The rapid development of spatial transcriptomics (ST) techniques has allowed the measurement of transcriptional levels across many genes together with the spatial positions of cells. This has led to an explosion of interest in computational methods and techniques for harnessing both spatial and transcriptional information in analysis of ST datasets. The wide diversity of approaches in aim, methodology and technology for ST provides great challenges in dissecting cellular functions in spatial contexts. Here, we synthesize and review the key problems in analysis of ST data and methods that are currently applied, while also expanding on open questions and areas of future development.
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http://dx.doi.org/10.1038/s42003-022-03175-5 | DOI Listing |
J Ovarian Res
January 2025
Department of Gynecology, Obstetrics and Gynecology Hospital of Fudan University, #128 Shenyang Road, Shanghai, 200090, People's Republic of China.
Background: Ovarian cancers (OC) and cervical cancers (CC) have poor survival rates. Tumor-infiltrating lymphocytes (TILs) play a pivotal role in prognosis, but shared immune mechanisms remain elusive.
Methods: We integrated single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) to explore immune regulation in OC and CC, focusing on the PI3K/AKT pathway and FLT3 as key modulators.
Mod Pathol
January 2025
Department of Pathology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan. Electronic address:
Oxyntic gland neoplasms typically arise in Helicobacter pylori-naïve stomachs and are composed predominantly of chief cells, with a smaller component of parietal cells. The pathologic diagnosis can be challenging due to minimal cellular atypia. Especially in biopsy specimens with limited tumor volume or when pathologists have limited experience in diagnosing this neoplasm, distinguishing it from normal oxyntic glands can be difficult, and no reliable diagnostic markers are currently available.
View Article and Find Full Text PDFDiscov Oncol
January 2025
Second Department of Oncology, The Second Hospital of Hebei Medical University, Shijiazhuang, 050000, Hebei, China.
Introduction: We conducted a panoramic analysis of GBN5 expression and prognosis in 33 cancers, aiming to deepen the systematic understanding of GBN5 in cancer.
Materials And Methods: We employed a multi-omics approach, including transcriptomic, genomic, proteomic, single-cell cytomic, spatial transcriptomic, and genomic data, to explore the prognostic value and potential oncogenic mechanisms of GBN5 across pan-cancers from multiple perspectives.
Results: We found that GBN5 was differentially expressed in multiple tumors and showed early diagnostic value.
Genomics Proteomics Bioinformatics
January 2025
Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Research Unit of Hematologic Malignancies Genomics and Translational Research of Chinese Academy of Medical Sciences, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
Single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) techniques hold great value in evaluating the heterogeneity and spatial characteristics of hematopoietic cells within tissues. These two techniques are highly complementary, with scRNA-seq offering single-cell resolution and ST retaining spatial information. However, there is an urgent demand for well-organized and user-friendly toolkits capable of handling single-cell and spatial information.
View Article and Find Full Text PDFPharmaceuticals (Basel)
January 2025
Department of Biomedicine, Texas A&M University, College Station, TX 77843, USA.
Recent developments in single-cell multi-omics technologies have provided the ability to identify diverse cell types and decipher key components of the tumor microenvironment (TME), leading to important advancements toward a much deeper understanding of how tumor microenvironment heterogeneity contributes to cancer progression and therapeutic resistance. These technologies are able to integrate data from molecular genomic, transcriptomic, proteomics, and metabolomics studies of cells at a single-cell resolution scale that give rise to the full cellular and molecular complexity in the TME. Understanding the complex and sometimes reciprocal relationships among cancer cells, CAFs, immune cells, and ECs has led to novel insights into their immense heterogeneity in functions, which can have important consequences on tumor behavior.
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