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RNA-seq

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...

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An Ultrahigh-throughput Microfluidic Platform for Single-cell Genome Sequencing

An Ultrahigh-throughput Microfluidic Platform for Single-cell Genome Sequencing

Published on: May 23, 2018

Protocol for analysis of single-cell sequencing data by Seqtometry.

Robert Kousnetsov¹, Daniel Hawiger¹

¹Department of Molecular Microbiology and Immunology, Saint Louis University School of Medicine, St. Louis, MO, USA.

|August 3, 2024

Summary

Seqtometry provides a new protocol for single-cell RNA sequencing (scRNA-seq) and single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) analysis. This method enables comprehensive cell characterization through biologically interpretable dimensions.

Keywords:

Bioinformatics Cell Biology Immunology Sequence analysis Systems biology

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Area of Science:

Genomics
Molecular Biology
Bioinformatics

Background:

Single-cell analysis is crucial for understanding cellular heterogeneity.
Existing methods for gene expression and chromatin accessibility profiling have limitations.
Seqtometry offers an integrated platform for multi-omic single-cell data.

Purpose of the Study:

To present a detailed protocol for scRNA-seq and scATAC-seq analysis using Seqtometry.
To demonstrate the platform's capability for preprocessing, imputation, scoring, and plotting.
To highlight extensions for large-scale and multi-dataset integration.

Main Methods:

Seqtometry platform for direct gene expression and accessibility profiling.
Advanced scoring with gene signatures for data interpretation.
Standardized protocol steps including preprocessing, imputation, scoring, and plotting.

Main Results:

A robust protocol for combined scRNA-seq and scATAC-seq analysis.
Generation of biologically interpretable dimensions for cell identification.
Successful application to large datasets and integration of multiple datasets.

Conclusions:

The Seqtometry protocol facilitates comprehensive single-cell characterization.
It enables direct identification and detailed profiling of specific cell populations.
This approach advances the analysis of complex single-cell multi-omic data.