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Related Concept Videos

RNA-seq03:21

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...

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Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
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GxP-Ready Single-Cell RNA-seq and Spatial Transcriptomics End-to-End Pipeline for Clinical Research.

Amaya Zaratiegui1, Timothy Burfield1, Helle Rus Povlsen1

  • 1Novo Nordisk A/S, Søborg, Denmark.

Computational and Structural Biotechnology Journal
|July 15, 2026
PubMed
Summary

NNclinSSOAP is a GxP-ready pipeline for single-cell and spatial transcriptomics data. It addresses regulatory challenges, enabling reproducible and scalable analysis for clinical applications.

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Area of Science:

  • Genomics
  • Computational Biology
  • Biotechnology

Background:

  • Single-cell/nucleus RNA-sequencing and Spatial Transcriptomics offer deep insights into cellular heterogeneity and tissue architecture.
  • Clinical adoption of these powerful omics tools is limited by regulatory hurdles like data integrity, reproducibility, and scalability.

Purpose of the Study:

  • To develop a modular, GxP-ready computational pipeline for integrated single-cell and spatial omics data analysis.
  • To facilitate the use of advanced omics technologies in clinical and regulated research settings.

Main Methods:

  • Development of NNclinSSOAP (Novo Nordisk Clinical Single-cell Spatial Omics Analytical Pipeline), a Nextflow-based end-to-end computational workflow.
  • Integration of established single-cell RNA sequencing analysis with a novel pipeline for Xenium spatial transcriptomics data.
  • Implementation of features ensuring data integrity, traceability, and scalability for high-performance computing (HPC) environments.

Main Results:

  • NNclinSSOAP transforms raw RNA sequencing and Xenium spatial data into annotated single-cell objects and spatially resolved tissue maps.
  • The pipeline is designed for mechanistic studies and clinical endpoint generation, supporting large-scale dataset processing.
  • A demo case demonstrates the pipeline's usability, executable within 1.5 hours on a standard laptop, with open-source code and data.

Conclusions:

  • NNclinSSOAP provides a robust, compliant, and scalable solution for analyzing single-cell and spatial transcriptomics data in regulated environments.
  • The pipeline enhances the potential of omics technologies for clinical research and disease understanding.
  • Open-source availability promotes broader adoption and reproducibility in the scientific community.