Dataset Information

Alternative normalization and analysis pipeline to address systematic bias in NanoString GeoMx Digital Spatial Profiling data.

ABSTRACT: Spatial transcriptomics is a novel technique that provides RNA-expression data with tissue-contextual annotations. Quality assessments of such techniques using end-user generated data are often lacking. Here, we evaluated data from the NanoString GeoMx Digital Spatial Profiling (DSP) platform and standard processing pipelines. We queried 72 ROIs from 12 glioma samples, performed replicate experiments of eight samples for validation, and evaluated five external datasets. The data consistently showed vastly different signal intensities between samples and experimental conditions that resulted in biased analysis. We evaluated the performance of alternative normalization strategies and show that quantile normalization can adequately address the technical issues related to the differences in data distributions. Compared to bulk RNA sequencing, NanoString DSP data show a limited dynamic range which underestimates differences between conditions. Weighted gene co-expression network analysis allowed extraction of gene signatures associated with tissue phenotypes from ROI annotations. Nanostring GeoMx DSP data therefore require alternative normalization methods and analysis pipelines.

SUBMITTER: van Hijfte L

PROVIDER: S-EPMC9800292 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Alternative normalization and analysis pipeline to address systematic bias in NanoString GeoMx Digital Spatial Profiling data.

van Hijfte Levi L Geurts Marjolein M Vallentgoed Wies R WR Eilers Paul H C PHC Sillevis Smitt Peter A E PAE Debets Reno R French Pim J PJ

iScience 20221209 1

Spatial transcriptomics is a novel technique that provides RNA-expression data with tissue-contextual annotations. Quality assessments of such techniques using end-user generated data are often lacking. Here, we evaluated data from the NanoString GeoMx Digital Spatial Profiling (DSP) platform and standard processing pipelines. We queried 72 ROIs from 12 glioma samples, performed replicate experiments of eight samples for validation, and evaluated five external datasets. The data consistently sho ...[more]

PMID: 36590163

Similar Datasets

Project description:Rhabdomyosarcoma (RMS) exhibits a complex prognostic algorithm based on histologic, biologic and clinical parameters. The embryonal (ERMS) and spindle cell-sclerosing RMS (SRMS) histologic subtypes warrant further studies due to their heterogenous genetic background and variable clinical behavior. NanoString digital profiling methods have been previously highlighted as robust novel methods to detect protein and microRNA expression in several cancers but not in RMS. To identify prognostic biomarkers, we categorized 12 ERMS and SRMS tumor cases into adverse (n = 5) or favorable (n = 7) prognosis groups and analyzed their signaling pathways and microRNA profiles. The digital spatial profiling of protein and microRNA analysis was performed on formalin-fixed, paraffin-embedded (FFPE) tumor tissue using NanoString technology. The detectable expression of several component members of the PI3K/AKT, MAPK and apoptosis signaling pathways was highlighted in RMS, including INPP4B, Pan-AKT, MET, Pan-RAS, EGFR, phospho-p90 RSK, p44/42 ERK1/2, BAD, BCL-XL, cleaved caspase-9, NF1, PARP and p53. Compared to cases with favorable prognosis, the adverse-prognosis tumor samples had significantly increased expression of INPP4B, which was confirmed with traditional immunohistochemistry. The analysis of microRNA profiles revealed that, out of 798 microRNAs assessed, 228 were overexpressed and 134 downregulated in the adverse prognosis group. Significant over-expression of oncogenic/tumor suppressor miR-3144-3p, miR-612, miR-302d-3p, miR-421, miR-548ar-5p and miR-548y (p < 0.05) was noted in the adverse prognosis group. This study highlights the utility of NanoString digital profiling methods in RMS, where it can detect distinct molecular signatures with the expression of signaling pathways and microRNAs from FFPE tumor tissue that may help identify prognostic biomarkers of interest. The overexpression of INPP4B and miR-3144-3p, miR-612, miR-302d-3p, miR-421, miR-548y and miR-548ar-5p may be associated with worse overall survival in ERMS and SRMS.

Dataset Information

Alternative normalization and analysis pipeline to address systematic bias in NanoString GeoMx Digital Spatial Profiling data.

Publications

Alternative normalization and analysis pipeline to address systematic bias in NanoString GeoMx Digital Spatial Profiling data.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets