32,789 research outputs found
Automated single-cell motility analysis on a chip using lensfree microscopy.
Quantitative cell motility studies are necessary for understanding biophysical processes, developing models for cell locomotion and for drug discovery. Such studies are typically performed by controlling environmental conditions around a lens-based microscope, requiring costly instruments while still remaining limited in field-of-view. Here we present a compact cell monitoring platform utilizing a wide-field (24 mm(2)) lensless holographic microscope that enables automated single-cell tracking of large populations that is compatible with a standard laboratory incubator. We used this platform to track NIH 3T3 cells on polyacrylamide gels over 20 hrs. We report that, over an order of magnitude of stiffness values, collagen IV surfaces lead to enhanced motility compared to fibronectin, in agreement with biological uses of these structural proteins. The increased throughput associated with lensfree on-chip imaging enables higher statistical significance in observed cell behavior and may facilitate rapid screening of drugs and genes that affect cell motility
Evaluation of Dynamic Cell Processes and Behavior Using Video Bioinformatics Tools
Just as body language can reveal a person’s state of well-being, dynamic changes in cell behavior and
morphology can be used to monitor processes in cultured cells. This chapter discusses how CL-Quant
software, a commercially available video bioinformatics tool, can be used to extract quantitative data on:
(1) growth/proliferation, (2) cell and colony migration, (3) reactive oxygen species (ROS) production, and
(4) neural differentiation. Protocols created using CL-Quant were used to analyze both single cells and
colonies. Time-lapse experiments in which different cell types were subjected to various chemical
exposures were done using Nikon BioStations. Proliferation rate was measured in human embryonic stem
cell colonies by quantifying colony area (pixels) and in single cells by measuring confluency (pixels).
Colony and single cell migration were studied by measuring total displacement (distance between the
starting and ending points) and total distance traveled by the colonies/cells. To quantify ROS production,
cells were pre-loaded with MitoSOX Red™, a mitochondrial ROS (superoxide) indicator, treated with
various chemicals, then total intensity of the red fluorescence was measured in each frame. Lastly, neural
stem cells were incubated in differentiation medium for 12 days, and time lapse images were collected
daily. Differentiation of neural stem cells was quantified using a protocol that detects young neurons. CLQuant
software can be used to evaluate biological processes in living cells, and the protocols developed in
this project can be applied to basic research and toxicological studies, or to monitor quality control in
culture facilities
An end-to-end software solution for the analysis of high-throughput single-cell migration data
The systematic study of single-cell migration requires the availability of software for assisting data inspection, quality control and analysis. This is especially important for high-throughput experiments, where multiple biological conditions are tested in parallel. Although the field of cell migration can count on different computational tools for cell segmentation and tracking, downstream data visualization, parameter extraction and statistical analysis are still left to the user and are currently not possible within a single tool. This article presents a completely new module for the open-source, cross-platform CellMissy software for cell migration data management. This module is the first tool to focus specifically on single-cell migration data downstream of image processing. It allows fast comparison across all tested conditions, providing automated data visualization, assisted data filtering and quality control, extraction of various commonly used cell migration parameters, and non-parametric statistical analysis. Importantly, the module enables parameters computation both at the trajectory-and at the step-level. Moreover, this single-cell analysis module is complemented by a new data import module that accommodates multiwell plate data obtained from high-throughput experiments, and is easily extensible through a plugin architecture. In conclusion, the end-to-end software solution presented here tackles a key bioinformatics challenge in the cell migration field, assisting researchers in their highthroughput data processing
Visualization and Correction of Automated Segmentation, Tracking and Lineaging from 5-D Stem Cell Image Sequences
Results: We present an application that enables the quantitative analysis of
multichannel 5-D (x, y, z, t, channel) and large montage confocal fluorescence
microscopy images. The image sequences show stem cells together with blood
vessels, enabling quantification of the dynamic behaviors of stem cells in
relation to their vascular niche, with applications in developmental and cancer
biology. Our application automatically segments, tracks, and lineages the image
sequence data and then allows the user to view and edit the results of
automated algorithms in a stereoscopic 3-D window while simultaneously viewing
the stem cell lineage tree in a 2-D window. Using the GPU to store and render
the image sequence data enables a hybrid computational approach. An
inference-based approach utilizing user-provided edits to automatically correct
related mistakes executes interactively on the system CPU while the GPU handles
3-D visualization tasks. Conclusions: By exploiting commodity computer gaming
hardware, we have developed an application that can be run in the laboratory to
facilitate rapid iteration through biological experiments. There is a pressing
need for visualization and analysis tools for 5-D live cell image data. We
combine accurate unsupervised processes with an intuitive visualization of the
results. Our validation interface allows for each data set to be corrected to
100% accuracy, ensuring that downstream data analysis is accurate and
verifiable. Our tool is the first to combine all of these aspects, leveraging
the synergies obtained by utilizing validation information from stereo
visualization to improve the low level image processing tasks.Comment: BioVis 2014 conferenc
Toward high-content/high-throughput imaging and analysis of embryonic morphogenesis
In vivo study of embryonic morphogenesis tremendously benefits from recent advances in live microscopy and computational analyses. Quantitative and automated investigation of morphogenetic processes opens the field to high-content and high-throughput strategies. Following experimental workflow currently developed in cell biology, we identify the key challenges for applying such strategies in developmental biology. We review the recent progress in embryo preparation and manipulation, live imaging, data registration, image segmentation, feature computation, and data mining dedicated to the study of embryonic morphogenesis. We discuss a selection of pioneering studies that tackled the current methodological bottlenecks and illustrated the investigation of morphogenetic processes in vivo using quantitative and automated imaging and analysis of hundreds or thousands of cells simultaneously, paving the way for high-content/high-throughput strategies and systems analysis of embryonic morphogenesis
Recommended from our members
A Video Bioinformatics Method to Quantify Cell Spreading and Its Application to Cells Treated with Rho-Associated Protein Kinase and Blebbistatin
Commercial software is available for performing video bioinformatics analysis on cultured cells. Such software is convenient and can often be used to create suitable protocols for quantitative analysis of video
data with relatively little background in image processing. This chapter demonstrates that CL-Quant software, a commercial program produced by DRVision, can be used to automatically analyze cell spreading in time-lapse videos of human embryonic stem cells (hESC). Two cell spreading protocols were developed and tested. One was professionally created by engineers at DRVision and adapted to this project. The other was created by an undergraduate student with 1 month of experience using CL-Quant.
Both protocols successfully segmented small spreading colonies of hESC, and, in general, were in good agreement with the ground truth which was measured using ImageJ. Overall the professional protocol
performed better segmentation, while the user-generated protocol demonstrated that someone who had relatively little background with CL-Quant can successfully create protocols. The protocols were applied to
hESC that had been treated with ROCK inhibitors or blebbistatin, which tend to cause rapid attachment and spreading of hESC colonies. All treatments enabled hESC to attach rapidly. Cells treated with the
ROCK inhibitors or blebbistatin spread more than controls and often looked stressed. The use of the spreading analysis protocol can provide a very rapid method to evaluate the cytotoxicity of chemical treatment and reveal effects on the cytoskeleton of the cell. While hESC are presented in this chapter, other cell types could also be used in conjunction with the spreading protocol
3D time series analysis of cell shape using Laplacian approaches
Background:
Fundamental cellular processes such as cell movement, division or food uptake critically depend on cells being able to change shape. Fast acquisition of three-dimensional image time series has now become possible, but we lack efficient tools for analysing shape deformations in order to understand the real three-dimensional nature of shape changes.
Results:
We present a framework for 3D+time cell shape analysis. The main contribution is three-fold: First, we develop a fast, automatic random walker method for cell segmentation. Second, a novel topology fixing method is proposed to fix segmented binary volumes without spherical topology. Third, we show that algorithms used for each individual step of the analysis pipeline (cell segmentation, topology fixing, spherical parameterization, and shape representation) are closely related to the Laplacian operator. The framework is applied to the shape analysis of neutrophil cells.
Conclusions:
The method we propose for cell segmentation is faster than the traditional random walker method or the level set method, and performs better on 3D time-series of neutrophil cells, which are comparatively noisy as stacks have to be acquired fast enough to account for cell motion. Our method for topology fixing outperforms the tools provided by SPHARM-MAT and SPHARM-PDM in terms of their successful fixing rates. The different tasks in the presented pipeline for 3D+time shape analysis of cells can be solved using Laplacian approaches, opening the possibility of eventually combining individual steps in order to speed up computations
- …