Pan-cancer single cell RNA-seq uncovers recurring programs of cellular heterogeneity

Kinker, Gabriela Sarti; Greenwald, Alissa C.; Tal, Rotem; Orlova, Zhanna; Cuoco, Michael S.; McFarland, James M.; Warren, Allison; Rodman, Christopher; Roth, Jennifer A.; Bender, Samantha; Kumar, Bhavna; Rocco, James W.; Fernandes, Pedro Acm; Mader, Christopher C.; Keren‐Shaul, Hadas; Plotnikov, A.N.; Barr, Haim; Tsherniak, Aviad; Rozenblatt-Rosen, Orit; Krizhanovsky, Valery; Puram, Sidharth V.; Regev, Aviv; Tirosh, Itay

doi:10.1101/807552

Cited by 16 publications

(32 citation statements)

References 46 publications

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“…When cell lines are composed of transcriptionally distinct subsets even in the absence of perturbations, as we showed in a recent study 31 , MIX-Seq can be used to examine whether different cell populations within a given sample exhibit differential treatment responses. For example, the lung cell line IALM had two distinct subpopulations at baseline, characterized by differential expression of epithelial-to-mesenchymal transition and integrin-related programs ( Supplementary Fig.…”

Section: Mix-seq Identifies Selective Perturbation Responses and Moamentioning

confidence: 98%

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: Mix-seq Identifies Selective Perturbation Responses and Moamentioning

confidence: 98%

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

et al. 2020

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“…We next leveraged the single-cell nature of MIX-Seq data to study how perturbations affect different sub-populations of cells, and potentially alter patterns of transcriptional heterogeneity within a cell line. Cancer cell lines exhibit substantial genetic [33][34][35] , epigenetic 31,36,37 , and transcriptional 35,36,38,39 heterogeneity. For example, we found that the lung cancer cell line IALM was composed of two distinct subpopulations at baseline, and these subpopulations showed significant differences in their transcriptional response to trametinib ( Fig.…”

Section: Single-cell Profiling Enables Characterization Of Heterogenementioning

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

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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.

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“…To determine the extent to which the diversity of the MNP populations are conserved across samples and diseases, we calculated the similarity of all states using the Jaccard distance, which represents the overlap of two gene lists (Jaccard distance = 0, perfect overlap) (Kinker et al, 2019). We focused on 151 clusters that reached a minimum Jaccard index of 0.2 (Jaccard distance of 0.8) with at least one other cluster ( Methods ).…”

Section: Pan-cluster Comparison Reveals Conserved Cell Subsets and Mamentioning

confidence: 99%

“…These GEPs aim to capture coregulated genes that describe cellular behavior both within and across cell types, such as a type I interferon response or general response to stress. Non-negative matrix factorization (NMF) has been established in single-cell RNA-sequencing analysis to identify gene expression programs descriptive of cellular identity and activity (Kinker et al, 2019;Kotliar et al, 2019;Welch et al, 2019). Thus, by comparing GEPs across datasets, we aimed to identify consensus GEPs which consistently describe variation in the MNP compartment.…”

Section: Integrated Gene Expression Programs Show Similar Concordancementioning

confidence: 99%

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Consensus transcriptional states describe human mononuclear phagocyte diversity in the lung across health and disease

Peters

Blainey

Bryson

2020

Preprint

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SUMMARYMonocytes, dendritic cells, and macrophages, commonly referred to as mononuclear phagocytes (MNPs), are innate immune cells capable of adopting diverse homeostatic and pathogenic phenotypes. Recent single-cell RNA-sequencing studies across many diseases in the lung have profiled this diversity transcriptionally, defining new cellular states and their association with disease. Despite these massive cellular profiling efforts, many studies have focused on defining myeloid dysfunction in specific diseases without identifying common pan-disease trends in the mononuclear phagocyte compartment within the lung. To address these gaps in our knowledge, we collate, process, and analyze 561,390 cellular transcriptomes from 12 studies of the human lung across multiple human diseases. We develop a computational framework to identify and compare dominant gene markers and gene expression programs and characterize MNP diversity in the lung, proposing a conserved dictionary of gene sets. Utilizing this reference, we efficiently identify disease-associated and rare MNP populations across multiple diseases and cohorts. Furthermore, we demonstrate the utility of this dictionary in characterizing a recently published dataset of bronchoalveolar lavage cells from COVID-19 patients and healthy controls which further reveal novel transcriptional shifts directly relatable to other diseases in the lung. These results underline conserved MNP transcriptional programs in lung disease, provide an immediate reference for characterizing the landscape of lung MNPs and establish a roadmap to dissecting MNP transcriptional complexity across tissues.

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Pan-cancer single cell RNA-seq uncovers recurring programs of cellular heterogeneity

Cited by 16 publications

References 46 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

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

Consensus transcriptional states describe human mononuclear phagocyte diversity in the lung across health and disease

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