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Immunofluorescence staining of a human kidney (#4, peri-tumor area) obtained by MELC

19 marker MELC run in a human peri-tumor kidney sample (#4). Each image shows the same field of view, sequentially stained with the depicted fluorescence labelled antibodies. Images contain 2024 x 2024 pixels and are generated using an inverted wide field fluorescence microscope with a 20 x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Scale bar 100 µm. All raw images were aligned based on the reference phase contrast image taken at the beginning of measurement. Each fluorescence MELC image was processed by background subtraction and illumination correction, based on bleaching images. Then an “Extended Depth of Field” algorithm was applied to the 3D fluorescence stack in each cycle. Images were at last normalized in ImageJ, where a rolling ball algorithm was used for background estimation, edges were removed (accounting for the maximum allowed shift during the autofocus procedure) and fluorescence intensities were stretched to the full intensity range (16 bit -> 216).

Immunofluorescence staining of a human kidney (#3, tumor area) obtained by MELC

19 marker MELC run in a human tumor kidney sample (#3). Each image shows the same field of view, sequentially stained with the depicted fluorescence labelled antibodies. Images contain 2024 x 2024 pixels and are generated using an inverted wide field fluorescence microscope with a 20 x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Scale bar 100 µm. All raw images were aligned based on the reference phase contrast image taken at the beginning of measurement. Each fluorescence MELC image was processed by background subtraction and illumination correction, based on bleaching images. Then an “Extended Depth of Field” algorithm was applied to the 3D fluorescence stack in each cycle. Images were at last normalized in ImageJ, where a rolling ball algorithm was used for background estimation, edges were removed (accounting for the maximum allowed shift during the autofocus procedure) and fluorescence intensities were stretched to the full intensity range (16 bit -> 216).

Immunofluorescence staining of a human kidney (#3, peri-tumor area) obtained by MELC

19 marker MELC run in a human peri-tumor kidney sample (#3). Each image shows the same field of view, sequentially stained with the depicted fluorescence labelled antibodies. Images contain 2024 x 2024 pixels and are generated using an inverted wide field fluorescence microscope with a 20 x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Scale bar 100 µm. All raw images were aligned based on the reference phase contrast image taken at the beginning of measurement. Each fluorescence MELC image was processed by background subtraction and illumination correction, based on bleaching images. Then an “Extended Depth of Field” algorithm was applied to the 3D fluorescence stack in each cycle. Images were at last normalized in ImageJ, where a rolling ball algorithm was used for background estimation, edges were removed (accounting for the maximum allowed shift during the autofocus procedure) and fluorescence intensities were stretched to the full intensity range (16 bit -> 216).

Immunofluorescence staining of a human kidney (#2, tumor area) obtained by MELC

19 marker MELC run in a human tumor kidney sample (#2). Each image shows the same field of view, sequentially stained with the depicted fluorescence labelled antibodies. Images contain 2024 x 2024 pixels and are generated using an inverted wide field fluorescence microscope with a 20 x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Scale bar 100 µm. All raw images were aligned based on the reference phase contrast image taken at the beginning of measurement. Each fluorescence MELC image was processed by background subtraction and illumination correction, based on bleaching images. Then an “Extended Depth of Field” algorithm was applied to the 3D fluorescence stack in each cycle. Images were at last normalized in ImageJ, where a rolling ball algorithm was used for background estimation, edges were removed (accounting for the maximum allowed shift during the autofocus procedure) and fluorescence intensities were stretched to the full intensity range (16 bit -> 216).

Immunofluorescence staining of a human kidney (#2, peri-tumor area) obtained by MELC

19 marker MELC run in a human peri-tumor kidney sample (#2). Each image shows the same field of view, sequentially stained with the depicted fluorescence labelled antibodies. Images contain 2024 x 2024 pixels and are generated using an inverted wide field fluorescence microscope with a 20 x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Scale bar 100 µm. All raw images were aligned based on the reference phase contrast image taken at the beginning of measurement. Each fluorescence MELC image was processed by background subtraction and illumination correction, based on bleaching images. Then an “Extended Depth of Field” algorithm was applied to the 3D fluorescence stack in each cycle. Images were at last normalized in ImageJ, where a rolling ball algorithm was used for background estimation, edges were removed (accounting for the maximum allowed shift during the autofocus procedure) and fluorescence intensities were stretched to the full intensity range (16 bit -> 216).

Immunofluorescence staining of a human kidney (#1, peri-tumor area) obtained by MELC

19 marker MELC run in a human peri-tumor kidney sample (#1). Each image shows the same field of view, sequentially stained with the depicted fluorescence labelled antibodies. Images contain 2024 x 2024 pixels and are generated using an inverted wide field fluorescence microscope with a 20 x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Scale bar 100 µm. All raw images were aligned based on the reference phase contrast image taken at the beginning of measurement. Each fluorescence MELC image was processed by background subtraction and illumination correction, based on bleaching images. Then an “Extended Depth of Field” algorithm was applied to the 3D fluorescence stack in each cycle. Images were at last normalized in ImageJ, where a rolling ball algorithm was used for background estimation, edges were removed (accounting for the maximum allowed shift during the autofocus procedure) and fluorescence intensities were stretched to the full intensity range (16 bit -> 216).

opencc-by-4.0Jun 2022View details →

Spatiotemporal multiplexed immunofluorescence imaging of living cells and tissues with bioorthogonal cycling of fluorescent probes

Raw multichannel and/or Z-stack source data from time series images in TIF format to accompany publication of: Spatiotemporal multiplexed immunofluorescence imaging of living cells and tissues with bioorthogonal cycling of fluorescent probes Jina Ko1, Martin Wilkovitsch2, Juhyun Oh1, Rainer Kohler1, Evangelia Bolli1,3, Mikael J. Pittet1,3,4,5, Claudio Vinegoni1, David B. Sykes6,7, Hannes Mikula2, Ralph Weissleder1,8*, Jonathan C. T. Carlson1,7* 1 Center for Systems Biology, Massachusetts General Hospital, 185 Cambridge St, CPZN 5206, Boston, MA 02114  2 Institute of Applied Synthetic Chemistry, TU Wien, 1060 Vienna, Austria  3 Department of Pathology and Immunology, University of Geneva, Geneva, Switzerland 4 Ludwig Institute for Cancer Research, Lausanne Branch, Switzerland 5 AGORA Cancer Center, Lausanne, Switzerland 6 Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA, USA 7 Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA 8 Department of Systems Biology, Harvard Medical School, 200 Longwood Ave, Boston, MA 02115

opencc-by-4.0Sep 2022View details →

hiPSC 3D immunofluorescence images, test data set 2x2, 10Z

Example dataset of human induced pluripotent stem cells, imaged at 40x magnification with a Yokogawa CV7000. This is a small subset of a larger experiment intended as a test dataset for Fractal: https://github.com/fractal-analytics-platform/fractal 3 Channels were imaged: - C01: DAPI, nuclear stain - C02: nanog, antibody staining with Bio-Techne AG, AF1997-SP, Lot KKJ0617121 for the stemness marker nanog - C03: Lamin B1, antibody staining with Abcam, ab16048, Lot GR3244890-2 for the nuclear envelope marker Lamin B1   This dataset contains 10 Z levels for 4 field of views for those 3 channels, as well as (manually adjusted) metadata files from the Yokogawa CV7000.   The data was acquired in the Pelkmans lab in August 2020. The images have been converted from TIFF into PNG (lossless).

opencc-by-4.0Nov 2022View details →

OME-Zarr hiPSC 3D immunofluorescence images, tiny test set

This dataset is intended to be used for automated testing of OME-Zarr processing.   Example dataset of human induced pluripotent stem cells, imaged at 40x magnification with a Yokogawa CV7000. This is a tiny subset of a larger experiment intended as a test dataset for the <a href="https://fractal-analytics-platform.github.io/">Fractal platform</a> and others experimenting with OME-Zarrs. 1 Channel is included: <ul> <li>C01: DAPI, nuclear stain</li> </ul> It is generated from this raw data: <a href="../records/8287221">https://zenodo.org/records/8287221</a> This dataset contains 2 Z levels for 2 field of views for this 1 channel, as well as (manually adjusted) metadata files from the Yokogawa CV7000. The data was acquired in the Pelkmans lab in August 2020. The images have been processed using Fractal, the workflow is attached as a json file. It ran with fractal-server==2.3.6, fractal-client==2.0.1, fractal-web==1.4.0 and fractal-tasks-core==1.2.1. Two versions of the OME-Zarr are added here: A 3D version with both Z planes. And a 2D version (MIP of the Z-planes) which also contains label images from cellpose segmentation, measurements and output ROI tables.

opencc-by-4.0Feb 2023View details →

Literature Datasets for the publication "Systematic Review: Prevalence and Practices of Immunofluorescent Cell Image Processing"

This dataset contains the CSV files returned from PubMed searches used to complete a Systematic Review of Image Processing Publication Practices for methods applied to immunofluorescent images of all CNS cells.  The file names are organized "date_supplementarytablenumber" followed by the appropriate search terms.

opencc-by-4.0Sep 2022View details →

hiPSC 3D immunofluorescence images, tiny test set

Example dataset of human induced pluripotent stem cells, imaged at 40x magnification with a Yokogawa CV7000. This is a tiny subset of a larger experiment intended as a test dataset for Fractal: https://github.com/fractal-analytics-platform/fractal It is a subset of this dataset: https://zenodo.org/record/7057076 1 Channel is included: - C01: DAPI, nuclear stain   This dataset also contains a small Fractal workflow for standard processing   This dataset contains 2 Z levels for 2 field of views for this 1 channel, as well as (manually adjusted) metadata files from the Yokogawa CV7000.   The data was acquired in the Pelkmans lab in August 2020. The images have been converted from TIFF into PNG (lossless).

opencc-by-4.0Jul 2019View details →

DS5_LH_Tullii et al._ACS Appl. Mater. Interfaces_2019_immunofluorescence

Neuron synaptic expression analysis; neurons and HEK cells morphological analysis

Immunofluorescence staining of a human kidney (#1, tumor area) obtained by MELC

opencc-by-4.0Feb 2022View details →

Figure 2-Immunofluorescence staining of regenerating nerves 7, 14, 21, and 28 days after the injury and repair.

Figure 2- Immunofluorescence staining of regenerating nerves 7, 14, 21, and 28 days after the injury and repair. One section every millimeter labeled with Reca1 (red, endothelial cell marker), S100β (green, Schwann cell marker), and Neurofilament/NF (white, axon marker) to follow the nerve regeneration progression. The single labeling is shown in Figures S1 (NF), S2 (S100β), and S3 (Reca1) in the published manuscript. The dotted line delimits the region containing cell nuclei identified with DAPI (as in Figure 1B). Scale bar: 400 µm. It is possible to zoom in on this high-resolution version of this figure to appreciate the interactions between the different structures.

opencc-by-4.0May 2024View details →

Immunofluorescence of human metastatic lymph node

Description: Immunofluorescence staining of a consecutive, 10 µm-thick section (IF section #1), consecutive to Open-ST metastatic lymph node section #2 Data is provided as a standard, non-compressed pyramidal OME.TIFF file with 3 channels, converted with QuPath. Channels: 0 (cyan): DAPI 1 (yellow): panCK 2 (magenta): VIM Methods: Immunofluorescent (IF) staining was performed on the first cryosection of the metastatic lymph node reserved for validations, as shown in the experimental setup in Figure 2D. This slide was reserved at -80°C for ~15 months, before proceeding to IF staining. Steps were performed at room temperature unless stated otherwise. Upon drying the slide, the OCT was removed in a 10-minute PBS wash. Next, the section was fixed with 4% formaldehyde (Sigma-Aldrich, F8775) for 15 minutes and then washed with DPBS (no calcium, no magnesium, Gibco™, 14190169) three times. Blocking and permeabilization was done by incubating in 0.25% Triton-X (Sigma-Aldrich, T8787) and 5% normal donkey serum (Biozol Diagnostica, SBA-0030-01) in DPBS for 1 hour.  The section was incubated overnight at 4°C in the dark, with primary-conjugated antibodies diluted in a DPBS buffer with 0.1% Triton-X and 5% normal donkey serum, as follows: 1:100 for Pan Cytokeratin (mouse mAb, clones AE1and AE3, eFluor™ 570 conjugate,  ThermoFisher, Cat# 41-9003-82), 1:50 for Vimentin (mouse mAb, clone V9, Alexa Fluor® 750 conjugate, Bio-Techne, Cat#  NBP1-97670AF750). Following three 5-minute DPBS washes, DAPI staining was performed with 1 ug/mL DAPI (Bio-Trend, #40011) in DPBS for 10 min in the dark. After three DPBS rinses, the section was dried and then mounted with 85% glycerol.  Images were acquired on the Leica Thunder DMi8 imager using a Leica DFC 9000GT sCMOS fluorescence camera, a 20X objective and the Leica Application Suite (LAS) X software (v.3.9.0.28093). Thunder instant computational clearing was performed on the image.

opencc-by-4.0Jul 2024View details →

Multi-modal image analysis for large scale cancer tissue studies within IMMUcan: multiplex immunofluorescence images

In cancer research, multiplexed imaging has enabled the in-depth characterization of the tumor microenvironment (TME) and how it relates to patient prognosis. However, standardized, multi-modal data from large numbers of patients to identify robust biomarkers is missing. To provide such data across five cancer indications, the IMMUcan consortium performs broad molecular and cellular spatial profiling of thousands of cancer samples. Two reproducible and scalable workflows have been developed for whole slide multiplexed immunofluorescence (mIF) and imaging mass cytometry (IMC) to overcome challenges of reproducibility and scalability. For mIF we developed IFQuant, a web-based tool optimized for user-friendliness and reproducibility. This Zenodo record contains the mIF images and IFQuant settings to reproduce the results presented in the referenced publication. The companion IMC dataset is available as a joint Zenodo record.

opencc-by-4.0Jan 2023View details →

Dual-modality imaging of immunofluorescence and imaging mass cytometry for high-resolution whole slide imaging with accurate single-cell segmentation

Imaging mass cytometry (IMC) is a powerful multiplexed tissue imaging technology that allows simultaneous detection of more than 30 makers on a single slide. It has been increasingly used for single-cell based spatial phenotyping in a wide range of samples. However, it only acquires a small, rectangle field of view (FOV) with a low image resolution that hinders downstream analysis. Here, we reported a highly practical dual-modality imaging method that combines high-resolution immunofluorescence (IF) and high-dementional IMC on the same tissue slide. Our computational pipeline uses the whole slide image (WSI) of IF as spatial reference,  integrates small FOV IMC into a WSI of IMC. The high-resolution IF images enable accurate single-cell segmentation to extract robust high-dimensional IMC features for downstream analysis. We applied this method in esophageal adenocarcinoma of different stages, identified the single-cell pathology landscape via reconstruction of WSI IMC images and demonstrated the advantage of the dual-modality imaging strategy.