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Georgieva, D. C.

Publications and source records attributed to Georgieva, D. C..

2 recordsLinked to original sources

Establishing cell-intrinsic limitations in cell cycle progression controls graft growth and promotes differentiation of pancreatic endocrine cells

Limitations in cell proliferation are a key barrier to reprogramming differentiated cells to pluripotent stem cells, and conversely, acquiring these limitations may be important to establish the differentiated state. The pancreas, and beta cells in particular have a low proliferative potential, which limits regeneration, but how these limitations are established is largely unknown. Understanding proliferation potential is important for the safty of cell replacement therapy with cell products made from pluripotent stem cell which have unlimited proliferative potential. Here we test a novel hypothesis, that these limitations are established through limitations in S-phase progression. We used a stem cell-based system to expose differentiating stem cells to small molecules that interfere with cell cycle progression either by inducing G1 arrest, impairing S-phase entry, or S-phase completion. Upon release from these molecules, we determined growth potential, differentiation and function of insulin-producing endocrine cells both in vitro and after grafting in vivo. We found that the combination of G1 arrest with a compromised ability to complete DNA replication promoted the differentiation of pancreatic progenitor cells towards insulin-producing cells, improved the stability of the differentiated state, and protected mice from diabetes without the formation of cystic growths. Therefore, a compromised ability to enter S-phase and replicate the genome is a functionally important property of pancreatic endocrine differentiation, and can be exploited to generate insulin-producing organoids with predictable growth potential after transplantation.

cell biology↗

NanoMod: a computational tool to detect DNA modifications using Nanopore long-read sequencing data

BackgroundRecent advances in single-molecule sequencing techniques, such as Nanopore sequencing, improved read length, increased sequencing throughput, and enabled direct detection of DNA modifications through the analysis of raw signals. These DNA modifications include naturally occurring modifications such as DNA methylations, as well as modifications that are introduced by DNA damage or through synthetic modifications to one of the four standard nucleotides.\n\nMethodsTo improve the performance of detecting DNA modifications, especially synthetically introduced modifications, we developed a novel computational tool called NanoMod. NanoMod takes raw signal data on a pair of DNA samples with and without modified bases, extracts signal intensities, performs base error correction based on a reference sequence, and then identifies bases with modifications by comparing the distribution of raw signals between two samples, while taking into account of the effects of neighboring bases on modified bases (\"neighborhood effects\").\n\nResultsWe evaluated NanoMod on simulation data sets, based on different types of modifications and different magnitudes of neighborhood effects, and found that NanoMod outperformed other methods in identifying known modified bases. Additionally, we demonstrated superior performance of NanoMod on an E. coli data set with 5mC (5-methylcytosine) modifications.\n\nConclusionsIn summary, NanoMod is a flexible tool to detect DNA modifications with single-base resolution from raw signals in Nanopore sequencing, and will greatly facilitate large-scale functional genomics experiments in the future that use modified nucleotides.

bioinformatics↗