Single-cell proteomics – same cell type, same amount of protein?

[Daniel_lferrer]

The number of cell types in a normal, healthy adult is estimated to be 200-400, according to Alberts et al¹, Molecular Biology of the Cell, and Vickaryous et.al², in Human Cell Diversity, as we have not been able to dissect every single cell in the human body. 

As technologies to isolate single cells have advanced, analysis of DNA and RNA content at single-cell resolution has accelerated significantly, bringing a new era from studying population of cells biology to single-cell biology. The single-cell resolution biology is highlighted in the figure below, with a picture of a map taken at different resolutions³.  Tabula Sapiens Consortium has undertaken the challenge of mapping all human cells^4,. The initial study started with analysis of 24 different tissues and organs corresponding to 400 cell types and 500,000 cells. We will be able to distinguish the roads and buildings of each different cell.

Environmental Science & Technology 2021 55 (5), 3368-3379.

Such detailed resolution of the road, building and landscape can only be achieved through proteomics analysis at the single-cell resolution. as subtle biological changes carry important insights. Furthermore, these platforms need to be able to measure proteins over a wide dynamic range across thousands of samples.

Combining such measurements with proper statistics enables the measurements of protein-protein interactions, post-translational modification (PTM) regulation, and activation/inhibition of regulatory mechanisms. This is further highlighted by the fact that different cell types differ in size and function, which translates into different amounts of proteins being expressed, modified and regulated.

Even if cells are clonally identical, the way each cell responds will differ depending on the environment and age. The best example of this is found in stem cells, where — depending on the activation of the cell — it will differentiate into a different type of cell.

Discovery of the Yamanaka factors (Oct3/4, Sox2, Klf4, c-Myc) further demonstrated this being able to induce pluripotency (induce to become stem cells) to enable somatic cells to become pluripotent⁵. Even if cells are localized to the same region in the same tissue, the environment that each cell faces at the single-cell resolution varies by the definition that one cell will never exactly face the same number of cells interacting with the same number and types of surface proteins. These subtle differences change how the cell understands its environment, and as a result, change to adopt to the environment for its role as part of a multicellular organism.

Thinking of the heterogeneity of a single-cell type is not so different from thinking about our own self. We never face exactly the same environment twice, and we constantly face different environments we must adjust to. Our age, physical location, health conditions, events happening in the world, and many other factors direct our actions and requirements to respond to the chronological time scale of our lives. This is the same way single cells face the world around them. If we think about ourselves as single-cellular units, we form many different specialized communities, i.e., tissues, for different purposes and functions.

Differential expression is one of the most-used quantification methods we employ to understand the biology and the disease state. With the higher-resolution analysis at the single cell level, the ability to measure more subtle changes becomes more important, because analysis at this resolution is more easily affected by the noise and contamination. The biological variabilities will also be pronounced, as each cell expression level is going to depend on the age, lineage, and other factors defining the cell and its environment. What is essential is to increase the signal-to-noise ratio and utilize quantitative measurements with high accuracy and precision. Additionally, significant numbers of cells need to be analyzed to resolve the biological picture they represent.

What do you think are the most accurate and sensitive quantitative methods and statistical tools we need to consider?

References:

1. Alberts et al., Molecular Biology of the Cell. Garland Science 1994 3rd edition.
2. Vickaryous et.al. Human cell type diversity, evolution, development, and classification with special reference to cells derived from the neural crest.  2006 Biological Reviews. PMID: 16790079.
3. Haberl et.al. Environ. Sci. Technol. 2021
4. Tabula Sapiens Consortium. The Tabula Sapiens: A multiple-organ, single-cell transcriptomic atlas of humans. 2022
5. Lie et.al. Yamanaka factors critically regulate the developmental signaling network in mouse embryonic stem cells. Cell Research 2008. PMID: 19030024