Pavillon, N. | bioRxiv Science

bioRxiv2021

Cellular adhesion is a controlling factor in neutrophil extracellular trap formation induced by antineutrophil cytoplasmic antibodies

Anti-neutrophil cytoplasmic antibody (ANCA) associated vasculitis (AAV) is a life-threatening condition characterized by improper activation of neutrophils and release of neutrophil extracellular traps (NETs) in small vessels. This study aimed to explain the role of NETs in AAV pathogenesis by investigating a link between neutrophil adhesion and NET release. We leveraged an imaging flow cytometry-based assay and three-dimensional culture to demonstrate that neutrophil adhesion is essential for ANCA induced NET formation. We confirmed this requirement for cell adhesion using standard microscopy on ultra-low attachment hydrogel surfaces and demonstrate that this depends on the focal adhesion kinase pathway as determined using inhibitors for multiple targets in this process. ANCA increased expression of {beta}2 integrins on neutrophils, and we confirmed that these integrins were required for NET formation using blocking antibodies. Finally, inhibitors for oxidative burst prevented NET formation, and this oxidative burst was mediated by the focal adhesion pathway. Overall, our findings reveal a central role for neutrophil attachment in NET formation in response to ANCA, helping to explain the restricted localization pattern of vessel damage, and suggesting that targeting neutrophil adhesion factors may be beneficial in preventing pathological damage from NETs during AAV.

immunology↗

bioRxiv2021

Deriving accurate molecular indicators of protein synthesis through Raman-based sparse classification

Raman spectroscopy has the ability to retrieve molecular information from live biological samples non-invasively through optical means. Coupled with machine learning, it is possible to use the large amount of information contained in a Raman spectrum to create models that can predict the state of new samples based on statistical analysis from previous measurements. Furthermore, in case of linear models, the separation coefficients can be used to interpret which bands are contributing to the discrimination between experimental conditions, which correspond here to single-cell measurements of macrophages under in vitro immune stimulation. We here evaluate a typical linear method using discriminant analysis and PCA, and compare it to regularized logistic regression (Lasso). We find that the use of PCA is not beneficial to the classification performance. Furthermore, the Lasso approach yields sparse separation vectors, since it suppresses spectral coefficients which do not improve classification, making interpretation easier. To further evaluate the approach, we apply the Lasso technique to a well-defined case where protein synthesis is inhibited, and show that the separating features are consistent with RNA accumulation and protein levels depletion. Surprisingly, when Raman features are selected purely in terms of their classification power (Lasso), the selected coefficients are contained in side bands, while typical strong Raman peaks are not present in the discrimination vector. We propose that this occurs because large Raman bands are representative of a wide variety of cellular molecules and are therefore less suited for accurate classification.

biochemistry↗