Trans-channel fluorescence learning improves high-content screening for Alzheimer’s disease therapeutics

Synthetic PET from CT improves diagnosis and prognosis for lung cancer: Proof of concept

[18F]Fluorodeoxyglucose positron emission tomography (FDG-PET) and computed tomography (CT) are indispensable components in modern medicine. Although PET can provide additional diagnostic value, it is costly and not universally accessible, particularly in low-income countries. To bridge this gap, we have developed a conditional generative adversarial network pipeline that can produce FDG-PET from diagnostic CT scans based on multi-center multi-modal lung cancer datasets (n = 1,478). Synthetic PET images are validated across imaging, biological, and clinical aspects. Radiologists confirm comparable imaging quality and tumor contrast between synthetic and actual PET scans. Radiogenomics analysis further proves that the dysregulated cancer hallmark pathways of synthetic PET are consistent with actual PET. We also demonstrate the clinical values of synthetic PET in improving lung cancer diagnosis, staging, risk prediction, and prognosis. Taken together, this proof-of-concept study testifies to the feasibility of applying deep learning to obtain high-fidelity PET translated from CT.

Screening Techniques for Drug Discovery in Alzheimer's Disease

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by progressive and irreversible impairment of memory and other cognitive functions of the aging brain. Pathways such as amyloid beta neurotoxicity, tau pathogenesis and neuroinflammatory have been used to understand AD, despite not knowing the definite molecular mechanism which causes this progressive disease. This review attempts to summarize the small molecules that target these pathways using various techniques involving high-throughput screening, molecular modeling, custom bioassays, and spectroscopic detection tools. Novel and evolving screening methods developed to advance drug discovery initiatives in AD research are also highlighted.

Microsnoop: A generalist tool for microscopy image representation

Accurate profiling of microscopy images from small scale to high throughput is an essential procedure in basic and applied biological research. Here, we present Microsnoop, a novel deep learning-based representation tool trained on large-scale microscopy images using masked self-supervised learning. Microsnoop can process various complex and heterogeneous images, and we classified images into three categories: single-cell, full-field, and batch-experiment images. Our benchmark study on 10 high-quality evaluation datasets, containing over 2,230,000 images, demonstrated Microsnoop's robust and state-of-the-art microscopy image representation ability, surpassing existing generalist and even several custom algorithms. Microsnoop can be integrated with other pipelines to perform tasks such as superresolution histopathology image and multimodal analysis. Furthermore, Microsnoop can be adapted to various hardware and can be easily deployed on local or cloud computing platforms. We will regularly retrain and reevaluate the model using community-contributed data to consistently improve Microsnoop.

CellDeathPred: a deep learning framework for ferroptosis and apoptosis prediction based on cell painting

Cell death, such as apoptosis and ferroptosis, play essential roles in the process of development, homeostasis, and pathogenesis of acute and chronic diseases. The increasing number of studies investigating cell death types in various diseases, particularly cancer and degenerative diseases, has raised hopes for their modulation in disease therapies. However, identifying the presence of a particular cell death type is not an obvious task, as it requires computationally intensive work and costly experimental assays. To address this challenge, we present CellDeathPred, a novel deep-learning framework that uses high-content imaging based on cell painting to distinguish cells undergoing ferroptosis or apoptosis from healthy cells. In particular, we incorporate a deep neural network that effectively embeds microscopic images into a representative and discriminative latent space, classifies the learned embedding into cell death modalities, and optimizes the whole learning using the supervised contrastive loss function. We assessed the efficacy of the proposed framework using cell painting microscopy data sets from human HT-1080 cells, where multiple inducers of ferroptosis and apoptosis were used to trigger cell death. Our model confidently separates ferroptotic and apoptotic cells from healthy controls, with an average accuracy of 95% on non-confocal data sets, supporting the capacity of the CellDeathPred framework for cell death discovery.

24-Hydroxycholesterol Moderates the Effects of Amyloid-β on Expression of HMG-CoA Reductase and ABCA1 Proteins in Mouse Astrocytes

Background

Elevated brain cholesterol increases the risk of Alzheimer's disease. Production of 24-hydroxycholesterol (24s-OHC) by neurons prevents cholesterol accumulation in the brain. In this study, we investigated the effect of 24s-OHC on the HMG-COA reductase and ABCA1 which are involved in the brain cholesterol homeostasis with or without β-amyloid in astrocytes.

Methods and Materials

Astrocytes were treated with 24s-OHC with or without Aβ. Western blot and real-time polymerase chain reaction were done to detect protein and gene expression of β-hydroxy-3-methyl-glutaryl-coenzyme A reductase (HMGCR) and ABCA1, respectively. Cholesterol release was determined using a quantitation kit.

Results

Protein levels of HMGCR and ABCA1 were significantly increased by Aβ; however, the 24s-OHC was able to restore their levels and diminish the effect of amyloid-β. Aβ did not have a significant effect on HMGCR expression, while 24s-OHC reduced it by 68%. Aβ-induced ABCA1 expression did not increase cholesterol efflux as the lower levels of cholesterol in conditioned medium of Aβ-treated cells were found.

Conclusion

Our novel findings show that Aβ affects two key elements in the brain cholesterol homeostasis, HMGCR and ABCA1, which are crucial in cholesterol synthesis and efflux. Since 24s-OHC could suppress the Aβ effects on enhancement of HMGCR and ABCA1, therefore the cytochrome P450 46A1 (Cyp46A1), which is exclusively expressed in the central nervous system and responsible for producing of 24s-OHC, could consider as a therapeutic target in the cholesterol-related neurodegenerative diseases such as Alzheimer's disease.