Accurate understanding of cells is a prerequisite for understanding their functions in physiological and pathological processes. Traditional research methods focus on analyzing cell populations and obtaining average results of a large number of cells, which cannot distinguish the precise contribution values of different cell individuals to sample heterogeneity, thus ignoring or masking individual differences in single cells. Single cell proteomics can not only provide gene expression of a single cell and provide big data reference for cell subgroup classification, but also avoid expression level changes caused by post transcriptional regulation and post translational regulation through protein level detection, which is closer to the regulatory essence of cells, and can more profoundly and accurately understand the root causes of diseases in tumors The fields of immunity and reproductive development have great research value, providing data references for revealing physiological and pathological phenotypes, developing drug targets, and achieving precision medicine.From a technical perspective, with the development of single-cell sequencing technology, its research scope is no longer limited to transcriptomics, but has expanded to multiple omics levels such as genomics, immunohistochemistry, epigmics, proteomics, etc. The research objects involve various molecular information such as chromatin, DNA, epigenetics, transcription factors, histones, and cell surface proteins. Based on single cell multi omics data, cell clustering can cluster cells using existing cell type annotations from reference datasets of similar cell groups, or perform unsupervised clustering to identify similar cell groups, and use other molecular level data for finer partitioning.For example, transcriptome data can be used to identify differentially expressed genes between different cell types or states, genomic data can detect gene copy number changes, single base mutations, insertion deletions, and other information at the single cell level, chromatin accessibility data can be used to identify accessible regions and enriched DNA motifs, and epigenetic data can be used to identify differentially methylated regions between different cell types or states, Proteomic data can be used to understand cellular interactions, among others. In addition, the newly emerged spatial transcriptome sequencing methods and their data integration algorithms in recent years have provided spatial information of cells, supplementing single-cell sequencing technology.From an application perspective, in complex biological processes such as tumorigenesis, heterogeneity exists at multiple levels such as genome, transcriptome, epigroup, and immunohistochemistry. Tumor cells with the same gene may have different DNA methylation, gene expression, and clone amplification patterns. Therefore, multiple omics techniques are often needed to more accurately classify them into different subgroups and reveal deeper biological mechanisms.
With the emergence of single-cell sequencing technology, the microscopic world we can explore has become increasingly precise. From visible tissues to invisible cells, traditional techniques process a piece of tissue at once, obtaining the average level of thousands of cells in that tissue. However, single-cell sequencing technology studies individual cells, revealing the characteristics of individual cells being masked and even the molecular mechanisms within the cells, Effectively avoiding the drawbacks of traditional sequencing and revealing the heterogeneity between cells. With the innovation of biotechnology, single cell sequencing technology has gone from being high cost and low flux at the beginning to being low cost and high flux now. It can capture up to 10000 cells at a time and can be used for cell type analysis clustering, immunology research, stem cell research, inter cell heterogeneity analysis, developmental biology research, etc., greatly promoting the development of genomics, proteomics, metabolomics, etc.Single cell RNA sequencing (scRNA seq) is a new technology for sequencing transcriptome at the single cell level. The single cell resolution provided by scRNA seq can directly measure transcriptome information at the single cell level, overcoming tissue level limitations and providing gene differential information at the cellular level. By comparing the differences between various cell transcriptomes, rare cell subpopulations can be identified, Heterogeneous tumor subpopulations reveal differences between immune cells in inflammatory response. This technology has broken through the existing research scale, thus attracting attention in the fields of life sciences and basic medical research, providing unprecedented insights for the development of cancer treatment.
Single Cell Proteomics Services
Single Cell Transcriptome Service
