Use of Single-Cell -Omic Technologies to Study the Gastrointestinal Tract and Diseases, From Single Cell Identities to Patient Features

Proteomics mining of cancer hallmarks on a single-cell resolution

Dysregulated proteome is an essential contributor in carcinogenesis. Protein fluctuations fuel the progression of malignant transformation, such as uncontrolled proliferation, metastasis, and chemo/radiotherapy resistance, which severely impair therapeutic effectiveness and cause disease recurrence and eventually mortality among cancer patients. Cellular heterogeneity is widely observed in cancer and numerous cell subtypes have been characterized that greatly influence cancer progression. Population-averaged research may not fully reveal the heterogeneity, leading to inaccurate conclusions. Thus, deep mining of the multiplex proteome at the single-cell resolution will provide new insights into cancer biology, to develop prognostic biomarkers and treatments. Considering the recent advances in single-cell proteomics, herein we review several novel technologies with particular focus on single-cell mass spectrometry analysis, and summarize their advantages and practical applications in the diagnosis and treatment for cancer. Technological development in single-cell proteomics will bring a paradigm shift in cancer detection, intervention, and therapy.

Control of Paneth cell function by HuR regulates gut mucosal growth by altering stem cell activity

Rapid self-renewal of the intestinal epithelium requires the activity of intestinal stem cells (ISCs) that are intermingled with Paneth cells (PCs) at the crypt base. PCs provide multiple secreted and surface-bound niche signals and play an important role in the regulation of ISC proliferation. Here, we show that control of PC function by RNA-binding protein HuR via mitochondria affects intestinal mucosal growth by altering ISC activity. Targeted deletion of HuR in mice disrupted PC gene expression profiles, reduced PC-derived niche factors, and impaired ISC function, leading to inhibited renewal of the intestinal epithelium. Human intestinal mucosa from patients with critical surgical disorders exhibited decreased levels of tissue HuR and PC/ISC niche dysfunction, along with disrupted mucosal growth. HuR deletion led to mitochondrial impairment by decreasing the levels of several mitochondrial-associated proteins including prohibitin 1 (PHB1) in the intestinal epithelium, whereas HuR enhanced PHB1 expression by preventing microRNA-195 binding to the Phb1 mRNA. These results indicate that HuR is essential for maintaining the integrity of the PC/ISC niche and highlight a novel role for a defective PC/ISC niche in the pathogenesis of intestinal mucosa atrophy.

Omics and Multi-Omics in IBD: No Integration, No Breakthroughs

The recent advent of sophisticated technologies like sequencing and mass spectroscopy platforms combined with artificial intelligence-powered analytic tools has initiated a new era of "big data" research in various complex diseases of still-undetermined cause and mechanisms. The investigation of these diseases was, until recently, limited to traditional in vitro and in vivo biological experimentation, but a clear switch to in silico methodologies is now under way. This review tries to provide a comprehensive assessment of state-of-the-art knowledge on omes, omics and multi-omics in inflammatory bowel disease (IBD). The notion and importance of omes, omics and multi-omics in both health and complex diseases like IBD is introduced, followed by a discussion of the various omics believed to be relevant to IBD pathogenesis, and how multi-omics "big data" can generate new insights translatable into useful clinical tools in IBD such as biomarker identification, prediction of remission and relapse, response to therapy, and precision medicine. The pitfalls and limitations of current IBD multi-omics studies are critically analyzed, revealing that, regardless of the types of omes being analyzed, the majority of current reports are still based on simple associations of descriptive retrospective data from cross-sectional patient cohorts rather than more powerful longitudinally collected prospective datasets. Given this limitation, some suggestions are provided on how IBD multi-omics data may be optimized for greater clinical and therapeutic benefit. The review concludes by forecasting the upcoming incorporation of multi-omics analyses in the routine management of IBD.

Single-cell metabolomics by mass spectrometry: ready for primetime?

Single-cell metabolomics (SCMs) is a powerful tool for studying cellular heterogeneity by providing insight into the differences between individual cells. With the development of a set of promising SCMs pipelines, this maturing technology is expected to be widely used in biomedical research. However, before SCMs is ready for primetime, there are some challenges to overcome. In this review, we summarize the trends and challenges in the development of SCMs. We also highlight the latest methodologies, applications, and sketch the perspective for integration with other omics and imaging approaches.

The landscape of immune dysregulation in Crohn's disease revealed through single-cell transcriptomic profiling in the ileum and colon

Crohn's disease (CD) is a chronic gastrointestinal disease that is increasing in prevalence worldwide. CD is multifactorial, involving the complex interplay of genetic, immune, and environmental factors, necessitating a system-level understanding of its etiology. To characterize cell-type-specific transcriptional heterogeneity in active CD, we profiled 720,633 cells from the terminal ileum and colon of 71 donors with varying inflammation status. Our integrated datasets revealed organ- and compartment-specific responses to acute and chronic inflammation; most immune changes were in cell composition, whereas transcriptional changes dominated among epithelial and stromal cells. These changes correlated with endoscopic inflammation, but small and large intestines exhibited distinct responses, which were particularly apparent when focusing on IBD risk genes. Finally, we mapped markers of disease-associated myofibroblast activation and identified CHMP1A, TBX3, and RNF168 as regulators of fibrotic complications. Altogether, our results provide a roadmap for understanding cell-type- and organ-specific differences in CD and potential directions for therapeutic development.

Molecular pathogenesis, targeted therapies, and future perspectives for gastric cancer

Gastric cancer is a major source of global cancer mortality with limited treatment options and poor patient survival. As our molecular understanding of gastric cancer improves, we are now beginning to recognize that these cancers are a heterogeneous group of diseases with incredibly unique pathogeneses and active oncogenic pathways. It is this molecular diversity and oftentimes lack of common oncogenic driver mutations that bestow the poor treatment responses that oncologists often face when treating gastric cancer. In this review, we will examine the treatments for gastric cancer including up-to-date molecularly targeted therapies and immunotherapies. We will then review the molecular subtypes of gastric cancer to highlight the diversity seen in this disease. We will then shift our discussion to basic science and gastric cancer mouse models as tools to study gastric cancer molecular heterogeneity. Furthermore, we will elaborate on a molecular process termed paligenosis and the cyclical hit model as key events during gastric cancer initiation that impart nondividing mature differentiated cells the ability to re-enter the cell cycle and accumulate disparate genomic mutations during years of chronic inflammation and injury. As our basic science understanding of gastric cancer advances, so too must our translational and clinical efforts. We will end with a discussion regarding single-cell molecular analyses and cancer organoid technologies as future translational avenues to advance our understanding of gastric cancer heterogeneity and to design precision-based gastric cancer treatments. Elucidation of interpatient and intratumor heterogeneity is the only way to advance future cancer prevention, diagnoses and treatment.

Multi-omics approaches for deciphering the complexity of traditional Chinese medicine syndromes in stroke: A systematic review

Background: Deciphering the biological basis of traditional Chinese medicine (TCM) syndromes in complex diseases is challenging. Rapid advances in multi-omics approaches provide new opportunities to unveil the biological basis of TCM syndromes. We intend to summarize the latest significant progress and highlight the crucial value of applying multi-omics approaches to reveal TCM syndromes of stroke in a new horizon. Methods: We systematically searched PubMed, EMBASE, Web of Science Core Collection (WOSCC), Cochrane Library, China National Knowledge Infrastructure (CNKI), Chinese Science and Technology Periodical Database (VIP), Wanfang database and China Biology Medicine Database (SinoMed) for relevant studies from their inception to 31 March 2022, and conducted a comprehensive systematic review (PROSPERO registration number: CRD42021285922). Results: A total of 43 relevant studies were included in the final systematic review, genomics, transcriptomics, proteomics, and metabolomics were all involved. Some gene polymorphisms, differential lncRNAs, mRNAs, miRNAs, proteins, and metabolites may be associated with TCM syndromes of stroke. In addition, some studies conducted a preliminary exploration on the different diseases with the same TCM syndrome. The results showed that thioredoxin-dependent peroxidase reductase may be the specific marker protein of Liver-yang transforming into wind syndrome, and the network formed by mir-146b-5p, -199a-5p, and 23 targeted mRNAs may be the biomarker of Blood-stasis syndrome. Conclusion: Multi-omics technologies have served as powerful tools to investigate the complexity of TCM syndromes and may hold the promise of promoting the modernization of TCM as well as personalized medicine of TCM in stroke.

Discovering Immune-Mediated Mechanisms of Gastric Carcinogenesis Through Single-Cell RNA Sequencing

Single-cell RNA sequencing (scRNAseq) technology is still relatively new in the field of gastric cancer immunology but gaining significant traction. This technology now provides unprecedented insights into the intratumoral and intertumoral heterogeneities at the immunological, cellular, and molecular levels. Within the last few years, a volume of publications reported the usefulness of scRNAseq technology in identifying thus far elusive immunological mechanisms that may promote and impede gastric cancer development. These studies analyzed datasets generated from primary human gastric cancer tissues, metastatic ascites fluid from gastric cancer patients, and laboratory-generated data from in vitro and in vivo models of gastric diseases. In this review, we overview the exciting findings from scRNAseq datasets that uncovered the role of critical immune cells, including T cells, B cells, myeloid cells, mast cells, ILC2s, and other inflammatory stromal cells, like fibroblasts and endothelial cells. In addition, we also provide a synopsis of the initial scRNAseq findings on the interesting epithelial cell responses to inflammation. In summary, these new studies have implicated roles for T and B cells and subsets like NKT cells in tumor development and progression. The current studies identified diverse subsets of macrophages and mast cells in the tumor microenvironment, however, additional studies to determine their roles in promoting cancer growth are needed. Some groups specifically focus on the less prevalent ILC2 cell type that may contribute to early cancer development. ScRNAseq analysis also reveals that stromal cells, e.g., fibroblasts and endothelial cells, regulate inflammation and promote metastasis, making them key targets for future investigations. While evaluating the outcomes, we also highlight the gaps in the current findings and provide an assessment of what this technology holds for gastric cancer research in the coming years. With scRNAseq technology expanding rapidly, we stress the need for periodic review of the findings and assess the available scRNAseq analytical tools to guide future work on immunological mechanisms of gastric carcinogenesis.  

Quantifying and correcting slide-to-slide variation in multiplexed immunofluorescence images

MOTIVATION

Multiplexed imaging is a nascent single-cell assay with a complex data structure susceptible to technical variability that disrupts inference. These in situ methods are valuable in understanding cell-cell interactions, but few standardized processing steps or normalization techniques of multiplexed imaging data are available.

RESULTS

We implement and compare data transformations and normalization algorithms in multiplexed imaging data. Our methods adapt the ComBat and functional data registration methods to remove slide effects in this domain, and we present an evaluation framework to compare the proposed approaches. We present clear slide-to-slide variation in the raw, unadjusted data and show that many of the proposed normalization methods reduce this variation while preserving and improving the biological signal. Furthermore, we find that dividing multiplexed imaging data by its slide mean, and the functional data registration methods, perform the best under our proposed evaluation framework. In summary, this approach provides a foundation for better data quality and evaluation criteria in multiplexed imaging.

AVAILABILITY AND IMPLEMENTATION

Source code is provided at: https://github.com/statimagcoll/MultiplexedNormalization and an R package to implement these methods is available here: https://github.com/ColemanRHarris/mxnorm.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Cell population-based framework of genetic epidemiology in the single-cell omics era

Genetic epidemiology is a rapidly advancing field due to the recent availability of large amounts of omics data. In recent years, it has become possible to obtain omics information at the single-cell level, so genetic epidemiological models need to be updated to integrate with single-cell expression data. In this perspective paper, we propose a cell population-based framework for genetic epidemiology in the single-cell era. In this framework, genetic diversity influences phenotypic diversity through the diversity of cell population profiles, which are defined as high-dimensional probability distributions of the state spaces of biomolecules of each omics layer. We discuss how biomolecular experimental measurement data can capture the different properties of this distribution. In particular, single-cell data constitute a sample from this population distribution where only some coordinate values are observable. From a data analysis standpoint, we introduce methodology for feature extraction from cell population profiles. Finally, we discuss how this framework can be applied not only to genetic epidemiology but also to systems biology.

Gastrointestinal mucosal biopsies in Parkinson's disease: beyond alpha-synuclein detection

Alpha-synuclein deposits, the pathological hallmarks of Parkinson's disease, are consistently found in the gastrointestinal tract of parkinsonian subjects. These observations have raised the potential that endoscopically obtainable mucosal biopsies can aid to a molecular diagnosis of the disease. The possible usefulness of mucosal biopsies is, however, not limited to the detection of alpha-synuclein, but also extends to other essential aspects underlying pathophysiological mechanisms of gastrointestinal manifestations in Parkinson's disease. The aim of the current review is to provide an appraisal of the existing studies showing that gastrointestinal biopsies can be used for the analysis of enteric neuronal and glial cell morphology, intestinal epithelial barrier function, and gastrointestinal inflammation in Parkinson's disease. A perspective on the generation of organoids with GI biopsies and the potential use of single-cell and spatial transcriptomic technologies will be also addressed.

The Pathogenesis and Clinical Management of Stricturing Crohn Disease

Stricturing of the gastrointestinal tract is a common complication in Crohn disease and is a significant cause of morbidity and mortality among this population. The inflammatory process initiates fibrosis, leading to aberrant wound healing and excess deposition of extracellular matrix proteins. Our understanding of this process has grown and encompasses cellular mechanisms, epigenetic modifications, and inherent genetic predisposition toward fibrosis. Although medications can improve inflammation, there is still no drug to attenuate scar formation. As such, management of stricturing disease requires a multidisciplinary and individualized approach including medical management, therapeutic endoscopy, and surgery. This review details the current understanding regarding the pathogenesis, detection, and management of stricturing Crohn disease.

Machine Learning for Future Subtyping of the Tumor Microenvironment of Gastro-Esophageal Adenocarcinomas

Tumor progression involves an intricate interplay between malignant cells and their surrounding tumor microenvironment (TME) at specific sites. The TME is dynamic and is composed of stromal, parenchymal, and immune cells, which mediate cancer progression and therapy resistance. Evidence from preclinical and clinical studies revealed that TME targeting and reprogramming can be a promising approach to achieve anti-tumor effects in several cancers, including in GEA. Thus, it is of great interest to use modern technology to understand the relevant components of programming the TME. Here, we discuss the approach of machine learning, which recently gained increasing interest recently because of its ability to measure tumor parameters at the cellular level, reveal global features of relevance, and generate prognostic models. In this review, we discuss the relevant stromal composition of the TME in GEAs and discuss how they could be integrated. We also review the current progress in the application of machine learning in different medical disciplines that are relevant for the management and study of GEA.

Automation of single-cell proteomic sample preparation

Molecular heterogeneity exists at different spatial scales in biological samples and is an important parameter in the development of pathologies and resistances to therapies. When aiming to reach molecular heterogeneity of cells at extremely low spatial scales, single-cell analysis can be the ultimate choice. Proteomics performed in bulk population of cells (macroproteomics) is prone to mask molecular heterogeneity. Mass spectrometry-based single cell proteomics (SCP-MS) is the right solution to overcome this issue. Three main problems can be identified using SCP-MS: (i) analytical loss during sample preparation, (ii) inefficient microinjection/delivery of proteins/peptides from samples to MS and (iii) low analytical throughput. Technologies for automation of SCP have recently gained attention to improve methods accuracy, sensitivity, throughput and in-depth and low-biased proteome analysis. In this minireview, we therefore overview the state-of-the-art of automation of SCP-MS sample preparation approaches.

Gastrointestinal Tract Disease Classification from Wireless Endoscopy Images Using Pretrained Deep Learning Model

Wireless capsule endoscopy is a noninvasive wireless imaging technology that becomes increasingly popular in recent years. One of the major drawbacks of this technology is that it generates a large number of photos that must be analyzed by medical personnel, which takes time. Various research groups have proposed different image processing and machine learning techniques to classify gastrointestinal tract diseases in recent years. Traditional image processing algorithms and a data augmentation technique are combined with an adjusted pretrained deep convolutional neural network to classify diseases in the gastrointestinal tract from wireless endoscopy images in this research. We take advantage of pretrained models VGG16, ResNet-18, and GoogLeNet, a convolutional neural network (CNN) model with adjusted fully connected and output layers. The proposed models are validated with a dataset consisting of 6702 images of 8 classes. The VGG16 model achieved the highest results with 96.33% accuracy, 96.37% recall, 96.5% precision, and 96.5% F1-measure. Compared to other state-of-the-art models, the VGG16 model has the highest Matthews Correlation Coefficient value of 0.95 and Cohen's kappa score of 0.96.

Lipidomic Typing of Colorectal Cancer Tissue Containing Tumour-Infiltrating Lymphocytes by MALDI Mass Spectrometry Imaging

Predicting the prognosis of colorectal cancer (CRC) patients remains challenging and a characterisation of the tumour immune environment represents one of the most crucial avenues when attempting to do so. For this reason, molecular approaches which are capable of classifying the immune environments associated with tumour infiltrating lymphocytes (TILs) are being readily investigated. In this proof of concept study, we aim to explore the feasibility of using spatial lipidomics by MALDI-MSI to distinguish CRC tissue based upon their TIL content. Formalin-fixed paraffin-embedded tissue from human thymus and tonsil was first analysed by MALDI-MSI to obtain a curated mass list from a pool of single positive T lymphocytes, whose putative identities were annotated using an LC-MS-based lipidomic approach. A CRC tissue microarray (TMA, n = 30) was then investigated to determine whether these cases could be distinguished based upon their TIL content in the tumour and its microenvironment. MALDI-MSI from the pool of mature T lymphocytes resulted in the generation of a curated mass list containing 18 annotated m/z features. Initially, subsets of T lymphocytes were then distinguished based on their state of maturation and differentiation in the human thymus and tonsil tissue. Then, when applied to a CRC TMA containing differing amounts of T lymphocyte infiltration, those cases with a high TIL content were distinguishable from those with a lower TIL content, especially within the tumour microenvironment, with three lipid signals being shown to have the greatest impact on this separation (p < 0.05). On the whole, this preliminary study represents a promising starting point and suggests that a lipidomics MALDI-MSI approach could be a promising tool for subtyping the diverse immune environments in CRC.