NEW YORK – A team led by researchers at the Max Planck Institute of Biochemistry has used spatially resolved single-cell mass spectrometry to characterize hepatocyte subpopulations in mice.
Detailed in a paper published this week in Nature Methods, the research provides new insights into how different liver cell types deal with different stresses, including alcohol, fat, toxins, and drugs, and could lead to better understanding of various liver diseases, said Florian Rosenberger, a postdoctoral fellow at Max Planck and first author on the study.
The liver, the study authors note, is organized such that hepatocytes exhibit different functional characteristics based on paracrine signaling and metabolic gradients running along the portal vein to central vein axis. They add that while researchers have previously used techniques including single-cell RNA sequencing, imaging, fluorescence-activated cell signaling (FACS), and mass spec-based proteomics to explore this phenomenon of "liver zonation," "the extent of spatial heterogeneity and proteome variation in hepatocyte remains an open question."
To get at this question, Rosenberger and his colleagues used the single-cell deep visual proteomics (sc-DVP) method developed in the lab of Max Planck researcher Matthias Mann, senior author on the study. The sc-DVP approach uses artificial intelligence-based image analysis to identify cells or tissue regions of interest. These cells or regions are then extracted using laser microdissection and analyzed using a single-cell mass spec proteomic workflow.
While researchers including Mann have used the technique to analyze small sets of cells extracted from tissues, they had not previously taken their analyses down to the single-cell level. With the workflow detailed in the Nature Methods paper, they were able to "analyze individual cellular shapes approximately one-third to half of a single cell in size," Rosenberg said, noting that this "gives unprecedented spatial resolution and biological insight."
The study, he added, "marks the first time single-cell proteomics has been performed within an intact tissue."
Using the sc-DVP approach, the Max Planck team was able to measure more than 1,700 proteins per individual hepatocyte shape. The proteomic data "correctly and accurately recapitulate[d] hepatocyte physiology by direction, extent and spatial organization of zonation," the authors wrote, adding that they "detected all of the previously used markers of liver zonation" and that "more than half of quantified proteins were significantly different between portal and central zones" of the liver, which correlated with previous work done using sc-RNA-seq and FACS-based proteomics.
Rosenberger said the results suggest that the technique could be useful for better understanding the biology of a variety of complex tissues.
"Broadly speaking, any situation where a cell's proteome depends on its location within the tissue is an ideal application for our method," he said, citing as an example the lab's finding that cells located near blood vessels in high-oxygen environments have different sets of proteins involved in energy metabolism than those located farther away.
"This insight is vital, for example, in understanding how migratory cells, such as immune cells, function relative to their immediate tissue surroundings," he said.
Currently, Rosenberger and his colleagues are using the approach to study two diseases in particular — Parkinson's and alpha-1 antitrypsin deficiency in the liver. Regarding the latter condition, he noted that the spatial distribution of cells in the liver is "undeniably significant," but the reasons for this impact on the disease "remains elusive."
The researchers are also working to improve the method's sensitivity. They noted that while they have made significant gains from the initial versions of the approach, more sensitivity is still desirable, particularly for analyzing smaller cells like resting lymphocytes, which, they wrote, have roughly tenfold less protein than the hepatocytes analyzed in the Nature Methods paper.