Ratiometric time-lapse FRET analysis requires a robust and accurate processing pipeline to eliminate bias in intensity measurements on fluorescent images before further quantitative analysis can be conducted. This level of robustness can only be achieved by supplementing automated tools with built-in flexibility for manual ad-hoc adjustments. FRET-IBRA is a modular and fully parallelized configuration file-based tool written in Python. It simplifies the FRET processing pipeline to achieve accurate, registered, and unified ratio image stacks. The flexibility of this tool to handle discontinuous image frame sequences with tailored configuration parameters further streamlines the processing of outliers and time-varying effects in the original microscopy images. FRET-IBRA offers cluster-based channel background subtraction, photobleaching correction, and ratio image construction in an all-in-one solution without the need for multiple applications, image format conversions, and/or plug-ins. The package accepts a variety of input formats and outputs TIFF image stacks along with performance measures to detect both the quality and failure of the background subtraction algorithm on a per frame basis. Furthermore, FRET-IBRA outputs images with superior signal-to-noise ratio and accuracy in comparison to existing background subtraction solutions, whilst maintaining a fast runtime. We have used the FRET-IBRA package extensively to quantify the spatial distribution of calcium ions during pollen tube growth under mechanical constraints. Benchmarks against existing tools clearly demonstrate the need for FRET-IBRA in extracting reliable insights from FRET microscopy images of dynamic physiological processes at high spatial and temporal resolution. The source code for Linux and Mac operating systems is released under the BSD license and, along with installation instructions, test images, example configuration files, and a step-by-step tutorial, is freely available at github.com/gmunglani/fret-ibra.

Particle tracking has emerged as a powerful tool for investigating the swarm control of microrobots and the dynamic biological processes in the life sciences. In seed plants, pollen tubes, a part of the male gametophyte, are excellent models for understanding plant growth and cellular behavior, because vesicle motion within pollen tubes reveals important information about vesicle function and interactions. Conventional vesicle tracking is based on spatiotemporal image analysis, which requires high-quality images and vesicles with constant velocity. For in vivo tracking, vesicles may disappear in some frames, and image sequences may have spatial and temporal distortions, which hamper vesicle tracking for broader applications. In this paper, we studied intracellular motion during pollen tube growth with an optical flow method. Streaming images from confocal and optical microscopes were recorded to study the intracellular motion of vesicles of different size. Local motion for each vesicle was detected using a local displacement vector field. The displacement from two adjacent frames was then calculated. The flow field shows information such as the dynamics of vesicle secretion, endocytosis, exocytosis, and cytoskeletal stability. Vesicles from different regions inside the tube were tracked simultaneously with a Kanade-Lucas-Tomasi (KLT) feature matching algorithm. The spatial and temporal characteristics of intracellular vesicles were evaluated. The proposed methods can be of great use for studying the dynamics of fluorescently tagged particles in biological systems.

SUMMARY: Analysis of differential gene expression by RNA sequencing (RNA-Seq) is frequently done using feature counts, i.e. the number of reads mapping to a gene. However, commonly used count algorithms (e.g. HTSeq) do not address the problem of reads aligning with multiple locations in the genome (multireads) or reads aligning with positions where two or more genes overlap (ambiguous reads). Rcount specifically addresses these issues. Furthermore, Rcount allows the user to assign priorities to certain feature types (e.g. higher priority for protein-coding genes compared to rRNA-coding genes) or to add flanking regions. AVAILABILITY AND IMPLEMENTATION: Rcount provides a fast and easy-to-use graphical user interface requiring no command line or programming skills. It is implemented in C++ using the SeqAn (www.seqan.de) and the Qt libraries (qt-project.org). Source code and 64 bit binaries for (Ubuntu) Linux, Windows (7) and MacOSX are released under the GPLv3 license and are freely available on github.com/MWSchmid/Rcount. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Test data, genome annotation files, useful Python and R scripts and a step-by-step user guide (including run-time and memory usage tests) are available on github.com/MWSchmid/Rcount.

BACKGROUND: The study of nuclear architecture using Chromosome Conformation Capture (3C) technologies is a novel frontier in biology. With further reduction in sequencing costs, the potential of Hi-C in describing nuclear architecture as a phenotype is only about to unfold. To use Hi-C for phenotypic comparisons among different cell types, conditions, or genetic backgrounds, Hi-C data processing needs to be more accessible to biologists. RESULTS: HiCdat provides a simple graphical user interface for data pre-processing and a collection of higher-level data analysis tools implemented in R. Data pre-processing also supports a wide range of additional data types required for in-depth analysis of the Hi-C data (e.g. RNA-Seq, ChIP-Seq, and BS-Seq). CONCLUSIONS: HiCdat is easy-to-use and provides solutions starting from aligned reads up to in-depth analyses. Importantly, HiCdat is focussed on the analysis of larger structural features of chromosomes, their correlation to genomic and epigenomic features, and on comparative studies. It uses simple input and output formats and can therefore easily be integrated into existing workflows or combined with alternative tools.

We present a microrobotic platform that combines MEMS-based capacitive force sensing technology, a dual-stage positioning system and a real-time control and acquisition architecture with computer vision automation to manipulate and mechanically characterize growing plant cells. The topography accuracy of the system, using a silicon wafer sample is measured to be 28 nm(1σ, 200Hz). With an SI-traceable stiffness reference we estimate the accuracy of the RT-CFM to be 3.49%. The target locations are selected from an interactive image of the workspace, and the sensing tip is positioned at each location using visual servoing techniques. Topography and stiffness maps were successfully obtained on growing pollen tubes. With the proposed system, cells can be mechanically stimulated at high speeds and with high precision while the intracellular components are visualized using confocal imaging. The system offers a versatile solution for dexterous and high-throughput characterization of biological specimen.