A computer-generated image of proteins. A new method for identifying proteins provides a much more comprehensive picture of proteins associated with cells.Credit: Design Cells/Getty Images
A powerful new method that combines high-resolution imaging and mass spectrometry is allowing the identification of proteins in a spatial context and providing a wealth of new insights into how cells function in health and disease (A. Mund et al. Nature Biotechnol. 40, 1231–1240; 2022).
Researchers using only imaging-based methods have generally been limited to identifying, at most, a subset of proteins within a given cell or tissue. Although recent years have seen great progress in the development of imaging techniques and methods to detect proteins within their spatial context, combining high-resolution imaging and proteomics techniques has been challenging.
“Identifying all the proteins associated with a cell — rather than a predefined selection — is crucial because it allows for a more comprehensive understanding of cellular functions and states,” says Matthias Mann, director at the Max Planck Institute of Biochemistry. “By not limiting analysis to known or expected proteins, researchers can discover new proteins and pathways, which can enhance our understanding of complex biological processes and diseases.”
The team headed by Mann has developed such a technique by integrating high-resolution microscopy, machine learning and ultrasensitive mass spectrometry. “This powerful combination allows us to quantify thousands of proteins with single-cell precision,” comments Mann.
The method involves four steps: a high-resolution image of a tissue slice is acquired using advanced microscopy; deep-learning software identifies and classifies single cells within the sample; the cells are precisely excised using Leica’s laser-microdissection microscope; and the proteins are analysed using ultrasensitive mass spectrometry.
Mann’s team demonstrated the power of their technique by using it to analyse skin-cancer tissue. They were able to divide a tumour into seven distinct regions and identified five classes of cellular phenotype. Their results revealed how normal cells can gradually morph into cancerous forms.
“We were able to uncover remarkably pronounced distinctions between cell types within the same sample of melanoma tissue,” says Mann. “Our analysis highlighted the unexpected complexity of tumours and the intricate pathways underlying the progression of cancer.” These insights should help to identify new ways to attack cancer cells, he adds.
Evidencing the popularity of the technique, the paper has been accessed more than 70,000 times and cited 97 times since its publication in May 2022. “The reaction from the scientific community has been very positive, with the high citation rate indicating that the technique is being actively discussed and potentially used by other researchers,” notes Mann.
The method has great potential in the field of personalized medicine. “Our technique moves the field closer to precision medicine, where treatments are tailored based on the protein profile of individual cells within a patient’s tissue,” says Mann.
To read the full paper in Nature Biotechnology, click here.