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Long-range intercellular communication is essential for multicellular biological systems to regulate multiscale cell–cell interactions and maintain life. Growing evidence suggests that intercellular calcium waves (ICWs) act as a class of long-range signals that influence a broad spectrum of cellular functions and behaviors. Importantly, mechanical signals, ranging from single-molecule-scale to tissue-scale in vivo, can initiate and modulate ICWs in addition to relatively well-appreciated biochemical and bioelectrical signals. Despite these recent conceptual and experimental advances, the full nature of underpinning mechanotransduction mechanisms by which cells convert mechanical signals into ICW dynamics remains poorly understood. This review provides a systematic analysis of quantitative ICW dynamics around three main stages: initiation, propagation, and regeneration/relay. We highlight the landscape of upstream molecules and organelles that sense and respond to mechanical stimuli, including mechanosensitive membrane proteins and cytoskeletal machinery. We clarify the roles of downstream molecular networks that mediate signal release, spread, and amplification, including adenosine triphosphate (ATP) release, purinergic receptor activation, and gap junction (GJ) communication. Furthermore, we discuss the broad pathophysiological implications of ICWs, covering pathophysiological processes such as cancer metastasis, tissue repair, and developmental patterning. Finally, we summarize recent advances in optical imaging and artificial intelligence (AI)/machine learning (ML) technologies that reveal the precise spatial-temporal-functional dynamics of ICWs and ATP waves. By synthesizing these insights, we offer a comprehensive framework of ICW mechanobiology and propose new directions for mechano-therapeutic strategies in disease diagnosis, cancer immunotherapies, and drug discovery. 

Abstract Evidence from physical sciences in oncology increasingly suggests that the interplay between the biophysical tumor microenvironment and genetic regulation has significant impact on tumor progression. Especially, tumor cells and the associated stromal cells not only alter their own cytoskeleton and physical properties but also remodel the microenvironment with anomalous physical properties. Together, these altered mechano-omics of tumor tissues and their constituents fundamentally shift the mechanotransduction paradigms in tumorous and stromal cells and activate oncogenic signaling within the neoplastic niche to facilitate tumor progression. However, current findings on tumor biophysics are limited, scattered, and often contradictory in multiple contexts. Systematic understanding of how biophysical cues influence tumor pathophysiology is still lacking. This review discusses recent different schools of findings in tumor biophysics that have arisen from multi-scale mechanobiology and the cutting-edge technologies. These findings range from the molecular and cellular to the whole tissue level and feature functional crosstalk between mechanotransduction and oncogenic signaling. We highlight the potential of these anomalous physical alterations as new therapeutic targets for cancer mechanomedicine. This framework reconciles opposing opinions in the field, proposes new directions for future cancer research, and conceptualizes novel mechanomedicine landscape to overcome the inherent shortcomings of conventional cancer diagnosis and therapies. 

Biomechanical forces are of fundamental importance in biology, diseases, and medicine. Mechanobiology is an emerging interdisciplinary field that studies how biological mechanisms are regulated by biomechanical forces and how physical principles can be leveraged to innovate new therapeutic strategies. This article reviews state-of-the-art mechanobiology knowledge about the yes-associated protein (YAP), a key mechanosensitive protein, and its roles in the development of drug resistance in human cancer. Specifically, the article discusses three topics: how YAP is mechanically regulated in living cells; the molecular mechanobiology mechanisms by which YAP, along with other functional pathways, influences drug resistance of cancer cells (particularly lung cancer cells); and finally, how the mechanical regulation of YAP can influence drug resistance and vice versa. By integrating these topics, we present a unified framework that has the potential to bring theoretical insights into the design of novel mechanomedicines and advance next-generation cancer therapies to suppress tumor progression and metastasis. 

Electronic devicesforrecording neuralactivityinthe nervoussyste m needto bescalableacrosslargespatialandte mporalscales whilealso providing millisecondandsingle-cellspatiote mporalresolution. H o w e v e r, e xi s ti n g hi g h- r e s ol u ti o n n e u r al r e c o r di n g d e vi c e s c a n n o t achievesi multaneousscalability on bothspatialandte mporallevels due toatrade-offbetweensensordensityand mechanicalflexibility. Here weintroduceathree-di mensional(3D)stackingi mplantableelectronic platfor m,basedonperfluorinateddielectricelasto mersandtissue-levelsoft multilayerelectrodes,thatenablesspatiote mporallyscalablesingle-cell neuralelectrophysiologyinthenervoussyste m. Ourelasto mersexhibit stable dielectric perfor mancefor overayearin physiologicalsolutions andare10,000ti messofterthanconventional plastic dielectrics. By leveragingthese uniquecharacteristics we developthe packaging of lithographednano metre-thickelectrodearraysina3Dconfiguration with across-sectionaldensityof7.6electrodesper100μ m2.Theresulting3D integrated multilayersoftelectrodearrayretainstissue-levelflexibility, reducingchronici m muneresponsesin mouse neuraltissues,and de monstratestheabilitytoreliablytrackelectricalactivityinthe mouse brain orspinalcord over months without disruptingani mal behaviour. 

Abstract Current biotechnologies can simultaneously measure multiple high-dimensional modalities (e.g., RNA, DNA accessibility, and protein) from the same cells. A combination of different analytical tasks (e.g., multi-modal integration and cross-modal analysis) is required to comprehensively understand such data, inferring how gene regulation drives biological diversity and functions. However, current analytical methods are designed to perform a single task, only providing a partial picture of the multi-modal data. Here, we present UnitedNet, an explainable multi-task deep neural network capable of integrating different tasks to analyze single-cell multi-modality data. Applied to various multi-modality datasets (e.g., Patch-seq, multiome ATAC + gene expression, and spatial transcriptomics), UnitedNet demonstrates similar or better accuracy in multi-modal integration and cross-modal prediction compared with state-of-the-art methods. Moreover, by dissecting the trained UnitedNet with the explainable machine learning algorithm, we can directly quantify the relationship between gene expression and other modalities with cell-type specificity. UnitedNet is a comprehensive end-to-end framework that could be broadly applicable to single-cell multi-modality biology. This framework has the potential to facilitate the discovery of cell-type-specific regulation kinetics across transcriptomics and other modalities. 

Introduction Studies have reported that antidiabetic medications (ADMs) were associated with lower risk of dementia, but current findings are inconsistent. This study compared the risk of dementia onset in patients with type 2 diabetes (T2D) treated with sulfonylurea (SU) or thiazolidinedione (TZD) to patients with T2D treated with metformin (MET). Research design and methods This is a prospective observational study within a T2D population using electronic medical records from all sites of the Veterans Affairs Healthcare System. Patients with T2D who initiated ADM from January 1, 2001, to December 31, 2017, were aged ≥60 years at the initiation, and were dementia-free were identified. A SU monotherapy group, a TZD monotherapy group, and a control group (MET monotherapy) were assembled based on prescription records. Participants were required to take the assigned treatment for at least 1 year. The primary outcome was all-cause dementia, and the two secondary outcomes were Alzheimer’s disease and vascular dementia, defined by International Classification of Diseases (ICD), 9th Revision, or ICD, 10th Revision, codes. The risks of developing outcomes were compared using propensity score weighted Cox proportional hazard models. Results Among 559 106 eligible veterans (mean age 65.7 (SD 8.7) years), the all-cause dementia rate was 8.2 cases per 1000 person-years (95% CI 6.0 to 13.7). After at least 1 year of treatment, TZD monotherapy was associated with a 22% lower risk of all-cause dementia onset (HR 0.78, 95% CI 0.75 to 0.81), compared with MET monotherapy, and 11% lower for MET and TZD dual therapy (HR 0.89, 95% CI 0.86 to 0.93), whereas the risk was 12% higher for SU monotherapy (HR 1.12 95% CI 1.09 to 1.15). Conclusions Among patients with T2D, TZD use was associated with a lower risk of dementia, and SU use was associated with a higher risk compared with MET use. Supplementing SU with either MET or TZD may partially offset its prodementia effects. These findings may help inform medication selection for elderly patients with T2D at high risk of dementia.