Genome-wide CRISPR interference screen identifies long non-coding RNA loci required for differentiation and pluripotency

Abstract

Although many long non-coding RNAs (lncRNAs) exhibit lineage-specific expression, the vast majority remain functionally uncharacterized in the context of development. Here, we report the first described human embryonic stem cell (hESC) lines to repress (CRISPRi) or activate (CRISPRa) transcription during differentiation into all three germ layers, facilitating the modulation of lncRNA expression during early development. We performed an unbiased, genome-wide CRISPRi screen targeting thousands of lncRNA loci expressed during endoderm differentiation. While dozens of lncRNA loci were required for proper differentiation, most differentially expressed lncRNAs were not, supporting the necessity for functional screening instead of relying solely on gene expression analyses. In parallel, we developed a clustering approach to infer mechanisms of action of lncRNA hits based on a variety of genomic features. We subsequently identified and validated FOXD3-AS1 as a functional lncRNA essential for pluripotency and differentiation. Taken together, the cell lines and methodology described herein can be adapted to discover and characterize novel regulators of differentiation into any lineage.

Integration of transcription regulation and functional genomic data reveals lncRNA SNHG6’s role in hematopoietic differentiation and leukemia

“…We then trained the ML algorithm using a set of functionally validated lncRNAs. Although many human lncRNAs have been demonstrated to be functional in various contexts, such as cell proliferation [ 24 , 57 ], differentiation [ 26 , 58 ], and disease [ 20 , 59 , 60 ], these lncRNAs have often been studied in different cell types and under varying experimental conditions, resulting in scattered data.…”

“…To train our algorithm, we incorporated data from three genetic screens [24][25][26] that targeted 16,401 lncR-NAs across seven cell lines. However, these data are imbalanced since only 9% (n = 1451) of the lncRNAs were identified as functional.…”

“…To bridge this gap, we developed an ML model known as INFLAMeR (Identifying Novel Functional LncRNAs with Advanced Machine Learning Resources). The model was based on 143 genetic features that focus on cell type-specific regulatory mechanisms and was trained on previous CRISPRi screens [ 24 – 26 ]. INFLAMeR successfully replicated the CRISPRi screening results and identified additional lncRNAs with high prediction scores.…”

Integration of transcription regulation and functional genomic data reveals lncRNA SNHG6’s role in hematopoietic differentiation and leukemia

“…(a) Workflow for designing the INFLAMeR (Identifying Novel Functional LncRNAs with Advanced Machine learning Resources) algorithm and selecting targets. Upper: The algorithm was trained on previous high-throughput pooled CRISPR interference (CRISPRi) screening data from three previous high-throughput CRISPRi screens [24–26]; the algorithm was computed based on a total of 71 features comprising ENCODE ChIP-Seq transcription factor (TF) binding data for the regions surrounding lncRNA promoters [50–52] and genomic features. Lower: targets predicted to be functional by INFLAMeR in the K562 cell line (INFLAMeR score > 0.5) were selected for validation after excluding lncRNAs that were functionally characterized in previous studies, those with low expression, those not annotated in the Ensembl database, and those neighboring a protein-coding (PC) gene; thirty-nine lncRNAs were selected for validation.…”

“…To train our algorithm, we incorporated data from three genetic screens [24][25][26] that targeted 16,401 lncRNAs across seven cell lines. However, these data are imbalanced since only 9% (n = 1,451) of the lncRNAs were identified as functional.…”