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Imaging Biological Samples with Optical Microscopy

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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Multiplexed Barcoding Image Analysis for Immunoprofiling and Spatial Mapping Characterization in the Single-Cell Analysis of Paraffin Tissue Samples

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SCIMAP: A Python Toolkit for Integrated Spatial Analysis of Multiplexed Imaging Data.

Ajit J Nirmal, Peter K Sorger

|May 15, 2024

Summary

This summary is machine-generated.

SCIMAP is a new Python package for analyzing multiplexed imaging data, enabling efficient exploration of cell spatial relationships in tissues and tumors. It integrates visualization and analysis for large datasets, advancing tissue profiling research.

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Cortical Actin Flow in T Cells Quantified by Spatio-temporal Image Correlation Spectroscopy of Structured Illumination Microscopy Data

Cortical Actin Flow in T Cells Quantified by Spatio-temporal Image Correlation Spectroscopy of Structured Illumination Microscopy Data

Published on: December 17, 2015

Area of Science:

Computational Biology
Bioinformatics
Pathology

Background:

Multiplexed imaging generates large-scale cellular data, crucial for understanding tissue and tumor composition.
Quantifying spatial relationships among cells is vital for tissue profiling but computationally intensive.
Existing tools often lack seamless integration of image visualization and analysis for complex multiplexed imaging data.

Approach:

Introduced SCIMAP, a Python package designed for the analysis of multiplexed imaging data.
SCIMAP enables efficient preprocessing, analysis, and visualization of large cell datasets (10^7+ cells, 100+ biomolecules).
The package facilitates exploration of spatial relationships and their statistical significance at multiple scales.

Key Points:

SCIMAP addresses the need for specialized toolkits for multiplexed imaging data analysis.
It seamlessly integrates image visualization with data analysis and exploration.
The modular design allows for the integration of new algorithms to enhance spatial analysis capabilities.

Conclusions:

SCIMAP empowers researchers to efficiently analyze complex multiplexed imaging datasets.
It advances the understanding of cellular composition and spatial organization in tissues and tumors.
The tool facilitates the discovery of spatial patterns and their statistical significance.