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Checkpoint Molecules Predict Response to Immunotherapy in Cancer

FEATURED PUBLICATION

Checkpoint Molecules Predict Response to Immunotherapy in Cancer

cells

Heij et al. investigated the expression of checkpoint molecules in the tumor microenvironment of intrahepatic cholangio-carcinoma. They found that co-expression patterns of checkpoint molecules such as PD-L1 and TIGIT correlate with metastases and poor prognosis. TissueFAXS and StrataQuest were used to image the mIF-stained samples and assess protein expression.

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Community-developed Checklists Enhance Reproducibility of Microscopy Images

FEATURED PUBLICATION

Community-developed checklists for publishing images and image analyses

Nature Methods

QUAREP-LiMi is an initiative focused on promoting quality assessment and reproducibility for instruments and images in light microscopy. Dr. Rupert Ecker, CEO of TissueGnostics, participated in developing standardized guidelines for best practices in image communication. These checklists offer key recommendations on image formatting and annotation among others.

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AI-Assisted Analysis of ‘Anti-/Pro-Tumor’ Profiles for Pancreatic Adenocarcinoma

FEATURED PUBLICATION

AI-Assisted Analysis of ‘Anti-/Pro-Tumor’ Profiles for Pancreatic Adenocarcinoma

Cancer Biology & Medicine

To predict survival of patients with pancreatic adenocarcinoma, Zhou et al. performed an AI-assisted comprehensive analysis of CD8+ T cells, cancer stem cells (CSCs), and tumor budding (TB). They used StrataQuest image analysis software to automatically detect and quantify tumor-infiltrating CD8+ T cells, CD133+ CSCs, and CK19+ TB.

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Characterization of the Spatial Immune Context of Lung Cancer

FEATURED PUBLICATION

Characterization of the Spatial Immune Context of Lung Cancer

International Journal of Molecular Sciences

In a recent study, Imamura et al. investigated the role of debrin-expressing tumor infiltration lymphocytes (TIL) in lung cancer. The high-end image analysis solution, StrataQuest, was used for the automatic tissue classification and localization of CD3+/debrin+ cellular phenotypes in the tumor cell nest, as well as in the stroma.

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COL8A1 Promotes Tumor in Pancreatic Ductal Adenocarcinoma

FEATURED PUBLICATION

COL8A1 Promotes Tumor in Pancreatic Ductal Adenocarcinoma

Matrix Biology

Yan et al. investigated several collagen subtypes involved in pancreatic ductal adenocarcinoma desmoplasia. Based on this study, COL8A1 was suggested as a new marker for predicting poor chemotherapy responsiveness and survival. The TissueFAXS imaging platform and StrataQuest analysis solution were used to image and segment morphological structures.

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Erk1/2-Dependent HNSCC Cell Susceptibility to Erastin-Induced Ferroptosis

FEATURED PUBLICATION

Erk1/2-Dependent HNSCC Cell Susceptibility to Erastin-Induced Ferroptosis

cells

Savic et al. investigated the Erk 1/2 pathway role in ferroptosis induction by targeting xCT protein during the treatment of HNSCC. They found erastin to be a potent ferroptosis inducer which is strongly dependent on Erk 1/2 kinases. TissueFAXS and HistoQuest were used to image the IHC-stained samples and calculate the percentage of xCT-positive cells.

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Tumor Microenvironment Characterization of Rectal Cancer

FEATURED PUBLICATION

Case Report: Tumor Microenvironment Characterization of Rectal Cancer

Frontiers in Immunology

Song et al. published a case study investigating the genetics and tumor microenvironment of a rectal cancer patient with MSS/PD-L1-negative recurrent hepatopulmonary metastasis. TissueFAXS and StrataQuest were used to image stained tissue samples and to assess tumor infiltrating immune cells using, among others, CD4, CD8, CD163, PD1, and PD-L1 as markers.

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Adverse Effects of Single

FEATURED PUBLICATION

The impact of HIV infection on the frequencies, function, spatial localization and heterogeneity of TFRs within human lymph nodes

BMC Immunology

Mahlobo et al. published a study in BMC Immunology addressing the effect of HIV infections on frequencies, function, spatial localization and heterogeneity of T follicular regulatory cells (TFRs) in human lymph nodes. TFRs within lymph node tissues were quantified using TissueFAXS i PLUS and TissueQuest.

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Temporal changes

FEATURED PUBLICATION

Temporal changes in T cell subsets and expansion of cytotoxic CD4+ T cells in the lungs in severe COVID-19

Clinical Immunology

Kaneko et al. published a study in the Clinical Immunology journal focusing on the quantitative analysis of T-cell subset alterations in thoracic lymph nodes and lung samples of COVID-19 patients. With the help of TissueFAXS and StrataQuest, the authors were first to show the expansion of cytotoxic CD4+ T cells in the lungs of patients with severe COVID-19.

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Spatial Distribution

FEATURED PUBLICATION

Spatial distribution and functional analysis define the action pathway of Tim-3/Tim-3 ligands in tumor development

Molecular Therapy

In a recent publication in the journal Molecular Therapy, Wang et al (Cheeloo Medical College of Shandong University) combine multiplexing and TissueGnostics’ image cytometry solutions for the assessment of the expression level of Tim-3, a well-known immune checkpoint, in tumor infiltrating lymphocytes. This study nominates Tim-3 as a potential target for cancer immunotherapy.

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Multi-Antigen Imaging

FEATURED PUBLICATION

Multi-Antigen Imaging Reveals Inflammatory DC, ADAM17 and Neprilysin as Effectors in Keloid Formation

International Journal of Molecular Sciences

In a recent publication in the MDPI - International Journal of Molecular Sciences, Rath et al (University Hospital Heidelberg) combine multiplexing, TissueGnostics’ image cytometry and in-depth data mining to characterize the immune microenvironment including spatial relationships within keloid lesions. Using this approach, the authors were able to nominate potential therapeutic targets.

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SynNotch-CAR T

FEATURED PUBLICATION

SynNotch-CAR T cells overcome challenges of specificity, heterogeneity, and persistence in treating glioblastoma

Science Translational Medicine

A recent publication from the group of Choe et al. (University of California, UCSF) published in Science Translational research is exploring the therapeutic effectiveness of SynNotch-CAR T cells in a PDX glioblastoma mouse model. T cells in-situ were imaged/assessed by TissueFAXS and quantified via StrataQuest.

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CpG Methylation Influence on Protease Serine 3 Isoforms in Lung Cancer

FEATURED PUBLICATION

CpG Methylation Influence on Protease Serine 3 Isoforms in Lung Cancer

Acta Pharmaceutica Sinica B

Lin et al. investigated the UHRF1/DNMT1–MZF1 axis in the context of CpG methylation of protease serine 3, a protein with several isoforms that show paradoxical effects. To differentiate the isoforms, the researchers employed 7-marker multiplex staining and performed a quantitative analysis using TissueFAXS SPECTRA and StrataQuest.

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Oncology (1174)
Neuroscience (384)
Immunology (305)
Cardiology (125)
Pharmacology (120)
Virology (100)
Reproductive Medicine (92)
Nanomedicine (89)
Gastroenterology (75)
Dermatology (73)
...more

Mouse (1267)
Human (1239)
Rat (289)
Fish (26)
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Top Ten
Bacteria (4)
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Human (1239)
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Customer Publications (A total of 3293 papers published)

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3251

Death receptor 5 mediated-apoptosis contributes to cholestatic liver disease

Kazuyoshi Takeda, Yuko Kojim, Kenichi Ikejim, Kenichi Harada, Shunhei Yamashina, Kyoko Okumur, Tomonori Aoyam, Steffen Fres, Hiroko Ieka, Nicole M Haynes, Erika Cretne, Hideo Yagita, Noriyoshi Sueyoshi, Nobuhiro Sato, Yasuni Nakanuma, Mark J Smyth, Ko Okumura

PNAS (PNAS) USA , 05 August 2008

Hepathology

Key words: Cholangitis primary, sclerosing cholangitis

3252

Development of pulmonary fibrosis through a pathway involving the transcription factor Fra-2/AP-1

Robert Eferl, Peter Hasselblatt, Martina Rath, Helmut Popper, Rainer Zenz, Vukoslav Komnenovic, Maria-Helena Idarraga, Lukas Kenner, Erwin F Wagner

PNAS (PNAS) USA , 29 July 2008

Pulmonology

Key words: Fra-2 transgenic mouse, idiopathic pulmonary, fibrosis, osteopontin, pulmonary arterial, hypertension fibrosis, mouse model

3253

Apoptosis resistance in breast cancer stem cells through the regulation of Bcl2

Z Madjd, A Zare Mehrjerdi, A Sharifi, S Molanaei, S Zohoorian, M Asadi Lari

European Journal of Cancer (Elsevier) , 10 July 2008

Oncology

3254

Regulation of matrix metalloproteinases and tissue inhibitor of metalloproteinases by cobalt chloride-stimulated hypoxia in primary and metastatic breast cancer cells

WY Lee, F Ou-Yang, MF Hou, SF Yang, SC Huang

European Journal of Cancer (Elsevier) , 10 July 2008

Oncology

3255

Biodistribution and kinetics of the novel selective oncolytic adenovirus M1 after systemic administration

X Huang, L Zhuang, Y Cao, Q Gao, Z Han, D Tang, H Xing, J Zhou, D Ma

European Journal of Cancer (Elsevier) , 10 July 2008

Oncology

3256

Melatonin Receptor (MT1) and Nestin Coexpression: an indicator of more advanced breast cancer?

O Rögelsperger, C Ekmekcioglu, M Svoboda, R Königsberg, M Klimpfinger, W Jäger, T Thalhammer

European Journal of Cancer (Elsevier) Austria , 10 July 2008

Oncology

3257

Insulin-like growth factor binding protein-3 (IGFBP-3) in the prostate and in prostate cancer: Local production, distribution and secretion pattern indicate a role in stromal-epithelial interaction

Petra Massoner, Petra Haag, Christof Seifarth, Andreas Jurgeit, Hermann Rogatsch, Wolfgang Doppler, Georg Bartsch, Helmut Klocker

The Prostate (Wiley) Austria , 05 May 2008

Oncology

Key words: IGFBP-3, prostate cancer, stromal-epithelial interaction, expression, secretion

3258

A standards based ontological approach to information handling for use by organizations providing human tissue for research

Mary E Edgerton, Carl Morrison, Virginia A LiVolsi, Christopher A Moskaluk, Stephen J Qualman, M Kay Washington, William E Grizzle

Cancer Informatics (SAGE Journals) USA , 10 April 2008

Biological Methods

3259

Acute anoxia stimulates proliferation in adult neural stem cells from the rat brain

Heinrich F Bürgers, Dominik W Schelshorn, Wolfgang Wagner, Wolfgang Kuschinsky, Martin H Maurer

Experimental Brain Research (Springer) Germany , 11 March 2008

Neuroscience

Key words: Stem cell marker, arythroid progenitor, ogd group, neural stem cell

3260

Lithium Promotes Adult Neural Progenitor Differentiation Via GSK3ß-Dependent Signaling Pathways

Heinrich F Bürgers, Dominik W Schelshorn, Wolfgang Kuschinsky and Martin H Maurer

The Open Enzyme Inhibition Journal () Germany , 02 January 2008

Cell Biology

Key words: Adult neural progenitor cell, Lithium, Two-dimensional gel electrophoresis, Rat.

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Results 3251 - 3260 of 3293

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Cytometry encompasses several methods for investigating cells, such as their count, cell cycle state, phenotype, morphology, size ...

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IHC IMMUNE SATUS IN SITU

The IHC Immune Status in Situ App uses the AI classifier to segment tissue into morphological entities such as tumor, stroma, and lymphocyte clusters. It further identifies single cells ...

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The Effect of Stem Cell Transplantation on Skin Microorganisms

Nadine Bayer, PhD
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Spatial Tissue Cytometry: Success Stories - Eurolab Article

Read about tissue cytometry's potential in understanding the complexities of cell populations in the tissue environment

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IF SKELETAL MUSCLE

04 ImageWebsite Original 60 muscleFibres overlay

The IF Skeletal Muscle App allows the segmentation of skeletal muscle tissue sections into muscle fibers and connective tissue based on specific IF staining. Outcome parameters are provided, such as the number of muscle fibers and area of the total tissue, muscle fibers, and connective tissue.

Image: Courtesy of Stefania Petrini, Bambino Gesù Children’s Hospital, Rome

App Category 2

IHC ADIPOCYTE

Adipocytes FatCellDetection 274x274

The IHC Adipocyte App quantifies adipocytes and their lumen in adequate HE samples. The App automatically mends small rips in adipocyte membranes and eliminates cell membrane artifacts in adipocyte lumina and lumina on sample borders. The App also outputs area measurements for all detected adipocyte lumina.

App Category 1

Example for IHC Adipocyte App

IF 2

IF2APP CellularMask UR 274x274

The IF 2 App provides single cell-based co-expression analysis for two IF markers. It segments cells into their nucleus, perinuclear area, and/or cytoplasm. Each segmented cell compartment is measured for up to 20 intensity, statistic, and morphometric parameters that can be displayed in and exported into scattergrams and histograms.

App Category 1

IF 3

IF3 APP 91per CellularMask original UR 274x274

The IF 3 App provides single cell-based co-expression analysis for three IF markers. It segments cells into their nucleus, perinuclear area, and/or cytoplasm. Each segmented cell compartment is measured for up to 20 intensity, statistic, and morphometric parameters that can be displayed in and exported into scattergrams and histograms.

App Category 1

IF 4

IF4 APP 85per Original 274x274

The IF 4 App provides single cell-based co-expression analysis for four IF markers. It segments cells into their nucleus, perinuclear area, and/or cytoplasm. Each segmented cell compartment is measured for up to 20 intensity, statistic, and morphometric parameters that can be displayed in and exported into scattergrams and histograms.

App Category 2

IF GLOMERULI

IFGlomeruli Original 31per proximityMask 274x274

The IF Glomeruli App provides the detection of tissue, cells, and glomeruli stained by a specific marker. It segments the cells into their nucleus and/or cytoplasm and determines the cellular phenotype of specific IF-stained cell populations. The detected cells can be classified as being either inside or outside the glomeruli within certain distances (distance ranges are definable). For each cell, the spatial information and up to 20 intensity, statistics, and morphometric parameters are measured. The data can be displayed in diagrams and exported.

App Category 3

IF HI-PLEX 50

IFHi Plex50 63per Original 3MarkerMasks NucleiMask 274x274

The IF Hi-Plex 50 App combines and analyses images of the same IF-stained tissue section, acquired up to 50 times with different markers. The App enables the detection of the cellular phenotypes of specific IF-stained cell populations. It segments cells into their nucleus, perinuclear area, and/or cytoplasm. Each segmented cell compartment is measured for up to 20 intensity, statistic, and morphometric parameters that can be displayed in and exported into scattergrams and histograms.

App Category 3

IF DOTS

IFDots APP 113per Original NucleiMask DotsMasks 274x274

The IF Dots App provides dot detection per cell within the cell compartments for up to four markers in a sample (e.g., FISH, RNA, oil droplets). Each segmented cell compartment is measured for up to 20 intensity, statistic, and morphometric parameters. Dot measurement parameters are provided per cell compartment (e.g., nucleus, cytoplasm) and per dot and include count, mean intensity, total dot area, the sum of intensity.

App Category 2

Example for IF Dots App

IF Immune Status in situ

IF ImmuneStatusInSitu 33per Original NucleiMask EptihteliumMask UR 25 50um 274x274

The IF Immune Status in Situ App provides a phenotypic characterization of immune cells in reference to detected metastructures (e.g., tumors, glands) and measures the distance of detected cellular objects to the metastructure boundary (within and/or outside). Distance ranges are also definable. Each segmented cell compartment is measured for up to 20 intensity, statistic, and morphometric parameters, as well as the distance of each cell to the areas boundary.

App Category 3

IF SKIN MORPHOLOGY

IFSkinMorphology 46per DermisMask EpidermisMask NucleiMask 274x274

The IF Skin Morphology App provides tissue detection, including the segmentation of epidermis and dermis, based on specific IF staining. It segments cells into their nucleus, perinuclear area, and/or cytoplasm and determines the cellular phenotype of specific IF-stained cell populations. The detected cells can be classified and visualized as being within or outside detected structures (epidermis and dermis). Each segmented cell compartment is measured for up to 20 intensity, statistic, and morphometric parameters that can then be displayed in diagrams and exported.

App Category 3

IF GRANULOMA

IFGranuloma 24per original nucleusMask GranulomaMask UR 274x274

The IF Granuloma App detects granulomas based on nuclear structure analysis and an adequate IF staining (e.g., CD11c, CD68). The App measures the number and area of Granulomas and their density. Each segmented cell compartment is measured for up to 20 intensity, statistic, and morphometric parameters.

App Category 2

IF CYTOSKELETON

IFCytoskeleton 101per Actin FilamentsMask Nucleusmask 274x274

The IF Cytoskeleton App detects cytoskeletal structures based on a specific stain. The cell cytoplasm can be detected using other stains. Data can also be exported, including the number of cytoskeletal filaments inside and outside the cell and on the cell membrane, filament length, and total filament area.

App Category 3

IF GLIAL CELLS

IFGlialCells 120per Original ALLMask 274x274

The IF Glial Cells App allows the detection of astrocytes and microglia based on specific IF staining. The measurements assessed by the App include the number of astrocytes and microglia and the number of branches of each cell (short and long attached).

App Category 3

IHC 2

IHC2 APP 154per Nucleus NucMask Original UR 274x274

The IHC 2 App unmixes two markers (e.g., chromogen and counterstain) in an IHC or HC digital slide and segments single cells into their nucleus, perinuclear area, and/or cytoplasm. Each segmented cell compartment is measured for up to 20 intensity, statistic, and morphometric parameters that can be displayed in and exported into scattergrams and histograms.

App Category 1

Example for IHC 2 App

IHC 3

IHC3 Original 67 BFMarker2 Shades data UR 274x274

The IHC 3 App unmixes three markers (e.g., two chromogens and a counterstain) in an IHC or HC digital slide and segments single cells into their nucleus, perinuclear area, and/or cytoplasm. Each segmented cell compartment is measured for up to 20 intensity, statistic, and morphometric parameters that can be displayed in and exported into scattergrams and histograms.

App Category 2

IHC MACROPHAGES

IHCMacrophages 73per Original NucleiMask AllMasks UR 274x274

The IHC Macrophages App detects macrophages based on adequately stained IHC samples. The App can be combined with area detection and distance range algorithms to determine the distance of Langerhans cells from the border of the epidermis inside and outside the epidermis (see above example). Each segmented cell compartment is measured for up to 20 parameters, as is the distance of each cell to the boundary.

App Category 3

IHC TUMOR-STROMA

IHC TumorStroma 30per Original TumorMask NucleiMask 274x274

The IHC Tumor-Stroma App combines the segmentation of tumor and stroma (based on the morphology) and the detection of specifically stained cell populations. It segments the cells into their nucleus, perinuclear area, and/or cytoplasm. Each segmented cell compartment in tumor and/or stroma is measured for up to 20 intensity, statistic, and morphometric parameters that can be displayed in and exported into scattergrams and histograms.

App Category 2

PULMO

Pulmo APP 52per Original BloodVessels Bronchiole Aveoli 274x274

The Pulmo App segments nuclei and the metastructure components of the lung, including tissue, bronchioles, blood vessels, and alveoles. Each segmented metastructure is measured for up to 20 morphometric parameters.

App Category 3

IHC ANGIO

IHCAngio 37per BloodvesselMask Original 274x274

The IHC Angio App detects blood vessels based on appropriate stains (e.g., CD31) and measures overall vessel and lumen areas. The vessel detection can be set to close open stained vessel walls and connect separated vessel sections within a definable distance. As well as vessel number, density, and areas, the App also outputs endothelium and lumina areas.

App Category 2

IHC META CELLS

IHCMetaCells 55per InsulinIslandNuclei274x274

The IHC Meta Cells App combines the detection of IHC/HC stained metastructures (e.g., Langerhans islets, Tumor - Stroma) with single-cell detection (segmentation of cells into nucleus, perinuclear area, and/or cytoplasm). Detected cells can be classified and visualized as being within or outside detected metastructures. Each detected area and cell compartment is measured for up to 20 intensity, statistic, and morphometric parameters.

App Category 2

RNA SCOPE

RNAScope 85per Nucleus OriginalGrowMask Dots 274x274

The RNA Scope App enables the detection of nuclei based on appropriate staining and dot detection per cell within nucleus and/or cytoplasm for one dot marker in CISH and SISH experiments . Each segmented cell compartment is measured for up to 20 intensity, statistic, and morphometric parameters. Dot parameters are provided per cell and per dot and include count, mean intensity, total dot area and the sum of intensity.

App Category 2

TUMOR FOCI

TumorFoci Tumor Original 274x274

The Tumor Foci App allows for the detection of the whole tissue and, more importantly, tumor foci based on nuclear structure analysis, mainly on HE staining. The number, area, and density of tumor foci are measured.

App Category 1

IHC MEMBRANE

IHC Membrane ROI01 Nuclei Membrane 274x274

The IHC Membrane App unmixes up to three markers in an IHC or HC digital slide and segments cells into nucleus, perinuclear area, and/or cytoplasm, as well as into membrane (e.g., HER2/neu). Each segmented cell compartment is measured for up to 20 intensity, statistic, and morphometric parameters. Three more parameters are measured for membrane intensity and angle of staining. All parameters are displayed in scattergrams and histograms and can be exported.

App Category 2

BONE GOLDNER

BoneGoldnerApp original tissuedetection OsteoidDetection 274x274

The Bone Tissue Analysis Goldner App allows for the detection of mineralized bone tissue and osteoid based on Goldner-stained bone tissue sections. The App assesses parameters such as BV (bone volume), TV (trabecular bone volume), OV (osteoid volume), OV/BV, OV/TV, OS (osteoid surface), BS (bone surface length), and the mean of osteoid width and thickness.

App Category 2

BONE MINERALIZATION

BoneMineralizationAPP Original boneM Cartilage minCart 274x274

The Bone Mineralization APP separates Safranin O-stained bone tissue into its morphological substructures (cartilage, mineralized cartilage, bone marrow, and mineralized bone). Measurements assessed with this App include TV (trabecular bone volume), BV (total bone volume), MCV (mineralized cartilage), CV (cartilage volume), and bone marrow (BM).

App Category 3

BONE VON KOSSA

BoneVonKossa original bone

The Bone Tissue Analysis Von Kossa App allows for the detection of mineralized bone tissue based on Von Kossa stained bone tissue sections. The App provides parameters such as TV (trabecular bone volume), BV (bone volume), BS (bone surface), BV/TV, BS/BV, Tb.N (trabecular number), tb.Th (trabecular thickness), and Tb.Sp (trabecular separation).

App Category 2

IF LEISHMANIASIS

IFLeishmaniasis NucGrey CombinedMask 274X274

The IF Leishmaniasis App detects intracellular Leishmania parasites and segments them in the detected host cells. The number of parasites per cell is determined, and living and dead parasites can be distinguished (live/dead assays). The App outputs 20 intensity, statistic, and morphometric parameters for each segmented cell compartment per marker, as well as the number, mean intensity, sum of intensity, and size of parasites.

App Category 2

Example for IF Leishmaniasis App

IF CELLULAR MICROENVIRONMENT

IFCellularMicroenvironment Orginal Connections 274x274

The IF Cellular Microenvironment App allows to determine the cellular phenotype of specific IF-stained cell populations and establishes their spatial relationship between each other and their neighboring cells/cell populations, including those with metastructures (e.g., blood vessels, tumors) in their vicinity. It is especially suited for proximity and infiltration analyses.

App Category 4

IF NEURITE

IFCellularMicroenvironment Orginal Connections 274x274

The IF Neurite App identifies neuronal cells and cell clusters and their neurites/dendrites. It quantifies the number of neurites/dendrites branching out from a specific neuron, identifies branch points, and exports total neurite/dendrite area, total neurite/dendrite length, average neurite/dendrite thickness, the number of branch points, and the number of endpoints.

App Category 3

IF PYKNOTIC NUCLEI

IFCellularMicroenvironment Orginal Connections 274x274

The IF Pyknotic Nuclei App provides tissue detection and cell segmentation in combination with the detection of pyknotic nuclei (defined as completely condensed, round, high-intensity nuclei) based on nuclei staining. Additionally, the App allows for the determination of the cellular phenotype of specific IF-stained cell populations and dot detection. It segments the detected cells into nucleus, perinuclear area, and/or cytoplasm. The App provides parameters such as the number, mean intensity, and percentage of specific cell populations (including cells containing pyknotic nuclei). It also outputs dot parameters per segmented cell and/or dot, including count, mean intensity, total dot area, and sum of intensity.

App Category 3

IF TUMOR VASCULARIZATION

IFCellularMicroenvironment Orginal Connections 274x274

The IF Tumor Vascularization App provides tissue detection, including the separation of tumor tissue and tumor stroma (healthy tissue). Additionally, it detects blood vessels based on appropriate stains (e.g., CD31) and measures the number, area, and density of these blood vessels. The vessel detection also can be set to close open stained vessel walls and to connect separated vessel sections within a definable distance.

App Category 3

IHC TUMOR MACROPHAGES

IFCellularMicroenvironment Orginal Connections 274x274

The IHC Tumor-Macrophages App provides tissue detection, including the separation of tumor and healthy tissue. It detects macrophages based on specific staining (e.g., CD68) and outputs the area of macrophages within tumor and healthy tissue.

Image: Courtesy of Dr. Patrick Michl, Dr. Maren Egidi, and Dr. Heidi Griesmann, Universitätsklinikum Halle (Saale).

App Category 3

IHC TUMOR VASCULARIZATION

IFCellularMicroenvironment Orginal Connections 274x274

The IHC Tumor Vascularization App provides tissue detection, including the separation of tumor tissue and tumor stroma (healthy tissue). Additionally, it detects blood vessels based on appropriate stains (e.g., CD31) and measures the number and area of these blood vessels. The vessel detection can also be programmed to close open stained vessel walls and to connect separated vessel sections within a definable distance. The App outputs the number, density, and areas of vessels within both tumor and healthy tissue.

Image: Courtesy of Dr. Patrick Michl, Dr. Maren Egidi, and Dr. Heidi Griesmann, Universitätsklinikum Halle (Saale).

App Category 3

IHC ANGIO TRICHOME

IHCAngioTrichome BVRingBrownCellsBVonOriginal 274x274

The IHC Angio Trichome App detects blood vessels based on trichome staining and measures the overall vessel and lumen area. Furthermore, it detects specifically IHC-stained single cell populations and establishes their spatial relationship to the detected blood vessels. The App outputs number and vessel density, vessel wall thickness and areas of vessels, endothelium and lumina, the number of IHC stained cells, proximity measurement, etc.

App Category 3

RNA SCOPE+

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The RNA Scope+ App provides detection of nuclei based on appropriate staining and dot detection per cell within the nucleus and/or cytoplasm for two dot markers in CISH and SISH experiments. Each segmented cell compartment is measured for up to 20 intensity, statistic, and morphometric parameters. It also outputs dot parameters per segmented cell and/or dot, including count, mean intensity, total dot area, and sum of intensity.

App Category 2

IHC ANGIO ELASTIN

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The IHC Angio Elastin App detects blood vessels in Verhoeffs van Geison-stained samples, elastin, and collagen. The outputs include the number and area of vessels, elastin, and collagen within a definable distance to the vessel.

App Category 2

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IF CARDIO CELL CULTURE

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The IF Cardio Cell Culture App provides cell segmentation, detection of cardiomyocytes (based on appropriate staining, e.g., Troponin Red), fibroblasts within cultured cardio cells, plus one additional marker. The App outputs parameters such as the number of cardiomyocytes, fibroblasts, and marker-positive cardiomyocytes and fibroblasts.

Image: Courtesy of Agatha Ribeiro da Silva, Prof. Jose E. Krieger (Heart Institute, University Sao Paulo)

App Category 3

IF CARDIO CELL CULTURE DOTS

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The IF Cardio Cell Culture Dot App provides cell segmentation and detection of cardiomyocytes (based on an appropriate stain, e.g., Troponin Red) and fibroblasts within cultured cardio cells, plus one dot marker (CISH, FISH). The App outputs parameters such as the number of cardiomyocytes and fibroblasts. It also outputs the number of dot-positive cardiomyocytes and fibroblasts and the number, area (μm²), and mean intensity of dots per cell.

Image: Courtesy of Agatha Ribeiro da Silva, Prof. Jose E. Krieger (Heart Institute, University Sao Paulo).

App Category 3

IF DENDRITES AND AXONS

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The IF Dendrites & Axons App identifies neuronal cells, their dendrites and axon, based on appropriate markers. It quantifies the number of dendrites branching out from a specific neuron. The App provides the total number of dendrites per neuron, including the length of these dendrites and their axons.

Image: Courtesy of Thomas Bastian, Ph.D., University of Minnesota.

App Category 3

IHC SMALL INTESTINE - DOTS

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The IHC Small Intestine - Dots App provides nuclei segmentation and detection of tissue and villi based on nuclei staining (crypts need to be defined manually). Furthermore, it allows dot detection for one dot markers (CISH, RNAScope, SISH) within villi and crypt areas. Dot parameters are provided for villi and crypts and for dots and include count, mean intensity, total dot area, the sum of intensity.

App Category 2

LIPID DROPLETS

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The Lipid Droplets App quantifies lipid droplets within H&E stained tissues (e.g., liver). The App automatically mends small rips in liver droplet membranes and eliminates cell membrane artifacts, including lumina on sample borders. The App also outputs area and number measurements for all detected lipid droplets.

App Category 1

ANGIO SIRIUS RED

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The Angio Sirius Red App detects collagen and blood vessels based on Sirius Red staining. The app outputs the area of Sirius Red stained collagen and the number of detected vessels.

App Category 2

IHC ANGIO DIAMETER

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The IHC Angio Diameter App detects blood vessels based on appropriate stains (e.g., CD31). The App outputs vessel area, number, density, and blood vessel diameter.

App Category 2

IF CULTURED CELLS & SUBSTRUCTURES

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The IF Cultured Cells & Substructures App detects cells based on nuclei staining, as well as one dot marker (FISH, CISH experiments) and cytoskeletal structures based on a specific stain. It outputs the number of detected cells, the number and intensity of dots per cell, and the density of cytoskeletal filaments.

App Category 3

IF MEMBRANE

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The IF Membrane App detects nuclei and segments the cells into different cellular compartments, including membrane, nuclei, and cytoplasm. It also detects one additional marker (e.g., HER2/neu). Each segmented cell compartment is measured for different parameters, such as staining intensity, stained area, and the number/percentage of marker-positive cells within the detected cellular compartments. Three more parameters are measured for the membrane, including membrane area, membrane length, and the angle of staining.

App Category 2

IF SPHEROIDS

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The IF Spheroids App allows a comprehensive analysis of spheroids (as well as organoids and embryoid bodies). It automatically identifies the spheroids and cells based on the nuclei staining and analyzes two additional IF markers. It segments the cells into different cellular compartments, including membrane, nuclei, and cytosol, and further measures the marker expression for each compartment. It can also measure dot markers (if available). It establishes proximity distances for the cells detected within the spheroids, bringing the IF-stained cell populations into spatial context.

App Category 3

IHC IMMUNE STATUS IN SITU

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The IHC Immune Status in Situ App uses the AI classifier to segment tissue into morphological entities such as tumor, stroma, and lymphocyte clusters. It further identifies single cells based on nuclei staining (hematoxylin), detects immune cells based on appropriated stains (CD45, CD3, CD20, etc.), and measures the distance of detected cells to the metastructure boundary. The App can also define distance ranges through outputting parameters, including the area of the detected morphological entities and the number/percentage of lymphocytes detected within the tissue entities, as well as in certain proximities.

App Category 3

WILMS TUMOR

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The Wilms Tumor App is based on the AI Classifier and allows for the segmentation of H&E stained Wilms tumor tissues into tumor, stroma, and blood vessels. It outputs the area (µm²) of the segmented tissue entities.

App Category 1

IHC NECROTIC TUMOR

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The IHC Necrotic Tumor App segments tumor tissues into tumor, necrotic tissue, and stroma using the AI Classifier. Furthermore, it identifies single cells as well as one additional cellular marker (e.g., neutrophils). It outputs the area of tumor, necrotic tissue, and stroma and measures the number and percentage of neutrophils within the morphological entities.

App Category 3

IHC NECROTIC TUMOR ANGIO

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The IHC Necrotic Tumor Angio App can segment tumor tissues into tumor, necrotic tissue, and blood vessels using the AI Classifier. It outputs the area of tumor, necrotic tissue, and blood vessels, as well as the number and percentage of blood vessels in total and within the two morphological entities.

App Category 3

IF RODS AND CONES IN RETINA

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The IF Rods & Cones in Retina App detects the rods and cones based on specific staining. It outputs the number, density, and length of detected structures, as well as the number, percentage, and density of marker-stained rods and cones.

App Category 2

IF TUMOR FOCI ANGIO

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The IF Tumor Foci Angio App identifies single cells and segments tissues into tumor foci and blood vessels based on appropriate markers. It applies proximity maps to identify nuclei close to blood vessels. It measures the number of nuclei located within a certain distance relative to blood vessels, the number of nuclei in the different morphological entities, and the area of these morphological entities.

App Category 3

IF RETINAL VASCULATURE

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The IF Retinal Vasculature App detects blood vessels in retinal tissue based on appropriate stains (e.g., CD31). The App outputs vessel area, density, and length.

App Category 2

ENDOMETRIUM HE

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The Endometrium HE App allows for the segmentation of hematoxylin and eosin-stained endometrium tissues into their morphological entities (glands, stroma, and blood vessels). The measurements provided by the App include the area of glands, stroma, and blood vessels.

App Category 2

IHC EXTRACELLULAR FILAMENTS

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The IHC Extracellular Filament App detects nuclei and extracellular filaments stained with specific markers. It outputs the number of nuclei, total filaments area, and the length of these filaments.

App Category 2

IF CELLULAR CONTACT

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The IF Cellular Contact App allows for the determination of the cellular phenotype of specific IF-stained cell populations and establishes the cellular contacts to their neighboring cells (the number of markers is technically unlimited). If needed, the App provides a separation of nuclei in tissues with high cellular densities. The App outputs parameters such as staining intensity per marker and morphometric parameters for each segmented cell/cell compartment, as well as the number and percentage of cells of different phenotypes in direct contact.

Images: courtesy of Naoki Kaneko/Shiv Pillai (PI), Ragon Institute of MGH, MIT and Harvard, Boston, MA USA.

App Category 3

The App was used in a CELL publication, read more

EBER-ISH

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The EBER-ISH App analyzes tissue samples stained by EBER-ISH probes (EBV-encoded RNA in-situ hybridization). These probes visualize the Epstein-Barr virus (EBV) EBER RNA. First, the App identifies nuclei, then detects EBER-ISH positive nuclei. The measurements provided by the App include the number of detected cells and the number, density, and percentage of EBER-ISH positive cells.

App Category 1

IF CELL CULTURE - OSTEOCLAST

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The IF Cell Culture - Osteoclast App allows for the segmentation of nuclei, the identification of cultured multinucleated osteoclasts (stained by a specific marker), and the quantification of one or two additional markers. Outputs include the number of detected cells, osteoclasts, and nuclei per osteoclast, the area of osteoclasts, and the intensity of markers within the osteoclasts.

App Category 2

read more about Automated Detection and Characterization of Osteoclasts in Microscopic Images

ORGANOID

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The Organiod App detects cultured organoids using the machine learning classifier. It outputs number and total area (µm2) of organiods and categorizes them into different size classes.

App Category 3

IF BRAIN

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The IF Brain App allows the classification (using the AI based classifier) of brain regions and detection of various cellular phenotypes, e.g. astrocytes, based on stained linage markers. The App outputs area (µm²) of detected tissues classes, count of total cells as well as in each detected area. Count and % of specific phenotype detected in total as well as in tissue classes.

App Category 3

IF EMBRYOID BODIES

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The IF Embryoid Bodies App automatically detects embryoid bodies/organoids based on IF staining. It identifies nuclei based on DAPI staining or other nuclei dye and identifies additional phenotype markers in the nuclei/cell and/or membrane of the detected cells. It outputs number and area (µm²) of detected embryoid bodies/organoids, count of nuclei and count/% of cellular phenotypes.

App Category 2

IF INSULIN ISLETS

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The IF Insulin Islet App allows for detection marker stained insulin islets, the whole tissue, and cellular phenotypes stained by specific markers within the insulin islets. The App outputs, whole tissue area (µm²), number and area (µm²) of detected insulin islets. Number of cells and marker-specific phenotypes in the whole tissue as well as within the insulin islets. Image provided by Emma Hamilton-Williams.

App Category 3

MUCIN SWISS ROLL

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The Mucin Swiss Roll App allows for detection of the swiss roll, and the segmentation into different subclasses (mucosa, immune cell follicles, connective tissue, background). Further it detects nuclei and (e.g. PAS stained) mucin. The App outputs area (µm²) of detected tissues/tissue classes, count of total cells and in each detected area as well as the area of stained mucin in the entire tissue and within the subclasses..

App Category 2

IHC SWISS ROLL

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The IHC Swiss Roll App allows for detection of the swiss roll, and the segmentation into different subclasses (mucosa, immune cell follicles, connective tissue, background). Further it detects nuclei and identifies phenotypes based on specific stains. The App outputs area (µm²) of detected tissues/tissue classes, count of total cells as well as in each detected area. Count and % of specific phenotype detected in total as well as in the tissue classes.

App Category 2

IF SWISS ROLL

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The IF Swiss Roll App allows for detection of the swiss roll, and the segmentation into different subclasses (mucosa, immune cell follicles, connective tissue, background). Further it detects nuclei and identifies phenotypes based on specific IF stains. The App outputs area (µm²) of detected tissues/tissue classes, count of total cells as well as in each detected area. Count and % of specific phenotype detected in total as well as in the tissue classes.

App Category 2

IHC INSULIN ISLETS

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The IHC Insulin Islet App allows for detection of marker stained insulin islets, the whole tissue, and cellular phenotypes stained by specific markers within the insulin islets and in the tissue. The App outputs, whole tissue area (µm²), number and area (µm²) of detected insulin islets. Number of cells and marker-specific phenotypes in the whole tissue as well as within the insulin islets.

App Category 2

IHC ADIPOCYTES +

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The IHC Adipocytes+ App identifies adipocytes and cellular aggregates inbetween the adipocytes. Small rips in adipocyte membranes are mended automatically and cell membrane artefacts in adipocyte lumina are automatically eliminated. The App outputs number and area measurements for all detected adipocytes as well as number and area of cellular aggregates.

App Category 2

IHC MICROGLIA

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The IHC Microglia App detects microglia soma based on a specifc staining and further identifies branches as well as primary and secondary branching points. It outputs number and area of cells, number of primary and secondary branching points as well as area and lenght of the detected branches..

App Category 3

IHC LUNG CANCER MOUSE

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The IHC Lung Cancer mouse App is using the machine learning base classifier to segment murine lung cancer tissue sections into tumor and non-cancerous tissue. Further it detects nuclei based on hematoxylin staining and identifies cellular phenotypes based on speicifc markers. It outputs number and area of tumor tissue as well as total number of cells and detected cellular phenotypes.

App Category 3

IHC MEGAKARYOCYTES

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The IHC Megakaryocytes App allows for detection of megakaryocytes based on specific marker staining. It outputs number and size of detected megakaryocytes as well as the number of megakaryocytes that contain neutrophils inside their cytoplasm and the number of neutrophils inside each megakaryocyte. Image courtesy: Prof Wendy Erber and A/Prof Kathy Fuller, The University of Western Australia

App Category 2