Abstract
Abstract Single-cell RNA sequencing (scRNA-seq) technology has made great strides in research over the last decade. Data analysis has been aided by developments in bioinformatics tools and artificial intelligence, allowing biological and clinical researchers to get a deeper understanding of the different cell clusters and their dynamics within tumours. Combining conventional treatment modalities like chemotherapy and radiation with immunotherapy is a growing trend in cancer treatment. Hence, knowledge of the tumour microenvironment and the effect of each treatment modality on the TME, at a single cell level can provide treating clinicians with better clues for patient stratification and prognostication. With this knowledge, immunotherapy could become successful in treating a wide range of cancers, opening the path for the creation of even more effective treatment strategies. Despite the widespread availability of scRNA-seq technology, computational analysis and data interpretation are still challenges. Worldwide, such challenges are being addressed by various researchers, strengthening the contribution of this technology towards cancer elimination. In this mini-review, we primarily focus on the technique, its workflow, and the computational aspects of scRNA technology, along with an overview of the current challenges in the analysis and interpretation of the data generated.
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