TMEM56 deficiency impairs the haem metabolism and cell cycle progression during human erythropoiesis.

TMEM56, a gene coding a transmembrane protein, is abundantly expressed in erythroid cells. Despite this, its role in erythropoiesis has not been well characterized. In this study, we sought to clarify the function of TMEM56 in erythroid development, focusing specifically on its involvement in haem biosynthesis and cell cycle progression. To do this, we used CD34+ haematopoietic stem cells derived from umbilical cord blood and differentiated them into erythroid cells in an ex vivo model. Our results indicate that the loss of TMEM56 disrupts haem biosynthesis and impairs erythroid differentiation. Furthermore, deletion of Tmem56 in the erythroid lineage in murine models using erythropoietin receptor (EpoR)-Cre revealed defects in erythroid progenitors within the bone marrow under both normal conditions and during haemolytic anaemia. These observations underscore the regulatory role of TMEM56 in maintaining erythroid lineage homeostasis. Taken together, our results unveil a previously unrecognized function of TMEM56 in erythroid differentiation and suggest its potential as an unfounded target for therapeutic strategies in the treatment of erythropoietic disorders.

Metabolite and protein shifts in mature erythrocyte under hypoxia.

As the only cell type responsible for oxygen delivery, erythrocytes play a crucial role in supplying oxygen to hypoxic tissues, ensuring their normal functions. Hypoxia commonly occurs under physiological or pathological conditions, and understanding how erythrocytes adapt to hypoxia is fundamental for exploring the mechanisms of hypoxic diseases. Additionally, investigating acute and chronic mountain sickness caused by plateaus, which are naturally hypoxic environments, will aid in the study of hypoxic diseases. In recent years, increasingly developed proteomics and metabolomics technologies have become powerful tools for studying mature enucleated erythrocytes, which has significantly contributed to clarifying how hypoxia affects erythrocytes. The aim of this article is to summarize the composition of the cytoskeleton and cytoplasmic proteins of hypoxia-altered erythrocytes and explore the impact of hypoxia on their essential functions. Furthermore, we discuss the role of microRNAs in the adaptation of erythrocytes to hypoxia, providing new perspectives on hypoxia-related diseases.

A potent GPX4 degrader to induce ferroptosis in HT1080 cells.

Glutathione peroxidase 4 (GPX4) is the most promising target for inducing ferroptosis. GPX4-targeting strategies primarily focus on inhibiting its activity or adjusting its cellular level. However, small inhibitors have limitations due to the covalent reactive alkyl chloride moiety, which could lead to poor selectivity and suboptimal pharmacokinetic properties. Herein, we designed and synthesized a series of proteolysis targeting chimeras (PROTACs) by connecting RSL3, a small molecule inhibitor of GPX4, with six different ubiquitin ligase ligands. As a highly effective degrader, compound 18a is a potent degrader (DC50, 48h = 1.68 µM, Dmax, 48h = 85 %). It also showed an obvious anti-proliferative effect with the IC50 value of 2.37 ± 0.17 µM in HT1080. Mechanism research showed that compound 18a formed a ternary complex with GPX4 and cIAP and induced the degradation of GPX4 through the ubiquitin-proteasome system pathway. Furthermore, compound 18a also induced the accumulation of lipid peroxides and mitochondrial depolarization, subsequently triggering ferroptosis. Our work demonstrated the practicality and efficiency of the PROTAC strategy and offered a promising avenue for designing degraders to induce ferroptosis in cancer cells.

High-resolution single-photon imaging with physics-informed deep learning.

High-resolution single-photon imaging remains a big challenge due to the complex hardware manufacturing craft and noise disturbances. Here, we introduce deep learning into SPAD, enabling super-resolution single-photon imaging with enhancement of bit depth and imaging quality. We first studied the complex photon flow model of SPAD electronics to accurately characterize multiple physical noise sources, and collected a real SPAD image dataset (64 × 32 pixels, 90 scenes, 10 different bit depths, 3 different illumination flux, 2790 images in total) to calibrate noise model parameters. With this physical noise model, we synthesized a large-scale realistic single-photon image dataset (image pairs of 5 different resolutions with maximum megapixels, 17250 scenes, 10 different bit depths, 3 different illumination flux, 2.6 million images in total) for subsequent network training. To tackle the severe super-resolution challenge of SPAD inputs with low bit depth, low resolution, and heavy noise, we further built a deep transformer network with a content-adaptive self-attention mechanism and gated fusion modules, which can dig global contextual features to remove multi-source noise and extract full-frequency details. We applied the technique in a series of experiments including microfluidic inspection, Fourier ptychography, and high-speed imaging. The experiments validate the technique's state-of-the-art super-resolution SPAD imaging performance.