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Three-Dimensional Microscopy in Microbiology01:28

Three-Dimensional Microscopy in Microbiology

Three-dimensional imaging techniques are essential in cell biology, allowing researchers to visualize intricate cellular structures with high resolution. Two prominent methods, Differential Interference Contrast Microscopy (DIC) and Confocal Scanning Laser Microscopy (CSLM), provide distinct advantages for imaging live and thick specimens, respectively.Differential Interference Contrast MicroscopyDIC microscopy enhances contrast in transparent, unstained samples by converting phase...
Two-Dimensional Microscopy in Microbiology01:29

Two-Dimensional Microscopy in Microbiology

Two-dimensional (2D) microscopy encompasses a range of optical techniques that capture images within a single focal plane, offering detailed representations of microscopic structures. These techniques are essential in biological and medical research, enabling the visualization of cellular and subcellular structures with different levels of contrast and specificity.There are several major types of 2D microscopy, each with strengths and applications.Bright-Field MicroscopyBright-field microscopy...
Imaging Biological Samples with Optical Microscopy01:18

Imaging Biological Samples with Optical Microscopy

Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...

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Related Experiment Video

Updated: Jun 30, 2026

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning
09:31

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

Published on: April 28, 2022

[Virtual microscopy in systems pathology].

N Grabe1

  • 1Institut für Medizinische Biometrie und Informatik, Sektion Medizinische Informatik, Universitätsklinikum Heidelberg, Im Neuenheimer Feld 305, 69120 Heidelberg. niels.grabe@med.uni-heidelberg.de

Der Pathologe
|September 30, 2008
PubMed
Summary
This summary is machine-generated.

Systems pathology integrates gene regulatory and spatial models for molecular disease insights. This approach, using fluorescent virtual microscopy, enhances diagnosis, prognosis, and therapy by modeling biomarker expression in tissues.

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Last Updated: Jun 30, 2026

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning
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Expanding the Comprehension of the Tumor Microenvironment using Mass Spectrometry Imaging of Formalin-Fixed and Paraffin-Embedded Tissue Samples
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Published on: January 19, 2018

Area of Science:

  • Integrative biology
  • Systems biology
  • Systems pathology

Context:

  • Genomics and proteomics have advanced towards systems biology, focusing on molecular network models for biological systems.
  • Systems biology aims to deepen functional understanding and improve interpretation of quantitative measurements.
  • Systems pathology extends systems biology by incorporating spatial modeling of tissue alterations alongside gene regulatory networks.

Purpose:

  • To create a generic process for systems pathology that integrates gene regulatory and morphological models.
  • To develop molecular disease models for improved diagnosis, prognosis, and therapy.
  • To demonstrate the utility of fluorescent virtual microscopy in capturing high-resolution spatial and molecular tissue data.

Summary:

  • A novel process for systems pathology is presented, merging gene regulatory and morphological models into comprehensive molecular disease models.
  • Fluorescent virtual microscopy is identified as a key technology for acquiring high-throughput, high-resolution morphological and molecular data from tissues.
  • The process is exemplified using epidermal differentiation, showing how spatiotemporal biomarker expression can be modeled by reconstructing protein networks from fluorescent tissue sections.

Impact:

  • Enables deeper functional understanding of biological systems through integrated modeling.
  • Facilitates improved diagnosis, prognosis, and therapeutic strategies by deploying advanced disease models.
  • Advances the field of systems pathology by providing a framework for integrating diverse biological data types for disease research.