Novel Techniques for Single-cell RNA Sequencing Data Imputation and Clustering

Advances in single-cell technologies have shifted genomics research from the analysis of bulk tissues toward a comprehensive characterization of individual cells. These cutting-edge approaches enable the in-depth analysis of individual cells, unveiling the remarkable heterogeneity and complexity of cellular systems. By unraveling the unique signatures and functions of distinct cell types, single-cell technologies have not only deepened our understanding of fundamental biological processes but also unlocked new avenues for disease diagnostics and therapeutic interventions.The applications of single-cell technologies extend beyond basic research, with significant implications for precision medicine, drug discovery, and regenerative medicine. By capturing the cellular heterogeneity within tumors, these methods have shed light on the mechanisms of tumor evolution, metastasis, and therapy resistance. Additionally, they have facilitated the identification of rare cell populations with specialized functions, such as stem cells and tissue-resident immune cells, which hold great promise for cell-based therapies.However, one of the major challenges in analyzing scRNA-seq data is the prevalence of dropouts, which are instances where gene expression is not detected despite being present in the cell. Dropouts occur due to technical limitations and can introduce excessive noise into the data, obscuring the true biological signals. As a result, imputation methods are used to estimate missing values and reduce the impact of dropouts on downstream analyses. Furthermore, the high-dimensionality of scRNA-seq data presents additional challenges in effectively partitioning cell populations. Thus, robust computational approaches are required to overcome these challenges and extract meaningful biological insights from single-cell data.There have been numerous imputation and clustering methods developed specifically to address the unique challenges associated with scRNA-seq data analysis. These methods aim to reduce the impact of dropouts and high dimensionality, allowing for accurate cell population partitioning and the discovery of meaningful biological insights. While these methods have unquestionably advanced the field of single-cell transcriptomics, they are not without limitations. Some methods may be computationally intensive, resulting in scalability issues with large datasets, whereas others may introduce biases or overfit the data, potentially affecting the accuracy of subsequent analyses. Furthermore, the performance of these methods can vary depending on the dataset's complexity and heterogeneity. As a result, ongoing research is required to improve existing methodologies and create new algorithms that address these limitations while retaining robustness and accuracy in scRNA-seq data analysis.In this work, we propose three imputation approaches which incorporate with statistical and deep learning framework. We robustly reconstruct the gene expression matrix, effectively mitigating dropout effects and reducing noise. This results in the enhanced recovery of true biological signals from scRNA-seq data and leveraging transcriptomic profiles of single cells. In addition, we introduce a clustering method, which exploits the scRNA-seq data to identify cellular subpopulations. Our method employs a combination of dimensionality reduction and network fusion algorithms to generate a cell similarity graph. This approach accounts for both local and global structure within the data, enabling the discovery of rare and previously unidentified cell populations.We plan to assess the imputation and clustering methods through rigorous benchmarking on simulated and more than 30 real scRNA-seq datasets against existing state-of-the-art techniques. We will show that the imputed data generated from our method can enhance the quality of downstream analyses. Also, we demonstrate that our clustering algorithm is efficient in accurately identifying the cells populations and capable of analyzing big datasets.In conclusion, this thesis propose an alternative approaches to advance current state of scRNA-seq data analysis by developing innovative imputation and clustering methods that enable a more comprehensive and accurate characterization of cellular subpopulations. These advancements potentially have broad applicability in diverse research fields, including developmental biology, immunology, and oncology, where understanding cellular heterogeneity is crucial

Antiinflammatory Therapy with Canakinumab for Atherosclerotic Disease

Background: Experimental and clinical data suggest that reducing inflammation without affecting lipid levels may reduce the risk of cardiovascular disease. Yet, the inflammatory hypothesis of atherothrombosis has remained unproved. Methods: We conducted a randomized, double-blind trial of canakinumab, a therapeutic monoclonal antibody targeting interleukin-1β, involving 10,061 patients with previous myocardial infarction and a high-sensitivity C-reactive protein level of 2 mg or more per liter. The trial compared three doses of canakinumab (50 mg, 150 mg, and 300 mg, administered subcutaneously every 3 months) with placebo. The primary efficacy end point was nonfatal myocardial infarction, nonfatal stroke, or cardiovascular death. RESULTS: At 48 months, the median reduction from baseline in the high-sensitivity C-reactive protein level was 26 percentage points greater in the group that received the 50-mg dose of canakinumab, 37 percentage points greater in the 150-mg group, and 41 percentage points greater in the 300-mg group than in the placebo group. Canakinumab did not reduce lipid levels from baseline. At a median follow-up of 3.7 years, the incidence rate for the primary end point was 4.50 events per 100 person-years in the placebo group, 4.11 events per 100 person-years in the 50-mg group, 3.86 events per 100 person-years in the 150-mg group, and 3.90 events per 100 person-years in the 300-mg group. The hazard ratios as compared with placebo were as follows: in the 50-mg group, 0.93 (95% confidence interval [CI], 0.80 to 1.07; P = 0.30); in the 150-mg group, 0.85 (95% CI, 0.74 to 0.98; P = 0.021); and in the 300-mg group, 0.86 (95% CI, 0.75 to 0.99; P = 0.031). The 150-mg dose, but not the other doses, met the prespecified multiplicity-adjusted threshold for statistical significance for the primary end point and the secondary end point that additionally included hospitalization for unstable angina that led to urgent revascularization (hazard ratio vs. placebo, 0.83; 95% CI, 0.73 to 0.95; P = 0.005). Canakinumab was associated with a higher incidence of fatal infection than was placebo. There was no significant difference in all-cause mortality (hazard ratio for all canakinumab doses vs. placebo, 0.94; 95% CI, 0.83 to 1.06; P = 0.31). Conclusions: Antiinflammatory therapy targeting the interleukin-1β innate immunity pathway with canakinumab at a dose of 150 mg every 3 months led to a significantly lower rate of recurrent cardiovascular events than placebo, independent of lipid-level lowering. (Funded by Novartis; CANTOS ClinicalTrials.gov number, NCT01327846.

Highly Sensitive Planar Hall Magnetoresistive Sensor for Magnetic Flux Leakage Pipeline Inspection

Magnetic flux leakage (MFL) detection is frequently used for oil and gas pipeline inspection, especially for evaluation of the integrity of pipelines. The success of the MFL technique depends on many parameters. However, a sensitive magnetic sensor is an important requirement. Therefore, magnetic field sensors based on different mechanisms have been developed and applied to the MFL technique. In this paper, we evidence for the first time the capability of an innovative device based on a planar Hall magnetoresistance sensor devoted to MFL detection. This promising prototype combines all the required qualifications such as high sensitivity, low thermal drift, and bipolar and linear responses to the magnetic field. New achievements are carried out on embedded sensors in a testing platform reflecting pipeline environments. The ultrasensitive magnetic mapping concludes to a convincing technical approach with a high potential application toward MFL inspection, especially for the detection of shallow defects appearing at near side, far side, and sub-surface of a pipe wall. © 2018 IEEE.1

Muscle ankyrin repeat proteins: their role in striated muscle function in health and disease

Remodeling is a stringently controlled process that enables adequate response of muscle cells to constant physical stresses. In this process, different kinds of stimuli have to be sensed and converted into biochemical signals that ultimately lead to alterations of muscle phenotype. Several multiprotein complexes located in the sarcomere and organized on the titin molecular spring have been identified as stress sensors and signal transducers. In this review, we focus on Ankrd1/CARP and Ankrd2/Arpp proteins, which belong to the muscle ankyrin repeat protein family (MARP) involved in a mechano-signaling pathway that links myofibrillar stress response to muscle gene expression. Apart from the mechanosensory function, they have an important role in transcriptional regulation, myofibrillar assembly, cardiogenesis and myogenesis. Their altered expression has been demonstrated in neuromuscular disorders, cardiovascular diseases, as well as in tumors, suggesting a role in pathological processes. Although analyzed in a limited number of patients, there is a considerable body of evidence that MARP proteins could be suitable candidates for prognostic and diagnostic biomarkers

Neuropeptide substance P and the immune response

Substance P is a peptide mainly secreted by neurons and is involved in many biological processes, including nociception and inflammation. Animal models have provided insights into the biology of this peptide and offered compelling evidence for the importance of substance P in cell-to-cell communication by either paracrine or endocrine signaling. Substance P mediates interactions between neurons and immune cells, with nerve-derived substance P modulating immune cell proliferation rates and cytokine production. Intriguingly, some immune cells have also been found to secrete substance P, which hints at an integral role of substance P in the immune response. These communications play important functional roles in immunity including mobilization, proliferation and modulation of activity of immune cells. This Review summarizes current knowledge of substance P and its receptors, as well as its physiological and pathological roles. We focus on recent developments in the immuno-biology of substance P and we discuss the clinical implications of its ability to modulate the immune response