From Santiago Ramón y Cajal 's time we have known that there are five types of neuronal cells in the retina: rods & cones, horizontal cells, bipolar cells, amacrine cells, and retinal ganglion cells.
With Sharpe et al. (L. T. Sharpe, A. Stockman, H. Jgle, and J. Nathans. opsin genes, cone photopigments and color vision. Color vision: From genes to perception, pages 3–51, 1999) we learned that the spectral sensitivity of the pigments in the cones is controlled by the not-so-robust order of the visual pigment genes in the sex chromosome and color vision deficiency had to do with the L peak moving towards the M peak or vice versa.
However, the genome only allowed to predict a predisposition for color vision deficiency, not a prediction of the spectral color performance. The reason is that not the genes determine the spectral peaks but their expression by the transcriptome, i.e., the messenger RNA (mRNA), which of course cannot be studied in vivo.
In a recent paper (E. Z. Macosko, A. Basu, R. Satija, J. Nemesh, K. Shekhar, M. Goldman, I. Tirosh, A. R. Bialas, N. Kamitaki, E. M. Martersteck, et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell, 161(5):1202–1214, 2015), Macosko et al. describe the application of a new technique called Drop-seq, which has allowed them to analyze the gene activity of 44,808 cells from 14-day-old mice retinae. "Gene activity" here means that they analyzed the transcriptomes of these 44,808 retinal cells and identified 39 transcriptionally distinct cell populations, each corresponding to one of a group of closely related cell types.
Drop-seq generates a library of STAMPs (single-cell transcriptomes attached to micro-particles). They used Seurat, a recently developed R package for single-cell analysis, to study this STAMP library. In a first step, they performed a principal component analysts on the largest libraries, then they reduced the 32 statistically significant principal components to two dimensions using t-distributed stochastic neighbor embedding (tSNE).
Subsequently they projected the remaining cells in the data into the tSNE analysis. Then they combined a density clustering approach with post hoc differential expression analysis to divide the 44,808 cells among 39 transcriptionally distinct clusters, obtaining this illustration:
Finally, they organized the 39 cell populations into larger categories (classes) by building a dendrogram of similarity relationships among the 39 cell populations. For now, the result is that they can say a lot about the amacrine cells that was not known before. However, it will take more research to formulate an interpretation for the visual system.