Rare Cell Detection by Single-Cell RNA Sequencing as Guided by Single-Molecule RNA FISH

Eduardo Torre; Hannah Dueck; Sydney Shaffer; Janko Gospocic; Rohit Gupte; Roberto Bonasio; Junhyong Kim; John Murray; Arjun Raj

doi:10.1016/j.cels.2018.01.014

A comparison between single cell RNA sequencing and single molecule RNA FISH for rare cell analysis

10.1101/138289 ◽

2017 ◽

Cited By ~ 6

Author(s):

Eduardo Torre ◽

Hannah Dueck ◽

Sydney Shaffer ◽

Janko Gospocic ◽

Rohit Gupte ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Single Molecule ◽

Single Cells ◽

Cycle Phase ◽

Expression Variability ◽

Sequencing Technologies ◽

Rna Fish ◽

Single Cell Rna Sequencing ◽

Sequencing Platforms

AbstractThe development of single cell RNA sequencing technologies has emerged as a powerful means of profiling the transcriptional behavior of single cells, leveraging the breadth of sequencing measurements to make inferences about cell type. However, there is still little understanding of how well these methods perform at measuring single cell variability for small sets of genes and what “transcriptome coverage” (e.g. genes detected per cell) is needed for accurate measurements. Here, we use single molecule RNA FISH measurements of 26 genes in thousands of melanoma cells to provide an independent reference dataset to assess the performance of the DropSeq and Fluidigm single cell RNA sequencing platforms. We quantified the Gini coefficient, a measure of rare-cell expression variability, and find that the correspondence between RNA FISH and single cell RNA sequencing for Gini, unlike for mean, increases markedly with per-cell library complexity up to a threshold of ∼2000 genes detected. A similar complexity threshold also allows for robust assignment of multi-genic cell states such as cell cycle phase. Our results provide guidelines for selecting sequencing depth and complexity thresholds for single cell RNA sequencing. More generally, our results suggest that if the number of genes whose expression levels are required to answer any given biological question is small, then greater transcriptome complexity per cell is likely more important than obtaining very large numbers of cells.

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A spatially restricted fibrotic niche in pulmonary fibrosis is sustained by M-CSF/M-CSFR signalling in monocyte-derived alveolar macrophages

European Respiratory Journal ◽

10.1183/13993003.00646-2019 ◽

2019 ◽

Vol 55 (1) ◽

pp. 1900646 ◽

Cited By ~ 30

Author(s):

Nikita Joshi ◽

Satoshi Watanabe ◽

Rohan Verma ◽

Renea P. Jablonski ◽

Ching-I Chen ◽

...

Keyword(s):

Pulmonary Fibrosis ◽

Single Cell ◽

Rna Sequencing ◽

Single Molecule ◽

Alveolar Macrophages ◽

Lineage Tracing ◽

Single Cell Rna Sequencing ◽

Pharmacological Blockade ◽

Macrophage Colony ◽

Druggable Targets

Ontologically distinct populations of macrophages differentially contribute to organ fibrosis through unknown mechanisms.We applied lineage tracing, single-cell RNA sequencing and single-molecule fluorescence in situ hybridisation to a spatially restricted model of asbestos-induced pulmonary fibrosis.We demonstrate that tissue-resident alveolar macrophages, tissue-resident peribronchial and perivascular interstitial macrophages, and monocyte-derived alveolar macrophages are present in the fibrotic niche. Deletion of monocyte-derived alveolar macrophages but not tissue-resident alveolar macrophages ameliorated asbestos-induced lung fibrosis. Monocyte-derived alveolar macrophages were specifically localised to fibrotic regions in the proximity of fibroblasts where they expressed molecules known to drive fibroblast proliferation, including platelet-derived growth factor subunit A. Using single-cell RNA sequencing and spatial transcriptomics in both humans and mice, we identified macrophage colony-stimulating factor receptor (M-CSFR) signalling as one of the novel druggable targets controlling self-maintenance and persistence of these pathogenic monocyte-derived alveolar macrophages. Pharmacological blockade of M-CSFR signalling led to the disappearance of monocyte-derived alveolar macrophages and ameliorated fibrosis.Our findings suggest that inhibition of M-CSFR signalling during fibrosis disrupts an essential fibrotic niche that includes monocyte-derived alveolar macrophages and fibroblasts during asbestos-induced fibrosis.

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Lung transplantation for patients with severe COVID-19

Science Translational Medicine ◽

10.1126/scitranslmed.abe4282 ◽

2020 ◽

Vol 12 (574) ◽

pp. eabe4282 ◽

Cited By ~ 1

Author(s):

Ankit Bharat ◽

Melissa Querrey ◽

Nikolay S. Markov ◽

Samuel Kim ◽

Chitaru Kurihara ◽

...

Keyword(s):

Respiratory Failure ◽

Pulmonary Fibrosis ◽

Lung Transplantation ◽

Single Cell ◽

Rna Sequencing ◽

Lung Tissue ◽

Single Molecule ◽

Sequencing Data ◽

Native Lung ◽

Single Cell Rna Sequencing

Lung transplantation can potentially be a life-saving treatment for patients with nonresolving COVID-19–associated respiratory failure. Concerns limiting lung transplantation include recurrence of SARS-CoV-2 infection in the allograft, technical challenges imposed by viral-mediated injury to the native lung, and the potential risk for allograft infection by pathogens causing ventilator-associated pneumonia in the native lung. Additionally, the native lung might recover, resulting in long-term outcomes preferable to those of transplant. Here, we report the results of lung transplantation in three patients with nonresolving COVID-19–associated respiratory failure. We performed single-molecule fluorescence in situ hybridization (smFISH) to detect both positive and negative strands of SARS-CoV-2 RNA in explanted lung tissue from the three patients and in additional control lung tissue samples. We conducted extracellular matrix imaging and single-cell RNA sequencing on explanted lung tissue from the three patients who underwent transplantation and on warm postmortem lung biopsies from two patients who had died from COVID-19–associated pneumonia. Lungs from these five patients with prolonged COVID-19 disease were free of SARS-CoV-2 as detected by smFISH, but pathology showed extensive evidence of injury and fibrosis that resembled end-stage pulmonary fibrosis. Using machine learning, we compared single-cell RNA sequencing data from the lungs of patients with late-stage COVID-19 to that from the lungs of patients with pulmonary fibrosis and identified similarities in gene expression across cell lineages. Our findings suggest that some patients with severe COVID-19 develop fibrotic lung disease for which lung transplantation is their only option for survival.

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bayNorm: Bayesian gene expression recovery, imputation and normalisation for single cell RNA-sequencing data

10.1101/384586 ◽

2018 ◽

Cited By ~ 7

Author(s):

Wenhao Tang ◽

François Bertaux ◽

Philipp Thomas ◽

Claire Stefanelli ◽

Malika Saint ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Rna Sequencing ◽

Single Molecule ◽

Empirical Bayes ◽

Missing Values ◽

Likelihood Function ◽

Differential Expression Analysis ◽

Sequencing Data ◽

Single Cell Rna Sequencing

Normalisation of single cell RNA sequencing (scRNA-seq) data is a prerequisite to their interpretation. The marked technical variability and high amounts of missing observations typical of scRNA-seq datasets make this task particularly challenging. Here, we introduce bayNorm, a novel Bayesian approach for scaling and inference of scRNA-seq counts. The method’s likelihood function follows a binomial model of mRNA capture, while priors are estimated from expression values across cells using an empirical Bayes approach. We demonstrate using publicly-available scRNA-seq datasets and simulated expression data that bayNorm allows robust imputation of missing values generating realistic transcript distributions that match single molecule FISH measurements. Moreover, by using priors informed by dataset structures, bayNorm improves accuracy and sensitivity of differential expression analysis and reduces batch effect compared to other existing methods. Altogether, bayNorm provides an efficient, integrated solution for global scaling normalisation, imputation and true count recovery of gene expression measurements from scRNA-seq data.

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Single-cell RNA Sequencing in Immunology

Current Genomics ◽

10.2174/1389202921999201020203249 ◽

2020 ◽

Vol 21 (8) ◽

pp. 564-575

Author(s):

Yirui Cao ◽

Yue Qiu ◽

Guowei Tu ◽

Cheng Yang

Keyword(s):

Immune System ◽

Single Cell ◽

Rna Sequencing ◽

Single Molecule ◽

Complex Cell ◽

Future Directions ◽

Immune Reactions ◽

Single Cell Rna Sequencing

The complex immune system is involved in multiple pathological processes. Single-cell RNA sequencing (scRNA-seq) is able to analyze complex cell mixtures correct to a single cell and single molecule, thus is qualified to analyze immune reactions in several diseases. In recent years, scRNA-seq has been applied in many researching fields and has presented many innovative results. In this review, we intend to provide an overview of single-cell RNA sequencing applications in immunology and a prospect of future directions.

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Genotyping Copy Number Alterations from single-cell RNA sequencing

10.1101/2021.02.02.429335 ◽

2021 ◽

Author(s):

Salvatore Milite ◽

Riccardo Bergamin ◽

Giulio Caravagna

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Single Molecule ◽

Copy Number ◽

Probabilistic Method ◽

Real Data ◽

Copy Number Alterations ◽

Sequencing Data ◽

Dna And Rna ◽

Single Cell Rna Sequencing

AbstractCancers are constituted by heterogeneous populations of cells that show complex genotypes and phenotypes which we can read out by sequencing. Many attempts at deciphering the clonal process that drives these populations are focusing on single-cell technologies to resolve genetic and phenotypic intra-tumour heterogeneity. While the ideal technologies for these investigations are multi-omics assays, unfortunately these types of data are still too expensive and have limited scalability. We can resort to single-molecule assays, which are cheaper and scalable, and statistically emulate a joint assay, only if we can integrate measurements collected from independent cells of the same sample. In this work we follow this intuition and construct a new Bayesian method to genotype copy number alterations on single-cell RNA sequencing data, therefore integrating DNA and RNA measurements. Our method is unsupervised, and leverages on a segmentation of the input DNA to determine the sample subclonal composition at the copy number level, together with clone-specific phenotypes defined from RNA counts. By design our probabilistic method works without a reference RNA expression profile, and therefore can be applied in cases where this information may not be accessible. We implement the method on a probabilistic backend that allows easy running on both CPUs and GPUs, and test it on both simulated and real data. Our analysis shows its ability to determine copy number associated clones and their RNA phenotypes in tumour data from 10x and Smart-Seq assays, as well as in data from the Human Cell Atlas project.

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