Cell Image Analysis Research Articles

Lymphedema-Associated Fibroblasts Are Related to Fibrosis and Stage Progression in Patients and a Murine Microsurgical Model.

The driver of secondary lymphedema (SL) progression is chronic inflammation, which promotes fibrosis. Despite advances in preclinical research, a specific effector cell subpopulation as a biomarker for therapy response or stage progression is still missing for SL. Whole skin samples of 35 murine subjects of a microsurgically induced SL model and 12 patients with SL were collected and their fibroblasts were isolated. These lymphedema-associated fibroblasts (LAFs) were cultured in a collagen I-poly-D-lysine 3-dimensional hydrogel to mimic skin conditions. Fibroblasts from nonlymphedema skin were used as negative control and transforming growth factor β (TGF-β)-stimulated fibroblasts were used to recreate profibrotic myofibroblasts. Quantitative immunocytofluorescence confocal microscopy analysis and invasion functional assays were performed in all subpopulations and statistically compared. In contrast to normal skin fibroblasts, LAFs exhibit α-smooth muscle actin-positive stress fibers and a reduced number of tight junctions in 3-dimensional hydrogel conditions. The switch from normal E-cadherin high phenotype to an N-cadherin high -E-cadherin low morphology suggests epithelial-to-mesenchymal transition for expansion and proliferation. This pathologic behavior of LAF was confirmed by live cell imaging analysis of invasion assays. The significant activation of markers of the TGF-β receptor 2-Smad pathway and collagen synthesis (HSP-47 [heat shock protein 47]) in LAFs supports epithelial-to-mesenchymal transition phenotypic changes and previous findings relating to TGF-β1 and fibrosis with lymphedema. A characteristic SL myofibroblast subpopulation was identified and translationally related to fibrosis and TGF-β1-associated stage progression. This SL-related subpopulation was termed LAFs. A comprehensive stage-related characterization is required to validate LAFs as a reliable biomarker for SL disease progression. The authors identify a cellular effector for fibrosis and stage progression of secondary lymphedema as a possible biomarker for surgical indication and therapy response.

Manifestation of Triploid Heterosis in the Root System after Crossing Diploid and Autotetraploid Energy Willow Plants.

Successful use of woody species in reducing climatic and environmental risks of energy shortage and spreading pollution requires deeper understanding of the physiological functions controlling biomass productivity and phytoremediation efficiency. Targets in the breeding of energy willow include the size and the functionality of the root system. For the combination of polyploidy and heterosis, we have generated triploid hybrids (THs) of energy willow by crossing autotetraploid willow plants with leading cultivars (Tordis and Inger). These novel Salix genotypes (TH3/12, TH17/17, TH21/2) have provided a unique experimental material for characterization of Mid-Parent Heterosis (MPH) in various root traits. Using a root phenotyping platform, we detected heterosis (TH3/12: MPH 43.99%; TH21/2: MPH 26.93%) in the size of the root system in soil. Triploid heterosis was also recorded in the fresh root weights, but it was less pronounced (MPH%: 9.63-19.31). In agreement with root growth characteristics in soil, the TH3/12 hybrids showed considerable heterosis (MPH: 70.08%) under in vitro conditions. Confocal microscopy-based imaging and quantitative analysis of root parenchyma cells at the division-elongation transition zone showed increased average cell diameter as a sign of cellular heterosis in plants from TH17/17 and TH21/2 triploid lines. Analysis of the hormonal background revealed that the auxin level was seven times higher than the total cytokinin contents in root tips of parental Tordis plants. In triploid hybrids, the auxin-cytokinin ratios were considerably reduced in TH3/12 and TH17/17 roots. In particular, the contents of cytokinin precursor, such as isopentenyl adenosine monophosphate, were elevated in all three triploid hybrids. Heterosis was also recorded in the amounts of active gibberellin precursor, GA19, in roots of TH3/12 plants. The presented experimental findings highlight the physiological basics of triploid heterosis in energy willow roots.

Evaluation of the Sysmex DI-60 digital morphology analyzer on Wright-stained samples with a focus on prevalence-dependent quality indicators.

This study aims to evaluate the trueness of the DI-60 Digital Cell Imaging Analyzer on Wright-stained samples with a focus on prevalence-dependent quality indicators for differential blood counts requested from non-hematology wards. Two hundred and ninety-nine samples were included into this performance evaluation study at the Department of Laboratory Medicine, Medical University of Vienna. The following aspects were verified: (a) the reliability of automatedly pre-classified differential counts, (b) the concordance of DI-60 counts with manual-microscopic differential counts and (c) the agreement of DI-60 and manual-microscopic results regarding clinically relevant findings. 82.3% of all leukocytes were correctly pre-classified. Cell categories with a low prevalence (eosinophils, basophils, progenitors/precursors) in non-hematological patients presented with a low positive predictive value (PPV), indicating a high frequency of false positives. Comparisons between visually adjusted results of the DI-60 and manual-microscopic differential counts revealed a good concordance for neutrophil and lymphocyte counts. Besides the detection of precursors/progenitors and normoblasts, no relevant systemic errors were detected. However, due to their low prevalence and technical aspects, the detection of basophilia, monocytosis or the presence of precursors/progenitors showed comparably low accuracies (error rates of 7.4%-24.1%). The DI-60 system works well for Wright-stained samples collected in the non-hematology ward. Due to the varying prevalence of cell categories found in peripheral blood, a low PPV can be expected with automatic assignment for those cells with low prevalence (e.g., basophils, eosinophils, precursor and progenitor cells, plasma cells). If the pre-test probability of these conditions is increased, manual microscopic processing may be recommended.

An innovative fluorescent probe for the detection of cyanide - enhanced sensitivity by controlling its electrostatic potential and suitable for applications such as cell imaging and food analysis

As cyanide is a huge hazard to the environment and human health, the study of the method of detecting low concentrations of cyanide is of great significance. In general, materials with strong positive electrostatic properties can use electrostatic attraction to enrich anions in the water near the materials, then realize rapid detection of low concentration anions by fluorescent probes. In this paper, fluorescent probes PI-S, PI-I and PI-N with cyanide-specific recognition and different charges were synthesized to study the relationship between the charge effect of probes and the sensing sensitivity. Through the zeta potential test and the calculation of the surface electrostatic potential, the positive electricity of PI-S, PI-I and PI-N gradually increased, the ΔG < 0 of the adsorption process gradually decreased, CN– could be aggregated to the vicinity of probes. As a result, the detection limit of the probe was gradually reduced from 1.07 × 10−6 to 5.03 × 10−8 M, the sensitivity was significantly enhanced. Therefore, this is expected to be a new strategy to improve the sensitivity of anion probes by increasing the positive electricity of molecules. In addition, PI-N has good anti-interference ability, short response time and certain application value in cell imaging and identification of endogenous cyanide in food.

Long-term, real-time and label-free live cell image processing and analysis based on a combined algorithm of CellPose and watershed segmentation

Developing a rapid and quantitative method to accurately evaluate the physiological abilities of living cells is critical for tumor control. Many experiments have been conducted in the field of biology in an attempt to measure the proliferation and movement abilities of cells, but existing methods cannot provide real-time and objective data for label-free cells. The quantitative imaging technique, including an automatic segmentation algorithm for individual label-free cells, has been a breakthrough in this regard. In this study, we develop a combined automatic image processing algorithm of CellPose and watershed segmentation for the long-term and real-time imaging of label-free cells. This method shows strong reliability in cell identification regardless of cell densities, allowing us to obtain accurate information about the number and proliferation ability of the target cells. Additionally, our results also suggest that this method is a reliable way to assess real-time data on drug cytotoxicity, cell morphology, and cell movement ability.

Application and progress of high content imaging in molecular biology.

Humans have adopted many different methods to explore matter imaging, among which High Content Imaging (HCI) could conduct automated imaging analysis of cells while maintaining its structural and functional integrity. Meanwhile, as one of the most important research tools for diagnosing human diseases, HCI is widely used in the frontier of medical research, and its future application has attracted researchers' great interests. Here, the meaning of HCI was briefly explained, the history of optical imaging and the birth of HCI were described, and the experimental methods of HCI were described. Furthermore, the directions of the application of HCI were highlighted in five aspects: protein localization changes, gene identification, chemical and genetic analysis, microbiology, and drug discovery. Most importantly, some challenges and future directions of HCI were discussed, and the application and optimization of HCI were expected to be further explored. This article is protected by copyright. All rights reserved.

Image analysis of brain cortex cells in vitro using deep learning method

The article presents a method for analyzing images of cultured cortical cells for a quantitative analysis of the parameters of development of biological neural networks using machine learning approaches. We have developed software modules for segmentation of images into cells, clusters, and neurites using the neural network model and the deep learning method; a training set of images of cultivated neurons and corresponding segmentation masks have been generated. The results were validated by analyzing the development of cultivated neurons in vitro based on the length count of neutrites at different growth stages of the culture. The developed methods for monitoring the processes of formation of biological neuronal networks based on the analysis of the neuronal growth under different conditions and on different substrates provide an opportunity to monitor the processes of stem cell differentiation in the neurogenic direction. The results can be used in monitoring the formation of organoids in bioengineering applications, as well as in modeling the processes of nerve tissue regeneration.

Novel T cell/organoid culture system allows ex vivo modeling of intestinal graft-versus-host disease.

Acute graft-versus-host disease (GvHD) remains the biggest clinical challenge and prognosis-determining complication after allogeneic hematopoietic stem cell transplantation (allo-HSCT). Donor T cells are acceptedly key mediators of alloreactivity against host tissues and here especially the gut. In support of previous studies, we found that the intestinal intra-epithelial lymphocyte (IEL) compartment was dynamically regulated in the course of MHC class I full mismatch allo-HSCT. However, while intestinal epithelial cell (IEC) damage endangers the integrity of the intestinal barrier and is a core signature of intestinal GvHD, the question whether and to what degree IELs are contributing to IEC dysregulation is poorly understood. To study lymphoepithelial interaction, we employed a novel ex vivo T cell/organoid co-culture model system. Here, allogeneic intra-epithelial T cells were superior in inducing IEC death compared to syngeneic IEL and allogeneic non-IEL T cells. The ability to induce IEC death was predominately confined to TCRβ+ T cells and was executed in a largely IFNγ-dependent manner. Alloreactivity required a diverse T cell receptor (TCR) repertoire since IELs genetically modified to express a TCR restricted to a single, non-endogenous antigen failed to mediate IEC pathology. Interestingly, minor histocompatibility antigen (miHA) mismatch was sufficient to elicit IEL-driven IEC damage. Finally, advanced live cell imaging analyses uncovered that alloreactive IELs patrolled smaller areas within intestinal organoids compared to syngeneic controls, indicating their unique migratory properties within allogeneic IECs. Together, we provide here experimental evidence for the utility of a co-culture system to model the cellular and molecular characteristics of the crosstalk between IELs and IEC in an allogeneic setting ex vivo. In the light of the emerging concept of dysregulated immune-epithelial homeostasis as a core aspect of intestinal GvHD, this approach represents a novel experimental system to e.g. screen therapeutic strategies for their potential to normalize T cell/IEC- interaction. Hence, analyses in pre-clinical in vivo allo-HSCT model systems may be restricted to hereby positively selected, promising approaches.

The autophagy protein Atg9 functions in glia and contributes to parkinsonian symptoms in a Drosophila model of Parkinson's disease.

Parkinson's disease is a progressive neurodegenerative disease characterized by motor deficits, dopaminergic neuron loss, and brain accumulation of α-synuclein aggregates called Lewy bodies. Dysfunction in protein degradation pathways, such as autophagy, has been demonstrated in neurons as a critical mechanism for eliminating protein aggregates in Parkinson's disease. However, it is less well understood how protein aggregates are eliminated in glia, the other cell type in the brain. In the present study, we show that autophagy-related gene 9 (Atg9), the only transmembrane protein in the autophagy machinery, is highly expressed in Drosophila glia from adult brain. Results from immunostaining and live cell imaging analysis reveal that a portion of Atg9 localizes to the trans-Golgi network, autophagosomes, and lysosomes in glia. Atg9 is persistently in contact with these organelles. Lacking glial atg9 reduces the number of omegasomes and autophagosomes, and impairs autophagic substrate degradation. This suggests that glial Atg9 participates in the early steps of autophagy, and hence the control of autophagic degradation. Importantly, loss of glial atg9 induces parkinsonian symptoms in Drosophila including progressive loss of dopaminergic neurons, locomotion deficits, and glial activation. Our findings identify a functional role of Atg9 in glial autophagy and establish a potential link between glial autophagy and Parkinson's disease. These results may provide new insights on the underlying mechanism of Parkinson's disease.

Classification of Acute Lymphoblastic Leukemia based on White Blood Cell Images using InceptionV3 Model

Acute lymphoblastic leukemia (ALL) is the most common form of leukemia that occurs in children. Detection of ALL through white blood cell image analysis can help with the prognosis and appropriate treatment. In this study, the author proposes an approach to classifying ALL based on white blood cell images using a convolutional neural network (CNN) model called InceptionV3. The dataset used in this research consists of white blood cell images collected from patients with ALL and healthy individuals. These images were obtained from The Cancer Imaging Archive (TCIA), which is a service that stores large-scale cancer medical images available to the public. During the evaluation phase, the author used training data evaluation metrics such as accuracy and loss to measure the model's performance. The research results show that the InceptionV3 model is capable of classifying white blood cell images with a high level of accuracy. This model achieves an average ALL recognition accuracy of 0.9896 with a loss of 0.031. The use of CNN models such as InceptionV3 in medical image analysis has the potential to improve the efficiency and precision of image-based disease diagnosis.

Reducing Cancer Cell Adhesion using Microtextured Surfaces.

For the past century, trypsin has been the primary method of cell dissociation, largely without any major changes to the process. Enzymatic cell detachment strategies for large-scale cell culturing processes are popular but can be labor-intensive, potentially lead to the accumulation of genetic mutations, and produce large quantities of liquid waste. Therefore, engineering surfaces to lower cell adhesion strength could enable the next generation of cell culture surfaces for delicate primary cells and automated, high-throughput workflows. In this study, a process for creating microtextured polystyrene (PS) surfaces to measure the impact of microposts on the adhesion strength of cells is developed. Cell viability and proliferation assays show comparable results in twocancer cell lines between micropost surfaces and standard cell culture vessels. However, cell image analysis on microposts reveals that cell area decreases by half, and leads to an average twofold increase in cell length per area. Using a microfluidic-based method up to a seven times greater percentage of cells are removed from micropost surfaces than the flat control surfaces. These results show that micropost surfaces enable decreased cell adhesion strength while maintaining similar cell viabilities and proliferation as compared to flat PS surfaces.

Automatic base-model selection for white blood cell image classification using meta-learning

Healthcare has benefited from the implementation of deep-learning models to solve medical image classification tasks. For example, White Blood Cell (WBC) image analysis is used to diagnose different pathologies like leukemia. However, medical datasets are mostly imbalanced, inconsistent, and costly to collect. Hence, it is difficult to select an adequate model to overcome the mentioned drawbacks. Therefore, we propose a novel methodology to automatically select models to solve WBC classification tasks. These tasks contain images collected using different staining methods, microscopes, and cameras. The proposed methodology includes meta- and base-level learnings. At the meta-level, we implemented meta-models based on prior-models to acquire meta-knowledge by solving meta-tasks using the shades of gray color constancy method. To determine the best models to solve new WBC tasks we developed an algorithm that uses the meta-knowledge and the Centered Kernel Alignment metric. Next, a learning rate finder method is employed to adapt the selected models. The adapted models (base-models) are used in an ensemble learning approach achieving accuracy and balanced accuracy scores of 98.29 and 97.69 in the Raabin dataset; 100 in the BCCD dataset; 99.57 and 99.51 in the UACH dataset, respectively. The results in all datasets outperform most of the state-of-the-art models, which demonstrates our methodology’s advantage of automatically selecting the best model to solve WBC tasks. The findings also indicate that our methodology can be extended to other medical image classification tasks where is difficult to select an adequate deep-learning model to solve new tasks with imbalanced, limited, and out-of-distribution data.

A new microfluidic model to study dendritic remodeling and mitochondrial dynamics during axonal regeneration of adult zebrafish retinal neurons.

Unlike mammals, adult zebrafish are able to fully regenerate axons and functionally recover from neuronal damage in the mature central nervous system (CNS). Decades of research have tried to identify the mechanisms behind their spontaneous regenerative capacity, but the exact underlying pathways and molecular drivers remain to be fully elucidated. By studying optic nerve injury-induced axonal regrowth of adult zebrafish retinal ganglion cells (RGCs), we previously reported transient dendritic shrinkage and changes in the distribution and morphology of mitochondria in the different neuronal compartments throughout the regenerative process. These data suggest that dendrite remodeling and temporary changes in mitochondrial dynamics contribute to effective axonal and dendritic repair upon optic nerve injury. To further elucidate these interactions, we here present a novel adult zebrafish microfluidic model in which we can demonstrate compartment-specific alterations in resource allocation in real-time at single neuron level. First, we developed a pioneering method that enables to isolate and culture adult zebrafish retinal neurons in a microfluidic setup. Notably, with this protocol, we report on a long-term adult primary neuronal culture with a high number of surviving and spontaneously outgrowing mature neurons, which was thus far only very limitedly described in literature. By performing time-lapse live cell imaging and kymographic analyses in this setup, we can explore changes in dendritic remodeling and mitochondrial motility during spontaneous axonal regeneration. This innovative model system will enable to discover how redirecting intraneuronal energy resources supports successful regeneration in the adult zebrafish CNS, and might facilitate the discovery of new therapeutic targets to promote neuronal repair in humans.

Comparison of an automated digital cell morphology analysis system with manual counting

AbstractBackgroundBionovation's CSFA800 is a new automated digital cell imaging analyzer. We evaluated the performance of the CSFA800 by comparing it with artificial peripheral blood white blood cell counting.MethodsAccording to inclusion and exclusion criteria, 131 randomly selected samples (77 abnormal samples and 54 normal samples) were compared. Correlations between automated and manual counting results were analyzed. Manual counting was carried out according to the guidelines of the Association of Clinical and Laboratory Standards.ResultsCounts of neutrophils, lymphocytes, monocytes, eosinophils, basophils, and immature granulocytes obtained from CSFA800 and artificial methods were linearly and positively correlated, with R values of 0.73, 0.65, 0.24, 0.2, 0.4, and 0.63, respectively, all p &lt; 0.05. Therefore, correlations between CSFA800 and manual counting are acceptable. Compared with the DI‐60 Automated Digital Cell Morphology System (DI‐60; Sysmex), CSFA800 is more efficient and can analyze 20,000 cells in 1 min. However, the overall accuracy of CSFA800 is not as good as DI‐60, although its counting performance is better for basophils.ConclusionsThe performance of CSFA800 for WBC counts is acceptable, and it displayed good performance for neutrophils, lymphocytes, and immature granulocytes. Compared to DI‐60, CSFA800 is more efficient but has slightly lower overall accuracy. To some extent, CSFA800 is helpful to optimize the clinical laboratory workflow and improve the working efficiency of inspectors.

A review of open-source image analysis tools for mammalian cell culture: algorithms, features and implementations.

Cell culture is undeniably important for multiple scientific applications, including pharmaceuticals, transplants, and cosmetics. However, cell culture involves multiple manual steps, such as regularly analyzing cell images for their health and morphology. Computer scientists have developed algorithms to automate cell imaging analysis, but they are not widely adopted by biologists, especially those lacking an interactive platform. To address the issue, we compile and review existing open-source cell image processing tools that provide interactive interfaces for management and prediction tasks. We highlight the prediction tools that can detect, segment, and track different mammalian cell morphologies across various image modalities and present a comparison of algorithms and unique features of these tools, whether they work locally or in the cloud. This would guide non-experts to determine which is best suited for their purposes and, developers to acknowledge what is worth further expansion. In addition, we provide a general discussion on potential implementations of the tools for a more extensive scope, which guides the reader to not restrict them to prediction tasks only. Finally, we conclude the article by stating new considerations for the development of interactive cell imaging tools and suggesting new directions for future research.

Abstract B014: Phenotypic CRISPR genome-wide screening for the discovery of genetic regulators of allele-specific mutant KRAS

Abstract Kirsten rat sarcoma viral oncogene homolog (KRAS) is the most frequently mutated oncogene detected in 30% of all human cancers. Drugs that can impede the function of oncogenic KRAS offer exceptional therapeutic value. However, direct targeting KRAS is challenging due to its protein structure that lacks deep binding pockets for small-molecule inhibitors. Despite the recent success in targeting the G12C allele, targeted therapy for another hotspot mutant – G12V mutant KRAS has not been described. Here, we utilize CRISPR-Cas9 based genome-wide knockout screens and leverage a high-throughput microfluidic cell sorting platform to identify genes that regulate mutant KRAS harboring G12V mutation. The screens were conducted in two colorectal cancer cell lines HT29 and SW480, expressing wild-type KRAS and homozygous G12V KRAS, respectively. We have identified a list of genes whose loss-of-function reduces wild-type or mutant G12V KRAS protein expression. Our top gene identified from the mutant KRAS screen - denoted as selective-mutant KRAS modulator (SMKM) has shown promising results in selectively decreasing G12V KRAS protein expression. Through multiple assays including live cell imaging, western blot, flow cytometry, and proteomic analysis in SMKM knockout cells and SMKM inhibitor-treated cells. We found highly selective reduction in G12V KRAS protein expression. More importantly, genetically or pharmacologically inhibiting SMKM has demonstrated anti-tumor activity. We conducted cell viability assays including CellTiter-glo, MTT, and clonogenic assay. We treated a panel of G12V KRAS mutant and wild-type KRAS cell lines with SMKM inhibitor and found significantly reduced cell viability in G12V KRAS mutant cell lines across colorectal and lung cancer. To further demonstrate the downstream impact on mutant KRAS signaling, secondary assays including western blot and Lumit immunoassay were conducted. We observe a reduction in the aberrant phospho-ERK and phospho-AKT signaling in SMKM inhibitor treated cells. Our in vivo efficacy trial using heterozygous G12V KRAS lung cancer NCI-H441 xenograft model has shown SMKM inhibition significantly reduces tumor growth and increases the survival rate. Together, these data indicate the feasibility of selectively targeting G12V KRAS with SMKM inhibition. Lastly, our results provide a first-in-class small molecule oral inhibitor based selective protein clearance as an effective anti-G12V mutant KRAS therapeutic strategy. Citation Format: Xiyue Hu, Randy Singh, Shana Kelley. Phenotypic CRISPR genome-wide screening for the discovery of genetic regulators of allele-specific mutant KRAS [abstract]. In: Proceedings of the AACR Special Conference: Targeting RAS; 2023 Mar 5-8; Philadelphia, PA. Philadelphia (PA): AACR; Mol Cancer Res 2023;21(5_Suppl):Abstract nr B014.

PO-04-166 DISTURBED PH CONTROL BY A NOVEL SLC4A3 VARIANT IDENTIFIED IN A SHORT QT SYNDROME FAMILY WITH A KCNH2 VARIANT

Short QT syndrome (SQTS) is one of the inherited primary arrhythmia syndromes and characterized by shortened QT intervals leading to ventricular fibrillation (VF) and sudden cardiac death, especially in young people. The major cause of SQTS is pathogenic variants in KCNH2 encoding Kv11.1 which produce IKr. Recently, a novel variant in SLC4A3 encoding the anion exchanger (AE3) has been identified in two SQTS families. However, the pathogenesis of SQTS caused by SLC4A3 variants in the presence of a KCNH2 variant have not been elucidated yet. We aimed to elucidate the functional change of a novel SLC4A3 variant in the interaction with a KCNH2 variant. We performed targeted gene sequencing (TGS) and whole exome sequencing (WES) in SQTS patients. Electrophysiological characteristics of the KCNH2 variant were analyzed using HEK293 cells by patch-clamp methods. We constructed stable HEK293 cell lines expressing SLC4A3 with or without the variant and measured intracellular pH (pHi) by ratiometric pH indicator, BCECF-AM. Using the stable cell lines, we transfected KCNH2 (WT or the variant), KCNQ1 and KCNJ2 and measured IKr, IKs and IK1. We identified a KCNH2 variant, c.280 c>t, p.H70Y by TGS in a 25-year-old man who suffered VF at night and was diagnosed with SQTS after resuscitation. We first performed electrophysiological analysis of KCNH2-H70Y alone, however, the variant affected a little of Ikr properties. Therefore, we performed WES in the proband and his family, and a novel SLC4A3 variant, c.1059 c>a, p.N353K was identified. Both variants were co-segregated in his family with short QT intervals. In the live cell imaging analysis, cells expressing SLC4A3-N353K showed significantly slower pHi alternations than those with WT (figure A), reflecting the loss-of-function effect of exchange kinetics in mutant AE3. In the electrophysiological analysis using stable cell lines expressing SLC4A3, IKs and IK1 did not show any changes in channel properties regardless the presence of SLC4A3-N353K. However, IKr with H70Y showed significantly slow deactivation time constant in the cells expressing SLC4A3-N353K (Figure B and C), which means the gain-of-function effect on Kv11.1. SLC4A3-N353K showed slower alteration of pHi, a loss-of-function effect on AE3. With the expression of SLC4A3-N353K,KCNH2-H70Y decelerated the deactivation kinetics of Ikr, which may lead to shortening of QT intervals in our patient.

Rhodamine B-Quinoline based schiff base as fluorescent ‘turn on’ sensor of Al3+, Cr3+, HSO4− and its cytotoxicity and cell imaging application on TPC-1 and HtH-7 cell lines

A designed Rhodamine-B-quinoline based Schiff base chemosensor (RhBQ) has been synthesized and characterized using various spectroscopic techniques. It shows ‘turn on’ fluorescence emission and also colorimetric sensitivity and selectivity towards trivalent metal ions (Al3+ and Cr3+) and anion (HSO4−) present in aqueous medium. Stoichiometry of both metal (Al3+ & Cr3+) - RhBQ complexes are found to be 1:1 using Job’s plot. A notable enhancement in emission intensity for RhBQ-Al3+ (∼341 fold), RhBQ-Cr3+ (∼292 fold), and RhBQ-HSO4− (∼136 fold) complexes compare to RhBQ are observed. Sensing mechanism i.e. opening of spirolactam ring of RhBQ has been confirmed through various spectroscopic techniques such as; 1H NMR, 13C NMR, ESI-MS, TRPL, FT-IR, and DFT based computational study. The limit of detection (LOD) for Al3+, Cr3+, and HSO4− are found to be 2.2 × 10−8 M, 2.12 × 10−8 M and 8.63 × 10−7 M respectively. An advanced level 3 input OR–INHIBIT logic gate are constructed using chelating ligand EDTA. For onsite detection, TLC paper strip spot of the probe, RhBQ is prepared and the detection of Al3+ in few antacid drug are presented. In addition, cell-imaging analysis of Al3+, Cr3+, and HSO4− ions in the Human papillary thyroid carcinoma cell line (TPC-1) & Human anaplastic thyroid cancer cell lines (HtH-7) are presented and shows promising concentration and time-dependent detection results.

Multisite assessment of reproducibility in high‐content cell migration imaging data

High‐content image‐based cell phenotyping provides fundamental insights into a broad variety of life science disciplines. Striving for accurate conclusions and meaningful impact demands high reproducibility standards, with particular relevance for high‐quality open‐access data sharing and meta‐analysis. However, the sources and degree of biological and technical variability, and thus the reproducibility and usefulness of meta‐analysis of results from live‐cell microscopy, have not been systematically investigated. Here, using high‐content data describing features of cell migration and morphology, we determine the sources of variability across different scales, including between laboratories, persons, experiments, technical repeats, cells, and time points. Significant technical variability occurred between laboratories and, to lesser extent, between persons, providing low value to direct meta‐analysis on the data from different laboratories. However, batch effect removal markedly improved the possibility to combine image‐based datasets of perturbation experiments. Thus, reproducible quantitative high‐content cell image analysis of perturbation effects and meta‐analysis depend on standardized procedures combined with batch correction.

Computer analysis of abnormal proliferation and transformation cells in gastric mucosa and its clinical significance

ABSTRACT To investigate the image computer analysis of abnormally proliferating transformed cells of gastric mucosa and its clinical significance. The pathological pictures of gastric adenomatous polyp cells, abnormally proliferating altered cells, and tubular adenocarcinoma cells in the stomach mucosa were assessed by image computer method on a total of 96 gastroscopic biopsy and ESD resection specimens. The data of cytoplasmic area, nuclear area, nuclear-cytoplasmic ratio, nuclear factor and N-heterotypic index of gastric adenomatous polyps, abnormal proliferative transformation and gastric intramucosal tubular adenocarcinoma were collected, and the mean, standard deviation and variance were calculated respectively. Standard Error, Maximum, Minimum Parameters and Statistical Structure. There were substantial discrepancies between gastric mucosal gastric adenomatous polyp cells and gastric mucosal abnormally proliferating transformed cells, according to the five data in the abnormal cells in the stomach mucosal proliferation area (p < 0.01); There was no significant difference between cells (p > 0.05). Computer analysis of cell images can provide quantitative values for the pathological diagnosis of gastric adenomatous polyp cells, abnormally proliferating transformed cells and tubular adenocarcinoma cells in the gastric mucosa, especially the degree of atypical proliferation. The monitoring of abnormally proliferated and transformed cells in gastric mucosa is of great significance for clinicians to accurately treat and track cell transformation, and to control the occurrence and development of gastric adenocarcinoma.