• Advancements and Future Outlook in Single-Cell Analysis
  • Mahla Sadat Hosseini,1 Ali Etemadi,2,*
    1. Medical Biotechnology, Tehran University of Medical Sciences
    2. Medical Biotechnology, Tehran University of Medical Sciences


  • Introduction: Single-cell sequencing is an approach to detect the gene sequence information at single-cell level, yielding a new understanding of the gene expression profiling of an individual cell between heterogeneous populations. It has been proved that both eukaryotic and prokaryotic cell populations are heterogeneous. This is to say individual cells in populations differ dramatically in size, protein levels, and expressed RNA transcripts. Hence it is not far enough to analyze the physiological characteristics on the level of homogenized cell population, which may ignore the critical changes occurring in individual cells. Single-cell analysis is performed to analyze the genomics, transcriptomics, proteomics, and metabolomics at the single-cell level, enabling it possible to discover mechanisms not seen when studying a bulk population of cells.
  • Methods: Here’s a brief overview of a typical single-cell RNA sequencing (scRNA-seq) workflow: Sample Preparation: Isolate single cells from the tissue or culture of interest. This can be done using methods like fluorescence-activated cell sorting (FACS) or microfluidics. Cell Lysis and RNA Capture: Lyse the cells to release their RNA. Capture the RNA using specialized beads or plates that contain oligonucleotides designed to bind RNA molecules. Reverse Transcription and Amplification: Convert the captured RNA into complementary DNA (cDNA) using reverse transcription. Amplify the cDNA to generate enough material for sequencing. Library Preparation: Prepare sequencing libraries from the amplified cDNA. This involves adding adapters and barcodes to the cDNA fragments to enable sequencing and identification of individual cells. Sequencing: Sequence the prepared libraries using high-throughput sequencing platforms like Illumina. Data Analysis: Process the sequencing data to align reads, quantify gene expression, and identify cell types and states. This step requires advanced bioinformatics tools and expertise. Interpretation: Analyze the results to gain insights into cellular heterogeneity, gene expression patterns, and potential biological mechanisms.
  • Results: The advances of single-cell analysis over the past 5 years have happened at a lightning pace, and the potential for their use in various fields is high. However, the novelty of these single-cell techniques also implies various limitations. Due to the heterogeneity present in cell populations, it is necessary to analyze the function of individual cells at high resolution. Single-cell function analysis provides a novel understanding of the function of an organ or tissue or system and the interaction of single cells on a global scale. For example, single tumor cell function analysis helps to explain the different responses of different patients to the same anti-cancer drug and the mechanism of anti-cancer drug resistance, promoting to develop more effective therapeutic strategies.
  • Conclusion: Single-cell analysis, while incredibly powerful, comes with several challenges like Technical Complexity, the procedures involved, such as isolating individual cells and preparing them for analysis, require highly specialized equipment and expertise. This can make the process time-consuming and costly. Despite these challenges, ongoing advancements in technology and methodology are continually improving the robustness and accessibility of single-cell analysis.
  • Keywords: RNA-Seq, single cell, Data Analysis, Sequencing