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Related Experiment Video

Updated: Oct 17, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

Efficient and precise single-cell reference atlas mapping with Symphony.

Joyce B Kang^1,2,3,4,5, Aparna Nathan^1,2,3,4,5, Kathryn Weinand^1,2,3,4,5

¹Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, USA.

Nature Communications

|October 8, 2021

Summary

This summary is machine-generated.

Symphony is a new algorithm for building and mapping single-cell atlases. It efficiently maps query cells to large reference atlases, enabling rapid cell state identification and annotation transfer.

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Area of Science:

Computational Biology
Genomics
Immunology

Background:

Single-cell technologies enable comprehensive atlases of diverse biological states.
Mapping query cells to these atlases is crucial for identifying cell types and phenotypes.
Existing methods face challenges with large-scale, integrated datasets.

Purpose of the Study:

To introduce Symphony, an algorithm for building and querying large-scale single-cell reference atlases.
To enable rapid and reproducible mapping of query cells onto complex atlases.
To facilitate the transfer of annotations from reference atlases to query datasets.

Main Methods:

Symphony algorithm for building integrated, portable reference atlases.
Low-dimensional embedding for stable query cell localization.
Efficient query mapping enabling rapid annotation transfer.

Main Results:

Demonstrated efficient mapping of query cells to multimillion-cell atlases within seconds.
Successfully predicted pancreatic cell types from multi-donor, multi-species data.
Localized query cells along developmental trajectories and inferred surface protein expression.

Conclusions:

Symphony provides a powerful and efficient solution for analyzing large-scale single-cell reference atlases.
The algorithm facilitates reproducible cell state identification and annotation transfer across diverse datasets.
Symphony advances the utility of single-cell atlases for biological discovery.