Healthcare analytics, AI solutions for biological big data, providing an AI platform for the biotech, life sciences, medical and pharmaceutical industries, as well as for related technological approaches, i.e., curation and text analysis with machine learning and other activities related to AI applications to these industries.
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
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.
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
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
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
4.1.3 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, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 4: Single Cell Genomics
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, PhD, the 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?”
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.
Emerging STAR in Molecular Biology, Synthetic Virology and Genomics: Clodagh C. O’Shea: ChromEMT – Visualizing 3D chromatin structure
Article ID #241: Emerging STAR in Molecular and Cell Biology, Synthetic Virology and Genomics: Clodagh C. O’Shea: ChromEMT – Visualizing 3D chromatin structure. Published on 8/31/2017
WordCloud Image Produced by Adam Tubman
Curator: Aviva Lev-Ari, PhD, RN
On 8/28/2017, I attend and covered in REAL TIME the CHI’s 5th Immune Oncology Summit – Oncolytic Virus Immunotherapy, August 28-29, 2017 Sheraton Boston Hotel | Boston, MA
I covered in REAL TIME this event and Clodagh C. O’Shea talk at the conference.
On that evening, I e-mailed my team that
“I believe that Clodagh C. O’Sheawill get the Nobel Prizebefore CRISPR
11:00Synthetic Virology: Modular Assembly of Designer Viruses for Cancer Therapy
Clodagh O’Shea, Ph.D., Howard Hughes Medical Institute Faculty Scholar; Associate Professor, William Scandling Developmental Chair, Molecular and Cell Biology Laboratory, Salk Institute for Biological Studies
Design is the ultimate test of understanding. For oncolytic therapies to achieve their potential, we need a deep mechanistic understanding of virus and tumor biology together with the ability to confer new properties.
orthogonal capsid functionalization technologies (RapAd) and
replication assays that have enabled the rational design, directed evolution, systematic assembly and screening of powerful new vectors and oncolytic viruses.
Clodagh O’Shea’s Talk In Real Time:
Future Cancer therapies to be sophisticated as Cancer is
Targer suppresor pathways (Rb/p53)
OV are safe their efficacy ishas been limited
MOA: Specify Oncolytic Viral Replication in Tumor cells Attenuate – lack of potency
SOLUTIONS: Assembly: Assmble personalized V Tx fro libraries of functional parts
Adenovirus – natural & clinical advantages
Strategy: Technology for Assmbling Novel Adenovirus Genomes using Modular Genomic Parts
BS, Biochemistry and Microbiology, University College Cork, Ireland PhD, Imperial College London/Imperial Cancer Research Fund, U.K. Postdoctoral Fellow, UCSF Comprehensive Cancer Center, San Francisco, U.S.A
Single-cell RNA sequencing (scRNA-seq) is a recent technology that enables fine-grained discovery of cellular subtypes and specific cell states. It routinely uses machine learning methods, such as feature learning, clustering, and classification, to assist in uncovering novel information from scRNA-seq data. However, current methods are not well suited to deal with the substantial amounts of noise that is created by the experiments or the variation that occurs due to differences in the cells of the same type. Here, we develop a new hybrid approach, Deep Unsupervised Single-cell Clustering (DUSC), that integrates feature generation based on a deep learning architecture with a model-based clustering algorithm, to find a compact and informative representation of the single-cell transcriptomic data generating robust clusters. We also include a technique to estimate an efficient number of latent features in the deep learning model. Our method outperforms both classical and state-of-the-art feature learning and clustering methods, approaching the accuracy of supervised learning. We applied DUSC to single-cell transcriptomics dataset obtained from a triple-negative breast cancer tumor to identify potential cancer subclones accentuated by copy-number variation and investigate the role of clonal heterogeneity. Our method is freely available to the community and will hopefully facilitate our understanding of the cellular atlas of living organisms as well as provide the means to improve patient diagnostics and treatment.
Jenny Mjösberg and Rickard Sandberg are principal investigators at Karolinska Institutet’s Department of Medicine, Huddinge and Department of Cell and Molecular Biology, respectively. Credit: Stefan Zimmerman.
A relatively newly discovered group of immune cells known as ILCs have been examined in detail in a new study published in the journal Nature Immunology. By analysing the gene expression in individual tonsil cells, scientists at Karolinska Institutet have found three previously unknown subgroups of ILCs, and revealed more about how these cells function in the human body.
Innate lymphoid cells (ILCs) are a group of immune cells that have only relatively recently been discovered in humans. Most of current knowledge about ILCs stems from animal studies of e.g. inflammation or infection in the gastrointestinal tract. There is therefore an urgent need to learn more about these cells in humans.
Previous studies have shown that ILCs are important for maintaining the barrier function of the mucosa, which serves as a first line of defence against microorganisms in the lungs, intestines and elsewhere. However, while there is growing evidence to suggest that ILCs are involved in diseases such as inflammatory bowel disease, asthma and intestinal cancer, basic research still needs to be done to ascertain exactly what part they play.
Two research groups, led by Rickard Sandberg and Jenny Mjösberg, collaborated on a study of ILCs from human tonsils. To date, three main groups of human ILCs are characterized. In this present study, the teams used a novel approach that enabled them to sort individual tonsil cells and measure their expression across thousands of genes. This way, the researchers managed to categorise hundreds of cells, one by one, to define the types of ILCs found in the human tonsils.
Unique gene expression profiles
Rickard Sandberg, credit: Stefan Zimmerman,
“We used cluster analyses to demonstrate that ILCs congregate into ILC1, ILC2, ILC3 and NK cells, based on their unique gene expression profiles,” says Professor Sandberg at Karolinska Institutet’sDepartment of Cell and Molecular Biology, and the Stockholm branch of Ludwig Cancer Research. “Our analyses also discovered the expression of numerous genes of previously unknown function in ILCs, highlighting that these cells are likely doing more than what we previously knew.”
By analysing the gene expression profiles (or transcriptome) of individual cells, the researchers found that one of the formerly known main groups could be subdivided.
Jenny Mjösberg, credit: Stefan Zimmerman.
“We’ve identified three new subgroups of ILC3s that evince different gene expression patterns and that differ in how they react to signalling molecules and in their ability to secrete proteins,” says Dr Mjösberg at Karolinska Institutet’s Department of Medicine in Huddinge, South Stockholm. “All in all, our study has taught us a lot about this relatively uncharacterised family of cells and our data will serve as an important resource for other researchers.”
The study was financed by grants from a number of bodies, including the Swedish Research Council, the Swedish Cancer Society, the EU Framework Programme for Research and Innovation, the Swedish Society for Medical Research, the Swedish Foundation for Strategic Research and Karolinska Institutet.
Using a new assay method to study tumor cells, researchers at the University of California, San Diego School of Medicine and UC San Diego Moores Cancer Center have found evidence of clonal evolution in chronic lymphocytic leukemia (CLL). The assay method distinguishes features of leukemia cells that indicate whether the disease will be aggressive or slow-moving, a key factor in when and how patients are treated.
The findings are published in the July 26, 2012 First Edition online issue of Blood.
The progression of CLL is highly variable, dependent upon the rate and effects of accumulating monoclonal B cells in the blood, marrow, and lymphoid tissues. Some patients are symptom-free for years and do not require treatment, which involves the use of drugs that can cause significant side effects and are not curative. In other patients, however, CLL is relatively aggressive and demands therapeutic intervention soon after diagnosis.
“Our study shows that there may not be a sharp dividing line between the more aggressive and less aggressive forms of CLL,” said Thomas J. Kipps, MD, PhD, Evelyn and Edwin Tasch Chair in Cancer Research and senior author of the study. “Instead, it seems that over time the leukemia cells of patients with indolent disease begin to use genes similar to those that are generally used by CLL cells of patients with aggressive disease. In other words, prior to requiring therapy, the patterns of genes expressed by CLL cells appear to converge, regardless of whether or not the patient had aggressive versus indolent disease at diagnosis.”
Existing markers for aggressive or indolent disease are mostly fixed and have declining predictive value the longer the patient is from his or her initial diagnosis. When the blood sample is collected, these markers cannot reliably predict whether a CLL patient will need therapy soon, particularly when the patient has had the diagnosis of CLL for many years.
Kipps and colleagues studied thousands of genes, particularly those that code for proteins, in a group of 130 CLL patients with varying risks of disease progression. They identified 38 prognostic subnetworks of interacting genes and proteins that, at the time of sample collection, indicate the relative the aggressiveness of the disease and predict when the patient will require therapy. They confirmed their work using the method on two other, smaller CLL patient cohorts in Germany and Italy.
The subnetworks offer greater predictive value because they are based not on expression levels of individual genes or proteins, but on how they dynamically interact and change over time, influencing the course of the CLL and patient symptoms.
“In a sense, we looked at families rather than individuals,” said Kipps. “If you find in an interconnected family where most genes or proteins are expressed at higher levels, it becomes more likely that these genes and proteins have functional significance.”
He added that while the subnetworks abound in data, their complexity actually makes them easy to interpret and understand. “It’s like when you look out of a window and see the sky, clouds, trees, people, cars. You’re getting tremendous amounts of information that individually doesn’t tell you much. But when you look at the scene as a whole, you see patterns and networks. This work is similar. We’re taking all of the individual gene expression patterns and making sense of them as a whole. We’re more able to more clearly see how they control and regulate function.”
The findings help define how CLL — and perhaps other cancers — evolve over time, becoming more aggressive and deadly. “It’s as if each tumor has a clock which determines how frequently it may acquire the chance changes that make it behave more aggressively. Although the rates can vary, it appears that tumors march down similar pathways, which converge over time to a point where they become aggressive enough to require therapy.”
The study may alter how scientists think about CLL and how clinicians treat the disease: whether it is better to wait for later stages of the disease when tumor cells are more fragile and easier to kill, or treat early-stage indolent tumor cells aggressively, when they are fewer in number but harder to find and more resistant to therapy.
Abstract:
The clinical course of patients with chronic lymphocytic leukemia (CLL) is heterogeneous. Several prognostic factors have been identified that can stratify patients into groups that differ in their relative tendency for disease progression and/or survival. Here, we pursued a subnetwork-based analysis of gene expression profiles to discriminate between groups of patients with disparate risks for CLL progression. From an initial cohort of 130 patients, we identified 38 prognostic subnetworks that could predict the relative risk for disease progression requiring therapy from the time of sample collection, more accurately than established markers. The prognostic power of these subnetworks then was validated on two other cohorts of patients. We noted reduced divergence in gene expression between leukemia cells of CLL patients classified at diagnosis with aggressive versus indolent disease over time. The predictive subnetworks vary in levels of expression over time but exhibit increased similarity at later time points prior to therapy, suggesting that degenerate pathways apparently converge into common pathways that are associated with disease progression. As such, these results have implications for understanding cancer evolution and for the development of novel treatment strategies for patients with CLL.
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