In the early 1960s, Mack Fulwyler joined the lab of Marvin Van Dilla at the Los Alamos National Laboratory as an engineer to study the effect of fallout radiation from nuclear weapons testing on biological material. However, following the ban on atmospheric nuclear weapons testing in 1963, this project shut down, and Fulwyler assisted other scientists on their projects.
One project was studying the applications of the Coulter counter, a device used to count cells and estimate their size based on changes in electrical impulses that created a distribution histogram. A data disagreement led to a technology that completely altered biological research.
The Start of Cell Sorting
One day, Fulwyler observed Clarence Lushbaugh, a pathologist also at the Los Alamos National Laboratory, adjusting the settings on the counter while investigating a blood sample. The change caused a shift in the distribution of a small population of red blood cells from the main distribution of cells. Lushbaugh concluded that the altered volume indicated that the cells were immature. Fulwyler disagreed with the scientist’s conclusion on the grounds that Lushbaugh was not using the device correctly.1
Fulwyler thought that if he could separate this cell population and reanalyze them, he could disprove Lushbaugh’s conclusion. He wanted to modify the cell counter so that after it measured a cell, that cell could be tagged in some way and sorted.
The original devices that you use to capture the fluorescence distribution, that’s straight out of nuclear physics.
—Joe Gray, Oregon Health and Science University
Luckily, he came across a paper from Stanford University researchers who developed a new technology for ink printers; the instrument vibrated to break a jet of liquid ink into individual droplets, then charged them individually and deflected them.2 Combining this droplet-making and charging technology with that of the Coulter counter, Fulwyler built the first cell sorting apparatus, which separated cells based on volume by applying electrical charges to the droplets.3
This device laid the foundation for flow cytometry and cell sorting. Alongside Fulwyler’s cell sorting development, other members in Van Dilla’s group explored incorporating DNA dyes and light scatter into early flow systems, albeit without sorting at first.4,5 The combination of fluorescence with cell sorting came about because of one scientist’s tired eyes.
Merging Fluorescence with Cell Sorting
In the mid-1960s, Leonard (Len) Herzenberg, an immunologist at Stanford University, studied antibody responses. At that time, studying antibody-producing cells required fractionating bulk samples and manually counting cells. “Len’s trouble was he had very poor vision in one eye, and not such good vision in the other eye,” recalled Leonore Herzenberg, a geneticist at Stanford University and Leonard Herzenberg’s wife and colleague.
“He kept thinking there must be a better way of counting cells,” recalled Stephen De Rosa, who was a postdoctoral researcher in the Herzenberg laboratory in the late 1990s and is currently an immunologist at the University of Washington.
Leonard Herzenberg was interested in sorting live cells, but he wanted to do it using fluorescence. In 1967, he visited Fulwyler to study his instrument, and ultimately asked him for its design plans. Herzenberg recruited engineers at his institution to help him adapt the cell sorter to separate cells based upon fluorescence.6 In 1969, fluorescence activated cell sorting (FACS) was born.
“[Leonard Herzenberg] had the concept of FACS and developing it further. And that was not something I would have done,” Leonore Herzenberg said. “But once he brought me there, I was doing that as well as everything else.”
“Ultimately, I took over from him,” Leonore Herzenberg said. “Len would only stay with something for about two or three years and then he was off into something new,” she joked.
In the subsequent years, lasers replaced the arc lamps that Herzenberg originally used, and soon groups coupled multiple lasers with light scatter information to make more measurements.7,8 “A lot of the early work was just trying to learn how to measure more cool things about cells,” said Joe Gray, a systems biologist and professor emeritus at Oregon Health and Science University who worked as an engineer building cell sorters at the Lawrence Livermore National Laboratory. Although the Herzenbergs developed FACS to sort antibody producing cells, the method also became popular to study DNA and sort chromosomes.9,10
“The whole tree of the development of the immunological system was really worked out with the help of the flow sorter,” said Donna Arndt-Jovin, a molecular cell biologist at Max Planck Institute who helped advance flow system instruments in the 1970s. Before FACS could lend this help, though, the instruments needed to get into research laboratories, and that was a challenge.
FACS Goes Commercial
When biologists like Leonard Herzenberg wanted to incorporate cell sorting into their laboratories, they had to build these instruments from scratch. “Who would you hire to build a cell sorter? A particle physicist,” Gray said. “The original devices that you use to capture the fluorescence distribution, that’s straight out of nuclear physics … I literally took the same electronics that I was using in physics and installed those on the cell sorter that we built.”
For cytometry, and especially FACS, to become more applicable in research, the instruments needed to be more readily available to consumers. “Len had reached out to Becton Dickinson, and they really commercialized it,” said De Rosa. Bernard Shoor, then a manager at Becton Dickinson, today BD Biosciences, saw the potential for Herzenberg’s instrument and the company began manufacturing them. The partnership between Herzenberg and BD Biosciences was crucial for expanding FACS into laboratories far and wide. “It really was a collaborative effort with this commercial company with an academic scientist,” De Rosa said.
In 1974, BD Biosciences released the FACS II as the first commercial fluorescent cell sorter. The same year, another company, Technicon Instruments Corporation, released a sorting instrument specific for blood samples that differentiated leukocytes, which used a halogen lamp instead of lasers like FACS instruments.11 BD trademarked the term FACS in 1985. Today, scientists commonly use the term FACS to refer to cell sorting with fluorescence, just like it was when Herzenberg first coined it in 1973.
Cell Sorting Gets an Upgrade
New lasers and dyes, as well as the introduction of monoclonal antibodies labeled with fluorescent markers, opened the floodgates to the potential for characterizing cells.12-14 “It just grew out of these things in the sense that people saw the use of it, they wanted … to have more parameters, more possibilities to look at this or that or the other thing,” Arndt-Jovin said.
As researchers added more fluorescent parameters to their samples, they also had to add more detectors and introduce compensation (i.e., correction for possible spectral overlap).15 Computers helped make the complicated and rapid calculations needed to sort cells, but computer memory was limited at that time.16,17 “You just can’t imagine how little memory there was and how expensive it was,” explained Arndt-Jovin. “And there was no capacity. Your cell phone has more than the computers we were using.”
When you think about being able to sort multiple directions at the same time, it’s really good for conserving cells.
—Stephen De Rosa, University of Washington
“Both microscopy and cytometry have always come along at the rate that the information processing technology allowed,” Gray said. However, microprocessor advancements in the 1980s improved the ability to acquire, store, and process data on cell sorters.18
It was around this time that the Herzenberg group began studying immune responses to the human immunodeficiency virus (HIV). Although tandem dyes like phycoerythrin (PE) and allophycocyanin (APC) expanded the capabilities of characterizing cells, Mario Roederer, who joined the Herzenberg lab in 1988 as a postdoctoral researcher, wanted to investigate more parameters simultaneously.19,20
Roederer led the team down the road of creating probes that conjugated indotricarbocyanine, commonly known as Cy7, to PE and APC dyes, and incorporated other recently developed conjugated dyes into their panels.21,22 “We pushed our capabilities from three or four colors to eleven colors, meaning that we could measure eleven different things on each cell, quantitatively and uniquely, as they went through the flow cytometer and then make sort decisions based on those different parameters,” said Roederer, who is now an immunologist at the National Institute of Allergy and Infectious Diseases.23
However, increasing the cell sorting capacity required overhauling the current sorters to accommodate more detectors.24 In order to analyze their data, Roederer and a colleague, Adam Treister, a scientific programmer at Stanford University at the time, also wrote software code that could parse multiple variables. “We needed software that could analyze data that was very complex and generate the publication quality graphics necessary for generating reports and presentations,” Roederer explained.
The software was FlowJo, which Roederer and Treister licensed to other interested users through Stanford University. “It [was] really the first time that people had access to how they analyzed the data over time, and then reapply that as a template or as a mode to analyze new data as it came in as well,” Roederer said.
With more labels and better analysis software, the next big leap for FACS was in the sorting itself. The majority of instruments deflected charged droplets.25 While four-way sorting was described in the early 1980s, this option only gained popularity in the early 2000s after companies built four-way sorters.26-28 Today, instruments can sort into six distinct populations.
Future Advances in FACS and Flow Cytometry
Today, scientists often use FACS in parallel with other methodologies to study cell populations and their functions. In addition to his work at the University of Washington, De Rosa leads the flow cytometry laboratory at the HIV Vaccine Trials Network at the Fred Hutch Cancer Center, where his team and others use FACS to identify rare antigen-specific T and B cells. “It’s absolutely critical,” he said. For example, his team uses FACS to sort vaccine-induced B cells to do B cell receptor sequencing. “When you think about being able to sort multiple directions at the same time, it’s really good for conserving cells.”
Aside from new labels and increased sorting capacity, FACS functions the same today as it did in the 1970s when it was commercialized. However, label-free methods and miniaturized lab-on-a-chip cell sorting are further expanding its capabilities.29-31 Additionally, microfluidic technologies are poised to advance not only cell sorting, but cell biology research.32 “The real advance comes when we can sort at a rate of a million cells per second,” Roederer said. “At a million cells per second, the whole technology, the whole bio laboratory changes fundamentally, because you don’t need to do centrifugation anymore.”
With these further expanded parameters to parse and sort data comes the need to overhaul its analysis once again. New algorithms and models are improving high-dimensional data analysis, and the insights potential by artificial intelligence methods to identify patterns not yet discernible is on the horizon.33-36
“It just has been fun to watch the technology advance and go into the hands of not just the flow cores but [also being] available to all the people around them.” Roederer said.
- Robinson JP. Mack Fulwyler in his own words. Cytom Part A. 2005;67A(2):61-67.
- Sweet RG. High frequency recording with electrostatically deflected ink jets. Rev Sci Instrum. 1965;36:131-136.
- Fulwyler MJ. Electronic separation of biological cells by volume. Science. 1965;150(3698):910-911.
- Van Dilla MA, et al. Cell microfluorometry: A method for rapid fluorescence measurement. Science. 1969;163(3872):1213-1214.
- Mullaney PF, et al. Cell sizing: A light scattering photometer for rapid volume determination. Rev Sci Instrum. 1969;40:1029-1032.
- Hulett HR, et al. Cell sorting: Automated separation of mammalian cells as a function of intracellular fluorescence. Science. 1969;166(3906):747-749.
- Hulett HR, et al. Development and application of a rapid cell sorter. Clin Chem. 1973;19(8):813-816.
- Steinkamp JA, et al. A new multiparameter separator for microscopic particles and biological cells. Rev Sci Instrum. 1973;44:1301-1310.
- Van Dilla MA, et al. High-speed cell analysis and sorting with flow systems: Biological applications and new approaches. IEEE Trans Nucl Sci. 1974;21(1):714-720.
- Carrano AV, et al. Measurement and purification of human chromosomes by flow cytometry and sorting. PNAS. 1979;76(3):1382-1384.
- Mansberg HP, et al. The Hemalog D white cell differential system. J Histochem Cytochem. 1974;22(7):711-724.
- Steinkamp JA, et al. Dual-laser flow cytometry of single mammalian cells. J Histochem Cytochem. 1979;27(1):273-276.
- Shapiro HM, et al. Combined blood cell counting and classification with fluorochrome stains and flow instrumentation. J Histochem Cytochem. 1976;24(1):396-401.
- Reinherz EL, et al. Separation of functional subsets of human T cells by a monoclonal antibody. Proc Natl Acad Sci USA. 1979;76(8):4061-4065.
- Loken MR, et al. Two-color fluorescence using a fluorescence activated cell sorter. J Histochem Cytochem. 1977;25(7):899-907.
- Arndt-Jovin DJ, Jovin TM. Computer-controlled multiparameter analysis and sorting of cells and particles. J Histochem Cytochem. 1974;22(7):622-625.
- Shapiro HM, et al. Computer-aided microspectrophotometry of biological specimens. Exp Cell Res. 1971;67(1):81-89.
- Voet L, et al. Data acquisition and control system for multiparameter cell sorting based on DEC LSI-11 microprocessor. Cytometry. 1981;2(6):383-389.
- Oi VT, et al. Fluorescent phycobiliprotein conjugates for analyses of cells and molecules. J. Cell Biol. 1982;93(3):981-986.
- Shapiro HM, et al. Immunofluorescence measurement in a flow cytometer using low-power helium—neon laser excitation. Cytometry. 1983;4(3):276-279.
- Roederer M, et al. Cy7PE and Cy7APC: Bright new probes for immunofluorescence. Cytometry. 1996;24(3):191-197.
- Waggoner AS, et al. A new fluorescent antibody label for three-color flow cytometry with a single laser. Ann N Y Acad Sci. 1993;677(1):185-193.
- De Rosa SC, et al. 11-color, 13-parameter flow cytometry: Identification of human naïve T cells by phenotype, function, and T-cell receptor diversity. Nat Med. 2001;7:245-248.
- Roederer, M, et al. 8 Color, 10-parameter flow cytometry to elucidate complex leukocyte heterogeneity. Cytometry. 1998;29(4):328-339.
- Chapman GV. Instrumentation for flow cytometry. J Immunol Methods. 2000;243(1-2):3-12.
- McCutcheon MJ, Miller RG. Flexible sorting decision and droplet charging control electronic circuitry for flow cytometer-cell sorters. Cytometry. 1982;2(4):219-225.
- Mellor AL, et al. Specific subsets of murine dendritic cells acquire potent T cell regulatory functions following CTLA4-mediated induction of indoleamine 2,3 dioxygenase. Int Immunol. 2004;16(10):1391-1401.
- Picot J, et al. Flow cytometry: Retrospective, fundamentals and recent instrumentation. Cytotechnology. 2012;64:109-130.
- Gosset DR, et al. Label-free cell separation and sorting in microfluidic systems. Anal Bioanal Chem. 2010;397:3249-3267.
- Wolff A, et al. Integrating advanced functionality in a microfabricated high-throughput fluorescent-activated cell sorter. Lab Chip. 2003;3:22-27.
- Cho SH, et al. Human mammalian cell sorting using a highly integrated micro-fabricated fluorescence-activated cell sorter (μFACS). Lab Chip. 2010;10:1567-1573.
- Carey TR, et al. Developments in label-free microfluidic methods for single-cell analysis and sorting. Wiley Interdiscip Rev Nanomed Nanobiotechnol. 2018;11(1):e1529.
- Quintelier K, et al. Analyzing high-dimensional cytometry data using FlowSOM. Nat Protoc. 2021;16:3775-2801.
- Qiu P, et al. Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE. Nat Biotechnol. 2011;29:886-891.
- Amir E-aD, et al. viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia. Nat Biotechnol. 2013;31:545-552.
- Becht E, et al. High-throughput single-cell quantification of hundreds of proteins using conventional flow cytometry and machine learning. Sci Adv. 2021;7(39):abg0505.