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Single-cell sequencing technologies such as 10X Genomics, BD Phrapsody, and Illumina Hiseq have emerged that allow us to profile the genome, transcriptome and epigenomics of individual cells, unravel the heterogeneity of cell genotype, phenotype, and function within a complex cell population. Currently, single-cell sequencing technologies have been applied in a wide of life science research, including in oncology, developmental biology, microbiology, neuroscience, botany, and other fields. As an expert in the field of single-cell sequencing and analysis, SingleX provides the best and most comprehensive single-cell sequencing and analysis services.

  • Single-cell DNA sequencing: It is an approach for investigating the genome information of a single cell. Based on this technique, the genomic heterogeneity of cell populations can be explored, including genetic point mutations and copy variation occurring in normal and disease development processes.
  • Single-cell DNA methylome sequencing: It is applied for identification and quantification for DNA methylation at single-cell level.
  • Single-cell RNA sequencing: It is used to investigate the expression profiles of individual cells at high resolution, reveal the heterogeneous gene expression patterns in a wide range of tissues and cell populations, and explain the response of an individual cell to different environmental signals and conditions.

Single Cell Copy Number Variation (CNV) Analysis

Copy number variation (CNV) is an important type of somatic aberration, which is associated with various diseases. Our powerful NGS technologies are reliable, efficient, high scale and resolution methods for analyzing CNVs at the single-cell level, and are applied in a variety of research areas, such as determining tumor heterogeneity, characterizing cell lines, uncovering neuronal mosaicism…

Copy number variation (CNVs) in a single tumor cell. Fig.1 Copy number variation (CNVs) in a single tumor cell.

Single Cell Single Nucleotide Variation (SNV) Analysis

Single nucleotide variations (SNVs) are genetic alterations of a single base observed in specific cells as compared to the population background. SNVs occurring in somatic cells are associated with many diseases, such as tumor, obesity, and diabetes mellitus and so on. SingleX provides reliable SNV detection and analysis at single-cell level, allowing for a new understanding of cellular heterogeneity.

Single Nucleotide Variation (SNV). Fig.2 Single Nucleotide Variation (SNV).

Single Cell Insertion and Deletion (InDel) Analysis

Insertions and deletions (InDels) are a type of genomic variations that refer to the insertion and/or deletion of nucleotides into genomic DNA. They can be used as genetic markers in disease diagnosis and cell-type identification. With the extensive experience in single-cell sequencing and analysis, SingleX leverages single-cell sequencing techniques and bioinformatics tools to offer high-quality services for single-cell InDel analysis.

Occurrence of indels in different types of solid tumour. Fig.3 Occurrence of indels in different types of solid tumour. (Turajlic, 2017)

Single Cell Lineage Tracing Analysis

Lineage tracing allows for the identification of all progeny of a single cell, which provides a detailed understanding of cellular hierarchies in tissue development, homeostasis, and disease. SingleX combines single-cell transcriptome sequencing with genetic lineage labels to provide the best single-cell lineage tracing analysis services.

Reconstruction of developmental trajectories. Fig.4 Reconstruction of developmental trajectories. (Farrell, 2018)

Single Cell Gene Expression Analysis

Single-cell gene expression profiling allows for understanding changes in gene expression of individual cells, helping to explain the cell heterogeneity in the context of tissue or tumor. Based on the 10x Genomics Chromium platform, we offer high-throughput and high-resolution functional gene expression analysis in tens of thousands of cells simultaneously, to characterize cell populations, cell types, and cell states.

RNA-Seq analysis revealed cell type-specific gene expression profiles. Fig.5 RNA-Seq analysis revealed cell type-specific gene expression profiles. (Zhang, 2014)

Single Cell Clustering Analysis

We employ the best clustering algorithms to perform single cell clustering analysis according to the gene expression data. Meanwhile, t-SNE analysis is used to visually display the results of cell clustering. The proportion of each cell subgroup in different samples can be statistically analyzed.

Identification of immune cells subgroup in samples of breast cancer tumor tissue, normal tissue, blood, and lymph nodes (top); Proportion of each cell subgroup in different tissue samples (bottom). Fig.6 Identification of immune cells subgroup in samples of breast cancer tumor tissue, normal tissue, blood, and lymph nodes (top); Proportion of each cell subgroup in different tissue samples (bottom). (Azizi, 2018)

Single Cell Type Identification Analysis

We support to analyze and process large amounts of RNA-seq data to identify cell type-specific gene signatures, providing analysis of cell type including complex and rare cell populations.

Cell-type analysis in AML patients. Fig.7 Cell-type analysis in AML patients. (DeTomaso, 2019)

Single Cell Marker Gene Detection Analysis

Identification of marker genes is of great importance for determining cell type and for understanding cell-specific gene function and the molecular mechanisms underlying complex diseases. SingleX can characterize marker genes of different cell subpopulations and visually display expression information of these genes.

Feature Plot diagram (top) and Violin diagram (bottom) of marker gene. Fig.8 Feature Plot diagram (top) and Violin diagram (bottom) of marker gene. (Dick, 2019)

Single Cell Different Gene Analysis Between Different Cell Populations

Based on RNA-seq data, we can profile the changes in gene expression levels between different cell subpopulations, unlocking the mystery in gene expression profiles between individual cells.

Cluster analysis diagram of differential genes between different cells subgroups. Fig.9 Cluster analysis diagram of differential genes between different cells subgroups. (Dick, 2019)

Single Cell Gene Ontology (GO) Analysis

Gene Ontology (GO) is a major bioinformatics initiative to unify the representation of gene and gene product attributes across all species. We can annotate functions of marker genes or differentially expressed genes based on NCBI/UNIPROT/SWISSPROT/AMIGO GO database, thereby extracting the key GO terms that are significantly enriched in these gene groups.

Gene Ontology analysis of differentially expressed genes in ovarian cancer. Fig.10 Gene Ontology analysis of differentially expressed genes in ovarian cancer. (Zhou, 2018)

Single Cell KEGG Pathway Analysis

Based on KEGG database, we can analyze gene pathways and find gene clusters of co-expressed genes sharing the same pathway.

Pathway entries with significant enrichment of differentially expressed genes between different clusters. Fig.11 Pathway entries with significant enrichment of differentially expressed genes between different clusters. (Dick, 2019)

Single Cell RNA Velocity Analysis

Velocyto algorithm is adopted to predict the direction of change of individual cells and obtain the transformation process between cells.

RNA velocity analysis results. The arrow direction in the figure represents the direction of cell evolution predicted by the algorithm. Fig.12 RNA velocity analysis results. The arrow direction in the figure represents the direction of cell evolution predicted by the algorithm. (Gioele, 2018)

Single Cell Pesudotime Analysis

By gene expressing data of cells as the research object, using TSCAN/reviewed/SLICER/Ouija algorithm, the change of cell model is analyzed on the virtual time axis. The dynamic change process of reconstructed cells is simulated, obtaining the state transition between cells, as well as the differential gene expression between cells under different states.


Pesudotime locus of state transitions between cells (right) and Heatmap (left). Fig.13 Pesudotime locus of state transitions between cells (right) and Heatmap (left). (Dick, 2019)

Single Cell Communication Analysis

By the gene expression data of cell subgroups as the research object, the expression information of ligand and receptor genes of cells is obtained, and the signal communication relationship between cell subgroups is obtained.

Overview of selected ligand-receptor interactions. Fig.14 Overview of selected ligand-receptor interactions. (Vento-Tormo, 2018)

Single Cell TCGA Prognostic Combination Analysis

By the clinical information of TCGA and the screened key genes as the research object, combining the clinical data of TCGA, we perform single-cell TCGA prognostic combination analysis. The relationship between the key genes and the clinical prognosis is obtained.

X axis represents over survival, Y axis represents proportion, and the different color curves represent different groups Fig.15 X-axis represents over survival, Y-axis represents proportion, and the different color curves represent different groups. (Guo, 2018)

Single Cell Chromatin Accessibility Analysis

We provide single-cell chromatin accessibility analysis to reveal cell state transcriptional regulators and cellular lineage relationships.

Single-cell ATAC-seq provides an accurate measure of chromatin accessibility genome-wide. Fig.16 Single-cell ATAC-seq provides an accurate measure of chromatin accessibility genome-wide. (Buenrostro, 2015)

Single Cell DNA Methylation Analysis

DNA methylation is the most stable epigenetic mark that is involved in several biological processes, including X-chromosome inactivation, repression of transposable elements, aging, and carcinogenesis. We provide detailed DNA methylation analysis, uncovering cell cycle regulation, differential gene expression, and epigenetic effects.

DNA methylation pattern in gene body regions as determined from HepG2 scTrio-seq data and RRBS data. Fig.17 DNA methylation pattern in gene body regions as determined from HepG2 scTrio-seq data and RRBS data. (Hou, 2016)

Single Cell Spatial Patterns of Gene Expression Analysis

Spatial localization is very important for cellular fate and behavior. We offer single-cell spatial patterns of gene expression analysis, aiming to deeply profile cellular function.

Spatial expression patterns. Fig.18 Spatial expression patterns. (Edsgärd, 2018)

Highlights

  • Support with experimental design and selection of appropriate single-cell workflow depending on cell types and number of cells
  • High-throughput cell sorting and capturing techniques (10X Genomics and BD Phrapsody)
  • The top-ranking technique for single-cell sequencing library construction (Completing the library construction of 1,000-10,000 cells at one time)
  • Extensive experience in single-cell sequencing and bioinformatics analysis

SingleX is the first all-encompassing single-cell sequencing and analysis service provides that employs advanced NGS technologies and bioinformatics platforms to offer top-quality research services for single-cell analysis, deeply profiling the secret of cellular heterogeneity. Please feel free to contact us for more details

References

  1. Guo, X.; et al. Global characterization of T cells in non-small-cell lung cancer by single-cell sequencing. Nature Medicine. 2018, 24(7):978-985.
  2. Azizi, E.; et al. Single-cell map of diverse immune phenotypes in the breast tumor microenvironment. Cell. 2018, 174(5):1293-1308.e36.
  3. Dick, S.A.; et al. Self-renewing resident cardiac macrophages limit adverse remodeling following myocardial infarction. Nat Immunol. 2019, 20(1):29-39.
  4. Turajlic, S.; et al. Insertion-and-deletion-derived tumour-specific neoantigens and the immunogenic phenotype: A pan-cancer analysis. The Lancet Oncology. 2017, 18(8).
  5. Farrell, J.A.; et al. Single-cell reconstruction of developmental trajectories during zebrafish embryogenesis. Science. 2018, 360(6392):eaar3131.
  6. Zhang, Y.; et al. An RNA-sequencing transcriptome and splicing database of Glia, neurons, and vascular cells of the cerebral cortex. Journal of Neuroscience. 2014, 34(36):11929-11947.
  7. Zhou, Y.; et al. Identification of genes and pathways involved in ovarian epithelial cancer by bioinformatics analysis. J Cancer. 2018, 9(17):3016-3022.
  8. Buenrostro, J.D.; et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature. 2015, 523(7561): 486-490.
  9. Hou, Y.; et al. Single-cell triple omics sequencing reveals genetic, epigenetic, and transcriptomic heterogeneity in hepatocellular carcinomas. Cell Research. 2016, 26:304-319.
  10. Vento-Tormo, R.; et al. Single-cell reconstruction of the early maternal-fetal interface in humans. Nature. 2018, 563: 347-353.
  11. Gioele, L.M.; et al. RNA velocity of single cells. Nature. 2018. 560: 494-498.
  12. DeTomaso, David, et al. "Functional interpretation of single cell similarity maps." Nature communications 10.1 (2019): 1-11.
  13. Edsgärd, Daniel, Per Johnsson, and Rickard Sandberg. "Identification of spatial expression trends in single-cell gene expression data." Nature methods 15.5 (2018): 339.
! ! For research purposes only. Not for clinical, therapeutic, or diagnostic purposes in animals or humans.

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