Organization of the human intestine at single-cell resolution

Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics

Single-cell transcriptomics has broadened our understanding of cellular diversity and gene expression dynamics in healthy and diseased tissues. Recently, spatial transcriptomics has emerged as a tool to contextualize single cells in multicellular neighbourhoods and to identify spatially recurrent phenotypes, or ecotypes. These technologies have generated vast datasets with targeted-transcriptome and whole-transcriptome profiles of hundreds to millions of cells. Such data have provided new insights into developmental hierarchies, cellular plasticity and diverse tissue microenvironments, and spurred a burst of innovation in computational methods for single-cell analysis. In this Review, we discuss recent advancements, ongoing challenges and prospects in identifying and characterizing cell states and multicellular neighbourhoods. We discuss recent progress in sample processing, data integration, identification of subtle cell states, trajectory modelling, deconvolution and spatial analysis. Furthermore, we discuss the increasing application of deep learning, including foundation models, in analysing single-cell and spatial transcriptomics data. Finally, we discuss recent applications of these tools in the fields of stem cell biology, immunology, and tumour biology, and the future of single-cell and spatial transcriptomics in biological research and its translation to the clinic.

Flexible and robust cell type annotation for highly multiplexed tissue images

Identifying cell types in highly multiplexed images is essential for understanding tissue spatial organization. Current cell type annotation methods often rely on extensive reference images and manual adjustments. In this work, we present a tool, Robust Image-Based Cell Annotator (RIBCA), that enables accurate, automated, unbiased, and fine-grained cell type annotation for images with a wide range of antibody panels, without requiring additional model training or human intervention. Our tool has successfully annotated over 3 million cells, revealing the spatial organization of various cell types across more than 40 different human tissues. It is open-source and features a modular design, allowing for easy extension to additional cell types.

Cell-type-resolved chromatin accessibility in the human intestine identifies complex regulatory programs and clarifies genetic associations in Crohn's disease

Crohn's disease (CD) is a complex inflammatory bowel disease resulting from an interplay of genetic, microbial, and environmental factors. Cell-type-specific contributions to CD etiology and genetic risk are incompletely understood. Here we built a comprehensive atlas of cell-type- resolved chromatin accessibility comprising 557,310 candidate cis-regulatory elements (cCREs) in terminal ileum and ascending colon from patients with active and inactive CD and healthy controls. Using this atlas, we identified cell-type-, anatomic location-, and context-specific cCREs and characterized the regulatory programs underlying inflammatory responses in the intestinal mucosa of CD patients. Genetic variants that disrupt binding motifs of cell-type-specific transcription factors significantly affected chromatin accessibility in specific mucosal cell types. We found that CD heritability is primarily enriched in immune cell types. However, using fine- mapped non-coding CD variants we identified 29 variants located within cCREs several of which were accessible in epithelial and stromal cells implicating cell types from additional lineages in mediating CD risk in some loci. Our atlas provides a comprehensive resource to study gene regulatory effects in CD and health, and highlights the cellular complexity underlying CD risk.

Single-cell transcriptomics of human organoid-derived enteroendocrine cell populations from the small intestine

Gut hormones control intestinal function, metabolism and appetite, and have been harnessed therapeutically to treat type 2 diabetes and obesity. Our understanding of the enteroendocrine axis arises largely from animal studies, but intestinal organoid models make it possible to identify, genetically modify and purify human enteroendocrine cells (EECs). This study aimed to map human EECs using single-cell RNA sequencing. Organoids derived from human duodenum and ileum were genetically modified using CRISPR-Cas9 to express the fluorescent protein Venus driven by the chromogranin-A promoter. Fluorescent cells from CHGA-Venus organoids were purified by flow cytometry and analysed by 10X single-cell RNA sequencing. Cluster analysis separated EEC populations, allowing an examination of differentially expressed hormones, nutrient-sensing machinery, transcription factors and exocytotic machinery. Bile acid receptor GPBAR1 was most highly expressed in L-cells (producing glucagon-like peptide 1 and peptide YY), long-chain fatty acid receptor FFAR1 was highest in I-cells (cholecystokinin), K-cells (glucose-dependent insulinotropic polypeptide) and L-cells, short-chain fatty acid receptor FFAR2 was highest in ileal L-cells and enterochromaffin cells, olfactory receptor OR51E1 was notably expressed in ileal enterochromaffin cells, and the glucose-sensing sodium glucose cotransporter SLC5A1 was highly and differentially expressed in K- and L-cells, reflecting their known responsiveness to ingested glucose. The organoid EEC atlas was merged with published data from human intestine and organoids, with good overlap between enteroendocrine datasets. Understanding the similarities and differences between human EEC types will facilitate the development of drugs targeting the enteroendocrine axis for the treatment of conditions such as diabetes, obesity and intestinal disorders. KEY POINTS: Gut hormones regulate intestinal function, nutrient homeostasis and metabolism and form the basis of the new classes of drugs for obesity and diabetes. As enteroendocrine cells (EECs) comprise only ∼1% of the intestinal epithelium, they are under-represented in current single-cell atlases. To identify, compare and characterise human EECs we generated chromogranin-A labelled organoids from duodenal and ileal biopsies by CRISPR-Cas9. Fluorescent chromogranin-A positive EECs were purified and analysed by single-cell RNA sequencing, revealing predominant cell clusters producing different gut hormones. Cell clusters exhibited differential expression of nutrient-sensing machinery including bile acid receptors, long- and short-chain fatty acid receptors and glucose transporters. Organoid-derived EECs mapped well onto data from native intestinal cell populations, extending coverage of EECs.

Spatially restricted immune and microbiota-driven adaptation of the gut

The intestine is characterized by an environment in which host requirements for nutrient and water absorption are consequently paired with the requirements to establish tolerance to the outside environment. To better understand how the intestine functions in health and disease, large efforts have been made to characterize the identity and composition of cells from different intestinal regions1-8. However, the robustness, nature of adaptability and extent of resilience of the transcriptional landscape and cellular underpinning of the intestine in space are still poorly understood. Here we generated an integrated resource of the spatial and cellular landscape of the murine intestine in the steady and perturbed states. Leveraging these data, we demonstrated that the spatial landscape of the intestine was robust to the influence of the microbiota and was adaptable in a spatially restricted manner. Deploying a model of spatiotemporal acute inflammation, we demonstrated that both robust and adaptable features of the landscape were resilient. Moreover, highlighting the physiological relevance and value of our dataset, we identified a region of the middle colon characterized by an immune-driven multicellular spatial adaptation of structural cells to the microbiota. Our results demonstrate that intestinal regionalization is characterized by robust and resilient structural cell states and that the intestine can adapt to environmental stress in a spatially controlled manner through the crosstalk between immunity and structural cell homeostasis.

Chromatin remodelling in damaged intestinal crypts orchestrates redundant TGFβ and Hippo signalling to drive regeneration

Cell state dynamics underlying successful tissue regeneration are undercharacterized. In the intestine, damage prompts epithelial reprogramming into revival stem cells (revSCs) that reconstitute Lgr5+ intestinal stem cells (ISCs). Here single-nuclear multi-omics of mouse crypts regenerating from irradiation shows revSC chromatin accessibility overlaps with ISCs and differentiated lineages. While revSC genes themselves are accessible throughout homeostatic epithelia, damage-induced remodelling of chromatin in the crypt converges on Hippo and the transforming growth factor-beta (TGFβ) signalling pathway, which we show is transiently activated and directly induces functional revSCs. Combinatorial gene expression analysis further suggests multiple sources of revSCs, and we demonstrate TGFβ can reprogramme enterocytes, goblet and paneth cells into revSCs and show individual revSCs form organoids. Despite this, loss of TGFβ signalling yields mild regenerative defects, whereas interference in both Hippo and TGFβ leads to profound defects and death. Intestinal regeneration is thus poised for activation by a compensatory system of crypt-localized, transient morphogen cues that support epithelial reprogramming and robust intestinal repair.

Engineering immune organoids to regenerate host immune system

Recent advances in immunotherapy have underscored the potential of harnessing the immune system to treat disorders associated with immune dysregulation, such as primary and secondary immunodeficiencies, cancer, transplantation rejection, and aging. Owing to the cellular and structural complexity and the dynamic nature of immune responses, engineering immune organoids that replicate the function and key features of their corresponding immune organs continues to be a formidable challenge. In this overview, we will discuss the recent progress in bioengineering organoids of key primary and secondary immune organs and tissues, focusing particularly on their contributions to the host's immune system in animal models and highlighting their potential roles in regenerative medicine.

LAMTOR1/mTORC1 promotes CD276 to induce immunosuppression via PI3K/Akt/MMP signaling pathway in Clostridium perfringens-induced necrotic enteritis of laying hens

Clostridium perfringens (C. perfringens) causes avian necrotic enteritis, leading to huge economic losses to the poultry industry. This pathogen induces host immunosuppression; however, the molecular mechanism is still unclear. Thus, we established a laying hen infection model to explore this mechanism. We randomly divided 20 one-old-day laying hens into the control and infection groups. The infection group was infected intragastrically with 1 × 109 colony-forming units of C. perfringens in 1 mL of sterile phosphate-buffered saline (PBS) once a day from d 17 to 20; the control group received the same volume of PBS without the bacterium. Twenty-four hours after the last challenge, we sacrificed the laying hens and collected the jejunum for analysis. The infection group presented alterations in blood biochemical parameters and necrotic lesion scores as well as damage to the jejunum. Proteomics revealed 427 upregulated and 291 downregulated proteins in the infection group. In the infection group, CD3, CD4, and CD8 messenger RNA expression (mRNA) expression was decreased; LAMTOR1 and mTORC1 mRNA expression was increased; CD276 protein expression was enhanced; and the PI3K/Akt/MMP pathway was activated in jejunum of laying hens. This is the first study to report CD276 expression in the jejunum related to immunosuppression in a laying hen model of necrotic enteritis. It provides some new key targets to potentially control avian necrotic enteritis.

The peripheral neuroimmune system

Historically, the nervous and immune systems were studied as separate entities. The nervous system relays signals between the body and the brain by processing sensory inputs and executing motor outputs, whereas the immune system provides protection against injury and infection through inflammation. However, recent developments have demonstrated that these systems mount tightly integrated responses. In particular, the peripheral nervous system acts in concert with the immune system to control reflexes that maintain and restore homeostasis. Notwithstanding their homeostatic mechanisms, dysregulation of these neuroimmune interactions may underlie various pathological conditions. Understanding how these two distinct systems communicate is an emerging field of peripheral neuroimmunology that promises to reveal new insights into tissue physiology and identify novel targets to treat disease.

Unraveling the gut: the pivotal role of intestinal mechanisms in Kawasaki disease pathogenesis

Kawasaki disease (KD), an acute systemic vasculitis that primarily affects children under 5 years of age, is the leading cause of acquired heart disease in this age group. Recent studies propose a novel perspective on KD's etiology, emphasizing the gastrointestinal (GI) tract, particularly the role of gut permeability. This review delves into how disruptions in gut barrier function trigger systemic inflammatory responses, exacerbate vascular inflammation, and contribute to coronary artery aneurysms. Evidence suggests that children with KD often exhibit increased gut permeability, leading to an imbalance in gut immunity and subsequent gut barrier damage. These changes impact vascular endothelial cells, promoting platelet aggregation and activation, thereby advancing severe vascular complications, including aneurysms. Additionally, this review highlights the correlation between GI symptoms and increased resistance to standard treatments like intravenous immunoglobulin (IVIG), indicating that GI involvement may predict therapeutic outcomes. Advocating for a new paradigm, this review calls for integrated research across gastroenterology, immunology, and cardiology to examine KD through the lens of GI health. The goal is to develop innovative therapeutic interventions targeting the intestinal barrier, potentially revolutionizing KD management and significantly improving patient outcomes.

Incretin hormones and obesity

The incretin hormones glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP) play critical roles in co-ordinating postprandial metabolism, including modulation of insulin secretion and food intake. They are secreted from enteroendocrine cells in the intestinal epithelium following food ingestion, and act at multiple target sites including pancreatic islets and the brain. With the recent development of agonists targeting GLP-1 and GIP receptors for the treatment of type 2 diabetes and obesity, and the ongoing development of new incretin-based drugs with improved efficacy, there is great interest in understanding the physiology and pharmacology of these hormones.

Generalized cell phenotyping for spatial proteomics with language-informed vision models

We present a novel approach to cell phenotyping for spatial proteomics that addresses the challenge of generalization across diverse datasets with varying marker panels. Our approach utilizes a transformer with channel-wise attention to create a language-informed vision model; this model's semantic understanding of the underlying marker panel enables it to learn from and adapt to heterogeneous datasets. Leveraging a curated, diverse dataset with cell type labels spanning the literature and the NIH Human BioMolecular Atlas Program (HuBMAP) consortium, our model demonstrates robust performance across various cell types, tissues, and imaging modalities. Comprehensive benchmarking shows superior accuracy and generalizability of our method compared to existing methods. This work significantly advances automated spatial proteomics analysis, offering a generalizable and scalable solution for cell phenotyping that meets the demands of multiplexed imaging data.

Chronic inflammation drives epididymal tertiary lymphoid structure formation and autoimmune fertility disorders

The incomplete understanding of epididymal mucosal immunity is a significant contributing factor to the classification of many male infertility cases as idiopathic. Conditions that disrupt the immune balance in the male reproductive tract, such as vasectomy and infections, can expose sperm to the immune system, leading to increased production of anti-sperm antibodies (ASAs) and subsequent reproductive challenges. Regulatory T cells (Tregs) regulate inflammation and maintain sperm tolerance. In a murine model, we demonstrated that disrupting sperm immunotolerance induces chronic autoimmune responses characterized by antibody production targeting sperm and reproductive tissue autoantigens and unique tissue-specific immune cell signatures in the epididymis and testis. Such inflammatory features impair sperm function, contribute to epididymal damage, and drive sustained male subfertility. Tertiary lymphoid structures (TLSs) were formed within the epididymis after Treg depletion, defined by clusters of heterogenous B and T cells, fibroblasts, and endothelial cells. These ectopic structures perpetuate inflammation and lower the activation threshold for future immune threats. Similar isotypes of autoantibodies were detected in the seminal plasma of infertile patients, suggesting shared mechanistic pathways between mice and humans. Overall, we provide an in-depth understanding of the diverse B- and T-cell dynamics and TLS formation during epididymitis to develop precision-targeted therapies for infertility and chronic inflammation. Additionally, this immunological characterization of the epididymal microenvironment has the potential to identify novel targets for the development of male contraceptives.

One Sentence Summary

Understanding the epididymal immune cell landscape dynamics aids in developing targeted therapies for infertility and contraception.

Predicting nanocarrier permeation across the human intestine in vitro: model matters

For clinical translation of oral nanocarriers, simulation of the intestinal microenvironment during in vitro testing is crucial to evaluate interactions with the intestinal mucosa. However, studies are often conducted using simplistic cell culture models, overlooking key physiological factors, and potentially leading to an overestimation of nanocarrier permeation. In this study, we systematically investigate different tissue models of the human intestine under static cultivation and dynamic flow conditions and analyze the impact of altered tissue characteristics on nanocarrier permeation. Our results reveal that the selection of cell types as well as the respective culture condition have a notable impact on the physiological characteristics of the resulting tissues. Tissue layer thickness, mucus secretion, and barrier impairment, all increase with increasing amounts of goblet cells and the application of dynamic flow conditions. Permeation studies with poly(lactic-co-glycolic acid) (PLGA) nanocarriers with and without polyethylene glycol (PEG) coating elucidate that the amount of mucus present in the respective model is the limiting factor for the permeation of PLGA nanocarriers, while tissue topography presents the key factor influencing PEG-PLGA nanocarrier permeation. Furthermore, both nanocarriers exhibit diametrically opposite permeation kinetics compared to soluble compounds. In summary, these findings reveal the critical role of the implemented test systems on permeation assessment and emphasize that, in the context of preclinical nanocarrier testing, the choice of in vitro model matters.

High-confidence calling of normal epithelial cells allows identification of a novel stem-like cell state in the colorectal cancer microenvironment

Single-cell analyses can be confounded by assigning unrelated groups of cells to common developmental trajectories. For instance, cancer cells and admixed normal epithelial cells could adopt similar cell states thus complicating analyses of their developmental potential. Here, we develop and benchmark CCISM (for Cancer Cell Identification using Somatic Mutations) to exploit genomic single nucleotide variants for the disambiguation of cancer cells from genomically normal non-cancer cells in single-cell data. We find that our method and others based on gene expression or allelic imbalances identify overlapping sets of colorectal cancer versus normal colon epithelial cells, depending on molecular characteristics of individual cancers. Further, we define consensus cell identities of normal and cancer epithelial cells with higher transcriptome cluster homogeneity than those derived using existing tools. Using the consensus identities, we identify significant shifts of cell state distributions in genomically normal epithelial cells developing in the cancer microenvironment, with immature states increased at the expense of terminal differentiation throughout the colon, and a novel stem-like cell state arising in the left colon. Trajectory analyses show that the new cell state extends the pseudo-time range of normal colon stem-like cells in a cancer context. We identify cancer-associated fibroblasts as sources of WNT and BMP ligands potentially contributing to increased plasticity of stem cells in the cancer microenvironment. Our analyses advocate careful interpretation of cell heterogeneity and plasticity in the cancer context and the consideration of genomic information in addition to gene expression data when possible.

Single-cell integration reveals metaplasia in inflammatory gut diseases

The gastrointestinal tract is a multi-organ system crucial for efficient nutrient uptake and barrier immunity. Advances in genomics and a surge in gastrointestinal diseases1,2 has fuelled efforts to catalogue cells constituting gastrointestinal tissues in health and disease3. Here we present systematic integration of 25 single-cell RNA sequencing datasets spanning the entire healthy gastrointestinal tract in development and in adulthood. We uniformly processed 385 samples from 189 healthy controls using a newly developed automated quality control approach (scAutoQC), leading to a healthy reference atlas with approximately 1.1 million cells and 136 fine-grained cell states. We anchor 12 gastrointestinal disease datasets spanning gastrointestinal cancers, coeliac disease, ulcerative colitis and Crohn's disease to this reference. Utilizing this 1.6 million cell resource (gutcellatlas.org), we discover epithelial cell metaplasia originating from stem cells in intestinal inflammatory diseases with transcriptional similarity to cells found in pyloric and Brunner's glands. Although previously linked to mucosal healing4, we now implicate pyloric gland metaplastic cells in inflammation through recruitment of immune cells including T cells and neutrophils. Overall, we describe inflammation-induced changes in stem cells that alter mucosal tissue architecture and promote further inflammation, a concept applicable to other tissues and diseases.

Intestinal organ chips for disease modelling and personalized medicine

Alterations in intestinal structure, mechanics and physiology underlie acute and chronic intestinal conditions, many of which are influenced by dysregulation of microbiome, peristalsis, stroma or immune responses. Studying human intestinal physiology or pathophysiology is difficult in preclinical animal models because their microbiomes and immune systems differ from those of humans. Although advances in organoid culture partially overcome this challenge, intestinal organoids still lack crucial features that are necessary to study functions central to intestinal health and disease, such as digestion or fluid flow, as well as contributions from long-term effects of living microbiome, peristalsis and immune cells. Here, we review developments in organ-on-a-chip (organ chip) microfluidic culture models of the human intestine that are lined by epithelial cells and interfaced with other tissues (such as stroma or endothelium), which can experience both fluid flow and peristalsis-like motions. Organ chips offer powerful ways to model intestinal physiology and disease states for various human populations and individual patients, and can be used to gain new insight into underlying molecular and biophysical mechanisms of disease. They can also be used as preclinical tools to discover new drugs and then validate their therapeutic efficacy and safety in the same human-relevant model.

Pseudo-spectral angle mapping for pixel and cell classification in highly multiplexed immunofluorescence images

Purpose

The rapid development of highly multiplexed microscopy has enabled the study of cells embedded within their native tissue. The rich spatial data provided by these techniques have yielded exciting insights into the spatial features of human disease. However, computational methods for analyzing these high-content images are still emerging; there is a need for more robust and generalizable tools for evaluating the cellular constituents and stroma captured by high-plex imaging. To address this need, we have adapted spectral angle mapping-an algorithm developed for hyperspectral image analysis-to compress the channel dimension of high-plex immunofluorescence (IF) images.

Approach

Here, we present pseudo-spectral angle mapping (pSAM), a robust and flexible method for determining the most likely class of each pixel in a high-plex image. The class maps calculated through pSAM yield pixel classifications which can be combined with instance segmentation algorithms to classify individual cells.

Results

In a dataset of colon biopsies imaged with a 13-plex staining panel, 16 pSAM class maps were computed to generate pixel classifications. Instance segmentations of cells with Cellpose2.0 ( F 1 -score of 0.83 ± 0.13 ) were combined with these class maps to provide cell class predictions for 13 cell classes. In addition, in a separate unseen dataset of kidney biopsies imaged with a 44-plex staining panel, pSAM plus Cellpose2.0 ( F 1 -score of 0.86 ± 0.11 ) detected a diverse set of 38 classes of structural and immune cells.

Conclusions

In summary, pSAM is a powerful and generalizable tool for evaluating high-plex IF image data and classifying cells in these high-dimensional images.

Consensus tissue domain detection in spatial omics data using multiplex image labeling with regional morphology (MILWRM)

Spatially resolved molecular assays provide high dimensional genetic, transcriptomic, proteomic, and epigenetic information in situ and at various resolutions. Pairing these data across modalities with histological features enables powerful studies of tissue pathology in the context of an intact microenvironment and tissue structure. Increasing dimensions across molecular analytes and samples require new data science approaches to functionally annotate spatially resolved molecular data. A specific challenge is data-driven cross-sample domain detection that allows for analysis within and between consensus tissue compartments across high volumes of multiplex datasets stemming from tissue atlasing efforts. Here, we present MILWRM (multiplex image labeling with regional morphology)-a Python package for rapid, multi-scale tissue domain detection and annotation at the image- or spot-level. We demonstrate MILWRM's utility in identifying histologically distinct compartments in human colonic polyps, lymph nodes, mouse kidney, and mouse brain slices through spatially-informed clustering in two different spatial data modalities from different platforms. We used tissue domains detected in human colonic polyps to elucidate the molecular distinction between polyp subtypes, and explored the ability of MILWRM to identify anatomical regions of the brain tissue and their respective distinct molecular profiles.

In vivo detection of endogenous toxic phenolic compounds of intestine

Phenol and p-cresol are two common toxic small molecules related to various diseases. Existing reports confirmed that high L-tyrosine in the daily diet can increase the concentration of phenolic compounds in blood and urine. L-tyrosine is a common component of protein-rich foods. Some anaerobic bacteria in the gut can convert non-toxic l-tyrosine into these two toxic phenolic compounds, phenol and p-cresol. Existing methods have been constructed for measuring the concentration of phenolic compound in feces. However, there is still a lack of direct visual evidence to measure the phenolic compounds in the intestine. In this study, we aimed to construct a whole-cell biosensor for phenolic compounds detection based on the dmpR, the regulator from the phenol metabolism cluster. The commensal bacterium Citrobacter amalonaticus PS01 was selected and used as the chassis. Compared with the biosensor based on ECN1917, the biosensor PS01[dmpR] could better implant into the mouse gut through gavage and showed a higher sensitive to phenolic compound. And the concentration of phenolic compounds in the intestines could be observed with the help of in vivo imaging system using PS01[dmpR]. This paper demonstrated endogenous phenol synthesis in the gut and the strategy of using commensal bacteria to construct whole-cell biosensors for detecting small molecule compounds in the intestines.

Colon or semicolon: gut sampling microdevices for omics insights

Ingestible microdevices represent a breakthrough in non-invasive sampling of the human gastrointestinal (GI) tract. By capturing the native spatiotemporal microbiome and intricate biochemical gradients, these devices allow a non-invasive multi-omic access to the unperturbed host-microbiota crosstalk, immune/nutritional landscapes and gut-organ connections. We present the current progress of GI sampling microdevices towards personalized metabolism and fostering collaboration among clinicians, engineers, and data scientists.

Spatial transcriptomics in health and disease

The ability to localize hundreds of macromolecules to discrete locations, structures and cell types in a tissue is a powerful approach to understand the cellular and spatial organization of an organ. Spatially resolved transcriptomic technologies enable mapping of transcripts at single-cell or near single-cell resolution in a multiplex manner. The rapid development of spatial transcriptomic technologies has accelerated the pace of discovery in several fields, including nephrology. Its application to preclinical models and human samples has provided spatial information about new cell types discovered by single-cell sequencing and new insights into the cell-cell interactions within neighbourhoods, and has improved our understanding of the changes that occur in response to injury. Integration of spatial transcriptomic technologies with other omics methods, such as proteomics and spatial epigenetics, will further facilitate the generation of comprehensive molecular atlases, and provide insights into the dynamic relationships of molecular components in homeostasis and disease. This Review provides an overview of current and emerging spatial transcriptomic methods, their applications and remaining challenges for the field.

Cellular and molecular basis of proximal small intestine disorders

The proximal part of the small intestine, including duodenum and jejunum, is not only dedicated to nutrient digestion and absorption but is also a highly regulated immune site exposed to environmental factors. Host-protective responses against pathogens and tolerance to food antigens are essential functions in the small intestine. The cellular ecology and molecular pathways to maintain those functions are complex. Maladaptation is highlighted by common immune-mediated diseases such as coeliac disease, environmental enteric dysfunction or duodenal Crohn's disease. An expanding spectrum of more than 100 rare monogenic disorders inform on causative molecular mechanisms of nutrient absorption, epithelial homeostasis and barrier function, as well as inflammatory immune responses and immune regulation. Here, after summarizing the architectural and cellular traits that underlie the functions of the proximal intestine, we discuss how the integration of tissue immunopathology and molecular mechanisms can contribute towards our understanding of disease and guide diagnosis. We propose an integrated mechanism-based taxonomy and discuss the latest experimental approaches to gain new mechanistic insight into these disorders with large disease burden worldwide as well as implications for therapeutic interventions.

Tuft cells act as regenerative stem cells in the human intestine

In mice, intestinal tuft cells have been described as a long-lived, postmitotic cell type. Two distinct subsets have been identified: tuft-1 and tuft-2 (ref. 1). By combining analysis of primary human intestinal resection material and intestinal organoids, we identify four distinct human tuft cell states, two of which overlap with their murine counterparts. We show that tuft cell development depends on the presence of Wnt ligands, and that tuft cell numbers rapidly increase on interleukin-4 (IL-4) and IL-13 exposure, as reported previously in mice2-4. This occurs through proliferation of pre-existing tuft cells, rather than through increased de novo generation from stem cells. Indeed, proliferative tuft cells occur in vivo both in fetal and in adult human intestine. Single mature proliferating tuft cells can form organoids that contain all intestinal epithelial cell types. Unlike stem and progenitor cells, human tuft cells survive irradiation damage and retain the ability to generate all other epithelial cell types. Accordingly, organoids engineered to lack tuft cells fail to recover from radiation-induced damage. Thus, tuft cells represent a damage-induced reserve intestinal stem cell pool in humans.

Gut mucosal microbiota profiles linked to development of positional-specific human colorectal cancer

Colorectal cancer (CRC) continuously ranks as the third most common cause of cancer-related deaths worldwide. Based on anatomical classifications and clinical diagnoses, CRC is classified into right-sided, left-sided, and rectal CRC. Importantly, the three types of positional-specific CRC affect the prognosis outcomes, thus indicating that positional-specific treatments for CRC are required. Emerging evidence suggests that besides host genetic and epigenetic alterations, gut mucosal microbiota is linked to gut inflammation, CRC occurrence, and prognoses. However, gut mucosal microbiota associated with positional-specific CRC are poorly investigated. Here, we report the gut mucosal microbiota profiles associated with these three types of CRC. Our analysis showed that the unique composition and biodiversity of bacterial taxa are linked to positional-specific CRC. We found that a combination of bacterial taxa can serve as potential biomarkers to distinguish the three types of CRC. Further investigations of the physiological roles of bacteria associated with positional-specific CRC may help understand the mechanism of CRC progression in different anatomical locations under the impact of gut mucosal microbiota.

Basal-to-inflammatory transition and tumor resistance via crosstalk with a pro-inflammatory stromal niche

Cancer-associated inflammation is a double-edged sword possessing both pro- and anti-tumor properties through ill-defined tumor-immune dynamics. While we previously identified a carcinoma tumor-intrinsic resistance pathway, basal-to-squamous cell carcinoma transition, here, employing a multipronged single-cell and spatial-omics approach, we identify an inflammation and therapy-enriched tumor state we term basal-to-inflammatory transition. Basal-to-inflammatory transition signature correlates with poor overall patient survival in many epithelial tumors. Basal-to-squamous cell carcinoma transition and basal-to-inflammatory transition occur in adjacent but distinct regions of a single tumor: basal-to-squamous cell carcinoma transition arises within the core tumor nodule, while basal-to-inflammatory transition emerges from a specialized inflammatory environment defined by a tumor-associated TREM1 myeloid signature. TREM1 myeloid-derived cytokines IL1 and OSM induce basal-to-inflammatory transition in vitro and in vivo through NF-κB, lowering sensitivity of patient basal cell carcinoma explant tumors to Smoothened inhibitor treatment. This work deepens our knowledge of the heterogeneous local tumor microenvironment and nominates basal-to-inflammatory transition as a drug-resistant but targetable tumor state driven by a specialized inflammatory microenvironment.

Characterizing cell-type spatial relationships across length scales in spatially resolved omics data

Spatially resolved omics (SRO) technologies enable the identification of cell types while preserving their organization within tissues. Application of such technologies offers the opportunity to delineate cell-type spatial relationships, particularly across different length scales, and enhance our understanding of tissue organization and function. To quantify such multi-scale cell-type spatial relationships, we developed CRAWDAD, Cell-type Relationship Analysis Workflow Done Across Distances, as an open-source R package with source code and additional documentation at https://jef.works/CRAWDAD/. To demonstrate the utility of such multi-scale characterization, recapitulate expected cell-type spatial relationships, and evaluate against other cell-type spatial analyses, we applied CRAWDAD to various simulated and real SRO datasets of diverse tissues assayed by diverse SRO technologies. We further demonstrate how such multi-scale characterization enabled by CRAWDAD can be used to compare cell-type spatial relationships across multiple samples. Finally, we applied CRAWDAD to SRO datasets of the human spleen to identify consistent as well as patient and sample-specific cell-type spatial relationships. In general, we anticipate such multi-scale analysis of SRO data enabled by CRAWDAD will provide useful quantitative metrics to facilitate the identification, characterization, and comparison of cell-type spatial relationships across axes of interest.

Enterococcal-host interactions in the gastrointestinal tract and beyond

The gastrointestinal tract (GIT) is typically considered the natural niche of enterococci. However, these bacteria also inhabit extraintestinal tissues, where they can disrupt organ physiology and cause life-threatening infections. Here, we discuss how enterococci, primarily Enterococcus faecalis, interact with the intestine and other host anatomical locations such as the oral cavity, heart, liver, kidney, and vaginal tract. The metabolic flexibility of these bacteria allows them to quickly adapt to new environments, promoting their persistence in diverse tissues. In transitioning from commensals to pathogens, enterococci must overcome harsh conditions such as nutrient competition, exposure to antimicrobials, and immune pressure. Therefore, enterococci have evolved multiple mechanisms to adhere, colonize, persist, and endure these challenges in the host. This review provides a comprehensive overview of how enterococci interact with diverse host cells and tissues across multiple organ systems, highlighting the key molecular pathways that mediate enterococcal adaptation, persistence, and pathogenic behavior.

Identification and Characterization of Multiple Paneth Cell Types in the Mouse Small Intestine

The small intestinal crypts harbor secretory Paneth cells (PCs) which express bactericidal peptides that are crucial for maintaining intestinal homeostasis. Considering the diverse environmental conditions throughout the course of the small intestine, multiple subtypes of PCs are expected to exist. We applied single-cell RNA-sequencing of PCs combined with deep bulk RNA-sequencing on PC populations of different small intestinal locations and discovered several expression-based PC clusters. Some of these are discrete and resemble tuft cell-like PCs, goblet cell (GC)-like PCs, PCs expressing stem cell markers, and atypical PCs. Other clusters are less discrete but appear to be derived from different locations along the intestinal tract and have environment-dictated functions such as food digestion and antimicrobial peptide production. A comprehensive spatial analysis using Resolve Bioscience was conducted, leading to the identification of different PC's transcriptomic identities along the different compartments of the intestine, but not between PCs in the crypts themselves.

Application of Pan-Omics Technologies in Research on Important Economic Traits for Ruminants

The economic significance of ruminants in agriculture underscores the need for advanced research methodologies to enhance their traits. This review aims to elucidate the transformative role of pan-omics technologies in ruminant research, focusing on their application in uncovering the genetic mechanisms underlying complex traits such as growth, reproduction, production performance, and rumen function. Pan-omics analysis not only helps in identifying key genes and their regulatory networks associated with important economic traits but also reveals the impact of environmental factors on trait expression. By integrating genomics, epigenomics, transcriptomics, metabolomics, and microbiomics, pan-omics enables a comprehensive analysis of the interplay between genetics and environmental factors, offering a holistic understanding of trait expression. We explore specific examples of economic traits where these technologies have been pivotal, highlighting key genes and regulatory networks identified through pan-omics approaches. Additionally, we trace the historical evolution of each omics field, detailing their progression from foundational discoveries to high-throughput platforms. This review provides a critical synthesis of recent advancements, offering new insights and practical recommendations for the application of pan-omics in the ruminant industry. The broader implications for modern animal husbandry are discussed, emphasizing the potential for these technologies to drive sustainable improvements in ruminant production systems.

Label-free active single-cell encapsulation enabled by microvalve-based on-demand droplet generation and real-time image processing

Droplet microfluidics-based single-cell encapsulation is a critical technology that enables large-scale parallel single-cell analysis by capturing and processing thousands of individual cells. As the efficiency of passive single-cell encapsulation is limited by Poisson distribution, active single-cell encapsulation has been developed to theoretically ensure that each droplet contains one cell. However, existing active single-cell encapsulation technologies still face issues related to fluorescence labeling and low throughput. Here, we present an active single-cell encapsulation technique by using microvalve-based drop-on-demand technology and real-time image processing to encapsulate single cells with high throughput in a label-free manner. Our experiments demonstrated that the single-cell encapsulation system can encapsulate individual polystyrene beads with 96.3 % efficiency and HeLa cells with 94.9 % efficiency. The flow speed of cells in this system can reach 150 mm/s, resulting in a corresponding theoretical encapsulation throughput of 150 Hz. This technology has significant potential in various biomedical applications, including single-cell omics, secretion detection, and drug screening.

Human BioMolecular Atlas Program (HuBMAP): 3D Human Reference Atlas Construction and Usage

The Human BioMolecular Atlas Program (HuBMAP) aims to construct a reference 3D structural, cellular, and molecular atlas of the healthy adult human body. The HuBMAP Data Portal (https://portal.hubmapconsortium.org) serves experimental datasets and supports data processing, search, filtering, and visualization. The Human Reference Atlas (HRA) Portal (https://humanatlas.io) provides open access to atlas data, code, procedures, and instructional materials. Experts from more than 20 consortia are collaborating to construct the HRA's Common Coordinate Framework (CCF), knowledge graphs, and tools that describe the multiscale structure of the human body (from organs and tissues down to cells, genes, and biomarkers) and to use the HRA to understand changes that occur at each of these levels with aging, disease, and other perturbations. The 6th release of the HRA v2.0 covers 36 organs with 4,499 unique anatomical structures, 1,195 cell types, and 2,089 biomarkers (e.g., genes, proteins, lipids) linked to ontologies and 2D/3D reference objects. New experimental data can be mapped into the HRA using (1) three cell type annotation tools (e.g., Azimuth) or (2) validated antibody panels (OMAPs), or (3) by registering tissue data spatially. This paper describes the HRA user stories, terminology, data formats, ontology validation, unified analysis workflows, user interfaces, instructional materials, application programming interface (APIs), flexible hybrid cloud infrastructure, and previews atlas usage applications.

Circulating pancreatic enzyme levels are a causal biomarker of type 1 diabetes

Novel biomarkers of type 1 diabetes (T1D) are needed for earlier detection of disease and identifying therapeutic targets. We identified biomarkers of T1D by combining plasma cis and trans protein QTLs (pQTLs) for 2,922 proteins in the UK Biobank with a T1D genome-wide association study (GWAS) in 157k samples. T1D risk variants at over 20% of known loci colocalized with cis or trans pQTLs, and distinct sets of T1D loci colocalized with immune, pancreatic secretion, or gut-related proteins. We identified 23 proteins with evidence for a causal role in using pQTLs as genetic instruments in Mendelian Randomization which included multiple sensitivity analyses. Proteins increasing T1D risk were involved in immune processes (e.g. HLA-DRA) and, more surprisingly, T1D protective proteins were enriched in pancreatic secretions (e.g. CPA1), cholesterol metabolism (e.g. APOA1), and gut homeostasis. Genetic variants associated with plasma levels of T1D-protective pancreatic enzymes such as CPA1 were enriched in cis-regulatory elements in pancreatic exocrine and gut enteroendocrine cells, and the protective effects of CPA1 and other enzymes on T1D were consistent when using instruments specific to acinar cells. Finally, pancreatic enzymes had decreased acinar expression in T1D, including CPA1 which was altered prior to onset. Together, these results reveal causal biomarkers and highlight processes in the exocrine pancreas, immune system, and gut that modulate T1D risk.

Spatially Segregated Macrophage Populations Predict Distinct Outcomes in Colon Cancer

Tumor-associated macrophages are transcriptionally heterogeneous, but the spatial distribution and cell interactions that shape macrophage tissue roles remain poorly characterized. Here, we spatially resolve five distinct human macrophage populations in normal and malignant human breast and colon tissue and reveal their cellular associations. This spatial map reveals that distinct macrophage populations reside in spatially segregated micro-environmental niches with conserved cellular compositions that are repeated across healthy and diseased tissue. We show that IL4I1+ macrophages phagocytose dying cells in areas with high cell turnover and predict good outcome in colon cancer. In contrast, SPP1+ macrophages are enriched in hypoxic and necrotic tumor regions and portend worse outcome in colon cancer. A subset of FOLR2+ macrophages is embedded in plasma cell niches. NLRP3+ macrophages co-localize with neutrophils and activate an inflammasome in tumors. Our findings indicate that a limited number of unique human macrophage niches function as fundamental building blocks in tissue. Significance: This work broadens our understanding of the distinct roles different macrophage populations may exert on cancer growth and reveals potential predictive markers and macrophage population-specific therapy targets.

The impact of GLP-1 signalling on the energy metabolism of pancreatic islet β-cells and extrapancreatic tissues

Glucagon-like peptide-1 signalling impacts glucose homeostasis and appetite thereby indirectly affecting substrate availability at the whole-body level. The incretin canonically produces an insulinotropic effect, thereby lowering blood glucose levels by promoting the uptake and inhibiting the production of the sugar by peripheral tissues. Likewise, GLP-1 signalling within the central nervous system reduces the appetite and food intake, whereas its gastric effect delays the absorption of nutrients, thus improving glycaemic control and reducing the risk of postprandial hyperglycaemia. We review the molecular aspects of the GLP-1 signalling, focusing on its impact on intracellular energy metabolism. Whilst the incretin exerts its effects predominantly via a Gs receptor, which decodes the incretin signal into the elevation of intracellular cAMP levels, the downstream signalling cascades within the cell, acting on fast and slow timescales, resulting in an enhancement or an attenuation of glucose catabolism, respectively.

A spatial expression atlas of the adult human proximal small intestine

The mouse small intestine shows profound variability in gene expression along the crypt-villus axis1,2. Whether similar spatial heterogeneity exists in the adult human gut remains unclear. Here we use spatial transcriptomics, spatial proteomics and single-molecule fluorescence in situ hybridization to reconstruct a comprehensive spatial expression atlas of the adult human proximal small intestine. We describe zonated expression and cell type representation for epithelial, mesenchymal and immune cell types. We find that migrating enterocytes switch from lipid droplet assembly and iron uptake at the villus bottom to chylomicron biosynthesis and iron release at the tip. Villus tip cells are pro-immunogenic, recruiting γδ T cells and macrophages to the tip, in contrast to their immunosuppressive roles in mouse. We also show that the human small intestine contains abundant serrated and branched villi that are enriched at the tops of circular folds. Our study presents a detailed resource for understanding the biology of the adult human small intestine.

Mapping the Multiscale Proteomic Organization of Cellular and Disease Phenotypes

While the primary sequences of human proteins have been cataloged for over a decade, determining how these are organized into a dynamic collection of multiprotein assemblies, with structures and functions spanning biological scales, is an ongoing venture. Systematic and data-driven analyses of these higher-order structures are emerging, facilitating the discovery and understanding of cellular phenotypes. At present, knowledge of protein localization and function has been primarily derived from manual annotation and curation in resources such as the Gene Ontology, which are biased toward richly annotated genes in the literature. Here, we envision a future powered by data-driven mapping of protein assemblies. These maps can capture and decode cellular functions through the integration of protein expression, localization, and interaction data across length scales and timescales. In this review, we focus on progress toward constructing integrated cell maps that accelerate the life sciences and translational research.

Endogenous ethanol production in health and disease

The gut microbiome exerts metabolic actions on distal tissues and organs outside the intestine, partly through microbial metabolites that diffuse into the circulation. The disruption of gut homeostasis results in changes to microbial metabolites, and more than half of the variance in the plasma metabolome can be explained by the gut microbiome. Ethanol is a major microbial metabolite that is produced in the intestine of nearly all individuals; however, elevated ethanol production is associated with pathological conditions such as metabolic dysfunction-associated steatotic liver disease and auto-brewery syndrome, in which the liver's capacity to metabolize ethanol is surpassed. In this Review, we describe the mechanisms underlying excessive ethanol production in the gut and the role of ethanol catabolism in mediating pathogenic effects of ethanol on the liver and host metabolism. We conclude by discussing approaches to target excessive ethanol production by gut bacteria.

Reprogramming human urine cells into intestinal organoids with long-term expansion ability and barrier function

Generation of intestinal organoids from human somatic cells by reprogramming would enable intestinal regeneration, disease modeling, and drug screening in a personalized pattern. Here, we report a direct reprogramming protocol for the generation of human urine cells induced intestinal organoids (U-iIOs) under a defined medium. U-iIOs expressed multiple intestinal specific genes and showed resembling gene expression profiles to primary small intestines. U-iIOs can be stably long-term expanded and further differentiated into more mature intestinal lineage cells with high expression of metallothionein and cytochrome P450 (CYP450) genes. These specific molecular features of U-iIOs differ from human pluripotent stem cells derived intestinal organoids (P-iIOs) and intestinal immortalized cell lines. Furthermore, U-iIOs exhibit intestinal barriers indicated by blocking FITC-dextran permeation and uptaking of the specific substrate rhodamine 123. Our study provides a novel platform for patient-specific intestinal organoid generation, which may lead to precision treatment of intestinal diseases and facilitate drug discovery.

Self-supervised learning for characterising histomorphological diversity and spatial RNA expression prediction across 23 human tissue types

As vast histological archives are digitised, there is a pressing need to be able to associate specific tissue substructures and incident pathology to disease outcomes without arduous annotation. Here, we learn self-supervised representations using a Vision Transformer, trained on 1.7 M histology images across 23 healthy tissues in 838 donors from the Genotype Tissue Expression consortium (GTEx). Using these representations, we can automatically segment tissues into their constituent tissue substructures and pathology proportions across thousands of whole slide images, outperforming other self-supervised methods (43% increase in silhouette score). Additionally, we can detect and quantify histological pathologies present, such as arterial calcification (AUROC = 0.93) and identify missing calcification diagnoses. Finally, to link gene expression to tissue morphology, we introduce RNAPath, a set of models trained on 23 tissue types that can predict and spatially localise individual RNA expression levels directly from H&E histology (mean genes significantly regressed = 5156, FDR 1%). We validate RNAPath spatial predictions with matched ground truth immunohistochemistry for several well characterised control genes, recapitulating their known spatial specificity. Together, these results demonstrate how self-supervised machine learning when applied to vast histological archives allows researchers to answer questions about tissue pathology, its spatial organisation and the interplay between morphological tissue variability and gene expression.

Transcription factor dynamics, oscillation, and functions in human enteroendocrine cell differentiation

Enteroendocrine cells (EECs) secrete serotonin (enterochromaffin [EC] cells) or specific peptide hormones (non-EC cells) that serve vital metabolic functions. The basis for terminal EEC diversity remains obscure. By forcing activity of the transcription factor (TF) NEUROG3 in 2D cultures of human intestinal stem cells, we replicated physiologic EEC differentiation and examined transcriptional and cis-regulatory dynamics that culminate in discrete cell types. Abundant EEC precursors expressed stage-specific genes and TFs. Before expressing pre-terminal NEUROD1, post-mitotic precursors oscillated between transcriptionally distinct ASCL1+ and HES6hi cell states. Loss of either factor accelerated EEC differentiation substantially and disrupted EEC individuality; ASCL1 or NEUROD1 deficiency had opposing consequences on EC and non-EC cell features. These TFs mainly bind cis-elements that are accessible in undifferentiated stem cells, and they tailor subsequent expression of TF combinations that underlie discrete EEC identities. Thus, early TF oscillations retard EEC maturation to enable accurate diversity within a medically important cell lineage.

SEraster: a rasterization preprocessing framework for scalable spatial omics data analysis

MOTIVATION

Spatial omics data demand computational analysis but many analysis tools have computational resource requirements that increase with the number of cells analyzed. This presents scalability challenges as researchers use spatial omics technologies to profile millions of cells.

RESULTS

To enhance the scalability of spatial omics data analysis, we developed a rasterization preprocessing framework called SEraster that aggregates cellular information into spatial pixels. We apply SEraster to both real and simulated spatial omics data prior to spatial variable gene expression analysis to demonstrate that such preprocessing can reduce computational resource requirements while maintaining high performance, including as compared to other down-sampling approaches. We further integrate SEraster with existing analysis tools to characterize cell-type spatial co-enrichment across length scales. Finally, we apply SEraster to enable analysis of a mouse pup spatial omics dataset with over a million cells to identify tissue-level and cell-type-specific spatially variable genes as well as spatially co-enriched cell types that recapitulate expected organ structures.

AVAILABILITY AND IMPLEMENTATION

SEraster is implemented as an R package on GitHub (https://github.com/JEFworks-Lab/SEraster) with additional tutorials at https://JEF.works/SEraster.

Integration of spatial and single-cell data across modalities with weakly linked features

Although single-cell and spatial sequencing methods enable simultaneous measurement of more than one biological modality, no technology can capture all modalities within the same cell. For current data integration methods, the feasibility of cross-modal integration relies on the existence of highly correlated, a priori 'linked' features. We describe matching X-modality via fuzzy smoothed embedding (MaxFuse), a cross-modal data integration method that, through iterative coembedding, data smoothing and cell matching, uses all information in each modality to obtain high-quality integration even when features are weakly linked. MaxFuse is modality-agnostic and demonstrates high robustness and accuracy in the weak linkage scenario, achieving 20~70% relative improvement over existing methods under key evaluation metrics on benchmarking datasets. A prototypical example of weak linkage is the integration of spatial proteomic data with single-cell sequencing data. On two example analyses of this type, MaxFuse enabled the spatial consolidation of proteomic, transcriptomic and epigenomic information at single-cell resolution on the same tissue section.

Using Human Intestinal Organoids to Understand the Small Intestine Epithelium at the Single Cell Transcriptional Level

Single cell transcriptomics has revolutionized our understanding of the cell biology of the human body. State-of-the-art human small intestinal organoid cultures provide ex vivo model systems that bridge the gap between animal models and clinical studies. The application of single cell transcriptomics to human intestinal organoid (HIO) models is revealing previously unrecognized cell biology, biochemistry, and physiology of the GI tract. The advanced single cell transcriptomics platforms use microfluidic partitioning and barcoding to generate cDNA libraries. These barcoded cDNAs can be easily sequenced by next generation sequencing platforms and used by various visualization tools to generate maps. Here, we describe methods to culture and differentiate human small intestinal HIOs in different formats and procedures for isolating viable cells from these formats that are suitable for use in single-cell transcriptional profiling platforms. These protocols and procedures facilitate the use of small intestinal HIOs to obtain an increased understanding of the cellular response of human intestinal epithelium at the transcriptional level in the context of a variety of different environments.

Spatial Deconvolution of Cell Types and Cell States at Scale Utilizing TACIT

Identifying cell types and states remains a time-consuming, error-prone challenge for spatial biology. While deep learning is increasingly used, it is difficult to generalize due to variability at the level of cells, neighborhoods, and niches in health and disease. To address this, we developed TACIT, an unsupervised algorithm for cell annotation using predefined signatures that operates without training data. TACIT uses unbiased thresholding to distinguish positive cells from background, focusing on relevant markers to identify ambiguous cells in multiomic assays. Using five datasets (5,000,000-cells; 51-cell types) from three niches (brain, intestine, gland), TACIT outperformed existing unsupervised methods in accuracy and scalability. Integrating TACIT-identified cell types with a novel Shiny app revealed new phenotypes in two inflammatory gland diseases. Finally, using combined spatial transcriptomics and proteomics, we discovered under- and overrepresented immune cell types and states in regions of interest, suggesting multimodality is essential for translating spatial biology to clinical applications.

Automated classification of cellular expression in multiplexed imaging data with Nimbus

Multiplexed imaging offers a powerful approach to characterize the spatial topography of tissues in both health and disease. To analyze such data, the specific combination of markers that are present in each cell must be enumerated to enable accurate phenotyping, a process that often relies on unsupervised clustering. We constructed the Pan-Multiplex (Pan-M) dataset containing 197 million distinct annotations of marker expression across 15 different cell types. We used Pan-M to create Nimbus, a deep learning model to predict marker positivity from multiplexed image data. Nimbus is a pre-trained model that uses the underlying images to classify marker expression across distinct cell types, from different tissues, acquired using different microscope platforms, without requiring any retraining. We demonstrate that Nimbus predictions capture the underlying staining patterns of the full diversity of markers present in Pan-M. We then show how Nimbus predictions can be integrated with downstream clustering algorithms to robustly identify cell subtypes in image data. We have open-sourced Nimbus and Pan-M to enable community use at https://github.com/angelolab/Nimbus-Inference.

Nutrient metabolism in regulating intestinal stem cell homeostasis

Intestinal stem cells (ISCs) are known for their remarkable proliferative capacity, making them one of the most active cell populations in the body. However, a high turnover rate of intestinal epithelium raises the likelihood of dysregulated homeostasis, which is known to cause various diseases, including cancer. Maintaining precise control over the homeostasis of ISCs is crucial to preserve the intestinal epithelium's integrity during homeostasis or stressed conditions. Recent research has indicated that nutrients and metabolic pathways can extensively modulate the fate of ISCs. This review will explore recent findings concerning the influence of various nutrients, including lipids, carbohydrates, and vitamin D, on the delicate balance between ISC proliferation and differentiation.

Proteomic and Cellular Characterization of Omicron Breakthrough Infections and a Third Homologous or Heterologous Boosting Vaccination in a Longitudinal Cohort

The understanding of dynamic plasma proteome features in hybrid immunity and breakthrough infection is limited. A deeper understanding of the immune differences between heterologous and homologous immunization could assist in the future establishment of vaccination strategies. In this study, 40 participants who received a third dose of either a homologous BBIBP-CorV or a heterologous ZF2001 protein subunit vaccine following two doses of inactivated coronavirus disease 2019 vaccines and 12 patients with BA2.2 breakthrough infections were enrolled. Serum samples were collected at days 0, 28, and 180 following the boosting vaccination and breakthrough and then analyzed using neutralizing antibody tests and mass spectrometer-based proteomics. Mass cytometry of peripheral blood mononuclear cell samples was also performed in this cohort. The chemokine signaling pathway and humoral response markers (IgG2 and IgG3) associated with infection were found to be upregulated in breakthrough infections compared to vaccination-induced immunity. Elevated expression of IGKV, IGHV, IL-17 signaling, and the phagocytosis pathway, along with lower expression of FGL2, were correlated with higher antibody levels in the boosting vaccination groups. The MAPK signaling pathway and Fc gamma R-mediated phagocytosis were more enriched in the heterologous immunization groups than in the homologous immunization groups. Breakthrough infections can trigger more intensive inflammatory chemokine responses than vaccination. T-cell and innate immune activation have been shown to be closely related to enhanced antibody levels after vaccination and therefore might be potential targets for vaccine adjuvant design.

LTA and PGN from Bacillus siamensis can alleviate soybean meal-induced enteritis and microbiota dysbiosis in Lateolabrax maculatus

An eight-week feeding trial was designed to assess which component of commensal Bacillus siamensis LF4 can mitigate SBM-induced enteritis and microbiota dysbiosis in spotted seabass (Lateolabrax maculatus) based on TLRs-MAPKs/NF-кB signaling pathways. Fish continuously fed low SBM (containing 16 % SBM) and high SBM (containing 40 % SBM) diets were used as positive (FM group) and negative (SBM group) control, respectively. After feeding high SBM diet for 28 days, fish were supplemented with B. siamensis LF4-derived whole cell wall (CW), cell wall protein (CWP), lipoteichoic acid (LTA) or peptidoglycan (PGN) until 56 days. The results showed that a high inclusion of SBM in the diet caused enteritis, characterized with significantly (P < 0.05) decreased muscular thickness, villus height, villus width, atrophied and loosely arranged microvillus. Moreover, high SBM inclusion induced an up-regulation of pro-inflammatory cytokines and a down-regulation of occludin, E-cadherin, anti-inflammatory cytokines, apoptosis related genes and antimicrobial peptides. However, dietary supplementation with CW, LTA, and PGN of B. siamensis LF4 could effectively alleviate enteritis caused by a high level of dietary SBM. Additionally, CWP and PGN administration increased beneficial Cetobacterium and decreased pathogenic Plesiomonas and Brevinema, while dietary LTA decreased Plesiomonas and Brevinema, suggesting that CWP, LTA and PGN positively modulated intestinal microbiota in spotted seabass. Furthermore, CW, LTA, and PGN application significantly stimulated TLR2, TLR5 and MyD88 expressions, and inhibited the downstream p38 and NF-κB signaling. Taken together, these results suggest that LTA and PGN from B. siamensis LF4 could alleviate soybean meal-induced enteritis and microbiota dysbiosis in L. maculatus, and p38 MAPK/NF-κB pathways might be involved in those processes.