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Updated: Jun 21, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Panpipes: a pipeline for multiomic single-cell and spatial transcriptomic data analysis.

Fabiola Curion^1,2, Charlotte Rich-Griffin³, Devika Agarwal^3,4

¹Department of Computational Health, Institute of Computational Biology, Helmholtz Munich, Germany.

|July 8, 2024

Summary

This summary is machine-generated.

This study introduces Panpipes, a computational workflow for automating multimodal single-cell and spatial transcriptomic analyses. It enables systematic joint evaluation of epigenome, transcriptome, and proteome data for deeper biological insights.

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Area of Science:

Computational Biology
Genomics
Molecular Biology

Background:

Single-cell multiomic analysis offers deep insights into cellular molecular circuitry.
Interpreting multiomic datasets is challenging due to limited joint evaluation methods.

Purpose of the Study:

To present Panpipes, a computational workflow for automating multimodal single-cell and spatial transcriptomic analyses.
To enable systematic, joint evaluation of diverse molecular data modalities.

Main Methods:

Developed Panpipes, a Python-based computational workflow.
Integrated quality control, preprocessing, integration, clustering, and reference mapping tools.
Applied to large-scale single-cell and spatial transcriptomic datasets.

Main Results:

Panpipes automates the analysis of multimodal single-cell and spatial transcriptomic data.
The workflow allows for reliable and customizable analysis of individual and integrated modalities.
Facilitates informed decision-making for downstream biological investigations.

Conclusions:

Panpipes provides a scalable solution for multimodal single-cell data analysis.
Empowers researchers to systematically interpret complex molecular circuitry.
Enhances the understanding of cell identity and state through integrated omics.