scholarly journals Connectivity analysis of single cell RNA-sequencing derived transcriptional signature of lymphangioleiomyomatosis

2020 ◽  
Author(s):  
Naim Al Mahi ◽  
Erik Y. Zhang ◽  
Susan Sherman ◽  
Jane J. Yu ◽  
Mario Medvedovic

ABSTRACTLymphangioleiomyomatosis (LAM) is a rare pulmonary disease affecting women of childbearing age that is characterized by the aberrant proliferation of smooth-muscle (SM)-like cells and emphysema-like lung remodeling. In LAM, mutations in TSC1 or TSC2 genes results in the activation of the mechanistic target of rapamycin complex 1 (mTORC1) and thus sirolimus, an mTORC1 inhibitor, has been approved by FDA to treat LAM patients. Sirolimus stabilizes lung function and improves symptoms. However, the disease recurs with discontinuation of the drug, potentially because of the sirolimus-induced refractoriness of the LAM cells. Therefore, there is a critical need to identify remission inducing cytocidal treatments for LAM. Recently released Library of Integrated Network-based Cellular Signatures (LINCS) L1000 transcriptional signatures of chemical perturbations has opened new avenues to study cellular responses to existing drugs and new bioactive compounds. Connecting transcriptional signature of a disease to these chemical perturbation signatures to identify bioactive chemicals that can “revert” the disease signatures can lead to novel drug discovery. We developed methods for constructing disease transcriptional signatures and performing connectivity analysis using single cell RNA-seq data. The methods were applied in the analysis of scRNA-seq data of naïve and sirolimus-treated LAM cells. The single cell connectivity analyses implicated mTORC1 inhibitors as capable of reverting the LAM transcriptional signatures while the corresponding standard bulk analysis did not. This indicates the importance of using single cell analysis in constructing disease signatures. The analysis also implicated other classes of drugs, CDK, MEK/MAPK and EGFR/JAK inhibitors, as potential therapeutic agents for LAM.

2020 ◽  
Vol 48 (W1) ◽  
pp. W403-W414
Author(s):  
Fabrice P A David ◽  
Maria Litovchenko ◽  
Bart Deplancke ◽  
Vincent Gardeux

Abstract Single-cell omics enables researchers to dissect biological systems at a resolution that was unthinkable just 10 years ago. However, this analytical revolution also triggered new demands in ‘big data’ management, forcing researchers to stay up to speed with increasingly complex analytical processes and rapidly evolving methods. To render these processes and approaches more accessible, we developed the web-based, collaborative portal ASAP (Automated Single-cell Analysis Portal). Our primary goal is thereby to democratize single-cell omics data analyses (scRNA-seq and more recently scATAC-seq). By taking advantage of a Docker system to enhance reproducibility, and novel bioinformatics approaches that were recently developed for improving scalability, ASAP meets challenging requirements set by recent cell atlasing efforts such as the Human (HCA) and Fly (FCA) Cell Atlas Projects. Specifically, ASAP can now handle datasets containing millions of cells, integrating intuitive tools that allow researchers to collaborate on the same project synchronously. ASAP tools are versioned, and researchers can create unique access IDs for storing complete analyses that can be reproduced or completed by others. Finally, ASAP does not require any installation and provides a full and modular single-cell RNA-seq analysis pipeline. ASAP is freely available at https://asap.epfl.ch.


2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Greg Holmes ◽  
Ana S. Gonzalez-Reiche ◽  
Madrikha Saturne ◽  
Susan M. Motch Perrine ◽  
Xianxiao Zhou ◽  
...  

AbstractCraniofacial development depends on formation and maintenance of sutures between bones of the skull. In sutures, growth occurs at osteogenic fronts along the edge of each bone, and suture mesenchyme separates adjacent bones. Here, we perform single-cell RNA-seq analysis of the embryonic, wild type murine coronal suture to define its population structure. Seven populations at E16.5 and nine at E18.5 comprise the suture mesenchyme, osteogenic cells, and associated populations. Expression of Hhip, an inhibitor of hedgehog signaling, marks a mesenchymal population distinct from those of other neurocranial sutures. Tracing of the neonatal Hhip-expressing population shows that descendant cells persist in the coronal suture and contribute to calvarial bone growth. In Hhip−/− coronal sutures at E18.5, the osteogenic fronts are closely apposed and the suture mesenchyme is depleted with increased hedgehog signaling compared to those of the wild type. Collectively, these data demonstrate that Hhip is required for normal coronal suture development.


2021 ◽  
Vol 2021 ◽  
pp. 1-21
Author(s):  
Xinbing Liu ◽  
Wei Gao ◽  
Wei Liu

Background. To further understand the development of the spinal cord, an exploration of the patterns and transcriptional features of spinal cord development in newborn mice at the cellular transcriptome level was carried out. Methods. The mouse single-cell sequencing (scRNA-seq) dataset was downloaded from the GSE108788 dataset. Single-cell RNA-Seq (scRNA-Seq) was conducted on cervical and lumbar spinal V2a interneurons from 2 P0 neonates. Single-cell analysis using the Seurat package was completed, and marker mRNAs were identified for each cluster. Then, pseudotemporal analysis was used to analyze the transcription changes of marker mRNAs in different clusters over time. Finally, the functions of these marker mRNAs were assessed by enrichment analysis and protein-protein interaction (PPI) networks. A transcriptional regulatory network was then constructed using the TRRUST dataset. Results. A total of 949 cells were screened. Single-cell analysis was conducted based on marker mRNAs of each cluster, which revealed the heterogeneity of neonatal mouse spinal cord neuronal cells. Functional analysis of pseudotemporal trajectory-related marker mRNAs suggested that pregnancy-specific glycoproteins (PSGs) and carcinoembryonic antigen cell adhesion molecules (CEACAMs) were the core mRNAs in cluster 3. GSVA analysis then demonstrated that the different clusters had differences in pathway activity. By constructing a transcriptional regulatory network, USF2 was identified to be a transcriptional regulator of CEACAM1 and CEACAM5, while KLF6 was identified to be a transcriptional regulator of PSG3 and PSG5. This conclusion was then validated using the Genotype-Tissue Expression (GTEx) spinal cord transcriptome dataset. Conclusions. This study completed an integrated analysis of a single-cell dataset with the utilization of marker mRNAs. USF2/CEACAM1&5 and KLF6/PSG3&5 transcriptional regulatory networks were identified by spinal cord single-cell analysis.


2019 ◽  
Vol 36 (7) ◽  
pp. 2288-2290 ◽  
Author(s):  
Shian Su ◽  
Luyi Tian ◽  
Xueyi Dong ◽  
Peter F Hickey ◽  
Saskia Freytag ◽  
...  

Abstract Motivation Bioinformatic analysis of single-cell gene expression data is a rapidly evolving field. Hundreds of bespoke methods have been developed in the past few years to deal with various aspects of single-cell analysis and consensus on the most appropriate methods to use under different settings is still emerging. Benchmarking the many methods is therefore of critical importance and since analysis of single-cell data usually involves multi-step pipelines, effective evaluation of pipelines involving different combinations of methods is required. Current benchmarks of single-cell methods are mostly implemented with ad-hoc code that is often difficult to reproduce or extend, and exhaustive manual coding of many combinations is infeasible in most instances. Therefore, new software is needed to manage pipeline benchmarking. Results The CellBench R software facilitates method comparisons in either a task-centric or combinatorial way to allow pipelines of methods to be evaluated in an effective manner. CellBench automatically runs combinations of methods, provides facilities for measuring running time and delivers output in tabular form which is highly compatible with tidyverse R packages for summary and visualization. Our software has enabled comprehensive benchmarking of single-cell RNA-seq normalization, imputation, clustering, trajectory analysis and data integration methods using various performance metrics obtained from data with available ground truth. CellBench is also amenable to benchmarking other bioinformatics analysis tasks. Availability and implementation Available from https://bioconductor.org/packages/CellBench.


2021 ◽  
Author(s):  
Greg Holmes ◽  
Ana S. Gonzalez-Reiche ◽  
Madrikha Saturne ◽  
Xianxiao Zhou ◽  
Ana C. Borges ◽  
...  

AbstractCraniofacial development depends on proper formation and maintenance of sutures between adjacent bones of the skull. In sutures, bone growth occurs at the edge of each bone, and suture mesenchyme maintains the separation between them. We performed single-cell RNA-seq analyses of the embryonic, murine coronal suture. Analyzing replicate libraries at E16.5 and E18.5, we identified 14 cell populations. Seven populations at E16.5 and nine at E18.5 comprised the suture mesenchyme, osteogenic cells, and associated populations. Through an integrated analysis with bulk RNA-seq data, we found a distinct coronal suture mesenchyme population compared to other neurocranial sutures, marked by expression ofHhip, an inhibitor of hedgehog signaling. We found that at E18.5,Hhip-/-coronal osteogenic fronts are closely apposed and suture mesenchyme is depleted, demonstrating thatHhipis required for coronal suture development. Our transcriptomic approach provides a rich resource for insight into normal and abnormal development.


2021 ◽  
Author(s):  
Jing Liu ◽  
Shengyong Yu ◽  
Chunhua Zhou ◽  
Jiangping He ◽  
Xingnan Huang ◽  
...  

Abstract Single cell analysis provides clarity unattainable with bulk approaches. Here we apply single cell RNA-seq to a newly established BMP4 induced mouse primed to naive transition (Bi-PNT) system and show that the reset is not a direct reversal of cell fate but through developmental intermediates. We first show that mEpiSCs bifurcate into c-Kit+ naïve and c-Kit- placenta-like cells, among which, the naive branch undergoes further transition through a primordial germ cell-like cells (PGCLCs) intermediate capable of spermatogenesis in vivo. Indeed, deficiency of Prdm1/Blimp1, the key regulator for PGC specification, blocks the induction of PGCLCs and naïve cells. Instead, Gata2 knockout arrests placenta-like fate, but facilitates the generation of PGCLCs. Our results not only reveal a newly cell fate dynamics between primed and naive states at single-cell resolution, but also provide a model system to explore mechanisms involved in regaining germline competence from primed pluripotency.


2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Walter Muskovic ◽  
Joseph E. Powell

Abstract Background Advances in droplet-based single-cell RNA-sequencing (scRNA-seq) have dramatically increased throughput, allowing tens of thousands of cells to be routinely sequenced in a single experiment. In addition to cells, droplets capture cell-free “ambient” RNA predominantly caused by lysis of cells during sample preparation. Samples with high ambient RNA concentration can create challenges in accurately distinguishing cell-containing droplets and droplets containing ambient RNA. Current methods to separate these groups often retain a significant number of droplets that do not contain cells or empty droplets. Additionally, there are currently no methods available to detect droplets containing damaged cells, which comprise partially lysed cells, the original source of the ambient RNA. Results Here, we describe DropletQC, a new method that is able to detect empty droplets, damaged, and intact cells, and accurately distinguish them from one another. This approach is based on a novel quality control metric, the nuclear fraction, which quantifies for each droplet the fraction of RNA originating from unspliced, nuclear pre-mRNA. We demonstrate how DropletQC provides a powerful extension to existing computational methods for identifying empty droplets such as EmptyDrops. Conclusions We implement DropletQC as an R package, which can be easily integrated into existing single-cell analysis workflows.


2017 ◽  
Author(s):  
Bo Wang ◽  
Daniele Ramazzotti ◽  
Luca De Sano ◽  
Junjie Zhu ◽  
Emma Pierson ◽  
...  

AbstractMotivationWe here present SIMLR (Single-cell Interpretation via Multi-kernel LeaRning), an open-source tool that implements a novel framework to learn a cell-to-cell similarity measure from single-cell RNA-seq data. SIMLR can be effectively used to perform tasks such as dimension reduction, clustering, and visualization of heterogeneous populations of cells. SIMLR was benchmarked against state-of-the-art methods for these three tasks on several public datasets, showing it to be scalable and capable of greatly improving clustering performance, as well as providing valuable insights by making the data more interpretable via better a visualization.Availability and ImplementationSIMLR is available on GitHub in both R and MATLAB implementations. Furthermore, it is also available as an R package on [email protected] or [email protected] InformationSupplementary data are available at Bioinformatics online.


2021 ◽  
Author(s):  
Anne Bertolini ◽  
Michael Prummer ◽  
Mustafa Anil Tuncel ◽  
Ulrike Menzel ◽  
María Lourdes Rosano-González ◽  
...  

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful technique to decipher tissue composition at the single-cell level and to inform on disease mechanisms, tumor heterogeneity, and the state of the immune microenvironment. Although multiple methods for the computational analysis of scRNA-seq data exist, their application in a clinical setting demands standardized and reproducible workflows, targeted to extract, condense, and display the clinically relevant information. To this end, we designed scAmpi (Single Cell Analysis mRNA pipeline), a workflow that facilitates scRNA-seq analysis from raw read processing to informing on sample composition, clinically relevant gene and pathway alterations, and in silico identification of personalized candidate drug treatments. We demonstrate the value of this workflow for clinical decision making in a molecular tumor board as part of a clinical study.


Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 19-20
Author(s):  
Yael C Cohen ◽  
Mor Zada ◽  
Shuang-Yin Wang ◽  
Chamutal Bornstein ◽  
Eyal David ◽  
...  

Substantial progress in the treatment of Multiple Myeloma (MM) extends survival for many patients (Pts), though most Pts eventually relapse and become therapy refractory. Patients with induction resistant multiple myeloma (IRMM), either primary refractory or early (≤18 months) relapse, have a particularly compromised survival. New treatment strategies and molecular biomarkers for patient stratification and effective clinical care are needed. We previously reported outcomes of KYDAR (NCT04065789) single-arm prospective clinical trial, in which pts primary resistant to a bortezomib-based induction achieved high rates of durable responses when treated with carfilzomib/daratumumab/lenalidomide/dexamethasone (Cohen YC et al. Blood (2019) 134 (Suppl 1): 982). We applied comprehensive single cell RNA-seq analysis of plasma cells (PCs) obtained from longitudinal bone marrow aspirate samples, taken from KYDAR participants (n=34), compared to newly diagnosed MM Pts (n=15) and to healthy controls (n=11). We discovered a novel MM resistance signature differentially expressed between IRMM and newly diagnosed MM groups. This "gene module is enriched for several pathways that were perturbed in the IRMM Pts, including mitochondrial stress genes, the ER and UPR pathway, and the proteasome machinery. Furthermore, differential gene expression analysis between KYDAR responders and non-responders unveil potentially druggable escape mechanisms. These include upregulation of genes associated with immune regulation, proteasome, apoptotic and ER-stress pathways, e.g. Cyclophilin A (PPIA) creating an elaborated signature and potential target list of pathways and escape mechanisms from a highly potent quadruple therapy. This signature includes many novel genes which were not previously described in the context of MM (Fig 1A). Here we report external validation of this novel resistance signature among 908 MM Pts in the MMRF CoMMpass dataset. We found that our genes signature expression follows a normal distribution with no apparent sub-populations in naïve patients, but when examining Pts after multiple relapses, we detected gradient increase in our signature with a clear bi-model distribution (Fig. 1B). The prevalence of high module-1 expression was 5% in newly diagnosed Pts vs 14% in Pts in 3rd or subsequent relapse (p<0.001). Survival analysis on MMRF "module 1 high" (module 1 score > 200) Pts (n = 68) compared with the rest of the population (n = 711) revealed a striking hazard-ratio of 3.9 (2.22 - 6.87) with p-value = 4.57x10-17 (Fig 1C). Module-1 was highly predictive of treatment outcome in KYDAR trials, beyond FISH cytogenetics (Fig 1D). We hypothesized that PPIA may function as a protective resistance gene in MM malignant cells, by accelerating protein folding pathways and reducing stress associated to proteasome inhibitors. In order to test whether PPIA is merely a marker for highly resistant patients or has a causal role in MM resistance to proteasome inhibitors, we used Cyclosporine A (CsA), a known inhibitor of PPIA, in a series of in vitro experiments, to explore it's potential synergy with carfilzomib, a proteasome inhibitor, on RPMI-8226 and U266B MM cell lines, expressing high levels of PPIA. Using MTS proliferation assay, we found that the combined CsA and carfilzomib therapy was significantly more effective than carfilzomib alone. Apoptosis as measured by Propidium Iodide, DAPI and Annexin V FITC staining, was dramatically increased in the combination therapy setting compared to carfilzomib or CsA monotherapy (Fig 1E). In summary, our study defines a roadmap for combining single cell RNA-seq profiling with clinical trials. We reveal and externally-validate a novel transcriptional signature for therapy resistance. We show inhibition of PPIA, a potential target identified, by CsA, overcomes relative resistance of MM cell lines to carfilzomib. We anticipate that such studies will significantly improve the ability to define mechanism of action of treatment, molecularly characterize the Pts that may benefit from the treatment, and reveal potential novel targets. Disclosures Tadmor: AbbVie: Consultancy, Speakers Bureau; Janssen: Consultancy, Speakers Bureau; Takeda: Consultancy, Speakers Bureau; Sanofi: Consultancy, Speakers Bureau; Medison: Consultancy, Speakers Bureau; Neopharm: Consultancy, Speakers Bureau; 6. Novartis Israel Ltd., a company wholly owned by Novartis Pharma AG: Consultancy, Speakers Bureau.


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