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

Islam, Mirazul; Chen, Bob; Spraggins, Jeffrey M.; Lau, Ken S.

doi:10.1053/j.gastro.2020.04.073

Cited by 19 publications

(15 citation statements)

References 160 publications

(190 reference statements)

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“…For this reason, molecular approaches that are capable of classifying the immune environments associated with TIL infiltration are being readily investigated, with a particular focus on the proteome [ 8 , 9 ]. However, dysregulated lipid metabolism of tumour-infiltrating immune cells and their surrounding environment has also been demonstrated to be a driver of responsiveness and tolerance to immunosuppressive treatment [ 10 ], including in CRC [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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.

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Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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“…However, within data exploration, unsupervised learning may be applied to study relationships and understand connections that need to be uncovered in each dataset, including gene network analyses [ 113 , 114 ]. For instance, visualization techniques using principal component analysis (PCA) and t -statistic stochastic neighbor embedding (t-SNE) are widely applied in the biomedical field, especially given the growing interest in single-cell RNA/DNA sequencing [ 115 , 116 , 117 , 118 ].…”

Section: Machine Learning—basic Concepts Specific Applications and Future Directions In Geamentioning

confidence: 99%

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

Klein

Duda

2021

Cancers

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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.

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“…The accuracy of diagnosing tumours and gastrointestinal bleeding, especially in the small intestine, has improved. The overall process is very time-consuming to analyze all the frames extracted from each patient [ 9 ]. Furthermore, even the most experienced physicians confront difficulties that necessitate a large amount of time to analyze all of the data because the contaminated zone in one frame will not emerge in the next.…”

Section: Introductionmentioning

confidence: 99%

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

Yogapriya¹,

Sumithra

Anitha³

et al. 2021

Computational and Mathematical Methods in Medicine

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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.

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

Cited by 19 publications

References 160 publications

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

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

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

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

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