CEL-Seq 2

CEL-Seq 2 processes CEL-Seq2 sequencing data and generates Unique Molecular Identifier (UMI) count matrices to quantify gene expression at single-cell resolution.

Key Features:

• Implementation: Python-based framework for processing and analyzing CEL-Seq2 sequencing data.
• UMI count matrix generation: Produces Unique Molecular Identifier (UMI) count matrices for accurate single-cell gene expression quantification.
• Enhanced sensitivity: Builds on the CEL-Seq2 method to provide approximately threefold higher sensitivity in detecting gene expression changes.
• Cost-effectiveness and efficiency: Reduces financial costs and hands-on time associated with single-cell RNA sequencing experiments.
• Fluidigm C1 integration: Compatible with Fluidigm's C1 system and its on-chip barcoding method for cell capture and barcode assignment.
• Comparative advantage: Demonstrates superior sensitivity in comparative analyses versus Smart-Seq, exemplified by studies of cell-cycle progression in mouse fibroblast cells.

Scientific Applications:

• Single-cell RNA-Seq analysis: High-resolution quantification of transcriptomes in individual cells using CEL-Seq2 data.
• Detection of nuanced expression changes: Identification of subtle transcriptional variations across individual cells.
• Cellular differentiation studies: Analysis of gene expression programs underlying differentiation processes.
• Disease mechanism investigation: Profiling cellular gene expression relevant to disease mechanisms.
• Developmental biology: Examination of developmental transcriptional dynamics at single-cell resolution.
• Cellular heterogeneity and dynamics: Investigation of heterogeneity and dynamic gene expression changes such as cell-cycle progression in mouse fibroblast cells.

Methodology:

Processes CEL-Seq2 data to generate Unique Molecular Identifier (UMI) count matrices and accounts for amplification biases; implemented in Python.