The proteome is the entire set of proteins generated by an organism under discussion. Here we'll use it in the more restricted sense of the entire set of proteins generated by a specific type of cell, the common fibroblast. Fibroblasts are connective tissue cells responsible in part for building the extracellular matrix that supports cell populations in a three-dimensional structure. We live in an age of rapidly advancing biotechnology, and the areas in which progress is most rapid are those related to genetics and proteomics: measuring, cataloging, and altering the thousands of different types of complex molecule present in and around cells. As the costs fall and the tools become ever more capable, researchers can now easily amass a great deal of data on the abundance of all proteins in specific tissues and under specific circumstances.

So to aging: it is now possible to compare cells from young tissue and old tissue in great detail - at least insofar as relative protein levels are concerned. From the perspective of aging considered as a process of damage accumulation, a snapshot of an old cell and a young cell created in this way is a comparison that shows the high level outcome of low level underlying processes. It tells us something about how the cell has altered its behavior, the pace at which it synthesizes different proteins, but not in great detail. That detail must be painstakingly inferred, a process that involves taking the catalog of changes in protein abundance and working backwards through what is presently known of what these proteins actually do.

Interestingly, we know already where researchers will end up at the end of this process should they follow it through to the very end, tracing back every change through nested layers of cause and effect. There is already a good, well-established list of the forms of damage to cells and tissues that are fundamental, not caused by some other change, but rather occurring as a natural result of the normal operation of cellular metabolism. There is a starting point and an ending, and a vast and very, very complicated blank space on the map in between.

Fortunately that blank space doesn't matter from the practical perspective of producing treatments: what researchers should do is to find ways to fix the fundamental damage and work forward to see what happens when it is fixed. That strategy should along the way generate effective treatments for aging. Unfortunately, this is not what 99% of the research community is actually doing. Rather, they are working backwards from the end, a process that will in the end come to the same filling in of the map, but has very little chance of generating effective treatments for aging along the way. Why little chance? Because their discoveries relate to proximate causes, changes in protein levels that are happening for very complicated reasons and are consequently hard to safely alter to try to make the situation less bad. Even if they are altered safely, that fails to address the underlying causes, which march on, and no doubt lead to all sorts of other forms of harm.

Here is a paper that demonstrates just how far the tools have come in the past two decades. Consider that the Human Genome Project kicked off in 1990 with a very long timeline, and then the whole thing was basically completed in a couple of years by Celera between 1998 and 2001 using newer technologies. The costs were staggering. Yet less than fifteen years later it is entirely unremarkable for genomes to be sequenced and the costs are small and falling rapidly. Proteome analysis is a much more complex affair, but the advance in capabilities has been similarly relentless. Today's machinery allows thousands of different proteins to be efficiently assessed and analysed per sample, and at costs that are tiny in comparison to event just a few years past. This paper isn't unusual at all in terms of what is taking place in the laboratory these days; be sure to read in far enough to find the diagrams:

Proteome-wide analysis reveals an age-associated cellular phenotype of in situ aged human fibroblasts

We analyzed an ex vivo model of in situ aged human dermal fibroblasts, obtained from 15 adult healthy donors from three different age groups using an unbiased quantitative proteome-wide approach applying label-free mass spectrometry. Thereby, we identified 2409 proteins, including 43 proteins with an age-associated abundance change. Most of the differentially abundant proteins have not been described in the context of fibroblasts' aging before, but the deduced biological processes confirmed known hallmarks of aging and led to a consistent picture of eight biological categories involved in fibroblast aging, namely proteostasis, cell cycle and proliferation, development and differentiation, cell death, cell organization and cytoskeleton, response to stress, cell communication and signal transduction, as well as RNA metabolism and translation. Our present analyses showed 43 proteins with altered expression in these cells according to the different donor age groups. Remarkably, we found no overlap between the mRNA and protein expression data for these 43 proteins. This could be due to the fact that individual proteins or transcripts may not meet the threshold for statistical significance as the used technologies have different noise levels. On the other hand, it has been shown and confirmed by our data that in mammalian cells approximately only one third of the mRNA abundance is reflected in the proteome. However, the fact that 77% of the age-associated proteins were not linked to expression changes of the corresponding transcripts suggested that most of the age-associated alterations detected at the proteome level are likely caused by other processes, such as post-transcriptional regulation, translation efficiency, protein stability or modifications, rather than by differential regulation of gene expression.

There is a lot more theorizing in that vein in the paper; this is characteristic of this approach of working down from the top. It generates as many questions and new leads to follow as it does answers. As you might note the generation of proteins from genetic blueprints is a process with a lot of distinct stages, all of which are quite capable of reacting to circumstances independently from the others, producing a net change in abundance. Personally I think the "protein stability or modifications" segment is worth looking at in more detail given the apparently falling levels of chaperone proteins and decline in other parts of the cellular housekeeping processes with age.