Biology subjects

Lund, E.

Publications and source records attributed to Lund, E..

4 recordsLinked to original sources

bioRxiv2018

Spatiotemporally controlled Myosin relocalization and internal pressure cause biased cortical extension to generate sibling cell size asymmetry

Metazoan cells can generate unequal sized sibling cells during cell division. This form of asymmetric cell division depends on spindle geometry and Myosin distribution but the underlying mechanics are unclear. Here, we use atomic force microscopy and live cell imaging to elucidate the biophysical forces involved in the establishment of physical asymmetry in Drosophila neural stem cells. We show that the force driving initial apical membrane expansion is provided by hydrostatic pressure, peaking shortly after anaphase onset, and enabled by a relieve of actomyosin contractile tension on the apical cell cortex. The subsequent increase in contractile forces at the cleavage furrow, combined with the relocalization of basally located Myosin results in basal membrane extension and sustained apical expansion. We propose that spatiotemporally controlled actomyosin contractile tension and hydrostatic pressure enables stereotypic biased membrane expansion to generate sibling cell size asymmetry.

cell biology↗

bioRxiv2017

A Standard Operating Procedure For Outlier Removal In Large-Sample Epidemiological Transcriptomics Datasets

Transcriptome measurements and other -omics type data are increasingly more used in epidemiological studies. Most of omics studies to date are small with samples sizes in the tens, or sometimes low hundreds, but this is changing. Our Norwegian Woman and Cancer (NOWAC) datasets are to date one or two orders of magnitude larger. The NOWAC biobank contains about 50000 blood samples from a prospective study. Around 125 breast cancer cases occur in this cohort each year. The large biological variation in gene expression means that many observations are needed to draw scientific conclusions. This is true for both microarray and RNA-seq type data. Hence, larger datasets are likely to become more common soon.\n\nTechnical outliers are observations that somehow were distorted at the lab or during sampling. If not removed these observations add bias and variance in later statistical analyses, and may skew the results. Hence, quality assessment and data cleaning are important. We find common quality assessment libraries difficult to work with for large datasets for two reasons: slow execution speed and unsuitable visualizations.\n\nIn this paper, we present our standard operating procedure (SOP) for large-sample transcriptomics datasets. Our SOP combines automatic outlier detection with manual evaluation to avoid removing valuable observations. We use laboratory quality measures and statistical measures of deviation to aid the analyst. These are available in the nowaclean R package, currently available on GitHub (https://github.com/3inar/nowaclean). Finally, we evaluate our SOP on one of our larger datasets with 832 observations.

epidemiology↗

bioRxiv2017

Building Applications For Interactive Data Exploration In Systems Biology

As the systems biology community generates and collects data at an unprecedented rate, there is a growing need for interactive data exploration tools to explore the datasets. These tools need to combine advanced statistical analyses, relevant knowledge from biological databases, and interactive visualizations in an application with clear user interfaces. To answer specific research questions tools must provide specialized user interfaces and visualizations. While these are application-specific, the underlying components of a data analysis tool can be shared and reused later. Application developers can therefore compose applications of reusable services rather than implementing a single monolithic application from the ground up for each project.\n\nOur approach for developing data exploration applications in systems biology builds on the microservice architecture. Microservice architectures separates an application into smaller components that communicate using language-agnostic protocols. We show that this design is suitable in bioinformatics applications where applications often use different tools, written in different languages, by different research groups. Packaging each service in a software container enables re-use and sharing of key components between applications, reducing development, deployment, and maintenance time.\n\nWe demonstrate the viability of our approach through a web application, MIxT blood-tumor, for exploring and comparing transcriptional profiles from blood and tumor samples in breast cancer patients. The application integrates advanced statistical software, up-to-date information from biological databases, and modern data visualization libraries.\n\nThe web application for exploring transcriptional profiles, MIxT, is online at mixt-blood-tumor.bci.mcgill.ca and open-sourced at github.com/fjukstad/mixt. Packages to build the supporting microservices are open-sourced as a part of Kvik at github.com/fjukstad/kvik.

bioinformatics↗

bioRxiv2017

Curve Selection For Predicting Breast Cancer Metastasis From Prospective Gene Expression In Blood

We investigate whether there is information in gene expression levels in blood that predicts breast cancer metastasis. Our data comes from the NOWAC epidemiological cohort study where blood samples were provided at enrollment. This could be anywhere from years to weeks before any cancer diagnosis. When and if a cancer is diagnosed, it could be so in different ways: at a screening, between screenings, or in the clinic, outside of the screening program. To build predictive models we propose that variable selection should include followup time and stratify by detection method. We show by simulations that this improves the probability of selecting relevant predictor genes. We also demonstrate that it leads to improved predictions and more stable gene signatures in our data. There is some indication that blood gene expression levels hold predictive information about metastasis. With further development such information could be used for early detection of metastatic potential and as such aid in cancer treatment.

bioinformatics↗