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Cell Size

Cell Size

Cell sizes vary widely among and within organisms. Bacterial cells range between 1-10 micrometers (μm)and are considerably smaller than most eukaryotic cells. The smallest bacteria are 0.1 μm in diameter—about a thousand times smaller than eukaryotic cells, which typically range from 10-100 μm.
Surface Area
Cells can take in nutrients and water via diffusion through the plasma membrane itself or through specific channels in the membrane. The area of the membrane surrounding...

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Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone

Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone

Published on: April 8, 2015

Estimating real cell size distribution from cross-section microscopy imaging.

Michael Lenz¹, Nadia J T Roumans², Roel G Vink²

¹Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands.

Bioinformatics (Oxford, England)

|September 3, 2016

Summary

This summary is machine-generated.

Microscopy imaging can be biased by cell size. The EstiTiCS algorithm corrects for this bias, improving cell size and type distribution estimations in biological samples like adipose tissue. This method enhances accuracy in medical diagnosis and molecular biology research.

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Area of Science:

Microscopy and computational biology
Biomedical imaging analysis
Quantitative pathology

Background:

Microscopy is vital for medical diagnosis and molecular biology, providing insights into disease states and cellular parameters.
Existing imaging methods are prone to sampling and observational biases, particularly affecting the accurate estimation of cell size and type distributions.
These biases can lead to underestimation of smaller cells and inaccurate measurements of cell size.

Purpose of the Study:

To introduce an algorithm, Estimate Tissue Cell Size/Type Distribution (EstiTiCS), to correct for biases in microscopy-based cell size and type estimations.
To account for section thickness independently of tissue type, thereby improving the accuracy of cell distribution analysis.
To validate the algorithm's performance using simulation experiments and real-world histological data.

Main Methods:

Development of the EstiTiCS algorithm for bias adjustment in cell size and type distribution analysis.
Utilizing simulation experiments to model and understand bias sources in various tissue distributions.
Application of the algorithm to histological sections of adipose tissue from a dietary intervention study.

Main Results:

The EstiTiCS algorithm effectively adjusts for the underestimation of small cells and the size of measured cells.
Bias correction using EstiTiCS resulted in a distribution closer to the true/expected adipocyte size.
The method demonstrated robustness across different tissue types and section thicknesses.

Conclusions:

EstiTiCS provides a reliable method for correcting observational bias in microscopy imaging of cell size and type.
The algorithm is suitable as a final step in pipelines for estimating tissue-wide cell distributions.
Accurate cell size and type distribution analysis is crucial for advancing medical diagnosis and molecular biology research.