Elucidating the mechanism of reprogramming is confounded by heterogeneity due to the low efficiency and differential kinetics of obtaining induced pluripotent stem cells iPSCs from somatic cells.
Therefore, we increased the efficiency with a combination of epigenomic modifiers and signaling molecules and profiled the transcriptomes of individual reprogramming cells. Contrary to the established temporal order, somatic gene inactivation and upregulation of cell cycle, epithelial, and early pluripotency genes can be triggered independently such that any combination of these events can occur in single cells.
Ehf, Phlda2, and translation initiation factor Eif4a1 play functional roles in robust iPSC generation.
Using regulatory network analysis, we identify a critical role for signaling inhibition by 2i in repressing somatic expression and synergy between the epigenomic modifiers ascorbic acid and a Dot1L inhibitor for pluripotency gene activation.
Tran et al. With single-cell transcriptomes, they define the transcriptional signature and key regulators of reprogramming cells. Using network analysis, they find 2i suppresses somatic while ascorbic acid and Dot1l inhibitor collaboratively upregulate pluripotency genes. Mouse iPSCs are functionally equivalent to embryonic stem cells ESCs because they pass all the tests of pluripotency, including tetraploid complementation Zhao et al.
In addition, iPSC colonies appear at different times during the reprogramming process Apostolou and Hochedlinger, ; Buganim et al. Identifying only those cells that successfully complete the reprogramming process versus those that fail to do so can reveal key mechanisms that make the reprogramming process inefficient.
Transcriptional profiling of bulk reprogramming populations over time has led to the description of a temporal series of events with early downregulation of somatic cell expression followed by metabolic and cell cycle changes that culminates in the activation of the pluripotency gene regulatory network Apostolou and Hochedlinger, ; Apostolou and Stadtfeld, Mouse embryonic fibroblasts MEFs undergo a mesenchymal-to-epithelial transition MET before pluripotency gene activation during reprogramming Hussein et al.
Importantly, whether all cells undergoing reprogramming have to trigger these programs in the same temporal order remains unknown. Due to the low efficiency and variable kinetics of obtaining iPSCs, reprogramming cultures will have heterogeneous expression profiles. Therefore, in population-based analyses of unsorted cells, expression signatures from cells that will successfully reprogram are obscured.
To overcome these issues with ensemble profiling, single-cell analysis of candidate factors in reprogramming MEFs has been performed both at the RNA and protein level. These studies have uncovered intermediate markers, a role for Ras-signaling, and a role for Sox2 in the deterministic activation of the pluripotency network. Buganim et al. More recent experiments have focused on profiling cells during reprogramming in low-efficiency systems, including non-transgenic chemical reprogramming Zhao et al.
Reprogramming efficiency can be increased by the modulation of regulators that decrease chromatin compaction and those that perturb signaling pathways Esteban et al.
We and others have combined such epigenomic and signaling modulators and found that they synergistically increase reprogramming efficiency from OSKM-expressing cells Bar-Nur et al. Although each small molecule has been used previously, to our knowledge this particular combination called A2S [ascorbic acid, 2i, SGC] henceforth has not been reported.
We found that early events, such as epithelial and cell cycle activation, are turned on independently. Surprisingly, all mesenchymal genes are not downregulated together in the same cells, and some genes, such as Twist1, can even be found expressed with early pluripotency marker Nanog. A large majority of the cells in FBS stop cycling partly due to senescence, which can be overcome by the addition of A2S. Nanog, Oct4, and even Sox2 could be activated in individual cells, but what distinguished successful reprogramming was the detectable coexpression of these genes in different modules.
Chromosome conformation capture methods are being increasingly used to study three-dimensional genome architecture in multiple cell types and species.
An important challenge is to examine changes in three-dimensional architecture across cell types and species. We present Arboretum-Hi-C, a multi-task spectral clustering method, to identify common and context-specific aspects of genome architecture.
Compared to standard clustering, Arboretum-Hi-C produced more biologically consistent patterns of conservation. Most clusters are conserved and enriched for either high- or low-activity genomic signals.Ulti rokne ke totke
Most genomic regions diverge between clusters with similar chromatin state except for a few that are associated with lamina-associated domains and open chromatin. The online version of this article doi The three-dimensional 3D organization of the genome is emerging as an important layer in the regulation of gene expression [ 1 — 10 ]. Recent advances in high-throughput chromosome conformation capture 3C, particularly 4C, 5C, and Hi-C technology allow us to examine the 3D organization of a genome in an unbiased and comprehensive manner [ 18 ].
Genome-wide 3C data sets are becoming increasingly available for multiple species and tissues and have enabled us to examine the folding and organizational principles of the genome and identify long-range interactions among genomic loci [ 111 ]. In particular, studies in yeast have shown that such long-range interactions are enriched for loci involving tRNA genes, centromeres, early origins of replication [ 4 ], and transcription factories for regulation of gene expression [ 12 ].
In mammalian systems, such interactions are organized into architectural units known as compartments and topologically associated domains TADs. While the interactions can be cell-type- [ 13 ] or species-specific [ 1415 ], the compartments and TADs are likely conserved across developmental stages [ 316 ] and across species [ 14 ]. However, our understanding of the extent of conservation and context-specificity of these interactions is incomplete.
The availability of genome-wide 3C data sets for multiple species and tissues gives us the unique opportunity to compare chromatin organization across tissues and organisms to identify the principles of this organization. In parallel, statistical techniques have been developed to normalize these data, identify significant interacting genomic loci [ 17 — 19 ], and identify different types of organizational units from these data [ 20 ].
Clustering and dimensionality reduction approaches, in particular, have emerged as important analytical tools for Hi-C data [ 891921 ].Baby swing recall 2019
Rao et al. Principal component analyses of Hi-C data for each chromosome revealed a compartment structure [ 8 ], where regions within each compartment are more likely to interact than regions from two different compartments. Imakaev et al. The second and third eigenvectors exhibited variation along the chromosomal arms, with increased magnitude in the centromeric and telomeric regions for the second and third eigenvectors, respectively.
While current clustering and dimensionality reduction techniques have provided useful insights into genome organization, there are several key issues that need to be addressed. First, unlike traditional functional genomics data such as genome-wide mRNA level or histone modification measurements, 3C data specify contact counts among pairs of genomic loci. A graph-based representation provides a natural representation of these Hi-C data [ 22 ] and incorporating Hi-C interaction information as a graph prior was recently shown to improve chromatin-mark-based genome segmentation and annotation [ 23 ].
Graph-clustering methods, such as spectral clustering [ 2425 ], when applied to graph data, are more advantageous than using conventional clustering. However, to our knowledge, graph-clustering methods, especially across multiple cell types and species, have not been explored with Hi-C data. It is currently unknown whether such methods have any advantages over traditional clustering methods that do not capture the graph nature of 3C data.
The second issue is that methods that systematically compare these maps across multiple tissues or multiple organisms are scarce [ 3 ]. In particular, given such contact count matrices from two or more cell types, tissues, or organisms, it is not immediately clear how to identify clusters simultaneously in both cell types and also compare them to identify common and context-specific patterns.
The systematic comparison of the general 3D organization of the genome across multiple conditions, cell types, and organisms is still a largely unexplored computational challenge. In this paper, we first perform a comprehensive analysis of different clustering approaches hierarchical, k -means, and spectral using different distance measures. Our analysis shows that spectral clustering methods tend to outperform existing non-graph-based methods, producing higher quality clusters based on statistical enrichment of multiple one-dimensional regulatory genomic signals.
We next develop a multi-task version of our spectral clustering algorithm and apply it to Hi-C data in four cell lines, two each from human and mouse. Compared to an independent clustering method, our multi-task clustering method finds more biologically consistent patterns of conservation and divergence. Using the inferred clusters, we perform a systematic comparative study of the extent of conservation and divergence in chromosome contact preferences between matched cell lines of different species, and between cell lines of the same species.How to block ads on youtube app iphone
Our results indicate that most regions maintain their chromosome contact preferences between cell lines, and regions that diverge between species and cell lines are enriched for lamina-associated domains LADs and architectural proteins.Mark your calendars! Agendas subject to change. Click here to download a pdf of the program.
Oral Presentations Schedule Posters Schedule. Go directly to: Sunday, Dec 9 - Monday, Dec Welcome and Introductory Remarks. Pablo Meyer and Gustavo Stolovitzky. Submitted Abstracts Chair: Pablo Meyer. Mi Yang Target functional similarity based workflows for drug synergy prediction and stratification.
Michael Banf Enhancing gene regulatory network inference through data integration with markov random fields. Keynote - Daphne Koller A fireside chat. Challenge Updates Chair: Jim Costello. Geoffrey Siwo Malaria Challenge. Avner Schlessinger Challenge Overview Talk. Minji Jeon Best Performance Talk. Shengbao Suo Revealing the critical regulators of cell identity in the mouse cell atlas.
Thin Nguyen Best Performer Talk 1. Peng Qiu Best Performer Talk 2. RSG Schedule. Keynote - Aravinda Chakravarti. Alireza Fotuhi Siahpirani, Rupa Sridharan and Sushmita Roy Incorporating noisy prior networks for estimating latent transcription factor activities and inferring genome-scale regulatory network in yeast and mammalian systems.
Keynote - Shirley Liu. Special Session Welcome. Keynote - Peter Kharchenko Analysis of transcriptional dynamics with single-cell transcriptomics.
Joseph A. Bryson and Emily R. Miraldi Benchmarked methods for transcriptional regulatory network inference from single-cell RNA-seq data. Keynote - Miriam Merad. Keynote - Adam Siepel An evolutionary framework for measuring epigenomic information and estimating cell-type specific fitness consequences.
Akpeli Nordor, Martin Aryee and Geoffrey Siwo Predicting interactions between small molecules and genome editing technologies.
Svetlana Shabalina Complexity and evolution of the mammalian transcriptome: the architecture of alternative transcription and splicing.
Keynote - Itai Yanai Single-cell and spatial gene expression analysis of tumorigenesis. Keynote - Ana Pombo.Our goal is to understand, model, and recapitulate in vitro the instructive signals utilized by human embryos to pattern tissue-specific differentiation of pluripotent stem cells, and apply this knowledge towards the rational design of tissue engineered scaffolds and other regenerative therapeutic strategies.
Currently, we primarily focus on generating tissues and therapies for the central nervous system.Incorporating noisy prior networks for estimating... - Alireza Fotuhi Siahpirani - RECOMB/RSG 2018
Governor Mandela Barnes! Micro-patterned substrate tech allows for single neural rosette formation. Tim Kamp middle! Graduate student Alireza Aghayee instructing new undergraduate researcher Lexi Doersch with a new experimental practice.
Thomson, Randolph S. Ashton, Sushmita Roy. Cell Systems. JD McNulty. Acta Biomaterialia. A 3D culture model of innervated human skeletal muscle enables studies of the adult neuromuscular junction. Engineering induction of singular neural rosette emergence within hPSC-derived tissues. Single-injection ex ovo transplantation method for broad spinal cord engraftment of human pluripotent stem cell-derived motor neurons.
Journal of Neuroscience Methods. High-content imaging with micropatterned multiwell plates reveals influence of cell geometry and cytoskeleton on chromatin dynamics Harkness.
You can read this work and learn more about our collaborators from our publications page and the news story through the College of Engineering!Youtube blacklist season 7 episode 1
March Dr. Please read news story here. December Attention Undergraduates! Application deadline is February 15th, Please see this document for details! Congrats Dr. View WIDs press release for the technology. Also, Dr. October Axosim, Inc. They will be assisting Carlos and Nisha with various research-related projects. More announcements!!! U niversity of W isconsin —Madison. Public Advocacy Dr.
Neural Rosette Cutaway Micro-patterned substrate tech allows for single neural rosette formation. Neural rosettes 2D neural rosettes grown in cell culture. N-Cadherin red ; Cell nuclei blue ; Laminin green. Mentorships Graduate student Alireza Aghayee instructing new undergraduate researcher Lexi Doersch with a new experimental practice.A predictive modeling approach for cell-line specific long-range regulatory interactions.
Long range regulatory interactions among distal enhancers and target genes are important determinants of tissue-specific gene expression. Genome-scale identification of these interactions in a cell type-specific manner, especially using the fewest possible datasets, is a significant challenge. We develop a novel computational approach, Regulatory Interaction Prediction for Promoters and Long-range Enhancers RIPPLEthat combines Random forests and structured-sparsity based multi-task learning to predict cell-type specific enhancer-promoter interactions.
RIPPLE integrates published 5C data with diverse regulatory genomic datasets is based on a supervised learning framework to first identify a minimal set of features needed to predict long range regulatory interactions. We used RIPPLE within an ensemble approach to predict genome-wide interaction maps in cell lines with available 5C data as well as new cell lines. We identified different classes of interacting enhancers and promoters that represent different combinations of architectural proteins and chromatin marks.
Enhancer-promoter interactions in the high confidence interaction networks tend to be organized into subnetworks that are significantly enriched in house keeping and cell-type specific functions. Overall, our approach and associated genome-wide predictions provides a useful resource to understand long range gene regulation across multiple cell types.
Designed by Web Page Templates.The overall goal and mission for the HMAP Center has not changed throughout the course of the year and has continued to push boundaries developing the next generation of human organotypic culture models. The need for human, organotypic culture models coupled with the requirements of contemporary toxin screening i. The H-MAPs Center is committed to transforming chemical toxicity testing by taking advantage of advances in biology, biotechnology, and computer modeling.
The overall objective of this program is to create transformative organotypic human models in formats that offer unique practical capabilities for toxin screening and pathway analysis.
The H-MAPs Center brings together leading experts in human pluripotent stem cell biology, human development, and microscale tissue engineering to develop organotypic human models. The Center has demonstrated the ability to form organotypic human models in robust, innovative high throughput screening systems across center projects and aims to identify mechanisms of action associated with toxicity using bioinformatics-based pathway analysis. The center has continued to grow and demonstrate excellence in its scientific achievements and progress.
The increased weekly interactions with the Environmental Protection Agency during the Virtual Tissue Models Meeting has not only strengthened research within the center itself but the value of these interactions has been broadly disseminated throughout the University. Significant progress has been made pursuant to research milestones laid down in the original grant proposal. In addition, there have been over invited talks, podium presentations and disclosed technologies for further dissemination of research carried out in the center.
Increased interactions at the annual satellite meeting at the Society of Toxicology conference in Washington DC, at the Alternatives and Animal Use in the Life Sciences and subsequent attendance and presentation at Virtual Tissue Models meetings with EPA staff have drastically improved research into the toxicological sciences within the center.
Additionally, efforts are being made to align experimental in vitro models with in silico models at the EPA. Further, activities aimed at translating technologies developed in the H-MAP center to pharmaceutical use through direct interactions with pharmaceutical companies and the formation of local spinout companies.
The synthetic matrices core in partnership with StemPharm Inc. Further, the same 3D neurovascular tissues are being co-developed in a collaboration between StemPharm Inc and Cellular Dynamics International for their broader dissemination and use.
Devices developed by Dr. Johnson have similarly resulted in the formation of a local spinout company Onexio. Brain MAPs has patented their technology and is forming a local spinout company. Outputs and outcomes from individual projects are highlighted in individual project reports. First review of center activities held by the Scientific Advisory Committee meeting in March.I contenuti vr di rai cinema disponibili su playstation store
Continuation of intra-project biweeklyinter-project biweeklyand Center-wide quarterly meetings to ensure connectivity and quality assurance throughout the Center. Molly Morgan and Dr. Planned activities include all activities proposed in the initial Center proposal. In addition, Center activities have led to initiation of a series of new collaborative projects, including:.
The average expression values for genes in consensus modules associated with the immune system are mentioned below each box plot. Genes in modules associated with immune system function red have induced gene expression relative to genes in the other consensus modules blue. Supplementary Figure S3. Summary of consensus module enrichments for regulators from DNase I—filtered motif instances for six cancer cell lines.
The figure follows the same legend as Figure S1 replacing the annotation terms with the name of the TF with DNase I—filtered motif instances. Each panel corresponds to one of the cancers studied in this paper. The rows are ordered using optimal leaf order clustering using the number of modules enriched across all cancers as a distance measure.
Supplementary Figure S4. Summary of consensus module enrichments for regulators from DNase I—filtered motif instances from the H1 ES cell line. This figure follows the legend of Figure S1replacing the annotation terms with the name of the TF with DNase I—filtered motif instances.
The enrichments are ordered by TF name in alphabetical order from top to bottom shown in three panels for clarity. Supplementary Figure S5. Five-fold cross-validation study of predictive power of consensus networks. Shown are the distributions of average correlation values between observed and predicted expression levels from five-fold cross-validation on the consensus network red and random networks blue for each of the cancer studies.
Many human diseases including cancer are the result of perturbations to transcriptional regulatory networks that control context-specific expression of genes.
A comparative approach across multiple cancer types is a powerful approach to illuminate the common and specific network features of this family of diseases. An emerging challenge is to devise computational approaches that systematically compare these genomic data sets across different cancer types that identify common and cancer-specific network components.
We present a module- and network-based characterization of transcriptional patterns in six different cancers being studied in TCGA: breast, colon, rectal, kidney, ovarian, and endometrial. Our approach uses a recently developed regulatory network reconstruction algorithm, modular regulatory network learning with per gene information MERLINwithin a stability selection framework to predict regulators for individual genes and gene modules.
Our module-based analysis identifies a common theme of immune system processes in each cancer study, with modules statistically enriched for immune response processes as well as targets of key immune response regulators from the interferon regulatory factor IRF and signal transducer and activator of transcription STAT families.
Comparison of the inferred regulatory networks from each cancer type identified a core regulatory network that included genes involved in chromatin remodeling, cell cycle, and immune response. Regulatory network hubs included genes with known roles in specific cancer types as well as genes with potentially novel roles in different cancer types. Overall, our integrated module and network analysis recapitulated known themes in cancer biology and additionally revealed novel regulatory hubs that suggest a complex interplay of immune response, cell cycle, and chromatin remodeling across multiple cancers.
Transcriptional regulatory networks are networks of regulatory proteins, such as transcription factors TFs and signaling proteins, and target genes that control the context-specific expression profiles of genes.
Many human diseases, including cancer, are the result of perturbations to transcriptional regulatory networks. However, a major challenge is the lack of systematic ways to compare multiple cancers that can provide deeper insight into the aberrant network patterns in disease cell types.
Author: Alireza Fotuhi Siahpirani
Network-based characterization 4 of complex diseases, including cancer, has been invaluable to integrate and interpret functional genomics data sets and identify new biomarkers that can be used to better classify patients into subtypes, and such approaches are much more powerful than approaches that examine a single gene 5 — 10 at a time.
However, most of these approaches have relied on known and curated pathways and single-reference interaction maps. Due to our limited understanding of regulator target relationships in mammalian systems, the role of transcriptional regulatory networks has not been examined as extensively as the role the protein—protein interaction networks has. On the other hand, module-based methods that characterize complex transcriptional programs using sets of genes that are coherently changing have been remarkably useful to identify cancer-specific signatures that are often correlated with clinical traits.
Recent approaches to pan-cancer studies enable comparisons of multiple cancers; however, these approaches have focused primarily on genomic sequence mutations. For example, the approach by Ciriello et al. It was found that cancers can be grouped into those driven by copy number variations and those driven by somatic mutations.
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