Single Cell RNA Sequencing Service

Catalogue

Creative Biolabs provides a comprehensive range of customized, high-quality services in single cell RNA sequencing to support sciences research in single-cell transcriptomics and related biomedical industry worldwide, with proven experience and expertise to help researchers discover new or rare cell types and build gene atlases.

Single Cell RNA Sequencing

Single Cell RNA Sequencing (scRNA-seq), as a novel field in biology, provides the sequence information of individual cells and reveals the uniqueness of individual cells. scRNA-seq can address questions that are unobtainable in bulk analysis by the understanding of the function of an individual cell. The following is the workflow of scRNA-seq, which is similar to the methods used for bulk RNA-seq: reverse transcription (RT), amplification, library construction and sequencing.

Fig.1 The base workflow of scRNA-seq. (Source: Wikipedia)

10x Genomics Technology

The core of 10x technology is Gel bead in Emulsion (GEM). 10x genomics uses gel beads to introduce the oligonucleotides, and both lysis and RT are performed in droplets, which greatly simplify the entire cell lysis-to-PCR processing time and significantly increase throughput (Choi, 2020). It allowed thousands of cells to be processed in parallel for scRNA-seq.

Fig.2 10x scRNA-seq technology. (Choi, 2020)

Our Single Cell RNA Sequencing Service

We offer scientific and meticulous design for material selection, cell isolation, library construction, sequencing and data analysis to ensure high-quality research results.

Fig.3 Our Single Cell RNA Sequencing Service. (Creative Biolabs)

Features & Benefits

We can provide a high throughput platform to capture the full heterogeneity of a sample, in hundreds of thousands of cells. The transcriptome information can be obtained at a single-cell level. Creative Biolabs is committed to efficiency, confidentiality, and rapid, accurate service regarding the research of scRNA-seq.

Fig.4 Our features and benefits. (Creative Biolabs)

Sample Requirements

1. 5x10⁵ to 1x10⁶ cells in 1 ml of freezing media

2. 1x10⁶ to 1x10⁷ cells for blood samples

3. The size of frozen tissue is approximate 100 mg

4. Use standard freezing media without Mg²⁺ and Ca²⁺

5. The number of living cells is more than 90%

Before delivering cells, please contact us to discuss the sample preparation.

Results Display

• Detection of cell types

Fig.5 tSNE plot of cell clusters. (Wang, 2019)

• Gene expression heatmaps

Fig.6 Heatmap of marker genes expression level in different cell types. (Wang, 2019)

• Differential expression gene analysis

Fig.7 Differential expression gene analysis between alpha and beta cells from the same donor. (Wang, 2019)

• Pseudo-time analysis

Fig.8 Pseudo-time trajectory of cells. (Ashwinikumar, 2019)

• Gene set and pathway analysis

Fig.9 The enriched biological processes of DEGs. (Ma, 2020)

Application Prospect

scRNA-seq provides single cell transcriptome 3' gene expression to profile tens of thousands of cells, which can help researchers explore cellular heterogeneity, novel targets, and biomarkers with combined gene expression, and have a wide range of applications in biological research and medicine.

Fig.10 Applications of single cell RNA sequencing. (Source: Wikipedia)

Frequently Asked Questions

Q: Does dead cell impact data quality?

A: Dead cells can release ambient RNA, increase background noise and lead to missed sequencing targets and suboptimal results. We recommend the number of living cells is more than 90%.

Q: How to isolate single cells?

A: We use microfluidic partitioning to capture single cells, and maintain a high cell recovery rate.

Q: How many reads do I need for my experiment?

A: We provide 100,000 reads per cell to maximize the identification of transcripts.

Q:Can I acquire the samples in culture medium?

A:Samples can directly acquire from culture medium if your medium doesn't contain components that inhibit cDNA, such as Mg²⁺ and Ca²⁺.

Published Articles

• Paper Title: Single-cell RNA-sequencing reveals distinct patterns of cell state heterogeneity in mouse models of breast cancer

Technology: 10x scRNA-seq

Sample: Mouse models of breast cancer

Journal: eLife

IF: 7

Published Time: 2020

Background: There is a milieu of long-lived SCs/progenitors within the mouse mammary gland that could be candidates for transformation and potential tumor-initiating cells, but each with properties that are unique to a particular lineage or developmental timepoint.

Methods:

Fig.11 The article's method.

Results:

Clustering and differential expression analysis: these three main groups could be further subdivided into six clusters upon unsupervised clustering analysis by a shared nearest neighbor clustering approach.

Fig.12 t-SNE plots of mammary tumor cells colored by clusters.

Gene set and pathway analysis: they focused on dissecting the single cell transcriptomes of tumor cells within individual models of mammary tumors. Both replicates were included for each tumor model and the distribution of cells was even amongst the clusters for all tumor types.

Fig.13 Tables summarizing the sub-clusters, putative identities and key cluster defining genes.

Intrinsic breast cancer molecular subtype assignment: the Neu, PyMT and BRCA1-null tumors each comprised of cells which corresponded to several intrinsic breast cancer subtypes . The BRCA1-null tumors had larger proportions of claudin-low and basal-like tumor cells, whereas Neu and PyMT tumors contained more luminal A and normal-like subtype cells.

Fig.14 Single-cell intrinsic molecular subtype assignment of mammary tumor cells.

• Paper Title: Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma

Technology: ScRNA-seq

Sample: Primary glioblastomas

Journal:Science

IF:43

Published:2014

Background:Tumor heterogeneity can manifest as variability between tumors, wherein different stages, genetic lesions, or expression programs are associated with distinct outcomes or therapeutic responses and become major challenge to cancer diagnosis and treatment.

Method:

Fig.15 The article's method.

Results:The research has leveraged single-cell transcriptomics to characterize heterogeneous gene expression programs within five glioblastoma tumors and interrelate their transcriptional, functional, and genetic diversity. Research analysis reveals that tumors contain multiple cell states with distinct transcriptional programs and provides inferential evidence for dynamic transitions.

Fig.16 (A): Variation in expression of receptor and ligand; (B): Identification of oligodendrocytes; (C): Subtype classification of primary tumors based on population averaged data. (Anoop, 2014)

Please contact us to learn how we can be involved in your single cell RNA sequencing project.

References

1. Wang Y; Navin N. Advances and Applications of Single-Cell Sequencing Technologies. Molecular Cell. 2015, 58(4):598-609.
2. Choi J R.; et al. Single-Cell RNA Sequencing and Its Combination with Protein and DNA Analyses. Cells. 2020, 9(5):1130.
3. Wang Y J.; et al. Comparative analysis of commercially available single-cell RNA sequencing platforms for their performance in complex human tissues. bioRxiv preprint, 2019.
4. Ashwinikumar K.; et al. Beyond bulk: a review of single cell transcriptomics methodologies and applications. Current Opinion in Biotechnology. 2019, 58:129-136.
5. Yeo S K.; et al. Single-cell RNA-sequencing reveals distinct patterns of cell state heterogeneity in mouse models of breast cancer. eLife Sciences. 2020, 9: e58810.
6. Ma X S.; et al. Identification of a distinct luminal subgroup diagnosing and stratifying early stage prostate cancer by tissue-based single-cell RNA sequencing. Molecular cancer. 2020, 19(1):147.
7. Anoop P. Patel.; et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science. 2014, 344(6190):1396-1401.