Cells come in many different shapes and sizes. Our blood alone carries a rich assortment—from the flat, doughnut-shaped red blood cell to the more globular, foreign-particle-guzzling macrophage—one of the largest cells in the body. The field of cytometry, or cell measurement—which helps doctors diagnose problems including cancer, in which cells morph into unusual forms—has long depended on the ability to sort cells into their biological components such as DNA, RNA and proteins.

But currently available cell-sorting techniques are limited, says Sadao Ota, an applied physicist and bioengineer at the University of Tokyo. Scientists typically use flow cytometers—a subset of these devices can identify and separate cells based on fluorescently labeled molecules carried inside or on them as they pass through a fluid-filled machine that keeps them alive. This decades-old approach allows researchers to classify large numbers of cells at once. But there is a catch: it lacks the ability to assess the specific physical shape, or morphology, of the cells. That means spotting something like a tumor cell would hinge on finding specific molecular markers, which can vary among cells and may be difficult to identify because they are not always known. Scientists could also categorize cells based on structure, but that unwieldy approach typically involves human experts peering through microscopes, which is much slower and does not enable many cells to be analyzed at once.

To address these problems, Ota and his colleagues have developed a new technique they call “ghost” cytometry, which can rapidly sort many cells based on physical characteristics such as size and shape—but without the need to generate images first. They tweaked the typical cytometry setup and added a single-pixel detector—a camera that images one pixel at a time rather than thousands at once—creating a device that can generate a unique signature for fluorescently labeled cells based on the light they emit. Essentially this approach produces a “ghost” depiction of a cell’s structure, an identifiable pseudo-image based on the activated light particles.

To make this work, scientists first label the cells with a fluorescent dye and then introduce them into a fluid-filled apparatus called a microfluidic device. As the cells flow through the device they pass a light source that activates the fluorescent chemicals in the dye. This releases photons (particles of light) that are picked up by a single-pixel detector and converted into a unique signature, based on a given cell’s physical features. A machine-learning algorithm then uses these ghost images to categorize the cells in real time, and another device sorts the incoming cells into separate compartments.

To test their new technique Ota and his team trained a machine-learning algorithm to recognize the signatures of breast and pancreatic cancer cells, both from humans. The program was able to accurately distinguish between the two types of cells—which have similar sizes and structures—when they were mixed together and passed through the Ota team’s device at a rate of 10,000 cells per second (a speed similar to that of currently available cytometry-based sorting techniques). Their findings were published Thursday in Science.

Although some flow cytometers have been able to image cells for several years, “this is the first instrument that allows the physical sorting of cells based on their morphology,” Anne Carpenter, a computational biologist at the Broad Institute of MIT and Harvard who was not involved in the work, wrote in an e-mail. “This is revolutionary.”

Ghost cytometry could be useful in a variety of applications, both in the clinic and the lab. For example, the team demonstrated this system was also able to pinpoint breast cancer cells among a mixture of blood cells. This suggests the method could aid cancer diagnosis and treatment by improving the ability to identify circulating diseased cells, Ota says.

The new technique may also help scientists better investigate fundamental biological processes such as cell division. Current flow cytometers are unable to sort cells based on the specific phases of their life cycles, but ghost cytometry could make this possible by pinpointing features such as the size and shape of the nucleus, says Andrew Filby, a cell biologist at Newcastle University in England who did not take part in the study. “There’s an endless number of experiments” that can be done with this method, Filby adds. If it takes off, he says, “it will change the field of cell sorting and cytometry permanently.”

Hoping to commericialize the new method, Ota and his colleagues have founded a company called THINKCYTE. Ota says that a prototype of the ghost cytometry device is slated for release in Japan and the U.S. in 2019.