Cumulus: a cloud-based data analysis framework for large-scale single-cell and single-nucleus RNA-seq

Li, Bo; Gould, Joshua; Yang, Yiming; Sarkizova, Siranush; Tabaka, Marcin; Ashenberg, Orr; Rosen, Yanay; Slyper, Michal; Kowalczyk, Monika S.; Villani, Alexandra‐Chloé; Tickle, Timothy L.; Hacohen, Nir; Rozenblatt-Rosen, Orit; Regev, Aviv

doi:10.1101/823682

Cited by 15 publications

(13 citation statements)

References 56 publications

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“…Data processing. Sequencing data were processed using the 10x Cell Ranger software, run using the Cumulus cloud-based analysis framework 40 . Our initial experiments were done with 10x Single Cell 3' v2 chemistry and were processed using version 2 of the Cell Ranger software.…”

Section: Methodsmentioning

confidence: 99%

Multiplexed single-cell transcriptional response profiling to define cancer vulnerabilities and therapeutic mechanism of action

et al. 2020

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Assays to study cancer cell responses to pharmacologic or genetic perturbations are typically restricted to using simple phenotypic readouts such as proliferation rate. Information-rich assays, such as gene-expression profiling, have generally not permitted efficient profiling of a given perturbation across multiple cellular contexts. Here, we develop MIX-Seq, a method for multiplexed transcriptional profiling of post-perturbation responses across a mixture of samples with single-cell resolution, using SNP-based computational demultiplexing of singlecell RNA-sequencing data. We show that MIX-Seq can be used to profile responses to chemical or genetic perturbations across pools of 100 or more cancer cell lines. We combine it with Cell Hashing to further multiplex additional experimental conditions, such as posttreatment time points or drug doses. Analyzing the high-content readout of scRNA-seq reveals both shared and context-specific transcriptional response components that can identify drug mechanism of action and enable prediction of long-term cell viability from shortterm transcriptional responses to treatment.

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Section: Methodsmentioning

confidence: 99%

Multiplexed single-cell transcriptional response profiling to define cancer vulnerabilities and therapeutic mechanism of action

et al. 2020

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“…For the smaller subset of 252,871 cells we then manually annotated cell clusters with broad cell type classes using marker genes. These clusters were generated using the harmony-pytorch Python implementation (version 0.1.1 (https://github.com/lilab-bcb/harmony-pytorch 163 ) of the Harmony scRNA-seq integration method 164 for batch correction and leiden clustering from the Scanpy package (version 1.4.5) 165 . Clusters without clear markers distinguishing types were excluded from further analysis.…”

Section: Integrated Analysis Of Published Datasetsmentioning

confidence: 99%

Integrated analyses of single-cell atlases reveal age, gender, and smoking status associations with cell type-specific expression of mediators of SARS-CoV-2 viral entry and highlights inflammatory programs in putative target cells

Muus

Luecken

Eraslan

et al. 2020

Preprint

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The COVID-19 pandemic, caused by the novel coronavirus SARS-CoV-2, creates an urgent need for identifying molecular mechanisms that mediate viral entry, propagation, and tissue pathology. Cell membrane bound angiotensin-converting enzyme 2 (ACE2) and associated proteases, transmembrane protease serine 2 (TMPRSS2) and Cathepsin L (CTSL), were previously identified as mediators of SARS-CoV2 cellular entry. Here, we assess the cell type-specific RNA expression of ACE2, TMPRSS2, and CTSL through an integrated analysis of 107 single-cell and single-nucleus RNA-Seq studies, including 22 lung and airways datasets (16 unpublished), and 85 datasets from other diverse organs. Joint expression of ACE2 and the accessory proteases identifies specific subsets of respiratory epithelial cells as putative targets of viral infection in the nasal passages, airways, and alveoli. Cells that co-express ACE2 and proteases are also identified in cells from other organs, some of which have been associated with COVID-19 transmission or pathology, including gut enterocytes, corneal epithelial cells, cardiomyocytes, heart pericytes, olfactory sustentacular cells, and renal epithelial cells. Performing the first meta-analyses of scRNA-seq studies, we analyzed 1,176,683 cells from 282 nasal, airway, and lung parenchyma samples from 164 donors spanning fetal, childhood, adult, and elderly age groups, associate increased levels of ACE2, TMPRSS2, and CTSL in specific cell types with increasing age, male gender, and smoking, all of which are epidemiologically linked to COVID-19 susceptibility and outcomes. Notably, there was a particularly low expression of ACE2 in the few young pediatric samples in the analysis. Further analysis reveals a gene expression program shared by ACE2 + TMPRSS2 + cells in nasal, lung and gut tissues, including genes that may mediate viral entry, subtend key immune functions, and mediate epithelial-macrophage cross-talk. Amongst these are IL6, its receptor and co-receptor, IL1R, TNF response pathways, and complement genes. Cell type specificity in the lung and airways and smoking effects were conserved in mice. Our analyses suggest that differences in the cell type-specific expression of mediators of SARS-CoV-2 viral entry may be responsible for aspects of COVID-19 epidemiology and clinical course, and point to putative molecular pathways involved in disease susceptibility and pathogenesis.

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“…Sequencing data were processed using 10x Cell Ranger software, run using the 'Cumulus' cloudbased analysis framework 51 . Our initial experiments were done with 10x Single Cell 3' v2 chemistry, and were processed using version 2 of the Cell Ranger software.…”

Section: Data Processingmentioning

confidence: 99%

Multiplexed single-cell profiling of post-perturbation transcriptional responses to define cancer vulnerabilities and therapeutic mechanism of action

McFarland

Warren

et al. 2019

Preprint

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Assays to study cancer cell responses to pharmacologic or genetic perturbations are typically restricted to using simple phenotypic readouts such as proliferation rate or the expression of a marker gene. Information-rich assays, such as gene-expression profiling, are generally not amenable to efficient profiling of a given perturbation across multiple cellular contexts. Here, we developed MIX-Seq, a method for multiplexed transcriptional profiling of post-perturbation responses across a mixture of samples with single-cell resolution, using SNP-based computational demultiplexing of single-cell RNAsequencing data. We show that MIX-Seq can be used to profile responses to chemical or genetic perturbations across pools of 100 or more cancer cell lines, and combine it with Cell Hashing to further multiplex additional experimental conditions, such as multiple post-treatment time points or drug doses. Analyzing the high-content readout of scRNA-seq reveals both shared and context-specific transcriptional response components that can identify drug mechanism of action and can be used to predict long-term cell viability from short-term transcriptional responses to treatment.

show abstract

Cumulus: a cloud-based data analysis framework for large-scale single-cell and single-nucleus RNA-seq

Cited by 15 publications

References 56 publications

Multiplexed single-cell transcriptional response profiling to define cancer vulnerabilities and therapeutic mechanism of action

Multiplexed single-cell transcriptional response profiling to define cancer vulnerabilities and therapeutic mechanism of action

Integrated analyses of single-cell atlases reveal age, gender, and smoking status associations with cell type-specific expression of mediators of SARS-CoV-2 viral entry and highlights inflammatory programs in putative target cells

Multiplexed single-cell profiling of post-perturbation transcriptional responses to define cancer vulnerabilities and therapeutic mechanism of action

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