The success of imaging technologies
The molecular and functional differences among the cells making our bodies have been appreciated for many decades. Yet, the tools to study them were very limited. In the last couple of decades, we have began developing increasingly powerful technologies for molecular single-cell measurements. Currently, the most widely used high-throughput methods for molecular single-cell analysis have two things in common: (1) they quantify nucleic acids and 2) they are based on imagining. The imaging can be done in situ (e.g., fluorescent in situ hybridization, FISH) or in vitro (e.g., single-cell RNA-seq based on next gen DNA sequencing). Imaging has been applied to single-cell protein analysis as well, though most applications have been hampered by their dependance on antibodies. A recent break away from this antibody-dependance is the single-molecule Edman degradation developed by the group of Edward Marcotte. If this is developed further, imaging could become a workhorse for single-cell protein analysis as well.
Emerging mass-spec methods
Efforts to apply mass-spectrometry to single-cell analysis started in the 1990s. As comprehensively reviewed by Rubakhin et al., these efforts focused on ionizing biological molecules via Secondary Ion MS (SIMS) or via Matrix Assisted Laser Desorption/Ionization (MALDI). These methods allow to ionize biological molecules with minimal processing and losses but remain rather limited in their quantification accuracy and in identifying the chemical composition of the analyzed ions. In contrast, the methods that afford robust high-throughput identification (based on analyte separation and tandem MS analysis, e.g., LC-MS/MS or CE-MS/MS) have been very challenging to apply to small samples. Still, the typical mammalian cell contains thousands of metabolites and proteins whose abundance is much higher than the sensitivity of mass-spec instruments. Based on this realization, we outlined directions for multiplexed analysis of single cells by LC-MS/MS that can enable quantifying thousands of proteins across many thousands of single cells. We recently published a proof of principle that has been superseded by a higher throughput single-cell proteomics method. These initial steps need much further developments, both experimental and computational, before they reach the transformative potential that single-cell mass-spec could have.
Single-cell analysis is not merely about measurements. It’s about understanding them. Our progress in understanding single-cell data has been limited, even for the data coming from the more mature technologies. Conceptual progress has been much slower than technological progress. So, how do we make sense of the data?
I will reserve my musings on this question for a forthcoming post. For now, I’ll just say that I like an idea articulated by Munsky et al., 2012 and Padovan-Merhar and Raj, 2013: Using the variability between single cells as a natural perturbation for studying gene regulation. I think that this approach can be a very powerful. More thoughts on that coming soon.