NeuroLINCS
Ontology highlight
ABSTRACT: The NeuroLINCS Center is part of the NIH Common Fund's Library of Integrated Network-based Cellular Signatures (LINCS) program, which aims to characterize how a variety of human cells, tissues and the entire organism respond to perturbations by drugs and other molecular factors. As Part of the LINCS program, the NeuroLINCS study concentrates on human brain cells, which are far less understood than other cells in the body. Our initial focus is to produce diseased motor neurons from patients by utilizing high-quality induced pluripotent stem cell (iPSC) lines from Amyotrophic Lateral Sclerosis (ALS) and Spinal Muscular Atrophy (SMA) patients in addition to unaffected normal healthy controls. Using state-of-the-art OMICS methods (genomics, epigenomics, transcriptomics, and proteomics), we intend to create a wealth of cellular data that is patient-specific in the context of their baseline genetic perturbations and in the presence of other genetic and environmental perturbagens (e.g. endoplasmic reticulum stress). The primary data will be used to build cell signatures that convey the key features that distinguish the state of a cell and determine its behavior. Ultimately, the analysis of these datasets will lead to the identification of a network of unique signatures relevant to each of these motor neuron diseases. The datasets represented in this study are generated from assays interrogating RNA expression (RNA-seq), chromatin accessibility (ATAC-seq) and whole genome sequencing.
SECONDARY ACCESSION(S): PRJNA349770PRJNA349769
REPOSITORIES: dbGaP
Similar Datasets
Project description:The National Institute of Health (NIH) Library of integrated network-based cellular signatures (LINCS) program is premised on generation of a publicly available data resource composed of cell-based biochemical responses or “signatures” to genetic or environmental perturbations. The NeuroLINCS center focuses on human induced pluripotent stem cells (iPSCs) derived from patients with motor neuron disease and the relevant differentiated neuronal cell cultures originating from patients and healthy controls. To establish a robust data generation process we strive to provide i) workflows for the generation of various multi-omic and functional assays for iPSC-derived motor and cortical neurons, ii) public annotated data sets and iii) relevant and targetable biological pathways of two motor neuron disorders, spinal muscular atrophy (SMA) and amyotrophic lateral sclerosis (ALS). Multi-omic assays are performed on aliquots of the same biological specimen and omic data integration analyses have been performed on both iPSCs and iPSC-derived motor neuron (iMN) cultures (unpublished results) Epigenomics, transcriptomics and proteomics data were collected for iPSC cultures originating from 12 individual human iPSC lines. Although iPSCs do not manifest a neurological disease state, they may be explored for asymptomatic biological defects that could ultimately be involved in disease in the context of neuronal tissues. Here we describe the biological diversity of the proteome among 12 individual genetic backgrounds and multiple cell growth replicates of iPSCs. By first defining aspects of non-disease specific biological and technical variability, true disease specific signatures of each omic assay may be extracted with greater clarity. These experiments are part of the transition of discovery proteomics to large data sets consisting of genetically diverse specimens for whole proteome analyses. Through the NIH LINCS program, the multi-omic data sets generated by NeuroLINCS are a public resource provided in tiers of data levels to enable broad applicability throughout the scientific community. From the DIA-MS proteomic analysis of iPSCs originating from 6 cell lines of district genetic backgrounds, 39 proteins recognized iPSC or embryonic stem cell markers were reproducibly quantified with consistent expression levels across all lines analyzed. Another set of consistently quantified proteins extracted from the analyses are more diverse, with little or no known direct associations to iPSCs. Interestingly, some are associated with cancer in the literature, not surprising in that malignancies are known to transition to a less differentiated, more pluripotent biological state to enable increased cell division and metastasis. Possibly even more interesting, as it is less well studied than cancer is the appearance of iPSC proteins quantified that are associated with oocyte health and fertility. Another sub-category of iPSC quantified proteins maybe considered typical housekeeping proteins of cellular metabolism, cell division and cell structure and morphology though as a public resource to be compared with other human tissue or organ cell types many allow the level of expression or isoform specificity to be unique of iPSCs. The data quality assessments and metadata provided make these DIA-MS analyses of 6 cell lines as iPSCs a rich public resource.
2020-09-15 | PXD021497 | panorama
Project description:The Library of Integrated Cellular Signatures (LINCS) is an NIH program which funds the generation of perturbational profiles across multiple cell and perturbation types, as well as read-outs, at a massive scale. The LINCS Center for Transcriptomics at the Broad Institute uses the L1000 high-throughput gene-expression assay to profile a range of cellular models and perturbations of cellular state. These data relate to using RNA-Seq datasets from the GTEx consortium (http://www.gtexportal.org/) to validate the L1000 assay and to improve the statistical model used in imputing the transcriptome. The Platform is GPL20573: Broad Institute Human L1000 epsilon https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GPL20573 For questions or assistance with this dataset, please email the CMap support team at: clue@broadinstitute.org
2017-03-03 | GSE92743 | GEO
Project description:Transcriptional programming of cell identity promises to open up new frontiers in regenerative medicine by enabling the efficient production of clinically relevant cell types. We examine if such cellular programming is accomplished by transcription factors that each have an independent and additive effect on cellular identity, or if programming factors synergize to produce an effect that is not independently obtainable. The combinations of Ngn2-Isl1-Lhx3 and Ngn2-Isl1-Phox2a transcription factors program embryonic stem cells to express a spinal or cranial motor neuron identity respectively. The two alternate expression programs are determined by recruitment of Isl1/Lhx3 and Isl1/Phox2a pairs to distinct genomic locations characterized by two alternative dimeric homeobox motifs. These results suggest that the function of programming modules relies on synergistic interactions among transcription factors and thus cannot be extrapolated from the study of individual transcription factors in a different cellular context. In this study, we functionally characterize induced motor neurons that have been directly generated from ES cells via the forced expression of two different combinations of three transcription factors. Spinal motor neurons are induced via the expression of Ngn2, Isl1, and Lhx3 (iNIL), while cortical motor neurons are induced via the expression of Ngn2, Isl1, and Phox2a (iNIP). Here we profile the gene expression patterns of both types of induced motor neurons, directed differentiation motor neurons, and control cells. In all, 20 microarray experiments are provided in this submission, including 3 replicates of a control condition, 3 replicates of cells that have 24hrs induction of iNIL, 2 replicates of induced spinal motor neurons (induction of iNIL for 48hrs) that have been Hb9-GFP sorted, 3 replicates of induced spinal motor neurons exposed to retinoic acid that have been Hb9-GFP sorted, 3 replicates of motor neurons that have been differentiated in vitro using RA and Hh signalling, 3 replicates of induced cortical motor neurons (induction of iNIP for 48hrs), and 3 replicates of cells in which Isl1 in induced alone (induction of iI for 48hrs). For ChIP-Seq Samples: In this study, we functionally characterize induced motor neurons that have been directly generated from ES cells via the forced expression of two different combinations of three transcription factors. Spinal motor neurons are induced via the expression of Ngn2, Isl1, and Lhx3 (iNIL), while cortical motor neurons are induced via the expression of Ngn2, Isl1, and Phox2a (iNIP). The genome-wide binding of some of the programming factors is characterized here using ChIP-seq. We characterize the binding of Lhx3 and Isl1/2 in iNIL cells, Phox2a and Isl1/2 in iNIP cells, and Isl1/2 in cells in which Isl1 is induced alone (iI). There are 7 Illumina sequence datasets in this submission; one replicate for each of iLhx3-V5 and Isl1/2 in iNIL cells, two replicates for each of iPhox2a-V5 and Isl1/2 in iNIP cells, and one replicate for Isl1/2 in iI cells. An appropriate pseudo-IP control experiment is included.
2013-07-04 | E-GEOD-31456 | biostudies-arrayexpress
Project description:Cell type-specific gene expression patterns are outputs of transcriptional gene regulatory networks (GRNs) that dynamically reconfigure to drive diverse cellular states. Single-cell transcriptomic technologies, such as single cell RNA-sequencing (scRNA-seq) and single cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq), can examine the transcriptional state at individual cell resolution, allowing the study of cellular heterogeneity and cell-type specific gene regulation in dynamic processes such as cell fate specification. However, current approaches to infer cell type-specific gene regulatory networks from these datasets are limited in their ability to integrate scRNA-seq and scATACseq measurements and to model network dynamics on a cell lineage. To address this challenge, we have developed single-cell Multi-Task Network Inference (scMTNI), a multi-task learning framework to infer the gene regulatory network for each cell type on a lineage from scRNA-seq and scATAC-seq data. Using simulated, published, and newly collected single cell omic datasets, we show that scMTNI is able to accurately infer gene regulatory networks and captures meaningful network dynamics that identify GRN components associated with cell type transitions. Application of our method to mouse cellular reprogramming identified key regulators associated with cell populations that reprogram versus those that are stalled. Taken together, scMTNI is a powerful framework to infer cell type-specific gene regulatory networks and their dynamics from scRNA-seq and scATAC-seq datasets.
2023-04-14 | GSE208620 | GEO
Project description:Current methods for quantification of differences between scRNA-seq datasets collected in multiple conditions focus on discrete regions of the transcriptional state space such as clusters of cells. Here, we describe a continuous measure of the effect of a perturbation across the transcriptomic space with single cell resolution. We describe the cellular state space as manifold, use a heat filter to estimate the density of each sample over the manifold, and normalize these densities to obtain the relative likelihood of observing each cell in each of the observed conditions. We develop vertex frequency clustering to identify populations of affected cells at the appropriate level of granularity. Using these selected populations we can derive gene signatures of affected populations of cells. We demonstrate both algorithms using a combination of biological and synthetic datasets.
2020-11-14 | GSE161465 | GEO
Project description:<p>The adoptive transfer of autologous T cells genetically modified to express a CD19-specific, 4-1BB/CD3zeta-signaling chimeric antigen receptor (CAR; CTL019) has shown remarkable activity in patients with B acute lymphoblastic leukemia. Similar therapy can induce long-term remissions for relapsed/refractory chronic lymphocytic leukemia (CLL) patients, but in only a small subset of subjects. The determinants of response and resistance to CTL019 therapy of CLL are not fully understood. We employed next generation sequencing of RNA (RNA-seq) to identify predictive indicators of response to CTL019 treatment. We performed RNA-seq on leukapheresis and manufactured infusion product T cells from patients with heavily pre-treated and high-risk disease. To characterize potency, we also performed RNA-seq on the cellular infusion product after CAR-specific stimulation. Our findings indicate that durable remission in CLL is associated with gene expression signatures of early memory T cell differentiation (e.g., STAT3), while T cells from poorly- or non-responding patients exhibited elevated expression of key regulators of late memory as well as effector T cell differentiation, apoptosis, aerobic glycolysis, hypoxia and exhaustion. These gene expression signatures, along with additional immunological biomarkers, may be used to identify which patients are most likely to respond to cellular therapies and suggest manufacturing modifications that might potentiate the generation of maximally efficacious infusion products.</p>
| phs001707 | dbGaP
Project description:Th17 cells have critical roles in mucosal defense and are major contributors to inflammatory disease. Their differentiation requires the nuclear hormone receptor RORγt working with multiple other essential transcription factors (TFs). We have used an iterative systems approach, combining genome-wide TF occupancy, expression profiling of TF mutants, and expression time series to delineate the Th17 global transcriptional regulatory network. We find that cooperatively-bound BATF and IRF4 contribute to initial chromatin accessibility, and with STAT3 initiate a transcriptional program that is then globally tuned by the lineage-specifying TF RORγt, which plays a focal deterministic role at key loci. Integration of multiple datasets allowed inference of an accurate predictive model that we computationally and experimentally validated, identifying multiple new Th17 regulators, including Fosl2, a key determinant of cellular plasticity. This interconnected network can be used to investigate new therapeutic approaches to manipulate Th17 functions in the setting of inflammatory disease. 143 RNA-seq, 83 ChIP-seq, 65 ChIP-seq controls, and 16 FAIRE-seq
2012-09-20 | E-GEOD-40918 | biostudies-arrayexpress
Project description:We aim to understand the role that Cdx2 plays in specifying the rostro-caudal identity of differentiating motor neurons. We find that expressing Cdx2 in combination with FGF signaling is sufficient to produce motor neurons with a more caudal identity. ChIP-seq analysis of Cdx2 finds that it binds extensively throughout the Hox regions in progenitor motor neurons. Analysis of polycomb-associated chromatin over Hox regions in the subsequently generated motor neurons finds that Cdx2 binding corresponds to chromatin domains encompassing de-repressed caudal Hox genes. These results suggest a direct role for Cdx2 in specifying caudal motor neuron identity. ChIP-seq studies: We characterize the binding of Cdx2 in progenitor motor neurons using a V5 tagged doxycycline inducible Cdx2 ESC line (iCdx2). Progenitor motor neurons were generated after 4 days of in vitro differentiation of mouse embryonic stem cells using retinoic acid (RA) and hedgehog (Hh) signaling exposure at day 2. On day 3, the cells are exposed to Dox with and without accompanying FGF signaling. The genome-wide binding of the induced Cdx2 transcription factor is profiled using ChIP-seq with an anti-V5 antibody. An appropriate whole-cell extract control experiment for these ChIP-seq experiments is also included. We also examine the effect of induced Cdx2 expression on polycomb-associated chromatin structure in the resulting cellular populations by profiling the H3K27me3 chromatin mark using ChIP-seq. H3K27me3 experiments were performed after 5 days of in vitro differentiation using cells exposed to either: 1) RA & Hh to derive progenitor motor neurons, followed by Dox & FGF; 2) Dox & FGF alone; or 3) RA and Hh alone. There are 6 Illumina sequencing datasets included in this submission: two biological replicates of iCdx2 ChIP-seq in the presence of FGF; one sample of iCdx2 ChIP-seq in the absence of FGF; one H3K27me3 ChIP-seq in the presence of RA, Hh, Dox, and FGF; one H3K27me3 ChIP-seq in the presence of Dox and FGF; and one H3K27me3 ChIP-seq in the presence of RA and Hh.
2013-07-04 | E-GEOD-39433 | biostudies-arrayexpress
Project description:Enhancing single-cell cellular state inference by incorporating molecular network features
| PRJNA589097 | ENA
Project description:Single cell genomics enables characterization of disease specific cell states, while improvements in mass spectrometry workflows bring the clinical use of body fluid proteomics within sight. However, the correspondence of peripheral protein signatures with cell state changes in diseased organs is currently unknown. Here, we use single cell RNA-seq and proteomics from large patient cohorts of pulmonary fibrosis and establish that predictive protein signatures in body fluids correspond to specific cellular changes in the lung. We determined transcriptional changes in 45 cell types across three patient cohorts and quantified bronchoalveolar lavage fluid and plasma proteins to discover protein signatures and associated cell state changes that were linked to diagnosis, lung function, smoking and injury status. Altered expression of the novel marker of lung health CRTAC1 in alveolar epithelium is robustly reported in patient plasma. With further improvements of this concept and deeper coverage of plasma proteomes, we envision future longitudinal profiling of body fluid signatures coupled to deep learning for non-invasive prediction and monitoring of pathological cell state changes in patient organs.
2020-09-18 | PXD017145 | Pride