inForm Tissue Finder

Part Number: CLS135783

inForm® Tissue Finder adds exceptional functionality to inForm Cell Analysis to automate the detection and segmentation of specific tissues through powerful patented pattern recognition algorithms. Automation provides consistent reproducible results and enables comparative studies of multiple markers and specimens, supporting researchers to make faster discoveries of the indicators of disease.

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Detail Information

Once trained, inForm will locate and analyze user-specified regions automatically across an entire image or multiple images. Large numbers of images can be rapidly batch processed, allowing analysis that might have taken days to be done in a matter of minutes.

Key Features:

  • Visualizes, analyzes, quantifies and phenotypes immune and other cells in situ in solid tissue and TMAs (H&E, IHC, and IF)
  • Supports MOTiF(TM) workflow solution for whole slide multispectral analysis
  • Pathology Views ™ renders immunofluorescence (IF) images as simulated H&E or DAB and hematoxylin, to provide views more familiar to you
  • Powerful unmixing algorithm enables identification and separation of weakly expressing and overlapping signals from background autofluorescence
  • User-trained feature recognition algorithms allow automatic identification of specific tissue types based on tissue morpholog
  • Adaptive Cell Segmentation reliably identifies individual cell types in densely packed, complex morphologies regardless of staining heterogeneity and background levels for accurate phenotyping and downstream spatial analysis in phenoptrReports
  • Automatically classifies cell phenotypes using machine-learning algorithms to differentiate cell types across a tissue section
  • Scoring (% positivity, 0/1+/2+/3+, co-localization and more)
  • Batch processing of images using customizable image analysis workflows
  • Review and merge data from a set of images or slides into summary data files to assure data quality

Resources, Events & More

Application Note

Detecting Phenotypical Subgroups in Breast Cancer using Multiplexed Protein Expression Analysis in Intact Tissue Sections

Cancer immunotherapy is rapidly, changing the landscape of cancer,treatment, with checkpoint inhibitors,such as those targeting CTLA-4 and PD-L1, activated T cell therapies and a range of,other combinatorial approaches providing significantly longer benefits for patients,compared to small molecule inhibitor and targeted therapy approaches1,2. However,response rates range from 15 – 30%, pointing to the need for the use of biomarkers for,better stratification. “A more complete understanding of the cellular and molecular,components of the tumor-immune system interaction is crucial to the development of,rational and efficacious immunotherapies in the future3.” While quantifying the number,of specific subsets of immune cells in blood is routine using multimarker flow cytometry,monitoring these same subsets of immune cells in solid tumors remains unobtainable,with standard methods. Methods that involve decomposing the tumor and releasing the,immune cells for flow cytometric analyses are difficult. Even when working properly with,no biasing of the cell populations, these methods cannot reveal the contextual spatial,relationships and the functional states of the immune cells within and around the tumor,which leading researchers today believe may contain vital information needed to predict,response to specific immunotherapies.

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Scientific Paper