Reconstructing Differentiation Trajectories
“A key limitation at the moment is that we are constrained by the number of proteins that we can measure by flow cytometry in a single cell,” says Berthold Göttgens, Ph.D., professor of molecular hematology at the University of Cambridge. Current flow cytometry approaches allow only up to 20 to 30 protein species to be simultaneously profiled in a cell, and the technology typically provides a snapshot of cellular information.
Dr. Göttgens’ laboratory uses both experimental and computational approaches to study the transcriptional control of normal and leukemic blood stem/progenitor cells. Transcriptional control, Dr. Göttgens reasons, needs to be assessed at the level of single cells, not the bulk of cells. Fortunately, cells of different types—which are, presumably, transcriptionally distinct—may be isolated from the bulk according to their surface
maker proteins. In stem cell biology, population-level profiling provides only population averages and is uninformative about the biology of single cells. The importance of capturing single-cell processes, increasingly recognized in many areas of life sciences, is particularly critical for stem cell research, where progression between distinct cellular states is fundamental for understanding the biology and for developing therapeutic interventions.
In a recent study stemming from practical challenges caused by molecular and functional heterogeneities of murine hematopoietic stem cells, Dr. Göttgens and colleagues combined single-cell gene-expression analyses, flow cytometric sorting, and functional assays to better understand the gene-expression program at the single-cell level. “Establishing a connection between surface marker protein profiles and a whole transcriptome helps use the surface markers to purify viable cells,” explains Dr. Göttgens. “Ultimately, this approach can be used for applications with potential therapeutic benefits.”
By taking advantage of recent molecular-profiling technologies, Dr. Göttgens and colleagues interrogated early hematopoietic stem cell differentiation at the single-cell level in mouse hematopoietic progenitor stem cells. Surface-based cell sorting was used to retrospectively assign cells to one of twelve different phenotypes.
“From these same cells, we also recorded mRNA expression for the entire transcriptome,” details Dr. Göttgens. This helped link single-cell gene-expression profiles with single-cell function. An online repository that incorporated the data provided a resource to visualize lineage-specific transcriptional programs and helped generate an atlas of the early hematopoietic stem cell differentiation at the single-cell resolution.
“Linking molecular profiles with surface marker profiles enabled us to detect protein expression levels without the need to lyse the cells,” explains Dr. Göttgens. Measuring surface protein markers and gene expression in the same single cells, and connecting the two, allowed investigators to use the surface markers to specifically purify distinct populations of cells.
It is relatively easy to generate raw data with the new experimental approaches. The new approaches, however, require laboratories to deal with new data types. “There are no established methods on how to deal with the new data types,” warns Dr. Göttgens. “The next couple of years will see a bottleneck on the computational side.”