Scientist Stories: Gary Bader, An Atlas of Human Liver Diversity over a Lifespan

So we're now having our final talk in this really, really great session, first session. So I'll introduce you to Gary Beider. Gary is a computational biology professor at the University of Toronto in Canada. He's a member of the HCA organising committee, and he has been active in contributing to the Human Liver Atlas. Atlas, including community building around this project, and he will discuss this today. Welcome, Gary.

Hello everyone.

Hola.

I'd like to thank the organizers for giving me the opportunity to tell you about an atlas of human diversity over a lifespan. The human liver is the largest solid organ in the human body. It has many functions. It's a metabolic factory with over 500 known functions. It's an immune center that contains the majority of the body's macrophages. It has a famous capacity to regenerate. Even after 80% of it is removed, it can grow back to its normal volume. And it's affected by important diseases that are increasingly prevalent. Notably, nonalcoholic fatty liver disease is estimated to affect 1.7 billion people worldwide, and an estimated 500 million people are affected by viral hepatitis, which can lead to liver cancer. The human liver is highly structured, formed by these hexagonal-shaped hepatic lobules, which are repeated approximately 1 million times in one liver. The lobules themselves are made up of specialized capillaries called sinusoids, and all this plumbing serves to process and filter the blood at a rate of about 1.5 liters per minute. The liver contains 10% of your blood volume at any given moment. While we know much about liver physiology and anatomy, we still don't know very little about how its cells work together to carry out its normal functions and how inflammation, fibrosis, and eventual liver failure work. This became very apparent with the first single-cell resolution transcriptomics map of the human liver in a 2017 and 2018 project led by liver transplant surgeon Ian McGilvray, immunologist Sonya McParland, and myself contributing computational biology expertise in Toronto, this map was able to discover previously unknown functional specialization of a range of liver cells. For example, the map revealed that the traditionally defined liver macrophage, or Kupffer cell, could be split into two subtypes: one inflammatory and one non-inflammatory. This of course has important medical implications as liver inflammation is a hallmark of many liver diseases. All of this data was immediately shared and made public as part of the Human Cell Atlas and can be accessed at the HCA data portal website. In parallel to the work I've just described, a number of other groups have developed outstanding human liver maps from a diverse range of tissue sources using different methods and studying different aspects of liver biology. Dominik Grun and Thomas Baumert from Germany mapped healthy human tissue from 9 individuals focusing on stem and progenitor cell biology. Shalev Itzkovitz from the Weizmann Institute used laser capture microdissection RNA-seq to study zonation, or expression and function gradients along the sinusoid. And Neil Henderson in the UK examined immune cells in 5 healthy liver samples. Through HCA meetings, these liver researchers and others met each other and came together to share their findings, collaborate, and ultimately define the shared goal of mapping the human liver over a lifespan, covering fetal, pediatric, and adult life stages, creating male and female maps, and exploring how maps differ over ancestry and other factors. Ultimately, 19 labs interested in human liver research and representing 6 scientific specialty areas such as genomics, immunology, and computational biology were able to find each other at these meetings and form a collaborative network, successfully receive funding from the Chan Zuckerberg Initiative Seed Networks funding program with a grant led by Alan Mullin at the Mass General Hospital and the Harvard Stem Cell Institute in Boston, and embark on a large combined effort to extensively map the human liver. This network has recently been extended to two additional groups studying liver funded under the CZI Inflammation Program. Thank you very much to CZI. This project is receiving liver tissue from multiple sources, including biopsies, whole organs, parts of organs discarded from surgeries, and other tissue sources and are using a range of single-cell genomics, spatial, and imaging technologies, including single-cell and single-nuclei RNA-seq, spatial transcriptomics, technology for mapping immune cell subsets such as CITE-seq, and technology for mapping T-cell epitopes, and 3D volumetric electron microscopy to ultimately be able to create a single-cell resolution, 3D spatial and molecular human liver map. A major challenge with single-cell liver genomics is that some liver cells, such as hepatocytes, which make up most of the liver, are very sensitive to experimental processing. Standard tissue dissociation protocols and cell processing methods like flow cytometry fail terribly as they kill all or most of the hepatocytes along with other sensitive cells like stellate cells and cholangiocytes. Sonya and Ian spent almost a year optimizing a tissue dissociation protocol that maintained sensitive cells. This was done by taking advantage of the natural anatomy of the liver and the fortunate availability of entire caudate lobes from the liver transplant surgical procedure that is the source of the tissue for RMAP. This video demonstrates the protocol. First, the caudate lobe is removed during the liver transplant surgery. This is done as a normal part of the surgery. Then, the vasculature of the caudate lobe itself is used to deliver reagents to all parts of the tissue. First, blood cells are flushed out of the vasculature. Then collagenase is distributed through the same vasculature to dissociate the cells for a very short amount of time, and then the glycans capsule which surrounds the caudate lobe is cut, releasing all of the cells. Because tissue dissociation protocols have been very time-consuming to develop and are critical for single-cell mapping success, The Human Liver Network made an early investment in sharing knowledge to save time and resources, as well as speed science across many groups. For example, all groups freely shared their tissue dissociation protocols on the protocols.io website. Our group in Toronto also benefited greatly by learning how to implement single-nuclei RNA-seq in human liver, knowledge shared by Aviv and Orit's group at the Broad Institute in Boston. And which we are now sharing with other groups in the UK, Germany, and Singapore. Interestingly, this single nuclei RNA sequencing technology alleviates some of the problems that we had from tissue dissociation protocols for single cell RNA-seq. The reason is that our single nuclei RNA-seq can be applied to frozen samples and we don't have to worry about dissociating cells from the samples. It turns out even this improved technology needs to be optimized for each tissue. And even though we got much improved cell representation, there's still some cells that have better representation than others. So what we're doing right now is comparing the single-cell RNA-seq map and the single-nuclei RNA-seq map to identify which technology is best for which cells. After we do this, we can make recommendations that if you're interested in hepatocytes, you should use single-cell RNA-seq with this particular protocol. If you're interested in stellate cells, then you should use single-nuclei RNA-seq with this particular protocol. And we will share all this information with the community so that they, they can benefit from that information. It's pretty clear that the more we can freely share information, the faster we can all achieve our scientific goals. Together, the network is making great progress. One highlight is that Maz Hanifa's lab, with colleagues including Ludovic Vallier, Sarah Teichmann, and Sam Bajati in the UK, published the first fetal liver map last year, covering 7 to 17 post-conception weeks and focusing on blood system development in the liver. A second map led by Ludovic Vallier is in progress, extending this to as early as 5 post-conception weeks and focusing on liver cell development. As an update on the human adult MAP project, the Toronto group has now collected over 160,000 cell transcriptomes from 20 healthy individuals, all liver transplant donors using Ian and Sonya's protocol that was described in the video. This MAP covers age ranges from earlier than 20 years old to almost 70 years old, has equal sex representation, and covers diverse body mass index values. So we finally have enough samples to start understanding how these factors, as well as various technical factors, relate to the transcriptomics map. Research Associate Tallulah Andrews, part of the Toronto team, is leading the analysis effort of this map. With this much data, there are many opportunities for computational analysis and to develop novel computational methods, both within the groups generating the data, as well as by any scientist who can freely access the large amount of public data that's being shared. As an example, Gerald Kwon's lab at UC Davis on the Human Liver Map team is developing deep learning-based methods to integrate data across labs, technologies, and biological factors such as sex and age. My group is also thinking about how to develop methods that can analyze and interpret single-cell maps that change over time, like the maps of the human lifespan that are being generated as part of the HCA Human Liver Project. Like other Human Cell Atlas projects, the Liver Project is open and shared. That means that we hope the network of scientists will grow over time to help speed progress to reach the goal of completing the map. And we encourage anyone interested in human liver single-cell genomics to reach out and connect with the Liver Network. We're very happy to share experiences and to welcome new community members. To connect, just reach out to anyone in the HCA, any of the liver scientists I mentioned, or email this email address, liver@humancellatlas.org. I want to thank all the HCA Liver Network members that I mentioned throughout the talk. They're doing amazing science, and I just have the pleasure to summarize some of it for you today. I also want to thank CZI, the Wellcome Trust, the NIH, and many other funding agencies that are making this work possible. Thank you as well for listening, and I look forward to taking questions.

Gary, this was a fantastic talk, a fantastic example and illustration of one of the biological networks of the HCA. I guess participants are curious on how can they really join the effort? How do you select members for this community for the liver project?

That's a great question. So the project is really open, and I think the whole HTA is really open. So just reach out to people that are involved to learn more. You can email me at liver@humancellalice.org. Address goes to me and Alan Mullin, and we'll gladly write back and schedule a call with you to tell you all about anything that is new or interesting. So just reach out, everyone is welcome.

Gary, thank you so much. Thank you for so much for your talk and for sharing all this information that you have already collected throughout this few years, if you think about it, right? Very few years. It's really amazing. Yeah, the amount of data, it's really, really fascinating. Fascinating. Thank you.

Thank you.

We have to wrap up this first session now. I believe it was a really great session, session with great speakers, fantastic talks. And we're gonna have a break now, a 15-minute break. So we're gonna be back here at, um, in, in, uh, 15 minutes, right? So see you guys back in 15 minutes for session 2. Session 2 is going to be on regional networks of the HCA. Thank you.