Human macrophages are innate immune cells with diverse, functionally distinct phenotypes, namely, pro-inflammatory M1 and anti-inflammatory M2 macrophages. Both are involved in multiple... Show moreHuman macrophages are innate immune cells with diverse, functionally distinct phenotypes, namely, pro-inflammatory M1 and anti-inflammatory M2 macrophages. Both are involved in multiple physiological and pathological processes, including would healing, infection, and cancer. However, the metabolic differences between these phenotypes are largely unexplored at single-cell resolution. To address this knowledge gap, an untargeted live single-cell mass spectrometry-based metabolomic profiling coupled with a machine-learning data analysis approach was developed to investigate the metabolic profile of each phenotype at the single-cell level. Results show that M1 and M2 macrophages have distinct metabolic profiles, with differential levels of fatty acyls, glycerophospholipids, and sterol lipids, which are important components of plasma membrane and involved in multiple biological processes. Furthermore, we could discern several putatively annotated molecules that contribute to inflammatory response of macrophages. The combination of random forest and live single-cell metabolomics provided an in-depth profile of the metabolome of primary human M1 and M2 macrophages at the single-cell level for the first time, which will pave the way for future studies targeting the differentiation of other immune cells. Show less
Harish, P.; Malerba, A.; Kroon, R.H.M.J.M.; Shademan, M.; Engelan, B. van; Raz, V.; ... ; Snowden, S.G. 2023
The identification of metabolomic biomarkers relies on the analysis of large cohorts of patients compared to healthy controls followed by the validation of markers in an independent sample set.... Show moreThe identification of metabolomic biomarkers relies on the analysis of large cohorts of patients compared to healthy controls followed by the validation of markers in an independent sample set. Indeed, circulating biomarkers should be causally linked to pathology to ensure that changes in the marker precede changes in the disease. However, this approach becomes unfeasible in rare diseases due to the paucity of samples, necessitating the development of new methods for biomarker identification. The present study describes a novel approach that combines samples from both mouse models and human patients to identify biomarkers of OPMD. We initially identified a pathology-specific metabolic fingerprint in murine dystrophic muscle. This metabolic fingerprint was then translated into (paired) murine serum samples and then to human plasma samples. This study identified a panel of nine candidate biomarkers that could predict muscle pathology with a sensitivity of 74.3% and specificity of 100% in a random forest model. These findings demonstrate that the proposed approach can identify biomarkers with good predictive performance and a higher degree of confidence in their relevance to pathology than markers identified in a small cohort of human samples alone. Therefore, this approach has a high potential utility for identifying circulating biomarkers in rare diseases. Show less
Gorkom, T. van; Voet, W.; Arkel, G.H.J. van; Heron, M.; Hoeve-Bakker, B.J.A.; Thijsen, S.F.T.; Kremer, K. 2022
The diagnosis of LNB is established by clinical symptoms, pleocytosis, and proof of intrathecal synthesis of Borrelia-specific antibodies. Laboratory diagnosis of LNB is challenging, and validated... Show moreThe diagnosis of LNB is established by clinical symptoms, pleocytosis, and proof of intrathecal synthesis of Borrelia-specific antibodies. Laboratory diagnosis of LNB is challenging, and validated diagnostic algorithms are lacking.Laboratory diagnosis of Lyme neuroborreliosis (LNB) is challenging, and validated diagnostic algorithms are lacking. Therefore, this retrospective cross-sectional study aimed to compare the diagnostic performance of seven commercial antibody assays for LNB diagnosis. Random forest (RF) modeling was conducted to investigate whether the diagnostic performance using the antibody assays could be improved by including several routine cerebrospinal fluid (CSF) parameters (i.e., leukocyte count, total protein, blood-CSF barrier functionality, and intrathecal total antibody synthesis), two-tier serology on serum, the CSF level of the B-cell chemokine (C-X-C motif) ligand 13 (CXCL13), and a Borrelia species PCR on CSF. In total, 156 patients were included who were classified as definite LNB (n = 10), possible LNB (n = 7), or non-LNB patient (n = 139) according to the criteria of the European Federation of Neurological Societies using a consensus strategy for intrathecal Borrelia-specific antibody synthesis. The seven antibody assays showed sensitivities ranging from 47.1% to 100% and specificities ranging from 95.7% to 100%. RF modeling demonstrated that the sensitivities of most antibody assays could be improved by including other parameters to the diagnostic repertoire for diagnosing LNB (range: 94.1% to 100%), although with slightly lower specificities (range: 92.8% to 96.4%). The most important parameters for LNB diagnosis are the detection of intrathecally produced Borrelia-specific antibodies, two-tier serology on serum, CSF-CXCL13, Reibergram classification, and pleocytosis. In conclusion, this study shows that LNB diagnosis is best supported using multiparameter analysis. Furthermore, a collaborative prospective study is proposed to investigate if a standardized diagnostic algorithm can be developed for improved LNB diagnosis. IMPORTANCE The diagnosis of LNB is established by clinical symptoms, pleocytosis, and proof of intrathecal synthesis of Borrelia-specific antibodies. Laboratory diagnosis of LNB is challenging, and validated diagnostic algorithms are lacking. Therefore, this retrospective cross-sectional study aimed to compare the diagnostic performance of seven commercial antibody assays for LNB diagnosis. Multiparameter analysis was conducted to investigate whether the diagnostic performance using the antibody assays could be improved by including several routine (CSF) parameters. The results of this study show that LNB diagnosis is best supported using the detection of intrathecally produced Borrelia-specific antibodies, two-tier serology on serum, CSF-CXCL13, Reibergram classification, and pleocytosis. Furthermore, we propose a collaborative prospective study to investigate the potential role of constructing a diagnostic algorithm using multiparameter analysis for improved LNB diagnosis. Show less
This study investigates the extent to which there is individuality in how structural variation is conditioned over time. Earlier research already classified the diachronically unstable gerund... Show moreThis study investigates the extent to which there is individuality in how structural variation is conditioned over time. Earlier research already classified the diachronically unstable gerund variation as involving a high fraction of mixed-usage speakers throughout the change, whereby the proportion of the conservative variant versus the progressive variant as observable in the linguistic output of individual language users superficially resembles the mean proportion as observable at the population level. However, this study sets out to show that there can still be heterogeneity within such a centralized population in terms of how each individual conditions the observed variation. A random forest and conditional inference tree analysis of over 14,000 gerunds uttered by nineteen seventeenth-century authors is presented to show that, while the most important language-internal factors conditioning the gerund variation are adopted by (and shared between) all authors, we can still attest inter-individual variation (i) at lower levels of variable importance, and (ii) in the breadth of the range of contexts individual authors employ to condition the attested variation. Show less
