psychological factors such as body image and postoperative complications. The present study analyzed the association between preoperative body image factors and postoperative complications following breast reconstruction. METHODS: This was a prospective cohort study of 302 breast cancer patients undergoing breast reconstruction from 2011 to 2015. All patients completed the BREAST-Q; demographics, surgical details, and postoperative complications were recorded. The association of body image factors by means of the BREAST-Q and postoperative complications was analyzed. RESULTS: On univariate analysis, patients who reported lower preoperative satisfaction with how they appeared in the mirror unclothed, or felt less self-confident or attractive, were significantly more likely to develop an infection postoperatively. Preoperative satisfaction scores were not associated with complications when analyzed in a multivariate fashion. On binomial logistic regression analysis, after controlling for age, body mass index, reconstruction technique, and use of radiotherapy, patients who reported less preoperative satisfaction with how comfortably bras fit or how they appeared in a mirror unclothed were at an increased risk for delayed wound healing. CONCLUSIONS: Patients with lower preoperative body satisfaction were found to have an increased incidence of infections and delayed wound healing. Although postoperative outcomes are multifactorial, the data suggest that baseline psychological factors such as body image may play a role in postoperative outcomes. Broader use of prehabilitative therapies, targeted at psychosocial factors, may warrant further investigation to optimize postoperative outcomes. CLINICAL QUESTION/LEVEL OF EVIDENCE: Risk, III.

Systematic review with meta-analysis of diagnostic test accuracy for COVID-19 by mass spectrometry.

BACKGROUND: The global COVID-19 pandemic has led to extensive development in many fields, including the diagnosis of COVID-19 infection by mass spectrometry. The aim of this systematic review and meta-analysis was to assess the accuracy of mass spectrometry diagnostic tests developed so far, across a wide range of biological matrices, and additionally to assess risks of bias and applicability in studies published to date. METHOD: 23 retrospective observational cohort studies were included in the systematic review using the PRISMA-DTA framework, with a total of 2858 COVID-19 positive participants and 2544 controls. Risks of bias and applicability were assessed via a QUADAS-2 questionnaire. A meta-analysis was also performed focusing on sensitivity, specificity, diagnostic accuracy and Youden's Index, in addition to assessing heterogeneity. FINDINGS: Sensitivity averaged 0.87 in the studies reviewed herein (interquartile range 0.81-0.96) and specificity 0.88 (interquartile range 0.82-0.98), with an area under the receiver operating characteristic summary curve of 0.93. By subgroup, the best diagnostic results were achieved by viral proteomic analyses of nasopharyngeal swabs and metabolomic analyses of plasma and serum. The performance of other sampling matrices (breath, sebum, saliva) was less good, indicating that these protocols are currently insufficiently mature for clinical application. CONCLUSIONS: This systematic review and meta-analysis demonstrates the potential for mass spectrometry and 'omics in achieving accurate test results for COVID-19 diagnosis, but also highlights the need for further work to optimize and harmonize practice across laboratories before these methods can be translated to clinical applications.

An integrated analysis and comparison of serum, saliva and sebum for COVID-19 metabolomics.

The majority of metabolomics studies to date have utilised blood serum or plasma, biofluids that do not necessarily address the full range of patient pathologies. Here, correlations between serum metabolites, salivary metabolites and sebum lipids are studied for the first time. 83 COVID-19 positive and negative hospitalised participants provided blood serum alongside saliva and sebum samples for analysis by liquid chromatography mass spectrometry. Widespread alterations to serum-sebum lipid relationships were observed in COVID-19 positive participants versus negative controls. There was also a marked correlation between sebum lipids and the immunostimulatory hormone dehydroepiandrosterone sulphate in the COVID-19 positive cohort. The biofluids analysed herein were also compared in terms of their ability to differentiate COVID-19 positive participants from controls; serum performed best by multivariate analysis (sensitivity and specificity of 0.97), with the dominant changes in triglyceride and bile acid levels, concordant with other studies identifying dyslipidemia as a hallmark of COVID-19 infection. Sebum performed well (sensitivity 0.92; specificity 0.84), with saliva performing worst (sensitivity 0.78; specificity 0.83). These findings show that alterations to skin lipid profiles coincide with dyslipidaemia in serum. The work also signposts the potential for integrated biofluid analyses to provide insight into the whole-body atlas of pathophysiological conditions.

Microbial-driven preterm labour involves crosstalk between the innate and adaptive immune response.

There has been a surge in studies implicating a role of vaginal microbiota in spontaneous preterm birth (sPTB), but most are associative without mechanistic insight. Here we show a comprehensive approach to understand the causative factors of preterm birth, based on the integration of longitudinal vaginal microbiota and cervicovaginal fluid (CVF) immunophenotype data collected from 133 women at high-risk of sPTB. We show that vaginal depletion of Lactobacillus species and high bacterial diversity leads to increased mannose binding lectin (MBL), IgM, IgG, C3b, C5, IL-8, IL-6 and IL-1ß and to increased risk of sPTB. Cervical shortening, which often precedes preterm birth, is associated with Lactobacillus iners and elevated levels of IgM, C3b, C5, C5a and IL-6. These data demonstrate a role for the complement system in microbial-driven sPTB and provide a scientific rationale for the development of live biotherapeutics and complement therapeutics to prevent sPTB.

Metabolomics Markers of COVID-19 Are Dependent on Collection Wave.

The effect of COVID-19 infection on the human metabolome has been widely reported, but to date all such studies have focused on a single wave of infection. COVID-19 has generated numerous waves of disease with different clinical presentations, and therefore it is pertinent to explore whether metabolic disturbance changes accordingly, to gain a better understanding of its impact on host metabolism and enable better treatments. This work used a targeted metabolomics platform (Biocrates Life Sciences) to analyze the serum of 164 hospitalized patients, 123 with confirmed positive COVID-19 RT-PCR tests and 41 providing negative tests, across two waves of infection. Seven COVID-19-positive patients also provided longitudinal samples 2-7 months after infection. Changes to metabolites and lipids between positive and negative patients were found to be dependent on collection wave. A machine learning model identified six metabolites that were robust in diagnosing positive patients across both waves of infection: TG (22:1_32:5), TG (18:0_36:3), glutamic acid (Glu), glycolithocholic acid (GLCA), aspartic acid (Asp) and methionine sulfoxide (Met-SO), with an accuracy of 91%. Although some metabolites (TG (18:0_36:3) and Asp) returned to normal after infection, glutamic acid was still dysregulated in the longitudinal samples. This work demonstrates, for the first time, that metabolic dysregulation has partially changed over the course of the pandemic, reflecting changes in variants, clinical presentation and treatment regimes. It also shows that some metabolic changes are robust across waves, and these can differentiate COVID-19-positive individuals from controls in a hospital setting. This research also supports the hypothesis that some metabolic pathways are disrupted several months after COVID-19 infection.