Single-cell RNA sequencing data imputation using bi-level feature propagation.

Single-cell RNA sequencing (scRNA-seq) enables the exploration of cellular heterogeneity by analyzing gene expression profiles in complex tissues. However, scRNA-seq data often suffer from technical noise, dropout events and sparsity, hindering downstream analyses. Although existing works attempt to mitigate these issues by utilizing graph structures for data denoising, they involve the risk of propagating noise and fall short of fully leveraging the inherent data relationships, relying mainly on one of cell-cell or gene-gene associations and graphs constructed by initial noisy data. To this end, this study presents single-cell bilevel feature propagation (scBFP), two-step graph-based feature propagation method. It initially imputes zero values using non-zero values, ensuring that the imputation process does not affect the non-zero values due to dropout. Subsequently, it denoises the entire dataset by leveraging gene-gene and cell-cell relationships in the respective steps. Extensive experimental results on scRNA-seq data demonstrate the effectiveness of scBFP in various downstream tasks, uncovering valuable biological insights.

Stricturing Crohn's Disease Single-Cell RNA Sequencing Reveals Fibroblast Heterogeneity and Intercellular Interactions.

BACKGROUND & AIMS: Fibroblasts play a key role in stricture formation in Crohn's disease (CD) but understanding its pathogenesis requires a systems-level investigation to uncover new treatment targets. We studied full-thickness CD tissues to characterize fibroblast heterogeneity and function by generating the first single-cell RNA sequencing (scRNAseq) atlas of strictured bowel and providing proof of principle for therapeutic target validation. METHODS: We performed scRNAseq of 13 fresh full-thickness CD resections containing noninvolved, inflamed nonstrictured, and strictured segments as well as 7 normal non-CD bowel segments. Each segment was separated into mucosa/submucosa or muscularis propria and analyzed separately for a total of 99 tissue samples and 409,001 cells. We validated cadherin-11 (CDH11) as a potential therapeutic target by using whole tissues, isolated intestinal cells, NanoString nCounter, next-generation sequencing, proteomics, and animal models. RESULTS: Our integrated dataset revealed fibroblast heterogeneity in strictured CD with the majority of stricture-selective changes detected in the mucosa/submucosa, but not the muscle layer. Cell-cell interaction modeling revealed CXCL14+ as well as MMP/WNT5A+ fibroblasts displaying a central signaling role in CD strictures. CDH11, a fibroblast cell-cell adhesion molecule, was broadly expressed and up-regulated, and its profibrotic function was validated using NanoString nCounter, RNA sequencing, tissue target expression, in vitro gain- and loss-of-function experiments, proteomics, and knock-out and antibody-mediated CDH11 blockade in experimental colitis. CONCLUSIONS: A full-thickness bowel scRNAseq atlas revealed previously unrecognized fibroblast heterogeneity and interactions in CD strictures and CDH11 was validated as a potential therapeutic target. These results provide a new resource for a better understanding of CD stricture formation and open potential therapeutic developments. This work has been posted as a preprint on Biorxiv under doi: 10.1101/2023.04.03.534781.

Deep single-cell RNA-seq data clustering with graph prototypical contrastive learning.

MOTIVATION: Single-cell RNA sequencing enables researchers to study cellular heterogeneity at single-cell level. To this end, identifying cell types of cells with clustering techniques becomes an important task for downstream analysis. However, challenges of scRNA-seq data such as pervasive dropout phenomena hinder obtaining robust clustering outputs. Although existing studies try to alleviate these problems, they fall short of fully leveraging the relationship information and mainly rely on reconstruction-based losses that highly depend on the data quality, which is sometimes noisy. RESULTS: This work proposes a graph-based prototypical contrastive learning method, named scGPCL. Specifically, scGPCL encodes the cell representations using Graph Neural Networks on cell-gene graph that captures the relational information inherent in scRNA-seq data and introduces prototypical contrastive learning to learn cell representations by pushing apart semantically dissimilar pairs and pulling together similar ones. Through extensive experiments on both simulated and real scRNA-seq data, we demonstrate the effectiveness and efficiency of scGPCL. AVAILABILITY AND IMPLEMENTATION: Code is available at https://github.com/Junseok0207/scGPCL.

Noncanonical NF-κB factor p100/p52 regulates homologous recombination and modulates sensitivity to DNA-damaging therapy.

Homologous recombination (HR) serves multiple roles in DNA repair that are essential for maintaining genomic stability, including double-strand DNA break (DSB) repair. The central HR protein, RAD51, is frequently overexpressed in human malignancies, thereby elevating HR proficiency and promoting resistance to DNA-damaging therapies. Here, we find that the non-canonical NF-κB factors p100/52, but not RelB, control the expression of RAD51 in various human cancer subtypes. While p100/p52 depletion inhibits HR function in human tumor cells, it does not significantly influence the proficiency of non-homologous end joining, the other key mechanism of DSB repair. Clonogenic survival assays were performed using a pair DLD-1 cell lines that differ only in their expression of the key HR protein BRCA2. Targeted silencing of p100/p52 sensitizes the HR-competent cells to camptothecin, while sensitization is absent in HR-deficient control cells. These results suggest that p100/p52-dependent signaling specifically controls HR activity in cancer cells. Since non-canonical NF-κB signaling is known to be activated after various forms of genomic crisis, compensatory HR upregulation may represent a natural consequence of DNA damage. We propose that p100/p52-dependent signaling represents a promising oncologic target in combination with DNA-damaging treatments.

Origami and Kirigami Structure for Impact Energy Absorption: Its Application to Drone Guards.

As the use of drones grows, so too does the demand for physical protection against drone damage resulting from collisions and falls. In addition, as the flight environment becomes more complicated, a shock absorption system is required, in which the protective structure can be deformed based on the circumstances. Here, we present an origami- and kirigami-based structure that provides protection from various directions. This research adds a deformation capacity to existing fixed-shape guards; by using shape memory alloys, the diameter and height of the protective structure are controlled. We present three protective modes (1: large diameter/low height; 2: small diameter/large height; and 3: lotus shaped) that mitigate drone falls and side collisions. From the result of the drop impact test, mode 2 showed a 78.2% reduction in the maximum impact force at side impact. We incorporated kirigami patterns into the origami structures in order to investigate the aerodynamic effects of the hollow patterns. Airflow experiments yielded a macro understanding of flow-through behaviors on each kirigami pattern. In the wind speed experiment, the change in airflow velocity induced by the penetration of the kirigami pattern was measured, and in the force measurement experiment, the air force applied to the structure was determined.

JULGI-mediated increment in phloem transport capacity relates to fruit yield in tomato.

The continuous growth of the global population and the increase in the amount of arid land has severely constrained agricultural crop production. To solve this problem, many researchers have attempted to increase productivity through the efficient distribution of energy; however, the direct relationship between the plant vasculature, specifically phloem development, and crop yield is not well established. Here, we demonstrate that an optimum increase in phloem-transportation capacity by reducing SIJUL expression leads to improved sink strength in tomato (Solanum lycopersicum L.). SIJUL, a negative regulator of phloem development, suppresses the translation of a positive regulator of phloem development, SlSMXL5. The suppression of SlJUL increases the number of phloem cells and sucrose transport, but only an optimal reduction of SlJUL function greatly enhances sink strength in tomato, improving fruit setting, and yield contents by 37% and 60%, respectively. We show that the increment in phloem cell number confers spare transport capacity. Our results suggest that the control of phloem-transport capacity within the threshold could enhance the commitment of photosynthates to instigate yield improvement.

Association of Depression With Susceptibility and Adaptation to Seasickness in the Military Seafarers.

BACKGROUND: Although depression and motion sickness are prevalent in military personnel and seafarers, the association between depression and seasickness has been not yet elucidated. We aimed to evaluate the relationship of depression with initial susceptibility and adaptation to seasickness amongst military seafarers. METHODS: This retrospective cohort enrolled Navy seafarers who started seafaring between 2017 and 2019. Three groups were established according to the Beck Depression Inventory (BDI) score: no depression (BDI score of 0), minimal depression (BDI score 1-9), and mild-to-moderate depression (BDI score 10-29). The occurrence of seasickness requiring treatment was observed as the prescription of medication for the first 30 distant seafaring days. Considering adjustment period, the two different outcomes were defined. The susceptibility to seasickness was evaluated via at least one day suffered from seasickness requiring treatment during the early period (the first 5 seafaring days), and adaptation ability to seasickness was defined by more than 10% of the ratio, calculated days suffered from seasickness requiring treatment/days of seafaring during the late period (the 6-30th seafaring days). Binary logistic regression was further evaluated to estimate the odds of BDI groups and BDI score adjusted for age and workplace whether outside visual perception was possible. RESULTS: Among the 185 recruits, 179 participants (97%) sailed for more than 5 days were included in the study. Of the participants, 36% was susceptible to seasickness in the early and 17% was poorly adapted to seasickness in the late period. Multivariable model revealed that mild-to-moderate depression had elevated risk of poor adaptation (odds ratio [OR], 4.63; 95% confidence interval [CI], 1.31-16.98) whereas the results were not statistically significant for susceptibility to seasickness in the early period BDI score was independently associated with increased odds of poor adaptation (OR, 1.10; 95% CI, 1.04-1.18). CONCLUSION: The present study suggests that depression is associated with poor adaptation to seasickness in Navy seafarers. Depression screening tool might be helpful for providing preventable strategies for population at risk.

Compact Camera Fluorescence Detector for Parallel-Light Lens-Based Real-Time PCR System.

The polymerase chain reaction is an important technique in biological research. However, it is time consuming and has a number of disadvantages. Therefore, real-time PCR technology that can be used in real-time monitoring has emerged, and many studies are being conducted regarding its use. Real-time PCR requires many optical components and imaging devices such as expensive, high-performance cameras. Therefore, its cost and assembly process are limitations to its use. Currently, due to the development of smart camera devices, small, inexpensive cameras and various lenses are being developed. In this paper, we present a Compact Camera Fluorescence Detector for use in parallel-light lens-based real-time PCR devices. The proposed system has a simple optical structure, the system cost can be reduced, and the size can be miniaturized. This system only incorporates Fresnel lenses without additional optics in order for the same field of view to be achieved for 25 tubes. In the center of the Fresnel lens, one LED and a complementary metal-oxide semiconductor camera were placed in directions that were as similar as possible. In addition, to achieve the accurate analysis of the results, image processing was used to correct them. As a result of an experiment using a reference fluorescent substance and double-distilled water, it was confirmed that stable fluorescence detection was possible.

Electrochemical Gene Amplification Signal Detection of Disposable Biochips Using Electrodes.

Real-time Polymerase Chain Reaction (RT-PCR), a molecular diagnostic technology, is spotlighted as one of the quickest and fastest diagnostic methods for the actual coronavirus (SARS-CoV-2). However, the fluorescent label-based technology of the RT-PCR technique requires expensive equipment and a sample pretreatment process for analysis. Therefore, this paper proposes a biochip based on Electrochemical Impedance Spectroscopy (EIS). In this paper, it was possible to see the change according to the concentration by measuring the impedance with a chip made of two electrodes with different shapes of sample DNA.

Quantitative Analysis of Fluorescence Detection Using a Smartphone Camera for a PCR Chip.

Most existing commercial real-time polymerase chain reaction (RT-PCR) instruments are bulky because they contain expensive fluorescent detection sensors or complex optical structures. In this paper, we propose an RT-PCR system using a camera module for smartphones that is an ultra small, high-performance and low-cost sensor for fluorescence detection. The proposed system provides stable DNA amplification. A quantitative analysis of fluorescence intensity changes shows the camera's performance compared with that of commercial instruments. Changes in the performance between the experiments and the sets were also observed based on the threshold cycle values in a commercial RT-PCR system. The overall difference in the measured threshold cycles between the commercial system and the proposed camera was only 0.76 cycles, verifying the performance of the proposed system. The set calibration even reduced the difference to 0.41 cycles, which was less than the experimental variation in the commercial system, and there was no difference in performance.

Open Platform Cameras Based Bio-Imaging Evaluation System.

With the active development of mobile devices, a variety of ultra-small, high-definition, and open platform-based cameras are being mass-produced. In this paper, we established an emulation system to verify the bio-imaging performance of the bulky and expensive high-performance cameras and various smartphone cameras that have been used in bio-imaging devices. In the proposed system, the linearity of the brightness gradient change of four types of cameras was compared and analyzed. Based on these results, three cameras were selected in order of excellent linearity, and gel image analysis results were compared.

Cost-Effective Multiplex Fluorescence Detection System for PCR Chip.

The lack of portability and high cost of multiplex real-time PCR systems limits the device to be used in POC. To overcome this issue, this paper proposes a compact and cost-effective fluorescence detection system that can be integrated to a multiplex real-time PCR equipment. An open platform camera with embedded lens was used instead of photodiodes or an industrial camera. A compact filter wheel using a sliding tape is integrated, and the excitation LEDs are fixed at a 45° angle near the PCR chip, eliminating the need of additional filter wheels. The results show precise positioning of the filter wheel with an error less than 20 µm. Fluorescence detection results using a reference dye and standard DNA amplification showed comparable performance to that of the photodiode system.

Cloud-Based Software Architecture for Fully Automated Point-of-Care Molecular Diagnostic Device.

This paper proposes a cloud-based software architecture for fully automated point-of-care molecular diagnostic devices. The target system operates a cartridge consisting of an extraction body for DNA extraction and a PCR chip for amplification and fluorescence detection. To facilitate control and monitoring via the cloud, a socket server was employed for fundamental molecular diagnostic functions such as DNA extraction, amplification, and fluorescence detection. The user interface for experimental control and monitoring was constructed with the RESTful application programming interface, allowing access from the terminal device, edge, and cloud. Furthermore, it can also be accessed through any web-based user interface on smart computing devices such as smart phones or tablets. An emulator with the proposed software architecture was fabricated to validate successful operation.

Multiple Compact Camera Fluorescence Detector for Real-Time PCR Devices.

The polymerase chain reaction is an important technique in biological research because it tests for diseases with a small amount of DNA. However, this process is time consuming and can lead to sample contamination. Recently, real-time PCR techniques have emerged which make it possible to monitor the amplification process for each cycle in real time. Existing camera-based systems that measure fluorescence after DNA amplification simultaneously process fluorescence excitation and emission for dozens of tubes. Therefore, there is a limit to the size, cost, and assembly of the optical element. In recent years, imaging devices for high-performance, open platforms have benefitted from significant innovations. In this paper, we propose a fluorescence detector for real-time PCR devices using an open platform camera. This system can reduce the cost, and can be miniaturized. To simplify the optical system, four low-cost, compact cameras were used. In addition, the field of view of the entire tube was minimized by dividing it into quadrants. An effective image processing method was used to compensate for the reduction in the signal-to-noise ratio. Using a reference fluorescence material, it was confirmed that the proposed system enables stable fluorescence detection according to the amount of DNA.

IMIPMF: Inferring miRNA-disease interactions using probabilistic matrix factorization.

Recently, increasing evidence have reported that microRNAs (miRNAs) play key roles in a variety of biological processes. Therefore, the identification of novel miRNA-disease associations can shed new light on disease etiology and pathogenesis. Till now, various computational methods have been proposed to predict potential miRNA-disease associations by reducing the experimental costs and time consumption. However, most existing methods are highly dependent on known miRNA-disease associations. Therefore, the prediction of new miRNAs (i.e., miRNAs without known associated diseases) and new diseases (i.e., diseases without known associated miRNAs) has become challenging. In this paper, we present IMIPMF, a novel method for predicting miRNA-disease associations using probabilistic matrix factorization (PMF), which is a machine learning technique that is widely used in recommender systems. Predicting the rating scores that a user may assign to each item in a recommender system is analogous to predicting miRNA-disease associations. By applying PMF, our model not only identifies novel miRNA-disease associations, but also overcomes the common problem of incompatibility with miRNAs without any known associated disease, which was a limitation of most previous computational methods. We demonstrated that our proposed model achieved a high performance with a reliable AUC value of 0.891 by performing 5-fold cross-validation. Overall, IMIPMF is a high-performance machine-learning-based model for predicting miRNA-disease associations, although it only considers known miRNA-disease associations and miRNA expression data.

Synergistic cellular responses to heavy metal exposure: A minireview.

BACKGROUND: Metal-responsive transcription factor 1 (MTF-1) induces the expression of metallothioneins (MTs) which bind and sequester labile metal ions. While MTF-1 primarily responds to excess metal exposure, additional stress response mechanisms are activated by excess metals. Evidence suggests potential crosstalk between responses mediated by MTF-1 and stress signaling enhances cellular tolerance to metal exposure. SCOPE OF REVIEW: This review aims to summarize the current understanding of interaction between the stress response mediated by MTF-1 and other cellular mechanisms, notably the nuclear factor κB (NF-κB) and heat shock response (HSR). MAJOR CONCLUSIONS: Crosstalk between MTF-1 mediated metal response and NF-κB signaling or HSR can modulate expression of stress proteins in response to metal exposure via effects on precursor signals or direct interaction of transcriptional activators. The interaction between stress signaling pathways can enhance cell survival and tolerance through a unified response system. GENERAL SIGNIFICANCE: Elucidating the interactions between MTF-1 and cell stress response mechanisms is critical to a comprehensive understanding of metal-based cellular effects. Co-activation of HSR and NF-κB signaling allows the cell to detect metal contamination in the environment and improve survival outcomes.

Comparison of named entity recognition methodologies in biomedical documents.

BACKGROUND: Biomedical named entity recognition (Bio-NER) is a fundamental task in handling biomedical text terms, such as RNA, protein, cell type, cell line, and DNA. Bio-NER is one of the most elementary and core tasks in biomedical knowledge discovery from texts. The system described here is developed by using the BioNLP/NLPBA 2004 shared task. Experiments are conducted on a training and evaluation set provided by the task organizers. RESULTS: Our results show that, compared with a baseline having a 70.09% F1 score, the RNN Jordan- and Elman-type algorithms have F1 scores of approximately 60.53% and 58.80%, respectively. When we use CRF as a machine learning algorithm, CCA, GloVe, and Word2Vec have F1 scores of 72.73%, 72.74%, and 72.82%, respectively. CONCLUSIONS: By using the word embedding constructed through the unsupervised learning, the time and cost required to construct the learning data can be saved.

Best serum biomarker combination for ovarian cancer classification.

BACKGROUND: Screening test using CA-125 is the most common test for detecting ovarian cancer. However, the level of CA-125 is diverse by variable condition other than ovarian cancer. It has led to misdiagnosis of ovarian cancer. METHODS: In this paper, we explore the 16 serum biomarker for finding alternative biomarker combination to reduce misdiagnosis. For experiment, we use the serum samples that contain 101 cancer and 92 healthy samples. We perform two major tasks: Marker selection and Classification. For optimal marker selection, we use genetic algorithm, random forest, T-test and logistic regression. For classification, we compare linear discriminative analysis, K-nearest neighbor and logistic regression. RESULTS: The final results show that the logistic regression gives high performance for both tasks, and HE4-ELISA, PDGF-AA, Prolactin, TTR is the best biomarker combination for detecting ovarian cancer. CONCLUSIONS: We find the combination which contains TTR and Prolactin gives high performance for cancer detection. Early detection of ovarian cancer can reduce high mortality rates. Finding a combination of multiple biomarkers for diagnostic tests with high sensitivity and specificity is very important.

A method of inferring the relationship between Biomedical entities through correlation analysis on text.

BACKGROUND: One of the most important processes in a machine learning-based natural language processing is to represent words. The one-hot representation that has been commonly used has a large size of vector and assumes that the features that make up the vector are independent of each other. On the other hand, it is known that word embedding has a great effect in estimating the similarity between words because it expresses the meaning of the word well. In this study, we try to clarify the correlation between various terms in the biomedical texts based on the excellent ability of estimating similarity between words shown by word embedding. Therefore, we used word embedding to find new biomarkers and microorganisms related to a specific diseases. METHODS: In this study, we try to analyze the correlation between diseases-markers and diseases-microorganisms. First, we need to construct a corpus that seems to be related to them. To do this, we extract the titles and abstracts from the biomedical texts on the PubMed site. Second, we express diseases, markers, and microorganisms' terms in word embedding using Canonical Correlation Analysis (CCA). CCA is a statistical based methodology that has a very good performance on vector dimension reduction. Finally, we tried to estimate the relationship between diseases-markers pairs and diseases-microorganisms pairs by measuring their similarity. RESULTS: In the experiment, we tried to confirm the correlation derived through word embedding using Google Scholar search results. Of the top 20 highly correlated disease-marker pairs, about 85% of the pairs have actually undergone a lot of research as a result of Google Scholars search. Conversely, for 85% of the 20 pairs with the lowest correlation, we could not actually find any other study to determine the relationship between the disease and the marker. This trend was similar for disease-microbe pairs. CONCLUSIONS: The correlation between diseases and markers and diseases and microorganisms calculated through word embedding reflects actual research trends. If the word-embedding correlation is high, but there are not many published actual studies, additional research can be proposed for the pair.

Performance evaluation of optimal real-time polymerase chain reaction achieved with reduced voltage.

BACKGROUND: Polymerase chain reaction (PCR) is used in nucleic acid tests of infectious diseases in point-of-care testing. Previous studies have demonstrated real-time PCR that uses a micro-PCR chip made of packing tape, double-sided tape, and a plastic cover with polycarbonate or polypropylene on a black matte printed circuit board substrate. Despite the success of DNA amplification and fluorescence detection using an early version of the micro-PCR chip, reaching the target temperature was fairly slow and, as a result, the total running time was getting longer. To reduce this runtime, the micro-PCR chip was modified by reducing the heater pattern size of the PCB substrate to one-quarter of the original size or less, while maintaining the ability of the heating pattern to cover the reservoir area of the microfluidic channel. In subsequent experiments, DNA amplification failed several times. During the analysis of the cause of this failure, it was found that the reagent was boiling with the heating range from 25 to 95 °C. METHODS: As a method of DNA amplification verification, images were captured by digital single-lens reflex camera to detect FAM fluorescence using diagonal illumination from a blue LED light source. The images were automatically captured at 72 °C (the extension step in nucleic acid amplification) and the brightness of the captured images was analyzed to con-firm the success of DNA amplification. RESULTS: Compared to the previous chip with a larger heating pattern size, the current chip appears to generate excess energy as the size of the heating pattern was reduced. To reduce this excess energy, the initial voltage was lowered to 2 V and 2.5 V, which is equivalent to a one-fifth and one-quarter voltage-power reduction in pulse width modulation control, respectively. In both voltage reduction cases, the DNA amplification was successful. CONCLUSIONS: DNA amplification tests may fail due to the excess energy generated by reducing the heater pattern size of the PCB substrate. However, the tests succeeded when the voltage was reduced to 2 V or 2.5 V. The 2.5 V power test was more efficient for reducing the overall running time.