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Archive for the ‘Variation in human protein-coding regions’ Category


Medicine in 2045 – Perspectives by World Thought Leaders in the Life Sciences & Medicine

Reporter: Aviva Lev-Ari, PhD, RN

 

This report is based on an article in Nature Medicine | VOL 25 | December 2019 | 1800–1809 | http://www.nature.com/naturemedicine

Looking forward 25 years: the future of medicine.

Nat Med 25, 1804–1807 (2019) doi:10.1038/s41591-019-0693-y

 

Aviv Regev, PhD

Core member and chair of the faculty, Broad Institute of MIT and Harvard; director, Klarman Cell Observatory, Broad Institute of MIT and Harvard; professor of biology, MIT; investigator, Howard Hughes Medical Institute; founding co-chair, Human Cell Atlas.

  • millions of genome variants, tens of thousands of disease-associated genes, thousands of cell types and an almost unimaginable number of ways they can combine, we had to approximate a best starting point—choose one target, guess the cell, simplify the experiment.
  • In 2020, advances in polygenic risk scores, in understanding the cell and modules of action of genes through genome-wide association studies (GWAS), and in predicting the impact of combinations of interventions.
  • we need algorithms to make better computational predictions of experiments we have never performed in the lab or in clinical trials.
  • Human Cell Atlas and the International Common Disease Alliance—and in new experimental platforms: data platforms and algorithms. But we also need a broader ecosystem of partnerships in medicine that engages interaction between clinical experts and mathematicians, computer scientists and engineers

Feng Zhang, PhD

investigator, Howard Hughes Medical Institute; core member, Broad Institute of MIT and Harvard; James and Patricia Poitras Professor of Neuroscience, McGovern Institute for Brain Research, MIT.

  • fundamental shift in medicine away from treating symptoms of disease and toward treating disease at its genetic roots.
  • Gene therapy with clinical feasibility, improved delivery methods and the development of robust molecular technologies for gene editing in human cells, affordable genome sequencing has accelerated our ability to identify the genetic causes of disease.
  • 1,000 clinical trials testing gene therapies are ongoing, and the pace of clinical development is likely to accelerate.
  • refine molecular technologies for gene editing, to push our understanding of gene function in health and disease forward, and to engage with all members of society

Elizabeth Jaffee, PhD

Dana and Albert “Cubby” Broccoli Professor of Oncology, Johns Hopkins School of Medicine; deputy director, Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins.

  • a single blood test could inform individuals of the diseases they are at risk of (diabetes, cancer, heart disease, etc.) and that safe interventions will be available.
  • developing cancer vaccines. Vaccines targeting the causative agents of cervical and hepatocellular cancers have already proven to be effective. With these technologies and the wealth of data that will become available as precision medicine becomes more routine, new discoveries identifying the earliest genetic and inflammatory changes occurring within a cell as it transitions into a pre-cancer can be expected. With these discoveries, the opportunities to develop vaccine approaches preventing cancers development will grow.

Jeremy Farrar, OBE FRCP FRS FMedSci

Director, Wellcome Trust.

  • shape how the culture of research will develop over the next 25 years, a culture that cares more about what is achieved than how it is achieved.
  • building a creative, inclusive and open research culture will unleash greater discoveries with greater impact.

John Nkengasong, PhD

Director, Africa Centres for Disease Control and Prevention.

  • To meet its health challenges by 2050, the continent will have to be innovative in order to leapfrog toward solutions in public health.
  • Precision medicine will need to take center stage in a new public health order— whereby a more precise and targeted approach to screening, diagnosis, treatment and, potentially, cure is based on each patient’s unique genetic and biologic make-up.

Eric Topol, MD

Executive vice-president, Scripps Research Institute; founder and director, Scripps Research Translational Institute.

  • In 2045, a planetary health infrastructure based on deep, longitudinal, multimodal human data, ideally collected from and accessible to as many as possible of the 9+ billion people projected to then inhabit the Earth.
  • enhanced capabilities to perform functions that are not feasible now.
  • AI machines’ ability to ingest and process biomedical text at scale—such as the corpus of the up-to-date medical literature—will be used routinely by physicians and patients.
  • the concept of a learning health system will be redefined by AI.

Linda Partridge, PhD

Professor, Max Planck Institute for Biology of Ageing.

  • Geroprotective drugs, which target the underlying molecular mechanisms of ageing, are coming over the scientific and clinical horizons, and may help to prevent the most intractable age-related disease, dementia.

Trevor Mundel, MD

President of Global Health, Bill & Melinda Gates Foundation.

  • finding new ways to share clinical data that are as open as possible and as closed as necessary.
  • moving beyond drug donations toward a new era of corporate social responsibility that encourages biotechnology and pharmaceutical companies to offer their best minds and their most promising platforms.
  • working with governments and multilateral organizations much earlier in the product life cycle to finance the introduction of new interventions and to ensure the sustainable development of the health systems that will deliver them.
  • deliver on the promise of global health equity.

Josep Tabernero, MD, PhD

Vall d’Hebron Institute of Oncology (VHIO); president, European Society for Medical Oncology (2018–2019).

  • genomic-driven analysis will continue to broaden the impact of personalized medicine in healthcare globally.
  • Precision medicine will continue to deliver its new paradigm in cancer care and reach more patients.
  • Immunotherapy will deliver on its promise to dismantle cancer’s armory across tumor types.
  • AI will help guide the development of individually matched
  • genetic patient screenings
  • the promise of liquid biopsy policing of disease?

Pardis Sabeti, PhD

Professor, Harvard University & Harvard T.H. Chan School of Public Health and Broad Institute of MIT and Harvard; investigator, Howard Hughes Medical Institute.

  • the development and integration of tools into an early-warning system embedded into healthcare systems around the world could revolutionize infectious disease detection and response.
  • But this will only happen with a commitment from the global community.

Els Toreele, PhD

Executive director, Médecins Sans Frontières Access Campaign

  • we need a paradigm shift such that medicines are no longer lucrative market commodities but are global public health goods—available to all those who need them.
  • This will require members of the scientific community to go beyond their role as researchers and actively engage in R&D policy reform mandating health research in the public interest and ensuring that the results of their work benefit many more people.
  • The global research community can lead the way toward public-interest driven health innovation, by undertaking collaborative open science and piloting not-for-profit R&D strategies that positively impact people’s lives globally.

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Evolution of the Human Cell Genome Biology Field of Gene Expression, Gene Regulation, Gene Regulatory Networks and Application of Machine Learning Algorithms in Large-Scale Biological Data Analysis

Curator & Reporter: Aviva Lev-Ari, PhD, RN

 

Subjects:

The Scientific Frontier is presented in Deciphering eukaryotic gene-regulatory logic with 100 million random promoters

Boer, C.G., Vaishnav, E.D., Sadeh, R. et al. Deciphering eukaryotic gene-regulatory logic with 100 million random promotersNat Biotechnol (2019) doi:10.1038/s41587-019-0315-8

Abstract

How transcription factors (TFs) interpret cis-regulatory DNA sequence to control gene expression remains unclear, largely because past studies using native and engineered sequences had insufficient scale. Here, we measure the expression output of >100 million synthetic yeast promoter sequences that are fully random. These sequences yield diverse, reproducible expression levels that can be explained by their chance inclusion of functional TF binding sites. We use machine learning to build interpretable models of transcriptional regulation that predict ~94% of the expression driven from independent test promoters and ~89% of the expression driven from native yeast promoter fragments. These models allow us to characterize each TF’s specificity, activity and interactions with chromatin. TF activity depends on binding-site strand, position, DNA helical face and chromatin context. Notably, expression level is influenced by weak regulatory interactions, which confound designed-sequence studies. Our analyses show that massive-throughput assays of fully random DNA can provide the big data necessary to develop complex, predictive models of gene regulation.

The Evolution of the Human Cell Genome Biology Field of Gene Expression, Gene Regulation, Gene Regulatory Networks and Application of Machine Learning Algorithms in Large-Scale Biological Data Analysis is presented in the following Table

 

50 Liu, X., Li, Y. I. & Pritchard, J. K. Trans effects on gene expression can drive omnigenic inheritance. Cell 177, 1022–1034 e1026 (2019).
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6 Wang, X. et al. High-resolution genome-wide functional dissection of transcriptional regulatory regions and nucleotides in human. Nat. Commun. 9, 5380 (2018).
15 Yona, A. H., Alm, E. J. & Gore, J. Random sequences rapidly evolve into de novo promoters. Nat. Commun. 9, 1530 (2018).
4 van Arensbergen, J. et al. Genome-wide mapping of autonomous promoter activity in human cells. Nat. Biotechnol. 35, 145–153 (2017).
14 Cuperus, J. T. et al. Deep learning of the regulatory grammar of yeast 5’ untranslated regions from 500,000 random sequences. Genome Res. 27, 2015–2024 (2017).
31 Levo, M. et al. Systematic investigation of transcription factor activity in the context of chromatin using massively parallel binding and expression assays. Mol. Cell 65, 604–617 e606 (2017).
49 Boyle, E. A., Li, Y. I. & Pritchard, J. K. An expanded view of complex traits: from polygenic to omnigenic. Cell 169, 1177–1186 (2017).
54 de Boer, C. High-efficiency S. cerevisiae lithium acetate transformation. protocols.io https://doi.org/10.17504/protocols.io.j4tcqwn (2017).
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20 Shalem, O. et al. Systematic dissection of the sequence determinants of gene 3’ end mediated expression control. PLoS Genet. 11, e1005147 (2015).
55 Deng, C., Daley, T. & Smith, A. D. Applications of species accumulation curves in large-scale biological data analysis. Quant. Biol. 3, 135–144 (2015).
9 Hughes, T. R. & de Boer, C. G. Mapping yeast transcriptional networks. Genetics 195, 9–36 (2013).
10 Jolma, A. et al. DNA-binding specificities of human transcription factors. Cell 152, 327–339 (2013).
19 Kosuri, S. et al. Composability of regulatory sequences controlling transcription and translation in Escherichia coli. Proc. Natl Acad. Sci. USA 110, 14024–14029 (2013).
7 Sharon, E. et al. Inferring gene regulatory logic from high-throughput measurements of thousands of systematically designed promoters. Nat. Biotechnol. 30, 521–530 (2012).
18 de Boer, C. G. & Hughes, T. R. YeTFaSCo: a database of evaluated yeast transcription factor sequence specificities. Nucleic Acids Res. 40, D169–D179 (2012).
56 Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).
61 Cherry, J. M. et al. Saccharomyces Genome Database: the genomics resource of budding yeast. Nucleic Acids Res. 40, D700–D705 (2012).
11 Nutiu, R. et al. Direct measurement of DNA affinity landscapes on a high-throughput sequencing instrument. Nat. Biotechnol. 29, 659–664 (2011).
26 Zhang, Z. et al. A packing mechanism for nucleosome organization reconstituted across a eukaryotic genome. Science 332, 977–980 (2011).
30 Ganapathi, M. et al. Extensive role of the general regulatory factors, Abf1 and Rap1, in determining genome-wide chromatin structure in budding yeast. Nucleic Acids Res. 39, 2032–2044 (2011).
52 Erb, I. & van Nimwegen, E. Transcription factor binding site positioning in yeast: proximal promoter motifs characterize TATA-less promoters. PloS One 6, e24279 (2011).
3 Kinney, J. B., Murugan, A., Callan, C. G. Jr. & Cox, E. C. Using deep sequencing to characterize the biophysical mechanism of a transcriptional regulatory sequence. Proc. Natl Acad. Sci. USA107, 9158–9163 (2010).
8 Gertz, J., Siggia, E. D. & Cohen, B. A. Analysis of combinatorial cis-regulation in synthetic and genomic promoters. Nature 457, 215–218 (2009).
16 Wunderlich, Z. & Mirny, L. A. Different gene regulation strategies revealed by analysis of binding motifs. Trends Genet. 25, 434–440 (2009).
27 Hesselberth, J. R. et al. Global mapping of protein–DNA interactions in vivo by digital genomic footprinting. Nat. Methods 6, 283–289 (2009).
29 Hartley, P. D. & Madhani, H. D. Mechanisms that specify promoter nucleosome location and identity. Cell 137, 445–458 (2009).
51 Gibson, D. G. et al. Enzymatic assembly of DNA molecules up to several hundred kilobases. Nat. Methods 6, 343–345 (2009).
58 Segal, E. & Widom, J. From DNA sequence to transcriptional behaviour: a quantitative approach. Nat. Rev. Genet. 10, 443–456 (2009).
2 Yuan, Y., Guo, L., Shen, L. & Liu, J. S. Predicting gene expression from sequence: a reexamination. PLoS Comput. Biol. 3, e243 (2007).
46 Hibbs, M. A. et al. Exploring the functional landscape of gene expression: directed search of large microarray compendia. Bioinformatics 23, 2692–2699 (2007).
25 Liu, X., Lee, C. K., Granek, J. A., Clarke, N. D. & Lieb, J. D. Whole-genome comparison of Leu3 binding in vitro and in vivo reveals the importance of nucleosome occupancy in target site selection. Genome Res. 16, 1517–1528 (2006).
34 Roberts, G. G. & Hudson, A. P. Transcriptome profiling of Saccharomyces cerevisiae during a transition from fermentative to glycerol-based respiratory growth reveals extensive metabolic and structural remodeling. Mol. Genet. Genomics 276, 170–186 (2006).
48 Tanay, A. Extensive low-affinity transcriptional interactions in the yeast genome. Gen. Res. 16, 962–972 (2006).
53 Tong, A. H. & Boone, C. Synthetic genetic array analysis in Saccharomyces cerevisiae. Methods Mol. Biol. 313, 171–192 (2006).
57 Li, W. & Godzik, A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22, 1658–1659 (2006).
62 Chua, G. et al. Identifying transcription factor functions and targets by phenotypic activation. Proc. Natl Acad. Sci. USA 103, 12045–12050 (2006).
17 Arnosti, D. N. & Kulkarni, M. M. Transcriptional enhancers: intelligent enhanceosomes or flexible billboards? J. Cell. Biochem. 94, 890–898 (2005).
21 Granek, J. A. & Clarke, N. D. Explicit equilibrium modeling of transcription-factor binding and gene regulation. Genome Biol. 6, R87 (2005).
1 Beer, M. A. & Tavazoie, S. Predicting gene expression from sequence. Cell 117, 185–198 (2004).
28 Bernstein, B. E., Liu, C. L., Humphrey, E. L., Perlstein, E. O. & Schreiber, S. L. Global nucleosome occupancy in yeast. Genome Biol. 5, R62 (2004).
44 Kim, T. S., Kim, H. Y., Yoon, J. H. & Kang, H. S. Recruitment of the Swi/Snf complex by Ste12-Tec1 promotes Flo8-Mss11-mediated activation of STA1 expression. Mol. Cell. Biol. 24, 9542–9556 (2004).
45 Harbison, C. T. et al. Transcriptional regulatory code of a eukaryotic genome. Nature 431, 99–104 (2004).
60 Kent, N. A., Eibert, S. M. & Mellor, J. Cbf1p is required for chromatin remodeling at promoter-proximal CACGTG motifs in yeast. J. Biol. Chem. 279, 27116–27123 (2004).
22 Kulkarni, M. M. & Arnosti, D. N. Information display by transcriptional enhancers. Development 130, 6569–6575 (2003).
24 Conlon, E. M., Liu, X. S., Lieb, J. D. & Liu, J. S. Integrating regulatory motif discovery and genome-wide expression analysis. Proc. Natl Acad. Sci. USA 100, 3339–3344 (2003).
43 Neely, K. E., Hassan, A. H., Brown, C. E., Howe, L. & Workman, J. L. Transcription activator interactions with multiple SWI/SNF subunits. Mol. Cell. Biol. 22, 1615–1625 (2002).
23 Bussemaker, H. J., Li, H. & Siggia, E. D. Regulatory element detection using correlation with expression. Nat. Genet. 27, 167–171 (2001).
37 Haurie, V. et al. The transcriptional activator Cat8p provides a major contribution to the reprogramming of carbon metabolism during the diauxic shift in Saccharomyces cerevisiae. J. Biol. Chem. 276, 76–85 (2001).
39 Grauslund, M. & Ronnow, B. Carbon source-dependent transcriptional regulation of the mitochondrial glycerol-3-phosphate dehydrogenase gene, GUT2, from Saccharomyces cerevisiae. Can. J. Microbiol. 46, 1096–1100 (2000).
42 Cullen, P. J. & Sprague, G. F. Jr. Glucose depletion causes haploid invasive growth in yeast. Proc. Natl Acad. Sci. USA 97, 13619–13624 (2000).
38 Sato, T. et al. TheE-box DNA binding protein Sgc1p suppresses the gcr2 mutation, which is involved in transcriptional activation of glycolytic genes in Saccharomyces cerevisiae. FEBS Lett. 463, 307–311 (1999).
40 Madhani, H. D. & Fink, G. R. Combinatorial control required for the specificity of yeast MAPK signaling. Science 275, 1314–1317 (1997).
41 Gavrias, V., Andrianopoulos, A., Gimeno, C. J. & Timberlake, W. E. Saccharomyces cerevisiae TEC1 is required for pseudohyphal growth. Mol. Microbiol. 19, 1255–1263 (1996).
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35 Forsburg, S. L. & Guarente, L. Identification and characterization of HAP4: a third component of the CCAAT-bound HAP2/HAP3 heteromer. Genes Dev. 3, 1166–1178 (1989).
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To access each reference as a live link, go to the number in the first column in the Table and look it up in the List of References in the Link, below

https://www.nature.com/articles/s41587-019-0315-8

Author information

C.G.D. and A.R. drafted the manuscript, with all authors contributing. C.G.D. analyzed the data. C.G.D., E.D.V., E.L.A. and R.S. performed the experiments. A.R. and N.F. supervised the research.

Correspondence to Carl G. de Boer or Aviv Regev.

Ethics declarations

Competing interests

A.R. is an SAB member of Thermo Fisher Scientific, Neogene Therapeutics, Asimov, and Syros Pharmaceuticals, an equity holder of Immunitas, and a founder of and equity holder in Celsius Therapeutics. All other authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Cite this article

Boer, C.G., Vaishnav, E.D., Sadeh, R. et al. Deciphering eukaryotic gene-regulatory logic with 100 million random promoters. Nat Biotechnol (2019) doi:10.1038/s41587-019-0315-8

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NSPR1 and DEC2 genes: Survival on 4.5 hours of Sleep per night: A mutation in the β1-adrenergic receptor gene in humans who require fewer hours of sleep than most, ADRB1 + neurons are active during rapid eye movement (REM) sleep and wakefulness

 

Reporter: Aviva Lev-Ari, PhD, RN

 

10/2019 RESEARCH ARTICLE SLEEP

Mutant neuropeptide S receptor reduces sleep duration with preserved memory consolidation

 See all authors and affiliations

Science Translational Medicine  16 Oct 2019:
Vol. 11, Issue 514, eaax2014
DOI: 10.1126/scitranslmed.aax2014

Abstract

Sleep is a crucial physiological process for our survival and cognitive performance, yet the factors controlling human sleep regulation remain poorly understood. Here, we identified a missense mutation in a G protein–coupled neuropeptide S receptor 1 (NPSR1) that is associated with a natural short sleep phenotype in humans. Mice carrying the homologous mutation exhibited less sleep time despite increased sleep pressure. These animals were also resistant to contextual memory deficits associated with sleep deprivation. In vivo, the mutant receptors showed increased sensitivity to neuropeptide S exogenous activation. These results suggest that the NPS/NPSR1 pathway might play a critical role in regulating human sleep duration and in the link between sleep homeostasis and memory consolidation.

It is possible that drugs could be developed to target either the NSPR1 or DEC2 genes, as a treatment for insomnia or other sleep disorders. However, further understanding of exactly how these genes function would be required before this stage. Both are involved in brain function, so targeting them could lead to negative neural side effects.

 

Neuron

Volume 103, Issue 6, 25 September 2019, Pages 1044-1055.e7

A Rare Mutation of β1-Adrenergic Receptor Affects Sleep/Wake Behaviors

Highlights

  • A mutation in ADRB1 leads to natural short sleep trait in humans
  • Mice engineered with same mutation have similar short sleep behavior as humans
  • Activity of dorsal pons ADRB1 + neurons associates with REM sleep and wakefulness
  • Mutation increases the population activity of dorsal pons ADRB1 + neurons

Summary

Sleep is crucial for our survival, and many diseases are linked to long-term poor sleep quality. Before we can use sleep to enhance our health and performance and alleviate diseases associated with poor sleep, a greater understanding of sleep regulation is necessary. We have identified a mutation in the β 1-adrenergic receptor gene in humans who require fewer hours of sleep than most. In vitro, this mutation leads to decreased protein stability and dampened signaling in response to agonist treatment. In vivo, the mice carrying the same mutation demonstrated short sleep behavior. We found that this receptor is highly expressed in the dorsal pons and that these ADRB1 + neurons are active during rapid eye movement (REM) sleep and wakefulness. Activating these neurons can lead to wakefulness, and the activity of these neurons is affected by the mutation. These results highlight the important role of β 1-adrenergic receptors in sleep/wake regulation.

Keywords

Additional SOURCES

Second Gene Mutation that Lets People Survive on Less Sleep

 

Other related articles on Circadian Rhythm and Sleep published in this Open Access Online Scientific Journal include the following:

 

2017 Nobel Prize in Physiology or Medicine jointly to Jeffrey C. Hall (ex-Brandeis, University of Maine), Michael Rosbash (Brandeis University) and Michael W. Young (Rockefeller University in New York) for their discoveries of molecular mechanisms controlling the circadian rhythm

Curator: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2017/10/02/2017-nobel-prize-in-physiology-or-medicine-jointly-to-jeffrey-c-hall-michael-rosbash-and-michael-w-young-for-their-discoveries-of-molecular-mechanisms-controlling-the-circadian-rhythm/

 

Patient-Reported Outcomes Study, Presented at SLEEP 2018, Provides Confirmatory Real-World Evidence of the Previously Presented 7-hour Action of REMfresh®, the First Continuous Release and Absorption Melatonin™

Reporter: Gail S. Thornton, PhD(c)

https://pharmaceuticalintelligence.com/2018/06/10/patient-reported-outcomes-study-presented-at-sleep-2018-provides-confirmatory-real-world-evidence-of-the-previously-presented-7-hour-action-of-remfresh-the-first-continuous-release-and-absorp/

 

Clinically Studied, Continuous Release and Absorption Melatonin, REMfresh, Designed to Give Patients Up to 7 Hours of Sleep Support

Reporter: Gail S. Thornton, M.A.

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2017 award recipients including Thomas S. Kilduff, PhD, Director, Center for Neuroscience at SRI International in Menlo Park, California

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2017/04/28/sleep-research-society-announces-2017-award-recipients-including-thomas-s-kilduff-phd-director-center-for-neuroscience-at-sri-international-in-menlo-park-california/

 

Sleep and Memory

Curator: Larry H. Bernstein, MD, FCAP

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https://pharmaceuticalintelligence.com/2016/02/12/sleep-apnea-insular-glutamate-and-gaba-levels/

 

Fat, Sleep and the Gut

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23andMe Genome-Wide Association Study on Human propensity to Get up early or Sleep in the Morning

Reporter: Aviva Lev-Ari, PhD, RN

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Study Shows Learning Is Best Enhanced During Sleep – Jewish Business News

Reporter: Aviva Lev-Ari, PhD, RN

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Beta-Blockers Cause Lack Of Restful Sleep – Life Extension

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2014/08/04/beta-blockers-cause-lack-of-restful-sleep-life-extension/

 

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Prolonged Wakefulness: Lack of Sufficient Duration of Sleep as a Risk Factor for Cardiovascular Diseases – Indications for Cardiovascular Chrono-therapeutics

Curator: Aviva Lev-Ari, PhD, RN

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Author: Larry H Bernstein, MD, FCAP

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2019 Warren Alpert Foundation Award goes to Four Scientists for Seminal Discoveries in OptoGenetics – Illuminating the Human Brain

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2019/07/18/2019-warren-alpert-foundation-award-goes-to-four-scientists-for-seminal-discoveries-in-optogenetics-illuminating-the-human-brain/

 

 

 

 

 

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Featuring Computational and Systems Biology Program at Memorial Sloan Kettering Cancer Center, Sloan Kettering Institute (SKI), The Dana Pe’er Lab

 

Reporter: Aviva Lev-Ari, PhD, RN

A lecture by Dana Pe’er is included, below in the eProceedings which I generated in Real Time on 6/14/2019 @MIT

eProceeding 2019 Koch Institute Symposium – 18th Annual Cancer Research Symposium – Machine Learning and Cancer, June 14, 2019, 8:00 AM-5:00 PM ET MIT Kresge Auditorium, 48 Massachusetts Ave, Cambridge, MA

https://pharmaceuticalintelligence.com/2019/03/12/2019-koch-institute-symposium-machine-learning-and-cancer-june-14-2019-800-am-500-pmet-mit-kresge-auditorium-48-massachusetts-ave-cambridge-ma/

 

 

Memorial Sloan Kettering Cancer Center, Sloan Kettering Institute (SKI

https://www.mskcc.org/research/ski/about

 

Research Programs

Cancer Biology & Genetics Program

Our scientists study the molecular and genetic determinants of cancer predisposition, tumor development, and metastasis.

Cell Biology Program

Our researchers explore the molecular mechanisms that control normal cell behavior and how these mechanisms are disrupted in cancer.

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Our scientists use chemical principles to investigate cutting-edge topics in biology and medicine.

Computational & Systems Biology Program

The goal of our research is to build computer models that simulate biological processes, from the molecular level up to the organism as a whole.

Developmental Biology Program

Our investigators study the mechanisms that control cell proliferation, cell differentiation, tissue patterning, and tissue morphogenesis.

Immunology Program

Our research is geared toward understanding how the immune system functions in all its complexity and how it can be harnessed to fight disease.

Molecular Biology Program

Our research is directed at understanding how cell growth is regulated and how the integrity of the genome is maintained.

Molecular Pharmacology Program

Our research program serves as a conduit for bringing basic science discoveries to preclinical and clinical evaluation.

Structural Biology Program

Our researchers are dedicated to understanding biology at the structural and mechanistic levels, and aiding the development of new cancer therapies.

Book traversal links for Research

 

The Dana Pe’er Lab

 

The Dana Pe'er Lab

The Pe’er lab combines single cell technologies, genomic datasets and machine learning algorithms to address fundamental questions in biomedical science. Empowered by recent breakthrough technologies like massive parallel single cell RNA-sequencing, we ask questions such as: How do multi-cellular organisms develop from a single cell, resulting in the vast diversity of progenitor and terminal cell types? How does a cell’s regulatory circuit control the dynamics of signal processing and how do these circuits rewire over the course of development? How does an ensemble of cells function together to execute a multi-cellular response, such as an immune response to pathogen or cancer? We will also address more medically oriented questions such as: How do regulatory circuits go awry in disease? What is the consequence of intra-tumor heterogeneity? Can we characterize the tumor immune eco-system to gain a better understanding of when or why immunotherapy works or does not work? A key goal is to use this characterization of the tumor immune eco-system to personalize immunotherapy.

Dana Pe'er, PhD

Dana Pe’er, PhD

Chair, Computational and Systems Biology Program, SKI; Scientific Director, Metastasis & Tumor Ecosystems Center

Research Focus

Computational Biologist Dana Pe’er combines single cell technologies, genomic datasets and machine learning techniques to address fundamental questions addressing regulatory cell circuits, cellular development, tumor immune eco-system, genotype to phenotype relations and precision medicine.

Education

PhD, Hebrew University, Jerusalem Israel

 

The Dana Pe’er Lab: Publications

View a full listing of Dana Pe’er’s journal articles.


Palantir characterizes cell fate continuities in human hematopoiesis. Setty M, Kiseliovas V, Levine J, Gayoso A, Mazutis L, Pe’er D. 2019, in press. Nature Biotechnology.

Single-cell map of diverse immune phenotypes in the breast tumor microenvironment. Azizi E, Carr AJ, Plitas G, Cornish AE, Konopacki C, Prabhakaran S, Nainys J, Wu K, Kiseliovas V, Setty M, Choi K, Fromme RM, Dao P, McKenney PT, Wasti RC, Kadaveru K, Mazutis L, Rudensky AY, Pe’er D. Cell. 2018 Aug 23;174(5):1293-1308.e36. doi: 10.1016/j.cell.2018.05.060. PMID: 29961579

Recovering gene interactions from single-cell data using data diffusion. van Dijk D, Sharma R, Nainys J, Yim K, Kathail P, Carr AJ, Burdziak C, Moon KR, Chaffer CL, Pattabiraman D, Bierie B, Mazutis L, Wolf G, Krishnaswamy S, Pe’er D. Cell. 2018 Jul 26;174(3):716-729.e27. doi: 10.1016/j.cell.2018.05.061. PubMed PMID: 29961576

The Human Cell Atlas. Regev A et al. Elife. 2017 Dec 5;6. pii: e27041. doi: 10.7554/eLife.27041. PubMed PMID: 29206104

Distinct cellular mechanisms underlie anti-CTLA-4 and anti-PD-1 checkpoint blockade. Wei SC, Levine JH, Cogdill AP, Zhao Y, Anang NAS, Andrews MC, Sharma P, Wang J, Wargo JA, Pe’er D, Allison JP. Cell. 2017 Sep 7;170(6):1120-1133.e17. doi: 10.1016/j.cell.2017.07.024. PMID: 28803728

Wishbone identifies bifurcating developmental trajectories from single-cell data. Setty M, Tadmor MD, Reich-Zeliger S, Angel O, Salame TM, Kathail P, Choi K, Bendall S, Friedman N, Pe’er D. Nat Biotechnol. 2016 Jun;34(6):637-45. doi: 10.1038/nbt.3569. PMID: 27136076

Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis. Levine JH, Simonds EF, Bendall SC, Davis KL, Amir el-AD, Tadmor MD, Litvin O, Fienberg HG, Jager A, Zunder ER, Finck R, Gedman AL, Radtke I, Downing JR, Pe’er D, Nolan GP. Cell. 2015 Jul 2;162(1):184-97. doi: 10.1016/j.cell.2015.05.047. PMID: 26095251

Interferon α/β enhances the cytotoxic response of MEK inhibition in melanoma. Litvin O, Schwartz S, Wan Z, Schild T, Rocco M, Oh NL, Chen BJ, Goddard N, Pratilas C, Pe’er D. Mol Cell. 2015 Mar 5;57(5):784-796. doi: 10.1016/j.molcel.2014.12.030. PMID: 25684207

Integration of genomic data enables selective discovery of breast cancer drivers. Sanchez-Garcia F, Villagrasa P, Matsui J, Kotliar D, Castro V, Akavia UD, Chen BJ, Saucedo-Cuevas L, Rodriguez Barrueco R, Llobet-Navas D, Silva JM, Pe’er D. Cell. 2014 Dec 4;159(6):1461-75. doi: 10.1016/j.cell.2014.10.048. PMID: 25433701

Conditional density-based analysis of T cell signaling in single-cell data. Krishnaswamy S, Spitzer MH, Mingueneau M, Bendall SC, Litvin O, Stone E, Pe’er D, Nolan GP. Systems biology. Science. 2014 Nov 28;346(6213):1250689. doi: 10.1126/science.1250689. PMID: 25342659

Single-cell trajectory detection uncovers progression and regulatory coordination in human B cell development. Bendall SC, Davis KL, Amir el-AD, Tadmor MD, Simonds EF, Chen TJ, Shenfeld DK, Nolan GP, Pe’er D. Cell. 2014 Apr 24;157(3):714-25. doi: 10.1016/j.cell.2014.04.005. PMID: 24766814

Book traversal links for The Dana Pe’er Lab

SOURCE

https://www.mskcc.org/research/ski/labs/dana-pe-er/publications

The Dana Pe’er Lab is one of four Labs of the Computational & Systems Biology Program

Computational biologists combine findings in biology with computer algorithms and databases to conduct biological research on powerful computers, using sophisticated software — so-called “dry” laboratories — in ways that complement and strengthen traditional laboratory and clinical research. The aim is to build computer models that simulate biological processes from the molecular level up to the organism as a whole and to use these models to make useful predictions.

 

Computational biology can help interpret detailed molecular profiles of cancerous and noncancerous cells, molecular response profiles of therapeutic agents, and a person’s genetic profile to assist in the development of better diagnostics and prognostics, as well as improved therapies. Intelligent use of computational methods using detailed molecular and genomic data is expected to reduce the trial and error of drug development and possibly lead to shorter, more accurate clinical trials.

 

The Christina Leslie Lab

The John Chodera Lab

The Dana Pe'er Lab

The Joao Xavier Lab

 

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SINGLE CELL GENOMICS 2019, September 24-26, 2019, Djurönäset, Stockholm, Sweden

Reporter: Aviva Lev-Ari, PhD, RN

 

http://www.weizmann.ac.il/conferences/SCG2019/single-cell-genomics-2019

 

Organizing committee

  • Ido Amit
  • Amos Tanay
  • Sten Linnarsson
  • Rickard Sandberg
  • Aviv Regev
  • John Marioni
  • Alexander van Oudenaarden

Sponsored by:


Single cell genomics has emerged as a revolutionary technology transforming nearly every field of biomedical research. Through its many applications (single cell genome sequencing, single cell transcriptomics, various single cell epigenetic profiling approaches, and spatially resolved methods), researchers can characterize the genetic and functional properties of individual cells in their native conditions, leading to numerous experimental and clinical opportunities. As technology is leaping forward, many critical questions are arising:

• How can the behavior of groups of thousands or tens of thousands of single cells be analyzed and modeled?

• How can samples of precise single-cell-states be converted to inferred cellular behaviour, in space and time?

• How can multimodal single-cell datasets be integrated?

• What can we learn about cell-cell interactions?

• What are the immediate implications to fields like neuroscience, immunology, cancer research and stem cells?

• What will the longer-term impacts be for clinical research and practice?

The conference will bring together many of the pioneers and leading experts in the field to three days of extensive, interdisciplinary and informal discussion. Our goal is to create a forum where knowledge is shared, hoping to define together the agenda of this new community. The meeting will include presentations from invited leaders and several selected abstracts, a poster session and many opportunities for interaction. We encourage students and postdocs to participate by presenting abstracts.

Speakers

  • Ed Boyden, MIT
  • Long Cai, CalTech
  • Joe Ecker, Salk Institute
  • Guoji Guo, Zhejiang University
  • Shalev Itzkovitz, Weizmann Institute of Science
  • Maria Kasper, Karolinska Institutet
  • Job Kind, Hubrecht institute
  • Allon Klein, Harvard
  • Keren Leeat, Standford
  • Ed Lein, Allen Institute
  • Evan Macosko, Broad Institute
  • Dana Pe’er, MSKCC
  • Nikolaus Rajewsky, Max Delbrück
  • Alex Shalek, MIT
  • Fabian Theis, Helmholtz Munich
  • Barbara Treutlein, Max Planck Institute
  • Hongkui Zeng, Allen Institute
  • Xiaowei Zhuang, Harvard

Program – pending

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The second annual PureTech Health BIG (Brain-Immune-Gut) Summit 2019 – By invitation only –

Selected Tweets from  #BIGAxisSummit

by @pharma_BI @AVIVA1950

for @pharmaceuticalintelligence.com

Reporter: Aviva Lev-Ari, PhD, RN

 

January 30 – February 1, 2019

The second annual PureTech Health BIG Summit brings together an elite ensemble of leading scientific researchers, investors, and CEOs and R&D leaders from major pharmaceutical, technology, and biotech companies.

The BIG Summit is designed to stimulate ideas that will have an impact on existing pipelines and catalyze future interactions among a group of delegates that represent leaders and innovators in their fields.

Please follow the discussion on Twitter using #BIGAxisSummit

By invitation only; registration is non-transferable.

For more information, please contact PureTechHealthSummit@PureTechHealth.com

 

HOST COMMITTEE

Participants

 

BIG SUMMIT AGENDA

(Subject to Change)

PureTech Health BIG Summit 2019 Agenda_FINALv2_WEBSITE.jpg

“Almost starting to understand immunology at this thought-provoking @PureTechh #BIGAxisSummit. Great Speakers.”

-tweet by Simone Fishburn, BioCentury @SimoneFishburn

SOURCE

https://bigsummit2019.com/agenda/

 

Selected Tweets from  #BIGAxisSummit

by @pharma_BI @AVIVA1950

for @pharmaceuticalintelligence.com

Gail S. Thornton Selections

Luke Timmerman‏ @ldtimmerman 7h7 hours ago

Back for final sessions at #BIGAxisSummit. @PureTechH Jim Harper of Sonde Health talking about how voice data — pacing, fine motor articulation, oscillation — can point the way to objective, quantitative measures for detecting and monitoring depression.

 

Eddie Martucci

 @EddieMartucci 5h5 hours ago

Paul Biondi at #BIGAxisSummit : What makes big deals happen is financial, and *deep conviction* of a big future fit. Disproportionate valuation from bidders is expected.

Love this. We often reduce everything to mathematical analyses to champion or ridicule deals. Not that simple

 

PureTech Health Plc‏ @PureTechH Jan 31

Bob Langer (@MIT) asks how #lymphatics affected by #aging. Santambrogio: typically blame aging #immune cells for increased disease, but aging affects lymphatics too (less efficient trafficking shown). Rejuvenating these could affect several aging-related diseases #BigAxisSummit

 

PureTech Health Plc‏ @PureTechH Jan 31

Viviane Labrie (@VAInstitute) discusses why the appendix has been identified as a potential starting point for #parkinsons #BIGAxisSummit

 

PureTech Health Plc‏ @PureTechH Jan 31

Chris Porter (@MIPS_Australia) notes #lymphatics is major route for trafficking #immune cells that surveil gut and respond to immune & #autoimmune stimuli. This is key in #BIGAxis interactions and why lymphatics-targeted therapies could enhance #immunomodulation #BIGAxisSummit

 

Dr. Stephen J. Williams Selections

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4.

5.

Dr. Irina Robu Selection

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2.

3.

4.

5.

Dr. Sudipta Saha Selection

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Single-cell Genomics: Directions in Computational and Systems Biology – Contributions of Prof. Aviv Regev @Broad Institute of MIT and Harvard, Cochair, the Human Cell Atlas Organizing Committee with Sarah Teichmann of the Wellcome Trust Sanger Institute

 

Curator: Aviva Lev-Ari, PhD, RN

 

Dana Pe’er, PhD, now chair of computational and systems biology at the Sloan Kettering Institute at the Memorial Sloan Kettering Cancer Center and a member of the Human Cell Atlas Organizing Committee,

what really sets Regev apart is the elegance of her work. Regev, says Pe’er, “has a rare, innate ability of seeing complex biology and simplifying it and formalizing it into beautiful, abstract, describable principles.”

Dr. Aviv Regev, an MIT biology professor who is also chair of the faculty of the Broad and director of its Klarman Cell Observatory and Cell Circuits Program, was reviewing a newly published white paper detailing how the Human Cell Atlas is expected to change the way we diagnose, monitor, and treat disease at a gathering of international scientists at Israel’s Weizmann Institute of Science, 10/2017.

For Regev, the importance of the Human Cell Atlas goes beyond its promise to revolutionize biology and medicine. As she once put it, without an atlas of our cells, “we don’t really know what we’re made of.”

Regev, turned to a technique known as RNA interference (she now uses CRISPR), which allowed her to systematically shut genes down. Then she looked at which genes were expressed to determine how the cells’ response changed in each case. Her team singled out 100 different genes that were involved in regulating the response to the pathogens—some of which weren’t previously known to be involved in immune function. The study, published in Science, generated headlines.

The project, the Human Cell Atlas, aims to create a reference map that categorizes all the approximately 37 trillion cells that make up a human. The Human Cell Atlas is often compared to the Human Genome Project, the monumental scientific collaboration that gave us a complete readout of human DNA, or what might be considered the unabridged cookbook for human life. In a sense, the atlas is a continuation of that project’s work. But while the same DNA cookbook is found in every cell, each cell type reads only some of the recipes—that is, it expresses only certain genes, following their DNA instructions to produce the proteins that carry out a cell’s activities. The promise of the Human Cell Atlas is to reveal which specific genes are expressed in every cell type, and where the cells expressing those genes can be found.

Regev says,

The final product, will amount to nothing less than a “periodic table of our cells,” a tool that is designed not to answer one specific question but to make countless new discoveries possible.

Sequencing the RNA of the cells she’s studying can tell her only so much. To understand how the circuits change under different circumstances, Regev subjects cells to different stimuli, such as hormones or pathogens, to see how the resulting protein signals change.

“the modeling step”—creating algorithms that try to decipher the most likely sequence of molecular events following a stimulus. And just as someone might study a computer by cutting out circuits and seeing how that changes the machine’s operation, Regev tests her model by seeing if it can predict what will happen when she silences specific genes and then exposes the cells to the same stimulus.

By sequencing the RNA of individual cancer cells in recent years—“Every cell is an experiment now,” she says—she has found remarkable differences between the cells of a single tumor, even when they have the same mutations. (Last year that work led to Memorial Sloan Kettering’s Paul Marks Prize for Cancer Research.) She found that while some cancers are thought to develop resistance to therapy, a subset of melanoma cells were resistant from the start. And she discovered that two types of brain cancer, oligodendroglioma and astrocytoma, harbor the same cancer stem cells, which could have important implications for how they’re treated.

As a 2017 overview of the Human Cell Atlas by the project’s organizing committee noted, an atlas “is a map that aims to show the relationships among its elements.” Just as corresponding coastlines seen in an atlas of Earth offer visual evidence of continental drift, compiling all the data about our cells in one place could reveal relationships among cells, tissues, and organs, including some that are entirely unexpected. And just as the periodic table made it possible to predict the existence of elements yet to be observed, the Human Cell Atlas, Regev says, could help us predict the existence of cells that haven’t been found.

This year alone it will fund 85 Human Cell Atlas grants. Early results are already pouring in.

  • In March, Swedish researchers working on cells related to human development announced they had sequenced 250,000 individual cells.
  • In May, a team at the Broad made a data set of more than 500,000 immune cells available on a preview site.

The goal, Regev says, is for researchers everywhere to be able to use the open-source platform of the Human Cell Atlas to perform joint analyses.

Eric Lander, PhDthe founding director and president of the Broad Institute and a member of the Human Cell Atlas Organizing Committee, likens it to genomics.

“People thought at the beginning they might use genomics for this application or that application,” he says. “Nothing has failed to be transformed by genomics, and nothing will fail to be transformed by having a cell atlas.”

“How did we ever imagine we were going to solve a problem without single-cell resolution?”

SOURCE

https://www.technologyreview.com/s/611786/the-cartographer-of-cells/?utm_source=MIT+Technology+Review&utm_campaign=Alumni-Newsletter_Sep-Oct-2018&utm_medium=email

Other related articles published in this Open Access Online Scientific Journal include the following:

 

University of California Santa Cruz’s Genomics Institute will create a Map of Human Genetic Variations

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2015/01/13/university-of-california-santa-cruzs-genomics-institute-will-create-a-map-of-human-genetic-variations/

 

Recognitions for Contributions in Genomics by Dan David Prize Awards

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2017/07/31/recognitions-for-contributions-in-genomics-by-dan-david-prize-awards/

 

ENCODE (Encyclopedia of DNA Elements) program: ‘Tragic’ Sequestration Impact on NHGRI Programs

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2013/09/18/encode-encyclopedia-of-dna-elements-program-tragic-sequestration-impact-on-nhgri-programs/

 

Single-cell Sequencing

Genomic Diagnostics: Three Techniques to Perform Single Cell Gene Expression and Genome Sequencing Single Molecule DNA Sequencing

Curator: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2017/07/04/genomic-diagnostics-three-techniques-to-perform-single-cell-gene-expression-and-genome-sequencing-single-molecule-dna-sequencing/

 

LIVE – Real Time – 16th Annual Cancer Research Symposium, Koch Institute, Friday, June 16, 9AM – 5PM, Kresge Auditorium, MIT – See, Aviv Regev

REAL TIME PRESS COVERAGE & Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2017/03/13/16th-annual-cancer-research-symposium-koch-institute-friday-june-16-9am-5pm-kresge-auditorium-mit/

 

LIVE 11/3/2015 1:30PM @The 15th Annual EmTech MIT – MIT Media Lab: Top 10 Breakthrough Technologies & 2015 Innovators Under 35 – See, Gilead Evrony

REAL TIME PRESS COVERAGE & Reporter: Aviva Lev-Ari, PhD, RN
https://pharmaceuticalintelligence.com/2015/11/03/live-1132015-130pm-the-15th-annual-emtech-mit-mit-media-lab-top-10-breakthrough-technologies-2015-innovators-under-35/

 

Cellular Guillotine Created for Studying Single-Cell Wound Repair

Reporter: Irina Robu, PhD

https://pharmaceuticalintelligence.com/2017/06/29/cellular-guillotine-created-for-studying-single-cell-wound-repair/

 

New subgroups of ILC immune cells discovered through single-cell RNA sequencing

Reporter: Stephen J Williams, PhD

https://pharmaceuticalintelligence.com/2016/02/17/new-subgroups-of-ilc-immune-cells-discovered-through-single-cell-rna-sequencing-from-karolinska-institute/

 

#JPM16: Illumina’s CEO on new genotyping array called Infinium XT and Bio-Rad Partnership for single-cell sequencing workflow

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2016/01/12/jpm16-illuminas-ceo-on-new-genotyping-array-called-infinium-xt-and-bio-rad-partnership-for-single-cell-sequencing-workflow/

 

Juno Acquires AbVitro for $125M: high-throughput and single-cell sequencing capabilities for Immune-Oncology Drug Discovery

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2016/01/12/juno-acquires-abvitro-for-125m-high-throughput-and-single-cell-sequencing-capabilities-for-immune-oncology-drug-discovery/

 

NIH to Award Up to $12M to Fund DNA, RNA Sequencing Research: single-cell genomics,  sample preparation,  transcriptomics and epigenomics, and  genome-wide functional analysis.

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2015/10/27/nih-to-award-up-to-12m-to-fund-dna-rna-sequencing-research-single-cell-genomics-sample-preparation-transcriptomics-and-epigenomics-and-genome-wide-functional-analysis/

 

Genome-wide Single-Cell Analysis of Recombination Activity and De Novo Mutation Rates in Human Sperm

Reporter and Curator: Dr. Sudipta Saha, Ph.D.

https://pharmaceuticalintelligence.com/2012/08/07/genome-wide-single-cell-analysis-of-recombination-activity-and-de-novo-mutation-rates-in-human-sperm/

REFERENCES to Original studies

In Science, 2018

Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors

 See all authors and affiliations

Science  21 Apr 2017:
Vol. 356, Issue 6335, eaah4573
DOI: 10.1126/science.aah4573
Single-cell reconstruction of developmental trajectories during zebrafish embryogenesis

See all authors and affiliations

Science  26 Apr 2018:
eaar3131
DOI: 10.1126/science.aar3131

In Nature, 2018 and 2017

How to build a human cell atlas

Aviv Regev is a maven of hard-core biological analyses. Now she is part of an effort to map every cell in the human body.

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  6. Amendments and Corrections | 

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