Deep Learning-Based Morphological Classification of Endoplasmic Reticulum Under Stress

Flaring Inflammation and ER Stress by an Organelle-Specific Fluorescent Cage

The endoplasmic reticulum (ER) plays an important role in protein synthesis and its disruption can cause protein unfolding and misfolding. Accumulation of such proteins leads to ER stress, which ultimately promotes many diseases. Routine screening of ER activity in immune cells can flag serious conditions at early stages, but the current clinically used bio-probes have limitations. Herein, an ER-specific fluorophore based on a biocompatible benzothiadiazole-imine cage (BTD-cage) with excellent photophysical properties is developed. The cage outperforms commercially available ER stains in long-term live cell imaging with no fading or photobleaching over time. The cage is responsive to different levels of ER stress where its fluorescence increases accordingly. Incorporating the bio-probe into an immune disorder model, a 6-, 21-, and 48-fold increase in intensity is shown in THP-1, Raw 246.7, and Jurkat cells, respectively (within 15 min). These results strongly support that this system can be used for rapid visual and selective detection of ER stress. It is envisaged that tailoring molecular interactions and molecular recognition using supramolecular improved fluorophores can expand the library of biological probes for enhanced selectivity and targetability toward cellular organelles.

Intracellular remodeling associated with endoplasmic reticulum stress modifies biomechanical compliance of bladder cells

Bladder cells face a challenging biophysical environment: mechanical cues originating from urine flow and regular contraction to enable the filling voiding of the organ. To ensure functional adaption, bladder cells rely on high biomechanical compliance, nevertheless aging or chronic pathological conditions can modify this plasticity. Obviously the cytoskeletal network plays an essential role, however the contribution of other, closely entangled, intracellular organelles is currently underappreciated. The endoplasmic reticulum (ER) lies at a crucial crossroads, connected to both nucleus and cytoskeleton. Yet, its role in the maintenance of cell mechanical stability is less investigated. To start exploring these aspects, T24 bladder cancer cells were treated with the ER stress inducers brefeldin A (10-40nM BFA, 24 h) and thapsigargin (0.1-100nM TG, 24 h). Without impairment of cell motility and viability, BFA and TG triggered a significant subcellular redistribution of the ER; this was associated with a rearrangement of actin cytoskeleton. Additional inhibition of actin polymerization with cytochalasin D (100nM CytD) contributed to the spread of the ER toward cell periphery, and was accompanied by an increase of cellular stiffness (Young´s modulus) in the cytoplasmic compartment. Shrinking of the ER toward the nucleus (100nM TG, 2 h) was related to an increased stiffness in the nuclear and perinuclear areas. A similar short-term response profile was observed also in normal human primary bladder fibroblasts. In sum, the ER and its subcellular rearrangement seem to contribute to the mechanical properties of bladder cells opening new perspectives in the study of the related stress signaling cascades. Video Abstract.

Induced Pluripotent Stem Cell-Based Drug Screening by Use of Artificial Intelligence

Induced pluripotent stem cells (iPSCs) are terminally differentiated somatic cells that differentiate into various cell types. iPSCs are expected to be used for disease modeling and for developing novel treatments because differentiated cells from iPSCs can recapitulate the cellular pathology of patients with genetic mutations. However, a barrier to using iPSCs for comprehensive drug screening is the difficulty of evaluating their pathophysiology. Recently, the accuracy of image analysis has dramatically improved with the development of artificial intelligence (AI) technology. In the field of cell biology, it has become possible to estimate cell types and states by examining cellular morphology obtained from simple microscopic images. AI can evaluate disease-specific phenotypes of iPS-derived cells from label-free microscopic images; thus, AI can be utilized for disease-specific drug screening using iPSCs. In addition to image analysis, various AI-based methods can be applied to drug development, including phenotype prediction by analyzing genomic data and virtual screening by analyzing structural formulas and protein-protein interactions of compounds. In the future, combining AI methods may rapidly accelerate drug discovery using iPSCs. In this review, we explain the details of AI technology and the application of AI for iPSC-based drug screening.