The recent advances in single-cell analysis have led to the development of multiple platforms for single-cell isolation. The conventional method of manual single-cell isolation was slow, laborious, and prone to contamination. Therefore, microfluidic devices have emerged as a promising alternative to conventional methods. The devices are designed to trap single cells in chambers or channels, providing a controlled environment that will favor single-cell isolation. The devices can handle several cells simultaneously, and the analysis is automated, saving time and reducing the risk of contamination. The devices can also be customized to ensure the isolation of specific cells.
Single-cell transcriptomics has become a popular way to explore cellular heterogeneity. The conventional bulk RNA sequencing methods only give an average gene expression and cannot capture heterogeneity at the single-cell level. Therefore, several single-cell RNA sequencing protocols have been developed. The protocols differ in sequencing strategies, priming, and library preparation. The use of droplet-based technology has revolutionized the process of single-cell RNA sequencing. The devices generate an array of droplets where each droplet captures an individual cell. The RNA from each cell within the droplet is then converted into cDNA, and the library is prepared for sequencing. The technology has high throughput and is ideal for analyzing thousands of cells in a short time.
Proteomics is another essential tool in life science research. The analysis of proteins from single cells can give insight into cellular behavior. But, proteomics analysis faces the challenge of the low amount of protein and high level of variation in protein expression within cells. Therefore, a new generation of tools has been developed to overcome these challenges. The tools rely on micro- and nanoscale techniques to handle a few proteins, minimizing the loss of a small amount of proteins. The new generation of tools also uses several separation techniques, such as liquid chromatography and electrophoresis, to improve separation before mass spectrometry.
The process of single-cell analysis generates large amounts of data; hence, data analysis tools have become essential to unlock biological insights. Several computational tools have been designed to handle different tasks. The tools can be used for data analysis, visualization, and modeling. Currently, the major challenge in data analysis is designing tools to handle the complexity of the data, noise, and handle variability across different platforms. Therefore, the demand for big data analytics, machine learning, and artificial intelligence tools to handle these challenges has emerged.
The recent progress in life-science tools has drastically changed the study of biological systems. The development of single-cell isolation and analysis tools has facilitated deep analysis of individual cells, providing a detailed view of heterogeneity at the cellular level. The single-cell analysis has become an essential tool in various fields, including neuroscience, cancer research, developmental biology, and stem cell research. The advances in single-cell isolation, sequencing, proteomics, and data analytics have created a new era in life-science research where new biological insights are discovered. The future of life sciences will depend on how well we can harness the power of single-cell analysis and the integration of life science tools with big data analytics, machine learning, and artificial intelligence.