Single Cell Transcriptomics of Tissue-specific Macrophages

Single Cell Transcriptomics

Single-cell transcriptome sequencing is a new technology for sequencing the transcriptome at the single-cell level, which can study gene expression within a single cell, and at the same time solve the problem of cell heterogeneity that cannot be solved by sequencing tissue samples, making it possible to analyze the behavior, mechanism and relationship of individual cells to the body. When applied to tissue-specific macrophages, single-cell transcriptomics allows researchers to characterize the diversity and functional states of macrophages within different tissues.

Fig.1 Single-cell analysis.¹

Our Single Cell Transcriptomics of Tissue-specific Macrophage Service

• Single Cell Transcriptome Sequencing Platform

Creative Biolabs' single-cell transcriptome sequencing platform utilizes microfluidics, oil droplet encapsulation, and Barcode labeling to enable high-throughput cell harvesting techniques that enable the isolation and labeling of 500-10,000 single cells at a time to obtain transcriptome information at the 3' end of each cell. It has the advantages of high cell throughput, low cost of library construction, and short capture cycle. In addition, this technology can also predict the development trajectory of cell differentiation and research, and play an increasingly important role in the current fields of disease, immunity, and tumors, as well as tissues, organs, and developmental research.

Fig.2 High-throughput single-cell transcriptome technology platform.²

• Single Cell Transcriptome Sequencing Workflow

Our macrophage single-cell sequencing platform includes cell isolation and capture, microfluidic chip amplification, and data analysis. The platform solves two major challenges of single-cell sequencing: single-cell isolation and capture, and reverse transcription amplification of very low amounts of RNA. The overall project process, from sample receipt to project delivery, is as follows:

Display Results

Cell differentiation trajectory analysis
	Fig.4 Results of monocyte/macrophage clusters superimposed on pseudo-time branches.3
Marker gene identification
	Fig.5 Results of monocyte/macrophage gene identification.3
Cell subsets identification
	Fig.6 Results of monocyte/macrophage cell subsets identification.3
tSNE Analysis
	Fig.7 Results of tSNE analysis of monocyte/macrophage.3

Frequently Asked Questions

Q1: How to identify specific cell types?

A1: Known marker gene; Expression of the marker gene in cluster cells; Differentially expressed genes or marker genes identified by the software; Functional pathways where genes reside.

Q2: What is the use of pseudo-time analysis?

A2: In many biological processes, not all cells are at the same time at the same time. When studying cell differentiation using single-cell gene expression, captured cells are typically in a wide range of different phases: some cells have not yet begun to differentiate, some are in an intermediate state, and others may have finished differentiating. Therefore, on the whole, the gene expression of individual cells in the sample fluctuates greatly, which is very complicated to study. Pseudo-time analysis calculates the distance between cells and cell time based on gene expression information and estimates the total shortest path after arranging all cells into their quasi-time course, which helps to understand the type of cells in the sample and the process of cell differentiation.