Genomics Initiative

A Month of CIRM: Where we’ve been, where we’re going

/ Kevin McCormack / Leave a comment

All this month we are using our blog and social media to highlight a new chapter in CIRM’s life, thanks to the voters approving Proposition 14. We are looking back at what we have done since we were created in 2004, and also looking forward to the future. We kick off this event with a letter from our the Chair of our Board, Jonathan Thomas.

When voters approved Proposition 14 last November, they gave the Stem Cell Agency a new lease on life and a chance to finish the work we began with the approval of Proposition 71 in 2004. It’s a great honor and privilege. It’s also a great responsibility. But I think looking back at what we have achieved over the last 16 years shows we are well positioned to seize the moment and take CIRM and regenerative medicine to the next level and beyond.

When we started, we were told that if we managed to get one project into a clinical trial by the time our money ran out we would have done a good job. As of this moment we have 68 clinical trials that we have funded plus another 31 projects in clinical trials where we helped fund crucial early stage research. That inexorable march to therapies and cures will resume when we take up our first round of Clinical applications under Prop 14 in March.

But while clinical stage projects are the end game, where we see if therapies really work and are safe in people, there’s so much more that we have achieved since we were created. We have invested $900 million in  basic research, creating a pipeline of the most promising stem cell research programs, as well as investing heavily on so-called “translational” projects, which move projects from basic science to where they’re ready to apply to the Food and Drug Administration (FDA) to begin clinical trials.

We have funded more than 1,000 projects, with each one giving us valuable information to help advance the science. Our funding has helped attract some of the best stem cell scientists in the world to California and, because we only fund research in California, it has persuaded many companies to either move here or open offices here to be eligible for our support. We have helped create the Alpha Stem Cell Clinics, a network of leading medical centers around the state that have the experience and expertise to deliver stem cell therapies to patients. All of those have made California a global center in the field.

That result is producing big benefits for the state. An independent Economic Impact Analysis reported that by the end of 2018 we had already helped generate an extra $10.7 billion in new sales revenue and taxes for California, hundreds of millions more in federal taxes and created more than 56,000 new jobs.

As if that wasn’t enough, we have also:

  • Helped develop the largest iPSC research bank in the world.
  • Created the CIRM Center of Excellence in Stem Cell Genomics to accelerate fundamental understanding of human biology and disease mechanisms.
  • Helped fund the construction of 12 world class stem cell institutes throughout the state.
  • Reached a unique partnership with the National Heart, Lung and Blood Institutes to find a cure for sickle cell disease.
  • Used our support for stem cell research to leverage an additional $12 billion in private funding for the field.
  • Enrolled more than 2700 patients in CIRM funded clinical trials

In many ways our work is just beginning. We have laid the groundwork, helped enable an extraordinary community of researchers and dramatically accelerated the field. Now we want to get those therapies (and many more) over the finish line and get them approved by the FDA so they can become available to many more people around the state, the country and the world.

We also know that we have to make these therapies available to all people, regardless of their background and ability to pay. We have to ensure that underserved communities, who were often left out of research in the past, are an integral part of this work and are included in every aspect of that research, particularly clinical trials. That’s why we now require anyone applying to us for funding to commit to engaging with underserved communities and to have a written plan to show how they are going to do that.

Over the coming month, you will hear more about some of the remarkable things we have managed to achieve so far and get a better sense of what we hope to do in the future. We know there will be challenges ahead and that not everything we do or support will work. But we also know that with the team we have built at CIRM, the brilliant research community in California and the passion and drive of the patient advocate community we will live up to the responsibility the people of California placed in us when they approved Proposition 14.

Scientists develop faster, smarter way to classify tumors using single-cell technology

/ Kevin McCormack / Leave a comment
Dr. Stephen Lin, CIRM Senior Science Officer

By Dr. Stephen Lin

Single-cell.  It is the new buzzword in biology.  Single-cell biology refers to the in-depth characterization of individual cells in an organ or similar microenvironment.  Every organ, like the brain or heart, is composed of thousands to millions of cells.  Single-cell biology breaks those organs down into their individual cell components to study the diversity within those cells.  For example, the heart is composed of cardiomyocytes, but within that bulk population of cardiomyocytes there are specialized cardiomyocytes for the different chambers of the heart and others that control beating, plus others not even known yet.  Single-cell studies characterize cell-to-cell variability in the body down to this level of detail to gain knowledge of tissues in a way that was not possible before.   

The majority of single-cell studies are based on next generation sequencing technologies of genetic material such as DNA or RNA.  The cost of sequencing each base of DNA or RNA has dropped precipitously since the first human genome was published in 2000, often compared to the trend seen with Moore’s Law in computing.  As a result it is now possible to sequence every gene that is expressed in an individual cell, called the transcriptome, for thousands and thousands of cells.   

The explosion of data coming from these technologies requires new approaches to study and analyze the information.  The scale of the genetic sequences that can be generated is so big that it is often not possible anymore for scientists to interpret the data manually as had been traditionally done.  To apply this exciting field to stem cell research and therapies, CIRM funded the Genomics Initiative which created the Centers of Excellence in Stem Cell Genomics (CESCG).  The goal of the CESCG is to create novel genomic information and create new bioinformatics tools (i.e. computer software) specifically for stem cell research, some of which was highlighted in past blogs.  Some of the earliest single-cell gene expression atlases of the human body were created under the CESCG. 

The latest study from CESCG investigators creates both new information and new tools for single-cell genomics.  In work funded by the Genomics Initiative, Stephen Quake and colleagues at Stanford University and the Chan-Zuckerberg Biohub studied tumor formation using single-cell approaches.  Drawing from one of the earliest published single-cell studies, the team had surveyed human brain transcriptome diversity that included samples from the brain cancer, glioblastoma. 

Recognizing that the data coming from these studies would eventually become too large and numerous to classify all of the cell types by hand, they created a new bioinformatics tool called Northstar to apply artificial intelligence to automatically classify cell types generated by single-cell studies.  The cell classifications generated by Northstar were similar to the original classifications created manually several years ago including the identification of specific cancerous cells. 

Some of the features that make Northstar a powerful bioinformatics tool for these studies are that the software is scalable for large numbers of cells, it performs the computations to classify cells very fast, and it requires relatively low computer processing power to go through literally millions of data points. 

The scalability of the tool was demonstrated on the Tabula Muris data collection, a single-cell compendium of 20 mouse organs with over 200,000 cells of data.  Finally, Northstar was used to classify the tumors from new single-cell data generated by the CESCG via samples of 11 patient pancreatic cancer patients obtained from Stanford Hospital.  Northstar correctly found the origins of cancerous cells from the specific diagnoses of pancreatic cancer that the patients had, for example cancerous cells in the endocrine cell lineage from a patient diagnosed with neuroendocrine pancreas cancer.  Furthermore, Northstar identified previously unknown origins of cancerous cell clusters from other patients with pancreatic cancer.  These new computational tools demonstrate how big data from genomic studies can become important contributors to personalized medicine.

The full study was published in Nature.

CIRM & CZI & MOU for COVID-19

/ Kevin McCormack / Leave a comment

Too many acronyms? Not to worry. It is all perfectly clear in the news release we just sent out about this.

A new collaboration between the California Institute for Regenerative Medicine (CIRM) and the Chan Zuckerberg Initiative (CZI) will advance scientific efforts to respond to the COVID-19 pandemic by collaborating on disseminating single-cell research that scientists can use to better understand the SARS-CoV-2 virus and help develop treatments and cures.

CIRM and CZI have signed a Memorandum of Understanding (MOU) that will combine CIRM’s infrastructure and data collection and analysis tools with CZI’s technology expertise. It will enable CIRM researchers studying COVID-19 to easily share their data with the broader research community via CZI’s cellxgene tool, which allows scientists to explore and visualize measurements of how the virus impacts cell function at a single-cell level. CZI recently launched a new version of cellxgene and is supporting the single-cell biology community by sharing COVID-19 data, compiled by the global Human Cell Atlas effort and other related efforts, in an interactive and scalable way.

“We are pleased to be able to enter into this partnership with CZI,” said Dr. Maria T. Millan, CIRM’s President & CEO. “This MOU will allow us to leverage our respective investments in genomics science in the fight against COVID-19. CIRM has a long-standing commitment to generation and sharing of sequencing and genomic data from a wide variety of projects. That’s why we created the CIRM genomics award and invested in the Stem Cell Hub at the University of California, Santa Cruz, which will process the large complex datasets in this collaboration.”  

“Quickly sharing scientific data about COVID-19 is vital for researchers to build on each other’s work and accelerate progress towards understanding and treating a complex disease,” said CZI Single-Cell Biology Program Officer Jonah Cool. “We’re excited to partner with CIRM to help more researchers efficiently share and analyze single-cell data through CZI’s cellxgene platform.”

In March 2020, the CIRM Board approved $5 million in emergency funding to target COVID-19. To date, CIRM has funded 17 projects, some of which are studying how the SARS-CoV-2 virus impacts cell function at the single-cell level.

Three of CIRM’s early-stage COVID-19 research projects will plan to participate in this collaborative partnership by sharing data and analysis on cellxgene.   

  • Dr. Evan Snyder and his team at Sanford Burnham Prebys Medical Discovery Institute are using induced pluripotent stem cells (iPSCs), a type of stem cell that can be created by reprogramming skin or blood cells, to create lung organoids. These lung organoids will then be infected with the novel coronavirus in order to test two drug candidates for treating the virus.
  • Dr. Brigitte Gomperts at UCLA is studying a lung organoid model made from human stem cells in order to identify drugs that can reduce the number of infected cells and prevent damage in the lungs of patients with COVID-19.
  • Dr. Justin Ichida at the University of Southern California is trying to determine if a drug called a kinase inhibitor can protect stem cells in the lungs and other organs, which the novel coronavirus selectively infects and kills.

“Cumulative data into how SARS-CoV-2 affects people is so powerful to fight the COVID-19 pandemic,” said Stephen Lin, PhD, the Senior CIRM Science Officer who helped develop the MOU. “We are grateful that the researchers are committed to sharing their genomic data with other researchers to help advance the field and improve our understanding of the virus.”

CZI also supports five distinct projects studying how COVID-19 progresses in patients at the level of individual cells and tissues. This work will generate some of the first single-cell biology datasets from donors infected by SARS-CoV-2 and provide critical insights into how the virus infects humans, which cell types are involved, and how the disease progresses. All data generated by these grants will quickly be made available to the scientific community via open access datasets and portals, including CZI’s cellxgene tool.

Stem Cell Tools: Helping Scientists Understand Complex Diseases

/ Karen Ring / Leave a comment

Yesterday, we discussed a useful stem cell tool called the CIRM iPSC Repository, which will contain over 3000 human induced pluripotent stem cell (iPSC) lines – from patients and healthy individuals – that contain a wealth of information about human diseases. Now that scientists have access to these lines, they need the proper tools to study them. This is where CIRM’s Genomics Initiative comes into play.

Crunching stem cell data

In 2014, CIRM funded the Genomics Initiative, which created the Center of Excellence in Stem Cell Genomics (CESCG). The goal of the CESCG is to develop novel genomics and bioinformatics tools specifically for stem cell research. These technologies aim to advance our fundamental understanding of human development and disease mechanisms, improve current cell and tissue production methods, and accelerate personalized stem cell-based therapies.

The CESCG is a consortium between Stanford University, the Salk Institute and UC Santa Cruz. Together, the groups oversee or support more than 20 different research projects throughout California focused on generating and analyzing sequencing data from stem or progenitor cells. Sequencing technology today is not only used to decode DNA, but also used to study other genomic data like that provides information about how gene activity is regulated.

Many of the projects within the CESCG are using these sequencing techniques to define the basic genetic properties of specific cell types, and will use this information to create better iPSC-based tissue models. For example, scientists can determine what genes are turned on or off in cells by analyzing raw data from RNA sequencing experiments (RNA is like a photocopy of DNA sequences and is the cell’s way of carrying out the instructions contained in the DNA. This technology sequences and identifies all the RNA that is generated in a tissue or cell at a specific moment).  Single cell RNA sequencing, made possible by techniques such as Drop-seq mentioned in yesterday’s blog, are now further revealing the diversity of cell types within tissues and creating more exact reference RNA sequences to identify a specific cell type.  By comparing RNA sequencing data from single cells of stem cell-based models to previously referenced cell types, researchers can estimate how accurate, or physiologically relevant, those stem cell models are.

Such comparative analyses can only be done using powerful software that can compare millions of sequence data at the same time. Part of a field termed bioinformatics, these activities are a significant portion of the CESCG and several software tools are being created within the Initiative.  Josh Stuart, a faculty member at UC Santa Cruz School of Engineering and a primary investigator in the CESCG, explained their team’s vision:

Josh Stuart

“A major challenge in the field is recognizing cell types or different states of the same cell type from raw data. Another challenge is integrating multiple data sets from different labs and figuring out how to combine measurements from different technologies. At the CESCG, we’re developing bioinformatics models that trace through all this data. Our goal is to create a database of these traces where each dot is a cell and the curves through these dots explain how the cells are related to one another.”

Stuart’s hope is that scientists will input their stem cell data into the CESCG database and receive a scorecard that explains how accurate their cell model is based on a specific genetic profile. The scorecard will help will not only provide details on the identity of their cells, but will also show how they relate to other cell types found in their database.

The Brain of Cells

An image of a 3D brain organoid grown from stem cells in the Kriegstein Lab at UCSF. (Photo by Elizabeth DiLullo)

A good example of how this database will work is a project called the Brain of Cells (BOC). It’s a collection of single cell RNA sequencing data from thousands of fetal-derived brain cells provided by multiple labs. The idea is that researchers will input RNA sequencing data from the stem cell-derived brain cells they make in their labs and the BOC will give them back a scorecard that describes what types of cells they are and their developmental state by comparing them to the referenced brain cells.

One of the labs that is actively involved in this project and is providing the bulk of the BOC datasets is Arnold Kriegstein’s lab at UC San Francisco. Aparna Bhaduri, a postdoctoral fellow in the Kriegstein lab working on the BOC project, outlined the goal of the BOC and how it will benefit researchers:

“The goal of the Brain of Cells project is to find ways to leverage existing datasets to better understand the cells in the developing human brain. This tool will allow researchers to compare cell-based models (such as stem cell-derived 3D organoids) to the actual developing brain, and will create a query-able resource for researchers in the stem cell community.”

Pablo Cordero, a former postdoc in Josh Stuart’s lab who designed a bioinformatics tool used in BOC called SCIMITAR, explained how the BOC project is a useful exercise in combining single cell data from different external researchers into one map that can predict cell type or cell fate.

“There is no ‘industry standard’ at the moment,” said Cordero. “We have to find various ways to perform these analyses. Approximating the entire human cell lineage is the holy grail of regenerative medicine since in theory, we would have maps of gene circuits that guide cell fate decisions.”

Once the reference data from BOC is ready, the group will use a bioinformatics program called Sample Psychic to create the scorecards for outside researchers. Clay Fischer, project manager of the CESCG at UC Santa Cruz, described how Sample Psychic works:

Clay Fischer

“Sample Psychic can look at how often genes are being turned off and on in cells. It uses this information to produce a scorecard, which shows how closely the data from your cells maps up to the curated cell types and can be used to infer the probability of the cell type.”

The BOC group believes that the analyses and data produced in this effort will be of great value to the research community and scientists interested in studying developmental neuroscience or neurodegeneration.

What’s next?

The Brain of Cells project is still in its early stages, but soon scientists will be able to use this nifty tool to help them build better and more accurate models of human brain development and brain-related diseases.

CESCG is also pursuing stem cell data driven projects focused on developing similar databases and scorecards for heart cells and pancreatic cells. These genomics and bioinformatics tools are pushing the envelope to a day when scientists can connect the dots between how different cell states and cell fates are determined by computational analysis and leverage this information to generate better iPSC-based systems for disease modeling in the lab or therapeutics in the clinic.

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