Single Cell Analysis

  • Single cell-omics
  • Single-cell analysis and application
  • Recent advancements in single-cell analysis
  • Single-cell analysis of tumour
  • An alternative option – Single nucleus RNA sequencing
  • Status quo and current challenges on solid tissue dissociation
  • Selected tissue dissociation tools by LAB-A-PORTER

Single cell analysis

Why are cells studied?

    • Cells are known to be the most fundamental building block of life. [1]
    • Cells form our bodies grow, reproduce, and process information, and they can respond to stimuli and process different chemical reactions. [1]
    • However, to date, the interactions between cells, the responses of their organelles to molecules and intracellular behaviour remain unknown. [1]

Why single-cell analysis instead of bulk cell analysis?

    • For the understanding of cellular physiological interactions with different molecules and organelles, an average ensemble measurement of millions of cells together is not enough to provide detailed information about cells. [1]
    • Bulk analysis of millions of cells cannot explore cellular heterogeneity characteristics and dynamics at the molecular level, in a particular cell population. [1]
    • The investigation of the progress of any disease has remained challenging because of the physiological states of cells and the heterogeneous nature of cells in a specific given population. [1]
    • The single-cell analysis provides clear information about each cell, such as specific biological factors of a cell, which is helpful in understanding stem cells’ behaviour or tumour progression. [1]
    • It can characterize in detail the molecular contents of cells, related to cell state and type, spatial and temporal transformations, and micro-environment. [1]

What are single-cell omics?

    • Single-cell omics such as genomics, proteomics, transcriptomics, and metabolomics.
    • It can promote unrevealed information regarding functional mutation and cell copy number variations. [1]
    • All these omics analyses may involve mathematical and computational modelling to investigate cellular functions ranging from the complete genome to living organisms. [1]
    • Single-cell analysis helps us to understand cellular organelle responses to the environment and intracellular interactions at a more profound level than bulk analysis, accelerating the development of the therapeutic and diagnostic process. [1]

Single cell analysis and applications

What are the different types of single-cell analysis and their applications?


    1. Single-cell genomics and gene expression profiling
      • They are used widely in medicine. [1]
      • For optimizing several biological, biomedical, and pathological conditions for various disease analyses. [1]
    1. Single-cell proteomics and transcriptomics analyses
      • Play a significant role in studying cellular heterogeneity characteristics and complex diseases such as cancers. [1]
      • Evolve the dynamics of differentiation and quantify transcriptional stochasticity. [1]
      • Help us to study some biological phenomena such as tissue regeneration, immune response and embryonic development. [1]


    1. Single-cell RNA sequencing (scRNA-seq)
      • It can unlock the mystery of gene expression. [1]
      • Track the trajectories of distinct cell lineages in development. [1]


    1. Single-cell metabolomics
      • Elucidate in detail the phenotypical variations of cells. [1]
      • Understanding single-cell metabolic reactions across organelles helps us to identify any disease pathologies. [1]
      • Provide holistic and comprehensive insights at the cellular or even subcellular level to understand the cellular organism response, under the different pathophysiological conditions. [1]

    Recent advancements in single-cell analysis

    What are the examples of recent advancements in single-cell analysis using different approaches?


    1. Using single cell RNA sequencing (scRNA-seq) profiles integrated along proteomic or genomic data to reveal complex cellular heterogeneity characteristics by Chio et al. [1]


    2. Using single nucleus RNA sequencing (snRNA-seq) to explore cellular composition and cell features of the whole mammalian heart by Wolfien et al. [1]

      • Conveyed precise cell type affirmation. [1]
      • Identified cell clusters interconnectedness. [1]
      • Identified the cardiomyocytes subgroups with distinct marker profiles. [1]


    3. Developed an easy-to-use process for quantification of mRNA study at the single-cell level by Jonasson et al. [1]

      • Quantification study can be widely applicable for cellular characterization, immune responses, and mapping of intra-tumoral heterogeneity etc. [1]


    4. Using single-cell genome sequencing to study localized high-risk prostate cancer circulating tumour cells (CTC) by Rangel-Pozzo et al. [1]

      • Tumour heterogeneity is one of the major causes failures in prostate cancer prediction and prognosis. [1]
      • Circulating tumour cell analysis can provide individual patient-specific clinical assessment, due to unlocking the mystery of tumour-derived and germline-specific genetic information more precisely in comparison with a single diagnostic biopsy. [1]


    5. Using single-cell transcriptome analysis to study heterogeneity characteristics of mouse hepatocytes and their distinctive functions during cholestatic liver injury by Chang et al. [1]


    6. Proposed various methods of single-cell proteome analysis to understand cellular heterogeneity, the molecular dynamics of the cell, as well as the clinical applications for tumour treatment and drug development by Liu et al. [1]


    7. Distinguished the individuality of 2D, 3D and in-vivo models by using the multi-parametric single-cell mass cytometry technique by Alfold et al. [1]

      • Single-cell genomics and proteomics in integration with the 3D cell culture technique can execute the new prospects for the unveiling of tumour heterogeneity. [1]


    8. Using single-cell RNA analysis to explore the fidelity of different stages of organoid culture by Eze et al. [4]


    9. Using single-cell RNA analysis to evaluate the cultural conditions and the biological features of organoids of colorectal cancer (CRC) patients by Wang et. Al [3]


    10. Investigated how single-cell technology has been applied to inspect several infectious diseases by Lin et al. [1]

      • They studied how cellular heterogeneity is linked substantially with the profession of infectious disease using fluorescence-activated cell sorting and next-generation sequencing. [1]
      • The genomic and phenotypic biomarker characterization, as well as host-pathogen interaction at the single-cell level, can help us to understand the unknown infection mechanisms and potential disease treatment. [1]
      • Single-cell information from primary cells can identify rare but important cell subtypes, which help us to understand the complex interplay between cells and the immune system. [1]


    11. Reviewed various single-neuron models, single-neuron behaviour, and their analysis by Gupta et al. [1]

      • Single-neuron analysis can emphasize the detail of pathophysiology, electrophysiology, anatomical differences, structural and functional features etc. in comparison with the bulk analysis of millions of neurons together. [1]

    Single-cell analysis of tumour

    Why is the single-cell analysis used to study the tumour ecosystem?

      • Tumours encompass a complex cellular ecosystem of malignant and non-malignant cells, whose diversity and interactions affect cancer progression and drug response and resistance. [5]
      • Recent advances in single-cell genomics, especially single-cell RNA sequencing, have transformed our ability to analyse tumours, revealing cell types, states, genetic diversity and interaction in the complex tumour ecosystem. [5]


    What are the challenges in single-cell RNA sequencing?


    Inherent methodological issues, such as artificial transcriptional stress response. [2]


      • It means that the dissociation process could induce the expression of stress genes, which lead to artificially changes in cell transcription patterns [2]
      • Brink et al. found that the process of protease dissociation at 37 degrees Celsius could induce the expression of stress genes, introduce technical error, and cause inaccurate cell type identification. [2]
      • Adam et al also found that dissociation at 37 degrees Celsius can cause artifact changes of cell transcriptome, resulting in inaccurate results; dissociation of tissue at 4 degrees Celsius has thus been suggested to minimize the isolation procedure-induced gene expression changes. [2]

    What are the challenges in single-cell RNA sequencing of clinical tumour specimens?

    Single-cell RNA sequencing of clinical tumour specimens:


      • Requires quick dissociation tailored to the tumour type, and involves enzymatic digestion, which can lead to loss of sensitive cells or changes in gene expression. [5]
      • Obtaining fresh tissue is time-sensitive and requires tight coordination between tissue acquisition and processing teams, posing a challenge in the clinical setting. [5]

    An alternative method - Single nucleus RNA sequencing

    What is single nucleus RNA sequencing?

      • Single nucleus RNA sequencing (snRNA-seq) is an alternative single-cell sequencing method.[2]
      • Instead of sequencing all the mRNA in the cytoplasm of cells, snRNA-seq is a methodology that only captures the mRNAs in the nucleus of cells. [2]

      What are the advantages of single nucleus RNA sequencing comparing with single cell RNA sequencing?

        • The snRNA sequencing solves the problems related to tissue preservation and cell isolation that are not easily separated into single cell suspension, is applicable for frozen samples, and minimizes artificial transcriptional stress response as compared to scRNA-seq. [2]
        • SnRNA-seq become very useful in many tissue types, such as muscle tissue, heart, kidney, lung, pancreas and various tumour tissues. It is particularly applicable in brain tissues, which are difficult to dissociate to obtain intact cells. [2]
        • Grindberg et al. demonstrated that single-cell transcriptomic analysis can be done using the extremely low levels of mRNA in a single nucleus of brain tissue. [2]
        • Compare with intact cells, the nucleus has the advantage of being easily separated from complex tissues and organs, such as those in the central nervous system. [2]
        • snRNA seq can be widely used for eukaryotic species, including species from different kingdoms. [2]
        • Single nucleus RNA sequencing allows profiling of single nuclei isolated from frozen tissues, decoupling tissue acquisition from immediate sample processing. [2]
        • Can handle samples that cannot be successfully dissociated even when fresh, due to size or cell fragility, as well as multiplexed analysis of longitudinal samples from the same individual. [2]

        Limitations of single nucleus RNA (snRNA) sequencing?

          • Only capture transcripts in the nucleus, which might fail to capture important biological processes related to mRNA processing, RNA stability and metabolism. [2]
          • However, nuclei have lower amounts of mRNA compared to cells and more challenging to enrich or deplete for specific types of interest. [5]
          • Both single-cell RNA sequencing and single nucleus RNA sequencing poses experimental challenges when applied to different tumour types, due to distinct cellular composition and extracellular matrix (ECM) indifferent tumours, and thus each assay requires dedicated customizations. [5]
          • Both technologies have been demonstrated by a large number of scientific publications in better understanding the cellular and biological processes in organogenesis, gaining novel biomedical and cellular insights into disease pathogenesis [2]

          The status quo and current challenges on solid tissue dissociation

          The status quo and current challenges on solid tissue dissociation

            • Optimal dissociation needs to achieve a balance between releasing cell types that are difficult to dissociate while avoiding damage to those that are fragile. [7]
            • Tissue dissociation is most commonly conducted using enzymes which require incubation at 37 degrees Celsius for variable times based on tissue type. [7]
            • A recent alternative approach to minimizing this artifact uses cold-active protease to conduct tissue dissociation on ice. [7]
            • In the case of single-nucleus RNA sequencing, the protocol uses much harsher conditions to release nuclei from tissue and can be applied to snap-frozen samples, thus avoiding many of the dissociation-related artifacts. [7]
            • The single nuclei method should also permit profiling of nuclei from the large cell (>40um) that do not fit through the microfluidics. [7]
            • Additional restrictions and challenges are faced by complex experimental designs where specimens cannot be processed immediately.[7]
              • Samples need to be preserved either as intact tissue or in a dissociated form as a single-cell suspension. [7]
            • Each of the approaches mentioned introduces specific biases and artifacts that can manifest themselves in altered transcriptional profiles or altered representations of cell types. [7]
            • Biases need to be considered when designing and analyzing data from a single-cell experiment. [7]

            Selected tissue dissociation tools by LAB-A-PORTER

            Tissue dissociation – Automation can help


            Tissue dissociation is tedious and challenging. Automation can potentially overcome manual cell and nuclei isolation challenges by:


              • Easily producing high-quality cells and nuclei.
              • Increasing cell viability.
              • Improving isolation reproducibility.
              • Reducing isolation failures.
                • Save precious samples
                • Prevent costly downstream sequencing repetition
              • Enabling processing of flash frozen tissue banks by improved efficiency.
              • Making protocols replicable.

            Tissue dissociation system – Singular 100 System from S2 Genomics


            S2 Genomics is developing integrated sample preparation systems for processing tissues into genomic samples for single-cell genomics and cell biology studies.


            The Singulator™ 100 enables rapid and hands-off tissue dissociations, making it easy for researchers to reproducibly prepare suspensions of nuclei or highly viable cells from small samples in high yield, for a wide range of single-cell analyses.


            The Singulator 100 overcomes the challenges with manual tissue preparation methods by producing consistent cell or nuclei isolations from a variety of solid tissue samples, reducing hours of hands-on processing to minutes.

            “The Singulator 100’s automated isolation workflows allow researchers to generate reproducible results by maintaining control over protocol variables that might otherwise be difficult to regulate when performing manual methods. While the protocols require testing to maximize output and quality for each organism or tissue, the ability to edit the protocols allows for precise adjustments of run parameters, which expedites troubleshooting and optimization.”
            Michael Peterson, Allison Scott, Cathleen Lake, Lu Deng, Anoja Perera, The Stowers Institute for Medical Research. [9]

            Nuclei in 6 to 10 minute

            Through Singulator 100, researchers could obtain single nuclei suspension from fresh tissue, frozen tissue and OCT tissues in 6 to 10 minutes with yield 10,000 to >600,00/mg, depending on tissue types.
            Merged DAPI-stained and bright-field images of small intestine, brain and heart tissue nuclei; DAPI stained liver nuclei. Courtesy of Dr. Minoda, Laboratory for Cellular Epigenomics, RIKEN Yokohama, Japan. Reference: S2 Genomics, 2022.

            Say Goodbye To Manual Tissue Dissociation.


            From tissue to single cells or nuclei in minutes with walk-away operation.

            Reference: S2 Genomics, 2022.

            Intuitive software. Customizable protocols.


            • Singular 100 System from S2 Genomics offers personalized protocol options, including:


                  • Flexible dissociation time
                    • ranging from 0 to 1440 minutes
                  • Flexible dissociation temperature
                    • Cold, room temperature and 37 degrees Celsius
                  • Flexible dissociation mixing profiles
                    • 3 Mixing type options – top, immersion, triturate or none
                    • 5 Mixing speed options – slowest, slow, medium, fast and fastest
                  • Flexible mechanical disruption
                    • 3 Disruption type: default, dounce or none
                    • 5 Disruption speed: slowest, slow, medium, fast or fastest
            • Included with on board database to track protocols and log events

            High nuclei yield

            Reference: S2 Genomics, 2022.

            1. Santra, Tuhin Subhra, and Fan-Gang Tseng. “Single-Cell Analysis.” Cells 9,9 1993. 29 Aug. 2020, doi:10.3390/cells9091993
            2. Jovic, Dragomirka et al. “Single-cell RNA sequencing technologies and applications: A brief overview.” Clinical and translational medicine 12,3 (2022): e694. doi:10.1002/ctm2.694
            3. Wang, Rui et al. “Systematic evaluation of colorectal cancer organoid system by single-cell RNA-Seq analysis.” Genome biology 23,1 106. 28 Apr. 2022, doi:10.1186/s13059-022-02673-3
            4. Eze, Ugomma C et al. “Single-cell atlas of early human brain development highlights heterogeneity of human neuroepithelial cells and early radial glia.” Nature neuroscience 24,4 (2021): 584-594. doi:10.1038/s41593-020-00794-1
            5. Slyper, Michal et al. “A single-cell and single-nucleus RNA-Seq toolbox for fresh and frozen human tumors.” Nature medicine 26,5 (2020): 792-802. doi:10.1038/s41591-020-0844-1
            6. Liu, Yang et al. “Tumour heterogeneity and intercellular networks of nasopharyngeal carcinoma at single cell resolution.” Nature communications 12,1 741. 2 Feb. 2021, doi:10.1038/s41467-021-21043-4
            7. Denisenko, Elena et al. “Systematic assessment of tissue dissociation and storage biases in single-cell and single-nucleus RNA-seq workflows.” Genome biology 21,1 130. 2 Jun. 2020, doi:10.1186/s13059-020-02048-6.
            8. S2 Genomics, ‘Singular 100’, Available at
            9. Peterson, Michael et al. ‘Improving single cell workflows using the Singular 100’ Stowers Institute. Available at

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