micronuclAI: Automated quantification of micronuclei for assessment of chromosomal instability.

Chromosomal instability (CIN) is a hallmark of cancer that drives metastasis, immune evasion and treatment resistance. CIN results from chromosome mis-segregation events during anaphase, as excessive chromatin is packaged in micronuclei (MN), that can be enumerated to quantify CIN. Despite recent advancements in automation through computer vision and machine learning, the assessment of CIN remains a predominantly manual and time-consuming task, thus hampering important work in the field. Here, we present micronuclAI , a novel pipeline for automated and reliable quantification of MN of varying size, morphology and location from DNA-only stained images. In micronucleAI , single-cell crops are extracted from high-resolution microscopy images with the help of segmentation masks, which are then used to train a convolutional neural network (CNN) to output the number of MN associated with each cell. The pipeline was evaluated against manual single-cell level counts by experts and against routinely used MN ratio within the complete image. The classifier was able to achieve a weighted F1 score of 0.937 on the test dataset and the complete pipeline can achieve close to human-level performance on various datasets derived from multiple human and murine cancer cell lines. The pipeline achieved a root-mean-square deviation (RMSE) value of 0.0041, an R 2 of 0.87 and a Pearson's correlation of 0.938 on images obtained at 10X magnification. We tested the approach in otherwise isogenic cell lines in which we genetically dialed up or down CIN rates, and also on a publicly available image data set (obtained at 100X) and achieved an RMSE value of 0.0159, an R 2 of 0.90, and a Pearson's correlation of 0.951. Given the increasing interest in developing therapies for CIN-driven cancers, this method provides an important, scalable, and rapid approach to quantifying CIN on routinely obtained images. We release a GUI-implementation for easy access and utilization of the pipeline.

Structure-activity relationships of GPX4 inhibitor warheads.

Direct inhibition of GPX4 requires covalent modification of the active-site selenocysteine. While phenotypic screening has revealed that activated alkyl chlorides and masked nitrile oxides can inhibit GPX4 covalently, a systematic assessment of potential electrophilic warheads with the capacity to inhibit cellular GPX4 has been lacking. Here, we survey more than 25 electrophilic warheads across several distinct GPX4-targeting scaffolds. We find that electrophiles with attenuated reactivity compared to chloroacetamides are unable to inhibit GPX4 despite the expected nucleophilicity of the selenocysteine residue. However, highly reactive propiolamides we uncover in this study can substitute for chloroacetamide and nitroisoxazole warheads in GPX4 inhibitors. Our observations suggest that electrophile masking strategies, including those we describe for propiolamide- and nitrile-oxide-based warheads, may be promising for the development of improved covalent GPX4 inhibitors.

Selective covalent targeting of GPX4 using masked nitrile-oxide electrophiles.

We recently described glutathione peroxidase 4 (GPX4) as a promising target for killing therapy-resistant cancer cells via ferroptosis. The onset of therapy resistance by multiple types of treatment results in a stable cell state marked by high levels of polyunsaturated lipids and an acquired dependency on GPX4. Unfortunately, all existing inhibitors of GPX4 act covalently via a reactive alkyl chloride moiety that confers poor selectivity and pharmacokinetic properties. Here, we report our discovery that masked nitrile-oxide electrophiles, which have not been explored previously as covalent cellular probes, undergo remarkable chemical transformations in cells and provide an effective strategy for selective targeting of GPX4. The new GPX4-inhibiting compounds we describe exhibit unexpected proteome-wide selectivity and, in some instances, vastly improved physiochemical and pharmacokinetic properties compared to existing chloroacetamide-based GPX4 inhibitors. These features make them superior tool compounds for biological interrogation of ferroptosis and constitute starting points for development of improved inhibitors of GPX4.

The Human TET2 Gene Contains Three Distinct Promoter Regions With Differing Tissue and Developmental Specificities.

Tet methylcytosine dioxygenase 2 (TET2) is a tumor suppressor gene that is inactivated in a wide range of hematological cancers. TET2 enzymatic activity converts 5-methylcytosine (5-mC) into 5-hydroxymethylcytosine (5-hmC), an essential step in DNA demethylation. Human TET2 is highly expressed in pluripotent cells and down-regulated in differentiated cells: however, transcriptional regulation of the human TET2 gene has not been investigated in detail. Here we define three promoters within a 2.5 kb region located ∼ 87 kb upstream of the first TET2 coding exon. The three promoters, designated as Pro1, Pro2, and Pro3, generate three alternative first exons, and their presence in TET2 mRNAs varies with cell type and developmental stage. In general, all three TET2 transcripts are more highly expressed in human tissues rich in hematopoietic stem cells, such as spleen and bone marrow, compared to other tissues, such as brain and kidney. Transcripts from Pro2 are expressed by a broad range of tissues and at a significantly higher level than Pro1 or Pro3 transcripts. Pro3 transcripts were highly expressed by embryoid bodies generated from the H9 ES cell line, and the major Pro3 transcript is an alternatively spliced mRNA isoform that produces a truncated TET2 protein lacking the catalytic domain. Our study demonstrates distinct tissue-specific mechanisms of TET2 transcriptional regulation during early pluripotent states and in differentiated cell types.