I wonder if 'high-throughput proteomics' and 'large-scale proteomics' are generally used synonymously? It would make sense to me to distinguish between 'high-throughput' as in a 'large number of samples' vs. 'large-scale' as in 'big samples with thousands and more of proteins'. However, I have not found any article on this so far and it seems to me that the community uses both terms interchangeably. Are there other terms commonly used to refer to these properties?
I often see the two terms used hand-in-hand, although I'd say they're not quite synonymous. Often the term high-throughput refers to the number of features being analyzed, with high-throughput methods quantifying many features in parallel. Because of the parallelization, the time needed to quantify all features is reduced, so you can process more samples in a fixed amount of time - the method has high throughput since many features/samples can be analyzed quickly. "High-throughput" typically refers to the technology being used to analyze a sample. Different proteomics technologies may be classified as high-throughput or not, regardless of how those technologies are applied.
Since a high-throughput method can process samples quickly and cheaply, it often goes hand-in-hand with large-scale analyses, which often refers to the number of samples (but sometimes the number of features) being processed. A "large-scale" analysis typically refers to the design of the experiment.
Older experiments were generally limited to small-scale by low-throughput technology, while high-throughput technology has enabled large-scale experiments. You could use a high-throughput technology to do a small-scale analysis, but it wouldn't leverage the power of high-throughput methods to run many samples in a short amount of time. Applying a high-throughput method to a single sample would be an example of such a case. You could also theoretically use a low-throughput technology to do a large-scale analysis, but it would take a very long time and be very costly, and would likely be infeasible without significant resources. Applying a low-throughput method to many samples would be an example, although this is not commonly seen in practice.
Usually you'd use a high-throughput method to achieve a large-scale analysis, which could not be feasibly achieved with a lower-throughput method. An experiment is often both high-throughput and large-scale so the terms are often seen together, but I'd say they have distinct connotations of referring to the technology and the overall experimental design, respectively.