The work has focused on two facets. The first being development of technologies aimed at measuring transcription factor binding dynamics across entire genomes in living organisms. Measurement of transcription factor off-rates determination across genomes has worked well. In addition, during the course of the project and in part based on review, more focus was put on to single cell gene expression analyses also because this was a logical step that the field has been taking and has resulted in a variety of advances as detailed below.
For the off-rate technique work has included optimisation of a plethora of steps. This has resulted in optimised protocols for each of the substeps. The sensitivity of our measurements has improved vastly due to these efforts, with in some cases more than 100-fold improvement in our signal to noise. As a spin-off we have published a highly improved protocol for chromatin immunoprecipitation. We have also learnt that not all transcription factors will perform equally well with these techniques. The off-rate procedure is now publicly available along with extended protocols. The analyses show that protein–DNA interactions are indeed dynamic, and these dynamics are an important aspect of chromatin-associated processes such as transcription or replication. The results indicate a large range of different residence times for individual transcription factors for example varying between 4.2 and 33 min depending on which binding site in the genome. Sites with different off-rates are associated with different functional characteristics. This includes their transcriptional dependency, nucleosome positioning and the size of the nucleosome-free region, as well as the ability to roadblock RNA polymerase II for termination. The results show how off-rates contribute to transcription factor function and that DIVORSEQ (Determining In Vivo Off-Rates by SEQuencing) is a meaningful way of investigating protein–DNA binding dynamics genome-wide. With regard to the on-rate, since this is a genomic adaptation of a protocol published by another research group, its adaptation was started later. We ran into several technical hurdles that were unforseen. On the one hand, this was surprising given that the starting point was a published protocol, in a reputable scientific journal. On the other hand, the project was high risk/high gain and although we have worked extensively on this, a shift in focus towards single cell approaches was an excellent alternative given the direction that the field was taking, also taking into account previous review remarks. The various analyses of single cell gene expression resulted in technical as well as conceptual advances. The technical advances included methods for disrupting tumor cell biopsies without excessive loss of cell viability (and cell type selection) and also selection methods for purifying viable cells after tissue disruption, as well as data analysis methods for identifying known and unknown cell types using single cell gene expression data for example, as well as a pipeline for processing the data. Applying such expertise to acute lymphatic leukaemia in infants (iALL) has resulted in the conceptual advance that it is possible to predict disease outcome based on the ratio of different tumor cell types found in different patients. This finding has been written up, deposited in medRxiv and is currently under review at a scientific journal. A concise overview of results is presented in the next section.
