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Columbus 2

Manufactured by PerkinElmer
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The Columbus 2.7 is a high-performance imaging system designed for advanced cell-based assays. It features a flexible and modular architecture, allowing users to configure the system to meet their specific research needs. The system provides advanced image acquisition and analysis capabilities, enabling researchers to obtain detailed insights into cellular processes and behaviors.

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Lab products found in correlation

Rnaimax, by Thermo Fisher Scientific (4 mentions) Hoechst 33342, by Thermo Fisher Scientific (4 mentions) Incucyte live cell analysis imaging, by Sartorius (2 mentions) Opera phenix, by Spirochrome (2 mentions) Sir dna, by PerkinElmer (2 mentions) Opera phenix high content screening system, by PerkinElmer (2 mentions) Sir dna, by Spirochrome (2 mentions) Messengermax, by Thermo Fisher Scientific (2 mentions) Egfp mrna, by TriLink (2 mentions) Pe mouse anti human cd54, by BD (2 mentions) Operetta hts imaging system, by PerkinElmer (2 mentions) Cellinsight cx7, by Thermo Fisher Scientific (1 mentions) Cellinsight cx7 high content screening platform, by Thermo Fisher Scientific (1 mentions) Bodipy 493 503, by Thermo Fisher Scientific (1 mentions) Cq1 confocal quantitative image cytometer, by Yokogawa (1 mentions) Dcfh da, by Merck Group (1 mentions) Paraformaldehyde (pfa), by Avantor (1 mentions) Imagexpress pico, by Molecular Devices (1 mentions) Dapi d1306, by Thermo Fisher Scientific (1 mentions) Operetta high content imaging system 1483, by PerkinElmer (1 mentions)

Close spelling variants of this product

Columbus 2.8 analysis system (5 citations) Columbus Software 2.8.0 (2 citations) Columbus software version 2.8.0 (2 citations)

20 protocols using columbus 2

1

Automated Imaging of UPR Activation

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Immunofluorescence and direct fluorescence in SK‐N‐SH and Mif‐inducible IgM heavy chain HeLa cells and co‐culture UPR reporter assays were imaged using an automated microscopy platform (CellInsight CX7; ThermoFisher Scientific, Waltham, MA, USA). DAPI (nuclear marker) was used for autofocus and immunofluorescence experiments were imaged with a 10× objective and 25 fields of view per well and UPR reporter assays with a 20× objective and 20 fields of view per well. Image analysis was performed with Columbus 2.5 software (PerkinElmer, Groningen, The Netherlands). For both ATF4 and XBP1 immunostainings as well as UPR reporters, fluorescence intensity in the nucleus (DAPI mask) followed by background subtraction (ring around nucleus) was used as output data. BiP immunofluorescence was measured in a whole cell mask [either MAP2, Tubulin beta III isoform (SK‐N‐SH cells) or GAPDH (IgM heavy chain HeLa cells)]. Negative stainings (secondary antibody only incubation) were also used to determine background values for immunofluorescence experiments and values were subtracted from output data of ATF4, XBP1, and BiP analyses. In experiments with SK‐N‐SH cells, the highest and lowest data points (one pair/experiment) were excluded from the datasets. Data of UPR reporter experiments were normalized by rescaling between 1 (minimum; EtOH/Mif 48 h) and 10 (maximum; TG) per experiment.
van Ziel A.M., Wolzak K., Nölle A., Hoetjes P.J., Berenjeno‐Correa E., van Anken E., Struys E.A, & Scheper W. (2019). No evidence for cell‐to‐cell transmission of the unfolded protein response in cell culture. Journal of Neurochemistry, 152(2), 208-220.
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2

Quantifying Neuronal GFP Expression

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At DIV1 neurons were infected with deltaCre-GFP lentivirus, the deltaCre targets the GFP to the nucleus. Treatments as indicated in figure legend, were performed at DIV14 followed by formaldehyde fixation (as described in the immunocytochemistry section). Neurons were imaged using a cellomics array scan (CellInsight CX7 High-Content Screening (HCS) Platform; ThermoFisher Scientific) with a 10x objective, 15 fields of view per well. Quantification was performed with Columbus 2.5 software (PerkinElmer).
van Ziel A.M., Largo-Barrientos P., Wolzak K., Verhage M, & Scheper W. (2019). Unconventional secretion factor GRASP55 is increased by pharmacological unfolded protein response inducers in neurons. Scientific Reports, 9, 1567.
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3

High-throughput Microscopy-based Screening for HAC Loss

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TIFF files generated by the CV7000S microscope were imported and analyzed using PerkinElmer Columbus 2.7. The DAPI channel was used to segment a nuclear ROI mask, which was then used to measure the mean fluorescence intensity in the nucleus in the GFP channel. Nuclei touching the image borders and nuclei with a roundness value <0.7, often representing nuclear segmentation errors, were excluded from the subsequent analysis steps. The cells with values of GFP mean fluorescence intensity <100 AU, an empirically determined threshold that was kept constant for all plates in the screen, were classified as GFP−. The percentage of GFP− cells was used as a proxy for measuring HAC loss. Well-level data were exported as tab-separated text files.
Liskovykh M., Goncharov N.V., Petrov N., Aksenova V., Pegoraro G., Ozbun L.L., Reinhold W.C., Varma S., Dasso M., Kumeiko V., Masumoto H., Earnshaw W.C., Larionov V, & Kouprina N. (2019). A novel assay to screen siRNA libraries identifies protein kinases required for chromosome transmission. Genome Research, 29(10), 1719-1732.
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4

Quantitative Analysis of Protein Expression

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Customized image analysis software (Columbus 2.7, PerkinElmer) was used for the image analysis and the quantification. Hoechst staining was used for the segmentation of both nuclei and cell. Next, the mean fluorescence intensity of each mCherry and YFP signals in the cytoplasm were quantified. The mean fluorescence intensity of each signals were then normalized to the fold-change based on the non-targeting siRNA in the same plate. To evaluate the impact of each siRNA, an average of 1000 cells was analyzed per run (n = 3). For the quality control of HTS, we used strictly standardized mean difference with more than 3 of beta-score of two positive control (siRNA-APP and siRNA-PS1, n = 14 each per plate) (Supplemental Fig. 1).
Chapuis J., Flaig A., Grenier-Boley B., Eysert F., Pottiez V., Deloison G., Vandeputte A., Ayral A.M., Mendes T., Desai S., Goate A.M., Kauwe J.S., Leroux F., Herledan A., Demiautte F., Bauer C., Checler F., Petersen R.C., Blennow K., Zetterberg H., Minthon L., Van Deerlin V.M., Lee V.M., Shaw L.M., Trojanowski J.Q., Albert M., Moghekar A., O’Brien R., Peskind E.R., Malmanche N., Schellenberg G.D., Dourlen P., Song O.R., Cruchaga C., Amouyel P., Deprez B., Brodin P, & Lambert J.C. (2016). Genome-wide, high-content siRNA screening identifies the Alzheimer’s genetic risk factor FERMT2 as a major modulator of APP metabolism. Acta Neuropathologica, 133(6), 955-966.
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5

Automated quantitative image analysis

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Customized image analysis software (Columbus 2.7, PerkinElmer) was used for the image analysis and the quantification. Hoechst staining was used for the segmentation of both nuclei and cell. Next, the mean fluorescence intensity of each mCherry and YFP signals in the cytoplasm were quantified. The mean fluorescence intensity of each signals were then normalized to the fold change based on the non-targeting siRNA in the same plate. To evaluate the impact of each siRNA, an average of 1,000 cells was analyzed per run (n=3). For the quality control of HTS, we used strictly standardized mean difference with more than 3 of beta-score of two positive control (siRNA-APP and siRNA-PS1, n=14 each per plate) (Supplemental Fig. 1).
Chapuis J., Flaig A., Grenier-Boley B., Eysert F., Pottiez V., Deloison G., Vandeputte A., Ayral A.M., Mendes T., Desai S., Goate A.M., Kauwe J.S., Leroux F., Herledan A., Demiautte F., Bauer C., Checler F., Petersen R.C., Blennow K., Zetterberg H., Minthon L., Van Deerlin V.M., Lee V.M., Shaw L.M., Trojanowski J.Q., Albert M., Moghekar A., O’Brien R., Peskind E.R., Malmanche N., Schellenberg G.D., Dourlen P., Song O.R., Cruchaga C., Amouyel P., Deprez B., Brodin P, & Lambert J.C. (2016). Genome-wide, high-content siRNA screening identifies the Alzheimer’s genetic risk factor FERMT2 as a major modulator of APP metabolism. Acta Neuropathologica, 133(6), 955-966.
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6

Automated Single-Cell Transcriptomics Analysis

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Images were imported and analyzed in Columbus 2.7 or 2.8 (PerkinElmer, Waltham, MA). Briefly, nuclei were segmented using the DAPI channel, and dilated by a fix percentage to generate an approximate cell body region of interest. For digital HCR (dHCR, Choi, 2018) HCR foci were first detected over the cell body region using Columbus spot finding algorithm C, and then filtered using a user-trained Fisher Linear Discriminant classifier based on fluorescence intensity and contrast. The output of dHCR was number of HCR spots per cell. For quantitative HCR (qHCR, Choi et al. 2018 ) the mean fluorescence intensity in the HCR channel was measured over the cell body region and used as the output measurement. Single cell results were exported from Columbus as text files.
Gadkari M., Sun J., Carcamo A., Alessi H., Hu Z., Fraser I.D.C., Pegoraro G., & Franco L.M. (2020). High-throughput imaging of mRNA at the single-cell level in human primary immune cells.
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7

Time-lapse Imaging of Cell Death

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Percentage cell death was assayed every 30-45 mins by time-lapse imaging using the IncuCyte® live cell analysis imaging (Essenbioscience) or the Opera Phenix™ High Content Screening System (PerkinElmer, USA) for 16 hours with 5%CO2 and 37°C climate control. For the IncuCyte® and Opera Phenix™ dead cells were identified by propidium iodide (PI; 0.25 μg/ml) staining and for the Opera Phenix™ all cells were stained with 250nM of SiR-DNA (Spirochrome, Switzerland). Dyes were added to the cells 2 hours before imaging and compounds were added 10 minutes before the start of imaging. For the Opera Phenix™, images were analysed using the server based Columbus 2.8.0 software (PerkinElmer, USA) to identify nuclei based on SiR-DNA staining and dead cells using PI staining. Results were exported as counts per well to be processed and graphed using R Studio (https://www.R-project.org/) with the tidyverse package (https://CRAN.R-project.org/package=tidyverse).
Lalaoui N., Boyden S.E., Oda H., Wood G.M., Stone D.L., Chau D., Liu L., Stoffels M., Kratina T., Lawlor K.E., Zaal K.J., Hoffmann P.M., Etemadi N., Shield-Artin K., Biben C., Tsai W.L., Blake M.D., Kuehn H.S., Yang D., Anderton H., Silke N., Wachsmuth L., Zheng L., Moura N.S., Beck D.B., Gutierrez-Cruz G., Ombrello A.K., Pinto-Patarroyo G.P., Kueh A.J., Herold M.J., Hall C., Wang H., Chae J.J., Dmitrieva N.I., McKenzie M., Light A., Barham B.K., Jones A., Romeo T.M., Zhou Q., Aksentijevich I., Mullikin J.C., Gross A.J., Shum A.K., Hawkins E.D., Masters S.L., Lenardo M.J., Boehm M., Rosenzweig S.D., Pasparakis M., Voss A.K., Gadina M., Kastner D.L, & Silke J. (2019). Mutations that prevent caspase cleavage of RIPK1 cause autoinflammatory disease. Nature, 577(7788), 103-108.
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8

Time-lapse Imaging of Cell Death

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Percentage cell death was assayed every 30-45 mins by time-lapse imaging using the IncuCyte® live cell analysis imaging (Essenbioscience) or the Opera Phenix™ High Content Screening System (PerkinElmer, USA) for 16 hours with 5%CO2 and 37°C climate control. For the IncuCyte® and Opera Phenix™ dead cells were identified by propidium iodide (PI; 0.25 μg/ml) staining and for the Opera Phenix™ all cells were stained with 250nM of SiR-DNA (Spirochrome, Switzerland). Dyes were added to the cells 2 hours before imaging and compounds were added 10 minutes before the start of imaging. For the Opera Phenix™, images were analysed using the server based Columbus 2.8.0 software (PerkinElmer, USA) to identify nuclei based on SiR-DNA staining and dead cells using PI staining. Results were exported as counts per well to be processed and graphed using R Studio (https://www.R-project.org/) with the tidyverse package (https://CRAN.R-project.org/package=tidyverse).
Lalaoui N., Boyden S.E., Oda H., Wood G.M., Stone D.L., Chau D., Liu L., Stoffels M., Kratina T., Lawlor K.E., Zaal K.J., Hoffmann P.M., Etemadi N., Shield-Artin K., Biben C., Tsai W.L., Blake M.D., Kuehn H.S., Yang D., Anderton H., Silke N., Wachsmuth L., Zheng L., Moura N.S., Beck D.B., Gutierrez-Cruz G., Ombrello A.K., Pinto-Patarroyo G.P., Kueh A.J., Herold M.J., Hall C., Wang H., Chae J.J., Dmitrieva N.I., McKenzie M., Light A., Barham B.K., Jones A., Romeo T.M., Zhou Q., Aksentijevich I., Mullikin J.C., Gross A.J., Shum A.K., Hawkins E.D., Masters S.L., Lenardo M.J., Boehm M., Rosenzweig S.D., Pasparakis M., Voss A.K., Gadina M., Kastner D.L, & Silke J. (2019). Mutations that prevent caspase cleavage of RIPK1 cause autoinflammatory disease. Nature, 577(7788), 103-108.
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9

Optimizing mRNA Delivery for CRISPR

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For optimization of mRNA delivery, cells were seeded at ∼70% confluency and reverse transfected with indicated concentrations of mRNA-Cas9-HA containing 5-methoxyuridine (5moU) or mRNA-eGFP 5moU (TriLink) in MessengerMAX (Thermo Fisher Scientific) or RNAiMAX (Thermo Fisher Scientific). Samples were fixed either 6 or 24 h after transfection and processed for immunofluorescence. Cas9 expression was monitored by the intensity of the HA signal, which was quantified using Columbus 2.9.1 (Perkin Elmer). Unless stated otherwise, the concentration of mRNA-Cas9-HA was fixed at 40 ng per well of a 384-well plate. Cells were reverse transfected with mRNA-Cas9-HA coupled with 1% (vol/vol) MessengerMAX in OptiMEM for 6 h prior to crRNA transfection.
Wong C.H., Wingett S.W., Qian C., Hunter M.R., Taliaferro J.M., Ross-Thriepland D, & Bullock S.L. (2024). Genome-scale requirements for dynein-based transport revealed by a high-content arrayed CRISPR screen. The Journal of Cell Biology, 223(5), e202306048.
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10

Optimizing mRNA-Cas9 Delivery and Expression

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For optimisation of mRNA delivery, cells were seeded at ~70% confluency and reverse transfected with indicated concentrations of mRNA-Cas9-HA containing 5-methoxyuridine (5moU) or mRNA-eGFP 5moU (TriLink) in MessengerMAX (Thermo Fisher) or RNAiMAX (Thermo Fisher). Samples were fixed either 6- or 24-h post-transfection and processed for immunofluorescence. Cas9 expression was monitored by the intensity of HA signal, which was quantified using Columbus 2.9.1 (Perkin Elmer). For screening and all other experiments, the concentration of mRNA-Cas9-HA was fixed at 40 ng per well of a 384-well plate. Cells were reverse transfected with mRNA-Cas9-HA coupled with 1% (v/v) MessengerMAX in OptiMEM for 6 h prior to crRNA transfection.
Wong C.H., Wingett S.W., Qian C., Taliaferro J.M., Ross-Thriepland D, & Bullock S.L. (2023). Genome-scale requirements for dynein-based trafficking revealed by a high-content arrayed CRISPR screen. bioRxiv.
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