Scientists Hear Cell Conversation for First Time
Friday 11 December 2009 - A cutting edge technique that allows scientists to monitor communication between cells could transform the way laboratory medical experiments are conducted.
The method, published in the journal Science, is likely to improve the accuracy of laboratory studies of cancers and other human diseases and the assessment of new drugs to target them.
Dr Rune Linding, the ICR’s Cellular and Molecular Logic Team Leader, says that understanding communication between cells is crucial, as many cancers and other diseases are caused by a breakdown in communications systems.
Until now, scientists have generally studied cell communication by taking a single population of cells, adding a molecule to stimulate the cells and measuring the level of signalling molecules produced. But this technique does not take into account that cells respond to signals they receive and feedback to each other, like a conversation between people.
The new method involves growing cells in media containing labelled amino acids (the fundamental building blocks of proteins) that are incorporated into the cells’ proteins. Two cell types, grown with different labels, are then combined for a short time to allow them to talk to each other. The cells are then broken open so the proteins produced can be examined and mass spectrometry is used to measure the level of each label, showing from which cell type the proteins originated.
The team used small interfering RNA molecules to look for genes involved in the conversation, and used the information about genes and proteins to create a computer model of the signalling networks involved.
They initially studied two cell types involved in a key communications system, EphB2, which is mutated in many forms of cancer. They found that both cell types responded differently to the conversation, demonstrating that future research on how cells respond to signals - including signals that trigger cancer, or signals from drugs – should be carried out on mixed populations of cells to get accurate results.
