RetFM-J, an ImageJ-based module for automated counting and quantifying features of nuclei in retinal whole-mounts.
Ontology highlight
ABSTRACT:
SUBMITTER: Hedberg-Buenz A
PROVIDER: S-EPMC4753132 | biostudies-literature | 2016 May
REPOSITORIES: biostudies-literature
Publications
RetFM-J, an ImageJ-based module for automated counting and quantifying features of nuclei in retinal whole-mounts.
Hedberg-Buenz Adam A Christopher Mark A MA Lewis Carly J CJ Meyer Kacie J KJ Rudd Danielle S DS Dutca Laura M LM Wang Kai K Garvin Mona K MK Scheetz Todd E TE Abràmoff Michael D MD Harper Matthew M MM Anderson Michael G MG
Experimental eye research 20150814
The present article introduces RetFM-J, a semi-automated ImageJ-based module that detects, counts, and collects quantitative data on nuclei of the inner retina from H&E-stained whole-mounted retinas. To illustrate performance, computer-derived outputs were analyzed in inbred C57BL/6J mice. Automated characterization yielded computer-derived outputs that closely matched manual counts. As a method using open-source software that is freely available, inexpensive staining reagents that are robust, a ...[more]
Similar Datasets
Project description:Automated image quantification workflows have dramatically improved over the past decade, enriching image analysis and enhancing the ability to achieve statistical power. These analyses have proved especially useful for studies in organisms such as Drosophila melanogaster, where it is relatively simple to obtain high sample numbers for downstream analyses. However, the developing wing, an intensively utilized structure in developmental biology, has eluded efficient cell counting workflows due to its highly dense cellular population. Here, we present efficient automated cell counting workflows capable of quantifying cells in the developing wing. Our workflows can count the total number of cells or count cells in clones labeled with a fluorescent nuclear marker in imaginal discs. Moreover, by training a machine-learning algorithm we have developed a workflow capable of segmenting and counting twin-spot labeled nuclei, a challenging problem requiring distinguishing heterozygous and homozygous cells in a background of regionally varying intensity. Our workflows could potentially be applied to any tissue with high cellular density, as they are structure-agnostic, and only require a nuclear label to segment and count cells.
| S-EPMC10903266 | biostudies-literature
Project description:Microglia are immune cells of the central nervous system capable of distinct phenotypic changes and migration in response to injury. These changes most notably include the retraction of fine dendritic structures and adoption of a globular, phagocytic morphology. Due to their characteristic responses, microglia frequently act as histological indicators of injury progression. While algorithms seeking to automate microglia counts and morphological analysis are becoming increasingly popular, few exist that are adequate for use within the retina and manual analysis remains prevalent. To address this, we propose a novel segmentation routine, implemented within FIJI-ImageJ, to perform automated segmentation and cell counting of retinal microglia. We show that our routine could perform cell counts with accuracy similar to manual observers using the I307N Rho model. Tracking cell position relative to retinal vasculature, we observed population migration towards the photoreceptor layer beginning 12 h post light damage. Using feature selection with Chi2 and principal component analysis, we resolved cells along a morphological gradient, demonstrating that extracted features were sufficiently descriptive to capture subtle morphological changes within cell populations in I307N Rho and Balb/c TLR2-/- retinal degeneration models. Taken together, we introduce a novel automated routine capable of efficient image processing and segmentation. Using data retrieved following segmentation, we perform morphological analysis simultaneously on whole populations of cells, rather than individually. Our algorithm was built entirely with open-source software, for use on retinal microglia.
| S-EPMC7867634 | biostudies-literature
Project description:To develop an automated retina layer thickness measurement tool for the ImageJ platform, to quantitate nuclear layers following the retina contour. We developed the ThicknessTool (TT), an automated thickness measurement plugin for the ImageJ platform. To calibrate TT, we created a calibration dataset of mock binary skeletonized mask images with increasing thickness masks and different rotations. Following, we created a training dataset and performed an agreement analysis of thickness measurements between TT and two masked manual observers. Finally, we tested the performance of TT measurements in a validation dataset of retinal detachment images. In the calibration dataset, there were no differences in layer thickness between measured and known thickness masks, with an overall coefficient of variation of 0.00%. Training dataset measurements of immunofluorescence retina nuclear layers disclosed no significant differences between TT and any observer's average outer nuclear layer (ONL) (p = 0.998), inner nuclear layer (INL) (p = 0.807), and ONL/INL ratio (p = 0.944) measurements. Agreement analysis showed that bias between TT vs. observers' mean was lower than between any observers' mean against each other in the ONL (0.77 ± 0.34 µm vs 3.25 ± 0.33 µm) and INL (1.59 ± 0.28 µm vs 2.82 ± 0.36 µm). Validation dataset showed that TT can detect significant and true ONL thinning (p = 0.006), more sensitive than manual measurement capabilities (p = 0.069). ThicknessTool can measure retina nuclear layers thickness in a fast, accurate, and precise manner with multi-platform capabilities. In addition, the TT can be customized to user preferences and is freely available to download.
| S-EPMC7595229 | biostudies-literature
Project description:Measuring the concentration and viability of fungal cells is an important and fundamental procedure in scientific research and industrial fermentation. In consideration of the drawbacks of manual cell counting, large quantities of fungal cells require methods that provide easy, objective and reproducible high-throughput calculations, especially for samples in complicated backgrounds. To answer this challenge, we explored and developed an easy-to-use fungal cell counting pipeline that combined the machine learning-based ilastik tool with the freeware ImageJ, as well as a conventional photomicroscope. Briefly, learning from labels provided by the user, ilastik performs segmentation and classification automatically in batch processing mode and thus discriminates fungal cells from complex backgrounds. The files processed through ilastik can be recognized by ImageJ, which can compute the numeric results with the macro 'Fungal Cell Counter'. Taking the yeast Cryptococccus deneoformans and the filamentous fungus Pestalotiopsis microspora as examples, we observed that the customizable software algorithm reduced inter-operator errors significantly and achieved accurate and objective results, while manual counting with a haemocytometer exhibited some errors between repeats and required more time. In summary, a convenient, rapid, reproducible and extremely low-cost method to count yeast cells and fungal spores is described here, which can be applied to multiple kinds of eucaryotic microorganisms in genetics, cell biology and industrial fermentation.
| S-EPMC8576076 | biostudies-literature
Project description:BackgroundDuring development, planes of cells give rise to complex tissues and organs. The proper functioning of these tissues is critically dependent on proper inter- and intra-cellular spatial orientation, a feature known as planar cell polarity (PCP). To study the genetic and environmental factors affecting planar cell polarity investigators must often manually measure cell orientations, which is a time-consuming endeavor.MethodologyTo automate cell counting and planar cell polarity data collection we developed a Fiji/ImageJ plug-in called PCP Auto Count (PCPA). PCPA analyzes binary images and identifies "chunks" of white pixels that contain "caves" of infiltrated black pixels. Inner ear sensory epithelia including cochleae (P4) and utricles (E17.5) from mice were immunostained for βII-spectrin and imaged on a confocal microscope. Images were preprocessed using existing Fiji functionality to enhance contrast, make binary, and reduce noise. An investigator rated PCPA cochlear angle measurements for accuracy using a 1-5 agreement scale. For utricle samples, we directly compared PCPA derived measurements against manually derived angle measurements using concordance correlation coefficients (CCC) and Bland-Altman limits of agreement. Finally, PCPA was tested against a variety of images copied from publications examining PCP in various tissues and across various species.ResultsPCPA was able to recognize and count 99.81% of cochlear hair cells (n = 1,1541 hair cells) in a sample set, and was able to obtain ideally accurate planar cell polarity measurements for over 96% of hair cells. When allowing for a <10° deviation from "perfect" measurements, PCPA's accuracy increased to >98%. When manual angle measurements for E17.5 utricles were compared, PCPA's measurements fell within -9 to +10 degrees of manually obtained mean angle measures with a CCC of 0.999. Qualitative examination of example images of Drosophila ommatidia, mouse ependymal cells, and mouse radial progenitors revealed a high level of accuracy for PCPA across a variety of stains, tissue types, and species. Altogether, the data suggest that the PCPA plug-in suite is a robust and accurate tool for the automated collection of cell counts and PCP angle measurements.
| S-EPMC10862842 | biostudies-literature
Project description:Here, we provide a protocol to automate the quantification of the number of phospho-histone H3-positive cells in the developing nervous system of zebrafish using a custom MATLAB script to identify labeled nuclei. We describe steps for fixation, immunolabeling, and imaging of zebrafish embryos. We then detail the analysis steps using Fiji and MATLAB. This protocol can be used for fixed, immunolabeled tissue, as shown here, or for live samples, such as cells expressing a histone-GFP fusion protein. For complete details on the use and execution of this protocol, please refer to Biswas et al.1.
| S-EPMC10519849 | biostudies-literature
Project description:Retinal ganglion cell (RGC) loss is one of the earliest and most important cellular changes in glaucoma. The DARC (Detection of Apoptosing Retinal Cells) technology enables in vivo real-time non-invasive imaging of single apoptosing retinal cells in animal models of glaucoma and Alzheimer's disease. To date, apoptosing RGCs imaged using DARC have been counted manually. This is time-consuming, labour-intensive, vulnerable to bias, and has considerable inter- and intra-operator variability.A semi-automated algorithm was developed which enabled automated identification of apoptosing RGCs labeled with fluorescent Annexin-5 on DARC images. Automated analysis included a pre-processing stage involving local-luminance and local-contrast "gain control", a "blob analysis" step to differentiate between cells, vessels and noise, and a method to exclude non-cell structures using specific combined 'size' and 'aspect' ratio criteria. Apoptosing retinal cells were counted by 3 masked operators, generating 'Gold-standard' mean manual cell counts, and were also counted using the newly developed automated algorithm. Comparison between automated cell counts and the mean manual cell counts on 66 DARC images showed significant correlation between the two methods (Pearson's correlation coefficient 0.978 (p < 0.001), R Squared = 0.956. The Intraclass correlation coefficient was 0.986 (95% CI 0.977-0.991, p < 0.001), and Cronbach's alpha measure of consistency = 0.986, confirming excellent correlation and consistency. No significant difference (p = 0.922, 95% CI: -5.53 to 6.10) was detected between the cell counts of the two methods.The novel automated algorithm enabled accurate quantification of apoptosing RGCs that is highly comparable to manual counting, and appears to minimise operator-bias, whilst being both fast and reproducible. This may prove to be a valuable method of quantifying apoptosing retinal cells, with particular relevance to translation in the clinic, where a Phase I clinical trial of DARC in glaucoma patients is due to start shortly.
| S-EPMC4063694 | biostudies-other
Project description:To describe and validate a semi-automated targeted sampling (SATS) method for quantifying optic nerve axons in a feline glaucoma model.Optic nerve cross sections were obtained from 15 cats, nine with mild to severe glaucoma and six with normal eyes. Optic nerves were dissected, fixed in paraformaldehyde and glutaraldehyde, and processed for light microscopy by resin embedding, sectioning, and staining of axon myelin sheaths with 1% p-phenylenediamine before axon quantification. Commercially available image analysis software was used as a semi-automated axon counting tool (SCT) and was first validated by comparison with a manual axon count (MAC). This counting tool was then used in a SATS method performed by three masked raters and in a semi-automated full count (SAFC) method performed by a single observer. Correlation was assessed between the SCT and MAC using a linear model and analysis of covariance (ANCOVA). Correlation between the SATS and SAFC methods was calculated and the bias, systematic errors, and variance component assessed. The intraclass correlation coefficient (ICC) was determined to establish inter-rater agreement. In addition, the time required to perform the SATS and SAFC methods was evaluated.Correlation between the axon counts obtained by the SCT and MAC was strong (r = 0.9985). There was evidence of an overcounting of axons by the SCT compared to the MAC with a percentage error rate of 13.0% (95% confidence interval [CI] 11.0%, 15.1%). Both the correlation of SATS count (average per rater) to SAFC (r = 0.9891) and inter-rater agreement (ICC = 0.986) were high. The SATS method presented an overall positive counting error (p<0.001) when compared to the SAFC, consistent with a fixed percentage overestimation of 11.2% (95% CI 8.3%, 14.2%) of the full count. The average time required to quantify axons by the SATS method was 10.9 min, only 27% of that required to conduct the SAFC.Our data demonstrate that the SATS method provides a practical, rapid, and reliable means of estimating axon counts in the optic nerves of cats with glaucoma.
| S-EPMC3976691 | biostudies-literature
Project description:While electron microscopy represents the gold standard for detection of synapses, a number of limitations prevent its broad applicability. A key method for detecting synapses is immunostaining for markers of pre- and post-synaptic proteins, which can infer a synapse based upon the apposition of the two markers. While immunostaining and imaging techniques have improved to allow for identification of synapses in tissue, analysis and identification of these appositions are not facile, and there has been a lack of tools to accurately identify these appositions. Here, we delineate a macro that uses open-source and freely available ImageJ or FIJI for analysis of multichannel, z-stack confocal images. With use of a high magnification with a high NA objective, we outline two methods to identify puncta in either sparsely or densely labeled images. Puncta from each channel are used to eliminate non-apposed puncta and are subsequently linked with their cognate from the other channel. These methods are applied to analysis of a pre-synaptic marker, bassoon, with two different post-synaptic markers, gephyrin and N-methyl-d-aspartate (NMDA) receptor subunit 1 (NR1). Using gephyrin as an inhibitory, post-synaptic scaffolding protein, we identify inhibitory synapses in basolateral amygdala, central amygdala, arcuate and the ventromedial hypothalamus. Systematic variation of the settings identify the parameters most critical for this analysis. Identification of specifically overlapping puncta allows for correlation of morphometry data between each channel. Finally, we extend the analysis to only examine puncta overlapping with a cytoplasmic marker of specific cell types, a distinct advantage beyond electron microscopy. Bassoon puncta are restricted to virally transduced, pedunculopontine tegmental nucleus (PPN) axons expressing yellow fluorescent protein. NR1 puncta are restricted to tyrosine hydroxylase labeled dopaminergic neurons of the substantia nigra pars compacta (SNc). The macro identifies bassoon-NR1 overlap throughout the image, or those only restricted to the PPN-SNc connections. Thus, we have extended the available analysis tools that can be used to study synapses in situ. Our analysis code is freely available and open-source allowing for further innovation.
| S-EPMC8524137 | biostudies-literature
Project description:The retinal pigment epithelium (RPE) maintains photoreceptor viability and function, completes the visual cycle, and forms the outer blood-retinal barrier (oBRB). Loss of RPE function gives rise to several monogenic retinal dystrophies and contributes to age-related macular degeneration. Retinal detachment (RD) causes separation of the neurosensory retina from the underlying RPE, disrupting the functional and metabolic relationships between these layers. Although the retinal response to RD is highly studied, little is known about how the RPE responds to loss of this interaction. RNA sequencing (RNA-Seq) was used to compare normal and detached RPE in the C57BL6/J mouse. The naïve mouse RPE transcriptome was compared to previously published RPE signature gene lists and from the union of these 14 genes (Bmp4, Crim1, Degs1, Gja1, Itgav, Mfap3l, Pdpn, Ptgds, Rbp1, Rnf13, Rpe65, Slc4a2, Sulf1 and Ttr) representing a core signature gene set applicable across rodent and human RPE was derived. Gene ontology enrichment analysis (GOEA) of the mouse RPE transcriptome identified expected RPE features and functions, such as pigmentation, phagocytosis, lysosomal and proteasomal degradation of proteins, and barrier function. Differentially expressed genes (DEG) at 1 and 7 days post retinal detachment (dprd) were defined as mRNA with a significant (padj≤0.05) fold change (FC) of 0.67 ≥ FC ≥ 1.5 in detached versus naïve RPE. The RPE transcriptome exhibited dramatic changes at 1 dprd, with 2297 DEG identified. The KEGG pathways and biological process GO groups related to innate immune responses were significantly enriched. Lipocalin 2 (Lcn2) and several chemokines were upregulated, while numerous genes related to RPE functions, such as pigment synthesis, visual cycle, phagocytosis, and tight junctions were downregulated at 1 dprd. The response was largely transient, with only 18 significant DEG identified at 7 dprd, including upregulation of complement gene C4b. Validation studies confirmed RNA-Seq results. Thus, the RPE quickly downregulates cell-specific functions and mounts an innate immune defense response following RD. Our data demonstrate that the RPE contributes to the inflammatory response to RD and may play a role in attraction of immune cells to the subretinal space.
| S-EPMC10964713 | biostudies-literature