How are your antibodies validated?

In short: We validate antibodies by testing different antibody clones, concentrations, and cellular conditions to optimize signal-to-noise ratio and reduce background noise, ensuring accurate identification of cellular markers. For the full details, download our technical whitepaper ‘Panel and Custom Antibody Validation Process‘ to learn more!

Our customers often want to know that when we have a cellular marker on our panel, such as CD45, we can confidently distinguish this cellular marker signal from background noise. To detect a cellular marker, we need a corresponding clone.

But not all clones are created equal. For instance, here’s an example of two different CD45 antibody clones tested on human cells with very different results. Can you see the difference?

Bad signal: cellular population missing or too lowGood signal: cellular population found

To that end, our immunologists use their extensive expertise to reduce mass cytometry panel validation time from months to weeks, using proven techniques.

Problems with standard approaches:
Traditional approaches would use panel design software alone to assign markers to channels, however standard panel design software approaches have crucial limitations. Specifically, these approaches:

  • only work with markers sold by the manufacturer
  • leave the antibody clone, antibody concentration, and stimulation condition unspecified for markers not sold by the manufacturer

Imagine you need a marker that’s not sold by the manufacturer: at this point, you need an expert to figure out what the right clone and concentration is. And if you don’t happen to have an expert handy, and you get the wrong clone or concentration: that could yield “bad or missing signals” on a panel, and ultimately unuseable data for precious samples.

Now, let’s compare our approach.

Our process:

We pair the best of software design with expert scientist intuition. Teiko scientists and immunologists have collectively processed over 2,800+ samples and 25 panels, yielding many hard-won lessons about panel design.

Ultimately, for a 44-marker panel, the goal of this process is to produce a 44 X 5 table:

MarkerChannel [44 total]Antibody CloneAntibody Concentration (μg/ml)Cellular conditions for signal detection
CD45[89Y, 112Cd, …, 209Bi][C1, C2, … CN][6, 3, 1.5, … 0.1875][Stimulated PMA, Stimulated PHA, Unstimulated]
CD8a[89Y, 112Cd, …, 209Bi][C1, C2, … CN][6, 3, 1.5, … 0.1875][Stimulated PMA, Stimulated PHA, Unstimulated]
CD45RA[89Y, 112Cd, …, 209Bi][C1, C2, … CN][6, 3, 1.5, … 0.1875][Stimulated PMA, Stimulated PHA, Unstimulated]

The first thing we do is look at the literature and antibody vendors and see if a marker of interest has been tested before. For each marker, i.e. CD45, we’ll figure out which antibody clone (i.e. HI30) to use based on historical performance.

Then, we’ll determine which channel to use to get the “maximum” signal to noise ratio. In general, Teiko’s experts follow a few rules that sound simple but require a lot of knowledge to execute quickly:

  • If a marker is weakly expressed on the cells, we put that marker on a “bright” channel to get a good signal.
  • Similarly, if a marker is strongly expressed on the cells, we put that marker on a “dim” channel to prevent the signal from becoming too “overwhelming”.
  • Some metals shouldn’t be matched on the same cell population because of concerns for natural impurity, and we won’t put those metals together.

Our experience with channel placement is where our experts can save you weeks of time and frustration.

After finding the right channel, we determine the “optimal” antibody concentration to get the maximum separation between signal and noise. We plot this “stain index” on a curve to find the right concentration.

This is how we generate the stain index:

  • First, we determine whether the marker needs to be tested in unstimulated or stimulated cells based on whether the marker is expressed on immune cells at baseline. If stimulation is required, we always test two different biological stimulations.
  • Next, we test six different concentrations of antibody and at each concentration we check:
    • Strength of the signal
    • Presence of background noise
    • “Spillover” (false-positive) signal into other channels
  • When a marker calls for it, we focus our analysis on a specific population that expresses that marker, so we don’t underestimate the signal.
  • Finally, we check that our marker performance is comparable to what others have seen in literature.

The end result is a fully populated table and completed validation:

MarkerChannel [44 totalAntibody cloneAntibody Concentration (μg/ml)Cellular Conditions for Signal Detection
CD45RA112CdC33Stimulated PMA
Illustrative purposes only

What is your process for validating new antibodies and adding them into my customized panel?

Using a process very similar to how we validated our base panels, we verify the optimal staining concentration at which each metal-conjugated antibody demonstrates a detectable and accurate signal on the mass cytometer, while minimizing background signal and spillover into other channels. Once titrated and validated individually, these antibodies are added to the panel and the full panel is then validated.

In sum

That’s a lot of heavy lifting behind the scenes. And we do that in weeks, not months. Teiko scientists performed this process to design all of our current Custom backbone and Standard panels.

Want to dive into the technical details? Download our technical whitepaper ‘Panel and Custom Antibody Validation Process‘ to learn more!

CLIA Validation Data for Teiko Bio’s Mass Cytometry Immune Profiling Assay for PBMCs

About the Webinar

In this live webinar, Ramji Srinivasan, cofounder and CEO of Teiko Bio, will walk through the precision and stability data for our mass cytometry assay for pan-immune profiling of PBMCs isolated from healthy human clinical samples.

About the Speaker

Before Teiko, Ramji was Cofounder, CEO and Chairman of Counsyl, a women’s health genetic screening laboratory. Counsyl screened over 1M prospective parents, mothers-to-be and women at risk of hereditary cancer. In 2018, Counsyl was acquired by Myriad Genetics, Inc for $375M in cash and stock.

Ramji earned a B.S. in computer science and an M.S. in financial mathematics, both from Stanford University. Ramji also attended Stanford’s Graduate School of Business before dropping out to start Counsyl.

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The complementary capabilities of single-cell RNA seq and high-dimensional cytometry

Single-cell RNA sequencing (scRNA-seq) and high-dimensional cytometry (i.e. spectral flow cytometry or mass cytometry), are both popular approaches for high dimensional analysis of disease states with single cell resolution. Individually, each is a powerful tool for determining the mechanisms underlying disease development and assessing response to treatment. When combined, however, the two can deeply enrich mechanistic and biomarker therapeutic research by providing complementary breadth and depth of analysis.

Researchers have used scRNAseq and high-dimensional cytometry as complementary tools in a wide variety of published studies to provide additional breadth, depth, and coverage when analyzing cellular processes.  Below are a few specific examples that illustrate the diversity and range of these approaches in a variety of biomedical research settings.

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Finding Cells of Interest in the Tumor Microenvironment

Comparison of scRNAseq data and CyTOF data taken from Figure 2a of Kashima et al. Sci Rep (2021) without alterations under the Creative Commons Attribution 4.0 International License. Full citation can be found under References below.
Comparison of scRNAseq data and CyTOF data taken from Figure 2a of Kashima et al. Sci Rep (2021) without alterations under the Creative Commons Attribution 4.0 International License. Full citation can be found under References below.

A study published in Scientific Reports by Kashima et al. in 2021 used both scRNA-seq and high-dimensional mass cytometry to investigate the immune profiles of the tumor microenvironment in gastric cancer patients.1 Because scRNA-seq was not limited by predetermined marker selection, it was able to detect and characterize new cell populations and subgroups within those populations. For example, scRNA-seq was able to identify plasma cells that were not defined by mass cytometry markers. In contrast, mass cytometry showed greater precision in differentiating predefined cell types characterized by a specific pattern of protein expression. For instance, NK cells and T cells were differentiated with greater accuracy using mass cytometry than with scRNA-seq.

As a result of their research, Kashimi et al. concluded that the greatest strength of high-dimensional mass cytometry lies in its ability to provide “narrow and clear” detection of prespecified cell types, while scRNA-seq enables “wide and indistinct” characterization of cells of interest. Thus, one approach for using the techniques together would be to apply scRNA-seq for discovery of new cell types of interest given the wide-breadth of this approach and then applying high-dimensional cytometry for more rapid and granular characterization of these cell types.

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Brain Matters: Protein Provides the Persistent Signal

Fernández‐Zapata et al. published a review article in Brain Pathology in 2020 summarizing studies that made use of both high-dimensional mass cytometry and scRNA-seq to comprehensively analyze the structure and function of human microglia, the central nervous system’s native immune cells.3 ScRNA-seq was effectively utilized to characterize microglial subsets formed in response to CNS pathology, such as multiple sclerosis (MS).

A key point from the Fernández‐Zapata et al. article is that function is mediated at the protein level, and proteomic techniques like mass cytometry can directly measure protein expression, turnover, and post-translational modifications missed by scRNA-seq.  For example, the authors discovered that “for some molecules with low transcript levels such as CX3CR1, CSF1R and FCGR1A, [there was] no change or even an increase in protein expression.”  This means that for infrequently produced markers, measuring protein expression will provide a more accurate readout of the cell state. One should also be aware that there is also greater cellular “noise” in scRNA-seq since there are significant differences in gene expression from cell to cell at the level of mRNA transcription even in uniform cell populations.4  Transcription (mRNA production) occurs in pulses.5  ScRNA-seq captures a “snapshot” in the life of a cell that ebbs and flows in terms of transcription.  Lack of detection of a particular mRNA does not mean the cell never produces it, just that the cell does not require it at that specific point in time.  By contrast, proteins are longer-lived molecules allowing for a greater time span to detect them and to capture cell activity as cells transition from one state to another.

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The Nanoparticle Nuisance

Nanoparticles are frequently added to consumer and biomedical products, and their interactions with human immune cells are important to characterize to ensure their safety. To address this topic, Ha et al. published a study in the journal Small in 2020 exploring the interactions between silver nanoparticles (AgNPs) and primary immune cells using both high-dimensional cytometry and scRNA-seq.2 Using mass cytometry, the study authors were able to show that monocytes and B cells had more contact with AgNPs than other cell populations. ScRNA-seq was then able to further characterize the specific cellular reactions in each cell type induced by contact with AgNPs.

In this study, mass cytometry was used as an initial screen for quick and accurate identification of cell subsets of interest that physically interacted with the specific nanoparticles being studied. It was able to directly detect the cell-AgNP interactions on a single cell level.  ScRNA-seq was then applied to obtain additional transcriptomic characterization taking place within the different cell types downstream of the interaction. In this instance the researchers were interested in AgNP-cell interactions, however a similar approach could be used to detect physical interactions between immune cells and any heavy metal labeled molecule.

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Other Limitations to Consider

An additional consideration when applying scRNA-seq is to determine how “deep” or “shallow” the analysis should be, which will depend on time and budget constraints.1,6 A “shallow” analysis (low number of mRNAs read per cell relative to the total mRNA content of the cell) carries greater uncertainty about whether transcriptomic differences are due to biological processes or sampling variation. Deeper sequencing (reading more mRNA molecules) enables greater resolution in differentiating cells, but big-picture population dynamics can be lost. An initial “shallow” analysis is not necessarily a bad thing for early-stage exploration and mapping out broad networks and processes. Since mass cytometry is currently limited to around 45 parameters, it must cover a broad range of preselected protein markers that help to distinguish cell types amongst diverse populations. It is also constrained by the technical challenges involved in constructing marker-specific antibodies conjugated to metal-isotopes.1 On the flip side, the longer half-life of proteins means they go through fewer fluctuations in comparison to mRNA and thus are more stable and reliable cell markers.5 Furthermore, high-dimensional cytometry techniques like mass cytometry and spectral flow cytometry have higher throughput, with the ability to analyze hundreds of thousands or even millions of cells. Higher throughput translates to greater confidence that a complete picture of cell population changes is being captured.

Teiko Bio: A Natural Complement to scRNAseq

Whether using high-dimensional cytometry to identify large scale immune cell population changes, study subset interactions, explore specific subsets with greater depth and accuracy, or validate function at the protein level, high-dimensional cytometry with Teiko is a natural complement to scRNAseq.  Together the two techniques can form a powerful duo to accomplish your clinical research goals.

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  1. Kashima Y, Togashi Y, Fukuoka S, Kamada T, Irie T, Suzuki A, Nakamura Y, Shitara K, Minamide T, Yoshida T, Taoka N. Potentiality of multiple modalities for single-cell analyses to evaluate the tumor microenvironment in clinical specimens. Scientific reports. 2021 Jan 11;11(1):1-1.
  2. Ha MK, Kwon SJ, Choi JS, Nguyen NT, Song J, Lee Y, Kim YE, Shin I, Nam JW, Yoon TH. Mass Cytometry and Single‐Cell RNA‐seq Profiling of the Heterogeneity in Human Peripheral Blood Mononuclear Cells Interacting with Silver Nanoparticles. Small. 2020 May;16(21):1907674.
  3. Fernández‐Zapata C, Leman JK, Priller J, Böttcher C. The use and limitations of single‐cell mass cytometry for studying human microglia function. Brain Pathology. 2020 Nov;30(6):1178-91
  4. Choi YH, Kim JK. Dissecting cellular heterogeneity using single-cell RNA sequencing. Molecules and cells. 2019 Mar 31;42(3):189.
  5. Gohil SH, Iorgulescu JB, Braun DA, Keskin DB, Livak KJ. Applying high-dimensional single-cell technologies to the analysis of cancer immunotherapy. Nature Reviews Clinical Oncology. 2021 Apr;18(4):244-56.
  6. Zhang MJ, Ntranos V, Tse D. Determining sequencing depth in a single-cell RNA-seq experiment. Nature communications. 2020 Feb 7;11(1):1-1.

Flow vs. Spectral vs. Mass Cytometry: What’s the difference?

Immunotherapies are powerful new drugs that harness the immune system’s power to cure cancer, without the side effects of chemotherapy. To design these, drug developers need more powerful tools to understand the complex symphony of immune cells and their functions. But with hundreds of cell types and a long list of important parameters, it can be hard to gather enough data to see the full picture.

For decades, researchers have used cytometry to identify, characterize, and sort immune cells into their many distinct subsets. This technology has come a long way from the original single-parameter instrument, and cytometry has diversified into three detection-based methods: flow, spectral, and mass cytometry. 

We highlight the differences between these three technologies below to help you figure out which best suits your research and therapeutic development needs.

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Flow Cytometry: a workhorse for looking at a handful of markers

The process of flow cytometry involves incubating cells with fluorescent-tagged antibodies against unique cellular protein markers. With careful selection of antibodies and fluorophores, a researcher can detect on average 10-12 unique cellular markers at a time. Using these markers, they can identify a handful of different immune cell types, as well as identify a small number of features about the cell’s activation, function, or memory status. 

One of the traditional flow cytometers biggest assets is speed: it can sort as many as 50,000 cells per second. This speed means that flow cytometry can collect enough cell events to identify even rare cell types. 

Despite its speed, conventional flow cytometry has several major limitations. The biggest limitation is in the number of parameters that can be measured simultaneously. With a limited number of detectors and broad emission spectra of many fluorophores the overlap becomes difficult to distinguish beyond a dozen markers. 

While flow cytometry can collect enough data to detect rare cells, the limited number of markers means it can’t provide much information about those cells. Dividing a single sample across multiple panels is one potential workaround, but it’s not an option for precious samples or those with low cell counts.

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Spectral Flow Cytometry: more markers, but limited customization

Like conventional flow cytometry, spectral flow cytometry uses fluorochrome-conjugated antibodies to label cells. However, instead of measuring the light emission using a single detector for each fluorophore, spectral cytometry captures the full fluorescence spectrum for all fluorophores on each cell. 

The use of full fluorescence spectra also means that researchers can use closely related dyes in large panels. These overlapping spectra are then un-mixed using proprietary software and algorithms. This allows scientists to capture more parameters per experiment, on average about twice as many as traditional flow cytometry with a maximum of 40 parameters .

While full-spectrum flow cytometry can measure more parameters per experiment than conventional flow cytometry, the method still has limitations. Multiplexing to 40 parameters is possible, but it requires extensive panel optimization to achieve. Although spectral unmixing allows fluorophores with similar peak emissions to be used in the same panel, the spectra can still influence each other in the unmixing, and must be carefully validated. While spectral unmixing can accommodate sample autofluorescence, considerations must be made to optimize marker resolution in complex tissues. The algorithms used to perform this spectral unmixing are also something of a black box, making it more challenging to interpret results.

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Mass cytometry: more customization, requires special expertise

Mass cytometry combines the precision of mass spectrometry with the power of flow cytometry by allowing scientists to easily measure 44 different parameters simultaneously without the need for spectral compensation or unmixing. This means scientists can identify 25+ different immune cell subsets and profile cellular activation, exhaustion, and function using a single panel.

Instead of using fluorophores, mass cytometry utilizes metal isotopes to label antibodies. Most platforms use heavy metals as these do not naturally occur in biological systems. Instead of detecting fluorescence spectra, this system uses a time-of-flight mass spectrometer for detection which can quickly and efficiently differentiate isotopes without any loss of signal clarity. Since each metal has a distinct isotopic value instead of a broad spectrum, mass cytometry does not require the compensation or spectral unmixing needed for conventional and full-spectrum flow cytometry, respectively.

The approach does have some drawbacks, including slower throughput than flow cytometry. The cells are also vaporized during acquisition, which prevents the cells from being utilized in downstream analyses. Despite these drawbacks, mass cytometry is quickly becoming the go-to platform for immune profiling and biomarker validation due to its ease of panel customization, high-number of parameters per panel, and sensitive mass spectrometry-based detection method.

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Which Technique is Right For You?

All three techniques are used in both academic and industry labs, occasionally in tandem!

Flow cytometry is a good choice for those interested in profiling a handful of immune cell types or for looking at a handful of markers on a single cell type. 

Spectral cytometry is a good choice for those interested in profiling more cell types and who plan on using the same or similar panels with limited changes between experiments.

Mass cytometry is a good choice for those interested in profiling the full immune landscape, those with limited or precious samples, or those who prefer the ease of panel customization that mass spectrometry-based detection methods can provide.

Where to go for help

At Teiko, our end-to-end platform utilizes mass cytometry as it allows us to easily customize our 35-marker base panels with up to 12 additional custom markers tailored to each customer project. Our team has over a decade of experience in every stage of mass cytometry analysis including panel development, sample processing, computational analysis and data interpretation. Our scientists have also previously worked with conventional flow cytometry and spectral flow cytometry platforms and are available to answer your questions about our experiences with all three.

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  1. Bonilla, D. L., Reinin, G., & Chua, E. (2021). Full spectrum flow cytometry as a powerful technology for cancer immunotherapy research. Frontiers in Molecular Biosciences, 495.
  2. Hartmann, F. J., Babdor, J., Gherardini, P. F., Amir, E. A. D., Jones, K., Sahaf, B., … & Bendall, S. C. (2019). Comprehensive immune monitoring of clinical trials to advance human immunotherapy. Cell reports, 28(3), 819-831.
  3. Herzenberg LA, Parks D, Sahaf B, Perez O, Roederer M, Herzenberg LA. The history and future of the fluorescence activated cell sorter and flow cytometry: a view from Stanford. Clin Chem. 2002 Oct;48(10):1819-27. PMID: 12324512.
  4. Jameson, VJ, Luke, T, Yan, Y, Hind, A, Evrard, M, Man, K, et al. Unlocking autofluorescence in the era of full spectrum analysis: Implications for immunophenotype discovery projects. Cytometry. 2022; 101( 11): 922– 941.
  5. Levine, L. S., Hiam-Galvez, K. J., Marquez, D. M., Tenvooren, I., Madden, M. Z., Contreras, D. C., … & Spitzer, M. H. (2021). Single-cell analysis by mass cytometry reveals metabolic states of early-activated CD8+ T cells during the primary immune response. Immunity, 54(4), 829-844.
  6. Mardi, A., Meidaninikjeh, S., Nikfarjam, S., Majidi Zolbanin, N., & Jafari, R. (2021). Interleukin-1 in COVID-19 Infection: Immunopathogenesis and Possible Therapeutic Perspective. Viral immunology, 34(10), 679-688.
  7. McKinnon, K. M. (2018). Flow cytometry: an overview. Current protocols in immunology, 120(1), 5-1.
  8. Nolan, J. P., & Condello, D. (2013). Spectral flow cytometry. Current protocols in cytometry, 63(1), 1-27.Spitzer, M. H., & Nolan, G. P. (2016). Mass cytometry: single cells, many features. Cell, 165(4), 780-791.