scholarly journals Big knowledge from big data in functional genomics

2017 ◽  
Vol 1 (3) ◽  
pp. 245-248 ◽  
Author(s):  
Chris P. Ponting
Keyword(s):  
Big Data ◽  
Molecular Interactions ◽  
Human Population ◽  
Single Cells ◽  
Human Biology ◽  
Reverse Causation ◽  
Downstream Effects ◽  
Dna Variants ◽  
Cellular Phenotypes ◽  
Why Questions

With so much genomics data being produced, it might be wise to pause and consider what purpose this data can or should serve. Some improve annotations, others predict molecular interactions, but few add directly to existing knowledge. This is because sequence annotations do not always implicate function, and molecular interactions are often irrelevant to a cell's or organism's survival or propagation. Merely correlative relationships found in big data fail to provide answers to the Why questions of human biology. Instead, those answers are expected from methods that causally link DNA changes to downstream effects without being confounded by reverse causation. These approaches require the controlled measurement of the consequences of DNA variants, for example, either those introduced in single cells using CRISPR/Cas9 genome editing or that are already present across the human population. Inferred causal relationships between genetic variation and cellular phenotypes or disease show promise to rapidly grow and underpin our knowledge base.

scholarly journals Multi-Level Relationships between Satellite-Derived Nighttime Lighting Signals and Social Media–Derived Human Population Dynamics

2018 ◽  
Vol 10 (7) ◽  
pp. 1128 ◽  
Author(s):  
Ting Ma
Keyword(s):  
Social Media ◽  
Population Dynamics ◽  
Big Data ◽  
Human Activities ◽  
Human Population ◽  
City Size ◽  
Human Settlements ◽  
Good Opportunity ◽  
Nighttime Light ◽  
Multi Level

Satellite-based measurements of the artificial nighttime light brightness (NTL) have been extensively used for studying urbanization and socioeconomic dynamics in a temporally consistent and spatially explicit manner. The increasing availability of geo-located big data detailing human population dynamics provides a good opportunity to explore the association between anthropogenic nocturnal luminosity and corresponding human activities, especially at fine time/space scales. In this study, we used Visible Infrared Imaging Radiometer Suite (VIIRS) day/night band (DNB)–derived nighttime light images and the gridded number of location requests (NLR) from China’s largest social media platform to investigate the quantitative relationship between nighttime light radiances and human population dynamics across China at four levels: the provincial, city, county, and pixel levels. Our results show that the linear relationship between the NTL and NLR might vary with the observation level and magnitude. The dispersion between the two variables likely increases with the observation scale, especially at the pixel level. The effect of spatial autocorrelation and other socioeconomic factors on the relationship should be taken into account for nighttime light-based measurements of human activities. Furthermore, the bivariate relationship between the NTL and NLR was employed to generate a partition of human settlements based on the combined features of nighttime lights and human population dynamics. Cross-regional comparisons of the partitioned results indicate a diverse co-distribution of the NTL and NLR across various types of human settlements, which could be related to the city size/form and urbanization level. Our findings may provide new insights into the multi-level responses of nighttime light signals to human activity and the potential application of nighttime light data in association with geo-located big data for investigating the spatial patterns of human settlement.

scholarly journals Single-cell tumor-immune microenvironment of BRCA1/2 mutated high-grade serous ovarian cancer

2021 ◽  
Author(s):  
Inga-Maria Launonen ◽  
Nuppu Lyytikäinen ◽  
Julia Casado ◽  
Ella Anttila ◽  
Angéla Szabó ◽  
...  
Keyword(s):  
Tumor Microenvironment ◽  
Single Cell ◽  
Single Cells ◽  
High Grade ◽  
Prognostic Role ◽  
Immune Microenvironment ◽  
Proteomic Data ◽  
Tumor Immune Microenvironment ◽  
Cellular Phenotypes

Abstract The majority of high-grade serous ovarian cancers (HGSCs) are deficient in homologous recombination (HR) DNA repair, most commonly due to mutations or hypermethylation of the BRCA1/2 genes. We aimed to discover how BRCA1/2 mutations shape the cellular phenotypes and spatial interactions of the tumor microenvironment. Using a highly multiplex immunofluorescence and image analysis we generated spatial proteomic data for 21 markers in 124,623 single cells from 112 tumor cores originating from 31 tumors with BRCA1/2 mutation (BRCA1/2mut), and from 13 tumors without alterations in HR genes (HRwt). We identified a phenotypically distinct tumor microenvironment in the BRCA1/2mut tumors with evidence of increased immunosurveillance. Importantly, we found an opposing prognostic role of a proliferative tumor-cell phenotypic subpopulation in the HR-genotypes, which associated with enhanced spatial tumor-immune interactions by the CD8+ and CD4+T-cells in BRCA1/2mut tumors. The single-cell spatial landscapes indicate distinct patterns of spatial immunosurveillance with the premise to improve immunotherapeutic strategies and patient stratification in HGSC.

Precision Medicine Versus Population Medicine in Colon Cancer: From Prospects of Prevention, Adjuvant Chemotherapy, and Surveillance

2018 ◽  
pp. 220-230 ◽  
Author(s):  
Michael J. Hall ◽  
Arden M. Morris ◽  
Weijing Sun
Keyword(s):  
Colorectal Cancer ◽  
Colon Cancer ◽  
Big Data ◽  
Cancer Diagnosis ◽  
Human Biology ◽  
Stage Iii Colon Cancer ◽  
Treatment And Prevention ◽  
Combining Data ◽  
Prevention And Treatment ◽  
Colorectal Cancer Prevention

With the advances of technologic revolution that provides new insights into human biology, genetics and cancer, as well as advantages of big data which amasses large amounts of information for us to approach cancer treatment and prevention, we are facing challenges of organically combining data from studies based on general population and information from individual testing and setting out precisional recommendations in cancer diagnosis, prevention, and treatment. We are obligated to accelerate the adaptation of new scientific discoveries into effective treatments and prevention for cancer. In this review, we introduce our opinions on bringing knowledge of precision and population medicine together to guide our clinical practice from the prospects of colorectal cancer prevention, stage III colon cancer adjuvant therapy, and postsurgery surveillance.

scholarly journals Voltage-gated calcium flux mediates Escherichia coli mechanosensation

2017 ◽  
Vol 114 (35) ◽  
pp. 9445-9450 ◽  
Author(s):  
Giancarlo N. Bruni ◽  
R. Andrew Weekley ◽  
Benjamin J. T. Dodd ◽  
Joel M. Kralj
Keyword(s):  
Escherichia Coli ◽  
Calcium Influx ◽  
Single Cells ◽  
Calcium Flux ◽  
Excitable Cells ◽  
Intracellular Signals ◽  
Downstream Effects ◽  
New Findings ◽  
Dynamic Voltage ◽  
Induced Changes

Electrically excitable cells harness voltage-coupled calcium influx to transmit intracellular signals, typically studied in neurons and cardiomyocytes. Despite intense study in higher organisms, investigations of voltage and calcium signaling in bacteria have lagged due to their small size and a lack of sensitive tools. Only recently were bacteria shown to modulate their membrane potential on the timescale of seconds, and little is known about the downstream effects from this modulation. In this paper, we report on the effects of electrophysiology in individual bacteria. A genetically encoded calcium sensor expressed in Escherichia coli revealed calcium transients in single cells. A fusion sensor that simultaneously reports voltage and calcium indicated that calcium influx is induced by voltage depolarizations, similar to metazoan action potentials. Cytoplasmic calcium levels and transients increased upon mechanical stimulation with a hydrogel, and single cells altered protein concentrations dependent on the mechanical environment. Blocking voltage and calcium flux altered mechanically induced changes in protein concentration, while inducing calcium flux reproduced these changes. Thus, voltage and calcium relay a bacterial sense of touch and alter cellular lifestyle. Although the calcium effectors remain unknown, these data open a host of new questions about E. coli, including the identity of the underlying molecular players, as well as other signals conveyed by voltage and calcium. These data also provide evidence that dynamic voltage and calcium exists as a signaling modality in the oldest domain of life, and therefore studying electrophysiology beyond canonical electrically excitable cells could yield exciting new findings.

scholarly journals Single-cell tumor-immune microenvironment of BRCA1/2 mutated high-grade serous ovarian cancer

2021 ◽  
Author(s):  
Inga-Maria Launonen ◽  
Nuppu Lyytikäinen ◽  
Julia Casado ◽  
Ella Anttila ◽  
Angéla Szabó ◽  
...  
Keyword(s):  
Tumor Microenvironment ◽  
Single Cell ◽  
Spatial Data ◽  
Single Cells ◽  
High Grade ◽  
Spatial Interactions ◽  
Immune Microenvironment ◽  
Tumor Immune Microenvironment ◽  
Cellular Phenotypes

Abstract The majority of high-grade serous ovarian cancers (HGSCs) are deficient in homologous recombination (HR) DNA repair, most commonly due to mutations or hypermethylation of the BRCA1/2 genes. We aimed to discover how BRCA1/2 mutations shape the cellular phenotypes and spatial interactions of the tumor microenvironment. Using a highly multiplex immunofluorescence and image analysis on 112 tumor cores we generated single-cell spatial data for 21 markers in 124,623 single cells from 31 tumors with BRCA1/2 mutation (BRCA1/2mut), and 13 tumors without any alterations in HR genes (HRwt). We identified a phenotypically distinct tumor microenvironment in the BRCA1/2mut tumors with evidence of increased immunosurveillance. Importantly, we found an opposing prognostic role of a proliferative tumor-cell phenotypic subpopulation in the HR-genotypes, which associated with enhanced spatial interactions in the tumor-immune cellular communities. The single-cell spatial landscapes indicate distinct patterns of spatial immunosurveillance with the premise to improve immunotherapeutic strategies and patient stratification in HGSC.

scholarly journals Machine learning algorithms in big data analyses identify determinants of insulin gene transcription

2021 ◽  
Author(s):  
Wilson KM Wong ◽  
Vinod Thorat ◽  
Mugdha V Joglekar ◽  
Charlotte X Dong ◽  
Hugo Lee ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Single Cell ◽  
Gene Transcription ◽  
Single Cells ◽  
Machine Learning Algorithms ◽  
Insulin Gene ◽  
Validation Dataset ◽  
Β Cells ◽  
Data Analyses

Machine learning (ML) workflows enable unprejudiced and robust evaluation of complex datasets and are being increasingly sought in analyzing transcriptome-based big datasets. Here, we analysed over 490,000,000 data points to compare 10 different ML algorithms in a large (N=11,652) training dataset of single-cell RNA-sequencing of human pancreatic cells to identify features (genes) associated with the presence or absence of insulin gene transcript(s). Prediction accuracy and sensitivity of models were tested in a separate validation dataset (N=2,913 single-cell transcriptomes) and the efficacy of each ML workflow to accurately identify insulin-producing cells assessed. Overall, Ensemble ML workflows, and in particular, Random Forest ML algorithm delivered high predictive power in a receiver operator characteristic (ROC) curve analysis (AUC=0.83) at the highest sensitivity (0.98) as compared to the other nine algorithms. The top 10 features, (including IAPP, ADCYAP1, LDHA and SST) common to the three Ensemble ML workflows were identified to be localized to human islet-β cells as well as non-β cells and were significantly dysregulated in scRNA-seq datasets from Ire-1αβ-/- mice that demonstrate de-differentiation of pancreatic β-cells as well as in pancreatic single cells from individuals with Type 2 Diabetes. Our findings provide a direct comparison of ML workflows in big data analyses, identify key determinants of insulin transcription and provide workflows for other regulatory analyses to identify/validate novel genes/features of endocrine pancreatic gene transcription.

scholarly journals Exploring dimension-reduced embeddings with Sleepwalk

2019 ◽  
Author(s):  
Svetlana Ovchinnikova ◽  
Simon Anders
Keyword(s):  
Big Data ◽  
Dimension Reduction ◽  
Single Cell ◽  
Single Cells ◽  
High Dimensional ◽  
Rna Seq ◽  
Mouse Cursor ◽  
Sample Data ◽  
Reduction Methods ◽  
Full Power

AbstractDimension-reduction methods, such as t-SNE or UMAP, are widely used when exploring high-dimensional data describing many entities, e.g., RNA-seq data for many single cells. However, dimension reduction is commonly prone to introducing artefacts, and we hence need means to see where a dimension-reduced embedding is a faithful representation of the local neighbourhood and where it is not.We present Sleepwalk, a simple but powerful tool that allows the user to interactively explore an embedding, using colour to depict original or any other distances from all points to the cell under the mouse cursor. We show how this approach not only highlights distortions, but also reveals otherwise hidden characteristics of the data, and how Sleep-walk’s comparative modes help integrate multi-sample data and understand differences between embedding and preprocessing methods. Sleepwalk is a versatile and intuitive tool that unlocks the full power of dimension reduction and will be of value not only in single-cell RNA-seq but also in any other area with matrix-shaped big data.

scholarly journals Simultaneous quantification of mRNA and protein in single cells reveals post-transcriptional effects of genetic variation

2020 ◽  
Author(s):  
Christian Brion ◽  
Sheila Lutz ◽  
Frank W. Albert
Keyword(s):  
Statistical Power ◽  
Single Cells ◽  
Live Cells ◽  
Kinase Gene ◽  
Protein Levels ◽  
Simultaneous Quantification ◽  
Specific Effects ◽  
Recombinant Cells ◽  
Dna Variants ◽  
Large Populations

AbstractTrans-acting DNA variants may specifically affect mRNA or protein levels of genes located throughout the genome. However, prior work compared trans-acting loci mapped in studies performed separately or with limited statistical power. Here, we developed a CRISPR-based system for simultaneous quantification of mRNA and protein of a given gene via dual fluorescent reporters in single, live cells of the yeast Saccharomyces cerevisiae. In large populations of recombinant cells from a cross between two genetically divergent strains, we mapped 86 trans-acting loci affecting the expression of ten genes. Less than 20% of these loci had concordant effects on mRNA and protein of the same gene. Most loci influenced protein but not mRNA of a given gene. One such locus harbored a premature stop variant in the YAK1 kinase gene that had specific effects on protein or mRNA of dozens of genes. These results demonstrate complex, post-transcriptional genetic effects on gene expression.One sentence summaryA CRISPR-based dual reporter assay enables genetic mapping of DNA variants that specifically affect mRNA or protein levels in trans.

scholarly journals Single cells make big data: New challenges and opportunities in transcriptomics

2017 ◽  
Vol 4 ◽  
pp. 85-91 ◽  
Author(s):  
Philipp Angerer ◽  
Lukas Simon ◽  
Sophie Tritschler ◽  
F. Alexander Wolf ◽  
David Fischer ◽  
...  
Keyword(s):  
Big Data ◽  
Single Cells ◽  
Challenges And Opportunities ◽  
New Challenges
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