Journal: Cells

Article Title: Cyclin-Dependent Kinase 18 Controls Trafficking of Aquaporin-2 and Its Abundance through Ubiquitin Ligase STUB1, Which Functions as an AKAP

doi: 10.3390/cells9030673

Figure Lengend Snippet: CDK18 is necessary for the cAMP-induced redistribution of AQP2 from intracellular vesicles to the plasma membrane. ( A ) Schematic representation of the Kinome-wide siRNA screening approach. MCD4 cells were seeded in 384-well microtiter plates and the expression of 719 kinases was knocked down each with a pool of four siRNAs. The effects of the knockdown on the localization of AQP2 were detected with specific anti-AQP2 and secondary Cy3-coupled antibodies and automated immunofluorescence microscopic analysis. Image analysis was carried out with CellProfiler and KNIME software. ( B ) MCD4 cells were treated with 50 nM non-targeting siRNA (siNT), a pool of four different or a single CDK18 siRNA. The cells were treated with forskolin (Fsk; 30 µM, 60 min) or were left unstimulated (control) and the localization of AQP2 was analyzed with a confocal laser scanning microscope (40× magnification). AQP2 is in green and nuclei are in blue. Shown are representative images from n ≥ 3 independent experiments per condition; scale bar, 50 µm. ( C ) The efficacy of the CDK18 knockdown was evaluated by Western blot analysis. CDK18 and as a loading control Pan-cadherin were detected. Signals were quantified by densitometric analysis. Statistical analysis was performed using the unpaired t-test, significant differences are indicated, * p ≤ 0.05, ** p ≤ 0.01. Mean ± SEM are plotted, n = 3–6 independent experiments per condition.

Article Snippet: We used image analysis software CellProfiler [ ] and KNIME (knime.org) to identify candidates whose knockdown prevented the redistribution of AQP2.

Techniques: Expressing, Immunofluorescence, Software, Laser-Scanning Microscopy, Western Blot

Journal: Open Research Europe

Article Title: Methodological approaches in aggregate formation and microscopic analysis to assess pseudoislet morphology and cellular interactions

doi: 10.12688/openreseurope.14894.2

Figure Lengend Snippet: ( A – D ) Post-processed image of the aggregated INS1E cells (green) and nuclei (blue) with applied background noise reduction and immunofluorescence signal normalisation by each software. ( E – H ) The threshold segmentation of individual nuclei ID represents the thresholding success for all four software, Fiji, CellProfiler, GA3 and Imaris, respectively. ( I – L ) Focus on cell segmentation between single cells in the aggregate and how well each of the four different software could identify single cells. ( M ) The software was compared to manual counting for their performance to quantify nuclei and correct for multiple counts. The data represent an over– or underestimated count of nuclei in the different images containing aggregates of cells. The software GA3 and Imaris overestimated the count of nuclei with an average of 13% and 10%, respectively. Fiji and CellProfiler underestimated the count of nuclei with an average of 69% and 50%, respectively. ( N ) A comparison between the software for their success segmenting single cells in the cell clusters. GA3 and Imaris had close quantifications to the manual cell count with a slight underestimation of 8% and 6%, respectively. CellProfiler showed results close to GA3 and Imaris along with larger variation in over– and underestimations of the cell count with an average of 12% overestimation. Fiji showed a consistent underestimation of the cell count with an average value of 83% from the manual count. Results are calculated by the relative change, and the data comprised nine z-stack data sets.

Article Snippet: Cell quantification was completed on digital images using four different software packages: Fiji , CellProfiler (Broad Institute, Cambridge, MA, USA), NIS Elements GA3 (Nikon Instruments), Imaris 9.5.0 (Bitplane, South Windsor, CT, USA).

Techniques: Immunofluorescence, Software, Comparison, Cell Counting

Journal: Open Research Europe

Article Title: Methodological approaches in aggregate formation and microscopic analysis to assess pseudoislet morphology and cellular interactions

doi: 10.12688/openreseurope.14894.2

Figure Lengend Snippet: ( A ) GA3 and Imaris produced equal quantification of the nuclei except in image four, where GA3 slightly overestimated the count compared to Imaris. In most z-stacks, CellProfiler was closer to the manual count than Fiji except in dataset one and eight. However, overall, they underestimated the number of nuclei. ( B ) For the quantification of single cells, both GA3 and Imaris, resulted in equal counts as the manual count except in dataset four and seven. However, CellProfiler showed equal accuracy as GA3 and Imaris in many z-stacks, except in dataset two, three, four and eight. Fiji had a consistent underestimation of ~83% in all datasets. ( C ) Comparing the distinction between core and mantle in the aggregates. The manual counting had a lesser distinction between the core and mantle distribution of the INS1E cells compared to both GA3 and Imaris that used a percentage area distribution mask to quantify the distribution of the cells in each area. This resulted in an 89% and 17% core versus mantle distribution in GA3 and 83% and 17% in Imaris. ( D ) Another method was to quantify overlapping signals between two immunofluorescence channels within a 0 µm distance. The GA3 and Imaris software resulted in equal count except in datasets nine and seven. However, in most situations, the manual quantification had a higher count than GA3 and Imaris. Only dataset three, five, six and eight had comparable results as the software. Results are calculated by the relative change, and the data set included nine z-stack data sets.

Article Snippet: Cell quantification was completed on digital images using four different software packages: Fiji , CellProfiler (Broad Institute, Cambridge, MA, USA), NIS Elements GA3 (Nikon Instruments), Imaris 9.5.0 (Bitplane, South Windsor, CT, USA).

Techniques: Produced, Immunofluorescence, Software