Passion, Imagination & CyTOF
It's easy to imagine Sean Bendall, PhD, on a "40 Under 40" list. An assistant professor previously affiliated with Stanford's Nolan Lab at the National Heart Lung and Blood Institute (NHLBI) Proteomics Center in Palo Alto, California, Bendall is a young standout and proof that the status quo will no longer do. He's in the forefront of a new movement that uses technology to advance single-cell biology, specifically in drug and signaling responses in developing human systems.
In the Field: How Sean Bendall is advancing Stanford proteomics research
By simplifying complex workflows and zeroing in at the single-cell level using the Fluidigm® CyTOF® system, Bendall and his forward-thinking colleagues have gained new insights into human biology. Instead of focusing on the mature immune system, they're exploring its development--and, in turn, creating an entirely new way to look at scientific workflows.
While training under Garry Nolan, PhD, Bendall and his colleagues combined phospho-specific flow cytometry and mass spectrometry isotope-based analysis of signaling pathways at the single-cell level. The Nolan lab has been a leader in advancing flow cytometric analysis, establishing a range of flow assays and expression measurement systems for single-cell research. Combined with proteomics and advanced analyses like Bayesian mathematics, the lab's workflows represent a revolutionary method for studying pathways and identifying signaling structures in both normal and diseased cells. Bendall drew much of the inspiration for his independent lab from his time in the Nolan Lab and continues to partner with them as well as share access to the CyTOF system. Bendall's lab is part of Stanford's Department of Pathology at the Stanford Blood Center, where he focuses on the human hematopoietic immune system and regulatory signaling in healthy and diseased processes (oncogenic, allergic and autoimmune). With four other projects in the areas of pulmonary arterial hypertension (PAH) and autoimmunity, the NHLBI and Stanford are making significant strides through interdisciplinary partnering.
That collaboration starts with a growing trend toward bridging genomics and proteomics. Bendall explains that while genomic data provides a breadth of information, proteomic data measures more limited things about individual cells. In his own work, Bendall uses genomics and proteomics to start with broad ideas and then narrow his hypothesis. "I don't pretend to be an expert in single-cell molecular biology," he says, citing his ongoing collaborations in human allergic response with thought leaders such as Steve Galli, pathology department chair at Stanford and an expert in allergy and the function of allergic immune cells.
Bendall is, however, an expert in single-cell proteomics and cytometry-based analysis. Theoretically, he could run flow cytometry analysis in his lab, then cross campus and drop them into another lab's pipeline. So it's possible that he may soon bridge two dynamic worlds across one collegiate square.
The prospect of sharing knowledge, and having the means to achieve it, is exciting for Bendall, who has always loved technology. By training, he is a protein biochemist. His undergraduate studies focused on biochemistry and microbiology, evolving later into protein biochemistry, proteomics and mass spectrometry. As a PhD candidate he worked in a proteomics lab, partnering with another lab that researched hematopoietic and embryonic stem cells in humans.
Bendall found the labs to be something of a paradox: on the proteomics side, he analyzed up to 10 million cells, while the stem cell side was defined by the functions of individual cells.
It was hard to do assays on a million cells when really the fundamental questions were about the functions of a single cell, ”
– Sean Bendall, PhD
A lot of followup was required because bulk analysis is only effective if the cell of interest happens to be in the majority. These kinds of "technological deficits" brought Bendall to Stanford in search of "tools that could answer the same bulk questions, but on a cell-by-cell basis."
One of Bendall's challenges is applying his research in a human context. Fewer tools are available--and human subjects aren't easily modified. "We can't have a cage full of people that we just play around with," he quips. One could remove a gene from a worm, then compare it to another worm with that gene. But with human beings, one must compare different cells in a related system without modification. Bendall leverages single-cell analysis to remove such complexity, something that would normally be solved using molecular tools in lower organisms. His goal is to create a suite of technologies that illuminate drug action and gene function in a human context. In short, to "de-convolute the human system."
To achieve this, Bendall is reimagining experimental design with single-cell analysis. Repeating the same inquiry on two platforms can yield similar data, so Bendall leads with proteomic technologies, then uses another platform to drill down. He tailors each single-cell assay to the targets for that drug or disease trial. But current methods are flawed.
"Stem cell differentiation--especially in a dish--is a mess," he notes. Measuring cell by cell adds clarity to heterogeneity, creates order and allows Bendall to focus on specific cells, all while preventing inherent biological variability from negatively affecting results.
Bendall's lab currently has several projects. The first focuses on human allergic granulocyte populations in a deep proteomic context. On the one hand, he is looking at cells related to common allergic reactions in patients undergoing therapeutics. On the other, he is conducting basic biology studies with human embryonic stem cells that he can genetically manipulate to identify the roles of different regulators.
Although scientists have known about these types of cells for decades, Bendall explains, "We still don't actually understand … what is a normal level of heterogeneity and how that changes in the case of allergic disease." His research also has the potential to tie into allergic immunotherapy trials being conducted at Stanford. In its simplest form, Bendall's research is focused on "understanding normal so we can understand abnormal."
Sometimes normal is the first thing overlooked. When Bendall arrived at the Nolan Lab, many people were researching leukemia. While they studied cancerous bone marrow samples, Bendall looked at acute myeloid leukemia with CyTOF. His first question was straightforward enough: "What does healthy marrow look like?" But the lab hadn't yet profiled a healthy marrow sample, so Bendall suggested they begin there. Since things in pathology are scored based on how "far from normal" they are, quantifying normal is essential to understand and predict abnormality.
Bendall's second, yet-to-be funded project aims to show how single-cell technology aids blood cancer research. He combined years of human hematopoietic immune cell biology into one experiment to prove the value of looking at the whole, rather than running separate experiments on the parts. Expanding upon existing findings, Bendall hopes to understand what the mechanism of engraftment looks like in bone-marrow transplant patients when things work well and when things go wrong. And he plans to gain the highest resolution of understanding using single-cell technology.
"Let's not just stamp, collect and define the system based on, say, surface markers," Bendall says. "Let's define the system based on the intrinsic biochemical molecular regulators that are really affecting the cell's behavior." His research could lead to actionable clinical methodologies, meaning, for example, that if one has too much of one cell type and not enough of another, various therapies may be fine-tuned on a case-by-case basis through his research results.
Bendall's work may also help to redefine how scientists design experiments. Studies with traditional flow methodology are often "heavily hypothesis-driven," he states. "They're yes-or-no questions, or a checklist." But what happens when you don't know what you're looking for yet?
In his April 2014 paper in Cell , Bendall references the discovery of several factions of definitive B cells in normal human marrow that had never been seen before. This population of gatekeeper B cells, dubbed "fraction three," determines cell maturity and serves as a "human checkpoint." And it represents a significant discovery:
"These cells were as rare as 1 in 10,000 cells in the marrow," Bendall explains. "So unless you knew exactly what the definition in that cell was, there was no way you were going to randomly find it."
Using CyTOF data helped identify the right cell populations and enabled Bendall to perform genomic analyses to prove that B-cell genome rearrangements were increasing with cell development. This was more than a discovery--it became a whole new strategy. It gave Bendall a workflow that would leverage genomics and proteomics in a phased approach that refines his hypothesis with maximum clarity.
"In a population, maybe one-third to two-thirds of the cells are the most interesting ones," Bendall notes. "I don't know what separates those cells, but now I have a basic definition, and I can pre-purify them into the next assay that drills even more deeply into the cell using other metrics like gene expression." It's the combination of the different techniques that really delivers a one-two punch.
Bendall first phenotypes a novel cell population on CyTOF, then performs mRNA sequencing to find distinctive signatures that point to unique surface markers. These surface markers then became critical tools used to enrich the specific target population using flow cytometry, modify the cells via a stimulant or drug and finally characterize the biological effect through mRNA sequencing or mass cytometry.
Single-cell technology and the CyTOF system are on the front lines of these visionary types of phased strategies, allowing scientists to "deal with little amounts of material and ask many, many questions," he says.
In a way, Bendall and his colleagues are getting back to basics for the first time. "The cell is the simplest unit of life," he explains, "and when you can normalize things one cell at a time there's a lot more to understand."
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