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8 results for “Label-free Microscopy”
Comparison and practical review of segmentation approaches for label-free microscopy
<p>This dataset contains microscopic images of PNT1A cell line captured by multiple microcopic without use of any labeling and a manually annotated ground truth for subsequent use in segmentation algorithms. Dataset also includes images reconstructed according to the methods described below in order to ease further segmentation. </p> <p>See Vicar et al. Cell segmentation methods for label-free contrast microscopy: review and comprehensive comparison. BMC Bioinformatics (2019) 20:360. DOI <a href="https://doi.org/10.1186/s12859-019-2880-8">10.1186/s12859-019-2880-8</a></p> <p>Code using this dataset is available at <a href="https://github.com/tomasvicar/Cell-segmentation-methods-comparison">https://github.com/tomasvicar/Cell-segmentation-methods-comparison</a></p> <p><strong>Materials and methods </strong></p> <p>Cells were cultured in RPMI-1640 medium supplemented with antibiotics (penicillin 100 U/ml and streptomycin 0.1 mg/ml) with 10% fetal bovine serum. Prior microscopy acquisition, cells were maintained at 37 cenigrade in a humidified incubator with 5% CO2. Intentionally, high passage number of cells was used (>30) in order to describe distinct morphological heterogeneity of cells (rounded and spindle-shaped, relatively small to large polyploid cells). For acquisition purposes, cells were cultivated in Flow chambers µ-Slide I Luer Family (Ibidi, Martinsried, Germany).</p> <p>Quantitative phase imaging (QPI) microscopy was performed on Tescan Q-PHASE (Tescan, Brno, Czech republic), with objective Nikon CFI Plan Fluor 10x/0.30 captured by Ximea MR4021MC (Ximea, Münster, Germany). Imaging is based on the original concept of coherence-controlled holographic microscope \cite{Kolman:10,Slaby:13}, images are shown in grayscale with units of pg/µm2.</p> <p>DIC microscopy was performed on microscope Nikon A1R (Nikon, Tokyo, Japan), with objective Nikon CFI Plan Apo VC 20x/0.75 captured by CCD camera Jenoptik ProgRes MF (Jenoptik, Jena, Germany). </p> <p>HMC microscopy was performed on microscope Olympus IX71 (Olympus, Tokyo, Japan), with objective Olympus CplanFL N 10x/0.3 RC1 captured by CCD camera Hamamatsu Photonics ORCA-R2 (Hamamatsu Photonics K.K., Hamamatsu, Japan).</p> <p>PC microscopy was performed on a Nikon Eclipse TS100-F microscope, with a Nikon CFI Achro ADL 10x/0.25 objective captured by CCD camera Jenoptik ProgRes MF.</p> <p><strong>Folder structure and file and filename description</strong><br> <br> <em>folder "source data+groundtruth"</em><br> - includes raw microscopic data <br> (uncompressed 16-bit for DIC, HMC and PC, 32-bit for QPI)<br> - includes manualy annotated groundtruth (zip file - imageJ ROI file, 1bit png mask)</p> <p>e.g. <br> DIC_01_raw.tif<br> DIC_01_groundtruth_imagejROI.zip<br> DIC_01_groundtruth_mask.png</p> <p><br> <em>folder "reconstructions"</em></p> <p>includes reconstructed images using reconstructions with highest dice coefficient achieved. </p> <p>for DIC and HMC: rDIC-Koos, rDIC-Yin, and rWeka<br> for PC: rPC-Top-Hat, rDIC-Yin, and rWeka<br> for QPI: rWeka</p> <p>note that for rWeka images numbered 01 for DIC, HMC and PC and 01-03 for QPI were used for learning.</p> <p><strong>Abbreviations</strong><br> DIC, differential image contrast<br> HMC, Hoffman modulation contrast<br> PC, phase contrast<br> QPI, quantitative phase imaging<br> rDIC-Koos, DIC/HMC image reconstruction according to Koos et al, Sci Rep. 2016;6:30420<br> rDIC-Yin, DIC/HMC image reconstruction according to Yin et al, Inf Process Med Imaging. 2011;22:384-97.<br> rPC-Yin, PC image reconstruction according to Yin et al, Med Im Anal. 2012; 16(5):1047<br> rPC-Top-Hat, Top-Hat filter according to Dewan et al, IEEE Transactions on Biomedical Circuits and<br> Systems.2014;8(5):716-728<br> rWeka, probability map using Trainable Weka segmentation according to Arganda-Carreras et al. Bioinformatics. 2017</p>
Data from: Automated cell lineage reconstruction using label-free 4D microscopy
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The yin and yang of hsa-miR-1244 expression levels during activation of the UPR control cell fate - Raw data for label-free holographic microscopy
<p>Regulation of endoplasmic reticulum (ER) homeostasis plays a critical role in maintaining cell survival. When ER stress occurs, a network of three pathways called the unfolded protein response (UPR) is activated to reestablish homeostasis. While it is known that there is cross-talk between these pathways, how this complex network is regulated is not entirely clear. Using human cancer and non-cancer cell lines, two different genome-wide approaches, and two different ER stress models, we searched for miRNAs that were decreased during the UPR and surprisingly found only one, miR-1244, that was found in all these conditions. The activation of both UPR adaptive and apoptotic signaling pathway was confirmed by parallel genome-wide mRNA expression arrays for Calu 3 cells and next generation sequencing for 16HBE14o- cells. We also verified that ER-stress related downregulation of miR-1244 expression occurred with 5 different ER stressors and was confirmed in another human cell line (HeLa S3). These analyses demonstrated that the outcome of this reduction during ER stress supported both IRE1 signaling and elevated BIP expression. Further analysis also revealed that this novel miRNA impacted all three pathways of the UPR using inhibitors specific for IRE1, ATF6, and PERK. This is the first example of a complex mechanism by which this miRNA serves as a regulatory check point for all 3 pathways that is switched off after UPR activation. In summary, the results indicate that ER stress reduction of <em>miR-1244 </em>expression contributes to the pro-survival arm of UPR.</p> <p>For real-time monitoring of cell viability, we applied real-time and label-free holographic microscopy-based monitoring of cell death and viability using HoloMonitor M4<strong>®</strong> time-lapse cytometer (Phase Holographic Imaging PHI AB, Lund, Sweden). Holographic microscopy was used to follow the optical thickness and irregularity of cells exposed for up to 24 h to Tm or Tg in the presence or absence of <em>miR-1244 </em>mimic or antagomiR. The images from up to 8 independent optical fields were collected and analyzed according to manufacture instructions with HoloMonitor® App Suite software. Healthy cells are irregular in shape and thin, whereas dying cells are round and thick . For all analysis, the same cells parameters qualification was applied.</p> <p> </p> <p> </p> <p><strong>Figure 4. Overexpression of </strong><strong>miR-1244 promotes cell death. </strong>The results of real-time monitoring of cell viability are shown with the real time and label free holographic microscopy using a HoloMonitor M4<strong>®</strong> time-lapse cytometer of HeLa cells transfected with <em>miR-1244</em> mimic or inhibitor or the scramble control and 48 h later monitored up to 24 h. Images were collected every 15 min (from 8 independent optical fields), and the distribution of live (blue) and dying cells (red) based on their optical thickness (Y-axes) and irregularity (X-axes) is presented at the 0, 8, 16 and 24 h time points. The images from up to 5 independent optical fields were collected and analyzed according to manufacturer’s instructions with HoloMonitor® App Suite software. Representative samples are shown (<strong>A</strong>). For all analyses, the same cell parameter qualifications were applied. Experiments were performed in triplicate. Based on the cells irregularity and average optical thickness the percentages of healthy cells (<strong>B</strong>) and of dying cells (<strong>C</strong>) were calculated. Data represents the mean ± SE of three independent experiments. *<em>P</em>< 0.05, **<em>P</em>< 0.001, ***<em>P</em>< 0.0001 were considered significant.</p> <p><strong>Figure 5. </strong><strong>miR-1244 influences the fate of cells challenged with Tm induced ER stress. </strong>The results of real-time monitoring of cell viability with the real time and label free holographic microscopy are shown using a HoloMonitor M4<strong>®</strong> time-lapse cytometer of HeLa cells transfected with <em>miR-1244</em> mimic or inhibitor or the scramble control and 48 h later treated with Tm (2.5 µg/ml) up to 24 h. Images were collected every 15 min (from 5 independent optical fields), and the distribution of live (blue) and dying cells (red) based on their optical thickness (Y-axes) and irregularity (X-axes) is presented at the 0, 8, 16 and 24 h time points. The images from up to 5 independent optical fields were collected and analyzed according to manufacturer’s instructions with HoloMonitor® App Suite software. Representative samples are shown (<strong>A</strong>). For all analyses, the same cell parameter qualifications were applied. Experiments were performed in triplicate. Based on the cells irregularity and average optical thickness the percentages of healthy cells (<strong>B</strong>) and of dying cells (<strong>C</strong>) were calculated. <a name="OLE_LINK3"></a>Data represents the mean ± SE of three independent experiments. *<em>P</em>< 0.05, **<em>P</em>< 0.001, ***<em>P</em>< 0.0001 were considered significant.</p> <p><strong>Figure 6. Exogenous </strong><strong>miR-1244 influences the fate of cells challenged with Tg induced ER stress. </strong>The results of real-time monitoring of cell viability with the real time and label free holographic microscopy are shown using a HoloMonitor M4<strong>®</strong> time-lapse cytometer of HeLa cells transfected with <em>miR-1244</em> mimic or inhibitor or the scramble control and 48 h later treated with Tg (500 nM) up to 24 h. Images were collected every 15 min (from 5 independent optical fields), and the distribution of live (blue) and dying cells (red) based on their optical thickness (Y-axes) and irregularity (X-axes) is presented at the 0, 8, 16 and 24 h time points. The images from up to 5 independent optical fields were collected and analyzed according to manufacturer’s instructions with HoloMonitor® App Suite software. Representative samples are shown (<strong>A</strong>). For all analyses, the same cell parameter qualifications were applied. Experiments were performed in triplicate. Based on the cells irregularity and average optical thickness the percentages of healthy cells (<strong>B</strong>) and of dying cells (<strong>C</strong>) were calculated. Data represents the mean ± SE of three independent experiments. *<em>P</em>< 0.05, **<em>P</em>< 0.001, ***<em>P</em>< 0.0001 were considered significant.</p> <p> </p> <p> </p>
Label-free adaptive optics single-molecule localization microscopy for whole zebrafish
<p>The specimen-induced aberration has been a major factor limiting the imaging depth of single-molecule localization microscopy (SMLM). Here, we report the application of label-free wavefront sensing adaptive optics to SMLM for deep-tissue super-resolution imaging. The proposed system measures complex tissue aberrations from intrinsic reflectance rather than fluorescence emission and physically corrects the wavefront distortion more than three-fold stronger than the previous limit. This enables us to resolve sub-diffraction morphologies of cilia and oligodendrocytes in whole zebrafish as well as dendritic spines in thick mouse brain tissues at the depth of up to 102 μm with localization number enhancement by up to 37 times and localization precision comparable to aberration-free samples. The proposed approach can expand the application range of SMLM to whole zebrafish that cause the loss of localization points owing to severe tissue aberrations.</p>
Data compilation for "Label-free Imaging of Catalytic H2O2 Decomposition on Single Colloidal Pt Nanoparticles using Nanofluidic Scattering Microscopy"
<p>Data compilation for "Label-free Imaging of Catalytic H2O2 Decomposition on Single Colloidal Pt Nanoparticles using Nanofluidic Scattering Microscopy"</p> <p>This data set contains raw and evaluated data for the publication mentioned above and is structured into three parts which are packed as .zip files. The file labeled with MAIN 1 contains data for the figures 1 to 3 and MAIN 2 for figures 4 to 6 of the main manuscript. The file labeled with SI contains all data related to the supplementary information.</p> <p>The data itself has three main types. There are .tif images which contain the raw darkfield microscopy images, usually as image stack of several single pictures. Some of these .tif images have been processed according to the description in the publication and single frames exported as .png images.<br> The .xlsx files contain the evaluated data that has been extracted from the microscopic images and was used to draw the corresponding graphs in the publication. The individual tables and the respective columns/rows have a basic labeling identifying their contents.<br> In addition, the data set also contains SEM/TEM images that show mainly the platinum particles used in the experiments. There is also one Comsol simulation for the flow in a nanochannel that is being filled with a bubble, which was created with Comsol 5.5.</p>
Data from: Label-free imaging of intracellular structures in living mammalian cells via external apodization phase-contrast microscopy
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Supplementary material - Label-free multimodal nonlinear optical microscopy reveals hallmarks of bone composition in pathophysiological conditions
<p>The repository features Excel worksheets including all the data used through this work and reported in the manuscript figures and graphs. More precisely, we included the following: </p> <ul> <li>Figure 4. SHG intensity values measured from WT and KO murine spines counterparts, both in parallel and orthogonal light polarization with respect to the craniocaudal axis of the vertebrae.</li> <li>Figure 5A. Quantization of ColIa1 gene expression level after mRNA extraction from WT and Dpp3 KO mice.</li> <li>Figure 5B. Absorbance values at 540 nm after Sirius Red staining of primary osteoblast isolated from WT and Dpp3 KO neonatal calvaria, used to assess and compare the in-vitro collagen production of the two murine models.</li> <li>Figure 7. SRS intensity ratios measured between bone and bone marrow regions of WT and Dpp3 KO models, both at the 2850 cm-1 Raman mode of CH2 stretching in lipids and at the 2920 cm-1 Raman mode of CH3 stretching in proteins.</li> <li>Figure 8. Data of gene expression analysis of selected genes relevant for lipid transport, uptake, and metabolism (i.e., CD 36, Fabp4, Lrp1, Fatp1, PGC1, CPT1, Pex7 and Glut1), in the flushed bone of WT and Dpp3 KO mice.</li> </ul>
Multi-Spectral Reflection Matrix for Ultra-Fast 3D Label-Free Microscopy
<p>Optical data associated with the paper "Multi-Spectral Reflection Matrix for Ultra-Fast 3D Label-Free Microscopy<strong>". </strong></p> <p><br> </p>
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.