Human coronavirus disease 2019 (COVID-19) is a life-threatening and highly contagious disease caused by coronavirus SARS-CoV-2. Sensitive and specific detection of SARS-CoV-2 viral proteins in tissues and cells of COVID-19 patients will support investigations of the biologic behavior and tissue and cell tropism of this virus. We identified commercially available affinity-purified polyclonal antibodies raised against nucleocapsid and spike proteins of SARS-CoV-2 that provide sensitive and specific detection of the virus by immunohistochemistry in formalin-fixed, paraffin-embedded tissue. Two immunohistochemistry protocols are presented that are mutually validated by the matched detection patterns of the two distinct viral antigens in virus-infected cells within autopsy lung tissue of COVID-19 deceased patients. Levels of nucleocapsid protein in the lungs of COVID-19 decedents, as measured by quantitative histo-cytometry of immunohistochemistry images, showed an excellent log–linear relationship with levels of viral nucleocapsid RNA levels, as measured by qRT-PCR. Importantly, since the nucleocapsid protein sequence is conserved across all known viral strains, the nucleocapsid immunohistochemistry protocol is expected to recognize all common variants of SARS-CoV-2. Negative controls include autopsy lung tissues from patients who died from non-COVID-19 respiratory disease and control rabbit immunoglobulin. Sensitive detection of SARS-CoV-2 in human tissues will provide insights into viral tissue and cell distribution and load in patients with active infection, as well as provide insight into the clearance rate of virus in later COVID-19 disease stages. The protocols are also expected to be readily transferable to detect SARS-CoV-2 proteins in tissues of experimental animal models or animals suspected to serve as viral reservoirs.

Gastrointestinal (GI) tract graft-versus-host disease (GVHD) is a major complication after allogeneic hematopoietic stem cell transplantation and is attributable to dysregulation that occurs between the effector and regulatory arms of the immune system. Whereas regulatory T cells have a primary role in counterbalancing GVHD-induced inflammation, identifying and harnessing other pathways that promote immune tolerance remain major goals in this disease. Herein, we identified interleukin-34 (IL-34) as an intestinal epithelium–derived cytokine that was able to mitigate the severity of GVHD within the GI tract. Specifically, we observed that the absence of recipient IL-34 production exacerbated GVHD lethality, promoted intestinal epithelial cell death, and compromised barrier integrity. Mechanistically, the absence of host IL-34 skewed donor macrophages toward a proinflammatory phenotype and augmented the accumulation of pathogenic CD4 ⁺ granulocyte-macrophage colony-stimulating factor (GM-CSF) ⁺ T cells within the colon. Conversely, the administration of recombinant IL-34 substantially reduced GVHD mortality and inflammation, which was dependent on the expression of apolipoprotein E in donor macrophages. Complementary genetic and imaging approaches in mice demonstrated that intestinal epithelial cells were the relevant source of IL-34. These results were supported by colonic biopsies from patients with GVHD, which displayed IL-34 expression in intestinal epithelial cells and apolipoprotein E in lamina propria macrophages, validating similar cellular localization in humans. These studies indicate that IL-34 acts as a tissue-intrinsic cytokine that regulates GVHD severity in the GI tract and could serve as a potential therapeutic target for amelioration of this disease.

SignificanceAccurate transfer of annotations from histological images to fluorescence images is essential in developing deep learning (DL)-based optical imaging systems for intraoperative assessment of tumor margins. Manual annotation is time-consuming, prone to interobserver variability, and impractical for large-scale datasets.AimWe present a semi-automated method that can effectively transfer tumor annotations from pathologist-annotated hematoxylin and eosin (H&E) images to fluorescence images captured using microscopy with ultraviolet surface excitation (MUSE). This method is not intended for intraoperative use but rather to facilitate the creation of annotated datasets for DL model development.ApproachOur semi-automated method consists of nonrigid image registration, outline extraction and refinement, and annotation transfer. The method was applied to H&E and MUSE image pairs from 35 breast and lung tissue samples. Manual annotations in MUSE images were used as the ground truth for evaluation.ResultsThe proposed method achieved a Dice score coefficient of 0.87±0.07, convolutional-neural-network-based feature similarity of 0.94±0.04, and a normalized Hausdorff distance of 0.15±0.06 across the dataset.ConclusionThese results demonstrate that the method provides a fast and accurate solution for generating annotated MUSE datasets necessary for training DL algorithms for intraoperative tumor margin detection.