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Dissecting splicing decisions and cell-to-cell variability with designed sequence libraries.

Martin Mikl1,2,3, Amit Hamburg4,5, Yitzhak Pilpel6

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This summary is machine-generated.

Researchers engineered over 32,000 splicing events to understand gene regulation. This study reveals how DNA sequences and cellular variability influence alternative splicing, enabling accurate prediction of splice isoform ratios.

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Area of Science:

  • Molecular Biology
  • Genomics
  • Bioinformatics

Background:

  • Alternative splicing significantly expands the human proteome, but its regulation is complex.
  • Investigating native sequences is challenging due to numerous regulatory inputs.
  • Understanding splicing regulation is crucial for deciphering gene expression and protein diversity.

Purpose of the Study:

  • To dissect the complexity of splicing regulation by creating a large, rationally designed library of splicing events.
  • To investigate the cause and effect of splicing decisions by measuring both RNA and protein splice isoforms.
  • To develop accurate predictive models for isoform ratios based on sequence and structure.

Main Methods:

  • Systematic sequence alterations were introduced to generate a library of >32,000 splicing events.
  • RNA and protein splice isoforms were measured to quantify regulatory inputs.
  • Machine learning models were used to predict isoform ratios (R² = 0.73-0.85) from sequence and secondary structure data.
  • Single-cell profiling was employed to measure cell-to-cell variability in splicing decisions.

Main Results:

  • Accurate prediction of isoform ratios was achieved using sequence and secondary structure information.
  • Diverse regulatory inputs influencing splicing were quantified.
  • Cell-to-cell variability in splicing decisions was measured and found to be encoded in DNA.
  • The study demonstrated that DNA sequence and regulatory inputs influence splicing variability.

Conclusions:

  • Systematic analysis of a large splicing library provides insights into splicing regulation complexity.
  • Predictive models can accurately forecast splice isoform ratios, advancing our understanding of gene expression.
  • Single-cell analysis reveals DNA-encoded variability in splicing, opening new avenues for research.