Single cell genomics breakthrough – RNA in single cells sequenced and up to 1000-fold variability in expression levels found

A team of scientists at the Klarman Cell Observatory at the Broad Institute recently completed an effort to read, or sequence, all the RNA — the “transcriptome” — in individual immune cells. Whereas DNA in a cell’s genome represents its blueprint for making the building blocks of cells, RNA is more like the cell’s contractor, turning that blueprint into proteins. By sequencing RNA in single cells, scientists can obtain a picture of what proteins each cell is actively making and in what amounts.

The Broad researchers sought to adapt a recently developed technique for single-cell RNA sequencing, known as SMART-Seq, and apply it to a model of immune cell response well-studied by Regev, Broad senior associate member Nir Hacohen, and their fellow researchers. In this model, immune cells known as bone-marrow derived dendritic cells (BMDCs) are exposed to a bacterial cell component that causes the cells to mount an immune response.

Working with scientists in the Broad’s Genomics Platform, notably research scientists Joshua Levin and Xian Adiconis, the team established the SMART-Seq method for use in their model system, using it to gather RNA sequence data from 18 BMDCs in this pilot phase.

The team first analyzed the data for differences in expression, or activity, of various genes among the cells, seen as alterations in RNA abundance. Although they were working with a single cell type — BDMCs — they did expect to see some variation in gene expression as cells activated various pathways during their immune response. But the team discovered that some genes varied greatly, with 1000-fold differences in the expression levels between cells. “We went after a narrowly defined cell type that has a specific function that we think of as being very uniform,” said Shalek. “What we saw was striking — a tremendous variability that wasn’t expected.”

Nature – Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells

This work demonstrates the power of single-cell RNA sequencing to reveal cellular diversity without using a genetic perturbation. “We took cells we thought were completely identical,” explained Satija, “and discovered how the naturally occurring variation between them could teach us something biologically.”

The biological insights from this effort, enabled by the diverse expertise and resources of the Broad, represent a first step in fulfilling the promise of unbiased, single-cell approaches to uncover biology. “It’s very rare to have people in one place who can generate this kind of data, analyze it, and validate it,” said Satija. “This collaboration is a very strong merging of a lot of different forms of expertise from many disciplines, and it’s been very successful.”

ABSTRACT – Recent molecular studies have shown that, even when derived from a seemingly homogenous population, individual cells can exhibit substantial differences in gene expression, protein levels and phenotypic output with important functional consequences. Existing studies of cellular heterogeneity, however, have typically measured only a few pre-selected RNAs or proteins simultaneously, because genomic profiling methods3 could not be applied to single cells until very recently. Here we use single-cell RNA sequencing to investigate heterogeneity in the response of mouse bone-marrow-derived dendritic cells (BMDCs) to lipopolysaccharide. We find extensive, and previously unobserved, bimodal variation in messenger RNA abundance and splicing patterns, which we validate by RNA-fluorescence in situ hybridization for select transcripts. In particular, hundreds of key immune genes are bimodally expressed across cells, surprisingly even for genes that are very highly expressed at the population average. Moreover, splicing patterns demonstrate previously unobserved levels of heterogeneity between cells. Some of the observed bimodality can be attributed to closely related, yet distinct, known maturity states of BMDCs; other portions reflect differences in the usage of key regulatory circuits. For example, we identify a module of 137 highly variable, yet co-regulated, antiviral response genes. Using cells from knockout mice, we show that variability in this module may be propagated through an interferon feedback circuit, involving the transcriptional regulators Stat2 and Irf7. Our study demonstrates the power and promise of single-cell genomics in uncovering functional diversity between cells and in deciphering cell states and circuits.

Nature Methods – Comparative analysis of RNA sequencing methods for degraded or low-input samples

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