“The information content of a single cell has been established as around 1.5 Gb, comparable to more than a hundred million pages of the Encyclopedia Britannica.”Carl Sagan, 1997.
If you’ve graduated from a molecular biology lab in the past 10 years, then you would have experienced that a large part of your training was aimed at gathering the right experimental evidence to support a small hypothesis. Typically, this hypothesis goes on to form a critical missing piece of the giant puzzle that groups of biologists across the world are trying to solve. Biologists are incentivized to zoom in to the problem and identify mechanisms that drive biological phenomena.
At first glance, this might seem antithetical to the very nature of data science-focused approaches of identifying patterns and trends of a higher order, choosing not to focus on singular events. In recent times, newer approaches in biology have enabled biologists and data science to find a compelling middle ground in Single-cell RNA sequencing.
Single-cell RNA sequencing has emerged as the technique of choice for researchers trying to understand the cellular heterogeneity of tissue systems under physiological and pathological conditions. In recent years this field has been marked by rapid advances in data generation techniques and algorithms for data analysis1.
Time to Look Beyond RNA-Seq?
Which cell type in the complex tumor microenvironment expresses your protein of interest? How does the expression of a gene change during differentiation? Are there biomarkers expressed in cell types that could distinguish tumor and stromal cells? Do immune cells in tissue systems express specific genes of interest? Does protein expression vary at the single cells level?
Unraveling Biological Responses with Single-Cell Data
Decades of biological research have made two points abundantly clear – biological systems, even within the same species, exhibit considerable heterogeneity at the tissue level. Consider the case of a gene MyoD. The MyoD gene is activated explicitly in satellite cells of the muscle and exhibits specific spatiotemporal gene expression patterns. Interestingly, expression of MyoD in a different cell type drives their differentiation towards a muscle lineage. Even at the level of tissues, cells making up a tissue have unique transcriptional signatures that drive their functions. For instance, Kupffer cells of the liver show a high expression of Arg1 compared to Hepatic stellate cells, which resemble fibroblasts at the transcriptional level.
Single-cell RNA-sequencing offers biologists the opportunity to understand the transcriptional signature of cells involved in physiological and pathological conditions at the resolution of a single cell. Since this technique’s commercial availability, a wide range of publications in Immunology, Immuno-oncology, Cancer research, and neuroscience have reiterated the potential of discovering novel cell populations, their gene expression patterns, and biological significance.
Traditional ensemble-based sequencing approaches, such as Bulk RNA seq methods, reflect an average of expression levels across a large population, which overlook unique biological behaviors of individual cells, conceal cell-to-cell variations, and do not explain the heterogeneity of single-cells radically. Whereas methods like flow cytometry are routinely used for detailed enumeration and characterizing of different cell types and cell states (phenotype) from a population of cells, identification is done based on a few canonical markers. Since these markers are limited in number, the dynamics of complicated diseases are not easily tracked through such methods2.
In addition to individual omics datasets, there has been an emergence of various data Atlases. These are large multi-omics databases that give you access to incredible quantities of data that allow you to explore different tissue systems at scale and granularity.
The evolution of droplet-based high-throughput microfluidic scRNA sequencing methods like CITE-seq, Cell Hashing, CyTOF, and more gave rise to projects like the Human Cell Atlas Project where scRNA seq methods contribute to the exploration of data-driven cell types, lineages, and trajectory analysis. Single-cell Atlases are an effort to catalog different kinds of cells in the human body and provide a roadmap to understanding human health and diseases. You can explore various types of Single-cell Atlases here. In addition, platforms like Polly provide organ-specific OmixAtlases that are powerful resources that enable biological discovery.
From Discrete States to Continuum
Single-cell data reveals that cell phenotypes are not static systems, rather a continuum of cell types and transitional states. Single-cell data allows R&D teams to recapitulate the dynamics, transitions, and development of individual cells against time. Scientists now trace cell development systems, step-by-step, & understand potential paths, intermediate transitions, and branch points based on the rise and fall of gene expressions of markers.
Detailed molecular information at the single-cell level that was once captured over years using a cellular model, is captured using a single experiment today. Novel methodologies of the likes of Monocle and Slingshot use single-cell data to capture cellular trajectories with high granularity. In the case of myoblast differentiation, for example, single-cell transcriptomics of stem cells can be used to trace the descendants of C2C12 cells in order to chart the gene expression during the progression from multipotent progenitors to a fully differentiated stage. The differentiation pattern of skeletal myoblasts revealed that their development into myocytes and mature myotubes follows a continuous lineage, rather than discrete steps. Inference of lineage structure has been referred to as pseudotemporal reconstruction and this resolution of information can help us understand how cells change state and how cell fate decisions are made.
The continuous innovation of scRNA-seq technologies and concomitant advances in bioinformatics approaches continue to facilitate biological and clinical researches and provide deep insights into the gene expression heterogeneity and dynamics of cells.
Thinking about exploring single-cell data for your current or future project? Access Polly’s single-cell data Atlas for all your needs.
Require training to use single-cell data? Our team of bioinformatics scientists can help you. Reach out to us at firstname.lastname@example.org.
- Nguyen QH, Pervolarakis N, Nee K, and Kessenbrock K (2018) Experimental Considerations for Single-Cell RNA Sequencing Approaches. Front. Cell Dev. Biol. 6:108. doi: 10.3389/fcell.2018.00108
- Oetjen, K. A., Lindblad, K. E., Goswami, M., Gui, G., Dagur, P. K., Lai, C., Dillon, L. W., McCoy, J. P., & Hourigan, C. S. (2018). Human bone marrow assessment by single-cell RNA sequencing, mass cytometry, and flow cytometry. JCI insight, 3(23), e124928. Human bone marrow assessment by single-cell RNA sequencing, mass cytometry, and flow cytometry