Ideal to

• Ensure the reliability and stability of an imaging system – obtain coherent measurements comparable over several years (including fluorescence quantities).
• Set-up and maintain quality management at a low cost for fluorescence measurements.

For microscopy platforms managers:

• Ensuring reliability and stability of an imaging system – obtaining coherent measurements comparable over several years (including fluorescence quantities)
• Setting-up and maintaining quality management at a low cost for fluorescence measurements.
• Help your users that need to perform studies over several days/weeks/month to compare their results with fluorescence properties variation of the microscope.
• Figuring out (in 5 min) when you REALLY need to ask for microscope manufacturer support or when it is just your user’s sample that is not good.

For microscopes manufacturers:

• Validating fluorescence-based microscopes performances during R&D and production.
• Validating fluorescence-based microscopes performances after installation and troubleshooting.
• Offering your customers a non-bleaching sample that they can use to do diagnostic and troubleshooting without necessarily having you visiting them.
• Custom slides available on demand to fit your need.

For microscopes developers:

• Validating fluorescence-based microscopes performances during R&D.
• Having a reliable sample where you know exactly what to observe/to expect!

Download the Application Guide here

Argo-POWER LM Fluorescent Patterns

ARGO-POWER HM Fluorescent Patterns

Argo-POWER SIM Fluorescent Patterns

Independent Publications:

• (In Progress) A. Millett-Sikking and A.G. York, “High NA single-objective light-sheet”,  https://andrewgyork.github.io/high_na_single_objective_lightsheet, June 2019

ARGO-SIM

• P. Montero Llopis, R. A. Senft, T. J. Ross-Elliott, et al., “Best practices and tools for reporting reproducible fluorescence microscopy methods,” Nature Methods 18, DOI: 10.1038/s41592-021-01156-w (June 2021).
• T. Mangeat, S. Labouesse, M. Allain, et. al, “Super-resolved live-cell imaging using random illumination microscopy,” Cell Reports Methods 1, DOI: 10.1016/j.crmeth.2021.100009, 1-15 (May 2021).
• M. Capek, M. Blazikova, I. Novotny, et. al, “The wavelet-based denoising of images in Fiji, with example applications in structured illumination microscopy,” Image Analysis & Stereology 40, DOI: 105566/ias.2432, 3-16 (April 2021).
• G. Wen, S. Li, L. Wang, X. Chen,  et al, “High-fidelity structured illumination microscopy by point-spread-function engineering” Light: Science & Applications 10, DOI: 10.1038/s41377-021-00513-w, 1-12 (April 2021).
• P. Liu, “Unified joint reconstruction approach for random illumination microscopy” Biomedical Optics Express 11, 5147-5165, DOI: 10.1364/BOE.399547 (June 2020).
• J. Xing, S. Chen, S. Becker, J.-Y. Yu, and C. Cogswell, “ℓ1-regularized maximum likelihood estimation with focused-spot illumination quadruples the diffraction-limited resolution in fluorescence microscopy,” Optics Express 28, 39413-39429, DOI: 10.1364/OE.41157 (December 2020).
• S. Qin, S. Isbaner, I. Gregor, and J. Enderlein, “Doubling the resolution of a confocal spinning-disk microscope using image scanning microscopy,” Nature Protocols 16, 164-181, DOI: 10.1038/s41596-020-00408-x (November 2020).
• X. Hu, S. Jalal, M. Sheetz, O. Bakke, and F. Margadant, “Micro-stepping Extended Focus reduces photobleaching and preserves structured illumination super-resolution features,” Journal of Cell Science 133, 1-12, DOI:10.1242/jcs.240796 (April 2020).
• C. Karras, M. Smedh, R. Förster, H. Deschout, J. Fernandez-Rodriguez, and R. Heintzmann, “Successful optimization of reconstruction parameters in structured illumination microscopy – a practical guide,” Optics Communication 436, 69-75, DOI: 10.1016/j.optcom.2018.12.005 (December 2018).
• P. Liu, “A unified joint reconstruction approach in structured illumination microscopy using unknown speckle patterns,” hosted on arXiv:1811.00283 [eess.IV] (November 2018).
• A. Millett-Sikking, N. H. Thayer, A. Bohnert, and A. G. York, “Remote refocus enables class-leading spatiotemporal resolution in 4D optical microscopy,” hosted on GitHub Pages, DOI:10.5281/zenodo.1146083, Jan 2018 ;
• K. Korobchevskaya, H. Colin-York, B. C. Lagerholm, and M. Fritzsche, “Exploring the Potential of Airyscan Microscopy for Live Cell Imaging,” Photonics 4, 41, DOI: 10.3390/photonics4030041, July 2017 ;
• J. Pospisil, “Hot Pixels Suppression in Structured Illumination Microscopy,” in Proceedings of the International Student Scientific Conference Poster, ISBN 978-80-01-06153-4 (May 2017).
• Talley J. Lambert and Jennifer C. Waters, “Navigating challenges in the application of super-resolution microscopy”, Journal of Cell Biology, DOI: 10.1083/jcb.201610011, Dec 2016 ;

ARGO-HM

• P. Mascalchi and F. P. Cordelières, “Which elements to build co-localization workflows? From metrology to analysis,” In: Rebollo E., Bosch M. (eds) Computer Optimized Microscopy. Methods in Molecular Biology 2040, Humana, New York, NY, DOI: 10.1007/978-1-4939-9686-5_10 (August 2019).
• G. N. McKay, A. Y. Trick, and N. J. Durr, “High-speed imaging of scattering particles flowing through turbid media with confocally aligned, oblique plane illumination,” Proceedings of SPIE 10890, Label-free Biomedical Imaging and Sensing (LBIS) 2019, 108902K, DOI: 10.1117/12.2509865 (March 2019).
• Y. Chen, W. Liu, Z. Zhang, C. Zheng, Y. Huang, R. Cao, D. Zhu, L. Xu, M. Zhang, Y.-H. Zhang, J. Fan, L. Jin, Y. Xu, C. Kuang, and X. Liu, “Multi-color live-cell super-resolution volume imaging with multi-angle interference microscopy,” DOI: 10.1038/s41467-018-07244-4, Nature Communications 9:4818, 1-8 (November 2018).

ARGO-M

• M. Butzlaff, A. Weigel, E. Ponimaskin, and A. Zeug, “eSIP: A Novel Solution-Based Sectioned Image Property Approach for Microscope Calibration”, PLOS ONE 10(8): e0134980, DOI:10.1371/journal.pone.0134980, August 2015.

Argolight Publications:

• A. Royon and N. Converset, “Quality Control of Fluorescence Imaging Systems: A new tool for performance assessment and monitoring”, Optik & Photonik 2, 22-25, DOI: 10.1002/opph.201700005, April 2017 ;
• A. Royon and N. Converset, “Quality Control of Fluorescence Imaging Systems. A New Tool for Performance Assessment and Monitoring”, Imaging & Microscopy, May 2016 (download here) ;
• A. Royon, “Quantitative imaging and fluorescence microscopy: Towards quantitative fluorescence microscopy: A new solution for standardization, monitoring, and quality management”, Laser Focus World, June 2015 (download here) ;
• A. Royon and G. Papon, “Calibration of Fluorescence Microscopes – A New Durable Multi-Dimensional Ruler”,  Imaging and Microscopy, Aug-Sept 2013 (download here).

Measure microscopes performance & detect issues

You don’t know if you don’t measure it.

Troubleshooting microscopes or performing quality control starts with you having the relevant information. You need reliable information, quickly! So you can take the right action and save the situation.

But time-efficiency does not mean cutting back on information. Daybook QC Software gives you the best information/time ratio possible from a quick 5 minutes check or a 1 hour in-depth performance assessment.

Click HERE to download Daybook QC Software

Analyze

More than 12 automated quality tests, several tens of relevant metrics.

1. Field uniformity

   The field uniformity is a well-known parameter related to the inhomogeneous spatial distribution of the intensity in the image of an even object.

   In any fluorescence microscope, the knowledge of the field uniformity is important when the image intensity information is aimed to be measured. For intensity quantification in images of biological samples, the field uniformity shall be known, and eventually corrected, to have access to accurate measurements.

   The “field uniformity” analysis provides the spatial distribution of the intensity, due to illumination and detection inhomogeneities, within the field of view.

   

   2. Field Distortion

   The field distortion is an optical aberration inducing a spatial deformation of the imaged object, usually at the corners of the image. In the presence of field distortion, the magnification is not constant over the field of view; it is dependent on the XY coordinates.

   In any fluorescence microscope, the knowledge of the field distortion is important when spatial information in an image is aimed to be measured. For positions, lengths, areas or volumes quantification in images of biological samples, the field distortion shall be known, and eventually corrected, to have access to accurate measurements.

   The “field distortion” analysis provides the lateral shifts, due to distortion, within the field of view.

   

   3. Lateral co-registration accuracy

   The co-registration accuracy is the ability of a fluorescence microscope to record images of a multi-labeled object without introducing additional shifts that would not be related to the object itself.

   In any fluorescence microscope, the knowledge of the co-registration accuracy, both in the lateral and axial directions, is important when color information in an image is aimed to be used. For co-localization quantification in images of biological samples stained with several labels, the co-registration accuracy between the different channels shall be known, and eventually corrected, to prevent from misinterpretation.

   The “lateral co-registration accuracy” analysis provides the lateral shifts between two channels, due to the co-registration inaccuracy of the system, within the entire field of view.

   

   4. Line Spread Function

   The line spread function (LSF) gives access to the spreading behavior of the light by a line object. Fitting the LSF with a mathematical function (double Gaussian, double Lorentzian, double Sech²) allows to extract the full width at half maximum (FWHM), which is a parameter related to the lateral resolution of the imaging system.

   The “line spread function” analysis provides information, in the XY plane, on how light spreads from a line, as well as quantitative parameters such as the FHWM of the LSF and the signal-to-noise ratio (SNR) value in the image.

   

   5. Ring Spread Function

   The ring spread function (RSF) gives access to the spreading behavior of the light by a ring object. Fitting the RSF with a mathematical function (double Gaussian, double Lorentzian, double Sech²) allows to extract the full width at half maximum (FWHM), which is a parameter related to the lateral resolution of the imaging system.

   The “ring spread function” analysis provides information, in the XY plane, on how light spreads from a ring, as well as quantitative parameters such as the FHWM of the RSF and the signal-to-noise ratio (SNR) value in the image.

   

   6. Point Spread Function

   The point spread function (PSF) gives access to the spreading behavior of the light by a point-like object. Fitting the PSF with a mathematical function (Gaussian, Lorentzian, Sech²) allows to extract the full width at half maximum (FWHM) along X, Y and Z, which is a parameter that commonly describes the resolution of the imaging system.

   The “point spread function” analysis provides information, in 3D, on how light spreads from a point-like object, as well as quantitative parameters such as the lateral and axial FHWM of the PSF and the signal-to-noise ratio (SNR) in the image.

   

   7. Lateral Resolution

   The lateral resolution is the minimal distance between two objects close to each other that an imaging system can clearly measure. There are different ways to measure the lateral resolution. The method used in this analysis relies on the contrast transfer function, i.e. the measured contrast vs the measured distance between gradually spaced lines.

   The “lateral resolution” analysis provides the minimal resolvable distance between lines close to each other, for a given contrast value, with an associated signal-to-noise ratio (SNR) value in the image.

   

   8. Optical sectioning strength

   The optical sectioning is the ability of an imaging system to acquire clear slices of a thick object, in order to reconstruct it in 3D from images captured at different focal plane. There are different ways to measure the optical sectioning strength. The method used in this analysis relies on the optical sectioning function, i.e. the measured contrast versus the specified distance between lines on the same plane and lines going deeper and deeper in the glass.

   The “optical sectioning” analysis provides the optical sectioning strength between lines at different depths, for a given contrast value.

   

   9. Stage Drift During Timelapse

   When performing timelapse imaging, it is important to ensure that the XY stage do not drift too much. Sources of stage drift can origin from the environmental conditions, such as temperature fluctuations and air flow.

   The “stage drift during timelapse” analysis allows to measure the XY stage drift during a timelapse imaging, to ensure it is within the specifications.

   Stage Repositioning Repeatability

   During the acquisition of image tiles, the lateral stage may not reposition to some location in a repeatable way. Therefore, it is important to ensure that the XY stage repositioning repeatability is sufficiently accurate when tiling and stitching large regions in 2D or 3D.

   The “stage repositioning repeatability” analysis allows to determine the XY motorized stage repositioning repeatability after many round trips, to ensure it is within the specifications.

   

   10. Stage Drift During Z Stacking

   This test concerns only the microscopes equipped with a motorized Z stage.

   During the acquisition of image Z-stacks, the lateral (XY) stage may drift. Therefore, it is important to ensure that the XY stage do not drift too much. Sources of stage drift can origin from the environmental conditions, such as temperature fluctuations and air flow.

   The “stage drift during Z-stacking” analysis allows to determine the XY stage drift during Z-stacking, to ensure it is within the specifications.

   

   11. Accuracy of 3D reconstruction

   This test concerns only the microscopes equipped with a motorized Z stage.

   Confocal fluorescence microscopes allow to perform 3D imaging, thanks to their ability to remove the out-of-focus light. 3D imaging is achieved by reconstructing a Z-stack of images, which accuracy may be altered by different issues. Sources of 3D reconstruction inaccuracy can origin from the Z-stage, from a wrong adjustment of the objective correction collar for coverslip thickness or from the environmental conditions, such as temperature fluctuations and air flow.

   The “accuracy of 3D reconstruction” analysis allows to check the ability of confocal fluorescence microscopes to acquire Z-stacks of images correctly, so that their reconstruction into 3D images is accurate, i.e. representative of the reality.

   

   12. Intensity Response

   The intensity response of a fluorescence microscope expresses the output digital signal to an input photon flux. It depends on many aspects of the microscope, either in the illumination path or the detection path.

   In any fluorescence microscope, the overall intensity response may evolve over time. For example, variations in the illumination power, detector sensitivity, optical alignment, etc. can lead to intensity response fluctuations. The knowledge of the intensity response is therefore important when the intensity quantification in images of a biological sample, acquired at different times, is aimed to be performed.

   The “intensity response” analysis provides the intensity response of the imaging system to 16 intensity levels following a linear evolution, as well as quantitative parameters such as intensity values and pattern dynamic range. Monitoring these parameters allows to observe how the intensity response evolves over time, with respect to reference values.

   

   13. Spectral Response

   This test concerns only the microscopes that can acquire fluorescence emission spectra.

   Fluorescence microscopes, although conceived preliminary to be imaging systems, can also acquire emission spectra, with a better spatial resolution but poorer spectral resolution than conventional spectrometers. The spectral response of a microscope expresses the responsivity of the imaging system versus the emission wavelengths.

   Correct interpretation of the emission spectra measured with a microscope requires the knowledge of its spectral response.

   The “spectral response” analysis provides the normalized detection efficiency versus the emission wavelengths, as well as quantitative parameters such as the spectral roll-off.

   

   14. Power Meter

   This test concerns only the Argo-POWER slide.

   The optical power measured at the sample plane is a key parameter in microscopy setups, either to troubleshoot any issue or to perform quantitative microscopy.

   Measuring the optical power at regular interval using the same illumination settings allows to troubleshoot, for example, changes in light sources power, drops in filters transmission, or laser-to-fiber coupling misalignment.

   Measuring the optical power prior and after an imaging campaign ensures that the light source power has not fluctuated in the meantime, evidencing it has not affected the intensity values in the images.

   Measuring the optical power prior an imaging campaign using the same illumination settings as for a previous imaging campaign ensures that the same optical power is applied on the sample, so that the illumination conditions are reproduced.

   Measuring the optical power allows to report this value in scientific publications, so that other researchers can reproduce these illumination conditions, thus contributing to the combat against the replication crisis.

   The “power meter” analysis allows to perform live optical power, live irradiance, time-lapse and power vs setting measurements.

    

    

   

Downloads

How to switch from Daybook 2 to Daybook 3

Daybook Analysis start guide

Daybook Analysis image quality

Daybook Analysis batch analysis

Daybook Analysis field uniformity

Daybook Analysis field distortion

Daybook Analysis lateral co  registration accuracy

Daybook Analysis line spread function

Daybook Analysis ring spread function

Daybook Analysis point spread function

Daybook Analysis lateral resolution

Daybook Analysis stage motion control

Daybook Analysis drift during Z stacking

Daybook Analysis intensity response

Daybook Analysis spectral response

Daybook Analysis accuracy of 3D reconstruction

Daybook Analysis power meter