Discovering cellular programs of intrinsic and extrinsic drivers of metabolic traits using LipocyteProfiler

Intracellular gene delivery is normally required to study gene functions. A versatile platform able to perform both chemical transfection and viral transduction to achieve efficient gene modification in most cell types is needed. Here we demonstrated that high throughput chemical transfection, virus packaging, and transduction can be conducted efficiently on our previously developed superhydrophobic microwell array chip (SMAR-chip). A total of 169 chemical transfections were successfully performed on the chip in physically separated microwells through a few simple steps, contributing to the convenience of DNA delivery and media change on the SMAR-chip. Efficiencies comparable to the traditional transfection in multi-well plates (~65%) were achieved while the manual operations were largely reduced. Two transfection procedures, the dry method amenable for the long term storage of the transfection material and the wet method for higher efficiencies were developed. Multiple transfections in a scheduled manner were performed to further increase the transfection efficiencies or deliver multiple genes at different time points. In addition, high throughput virus packaging integrated with target cell transduction were also proved which resulted in a transgene expression efficiency of >70% in NIH 3T3 cells. In summary, the SMAR-chip based high throughput gene delivery is efficient and versatile, which can be used for large scale genetic modifications in a variety of cell types.

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Therapeutic Strategies to Target Calcium Dysregulation in Alzheimer’s Disease

Cells ◽

10.3390/cells9112513 ◽

2020 ◽

Vol 9 (11) ◽

pp. 2513

Author(s):

Maria Calvo-Rodriguez ◽

Elizabeth K. Kharitonova ◽

Brian J. Bacskai

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cell Types ◽

Therapeutic Strategies ◽

Neuronal Function ◽

Calcium Dysregulation ◽

Disease Mechanisms ◽

Chemical Agents ◽

The Brain ◽

Ca2 Channels

Alzheimer’s disease (AD) is the most common form of dementia, affecting millions of people worldwide. Unfortunately, none of the current treatments are effective at improving cognitive function in AD patients and, therefore, there is an urgent need for the development of new therapies that target the early cause(s) of AD. Intracellular calcium (Ca2+) regulation is critical for proper cellular and neuronal function. It has been suggested that Ca2+ dyshomeostasis is an upstream factor of many neurodegenerative diseases, including AD. For this reason, chemical agents or small molecules aimed at targeting or correcting this Ca2+ dysregulation might serve as therapeutic strategies to prevent the development of AD. Moreover, neurons are not alone in exhibiting Ca2+ dyshomeostasis, since Ca2+ disruption is observed in other cell types in the brain in AD. In this review, we examine the distinct Ca2+ channels and compartments involved in the disease mechanisms that could be potential targets in AD.

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DeepBlueR: Large-scale epigenomic analysis in R

10.7287/peerj.preprints.3224v1 ◽

2017 ◽

Author(s):

Markus List ◽

Felipe Albrecht ◽

Christoph Bock ◽

Thomas Lengauer

Keyword(s):

Large Scale ◽

Cell Types ◽

Data Repository ◽

Complex Data ◽

Cellular Mechanisms ◽

Web Resource ◽

Typical Data ◽

Local Programming ◽

Computational Resources ◽

Epigenetic Research

Epigenetic research focuses on understanding non-inheritable factors influencing gene regulation and covers various cellular mechanisms such as DNA methylation, histone modification, miRNA function and transcription factor binding sites. Recent advances in high-throughput profiling technologies allow for systematically collecting data on each of these mechanisms in large-scale experiments. These efforts are fostered and concerted by international collaborations, such as the International Human Epigenome Consortium (IHEC) and its members. As a result of these collaborations, researchers can exploit massive amounts of publicly available epigenomic data on dozens of cell types, cell lines and tissues. Access to these data is streamlined by existing data portals and, in principle, allows for answering important biomedical questions. However, working with such data requires a suitable computational infrastructure not accessible ubiquitously. This creates a serious bottleneck in research and, as a result, data from these costly experiments are currently underused. To address this issue, we developed a new web resource, the DeepBlue Epigenomic Data Server to provide access to more than 40,000 experimental files from four major epigenome projects: ENCODE, ROADMAP, BLUEPRINT, the German Epigenome Program DEEP, the Canadian CEEHRC, and the Japanese CREST. A common challenge with this resources is that researchers are typically interested in a small fraction of the available epigenomic data to answer specific biomedical questions. Using a typical data repository to solve this task would require the user to download several files amounting to gigabytes of data that subsequently need to be filtered locally. In addition, it is often important to perform memory- and cpu-intensive operations to transform or aggregate these data, while the necessary computational resources are not accessible to every user. Therefore, the DeepBlue Data Server offers features beyond those of a centralized epigenomic data repository. It has a comprehensive programmatic interface (API) to enable users to perform complex data operations, such as searching, selecting, filtering, summarizing, and downloading of epigenomic data of interest. These operations can be combined into custom workflows, thus offering nearly the same degree of flexibility as a local programming environment. Here, we present DeepBlueR, a new R/Bioconductor package that enables users to engage with the DeepBlue server in a seamless fashion from within the R environment. DeepBlueR mirrors all DeepBlue data operations as R commands and provides additional features for compressing, downloading and transforming aggregated epigenomic data into suitable R data structures. A mechanism for local caching guarantees that complex scripts can be executed without the need to download previously requested data from the server.

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Deep learning-based predictive identification of neural stem cell differentiation

Nature Communications ◽

10.1038/s41467-021-22758-0 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Yanjing Zhu ◽

Ruiqi Huang ◽

Zhourui Wu ◽

Simin Song ◽

Liming Cheng ◽

...

Keyword(s):

Deep Learning ◽

Large Scale ◽

Deep Neural Network ◽

Early Stage ◽

Stem Cell Differentiation ◽

Cell Types ◽

Therapeutic Strategies ◽

Bright Field ◽

Independent Test ◽

Test Scenarios

AbstractThe differentiation of neural stem cells (NSCs) into neurons is proposed to be critical in devising potential cell-based therapeutic strategies for central nervous system (CNS) diseases, however, the determination and prediction of differentiation is complex and not yet clearly established, especially at the early stage. We hypothesize that deep learning could extract minutiae from large-scale datasets, and present a deep neural network model for predictable reliable identification of NSCs fate. Remarkably, using only bright field images without artificial labelling, our model is surprisingly effective at identifying the differentiated cell types, even as early as 1 day of culture. Moreover, our approach showcases superior precision and robustness in designed independent test scenarios involving various inducers, including neurotrophins, hormones, small molecule compounds and even nanoparticles, suggesting excellent generalizability and applicability. We anticipate that our accurate and robust deep learning-based platform for NSCs differentiation identification will accelerate the progress of NSCs applications.

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Network-based prediction of protein interactions

10.1101/275529 ◽

2018 ◽

Cited By ~ 4

Author(s):

István A. Kovács ◽

Katja Luck ◽

Kerstin Spirohn ◽

Yang Wang ◽

Carl Pollis ◽

...

Keyword(s):

High Throughput ◽

Protein Interactions ◽

Link Prediction ◽

Large Scale ◽

Molecular Mechanisms ◽

Biological Function ◽

Structural Constraints ◽

Closure Principle ◽

Cellular Mechanisms ◽

Interaction Partners

As biological function emerges through interactions between a cell’s molecular constituents, understanding cellular mechanisms requires us to catalogue all physical interactions between proteins [1–4]. Despite spectacular advances in high-throughput mapping, the number of missing human protein-protein interactions (PPIs) continues to exceed the experimentally documented interactions [5, 6]. Computational tools that exploit structural, sequence or network topology information are increasingly used to fill in the gap, using the patterns of the already known interactome to predict undetected, yet biologically relevant interactions [7–9]. Such network-based link prediction tools rely on the Triadic Closure Principle (TCP) [10–12], stating that two proteins likely interact if they share multiple interaction partners. TCP is rooted in social network analysis, namely the observation that the more common friends two individuals have, the more likely that they know each other [13, 14]. Here, we offer direct empirical evidence across multiple datasets and organisms that, despite its dominant use in biological link prediction, TCP is not valid for most protein pairs. We show that this failure is fundamental - TCP violates both structural constraints and evolutionary processes. This understanding allows us to propose a link prediction principle, consistent with both structural and evo-lutionary arguments, that predicts yet uncovered protein interactions based on paths of length three (L3). A systematic computational cross-validation shows that the L3 principle significantly outperforms existing link prediction methods. To experimentally test the L3 predictions, we perform both large-scale high-throughput and pairwise tests, finding that the predicted links test positively at the same rate as previously known interactions, suggesting that most (if not all) predicted interactions are real. Combining L3 predictions with experimen-tal tests provided new interaction partners of FAM161A, a protein linked to retinitis pigmentosa, offering novel insights into the molecular mechanisms that lead to the disease. Because L3 is rooted in a fundamental biological principle, we expect it to have a broad applicability, enabling us to better understand the emergence of biological function under both healthy and pathological conditions.SummaryWe unveil a fundamental organizing principle of biological networks and demonstrate its predictive power for uncovering novel protein interactions.

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Influence of genetic variants on gene expression in human pancreatic islets – implications for type 2 diabetes

10.1101/655670 ◽

2019 ◽

Cited By ~ 9

Author(s):

Ana Viñuela ◽

Arushi Varshney ◽

Martijn van de Bunt ◽

Rashmi B. Prasad ◽

Olof Asplund ◽

...

Keyword(s):

Type 2 Diabetes ◽

Genetic Variants ◽

Complex Traits ◽

Large Scale ◽

Association Studies ◽

Cell Types ◽

Genome Wide Association Studies ◽

Selective Enrichment ◽

Gwas Signal

AbstractMost signals detected by genome-wide association studies map to non-coding sequence and their tissue-specific effects influence transcriptional regulation. However, many key tissues and cell-types required for appropriate functional inference are absent from large-scale resources such as ENCODE and GTEx. We explored the relationship between genetic variants influencing predisposition to type 2 diabetes (T2D) and related glycemic traits, and human pancreatic islet transcription using RNA-Seq and genotyping data from 420 islet donors. We find: (a) eQTLs have a variable replication rate across the 44 GTEx tissues (<73%), indicating that our study captured islet-specific cis-eQTL signals; (b) islet eQTL signals show marked overlap with islet epigenome annotation, though eQTL effect size is reduced in the stretch enhancers most strongly implicated in GWAS signal location; (c) selective enrichment of islet eQTL overlap with the subset of T2D variants implicated in islet dysfunction; and (d) colocalization between islet eQTLs and variants influencing T2D or related glycemic traits, delivering candidate effector transcripts at 23 loci, including DGKB and TCF7L2. Our findings illustrate the advantages of performing functional and regulatory studies in tissues of greatest disease-relevance while expanding our mechanistic insights into complex traits association loci activity with an expanded list of putative transcripts implicated in T2D development.

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Large Scale Discoveries of Genes and Polymorphisms Associated with Variation in Hematologic Traits

Blood ◽

10.1182/blood.v128.22.sci-40.sci-40 ◽

2016 ◽

Vol 128 (22) ◽

pp. SCI-40-SCI-40

Author(s):

Nicole Soranzo

Keyword(s):

Blood Cell ◽

Genetic Variants ◽

Blood Cells ◽

Large Scale ◽

Genome Wide Association Study ◽

Conflicts Of Interest ◽

Cell Types ◽

The Novel ◽

Hematopoietic Stem ◽

Genome Wide

Abstract Hematopoiesis generates mature blood cells from hematopoietic stem cells (HSC) in distinct lineages to release of trillions of mature cells each day into the peripheral blood stream to perform essential functions such as oxygen transport, hemostasis and host defense. The formation and turnover of blood cells are tightly controlled and so the properties of blood cells, including their volume and count, have large heritabilities and are easily influenced by genetic variation. Here we describe the most statistically powerful genome wide association study (GWAS) of blood cell indices to date. We tested associations of 29.5 million polymorphic DNA sequence variants derived using the the Affymetrix axiom array with interpolation of 20 million variants using the UK 10000 genome data with 36 different hematological indices of red cells, white cells and platelets, some of which, such as the reticulocyte count, have been explored for the first time. We discovered significant associations at thousands of associated genetic variants, including hundreds of associations for low frequency genetic variants, thus identifying associations with larger effects on indices than those reported for common variants by previous discovery studies. We have described detailed follow-up studies of the novel associations. Using cell type-specific epigenome and gene expression data generated by the BLUEPRINT project and results from chromatin conformation capture in major blood cell types, we can identify the likely causal variants and their functional impact at a large number of the novel loci. Finally, we have evaluated the contribution of genetic variants to common and complex diseases. In conclusion, we have interrogated phenotypes across the whole hematopoietic tree and increased the number of traits associated with blood cell phenotypes by an order of magnitude. Overall, our results demonstrate widespread and powerful genetic influences on the formation and regulation of the major human blood cell types, identifying many novel genes involved and show the value of genome-wide functional annotation from relevant primary cell populations for interpreting genetic association results. Disclosures No relevant conflicts of interest to declare.

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DeepBlueR: Large-scale epigenomic analysis in R

10.7287/peerj.preprints.3224 ◽

2017 ◽

Author(s):

Markus List ◽

Felipe Albrecht ◽

Christoph Bock ◽

Thomas Lengauer

Keyword(s):

Large Scale ◽

Cell Types ◽

Data Repository ◽

Complex Data ◽

Cellular Mechanisms ◽

Web Resource ◽

Typical Data ◽

Local Programming ◽

Computational Resources ◽

Epigenetic Research

Epigenetic research focuses on understanding non-inheritable factors influencing gene regulation and covers various cellular mechanisms such as DNA methylation, histone modification, miRNA function and transcription factor binding sites. Recent advances in high-throughput profiling technologies allow for systematically collecting data on each of these mechanisms in large-scale experiments. These efforts are fostered and concerted by international collaborations, such as the International Human Epigenome Consortium (IHEC) and its members. As a result of these collaborations, researchers can exploit massive amounts of publicly available epigenomic data on dozens of cell types, cell lines and tissues. Access to these data is streamlined by existing data portals and, in principle, allows for answering important biomedical questions. However, working with such data requires a suitable computational infrastructure not accessible ubiquitously. This creates a serious bottleneck in research and, as a result, data from these costly experiments are currently underused. To address this issue, we developed a new web resource, the DeepBlue Epigenomic Data Server to provide access to more than 40,000 experimental files from four major epigenome projects: ENCODE, ROADMAP, BLUEPRINT, the German Epigenome Program DEEP, the Canadian CEEHRC, and the Japanese CREST. A common challenge with this resources is that researchers are typically interested in a small fraction of the available epigenomic data to answer specific biomedical questions. Using a typical data repository to solve this task would require the user to download several files amounting to gigabytes of data that subsequently need to be filtered locally. In addition, it is often important to perform memory- and cpu-intensive operations to transform or aggregate these data, while the necessary computational resources are not accessible to every user. Therefore, the DeepBlue Data Server offers features beyond those of a centralized epigenomic data repository. It has a comprehensive programmatic interface (API) to enable users to perform complex data operations, such as searching, selecting, filtering, summarizing, and downloading of epigenomic data of interest. These operations can be combined into custom workflows, thus offering nearly the same degree of flexibility as a local programming environment. Here, we present DeepBlueR, a new R/Bioconductor package that enables users to engage with the DeepBlue server in a seamless fashion from within the R environment. DeepBlueR mirrors all DeepBlue data operations as R commands and provides additional features for compressing, downloading and transforming aggregated epigenomic data into suitable R data structures. A mechanism for local caching guarantees that complex scripts can be executed without the need to download previously requested data from the server.

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Accelerated Discovery of High-Refractive-Index Polyimides via First-Principles Molecular Modeling, Virtual High-Throughput Screening, and Data Mining

10.26434/chemrxiv.7670903.v1 ◽

2019 ◽

Author(s):

Mohammad Atif Faiz Afzal ◽

Mojtaba Haghighatlari ◽

Sai Prasad Ganesh ◽

Chong Cheng ◽

Johannes Hachmann

Keyword(s):

Data Mining ◽

Refractive Index ◽

High Throughput ◽

First Principles ◽

High Throughput Screening ◽

Large Scale ◽

Computational Study ◽

High Refractive Index ◽

Structural Features ◽

Learning Program

<div>We present a high-throughput computational study to identify novel polyimides (PIs) with exceptional refractive index (RI) values for use as optic or optoelectronic materials. Our study utilizes an RI prediction protocol based on a combination of first-principles and data modeling developed in previous work, which we employ on a large-scale PI candidate library generated with the ChemLG code. We deploy the virtual screening software ChemHTPS to automate the assessment of this extensive pool of PI structures in order to determine the performance potential of each candidate. This rapid and efficient approach yields a number of highly promising leads compounds. Using the data mining and machine learning program package ChemML, we analyze the top candidates with respect to prevalent structural features and feature combinations that distinguish them from less promising ones. In particular, we explore the utility of various strategies that introduce highly polarizable moieties into the PI backbone to increase its RI yield. The derived insights provide a foundation for rational and targeted design that goes beyond traditional trial-and-error searches.</div>

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Next-Generation Sequencing: An Emerging Tool for Drug Designing

Current Pharmaceutical Design ◽

10.2174/1381612825666190911155508 ◽

2019 ◽

Vol 25 (31) ◽

pp. 3350-3357 ◽

Cited By ~ 1

Author(s):

Pooja Tripathi ◽

Jyotsna Singh ◽

Jonathan A. Lal ◽

Vijay Tripathi

Keyword(s):

Next Generation Sequencing ◽

High Throughput ◽

Large Scale ◽

Massively Parallel Sequencing ◽

Genomic Research ◽

Biological Research ◽

Next Generation ◽

Human Welfare ◽

Drug Designing ◽

Generation Sequencing

Background: With the outbreak of high throughput next-generation sequencing (NGS), the biological research of drug discovery has been directed towards the oncology and infectious disease therapeutic areas, with extensive use in biopharmaceutical development and vaccine production. Method: In this review, an effort was made to address the basic background of NGS technologies, potential applications of NGS in drug designing. Our purpose is also to provide a brief introduction of various Nextgeneration sequencing techniques. Discussions: The high-throughput methods execute Large-scale Unbiased Sequencing (LUS) which comprises of Massively Parallel Sequencing (MPS) or NGS technologies. The Next geneinvolved necessarily executes Largescale Unbiased Sequencing (LUS) which comprises of MPS or NGS technologies. These are related terms that describe a DNA sequencing technology which has revolutionized genomic research. Using NGS, an entire human genome can be sequenced within a single day. Conclusion: Analysis of NGS data unravels important clues in the quest for the treatment of various lifethreatening diseases and other related scientific problems related to human welfare.

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