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Live Notes, Real Time Conference Coverage AACR 2020: Tuesday June 23, 2020 3:00 PM-5:30 PM Educational Sessions

Reporter: Stephen J. Williams, PhD

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Register for FREE at https://www.aacr.org/

uesday, June 23

3:00 PM – 5:00 PM EDT

Virtual Educational Session
Tumor Biology, Bioinformatics and Systems Biology

The Clinical Proteomic Tumor Analysis Consortium: Resources and Data Dissemination

This session will provide information regarding methodologic and computational aspects of proteogenomic analysis of tumor samples, particularly in the context of clinical trials. Availability of comprehensive proteomic and matching genomic data for tumor samples characterized by the National Cancer Institute’s Clinical Proteomic Tumor Analysis Consortium (CPTAC) and The Cancer Genome Atlas (TCGA) program will be described, including data access procedures and informatic tools under development. Recent advances on mass spectrometry-based targeted assays for inclusion in clinical trials will also be discussed.

Amanda G Paulovich, Shankha Satpathy, Meenakshi Anurag, Bing Zhang, Steven A Carr

Methods and tools for comprehensive proteogenomic characterization of bulk tumor to needle core biopsies

Shankha Satpathy
  • TCGA has 11,000 cancers with >20,000 somatic alterations but only 128 proteins as proteomics was still young field
  • CPTAC is NCI proteomic effort
  • Chemical labeling approach now method of choice for quantitative proteomics
  • Looked at ovarian and breast cancers: to measure PTM like phosphorylated the sample preparation is critical

 

Data access and informatics tools for proteogenomics analysis

Bing Zhang
  • Raw and processed data (raw MS data) with linked clinical data can be extracted in CPTAC
  • Python scripts are available for bioinformatic programming

 

Pathways to clinical translation of mass spectrometry-based assays

Meenakshi Anurag

·         Using kinase inhibitor pulldown (KIP) assay to identify unique kinome profiles

·         Found single strand break repair defects in endometrial luminal cases, especially with immune checkpoint prognostic tumors

·         Paper: JNCI 2019 analyzed 20,000 genes correlated with ET resistant in luminal B cases (selected for a list of 30 genes)

·         Validated in METABRIC dataset

·         KIP assay uses magnetic beads to pull out kinases to determine druggable kinases

·         Looked in xenografts and was able to pull out differential kinomes

·         Matched with PDX data so good clinical correlation

·         Were able to detect ESR1 fusion correlated with ER+ tumors

Tuesday, June 23

3:00 PM – 5:00 PM EDT

Virtual Educational Session
Survivorship

Artificial Intelligence and Machine Learning from Research to the Cancer Clinic

The adoption of omic technologies in the cancer clinic is giving rise to an increasing number of large-scale high-dimensional datasets recording multiple aspects of the disease. This creates the need for frameworks for translatable discovery and learning from such data. Like artificial intelligence (AI) and machine learning (ML) for the cancer lab, methods for the clinic need to (i) compare and integrate different data types; (ii) scale with data sizes; (iii) prove interpretable in terms of the known biology and batch effects underlying the data; and (iv) predict previously unknown experimentally verifiable mechanisms. Methods for the clinic, beyond the lab, also need to (v) produce accurate actionable recommendations; (vi) prove relevant to patient populations based upon small cohorts; and (vii) be validated in clinical trials. In this educational session we will present recent studies that demonstrate AI and ML translated to the cancer clinic, from prognosis and diagnosis to therapy.
NOTE: Dr. Fish’s talk is not eligible for CME credit to permit the free flow of information of the commercial interest employee participating.

Ron C. Anafi, Rick L. Stevens, Orly Alter, Guy Fish

Overview of AI approaches in cancer research and patient care

Rick L. Stevens
  • Deep learning is less likely to saturate as data increases
  • Deep learning attempts to learn multiple layers of information
  • The ultimate goal is prediction but this will be the greatest challenge for ML
  • ML models can integrate data validation and cross database validation
  • What limits the performance of cross validation is the internal noise of data (reproducibility)
  • Learning curves: not the more data but more reproducible data is important
  • Neural networks can outperform classical methods
  • Important to measure validation accuracy in training set. Class weighting can assist in development of data set for training set especially for unbalanced data sets

Discovering genome-scale predictors of survival and response to treatment with multi-tensor decompositions

Orly Alter
  • Finding patterns using SVD component analysis. Gene and SVD patterns match 1:1
  • Comparative spectral decompositions can be used for global datasets
  • Validation of CNV data using this strategy
  • Found Ras, Shh and Notch pathways with altered CNV in glioblastoma which correlated with prognosis
  • These predictors was significantly better than independent prognostic indicator like age of diagnosis

 

Identifying targets for cancer chronotherapy with unsupervised machine learning

Ron C. Anafi
  • Many clinicians have noticed that some patients do better when chemo is given at certain times of the day and felt there may be a circadian rhythm or chronotherapeutic effect with respect to side effects or with outcomes
  • ML used to determine if there is indeed this chronotherapy effect or can we use unstructured data to determine molecular rhythms?
  • Found a circadian transcription in human lung
  • Most dataset in cancer from one clinical trial so there might need to be more trials conducted to take into consideration circadian rhythms

Stratifying patients by live-cell biomarkers with random-forest decision trees

Stratifying patients by live-cell biomarkers with random-forest decision trees

Guy Fish CEO Cellanyx Diagnostics

 

Tuesday, June 23

3:00 PM – 5:00 PM EDT

Virtual Educational Session
Tumor Biology, Molecular and Cellular Biology/Genetics, Bioinformatics and Systems Biology, Prevention Research

The Wound Healing that Never Heals: The Tumor Microenvironment (TME) in Cancer Progression

This educational session focuses on the chronic wound healing, fibrosis, and cancer “triad.” It emphasizes the similarities and differences seen in these conditions and attempts to clarify why sustained fibrosis commonly supports tumorigenesis. Importance will be placed on cancer-associated fibroblasts (CAFs), vascularity, extracellular matrix (ECM), and chronic conditions like aging. Dr. Dvorak will provide an historical insight into the triad field focusing on the importance of vascular permeability. Dr. Stewart will explain how chronic inflammatory conditions, such as the aging tumor microenvironment (TME), drive cancer progression. The session will close with a review by Dr. Cukierman of the roles that CAFs and self-produced ECMs play in enabling the signaling reciprocity observed between fibrosis and cancer in solid epithelial cancers, such as pancreatic ductal adenocarcinoma.

Harold F Dvorak, Sheila A Stewart, Edna Cukierman

 

The importance of vascular permeability in tumor stroma generation and wound healing

Harold F Dvorak

Aging in the driver’s seat: Tumor progression and beyond

Sheila A Stewart

Why won’t CAFs stay normal?

Edna Cukierman

 

Tuesday, June 23

3:00 PM – 5:00 PM EDT

 

 

 

 

 

 

 

Other Articles on this Open Access  Online Journal on Cancer Conferences and Conference Coverage in Real Time Include

Press Coverage
Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Symposium: New Drugs on the Horizon Part 3 12:30-1:25 PM
Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Session on NCI Activities: COVID-19 and Cancer Research 5:20 PM
Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Session on Evaluating Cancer Genomics from Normal Tissues Through Metastatic Disease 3:50 PM
Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Session on Novel Targets and Therapies 2:35 PM

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RNA from the SARS-CoV-2 virus taking over the cells it infects: Virulence – Pathogen’s ability to infect a Resistant Host: The Imbalance between Controlling Virus Replication versus Activation of the Adaptive Immune Response

Curator: Aviva Lev-Ari, PhD, RN – I added colors and bold face

 

UPDATED on 9/8/2020

What bats can teach us about developing immunity to Covid-19 | Free to read

Clive Cookson, Anna Gross and Ian Bott, London

https://www.ft.com/content/743ce7a0-60eb-482d-b1f4-d4de11182fa9?utm_source=Nature+Briefing&utm_campaign=af64422080-briefing-dy-20200908&utm_medium=email&utm_term=0_c9dfd39373-af64422080-43323101

 

UPDATED on 6/29/2020

Another duality and paradox in the Treatment of COVID-19 Patients in ICUs was expressed by Mike Yoffe, MD, PhD, David H. Koch Professor of Biology and Biological Engineering, Massachusetts Institute of Technology. Dr. Yaffe has a joint appointment in Acute Care Surgery, Trauma, and Surgical Critical Care, and in Surgical Oncology @BIDMC

on 6/29 at SOLUTIONS with/in/sight at Koch Institute @MIT

How Are Cancer Researchers Fighting COVID-19? (Part II)” Jun 29, 2020 11:30 AM EST

Mike Yoffe, MD, PhD 

In COVID-19 patients: two life threatening conditions are seen in ICUs:

  • Blood Clotting – Hypercoagulability or Thrombophilia
  • Cytokine Storm – immuno-inflammatory response
  • The coexistence of 1 and 2 – HINDERS the ability to use effectively tPA as an anti-clotting agent while the cytokine storm is present.

Mike Yoffe’s related domain of expertise:

Signaling pathways and networks that control cytokine responses and inflammation

Misregulation of cytokine feedback loops, along with inappropriate activation of the blood clotting cascade causes dysregulation of cell signaling pathways in innate immune cells (neutrophils and macrophages), resulting in tissue damage and multiple organ failure following trauma or sepsis. Our research is focused on understanding the role of the p38-MK2 pathway in cytokine control and innate immune function, and on cross-talk between cytokines, clotting factors, and neutrophil NADPH oxidase-derived ROS in tissue damage, coagulopathy, and inflammation, using biochemistry, cell biology, and mouse knock-out/knock-in models.  We recently discovered a particularly important link between abnormal blood clotting and the complement pathway cytokine C5a which causes excessive production of extracellular ROS and organ damage by neutrophils after traumatic injury.

SOURCE

https://www.bidmc.org/research/research-by-department/surgery/acute-care-surgery-trauma-and-surgical-critical-care/michael-b-yaffe

 

See

The Genome Structure of CORONAVIRUS, SARS-CoV-2

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2020/05/04/the-genome-structure-of-coronavirus-sars-cov-2-i-awaited-for-this-article-for-60-days/

 

Imbalanced Host Response to SARS-CoV-2 Drives Development of COVID-19

Open Access Published:May 15, 2020DOI:https://doi.org/10.1016/j.cell.2020.04.026

Highlights

  • SARS-CoV-2 infection induces low IFN-I and -III levels with a moderate ISG response
  • Strong chemokine expression is consistent across in vitroex vivo, and in vivo models
  • Low innate antiviral defenses and high pro-inflammatory cues contribute to COVID-19

Summary

Viral pandemics, such as the one caused by SARS-CoV-2, pose an imminent threat to humanity. Because of its recent emergence, there is a paucity of information regarding viral behavior and host response following SARS-CoV-2 infection. Here we offer an in-depth analysis of the transcriptional response to SARS-CoV-2 compared with other respiratory viruses. Cell and animal models of SARS-CoV-2 infection, in addition to transcriptional and serum profiling of COVID-19 patients, consistently revealed a unique and inappropriate inflammatory response. This response is defined by low levels of type I and III interferons juxtaposed to elevated chemokines and high expression of IL-6. We propose that reduced innate antiviral defenses coupled with exuberant inflammatory cytokine production are the defining and driving features of COVID-19.

Graphical Abstract

Keywords

Results

Defining the Transcriptional Response to SARS-CoV-2 Relative to Other Respiratory Viruses

To compare the transcriptional response of SARS-CoV-2 with other respiratory viruses, including MERS-CoV, SARS-CoV-1, human parainfluenza virus 3 (HPIV3), respiratory syncytial virus (RSV), and IAV, we first chose to focus on infection in a variety of respiratory cell lines (Figure 1). To this end, we collected poly(A) RNA from infected cells and performed RNA sequencing (RNA-seq) to estimate viral load. These data show that virus infection levels ranged from 0.1% to more than 50% of total RNA reads (Figure 1A).

Discussion

In the present study, we focus on defining the host response to SARS-CoV-2 and other human respiratory viruses in cell lines, primary cell cultures, ferrets, and COVID-19 patients. In general, our data show that the overall transcriptional footprint of SARS-CoV-2 infection was distinct in comparison with other highly pathogenic coronaviruses and common respiratory viruses such as IAV, HPIV3, and RSV. It is noteworthy that, despite a reduced IFN-I and -III response to SARS-CoV-2, we observed a consistent chemokine signature. One exception to this observation is the response to high-MOI infection in A549-ACE2 and Calu-3 cells, where replication was robust and an IFN-I and -III signature could be observed. In both of these examples, cells were infected at a rate to theoretically deliver two functional virions per cell in addition to any defective interfering particles within the virus stock that were not accounted for by plaque assays. Under these conditions, the threshold for PAMP may be achieved prior to the ability of the virus to evade detection through production of a viral antagonist. Alternatively, addition of multiple genomes to a single cell may disrupt the stoichiometry of viral components, which, in turn, may itself generate PAMPs that would not form otherwise. These ideas are supported by the fact that, at a low-MOI infection in A549-ACE2 cells, high levels of replication could also be achieved, but in the absence of IFN-I and -III induction. Taken together, these data suggest that, at low MOIs, the virus is not a strong inducer of the IFN-I and -III system, as opposed to conditions where the MOI is high.
Taken together, the data presented here suggest that the response to SARS-CoV-2 is imbalanced with regard to controlling virus replication versus activation of the adaptive immune response. Given this dynamic, treatments for COVID-19 have less to do with the IFN response and more to do with controlling inflammation. Because our data suggest that numerous chemokines and ILs are elevated in COVID-19 patients, future efforts should focus on U.S. Food and Drug Administration (FDA)-approved drugs that can be rapidly deployed and have immunomodulating properties.

SOURCE

https://www.cell.com/cell/fulltext/S0092-8674(20)30489-X

SARS-CoV-2 ORF3b is a potent interferon antagonist whose activity is further increased by a naturally occurring elongation variant

Yoriyuki KonnoIzumi KimuraKeiya UriuMasaya FukushiTakashi IrieYoshio KoyanagiSo NakagawaKei Sato

Abstract

One of the features distinguishing SARS-CoV-2 from its more pathogenic counterpart SARS-CoV is the presence of premature stop codons in its ORF3b gene. Here, we show that SARS-CoV-2 ORF3b is a potent interferon antagonist, suppressing the induction of type I interferon more efficiently than its SARS-CoV ortholog. Phylogenetic analyses and functional assays revealed that SARS-CoV-2-related viruses from bats and pangolins also encode truncated ORF3b gene products with strong anti-interferon activity. Furthermore, analyses of more than 15,000 SARS-CoV-2 sequences identified a natural variant, in which a longer ORF3b reading frame was reconstituted. This variant was isolated from two patients with severe disease and further increased the ability of ORF3b to suppress interferon induction. Thus, our findings not only help to explain the poor interferon response in COVID-19 patients, but also describe a possibility of the emergence of natural SARS-CoV-2 quasi-species with extended ORF3b that may exacerbate COVID-19 symptoms.

Highlights

  • ORF3b of SARS-CoV-2 and related bat and pangolin viruses is a potent IFN antagonist

  • SARS-CoV-2 ORF3b suppresses IFN induction more efficiently than SARS-CoV ortholog

  • The anti-IFN activity of ORF3b depends on the length of its C-terminus

  • An ORF3b with increased IFN antagonism was isolated from two severe COVID-19 cases

Competing Interest Statement

The authors have declared no competing interest.

Paper in collection COVID-19 SARS-CoV-2 preprints from medRxiv and bioRxiv

 

SOURCE

https://www.biorxiv.org/content/10.1101/2020.05.11.088179v1

 

 

A deep dive into how the new coronavirus infects cells has found that it orchestrates a hostile takeover of their genes unlike any other known viruses do, producing what one leading scientist calls “unique” and “aberrant” changes.Recent studies show that in seizing control of genes in the human cells it invades, the virus changes how segments of DNA are read, doing so in a way that might explain why the elderly are more likely to die of Covid-19 and why antiviral drugs might not only save sick patients’ lives but also prevent severe disease if taken before infection.“It’s something I have never seen in my 20 years of” studying viruses, said virologist Benjamin tenOever of the Icahn School of Medicine at Mount Sinai, referring to how SARS-CoV-2, the virus that causes Covid-19, hijacks cells’ genomes.The “something” he and his colleagues saw is how SARS-CoV-2 blocks one virus-fighting set of genes but allows another set to launch, a pattern never seen with other viruses. Influenza and the original SARS virus (in the early 2000s), for instance, interfere with both arms of the body’s immune response — what tenOever dubs “call to arms” genes and “call for reinforcement” genes.The first group of genes produces interferons. These proteins, which infected cells release, are biological semaphores, signaling to neighboring cells to activate some 500 of their own genes that will slow down the virus’ ability to make millions of copies of itself if it invades them. This lasts seven to 10 days, tenOever said, controlling virus replication and thereby buying time for the second group of genes to act.This second set of genes produce their own secreted proteins, called chemokines, that emit a biochemical “come here!” alarm. When far-flung antibody-making B cells and virus-killing T cells sense the alarm, they race to its source. If all goes well, the first set of genes holds the virus at bay long enough for the lethal professional killers to arrive and start eradicating viruses.

“Most other viruses interfere with some aspect of both the call to arms and the call for reinforcements,” tenOever said. “If they didn’t, no one would ever get a viral illness”: The one-two punch would pummel any incipient infection into submission.

SARS-CoV-2, however, uniquely blocks one cellular defense but activates the other, he and his colleagues reported in a study published last week in Cell. They studied healthy human lung cells growing in lab dishes, ferrets (which the virus infects easily), and lung cells from Covid-19 patients. In all three, they found that within three days of infection, the virus induces cells’ call-for-reinforcement genes to produce cytokines. But it blocks their call-to-arms genes — the interferons that dampen the virus’ replication.

The result is essentially no brakes on the virus’s replication, but a storm of inflammatory molecules in the lungs, which is what tenOever calls an “unique” and “aberrant” consequence of how SARS-CoV-2 manipulates the genome of its target.

In another new study, scientists in Japan last week identified how SARS-CoV-2 accomplishes that genetic manipulation. Its ORF3b gene produces a protein called a transcription factor that has “strong anti-interferon activity,” Kei Sato of the University of Tokyo and colleagues found — stronger than the original SARS virus or influenza viruses. The protein basically blocks the cell from recognizing that a virus is present, in a way that prevents interferon genes from being expressed.

In fact, the Icahn School team found no interferons in the lung cells of Covid-19 patients. Without interferons, tenOever said, “there is nothing to stop the virus from replicating and festering in the lungs forever.”

That causes lung cells to emit even more “call-for-reinforcement” genes, summoning more and more immune cells. Now the lungs have macrophages and neutrophils and other immune cells “everywhere,” tenOever said, causing such runaway inflammation “that you start having inflammation that induces more inflammation.”

At the same time, unchecked viral replication kills lung cells involved in oxygen exchange. “And suddenly you’re in the hospital in severe respiratory distress,” he said.

In elderly people, as well as those with diabetes, heart disease, and other underlying conditions, the call-to-arms part of the immune system is weaker than in younger, healthier people, even before the coronavirus arrives. That reduces even further the cells’ ability to knock down virus replication with interferons, and imbalances the immune system toward the dangerous inflammatory response.

The discovery that SARS-CoV-2 strongly suppresses infected cells’ production of interferons has raised an intriguing possibility: that taking interferons might prevent severe Covid-19 or even prevent it in the first place, said Vineet Menachery of the University of Texas Medical Branch.

In a study of human cells growing in lab dishes, described in a preprint (not peer-reviewed or published in a journal yet), he and his colleagues also found that SARS-CoV-2 “prevents the vast amount” of interferon genes from turning on. But when cells growing in lab dishes received the interferon IFN-1 before exposure to the coronavirus, “the virus has a difficult time replicating.”

After a few days, the amount of virus in infected but interferon-treated cells was 1,000- to 10,000-fold lower than in infected cells not pre-treated with interferon. (The original SARS virus, in contrast, is insensitive to interferon.)

Ending the pandemic and preventing its return is assumed to require an effective vaccine to prevent infectionand antiviral drugs such as remdesivir to treat the very sick, but the genetic studies suggest a third strategy: preventive drugs.

It’s possible that treatment with so-called type-1 interferon “could stop the virus before it could get established,” Menachery said.

Giving drugs to healthy people is always a dicey proposition, since all drugs have side effects — something considered less acceptable than when a drug is used to treat an illness. “Interferon treatment is rife with complications,” Menachery warned. The various interferons, which are prescribed for hepatitis, cancers, and many other diseases, can cause flu-like symptoms.

But the risk-benefit equation might shift, both for individuals and for society, if interferons or antivirals or other medications are shown to reduce the risk of developing serious Covid-19 or even make any infection nearly asymptomatic.

Interferon “would be warning the cells the virus is coming,” Menachery said, so such pretreatment might “allow treated cells to fend off the virus better and limit its spread.” Determining that will of course require clinical trials, which are underway.

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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).
5 Muerdter, F. et al. Resolving systematic errors in widely used enhancer activity assays in human cells. Nat. Methods 15, 141–149 (2018).
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).
59 Abadi, M. et al. TensorFlow: large-scale machine learning on heterogeneous systems. arXiv 1603.04467 (2016).
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).
36 Hedges, D., Proft, M. & Entian, K. D. CAT8, a new zinc cluster-encoding gene necessary for derepression of gluconeogenic enzymes in the yeast Saccharomyces cerevisiae. Mol. Cell. Biol. 15, 1915–1922 (1995).
47 Bednar, J. et al. Determination of DNA persistence length by cryo-electron microscopy. Separation of the static and dynamic contributions to the apparent persistence length of DNA. J. Mol. Biol. 254, 579–594 (1995).
32 Axelrod, J. D., Reagan, M. S. & Majors, J. GAL4 disrupts a repressing nucleosome during activation of GAL1 transcription in vivo. Genes Dev. 7, 857–869 (1993).
33 Morse, R. H. Nucleosome disruption by transcription factor binding in yeast. Science 262, 1563–1566 (1993).
12 Oliphant, A. R., Brandl, C. J. & Struhl, K. Defining the sequence specificity of DNA-binding proteins by selecting binding sites from random-sequence oligonucleotides: analysis of yeast GCN4 protein. Mol. Cell. Biol. 9, 2944–2949 (1989).
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).
13 Horwitz, M. S. & Loeb, L. A. Promoters selected from random DNA sequences. Proc. Natl Acad. Sci. USA 83, 7405–7409 (1986).

 

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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Cancer Genomics: Multiomic Analysis of Single Cells and Tumor Heterogeneity

Curator: Stephen J. Williams, PhD

 

scTrio-seq identifies colon cancer lineages

Single-cell multiomics sequencing and analyses of human colorectal cancer. Shuhui Bian et al. Science  30 Nov 2018:Vol. 362, Issue 6418, pp. 1060-1063

To better design treatments for cancer, it is important to understand the heterogeneity in tumors and how this contributes to metastasis. To examine this process, Bian et al. used a single-cell triple omics sequencing (scTrio-seq) technique to examine the mutations, transcriptome, and methylome within colorectal cancer tumors and metastases from 10 individual patients. The analysis provided insights into tumor evolution, linked DNA methylation to genetic lineages, and showed that DNA methylation levels are consistent within lineages but can differ substantially among clones.

Science, this issue p. 1060

Abstract

Although genomic instability, epigenetic abnormality, and gene expression dysregulation are hallmarks of colorectal cancer, these features have not been simultaneously analyzed at single-cell resolution. Using optimized single-cell multiomics sequencing together with multiregional sampling of the primary tumor and lymphatic and distant metastases, we developed insights beyond intratumoral heterogeneity. Genome-wide DNA methylation levels were relatively consistent within a single genetic sublineage. The genome-wide DNA demethylation patterns of cancer cells were consistent in all 10 patients whose DNA we sequenced. The cancer cells’ DNA demethylation degrees clearly correlated with the densities of the heterochromatin-associated histone modification H3K9me3 of normal tissue and those of repetitive element long interspersed nuclear element 1. Our work demonstrates the feasibility of reconstructing genetic lineages and tracing their epigenomic and transcriptomic dynamics with single-cell multiomics sequencing.

Fig. 1 Reconstruction of genetic lineages with scTrio-seq2.

Global SCNA patterns (250-kb resolution) of CRC01. Each row represents an individual cell. The subclonal SCNAs used for identifying genetic sublineages were marked and indexed; for details, see fig. S6B. On the top of the heatmap, the amplification or deletion frequency of each genomic bin (250 kb) of the non-hypermutated CRC samples from the TCGA Project and patient CRC01’s cancer cells are shown.

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Fig. 1 Reconstruction of genetic lineages with scTrio-seq2.

Global SCNA patterns (250-kb resolution) of CRC01. Each row represents an individual cell. The subclonal SCNAs used for identifying genetic sublineages were marked and indexed; for details, see fig. S6B. On the top of the heatmap, the amplification or deletion frequency of each genomic bin (250 kb) of the non-hypermutated CRC samples

 

 

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Single-cell RNA-seq helps in finding intra-tumoral heterogeneity in pancreatic cancer

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

 

Pancreatic cancer is a significant cause of cancer mortality; therefore, the development of early diagnostic strategies and effective treatment is essential. Improvements in imaging technology, as well as use of biomarkers are changing the way that pancreas cancer is diagnosed and staged. Although progress in treatment for pancreas cancer has been incremental, development of combination therapies involving both chemotherapeutic and biologic agents is ongoing.

 

Cancer is an evolutionary disease, containing the hallmarks of an asexually reproducing unicellular organism subject to evolutionary paradigms. Pancreatic ductal adenocarcinoma (PDAC) is a particularly robust example of this phenomenon. Genomic features indicate that pancreatic cancer cells are selected for fitness advantages when encountering the geographic and resource-depleted constraints of the microenvironment. Phenotypic adaptations to these pressures help disseminated cells to survive in secondary sites, a major clinical problem for patients with this disease.

 

The immune system varies in cell types, states, and locations. The complex networks, interactions, and responses of immune cells produce diverse cellular ecosystems composed of multiple cell types, accompanied by genetic diversity in antigen receptors. Within this ecosystem, innate and adaptive immune cells maintain and protect tissue function, integrity, and homeostasis upon changes in functional demands and diverse insults. Characterizing this inherent complexity requires studies at single-cell resolution. Recent advances such as massively parallel single-cell RNA sequencing and sophisticated computational methods are catalyzing a revolution in our understanding of immunology.

 

PDAC is the most common type of pancreatic cancer featured with high intra-tumoral heterogeneity and poor prognosis. In the present study to comprehensively delineate the PDAC intra-tumoral heterogeneity and the underlying mechanism for PDAC progression, single-cell RNA-seq (scRNA-seq) was employed to acquire the transcriptomic atlas of 57,530 individual pancreatic cells from primary PDAC tumors and control pancreases. The diverse malignant and stromal cell types, including two ductal subtypes with abnormal and malignant gene expression profiles respectively, were identified in PDAC.

 

The researchers found that the heterogenous malignant subtype was composed of several subpopulations with differential proliferative and migratory potentials. Cell trajectory analysis revealed that components of multiple tumor-related pathways and transcription factors (TFs) were differentially expressed along PDAC progression. Furthermore, it was found a subset of ductal cells with unique proliferative features were associated with an inactivation state in tumor-infiltrating T cells, providing novel markers for the prediction of antitumor immune response. Together, the findings provided a valuable resource for deciphering the intra-tumoral heterogeneity in PDAC and uncover a connection between tumor intrinsic transcriptional state and T cell activation, suggesting potential biomarkers for anticancer treatment such as targeted therapy and immunotherapy.

 

References:

 

https://www.ncbi.nlm.nih.gov/pubmed/31273297

 

https://www.ncbi.nlm.nih.gov/pubmed/21491194

 

https://www.ncbi.nlm.nih.gov/pubmed/27444064

 

https://www.ncbi.nlm.nih.gov/pubmed/28983043

 

https://www.ncbi.nlm.nih.gov/pubmed/24976721

 

https://www.ncbi.nlm.nih.gov/pubmed/27693023

 

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scPopCorn: A New Computational Method for Subpopulation Detection and their Comparative Analysis Across Single-Cell Experiments

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

 

Present day technological advances have facilitated unprecedented opportunities for studying biological systems at single-cell level resolution. For example, single-cell RNA sequencing (scRNA-seq) enables the measurement of transcriptomic information of thousands of individual cells in one experiment. Analyses of such data provide information that was not accessible using bulk sequencing, which can only assess average properties of cell populations. Single-cell measurements, however, can capture the heterogeneity of a population of cells. In particular, single-cell studies allow for the identification of novel cell types, states, and dynamics.

 

One of the most prominent uses of the scRNA-seq technology is the identification of subpopulations of cells present in a sample and comparing such subpopulations across samples. Such information is crucial for understanding the heterogeneity of cells in a sample and for comparative analysis of samples from different conditions, tissues, and species. A frequently used approach is to cluster every dataset separately, inspect marker genes for each cluster, and compare these clusters in an attempt to determine which cell types were shared between samples. This approach, however, relies on the existence of predefined or clearly identifiable marker genes and their consistent measurement across subpopulations.

 

Although the aligned data can then be clustered to reveal subpopulations and their correspondence, solving the subpopulation-mapping problem by performing global alignment first and clustering second overlooks the original information about subpopulations existing in each experiment. In contrast, an approach addressing this problem directly might represent a more suitable solution. So, keeping this in mind the researchers developed a computational method, single-cell subpopulations comparison (scPopCorn), that allows for comparative analysis of two or more single-cell populations.

 

The performance of scPopCorn was tested in three distinct settings. First, its potential was demonstrated in identifying and aligning subpopulations from single-cell data from human and mouse pancreatic single-cell data. Next, scPopCorn was applied to the task of aligning biological replicates of mouse kidney single-cell data. scPopCorn achieved the best performance over the previously published tools. Finally, it was applied to compare populations of cells from cancer and healthy brain tissues, revealing the relation of neoplastic cells to neural cells and astrocytes. Consequently, as a result of this integrative approach, scPopCorn provides a powerful tool for comparative analysis of single-cell populations.

 

This scPopCorn is basically a computational method for the identification of subpopulations of cells present within individual single-cell experiments and mapping of these subpopulations across these experiments. Different from other approaches, scPopCorn performs the tasks of population identification and mapping simultaneously by optimizing a function that combines both objectives. When applied to complex biological data, scPopCorn outperforms previous methods. However, it should be kept in mind that scPopCorn assumes the input single-cell data to consist of separable subpopulations and it is not designed to perform a comparative analysis of single cell trajectories datasets that do not fulfill this constraint.

 

Several innovations developed in this work contributed to the performance of scPopCorn. First, unifying the above-mentioned tasks into a single problem statement allowed for integrating the signal from different experiments while identifying subpopulations within each experiment. Such an incorporation aids the reduction of biological and experimental noise. The researchers believe that the ideas introduced in scPopCorn not only enabled the design of a highly accurate identification of subpopulations and mapping approach, but can also provide a stepping stone for other tools to interrogate the relationships between single cell experiments.

 

References:

 

https://www.sciencedirect.com/science/article/pii/S2405471219301887

 

https://www.tandfonline.com/doi/abs/10.1080/23307706.2017.1397554

 

https://ieeexplore.ieee.org/abstract/document/4031383

 

https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0927-y

 

https://www.sciencedirect.com/science/article/pii/S2405471216302666

 

 

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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.

Chemical Biology Program

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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Three Technology Leaders in Single Cell Sequencing: 10X Genomics, Illumina and MissionBio

 

Reporter: Aviva Lev-Ari, PhD, RN

We review below only Three Technology Leaders in Single Cell Sequencing. There are other players in this research technology space.

 

WHAT IS SINGLE CELL GENOMICS?

By Nicole Davis, Ph.D.

https://www.jax.org/news-and-insights/2015/december/single-cell-genomics

 


 

Platform Solution

Tapestri: The Precision Genomics Platform.

 

“Single-cell DNA analysis

allows us to actually observe

the clonal composition of

cancer, instead of guessing at

it. This opens up the possibility

of being able to make dynamic

changes in treatment.”

– Catherine Smith, MD, UCSF

 

Move Beyond Bulk NGS to Precision Genomics

The average read-out from conventional bulk sequencing misses the rare events and underlying genetic

diversity within and across cell populations. To resolve heterogeneity and improve patient stratification,

therapy selection, and disease monitoring we need insights into mutation co-occurrence cell population,

and zygosity within every single cell.

Clonal Resolution with Single-Cell Precision

Complex disease evolves, so understanding genetic variability — including mutation co-occurrence at the single-cell level — is vitally important for clinical researchers to break the cycle of treatment response, resistance and relapse.

CLONAL DIVERSITY REVEALED

High Sensitivity to Reveal True Heterogeneity

The Tapestri Platform revolutionizes the capability to directly assess the clonal architecture of a sample with detection of mutation co-occurrence patterns. Rather than inferring variants that co-occur within a subclone from comparable bulk variant allele frequencies, single-cell resolution uncovers the true distribution of genotypes and their segregation patterns across subclones.

Platform Features

  • Targeted and accurate SNVs and indel variant calling
  • Single-cell DNA throughput up to 10,000 cells
  • Simple workflow
  • User-friendly bioinformatics software
  • Customizable content
  • Detect rare subclones down to 0.1%
  • Resolve clonal architecture
  • Identify mutation co-occurrence
  • How the Tapestri Platform Works

    Step 3: After running your sequencer, proceed with our downstream analysis and visualization software. Mission Bio provides dedicated bioinformatics support to help you discover biological and clinical insights

     

A highly sensitive method for measuring gene expression from single cells

Generating RNA-Seq libraries from single cell and ultra-low-input samples

Illumina’s Single cell sequencing technologies

Single-Cell Research An Overview of Recent Single-Cell Research Publications Featuring Illumina® Technology

  • Quartz-Seq
  • Smart-Seq
  • Smart-Seq2
  • Single-Cell Methylome & Transcriptome Sequencing
  • Genome & Transcriptome Sequencing
  • Genomic DNA and mRNA Sequencing
  • T Cell–Receptor Chain Pairing
  • Unique Molecular Identifiers
  • Cell Expression by Linear Amplification Sequencing
  • Flow Cell–Surface Reverse-Transcription Sequencing
  • Single-Cell Tagged ReverseTranscription Sequencing
  • Fixed and Recovered Intact Single-Cell RNA Sequencing
  • Cell Labeling via Photobleaching Indexing Droplets
  • Drop-Seq
  • CytoSeq
  • Single-Cell RNA Barcoding and Sequencing
  • High-Throughput Single-Cell Labeling

 

TABLE OF CONTENTS

5 Introduction

7 Applications Cancer Metagenomics Stem Cells Developmental Biology Immunology Neurobiology Drug Discovery Reproductive Health Microbial Ecology and Evolution Plant Biology Forensics Allele-Specific Gene Expression 50 Sample Preparation

54 Data Analysis

60 DNA Methods Multiple-Strand Displacement Amplification Genome & Transcriptome Sequencing Multiple Annealing and Looping–Based Amplification Cycles Genomic DNA and mRNA Sequencing

68 Epigenomics Methods Single-Cell Assay for TransposaseAccessible Chromatin Using Sequencing Single-Cell Bisulfite Sequencing/ Single-Cell Whole-Genome Bisulfite Sequencing Single-Cell Methylome & Transcriptome Sequencing Single-Cell Reduced-Representation Bisulfite Sequencing Single-Cell Chromatin Immunoprecipitation Sequencing Chromatin Conformation Capture Sequencing Droplet-Based Chromatin Immunoprecipitation Sequencing

78 RNA Methods Designed Primer–Based RNA Sequencing Single-Cell Universal Poly(A)- Independent RNA Sequencing

 

PAGES 54 – 56 Data Analysis – most important pages in the Report, $669

Single-cell sequencing poses unique challenges for data analysis. Individual mammalian cells contain 50,000–300,000 transcripts, and gene expression values among individual cells can vary significantly.206 Although several hundred thousand transcripts may be expressed per individual cell, up to 85% of these are present at only 1–100 copies.207 Therefore, it is critically important in scRNA-Seq to capture low-abundance mRNA transcripts and amplify the synthesized cDNA to ensure that all transcripts are ultimately represented uniformly in the library.208,209 Spike-in quantification standards of known abundance can help distinguish technical variability/ noise from biologically meaningful gene expression changes.210 Molecular indexing can also correct for sequencing biases,211.212 and recent improvements in automated sample handling can reduce technical variability even more.213

PAGE 64

Genome & Transcriptome Sequencing

Genome & transcriptome sequencing (G&T-Seq) is a protocol that can separate and sequence genomic DNA and full-length mRNA from single cells.276 In this method, single cells are isolated and lysed. RNA is captured using biotinylated oligo(dT) capture primers and separated from DNA using streptavidin-coated magnetic beads. SmartSeq2 is used to amplify captured RNA on the bead, while MDA is used to amplify DNA. After sequencing, integrating DNA and RNA sequences provides insights into the gene-expression profile of single cells (Table 4)

PAGE 66

Genomic DNA and mRNA Sequencing

DR-Seq studies the genomic and transcriptomic relationship of single cells via sequencing. Nucleic acid amplification prior to physical separation reduces sample loss and the risk of contamination. DR-Seq involves multiple amplification steps, including the quasilinear amplification technique similar to MALBAC. First, mRNAs are reverse-transcribed from lysed single cells using poly(dT) primers with Ad-1x adapters, producing single-stranded cDNA (sscDNA). The Ad-1x adapter sequence contains cell-identifying barcodes, 5’ Illumina adapters, and a T7 promoter. Next, both gDNA and sscDNA are amplified simultaneously via quasilinear WGA with Ad-2 primers. These primers are similar to MALBAC adapters, containing 8 random nucleotides for random priming followed by a constant 27-nucleotide

PAGE 73

Single-Cell Methylome & Transcriptome Sequencing

scM&T-Seq allows parallel analysis of both epigenetic and gene expression patterns from single cells using Smart-Seq2 and scBS-Seq. scM&T-Seq is built upon G&T-Seq, but instead of using MDA for DNA sequencing, it uses scBS-Seq to interrogate DNA methylation patterns. First, single cells are isolated and individually lysed. Then, mRNAs are isolated using streptavidin-coupled mRNA capture primers, physically separating them from DNA strands. Smart-Seq2 is used to generate cDNA libraries from the mRNA, which involves reverse transcription with template switching and tagmentation. DNA libraries are prepared via scBS-Seq, which involves bisulfite conversion of DNA strands to identify methylated cytosines. Both libraries are now ready for sequencing (Table 9).

PAGE 78

RNA METHODS

Low-level RNA detection refers to both detection of rare RNA molecules in a cellfree environment (such as circulating tumor RNA) and the expression patterns of single cells. Tissues consist of a multitude of different cell types, each with a distinctly different set of functions. Even within a single cell type, the transcriptomes are highly dynamic and reflect temporal, spatial, and cell cycle–dependent changes. Cell harvesting, handling, and technical issues with sensitivity and bias during amplification add additional levels of complexity. To resolve this multitiered complexity would require analyzing many thousands of cells. The use of unique barcodes has greatly increased the number of samples that can be multiplexed and pooled at little to no decrease in reads associated with each sample. Recent improvements in cell capture and sample preparation will provide more information, faster, and at lower cost.310,311 This development promises to expand our understanding of cell function fundamentally, with significant implications for research and human health. 312

PAGR 87

Genome & Transcriptome Sequencing

G&T-Seq is a protocol that can separate and sequence genomic DNA and full-length mRNA from single cells.327 In this method, single cells are isolated and lysed. RNA is captured using biotinylated oligo(dT) capture primers and separated from DNA using streptavidin-coated magnetic beads. Smart-Seq2 is used to amplify captured RNA on the bead, while MDA is used to amplify DNA. After sequencing, integrating DNA and RNA sequences provides insights into the gene-expression profile of single cells.

PAGE 88

Genomic DNA and mRNA Sequencing

DR-Seq studies the genomic and transcriptomic relationship of single cells via sequencing. Nucleic acid amplification prior to physical separation reduces sample loss and the risk of contamination. DR-Seq involves multiple amplification steps, including the quasilinear amplification technique similar to MALBAC.

PAGE 89

T Cell–Receptor Chain Pairing

Functional TCRs are heterodimeric proteins composed of unique combinations of α and β chains. For an accurate functional analysis, both subunits must be sequenced together to avoid disrupting the α- and β-chain pairing during the cell lysis step.333

PAGE 91

Flow Cell–Surface Reverse-Transcription Sequencing

Flow cell–surface reverse-transcription sequencing (FRT-Seq) is a transcriptomesequencing technique developed in 2010.339 It is strand-specific, free of amplification, and is compatible with paired-end sequencing. To begin with, poly(A)+ RNA samples are fragmented by metal-ion hydrolysis and dephosphorylated.

PAGE 97

Single-Cell RNA Barcoding and Sequencing

Single-cell RNA barcoding and sequencing (SCRB-Seq) is a cost-efficient, multiplexed scRNA-Seq technique. SCRB-Seq isolates single cells into wells using FACS. After cell lysis, poly(A)+ mRNAs are annealed to a custom primer containing a poly(T) tract, UMI, well barcode, and biotin. Template-switching reverse transcription and PCR amplification are carried out on the mRNA, generating barcoded full-length cDNA. cDNA strands from all wells are pooled together to be purified. They are amplified by PCR and purified further. cDNA libraries are prepared using the Nextera XT kit with modified i5 primers. The resultant cDNA fragments are size-selected for 300–800 bp and sequenced (Table 31).

https://www.illumina.com/content/dam/illumina-marketing/documents/products/research_reviews/single-cell-sequencing-research-review.pdf

Scientific Publication Reviews can be accessed at

http://www.illumina.com/pubreviews

SOURCE

https://www.illumina.com/techniques/sequencing/rna-sequencing/ultra-low-input-single-cell-rna-seq.html

 

 

https://www.10xgenomics.com/company/#about

Our mission is to
accelerate science

10x Genomics is building tools for scientific discovery that reveal and address the true complexities of biology and disease. Through a combination of novel microfluidics, chemistry and bioinformatics, our award-winning Chromium System is enabling researchers around the world to more fully understand the fundamentals of biology at unprecedented resolution and scale.

Research Area

CLARIFY COMPLEX SYSTEMS

Complexity

Beyond Cell Type

For the first time, scRNA-Seq is enabling a cell-by-cell molecular and cellular characterization of hundreds of thousands of cells within the same sample. Complex systems, like those found in the immune system, can be explored without limits.

scRNA-Seq can now be applied to:
  • Immunology
  • Neurology
  • Stem Cell Biology
  • Oncology
  • Immuno-oncology
  • Functional Genomics
V(D)J Recombination

Clarify complex Systems

Single Cell Technology

Revolutionizing Gene Expression

WITH SINGLE-CELL RNA-SEQ

Transcriptome analysis has made the leap from bulk
population-based studies to the single cell, and scientists are harnessing
this new degree of resolution with remarkable ingenuity.

Single-cell RNA sequencing (scRNA-Seq) allows you to ask and answer questions that require
single-cell resolution on a scale that suits your experimental needs, from hundreds to millions of cells.
Are you truly tracking your cells’ transcriptomes, or are you just reading into the averages?

Get Started Today!

https://www.10xgenomics.com/product-list/#cnv

Solutions

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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

1.

2.

3.

4.

5.

Dr. Irina Robu Selection

1.

2.

3.

4.

5.

Dr. Sudipta Saha Selection

1.

2.

3.

4.

5.

 

 

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