One at a Time: New Tool Can Detect Genetic Changes in Thousands of Single Cancer Cells


Cancers don’t appear overnight. They arise over time as genetic changes accumulate in a population of cells. Those cells with certain genetic changes may eventually develop the capacity to grow out of control, ultimately forming a tumor.

To better understand how tumors evolve from one cell to billions, scientists want to study the genetic changes occurring in individual tumor cells. But techniques to do so have so far been limited.

Now, a team led by Sohrab Shah, Chief of Computational Oncology at Memorial Sloan Kettering, and Samuel Aparicio, of the University of British Columbia (UBC) and BC Cancer, has developed a new approach for high-throughput single-cell DNA sequencing. Their method, DLP+ (Direct Library Preparation+), combines automation, a powerful microscope, robust DNA sequencing chemistry, and sophisticated computational software to provide an unprecedented level of resolution to the understanding of tumor evolution.

According to Dr. Shah, DLP+ brings two main innovations to the table. One is scale. “DLP+ allows us to sequence the genomes of thousands of individual cells at one time,” he says.

The second is linking these data to imaging microscopy. “With DLP+, we can associate cell morphology — what cells look like — with properties of the genome at single-cell resolution,” Dr. Shah says. “That’s never really been possible before.”

With DLP+, we can associate cell morphology — what cells look like — with properties of the genome at single-cell resolution. That's never really been possible before.
Sohrab Shah Chair of Computational Oncology

The authors describe their approach in a paper published November 14 in Cell.

A Massive Data Set

Using DLP+, the MSK and UBC/BC Cancer researchers were able to sequence the genomes of nearly 52,000 individual cells and analyze how these genomes changed over time. This allowed them to pinpoint the first mutations that originally gave rise to a particular tumor as well as those changes that accumulated only in certain subpopulations of cells. Since human cancers are dynamic systems, with the capacity to change over time, this information is extremely valuable, particularly for tumors that metastasize or develop resistance to treatment.

Initially, such resistant cells can be very rare in a tumor. Methods of genome sequencing that rely on bulk DNA samples pooled from many cells will likely miss such rare events. DLP+, on the other hand, can capture them.

The investigators have made the data from these 52,000 cells available in an online database for other researchers to access and learn from.

Improving on Existing Methods

Drs. Shah and Aparicio are pioneers in the field of tumor evolution. A decade ago they compared mutations in bulk samples of a primary breast tumor with those in a metastatic tumor, defining how one person’s cancer had evolved over time. With DLP+, the genomes of thousands of individual cells in a tumor can now be studied, defining their mutations and drawing a much more accurate picture of tumor development.

DLP+ is also advantageous for small tissue samples, such as fine needle biopsies that yield only a few thousand cells. “Such small samples are difficult to process for bulk sequencing, but we were able to use DLP+ to study hundreds of cells from these specimens,” Dr. Aparicio explains. “This may provide a route to using DLP+ in a clinical context in future studies.”

Another feature of DLP+ important to the researchers is that the technology can easily be adopted by the wider research community. Previous whole-genome single-cell sequencing technologies have required expensive laboratory instruments that are not widely available. Such bespoke approaches are not practical for widespread implementation or scaling up.

In this paper we provide a recipe book for anyone to implement DLP+ technology.
Andrew McPherson Assistant Attending Computational Oncologist

“In this paper we provide a recipe book for anyone to implement DLP+ technology,” says Andrew McPherson, a computational biologist who started working on the project as a postdoctoral fellow at UBC and came to MSK with Dr. Shah to continue the project as an Assistant Attending Computational Oncologist.

The components necessary to build DLP+ are commercially available and the software to run it is open-source.

Finally, most current single-cell sequencing approaches analyze RNA and profile which genes are turned on in a cell, but they are limited in detecting the underlying changes in the DNA. “Combining results from these two approaches will yield important insights about tumor biology,” Dr. Shah says. “We’re excited to be able to present this technology to other scientists, both at MSK and beyond.”

This research was supported by the BC Cancer Foundation, the Canadian Institutes of Health Research, the Canadian Cancer Society Research Institute, the Terry Fox Research Institute, the Canadian Foundation for Innovation, the Canada Research Chairs Program, the Michael Smith Foundation for Health Research, Genome British Columbia, Genome Canada, CANARIE, Cancer Research UK, Cycle for Survival, and Microsoft Azure. Dr. Shah is a founder and shareholder of Contextual Genomics.