Identification and multimodal characterization of a specialized epithelial cell type associated with Crohn's disease.

Crohn's disease (CD) is a complex chronic inflammatory disorder with both gastrointestinal and extra-intestinal manifestations associated immune dysregulation. Analyzing 202,359 cells from 170 specimens across 83 patients, we identify a distinct epithelial cell type in both terminal ileum and ascending colon (hereon as 'LND') with high expression of LCN2, NOS2, and DUOX2 and genes related to antimicrobial response and immunoregulation. LND cells, confirmed by in-situ RNA and protein imaging, are rare in non-IBD controls but expand in active CD, and actively interact with immune cells and specifically express IBD/CD susceptibility genes, suggesting a possible function in CD immunopathogenesis. Furthermore, we discover early and late LND subpopulations with different origins and developmental potential. A higher ratio of late-to-early LND cells correlates with better response to anti-TNF treatment. Our findings thus suggest a potential pathogenic role for LND cells in both Crohn's ileitis and colitis.

Alleviating tiling effect by random walk sliding window in high-resolution histological whole slide image synthesis.

Multiplex immunofluorescence (MxIF) is an advanced molecular imaging technique that can simultaneously provide biologists with multiple (i.e., more than 20) molecular markers on a single histological tissue section. Unfortunately, due to imaging restrictions, the more routinely used hematoxylin and eosin (H&E) stain is typically unavailable with MxIF on the same tissue section. As biological H&E staining is not feasible, previous efforts have been made to obtain H&E whole slide image (WSI) from MxIF via deep learning empowered virtual staining. However, the tiling effect is a long-lasting problem in high-resolution WSI-wise synthesis. The MxIF to H&E synthesis is no exception. Limited by computational resources, the cross-stain image synthesis is typically performed at the patch-level. Thus, discontinuous intensities might be visually identified along with the patch boundaries assembling all individual patches back to a WSI. In this work, we propose a deep learning based unpaired high-resolution image synthesis method to obtain virtual H&E WSIs from MxIF WSIs (each with 27 markers/stains) with reduced tiling effects. Briefly, we first extend the CycleGAN framework by adding simultaneous nuclei and mucin segmentation supervision as spatial constraints. Then, we introduce a random walk sliding window shifting strategy during the optimized inference stage, to alleviate the tiling effects. The validation results show that our spatially constrained synthesis method achieves a 56% performance gain for the downstream cell segmentation task. The proposed inference method reduces the tiling effects by using 50% fewer computation resources without compromising performance. The proposed random sliding window inference method is a plug-and-play module, which can be generalized for other high-resolution WSI image synthesis applications. The source code with our proposed model are available at https://github.com/MASILab/RandomWalkSlidingWindow.git.

MTGR1 is required to maintain small intestinal stem cell populations.

Undifferentiated intestinal stem cells (ISCs) are crucial for maintaining homeostasis and resolving injury. Lgr5+ cells in the crypt base constantly divide, pushing daughter cells upward along the crypt axis where they differentiate into specialized cell types. Coordinated execution of complex transcriptional programs is necessary to allow for the maintenance of undifferentiated stem cells while permitting differentiation of the wide array of intestinal cells necessary for homeostasis. Previously, members of the myeloid translocation gene (MTG) family have been identified as transcriptional co-repressors that regulate stem cell maintenance and differentiation programs in multiple organ systems, including the intestine. One MTG family member, myeloid translocation gene related 1 (MTGR1), has been recognized as a crucial regulator of secretory cell differentiation and response to injury. However, whether MTGR1 contributes to the function of ISCs has not yet been examined. Here, using Mtgr1-/- mice, we have assessed the effects of MTGR1 loss specifically in ISC biology. Interestingly, loss of MTGR1 increased the total number of cells expressing Lgr5, the canonical marker of cycling ISCs, suggesting higher overall stem cell numbers. However, expanded transcriptomic and functional analyses revealed deficiencies in Mtgr1-null ISCs, including deregulated ISC-associated transcriptional programs. Ex vivo, intestinal organoids established from Mtgr1-null mice were unable to survive and expand due to aberrant differentiation and loss of stem and proliferative cells. Together, these results indicate that the role of MTGR1 in intestinal differentiation is likely stem cell intrinsic and identify a novel role for MTGR1 in maintaining ISC function.

Large-scale integration of omics and electronic health records to identify potential risk protein biomarkers and therapeutic drugs for cancer prevention and intervention.

Identifying risk protein targets and their therapeutic drugs is crucial for effective cancer prevention. Here, we conduct integrative and fine-mapping analyses of large genome-wide association studies data for breast, colorectal, lung, ovarian, pancreatic, and prostate cancers, and characterize 710 lead variants independently associated with cancer risk. Through mapping protein quantitative trait loci (pQTL) for these variants using plasma proteomics data from over 75,000 participants, we identify 365 proteins associated with cancer risk. Subsequent colocalization analysis identifies 101 proteins, including 74 not reported in previous studies. We further characterize 36 potential druggable proteins for cancers or other disease indications. Analyzing >3.5 million electronic health records, we uncover five drugs (Haloperidol, Trazodone, Tranexamic Acid, Haloperidol, and Captopril) associated with increased cancer risk and two drugs (Caffeine and Acetazolamide) linked to reduced colorectal cancer risk. This study offers novel insights into therapeutic drugs targeting risk proteins for cancer prevention and intervention.

GammaGateR: semi-automated marker gating for single-cell multiplexed imaging.

MOTIVATION: Multiplexed immunofluorescence (mIF) is an emerging assay for multichannel protein imaging that can decipher cell-level spatial features in tissues. However, existing automated cell phenotyping methods, such as clustering, face challenges in achieving consistency across experiments and often require subjective evaluation. As a result, mIF analyses often revert to marker gating based on manual thresholding of raw imaging data. RESULTS: To address the need for an evaluable semi-automated algorithm, we developed GammaGateR, an R package for interactive marker gating designed specifically for segmented cell-level data from mIF images. Based on a novel closed-form gamma mixture model, GammaGateR provides estimates of marker-positive cell proportions and soft clustering of marker-positive cells. The model incorporates user-specified constraints that provide a consistent but slide-specific model fit. We compared GammaGateR against the newest unsupervised approach for annotating mIF data, employing two colon datasets and one ovarian cancer dataset for the evaluation. We showed that GammaGateR produces highly similar results to a silver standard established through manual annotation. Furthermore, we demonstrated its effectiveness in identifying biological signals, achieved by mapping known spatial interactions between CD68 and MUC5AC cells in the colon and by accurately predicting survival in ovarian cancer patients using the phenotype probabilities as input for machine learning methods. GammaGateR is a highly efficient tool that can improve the replicability of marker gating results, while reducing the time of manual segmentation. AVAILABILITY AND IMPLEMENTATION: The R package is available at https://github.com/JiangmeiRubyXiong/GammaGateR.

Interleukin 13 Promotes Maturation and Proliferation in Metaplastic Gastroids.

BACKGROUND & AIMS: Type 2 innate lymphoid cells (ILC2s) and interleukin-13 (IL-13) promote the onset of spasmolytic polypeptide-expressing metaplasia (SPEM) cells. However, little is known about molecular effects of IL-13 in SPEM cells. We now sought to establish a reliable organoid model, Meta1 gastroids, to model SPEM cells in vitro. We evaluated cellular and molecular effects of ILC2s and IL-13 on maturation and proliferation of SPEM cells. METHODS: We performed single-cell RNA sequencing to characterize Meta1 gastroids, which were derived from stomachs of Mist1-Kras transgenic mice that displayed pyloric metaplasia. Cell sorting was used to isolate activated ILC2s from stomachs of IL-13-tdTomato reporter mice treated with L635. Three-dimensional co-culture was used to determine the effects of ILC2s on Meta1 gastroids. Mouse normal or metaplastic (Meta1) and human metaplastic gastroids were cultured with IL-13 to evaluate cell responses. Air-Liquid Interface culture was performed to test long-term culture effects of IL-13. In silico analysis determined possible STAT6-binding sites in gene promoter regions. STAT6 inhibition was performed to corroborate STAT6 role in SPEM cells maturation. RESULTS: Meta1 gastroids showed the characteristics of SPEM cell lineages in vitro even after several passages. We demonstrated that co-culture with ILC2s or IL-13 treatment can induce phosphorylation of STAT6 in Meta1 and normal gastroids and promote the maturation and proliferation of SPEM cell lineages. IL-13 up-regulated expression of mucin-related proteins in human metaplastic gastroids. Inhibition of STAT6 blocked SPEM-related gene expression in Meta1 gastroids and maturation of SPEM in both normal and Meta1 gastroids. CONCLUSIONS: IL-13 promotes the maturation and proliferation of SPEM cells consistent with gastric mucosal regeneration.

Intestinal tuft cells assemble a cytoskeletal superstructure composed of co-aligned actin bundles and microtubules.

Background & Aims: All tissues consist of a distinct set of cell types, which collectively support organ function and homeostasis. Tuft cells are a rare epithelial cell type found in diverse epithelia, where they play important roles in sensing antigens and stimulating downstream immune responses. Exhibiting a unique polarized morphology, tuft cells are defined by an array of giant actin filament bundles that support ∼2 µm of apical membrane protrusion and extend over 7 µm towards the cell's perinuclear region. Despite their established roles in maintaining intestinal epithelial homeostasis, tuft cells remain understudied due to their rarity (e.g. ∼ 1% in the small intestinal epithelium). Details regarding the ultrastructural organization of the tuft cell cytoskeleton, the molecular components involved in building the array of giant actin bundles, and how these cytoskeletal structures support tuft cell biology remain unclear. Methods: To begin to answer these questions, we used advanced light and electron microscopy to perform quantitative morphometry of the small intestinal tuft cell cytoskeleton. Results: We found that tuft cell core bundles consist of actin filaments that are crosslinked in a parallel "barbed-end out" configuration. These polarized structures are also supported by a unique group of tuft cell enriched actin-binding proteins that are differentially localized along the giant core bundles. Furthermore, we found that tuft cell actin bundles are co-aligned with a highly ordered network of microtubules. Conclusions: Tuft cells assemble a cytoskeletal superstructure that is well positioned to serve as a track for subcellular transport along the apical-basolateral axis and in turn, support the dynamic sensing functions that are critical for intestinal epithelial homeostasis. SYNOPSIS: This research leveraged advanced light and electron microscopy to perform quantitative morphometry of the intestinal tuft cell cytoskeleton. Three-dimensional reconstructions of segmented image data revealed a co-aligned actin-microtubule superstructure that may play a fundamental role in tuft cell function.

The Hallmarks of Precancer.

Research on precancers, as defined as at-risk tissues and early lesions, is of high significance given the effectiveness of early intervention. We discuss the need for risk stratification to prevent overtreatment, an emphasis on the role of genetic and epigenetic aging when considering risk, and the importance of integrating macroenvironmental risk factors with molecules and cells in lesions and at-risk normal tissues for developing effective intervention and health policy strategies.

Cross-scale multi-instance learning for pathological image diagnosis.

Analyzing high resolution whole slide images (WSIs) with regard to information across multiple scales poses a significant challenge in digital pathology. Multi-instance learning (MIL) is a common solution for working with high resolution images by classifying bags of objects (i.e. sets of smaller image patches). However, such processing is typically performed at a single scale (e.g., 20× magnification) of WSIs, disregarding the vital inter-scale information that is key to diagnoses by human pathologists. In this study, we propose a novel cross-scale MIL algorithm to explicitly aggregate inter-scale relationships into a single MIL network for pathological image diagnosis. The contribution of this paper is three-fold: (1) A novel cross-scale MIL (CS-MIL) algorithm that integrates the multi-scale information and the inter-scale relationships is proposed; (2) A toy dataset with scale-specific morphological features is created and released to examine and visualize differential cross-scale attention; (3) Superior performance on both in-house and public datasets is demonstrated by our simple cross-scale MIL strategy. The official implementation is publicly available at https://github.com/hrlblab/CS-MIL.

Feasibility of Universal Anomaly Detection without Knowing the Abnormality in Medical Images.

Many anomaly detection approaches, especially deep learning methods, have been recently developed to identify abnormal image morphology by only employing normal images during training. Unfortunately, many prior anomaly detection methods were optimized for a specific "known" abnormality (e.g., brain tumor, bone fraction, cell types). Moreover, even though only the normal images were used in the training process, the abnormal images were often employed during the validation process (e.g., epoch selection, hyper-parameter tuning), which might leak the supposed "unknown" abnormality unintentionally. In this study, we investigated these two essential aspects regarding universal anomaly detection in medical images by (1) comparing various anomaly detection methods across four medical datasets, (2) investigating the inevitable but often neglected issues on how to unbiasedly select the optimal anomaly detection model during the validation phase using only normal images, and (3) proposing a simple decision-level ensemble method to leverage the advantage of different kinds of anomaly detection without knowing the abnormality. The results of our experiments indicate that none of the evaluated methods consistently achieved the best performance across all datasets. Our proposed method enhanced the robustness of performance in general (average AUC 0.956).

Temporal recording of mammalian development and precancer.

Key to understanding many biological phenomena is knowing the temporal ordering of cellular events, which often require continuous direct observations [1, 2]. An alternative solution involves the utilization of irreversible genetic changes, such as naturally occurring mutations, to create indelible markers that enables retrospective temporal ordering [3-8]. Using NSC-seq, a newly designed and validated multi-purpose single-cell CRISPR platform, we developed a molecular clock approach to record the timing of cellular events and clonality in vivo , while incorporating assigned cell state and lineage information. Using this approach, we uncovered precise timing of tissue-specific cell expansion during murine embryonic development and identified new intestinal epithelial progenitor states by their unique genetic histories. NSC-seq analysis of murine adenomas and single-cell multi-omic profiling of human precancers as part of the Human Tumor Atlas Network (HTAN), including 116 scRNA-seq datasets and clonal analysis of 418 human polyps, demonstrated the occurrence of polyancestral initiation in 15-30% of colonic precancers, revealing their origins from multiple normal founders. Thus, our multimodal framework augments existing single-cell analyses and lays the foundation for in vivo multimodal recording, enabling the tracking of lineage and temporal events during development and tumorigenesis.