Author Correction: Combinatorial expression of GPCR isoforms affects signalling and drug responses

Martí-Solano, Maria; Crilly, Stephanie E; Malinverni, Duccio; Munk, Christian; Harris, Matthew; Pearce, Abigail; Quon, Tezz; Mackenzie, Amanda E.; Wang, Xusheng; Peng, Junmin; Tobin, Andrew B.; Ladds, Graham; Milligan, Graeme; Gloriam, David E.; Puthenveedu, Manojkumar A.; Babu, M. Madan

doi:10.1038/s41586-020-2999-9

Cited by 5 publications

(2 citation statements)

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“…In order to select interactors to include in model training, we mined the list of pruned BioGRID interactions and selected proteins interacting with at least one of the 78 TF characterized by CRISPRburst, and at least one TF absent from the CRISPRburst dataset. Out of this set, we only included proteins expressed in HeLa cells, defined based on their RNA expression levels above a log(pTPM+1) threshold of 0.1 in a public RNA-seq dataset (Human Protein Atlas cell line data) 65 . Self-interactions were assigned to each TF equal to the maximum number of evidence that TF has for any factor.…”

Section: Methodsmentioning

confidence: 99%

The kinetic landscape of human transcription factors

Mamrak

Alerasool

Griffith

et al. 2022

Preprint

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Cell-to-cell variability is shaped by transcription dynamics because genes are transcribed in bursts interspersed with inactive periods. The stochasticity of bursting means that genes transcribed in rare bursts exhibit more heterogeneity at the single cell level than genes that burst often1,2. Transcription starts with the binding of Transcription Factors (TFs) to specific sequence motifs where they recruit the transcription machinery3. In some systems, individual TF binding events temporally correlate with the firing of transcriptional bursts, defining the target gene's frequency and duration4-6. However, in the absence of methods that assess the impact of different TFs on transcription dynamics at the same genetic loci, it remains unclear whether DNA binding kinetics are the sole determinant of bursting. Here we develop an imaging-based synthetic recruitment assay, CRISPRburst, and measure how 92 human TFs impact bursting kinetics. We show that TFs recruited to chromatin under identical conditions generate diverse bursting signatures, some TFs increasing the probability of the gene turning on while others increase the number of mRNA molecules transcribed per burst. We find that the association of TFs with specific protein partners determines their bursting output, and train a model to predict the kinetic signatures of all human TFs. These kinetic signatures can be used as a TF classification system complementary to existing families based on DNA binding domains. Additionally, kinetic signatures provide a rational framework to design synthetic activators, model transcription regulation, and understand expression heterogeneity.

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Section: Methodsmentioning

confidence: 99%

The kinetic landscape of human transcription factors

Mamrak

Alerasool

Griffith

et al. 2022

Preprint

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“…We aggregated DINO features to build cell-line profiles (Methods) and reveal their similarities (Figure 4A). For comparison, we created the cell-line similarity matrix according to transcriptional (RNAseq) profiles available through HPA 61 and used the Mantel similarity 62 to estimate their correlation (Methods). The RNAseq matrix has a Mantel statistic of 0.47 with the DINO matrix; higher than the statistic with the matrix of supervised CNN features (0.31) and the matrix of ImageNet DINO features (0.40).…”

Section: Dino Enables Unbiased Profiling Of Cellular Morphologymentioning

confidence: 99%

Unbiased single-cell morphology with self-supervised vision transformers

Doron

Moutakanni²,

Chen

et al. 2023

Preprint

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Accurately quantifying cellular morphology at scale could substantially empower existing single-cell approaches. However, measuring cell morphology remains an active field of research, which has inspired multiple computer vision algorithms over the years. Here, we show that DINO, a vision-transformer based, self-supervised algorithm, has a remarkable ability for learning rich representations of cellular morphology without manual annotations or any other type of supervision. We evaluate DINO on a wide variety of tasks across three publicly available imaging datasets of diverse specifications and biological focus. We find that DINO encodes meaningful features of cellular morphology at multiple scales, from subcellular and single-cell resolution, to multi-cellular and aggregated experimental groups. Importantly, DINO successfully uncovers a hierarchy of biological and technical factors of variation in imaging datasets. The results show that DINO can support the study of unknown biological variation, including single-cell heterogeneity and relationships between samples, making it an excellent tool for image-based biological discovery.

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Assessing Conservation of Alternative Splicing with Evolutionary Splicing Graphs

Zea

Laskina

Richard

et al. 2020

Preprint

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Understanding how protein function has evolved and diversified is of great importance for human genetics and medicine. Here, we tackle the problem of describing the whole transcript variability observed in several species by generalising the definition of splicing graph. We provide a practical solution to building parsimonious evolutionary splicing graphs where each node is a minimal transcript building block defined across species. We show a clear link between the functional relevance, tissue-regulation and conservation of AS events on a set of 50 genes. By scaling up to the whole human protein-coding genome, we identify a few thousands of genes where alternative splicing modulates the number and composition of pseudo-repeats. We have implemented our approach in ThorAxe, an efficient, versatile, and robust computational tool freely available at https://github.com/PhyloSofS-Team/thoraxe. The results are accessible and can be browsed interactively at http://www.lcqb.upmc.fr/ThorAxe.

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Author Correction: Combinatorial expression of GPCR isoforms affects signalling and drug responses

Cited by 5 publications

References 0 publications

The kinetic landscape of human transcription factors

The kinetic landscape of human transcription factors

Unbiased single-cell morphology with self-supervised vision transformers

Assessing Conservation of Alternative Splicing with Evolutionary Splicing Graphs

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