Feeds:
Posts
Comments

Archive for the ‘Developmental biology’ Category


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

Researchers have classified a brand-new organ inside human body. Known as the mesentery, the new organ is found in our digestive systems, and was long thought to be made up of fragmented, separate structures. But recent research has shown that it’s actually one, continuous organ. The evidence for the organ’s reclassification is now published in The Lancet Gastroenterology & Hepatology. Although we now know about the structure of this new organ, its function is still poorly understood, and studying it could be the key to better understanding and treatment of abdominal and digestive disease.

mesentery

J Calvin Coffey, a researcher from the University Hospital Limerick in Ireland, who first discovered that the mesentery was an organ. In 2012, Coffey and his colleagues showed through detailed microscopic examinations that the mesentery is actually a continuous structure. Over the past four years, they’ve gathered further evidence that the mesentery should actually be classified as its own distinct organ, and the latest paper makes it official. Mesentery is a double fold of peritoneum – the lining of the abdominal cavity – that holds our intestine to the wall of our abdomen. It was described by the Italian polymath Leanardo da Vinci in 1508, but it has been ignored throughout the centuries, until now. Although there are generally considered to be five organs in the human body, there are in fact now 79, including the mesentery. The heart, brain, liver, lungs and kidneys are the vital organs, but there are another 74 that play a role in keeping us healthy. The distinctive anatomical and functional features of mesentery have been revealed that justify designation of the mesentery as an organ. Accordingly, the mesentery should be subjected to the same investigatory focus that is applied to other organs and systems. This provides a platform from which to direct future scientific investigation of the human mesentery in health and disease.

References:

http://www.thelancet.com/journals/langas/article/PIIS2468-1253(16)30026-7/abstract

http://www.sciencealert.com/it-s-official-a-brand-new-human-organ-has-been-classified

http://www.bbc.com/news/health-38506708

http://www.independent.co.uk/news/science/new-organ-mesentery-found-human-body-digestive-system-classified-abdominal-grays-anatomy-a7507396.html

https://in.news.yahoo.com/scientists-discover-human-organ-064207997.html

https://en.wikipedia.org/wiki/Mesentery

Advertisements

Read Full Post »


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

 

MicroRNAs (miRNAs) are a group of small non-coding RNA molecules that play a major role in posttranscriptional regulation of gene expression and are expressed in an organ-specific manner. One miRNA can potentially regulate the expression of several genes, depending on cell type and differentiation stage. They control every cellular process and their altered regulation is involved in human diseases. miRNAs are differentially expressed in the male and female gonads and have an organ-specific reproductive function. Exerting their affect through germ cells and gonadal somatic cells, miRNAs regulate key proteins necessary for gonad development. The role of miRNAs in the testes is only starting to emerge though they have been shown to be required for adequate spermatogenesis. In the ovary, miRNAs play a fundamental role in follicles’ assembly, growth, differentiation, and ovulation.

 

Deciphering the underlying causes of idiopathic male infertility is one of the main challenges in reproductive medicine. This is especially relevant in infertile patients displaying normal seminal parameters and no urogenital or genetic abnormalities. In these cases, the search for additional sperm biomarkers is of high interest. This study was aimed to determine the implications of the sperm miRNA expression profiles in the reproductive capacity of normozoospermic infertile individuals. The expression levels of 736 miRNAs were evaluated in spermatozoa from normozoospermic infertile males and normozoospermic fertile males analyzed under the same conditions. 57 miRNAs were differentially expressed between populations; 20 of them was regulated by a host gene promoter that in three cases comprised genes involved in fertility. The predicted targets of the differentially expressed miRNAs unveiled a significant enrichment of biological processes related to embryonic morphogenesis and chromatin modification. Normozoospermic infertile individuals exhibit a specific sperm miRNA expression profile clearly differentiated from normozoospermic fertile individuals. This miRNA cargo has potential implications in the individuals’ reproductive competence.

 

Circulating or “extracellular” miRNAs detected in biological fluids, could be used as potential diagnostic and prognostic biomarkers of several disease, such as cancer, gynecological and pregnancy disorders. However, their contributions in female infertility and in vitro fertilization (IVF) remain unknown. Polycystic ovary syndrome (PCOS) is a frequent endocrine disorder in women. PCOS is associated with altered features of androgen metabolism, increased insulin resistance and impaired fertility. Furthermore, PCOS, being a syndrome diagnosis, is heterogeneous and characterized by polycystic ovaries, chronic anovulation and evidence of hyperandrogenism, as well as being associated with chronic low-grade inflammation and an increased life time risk of type 2 diabetes. Altered miRNA levels have been associated with diabetes, insulin resistance, inflammation and various cancers. Studies have shown that circulating miRNAs are present in whole blood, serum, plasma and the follicular fluid of PCOS patients and that these might serve as potential biomarkers and a new approach for the diagnosis of PCOS. Presence of miRNA in mammalian follicular fluid has been demonstrated to be enclosed within microvesicles and exosomes or they can also be associated to protein complexes. The presence of microvesicles and exosomes carrying microRNAs in follicular fluid could represent an alternative mechanism of autocrine and paracrine communication inside the ovarian follicle. The investigation of the expression profiles of five circulating miRNAs (let-7b, miR-29a, miR-30a, miR-140 and miR-320a) in human follicular fluid from women with normal ovarian reserve and with polycystic ovary syndrome (PCOS) and their ability to predict IVF outcomes showed that these miRNAs could provide new helpful biomarkers to facilitate personalized medical care for oocyte quality in ART (Assisted Reproductive Treatment) and during IVF (In Vitro Fertilization).

 

References:

 

http://link.springer.com/chapter/10.1007%2F978-3-319-31973-5_12

 

http://onlinelibrary.wiley.com/doi/10.1111/andr.12276/abstract;jsessionid=F805A89DCC94BDBD42D6D60C40AD4AB0.f03t03

 

http://www.sciencedirect.com/science/article/pii/S0009279716302241

 

http://link.springer.com/article/10.1007%2Fs10815-016-0657-9

 

http://www.nature.com/articles/srep24976

 

 

Read Full Post »


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

 

Mitochondrial disease

 

Mitochondria are present in almost all human cells, and vary in number from a few tens to many thousands. They generate the majority of a cell’s energy supply which powers every part of our body. Mitochondria have their own separate DNA, which carries just a few genes. All of these genes are involved in energy production but determine no other characteristics. And so, any faults in these genes lead only to problems in energy production. Around 1 in 6500 children is thought to be born with a serious mitochondrial disorder due to faults in mitochondrial DNA.

 

Unlike nuclear genes, mitochondrial DNA is inherited only from our mothers. Mothers can carry abnormal mitochondria and be at risk of passing on serious disease to their children, even if they themselves show only mild or no symptoms. It is for such women who by chance have a high proportion of faulty mitochondrial DNA in their eggs for which the methods of mitochondrial replacement or “donation” have been developed. This technique is also referred as the three parent technique and it involves a couple and a donor.

 

Mitochondrial Donation

 

The most developed techniques, maternal spindle transfer (MST) and pro-nuclear transfer (PNT), are based on an IVF cycle but have additional steps. Other techniques are being developed.

 

In both MST and PNT, nuclear DNA is moved from a patient’s egg or embryo containing unhealthy mitochondria to a donor’s egg or embryo containing healthy mitochondria, from which the donor’s nuclear DNA has been removed.

 

mst

Maternal spindle transfer Bredenoord, A and P. Braude (2010) “Ethics of mitochondrial gene replacement: from bench to bedside” BMJ 341.

 

pnt

Pronuclear transfer Bredenoord, A and P. Braude (2010) “Ethics of mitochondrial gene replacement: from bench to bedside” BMJ 341.

 

Research Carried Out and Safety Issues

 

There have been many experiments conducted using MST and PNT in animals. PNT has been carried out since the mid-1980s in mice. MST has been carried out in a wide range of animals. More recently mice, monkeys and human embryos have been created with the specific aim of developing MST and PNT for avoiding mitochondrial disease.

 

  • There is no evidence to show that mitochondrial donation is unsafe
  • Research is progressing well and the recommended further experiments are expected to confirm this view.

 

The main area of research needed is to observe cells derived from embryos created by MST and PNT, to see how mitochondria behave.

 

Concerns about Mitochondrial Donation

 

The scientific evidence raises some potential concerns about mitochondrial donation. Just as we all have different blood groups, we also have different types of mitochondria, called haplotypes. Some scientists have suggested that if the patient and the mitochondria donor have different mitochondrial haplotypes, there is a theoretical risk that the donor’s mitochondria won’t be able to ‘talk’ properly to the patient’s nuclear DNA, which could cause problems in the embryo and resulting child. So, mitochondria haplotype matching in the process of selecting donors may be done to avoid problems.

 

Another potential concern is that a small amount of unhealthy mitochondrial DNA may be transferred into the donor’s egg along with the mother’s nuclear DNA. Studies carried out on MST and PNT show that some so-called mitochondrial ‘carry-over’ occurs. However, the carry-over is lower than 2% of the mitochondria in the resulting embryo, an amount which is very unlikely to be problematic for the children born.

 

References:

 

http://mitochondria.hfea.gov.uk/mitochondria/what-is-mitochondrial-disease/

 

http://mitochondria.hfea.gov.uk/mitochondria/what-is-mitochondrial-disease/new-techniques-to-prevent-mitochondrial-disease/

 

https://www.newscientist.com/article/2107219-exclusive-worlds-first-baby-born-with-new-3-parent-technique/

 

https://www.newscientist.com/article/2108549-exclusive-3-parent-baby-method-already-used-for-infertility/

 

http://www.frontlinegenomics.com/news/7889/ethical-concerns-raised-first-three-parent-ivf-baby/

 

http://www.hfea.gov.uk/docs/2011-04-18_Mitochondria_review_-_final_report.PDF

 

http://www.hfea.gov.uk/docs/Mito-Annex_VIII-science_review_update.pdf

 

http://www.hfea.gov.uk/docs/Third_Mitochondrial_replacement_scientific_review.pdf

 

https://pharmaceuticalintelligence.com/2014/02/26/three-parent-baby-making-practice-of-modifying-oocytes-for-use-in-in-vitro-fertilization-fda-hearing/

 

 

Read Full Post »

Milestones in Physiology & Discoveries in Medicine and Genomics: Request for Book Review Writing on Amazon.com


physiology-cover-seriese-vol-3individualsaddlebrown-page2

Milestones in Physiology

Discoveries in Medicine, Genomics and Therapeutics

Patient-centric Perspective 

http://www.amazon.com/dp/B019VH97LU 

2015

 

 

Author, Curator and Editor

Larry H Bernstein, MD, FCAP

Chief Scientific Officer

Leaders in Pharmaceutical Business Intelligence

Larry.bernstein@gmail.com

Preface

Introduction 

Chapter 1: Evolution of the Foundation for Diagnostics and Pharmaceuticals Industries

1.1  Outline of Medical Discoveries between 1880 and 1980

1.2 The History of Infectious Diseases and Epidemiology in the late 19th and 20th Century

1.3 The Classification of Microbiota

1.4 Selected Contributions to Chemistry from 1880 to 1980

1.5 The Evolution of Clinical Chemistry in the 20th Century

1.6 Milestones in the Evolution of Diagnostics in the US HealthCare System: 1920s to Pre-Genomics

 

Chapter 2. The search for the evolution of function of proteins, enzymes and metal catalysts in life processes

2.1 The life and work of Allan Wilson
2.2  The  evolution of myoglobin and hemoglobin
2.3  More complexity in proteins evolution
2.4  Life on earth is traced to oxygen binding
2.5  The colors of life function
2.6  The colors of respiration and electron transport
2.7  Highlights of a green evolution

 

Chapter 3. Evolution of New Relationships in Neuroendocrine States
3.1 Pituitary endocrine axis
3.2 Thyroid function
3.3 Sex hormones
3.4 Adrenal Cortex
3.5 Pancreatic Islets
3.6 Parathyroids
3.7 Gastointestinal hormones
3.8 Endocrine action on midbrain
3.9 Neural activity regulating endocrine response

3.10 Genomic Promise for Neurodegenerative Diseases, Dementias, Autism Spectrum, Schizophrenia, and Serious Depression

 

Chapter 4.  Problems of the Circulation, Altitude, and Immunity

4.1 Innervation of Heart and Heart Rate
4.2 Action of hormones on the circulation
4.3 Allogeneic Transfusion Reactions
4.4 Graft-versus Host reaction
4.5 Unique problems of perinatal period
4.6. High altitude sickness
4.7 Deep water adaptation
4.8 Heart-Lung-and Kidney
4.9 Acute Lung Injury

4.10 Reconstruction of Life Processes requires both Genomics and Metabolomics to explain Phenotypes and Phylogenetics

 

Chapter 5. Problems of Diets and Lifestyle Changes

5.1 Anorexia nervosa
5.2 Voluntary and Involuntary S-insufficiency
5.3 Diarrheas – bacterial and nonbacterial
5.4 Gluten-free diets
5.5 Diet and cholesterol
5.6 Diet and Type 2 diabetes mellitus
5.7 Diet and exercise
5.8 Anxiety and quality of Life
5.9 Nutritional Supplements

 

Chapter 6. Advances in Genomics, Therapeutics and Pharmacogenomics

6.1 Natural Products Chemistry

6.2 The Challenge of Antimicrobial Resistance

6.3 Viruses, Vaccines and immunotherapy

6.4 Genomics and Metabolomics Advances in Cancer

6.5 Proteomics – Protein Interaction

6.6 Pharmacogenomics

6.7 Biomarker Guided Therapy

6.8 The Emergence of a Pharmaceutical Industry in the 20th Century: Diagnostics Industry and Drug Development in the Genomics Era: Mid 80s to Present

6.09 The Union of Biomarkers and Drug Development

6.10 Proteomics and Biomarker Discovery

6.11 Epigenomics and Companion Diagnostics

 

Chapter  7

Integration of Physiology, Genomics and Pharmacotherapy

7.1 Richard Lifton, MD, PhD of Yale University and Howard Hughes Medical Institute: Recipient of 2014 Breakthrough Prizes Awarded in Life Sciences for the Discovery of Genes and Biochemical Mechanisms that cause Hypertension

7.2 Calcium Cycling (ATPase Pump) in Cardiac Gene Therapy: Inhalable Gene Therapy for Pulmonary Arterial Hypertension and Percutaneous Intra-coronary Artery Infusion for Heart Failure: Contributions by Roger J. Hajjar, MD

7.3 Diagnostics and Biomarkers: Novel Genomics Industry Trends vs Present Market Conditions and Historical Scientific Leaders Memoirs

7.4 Synthetic Biology: On Advanced Genome Interpretation for Gene Variants and Pathways: What is the Genetic Base of Atherosclerosis and Loss of Arterial Elasticity with Aging

7.5 Diagnosing Diseases & Gene Therapy: Precision Genome Editing and Cost-effective microRNA Profiling

7.6 Imaging Biomarker for Arterial Stiffness: Pathways in Pharmacotherapy for Hypertension and Hypercholesterolemia Management

7.7 Neuroprotective Therapies: Pharmacogenomics vs Psychotropic drugs and Cholinesterase Inhibitors

7.8 Metabolite Identification Combining Genetic and Metabolic Information: Genetic association links unknown metabolites to functionally related genes

7.9 Preserved vs Reduced Ejection Fraction: Available and Needed Therapies

7.10 Biosimilars: Intellectual Property Creation and Protection by Pioneer and by

7.11 Demonstrate Biosimilarity: New FDA Biosimilar Guidelines

 

Chapter 7.  Biopharma Today

8.1 A Great University engaged in Drug Discovery: University of Pittsburgh

8.2 Introduction – The Evolution of Cancer Therapy and Cancer Research: How We Got Here?

8.3 Predicting Tumor Response, Progression, and Time to Recurrence

8.4 Targeting Untargetable Proto-Oncogenes

8.5 Innovation: Drug Discovery, Medical Devices and Digital Health

8.6 Cardiotoxicity and Cardiomyopathy Related to Drugs Adverse Effects

8.7 Nanotechnology and Ocular Drug Delivery: Part I

8.8 Transdermal drug delivery (TDD) system and nanotechnology: Part II

8.9 The Delicate Connection: IDO (Indolamine 2, 3 dehydrogenase) and Cancer Immunology

8.10 Natural Drug Target Discovery and Translational Medicine in Human Microbiome

8.11 From Genomics of Microorganisms to Translational Medicine

8.12 Confined Indolamine 2, 3 dioxygenase (IDO) Controls the Homeostasis of Immune Responses for Good and Bad

 

Chapter 9. BioPharma – Future Trends

9.1 Artificial Intelligence Versus the Scientist: Who Will Win?

9.2 The Vibrant Philly Biotech Scene: Focus on KannaLife Sciences and the Discipline and Potential of Pharmacognosy

9.3 The Vibrant Philly Biotech Scene: Focus on Computer-Aided Drug Design and Gfree Bio, LLC

9.4 Heroes in Medical Research: The Postdoctoral Fellow

9.5 NIH Considers Guidelines for CAR-T therapy: Report from Recombinant DNA Advisory Committee

9.6 1st Pitch Life Science- Philadelphia- What VCs Really Think of your Pitch

9.7 Multiple Lung Cancer Genomic Projects Suggest New Targets, Research Directions for Non-Small Cell Lung Cancer

9.8 Heroes in Medical Research: Green Fluorescent Protein and the Rough Road in Science

9.9 Issues in Personalized Medicine in Cancer: Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing

9.10 The SCID Pig II: Researchers Develop Another SCID Pig, And Another Great Model For Cancer Research

Epilogue

Read Full Post »


Keystone Symposia on Molecular and Cellular Biology – 2016-2017 Forthcoming Conferences in Life Sciences

Reporter: Aviva Lev-Ari, PhD, RN

2016-2017 Forthcoming Conferences in Life Sciences by topic:

DNA Replication and Recombination (Z2)
April 2 – 6, 2017 | Santa Fe, New Mexico, USA
Scientific Organizers: John F.X. Diffley, Anja Groth and Scott Keeney

Immunology

Translational Vaccinology for Global Health (S1)
October 25 – 29, 2016 | London, United Kingdom
Scientific Organizers: Christopher L. Karp, Gagandeep Kang and Rino Rappuoli

Hemorrhagic Fever Viruses (S3)
December 4 – 8, 2016 | Santa Fe, New Mexico, USA
Scientific Organizers: William E. Dowling and Thomas W. Geisbert

Cell Plasticity within the Tumor Microenvironment (A1)
January 8 – 12, 2017 | Big Sky, Montana, USA
Scientific Organizers: Sergei Grivennikov, Florian R. Greten and Mikala Egeblad

TGF-ß in Immunity, Inflammation and Cancer (A3)
January 9 – 13, 2017 | Taos, New Mexico, USA
Scientific Organizers: Wanjun Chen, Joanne E. Konkel and Richard A. Flavell

New Developments in Our Basic Understanding of Tuberculosis (A5)
January 14 – 18, 2017 | Vancouver, British Columbia, Canada
Scientific Organizers: Samuel M. Behar and Valerie Mizrahi

PI3K Pathways in Immunology, Growth Disorders and Cancer (A6)
January 19 – 23, 2017 | Santa Fe, New Mexico, USA
Scientific Organizers: Leon O. Murphy, Klaus Okkenhaug and Sabina C. Cosulich

Biobetters and Next-Generation Biologics: Innovative Strategies for Optimally Effective Therapies (A7)
January 22 – 26, 2017 | Snowbird, Utah, USA
Scientific Organizers: Cherié L. Butts, Amy S. Rosenberg, Amy D. Klion and Sachdev S. Sidhu

Obesity and Adipose Tissue Biology (J4)
January 22 – 26, 2017 | Keystone, Colorado, USA
Scientific Organizers: Marc L. Reitman, Ruth E. Gimeno and Jan Nedergaard

Inflammation-Driven Cancer: Mechanisms to Therapy (J7)
February 5 – 9, 2017 | Keystone, Colorado, USA
Scientific Organizers: Fiona M. Powrie, Michael Karin and Alberto Mantovani

Autophagy Network Integration in Health and Disease (B2)
February 12 – 16, 2017 | Copper Mountain, Colorado, USA
Scientific Organizers: Ivan Dikic, Katja Simon and J. Wade Harper

Asthma: From Pathway Biology to Precision Therapeutics (B3)
February 12 – 16, 2017 | Keystone, Colorado, USA
Scientific Organizers: Clare M. Lloyd, John V. Fahy and Sally Wenzel-Morganroth

Viral Immunity: Mechanisms and Consequences (B4)
February 19 – 23, 2017 | Santa Fe, New Mexico, USA
Scientific Organizers: Akiko Iwasaki, Daniel B. Stetson and E. John Wherry

Lipidomics and Bioactive Lipids in Metabolism and Disease (B6)
February 26 – March 2, 2017 | Tahoe City, California, USA
Scientific Organizers: Alfred H. Merrill, Walter Allen Shaw, Sarah Spiegel and Michael J.O.Wakelam

Bile Acid Receptors as Signal Integrators in Liver and Metabolism (C1)
March 3 – 7, 2017 | Monterey, California, USA
Scientific Organizers: Luciano Adorini, Kristina Schoonjans and Scott L. Friedman

Cancer Immunology and Immunotherapy: Taking a Place in Mainstream Oncology (C7)
March 19 – 23, 2017 | Whistler, British Columbia, Canada
Scientific Organizers: Robert D. Schreiber, James P. Allison, Philip D. Greenberg and Glenn Dranoff

Pattern Recognition Signaling: From Innate Immunity to Inflammatory Disease (X5)
March 19 – 23, 2017 | Banff, Alberta, Canada
Scientific Organizers: Thirumala-Devi Kanneganti, Vishva M. Dixit and Mohamed Lamkanfi

Type I Interferon: Friend and Foe Alike (X6)
March 19 – 23, 2017 | Banff, Alberta, Canada
Scientific Organizers: Alan Sher, Virginia Pascual, Adolfo García-Sastre and Anne O’Garra

Injury, Inflammation and Fibrosis (C8)
March 26 – 30, 2017 | Snowbird, Utah, USA
Scientific Organizers: Tatiana Kisseleva, Michael Karin and Andrew M. Tager

Immune Regulation in Autoimmunity and Cancer (D1)
March 26 – 30, 2017 | Whistler, British Columbia, Canada
Scientific Organizers: David A. Hafler, Vijay K. Kuchroo and Jane L. Grogan

B Cells and T Follicular Helper Cells – Controlling Long-Lived Immunity (D2)
April 23 – 27, 2017 | Whistler, British Columbia, Canada
Scientific Organizers: Stuart G. Tangye, Ignacio Sanz and Hai Qi

Mononuclear Phagocytes in Health, Immune Defense and Disease (D3)
April 30 – May 4, 2017 | Austin, Texas, USA
Scientific Organizers: Steffen Jung and Miriam Merad

Modeling Viral Infections and Immunity (E1)
May 1 – 4, 2017 | Estes Park, Colorado, USA
Scientific Organizers: Alan S. Perelson, Rob J. De Boer and Phillip D. Hodgkin

Integrating Metabolism and Immunity (E4)
May 29 – June 2, 2017 | Dublin, Ireland
Scientific Organizers: Hongbo Chi, Erika L. Pearce, Richard A. Flavell and Luke A.J. O’Neill

Neuroinflammation: Concepts, Characteristics, Consequences (E5)
June 19 – 23, 2017 | Keystone, Colorado, USA
Scientific Organizers: Richard M. Ransohoff, Christopher K. Glass and V. Hugh Perry

Infectious Diseases

Translational Vaccinology for Global Health (S1)
October 25 – 29, 2016 | London, United Kingdom
Scientific Organizers: Christopher L. Karp, Gagandeep Kang and Rino Rappuoli

Hemorrhagic Fever Viruses (S3)
December 4 – 8, 2016 | Santa Fe, New Mexico, USA
Scientific Organizers: William E. Dowling and Thomas W. Geisbert

Cellular Stress Responses and Infectious Agents (S4)
December 4 – 8, 2016 | Santa Fe, New Mexico, USA
Scientific Organizers: Margo A. Brinton, Sandra K. Weller and Beth Levine

New Developments in Our Basic Understanding of Tuberculosis (A5)
January 14 – 18, 2017 | Vancouver, British Columbia, Canada
Scientific Organizers: Samuel M. Behar and Valerie Mizrahi

Autophagy Network Integration in Health and Disease (B2)
February 12 – 16, 2017 | Copper Mountain, Colorado, USA
Scientific Organizers: Ivan Dikic, Katja Simon and J. Wade Harper

Viral Immunity: Mechanisms and Consequences (B4)
February 19 – 23, 2017 | Santa Fe, New Mexico, USA
Scientific Organizers: Akiko Iwasaki, Daniel B. Stetson and E. John Wherry

Malaria: From Innovation to Eradication (B5)
February 19 – 23, 2017 | Kampala, Uganda
Scientific Organizers: Marcel Tanner, Sarah K. Volkman, Marcus V.G. Lacerda and Salim Abdulla

Type I Interferon: Friend and Foe Alike (X6)
March 19 – 23, 2017 | Banff, Alberta, Canada
Scientific Organizers: Alan Sher, Virginia Pascual, Adolfo García-Sastre and Anne O’Garra

HIV Vaccines (C9)
March 26 – 30, 2017 | Steamboat Springs, Colorado, USA
Scientific Organizers: Andrew B. Ward, Penny L. Moore and Robin Shattock

Modeling Viral Infections and Immunity (E1)
May 1 – 4, 2017 | Estes Park, Colorado, USA
Scientific Organizers: Alan S. Perelson, Rob J. De Boer and Phillip D. Hodgkin

Metabolic Diseases

Mitochondria Communication (A4)
January 14 – 18, 2017 | Taos, New Mexico, USA
Scientific Organizers: Jared Rutter, Cole M. Haynes and Marcia C. Haigis

Diabetes (J3)
January 22 – 26, 2017 | Keystone, Colorado, USA
Scientific Organizers: Jiandie Lin, Clay F. Semenkovich and Rohit N. Kulkarni

Obesity and Adipose Tissue Biology (J4)
January 22 – 26, 2017 | Keystone, Colorado, USA
Scientific Organizers: Marc L. Reitman, Ruth E. Gimeno and Jan Nedergaard

Microbiome in Health and Disease (J8)
February 5 – 9, 2017 | Keystone, Colorado, USA
Scientific Organizers: Julie A. Segre, Ramnik Xavier and William Michael Dunne

Bile Acid Receptors as Signal Integrators in Liver and Metabolism (C1)
March 3 – 7, 2017 | Monterey, California, USA
Scientific Organizers: Luciano Adorini, Kristina Schoonjans and Scott L. Friedman

Sex and Gender Factors Affecting Metabolic Homeostasis, Diabetes and Obesity (C6)
March 19 – 22, 2017 | Tahoe City, California, USA
Scientific Organizers: Franck Mauvais-Jarvis, Deborah Clegg and Arthur P. Arnold

Neuronal Control of Appetite, Metabolism and Weight (Z5)
May 9 – 13, 2017 | Copenhagen, Denmark
Scientific Organizers: Lora K. Heisler and Scott M. Sternson

Gastrointestinal Control of Metabolism (Z6)
May 9 – 13, 2017 | Copenhagen, Denmark
Scientific Organizers: Randy J. Seeley, Matthias H. Tschöp and Fiona M. Gribble

Integrating Metabolism and Immunity (E4)
May 29 – June 2, 2017 | Dublin, Ireland
Scientific Organizers: Hongbo Chi, Erika L. Pearce, Richard A. Flavell and Luke A.J. O’Neill

Neurobiology

Transcriptional and Epigenetic Control in Stem Cells (J1)
January 8 – 12, 2017 | Olympic Valley, California, USA
Scientific Organizers: Konrad Hochedlinger, Kathrin Plath and Marius Wernig

Neurogenesis during Development and in the Adult Brain (J2)
January 8 – 12, 2017 | Olympic Valley, California, USA
Scientific Organizers: Alysson R. Muotri, Kinichi Nakashima and Xinyu Zhao

Rare and Undiagnosed Diseases: Discovery and Models of Precision Therapy (C2)
March 5 – 8, 2017 | Boston, Massachusetts, USA
Scientific Organizers: William A. Gahl and Christoph Klein

mRNA Processing and Human Disease (C3)
March 5 – 8, 2017 | Taos, New Mexico, USA
Scientific Organizers: James L. Manley, Siddhartha Mukherjee and Gideon Dreyfuss

Synapses and Circuits: Formation, Function, and Dysfunction (X1)
March 5 – 8, 2017 | Santa Fe, New Mexico, USA
Scientific Organizers: Tony Koleske, Yimin Zou, Kristin Scott and A. Kimberley McAllister

Connectomics (X2)
March 5 – 8, 2017 | Santa Fe, New Mexico, USA
Scientific Organizers: Olaf Sporns, Danielle Bassett and Jeremy Freeman

Neuronal Control of Appetite, Metabolism and Weight (Z5)
May 9 – 13, 2017 | Copenhagen, Denmark
Scientific Organizers: Lora K. Heisler and Scott M. Sternson

Neuroinflammation: Concepts, Characteristics, Consequences (E5)
June 19 – 23, 2017 | Keystone, Colorado, USA
Scientific Organizers: Richard M. Ransohoff, Christopher K. Glass and V. Hugh Perry

Plant Biology

Phytobiomes: From Microbes to Plant Ecosystems (S2)
November 8 – 12, 2016 | Santa Fe, New Mexico, USA
Scientific Organizers: Jan E. Leach, Kellye A. Eversole, Jonathan A. Eisen and Gwyn Beattie

Structural Biology

Frontiers of NMR in Life Sciences (C5)
March 12 – 16, 2017 | Keystone, Colorado, USA
Scientific Organizers: Kurt Wüthrich, Michael Sattler and Stephen W. Fesik

Technologies

Cell Plasticity within the Tumor Microenvironment (A1)
January 8 – 12, 2017 | Big Sky, Montana, USA
Scientific Organizers: Sergei Grivennikov, Florian R. Greten and Mikala Egeblad

Precision Genome Engineering (A2)
January 8 – 12, 2017 | Breckenridge, Colorado, USA
Scientific Organizers: J. Keith Joung, Emmanuelle Charpentier and Olivier Danos

Transcriptional and Epigenetic Control in Stem Cells (J1)
January 8 – 12, 2017 | Olympic Valley, California, USA
Scientific Organizers: Konrad Hochedlinger, Kathrin Plath and Marius Wernig

Protein-RNA Interactions: Scale, Mechanisms, Structure and Function of Coding and Noncoding RNPs (J6)
February 5 – 9, 2017 | Banff, Alberta, Canada
Scientific Organizers: Gene W. Yeo, Jernej Ule, Karla Neugebauer and Melissa J. Moore

Lipidomics and Bioactive Lipids in Metabolism and Disease (B6)
February 26 – March 2, 2017 | Tahoe City, California, USA
Scientific Organizers: Alfred H. Merrill, Walter Allen Shaw, Sarah Spiegel and Michael J.O.Wakelam

Connectomics (X2)
March 5 – 8, 2017 | Santa Fe, New Mexico, USA
Scientific Organizers: Olaf Sporns, Danielle Bassett and Jeremy Freeman

Engineered Cells and Tissues as Platforms for Discovery and Therapy (K1)
March 9 – 12, 2017 | Boston, Massachusetts, USA
Scientific Organizers: Laura E. Niklason, Milica Radisic and Nenad Bursac

Frontiers of NMR in Life Sciences (C5)
March 12 – 16, 2017 | Keystone, Colorado, USA
Scientific Organizers: Kurt Wüthrich, Michael Sattler and Stephen W. Fesik

October 2016

Translational Vaccinology for Global Health (S1)
October 25 – 29, 2016 | London, United Kingdom
Scientific Organizers: Christopher L. Karp, Gagandeep Kang and Rino Rappuoli

November 2016

Phytobiomes: From Microbes to Plant Ecosystems (S2)
November 8 – 12, 2016 | Santa Fe, New Mexico, USA
Scientific Organizers: Jan E. Leach, Kellye A. Eversole, Jonathan A. Eisen and Gwyn Beattie

December 2016

Hemorrhagic Fever Viruses (S3)
December 4 – 8, 2016 | Santa Fe, New Mexico, USA
Scientific Organizers: William E. Dowling and Thomas W. Geisbert

Cellular Stress Responses and Infectious Agents (S4)
December 4 – 8, 2016 | Santa Fe, New Mexico, USA
Scientific Organizers: Margo A. Brinton, Sandra K. Weller and Beth Levine

January 2017

Cell Plasticity within the Tumor Microenvironment (A1)
January 8 – 12, 2017 | Big Sky, Montana, USA
Scientific Organizers: Sergei Grivennikov, Florian R. Greten and Mikala Egeblad

Precision Genome Engineering (A2)
January 8 – 12, 2017 | Breckenridge, Colorado, USA
Scientific Organizers: J. Keith Joung, Emmanuelle Charpentier and Olivier Danos

Transcriptional and Epigenetic Control in Stem Cells (J1)
January 8 – 12, 2017 | Olympic Valley, California, USA
Scientific Organizers: Konrad Hochedlinger, Kathrin Plath and Marius Wernig

Neurogenesis during Development and in the Adult Brain (J2)
January 8 – 12, 2017 | Olympic Valley, California, USA
Scientific Organizers: Alysson R. Muotri, Kinichi Nakashima and Xinyu Zhao

TGF-ß in Immunity, Inflammation and Cancer (A3)
January 9 – 13, 2017 | Taos, New Mexico, USA
Scientific Organizers: Wanjun Chen, Joanne E. Konkel and Richard A. Flavell

Mitochondria Communication (A4)
January 14 – 18, 2017 | Taos, New Mexico, USA
Scientific Organizers: Jared Rutter, Cole M. Haynes and Marcia C. Haigis

New Developments in Our Basic Understanding of Tuberculosis (A5)
January 14 – 18, 2017 | Vancouver, British Columbia, Canada
Scientific Organizers: Samuel M. Behar and Valerie Mizrahi

PI3K Pathways in Immunology, Growth Disorders and Cancer (A6)
January 19 – 23, 2017 | Santa Fe, New Mexico, USA
Scientific Organizers: Leon O. Murphy, Klaus Okkenhaug and Sabina C. Cosulich

Biobetters and Next-Generation Biologics: Innovative Strategies for Optimally Effective Therapies (A7)
January 22 – 26, 2017 | Snowbird, Utah, USA
Scientific Organizers: Cherié L. Butts, Amy S. Rosenberg, Amy D. Klion and Sachdev S. Sidhu

Diabetes (J3)
January 22 – 26, 2017 | Keystone, Colorado, USA
Scientific Organizers: Jiandie Lin, Clay F. Semenkovich and Rohit N. Kulkarni

Obesity and Adipose Tissue Biology (J4)
January 22 – 26, 2017 | Keystone, Colorado, USA
Scientific Organizers: Marc L. Reitman, Ruth E. Gimeno and Jan Nedergaard

Omics Strategies to Study the Proteome (A8)
January 29 – February 2, 2017 | Breckenridge, Colorado, USA
Scientific Organizers: Alan Saghatelian, Chuan He and Ileana M. Cristea

Epigenetics and Human Disease: Progress from Mechanisms to Therapeutics (A9)
January 29 – February 2, 2017 | Seattle, Washington, USA
Scientific Organizers: Johnathan R. Whetstine, Jessica K. Tyler and Rab K. Prinjha

Hematopoiesis (B1)
January 31 – February 4, 2017 | Banff, Alberta, Canada
Scientific Organizers: Catriona H.M. Jamieson, Andreas Trumpp and Paul S. Frenette

February 2017

Noncoding RNAs: From Disease to Targeted Therapeutics (J5)
February 5 – 9, 2017 | Banff, Alberta, Canada
Scientific Organizers: Kevin V. Morris, Archa Fox and Paloma Hoban Giangrande

Protein-RNA Interactions: Scale, Mechanisms, Structure and Function of Coding and Noncoding RNPs (J6)
February 5 – 9, 2017 | Banff, Alberta, Canada
Scientific Organizers: Gene W. Yeo, Jernej Ule, Karla Neugebauer and Melissa J. Moore

Inflammation-Driven Cancer: Mechanisms to Therapy (J7)
February 5 – 9, 2017 | Keystone, Colorado, USA
Scientific Organizers: Fiona M. Powrie, Michael Karin and Alberto Mantovani

Microbiome in Health and Disease (J8)
February 5 – 9, 2017 | Keystone, Colorado, USA
Scientific Organizers: Julie A. Segre, Ramnik Xavier and William Michael Dunne

Autophagy Network Integration in Health and Disease (B2)
February 12 – 16, 2017 | Copper Mountain, Colorado, USA
Scientific Organizers: Ivan Dikic, Katja Simon and J. Wade Harper

Asthma: From Pathway Biology to Precision Therapeutics (B3)
February 12 – 16, 2017 | Keystone, Colorado, USA
Scientific Organizers: Clare M. Lloyd, John V. Fahy and Sally Wenzel-Morganroth

Viral Immunity: Mechanisms and Consequences (B4)
February 19 – 23, 2017 | Santa Fe, New Mexico, USA
Scientific Organizers: Akiko Iwasaki, Daniel B. Stetson and E. John Wherry

Malaria: From Innovation to Eradication (B5)
February 19 – 23, 2017 | Kampala, Uganda
Scientific Organizers: Marcel Tanner, Sarah K. Volkman, Marcus V.G. Lacerda and Salim Abdulla

Lipidomics and Bioactive Lipids in Metabolism and Disease (B6)
February 26 – March 2, 2017 | Tahoe City, California, USA
Scientific Organizers: Alfred H. Merrill, Walter Allen Shaw, Sarah Spiegel and Michael J.O.Wakelam

March 2017

Bile Acid Receptors as Signal Integrators in Liver and Metabolism (C1)
March 3 – 7, 2017 | Monterey, California, USA
Scientific Organizers: Luciano Adorini, Kristina Schoonjans and Scott L. Friedman

Rare and Undiagnosed Diseases: Discovery and Models of Precision Therapy (C2)
March 5 – 8, 2017 | Boston, Massachusetts, USA
Scientific Organizers: William A. Gahl and Christoph Klein

mRNA Processing and Human Disease (C3)
March 5 – 8, 2017 | Taos, New Mexico, USA
Scientific Organizers: James L. Manley, Siddhartha Mukherjee and Gideon Dreyfuss

Kinases: Next-Generation Insights and Approaches (C4)
March 5 – 9, 2017 | Breckenridge, Colorado, USA
Scientific Organizers: Reid M. Huber, John Kuriyan and Ruth H. Palmer

Synapses and Circuits: Formation, Function, and Dysfunction (X1)
March 5 – 8, 2017 | Santa Fe, New Mexico, USA
Scientific Organizers: Tony Koleske, Yimin Zou, Kristin Scott and A. Kimberley McAllister

Connectomics (X2)
March 5 – 8, 2017 | Santa Fe, New Mexico, USA
Scientific Organizers: Olaf Sporns, Danielle Bassett and Jeremy Freeman

Tumor Metabolism: Mechanisms and Targets (X3)
March 5 – 9, 2017 | Whistler, British Columbia, Canada
Scientific Organizers: Brendan D. Manning, Kathryn E. Wellen and Reuben J. Shaw

Adaptations to Hypoxia in Physiology and Disease (X4)
March 5 – 9, 2017 | Whistler, British Columbia, Canada
Scientific Organizers: M. Celeste Simon, Amato J. Giaccia and Randall S. Johnson

Engineered Cells and Tissues as Platforms for Discovery and Therapy (K1)
March 9 – 12, 2017 | Boston, Massachusetts, USA
Scientific Organizers: Laura E. Niklason, Milica Radisic and Nenad Bursac

Frontiers of NMR in Life Sciences (C5)
March 12 – 16, 2017 | Keystone, Colorado, USA
Scientific Organizers: Kurt Wüthrich, Michael Sattler and Stephen W. Fesik

Sex and Gender Factors Affecting Metabolic Homeostasis, Diabetes and Obesity (C6)
March 19 – 22, 2017 | Tahoe City, California, USA
Scientific Organizers: Franck Mauvais-Jarvis, Deborah Clegg and Arthur P. Arnold

Cancer Immunology and Immunotherapy: Taking a Place in Mainstream Oncology (C7)
March 19 – 23, 2017 | Whistler, British Columbia, Canada
Scientific Organizers: Robert D. Schreiber, James P. Allison, Philip D. Greenberg and Glenn Dranoff

Pattern Recognition Signaling: From Innate Immunity to Inflammatory Disease (X5)
March 19 – 23, 2017 | Banff, Alberta, Canada
Scientific Organizers: Thirumala-Devi Kanneganti, Vishva M. Dixit and Mohamed Lamkanfi

Type I Interferon: Friend and Foe Alike (X6)
March 19 – 23, 2017 | Banff, Alberta, Canada
Scientific Organizers: Alan Sher, Virginia Pascual, Adolfo García-Sastre and Anne O’Garra

Injury, Inflammation and Fibrosis (C8)
March 26 – 30, 2017 | Snowbird, Utah, USA
Scientific Organizers: Tatiana Kisseleva, Michael Karin and Andrew M. Tager

HIV Vaccines (C9)
March 26 – 30, 2017 | Steamboat Springs, Colorado, USA
Scientific Organizers: Andrew B. Ward, Penny L. Moore and Robin Shattock

Immune Regulation in Autoimmunity and Cancer (D1)
March 26 – 30, 2017 | Whistler, British Columbia, Canada
Scientific Organizers: David A. Hafler, Vijay K. Kuchroo and Jane L. Grogan

Molecular Mechanisms of Heart Development (X7)
March 26 – 30, 2017 | Keystone, Colorado, USA
Scientific Organizers: Benoit G. Bruneau, Brian L. Black and Margaret E. Buckingham

RNA-Based Approaches in Cardiovascular Disease (X8)
March 26 – 30, 2017 | Keystone, Colorado, USA
Scientific Organizers: Thomas Thum and Roger J. Hajjar

April 2017

Genomic Instability and DNA Repair (Z1)
April 2 – 6, 2017 | Santa Fe, New Mexico, USA
Scientific Organizers: Julia Promisel Cooper, Marco F. Foiani and Geneviève Almouzni

DNA Replication and Recombination (Z2)
April 2 – 6, 2017 | Santa Fe, New Mexico, USA
Scientific Organizers: John F.X. Diffley, Anja Groth and Scott Keeney

B Cells and T Follicular Helper Cells – Controlling Long-Lived Immunity (D2)
April 23 – 27, 2017 | Whistler, British Columbia, Canada
Scientific Organizers: Stuart G. Tangye, Ignacio Sanz and Hai Qi

Mononuclear Phagocytes in Health, Immune Defense and Disease (D3)
April 30 – May 4, 2017 | Austin, Texas, USA
Scientific Organizers: Steffen Jung and Miriam Merad

May 2017

Modeling Viral Infections and Immunity (E1)
May 1 – 4, 2017 | Estes Park, Colorado, USA
Scientific Organizers: Alan S. Perelson, Rob J. De Boer and Phillip D. Hodgkin

Angiogenesis and Vascular Disease (Z3)
May 8 – 12, 2017 | Santa Fe, New Mexico, USA
Scientific Organizers: M. Luisa Iruela-Arispe, Timothy T. Hla and Courtney Griffin

Mitochondria, Metabolism and Heart (Z4)
May 8 – 12, 2017 | Santa Fe, New Mexico, USA
Scientific Organizers: Junichi Sadoshima, Toren Finkel and Åsa B. Gustafsson

Neuronal Control of Appetite, Metabolism and Weight (Z5)
May 9 – 13, 2017 | Copenhagen, Denmark
Scientific Organizers: Lora K. Heisler and Scott M. Sternson

Gastrointestinal Control of Metabolism (Z6)
May 9 – 13, 2017 | Copenhagen, Denmark
Scientific Organizers: Randy J. Seeley, Matthias H. Tschöp and Fiona M. Gribble

Aging and Mechanisms of Aging-Related Disease (E2)
May 15 – 19, 2017 | Yokohama, Japan
Scientific Organizers: Kazuo Tsubota, Shin-ichiro Imai, Matt Kaeberlein and Joan Mannick

Single Cell Omics (E3)
May 26 – 30, 2017 | Stockholm, Sweden
Scientific Organizers: Sarah Teichmann, Evan W. Newell and William J. Greenleaf

Integrating Metabolism and Immunity (E4)
May 29 – June 2, 2017 | Dublin, Ireland
Scientific Organizers: Hongbo Chi, Erika L. Pearce, Richard A. Flavell and Luke A.J. O’Neill

Cell Death and Inflammation (K2)
May 29 – June 2, 2017 | Dublin, Ireland
Scientific Organizers: Seamus J. Martin and John Silke

June 2017

Neuroinflammation: Concepts, Characteristics, Consequences (E5)
June 19 – 23, 2017 | Keystone, Colorado, USA
Scientific Organizers: Richard M. Ransohoff, Christopher K. Glass and V. Hugh Perry

SOURCE

Read Full Post »


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

 

In an African cichlid fish, Astatotilapia burtoni, fertile females select a mate and perform a stereotyped spawning / mating routine, offering quantifiable behavioral outputs of neural circuits. A male fish attracts a fertile female by rapidly quivering his brightly colored body. If she chooses him, he guides her back to his territory, where he quivers some more as she pecks at fish egg–colored spots on his anal fin. Next, she lays eggs and quickly scoops them up in her mouth. With a mouthful of eggs, she continues pecking at the male’s spots, “believing” them to be eggs to be collected. As she does, he releases sperm from near his anal fin, which she also gathers. This fertilizes the eggs, and she carries the embryos in her mouth for two weeks as they develop.

 

But, the question was how these females can time their reproduction to coincide with when they are fertile. The female fish will not approach or choose males until they are ready to reproduce, so there must be something in their brains that signals when sexual behavior will be required. The scientists began by considering signaling molecules previously associated with sexual behavior and reproduction, and showed that PGF2α injection activates a naturalistic pattern of sexual behavior in female Astatotilapia burtoni. They would engage in mating behavior even if they were non-fertile, doing the quiver dance with males, but wouldn’t actually lay eggs since they had none.

 

The scientists also identified cells in the brain that transduce the prostaglandin signal to mate and showed that the gonadal steroid 17α, 20β-dihydroxyprogesterone modulates mRNA levels of the putative receptor for PGF2α. The scientists keyed in on a receptor for PGF2α in the preoptic area (POA) within the hypothalamus of the brain, a region involved in sexual behavior across animals. They suspected that when PGF2α levels elevated in the fish, the molecule attaches to this receptor and triggers sexual behavior. Then they used CRISPR/Cas9 to generate PGF2α receptor knockout fish. This gene deletion or knockout uncoupled the sexual behavior from fertility status to prove that the receptor of PGF2α is necessary for the initiation of sexual behavior.

 

The finding has parallels across all vertebrates, and might influence the understanding of social behavior in humans. The next steps for this work will involve understanding other behaviors that are regulated by this receptor, and the finding provides insight into both the evolution of reproduction and sexual behaviors. In mammals and other vertebrates, PGF2α promotes the onset of labor and motherly behaviors, and this present research, coupled with other studies, suggests that PGF2α signaling has a common ancestral function associated with birth and its related behaviors.

 

References:

 

http://www.ncbi.nlm.nih.gov/pubmed/26996507

 

http://news.stanford.edu/news/2016/march/fish-mating-behavior-031716.html

 

 

http://www.academia.edu/676252/The_Genetics_of_Female_Sexual_Behaviour

 

https://scifeeds.com/news/scientists-identify-genetic-switch-for-female-sexual-behavior/

Read Full Post »


Imaging of Cancer Cells

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

Microscope uses nanosecond-speed laser and deep learning to detect cancer cells more efficiently

April 13, 2016

Scientists at the California NanoSystems Institute at UCLA have developed a new technique for identifying cancer cells in blood samples faster and more accurately than the current standard methods.

In one common approach to testing for cancer, doctors add biochemicals to blood samples. Those biochemicals attach biological “labels” to the cancer cells, and those labels enable instruments to detect and identify them. However, the biochemicals can damage the cells and render the samples unusable for future analyses. There are other current techniques that don’t use labeling but can be inaccurate because they identify cancer cells based only on one physical characteristic.

Time-stretch quantitative phase imaging (TS-QPI) and analytics system

The new technique images cells without destroying them and can identify 16 physical characteristics — including size, granularity and biomass — instead of just one.

The new technique combines two components that were invented at UCLA:

A “photonic time stretch” microscope, which is capable of quickly imaging cells in blood samples. Invented by Barham Jalali, professor and Northrop-Grumman Optoelectronics Chair in electrical engineering, it works by taking pictures of flowing blood cells using laser bursts (similar to how a camera uses a flash). Each flash only lasts nanoseconds (billionths of a second) to avoid damage to cells, but that normally means the images are both too weak to be detected and too fast to be digitized by normal instrumentation. The new microscope overcomes those challenges by using specially designed optics that amplify and boost the clarity of the images, and simultaneously slow them down enough to be detected and digitized at a rate of 36 million images per second.

A deep learning computer program, which identifies cancer cells with more than 95 percent accuracy. Deep learning is a form of artificial intelligence that uses complex algorithms to extract patterns and knowledge from rich multidimenstional datasets, with the goal of achieving accurate decision making.

The study was published in the open-access journal Nature Scientific Reports. The researchers write in the paper that the system could lead to data-driven diagnoses by cells’ physical characteristics, which could allow quicker and earlier diagnoses of cancer, for example, and better understanding of the tumor-specific gene expression in cells, which could facilitate new treatments for disease.

The research was supported by NantWorks, LLC.

 

Abstract of Deep Learning in Label-free Cell Classification

Label-free cell analysis is essential to personalized genomics, cancer diagnostics, and drug development as it avoids adverse effects of staining reagents on cellular viability and cell signaling. However, currently available label-free cell assays mostly rely only on a single feature and lack sufficient differentiation. Also, the sample size analyzed by these assays is limited due to their low throughput. Here, we integrate feature extraction and deep learning with high-throughput quantitative imaging enabled by photonic time stretch, achieving record high accuracy in label-free cell classification. Our system captures quantitative optical phase and intensity images and extracts multiple biophysical features of individual cells. These biophysical measurements form a hyperdimensional feature space in which supervised learning is performed for cell classification. We compare various learning algorithms including artificial neural network, support vector machine, logistic regression, and a novel deep learning pipeline, which adopts global optimization of receiver operating characteristics. As a validation of the enhanced sensitivity and specificity of our system, we show classification of white blood T-cells against colon cancer cells, as well as lipid accumulating algal strains for biofuel production. This system opens up a new path to data-driven phenotypic diagnosis and better understanding of the heterogeneous gene expressions in cells.

references:

Claire Lifan Chen, Ata Mahjoubfar, Li-Chia Tai, Ian K. Blaby, Allen Huang, Kayvan Reza Niazi & Bahram Jalali. Deep Learning in Label-free Cell Classification. Scientific Reports 6, Article number: 21471 (2016); doi:10.1038/srep21471 (open access)

Supplementary Information

 

Deep Learning in Label-free Cell Classification

Claire Lifan Chen, Ata Mahjoubfar, Li-Chia Tai, Ian K. Blaby, Allen Huang,Kayvan Reza Niazi & Bahram Jalali

Scientific Reports 6, Article number: 21471 (2016)    http://dx.doi.org:/10.1038/srep21471

Deep learning extracts patterns and knowledge from rich multidimenstional datasets. While it is extensively used for image recognition and speech processing, its application to label-free classification of cells has not been exploited. Flow cytometry is a powerful tool for large-scale cell analysis due to its ability to measure anisotropic elastic light scattering of millions of individual cells as well as emission of fluorescent labels conjugated to cells1,2. However, each cell is represented with single values per detection channels (forward scatter, side scatter, and emission bands) and often requires labeling with specific biomarkers for acceptable classification accuracy1,3. Imaging flow cytometry4,5 on the other hand captures images of cells, revealing significantly more information about the cells. For example, it can distinguish clusters and debris that would otherwise result in false positive identification in a conventional flow cytometer based on light scattering6.

In addition to classification accuracy, the throughput is another critical specification of a flow cytometer. Indeed high throughput, typically 100,000 cells per second, is needed to screen a large enough cell population to find rare abnormal cells that are indicative of early stage diseases. However there is a fundamental trade-off between throughput and accuracy in any measurement system7,8. For example, imaging flow cytometers face a throughput limit imposed by the speed of the CCD or the CMOS cameras, a number that is approximately 2000 cells/s for present systems9. Higher flow rates lead to blurred cell images due to the finite camera shutter speed. Many applications of flow analyzers such as cancer diagnostics, drug discovery, biofuel development, and emulsion characterization require classification of large sample sizes with a high-degree of statistical accuracy10. This has fueled research into alternative optical diagnostic techniques for characterization of cells and particles in flow.

Recently, our group has developed a label-free imaging flow-cytometry technique based on coherent optical implementation of the photonic time stretch concept11. This instrument overcomes the trade-off between sensitivity and speed by using Amplified Time-stretch Dispersive Fourier Transform12,13,14,15. In time stretched imaging16, the object’s spatial information is encoded in the spectrum of laser pulses within a pulse duration of sub-nanoseconds (Fig. 1). Each pulse representing one frame of the camera is then stretched in time so that it can be digitized in real-time by an electronic analog-to-digital converter (ADC). The ultra-fast pulse illumination freezes the motion of high-speed cells or particles in flow to achieve blur-free imaging. Detection sensitivity is challenged by the low number of photons collected during the ultra-short shutter time (optical pulse width) and the drop in the peak optical power resulting from the time stretch. These issues are solved in time stretch imaging by implementing a low noise-figure Raman amplifier within the dispersive device that performs time stretching8,11,16. Moreover, warped stretch transform17,18can be used in time stretch imaging to achieve optical image compression and nonuniform spatial resolution over the field-of-view19. In the coherent version of the instrument, the time stretch imaging is combined with spectral interferometry to measure quantitative phase and intensity images in real-time and at high throughput20. Integrated with a microfluidic channel, coherent time stretch imaging system in this work measures both quantitative optical phase shift and loss of individual cells as a high-speed imaging flow cytometer, capturing 36 million images per second in flow rates as high as 10 meters per second, reaching up to 100,000 cells per second throughput.

Figure 1: Time stretch quantitative phase imaging (TS-QPI) and analytics system; A mode-locked laser followed by a nonlinear fiber, an erbium doped fiber amplifier (EDFA), and a wavelength-division multiplexing (WDM) filter generate and shape a train of broadband optical pulses. http://www.nature.com/article-assets/npg/srep/2016/160315/srep21471/images_hires/m685/srep21471-f1.jpg

 

Box 1: The pulse train is spatially dispersed into a train of rainbow flashes illuminating the target as line scans. The spatial features of the target are encoded into the spectrum of the broadband optical pulses, each representing a one-dimensional frame. The ultra-short optical pulse illumination freezes the motion of cells during high speed flow to achieve blur-free imaging with a throughput of 100,000 cells/s. The phase shift and intensity loss at each location within the field of view are embedded into the spectral interference patterns using a Michelson interferometer. Box 2: The interferogram pulses were then stretched in time so that spatial information could be mapped into time through time-stretch dispersive Fourier transform (TS-DFT), and then captured by a single pixel photodetector and an analog-to-digital converter (ADC). The loss of sensitivity at high shutter speed is compensated by stimulated Raman amplification during time stretch. Box 3: (a) Pulse synchronization; the time-domain signal carrying serially captured rainbow pulses is transformed into a series of one-dimensional spatial maps, which are used for forming line images. (b) The biomass density of a cell leads to a spatially varying optical phase shift. When a rainbow flash passes through the cells, the changes in refractive index at different locations will cause phase walk-off at interrogation wavelengths. Hilbert transformation and phase unwrapping are used to extract the spatial phase shift. (c) Decoding the phase shift in each pulse at each wavelength and remapping it into a pixel reveals the protein concentration distribution within cells. The optical loss induced by the cells, embedded in the pulse intensity variations, is obtained from the amplitude of the slowly varying envelope of the spectral interferograms. Thus, quantitative optical phase shift and intensity loss images are captured simultaneously. Both images are calibrated based on the regions where the cells are absent. Cell features describing morphology, granularity, biomass, etc are extracted from the images. (d) These biophysical features are used in a machine learning algorithm for high-accuracy label-free classification of the cells.

On another note, surface markers used to label cells, such as EpCAM21, are unavailable in some applications; for example, melanoma or pancreatic circulating tumor cells (CTCs) as well as some cancer stem cells are EpCAM-negative and will escape EpCAM-based detection platforms22. Furthermore, large-population cell sorting opens the doors to downstream operations, where the negative impacts of labels on cellular behavior and viability are often unacceptable23. Cell labels may cause activating/inhibitory signal transduction, altering the behavior of the desired cellular subtypes, potentially leading to errors in downstream analysis, such as DNA sequencing and subpopulation regrowth. In this way, quantitative phase imaging (QPI) methods24,25,26,27 that categorize unlabeled living cells with high accuracy are needed. Coherent time stretch imaging is a method that enables quantitative phase imaging at ultrahigh throughput for non-invasive label-free screening of large number of cells.

In this work, the information of quantitative optical loss and phase images are fused into expert designed features, leading to a record label-free classification accuracy when combined with deep learning. Image mining techniques are applied, for the first time, to time stretch quantitative phase imaging to measure biophysical attributes including protein concentration, optical loss, and morphological features of single cells at an ultrahigh flow rate and in a label-free fashion. These attributes differ widely28,29,30,31 among cells and their variations reflect important information of genotypes and physiological stimuli32. The multiplexed biophysical features thus lead to information-rich hyper-dimensional representation of the cells for label-free classification with high statistical precision.

We further improved the accuracy, repeatability, and the balance between sensitivity and specificity of our label-free cell classification by a novel machine learning pipeline, which harnesses the advantages of multivariate supervised learning, as well as unique training by evolutionary global optimization of receiver operating characteristics (ROC). To demonstrate sensitivity, specificity, and accuracy of multi-feature label-free flow cytometry using our technique, we classified (1) OT-IIhybridoma T-lymphocytes and SW-480 colon cancer epithelial cells, and (2) Chlamydomonas reinhardtii algal cells (herein referred to as Chlamydomonas) based on their lipid content, which is related to the yield in biofuel production. Our preliminary results show that compared to classification by individual biophysical parameters, our label-free hyperdimensional technique improves the detection accuracy from 77.8% to 95.5%, or in other words, reduces the classification inaccuracy by about five times.     ……..

 

Feature Extraction

The decomposed components of sequential line scans form pairs of spatial maps, namely, optical phase and loss images as shown in Fig. 2 (see Section Methods: Image Reconstruction). These images are used to obtain biophysical fingerprints of the cells8,36. With domain expertise, raw images are fused and transformed into a suitable set of biophysical features, listed in Table 1, which the deep learning model further converts into learned features for improved classification.

The new technique combines two components that were invented at UCLA:

A “photonic time stretch” microscope, which is capable of quickly imaging cells in blood samples. Invented by Barham Jalali, professor and Northrop-Grumman Optoelectronics Chair in electrical engineering, it works by taking pictures of flowing blood cells using laser bursts (similar to how a camera uses a flash). Each flash only lasts nanoseconds (billionths of a second) to avoid damage to cells, but that normally means the images are both too weak to be detected and too fast to be digitized by normal instrumentation. The new microscope overcomes those challenges by using specially designed optics that amplify and boost the clarity of the images, and simultaneously slow them down enough to be detected and digitized at a rate of 36 million images per second.

A deep learning computer program, which identifies cancer cells with more than 95 percent accuracy. Deep learning is a form of artificial intelligence that uses complex algorithms to extract patterns and knowledge from rich multidimenstional datasets, with the goal of achieving accurate decision making.

The study was published in the open-access journal Nature Scientific Reports. The researchers write in the paper that the system could lead to data-driven diagnoses by cells’ physical characteristics, which could allow quicker and earlier diagnoses of cancer, for example, and better understanding of the tumor-specific gene expression in cells, which could facilitate new treatments for disease.

The research was supported by NantWorks, LLC.

 

http://www.nature.com/article-assets/npg/srep/2016/160315/srep21471/images_hires/m685/srep21471-f2.jpg

The optical loss images of the cells are affected by the attenuation of multiplexed wavelength components passing through the cells. The attenuation itself is governed by the absorption of the light in cells as well as the scattering from the surface of the cells and from the internal cell organelles. The optical loss image is derived from the low frequency component of the pulse interferograms. The optical phase image is extracted from the analytic form of the high frequency component of the pulse interferograms using Hilbert Transformation, followed by a phase unwrapping algorithm. Details of these derivations can be found in Section Methods. Also, supplementary Videos 1 and 2 show measurements of cell-induced optical path length difference by TS-QPI at four different points along the rainbow for OT-II and SW-480, respectively.

Table 1: List of extracted features.

Feature Name    Description         Category

 

Figure 3: Biophysical features formed by image fusion.

(a) Pairwise correlation matrix visualized as a heat map. The map depicts the correlation between all major 16 features extracted from the quantitative images. Diagonal elements of the matrix represent correlation of each parameter with itself, i.e. the autocorrelation. The subsets in box 1, box 2, and box 3 show high correlation because they are mainly related to morphological, optical phase, and optical loss feature categories, respectively. (b) Ranking of biophysical features based on their AUCs in single-feature classification. Blue bars show performance of the morphological parameters, which includes diameter along the interrogation rainbow, diameter along the flow direction, tight cell area, loose cell area, perimeter, circularity, major axis length, orientation, and median radius. As expected, morphology contains most information, but other biophysical features can contribute to improved performance of label-free cell classification. Orange bars show optical phase shift features i.e. optical path length differences and refractive index difference. Green bars show optical loss features representing scattering and absorption by the cell. The best performed feature in these three categories are marked in red.

Figure 4: Machine learning pipeline. Information of quantitative optical phase and loss images are fused to extract multivariate biophysical features of each cell, which are fed into a fully-connected neural network.

The neural network maps input features by a chain of weighted sum and nonlinear activation functions into learned feature space, convenient for classification. This deep neural network is globally trained via area under the curve (AUC) of the receiver operating characteristics (ROC). Each ROC curve corresponds to a set of weights for connections to an output node, generated by scanning the weight of the bias node. The training process maximizes AUC, pushing the ROC curve toward the upper left corner, which means improved sensitivity and specificity in classification.

….   How to cite this article: Chen, C. L. et al. Deep Learning in Label-free Cell Classification.

Sci. Rep. 6, 21471; http://dx.doi.org:/10.1038/srep21471

 

Computer Algorithm Helps Characterize Cancerous Genomic Variations

http://www.genengnews.com/gen-news-highlights/computer-algorithm-helps-characterize-cancerous-genomic-variations/81252626/

To better characterize the functional context of genomic variations in cancer, researchers developed a new computer algorithm called REVEALER. [UC San Diego Health]

Scientists at the University of California San Diego School of Medicine and the Broad Institute say they have developed a new computer algorithm—REVEALER—to better characterize the functional context of genomic variations in cancer. The tool, described in a paper (“Characterizing Genomic Alterations in Cancer by Complementary Functional Associations”) published in Nature Biotechnology, is designed to help researchers identify groups of genetic variations that together associate with a particular way cancer cells get activated, or how they respond to certain treatments.

REVEALER is available for free to the global scientific community via the bioinformatics software portal GenePattern.org.

“This computational analysis method effectively uncovers the functional context of genomic alterations, such as gene mutations, amplifications, or deletions, that drive tumor formation,” said senior author Pablo Tamayo, Ph.D., professor and co-director of the UC San Diego Moores Cancer Center Genomics and Computational Biology Shared Resource.

Dr. Tamayo and team tested REVEALER using The Cancer Genome Atlas (TCGA), the NIH’s database of genomic information from more than 500 human tumors representing many cancer types. REVEALER revealed gene alterations associated with the activation of several cellular processes known to play a role in tumor development and response to certain drugs. Some of these gene mutations were already known, but others were new.

For example, the researchers discovered new activating genomic abnormalities for beta-catenin, a cancer-promoting protein, and for the oxidative stress response that some cancers hijack to increase their viability.

REVEALER requires as input high-quality genomic data and a significant number of cancer samples, which can be a challenge, according to Dr. Tamayo. But REVEALER is more sensitive at detecting similarities between different types of genomic features and less dependent on simplifying statistical assumptions, compared to other methods, he adds.

“This study demonstrates the potential of combining functional profiling of cells with the characterizations of cancer genomes via next-generation sequencing,” said co-senior author Jill P. Mesirov, Ph.D., professor and associate vice chancellor for computational health sciences at UC San Diego School of Medicine.

 

Characterizing genomic alterations in cancer by complementary functional associations

Jong Wook Kim, Olga B Botvinnik, Omar Abudayyeh, Chet Birger, et al.

Nature Biotechnology (2016)              http://dx.doi.org:/10.1038/nbt.3527

Systematic efforts to sequence the cancer genome have identified large numbers of mutations and copy number alterations in human cancers. However, elucidating the functional consequences of these variants, and their interactions to drive or maintain oncogenic states, remains a challenge in cancer research. We developed REVEALER, a computational method that identifies combinations of mutually exclusive genomic alterations correlated with functional phenotypes, such as the activation or gene dependency of oncogenic pathways or sensitivity to a drug treatment. We used REVEALER to uncover complementary genomic alterations associated with the transcriptional activation of β-catenin and NRF2, MEK-inhibitor sensitivity, and KRAS dependency. REVEALER successfully identified both known and new associations, demonstrating the power of combining functional profiles with extensive characterization of genomic alterations in cancer genomes

 

Figure 2: REVEALER results for transcriptional activation of β-catenin in cancer.close

(a) This heatmap illustrates the use of the REVEALER approach to find complementary genomic alterations that match the transcriptional activation of β-catenin in cancer. The target profile is a TCF4 reporter that provides an estimate of…

 

An imaging-based platform for high-content, quantitative evaluation of therapeutic response in 3D tumour models

Jonathan P. Celli, Imran Rizvi, Adam R. Blanden, Iqbal Massodi, Michael D. Glidden, Brian W. Pogue & Tayyaba Hasan

Scientific Reports 4; 3751  (2014)    http://dx.doi.org:/10.1038/srep03751

While it is increasingly recognized that three-dimensional (3D) cell culture models recapitulate drug responses of human cancers with more fidelity than monolayer cultures, a lack of quantitative analysis methods limit their implementation for reliable and routine assessment of emerging therapies. Here, we introduce an approach based on computational analysis of fluorescence image data to provide high-content readouts of dose-dependent cytotoxicity, growth inhibition, treatment-induced architectural changes and size-dependent response in 3D tumour models. We demonstrate this approach in adherent 3D ovarian and pancreatic multiwell extracellular matrix tumour overlays subjected to a panel of clinically relevant cytotoxic modalities and appropriately designed controls for reliable quantification of fluorescence signal. This streamlined methodology reads out the high density of information embedded in 3D culture systems, while maintaining a level of speed and efficiency traditionally achieved with global colorimetric reporters in order to facilitate broader implementation of 3D tumour models in therapeutic screening.

The attrition rates for preclinical development of oncology therapeutics are particularly dismal due to a complex set of factors which includes 1) the failure of pre-clinical models to recapitulate determinants of in vivo treatment response, and 2) the limited ability of available assays to extract treatment-specific data integral to the complexities of therapeutic responses1,2,3. Three-dimensional (3D) tumour models have been shown to restore crucial stromal interactions which are missing in the more commonly used 2D cell culture and that influence tumour organization and architecture4,5,6,7,8, as well as therapeutic response9,10, multicellular resistance (MCR)11,12, drug penetration13,14, hypoxia15,16, and anti-apoptotic signaling17. However, such sophisticated models can only have an impact on therapeutic guidance if they are accompanied by robust quantitative assays, not only for cell viability but also for providing mechanistic insights related to the outcomes. While numerous assays for drug discovery exist18, they are generally not developed for use in 3D systems and are often inherently unsuitable. For example, colorimetric conversion products have been noted to bind to extracellular matrix (ECM)19 and traditional colorimetric cytotoxicity assays reduce treatment response to a single number reflecting a biochemical event that has been equated to cell viability (e.g. tetrazolium salt conversion20). Such approaches fail to provide insight into the spatial patterns of response within colonies, morphological or structural effects of drug response, or how overall culture viability may be obscuring the status of sub-populations that are resistant or partially responsive. Hence, the full benefit of implementing 3D tumour models in therapeutic development has yet to be realized for lack of analytical methods that describe the very aspects of treatment outcome that these systems restore.

Motivated by these factors, we introduce a new platform for quantitative in situ treatment assessment (qVISTA) in 3D tumour models based on computational analysis of information-dense biological image datasets (bioimage-informatics)21,22. This methodology provides software end-users with multiple levels of complexity in output content, from rapidly-interpreted dose response relationships to higher content quantitative insights into treatment-dependent architectural changes, spatial patterns of cytotoxicity within fields of multicellular structures, and statistical analysis of nodule-by-nodule size-dependent viability. The approach introduced here is cognizant of tradeoffs between optical resolution, data sampling (statistics), depth of field, and widespread usability (instrumentation requirement). Specifically, it is optimized for interpretation of fluorescent signals for disease-specific 3D tumour micronodules that are sufficiently small that thousands can be imaged simultaneously with little or no optical bias from widefield integration of signal along the optical axis of each object. At the core of our methodology is the premise that the copious numerical readouts gleaned from segmentation and interpretation of fluorescence signals in these image datasets can be converted into usable information to classify treatment effects comprehensively, without sacrificing the throughput of traditional screening approaches. It is hoped that this comprehensive treatment-assessment methodology will have significant impact in facilitating more sophisticated implementation of 3D cell culture models in preclinical screening by providing a level of content and biological relevance impossible with existing assays in monolayer cell culture in order to focus therapeutic targets and strategies before costly and tedious testing in animal models.

Using two different cell lines and as depicted in Figure 1, we adopt an ECM overlay method pioneered originally for 3D breast cancer models23, and developed in previous studies by us to model micrometastatic ovarian cancer19,24. This system leads to the formation of adherent multicellular 3D acini in approximately the same focal plane atop a laminin-rich ECM bed, implemented here in glass-bottom multiwell imaging plates for automated microscopy. The 3D nodules resultant from restoration of ECM signaling5,8, are heterogeneous in size24, in contrast to other 3D spheroid methods, such as rotary or hanging drop cultures10, in which cells are driven to aggregate into uniformly sized spheroids due to lack of an appropriate substrate to adhere to. Although the latter processes are also biologically relevant, it is the adherent tumour populations characteristic of advanced metastatic disease that are more likely to be managed with medical oncology, which are the focus of therapeutic evaluation herein. The heterogeneity in 3D structures formed via ECM overlay is validated here by endoscopic imaging ofin vivo tumours in orthotopic xenografts derived from the same cells (OVCAR-5).

 

Figure 1: A simplified schematic flow chart of imaging-based quantitative in situ treatment assessment (qVISTA) in 3D cell culture.

(This figure was prepared in Adobe Illustrator® software by MD Glidden, JP Celli and I Rizvi). A detailed breakdown of the image processing (Step 4) is provided in Supplemental Figure 1.

A critical component of the imaging-based strategy introduced here is the rational tradeoff of image-acquisition parameters for field of view, depth of field and optical resolution, and the development of image processing routines for appropriate removal of background, scaling of fluorescence signals from more than one channel and reliable segmentation of nodules. In order to obtain depth-resolved 3D structures for each nodule at sub-micron lateral resolution using a laser-scanning confocal system, it would require ~ 40 hours (at approximately 100 fields for each well with a 20× objective, times 1 minute/field for a coarse z-stack, times 24 wells) to image a single plate with the same coverage achieved in this study. Even if the resources were available to devote to such time-intensive image acquisition, not to mention the processing, the optical properties of the fluorophores would change during the required time frame for image acquisition, even with environmental controls to maintain culture viability during such extended imaging. The approach developed here, with a mind toward adaptation into high throughput screening, provides a rational balance of speed, requiring less than 30 minutes/plate, and statistical rigour, providing images of thousands of nodules in this time, as required for the high-content analysis developed in this study. These parameters can be further optimized for specific scenarios. For example, we obtain the same number of images in a 96 well plate as for a 24 well plate by acquiring only a single field from each well, rather than 4 stitched fields. This quadruples the number conditions assayed in a single run, at the expense of the number of nodules per condition, and therefore the ability to obtain statistical data sets for size-dependent response, Dfrac and other segmentation-dependent numerical readouts.

 

We envision that the system for high-content interrogation of therapeutic response in 3D cell culture could have widespread impact in multiple arenas from basic research to large scale drug development campaigns. As such, the treatment assessment methodology presented here does not require extraordinary optical instrumentation or computational resources, making it widely accessible to any research laboratory with an inverted fluorescence microscope and modestly equipped personal computer. And although we have focused here on cancer models, the methodology is broadly applicable to quantitative evaluation of other tissue models in regenerative medicine and tissue engineering. While this analysis toolbox could have impact in facilitating the implementation of in vitro 3D models in preclinical treatment evaluation in smaller academic laboratories, it could also be adopted as part of the screening pipeline in large pharma settings. With the implementation of appropriate temperature controls to handle basement membranes in current robotic liquid handling systems, our analyses could be used in ultra high-throughput screening. In addition to removing non-efficacious potential candidate drugs earlier in the pipeline, this approach could also yield the additional economic advantage of minimizing the use of costly time-intensive animal models through better estimates of dose range, sequence and schedule for combination regimens.

 

Microscope Uses AI to Find Cancer Cells More Efficiently

Thu, 04/14/2016 – by Shaun Mason

http://www.mdtmag.com/news/2016/04/microscope-uses-ai-find-cancer-cells-more-efficiently

Scientists at the California NanoSystems Institute at UCLA have developed a new technique for identifying cancer cells in blood samples faster and more accurately than the current standard methods.

In one common approach to testing for cancer, doctors add biochemicals to blood samples. Those biochemicals attach biological “labels” to the cancer cells, and those labels enable instruments to detect and identify them. However, the biochemicals can damage the cells and render the samples unusable for future analyses.

There are other current techniques that don’t use labeling but can be inaccurate because they identify cancer cells based only on one physical characteristic.

The new technique images cells without destroying them and can identify 16 physical characteristics — including size, granularity and biomass — instead of just one. It combines two components that were invented at UCLA: a photonic time stretch microscope, which is capable of quickly imaging cells in blood samples, and a deep learning computer program that identifies cancer cells with over 95 percent accuracy.

Deep learning is a form of artificial intelligence that uses complex algorithms to extract meaning from data with the goal of achieving accurate decision making.

The study, which was published in the journal Nature Scientific Reports, was led by Barham Jalali, professor and Northrop-Grumman Optoelectronics Chair in electrical engineering; Claire Lifan Chen, a UCLA doctoral student; and Ata Mahjoubfar, a UCLA postdoctoral fellow.

Photonic time stretch was invented by Jalali, and he holds a patent for the technology. The new microscope is just one of many possible applications; it works by taking pictures of flowing blood cells using laser bursts in the way that a camera uses a flash. This process happens so quickly — in nanoseconds, or billionths of a second — that the images would be too weak to be detected and too fast to be digitized by normal instrumentation.

The new microscope overcomes those challenges using specially designed optics that boost the clarity of the images and simultaneously slow them enough to be detected and digitized at a rate of 36 million images per second. It then uses deep learning to distinguish cancer cells from healthy white blood cells.

“Each frame is slowed down in time and optically amplified so it can be digitized,” Mahjoubfar said. “This lets us perform fast cell imaging that the artificial intelligence component can distinguish.”

Normally, taking pictures in such minuscule periods of time would require intense illumination, which could destroy live cells. The UCLA approach also eliminates that problem.

“The photonic time stretch technique allows us to identify rogue cells in a short time with low-level illumination,” Chen said.

The researchers write in the paper that the system could lead to data-driven diagnoses by cells’ physical characteristics, which could allow quicker and earlier diagnoses of cancer, for example, and better understanding of the tumor-specific gene expression in cells, which could facilitate new treatments for disease.   …..  see also http://www.nature.com/article-assets/npg/srep/2016/160315/srep21471/images_hires/m685/srep21471-f1.jpg

Chen, C. L. et al. Deep Learning in Label-free Cell Classification.    Sci. Rep. 6, 21471;   http://dx.doi.org:/10.1038/srep21471

 

 

Read Full Post »

Older Posts »