Wavelet-based background and noise subtraction for fluorescence microscopy images

Hüpfel, Manuel; Kobitski, Andrei Yu; Zhang, Weichun; Nienhaus, G. Ulrich

doi:10.1364/boe.413181

Cited by 24 publications

(23 citation statements)

References 26 publications

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“…To reduce the intensity variations and amplify weak signals, we chose to incorporate the novel Wavelet-Based Background Noise Subtraction (WBNS) algorithm as a pre-processing step. Through WBNS we achieve noise reduction and enhancement of the visual appearance of fluorescent signals without deteriorating their content [ 36 ]. By combining cell body segmentation to define the ROI, Gaussian filtering and the WBNS as a set of pre-processing steps to the quantitative analysis, we succeed in generating relatively noise-free fluorescent images that subsequently are used for particle tracking (Fig.…”

Section: Resultsmentioning

confidence: 99%

SpotitPy: a semi-automated tool for object-based co-localization of fluorescent labels in microscopy images

Akalestou-Clocher

Kalamara²,

Garinis³

2022

BMC Bioinformatics

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Background In fluorescence microscopy, co-localization refers to the spatial overlap between different fluorescent labels in cells. The degree of overlap between two or more channels in a microscope may reveal a physical interaction or topological functional interconnection between molecules. Recent advances in the imaging field require the development of specialized computational analysis software for the unbiased assessment of fluorescently labelled microscopy images. Results Here we present SpotitPy, a semi-automated image analysis tool for 2D object-based co-localization. SpotitPy allows the user to select fluorescent labels and perform a semi-automated and robust segmentation of the region of interest in distinct cell types. The workflow integrates advanced pre-processing manipulations for de-noising and in-depth semi-automated quantification of the co-localized fluorescent labels in two different channels. We validated SpotitPy by quantitatively assessing the presence of cytoplasmic ribonucleoprotein granules, e.g. processing (P) bodies, under conditions that challenge mRNA translation, thus highlighting SpotitPy benefits for semi-automatic, accurate analysis of large image datasets in eukaryotic cells. SpotitPy comes in a command line interface or a simple graphical user interphase and can be used as a standalone application. Conclusions Overall, we present a novel and user-friendly tool that performs a semi-automated image analysis for 2D object-based co-localization. SpotitPy can provide reproducible and robust quantifications for large datasets within a limited timeframe. The software is open-source and can be found in the GitHub project repository: (https://github.com/alexiaales/SpotitPy).

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Section: Resultsmentioning

confidence: 99%

SpotitPy: a semi-automated tool for object-based co-localization of fluorescent labels in microscopy images

Akalestou-Clocher

Kalamara²,

Garinis³

2022

BMC Bioinformatics

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“…For further processing images were z-projected by the averaging method in ImageJ [ 18, 19 ]. Wavelet-based background subtraction method [ 20 ] was used to remove intracellular signal in the case of membrane AβPP quantification.…”

Section: Methodsmentioning

confidence: 99%

Expression and Localization of AβPP in SH-SY5Y Cells Depends on Differentiation State

Riegerová

Brejcha

Bezděková

et al. 2021

JAD

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Neuroblastoma cell line SH-SY5Y, due to its capacity to differentiate into neurons, easy handling, and low cost, is a common experimental model to study molecular events leading to Alzheimer’s disease (AD). However, it is prevalently used in its undifferentiated state, which does not resemble cells affected by the disease. Here, we show that the expression and localization of amyloid-β protein precursor (AβPP), one of the key molecules involved in AD pathogenesis, is dramatically altered in SH-SY5Y cells fully differentiated by combined treatment with retinoic acid and BDNF. We show that insufficient differentiation of SH-SY5Y cells also results in AβPP mislocalization.

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“…3D multi-view light sheet images were taken on our custom-built, digital scanned laser light sheet microscope (DSLM) with two-sided illumination described in Ref. [ 21 ]. In all measurements, two-sided Bessel beam illumination with 488-nm (enhanced green fluorescent protein (EGFP)) and 561-nm (red fluorescent protein (RFP), fluorescent beads) laser light was used.…”

Section: Methodsmentioning

confidence: 99%

“…An imaging phantom was prepared inside a fluorinated ethylene propylene (FEP) tube (diameter 2.34 mm, wall thickness 0.23 mm, Reichelt Chemietechnik GmbH, Heidelberg, Germany), consisting of fluorescent polystyrene beads (F8801, FluoSpheres, Invitrogen, Eugene, OR, excitation/emission peaks 580/605 nm, diameter 100 nm) embedded in an agarose gel, as described in Ref. [ 21 ]. Here, however, the gel was kept inside the FEP tube for imaging to ensure equal optical conditions for bead and zebrafish/ Hydra measurements .…”

Section: Methodsmentioning

confidence: 99%

Two plus one is almost three: a fast approximation for multi-view deconvolution

Hüpfel

Merino

Bennemann

et al. 2021

Biomed. Opt. Express

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Multi-view deconvolution is a powerful image-processing tool for light sheet fluorescence microscopy, providing isotropic resolution and enhancing the image content. However, performing these calculations on large datasets is computationally demanding and time-consuming even on high-end workstations. Especially in long-time measurements on developing animals, huge amounts of image data are acquired. To keep them manageable, redundancies should be removed right after image acquisition. To this end, we report a fast approximation to three-dimensional multi-view deconvolution, denoted 2D+1D multi-view deconvolution, which is able to keep up with the data flow. It first operates on the two dimensions perpendicular and subsequently on the one parallel to the rotation axis, exploiting the rotational symmetry of the point spread function along the rotation axis. We validated our algorithm and evaluated it quantitatively against two-dimensional and three-dimensional multi-view deconvolution using simulated and real image data. 2D+1D multi-view deconvolution takes similar computation time but performs markedly better than the two-dimensional approximation only. Therefore, it will be most useful for image processing in time-critical applications, where the full 3D multi-view deconvolution cannot keep up with the data flow.

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Wavelet-based background and noise subtraction for fluorescence microscopy images

Cited by 24 publications

References 26 publications

SpotitPy: a semi-automated tool for object-based co-localization of fluorescent labels in microscopy images

SpotitPy: a semi-automated tool for object-based co-localization of fluorescent labels in microscopy images

Expression and Localization of AβPP in SH-SY5Y Cells Depends on Differentiation State

Two plus one is almost three: a fast approximation for multi-view deconvolution

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