Tools & Technology

A Simultaneous Picture of Gene Expression and Chromatin Landscape in Single Cells

Simultaneous Gene Expression and Chromatin Landscape in Single CellsAs we move through the day, it’s easy to forget that individual cells, organized into tissues and tissues into organs, act in harmony to give us life. Over the years, scientists have developed new techniques to investigate how gene expression, chromatin state, and methylation determines developmental behavior, at the level of a single cell. Analyzing all of these characteristics at the same time in one single cell has proved difficult, both due to the small amount of biological material in one cell and the high cost of sequencing many single cells [1]. Recent work from Cao et al, however, describes a new method called sci-CAR (single-cell combinatorial indexing of chromatin accessibility and mRNA) which enables the analysis of both transcriptome data (RNA-seq) and chromatin accessibility data (ATAC-seq) from a single cell.

The new technique is based on single-cell combinatorial indexing (sci) [2]. Rather than physically separating cells or nuclei from one another, cells are placed in different wells of a multi-well plate. Each well, containing multiple cells, is given a specific barcode that labels the cells as having come from that well. In this first plate, all of the cells are given a well-specific RNA-seq index from a poly-T primer with a barcode and a unique molecular identifier (UMI) in addition to their first ATAC-seq index by Tn5 transposase, also with a well-specific barcode. From there, all of the cells are pooled together and then re-sorted to just a few cells per well in a new multi-well plate. The cell contents or nuclei are lysed and divided in half. One will be used for sci-RNA-seq and the other half for sci-ATAC-seq. These lysates are then given a second round of well-specific indexes followed by sequencing. By first labeling the cells, mixing them all together, and then resorting and labeling them again, each cell will have a unique combination of barcodes, which allows for the analysis of gene expression and chromatin accessibility data from a single cell.

As a control to show that the transcriptome and epigenetic information obtained from sci-CAR came from the same cell, the researchers mixed a few wells with both human and mouse cells and found, reassuringly, that they each mapped specifically to their respective species. Additionally, as a proof of principle that that the sci-CAR technique works, the researchers assessed a well-established tissue culture model that results in large changes in gene expression. They treated human lung adenocarcinoma derived cells (A549) with a cortisol mimicking drug, dexamethasone (DEX), resulting in the alteration of expression in hundreds of genes [3]. They found that sci-CAR analysis of these cells indicated that the genes that were expected to increase or decrease expression upon addition of cortisol were in fact upregulated and downregulated respectively, leading to a specific clustering of cells that changed expression upon treatment with DEX.

Next, they applied sci-CAR to mouse kidney cells, which have been well-characterized by single-cell transcriptomes, but their epigenetic regulation is not as well understood. From sci-CAR analysis of whole mouse kidneys, they identified 14 different cell types by their transcriptome profiles using known markers, while simultaneously identifying new markers. By aggregating their sci-ATAC-seq data from the cell types identified in the sci-RNA-seq portion of the sci-CAR analysis, they could assess the chromatin accessibility differences between the identified cell types.  Additionally, they were able to use pseudocells generated from this sci-CAR data and a custom algorithm to correlate cis-regulatory elements with their target genes in single cells.

This new technique to profile both the transcriptome and epigenetic landscape in single cells allows for a deeper understanding of the unique cell types in a particular tissue. It also sets the stage for one day characterizing the DNA, RNA, epigenetic, and protein readouts of many different cell types at once, painting an even clearer picture of the cells that keep us going.

 

References:

Original article: Cao J, Cusanovich DA, Ramani V, Aghamirzaie D, Pliner HA, Hill AJ, Daza RM, McFaline-Figueroa JL, Packer JS, Christiansen L, Steemers F, Adey A, Trapnell C, Shendure J (2018). Joint profiling of chromatin accessibility and gene expression in thousands of single cells. Science, 361 (6409): 1380-1385. DOI: 10.1126/science.aau0730.

[1] Clyde D (2017). Barcoding the nucleus. Nat Rev Genet, 8 (4): 211. DOI: 10.1038/nrg.2017.11.

[2] Amini S, Pushkarev D, Christiansen L, Kostem E, Royce T, Turk C, Pignatelli N, Adey A, Kitzman JO, Vijayan K, Ronaghi M, Shendure J, Gunderson KL, Steemers FJ (2014). Haplotype-resolved whole-genome sequencing by contiguity-preserving transposition and combinatorial indexing. Nat Genet, 46 (12): 1343-9. DOI: 10.1038/ng.3119.

[3] Reddy TE, Pauli F, Sprouse RO, Neff NF, Newberry KM, Garabedian MJ, Myers RM (2009). Genomic determination of the glucocorticoid response reveals unexpected mechanisms of gene regulation. Genome Res, 19 (12): 2163-71. DOI: 10.1101/gr.097022.109.

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Stephanie DeMarco

Stephanie DeMarco