Background Prominent clinical symptoms of COVID-19 include CNS manifestations. However, it is unclear whether severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of COVID-19, gains access to the CNS and whether it causes neuropathological changes. We investigated the brain tissue of patients who died from COVID-19 for glial responses, inflammatory changes, and the presence of SARS-CoV-2 in the CNS. MethodsIn this post-mortem case series, we investigated the neuropathological features in the brains of patients who died between March 13 and April 24, 2020, in Hamburg, Germany. Inclusion criteria comprised a positive test for SARS-CoV-2 by quantitative RT-PCR (qRT-PCR) and availability of adequate samples. We did a neuropathological workup including histological staining and immunohistochemical staining for activated astrocytes, activated microglia, and cytotoxic T lymphocytes in the olfactory bulb, basal ganglia, brainstem, and cerebellum. Additionally, we investigated the presence and localisation of SARS-CoV-2 by qRT-PCR and by immunohistochemistry in selected patients and brain regions. Findings 43 patients were included in our study. Patients died in hospitals, nursing homes, or at home, and were aged between 51 years and 94 years (median 76 years [IQR 70-86]). We detected fresh territorial ischaemic lesions in six (14%) patients. 37 (86%) patients had astrogliosis in all assessed regions. Activation of microglia and infiltra tion by cytotoxic T lymphocytes was most pronounced in the brainstem and cerebellum, and meningeal cytotoxic T lymphocyte infiltration was seen in 34 (79%) patients. SARS-CoV-2 could be detected in the brains of 21 (53%) of 40 examined patients, with SARS-CoV-2 viral proteins found in cranial nerves originating from the lower brainstem and in isolated cells of the brainstem. The presence of SARS-CoV-2 in the CNS was not associated with the severity of neuropathological changes.Interpretation In general, neuropathological changes in patients with COVID-19 seem to be mild, with pronounced neuroinflammatory changes in the brainstem being the most common finding. There was no evidence for CNS damage directly caused by SARS-CoV-2. The generalisability of these findings needs to be validated in future studies as the number of cases and availability of clinical data were low and no age-matched and sex-matched controls were included.

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Gastric cancer (GC) is frequently diagnosed and treated in advanced tumour stages with poor prognosis. Recent studies have identified isoform 2 of the tight junction protein claudin-18 (CLDN18.2) as a promising target in GC therapy. In this study, we aimed to outline the expression of CLDN18.2 and its correlation with clinico-pathological patient characteristics in a large and well-characterized cohort of GC patients. The expression of CLDN18.2 was studied in 481 GCs by immunohistochemistry on whole tissue sections. Immunostained GCs were evaluated using the histoscore (H-score) and subsequently divided into two groups: tumours showing any or no expression. CLDN18.2 expression was investigated for correlation with various clinico-pathological patient characteristics, including survival. CLDN18.2 expression was found in 203 GCs (42.2%). Of these tumours, 71 (14.8%) showed solely weak immunostaining. CLDN18.2 expression correlated with mucin phenotype, EBV status, the integrin αvβ5, the EpCAM extracellular domain EpEX, and lysozyme. We found no correlation with survival, Laurén phenotype, or any other clinico-pathological patient characteristic. In conclusion, we demonstrate frequently decreased expression of CLDN18.2 in a GC cohort of appropriate size. Correlating CLDN18.2 expression with clinico-pathological patient characteristics reveals new linkages to αvβ5, EpEX, and lysozyme, which may pave the way for further investigations regarding the role of tight junction proteins in GC progression. Though CLDN18.2 continues to pose an attractive target candidate, we conclude that a rather low overall expression rate challenges its significance in advanced GC therapy and indicates the need for further investigations across different populations.Electronic supplementary materialThe online version of this article (10.1007/s00428-019-02624-7) contains supplementary material, which is available to authorized users.

Dataset integration is common practice to overcome limitations in statistically underpowered omics datasets. Proteome datasets display high technical variability and frequent missing values. Sophisticated strategies for batch effect reduction are lacking or rely on error-prone data imputation. Here we introduce HarmonizR, a data harmonization tool with appropriate missing value handling. The method exploits the structure of available data and matrix dissection for minimal data loss, without data imputation. This strategy implements two common batch effect reduction methods—ComBat and limma (removeBatchEffect()). The HarmonizR strategy, evaluated on four exemplarily analyzed datasets with up to 23 batches, demonstrated successful data harmonization for different tissue preservation techniques, LC-MS/MS instrumentation setups, and quantification approaches. Compared to data imputation methods, HarmonizR was more efficient and performed superior regarding the detection of significant proteins. HarmonizR is an efficient tool for missing data tolerant experimental variance reduction and is easily adjustable for individual dataset properties and user preferences.