Study: Retinal Bipolar Neuron Drop-seq 27499 cells
Retinal Bipolar Neuron Drop-Seq
Karthik Shekhar, Sylvain W. Lapan, Irene E. Whitney, Nicholas M. Tran, Evan Z. Macosko, Monika Kowalczyk, Xian Adiconis, Joshua Z. Levin, James Nemesh, Melissa Goldman, Steven A. McCarroll, Constance L. Cepko, Aviv Regev, Joshua R. Sanes. Comprehensive Classification of Retinal Bipolar Neurons by Single-Cell Transcriptomics. Cell. Volume 166, Issue 5, p1308–1323.e30, 25 August 2016. DOI: http://dx.doi.org/10.1016/j.cell.2016.07.054
Contact: Karthik Shekhar at firstname.lastname@example.org
Patterns of gene expression can be used to characterize and classify neuronal types. It is challenging, however, to generate taxonomies that fulfill the essential criteria of being comprehensive, harmonizing with conventional classification schemes, and lacking superfluous subdivisions of genuine types. To address these challenges, we used massively parallel single-cell RNA profiling and optimized computational methods on a heterogeneous class of neurons, mouse retinal bipolar cells (BCs). From a population of ∼25,000 BCs, we derived a molecular classification that identified 15 types, including all types observed previously and two novel types, one of which has a non-canonical morphology and position. We validated the classification scheme and identified dozens of novel markers using methods that match molecular expression to cell morphology. This work provides a systematic methodology for achieving comprehensive molecular classification of neurons, identifies novel neuronal types, and uncovers transcriptional differences that distinguish types within a class.
Figure 1 Clustering of Bipolar Cells by Drop-Seq. (A) Sketch of retinal cross-section depicting major resident cell classes. Rod and cone photoreceptors detect and transduce light stimuli into chemical signals, relaying this information to rod and cone bipolar cells (BCs), respectively (turquoise and purple/orange). BCs synapse on retinal ganglion cells (whose axons form the optic nerve) in the inner plexiform layer (IPL) at varying depths that depend on the BC type. (B) Overview of experimental strategy. Retinas from Vsx2-GFP mice were dissociated, followed by FAC sorting for GFP+cells. Single-cell libraries were prepared using Drop-seq and sequenced. Raw reads were processed to obtain a digital expression matrix (genes × cells). PCA, followed by graph clustering, was used to partition cells into clusters and identify cluster-specific markers, which were validated in vivo using methods that detect gene expression and cellular morphology in combination. (C–E) 2D visualization of single-cell clusters using tSNE. Individual points correspond to single cells colored according to clusters identified by the (C) Louvain-Jaccard and (D) Infomap algorithms and numbered in decreasing order of size. Arrows in (C) and (D) indicate a Louvain-Jaccard BC cluster that was partitioned by Infomap (examined in Figure 5). (E) Clustering output of Infomap when applied on cells from a single Drop-seq experiment (50% of the dataset). The tSNE representation was only used for visualization and not for defining clusters. (F) Gene expression patterns (columns) of major retinal class markers (left panels) and known BC type markers (right panels) in BC (upper panels) and non-BC clusters (lower panels) based on the clusters in (C). Clusters with cell-doublet signatures and/or that contained <50 cells are not shown. Putative cell type assignments, based on the expression of known genes, are indicated on the right (see Table S2). Nomenclature for BC types 1 and 5 are based on results in Figures 3 and 4. The size of each circle depicts the percentage of cells in the cluster in which the marker was detected (≥1 UMI), and its color depicts the average transcript count in expressing cells (nTrans). MG, Müller glia; AC, amacrine cells; PR, photoreceptors. (G) Hierarchical clustering of average gene signatures of BC clusters (Euclidean distance metric, average linkage). The confidence level of each split was assessed using bootstrap (STAR Methods). Relatedness between clusters was used in prospective cluster assignment to BC type in (F).