Removal of subsurface fluorescence in cryo-imaging using deconvolution

Krishnamurthi, Ganapathy; Wang, Charlie Y.; Steyer, Grant J.; Wilson, David L.

doi:10.1364/oe.18.022324

Cited by 22 publications

(21 citation statements)

References 19 publications

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“…Third, the brightness of cells can vary from one experiment to the next with more or less labeling. Fourth, in cryo-imaging, exceedingly bright cells can be visible in multiple image slices due to the presence of subsurface fluorescence, although this can be mitigated with processing [23–26]. Fifth, in a typical experiment, stem cells are really quite sparse, with 99.998% of a mouse occupied by other tissues.…”

Section: Introductionmentioning

confidence: 99%

Automatic Stem Cell Detection in Microscopic Whole Mouse Cryo-Imaging

Wuttisarnwattana

Gargesha

Hof

et al. 2016

IEEE Trans. Med. Imaging

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With its single cell sensitivity over volumes as large as or larger than a mouse, cryo-imaging enables imaging of stem cell biodistribution, homing, engraftment, and molecular mechanisms. We developed and evaluated a highly automated software tool to detect fluorescently labeled stem cells within very large (~200GB) cryo-imaging datasets. Cell detection steps are: preprocess, remove immaterial regions, spatially filter to create features, identify candidate pixels, classify pixels using bagging decision trees, segment cell patches, and perform 3D labeling. There are options for analysis and visualization. To train the classifier, we created synthetic images by placing realistic digital cell models onto cryo-images of control mice devoid of cells. Very good cell detection results were (precision=98.49%, recall=99.97%) for synthetic cryo-images, (precision=97.81%, recall=97.71%) for manually evaluated, actual cryo-images, and <1% false positives in control mice. An α-multiplier applied to features allows one to correct for experimental variations in cell brightness due to labeling. On dim cells (37% of standard brightness), with correction, we improved recall (49.26%→99.36%) without a significant drop in precision (99.99%→99.75%). With tail vein injection, multipotent adult progenitor cells in a graft-versus-host-disease model in the first days post injection were predominantly found in lung, liver, spleen, and bone marrow. Distribution was not simply related to blood flow. The lung contained clusters of cells while other tissues contained single cells. Our methods provided stem cell distribution anywhere in mouse with single cell sensitivity. Methods should provide a rational means of evaluating dosing, delivery methods, cell enhancements, and mechanisms for therapeutic cells.

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Section: Introductionmentioning

confidence: 99%

Automatic Stem Cell Detection in Microscopic Whole Mouse Cryo-Imaging

Wuttisarnwattana

Gargesha

Hof

et al. 2016

IEEE Trans. Med. Imaging

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“…Thus, the PSF is highly asymmetric, and not well estimated by blind deconvolution techniques or synthetically generated from widely used models. Whilst experimentally measuring the PSF is an option (15), the process is time-consuming and sometimes impossible, as it must be acquired for all wavelengths and magnifications and depends upon having high signal-to-noise ratio with small point-like sources and high similarity between the PSF measuring sample and the actual sample. This is impracticable for many MF-HREM experiments and hence we have used small structures from within the image stack to parameterise a synthetically generated PSF.…”

Section: Resultsmentioning

confidence: 99%

“…Block-facing serial-sectioning, overcomes slice alignment issues by imaging the surface of the exposed sample after each successive cut thereby creating an inherently aligned image stack and removing the need to retain the structural integrity of individual slices (Figure 1B). However, block-face imaging can suffer from loss of optical resolution in the z-axis, due to contamination by out of focus light from below the block surface (shine-through) (15). The addition of optical-sectioning capabilities such as two-photon and structured illumination into serial sectioning instruments has aimed to overcome this issue (3), (14), (16) (13,17), but at the cost of dramatically increasing the technical requirements for the imaging instrument require high powered lasers and often they are custom built.…”

Section: Introductionmentioning

confidence: 99%

Multi-Fluorescence High-Resolution Episcopic Microscopy (MF-HREM) for Three-Dimensional Imaging of Adult Murine Organs

Walsh

Holroyd

Finnerty

et al. 2020

Preprint

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10 Three-dimensional microscopy of large biological samples (>0.5 cm 3 ) is transforming 11 biological research. Many existing techniques require trade-offs between image resolution 12 and sample size, require clearing or use optical sectioning. These factors complicate the 13 implementation of large volume 3D imaging. Here we present Multi-fluorescent High 14 Resolution Episcopic Microscopy (MF-HREM) which allows 3D imaging of large samples 15 without the need for clearing or optical sectioning. 16MF-HREM uses serial-sectioning and block-facing wide-field fluorescence, without the need 17 for tissue clearing or optical sectioning. We detail developments in sample processing 18 including stain penetration, resin embedding and imaging. In addition, we describe image 19 post-processing methods needed to segment and further quantify these data. Finally, we 20 demonstrate the wide applicability of MF-HREM by: 1) quantifying adult mouse glomeruli. 2) 21 identifying injected cells and vascular networks in tumour xenograft models; 3) quantifying 22 vascular networks and white matter track orientation in mouse brain.

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“…4c, d, we show magnified versions of the unprocessed and deconvolved fluorescence images, respectively. We utilized a previously described deconvolution algorithm to confirm that tumor cell clusters were distinct from the main tumor mass [10]. Following deconvolution, scattered light was reduced and we more clearly discerned cell clusters not connected to the main tumor mass.…”

Section: Resultsmentioning

confidence: 99%

Cryo-image Analysis of Tumor Cell Migration, Invasion, and Dispersal in a Mouse Xenograft Model of Human Glioblastoma Multiforme

et al. 2011

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Purpose The goals of this study were to create cryo-imaging methods to quantify characteristics (size, dispersal, and blood vessel density) of mouse orthotopic models of glioblastoma multiforme (GBM) and to enable studies of tumor biology, targeted imaging agents, and theranostic nanoparticles. Procedures Green fluorescent protein-labeled, human glioma LN-229 cells were implanted into mouse brain. At 20–38 days, cryo-imaging gave whole brain, 4-GB, 3D microscopic images of bright field anatomy, including vasculature, and fluorescent tumor. Image analysis/visualization methods were developed. Results Vessel visualization and segmentation methods successfully enabled analyses. The main tumor mass volume, the number of dispersed clusters, the number of cells/cluster, and the percent dispersed volume all increase with age of the tumor. Histograms of dispersal distance give a mean and median of 63 and 56 μm, respectively, averaged over all brains. Dispersal distance tends to increase with age of the tumors. Dispersal tends to occur along blood vessels. Blood vessel density did not appear to increase in and around the tumor with this cell line. Conclusion Cryo-imaging and software allow, for the first time, 3D, whole brain, microscopic characterization of a tumor from a particular cell line. LN-229 exhibits considerable dispersal along blood vessels, a characteristic of human tumors that limits treatment success.

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Removal of subsurface fluorescence in cryo-imaging using deconvolution

Cited by 22 publications

References 19 publications

Automatic Stem Cell Detection in Microscopic Whole Mouse Cryo-Imaging

Automatic Stem Cell Detection in Microscopic Whole Mouse Cryo-Imaging

Multi-Fluorescence High-Resolution Episcopic Microscopy (MF-HREM) for Three-Dimensional Imaging of Adult Murine Organs

Cryo-image Analysis of Tumor Cell Migration, Invasion, and Dispersal in a Mouse Xenograft Model of Human Glioblastoma Multiforme

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