Journal of Pathology and Translational Medicine

Original Article

Identification of invasive subpopulations using spatial transcriptome analysis in thyroid follicular tumors: Ayana Suzuki, Satoshi Nojima, Shinichiro Tahara, Daisuke Motooka, Masaharu Kohara, Daisuke Okuzaki, Mitsuyoshi Hirokawa, Eiichi Morii; J Pathol Transl Med. 2024;58(1):22-28. Published online January 10, 2024; DOI: https://doi.org/10.4132/jptm.2023.11.21

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Background
Follicular tumors include follicular thyroid adenomas and carcinomas; however, it is difficult to distinguish between the two when the cytology or biopsy material is obtained from a portion of the tumor. The presence or absence of invasion in the resected material is used to differentiate between adenomas and carcinomas, which often results in the unnecessary removal of the adenomas. If nodules that may be follicular thyroid carcinomas are identified preoperatively, active surveillance of other nodules as adenomas is possible, which reduces the risk of surgical complications and the expenses incurred during medical treatment. Therefore, we aimed to identify biomarkers in the invasive subpopulation of follicular tumor cells.
Methods
We performed a spatial transcriptome analysis of a case of follicular thyroid carcinoma and examined the dynamics of CD74 expression in 36 cases.
Results
We identified a subpopulation in a region close to the invasive area, and this subpopulation expressed high levels of CD74. Immunohistochemically, CD74 was highly expressed in the invasive and peripheral areas of the tumor.
Conclusions
Although high CD74 expression has been reported in papillary and anaplastic thyroid carcinomas, it has not been analyzed in follicular thyroid carcinomas. Furthermore, the heterogeneity of CD74 expression in thyroid tumors has not yet been reported. The CD74-positive subpopulation identified in this study may be useful in predicting invasion of follicular thyroid carcinomas.

Citations

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Carbonic Anhydrase 12 as a Novel Prognostic Biomarker and Therapeutic Target for High‐Risk Follicular Thyroid Carcinoma
Masashi Tanida, Tsuyoshi Takashima, Shinichiro Tahara, Masaharu Kohara, Haruka Kanai, Masami Suzuki, Motoyuki Suzuki, Mitsuyoshi Hirokawa, Ayana Suzuki, Shinya Sato, Daisuke Okuzaki, Satoshi Nojima, Takahiro Matsui, Hidenori Inohara, Eiichi Morii
Cancer Science.2026; 117(1): 257. CrossRef
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Spatial Transcriptomics in Thyroid Cancer: Applications, Limitations, and Future Perspectives
Chaerim Song, Hye-Ji Park, Man S. Kim
Cells.2025; 14(12): 936. CrossRef
A New Tool to Decrease Interobserver Variability in Biomarker Annotation in Solid Tumor Tissue for Spatial Transcriptomic Analysis
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Reviews

Perspectives on single-nucleus RNA sequencing in different cell types and tissues: Nayoung Kim, Huiram Kang, Areum Jo, Seung-Ah Yoo, Hae-Ock Lee; J Pathol Transl Med. 2023;57(1):52-59. Published online January 10, 2023; DOI: https://doi.org/10.4132/jptm.2022.12.19

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Abstract PDF

Single-cell RNA sequencing has become a powerful and essential tool for delineating cellular diversity in normal tissues and alterations in disease states. For certain cell types and conditions, there are difficulties in isolating intact cells for transcriptome profiling due to their fragility, large size, tight interconnections, and other factors. Single-nucleus RNA sequencing (snRNA-seq) is an alternative or complementary approach for cells that are difficult to isolate. In this review, we will provide an overview of the experimental and analysis steps of snRNA-seq to understand the methods and characteristics of general and tissue-specific snRNA-seq data. Knowing the advantages and limitations of snRNA-seq will increase its use and improve the biological interpretation of the data generated using this technique.

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Single-cell and spatial sequencing application in pathology: Yoon-Seob Kim, Jinyong Choi, Sug Hyung Lee; J Pathol Transl Med. 2023;57(1):43-51. Published online January 10, 2023; DOI: https://doi.org/10.4132/jptm.2022.12.12

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Abstract PDF

Traditionally, diagnostic pathology uses histology representing structural alterations in a disease’s cells and tissues. In many cases, however, it is supplemented by other morphology-based methods such as immunohistochemistry and fluorescent in situ hybridization. Single-cell RNA sequencing (scRNA-seq) is one of the strategies that may help tackle the heterogeneous cells in a disease, but it does not usually provide histologic information. Spatial sequencing is designed to assign cell types, subtypes, or states according to the mRNA expression on a histological section by RNA sequencing. It can provide mRNA expressions not only of diseased cells, such as cancer cells but also of stromal cells, such as immune cells, fibroblasts, and vascular cells. In this review, we studied current methods of spatial transcriptome sequencing based on their technical backgrounds, tissue preparation, and analytic procedures. With the pathology examples, useful recommendations for pathologists who are just getting started to use spatial sequencing analysis in research are provided here. In addition, leveraging spatial sequencing by integration with scRNA-seq is reviewed. With the advantages of simultaneous histologic and single-cell information, spatial sequencing may give a molecular basis for pathological diagnosis, improve our understanding of diseases, and have potential clinical applications in prognostics and diagnostic pathology.

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