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Stereotyping groups of people according to the behaviors and characteristics of a few may not accurately reflect reality – it ignores diversity within these groups. Could this also be true for cells? Traditionally, cells have been classified according to type depending on their looks (round, pointed, etc.) and broad functional characteristics – for example, the immune system’s T and dendritic cells. But new research from the Weizmann Institute suggests that such generalization of cells may be misleading; rather, cells should be judged on their individual merit. “A cell has the potential to independently change its behavior by switching genes ‘on and off’ in response to various developmental signals and cues it receives from its environment. We therefore need a way to distinguish their individual characteristics and function,” says Dr. Ido Amit of the Immunology Department.
This is easier said than done. The average biological tissue contains billions of cells, comprising hundreds – if not thousands – of different “types.” How to sample each individual? A relatively new tool holds promise: single-cell sequencing. This method sequences the DNA and RNA of single cells, allowing scientists to trace and characterize their unique individual behavior – without cell-stereotype prejudices.
It can reveal which genes are being expressed, at what levels; whether proteins are being made, and under what conditions. This information will ultimately lead to a better understanding of how the cells work together to ensure the healthy functioning – or, in cases of disease, the malfunctioning – of tissues.
But single-cell sequencing is still in its infancy and scientists have so far been able to sequence the genetic material in only a small quantity of cells from tissue cultures consisting of one “type.” Now, as reported in Science, Amit, Prof. Amos Tanay of the Computer Science and Applied Mathematics, and Biological Regulation Departments, and their teams, led by Dr. Diego Jaitin, Dr.Hadas Keren-Shaul and PhD student Ephraim Kenigsberg, together with Prof. Steffen Jung’s laboratory in the Immunology Department, have developed a new single-cell sequencing method that is able to automatically sequence and analyze the RNA from thousands of single cells at once from living samples of complex tissues.
To test the robustness of their new technique, the scientists sampled mouse spleen – an immune system organ whose cells are very well described – to see whether they could reverse engineer and correctly identify its cellular composition. After computationally dividing the cells into groups expressing similar genes, they ended up with seven “types” of cells in total. Some of these groups were comparable to those already described, including B cells, natural killer cells, macrophages, monocytes and dendritic cells. But to their surprise, they discovered new subpopulations of dendritic cells, with diverse functions that have, until now, gone unrecognized.
“The use of traditional methods can be likened to observing a football game from afar,” says Tanay. “All players in a team seem identical, both in terms of outward appearance and behavior, and, for the most part, they all seem to ‘hang around’ in the same spot in the middle of the pitch. Single-cell sequencing, however, allows you to ‘zoom in’ on the action and distinguish the unique position and role of each player within the team – defense, attack, goalkeeper.”
In a follow-up experiment intended to simulate an immune response, the scientists exposed mice to a substance that mimics infection in order to test whether their method is able to correctly identify immune cells “in action.”
In response to changes in environmental cues, the same “types” of immune cells were identified, but the relative proportion of each changed, as did the genes expressed and the amount of RNA produced within each cell, both within and between cell groups. “Taken together, these findings paint a more complex picture of cell identity, suggesting that ultimately, defining cells according to ‘type’ is somewhat irrelevant and we should be letting individual cells ‘speak for themselves’,” says Kenigsberg.
This is just the beginning. Jaitin: “Single-cell RNA sequencing has huge potential in answering as yet unsolvable questions in biology, as well as having clinical implications in areas ranging from immune disorders to neurodegeneration, metabolism, stem cells and cancer. For example, we could apply these new tools to discover exactly which tumor cells are resistant to therapy.”
“The experimental protocol and analysis algorithms we developed make single-cell RNA sequencing of thousands of cells efficient, reliable and affordable,” says Keren-Shaul. Indeed, achieving a throughput about 30 times higher than current techniques, the team believes that the single-cell characterization of complex tissues is poised to increase the amount of detail able to be ascertained and bring new insights into multiple fields in biology and medicine.
Dr. Ido Amit's research is supported by M.D. Moross Institute for Cancer Research; the Abramson Family Center for Young Scientists; the Wolfson Family Charitable Trust; the Abisch Frenkel Foundation for the Promotion of Life Sciences; the Leona M. and Harry B. Helmsley Charitable Trust; Sam Revusky, Canada; Drs. Herbert and Esther Hecht, Beverly Hills, CA; the estate of Ernst and Anni Deutsch; and the Estate of Irwin Mandel. Dr. Amit is the incumbent of the Alan and Laraine Fischer Career Development Chair.
Prof. Steffen Jung's research is supported by the Leir Charitable Foundations; the Leona M. and Harry B. Helmsley Charitable Trust; the Maurice and Vivienne Wohl Biology Endowment; the Adelis Foundation; Lord David Alliance, CBE; the Wolfson Family Charitable Trust; the estate of Olga Klein Astrachan; and the European Research Council.
Prof. Amos Tanay's research is supported by the Helen and Martin Kimmel Award for Innovative Investigation; Pascal and Ilana Mantoux, Israel/France; the Wolfson Family Charitable Trust; the Rachel and Shaul Peles Fund for Hormone Research; and the estate of Evelyn Wellner.
All cell images: Thinkstock
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