Biology subjects

Carsten Marr

Publications and source records attributed to Carsten Marr.

4 recordsLinked to original sources

bioRxiv2016

cgCorrect: A method to correct for confounding cell-cell variation due to cell growth in single-cell transcriptomics

Motivation: Accessing gene expression at the single cell level has unraveled often large heterogeneity among seemingly homogeneous cells, which remained obscured in traditional population based approaches. The computational analysis of single-cell transcriptomics data, however, still imposes unresolved challenges with respect to normalization, visualization and modeling the data. One such issue are differences in cell size, which introduce additional variability into the data, for which appropriate normalization techniques are needed. Otherwise, these differences in cell size may obscure genuine heterogeneities among cell populations and lead to overdispersed steady-state distributions of mRNA transcript numbers.\n\nResults: We present cgCorrect, a statistical framework to correct for differences in cell size that are due to cell growth in single-cell transcriptomics data. We derive the probability for the cell growth corrected mRNA transcript number given the measured, cell size dependent mRNA transcript number, based on the assumption that the average number of transcripts in a cell increases proportional to the cells volume during cell cycle. cgCorrect can be used for both data normalization, and to analyze steady-state distributions used to infer the gene expression mechanism. We demonstrate its applicability on both simulated data and single-cell quantitative real-time PCR data from mouse blood stem and progenitor cells. We show that correcting for differences in cell size affects the interpretation of the data obtained by typically performed computational analysis.\n\nAvailability: A Matlab implementation of cgCorrect is available at http://icb.helmholtz-muenchen.de/cgCorrect\n\nSupplementary information: Supplementary information are available online. The simulated data set is available at http://icb.helmholtz-muenchen.de/cgCorrect

Bioinformatics↗

bioRxiv2016

Exact Bayesian lineage tree-based inference identifies Nanog negative autoregulation in mouse embryonic stem cells

The autoregulatory motif of Nanog, a heterogeneously expressed core pluripotency factor in mouse embryonic stem cells, remains debated. Although recent time-lapse microscopy data provide the unparalleled ability to monitor Nanog expression at the single-cell level, the extraction of mechanistic knowledge is precluded by the lack of inference techniques suitable for noisy, incomplete and heterogeneous data obtained from proliferating cell populations.\n\nThis work identifies Nanogs autoregulatory motif from quantified time-lapse fluorescence line-age trees with STILT (Stochastic Inference on Lineage Trees), a novel particle-filter based algorithm for exact Bayesian parameter inference and model selection of stochastic models. We first verify STILTs ability to accurately infer parameters and select the correct autoregulatory motif from simulated data. We then apply STILT to time-lapse microscopy movies of a fluorescent Nanog fusion protein reporter and reject the possibility of positive autoregulation. Finally, we use STILT for experimental design, perform in silico overexpression simulations, and experimentally validate model predictions via exogenous Nanog overexpression. We finally conclude that the protein expression dynamics and overexpression experiments strongly suggest a weak negative feedback from the protein on the DNA activation rate.\n\nWe find that a simple autoregulatory mechanism can explain the observed heterogeneous Nanog dynamics. This finding has implications on the understanding of the core pluripotency network, such as supporting the ability of mESC populations to diversify their proteomic profile to respond to a spectrum of differentiation cues. Beyond this application STILT constitutes a generally applicable fully Bayesian approach for model selection of gene regulatory models on the basis of time-lapse imaging data of proliferating cell populations. STILT is freely available at: http://www.imsb.ethz.ch/research/claassen/Software/stilt--stochastic-inference-on-lineage-trees.html

Systems Biology↗

bioRxiv2016

Single-Cell Gene Expression Profi ling and Cell State Dynamics: Collecting Data, Correlating Data Points and Connecting the Dots

Single-cell analyses of transcript and protein expression profiles - more precisely, single-cell resolution analysis of molecular profiles of cell populations - have now entered the center stage with widespread applications of single-cell qPCR, single-cell RNA-Seq and CyTOF. These high-dimensional population snapshot techniques are complemented by low-dimensional time-resolved, microscopy-based monitoring methods. Both fronts of advance have exposed a rich heterogeneity of cell states within uniform cell populations in many biological contexts, producing a new kind of data that has stimulated a series of computational analysis methods for data visualization, dimensionality reduction, and cluster (subpopulation) identification. The next step is now to go beyond collecting data and correlating data points: to connect the dots, that is, to understand what actually underlies the identified data patterns. This entails interpreting the \"clouds of points\" in state space as a manifestation of the underlying molecular regulatory network. In that way control of cell state dynamics can be formalized as a quasi-potential landscape, as first proposed by Waddington. We summarize key methods of data acquisition and computational analysis and explain the principles that link the single-cell resolution measurements to dynamical systems theory.

Systems Biology↗

bioRxiv2015

destiny – diffusion maps for large-scale single-cell data in R

SummaryDiffusion maps are a spectral method for non-linear dimension reduction and have recently been adapted for the visualization of single cell expression data. Here we present destiny, an efficient R implementation of the diffusion map algorithm. Our package includes a single-cell specific noise model allowing for missing and censored values. In contrast to previous implementations, we further present an efficient nearest-neighbour approximation that allows for the processing of hundreds of thousands of cells and a functionality for projecting new data on existing diffusion maps. We exemplarily apply destiny to a recent time-resolved mass cytometry dataset of cellular reprogramming.\n\nAvailability and implementationdestiny is an open-source R/Bioconductor package http://bioconductor.org/packages/ destiny also available at https://www.helmholtz-muenchen.de/icb/destiny. A detailed vignette describing functions and workflows is provided with the package.\n\nContactcarsten.marr@helmholtz-muenchen.de, f.buettner@helmholtz-muenchen.de

Bioinformatics↗