Assessing Spatial Sequencing and Imaging Approaches to Capture the Molecular and Pathological Heterogeneity of Archived Cancer Tissues.
JOURNAL OF PATHOLOGY(2025)
Univ Queensland | Akoya Biosci Inc | Univ Queensland IIT Delhi Acad Res UQIDRA
Abstract
Spatial transcriptomics (ST) offers enormous potential to decipher the biological and pathological heterogeneity in precious archival cancer tissues. Traditionally, these tissues have rarely been used and only examined at a low throughput, most commonly by histopathological staining. ST adds thousands of times as many molecular features to histopathological images, but critical technical issues and limitations require more assessment of how ST performs on fixed archival tissues. In this work, we addressed this in a cancer-heterogeneity pipeline, starting with an exploration of the whole transcriptome by two sequencing-based ST protocols capable of measuring coding and non-coding RNAs. We optimised the two protocols to work with challenging formalin-fixed paraffin-embedded (FFPE) tissues, derived from skin. We then assessed alternative imaging methods, including multiplex RNAScope single-molecule imaging and multiplex protein imaging (CODEX). We evaluated the methods' performance for tissues stored from 4 to 14 years ago, covering a range of RNA qualities, allowing us to assess variation. In addition to technical performance metrics, we determined the ability of these methods to quantify tumour heterogeneity. We integrated gene expression profiles with pathological information, charting a new molecular landscape on the pathologically defined tissue regions. Together, this work provides important and comprehensive experimental technical perspectives to consider the applications of ST in deciphering the cancer heterogeneity in archived tissues. (c) 2025 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
MoreTranslated text
Key words
dysplastic naevus,melanoma,spatial transcriptomics,pathological annotation,formalin-fixed paraffin-embedded,poly(A)-capture,probe-capture,CODEX,RNAScope
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Related Papers
1993
被引用144 | 浏览
2012
被引用22 | 浏览
2012
被引用65 | 浏览
2013
被引用205 | 浏览
2016
被引用4646 | 浏览
2015
被引用56 | 浏览
2016
被引用57 | 浏览
2018
被引用102 | 浏览
2018
被引用232 | 浏览
2021
被引用8 | 浏览
2022
被引用52 | 浏览
2022
被引用214 | 浏览
2020
被引用32 | 浏览
2023
被引用96 | 浏览
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper