Severity: Warning
Message: file_get_contents(https://...@gmail.com&api_key=61f08fa0b96a73de8c900d749fcb997acc09&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 197
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 197
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 271
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3165
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
Line: 597
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 511
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 317
Function: require_once
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Background: Spatial transcriptomics technologies are revolutionizing our understanding of intra-tumor heterogeneity and the tumor microenvironment by revealing single-cell molecular profiles within their spatial tissue context. The rapid development of spatial transcriptomics methods, each with unique characteristics, makes it challenging to select the most suitable technology for specific research objectives. Here, we compare four imaging-based approaches-RNAscope HiPlex, Molecular Cartography, Merscope, and Xenium-alongside Visium, a sequencing-based method. These technologies were employed to study cryosections of medulloblastoma with extensive nodularity (MBEN), a tumor chosen for its distinct microanatomical features.
Results: Our analysis reveals that automated imaging-based spatial transcriptomics methods are well-suited to delineate the intricate MBEN microanatomy and capture cell-type-specific transcriptome profiles. We devise approaches to compare the sensitivity and specificity of different methods, along with their unique attributes, to guide method selection based on the research objective. Furthermore, we demonstrate how reimaging slides after the spatial transcriptomics analysis can significantly improve cell segmentation accuracy and integrate additional transcript and protein readouts, expanding the analytical possibilities and depth of insight.
Conclusions: This study underscores important distinctions between spatial transcriptomics technologies and offers a framework for evaluating their performance. Our findings support informed decisions regarding methods and outline strategies to improve the resolution and scope of spatial transcriptomic analyses, ultimately advancing spatial transcriptomics applications in solid tumor research.
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Source |
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12180266 | PMC |
http://dx.doi.org/10.1186/s13059-025-03624-4 | DOI Listing |