Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Data for automated, high-throughput microscopy analysis of intracellular bacterial colonies using spot detection.

ABSTRACT: Quantification of intracellular bacterial colonies is useful in strategies directed against bacterial attachment, subsequent cellular invasion and intracellular proliferation. An automated, high-throughput microscopy-method was established to quantify the number and size of intracellular bacterial colonies in infected host cells (Detection and quantification of intracellular bacterial colonies by automated, high-throughput microscopy, Ernstsen et al., 2017 [1]). The infected cells were imaged with a 10× objective and number of intracellular bacterial colonies, their size distribution and the number of cell nuclei were automatically quantified using a spot detection-tool. The spot detection-output was exported to Excel, where data analysis was performed. In this article, micrographs and spot detection data are made available to facilitate implementation of the method.

SUBMITTER: Ernstsen CL

PROVIDER: S-EPMC5587884 | biostudies-other | 2017 Oct

REPOSITORIES: biostudies-other

ACCESS DATA

Json Xml

Publications

Data for automated, high-throughput microscopy analysis of intracellular bacterial colonies using spot detection.

Ernstsen Christina L CL Login Frédéric H FH Jensen Helene H HH Nørregaard Rikke R Nørregaard Rikke R Møller-Jensen Jakob J Nejsum Lene N LN

Data in brief 20170901

Quantification of intracellular bacterial colonies is useful in strategies directed against bacterial attachment, subsequent cellular invasion and intracellular proliferation. An automated, high-throughput microscopy-method was established to quantify the number and size of intracellular bacterial colonies in infected host cells (Detection and quantification of intracellular bacterial colonies by automated, high-throughput microscopy, Ernstsen et al., 2017 [1]). The infected cells were imaged wi ...[more]

PMID: 28913393

Similar Datasets

High-Throughput Automated Microscopy of Circulating Tumor Cells.

Project description:Circulating tumor cells (CTCs) have the potential of becoming the gold standard marker for cancer diagnosis, prognosis and monitoring. However, current methods for its isolation and characterization suffer from equipment variability and human operator error that hinder its widespread use. Here we report the design and construction of a fully automated high-throughput fluorescence microscope that enables the imaging and classification of cancer cells that were labeled by immunostaining procedures. An excellent agreement between our machine vision-based approach and a state-of-the-art microscopy equipment was achieved. Our integral approach provides a path for operator-free and robust analysis of cancer cells as a standard clinical practice.

| S-EPMC6760523 | biostudies-literature

Automated high-throughput light-sheet fluorescence microscopy of larval zebrafish.

Project description:Light sheet fluorescence microscopy enables fast, minimally phototoxic, three-dimensional imaging of live specimens, but is currently limited by low throughput and tedious sample preparation. Here, we describe an automated high-throughput light sheet fluorescence microscope in which specimens are positioned by and imaged within a fluidic system integrated with the sheet excitation and detection optics. We demonstrate the ability of the instrument to rapidly examine live specimens with minimal manual intervention by imaging fluorescent neutrophils over a nearly 0.3 mm3 volume in dozens of larval zebrafish. In addition to revealing considerable inter-individual variability in neutrophil number, known previously from labor-intensive methods, three-dimensional imaging allows assessment of the correlation between the bulk measure of total cellular fluorescence and the spatially resolved measure of actual neutrophil number per animal. We suggest that our simple experimental design should considerably expand the scope and impact of light sheet imaging in the life sciences.

| S-EPMC6235235 | biostudies-literature

Quantitative high-throughput population dynamics in continuous-culture by automated microscopy.

Project description:We present a high-throughput method to measure abundance dynamics in microbial communities sustained in continuous-culture. Our method uses custom epi-fluorescence microscopes to automatically image single cells drawn from a continuously-cultured population while precisely controlling culture conditions. For clonal populations of Escherichia coli our instrument reveals history-dependent resilience and growth rate dependent aggregation.

| S-EPMC5018735 | biostudies-literature

Strain Library Imaging Protocol for high-throughput, automated single-cell microscopy of large bacterial collections arrayed on multiwell plates.

Project description:Single-cell microscopy is a powerful tool for studying gene functions using strain libraries, but it suffers from throughput limitations. Here we describe the Strain Library Imaging Protocol (SLIP), which is a high-throughput, automated microscopy workflow for large strain collections that requires minimal user involvement. SLIP involves transferring arrayed bacterial cultures from multiwell plates onto large agar pads using inexpensive replicator pins and automatically imaging the resulting single cells. The acquired images are subsequently reviewed and analyzed by custom MATLAB scripts that segment single-cell contours and extract quantitative metrics. SLIP yields rich data sets on cell morphology and gene expression that illustrate the function of certain genes and the connections among strains in a library. For a library arrayed on 96-well plates, image acquisition can be completed within 4 min per plate.

| S-EPMC5831406 | biostudies-literature

Label-Free High-Throughput Leukemia Detection by Holographic Microscopy.

Project description:Complete blood count and differentiation of leukocytes (DIFF) belong to the most frequently performed laboratory diagnostic tests. Here, a flow cytometry-based method for label-free DIFF of untouched leukocytes by digital holographic microscopy on the rich phase contrast of peripheral leukocyte images, using highly controlled 2D hydrodynamic focusing conditions is reported. Principal component analysis of morphological characteristics of the reconstructed images allows classification of nine leukocyte types, in addition to different types of leukemia and demonstrates disappearance of acute myeloid leukemia cells in remission. To exclude confounding effects, the classification strategy is tested by the analysis of 20 blinded clinical samples. Here, 70% of the specimens are correctly classified with further 20% classifications close to a correct diagnosis. Taken together, the findings indicate a broad clinical applicability of the cytometry method for automated and reagent-free diagnosis of hematological disorders.

| S-EPMC6299719 | biostudies-literature

Automated high-throughput chromatin immunoprecipitation

Project description:The submitted files contain ChIP-seq data for the p300 transcriptional coactivator in GM12878 cells and for the NRSF transcription factor in GM12878 and Jurkat cells generated using a fully automated robotic chromatin immunoprecipitation protocol. Cells were fixed using 1% formaldehyde (NRSF samples) or 1% formaldehyde at 37C (p300 samples).

2014-06-01 | GSE53366 | GEO

Automated counting of airborne asbestos fibers by a high-throughput microscopy (HTM) method.

Project description:Inhalation of airborne asbestos causes serious health problems such as lung cancer and malignant mesothelioma. The phase-contrast microscopy (PCM) method has been widely used for estimating airborne asbestos concentrations because it does not require complicated processes or high-priced equipment. However, the PCM method is time-consuming and laborious as it is manually performed off-site by an expert. We have developed a high-throughput microscopy (HTM) method that can detect fibers distinguishable from other spherical particles in a sample slide by image processing both automatically and quantitatively. A set of parameters for processing and analysis of asbestos fiber images was adjusted for standard asbestos samples with known concentrations. We analyzed sample slides containing airborne asbestos fibers collected at 11 different workplaces following PCM and HTM methods, and found a reasonably good agreement in the asbestos concentration. Image acquisition synchronized with the movement of the robotic sample stages followed by an automated batch processing of a stack of sample images enabled us to count asbestos fibers with greatly reduced time and labors. HTM should be a potential alternative to conventional PCM, moving a step closer to realization of on-site monitoring of asbestos fibers in air.

| S-EPMC3231659 | biostudies-literature

Automated analysis of high-content microscopy data with deep learning.

Project description:Existing computational pipelines for quantitative analysis of high-content microscopy data rely on traditional machine learning approaches that fail to accurately classify more than a single dataset without substantial tuning and training, requiring extensive analysis. Here, we demonstrate that the application of deep learning to biological image data can overcome the pitfalls associated with conventional machine learning classifiers. Using a deep convolutional neural network (DeepLoc) to analyze yeast cell images, we show improved performance over traditional approaches in the automated classification of protein subcellular localization. We also demonstrate the ability of DeepLoc to classify highly divergent image sets, including images of pheromone-arrested cells with abnormal cellular morphology, as well as images generated in different genetic backgrounds and in different laboratories. We offer an open-source implementation that enables updating DeepLoc on new microscopy datasets. This study highlights deep learning as an important tool for the expedited analysis of high-content microscopy data.

| S-EPMC5408780 | biostudies-literature

Automated detection of apoptotic bodies and cells in label-free time-lapse high-throughput video microscopy using deep convolutional neural networks.

Project description:MotivationReliable label-free methods are needed for detecting and profiling apoptotic events in time-lapse cell-cell interaction assays. Prior studies relied on fluorescent markers of apoptosis, e.g. Annexin-V, that provide an inconsistent and late indication of apoptotic onset for human melanoma cells. Our motivation is to improve the detection of apoptosis by directly detecting apoptotic bodies in a label-free manner.ResultsOur trained ResNet50 network identified nanowells containing apoptotic bodies with 92% accuracy and predicted the onset of apoptosis with an error of one frame (5 min/frame). Our apoptotic body segmentation yielded an IoU accuracy of 75%, allowing associative identification of apoptotic cells. Our method detected apoptosis events, 70% of which were not detected by Annexin-V staining.Availability and implementationOpen-source code and sample data provided at https://github.com/kwu14victor/ApoBDproject.

| S-EPMC10563152 | biostudies-literature

A method for automated High throughput ChIPseq

Project description:Setup and optimisation of a high throughput pipeline for ChIPseq. The protocol used is ChIP carried out using the Agilent Bravo robot with subsequent Ilumina sequencing library preparation.This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

2013-02-12 | E-ERAD-77 | biostudies-arrayexpress

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data