Single Nuclei RNA Sequencing Service

Catalogue

Creative Biolabs provides a comprehensive range of customized, high-quality services in single cell nuclei RNA sequencing service to support scientific research in related biomedical industries worldwide, as well as a single-cell level research application platform for complex tissues such as brain tissue, heart, kidney, or some rare cryopreserved samples, making it easier to explore tumor cell heterogeneity and pathogenic mechanisms.

Single Cell Nuclei RNA Sequencing

Single Nuclei RNA Sequencing (snRNA-seq), is an RNA sequencing method for profiling difficult-to-isolate cells, such as those from archived tissues or those that are hard to be dissociated. snRNA-seq methods avoid strong biases against cells of complex morphology and large size and reduce dissociation bias, compatibility with frozen samples, elimination of dissociation-induced transcriptional stress responses. The workflow of snRNA-seq is as follows: isolate nuclei from cells or tissues, cDNA libraries preparation, snRNA-seq, data analysis.

Fig.1 The workflow of snRNA-seq. (Hu, 2017)

Our Single Cell Nuclei RNA Sequencing Service

Our scientists have accumulated extensive experience in snRNA-seq. We can offer scientific and meticulous design for snRNA-seq and data analysis to ensure high-quality research results.

Fig.2 Our Single Cell Nuclei RNA Sequencing Service. (Creative Biolabs)

Features & Benefits

Advantages of snRNA-seq

1. The nuclear membrane is more stable than the cell membrane, hence the direct nuclear extraction approach is relatively simple.

2. snRNA-seq can be used with a wide range of materials, including fresh/frozen, complicated tissue types, and no limitation of cell size and activity.

3. There is no "transcription bias" artificially introduced during the dissociation process.

4. The identification of cell types is more comprehensive and complete.

With proven experience and expertise, Creative Biolabs has become a leader in the research of snRNA-seq.

Fig.3 Our benefits. (Creative Biolabs)

Sample Requirements

• 5x10⁵ to 1x10⁶ cells in 1 ml of freezing media
• The size of frozen tissue is approximate 100mg
• Use standard freezing media without Mg²⁺ and Ca²⁺

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

Results Display

• Cell types identified from snRNA-seq data

Fig.4 t-SNE map of cell clusters and marker gene expression. (Hu, 2017)

• Marker gene identification analysis

Fig.5 Heatmap showing expression of specific marker genes. (Zhou, 2020)

Fig.6 Differential expression between subclusters. (Hu, 2017)

Fig.7 Gene Set Enrichment Analysis between subclusters. (Hu, 2017)

Frequently Asked Questions

Q: Which is better: scRNA-seq, or snRNA-seq

A: scRNA-seq measures both cytoplasmic and nuclear transcripts, while snRNA-seq mainly measures nuclear transcripts. Compared to scRNA-seq, snRNA-Seq is more appropriate to profile gene expression in cells that are difficult to isolate (e.g. adipocytes, neurons).

Q: How to isolate nuclei?

A: We use a quick and mild nuclear dissociation, to minimize technical issues that can affect studies.

Published Article

Paper Title: Human and mouse single-nucleus transcriptomics reveal TREM2-dependent and TREM2-independent cellular responses in Alzheimer's disease

Technology: snRNA-seq

Sample: Human and mouse brain samples

Journal: Nature Medicine

IF: 36.13

Published: 2020

Background: Alzheimer's disease (AD) is the most common dementia. Glia have been implicated in AD pathogenesis.  Pathologically, amyloid beta (Aβ) peptides produced by neurons form extracellular aggregates that initiate disease; intraneuronal tau hyperphosphorylation and aggregation ensue, causing neuronal and synaptic dysfunction and cell death.

Methods:

Fig.8 The article's method.

Results:

• A total of 73,419 individual nuclei were arranged by t-distributed stochastic neighbor embedding (t-SNE) in 2 dimensions. Unsupervised clustering revealed a total of 11 distinct clusters across all samples with cell-type identities as determined by expression of specific markers.

Fig.9 t-SNE plot of cell cluster.

• Heat map showing the average gene expression of top DEGs in the microglia cluster for each sample. DAM signature is present in all 5XFAD mice in a Trem2-dependent manner. Color scheme shows row max and row min, which represents relative expression of each gene among all samples.

Fig.10 Heat map of top DEGs.

• Volcano plot showing DEGs of AD versus control from Rush samples analyzed by NanoString. n = 13 subjects with AD and 12 controls. P value was determined by multivariate linear regression with Benjamini-Yekutieli adjustment.

Fig.11 Volcano plot of DEGs.

Please contact us to learn how we can be involved in your single cell nuclei RNA sequencing project. We're committed to be with you in every step of your research.

References

1. Hu P.; et al. Dissecting cell-type composition and activity-dependent transcriptional state in mammalian brains by massively parallel single-nucleus RNA-Seq. Molecular Cell. 2017, 68:1006-1015
2. Habib N.; et al. Div-Seq: Single-nucleus RNA-Seq reveals dynamics of rare adult newborn neurons. Science. 2016, 353(6302):925-928.
3. Zhou Y.; et al. Human and mouse single-nucleus transcriptomics reveal TREM2-dependent and TREM2-independent cellular responses in Alzheimer's disease. Nature medicine. 2020, 26(6).