16 Mar 2021
A team of researchers at the University of California, Irvine has developed a new computational tool called CellChat to help decipher the language skin cells use to communicate with one another.
What are the key differences between healthy skin and diseased skin such as psoriatic or eczema skin? Researchers have previously shown that differences are often in the signaling molecules that transmit information and commands between the skin cells – and it is well established that signaling defects such as too much or too little signaling is the key component of most of skin diseases. For example, in skin diseases with an autoimmune component, there are numerous defects in cytokine signaling and in fibrotic skin diseases there are defects in fibroblast-activating signaling.
This means, that upon disease, the entire and very complex signaling network of the skin re-structures and network goes from a normal state to a disease state. Thus, studying skin signaling network is important to improve the understanding of the skin and its diseases.
In a paper recently published in Nature Communications, researchers introduce a new tool – CellChat – that enables the decoding of signaling molecules. The work is supported by the LEO Foundation.
“Just like in our world, where we are constantly bombarded with information, all cells experience a lot of molecular words coming at them simultaneously. What they choose to do is dependent on this steady flow of molecular information and on what words and sentences are being heard the loudest,” said Maksim Plikus, professor of developmental and cell biology at the University of California, Irvine (UCI) and one of the researchers behind the new CellChat tool. He is also a LEO Foundation awardee and received the LEO Foundation Award 2019 in Region Americas for his extraordinary contribution to skin research.
“To properly understand why cells do certain things, and to predict their future actions, we need to be able to listen to what they are saying to one another; mathematical and machine learning tools enable the translation of such messages,” said Qing Nie, UCI Chancellor’s professor of mathematics and developmental and cell biology and co-senior author of the new paper.
CellChat breaks down the complexity of cellular communication patterns
To use CellChat to translate molecular messages between cells, researchers feed in a single-cell gene expression, and out comes a detailed report on signaling communication features of a given tissue or organ.
“For each distinct group of cells, CellChat shows what significant signals are being sent to their neighbors and what signals they have the ability to receive,” Plikus said. “As an interpreter of cellular language, CellChat provides scientists with a valuable insight into signaling patterns that guide function of the entire organ.”
In developing CellChat, the researchers borrowed heavily from machine learning tools and social network theory, which allows the platform to predict a higher level meaning of cellular language and identify contextual similarities that are otherwise not obvious. It breaks down the immense complexity of cellular communication patterns – e.g. communication patterns between skin cells during wound repair.
Cells produce modifier molecules to add emphasis to a certain command, transforming “do this” to “do this now.” CellChat automatically calibrates the strength of signaling communication between cells by considering all significantly present modifier molecules. As a result, its translation becomes more nuanced and helps to minimize inaccuracies that plague other similar yet less sophisticated computational tools.
Google Translator for the lexicon of cells
Beyond the purely fundamental research enterprise of interpreting these biological messages, Nie said CellChat can also be used to compare communication networks in different states of an organ, such as sickness and health. Calling it a “Google Translator for the lexicon of cells,” Nie said one of tool’s most significant capabilities is that it can be used to uncover molecular drivers in a broad spectrum of maladies including cancer and autoimmune disorders.
“In our paper, we showcase the power of CellChat using atopic dermatitis, a human skin condition, but the tool can be used on any tissue with the same success,” Plikus added.
CellChat is available as an open-source application .
Researchers from National Taiwan University and Yale University also contributed to the study.