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New tool is a roadmap of how cells develop

Researchers at Columbia University Medical Center created a new tool to map the many possible ways a cell can develop. Using the mathematics of topology, they now have a detailed roadmap of how stem cells become specialized into tissues.

The study is published in Nature Biotechnology.

Every organism begins from one cell. As that cell divides, copies of it branch off to become specialized cells, heart, bone, brain cells and etc., in a process called "differentiation." To understand internal and external cues that move cells along a path, scientists sequence their RNA — a molecular messenger that translates DNA into proteins and other products.

Sequencing RNA from a batch of cells is not ideal, however, as cells are usually in different states of development. So, scientists developed single-cell RNA sequencing. "It's like a new microscope, giving us the ability to study many biological phenomena at once," said Raul Rabadan, PhD, associate professor of systems biology and biomedical informatics at Columbia and co-author of the paper. "However, researchers are still left with the problem of understanding the relationships between different cell states, which drive the process of development."

To study cellular development, scientists use mathematical tools to analyze massive amounts of sequencing data. But these tools rely on underlying assumptions to narrow the possible results. "Due to the complexity involved in cellular development, models that make assumptions actually limit your ability to make new discoveries," said Abbas Rizvi PhD, a postdoctoral research scientist in Columbia's Department of Biochemistry and Molecular Biophysics and the lead author of the paper.

Dr. Rizvi, together with Pablo G. Camara, PhD, a postdoctoral fellow and theoretical physicist in the Departments of Biomedical Informatics and Systems Biology, looked to topology, an area of math that studies the spatial relationships between surfaces and shapes, to identify connections between different cellular states and the genes active while cells are in those states.

Rizvi and Camara together developed an algorithm, called single-cell topological data analysis (scTDA). The algorithm analyzes RNA sequences of individual cells, reconstructing their underlying trajectories, and captured the progress of different transcriptional programs over time.

Researchers then used scTDA to map the path of mouse stem cells, which had been coaxed into becoming motor neuron cells.

The scTDA map correctly indicated their possible developmental trajectory — starting as stem cells and finally becoming neurons. By looking at which genes were active near particular forks in the map, researchers were able to identify proteins that appear to guide cellular development at different points along the path . The method was also applied to developmental paths of mouse lung stem cells, human embryos, and mouse brains.

"We expect many more discoveries to come to light as scientists mine this data set. It really opens up possibilities for a very deep analysis of individual cells at very specific stages of development," adds Tom Maniatis PhD, the Isidore S. Edelman Professor of Biochemistry, Chair of the Department of Biochemistry and Molecular Biophysics at Columbia, and co-author.

"This approach provides deep insights into the potential fate of a cell, giving us access to the pivotal regulators and molecular transitions that govern a cell's identity — and presenting the opportunity to steer cells away from paths that have a negative effect on its development."

Abbas Rizvi, PhD, Department of Biochemistry and Molecular Biophysics, Columbia University Medical Center, New York, New York, USA.

The approach is currently being applied to uncover the dynamics and cell makeup of complex biological processes, including cancer.

Transcriptional programs control cellular lineage commitment and differentiation during development. Understanding of cell fate has been advanced by studying single-cell RNA-sequencing (RNA-seq) but is limited by the assumptions of current analytic methods regarding the structure of data. We present single-cell topological data analysis (scTDA), an algorithm for topology-based computational analyses to study temporal, unbiased transcriptional regulation. Unlike other methods, scTDA is a nonlinear, model-independent, unsupervised statistical framework that can characterize transient cellular states. We applied scTDA to the analysis of murine embryonic stem cell (mESC) differentiation in vitro in response to inducers of motor neuron differentiation. scTDA resolved asynchrony and continuity in cellular identity over time and identified four transient states (pluripotent, precursor, progenitor, and fully differentiated cells) based on changes in stage-dependent combinations of transcription factors, RNA-binding proteins, and long noncoding RNAs (lncRNAs). scTDA can be applied to study asynchronous cellular responses to either developmental cues or environmental perturbations. Subject terms: Network topology Transcriptomics

Other authors Abbas H Rizvi, Pablo G Camara, Elena K Kandror, Thomas J Roberts, Ira Schieren, Tom Maniatis & Raul Rabadan. These authors contributed equally to this work: Abbas H Rizvi & Pablo G Camara

The paper is titled, "Single-cell topological RNA-Seq analysis reveals insights into cellular differentiation and development." Authors included Abbas H. Rizvi (Columbia University Medical Center, New York, NY), Pablo G. Camara (CUMC), Elena K. Kandror (CUMC) Thomas J. Roberts (CUMC), Ira Schieren (Howard Hughes Medical Institute, New York, NY), Tom Maniatis (CUMC), and Raul Rabadan (CUMC).

The study was supported by grants from the National Institutes of Health (U54-CA193313-01, R01GM117591, and NS088992) and the ALS Therapy Alliance (ATA-2013-F-056).

The authors declare no financial or other conflicts of interest.

Columbia University Medical Center provides international leadership in basic, preclinical, and clinical research; medical and health sciences education; and patient care. The medical center trains future leaders and includes the dedicated work of many physicians, scientists, public health professionals, dentists, and nurses at the College of Physicians and Surgeons, the Mailman School of Public Health, the College of Dental Medicine, the School of Nursing, the biomedical departments of the Graduate School of Arts and Sciences, and allied research centers and institutions. Columbia University Medical Center is home to the largest medical research enterprise in New York City and State and one of the largest faculty medical practices in the Northeast. The campus that Columbia University Medical Center shares with its hospital partner, NewYork-Presbyterian, is now called the Columbia University Irving Medical Center. For more information, visit cumc.columbia.edu or columbiadoctors.org.
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Topological map of four cell populations during motor neuron differentiation.
Image Credit:
Rabadan lab/Columbia University Medical Center


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