Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Computational identification of clonal cells in single-cell CRISPR screens.

ABSTRACT:

Background

Single-cell CRISPR screens are powerful tools to understand genome function by linking genetic perturbations to transcriptome-wide phenotypes. However, since few cells can be affordably sequenced in these screens, biased sampling of cells could affect data interpretation. One potential source of biased sampling is clonal cell expansion.

Results

Here, we identify clonal cells in single cell screens using multiplexed sgRNAs as barcodes. We find that the cells in each clone share transcriptional similarities and bear segmental copy number changes. These analyses suggest that clones are genetically distinct. Finally, we show that the transcriptional similarities of clonally expanded cells contribute to false positives in single-cell CRISPR screens.

Conclusions

Experimental conditions that reduce clonal expansion or computational filtering of clonal cells will improve the reliability of single-cell CRISPR screens.

SUBMITTER: Wang Y

PROVIDER: S-EPMC8845350 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Computational identification of clonal cells in single-cell CRISPR screens.

Wang Yihan Y Xie Shiqi S Armendariz Daniel D Hon Gary C GC

BMC genomics 20220215 1

<h4>Background</h4>Single-cell CRISPR screens are powerful tools to understand genome function by linking genetic perturbations to transcriptome-wide phenotypes. However, since few cells can be affordably sequenced in these screens, biased sampling of cells could affect data interpretation. One potential source of biased sampling is clonal cell expansion.<h4>Results</h4>Here, we identify clonal cells in single cell screens using multiplexed sgRNAs as barcodes. We find that the cells in each clon ...[more]

PMID: 35168568

Similar Datasets

Computational identification of clonal cells in single-cell CRISPR screens

Project description:Single-cell CRISPR screens are powerful tools to understand genome function by linking genetic perturbations to transcriptome-wide phenotypes. However, since few cells can be affordably sequenced in these screens, biased sampling of cells could affect data interpretation. One potential source of biased sampling is clonal cell expansion. Here, we present a computational pipeline for clonal cell identification in single cell screens using multiplexed sgRNA barcodes. We find that the cells in each clone share transcriptional similarities and we infer the segmental copy number changes in clonal cells. These analyses suggest that clones are genetically distinct. Finally, we show that the transcriptional similarities of clonally expanded cells contribute to false positives in single-cell CRISPR screens. As a result, experimental conditions that reduce clonal expansion or computational filtering of clonal cells will improve the reliability of single-cell CRISPR screens.

2022-01-25 | GSE185995 | GEO

Computational identification of clonal cells in single-cell CRISPR screens

Project description:Computational identification of clonal cells in single-cell CRISPR screens

| PRJNA771674 | ENA

Protein Barcodes Enable High-Dimensional Single-Cell CRISPR Screens.

Project description:CRISPR pools are being widely employed to identify gene functions. However, current technology, which utilizes DNA as barcodes, permits limited phenotyping and bulk-cell resolution. To enable novel screening capabilities, we developed a barcoding system operating at the protein level. We synthesized modules encoding triplet combinations of linear epitopes to generate >100 unique protein barcodes (Pro-Codes). Pro-Code-expressing vectors were introduced into cells and analyzed by CyTOF mass cytometry. Using just 14 antibodies, we detected 364 Pro-Code populations; establishing the largest set of protein-based reporters. By pairing each Pro-Code with a different CRISPR, we simultaneously analyzed multiple phenotypic markers, including phospho-signaling, on dozens of knockouts. Pro-Code/CRISPR screens found two interferon-stimulated genes, the immunoproteasome component Psmb8 and a chaperone Rtp4, are important for antigen-dependent immune editing of cancer cells and identified Socs1 as a negative regulator of Pd-l1. The Pro-Code technology enables simultaneous high-dimensional protein-level phenotyping of 100s of genes with single-cell resolution.

| S-EPMC6319269 | biostudies-literature

High-throughput single-cell chromatin accessibility CRISPR screens enable unbiased identification of regulatory networks in cancer.

Project description:Chromatin accessibility profiling can identify putative regulatory regions genome wide; however, pooled single-cell methods for assessing the effects of regulatory perturbations on accessibility are limited. Here, we report a modified droplet-based single-cell ATAC-seq protocol for perturbing and evaluating dynamic single-cell epigenetic states. This method (Spear-ATAC) enables simultaneous read-out of chromatin accessibility profiles and integrated sgRNA spacer sequences from thousands of individual cells at once. Spear-ATAC profiling of 104,592 cells representing 414 sgRNA knock-down populations reveals the temporal dynamics of epigenetic responses to regulatory perturbations in cancer cells and the associations between transcription factor binding profiles.

| S-EPMC8137922 | biostudies-literature

On the design of CRISPR-based single-cell molecular screens.

Project description:Several groups recently coupled CRISPR perturbations and single-cell RNA-seq for pooled genetic screens. We demonstrate that vector designs of these studies are susceptible to ?50% swapping of guide RNA-barcode associations because of lentiviral template switching. We optimized a published alternative, CROP-seq, in which the guide RNA also serves as the barcode, and here confirm that this strategy performs robustly and doubled the rate at which guides are assigned to cells to 94%.

| S-EPMC5882576 | biostudies-literature

GiRAFR improves gRNA detection and annotation in single-cell CRISPR screens.

Project description:Novel methods that combine single cell RNA-seq with CRISPR screens enable high-throughput characterization of transcriptional changes caused by genetic perturbations. Dedicated software is however lacking to annotate CRISPR guide RNA (gRNA) libraries and associate them with single cell transcriptomes. Here, we describe a CRISPR droplet sequencing (CROP-seq) dataset. During analysis, we observed that the most commonly used method fails to detect mutant gRNAs. We therefore developed a python tool to identify and characterize intact and mutant gRNAs, called GiRAFR. We show that mutant gRNAs are dysfunctional, and failure to detect and annotate them leads to an inflated estimate of the number of untransformed cells, attenuated downregulation of target genes, as well as an underestimated multiplet frequency. These findings are mirrored in publicly available datasets, where we find that up to 35% of cells are transduced with a mutant gRNA. Applying GiRAFR hence stands to improve the annotation and quality of single cell CRISPR screens.

| S-EPMC10518011 | biostudies-literature

scMAGeCK links genotypes with multiple phenotypes in single-cell CRISPR screens.

Project description:We present scMAGeCK, a computational framework to identify genomic elements associated with multiple expression-based phenotypes in CRISPR/Cas9 functional screening that uses single-cell RNA-seq as readout. scMAGeCK outperforms existing methods, identifies genes and enhancers with known and novel functions in cell proliferation, and enables an unbiased construction of genotype-phenotype network. Single-cell CRISPR screening on mouse embryonic stem cells identifies key genes associated with different pluripotency states. Applying scMAGeCK on multiple datasets, we identify key factors that improve the power of single-cell CRISPR screening. Collectively, scMAGeCK is a novel tool to study genotype-phenotype relationships at a single-cell level.

| S-EPMC6979386 | biostudies-literature

Single-cell CRISPR screens in vivo map T cell fate regulomes in cancer.

Project description:CD8+ cytotoxic T cells (CTLs) orchestrate antitumour immunity and exhibit inherent heterogeneity1,2, with precursor exhausted T (Tpex) cells but not terminally exhausted T (Tex) cells capable of responding to existing immunotherapies3-7. The gene regulatory network that underlies CTL differentiation and whether Tex cell responses can be functionally reinvigorated are incompletely understood. Here we systematically mapped causal gene regulatory networks using single-cell CRISPR screens in vivo and discovered checkpoints for CTL differentiation. First, the exit from quiescence of Tpex cells initiated successive differentiation into intermediate Tex cells. This process is differentially regulated by IKAROS and ETS1, the deficiencies of which dampened and increased mTORC1-associated metabolic activities, respectively. IKAROS-deficient cells accumulated as a metabolically quiescent Tpex cell population with limited differentiation potential following immune checkpoint blockade (ICB). Conversely, targeting ETS1 improved antitumour immunity and ICB efficacy by boosting differentiation of Tpex to intermediate Tex cells and metabolic rewiring. Mechanistically, TCF-1 and BATF are the targets for IKAROS and ETS1, respectively. Second, the RBPJ-IRF1 axis promoted differentiation of intermediate Tex to terminal Tex cells. Accordingly, targeting RBPJ enhanced functional and epigenetic reprogramming of Tex cells towards the proliferative state and improved therapeutic effects and ICB efficacy. Collectively, our study reveals that promoting the exit from quiescence of Tpex cells and enriching the proliferative Tex cell state act as key modalities for antitumour effects and provides a systemic framework to integrate cell fate regulomes and reprogrammable functional determinants for cancer immunity.

| S-EPMC10700132 | biostudies-literature

Clonal dynamics limits detection of selection in tumour xenograft CRISPR/Cas9 screens.

Project description:Transplantable in vivo CRISPR/Cas9 knockout screens, in which cells are edited in vitro and inoculated into mice to form tumours, allow evaluation of gene function in a cancer model that incorporates the multicellular interactions of the tumour microenvironment. To improve our understanding of the key parameters for success with this method, we investigated the choice of cell line, mouse host, tumour harvesting timepoint and guide RNA (gRNA) library size. We found that high gRNA (80-95%) representation was maintained in a HCT116 subline transduced with the GeCKOv2 whole-genome gRNA library and transplanted into NSG mice when tumours were harvested at early (14 d) but not late time points (38-43 d). The decreased representation in older tumours was accompanied by large increases in variance in gRNA read counts, with notable expansion of a small number of random clones in each sample. The variable clonal dynamics resulted in a high level of 'noise' that limited the detection of gRNA-based selection. Using simulated datasets derived from our experimental data, we show that considerable reductions in count variance would be achieved with smaller library sizes. Based on our findings, we suggest a pathway to rationally design adequately powered in vivo CRISPR screens for successful evaluation of gene function.

| S-EPMC10721547 | biostudies-literature

Single-cell CRISPR immune screens reveal immunological roles of tumor intrinsic factors.

Project description:Genetic screens are widely exploited to develop novel therapeutic approaches for cancer treatment. With recent advances in single-cell technology, single-cell CRISPR screen (scCRISPR) platforms provide opportunities for target validation and mechanistic studies in a high-throughput manner. Here, we aim to establish scCRISPR platforms which are suitable for immune-related screens involving multiple cell types. We integrated two scCRISPR platforms, namely Perturb-seq and CROP-seq, with both in vitro and in vivo immune screens. By leveraging previously generated resources, we optimized experimental conditions and data analysis pipelines to achieve better consistency between results from high-throughput and individual validations. Furthermore, we evaluated the performance of scCRISPR immune screens in determining underlying mechanisms of tumor intrinsic immune regulation. Our results showed that scCRISPR platforms can simultaneously characterize gene expression profiles and perturbation effects present in individual cells in different immune screen conditions. Results from scCRISPR immune screens also predict transcriptional phenotype associated with clinical responses to cancer immunotherapy. More importantly, scCRISPR screen platforms reveal the interactive relationship between targeting tumor intrinsic factors and T cell-mediated antitumor immune response which cannot be easily assessed by bulk RNA-seq. Collectively, scCRISPR immune screens provide scalable and reliable platforms to elucidate molecular determinants of tumor immune resistance.

| S-EPMC9732527 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data