Modeling type 1 diabetes progression using machine learning and single-cell transcriptomic measurements in human islets.

Type 1 diabetes (T1D) is a chronic condition in which beta cells are destroyed by immune cells. Despite progress in immunotherapies that could delay T1D onset, early detection of autoimmunity remains challenging. Here, we evaluate the utility of machine learning for early prediction of T1D using single-cell analysis of islets.

Disease pathology signatures in a mouse model of Mucopolysaccharidosis type IIIB.

Mucopolysaccharidosis type IIIB (MPS IIIB) is a rare and devastating childhood-onset lysosomal storage disease caused by complete loss of function of the lysosomal hydrolase α-N-acetylglucosaminidase. The lack of functional enzyme in MPS IIIB patients leads to the progressive accumulation of heparan sulfate throughout the body and triggers a cascade of neuroinflammatory and other biochemical processes ultimately resulting in severe mental impairment and early death in adolescence or young adulthood. The low prevalence and severity of the disease has necessitated the use of animal models to improve our knowledge of the pathophysiology and for the development of therapeutic treatments.

scViewer: An Interactive Single-Cell Gene Expression Visualization Tool.

Single-cell RNA sequencing (scRNA-seq) is an attractive technology for researchers to gain valuable insights into the cellular processes and cell type diversity present in all tissues. The data generated by the scRNA-seq experiment are high-dimensional and complex in nature. Several tools are now available to analyze the raw scRNA-seq data from public databases; however, simple and easy-to-explore single-cell gene expression visualization tools focusing on differential expression and co-expression are lacking.

Alternative splicing of the SUMO1/2/3 transcripts affects cellular SUMOylation and produces functionally distinct SUMO protein isoforms.

Substantial increases in the conjugation of the main human SUMO paralogs, SUMO1, SUMO2, and SUMO3, are observed upon exposure to different cellular stressors, and such increases are considered important to facilitate cell survival to stress. Despite their critical cellular role, little is known about how the levels of the SUMO modifiers are regulated in the cell, particularly as it relates to the changes observed upon stress. Here we characterize the contribution of alternative splicing towards regulating the expression of the main human SUMO paralogs under normalcy and three different stress conditions, heat-shock, cold-shock, and Influenza A Virus infection.

Computational workflow and interactive analysis of single-cell expression profiling of islets generated by the Human Pancreas Analysis Program.

Type 1 and Type 2 diabetes are distinct genetic diseases of the pancreas which are defined by the abnormal level of blood glucose. Understanding the initial molecular perturbations that occur during the pathogenesis of diabetes is of critical importance in understanding these disorders. The inability to biopsy the human pancreas of living donors hampers insights into early detection, as the majority of diabetes studies have been performed on peripheral leukocytes from the blood, which is not the site of pathogenesis.

Single-cell multi-omics analysis of human pancreatic islets reveals novel cellular states in type 1 diabetes.

Type 1 diabetes (T1D) is an autoimmune disease in which immune cells destroy insulin-producing beta cells. The aetiology of this complex disease is dependent on the interplay of multiple heterogeneous cell types in the pancreatic environment. Here, we provide a single-cell atlas of pancreatic islets of 24 T1D, autoantibody-positive and nondiabetic organ donors across multiple quantitative modalities including ~80,000 cells using single-cell transcriptomics, ~7,000,000 cells using cytometry by time of flight and ~1,000,000 cells using in situ imaging mass cytometry.

Identification of Hub Genes in Different Stages of Colorectal Cancer through an Integrated Bioinformatics Approach.

Colorectal cancer (CRC) is the third most common cancer that contributes to cancer-related morbidity. However, the differential expression of genes in different phases of CRC is largely unknown. Moreover, very little is known about the role of stress-survival pathways in CRC.

Role of Stress-Survival Pathways and Transcriptomic Alterations in Progression of Colorectal Cancer: A Health Disparities Perspective.

Every year, more than a million individuals are diagnosed with colorectal cancer (CRC) across the world. Certain lifestyle and genetic factors are known to drive the high incidence and mortality rates in some groups of individuals. The presence of enormous amounts of reactive oxygen species is implicated for the on-set and carcinogenesis, and oxidant scavengers are thought to be important in CRC therapy.

A network-based zoning for parallel whole-cell simulation.

Motivation: In Computational Cell Biology, whole-cell modeling and simulation is an absolute requirement to analyze and explore the cell of an organism. Despite few individual efforts on modeling, the prime obstacle hindering its development and progress is its compute-intensive nature. Towards this end, little knowledge is available on how to reduce the enormous computational overhead and which computational systems will be of use.