Published on 21.05.2021
What is this new methodology?
Akira Cortal, first author of the publication: In our laboratory, we are trying to develop methods to explore and characterize the heterogeneity of cells in the body, with the aim of identifying those that may be involved in the onset and progression of a disease. In order to characterize all the cells, it is necessary to highlight the molecular signatures and cellular functions specific to each of them. To this end, the analysis of cellular RNA, known as transcriptomic analysis, makes it possible to identify the genes that are used by the cells of a tissue or organ at a given moment.
Each cell contains a great diversity of RNA molecules, but in very small quantities. It is therefore difficult to detect the molecular signals specific to each cell, i.e. a subset of molecules that is unique and exclusive to each cell and that is blurred in a kind of background noise. Numerical approaches are needed to distinguish these signals. Until now, even though new technologies allowed RNA sequencing at the scale of the individual cell, data analysis could only be done in a robust way for subgroups of cells (so-called clusters).
In this work, we have created a method of data analysis from single-cell sequencing, which automatically derives information for each individual cell, while taking into account the global heterogeneity.
This is a real revolution in single-cell analysis. It extracts a gene signature specific to each cell, a "molecular fingerprint" or identity card for each cell, called Cell-ID.
What new features does it bring to cell analysis?
This methodology has three main novelties. First, it allows to automatically identify the cell type of each cell (for example, is it a blood cell, and if so, is it a monocyte, a T cell or a neutrophil among a large variety of types). Secondly, it allows to characterize their cellular state (for example, if the cell shows signs of stress, inflammation, or activation of certain metabolic or signaling pathways). Also, it is now possible to map cells one by one which allows the identification of new cell types and cellular states that can be found reproduced in different individuals who share clinical symptoms, while they are not observed, or to a lesser extent, in healthy individuals. Finally, cell identity cards now allow the creation of reference libraries that can be used to scan new patients and establish similarities between cells from different organs or animal models.
Antonio Rausell: These signatures allow unbiased recognition of cellular identity between different donors, tissues of origin, model organisms and single-cell omics technologies. They enrich databases of cell types and states, and can help identify cells involved in pathologies and understand their evolution. This is a very fine level of single-cell analysis and a real advantage to characterize rare cells that we could not identify by analyzing subgroups of cells.
What prospects does it offer?
In addition to the new possibilities and the finesse of single-cell analysis that it allows, the methodology offers real clinical perspectives for rare diseases. Understanding the cellular signature at the origin of diseases is a crucial step in understanding the underlying molecular mechanisms and proposing a possible treatment.
We are already using this method in the analysis of severe hereditary immunodeficiencies and in the field of gene therapy, in close collaboration with Dr. Emmanuelle Six, researcher in the Human Lymphohematopoiesis laboratory of Institut Imagine and Professor Marina Cavazzana, Director of the Department of Biotherapy and Innovative Therapies of Hôpital Necker-Enfants Malades AP-HP, and head of the CIC of Biotherapy of Institut Imagine. We are studying, in the context of gene therapy, the factors that will allow or not the reconstitution of damaged cells, and therefore the success of the gene therapy.
The Cell-ID software is available in open source. Thus, the methodology is open and available to the entire scientific community so that it can benefit the maximum number of researchers and physicians, and ultimately, patients.
Read the details in Nature Biotechnology : https://rdcu.be/cjFWE