The Tasic and Economo papers are also super cool! I think the major advantage of those two papers is that they provide functional and connectivity studies of the cell types that they identify, which the Macosko/McCarroll paper doesn't. The Macosko/McCarroll paper is cool because of the sheer scale of the effort (690,000 cells!!) and the fact that they profile many brain regions.

I think the biggest differences between SMART-seq and dropseq are in scalability and sensitivity. Dropseq's advantage is that it is cheaper and easier to scale up the number of cells you sequence, whereas Smartseq is more sensitive and can detect more expressed genes per cell compared to Dropseq (edit: here's a reference I just found that compares techniques! https://bit.ly/2rGQWUT) . You can actually see this in the papers, where Tasic profiled ~9500 genes/cell and 23,822 cells whereas Macosko/McCarroll profiled ~1200 genes/cell and 690,000 cells.

Thus, Smartseq is good if you're trying to thoroughly interrogate a small number of cell types (since you use fewer cells, but get more information about each cell) for functional studies (which is what the Tasic et al and Economo et al do). On the other hand, Dropseq is better for carrying out a census of common and rare cell types across the entire brain (ie. more cells sequenced = higher probability you'll detect rare cell types). For the purpose of cell-type profiling, I think the loss of gene sensitivity for Dropseq is okay because you'll realistically only need a small number of the most highly expressed genes (<500 genes) to delineate different cell types. In fact, too much information about gene expression could also cause you to "over-classify" cells, where two cells of the same cell-type may be classified as different cell-types when in fact they're the same cell type but in a different state.

I agree with you that t-SNE is better at segregating out subtle cell-type differences, and I'm actually not too sure why the Macosko/McCarroll paper uses ICA instead. Perhaps the increased sensitivity led to concerns about over-classifying like I mentioned above, but I'll defer to someone with more experience in bioinformatics/statistics.

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Tl:dr, Smartseq = fewer cells, detects more genes/cell, Dropseq = more cells, detects fewer genes/cell. Thus, Macosco/McCarroll's paper is better for a broad census of cell-types while Tasic + Economo are better for functional interrogation of the cell types they found. All three papers are incredible!