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Updated: Feb 24, 2026

Isolation of Adult Spinal Cord Nuclei for Massively Parallel Single-nucleus RNA Sequencing

Isolation of Adult Spinal Cord Nuclei for Massively Parallel Single-nucleus RNA Sequencing

Published on: October 12, 2018

Massively parallel single-nucleus RNA-seq with DroNc-seq.

Naomi Habib^1,2,3, Inbal Avraham-Davidi¹, Anindita Basu^1,4

¹Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.

|August 29, 2017

Summary

This summary is machine-generated.

We developed DroNc-seq, a high-throughput method for single-nucleus RNA sequencing, enabling efficient cell type classification from archived brain tissues. This technology advances the creation of comprehensive cell atlases.

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Published on: May 23, 2018

Area of Science:

Genomics
Neuroscience
Biotechnology

Background:

Single-nucleus RNA sequencing (sNuc-seq) enables RNA profiling from preserved or difficult-to-dissociate tissues.
Current sNuc-seq methods lack high-throughput capabilities, limiting large-scale analyses.
Archived human and mouse brain samples are valuable resources for cellular and molecular studies.

Purpose of the Study:

To develop a massively parallel single-nucleus RNA sequencing method.
To enable high-throughput profiling of nuclei from archived tissues.
To demonstrate the utility of this method for cell type classification and atlas generation.

Main Methods:

Development of DroNc-seq, integrating droplet technology with sNuc-seq.
Profiling of 39,111 nuclei from archived mouse and human brain samples.
Application of sensitive, efficient, and unbiased cell type classification algorithms.

Main Results:

DroNc-seq achieves massively parallel single-nucleus RNA sequencing.
Sensitive, efficient, and unbiased classification of cell types was demonstrated.
Successful profiling of 39,111 nuclei from archived brain samples.

Conclusions:

DroNc-seq significantly enhances the throughput of sNuc-seq.
This method facilitates systematic charting of cell atlases from archived tissues.
DroNc-seq is a powerful tool for large-scale neuroscience research.