Centromeres, the specialized chromatin structures that are responsible for equal segregation of chromosomes at mitosis, are epigenetically maintained by a centromere-specific histone H3 variant (CenH3). However, the mechanistic basis for centromere maintenance is unknown. We investigated biochemical properties of CenH3 nucleosomes from Drosophila melanogaster cells. Cross-linking of CenH3 nucleosomes identifies heterotypic tetramers containing one copy of CenH3, H2A, H2B, and H4 each. Interphase CenH3 particles display a stable association of approximately 120 DNA base pairs. Purified centromeric nucleosomal arrays have typical “beads-on-a-string” appearance by electron microscopy but appear to resist condensation under physiological conditions. Atomic force microscopy reveals that native CenH3-containing nucleosomes are only half as high as canonical octameric nucleosomes are, confirming that the tetrameric structure detected by cross-linking comprises the entire interphase nucleosome particle. This demonstration of stable half-nucleosomes in vivo provides a possible basis for the instability of centromeric nucleosomes that are deposited in euchromatic regions, which might help maintain centromere identity.

Lineage transition in adenocarcinoma (ADC) and squamous cell carcinoma (SCC) of non-small cell lung cancer, as implicated by clinical observation of mixed ADC and SCC pathologies in adenosquamous cell carcinoma, remains a fundamental yet unsolved question. Here we provide in vivo evidence showing the transdifferentiation of lung cancer from ADC to SCC in mice: Lkb1-deficient lung ADC progressively transdifferentiates into SCC, via a pathologically mixed mAd-SCC intermediate. We find that reduction of lysyl oxidase (Lox) in Lkb1-deficient lung ADC decreases collagen disposition and triggers extracellular matrix remodelling and upregulates p63 expression, a SCC lineage survival oncogene. Pharmacological Lox inhibition promotes the transdifferentiation, whereas ectopic Lox expression significantly inhibits this process. Notably, ADC and SCC show differential responses to Lox inhibition. Collectively, our findings demonstrate the de novo transdifferentiation of lung ADC to SCC in mice and provide mechanistic insight that may have important implications for lung cancer treatment.

We have found that mica surfaces functionalized with aminopropyltriethoxysilane and aldehydes bind chromatin strongly enough to permit stable and reliable solution imaging by atomic force microscopy. The method is highly reproducible, uses very small amounts of material, and is successful even with very light degrees of surface modification. This surface is far superior to the widely used aminopropyltriethoxysilane-derivatized mica surface and permits resolution of structure on the nanometer-scale in an aqueous environment, conditions that are particularly important for chromatin studies. For example, bound nucleosomal arrays demonstrate major structural changes in response to changes in solution conditions, despite their prior fixation (to maintain nucleosome loading) and tethering to the surface with glutaraldehyde. By following individual molecules through a salt titration in a flow-through cell, one can observe significant changes in apparent nucleosome size at lower [salt] and complete loss of DNA from the polynucleosomal array at high salt. The latter result demonstrates that the DNA component in these arrays is not constrained by the tethering. The former result is consistent with the salt-induced loss of histones observed in bulk solution studies of chromatin and demonstrates that even histone components of the nucleosome are somewhat labile in these fixed and tethered arrays. We foresee many important applications for this surface in future atomic force microscopy studies of chromatin.

Mobile-phones have facilitated the creation of field-portable, cost-effective imaging and sensing technologies that approach laboratory-grade instrument performance. However, the optical imaging interfaces of mobile-phones are not designed for microscopy and produce spatial and spectral distortions in imaging microscopic specimens. Here, we report on the use of deep learning to correct such distortions introduced by mobile-phone-based microscopes, facilitating the production of high-resolution, denoised and colour-corrected images, matching the performance of benchtop microscopes with high-end objective lenses, also extending their limited depth-of-field. After training a convolutional neural network, we successfully imaged various samples, including blood smears, histopathology tissue sections, and parasites, where the recorded images were highly compressed to ease storage and transmission for telemedicine applications. This method is applicable to other low-cost, aberrated imaging systems, and could offer alternatives for costly and bulky microscopes, while also providing a framework for standardization of optical images for clinical and biomedical applications. enhancement and aberration correction were performed computationally using a deep convolutional neural network (see Fig. 1, Supplementary Fig. 1, and the Methods section). Deep learning 12 is a powerful machine learning technique that can perform complex operations using a multi-layered artificial neural network and has shown great success in various tasks for which data are abundant [13][14][15][16] . The use of deep learning has also been demonstrated in numerous biomedical applications, such as diagnosis 17,18 , image classification 19 , among others 20-24 . In our method, a supervised learning approach is first applied by feeding the designed deep network with input (smartphone microscope images) and labels (gold standard benchtop microscope images obtained for the same samples) and optimizing a cost function that guides the network to learn the statistical transformation between the input and label. Following the deep network training phase, the network remains fixed and a smartphone microscope image input into the deep network is rapidly enhanced in terms of spatial resolution, signal-to-noise ratio, and colour response, attempting to match the overall image quality and the field of view (FOV) that would result from using a 20× objective lens on a high-end benchtop microscope. In addition, we demonstrate that the image output by the network will have a larger depth of field (DOF) than the corresponding image acquired using a high-NA objective lens on a benchtop microscope. Each enhanced image of the mobile microscope is inferred by the deep network in a non-iterative, feed-forward manner. For example, the deep network generates an enhanced output image with a FOV of ~0.57 mm 2 (the same as that of a 20× objective lens), from a smartphone microscope image within ~0.42 s, using a standard personal computer equipped with a dual graphics-processing un...