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Peter Sims & Jinzhou Yuan

Assistant Professor Peter Sims and postdoctoral research scientist Jinzhou Yuan displaying their platform for automated single cell RNA-Seq. Photo: Lynn Saville.

RNA sequencing (RNA-Seq) has become a workhorse technology for research in systems biology. Unlike genome sequencing, which reveals a sample’s DNA blueprint, RNA-Seq catalogs the constantly changing transcriptome; that is, it itemizes and quantifies the complete set of messenger RNA transcripts that are present in cells at a specific time and under specific conditions. In this way, RNA-Seq makes it possible to investigate how the information encoded in the genome is functionally transformed into observable traits, and provides valuable data for defining and comparing different biological states.

Conventional RNA-Seq generates an average summary of mRNA abundance across all of the cells in a sample. Recent research, however, has created a demand for higher resolution technologies capable of generating mRNA profiles at the level of single cells. In cancer biology, for example, there is an increasingly acute awareness that gene expression in the cells that make up malignant tumors is highly heterogeneous. This suggests that in order to understand how the cells work together to drive a tumor’s cancerous behavior, scientists need better methods for characterizing the entire ecology of cells of which it is made. Being able to quantify differences in gene expression cell by cell could be one valuable way to explore such complex environments and understand how they sustain malignancy.

Although several single cell RNA-seq technologies have been unveiled in the past two years, they are expensive to operate and are not optimized to produce data on the scale that is required for systems biology research, particularly in tissue specimens with limited numbers of cells. In a new paper just published in the journal Scientific Reports, however, researchers in the laboratory of Department of Systems Biology Assistant Professor Peter Sims describe a novel approach that offers several important advantages over other existing methods.

The new, automated platform builds on previous innovations in the Sims Lab to offer a cheap, efficient, and reliable way to simultaneously measure gene expression in thousands of individual cells from a single tissue sample. Using custom designed microwell plates, microfluidics, temperature control systems, and software, the technology captures, tags, and generates a readout of the complete transcriptome in each cell, providing robust data that can then be analyzed to distinguish functional diversity among the cells in the sample. Already, the technology is playing a key role in several research projects being conducted in the Department of Systems Biology and promises to become even more powerful as the field of single cell genomics continues to evolve.

Master regulators of tumor homeostasis

In this rendering, master regulators of tumor homeostasis (white) integrate upstream genetic and epigenetic events (yellow) and regulate downstream genes (purple) responsible for implementing cancer programs such as proliferation and migration. CaST aims to develop systematic methods for identifying drugs capable of disrupting master regulator activity.

The Columbia University Department of Systems Biology has been named one of four inaugural centers in the National Cancer Institute’s (NCI) new Cancer Systems Biology Consortium. This five-year grant will support the creation of the Center for Cancer Systems Therapeutics (CaST), a collaborative research center that will investigate the general principles and functional mechanisms that enable malignant tumors to grow, evade treatment, induce disease progression, and develop drug resistance. Using this knowledge, the Center aims to identify new cancer treatments that target master regulators of tumor homeostasis.

CaST will build on previous accomplishments in the Department of Systems Biology and its Center for Multiscale Analysis of Genomic and Cellular Networks (MAGNet), which developed several key systems biology methods for characterizing the complex molecular machinery underlying cancer. At the same time, however, the new center constitutes a step forward, as it aims to move beyond a static understanding of cancer biology toward a time-dependent framework that can account for the dynamic, ever-changing nature of the disease. This more nuanced understanding could eventually enable scientists to better predict how individual tumors will change over time and in response to treatment.

Andrea CalifanoAndrea Califano, the Clyde and Helen Wu Professor of Chemical Systems Biology and Chair of the Columbia University Department of Systems Biology, has been named a recipient of a National Cancer Institute Outstanding Investigator Award. The seven-year grant will support the development of systematic approaches for identifying the molecular factors that lead to cancer progression and to the emergence of drug resistance at the single-cell level. 

Alex Lachmann
Alex Lachmann during his presentation to the RNA-Seq "boot camp."

In June 2015, the Columbia University Department of Systems Biology held a five-part lecture series focusing on advanced applications of RNA-Seq in biological research. The talks covered topics such as the use of RNA-Seq for studying heterogeneity among single cells, RNA-Seq experimental design, statistical approaches for analyzing RNA-Seq data, and the utilization of RNA-Seq for the prediction of molecular interaction networks. The speakers and organizers have compiled a list of lecture notes and study materials for those wishing to learn more. Click on the links below for more information.

PhenoGraph

PhenoGraph, a new algorithm developed in Dana Pe'er's laboratory, proved capable of accurately identifying AML stem cells, reducing high-dimensional single cell mass cytometry data to an interpretable two-dimensional graph. Image courtesy of Dana Pe'er.

A key problem that has emerged from recent cancer research has been how to deal with the enormous heterogeneity found among the millions of cells that make up an individual tumor. Scientists now know that not all tumor cells are the same, even within an individual, and that these cells diversify into subpopulations, each of which has unique properties, or phenotypes. Of particular interest are cancer stem cells, which are typically resistant to existing cancer therapies and lead to relapse and recurrence of cancer following treatment. Finding better ways to distinguish and characterize cancer stem cells from other subpopulations of cancer cells has therefore become an important goal, for once these cells are identified, their vulnerabilities could be studied with the aim of developing better, long lasting cancer therapies.

In a paper just published online in Cell, investigators in the laboratories of Columbia University’s Dana Pe’er and Stanford University’s Garry Nolan describe a new method that takes an important step toward addressing this challenge. As Dr. Pe’er explains, “Biology has come to a point where we suddenly realize there are many more cell types than we ever imagined possible. In this paper, we have created an algorithm that can very robustly identify such subpopulations in a completely automatic and unsupervised way, based purely on high-dimensional single-cell data. This new method makes it possible to discover many new cell subpopulations that we have never seen before.”

Topology of cancer

The Columbia University Center for Topology of Cancer Evolution and Heterogeneity will combine mathematical approaches from topological data analysis with new single-cell experimental technologies to study cellular diversity in solid tumors. Image courtesy of Raul Rabadan.

The National Cancer Institute’s Physical Sciences in Oncology program has announced the creation of a new center for research and education based at Columbia University. The Center for Topology of Cancer Evolution and Heterogeneity will develop and utilize innovative mathematical and experimental techniques to explore how genetic diversity emerges in the cells that make up solid tumors. In this way it will address a key challenge facing cancer research in the age of precision medicine — how to identify the clonal variants within a tumor that are responsible for its growth, spread, and resistance to therapy. Ultimately, the strategies the Center develops could be used to identify more effective biomarkers of disease and new therapeutic strategies.

Tracking clones

After identifying T cell clones that react against donated kidney tissue in vitro, new computational methods developed in Yufeng Shen's Lab are used to track their frequency following organ transplant. The findings can help to predict transplant rejection or tolerance.

When a patient receives a kidney transplant, a battle often ensues. In many cases, the recipient’s immune system identifies the transplanted kidney as a foreign invader and mounts an aggressive T cell response to eliminate it, leading to a variety of destructive side effects. To minimize complications, many transplant recipients receive drugs that suppress the immune response. These have their own consequences, however, as they can lead to increased risk of infections. For these reasons, scientists have been working to gain a better understanding of the biological mechanisms that determine transplant tolerance and rejection. This knowledge could potentially improve physicians’ ability to predict the viability of an organ transplant and to provide the best approach to immunosuppression therapy based on individual patients’ immune system profiles.

Yufeng Shen, an assistant professor in the Columbia University Department of Systems Biology and JP Sulzberger Columbia Genome Center, together with Megan Sykes, director of the Columbia Center for Translational Immunology at the Columbia University College of Physicians and Surgeons, recently took an encouraging step toward this goal. In a paper published in Science Translational Medicine, they report that the deletion of specific donor-resistant T cell clones in the transplant recipient can support tolerance of a new kidney. Critical to this discovery was the development of a new computational genomics approach by the Shen Lab, which makes it possible to track how frequently rare T cell clones develop and how their frequencies change following transplantation. The paper suggests both a general strategy for understanding the causes of transplant rejection and a means of identifying biomarkers for predicting how well a transplant recipient will tolerate a new kidney.

Fluidigm C1 Single-Cell Plate

At the core of the Fluidigm C1 Single-Cell Auto Prep System is a 96-well plate containing microfluidics. After individual cells are isolated in their own wells, the device amplifies their cDNA for genome-wide gene expression profiling. Scientists at the Columbia Genome Center are developing methods for addressing the technical and analytical challenges of single-cell RNA sequencing, and have begun generating some exciting data.

Since the invention of the first microscope, a procession of new technologies has enabled scientists to study individual cells at increasingly fine levels of detail. The last two years have witnessed an important next stage in this evolution, with the arrival of the first devices for genetically profiling single cells on a genome-wide scale.

The first commercial product in this field is the Fluidigm C1 Single-Cell Auto Prep System, which uses microfluidics to isolate single cells and offers the ability to generate gene expression profiles for up to 96 cells at a time. But because of the novelty of the technology and the inherent difficulties of working with single cells, it has presented a number of technical challenges for researchers interested in exploring biology at this level.

Now, scientists at the JP Sulzberger Columbia Genome Center led by Assistant Professors Peter Sims and Yufeng Shen have developed an experimental and computational pipeline that optimizes the C1’s capabilities. And even as they work to solve some of the challenges that are inherent to single-cell research, their approach has begun generating some exciting data for studying genetics in a variety of cell types.

Dana Pe'er and Kyle Allison

Dana Pe'er has received the Pioneer Award for high-risk, high-reward research, and postdoctoral scientist Kyle Allison has won an Early Independence Award.

Two members of the Columbia University Department of Systems Biology have been named recipients of NIH Director’s Awards from the National Institutes of Health Common Fund.

Associate Professor Dana Pe’er is one of 10 winners of the 2014 NIH Director’s Pioneer Awards. The Pioneer Awards provide up to $2.5 million over 5 years to support exceptionally creative investigators who are pursuing “high risk, high reward” science that holds great potential to transform biomedical or behavioral research. The award will support an ambitious new project to develop the technological and computational methods necessary to create a comprehensive, high-resolution atlas of development for all cell types in the human body.

In addition, Kyle Allison, a postdoctoral scientist in the laboratory of Professor Saeed Tavazoie, has received the NIH Director’s Early Independence Award. (Dr. Tavazoie is also a past winner of the Pioneer Award.) This program enables outstanding young investigators who have recently completed their PhD’s to move rapidly into independent research positions. Dr. Allison is one of just 17 scientists to receive this award this year. In combination with the Department of Systems Biology Fellows program, this five-year, $1.25 million grant will allow him to open his own laboratory at Columbia and pursue independent research to investigate the problem of bacterial persistence. He is the second Department of Systems Biology investigator to receive the Early Independence Award, joining Assistant Professor Harris Wang in being recognized with this honor.

“Having four recipients of NIH Director’s Awards within the Department of Systems Biology — and particularly two in one year — is quite remarkable,” said Department Chair Andrea Califano. “I think it’s a testimony to the timeliness of the perspectives and tools that systems biology offers and to the high quality of research being conducted at Columbia. I look forward to the discoveries that will undoubtedly come from Dana’s and Kyle’s extremely exciting efforts.”

Distribution of marker expression across development

A new algorithm called Wanderlust uses single-cell measurements to detect how marker expression changes across development.

In a new paper published in the journal Cell, a team of researchers led by Dana Pe’er at Columbia University and Garry Nolan at Stanford University describes a powerful new method for mapping cellular development at the single cell level. By combining emerging technologies for studying single cells with a new, advanced computational algorithm, they have designed a novel approach for mapping development and created the most comprehensive map ever made of human B cell development. Their approach will greatly improve researchers’ ability to investigate development in cells of all types, make it possible to identify rare aberrations in development that lead to disease, and ultimately help to guide the next generation of research in regenerative medicine.

Pointing out why being able to generate these maps is an important advance, Dr. Pe’er, an associate professor in the Columbia University Department of Systems Biology and Department of Biological Sciences, explains, “There are so many diseases that result from malfunctions in the molecular programs that control the development of our cell repertoire and so many rare, yet important, regulatory cell types that we have yet to discover. We can only truly understand what goes wrong in these diseases if we have a complete map of the progression in normal development. Such maps will also act as a compass for regenerative medicine, because it’s very difficult to grow something if you don’t know how it develops in nature. For the first time, our method makes it possible to build a high-resolution map, at the single cell level, that can guide these kinds of research.”

viSNE

viSNE reveals the progression of cancer in a sample of cells taken from a patient with acute myeloid leukemia. Cells are colored according to intensity of expression of the indicated cell markers, enabling the comparison of expression patterns before and after relapse. For example, Fit3 is expressed primarily in the diagnosis sample, while CD34 emerges in the relapse sample.

Researchers in the Columbia Initiative in Systems Biology have developed a computational method that enables scientists to visualize and interpret high-dimensional data produced by single-cell measurement technologies such as mass cytometry. The method, called viSNE (visual interactive Stochastic Neighbor Embedding), has just been published in the online edition of Nature Biotechnology. It has particular relevance to cancer research and therapeutics. As Columbia University Medical Center reports:

Researchers now understand that cancer within an individual can harbor subpopulations of cells with different molecular characteristics. Groups of cells may behave differently from one another, including in how they respond to treatment. The ability to study single cells, as well as to identify and characterize subpopulations of cancerous cells within an individual, could lead to more precise methods of diagnosis and treatment.

“Our method not only will allow scientists to explore the heterogeneity of cancer cells and to characterize drug-resistant cancer cells, but also will allow physicians to track tumor progression, identify drug-resistant cancer cells, and detect minute quantities of cancer cells that increase the risk of relapse,” said co-senior author Dana Pe’er, associate professor of biological sciences and systems biology at Columbia.