[Corrected 2/13/15, 4:45 pm. See below.] Someday, we might have instruments that peer inside a human to record what each cell in the body is doing. That sounds like science fiction, but it’s no more outlandish than the notion, fifty years ago, that one day we would be able to analyze a tissue sample and know what thousands of cells are doing at any given moment.
That is not science fiction. Taking a snapshot of cell activity is possible, thanks to the emerging field of single cell analysis. And one of biotechnology’s pioneers has just debuted a process that he says can produce individual profiles of up to 10,000 cells at a time.
Stephen Fodor (pictured) was founder of Affymetrix (NASDAQ: [[ticker:AFFX]]), the Silicon Valley company that pioneered the DNA microarray in the 1990s. He recently relinquished the chair of Affymetrix, and is now founder and CEO of Palo Alto, CA-based Cellular Research, which aims to compete with South San Francisco, CA-based Fluidigm (NASDAQ: [[ticker:FLDM]]) and others pushing the frontiers of single cell analysis.
There are many potential benefits of understanding the cell-by-cell composition of a tissue sample, not to mention what any given cell in that sample is doing.
If a population of cells is expressing a high level of a certain gene that’s causing disease, for instance, “is it one or two cells doing all that business, or a bunch of them?” asks Fodor. “This is the type of thing we want to go after.”
Perhaps the nearest-term need for such technology is in cancer, where tumor profiles vary from person to person, and where cell diversity within a single tumor can drive resistance to treatments. “Right now we give therapies based on bulk characterization, and it’s completely inaccurate,” says Holbrook Kohrt, a Stanford University immunotherapy researcher and physician who treats people with hematological cancers.
Kohrt wants to steer patients toward immunotherapy trials using a single drug or combinations of drugs, based on the cell-by-cell activity of their tumors, but he says the technology to date doesn’t allow for it. He’s particularly interested in the burgeoning field of checkpoint inhibitors, which have so far gone farther in treating solid tumors than hematological cancers.
Fodor’s new system, called Resolve, might be the answer. Kohrt is “kicking the tires” but says it’s still early. The promise is that Resolve will be able to identify a much higher number of genes, switched on and off, in a higher number of cells with much greater accuracy than before.
“Fodor’s new technology is impressive,” says Cytobank CEO Nikesh Kotecha, whose company is building a cloud service for single-cell data analysis and management.
Here’s a simplified version of how Resolve works. Cells drawn from a tissue sample are placed in a suspension and allowed to settle into wells—tiny indentations—at the bottom of a plate, which is standard lab equipment. Other single-cell systems use robotics or microfluidic chips to isolate cells into their own wells. Resolve uses gravity and math. That is, there are far fewer cells (10,000) than wells (150,000), so the odds of cells doubling up in a well are extremely low.
And once they’ve settled, each cell is joined in its well by a synthetic bead studded with very sci-fi sounding “molecular capture probes.” These probes, which are in fact made of easily fabricated chains of nucleic acids, contain one barcode to tag the cell and another to tag all the molecules in the cell. [An earlier version of this paragraph mistakenly described the probes as made of amino acids instead of nucleic acids. We regret the error.]
From there, the math gets more complicated. Essentially the two types of barcodes, and the high ratio of beads (about a million) and their molecular tags (hundreds of thousands per bead) to cells (about 10,000) means it’s exceedingly likely that each cell gets a unique barcode, and that each messenger RNA within each cell gets its own unique ID, too. It’s all based on probability theory advanced by a 19th century French mathematician named Poisson.
Why track and measure messenger RNA, also known as mRNA? They are the carriers of genetic information that is destined to become proteins; the presence of a particular mRNA means
