Since the first scRNA-seq protocol was published in 2009 ( 17), there has been an expansion of scRNA-seq methods that differ in how the mRNA transcripts are amplified to generate either full-length cDNA or cDNA with a unique molecular identifier (UMI) at either the 5′ or 3′ end. We also demonstrated how unbiased single cell identification could be performed, and how data obtained from different scRNA-seq protocols could be integrated prior to downstream analysis. Here, we list four of the most commonly-used scRNA-seq methods and discuss their strengths and limitations in terms of workflow, sensitivity, data quality, and cost (Table 1), thus providing a guide that could help immunologists make an informed choice for their scRNA-seq studies. As a result, it is becoming challenging for non-experts to select the most appropriate method to address a specific research question, or to assess whether a single cell approach is even suitable for a given investigation. The rapid development of low-input RNA-seq methods has led to an explosion of scRNA-seq protocols, each with their own advantages and limitations. A static snapshot of single-cell transcriptomes can provide a powerful window onto the various stages of differentiation and activation states which are rarely synchronized between cells. Single-cell RNA-sequencing (scRNA-seq) is now widely employed in immunological studies seeking to resolve previously under-recognized cellular heterogeneity ( 7, 8), define key processes in cell development and differentiation ( 9, 10), unravel critical pathways of hematopoiesis ( 11– 13), and understand the gene regulatory networks that predict immune function ( 14– 16). Bulk approaches to the analysis of cells that exist in a continuum of differentiation and activation states leads to averaging of their distinct characteristics and a corresponding loss of biologically important information.Īdvances in next-generation sequencing technologies have recently made it possible to interrogate the immune system at the level of individual cells. This is particularly critical when studying temporally dynamic processes, such as progenitor cell development into terminally differentiated populations via multiple transitional stages. Consequently, biologically significant heterogeneity within a population can be masked, and relevant information averaged with irrelevant signals from contaminating cells ( 6). Nonetheless, this type of analysis does not consider variability in gene expression between individual cells, or the influence of sample contamination with unrelated cell types that share overlapping phenotypic characteristics. During this process, new and unique population markers were identified that can more effectively resolve different immune cell compartments. Until recently, gene expression studies were performed on bulk populations of sorted or purified immune cells in attempt to better understand their transcriptomes. However, not all immune cell types can be fully resolved by the sole analysis of phenotypic markers, since many of these are expressed by multiple cell lineages, or are differentially regulated during inflammation ( 3– 5). With the aid of microscopy and flow cytometry, immune cells can be readily classified into distinct types based on specific surface markers. The immune system comprises a network of cells, tissues and organs that mediate host defense against pathogens, but this network also plays a critical role in homeostatic activities, such as tissue development ( 1), and metabolism ( 2).
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