Journal: bioRxiv

Article Title: GIANI: open-source software for automated analysis of 3D microscopy images

doi: 10.1101/2020.10.15.340810

Figure Lengend Snippet: All images represent a single slice of a 3D stack and are for illustrative purposes only. A: An example dataset, consisting of a mouse embryo showing DAPI (blue) and E-Cadherin (red). GIANI can accept as input any image data that is readable by Bio-Formats . B: Nuclear are first approximated using one of two blob detectors - Laplacian of Gaussian (left) or Hessian (right). C: Gaussian filtering is used to suppress noise in the channels used for nuclear and cell segmentation. D: The nuclear channel is then subjected to top-hat filtering to remove background - contrast has been increased to illustrate the effect of the filter. E: Nuclear segmentation is achieved using a marker-controlled watershed approach, with the background-subtracted image from D serving as the input and centroids from B serving as the seeds. F: Cell segmentation is achieved using the same marker-controlled watershed approach, with the filtered image from C serving as the input and nuclei segmentations from E serving as the seeds.

Article Snippet: Available to download from: https://dx.doi.org/10.5281/zenodo.5270323 File S3 GIANI settings used to produce the data in File S4 GIANI settings used to produce the data in File S5 GIANI and Imaris settings used to produce the data in File S6 GIANI and Imaris settings used to produce the data in File S7 GIANI and Imaris settings used to produce the data in File S8 CellProfiler pipeline and data used to produce .

Techniques: Marker

Journal: bioRxiv

Article Title: GIANI: open-source software for automated analysis of 3D microscopy images

doi: 10.1101/2020.10.15.340810

Figure Lengend Snippet: In each of the heat maps, a single tile represents the average of three simulated embryos. The GIANI settings used to produce this data are provided as File S3. A: A 2D slice of an exemplar 3D simulated embryo. B: The ground truth segmentation of ‘A’. C - H: Absolute errors in cell counts ( E c , calculated according to ), nuclear centroid localisation error ( E nl ) and cell centroid localisation error ( E cl ) produced by GIANI for simulated embryos with the indicated number of cells and signal-to-noise ratios (SNR). Results were obtained using either GIANI’s basic (C - E) or advanced (F - H) nuclear detector.

Article Snippet: Available to download from: https://dx.doi.org/10.5281/zenodo.5270323 File S3 GIANI settings used to produce the data in File S4 GIANI settings used to produce the data in File S5 GIANI and Imaris settings used to produce the data in File S6 GIANI and Imaris settings used to produce the data in File S7 GIANI and Imaris settings used to produce the data in File S8 CellProfiler pipeline and data used to produce .

Techniques: Produced

Journal: bioRxiv

Article Title: GIANI: open-source software for automated analysis of 3D microscopy images

doi: 10.1101/2020.10.15.340810

Figure Lengend Snippet: In each of the heat maps, a single tile represents the average of three simulated embryos. The GIANI and Imaris settings used to produce this data are provided as File S7. A: Absolute errors in cell counts ( E c , calculated according to ) produced by GIANI for simulated embryos with the indicated number of cells and signal-to-noise ratios (SNR). B: Absolute errors in nuclear centroid localisation ( E nl ) produced by GIANI. C: Absolute errors in cell counts produced by Imaris. D: Absolute errors in nuclear centroid localisation produced by Imaris.

Article Snippet: Available to download from: https://dx.doi.org/10.5281/zenodo.5270323 File S3 GIANI settings used to produce the data in File S4 GIANI settings used to produce the data in File S5 GIANI and Imaris settings used to produce the data in File S6 GIANI and Imaris settings used to produce the data in File S7 GIANI and Imaris settings used to produce the data in File S8 CellProfiler pipeline and data used to produce .

Techniques: Produced

Journal: bioRxiv

Article Title: GIANI: open-source software for automated analysis of 3D microscopy images

doi: 10.1101/2020.10.15.340810

Figure Lengend Snippet: The data shown in A - C is a subset of that shown in . In D - F, each tile represents a single simulated embryo, which was reduced in size to 512 × 512 × 112 voxels prior to running the pipeline. A: Absolute errors in cell counts ( E c , calculated according to ) produced by GIANI for simulated embryos with the indicated number of cells and signal-to-noise ratios (SNR). B: Absolute errors in nuclear centroid localisation ( E nl ) produced by GIANI. C: Absolute errors in cell centroid localisation error ( E cl ) produced by GIANI. D: Absolute errors in cell counts produced by CellProfiler. E: Absolute errors in nuclear centroid localisation produced by CellProfiler. F: Absolute errors in cell centroid localisation error produced by CellProfiler. The CellProfiler pipeline used and raw data are provided in File S8.

Article Snippet: Available to download from: https://dx.doi.org/10.5281/zenodo.5270323 File S3 GIANI settings used to produce the data in File S4 GIANI settings used to produce the data in File S5 GIANI and Imaris settings used to produce the data in File S6 GIANI and Imaris settings used to produce the data in File S7 GIANI and Imaris settings used to produce the data in File S8 CellProfiler pipeline and data used to produce .

Techniques: Produced

Journal: bioRxiv

Article Title: GIANI: open-source software for automated analysis of 3D microscopy images

doi: 10.1101/2020.10.15.340810

Figure Lengend Snippet: Unless otherwise stated, each dot represents a single cell. The GIANI settings used to produce this data are provided as File S4. A. Illustration of the division of embryo cells into ‘outer’ (red) and ‘inner’ (green) sub-populations. The embryo centroid is indicated by the white square. The blue circle has radius D m (see ) and indicates the distance from the embryo centroid to the most distant nucleus centroid. The radius of the yellow circle is D T × D m . B. The number of cells in each embryo divided into outer and inner sub-populations using a value of 0.5 for D T in control embryos. Each dot represents a single embryo ( n control = 18). C: Volume of nuclei in inner/outer populations in control embryos. D: Volume of cells in inner/outer populations in control embryos. E: Ratio of cell-to-nuclear volume in inner and outer cells in control embryos. F: Nuclear/cytoplasmic ratio of YAP1 expression in control embryos. G: Difference in expression profiles of nuclear GATA3 expression, normalised to DAPI, in control and treated embryos ( n treated = 20). H: Difference in nuclear/cytoplasmic ratiometric expression profiles of YAP1 in control and treated embryos.

Article Snippet: Available to download from: https://dx.doi.org/10.5281/zenodo.5270323 File S3 GIANI settings used to produce the data in File S4 GIANI settings used to produce the data in File S5 GIANI and Imaris settings used to produce the data in File S6 GIANI and Imaris settings used to produce the data in File S7 GIANI and Imaris settings used to produce the data in File S8 CellProfiler pipeline and data used to produce .

Techniques: Control, Expressing

Journal: bioRxiv

Article Title: GIANI: open-source software for automated analysis of 3D microscopy images

doi: 10.1101/2020.10.15.340810

Figure Lengend Snippet: A single slice of (A) expanded and (B) hatching blastocysts are shown, together with the segmentations produced by Imaris and GIANI. Scale bars are all equivalent to 20 µ m. The GIANI and Imaris settings used to produce this data, together with the raw image data, are provided as File S9.

Article Snippet: Available to download from: https://dx.doi.org/10.5281/zenodo.5270323 File S3 GIANI settings used to produce the data in File S4 GIANI settings used to produce the data in File S5 GIANI and Imaris settings used to produce the data in File S6 GIANI and Imaris settings used to produce the data in File S7 GIANI and Imaris settings used to produce the data in File S8 CellProfiler pipeline and data used to produce .

Techniques: Produced

Journal: bioRxiv

Article Title: GIANI: open-source software for automated analysis of 3D microscopy images

doi: 10.1101/2020.10.15.340810

Figure Lengend Snippet: The GIANI and Imaris settings used to produce this data are provided as File S5. A: Illustration of the segmentations produced by GIANI and Imaris on a large simulated dataset (File S1). The top row shows a single slice of each 3D volume, while the bottom row shows the magnified views of the boxes in the top row images. Scale bars are all equivalent to 20 µ m. B: Distribution of localisation errors produced by both Imaris and GIANI in detecting the simulated nuclei in File S1. C: Relationship between localisation error for detected nuclei and the distance of each nucleus to its nearest neighbour. D: Influence of distance of nuclei to their nearest neighbour on successful detection.

Article Snippet: Available to download from: https://dx.doi.org/10.5281/zenodo.5270323 File S3 GIANI settings used to produce the data in File S4 GIANI settings used to produce the data in File S5 GIANI and Imaris settings used to produce the data in File S6 GIANI and Imaris settings used to produce the data in File S7 GIANI and Imaris settings used to produce the data in File S8 CellProfiler pipeline and data used to produce .

Techniques: Produced

Journal: bioRxiv

Article Title: GIANI: open-source software for automated analysis of 3D microscopy images

doi: 10.1101/2020.10.15.340810

Figure Lengend Snippet: Illustrations of the segmentations produced by GIANI and Imaris on a Tribolium castaneum embryo dataset derived from light sheet microscopy (File S2). Two different slices of the 3D volumes are shown, at approximately 46 µ m (A) and 247 µ m (B), to illustrate the variation in nuclei morphologies at different depths. In each case, the top row shows the relevant slice of each 3D volume, while the bottom row shows the magnified views of the boxes in the top row images. Approximately 5,600 nuclei were detected in the full volume. Scale bars are all equivalent to 20 µ m. The GIANI and Imaris settings used to produce this data are provided as File S6.

Article Snippet: Available to download from: https://dx.doi.org/10.5281/zenodo.5270323 File S3 GIANI settings used to produce the data in File S4 GIANI settings used to produce the data in File S5 GIANI and Imaris settings used to produce the data in File S6 GIANI and Imaris settings used to produce the data in File S7 GIANI and Imaris settings used to produce the data in File S8 CellProfiler pipeline and data used to produce .

Techniques: Produced, Derivative Assay, Microscopy

Journal: bioRxiv

Article Title: GIANI: open-source software for automated analysis of 3D microscopy images

doi: 10.1101/2020.10.15.340810

Figure Lengend Snippet: A: The length of time taken by GIANI to analyse simulated embryos versus the number of cells in the embryo. Each point represents the execution time for a single embryo - approximately 20 embryos were analysed for each cell number. The blue line represents a moving average calculated with LOESS smoothing. The grey bands represent the 95% confidence interval. B: The length of time taken by GIANI to analyse simulated embryos consisting of 30 cells versus the number of available CPUs. Each point represents the execution time for a single embryo - approximately 20 embryos were analysed for each CPU number. The blue line represents a moving average calculated with LOESS smoothing. The grey bands represent the 95% confidence interval.

Article Snippet: Available to download from: https://dx.doi.org/10.5281/zenodo.5270323 File S3 GIANI settings used to produce the data in File S4 GIANI settings used to produce the data in File S5 GIANI and Imaris settings used to produce the data in File S6 GIANI and Imaris settings used to produce the data in File S7 GIANI and Imaris settings used to produce the data in File S8 CellProfiler pipeline and data used to produce .

Techniques:

Journal: Computational and Structural Biotechnology Journal

Article Title: Spatial patterns of the cap-binding complex eIF4F in human melanoma cells

doi: 10.1016/j.csbj.2023.01.040

Figure Lengend Snippet: In situ detection of eIF4F subcellular distribution. (A) Left panel: schematic view of the proximity ligation assay for the eIF4E-eIF4G complex. 4E: eIF4E, 4 G: eIF4G, 4 A: eIF4A, AAA: polyA tail. Right panel: example images of the eIF4E-eIF4G PLA assay and cell segmentation. The eIF4E-eIF4G complex was stained following the proximity ligation assay protocol, the cytoskeleton was stained by Phalloidin-Alexa 488, and the nucleus was stained with Hoechst 33342. (B) Polysome profile and eIF4E-eIF4G PLA assay of A375 cells upon treatment with PP242. Cells were treated with PP424 at 1 μM for 24 h, and then lysed by polysome hypotonic buffer or fixed with 4% PFA. (C) Polysome profile and eIF4E-eIF4G PLA assay of QBC989 cholangiocarcinoma cells upon nutrient deprivation. Cells were cultured with HBSS solution for nutrient starvation for 16 h, followed by hypotonic buffer lysis or 4% PFA fixation. (D-E) A375 cells expressing the ERK-KTR reporter gene were treated with 1 μM vemurafenib for 24 h and the eIF4E-eIF4G PLA assay was performed. Single cell quantification of ERK-KTR nuclear translocation (p-ERK1/2) and eIF4E-eIF4G spot count are plotted. (F) Pipeline of the eIF4E-eIF4G PLA image analysis. The eIF4E-eIF4G complex was stained following the proximity ligation assay protocol, the cytoskeleton was stained with Phalloidin-Alexa 488, and the nucleus was stained with Hoechst 33342. Cell images were then subjected to Cellprofiler 2.0 analysis to identify the nucleus and cytoplasm. The eIF4E-eIF4G spots were identified by using Cellprofiler module ‘IdentifySpots.m′ followed by correlation with each cell. The localization pattern of the eIF4E-eIF4G spots was calculated with Cellprofiler module ‘MeasureLocalizationOfSpots.m′. (G) Example images of the cell segmentation and spot localization analysis. (H) Summary of the spot features and cell features measured by Cellprofiler 2.0.

Article Snippet: For the MATLAB-based Cellprofiler pipeline, we used the Cellprofiler modules in combination with the modules developed by Lucas Pelkmans’s lab . We shared the ‘MeasureLocalizationOfSpots.m′ file in the supplemental files, however, please refer to the work from Battich et al. Battich et al., when using this code.

Techniques: In Situ, Proximity Ligation Assay, Staining, Cell Culture, Lysis, Expressing, Translocation Assay

Journal: PLoS ONE

Article Title: High-Throughput Method for Automated Colony and Cell Counting by Digital Image Analysis Based on Edge Detection

doi: 10.1371/journal.pone.0148469

Figure Lengend Snippet: (A) RGB image showing DAPI staining of nuclei in blue, and phospho-histone3 staining in red. (B) shows the red channel processed with IMJ Edge to calculate the number of dividing cells. (C) shows the blue channel processed by IMJ Edge to identify all the nuclei. (D) shows the blue channel of (A) processed by IMJ Cai, OpenCFU and Cell Profiler. The detected nuclei are outlined in blue boxes by OpenCFU, and in red circles by CellProfiler. (E) shows the original Nomarski image of yeast cells, and the processed images by various methods next to it. (F) shows the Normarski image of a 293 cell and its processed versions next to it. The segmentation step was skipped in IMJ Edge to prevent segmentation of cell processes.

Article Snippet: The IMJ macro, CellProfiler pipelines, and images are currently available from https://sourceforge.net/projects/cell-colony-edge/files/ .

Techniques: Staining

Journal: BMC Neuroscience

Article Title: Histological characterization of orphan transporter MCT14 (SLC16A14) shows abundant expression in mouse CNS and kidney

doi: 10.1186/s12868-016-0274-7

Figure Lengend Snippet: A majority of MCT14 positive neurons are non-GAD67. Double IHC on 7-µm paraffin sections with MCT14/NeuN and MCT14/GAD67 for determination of the type of neuron MCT14 is expressed in. MCT14 is seen in red , markers in green , and DAPI is seen in blue . a Colocalization of MCT14 with NeuN, a neuronal marker, in most MCT14-expressing cells. b Colocalization of MCT14 with neurons expressing GAD67, a marker for inhibitory neurons. c A total of six (n = 2) double IHC images with MCT14/NeunN and MCT14/GAD67 from retrosplenial cortex, hypothalamus and piriform cortex were collected and analyzed using a colocalization analysis pipeline from CellProfiler.org (Additional file : Figure S1). Fraction of MCT14-positive cells that also expressed respective marker was plotted. All images were acquired on a Zeiss AxioImager at ×20 magnification. Statistics were performed with Students’ t test, *p < 0.05

Article Snippet: All images were analyzed using a specialized pipeline in the automated open-source cell segmentation software CellProfiler (Additional file : Figure S1) (Broad Institute Imaging Platform, Cambridge, MA, USA) [ ].

Techniques: Marker, Expressing

Journal: Cell reports

Article Title: AP-1 transcription factor network explains diverse patterns of cellular plasticity in melanoma cells

doi: 10.1016/j.celrep.2022.111147

Figure Lengend Snippet: KEY RESOURCES TABLE

Article Snippet: This paper N/A Software and algorithms Source code for analyses This paper https://github.com/fallahi-sichani-lab/AP1-networkPlasticityMelanoma CellProfiler (3.1.9) ( McQuin et al., 2018 ) https://cellprofiler.org/ ImageJ (2.3.0) Public Domain Software https://imagej.nih.gov/ij/index.html MATLAB (2020b) Mathworks https://matlab.mathworks.com/ R (4.0.4) The Comprehensive R Archive Network (CRAN) https://www.r-project.org/ stats R package (4.1.2) The R Project https://stat.ethz.ch/R-manual/R-devel/library/stats/html/00Index.html AUCell R package (1.16.0) (Aibaretal., 2017) https://bioconductor.org/packages/release/bioc/vignettes/AUCell/inst/doc/AUCell.html umap R package (0.2.7.0) CRAN https://cran.r-project.org/web/packages/umap/ SCopeLoomR R package (0.13.0) Aerts Lab https://github.com/aertslab/SCopeLoomR Python (3.9.2) N/A https://www.python.org/downloads/ Scanpy (1.7.1) ( Wolf et al., 2018 ) https://github. com/scverse/scanpy pySCENIC (0.11.0) ( Van de Sande et al., 2020 ) https://github.com/aertslab/SCENICprotocol Nextflow (20.10.0) Seqera Labs https://www.nextflow.io/ Scikit-learn Library (0.24.1) ( Pedregosa et al., 2018 ) https://scikit-learn.org/stable/ SHAP ( Lundberg and Lee, 2017 ) https://github.com/slundberg/shap Other 96-well plates Corning Cat# 3904 Differentiation signature gene sets ( Tsoi et al., 2018 ) N/A Proliferative and Invasive phenotype gene sets ( Hoek et al., 2006 ) http://www.jurmo.ch/work_97.php List of bZIP transcription factor genes ( Vinson et al., 2002 ) https://github.com/fallahi-sichani-lab/AP1-networkPlasticityMelanoma List of transcription factor genes ( Van de Sande et al., 2020 ) https://raw.githubusercontent.com/aertslab/pySCENIC/master/resources/hs_hgnc_tfs.txt Nextflow pipeline adapted for running SCENIC iteratively ( Wouters et al., 2020 ) https://github.com/aertslab/singlecellRNA_melanoma_paper R scripts adapted for extracting and filtering regulons from multiple SCENIC runs ( Wouters et al., 2020 ) https://github.com/aertslab/singlecellRNA_melanoma_paper Homo sapiens whole-genome motif ranking databases for SCENIC (motif collection v9) Aerts Lab https://resources.aertslab.org/cistarget/ Motif annotation file for SCENIC (motif collection v9) Aerts Lab https://resources.aertslab.org/cistarget/ Open in a separate window KEY RESOURCES TABLE The original codes for data analysis performed in this paper are publicly available at GitHub: https://github.com/fallahi-sichani-lab/AP1-networkPlasticityMelanoma ( https://doi.org/10.5281/zenodo.6741989 ).

Techniques: Recombinant, Blocking Assay, Expressing, Activity Assay, Software

Journal: Cell reports

Article Title: AP-1 transcription factor network explains diverse patterns of cellular plasticity in melanoma cells

doi: 10.1016/j.celrep.2022.111147

Figure Lengend Snippet: KEY RESOURCES TABLE

Article Snippet: This paper N/A Software and algorithms Source code for analyses This paper https://github.com/fallahi-sichani-lab/AP1-networkPlasticityMelanoma CellProfiler (3.1.9) ( McQuin et al., 2018 ) https://cellprofiler.org/ ImageJ (2.3.0) Public Domain Software https://imagej.nih.gov/ij/index.html MATLAB (2020b) Mathworks https://matlab.mathworks.com/ R (4.0.4) The Comprehensive R Archive Network (CRAN) https://www.r-project.org/ stats R package (4.1.2) The R Project https://stat.ethz.ch/R-manual/R-devel/library/stats/html/00Index.html AUCell R package (1.16.0) (Aibaretal., 2017) https://bioconductor.org/packages/release/bioc/vignettes/AUCell/inst/doc/AUCell.html umap R package (0.2.7.0) CRAN https://cran.r-project.org/web/packages/umap/ SCopeLoomR R package (0.13.0) Aerts Lab https://github.com/aertslab/SCopeLoomR Python (3.9.2) N/A https://www.python.org/downloads/ Scanpy (1.7.1) ( Wolf et al., 2018 ) https://github. com/scverse/scanpy pySCENIC (0.11.0) ( Van de Sande et al., 2020 ) https://github.com/aertslab/SCENICprotocol Nextflow (20.10.0) Seqera Labs https://www.nextflow.io/ Scikit-learn Library (0.24.1) ( Pedregosa et al., 2018 ) https://scikit-learn.org/stable/ SHAP ( Lundberg and Lee, 2017 ) https://github.com/slundberg/shap Other 96-well plates Corning Cat# 3904 Differentiation signature gene sets ( Tsoi et al., 2018 ) N/A Proliferative and Invasive phenotype gene sets ( Hoek et al., 2006 ) http://www.jurmo.ch/work_97.php List of bZIP transcription factor genes ( Vinson et al., 2002 ) https://github.com/fallahi-sichani-lab/AP1-networkPlasticityMelanoma List of transcription factor genes ( Van de Sande et al., 2020 ) https://raw.githubusercontent.com/aertslab/pySCENIC/master/resources/hs_hgnc_tfs.txt Nextflow pipeline adapted for running SCENIC iteratively ( Wouters et al., 2020 ) https://github.com/aertslab/singlecellRNA_melanoma_paper R scripts adapted for extracting and filtering regulons from multiple SCENIC runs ( Wouters et al., 2020 ) https://github.com/aertslab/singlecellRNA_melanoma_paper Homo sapiens whole-genome motif ranking databases for SCENIC (motif collection v9) Aerts Lab https://resources.aertslab.org/cistarget/ Motif annotation file for SCENIC (motif collection v9) Aerts Lab https://resources.aertslab.org/cistarget/ Open in a separate window KEY RESOURCES TABLE The original codes for data analysis performed in this paper are publicly available at GitHub: https://github.com/fallahi-sichani-lab/AP1-networkPlasticityMelanoma ( https://doi.org/10.5281/zenodo.6741989 ).

Techniques: Recombinant, Blocking Assay, Expressing, Activity Assay, Software