scholarly journals Combinatorial prediction of marker panels from single-cell transcriptomic data

2019 ◽  
Author(s):  
Conor Delaney ◽  
Alexandra Schnell ◽  
Louis V. Cammarata ◽  
Aaron Yao-Smith ◽  
Aviv Regev ◽  
...  

AbstractSingle-cell transcriptomic studies are identifying novel cell populations with exciting functional roles in various in vivo contexts, but identification of succinct gene-marker panels for such populations remains a challenge. In this work we introduce COMET, a computational framework for the identification of candidate marker panels consisting of one or more genes for cell populations of interest identified with single-cell RNA-seq data. We show that COMET outperforms other methods for the identification of single-gene panels, and enables, for the first time, prediction of multi-gene marker panels ranked by relevance. Staining by flow-cytometry assay confirmed the accuracy of COMET’s predictions in identifying marker-panels for cellular subtypes, at both the single- and multi-gene levels, validating COMET’s applicability and accuracy in predicting favorable marker-panels from transcriptomic input. COMET is a general non-parametric statistical framework and can be used as-is on various high-throughput datasets in addition to single-cell RNA-sequencing data. COMET is available for use via a web interface (http://www.cometsc.com) or a standalone software package (https://github.com/MSingerlab/COMETSC).

2021 ◽  
Vol 7 (10) ◽  
pp. eabc5464
Author(s):  
Kiya W. Govek ◽  
Emma C. Troisi ◽  
Zhen Miao ◽  
Rachael G. Aubin ◽  
Steven Woodhouse ◽  
...  

Highly multiplexed immunohistochemistry (mIHC) enables the staining and quantification of dozens of antigens in a tissue section with single-cell resolution. However, annotating cell populations that differ little in the profiled antigens or for which the antibody panel does not include specific markers is challenging. To overcome this obstacle, we have developed an approach for enriching mIHC images with single-cell RNA sequencing data, building upon recent experimental procedures for augmenting single-cell transcriptomes with concurrent antigen measurements. Spatially-resolved Transcriptomics via Epitope Anchoring (STvEA) performs transcriptome-guided annotation of highly multiplexed cytometry datasets. It increases the level of detail in histological analyses by enabling the systematic annotation of nuanced cell populations, spatial patterns of transcription, and interactions between cell types. We demonstrate the utility of STvEA by uncovering the architecture of poorly characterized cell types in the murine spleen using published cytometry and mIHC data of this organ.


2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi5-vi5
Author(s):  
Robert Suter ◽  
Vasileios Stathias ◽  
Anna Jermakowicz ◽  
Hari Pradhyumnan ◽  
Maurizio Affer ◽  
...  

Abstract Glioblastoma (GBM) remains the most common adult brain cancer, with a dismal average patient survival of less than two years. No new treatments have been approved for GBM since the introduction of the alkylating agent temozolomide in 2005. Even then, temozolomide treatment only increases the average survival of GBM patients by a few months. Thus, novel therapeutic options are direly needed. The aurora kinases A and B are targetable and overexpressed in GBM, and their expression is highly correlated with patient survival outcomes. Our lab has found that small molecule aurora kinase inhibition reduces GBM tumor growth in vitro and in vivo, however, eventually tumors still grow. Computational analysis integrating compound transcriptional response signatures from the LINCS L1000 dataset with the single-cell RNA-sequencing data of patient GBM tumors resected at the University of Miami predicts that aurora inhibition targets a subset of cells present within any GBM tumor. Results of in vivo single-cell perturbation experiments with the aurora kinase inhibitor alisertib coincide with our predictions and reveal a cellular transcriptional phenotype resistant to aurora kinase inhibition, characterized by a mesenchymal expression program. We find that small molecules that are predicted to target different cell populations from alisertib, including this resistant mesenchymal population, synergize with alisertib to kill GBM cells. As a whole, we have identified the cellular population resistant to aurora kinase inhibition and have developed an analytical framework that identifies synergistic small molecule combinations by identifying compounds that target transcriptionally distinct cellular populations within GBM tumors.


2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Yuxuan Liu ◽  
Zhimin Gu ◽  
Hui Cao ◽  
Pranita Kaphle ◽  
Junhua Lyu ◽  
...  

AbstractCancers develop from the accumulation of somatic mutations, yet it remains unclear how oncogenic lesions cooperate to drive cancer progression. Using a mouse model harboring NRasG12D and EZH2 mutations that recapitulates leukemic progression, we employ single-cell transcriptomic profiling to map cellular composition and gene expression alterations in healthy or diseased bone marrows during leukemogenesis. At cellular level, NRasG12D induces myeloid lineage-biased differentiation and EZH2-deficiency impairs myeloid cell maturation, whereas they cooperate to promote myeloid neoplasms with dysregulated transcriptional programs. At gene level, NRasG12D and EZH2-deficiency independently and synergistically deregulate gene expression. We integrate results from histopathology, leukemia repopulation, and leukemia-initiating cell assays to validate transcriptome-based cellular profiles. We use this resource to relate developmental hierarchies to leukemia phenotypes, evaluate oncogenic cooperation at single-cell and single-gene levels, and identify GEM as a regulator of leukemia-initiating cells. Our studies establish an integrative approach to deconvolute cancer evolution at single-cell resolution in vivo.


Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 58-58
Author(s):  
Anna E. Marneth ◽  
Jonas S. Jutzi ◽  
Angel Guerra-Moreno ◽  
Michele Ciboddo ◽  
María José Jiménez Santos ◽  
...  

Abstract Somatic mutations in the ER chaperone calreticulin (CALR) are frequent and disease-initiating in myeloproliferative neoplasms (MPN). Although the mechanism of mutant CALR-induced MPN is known to involve pathogenic binding between mutant CALR and MPL, this insight has not yet been exploited therapeutically. Consequently, a major deficiency is the lack of clonally selective therapeutic agents with curative potential. Hence, we set out to discover and validate unique genetic dependencies for mutant CALR-driven oncogenesis. We first performed a whole-genome CRISPR knockout screen in CALR Δ52 MPL-expressing hematopoietic cells to identify genes that were differentially required for the growth of cytokine-independent, transformed CALR Δ52 cells as compared to control cells. Using gene-set enrichment analyses, we identified the N-glycan biosynthesis, unfolded protein response, and the protein secretion pathways to be amongst the most significantly differentially depleted pathways (FDR q values <0.001, 0.014, and 0.025, respectively) in CALR Δ52 cells. We performed a secondary CRISPR pooled screen focused on significant pathways from the primary screen and confirmed these findings. Strikingly, seven of the top ten hits in both screens were linked to protein N-glycosylation. Four of those genes encode proteins involved in the enzymatic activity of dolichol-phosphate mannose synthase (DPM1, DPM2, DPM3, and MPDU1). This enzyme synthesizes dolichol D-mannosyl phosphate, an essential substrate for protein N-glycosylation. Importantly, these findings from an unbiased whole-genome screen align with prior mechanistic studies demonstrating that both the N-glycosylation sites on MPL and the lectin-binding sites on CALR Δ52 are required for mutant CALR-driven oncogenesis. We next performed single gene CRISPR Cas9 validation studies and found that DPM2 is required for CALR Δ52-mediated transformation, as demonstrated by increased cell death, reduced p-STAT5 and decreased MPL cell-surface levels, when Dpm2 is knocked out. Importantly, cells cultured in cytokine-rich medium were unaffected by DPM2 loss. Upon cytokine withdrawal, a sub-clone of non-edited Dpm2WT CALR Δ52 cells grew out, further demonstrating requirement for DPM2 for the survival of CALR Δ52 cells. Additionally, we observed a >50% reduction in ex vivo myeloid colony formation of murine CalrΔ52 Dpm2 ko bone marrow (BM) compared with CRISPR-Cas9 non-targeting controls, with non-significant effects on CalrWT BM cells. To enable clinical translation, we performed a pharmacological screen targeting pathways significantly depleted in our CRISPR screens. Screening 70 drugs, we found that the N-glycosylation pathway was the only pathway in which all tested compounds preferentially killed CALR Δ52 transformed cells. We then treated primary Calr Δ52/+ mice with a clinical grade N-glycosylation (N-Gi) inhibitor and found platelet counts (Sysmex) to be significantly reduced (vehicle 3x10 6/mL, N-Gi 1x10 6/mL after 18 days, p<.0001). Concordantly, the proportion of megakaryocyte erythrocyte progenitors (MEPs) was significantly reduced in CalrΔ52 BM (p=0.03). We next performed competitive BM transplantation assays using CD45.2 UBC-GFP MxCre CalrΔ52 knockin and CD45.1 mice. We found that mice treated with N-Gi had significantly reduced platelet counts (vehicle 1440x10 6/mL, N-Gi 845x10 6/mL, p=0.005) as well as significantly reduced platelet chimerism (vehicle 55%, N-Gi 27%, p<0.001), indicating a distinct vulnerability of CalrΔ52 over WT cells. Finally, we interrogated RNA-sequencing data from primary human MPN platelets. We found N-glycosylation-related pathways to be significantly upregulated in CALR-mutated platelets (n = 13) compared to healthy control platelets (n = 21), highlighting the relevance of our findings to human MPN. In summary, using unbiased genetic and focused pharmacological screens, we identified the N-glycan biosynthesis pathway as essential for mutant CALR-driven oncogenesis. Using a pre-clinical MPN model, we found that in vivo inhibition of N-glycosylation normalizes key features of MPN and preferentially targets CalrΔ52 over WT cells. These findings have therapeutic implications through inhibiting N-glycosylation alone or in combination with other agents to advance the development of clonally selective therapeutic approaches in CALR-mutant MPN. AEM and JSJ contributed equally. Figure 1 Figure 1. Disclosures Mullally: Janssen, PharmaEssentia, Constellation and Relay Therapeutics: Consultancy.


2020 ◽  
Author(s):  
Leyi Zhang ◽  
Bingbing Wu

Abstract Background: Endometria undergo repeated repair and regeneration during the menstrual cycle. Using gene expression to define the menstrual cycle has been seen in a couple of researches, which shares little consistency. The possible reason lies in the fact that the composition of each specimen is different. The objective of this study was to reconstruct an integrated cell atlas of mouse uterus from the regenerative to maturational endometrium at the single-cell level.Methods: We used published single-cell RNA sequencing data of mice uteri to build an integrated cell atlas, including epithelial cells, stromal cells, and immune cells. Principal components analysis, hierarchical clustering, and other data mining procedures were performed with R software. Functional enrichment analyses were performed to identify the potential biological roles of molecular changes in different mice estrus states.Results: Based on the expression level of proliferating cell nuclear antigen and gene ontology terms, we reconstructed the cell atlas and delineated in detail the transitions that happened during the estrus cycle. We also depicted the transition and transcription factors that shaped the differentiation of ESCs and the mononuclear phagocyte system. Besides, the amounts and functions of immune cells varied sharply in two periods. We also found clues about uterus tissue-resident macrophages and Pdgfrb+ Aldh1a2+ Cd34+ endometrial mesenchymal stem cells in vivo.Conclusions: The cell atlas of mouse uterus presented here would improve our understanding of the functional changes that occurred in the endometrium and helped us identify abnormalities that have not been apparent histologically.


2016 ◽  
Author(s):  
Jack Kuipers ◽  
Katharina Jahn ◽  
Benjamin J. Raphael ◽  
Niko Beerenwinkel

The infinite sites assumption, which states that every genomic position mutates at most once over the lifetime of a tumor, is central to current approaches for reconstructing mutation histories of tumors, but has never been tested explicitly. We developed a rigorous statistical framework to test the assumption with single-cell sequencing data. The framework accounts for the high noise and contamination present in such data. We found strong evidence for recurrent mutations at the same site in 8 out of 9 single-cell sequencing datasets from human tumors. Six cases involved the loss of earlier mutations, five of which occurred at sites unaffected by large scale genomic deletions. Two cases exhibited parallel mutation, including the dataset with the strongest evidence of recurrence. Our results refute the general validity of the infinite sites assumption and indicate that more complex models are needed to adequately quantify intra-tumor heterogeneity.


2021 ◽  
Author(s):  
Ariel A. Hippen ◽  
Matias M. Falco ◽  
Lukas M. Weber ◽  
Erdogan Pekcan Erkan ◽  
Kaiyang Zhang ◽  
...  

AbstractMotivationSingle-cell RNA-sequencing (scRNA-seq) has made it possible to profile gene expression in tissues at high resolution. An important preprocessing step prior to performing downstream analyses is to identify and remove cells with poor or degraded sample quality using quality control (QC) metrics. Two widely used QC metrics to identify a ‘low-quality’ cell are (i) if the cell includes a high proportion of reads that map to mitochondrial DNA (mtDNA) encoded genes and (ii) if a small number of genes are detected. Current best practices use these QC metrics independently with either arbitrary, uniform thresholds (e.g. 5%) or biological context-dependent (e.g. species) thresholds, and fail to jointly model these metrics in a data-driven manner. Current practices are often overly stringent and especially untenable on lower-quality tissues, such as archived tumor tissues.ResultsWe propose a data-driven QC metric (miQC) that jointly models both the proportion of reads mapping to mtDNA genes and the number of detected genes with mixture models in a probabilistic framework to predict the low-quality cells in a given dataset. We demonstrate how our QC metric easily adapts to different types of single-cell datasets to remove low-quality cells while preserving high-quality cells that can be used for downstream analyses.AvailabilitySoftware available at https://github.com/greenelab/miQC. The code used to download datasets, perform the analyses, and reproduce the figures is available at https://github.com/greenelab/mito-filtering.ContactStephanie C. Hicks ([email protected]) and Anna Vähärautio ([email protected])


Author(s):  
Ni Huang ◽  
Paola Perez ◽  
Takafumi Kato ◽  
Yu Mikami ◽  
Kenichi Okuda ◽  
...  

ABSTRACTDespite signs of infection, the involvement of the oral cavity in COVID-19 is poorly understood. To address this, single-cell RNA sequencing data-sets were integrated from human minor salivary glands and gingiva to identify 11 epithelial, 7 mesenchymal, and 15 immune cell clusters. Analysis of SARS-CoV-2 viral entry factor expression showed enrichment in epithelia including the ducts and acini of the salivary glands and the suprabasal cells of the mucosae. COVID-19 autopsy tissues confirmed in vivo SARS-CoV-2 infection in the salivary glands and mucosa. Saliva from SARS-CoV-2-infected individuals harbored epithelial cells exhibiting ACE2 expression and SARS-CoV-2 RNA. Matched nasopharyngeal and saliva samples found distinct viral shedding dynamics and viral burden in saliva correlated with COVID-19 symptoms including taste loss. Upon recovery, this cohort exhibited salivary antibodies against SARS-CoV-2 proteins. Collectively, the oral cavity represents a robust site for COVID-19 infection and implicates saliva in viral transmission.


Author(s):  
Mirca S. Saurty-Seerunghen ◽  
Léa Bellenger ◽  
Elias A. El-Habr ◽  
Virgile Delaunay ◽  
Delphine Garnier ◽  
...  

Abstract Glioblastoma cell ability to adapt their functioning to microenvironment changes is a source of the extensive intra-tumor heterogeneity characteristic of this devastating malignant brain tumor. A systemic view of the metabolic pathways underlying glioblastoma cell functioning states is lacking. We analyzed public single cell RNA-sequencing data from glioblastoma surgical resections, which offer the closest available view of tumor cell heterogeneity as encountered at the time of patients’ diagnosis. Unsupervised analyses revealed that information dispersed throughout the cell transcript repertoires encoded the identity of each tumor and masked information related to cell functioning states. Data reduction based on an experimentally-defined signature of transcription factors overcame this hurdle. It allowed cell grouping according to their tumorigenic potential, regardless of their tumor of origin. The approach relevance was validated using independent datasets of glioblastoma cell and tissue transcriptomes, patient-derived cell lines and orthotopic xenografts. Overexpression of genes coding for amino acid and lipid metabolism enzymes involved in anti-oxidative, energetic and cell membrane processes characterized cells with high tumorigenic potential. Modeling of their expression network highlighted the very long chain polyunsaturated fatty acid synthesis pathway at the core of the network. Expression of its most downstream enzymatic component, ELOVL2, was associated with worsened patient survival, and required for cell tumorigenic properties in vivo. Our results demonstrate the power of signature-driven analyses of single cell transcriptomes to obtain an integrated view of metabolic pathways at play within the heterogeneous cell landscape of patient tumors.


2017 ◽  
Author(s):  
Julian Garneau ◽  
Florence Depardieu ◽  
Louis-Charles Fortier ◽  
David Bikard ◽  
Marc Monot

ABSTRACTBacteriophages are the most abundant viruses on earth and display an impressive genetic as well as morphologic diversity. Among those, the most common order of phages is the Caudovirales, whose viral particles packages linear double stranded DNA (dsDNA). In this study we investigated how the information gathered by high throughput sequencing technologies can be used to determine the DNA termini and packaging mechanisms of dsDNA phages. The wet-lab procedures traditionally used for this purpose rely on the identification and cloning of restriction fragment which can be delicate and cumbersome. Here, we developed a theoretical and statistical framework to analyze DNA termini and phage packaging mechanisms using next-generation sequencing data. Our methods, implemented in the PhageTerm software, work with sequencing reads in fastq format and the corresponding assembled phage genome.PhageTerm was validated on a set of phages with well-established packaging mechanisms representative of the termini diversity: 5’cos (lambda), 3’cos (HK97), pac (P1), headful without a pac site (T4), DTR (T7) and host fragment (Mu). In addition, we determined the termini of 9Clostridium difficilephages and 6 phages whose sequences where retrieved from the sequence read archive (SRA).A direct graphical interface is available as a Galaxy wrapper version athttps://galaxy.pasteur.frand a standalone version is accessible athttps://sourceforge.net/projects/phageterm/.


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