The in vitro 3D culture of intestinal epithelium is a valuable resource in the study of its function. Organoid culture exploits stem cells’ ability to regenerate and produce differentiated epithelium. Intestinal organoid models from rodent or human tissue are widely available whereas large animal models are not. Livestock enteric and zoonotic diseases elicit significant morbidity and mortality in animal and human populations. Therefore, livestock species-specific models may offer novel insights into host-pathogen interactions and disease responses. Bovine and porcine jejunum were obtained from an abattoir and their intestinal crypts isolated, suspended in Matrigel, cultured, cryopreserved and resuscitated. ‘Rounding’ of crypts occurred followed by budding and then enlargement of the organoids. Epithelial cells were characterised using immunofluorescent staining and confocal microscopy. Organoids were successfully infected with Toxoplasma gondii or Salmonella typhimurium . This 3D organoid model offers a long-term, renewable resource for investigating species-specific intestinal infections with a variety of pathogens.

When transmitted through the oral route, Toxoplasma gondii first interacts with its host at the small intestinal epithelium. This interaction is crucial to controlling initial invasion and replication, as well as shaping the quality of the systemic immune response. It is therefore an attractive target for the design of novel vaccines and adjuvants. However, due to a lack of tractable infection models, we understand surprisingly little about the molecular pathways that govern this interaction. The in vitro culture of small intestinal epithelium as 3D enteroids shows great promise for modeling the epithelial response to infection. However, the enclosed luminal space makes the application of infectious agents to the apical epithelial surface challenging. Here, we have developed three novel enteroid-based techniques for modeling T. gondii infection. In particular, we have adapted enteroid culture protocols to generate collagen-supported epithelial sheets with an exposed apical surface. These cultures retain epithelial polarization, and the presence of fully differentiated epithelial cell populations. They are susceptible to infection with, and support replication of, T. gondii . Using quantitative label-free mass spectrometry, we show that T. gondii infection of the enteroid epithelium is associated with up-regulation of proteins associated with cholesterol metabolism, extracellular exosomes, intermicrovillar adhesion, and cell junctions. Inhibition of host cholesterol and isoprenoid biosynthesis with Atorvastatin resulted in a reduction in parasite load only at higher doses, indicating that de novo synthesis may support, but is not required for, parasite replication. These novel models therefore offer tractable tools for investigating how interactions between T. gondii and the host intestinal epithelium influence the course of infection.

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Single cell RNA sequencing (scRNA-seq) is a powerful tool in detailing the cellular landscape within complex tissues. Large-scale single cell transcriptomics provide both opportunities and challenges for identifying rare cells playing crucial roles in development and disease. Here, we develop GapClust, a light-weight algorithm to detect rare cell types from ultra-large scRNA-seq datasets with state-of-the-art speed and memory efficiency. Benchmarking on diverse experimental datasets demonstrates the superior performance of GapClust compared to other recently proposed methods. When applying our algorithm to an intestine and 68 k PBMC datasets, GapClust identifies the tuft cells and a previously unrecognised subtype of monocyte, respectively.

Toxoplasma gondii and Cryptosporidium spp. can cause devastating pathological effects in humans and livestock, and in particular to young or immunocompromised individuals. The current treatment plans for these enteric parasites are limited due to long drug courses, severe side effects or simply a lack of efficacy. The study of the early interactions between the parasites and the site of infection in the small intestinal epithelium has been thwarted by the lack of accessible, physiologically relevant and species‐specific models. Increasingly, 3D stem cell‐derived enteroid models are being refined and developed into sophisticated models of infectious disease. In this review, we shall illustrate the use of enteroids to spearhead research into enteric parasitic infections, bridging the gap between cell line cultures and in vivo experiments.

The subjective effects and therapeutic potential of the shamanic practice of journeying is well known. However, previous research has neglected to provide a comprehensive assessment of the subjective effects of shamanic‐like journeying techniques on non‐shamans. Shamanic‐like techniques are those that demonstrate some similarity to shamanic practices and yet deviate from what may genuinely be considered shamanism. Furthermore, the personality traits that influence individual susceptibility to shamanic‐like techniques are unclear. The aim of the present study was, thus, to investigate experimentally the effect of shamanic‐like techniques and a personality trait referred to as “ego boundaries” on subjective experience including mood disturbance. Forty‐three non‐shamans were administered a composite questionnaire consisting of demographic items and a measure of ego boundaries (i.e., the Short Boundary Questionnaire; BQ‐Sh). Participants were randomly assigned to one of three conditions: listening to monotonous drumming for 15 minutes coupled with one of two sets of journeying instructions; or sitting quietly with eyes closed for 15 minutes. Participants' subjective experience and mood disturbance were retrospectively assessed using the Phenomenology of Consciousness Inventory (PCI) and the Profile of Mood States‐Short Form, respectively. The results indicated that there was a statistically significant difference between conditions with regard to the PCI major dimensions of visual imagery, attention and rationality, and minor dimensions of imagery amount and absorption. However, the shamanic‐like conditions were not associated with a major reorganization of the pattern of subjective experience compared to the sitting quietly condition, suggesting that what is typically referred to as an altered state of consciousness effect was not evident. One shamanic‐like condition and the BQ‐Sh subscales need for order, childlikeness, and sensitivity were statistically significant predictors of total mood disturbance. Implications of the findings for the anthropology of consciousness are also considered.

Identifying personalized driver genes is essential for discovering critical biomarkers and developing effective personalized therapies of cancers. However, few methods consider weights for different types of mutations and efficiently distinguish driver genes over a larger number of passenger genes. We propose MinNetRank (Minimum used for Network-based Ranking), a new method for prioritizing cancer genes that sets weights for different types of mutations, considers the incoming and outgoing degree of interaction network simultaneously, and uses minimum strategy to integrate multi-omics data. MinNetRank prioritizes cancer genes among multi-omics data for each sample. The sample-specific rankings of genes are then integrated into a population-level ranking. When evaluating the accuracy and robustness of prioritizing driver genes, our method almost always significantly outperforms other methods in terms of precision, F1 score, and partial area under the curve (AUC) on six cancer datasets. Importantly, MinNetRank is efficient in discovering novel driver genes. SP1 is selected as a candidate driver gene only by our method (ranked top three), and SP1 RNA and protein differential expression between tumor and normal samples are statistically significant in liver hepatocellular carcinoma. The top seven genes stratify patients into two subtypes exhibiting statistically significant survival differences in five cancer types. These top seven genes are associated with overall survival, as illustrated by previous researchers. MinNetRank can be very useful for identifying cancer driver genes, and these biologically relevant marker genes are associated with clinical outcome. The R package of MinNetRank is available at https://github.com/weitinging/MinNetRank.

Accurately predicting patient survival is essential for cancer treatment decision.However, the prognostic prediction model based on histopathological images of stomach cancer patients is still yet to be developed. We propose a deep learningbased model (MultiDeepCox-SC) that predicts overall survival in patients with stomach cancer by integrating histopathological images, clinical data, and gene expression data. The MultiDeepCox-SC not only automatedly selects patches with more information for survival prediction, without manual labeling for histopathological images, but also identifies genetic and clinical risk factors associated with survival in stomach cancer. The prognostic accuracy of the MultiDeepCox-SC (C-index = 0.744) surpasses the result only based on histopathological image (C-index = 0.660). The risk score of our model was still an independent predictor of survival outcome after adjustment for potential confounders, including pathologic stage, grade, age, race, and gender on The Cancer Genome Atlas dataset (hazard ratio 1.555, p = 3.53e-08) and the external test set (hazard ratio 2.912, p = 9.42e-4). Our fully automated online prognostic tool based on histopathological images, clinical data, and gene expression data could be utilized to improve pathologists' efficiency and accuracy (https://yu.life.sjtu.edu.cn/ DeepC oxSC).