Ixodes scapularis nymph saliva protein blocks host inflammation and complement-mediated killing of Lyme disease agent, Borrelia burgdorferi.

Tick serine protease inhibitors (serpins) play crucial roles in tick feeding and pathogen transmission. We demonstrate that Ixodes scapularis (Ixs) nymph tick saliva serpin (S) 41 (IxsS41), secreted by Borrelia burgdorferi (Bb)-infected ticks at high abundance, is involved in regulating tick evasion of host innate immunity and promoting host colonization by Bb. Recombinant (r) proteins were expressed in Pichia pastoris, and substrate hydrolysis assays were used to determine. Ex vivo (complement and hemostasis function related) and in vivo (paw edema and effect on Bb colonization of C3H/HeN mice organs) assays were conducted to validate function. We demonstrate that rIxsS41 inhibits chymase and cathepsin G, pro-inflammatory proteases that are released by mast cells and neutrophils, the first immune cells at the tick feeding site. Importantly, stoichiometry of inhibition analysis revealed that 2.2 and 2.8 molecules of rIxsS41 are needed to 100% inhibit 1 molecule of chymase and cathepsin G, respectively, suggesting that findings here are likely events at the tick feeding site. Furthermore, chymase-mediated paw edema, induced by the mast cell degranulator, compound 48/80 (C48/80), was blocked by rIxsS41. Likewise, rIxsS41 reduced membrane attack complex (MAC) deposition via the alternative and lectin complement activation pathways and dose-dependently protected Bb from complement killing. Additionally, co-inoculating C3H/HeN mice with Bb together with rIxsS41 or with a mixture (rIxsS41 and C48/80). Findings in this study suggest that IxsS41 markedly contributes to tick feeding and host colonization by Bb. Therefore, we conclude that IxsS41 is a potential candidate for an anti-tick vaccine to prevent transmission of the Lyme disease agent.

Publisher Correction to: Deep learning-based image analysis methods for brightfield-acquired multiplex immunohistochemistry images.

An amendment to this paper has been published and can be accessed via the original article.

Deep learning-based image analysis methods for brightfield-acquired multiplex immunohistochemistry images.

BACKGROUND: Multiplex immunohistochemistry (mIHC) permits the labeling of six or more distinct cell types within a single histologic tissue section. The classification of each cell type requires detection of the unique colored chromogens localized to cells expressing biomarkers of interest. The most comprehensive and reproducible method to evaluate such slides is to employ digital pathology and image analysis pipelines to whole-slide images (WSIs). Our suite of deep learning tools quantitatively evaluates the expression of six biomarkers in mIHC WSIs. These methods address the current lack of readily available methods to evaluate more than four biomarkers and circumvent the need for specialized instrumentation to spectrally separate different colors. The use case application for our methods is a study that investigates tumor immune interactions in pancreatic ductal adenocarcinoma (PDAC) with a customized mIHC panel. METHODS: Six different colored chromogens were utilized to label T-cells (CD3, CD4, CD8), B-cells (CD20), macrophages (CD16), and tumor cells (K17) in formalin-fixed paraffin-embedded (FFPE) PDAC tissue sections. We leveraged pathologist annotations to develop complementary deep learning-based methods: (1) ColorAE is a deep autoencoder which segments stained objects based on color; (2) U-Net is a convolutional neural network (CNN) trained to segment cells based on color, texture and shape; and ensemble methods that employ both ColorAE and U-Net, collectively referred to as (3) ColorAE:U-Net. We assessed the performance of our methods using: structural similarity and DICE score to evaluate segmentation results of ColorAE against traditional color deconvolution; F1 score, sensitivity, positive predictive value, and DICE score to evaluate the predictions from ColorAE, U-Net, and ColorAE:U-Net ensemble methods against pathologist-generated ground truth. We then used prediction results for spatial analysis (nearest neighbor). RESULTS: We observed that (1) the performance of ColorAE is comparable to traditional color deconvolution for single-stain IHC images (note: traditional color deconvolution cannot be used for mIHC); (2) ColorAE and U-Net are complementary methods that detect 6 different classes of cells with comparable performance; (3) combinations of ColorAE and U-Net into ensemble methods outperform using either ColorAE and U-Net alone; and (4) ColorAE:U-Net ensemble methods can be employed for detailed analysis of the tumor microenvironment (TME). We developed a suite of scalable deep learning methods to analyze 6 distinctly labeled cell populations in mIHC WSIs. We evaluated our methods and found that they reliably detected and classified cells in the PDAC tumor microenvironment. We also present a use case, wherein we apply the ColorAE:U-Net ensemble method across 3 mIHC WSIs and use the predictions to quantify all stained cell populations and perform nearest neighbor spatial analysis. Thus, we provide proof of concept that these methods can be employed to quantitatively describe the spatial distribution immune cells within the tumor microenvironment. These complementary deep learning methods are readily deployable for use in clinical research studies.