Citation: Campbell JN, Macosko EZ, Fenselau H, Pers TH, Lyubetskaya A, Tenen D, Goldman M, Verstegen AM, Resch JM, McCarroll SA, Rosen ED, Lowell BB, Tsai LT, A molecular census of arcuate hypothalamus and median eminence cell types. Nature Neuroscience. 06 February 2017; 20(3): 484–496 PMID: 28166221

Abstract: The hypothalamic arcuate–median eminence complex (Arc-ME) controls energy balance, fertility and growth through molecularly distinct cell types, many of which remain unknown. To catalog cell types in an unbiased way, we profiled gene expression in 20,921 individual cells in and around the adult mouse Arc-ME using Drop-seq. We identify 50 transcriptionally distinct Arc-ME cell populations, including a rare tanycyte population at the Arc-ME diffusion barrier, a new leptin-sensing neuron population, multiple agouti-related peptide (AgRP) and pro-opiomelanocortin (POMC) subtypes, and an orexigenic somatostatin neuron population. We extended Drop-seq to detect dynamic expression changes across relevant physiological perturbations, revealing cell type–specific responses to energy status, including distinct responses in AgRP and POMC neuron subtypes. Finally, integrating our data with human genome-wide association study data implicates two previously unknown neuron populations in the genetic control of obesity. This resource will accelerate biological discovery by providing insights into molecular and cell type diversity from which function can be inferred.

Expression values: All expression values are obtained by first batch correcting UMI counts for each gene using EdgeR. These batch corrected counts were normalized to a library size of 10,000 counts/cell. The natural logarithm was then applied to these values after adding 1 to avoid taking the log of 0. You may notice that the minimum expression level for many genes is above zero. This is a result of the batch correction.

Curated gene lists: Marker genes for each cluster were identified using Seurat v2.1 FindMarkers function with the following parameters: test.use="roc", min.pct = 0.25, only.pos = T. The expression threshold (the is.expr slot of the Seurat object) was set to 70 counts per 10,000 due to the effect of batch correction on minimum expression values. Markers were filtered to exclude those with <0.7 AUC for AllCell clusters (a##) and <0.6 AUC for NeuronOnly clusters (n##). Note that markers are shown in alphabetical order and that the heatmap settings must be set to "relative color scheme" due to the effect of batch correction on minimum expression values (see notes in "Expression values" section above).

Data download: If you would like to conduct more advanced analyses of this dataset, raw and processed data are available in the Download tab. You must be logged in to the Single Cell Portal to access the download tab. If you would like to perform a full reanalysis of the data, you can download gzipped FASTQ files from the primary data section. R1 files contain the Drop-seq cell and UMI barcodes. R2 files contain the actual read of RNA. The Drop-seq Tools pipeline can be used to process this data. You can also download our processed data. The file expression.txt.gz is a gzipped tab delimited file which contains the expression level of each gene in each cell. Rows are genes and columns are cells. The meta.txt file is a tab delimited file which contains the annotation data for all cells. Rows are cells and columns are annotations. The various Cluster.txt files contain tSNE coordinates for the cells in tab delimited format. Rows are cells and columns are tSNE dimensions. Note that the meta.txt and Cluster.txt files contain a second header row which specifies whether each column contains "group" (categorical) or "numeric" data. Many packages have been built to analyze single cell expression data. We recommend Seurat for R users or Scanpy for python users.