ArticleLaboratory investigation; a journal of technical methods and pathology2022
QuantISH: RNA in situ hybridization image analysis framework for quantifying cell type-specific target RNA expression and variability.
Article in Laboratory investigation; a journal of technical methods and pathology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed, 27 citations in OpenAlex.
- A survey of SARS-CoV-2 tropism.Infectious diseases & immunity · 2025Review
- Deep Learning Segmentation of Chromogenic Dye RNAscope From Breast Cancer Tissue.Journal of imaging informatics in medicine · 2025Article
- Review
- Alternative splicing regulates PACC1 function and promotes acidosis-induced cytotoxicity.Frontiers in cell and developmental biology · 2025Article
- Insights from an observational translational research program during the COVID-19 pandemic: Four years of experience.Vaccine · 2024Observational
- Comparative Analysis of Gene Expression Analysis Methods for RNA in Situ Hybridization Images.The Journal of molecular diagnostics : JMD · 2024Article
- Use of high-resolution fluorescence in situ hybridization for fast and robust detection of SARS-CoV-2 RNAs.Scientific reports · 2024Article
- Redefining clinical practice through spatial profiling: a revolution in tissue analysis.Annals of the Royal College of Surgeons of England · 2024Article
- ND630 controls ACACA and lipid reprogramming in prostate cancer by regulating the expression of circKIF18B_003.Journal of translational medicine · 2023Article
- Digital pathology systems enabling quality patient care.Genes, chromosomes & cancer · 2023Review
- Spatial RNA sequencing methods show high resolution of single cell in cancer metastasis and the formation of tumor microenvironment.Bioscience reports · 2023Review
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Authors and funding
11 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
RNA in situ hybridization (RNA-ISH) is a powerful spatial transcriptomics technology to characterize target RNA abundance and localization in individual cells. This allows analysis of tumor heterogeneity and expression localization, which are not readily obtainable through transcriptomic data analysis. RNA-ISH experiments produce large amounts of data and there is a need for automated analysis methods. Here we present QuantISH, a comprehensive open-source RNA-ISH image analysis pipeline that quantifies marker expressions in individual carcinoma, immune, and stromal cells on chromogenic or fluorescent in situ hybridization images. QuantISH is designed to be modular and can be adapted to various image and sample types and staining protocols. We show that in chromogenic RNA in situ hybridization images of high-grade serous carcinoma (HGSC) QuantISH cancer cell classification has high precision, and signal expression quantification is in line with visual assessment. We further demonstrate the power of QuantISH by showing that CCNE1 average expression and DDIT3 expression variability, as captured by the variability factor developed herein, act as candidate biomarkers in HGSC. Altogether, our results demonstrate that QuantISH can quantify RNA expression levels and their variability in carcinoma cells, and thus paves the way to utilize RNA-ISH technology.
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