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DNA Microarrays

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

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Related Experiment Video

Updated: Feb 16, 2026

Counting Proteins in Single Cells with Addressable Droplet Microarrays

Counting Proteins in Single Cells with Addressable Droplet Microarrays

Published on: July 6, 2018

Evaluating single molecule detection methods for microarrays with high dynamic range for quantitative single cell

Ali Salehi-Reyhani¹

¹Department Chemistry, Institute of Chemical Biology, Imperial College London, London, SW7 2AZ, UK. ali.salehi-reyhani@imperial.ac.uk.

Scientific Reports

|December 22, 2017

Summary

This summary is machine-generated.

Accurate single molecule counting in microarrays is vital for single cell proteomics. Super-resolution imaging enhances accuracy, especially with high protein variability and crowded spots, improving quantitative analysis.

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Last Updated: Feb 16, 2026

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Published on: July 6, 2018

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A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

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Published on: October 25, 2018

Area of Science:

Biochemistry
Proteomics
Microarray Technology

Background:

Single molecule microarrays enable high-sensitivity quantitative proteomics.
Accurate enumeration of single molecules is critical for single-cell analysis.
Significant protein abundance variations in single cells pose challenges for image analysis.

Purpose of the Study:

To evaluate image analysis methods for accurate single molecule enumeration on microarrays.
To assess method performance across a wide range of molecule counts and signal-to-noise ratios.
To determine the impact of super-resolution techniques on quantitative single-cell proteomics.

Main Methods:

Generation of synthetic microarray image datasets with known ground truth.
Inclusion of realistic single-cell protein abundance distributions in datasets.
Evaluation of various image analysis algorithms, including super-resolution methods.

Main Results:

Super-resolution image analysis significantly improves counting accuracy.
Advanced methods better handle high densities of single molecules per spot.
Performance was assessed across five orders of magnitude of molecule counts.

Conclusions:

Quantitative single-cell analysis using microarrays presents significant challenges.
Super-resolution imaging offers a promising approach to overcome limitations in molecule counting.
Method selection is crucial for reliable microarray-based single-cell proteomics.