Single Cell RNA-Seq analysis

Single Cell RNA-Seq analysis

Single Cell
RNA-Seq Analysis

Conventional gene expression analysis

= Analysis of cell populations together

➔ You can see the overall average.

scRNA-Seq

= Analysis of individual cells

➔ We can tell the difference between individual cells.

What is Single Cell RNA-Seq?

What is Single Cell RNA-Seq?

What is Single Cell RNA-Seq?

This method examines the gene expression of individual cells that make up a tissue.

Bulk RNA-Seq detects average gene expression in a cell population, whereas single cell RNA-seq analysis (scRNA-seq), which examines gene expression in individual cells, allows detailed analysis of the function and activation state of the various cells that make up a tissue. The scRNA-seq analysis is a method to analyze the function and activation state of various cells constituting a tissue.

ImmunoGeneTeqs

Features of Single Cell RNA-Seq

ImmunoGeneTeqs

Features of Single Cell RNA-Seq

ImmunoGeneTeqs

Features of Single Cell RNA-Seq

・ 

This is whole transcriptome scRNA-seq analysis using nanowells and magnetic beads on the BD Rhapsody system.

 

*Certified by Becton Dickinson Japan K.K. as a service provider using the BD Rhapsody™ Single Cell Analysis System

・ 

Our proprietary total cDNA amplification method from solid-phase beads (TAS-Seq method) realizes highly sensitive and accurate analysis.

・ 

Multiplex analysis is supported, allowing analysis of multiple samples without much cost. (The USB method allows labeling of any cell type or organism. Please refer to the Multiplex section in the "Technology " section.)

・ 

Simultaneous measurement of mRNA and cell surface protein expression (CITE-seq: Cellular Indexing of Transcriptomes and Epitopes by sequencing) is also available. Please contact us from the "Contact Us" page.

・ 

Multiplex analysis using BD's SampleTag, BioLegend's Hashtag, etc. is supported, allowing analysis of multiple samples without much cost.

・ 

BD Rhapsody Express systems can be rented and workers can be dispatched to the site (at an additional cost).

・ 

We have experience analyzing leukocytes derived from mouse lung, pancreatic la islet, kidney, human lung and gastric cancer biopsy samples, and human peripheral blood neutrophils.

・ 

Click here to see the analysis use paper .

Example of analysis of human rheumatic lung

S1:4055 cells and S2:4938 cells were pooled, clustered on PCA space, and the results were visualized by dimensional compression using tSNE.

The combination of ImmunoGeneTeqs Rhapsody and TAS-Seq method has almost no batch effect and basically does not require batch effect correction.

Frequency of cell detection by TAS-Seq is similar to FACS

Frequency of cell detection by TAS-Seq is similar to FACS

Comprehensive identification of even minor subpopulations without batch effect correction

Comprehensive identification of even minor subpopulations without batch effect correction

* Clustering refers to finding cells with similar gene expression patterns and dividing them into clusters (populations).

* In the case of data measured in different experimental environments (batches), there is a "batch effect" (inter-experimental error) that causes differences in data between batches, even for the same organ or cell. Batch effect correction may be necessary when these effects make data interpretation difficult, such as when interpreting clustering results. Batch effect correction may mask biological differences.

Comparison of cell detection accuracy between flow cytometers and various techniques in mouse lung cells

Compared to flow cytometer data, TAS-seq correlates well with flow cytometers and provides accurate cell composition data.

10X v2 data from other methods over-detected macrophages and under-detected fibroblast fractions. (See red box)

Smart-seq2 data over-detected endothelial cells and monocytes and lost alveolar macrophages (see green box).

Shichino S. et al. Communications Biology volume 5, Article number: 602 (2022) 

Accurate measurement of cell composition ratio by TAS-Seq method ( high reproducibility of cell presence frequency)

Accurate measurement of cell composition ratio by TAS-Seq method ( high reproducibility of cell presence frequency)

Detection of intercellular communication in mouse lung

TAS-Seq has the potential to detect important cell-to-cell communication more robustly than other technologies

TAS-Seq has the potential to detect important cell-to-cell communication more robustly than other technologies

Development of TAS-seq2

We have optimized the reaction system of TAS-Seq and developed TAS-Seq2 with even higher detection sensitivity.

10X v3: 10X Chromium v3 (GSE192930) include-intron mode (1.5 times more sensitive than non-intron mode 10X v3)

BD1-BD3 : BD Rhapsody WTA kit TAS-Seq1-1~1-3 : TAS-Seq TAS-Seq2-1~2-3 : TAS-Seq2

10X v3: 10X Chromium v3 (GSE192930) include-intron mode (1.5 times more sensitive than non-intron mode 10X v3)

BD1-BD3 : BD Rhapsody WTA kit TAS-Seq1-1~1-3 : TAS-Seq TAS-Seq2-1~2-3 : TAS-Seq2

10X v3: 10X Chromium v3 (GSE192930) include-intron mode (1.5 times more sensitive than non-intron mode 10X v3)

BD1-BD3 : BD Rhapsody WTA kit
TAS-Seq1-1~1-3 : TAS-Seq TAS-Seq2-1~2-3 : TAS-Seq2

TAS-Seq2detects 1.5-2x more genes than intron-containing 10X Chromium v3 (1.2-1.5x more genes detected than regular Chromium v3)

TAS-Seq2 detects 1.5-2x more genes than intron-containing 10X Chromium v3 (1.2-1.5x more genes detected than regular Chromium v3)

Example of nuclear analysis of cells derived from mouse liver specimen using TAS-Seq2

*10X nuclear conditioning kit with anti-nuclear pore complex hashtags approx. 20000-30000 reads / nucleus

TAS-Seq2 enables more sensitive detection of genes in cell nuclei than other methods

Detects hepatocytes that are difficult to analyze by single-cell RNA-seq due to losses during cell preparation. Hepatocyte clusters specific to histological regions are also identified.

Detects hepatocytes that are difficult to analyze by single-cell RNA-seq due to losses during cell preparation. Hepatocyte clusters specific to histological regions are also identified.

10X v3.1: Published mouse liver sample data (SRX14774301, SRX14774300)

Samples with analysis results

single-cell RNA-seq

Mouse

etc.

Human

etc.

Cultured Cell

etc.

Other Species

etc.

single-nucleus RNA-seq

Mouse

etc.

Using BD Rhapsody system and TAS-seq

Single Cell RNA-Seq Analysis Workflow

Single cell RNA-seq analysis workflow using BD Rhapsody system and TAS-seq

Customers perform everything from cell suspension to cDNA synthesis on-site (➊~➍) and send samples to us

*BD Rhapsody Express systems can be rented and workers can be dispatched to the site (at an additional cost).

Customers perform everything from cell suspension to cDNA synthesis on-site (➊~➍) and send samples to us

*BD Rhapsody Express systems can be rented and workers can be dispatched to the site (at an additional cost).

We amplify cDNA by TAS-seq, analyze sequence information, and deliver the data.

We amplify cDNA by TAS-seq, analyze sequence information, and deliver the data.

We send necessary equipment and reagents to you.


➊ Cell loading
(Spontaneous drop, Poisson distribution)


➋ Loading of excessive amounts of beads


➌ Cell solution, various cell-derived mRNAs supplemented with beads


➍ Bead recovery and reverse transcription

Customer sends us samples that have been ➊~➍ done onsite.

Matters to be checked in advance

* For multiplex analysis, please refer to the FAQ page "About Multiplex Analysis".

Sample acceptance

・ 

cDNA synthesized by our specified protocol (IGT will undertake cDNA amplification and subsequent steps)

・ 

Frozen cells (IGT is contracted to perform cell antibody staining and cDNA synthesis and beyond)

・ 

Frozen tissue (IGT is entrusted with the preparation of cell suspensions from tissue and beyond)

* Please contact us if you wish to make adjustments from frozen cells or tissues.

* When cryopreserving cells and tissues, please use the preservation solution and freezing method specified by us.

How to send samples

・ 

Please make sure that the sample is dry and leak-free, and send it to the shipping address below. (Available until 5:00 p.m. on weekdays)

・ 

When shipping BD Rhapsody beads that have been reverse-transcribed using our specified protocol, please ship refrigerated.

・ 

When shipping frozen cells/tissues, please include enough dry ice to maintain the frozen state and send via frozen delivery.

* Please send by next day except for remote areas.

Send samples to

1F Building 17, Tokyo University of Science, Noda Campus
2669 Yamazaki, Noda, Chiba 278-0022, Japan

ImmunoGeneTeqs, Inc.

Lead Time

From sample receipt

2.5 months

Deliverables (HDD)

・ 

Work report

・ 

Sequence raw data set

・ 

Mapping result files (gene expression tables, expression tables for RNA velocity analysis, analysis report files, etc.)

We can create a gene expression table for each cell from sequence data.

Here you can see the excerpts from the report.

The report on Seurat analysis is optional and will be charged separately.

An example report, including optional analysis, can be found at Latest Data

Additional analysis

Reference price

Number of samples

Number of samples

Number of cells/sample analyzed

Number of cells/sample analyzed

Price (incl. tax)

Price (incl. tax)

Single

Single

1

10,000

10,000

¥935,000

¥935,000

Multiplex

Multiplex

2

10,000

10,000

¥1,430,000

¥1,430,000

3

6,600

6,600

¥1,595,000

¥1,595,000

4

5,000

5,000

¥1,760,000

¥1,760,000

Analysis Report

Summary of single-cell RNA-seq analysis results report

Summary of single-cell RNA-seq analysis results report

Summary of single-cell RNA-seq analysis results report

Mapping Analysis Report

Mappinganalysis in brief

1.

Remove adapter sequences from the resulting sequence.

2.

Obtain quality sequences, check the base composition of each cycle, and confirm that there are no sequencing problems.

3.

The cDNA portion of the resulting sequence is mapped to a reference sequence.

4.

The mapped sequence data is divided based on the unique barcode sequence corresponding to each single cell to obtain gene expression data for each cell.

5.

Based on a barcode rank plot (Fig.1), it detects cells that are valid for analysis, and a scatter plot allows you to see the distribution of the number of reads per cell obtained and the number of genes detected. The scanter plot also allows you to check the distribution of the number of reads and genes detected per cell.

Examples of results

・ 

Table1 presents information on the mapping rate of cDNA portions, the efficiency of sequence read utilization, the final number of cells obtained, and the average number of genes detected in the cell population.

・ 

Fig.1 shows a Barcode rank plot, which is a plot to determine the effective cell count.

Table 1 Mapping statistics: number of cells and genes detected

Item

Number or Percentage (%)

Total raw reads: Total number of reads in the large source

Total raw reads: Total number of reads in the large source

640,067,050

640,067,050

Total reads after trimming and quality filtering:

Total number of reads after adapter removal and quality filtering

Total reads after trimming and quality filtering:

Total number of reads after adapter removal and quality filtering

601,568,258

601,568,258

Cell barcode calling rate:

Percentage assigned to cell barcodes

Cell barcode calling rate:

Percentage assigned to cell barcodes

90.36

90.36

Mapping rate against genome:

Mapping rate to genome

Mapping rate against genome:

Mapping rate to genome

99.72

99.72

Unique-mapping rate against genome:

Unique mapping rate to genome

Unique-mapping rate against genome:

Unique mapping rate to genome

92.28

92.28

Feature assignment rate: 1.0 to 1.0

Functional assignment ratio

Feature assignment rate: 1.0 to 1.0

Functional assignment ratio

82.86

82.86

Final usable reads: (1)

Final available leads

Final usable reads: (1)

Final available leads

470,344,073

470,344,073

Minimum read number of survived cells:

Minimum number of read reads for viable cells

Minimum read number of survived cells:

Minimum number of read reads for viable cells

6,293

6,293

survived cells:

Number of cells detected

survived cells:

Number of cells detected

5,640

5,640

Total reads of valid cells:

Total number of valid cell leads

Total reads of valid cells:

Total number of valid cell leads

398,748,815

398,748,815

Percent of total reads of valid cells:

Percentage of total effective cell leads

Percent of total reads of valid cells:

Percentage of total effective cell leads

84.78 

84.78 

Median reads per cell:

Median number of reads in valid cells

Median reads per cell:

Median number of reads in valid cells

59,859

59,859

Mean reads per cell:

Average number of reads in valid cells

Mean reads per cell:

Average number of reads in valid cells

70,700

70,700

Median symbols per cell:

Median number of genes detected in valid cells

Median symbols per cell:

Median number of genes detected in valid cells

4,098

4,098

Mean symbols per cell:

Average number of genes detected in valid cells

Mean symbols per cell:

Average number of genes detected in valid cells

4,373

4,373

total genes detected:

Total number of genes detected

total genes detected:

Total number of genes detected

53,098

53,098

Fig.1 Barcode rank plot

The vertical axis is the number of reads per cell barcode. The horizontal axis is the rank assigned to each cell barcode in order of number of reads per cell barcode. Inflection points are indicated by magenta vertical lines.

Seurat Analysis for Report on cell clustering and marker gene detection analysis

Report on cell clustering and marker gene detection analysis by Seurat analysis

1) Data preprocessing

・ 

When multiple samples are analyzed simultaneously using tags (BioLegend's Hashtag, BD's Sampletag, etc.), the amount of tag expression in each cell is determined, and based on the amount of tag expression in each cell Determine which cells are from which samples (Fig.2, Table2)

Table 2 Number of cells assigned to each sample tag

Doublet (14th-17th century men's jacket)

Doublet (14th-17th century men's jacket)

mouseSample

Tag10

mouseSample

Tag10

mouseSample

Tag6

mouseSample

Tag6

mouseSample

Tag7

mouseSample

Tag7

mouseSample

Tag8

mouseSample

Tag8

mouseSample

Tag9

mouseSample

Tag9

Cell count

Cell count

875

875

4,402

4,402

4,917

4,917

4,305

4,305

4,088

4,088

4,544

4,544

図2

Fig.2 Heatmap showing the relationship between sample tag expression in each cell barcode and the TAG assigned to each cell Fig.

2) Pretreatment in Seurat

・ 

Removal of cells with high mitochondrial gene expression (>0.25) (Fig.3)

・ 

Remove doublet cells and cells that did not express the tag (for analysis using tag)

・ 

Scaling using the ScaleData function

・ 

Pseudocolor density plots and Ridgeplots are used to determine the number of genes detected and the distribution of expression numbers per gene to generally confirm that there are no problems.

Fig.3 Pseudocolor density plots of statistical data

In this Fig., one point corresponds to one cell, and where the cells overlap at the same location, the density of the overlap is determined, corresponding to a blue to red spectrum from the lowest density to the highest density, and The cells are represented by colors in the spectrum corresponding to their density.

Top left: the Horizontal axis is the number of genes detected and the vertical axis is the ratio of the number of reads derived from mitochondrial genes to the total number of reads in each cell. Cells with this ratio greater than 0.25 were excluded from the calculation.

Top left: the Horizontal axis is the number of genes detected and the vertical axis is the ratio of the number of reads derived from mitochondrial genes to the total number of reads in each cell. Cells with this ratio greater than 0.25 were excluded from the calculation.

Bottom left: Horizontal axis shows the number of genes detected; vertical axis shows the number of reads derived from ribosomal RNA in each cell as a percentage of the total number of reads.

Bottom left: Horizontal axis shows the number of genes detected; vertical axis shows the number of reads derived from ribosomal RNA in each cell as a percentage of the total number of reads.

Top right: Horizontal axis, number of genes detected; vertical axis, number of reads derived from ribosomal protein genes in each cell as a percentage of the total number of reads.

Top right: Horizontal axis, number of genes detected; vertical axis, number of reads derived from ribosomal protein genes in each cell as a percentage of the total number of reads.

Bottom right: Horizontal axis shows the total number of reads in each cell and vertical axis shows the number of genes detected in each cell.

Bottom right: Horizontal axis shows the total number of reads in each cell and vertical axis shows the number of genes detected in each cell.

3) Principal component analysis and JackStraw plots

After the FindVariableFeatures function is used to detect genes with large expression variation, a Principal Component Analysis (PCA) is performed on these genes. The number of principal components (PCs) used for clustering cells based on gene expression patterns is determined by Jackstraw analysis

Fig.4 JackStraw plot

The number of principal components up to 71 with p-values less than 1 x 10-5 will be used in subsequent analyses.

4) Clustering

Perform cell clustering; cells with similar gene expression are in one cluster.

5) Seurat Clustering Silhouette Score Plot

Determine the resolution parameter. ( Fig.5) Here, the average silhouette score is maximum at resolution 1.5, so we assume that 1.5 is appropriate and proceed to the subsequent analysis.

Fig.5 Average silhouette score of clustering for each resolution parameter

6) Visualization of Seurat clustering results with Fit-SNE and UMAP plots

Visualize the clustering results done in 4) using FIt-SNE (George C. Linderman et al.Nat Methods 2019) (Fig.6) Also visualize with UMAP analysis (Fig.7)

Fig.6 Display of clustering results by Flt-SNE

50 clusters were detected.

Fig.7 Display of clustering results by UMAP

50 clusters were detected.

7) Analysis of cell composition in each sample

・ 

Find the number of cells in each cluster (Table 3)

・ 

Find differences in cell composition between samples (Fig.8, Fig.9)

Table 3: Number of cells in each cluster per tag (per sample)

Seurat_clusters

Seurat_clusters

mouseSampleTag10

mouseSampleTag10

mouseSampleTag6

mouseSampleTag6

mouseSampleTag7

mouseSampleTag7

mouseSampleTag8

mouseSampleTag8

mouseSampleTag9

mouseSampleTag9

0

529

785

558

526

587

1

672

640

458

352

651

2

284

272

334

273

338

3

228

243

198

172

208

4

182

150

167

135

176

5

114

232

164

138

120

6

171

130

150

158

149

7

167

148

100

213

113

8

113

210

159

110

123

9

121

146

140

110

157

・ 

Cluster numbers correspond to Suerat cluster numbers in Fig.6, Fig.7. Only 0 through 9 are shown in Table.

・ 

The original data is stored in .txt, or you can view the whole thing in an html file at Table.

図8

Percentage of total cells in each cell cluster is shown as a stacked bar graph for each sample Fig.

50 clusters were detected.

図9

Phylogenetic tree analysis of differences in cell composition percentages among samples

50 clusters were detected.

8) Detection of marker genes in each cell cluster

・ 

Find marker genes that are characteristically highly expressed in each cell cluster (Fig.10, Table4)

・ 

Expression patterns of the top 6 marker genes were visualized by FIt-SNE and UMAP (Fig.11, Fig.12)

Fig.10 Heatmap of expression patterns of the top 20 marker genes in each cluster Fig.

The vertical axis shows the 20 genes that are characteristic of each cluster, the horizontal axis shows the individual cells in each cluster, and the gene expression levels in the individual cells are indicated by the color of the spectrum on the right Table. The results can be used to determine the similarity of gene expression in individual cells within a cluster, or to compare gene expression in different clusters.

Table 4: All marker genes detected in each cluster

gene

gene

cluster

cluster

p_val_adj

p_val_adj

avg_logFC

avg_logFC

within_avg_exp

within_avg_exp

without_avg_exp

without_avg_exp

pct.1

pct.1

pct.2

pct.2

p_val

p_val

H2-Eb2

H2-Eb2

cluster0

cluster0

0

0

5.014

5.014

5.309

5.309

1.834

1.834

0.374

0.374

0.056

0.056

0

0

Fcer2a

Fcer2a

cluster0

cluster0

0

0

5.011

5.011

6.404

6.404

2.931

2.931

0.743

0.743

0.093

0.093

0

0

Cr2

Cr2

cluster0

cluster0

0

0

5.008

5.008

5.426

5.426

1.955

1.955

0.383

0.383

0.038

0.038

0

0

Ighd

Ighd

cluster0

cluster0

0

0

4.973

4.973

7.332

7.332

3.885

3.885

0.934

0.934

0.124

0.124

0

0

Bank1

Bank1

cluster0

cluster0

0

0

4.453

4.453

6.884

6.884

3.797

3.797

0.859

0.859

0.107

0.107

0

0

H2-Ob

H2-Ob

cluster0