ike to highlight a paper presented last May as an example of this concept. The present research presented by Cusanovich et. al. 1 aim the single-cell profiling of chromatin accessibility. Chromatin accessibility is a key but yet ill-defined controller for gene expression and genetic recombination. Current research for its understanding spans from studies about its three-dimensional structure to accessibility mappings using enzymatic digestion (e.g with DNAse I) or transposase-accessible chromatin assays (ATAC-seq; Figure 1). The rationale behind those mappings is that open chromatin is prone to bear more active genes because is more accessible for transcription factors and RNA-polymerases; and for the very same reason it is more accessible to any kind of DNA-related enzymatic activity susceptible of being quantified like DNA restriction or PCR.
In Cusanovich´s work they use ATAC-seq technic to map the accessible chromatin, not in the pooled chromatin from a cell population but in single cells by mean of barcoding their intact nuclei. They achieve that through tagging pools of nuclei with different barcoded Tn5 transposases and diluting then for later making PCRs with indexed primers complementary to the adapters introduced by transposase (Figure 2). Just by probability most nuclei will pass through a unique combination of tagged primers that will make them singular and traceable. Therefore, those PCR products coming from the same cell would have the same exclusive barcode, being a library of regions of a single-cell genome that are accessible and free of nucleosomes. Finally, for knowing the accessible regions on each cell, massive sequencing from the whole pooled set of PCR products is performed.
As proof of principle it is shown that the technique is able to properly assign the cell lineage in more than 90% of the single-cell libraries according with their chromatin accessibility profile, no matter if they come from equal mixtures of two cell types from two different species or different cell types from the same 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).
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