DomainFit: Identification of protein domains in cryo-EM maps at intermediate resolution using AlphaFold2-predicted models.

Cryoelectron microscopy (cryo-EM) has revolutionized the structural determination of macromolecular complexes. With the paradigm shift to structure determination of highly complex endogenous macromolecular complexes ex vivo and in situ structural biology, there are an increasing number of structures of native complexes. These complexes often contain unidentified proteins, related to different cellular states or processes. Identifying proteins at resolutions lower than 4 Å remains challenging because side chains cannot be visualized reliably. Here, we present DomainFit, a program for semi-automated domain-level protein identification from cryo-EM maps, particularly at resolutions lower than 4 Å. By fitting domains from AlphaFold2-predicted models into cryo-EM maps, the program performs statistical analyses and attempts to identify the domains and protein candidates forming the density. Using DomainFit, we identified two microtubule inner proteins, one of which contains a CCDC81 domain and is exclusively localized in the proximal region of the doublet microtubule in Tetrahymena thermophila.

DomainFit: Identification of Protein Domains in cryo-EM maps at Intermediate Resolution using AlphaFold2-predicted Models.

Cryo-electron microscopy (cryo-EM) has revolutionized our understanding of macromolecular complexes, enabling high-resolution structure determination. With the paradigm shift to in situ structural biology recently driven by the ground-breaking development of cryo-focused ion beam milling and cryo-electron tomography, there are an increasing number of structures at sub-nanometer resolution of complexes solved directly within their cellular environment. These cellular complexes often contain unidentified proteins, related to different cellular states or processes. Identifying proteins at resolutions lower than 4 Å remains challenging because the side chains cannot be visualized reliably. Here, we present DomainFit, a program for automated domain-level protein identification from cryo-EM maps at resolutions lower than 4 Å. By fitting domains from artificial intelligence-predicted models such as AlphaFold2-predicted models into cryo-EM maps, the program performs statistical analyses and attempts to identify the proteins forming the density. Using DomainFit, we identified two microtubule inner proteins, one of them, a CCDC81 domain-containing protein, is exclusively localized in the proximal region of the doublet microtubule from the ciliate Tetrahymena thermophila. The flexibility and capability of DomainFit makes it a valuable tool for analyzing in situ structures.

New Fluorophore and Its Applications in Visualizing Polystyrene Nanoplastics in Bean Sprouts and HeLa Cells.

In the domain of environmental science, pollutants of nanoscale plastic dimensions are acknowledged as subjects of intricate significance. Such entities, though minuscule, present formidable challenges to ecological systems and human health. The diminutive dimensions of these contaminants render their detection arduous, thus demanding the inception of avant-garde methodologies. The present manuscript postulates the employment of the tetraphenylethylene functional group with a fused xanthene (TPEF), a distinguished fluorophore, as an exemplary system for the discernment of nanoplastic particulates. The synthesis and characterization of TPEF have been exhaustively elucidated, revealing its paramount fluorescence attributes and inherent affinity for interaction with nanoplastics. When subjected to comparison with TPEF, nanoplastics are observed to manifest a more pronounced fluorescent luminescence than when associated with the conventional Nile Red (NR). Particularly, the TPEF has shown exceptional affinity for polystyrene (PS) nanoplastics. Further, the resilience of nanoplastics within the hypocotyl epidermis of soybeans, as well as their persistence in mung bean sprouts subsequent to rigorous rinsing protocols, has been meticulously examined. Additionally, this investigation furnishes empirical data signifying the existence of nano-dimensional plastic contaminants within HeLa cellular structures. The urgency of addressing the environmental ramifications engendered by these diminutive yet potent plastic constituents is emphatically highlighted in this manuscript. TPEF paves the way for prospective explorations, with the aspiration of devising efficacious mitigation strategies. Such strategies might encompass delineating the trajectories undertaken by nanoplastics within trophic networks or their ingress into human cellular architectures.

D-CryptO: deep learning-based analysis of colon organoid morphology from brightfield images.

Stem cell-derived organoids are a promising tool to model native human tissues as they resemble human organs functionally and structurally compared to traditional monolayer cell-based assays. For instance, colon organoids can spontaneously develop crypt-like structures similar to those found in the native colon. While analyzing the structural development of organoids can be a valuable readout, using traditional image analysis tools makes it challenging because of the heterogeneities and the abstract nature of organoid morphologies. To address this limitation, we developed and validated a deep learning-based image analysis tool, named D-CryptO, for the classification of organoid morphology. D-CryptO can automatically assess the crypt formation and opacity of colorectal organoids from brightfield images to determine the extent of organoid structural maturity. To validate this tool, changes in organoid morphology were analyzed during organoid passaging and short-term forskolin stimulation. To further demonstrate the potential of D-CryptO for drug testing, organoid structures were analyzed following treatments with a panel of chemotherapeutic drugs. With D-CryptO, subtle variations in how colon organoids responded to the different chemotherapeutic drugs were detected, which suggest potentially distinct mechanisms of action. This tool could be expanded to other organoid types, like intestinal organoids, to facilitate 3D tissue morphological analysis.

Spartan Face Mask Detection and Facial Recognition System.

According to the World Health Organization (WHO), wearing a face mask is one of the most effective protections from airborne infectious diseases such as COVID-19. Since the spread of COVID-19, infected countries have been enforcing strict mask regulation for indoor businesses and public spaces. While wearing a mask is a requirement, the position and type of the mask should also be considered in order to increase the effectiveness of face masks, especially at specific public locations. However, this makes it difficult for conventional facial recognition technology to identify individuals for security checks. To solve this problem, the Spartan Face Detection and Facial Recognition System with stacking ensemble deep learning algorithms is proposed to cover four major issues: Mask Detection, Mask Type Classification, Mask Position Classification and Identity Recognition. CNN, AlexNet, VGG16, and Facial Recognition Pipeline with FaceNet are the Deep Learning algorithms used to classify the features in each scenario. This system is powered by five components including training platform, server, supporting frameworks, hardware, and user interface. Complete unit tests, use cases, and results analytics are used to evaluate and monitor the performance of the system. The system provides cost-efficient face detection and facial recognition with masks solutions for enterprises and schools that can be easily applied on edge-devices.

Promotion of Bone Lesions Through the Myeloma Integrin α6-Mediated Osteolytic Signaling.

Osteolytic destruction is a hallmark of multiple myeloma and impairs myeloma patients' quality of life. However, the molecular mechanism underlying the pathogenesis of myeloma-associated bone disease remains unclear. In this study, we demonstrate the role of myeloma cell-expressed integrin α6 in bone. Integrin α6 binds to laminin 8 and epidermal growth factor receptor on mesenchymal stem cells (MSCs) to form a trimer complex and upregulates the secretion of osteolytic cytokines from both myeloma cells and MSCs, leading to enhanced bone resorption and reduced bone formation. Thus, this study elucidates an important mechanism for myeloma-induced bone lesions and implicates that targeting integrin α6 may be a viable approach for bone healing in myeloma patients.

The relationship of olfactory function and clinical traits in major depressive disorder.

People who have developed a good sense of smell could experience much more happiness and pleasure, which would trigger a discussion that olfactory disorder might correlate with the pathogenesis of major depressive disorder (MDD). Similar experiments conducted on rats have confirmed that nerve damage of olfactory pathway can induce a series of depression-like changes, including behavior, neurobiochemistry, and neuroimmunity. These changes will recover progressively with anti-depression treatment. While in similar studies on human beings, olfactory dysfunction has been found in people suffering from depression. This review briefly discusses the correlation between olfactory deficits and clinical traits of depression in different dimensions, such as the severity, duration and cognitive impairment of depression. Improving olfactory function may be expected to be a potential antidepressant therapy.

DiSNE Movie Visualization and Assessment of Clonal Kinetics Reveal Multiple Trajectories of Dendritic Cell Development.

A thorough understanding of cellular development is incumbent on assessing the complexities of fate and kinetics of individual clones within a population. Here, we develop a system for robust periodical assessment of lineage outputs of thousands of transient clones and establishment of bona fide cellular trajectories. We appraise the development of dendritic cells (DCs) in fms-like tyrosine kinase 3 ligand culture from barcode-labeled hematopoietic stem and progenitor cells (HSPCs) by serially measuring barcode signatures and visualize these multidimensional data using developmental interpolated t-distributed stochastic neighborhood embedding (DiSNE) time-lapse movies. We identify multiple cellular trajectories of DC development that are characterized by distinct fate bias and expansion kinetics and determine that these are intrinsically programmed. We demonstrate that conventional DC and plasmacytoid DC trajectories are largely separated already at the HSPC stage. This framework allows systematic evaluation of clonal dynamics and can be applied to other steady-state or perturbed developmental systems.

Deciphering the Innate Lymphoid Cell Transcriptional Program.

Innate lymphoid cells (ILCs) are enriched at mucosal surfaces, where they provide immune surveillance. All ILC subsets develop from a common progenitor that gives rise to pre-committed progenitors for each of the ILC lineages. Currently, the temporal control of gene expression that guides the emergence of these progenitors is poorly understood. We used global transcriptional mapping to analyze gene expression in different ILC progenitors. We identified PD-1 to be specifically expressed in PLZF+ ILCp and revealed that the timing and order of expression of the transcription factors NFIL3, ID2, and TCF-1 was critical. Importantly, induction of ILC lineage commitment required only transient expression of NFIL3 prior to ID2 and TCF-1 expression. These findings highlight the importance of the temporal program that permits commitment of progenitors to the ILC lineage, and they expand our understanding of the core transcriptional program by identifying potential regulators of ILC development.

Regulation of ERK-MAPK signaling in human epidermis.

BACKGROUND: The skin is largely comprised of keratinocytes within the interfollicular epidermis. Over approximately two weeks these cells differentiate and traverse the thickness of the skin. The stage of differentiation is therefore reflected in the positions of cells within the tissue, providing a convenient axis along which to study the signaling events that occur in situ during keratinocyte terminal differentiation, over this extended two-week timescale. The canonical ERK-MAPK signaling cascade (Raf-1, MEK-1/2 and ERK-1/2) has been implicated in controlling diverse cellular behaviors, including proliferation and differentiation. While the molecular interactions involved in signal transduction through this cascade have been well characterized in cell culture experiments, our understanding of how this sequence of events unfolds to determine cell fate within a homeostatic tissue environment has not been fully characterized. METHODS: We measured the abundance of total and phosphorylated ERK-MAPK signaling proteins within interfollicular keratinocytes in transverse cross-sections of human epidermis using immunofluorescence microscopy. To investigate these data we developed a mathematical model of the signaling cascade using a normalized-Hill differential equation formalism. RESULTS: These data show coordinated variation in the abundance of phosphorylated ERK-MAPK components across the epidermis. Statistical analysis of these data shows that associations between phosphorylated ERK-MAPK components which correspond to canonical molecular interactions are dependent upon spatial position within the epidermis. The model demonstrates that the spatial profile of activation for ERK-MAPK signaling components across the epidermis may be maintained in a cell-autonomous fashion by an underlying spatial gradient in calcium signaling. CONCLUSIONS: Our data demonstrate an extended phospho-protein profile of ERK-MAPK signaling cascade components across the epidermis in situ, and statistical associations in these data indicate canonical ERK-MAPK interactions underlie this spatial profile of ERK-MAPK activation. Using mathematical modelling we have demonstrated that spatially varying calcium signaling components across the epidermis may be sufficient to maintain the spatial profile of ERK-MAPK signaling cascade components in a cell-autonomous manner. These findings may have significant implications for the wide range of cancer drugs which therapeutically target ERK-MAPK signaling components.

A Stochastic Algorithm for Generating Realistic Virtual Interstitial Cell of Cajal Networks.

Interstitial cells of Cajal (ICC) play a central role in coordinating normal gastrointestinal (GI) motility. Depletion of ICC numbers and network integrity contributes to major functional GI motility disorders. However, the mechanisms relating ICC structure to GI function and dysfunction remains unclear, partly because there is a lack of large-scale ICC network imaging data across a spectrum of depletion levels to guide models. Experimental imaging of these large-scale networks remains challenging because of technical constraints, and hence, we propose the generation of realistic virtual ICC networks in silico using the single normal equation simulation (SNESIM) algorithm. ICC network imaging data obtained from wild-type (normal) and 5-HT2B serotonin receptor knockout (depleted ICC) mice were used to inform the algorithm, and the virtual networks generated were assessed using ICC network structural metrics and biophysically-based computational modeling. When the virtual networks were compared to the original networks, there was less than 10% error for four out of five structural metrics and all four functional measures. The SNESIM algorithm was then modified to enable the generation of ICC networks across a spectrum of depletion levels, and as a proof-of-concept, virtual networks were successfully generated with a range of structural and functional properties. The SNESIM and modified SNESIM algorithms, therefore, offer an alternative strategy for obtaining the large-scale ICC network imaging data across a spectrum of depletion levels. These models can be applied to accurately inform the physiological consequences of ICC depletion.

Human osteoclasts are inducible immunosuppressive cells in response to T cell-derived IFN-γ and CD40 ligand in vitro.

Osteoclasts (OCs) are bone resorbing cells whose activity can be regulated by activated T cells and their cytokines. However, the immune function of OCs is largely unknown. In this study, we found that as bystanders, human OCs effectively suppressed T-cell proliferation induced by allogeneic, microbial antigenic, and T-cell receptor stimuli in vitro. Mechanism studies revealed that T cell-derived IFN-γ and CD40 ligand (CD40L) induced the expression of indoleamine 2,3-dioxygenase (IDO) in OCs, which mediated the immunosuppressive function on T-cell proliferation through depleting tryptophan. Neutralizing IFN-γ and blocking CD40L, or silencing or inhibiting IDO in OCs restored T-cell proliferation in the presence of OCs. Our data reveal a novel function of human OCs as inducible immunosuppressive cells, and a feedback loop between OCs and activated T cells. Thus, this study provides new insight into the mechanism of the immunosuppressive function of OCs, and may be helpful for developing novel therapeutic strategies for human diseases involving both the bone and immune systems.

Developmental changes in postnatal murine intestinal interstitial cell of Cajal network structure and function.

The mammalian gastrointestinal (GI) tract undergoes rapid development during early postnatal life in order to transition from a milk to solid diet. Interstitial cells of Cajal (ICC) are the pacemaker cells that coordinate smooth muscle contractility within the GI tract, and hence we hypothesized that ICC networks undergo significant developmental changes during this early postnatal period. Numerical metrics for quantifying ICC network structural properties were applied on confocal ICC network imaging data obtained from the murine small intestine at various postnatal ages spanning birth to weaning. These imaging data were also coupled to a biophysically-based computational model to simulate pacemaker activity in the networks, to quantify how changes in structure may alter function. The results showed a pruning-like mechanism which occurs during postnatal development, and the temporal course of this phenomenon was defined. There was an initial ICC process overgrowth to optimize network efficiency and increase functional output volume. This was followed by a selective retaining and strengthening of processes, while others were discarded to further elevate functional output volume. Subsequently, new ICC processes were formed and the network was adjusted to its adult morphology. These postnatal ICC network developmental events may be critical in facilitating mature digestive function.

Automated classification and identification of slow wave propagation patterns in gastric dysrhythmia.

The advent of high-resolution (HR) electrical mapping of slow wave activity has significantly improved the understanding of gastric slow wave activity in normal and dysrhythmic states. One of the current limitations of this technique is it generates a vast amount of data, making manual analysis a tedious task for research and clinical development. In this study we present new automated methods to classify, identify, and locate patterns of interest in gastric slow wave propagation. The classification method uses a similarity metric to classify slow wave propagations, while the identification algorithm uses the divergence and mean curvature of the slow wave propagation to identify and regionalize patterns of interest. The methods were applied to synthetic and experimental datasets and were also compared to manual analysis. The methods classified and identified patterns of slow wave propagation in less than 1 s, compared to manual analysis which took up to 40 min. The automated methods achieved 96% accuracy in classifying AT maps, and 95% accuracy in identifying the propagation pattern with a mean spatial error of 1.5 mm in comparison to manual methods. These new methods will facilitate the efficient translation of gastrointestinal HR mapping techniques to clinical practice.

Are interstitial cells of Cajal involved in mechanical stress-induced gene expression and impairment of smooth muscle contractility in bowel obstruction?

BACKGROUND AND AIMS: The network of interstitial cells of Cajal (ICC) is altered in obstructive bowel disorders (OBD). However, whether alteration in ICC network is a cause or consequence of OBD remains unknown. This study tested the hypothesis that mechanical dilation in obstruction disrupts the ICC network and that ICC do not mediate mechanotranscription of COX-2 and impairment of smooth muscle contractility in obstruction. METHODS: Medical-grade silicon bands were wrapped around the distal colon to induce partial obstruction in wild-type and ICC deficient (W/W(v)) mice. RESULTS: In wild-type mice, colon obstruction led to time-dependent alterations of the ICC network in the proximal colon segment. Although unaffected on days 1 and 3, the ICC density decreased markedly and the network was disrupted on day 7 of obstruction. COX-2 expression increased, and circular muscle contractility decreased significantly in the segment proximal to obstruction. In W/W(v) control mice, COX-2 mRNA level was 4.0 (±1.1)-fold higher (n=4) and circular muscle contractility was lower than in wild-type control mice. Obstruction further increased COX-2 mRNA level in W/W(v) mice to 7.2 (±1.0)-fold vs. W/W(v) controls [28.8 (±4.1)-fold vs. wild-type controls] on day 3. Obstruction further suppressed smooth muscle contractility in W/W(v) mice. However, daily administration of COX-2 inhibitor NS-398 significantly improved muscle contractility in both W/W(v) sham and obstruction mice. CONCLUSIONS: Lumen dilation disrupts the ICC network. ICC deficiency has limited effect on stretch-induced expression of COX-2 and suppression of smooth muscle contractility in obstruction. Rather, stretch-induced COX-2 plays a critical role in motility dysfunction in partial colon obstruction.

Cellular automaton model for simulating tissue-specific intestinal electrophysiological activity.

Depletion of interstitial cell of Cajal (ICC) networks is known to occur in various gastrointestinal (GI) motility disorders. Although techniques for quantifying the structure of ICC networks are available, the ICC network structure-function relationships are yet to be well elucidated. Existing methods of relating ICC structure to function are computationally expensive, and it is difficult to up-scale them to larger multiscale simulations. A new cellular automaton model for simulating tissue-specific slow wave propagation was developed, and in preliminary studies the automaton model was applied on jejunal ICC network structures from wild-type and 5-HT2B receptor knockout (ICC depleted) mice. Two metrics were also developed to quantify the simulated propagation patterns: 1) ICC and 2) non-ICC activation lag metrics. These metrics measured the average delay in time taken for the slow wave to propagate across the ICC and non-ICC domain throughout the entire network compared to the theoretical fastest propagation, respectively. Slow wave propagation was successfully simulated across the ICC networks with greatly reduced computational time compared to previous methods, and the propagation pattern metrics quantitatively revealed an impaired propagation during ICC depletion. In conclusion, the developed slow wave propagation model and propagation pattern metrics offer a computationally efficient framework for relating ICC structure to function. These tools can now be further applied to define ICC structure-function relationships across various spatial and temporal scales.

A simplified biophysical cell model for gastric slow wave entrainment simulation.

Gastric electrical activity, also termed slow wave activity, is generated by a class of pacemaker cells called the interstitial cells of Cajal (ICC), which are organized with decreasing intrinsic frequencies along the stomach. In the healthy stomach, slow waves of different intrinsic frequencies converge to a single frequency with a constant phase-lag, in a process called entrainment. The main aim of this study was to develop a simplified biophysical ICC model that is capable of modeling the self-excitatory behavior and standard morphology of gastric slow waves. Entrainment of gastric slow waves was simulated in a one-dimensional (1D) model, with a linear gradient of intrinsic slow wave frequencies. In a coupled 1D model, the simulated slow waves were entrained to a single frequency; whereas in an uncoupled 1D model, the simulated slow waves occurred at different frequencies, resulting in loss of entrainment. The new cell model presents an option for future large multi-scale simulations of gastric slow waves in intact ICC network and diseased conditions where the loss of entrainment may lead to slow wave dysrhythmias and diminished gastric motility.

Automated classification of spatiotemporal characteristics of gastric slow wave propagation.

Gastric contractions are underpinned by an electrical event called slow wave activity. High-resolution electrical mapping has recently been adapted to study gastric slow waves at a high spatiotemporal detail. As more slow wave data becomes available, it is becoming evident that the spatial organization of slow wave plays a key role in the initiation and maintenance of gastric dsyrhythmias in major gastric motility disorders. All of the existing slow wave signal processing techniques deal with the identification and partitioning of recorded wave events, but not the analysis of the slow wave spatial organization, which is currently performed visually. This manual analysis is time consuming and is prone to observer bias and error. We present an automated approach to classify spatial slow wave propagation patterns via the use of Pearson cross correlations. Slow wave propagations were grouped into classes based on their similarity to each other. The method was applied to high-resolution gastric slow wave recordings from four pigs. There were significant changes in the velocity of the gastric slow wave wavefront and the amplitude of the slow wave event when there was a change in direction to the slow wave wavefront during dsyrhythmias, which could be detected with the automated approach.

Numerical metrics for automated quantification of interstitial cell of Cajal network structural properties.

Depletion of interstitial cells of Cajal (ICC) networks is known to occur in several gastrointestinal motility disorders. Although confocal microscopy can effectively image and visualize the spatial distribution of ICC networks, current descriptors of ICC depletion are limited to cell numbers and volume computations. Spatial changes in ICC network structural properties have not been quantified. Given that ICC generate electrical signals, the organization of a network may also affect physiology. In this study, six numerical metrics were formulated to automatically determine complex ICC network structural properties from confocal images: density, thickness, hole size, contact ratio, connectivity and anisotropy. These metrics were validated and applied in proof-of-concept studies to quantitatively determine jejunal ICC network changes in mouse models with decreased (5-HT2B receptor knockout (KO)) and normal (Ano1 KO) ICC numbers, and during post-natal network maturation. Results revealed a novel remodelling phenomenon occurring during ICC depletion, namely a spatial rearrangement of ICC and the preferential longitudinal alignment. In the post-natal networks, an apparent pruning of the ICC network was demonstrated. The metrics developed here enabled the first detailed quantitative analyses of structural changes that may occur in ICC networks during depletion and development.

Toward the virtual stomach: progress in multiscale modeling of gastric electrophysiology and motility.

Experimental progress in investigating normal and disordered gastric motility is increasingly being complimented by sophisticated multiscale modeling studies. Mathematical modeling has become a valuable tool in this effort, as there is an ever-increasing need to gain an integrative and quantitative understanding of how physiological mechanisms achieve coordinated functions across multiple biophysical scales. These interdisciplinary efforts have been particularly notable in the area of gastric electrophysiology, where they are beginning to yield a comprehensive and integrated in silico organ modeling framework, or 'virtual stomach'. At the cellular level, a number of biophysically based mathematical cell models have been developed, and these are now being applied in areas including investigations of gastric electrical pacemaker mechanisms, smooth muscle electrophysiology, and electromechanical coupling. At the tissue level, micro-structural models are being creatively developed and employed to investigate clinically significant questions, such as the functional effects of ICC degradation on gastrointestinal (GI) electrical activation. At the organ level, high-resolution electrical mapping and modeling studies are combined to provide improved insights into normal and dysrhythmic gastric electrical activation. These efforts are also enabling detailed forward and inverse modeling studies at the 'whole body' level, with implications for diagnostic techniques for gastric dysrhythmias. These recent advances, together with several others highlighted in this review, collectively demonstrate a powerful trend toward applying mathematical models to effectively investigate structure-function relationships and overcome multiscale challenges in basic and clinical GI research.