Deep Learning Image Analysis to Isolate and Characterize Different Stages of S-phase in Human Cells

Abstract. This research used deep learning for image analysis by isolating and characterizing distinct DNA replication patterns in human cells. By leveraging high-resolution microscopy images of multiple cells stained with 5-Ethynyl-2′-deoxyuridine (EdU), a replication marker, this analysis utilized Convolutional Neural Networks (CNNs) to perform image segmentation and to provide robust and reliable classification results. First multiple cells in a field of focus were identified using a pretrained CNN called Cellpose. After identifying the location of each cell in the image a python script was created to crop out each cell into individual .tif files. After careful annotation, a CNN was created from scratch using the TensorFlow Keras package and trained on those images to categorize them into five distinct replication patterns. Using a holdout test set our model was able to achieve an accuracy of 86.5%. This analysis method for segmentation and classification enhances the efficiency and reproducibility of DNA replication analysis, allowing for high-throughput processing and analysis of replication foci. This research can enhance image analysis in cell biology by providing a time-efficient and accurate tool to investigate replication dynamics, advance cancer research, and contribute to scientific discovery in various domains

Deep Learning Image Analysis to Isolate and Characterize Different Stages of S-phase in Human Cells

Abstract. This research used deep learning for image analysis by isolating and characterizing distinct DNA replication patterns in human cells. By leveraging high-resolution microscopy images of multiple cells stained with 5-Ethynyl-2′-deoxyuridine (EdU), a replication marker, this analysis utilized Convolutional Neural Networks (CNNs) to perform image segmentation and to provide robust and reliable classification results. First multiple cells in a field of focus were identified using a pretrained CNN called Cellpose. After identifying the location of each cell in the image a python script was created to crop out each cell into individual .tif files. After careful annotation, a CNN was created from scratch using the TensorFlow Keras package and trained on those images to categorize them into five distinct replication patterns. Using a holdout test set our model was able to achieve an accuracy of 86.5%. This analysis method for segmentation and classification enhances the efficiency and reproducibility of DNA replication analysis, allowing for high-throughput processing and analysis of replication foci. This research can enhance image analysis in cell biology by providing a time-efficient and accurate tool to investigate replication dynamics, advance cancer research, and contribute to scientific discovery in various domains

Altered RNA Editing in Mice Lacking ADAR2 Autoregulation

ADAR2 is a double-stranded-RNA-specific adenosine deaminase involved in the editing of mammalian RNAs by the site-selective conversion of adenosine to inosine. Previous studies from our laboratory have demonstrated that ADAR2 can modify its own pre-mRNA to create a proximal 3′ splice site containing a noncanonical adenosine-inosine dinucleotide. Alternative splicing to this proximal acceptor adds 47 nucleotides to the mature ADAR2 transcript, thereby resulting in the loss of functional ADAR2 protein expression due to premature translation termination in an alternate reading frame. To examine whether the editing of ADAR2 transcripts represents a negative autoregulatory strategy to modulate ADAR2 protein expression, we have generated genetically modified mice in which the ability of ADAR2 to edit its own pre-mRNA has been selectively ablated by deletion of a critical sequence (editing site complementary sequence [ECS]) required for adenosine-to-inosine conversion. Here we demonstrate that ADAR2 autoediting and subsequent alternative splicing are abolished in homozygous ΔECS mice and that ADAR2 protein expression is increased in numerous tissues compared to wild-type animals. The observed increases in ADAR2 protein expression correlate with the extent of ADAR2 autoediting observed with wild-type tissues and correspond to increases in the editing of ADAR2 substrates, indicating that ADAR2 autoediting is a key regulator of ADAR2 protein expression and activity in vivo

Correspondence

mediators; strand exchange; zebrafis

DTL/CDT2 is essential for both CDT1regulation and the early G2/M checkpoint

Checkpoint genes maintain genomic stability by arresting cells after DNA damage. Many of these genes also control cell cycle events in unperturbed cells. By conducting a screen for checkpoint genes in zebrafish, we found that dtl/cdt2 is an essential component of the early, radiation-induced G2/M checkpoint. We subsequently found that dtl/cdt2 is required for normal cell cycle control, primarily to prevent rereplication. Both the checkpoint and replication roles are conserved in human DTL. Our data indicate that the rereplication reflects a requirement for DTL in regulating CDT1, a protein required for prereplication complex formation. CDT1 is degraded in S phase to prevent rereplication, and following DNA damage to prevent origin firing. We show that DTL associates with the CUL4–DDB1 E3 ubiquitin ligase and is required for CDT1 down-regulation in unperturbed cells and following DNA damage. The cell cycle defects of Dtl-deficient zebrafish are suppressed by reducing Cdt1 levels. In contrast, the early G2/M checkpoint defect appears to be Cdt1-independent. Thus, DTL promotes genomic stability through two distinct mechanisms. First, it is an essential component of the CUL4–DDB1 complex that controls CDT1 levels, thereby preventing rereplication. Second, it is required for the early G2/M checkpoint

Oncogenesis caused by loss of the SNF5 tumor suppressor is dependent on activity of BRG1, the ATPase of the SWI/SNF chromatin remodeling complex

International audienceAlterations in chromatin play an important role in oncogenic transformation, although the underlying mechanisms are often poorly understood. The SWI/SNF complex contributes to epigenetic regulation by using the energy of ATP hydrolysis to remodel chromatin and thus regulate transcription of target genes. SNF5, a core subunit of the SWI/SNF complex, is a potent tumor suppressor that is specifically inactivated in several types of human cancer. However, the mechanism by which SNF5 mutation leads to cancer and the role of SNF5 within the SWI/SNF complex remain largely unknown. It has been hypothesized that oncogenesis in the absence of SNF5 occurs due to a loss of function of the SWI/SNF complex. Here, we show, however, distinct effects for inactivation of Snf5 and the ATPase subunit Brg1 in primary cells. Further, using both human cell lines and mouse models, we show that cancer formation in the absence of SNF5 does not result from SWI/SNF inactivation but rather that oncogenesis is dependent on continued presence of BRG1. Collectively, our results show that cancer formation in the absence of SNF5 is dependent on the activity of the residual BRG1-containing SWI/SNF complex. These findings suggest that, much like the concept of oncogene addiction, targeted inhibition of SWI/SNF ATPase activity may be an effective therapeutic approach for aggressive SNF5-deficient human tumors