Cells store genetic information in their genomes. However, this information is not sufficient to understand the complex behaviour of cells or to compute predictive models for human phenotypes and disease. The reason is that despite all cells in the body containing the same genome, each cell interprets this common set of instructions in its own way to define its specific identity. The last decades saw the blooming of a new discipline, epigenetics, that deals with the study of the non-genetic modifications of the genome (e.g. histone modifications amongst others) that critically determine which genes are expressed by each cell type. Epigenomic modifications are at the source of many disorders. For instance, the epigenome of cancer cells carries a fingerprint of the cell type that originated the cancer. Thus, understanding the epigenetic and global gene expression changes of cells in complex tissues will be key to determine underlying disease mechanisms and design targeted treatments. Measuring these changes require single-cell exploration of 3D chromosome reorganization and global transcriptional patterns while preserving spatial information within complex tissues. Imaging-based methods are ideally suited for these tasks, but their main limitation remains in the number of individual molecules (e.g. DNA/ RNA/ proteins) that can be simultaneously visualized with chemical specificity in single cells. We and others have recently developed imaging-based multi-omics methods that circumvent this limitation by relying on the combinations of microfluidics and imaging. The objective of NGI is to develop new computational and experimental approaches to improve imaging-based multi-omics.
Specifically, the NGI project has developed new multiplexing approaches to reduce image acquisition times. For this, we prototyped and benchmarked several microfluidics methods and labeling strategies. Second, NGI developed a new open-source software package (qudi-CBS) to control and automatize the operation of a robotized prototype to perform imaging-based multi-omics acquisitions. Third, NGI developed a new open-source, automatized and user-friendly software package (pyHiM) to analyze imaging-based multi-omics datasets.
We envision that, in future, these open-source initiatives will enable the widespread adoption of these technologies in the biology and medical communities, and encourage new collaborative frameworks to accelerate development in this rapidly changing technology. Combined, these should increase the throughput and reduce the cost associated with existing diagnostic methods, as well as lead to the development of new diagnosis tools. These abilities will be fundamental for the early detection of disease, such as cancer.
