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Related Concept Videos

CRISPR and crRNAs02:53

CRISPR and crRNAs

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Bacteria and archaea are susceptible to viral infections just like eukaryotes; therefore, they have developed a unique adaptive immune system to protect themselves. Clustered regularly interspaced short palindromic repeats and CRISPR-associated proteins (CRISPR-Cas) are present in more than 45% of known bacteria and 90% of known archaea.
The CRISPR-Cas system stores a copy of foreign DNA in the host genome and uses it to identify the foreign DNA upon reinfection. CRISPR-Cas has three different...
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Detecting subtle transcriptomic perturbations induced by lncRNAs knock-down in single-cell CRISPRi screening using a

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  • 1Université Côte d'Azur, IPMC, UMR CNRS 7275 Inserm 1323, IHU RespiERA, Valbonne, France.

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|March 19, 2024
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Summary

We developed a Sparse Supervised Autoencoder (SSAE) to detect subtle gene expression changes from single-cell CRISPR screens. This method enhances the analysis of long non-coding RNAs in lung cancer under hypoxia.

Keywords:
CRISPRihypoxialncRNAssingle-cell RNA-seqsparse supervised autoencoder

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Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • Single-cell CRISPR screens are powerful for gene function studies but struggle with detecting weak perturbations.
  • Existing methods are less sensitive for subtle gene expression changes, limiting the study of complex biological processes like hypoxia.
  • Master regulators like transcription factors are reliably studied, but other gene classes, such as long non-coding RNAs, remain challenging.

Purpose of the Study:

  • To develop a novel computational method for enhancing sensitivity in single-cell CRISPR screens.
  • To identify subtle transcriptomic perturbations induced by specific genetic modifications.
  • To improve the classification of cells most responsive to genetic perturbations, particularly focusing on long non-coding RNAs in lung adenocarcinoma.

Main Methods:

  • Development of a Sparse Supervised Autoencoder (SSAE) neural network for feature and cell selection.
  • Application of SSAE to CRISPR interference (CRISPRi)-based sequencing (CROP-Seq) data from lung adenocarcinoma cells.
  • Utilized a library targeting long non-coding RNAs and hypoxia-inducible factors (HIF1A, HIF2A) under varying hypoxic conditions.

Main Results:

  • The SSAE method successfully validated the effects of HIF1A and HIF2A knockdown on hypoxic response.
  • SSAE detected stable, hypoxia-dependent transcriptomic signatures induced by lncRNA knockdown.
  • The approach demonstrated superior performance compared to previous machine learning methods in identifying weak perturbations.

Conclusions:

  • The SSAE method significantly improves the detection of subtle transcriptomic changes in single-cell CRISPR screening data.
  • This approach is effective for deciphering the functional roles of long non-coding RNAs in complex biological contexts like hypoxia-driven lung cancer.
  • SSAE offers a robust tool for advancing genetic screening and understanding gene function from transcriptomic readouts.