Direct-seq: programmed gRNA scaffold for streamlined scRNA-seq in CRISPR screen.
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ABSTRACT:
SUBMITTER: Song Q
PROVIDER: S-EPMC7278172 | biostudies-literature | 2020 Jun
REPOSITORIES: biostudies-literature
Publications
Direct-seq: programmed gRNA scaffold for streamlined scRNA-seq in CRISPR screen.
Song Qingkai Q Ni Ke K Liu Min M Li Yini Y Wang Lixia L Wang Yingying Y Liu Yingzheng Y Yu Zhenxing Z Qi Yinyao Y Lu Zhike Z Ma Lijia L
Genome biology 20200608 1
CRISPR-based genome perturbation provides a new avenue to conveniently change DNA sequences, transcription, and epigenetic modifications in genetic screens. However, it remains challenging to assay the complex molecular readouts after perturbation at high resolution and at scale. By introducing an A/G mixed capture sequence into the gRNA scaffold, we demonstrate that gRNA transcripts could be directly reverse transcribed by poly (dT) primer together with the endogenous mRNA, followed by high-con ...[more]
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| S-EPMC7141853 | biostudies-literature
Project description:Single-cell technologies are emerging fast due to their ability to unravel the heterogeneity of biological systems. While scRNA-seq is a powerful tool that measures whole-transcriptome expression of single cells, it lacks their spatial localization. Novel spatial transcriptomics methods do retain cells spatial information but some methods can only measure tens to hundreds of transcripts. To resolve this discrepancy, we developed SpaGE, a method that integrates spatial and scRNA-seq datasets to predict whole-transcriptome expressions in their spatial configuration. Using five dataset-pairs, SpaGE outperformed previously published methods and showed scalability to large datasets. Moreover, SpaGE predicted new spatial gene patterns that are confirmed independently using in situ hybridization data from the Allen Mouse Brain Atlas.
| S-EPMC7544237 | biostudies-literature
Project description:BackgroundSingle-cell RNA sequencing (scRNA-seq) has emerged has a main strategy to study transcriptional activity at the cellular level. Clustering analysis is routinely performed on scRNA-seq data to explore, recognize or discover underlying cell identities. The high dimensionality of scRNA-seq data and its significant sparsity accentuated by frequent dropout events, introducing false zero count observations, make the clustering analysis computationally challenging. Even though multiple scRNA-seq clustering techniques have been proposed, there is no consensus on the best performing approach. On a parallel research track, self-supervised contrastive learning recently achieved state-of-the-art results on images clustering and, subsequently, image classification.ResultsWe propose contrastive-sc, a new unsupervised learning method for scRNA-seq data that perform cell clustering. The method consists of two consecutive phases: first, an artificial neural network learns an embedding for each cell through a representation training phase. The embedding is then clustered in the second phase with a general clustering algorithm (i.e. KMeans or Leiden community detection). The proposed representation training phase is a new adaptation of the self-supervised contrastive learning framework, initially proposed for image processing, to scRNA-seq data. contrastive-sc has been compared with ten state-of-the-art techniques. A broad experimental study has been conducted on both simulated and real-world datasets, assessing multiple external and internal clustering performance metrics (i.e. ARI, NMI, Silhouette, Calinski scores). Our experimental analysis shows that constastive-sc compares favorably with state-of-the-art methods on both simulated and real-world datasets.ConclusionOn average, our method identifies well-defined clusters in close agreement with ground truth annotations. Our method is computationally efficient, being fast to train and having a limited memory footprint. contrastive-sc maintains good performance when only a fraction of input cells is provided and is robust to changes in hyperparameters or network architecture. The decoupling between the creation of the embedding and the clustering phase allows the flexibility to choose a suitable clustering algorithm (i.e. KMeans when the number of expected clusters is known, Leiden otherwise) or to integrate the embedding with other existing techniques.
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