e species. This simple but smart setting easily reach a high number of possible tag combinations making possible to differentially barcode several thousands of cells per experiment. Nevertheless, in the PCR step, most of the barcodes receive only very few reads being discarded and only those barcode combinations having more than 500 reads are taken into account. Because of that, heterogeneous mixtures having different amounts cells for each cell type could be more difficult to handle, losing in the less represented population much of the information necessary to estimate its chromatin accessibility.
In order to solve that, they defined the DNase I hypersensitive sites (DHSs) for each cell-type mixture using the available published data and calculated their Jaccard distances between shared DHS for each cell type. Afterwards, using these distances, multidimensional scaling with the single-cell ATAC-seq data (that just to overlap with DHSs) could properly locate the single cells within their cell-types. All that leave the notion that the ability to discern between cell types is proportional to number of reads per cell but yet a successful clustering could be achieved just based on the use of very few shared DHS within a cell type. Thereby, optimizing the experimental conditions for increasing the number of reads per cell, applying latent semantic indexing for identify patterns within DHS distances matrix and linking differentially chromatin accessible regions from single cells to the genes they putatively regulate, they show that this method is able to separate two cell types in different clades identifying the subgroup of accessible chromatin regions behind that separation. (Figure 3, Left panel). Furthermore, the method is able to assess the variability between cells from the same type, whose panorama of chromatin accessibility (therefore, potentially expressed genes) is rather heterogeneous, reflecting the potentially unique state of that single cell within its population (Figure 3, Right panel).
All over, this work seemingly accomplishes several major interesting points. To the technical side, it brings a hitherto non-existent technology for characterising single-cell chromatin accessibility. In a magnificent example of what a smart and simple wet–lab setting helped by appropriate mathematical implementations may become, this single-cell barcoding method seems away cheaper and more scalable (able to afford more single cells) than its putative micro-compartmentalized equivalent. To the understanding of cell populations provides strong evidence of the expected heterogeneity regarding the accessibility of chromatin, which has important potential implications for understanding achitecture and control of the gene expression in complex tissues and organisms. Additionally, this work seems to consolidate the methodological concept of single-cell barcoding, which could be an extraordinary source of information about how cells work and how they are integrated into the tissue. Thus, having promising applications to human health as you can already see in another work2 last May. There, a different modality of single-cell barcoding is used for testing whether intratumoral heterogeneity could act as a source of therapeutic resistance or that resistance rises from de novo alterations during the treatment.
- Cusanovich, D. A. et al. (2015) Multiplex single-cell profiling of chromatin accessibility by combinatorial cellular indexing. Science DOI: 10.1126/science.aab1601. ↩
- Bhang, H. C. et al. (2015) Studying clonal dynamics in response to cancer therapy using high-complexity barcoding Nat. Med. DOI: 10.1038/nm.3841) ↩