scholarly journals Mitochondrial variant enrichment from high-throughput single-cell RNA-seq resolves clonal populations

2021 ◽  
Author(s):  
Tyler E. Miller ◽  
Caleb A. Lareau ◽  
Julia A. Verga ◽  
Daniel Ssozi ◽  
Leif S. Ludwig ◽  
...  
Keyword(s):  
Single Cell ◽  
High Throughput ◽  
Immune Cell ◽  
Rna Seq ◽  
High Confidence ◽  
Mitochondrial Mutations ◽  
Clonal Hematopoiesis ◽  
Single Cell Rna Sequencing ◽  
Variant Detection ◽  
Primary Human Cells

AbstractReconstructing lineage relationships in complex tissues can reveal mechanisms underlying development and disease. Recent methods combine single-cell transcriptomics with mitochondrial DNA variant detection to establish lineage relationships in primary human cells, but are not scalable to interrogate complex tissues. To overcome this limitation, here we develop a technology for high-confidence detection of mitochondrial mutations from high-throughput single-cell RNA-sequencing. We use the new method to identify skewed immune cell expansions in primary human clonal hematopoiesis.

scholarly journals Systematic comparison of high-throughput single-cell RNA-seq methods for immune cell profiling

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Tracy M. Yamawaki ◽  
Daniel R. Lu ◽  
Daniel C. Ellwanger ◽  
Dev Bhatt ◽  
Paolo Manzanillo ◽  
...  
Keyword(s):  
Single Cell ◽  
High Throughput ◽  
Immune Cell ◽  
Cell Types ◽  
Data Interpretation ◽  
Detection Sensitivity ◽  
Rna Seq ◽  
Cell Recovery

Abstract Background Elucidation of immune populations with single-cell RNA-seq has greatly benefited the field of immunology by deepening the characterization of immune heterogeneity and leading to the discovery of new subtypes. However, single-cell methods inherently suffer from limitations in the recovery of complete transcriptomes due to the prevalence of cellular and transcriptional dropout events. This issue is often compounded by limited sample availability and limited prior knowledge of heterogeneity, which can confound data interpretation. Results Here, we systematically benchmarked seven high-throughput single-cell RNA-seq methods. We prepared 21 libraries under identical conditions of a defined mixture of two human and two murine lymphocyte cell lines, simulating heterogeneity across immune-cell types and cell sizes. We evaluated methods by their cell recovery rate, library efficiency, sensitivity, and ability to recover expression signatures for each cell type. We observed higher mRNA detection sensitivity with the 10x Genomics 5′ v1 and 3′ v3 methods. We demonstrate that these methods have fewer dropout events, which facilitates the identification of differentially-expressed genes and improves the concordance of single-cell profiles to immune bulk RNA-seq signatures. Conclusion Overall, our characterization of immune cell mixtures provides useful metrics, which can guide selection of a high-throughput single-cell RNA-seq method for profiling more complex immune-cell heterogeneity usually found in vivo.

scholarly journals Systematic Comparison of High-Throughput Single-Cell RNAseq Methods for Immune Cell Profiling

2020 ◽  
Author(s):  
Chi-Ming Kevin Li ◽  
Tracy M Yamawaki ◽  
Daniel R Lu ◽  
Daniel C Ellwanger ◽  
Dev Bhatt ◽  
...  
Keyword(s):  
Single Cell ◽  
High Throughput ◽  
Immune Cell ◽  
Cell Types ◽  
Drop Out ◽  
Detection Sensitivity ◽  
Rna Seq ◽  
Cell Recovery

Abstract Background: Elucidation of immune populations with single-cell RNA-seq has greatly benefited the fieldof immunology by deepening the characterization of immune heterogeneity and leading to thediscovery of new subtypes. However, single-cell methods inherently suffer from limitations in therecovery of complete transcriptomes due to the prevalence of cellular and transcriptional dropoutevents. This issue is often compounded by limited sample availability and limited prior knowledge ofheterogeneity, which can confound data interpretation.Results: Here, we systematically benchmarked seven high-throughput single-cell RNA-seq methods. Weprepared 21 libraries under identical conditions of a defined mixture of two human and two murinelymphocyte cell lines, simulating heterogeneity across immune-cell types and cell sizes. We evaluatemethods by their cell recovery rate, library efficiency, sensitivity, and ability to recover expressionsignatures for each cell type. We observed higher mRNA detection sensitivity with the 10x Genomics 5’v1 and 3’ v3 methods. We demonstrate that these methods have fewer drop-out events whichfacilitates the identification of differentially-expressed genes and improves the concordance of singlecellprofiles to immune bulk RNA-seq signatures.Conclusion: Overall, our characterization of immune cell mixtures provides useful metrics, which canguide selection of a high-throughput single-cell RNA-seq method for profiling more complex immunecellheterogeneity usually found in vivo.

scholarly journals Power in Numbers: Single-Cell RNA-Seq Strategies to Dissect Complex Tissues

2018 ◽  
Vol 52 (1) ◽  
pp. 203-221 ◽  
Author(s):  
Kenneth D. Birnbaum
Keyword(s):  
Single Cell ◽  
High Throughput ◽  
Genetic Screening ◽  
Developmental Trajectories ◽  
Fluorescent Microscopy ◽  
The Novel ◽  
Rna Seq ◽  
Single Cell Rna Sequencing ◽  
Developmental Dynamics

The growing scale and declining cost of single-cell RNA-sequencing (RNA-seq) now permit a repetition of cell sampling that increases the power to detect rare cell states, reconstruct developmental trajectories, and measure phenotype in new terms such as cellular variance. The characterization of anatomy and developmental dynamics has not had an equivalent breakthrough since groundbreaking advances in live fluorescent microscopy. The new resolution obtained by single-cell RNA-seq is a boon to genetics because the novel description of phenotype offers the opportunity to refine gene function and dissect pleiotropy. In addition, the recent pairing of high-throughput genetic perturbation with single-cell RNA-seq has made practical a scale of genetic screening not previously possible.

scholarly journals Systematic comparison of high-throughput single-cell RNA-seq methods for immune cell profiling

2020 ◽  
Author(s):  
Tracy M Yamawaki ◽  
Daniel R Lu ◽  
Daniel C Ellwanger ◽  
Dev Bhatt ◽  
Paolo Manzanillo ◽  
...  
Keyword(s):  
Single Cell ◽  
High Throughput ◽  
Immune Cell ◽  
Cell Types ◽  
Drop Out ◽  
Detection Sensitivity ◽  
Rna Seq ◽  
Cell Recovery

Abstract Background: Elucidation of immune populations with single-cell RNA-seq has greatly benefited the field of immunology by deepening the characterization of immune heterogeneity and leading to the discovery of new subtypes. However, single-cell methods inherently suffer from limitations in the recovery of complete transcriptomes due to the prevalence of cellular and transcriptional dropout events. This issue is often compounded by limited sample availability and limited prior knowledge of heterogeneity, which can confound data interpretation. Results: Here, we systematically benchmarked seven high-throughput single-cell RNA-seq methods. We prepared 21 libraries under identical conditions of a defined mixture of two human and two murine lymphocyte cell lines, simulating heterogeneity across immune-cell types and cell sizes. We evaluate methods by their cell recovery rate, library efficiency, sensitivity, and ability to recover expression signatures for each cell type. We observed higher mRNA detection sensitivity with the 10x Genomics 5’ v1 and 3’ v3 methods. We demonstrate that these methods have fewer drop-out events which facilitates the identification of differentially-expressed genes and improves the concordance of single-cell profiles to immune bulk RNA-seq signatures.Conclusion: Overall, our characterization of immune cell mixtures provides useful metrics, which can guide selection of a high-throughput single-cell RNA-seq method for profiling more complex immune-cell heterogeneity usually found in vivo.

scholarly journals Systematic comparison of high-throughput single-cell RNA-seq methods for immune cell profiling

2020 ◽  
Author(s):  
Tracy M. Yamawaki ◽  
Daniel R. Lu ◽  
Daniel C. Ellwanger ◽  
Dev Bhatt ◽  
Paolo Manzanillo ◽  
...  
Keyword(s):  
Single Cell ◽  
High Throughput ◽  
Immune Cell ◽  
Cell Types ◽  
Drop Out ◽  
Detection Sensitivity ◽  
Rna Seq ◽  
Cell Recovery

AbstractBackgroundElucidation of immune populations with single-cell RNA-seq has greatly benefited the field of immunology by deepening the characterization of immune heterogeneity and leading to the discovery of new subtypes. However, single-cell methods inherently suffer from limitations in the recovery of complete transcriptomes due to the prevalence of cellular and transcriptional dropout events. This issue is often compounded by limited sample availability and limited prior knowledge of heterogeneity, which can confound data interpretation.ResultsHere, we systematically benchmarked seven high-throughput single-cell RNA-seq methods. We prepared 21 libraries under identical conditions of a defined mixture of two human and two murine lymphocyte cell lines, simulating heterogeneity across immune-cell types and cell sizes. We evaluate methods by their cell recovery rate, library efficiency, sensitivity, and ability to recover expression signatures for each cell type. We observed higher mRNA detection sensitivity with the 10x Genomics 5’ v1 and 3’ v3 methods. We demonstrate that these methods have fewer drop-out events which facilitates the identification of differentially-expressed genes and improves the concordance of single-cell profiles to immune bulk RNA-seq signatures.ConclusionOverall, our characterization of immune cell mixtures provides useful metrics, which can guide selection of a high-throughput single-cell RNA-seq method for profiling more complex immune-cell heterogeneity usually found in vivo.

scholarly journals Genotype-free demultiplexing of pooled single-cell RNA-seq

2019 ◽  
Author(s):  
Jun Xu ◽  
Caitlin Falconer ◽  
Quan Nguyen ◽  
Joanna Crawford ◽  
Brett D. McKinnon ◽  
...  
Keyword(s):  
Single Cell ◽  
Model Organisms ◽  
Genotype Data ◽  
Rna Seq ◽  
Batch Effects ◽  
High Confidence ◽  
Minimal Set ◽  
Genome Wide ◽  
Single Cell Rna Sequencing ◽  
Pooled Samples

AbstractA variety of experimental and computational methods have been developed to demultiplex samples from pooled individuals in a single-cell RNA sequencing (scRNA-Seq) experiment which either require adding information (such as hashtag barcodes) or measuring information (such as genotypes) prior to pooling. We introduce scSplit which utilises genetic differences inferred from scRNA-Seq data alone to demultiplex pooled samples. scSplit also extracts a minimal set of high confidence presence/absence genotypes in each cluster which can be used to map clusters to original samples. Using a range of simulated, merged individual-sample as well as pooled multi-individual scRNA-Seq datasets, we show that scSplit is highly accurate and concordant with demuxlet predictions. Furthermore, scSplit predictions are highly consistent with the known truth in cell-hashing dataset. We also show that multiplexed-scRNA-Seq can be used to reduce batch effects caused by technical biases. scSplit is ideally suited to samples for which external genome-wide genotype data cannot be obtained (for example non-model organisms), or for which it is impossible to obtain unmixed samples directly, such as mixtures of genetically distinct tumour cells, or mixed infections. scSplit is available at: https://github.com/jon-xu/scSplit

scholarly journals Systematic comparison of high-throughput single-cell RNA-seq methods for immune cell profiling

2021 ◽  
Author(s):  
Tracy M Yamawaki ◽  
Daniel R Lu ◽  
Daniel C Ellwanger ◽  
Dev Bhatt ◽  
Paolo Manzanillo ◽  
...  
Keyword(s):  
Single Cell ◽  
High Throughput ◽  
Immune Cell ◽  
Cell Types ◽  
Drop Out ◽  
Detection Sensitivity ◽  
Rna Seq ◽  
Cell Recovery

Abstract Background: Elucidation of immune populations with single-cell RNA-seq has greatly benefited the field of immunology by deepening the characterization of immune heterogeneity and leading to the discovery of new subtypes. However, single-cell methods inherently suffer from limitations in the recovery of complete transcriptomes due to the prevalence of cellular and transcriptional dropout events. This issue is often compounded by limited sample availability and limited prior knowledge of heterogeneity, which can confound data interpretation. Results: Here, we systematically benchmarked seven high-throughput single-cell RNA-seq methods. We prepared 21 libraries under identical conditions of a defined mixture of two human and two murine lymphocyte cell lines, simulating heterogeneity across immune-cell types and cell sizes. We evaluate methods by their cell recovery rate, library efficiency, sensitivity, and ability to recover expression signatures for each cell type. We observed higher mRNA detection sensitivity with the 10x Genomics 5’ v1 and 3’ v3 methods. We demonstrate that these methods have fewer drop-out events which facilitates the identification of differentially-expressed genes and improves the concordance of single-cell profiles to immune bulk RNA-seq signatures.Conclusion: Overall, our characterization of immune cell mixtures provides useful metrics, which can guide selection of a high-throughput single-cell RNA-seq method for profiling more complex immune-cell heterogeneity usually found in vivo.

scholarly journals Cryopreservation of human cancers conserves tumour heterogeneity for single-cell multi-omics analysis

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Sunny Z. Wu ◽  
Daniel L. Roden ◽  
Ghamdan Al-Eryani ◽  
Nenad Bartonicek ◽  
Kate Harvey ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
High Throughput ◽  
Single Cells ◽  
Cellular Heterogeneity ◽  
Tumour Heterogeneity ◽  
Fresh Tissue ◽  
Human Cancers ◽  
Cryopreserved Cell ◽  
Single Cell Rna Sequencing

Abstract Background High throughput single-cell RNA sequencing (scRNA-Seq) has emerged as a powerful tool for exploring cellular heterogeneity among complex human cancers. scRNA-Seq studies using fresh human surgical tissue are logistically difficult, preclude histopathological triage of samples, and limit the ability to perform batch processing. This hindrance can often introduce technical biases when integrating patient datasets and increase experimental costs. Although tissue preservation methods have been previously explored to address such issues, it is yet to be examined on complex human tissues, such as solid cancers and on high throughput scRNA-Seq platforms. Methods Using the Chromium 10X platform, we sequenced a total of ~ 120,000 cells from fresh and cryopreserved replicates across three primary breast cancers, two primary prostate cancers and a cutaneous melanoma. We performed detailed analyses between cells from each condition to assess the effects of cryopreservation on cellular heterogeneity, cell quality, clustering and the identification of gene ontologies. In addition, we performed single-cell immunophenotyping using CITE-Seq on a single breast cancer sample cryopreserved as solid tissue fragments. Results Tumour heterogeneity identified from fresh tissues was largely conserved in cryopreserved replicates. We show that sequencing of single cells prepared from cryopreserved tissue fragments or from cryopreserved cell suspensions is comparable to sequenced cells prepared from fresh tissue, with cryopreserved cell suspensions displaying higher correlations with fresh tissue in gene expression. We showed that cryopreservation had minimal impacts on the results of downstream analyses such as biological pathway enrichment. For some tumours, cryopreservation modestly increased cell stress signatures compared to freshly analysed tissue. Further, we demonstrate the advantage of cryopreserving whole-cells for detecting cell-surface proteins using CITE-Seq, which is impossible using other preservation methods such as single nuclei-sequencing. Conclusions We show that the viable cryopreservation of human cancers provides high-quality single-cells for multi-omics analysis. Our study guides new experimental designs for tissue biobanking for future clinical single-cell RNA sequencing studies.

scholarly journals Ultra-high-throughput single-cell RNA sequencing and perturbation screening with combinatorial fluidic indexing

2021 ◽  
Author(s):  
Paul Datlinger ◽  
André F. Rendeiro ◽  
Thorina Boenke ◽  
Martin Senekowitsch ◽  
Thomas Krausgruber ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
High Throughput ◽  
Single Cell Rna Sequencing
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