What is Spectral Flow Cytometry and Why Labs Should Use It
July 2, 2024
Flow cytometry has shifted the goalposts of what biomedical research can achieve, enabling widespread, rapid, and reliable assessment of several biomarkers simultaneously using fluorescent signals.
Historically, the implementation of high-dimensional analysis panels has been limited, in part, by the challenges of conventional flow cytometers to distinguish fluorochromes that have similar emission spectra. Furthermore, the spectral overlap between “closely-related” fluorochromes negatively impacts data resolution and subsequently complicates analysis.
By empowering the spectral overlap instead of attempting to eliminate it, spectral flow cytometry has emerged as a powerful alternative to conventional approaches.
But what is spectral flow cytometry, and why should labs embrace it?
What is spectral flow cytometry?
Both conventional and spectral flow cytometry use fluorescently labeled antibodies to detect specific markers or molecules on individual cells.
Unlike conventional, which detects broad overlapping peaks, spectral flow cytometry analyzes the entire spectrum of each fluorochrome used during an assay through spectral unmixing.
Spectral unmixing is the name given to the computerized process that allows the deconvolution of spectral signatures using complex mathematical algorithms to differentiate between fluorophores based on their unique spectral profiles. By leveraging their full-spectrum signatures, spectral flow cytometry can identify and quantify fluorescent signals even if they have substantial overlapping emission spectra, thereby allowing more flexibility in panel design and higher dimensionality of analysis.
Thus, analysts can identify and quantify over 40 fluorochromes simultaneously (compared to less than 30 markers for conventional cytometry ) for more detailed information about cell populations and biomarkers in a single sample.
Spectral flow cytometry can also help improve the resolution and accuracy of measurements whose resolutions are impacted by cellular autofluorescence. Indeed, spectral flow cytometry allows the identification of an autofluorescence signature from unstained samples which can subsequently be subtracted from the total fluorescence of stained samples.
Cytek® is the current market leader in the field of spectral flow cytometry with their Aurora instrument.
Key features of spectral and conventional flow cytometry
| Feature | Spectral Flow Cytometry | Conventional Flow Cytometry |
|---|---|---|
| Detection Method | Full spectrum emission capture | Discrete bandwidth with a single detector per fluorochrome |
| Fluorochrome Overlap Differentiation | Spectral unmixing | Compensation |
| Autofluorescence Handling | Yes, extracted as another color | Limited, through the use of calculation tools |
| Number of Detectable Fluorochromes | 40+ | Up to 30 |
| Flexibility in Fluorophore Selection | High, due to spectral signature analysis | Limited by optical filters |
Practical Applications of Spectral Flow Cytometry
Spectral flow cytometry continues to gain traction within the flow cytometry community, as both instruments and services become increasingly accessible.
Talks at CYTO 2023 highlighted three emerging areas for this approach:
1. Lung analysis
Lung is a very challenging tissue to analyze, owing to cellular complexity and high autofluorescence. Kewal Asosingh, Cleveland Clinic, highlighted how spectral flow cytometry handled autofluorescence and helped identify cellular subsets associated with impaired lung function in asthma.[i]
The study also showed a strong correlation between a decreased lung function and eosinophil — something more commonly thought to be associated with neutrophils.
2. Global clinical trials
Spectral flow cytometry provides detailed insights into immune cell identification, drug kinetics, and biomarker characterization.
Global standardization across laboratories ensuring consistency and high-quality data generation is a current need for this growing market. Standardized workflow achieved by several groups has shown spectral flow cytometry’s utility in global clinical trials[ii], offering deeper immune profiling.
3. High parameter analysis
A combination of an increasing number of dyes and advances in spectral technology opens new avenues for panel development.
As an example, Sandrine Schmutz, Institut Pasteur, reported a panel of 42 fluorescent markers, enabling deep phenotyping of most subsets of hematopoietic cells.[ii]
Deeper Insights into Complex Samples with Spectral Flow Cytometry
Spectral flow cytometry has several advantages over traditional analytical methods, offering a higher number of parameters and autofluorescence handling. Even small panels usually reserved for conventional flow cytometry could see their resolution increased with spectral flow cytometry.
CellCarta offers a broad range of services and biomarker expertise for deeper insights into your studies.
Contact the team to speak to an expert about how to leverage spectral flow cytometry into your clinical programs.
About the author:
Martin Turcotte (PhD) is a Senior Scientist at CellCarta, specializing in cytometry assay development. He led the implementation of the CYTEK Aurora spectral flow cytometry platform at the Montreal site. Martin has over 5 years of experience in development and validation of flow-based assays. He studied biopharmaceutical sciences and obtained a PhD in immuno-oncology.
[i] Asosingh, K. Four-dimensional functional pulmonary imaging in combination with high-dimensional flow cytometry identifies cellular subsets associated with impaired lung function in asthma (talk), CYTO 2023 (2023).
[ii] Schmutz, S. Full Spectrum Spectral Cytometry: evolution & new features for high parameter analysis (talk), CYTO 2023 (2023).
What is Spectral Flow Cytometry and Why Labs Should Use It
July 2, 2024
Flow cytometry has shifted the goalposts of what biomedical research can achieve, enabling widespread, rapid, and reliable assessment of several biomarkers simultaneously using fluorescent signals.
Historically, the implementation of high-dimensional analysis panels has been limited, in part, by the challenges of conventional flow cytometers to distinguish fluorochromes that have similar emission spectra. Furthermore, the spectral overlap between “closely-related” fluorochromes negatively impacts data resolution and subsequently complicates analysis.
By empowering the spectral overlap instead of attempting to eliminate it, spectral flow cytometry has emerged as a powerful alternative to conventional approaches.
But what is spectral flow cytometry, and why should labs embrace it?
What is spectral flow cytometry?
Both conventional and spectral flow cytometry use fluorescently labeled antibodies to detect specific markers or molecules on individual cells.
Unlike conventional, which detects broad overlapping peaks, spectral flow cytometry analyzes the entire spectrum of each fluorochrome used during an assay through spectral unmixing.
Spectral unmixing is the name given to the computerized process that allows the deconvolution of spectral signatures using complex mathematical algorithms to differentiate between fluorophores based on their unique spectral profiles. By leveraging their full-spectrum signatures, spectral flow cytometry can identify and quantify fluorescent signals even if they have substantial overlapping emission spectra, thereby allowing more flexibility in panel design and higher dimensionality of analysis.
Thus, analysts can identify and quantify over 40 fluorochromes simultaneously (compared to less than 30 markers for conventional cytometry ) for more detailed information about cell populations and biomarkers in a single sample.
Spectral flow cytometry can also help improve the resolution and accuracy of measurements whose resolutions are impacted by cellular autofluorescence. Indeed, spectral flow cytometry allows the identification of an autofluorescence signature from unstained samples which can subsequently be subtracted from the total fluorescence of stained samples.
Cytek® is the current market leader in the field of spectral flow cytometry with their Aurora instrument.
Key features of spectral and conventional flow cytometry
| Feature | Spectral Flow Cytometry | Conventional Flow Cytometry |
|---|---|---|
| Detection Method | Full spectrum emission capture | Discrete bandwidth with a single detector per fluorochrome |
| Fluorochrome Overlap Differentiation | Spectral unmixing | Compensation |
| Autofluorescence Handling | Yes, extracted as another color | Limited, through the use of calculation tools |
| Number of Detectable Fluorochromes | 40+ | Up to 30 |
| Flexibility in Fluorophore Selection | High, due to spectral signature analysis | Limited by optical filters |
Practical Applications of Spectral Flow Cytometry
Spectral flow cytometry continues to gain traction within the flow cytometry community, as both instruments and services become increasingly accessible.
Talks at CYTO 2023 highlighted three emerging areas for this approach:
1. Lung analysis
Lung is a very challenging tissue to analyze, owing to cellular complexity and high autofluorescence. Kewal Asosingh, Cleveland Clinic, highlighted how spectral flow cytometry handled autofluorescence and helped identify cellular subsets associated with impaired lung function in asthma.[i]
The study also showed a strong correlation between a decreased lung function and eosinophil — something more commonly thought to be associated with neutrophils.
2. Global clinical trials
Spectral flow cytometry provides detailed insights into immune cell identification, drug kinetics, and biomarker characterization.
Global standardization across laboratories ensuring consistency and high-quality data generation is a current need for this growing market. Standardized workflow achieved by several groups has shown spectral flow cytometry’s utility in global clinical trials[ii], offering deeper immune profiling.
3. High parameter analysis
A combination of an increasing number of dyes and advances in spectral technology opens new avenues for panel development.
As an example, Sandrine Schmutz, Institut Pasteur, reported a panel of 42 fluorescent markers, enabling deep phenotyping of most subsets of hematopoietic cells.[ii]
Deeper Insights into Complex Samples with Spectral Flow Cytometry
Spectral flow cytometry has several advantages over traditional analytical methods, offering a higher number of parameters and autofluorescence handling. Even small panels usually reserved for conventional flow cytometry could see their resolution increased with spectral flow cytometry.
CellCarta offers a broad range of services and biomarker expertise for deeper insights into your studies.
Contact the team to speak to an expert about how to leverage spectral flow cytometry into your clinical programs.
About the author:
Martin Turcotte (PhD) is a Senior Scientist at CellCarta, specializing in cytometry assay development. He led the implementation of the CYTEK Aurora spectral flow cytometry platform at the Montreal site. Martin has over 5 years of experience in development and validation of flow-based assays. He studied biopharmaceutical sciences and obtained a PhD in immuno-oncology.
[i] Asosingh, K. Four-dimensional functional pulmonary imaging in combination with high-dimensional flow cytometry identifies cellular subsets associated with impaired lung function in asthma (talk), CYTO 2023 (2023).
[ii] Schmutz, S. Full Spectrum Spectral Cytometry: evolution & new features for high parameter analysis (talk), CYTO 2023 (2023).
Global Instrument Alignment with Lyophilized BD™ CompBeads
May 3, 2024
CellCarta’s poster presents a robust method for multi-site instrument alignment using lyophilized BD™ CompBeads to ensure consistency in flow cytometry data across global locations. This method addresses the challenge of variability in mean fluorescence intensity (MFI) readouts by utilizing lyophilized beads that are stable at room temperature for 18 months.
The study confirmed that this approach allows for reliable data transfer and alignment of flow cytometric assays across different sites, reducing operator variability and shipping needs. The method was tested for stability, precision, and accuracy, demonstrating that it can maintain consistent performance over time and across various fluorochromes.
View the poster:
Multi-Site Instrument Alignment Using Lyophilized BD™ CompBeads
Global Instrument Alignment with Lyophilized BD™ CompBeads
May 3, 2024
CellCarta’s poster presents a robust method for multi-site instrument alignment using lyophilized BD™ CompBeads to ensure consistency in flow cytometry data across global locations. This method addresses the challenge of variability in mean fluorescence intensity (MFI) readouts by utilizing lyophilized beads that are stable at room temperature for 18 months.
The study confirmed that this approach allows for reliable data transfer and alignment of flow cytometric assays across different sites, reducing operator variability and shipping needs. The method was tested for stability, precision, and accuracy, demonstrating that it can maintain consistent performance over time and across various fluorochromes.
View the poster:
Multi-Site Instrument Alignment Using Lyophilized BD™ CompBeads
Enhance Flow Cytometry with Automatic Gating Tools
November 15, 2023
Gating can be one of the more time-consuming parts of cytometry analysis. When gate positions need to be changed for every sample in an experiment, it can take orders of magnitude longer.
Two automatic tools built into CellEngine, CellCarta’s cytometry analysis platform, can be leveraged to increase speed and efficiency:
- Automatic gate tailoring uses a machine learning algorithm to adjust gate placement. After defining a population hierarchy, select one or more gates to be adjusted to fit each sample’s data automatically. A set of manually gated reference files provide a model for the algorithm to position gates in the rest of the data. When faced with datasets with high variability, automatic gate tailoring is one of the most powerful tools to expedite analysis.
- Automatic percentile gating precisely moves a gate vertex or edge to a specified percentile of one or more control files. For example, you can move a range gate to the 99th percentile of an unstimulated or isotype control. This approach facilitates comparative analysis of samples; for example, when comparing normalized outcomes donor-by-donor.
In order to streamline the gating process most efficiently, there are several important points to remember when using these tools.
1 – Understanding Automatic Gating Tools in Flow Cytometry with CellEngine
Experimental design is a fundamental factor for successful cytometry analysis. Autogating in CellEngine streamlines the analysis process, but the best results come from well-designed trials and high-quality data.
Taking time to design clear objectives, include appropriate controls, and optimize experimental conditions ensures that the results from autogating are meaningful and useful.
For example, including an unstimulated control in an intracellular cytokine or phospho-flow panel provides an ideal control for using percentile gating.
Picking dyes that provide good separation of populations improves automatic gate tailoring’s performance. By creating a well-designed foundation, researchers can extract meaningful insights from complex datasets.
2 – Quality control is indispensable for Flow Cytometry Analysis
One of the most important aspects of high-quality analysis is high-quality data. To generate high-quality data, build quality control processes into your workflows to ensure reliability and accuracy, and identify issues promptly.
For example, verify staining via positive controls, use beads to calibrate and verify cytometer performance, and reduce technical variability as much as possible.
This maximizes the potential from each dataset, whether you use automatic tools in analysis or not.
3 – How to Implement Automatic Gating in CellEngine
Automatic gate tailoring uses one or more files as a reference for setting gate positions. Training data must be representative of the overall dataset, and carefully gated for best results.
An experiment with different cell types, disease states, or experimental conditions should include examples of all of those in the training data. This enhances the algorithm’s ability to properly adjust gates.
Consistent choices when gating the training data will also reduce variability.
Combining meticulous experiment design, trustworthy data, and carefully curated training data with automated tools increases efficiency and accuracy.
By incorporating these best practices, researchers can harness the full power of autogating tools to optimize their workflows.
If you’re interested in trying autogating, you can sign up for a free two-month trial of CellEngine at https://cellengine.com/.
About the author:
Anita Ray is a technical application specialist with CellEngine. She has ten years of experience in cytometry and worked in translational immuno-oncology prior to joining the CellEngine team.
Enhance Flow Cytometry with Automatic Gating Tools
November 15, 2023
Gating can be one of the more time-consuming parts of cytometry analysis. When gate positions need to be changed for every sample in an experiment, it can take orders of magnitude longer.
Two automatic tools built into CellEngine, CellCarta’s cytometry analysis platform, can be leveraged to increase speed and efficiency:
- Automatic gate tailoring uses a machine learning algorithm to adjust gate placement. After defining a population hierarchy, select one or more gates to be adjusted to fit each sample’s data automatically. A set of manually gated reference files provide a model for the algorithm to position gates in the rest of the data. When faced with datasets with high variability, automatic gate tailoring is one of the most powerful tools to expedite analysis.
- Automatic percentile gating precisely moves a gate vertex or edge to a specified percentile of one or more control files. For example, you can move a range gate to the 99th percentile of an unstimulated or isotype control. This approach facilitates comparative analysis of samples; for example, when comparing normalized outcomes donor-by-donor.
In order to streamline the gating process most efficiently, there are several important points to remember when using these tools.
1 – Understanding Automatic Gating Tools in Flow Cytometry with CellEngine
Experimental design is a fundamental factor for successful cytometry analysis. Autogating in CellEngine streamlines the analysis process, but the best results come from well-designed trials and high-quality data.
Taking time to design clear objectives, include appropriate controls, and optimize experimental conditions ensures that the results from autogating are meaningful and useful.
For example, including an unstimulated control in an intracellular cytokine or phospho-flow panel provides an ideal control for using percentile gating.
Picking dyes that provide good separation of populations improves automatic gate tailoring’s performance. By creating a well-designed foundation, researchers can extract meaningful insights from complex datasets.
2 – Quality control is indispensable for Flow Cytometry Analysis
One of the most important aspects of high-quality analysis is high-quality data. To generate high-quality data, build quality control processes into your workflows to ensure reliability and accuracy, and identify issues promptly.
For example, verify staining via positive controls, use beads to calibrate and verify cytometer performance, and reduce technical variability as much as possible.
This maximizes the potential from each dataset, whether you use automatic tools in analysis or not.
3 – How to Implement Automatic Gating in CellEngine
Automatic gate tailoring uses one or more files as a reference for setting gate positions. Training data must be representative of the overall dataset, and carefully gated for best results.
An experiment with different cell types, disease states, or experimental conditions should include examples of all of those in the training data. This enhances the algorithm’s ability to properly adjust gates.
Consistent choices when gating the training data will also reduce variability.
Combining meticulous experiment design, trustworthy data, and carefully curated training data with automated tools increases efficiency and accuracy.
By incorporating these best practices, researchers can harness the full power of autogating tools to optimize their workflows.
If you’re interested in trying autogating, you can sign up for a free two-month trial of CellEngine at https://cellengine.com/.
About the author:
Anita Ray is a technical application specialist with CellEngine. She has ten years of experience in cytometry and worked in translational immuno-oncology prior to joining the CellEngine team.
CellEngine Auto-Gate: Accurate Gating in COVID Study
May 22, 2023
CellCarta’s poster demonstrates the effectiveness of CellEngine’s automatic gate adjustment tool in cytometry analysis for a COVID-19 longitudinal clinical study. The tool uses a machine-learning algorithm trained on manually gated samples to automatically gate large datasets, significantly reducing analysis time while maintaining accuracy.
The study focused on two markers, CCR5 and perforin, known for high variability, and showed that the tool produced results highly similar to manual gating, with a time reduction of 69.3% for perforin and 88.5% for CCR5
View the full poster:
As presented in CYTO2023
Autogating
- Closely correlates with manual gating
- Reduces analysis time by 69% to 89%
- Adjusts gates for thousands of files in seconds
- Full deterministic operation based on a few manually gated files
CellEngine Auto-Gate: Accurate Gating in COVID Study
May 22, 2023
CellCarta’s poster demonstrates the effectiveness of CellEngine’s automatic gate adjustment tool in cytometry analysis for a COVID-19 longitudinal clinical study. The tool uses a machine-learning algorithm trained on manually gated samples to automatically gate large datasets, significantly reducing analysis time while maintaining accuracy.
The study focused on two markers, CCR5 and perforin, known for high variability, and showed that the tool produced results highly similar to manual gating, with a time reduction of 69.3% for perforin and 88.5% for CCR5
View the full poster:
As presented in CYTO2023
Autogating
- Closely correlates with manual gating
- Reduces analysis time by 69% to 89%
- Adjusts gates for thousands of files in seconds
- Full deterministic operation based on a few manually gated files
Next-Generation Flow Cytometry Analysis with CellEngine™
January 31, 2023
Next-Generation Flow Cytometry Analysis with CellEngine™
January 31, 2023