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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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Updated: May 3, 2026

Single-cell Profiling of Developing and Mature Retinal Neurons

Single-cell Profiling of Developing and Mature Retinal Neurons

Published on: April 19, 2012

scSemiProfiler: Advancing large-scale single-cell studies through semi-profiling with deep generative models and

Jingtao Wang^1,2, Gregory J Fonseca^1,2,3, Jun Ding^4,5,6,7,8

¹Meakins-Christe Laboratories, Research Institute of McGill University Health Centre, 1001 Decarie Blvd, Montreal, H4A 3J1, Quebec, Canada.

Nature Communications

|July 16, 2024

Summary

This summary is machine-generated.

scSemiProfiler offers a cost-effective solution for single-cell profiling by integrating bulk and limited single-cell sequencing data. This computational framework enables detailed cellular analysis in large biomedical studies, overcoming cost barriers.

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Last Updated: May 3, 2026

Single-cell Profiling of Developing and Mature Retinal Neurons

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Published on: April 19, 2012

A Versatile Automated Platform for Micro-scale Cell Stimulation Experiments

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An Ultrahigh-throughput Microfluidic Platform for Single-cell Genome Sequencing

An Ultrahigh-throughput Microfluidic Platform for Single-cell Genome Sequencing

Published on: May 23, 2018

Area of Science:

Genomics
Computational Biology
Biomedical Research

Background:

Single-cell sequencing is vital for understanding complex diseases but is often cost-prohibitive for large studies.
Existing bulk sequencing deconvolution methods lack single-cell resolution.

Purpose of the Study:

To introduce scSemiProfiler, a novel computational framework for cost-effective single-cell profiling.
To enable detailed cellular analysis in large-scale biomedical research.

Main Methods:

Integration of deep generative models and active learning strategies.
Fusion of bulk sequencing data with targeted single-cell sequencing from representative samples.

Main Results:

scSemiProfiler accurately infers single-cell profiles from combined data.
Validation across diverse datasets shows high concordance with true single-cell profiling.
The approach facilitates refined cellular analyses.

Conclusions:

scSemiProfiler provides a scalable and economical alternative for single-cell profiling.
This method empowers in-depth cellular investigation across various biological domains, especially in disease cohorts.