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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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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
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Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
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Related Experiment Video

Updated: Apr 24, 2026

Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging

Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging

Published on: April 30, 2019

CellMap: precision mapping of cellular landscape in spatial transcriptomics.

Hongjia Liu¹, Huamei Li², Amit Sharma³

¹State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing 211189, PR China.

Nucleic Acids Research

|January 8, 2026

Summary

This summary is machine-generated.

CellMap is a new computational tool that precisely maps single cells within spatial transcriptomic data. This method enhances the exploration of cellular mixtures, outperforming existing techniques for spatial biology research.

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Last Updated: Apr 24, 2026

Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging

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Published on: April 30, 2019

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Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

Area of Science:

Computational Biology
Genomics
Spatial Transcriptomics

Background:

Single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics are crucial for understanding cellular heterogeneity.
Integrating these technologies is essential for detailed cellular mixture analysis.
Current methods face challenges in resolving spatial transcriptomic data to single-cell resolution.

Purpose of the Study:

To develop a computational tool, CellMap, for resolving spatial transcriptomic spots to single-cell resolution.
To enable precise mapping of individual cells within their spatial context.
To improve the analysis of complex cellular microenvironments.

Main Methods:

CellMap integrates scRNA-seq and spatial transcriptomics data.
It employs seed gene co-linearity, a random forest model, and a linear assignment algorithm.
The tool assigns single cells to spatial spots with high accuracy.

Main Results:

CellMap successfully resolves spatial transcriptomic spots to single-cell resolution.
Benchmarking across diverse platforms and tissue types demonstrated superior performance.
The tool accurately maps cell types and their locations within tissues.

Conclusions:

CellMap provides a robust computational solution for single-cell resolution of spatial transcriptomics.
This advancement facilitates deeper insights into tissue architecture and cell-cell interactions.
CellMap outperforms existing methods, setting a new standard in spatial biology analysis.