Life Sciences
An Overview of Spatial Transcriptomics Methodologies in Traversing the Biological System
Transcriptomics covers the in-depth analysis of RNA molecules in cells or tissues and plays an essential role in understanding cellular functions and disease mechanisms. Advances in spatial transcriptomics (ST) in recent times have revolutionized the field by combining gene expression data with spatial information enabling the analysis of RNA molecules within their tissue context. The evolution of spatial transcriptomics particularly the integration of artificial intelligence (AI) in data analysis and its diverse applications have been found to be superior methods in developmental research. Spatial transcriptomics technologies along with single-cell RNA sequencing (scRNA-seq) offer unprecedented possibilities to unravel intricate cellular interactions within tissues. It emphasizes the importance of accurate cell localization for in-depth discoveries and developments via high-throughput spatial transcriptome profiling. The integration of artificial intelligence in spatial transcriptomics analysis is a key focus showcasing its role in detecting spatially variable genes clustering cell populations communication analysis and enhancing data interpretation. The evolution of AI methods tailored for spatial transcriptomics is highlighted addressing the unique challenges posed by spatially resolved transcriptomic data. Applications of spatial transcriptomics integrated with other omics data such as genomics proteomics and metabolomics provide a detailed view of molecular processes within tissues and emerge in diverse applications. Integrating spatial transcriptomics with AI represents a transformative approach to understanding tissue architecture and cellular interactions. This innovative synergy not only enhances our understanding of gene expression patterns but also offers a holistic view of molecular processes within tissues with profound implications for disease mechanisms and therapeutic development.
Explainable Colon Cancer Stage Prediction with Multimodal Biodata through the Attention-based Transformer and Squeeze-Excitation Framework
The heterogeneity in tumours poses significant challenges to the accurate prediction of cancer stages necessitating the expertise of highly trained medical professionals for diagnosis. Over the past decade the integration of deep learning into medical diagnostics particularly for predicting cancer stages has been hindered by the black-box nature of these algorithms which complicates the interpretation of their decision-making processes.
This study seeks to mitigate these issues by leveraging the complementary attributes found within functional genomics datasets (including mRNA miRNA and DNA methylation) and stained histopathology images. We introduced the Extended Squeeze- and-Excitation Multiheaded Attention (ESEMA) model designed to harness these modalities. This model efficiently integrates and enhances the multimodal features capturing biologically pertinent patterns that improve both the accuracy and interpretability of cancer stage predictions.
Our findings demonstrate that the explainable classifier utilised the salient features of the multimodal data to achieve an area under the curve (AUC) of 0.9985 significantly surpassing the baseline AUCs of 0.8676 for images and 0.995 for genomic data.
Furthermore the extracted genomics features were the most relevant for cancer stage prediction suggesting that these identified genes are promising targets for further clinical investigation.
Integrative Multi-Omics Approaches for Personalized Medicine and Health
Multi-omics data integration has transformed personalized medicine providing a comprehensive understanding of disease mechanisms and informed precision therapeutic options. Multi-omics data generated for the same samples/patients can help in getting insights into the flow of biological information at several levels thereby providing in-depth information regarding the molecular mechanisms underlying pathological conditions. Multi-omics integration plays a pivotal role in personalized medicine by providing comprehensive insights into the complex biological systems of individual patients. This review provides a comprehensive account of the current and future progress brought into multi-omics methodologies promising to refine diagnostics and therapeutic strategy by integrating genomic transcriptomic analyses proteomics approaches and metabolome screens.
A literature search was performed in PubMed using keywords like genomics proteomics transcriptomics metabolomics multi-omics and precision medicine to identify published research articles. A thorough review of all results was then conducted and their results and conclusions were compiled and summarized.
By analyzing various omics layers such as genomics transcriptomics proteomics and metabolomics multi-omics approaches enable the identification of patient-specific molecular traits and the discovery of new clinical therapeutics for diseases. Integration of various data types augments diagnostics optimizes therapeutic regimens and supports personalized medicine according to an individual patient profile.
Integration of multi-omics data and its applications in various fields such as cancer research helps in optimizing patient-specific treatment and improvement of patient health. With time as these technologies reach more people they stand to democratize precision medicine and hopefully bridge health disparities. In conclusion the present review highlights multiomics data integration as a transformative step towards personalized medicine and ultimately changing patient care from empirical-based to precision or individualized.
Integrative Analysis of Single Cell and Bulk RNA Sequencing Data Reveals T-Cell Specific Biomarkers for Diagnosis and Assessment of Celiac Disease: A Comprehensive Bioinformatics Approach
Celiac Disease (CD) is a common autoimmune disorder caused by the activation of CD4+ T cells that specifically target gluten and CD8+ T cells further causing cell death inside the epithelial layer despite no available established biomarkers of CD diagnosis. This work aimed to compare scRNA-seq and transcriptome data to find novel gene biomarkers linked to T cells that might potentially be utilized for the diagnosis and assessment of CD.
Collecting the scRNA and RNAseq datasets from the NCBI database the Seurat package of R studio and the statistical analysis tool GREIN server were employed to identify Differentially Expressed Genes (DEGs). Then DAVID FunRich STRING and NetworkAnalyst tools were utilized to explore significant pathways key hub proteins and gene regulators.
After integrating genes and conducting a comparative analysis a total of 115 genes were identified as DEGs. Exosomes MHC class II receptor activity immune response interferon gamma signaling and bystander B cell activation within the immune system pathways were the significant Gene Ontology (GO) and metabolic pathways identified. Besides eleven topological algorithms discovered two hub proteins namely HLA-DRA and HLA-DRB1 from the PPI network. Through the analysis of the regulatory network we have identified four crucial Transcription Factors (TFs) including YY1 FOXC1 GATA2 and USF2 and seven significant miRNAs (hsa-mir-129-2-3p and hsa-mir-155-5p etc.) in transcriptionally and post-transcriptionally regulated. Validation of hub proteins and transcription factors using Receiver Operating Characteristic (ROC) analysis indicates the acceptable value of the Area Under the Curve (AUC).
This study utilized single-cell RNA sequencing and transcriptomics data analysis to define unique protein biomarkers associated with T cells throughout the progression of CD. Furthermore wet lab studies will be needed to validate the potential hub proteins TFs and miRNAs as clinical biomarkers.
Single-Cell RNA Sequence Analysis to Identify Lymphatic Cell-Specific Biomarkers of Guillain-Barre Syndrome by Using Bioinformatics Approaches
An uncommon neurological condition known as Guillain-Barre syndrome (GBS) develops when the body's immunological system unintentionally targets peripheral nerves.
This work aimed to compare scRNA-seq and transcriptome data to find novel gene biomarkers linked to CD4+ T cells and B cells that might potentially be utilized for the diagnosis and assessment of GBS. It aimed to employ scRNA-seq data and bioinformatics tools analysis to identify cell-specific biomarkers for GBS diagnosis and prognosis.
scRNA-seq and microarray datasets from the GEO database were utilized to identify differentially expressed genes (DEGs). Pathway enrichment identification of potential hub genes and gene regulatory studies were employed using FunRich DAVID STRING and NetworkAnalyst tools.
After integrating the DEGs and performing a comparative analysis it was discovered that there were 84 DEGs shared between scRNA-seq and microarray datasets. The presence of signal transduction immune system cytokine signaling NOD-like receptor signaling and focal adhesion was detected in the most significant gene ontology and metabolic pathways. After generating a protein-protein interaction (PPI) network we used eleven topological algorithms of the cytoHubba plugin for identifying six key hub genes including CDC42 PTPRC SRSF1 HNRNPA2B1 NIPBL and FOS. Several crucial transcription factors (CHD1 IRF1 FOXC1 GATA2 YY1 E2F1 and CREB1) and two significant microRNAs (hsa-mir-20a-5p and hsa-mir-16-5p) were also discovered as hub gene regulators. The receiver operating characteristics (ROC) curve was used to evaluate the prognostic expression and diagnostic capabilities of the six major hub genes indicating a good scoring value.
Finally functional enrichment pathway analysis PPI and regulatory networks analysis demonstrated the critical functions of the identified key hub genes. After further wet lab research is validated our research work may offer useful predicted potential biomarkers for the diagnosis and prognosis of GBS.