Wednesday, April 30, 2025
4/30/2025
Genome-wide assessment of transcriptional state history - PROJECT SUMMARY The accurate description of cellular states—be it on the genomic, transcriptomic, or proteomic level—has been a major driver in the disentanglement of nature’s mysteries. Mainly supported by technological advances such as next-generation sequencing or single-cell technologies, we now can describe the distinct cellular populations within a tissue or collection of cells in intricate detail. This allows us to understand health and disease, and it often proves instrumental in our ability to develop effective treatments. However, our current technologies are mostly geared toward an understanding of current cellular states. While various technologies can trace the lineage of cells, even the combination with global cellular state readouts does not offer accurate insights into past cellular history. Moreover, current approaches that aim to combine a past transcriptional event with such tracing can only report the historic expression of one or few genes. However, this single moment in time may have comprised many critical developmental or adaptive ‘decisions’ or ‘inputs’ that shape the current function or even location of a cell. Thus, a global recording of a transcriptional state would support a more comprehensive picture of a cell’s history. This proposal’s aim is to provide such a genome-wide record of transcription by building on recently developed technological concepts. For this, we will develop the novel approach of transcriptional endpoint marking (TEM); this technology will introduce a permanent genetic mark at the locus of actively transcribed genes. Expressing the ‘marker’ construct transiently, will inscribe a ‘snapshot’ record of the transcriptomic profile that can later be assessed by targeted genome-wide retrieval of the genomic ‘marks’. Our proposed work will provide proof-of-concept and focus on the development of a genetic toolkit that enables this analysis. In addition, it will provide a computational basis for both the data processing and snapshot analysis. We will initially employ these methods to test cellular state differences that underlie adaptive responses, such as chemotherapy resistance in cell culture methods. We expect that this method will have a broad impact across several fields, which include our laboratory’s interests in vertebrate development. Moving this technology into in vivo systems, such as zebrafish or mouse, will enable the establishment of early developmental markers of defined cellular populations that remained elusive. Alternatively, it will also enable to understand lineage decisions for populations that share developmental history but present with distinct phenotypes. Likewise, snapshot analysis will aid in our understanding of metastasis and resistance in tumour models, resiliency in injury, or response to differentiation factors in vitro. TEM represents a fundamentally distinct approach to dissect development and adaption that complements current efforts in lineage tracing and state analysis. As such, it can serve as a technological driver of novel biological insights.
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