Immunophenotyping challenging tissue types using high-dimensional full spectrum flow cytometry.

Technological advancements in fluorescence flow cytometry and an ever-expanding understanding of the complexity of the immune system, have led to the development of large flow cytometry panels, reaching up to 40 markers at the single-cell level. Full spectrum flow cytometry, that measures the full emission range of all the fluorophores present in the panel instead of only the emission peaks is now routinely used in many laboratories internationally, and the demand for this technology is rapidly increasing. With the capacity to use larger and more complex staining panels, optimized protocols are required for the best panel design, panel validation and high-dimensional data analysis outcomes. In addition, for ex vivo experiments, tissue preparation methods for single-cell analysis should also be optimized to ensure that samples are of the highest quality and are truly representative of tissues in situ. Here we provide optimized step-by-step protocols for full spectrum flow cytometry panel design, tissue digestion and panel optimization to facilitate the analysis of challenging tissue types.

Panel Design and Optimization for Full Spectrum Flow Cytometry.

Technological advancements in fluorescence flow cytometry and an ever-expanding understanding of the complexity of the immune system have led to the development of large flow cytometry panels, reaching up to 40 markers at the single-cell level. Full spectrum flow cytometry, which measures the full emission range of all the fluorophores present in the panel instead of only the emission peaks, is now routinely used in laboratories around the world, and the demand for this technology is rapidly increasing. With the ability to use larger and more complex staining panels, optimized protocols are vital for achieving the best panel design, panel optimization, and high-dimensional data analysis outcomes. In addition, a better understanding of how to fully characterize the autofluorescence of the sample, coupled with an intelligent panel design approach, allows improved marker resolution on highly autofluorescent tissues or cells. Here, we provide optimized step-by-step protocols for full spectrum flow cytometry, covering panel design and optimization, autofluorescence evaluation and strategy selection, and methods for performing longitudinal studies.

Skin Whole-Mount Immunofluorescent Staining Protocol, 3D Visualization, and Spatial Image Analysis.

The use of polychromatic immunofluorescent staining on whole-mount skin enables cell type characterization and aids in the delineation of the physiological and immunological strategies used by the skin to combat pathogens. Using whole-mount skin for polychromatic immunofluorescent staining removes the need for histological sectioning and enables the visualization of anatomical structures and immune cell types in three dimensions. Here we present a detailed protocol for immunostaining with fluorescence-conjugated primary antibodies in whole-mount skin to reveal structural landmarks and specific immune cell types using confocal laser scanning microscopy (CLSM) (Basic Protocol 1). The optimized staining panel reveals structural features such as blood vessels (CD31 antibody) and the lymphatic network (LYVE-1 antibody), in combination with MHCII antibodies for antigen-presenting cells (APCs), CD64 for macrophages and monocytes, CD103 for dendritic epidermal T cells (DETC), and CD326 for Langerhans cells (LC). Basic Protocol 2 describes image visualization pipelines using open-source software (ImageJ/FIJI), enabling four visualization options (z-projections, orthogonal views, 3D visualization, and animation). Basic Protocol 3 describes a quantitative analysis pipeline using CellProfiler to characterize the spatial relationship between cell types using mathematical indices such as Spatial Distribution Index (SDI), Neighborhood Frequency (NF), and Normalized Median Evenness (NME). These protocols will enable researchers to stain, record, analyze, and interpret data from whole-mount skin using commercially available reagents in a CLSM-equipped laboratory and freely available analysis software. © 2023 Wiley Periodicals LLC. Basic Protocol 1: Immunofluorescent staining and imaging for whole-mount mouse skin Basic Protocol 2: File rendering and visualization using FIJI Basic Protocol 3: Spatial image analysis using CellProfiler.

Ensuring Full Spectrum Flow Cytometry Data Quality for High-Dimensional Data Analysis.

Full spectrum flow cytometry (FSFC) allows for the analysis of more than 40 parameters at the single-cell level. Compared to the practice of manual gating, high-dimensional data analysis can be used to fully explore single-cell datasets and reduce analysis time. As panel size and complexity increases so too does the detail and time required to prepare and validate the quality of the resulting data for use in downstream high-dimensional data analyses. To ensure data analysis algorithms can be used efficiently and to avoid artifacts, some important steps should be considered. These include data cleaning (such as eliminating variable signal change over time, removing cell doublets, and antibody aggregates), proper unmixing of full spectrum data, ensuring correct scale transformation, and correcting for batch effects. We have developed a methodical step-by-step protocol to prepare full spectrum high-dimensional data for use with high-dimensional data analyses, with a focus on visualizing the impact of each step of data preparation using dimensionality reduction algorithms. Application of our workflow will aid FSFC users in their efforts to apply quality control methods to their datasets for use in high-dimensional analysis, and help them to obtain valid and reproducible results. © 2023 Wiley Periodicals LLC. Basic Protocol 1: Data cleaning Basic Protocol 2: Validating the quality of unmixing Basic Protocol 3: Data scaling Basic Protocol 4: Batch-to-batch normalization.

A guideline for the appropriate recognition of shared resource laboratories in publication.

The issue of what level of contribution warrants authorship, determining a fair order of authors and when and whom to acknowledge in publications is often a cause of debate, and in some instances, has also been a focus of conflict at certain institutions. Shared resource laboratories (SRLs) play a fundamental role in supporting publications, and SRL staff scientists can contribute to numerous areas such as experimental design, sample preparation, data acquisition, data analysis and manuscript drafting and review. However, SRL staff scientists are often unfairly omitted from the author list. To avoid SRLs and SRL staff scientist contributions going unnoticed, the authors have formulated a set of guidelines to aid in the conceptualization and recognition of the technical and intellectual contributions of SRLs. As a better understanding of the role SRL staff scientists play in the achievement of the scientific lead's experimental aims will foster a positive feedback loop, where acknowledgements can lead to more support and funding for SRLs and more engaged SRL staff capable of supporting discoveries and technological innovations that underpin major advancements in the field of life sciences.

Implementing High Dimensional Reduction Analysis on Histocytometric Data.

In a previous protocol article, we demonstrated construction of a histocytometry pipeline that is capable of both segmenting highly aggregated cell populations and retaining the original intensity data range of the input microscopy images. In the protocol presented here, using the output from the aforementioned article, we demonstrate how to phenotype the data using the high dimensional reduction analysis technique optimized t-distributed stochastic neighbor embedding (opt-t-SNE) and compare it to traditional manual gating. Additionally, we present a protocol illustrating the advantage of the inclusion of cell junction/membrane markers for accurately segmenting highly aggregated cell populations in ilastik. © 2022 Wiley Periodicals LLC. Basic Protocol 1: Phenotyping lymph node populations using manual gating Basic Protocol 2: Phenotyping lymph node populations using t-SNE dimensional reduction Support Protocol: ilastik segmentation using a pan marker.

Creation of High-Dimensional Reduction Analysis-Compatible Histocytometry Files from Images of Densely-Packed Cells and/or Variable Stain Intensity.

The power of high-dimensional reduction techniques using multiparameter images has been demonstrated across a variety of different publications. Recently, we published an end-to-end low-cost GUI-based protocol for performing histocytometric spatial analysis on images derived from the most common microscope image formats. However, this protocol is limited by the normalized marker intensity outputs and the difficulty in processing images of highly aggregated and/or exceptionally heterogenous cell populations. Here we present the basic protocols required to construct an advanced histocytometric data file using only freeware. This data file is compatible with images containing cell nuclei clusters that are difficult to segment, and results in histocytometry files retaining the original marker intensity values of the microscopic images they were derived from. This is especially useful in cells that are phenotyped based on relative marker expression levels. Histocytometry data files produced by these protocols are compatible with high-dimensional reduction analysis using marker intensity data, such as tSNEs. This methodology is showcased using stitched microscopic images of murine lymph nodes, complex organs with highly aggregated heterogenous cell populations, that are typically difficult to segment. © 2022 Wiley Periodicals LLC. Basic Protocol 1: Image preprocessing and generation of nuclei marker probability maps Basic Protocol 2: Cell segmentation using ilastik-derived probability maps Basic Protocol 3: Generation of histocytometric .fcs files.

How to Build an Image-Processing Pipeline for Automating Multiparameter Histocytometry Analysis.

Until relatively recently, analysis of imaging data has been primarily quantitative and limited to 3-4 markers. The advancement of various technologies overcoming this marker limitation provided the capability of analyzing multiparameter imaging data down to the single cell level, termed histocytometry. Currently, most published end-to-end histocytometric analysis of imaging data is performed using expensive commercial programs or freely available analysis packages that require significant knowledge of programming languages for execution. Here we present a protocol that performs cell segmentation, phenotyping and spatial analysis, using software with easy-to-use GUIs (graphical user interfaces). These protocols allow the user to derive spatial and phenotypical data for the analysis of multiparameter microscopic images from most imaging platforms in a low-cost manner. © 2022 Wiley Periodicals LLC. Basic Protocol 1: Cell Segmentation and generation of histocytometric .csv file Basic Protocol 2: Phenotyping of cell populations Basic Protocol 3: Spatial relationship analyses of phenotyped populations Support Protocol 1: Nuclei Segmentation Accuracy Test Support Protocol 2: Correcting y-axis Inversion of Histocytometry Data Relative to Original Image File.

Panel Optimization for High-Dimensional Immunophenotyping Assays Using Full-Spectrum Flow Cytometry.

Technological advancements in fluorescence flow cytometry and an ever-expanding understanding of the complexity of the immune system have led to the development of large flow cytometry panels reaching up to 43 colors at the single-cell level. However, as panel size and complexity increase, so too does the detail involved in designing and optimizing successful high-quality panels fit for downstream high-dimensional data analysis. In contrast to conventional flow cytometers, full-spectrum flow cytometers measure the entire emission spectrum of each fluorophore across all lasers. This allows for fluorophores with very similar emission maxima but unique overall spectral fingerprints to be used in conjunction, enabling relatively straightforward design of larger panels. Although a protocol for best practices in full-spectrum flow cytometry panel design has been published, there is still a knowledge gap in going from the theoretically designed panel to the necessary steps required for panel optimization. Here, we aim to guide users through the theory of optimizing a high-dimensional full-spectrum flow cytometry panel for immunophenotyping using comprehensive step-by-step protocols. These protocols can also be used to troubleshoot panels when issues arise. A practical application of this approach is exemplified with a 24-color panel designed for identification of conventional T-cell subsets in human peripheral blood. © 2021 Malaghan Institute of Medical Research, Cytek Biosciences. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Preparation and evaluation of optimal spectral reference controls Support Protocol 1: Antibody titration Support Protocol 2: Changing instrument settings Basic Protocol 2: Unmixing evaluation of fully stained sample Basic Protocol 3: Evaluation of marker resolution Support Protocol 3: Managing heterogeneous autofluorescence Basic Protocol 4: Assessment of data quality using expert gating and dimensionality reduction algorithms.

High-Dimensional Image Analysis using Histocytometry.

Histocytometry is a technique for processing multiparameter microscopy images using computational approaches to identify and quantify cellular phenotypes. It allows for spatial analyses of cellular phenotypes in relation to each other and within defined spatial regions. The benefit of this technique over manual annotation and characterization of cells is a high degree of automation/throughput, significantly decreased user bias, and increased reproducibility. Recently, an increase in freely available software amenable to or deliberately designed for histocytometry has resulted in these complex analyses being available to a broader base of users who have amassed multi-component microscopic imaging data. This article provides an overview of a histocytometry pipeline, focusing on the strategic planning and software requirements to allow readers to perform cell segmentation, phenotyping, and spatial analyses to advance their research outputs. © 2021 Wiley Periodicals LLC.

Remote Training of SRL Users and Staff in a Global Pandemic.

The impact of the COVID-19 pandemic on training and Shared Resource Laboratory (SRL) operations such as staffing, facility access, and social distancing, has affected facilities around the globe to different degrees based on restrictions set by various geographical and institutional settings. With these restrictions come unique challenges regarding user and staff training and education, for both theory and practice. Most notably, limitations in facility access, occupancy, staffing availability, network restrictions and trainee engagement call for innovative solutions for training when traditional in-person options are not feasible. Through the use of remote access tools and prerecorded educational and training materials, SRLs are able to overcome these obstacles. Here, we focus on readily available technologies and general guidelines that SRLs in different environments can use for remote cytometry training and education, while highlighting key obstacles that still remain. Although SRLs may face initial struggles in transitioning trainings to a virtual format, remote technologies provide unique opportunities to advance current training programs. © 2020 International Society for Advancement of Cytometry.

High-Dimensional Data Analysis Algorithms Yield Comparable Results for Mass Cytometry and Spectral Flow Cytometry Data.

The arrival of mass cytometry (MC) and, more recently, spectral flow cytometry (SFC) has revolutionized the study of cellular, functional and phenotypic diversity, significantly increasing the number of characteristics measurable at the single-cell level. As a consequence, new computational techniques such as dimensionality reduction and/or clustering algorithms are necessary to analyze, clean, visualize, and interpret these high-dimensional data sets. In this small comparison study, we investigated splenocytes from the same sample by either MC or SFC and compared both high-dimensional data sets using expert gating, t-distributed stochastic neighbor embedding (t-SNE), uniform manifold approximation and projection (UMAP) analysis and FlowSOM. When we downsampled each data set to their equivalent cell numbers and parameters, our analysis yielded highly comparable results. Differences between the data sets only became apparent when the maximum number of parameters in each data set were assessed, due to differences in the number of recorded events or the maximum number of assessed parameters. Overall, our small comparison study suggests that mass cytometry and spectral flow cytometry both yield comparable results when analyzed manually or by high-dimensional clustering or dimensionality reduction algorithms such as t-SNE, UMAP, or FlowSOM. However, large scale studies combined with an in-depth technical analysis will be needed to assess differences between these technologies in more detail. © 2020 International Society for Advancement of Cytometry.

Panel Design and Optimization for High-Dimensional Immunophenotyping Assays Using Spectral Flow Cytometry.

Technological advances in fluorescence flow cytometry and an ever-expanding understanding of the complexity of the immune system have led to the development of large (20+ parameters) flow cytometry panels. However, as panel complexity and size increase, so does the difficulty involved in designing a high-quality panel, accessing the instrumentation capable of accommodating large numbers of parameters, and analyzing such high-dimensional data. A recent advancement is spectral flow cytometry, which in contrast to conventional flow cytometry distinguishes the full emission spectrum of each fluorophore across all lasers, rather than identifying only the peak of emission. Fluorophores with a similar emission maximum but distinct off-peak signatures can therefore be accommodated within the same flow cytometry panel, allowing greater flexibility in terms of panel design and fluorophore detection. Here, we highlight the specific characteristics of spectral flow cytometry and aim to guide users through the process of building, designing, and optimizing high-dimensional spectral flow cytometry panels using a comprehensive step-by-step protocol. Special considerations are also given for using highly overlapping dyes, and a logical selection process for optimal marker-fluorophore assignment is provided. © 2020 by John Wiley & Sons, Inc.

High-dimensional analysis of intestinal immune cells during helminth infection.

Single cell isolation from helminth-infected murine intestines has been notoriously difficult, due to the strong anti-parasite type 2 immune responses that drive mucus production, tissue remodeling and immune cell infiltration. Through the systematic optimization of a standard intestinal digestion protocol, we were able to successfully isolate millions of immune cells from the heavily infected duodenum. To validate that these cells gave an accurate representation of intestinal immune responses, we analyzed them using a high-dimensional spectral flow cytometry panel and confirmed our findings by confocal microscopy. Our cell isolation protocol and high-dimensional analysis allowed us to identify many known hallmarks of anti-parasite immune responses throughout the entire course of helminth infection and has the potential to accelerate single-cell discoveries of local helminth immune responses that have previously been unfeasible.

Parasitic worms known as helminths represent an important health problem in large parts of Africa, South America and Asia. Once their larvae enter the body, they head to the gut where they mature into adults and start laying eggs. In areas with poor sanitation, these may then get passed on to other individuals. To defend the body, the immune system sends large numbers of immune cells to the gut, but it usually struggles to eliminate the parasites. Without deworming medication, the infection can last for many years. Scientists study helminth infections in the laboratory by using worms that naturally infect mice. Understanding exactly how the immune system responds to the infection is essential to grasp why it fails to clear the worms. However, it is difficult to extract immune cells from an infected gut, as the infection creates strong local responses ­ such as an intense 'slime' production to try to flush out the worms. The standard procedure to obtain immune cells from the gut consists of three steps: collecting a gut segment and washing it, stripping away the surface layers with chemicals, and finally using enzymes to digest the tissues, which are then filtered to obtain individual cells. However, this protocol is not able to extract cells during infection. Ferrer-Font et al. therefore methodically refined every step of this method, and finally succeeded in obtaining millions of immune cells from infected guts. For the first time, these cells could then be studied and identified using a new technology called spectral flow cytometry. Over 40 immune cell types were followed throughout the course of infection, revealing that many 'first responders' immune cells were recruited to the gut early on, when the worms were still larvae. However, these cells disappeared once the worms developed into adults. These findings were confirmed by microscopy, which also showed that the first responder cells were found around the developing larvae, likely attacking them. When the adult worms developed, these cells were replaced by other immune cells, which also decreased the longer the worms were present in the gut. This new extraction process established by Ferrer-Font et al. can also be paired with other technologies that can, for example, reveal which genes are turned on in individual cells. This could help map out exactly how the body fights helminth infections, and how to improve this response. The method could also be useful to extract immune cells from the gut in other challenging scenarios, such food allergies or inflammatory bowel disorders.

Simultaneous Polychromatic Immunofluorescent Staining of Tissue Sections and Consecutive Imaging of up to Seven Parameters by Standard Confocal Microscopy.

Confocal microscopy has been an important imaging tool for life scientists for over 20 years. Early techniques focused on indirect staining processes that involved staining with an unconjugated primary antibody, followed by incubation with a secondary fluorescent antibody that would reveal and amplify the signal of the primary antibody. With more and more directly conjugated fluorescent primary antibodies becoming commercially available, staining with multiple fluorescent primary antibodies is now more frequent. To date, staining with up to three primary antibodies and a nuclear dye is widely practiced. Here, we describe an important improvement to the standard polychromatic immunofluorescent staining protocol that allows the simultaneous detection of seven fluorescent parameters using a standard confocal laser scanning microscope with four laser lines and four photomultiplier tubes. By incorporating recently available tandem dyes that emit in the blue and violet regions of the visible light spectrum (Brilliant Blue and Brilliant Violet), we were able to differentiate several additional fluorochromes simultaneously. Due to the added complexity of 7-color immunofluorescent imaging, we developed a clear methodology to optimize antibody concentrations and simple guidelines on how to identify and correct non-specific signals. These are detailed in the following protocol. © 2019 by John Wiley & Sons, Inc. Basic Protocol: 7-Color immunofluorescent staining protocol using directly conjugated antibodies Support Protocol 1: Antibody titration protocol Support Protocol 2: Spillover optimization protocol.

Large Particle Sorting to Isolate Live Parasitic Nematode Eggs.

Traditional jet-in-air cell sorters have been designed and optimized to isolate small particles such as mammalian lymphocytes with an average diameter of 10 µm. We discuss the practical considerations of setting up a conventional jet-in-air cell sorter, using a 200-µm nozzle, to isolate the large parasitic nematode eggs of Nippostrongylus brasiliensis, with a maximum size of 60 µm. The eggs were separated based on light scattering properties, no fluorescent dye or molecule was required.

Murine melanoma-infiltrating dendritic cells are defective in antigen presenting function regardless of the presence of CD4CD25 regulatory T cells.

Tumor-infiltrating dendritic cells are often ineffective at presenting tumor-derived antigen in vivo, a defect usually ascribed to the suppressive tumor environment. We investigated the effects of depleting CD4(+)CD25(+) "natural" regulatory T cells (Treg) on the frequency, phenotype and function of total dendritic cell populations in B16.OVA tumors and in tumor-draining lymph nodes. Intraperitoneal injection of the anti-CD25 monoclonal antibody PC61 reduced Treg frequency in blood and tumors, but did not affect the frequency of tumor-infiltrating dendritic cells, or their expression of CD40, CD86 and MHCII. Tumor-infiltrating dendritic cells from PC61-treated or untreated mice induced the proliferation of allogeneic T cells in vitro, but could not induce proliferation of OVA-specific OTI and OTII T cells unless specific peptide antigen was added in culture. Some proliferation of naïve, OVA-specific OTI T cells, but not OTII T cells, was observed in the tumor-draining LN of mice carrying B16.OVA tumors, however, this was not improved by PC61 treatment. Experiments using RAG1(-/-) hosts adoptively transferred with OTI and CD25-depleted OTII cells also failed to show improved OTI and OTII T cell proliferation in vivo compared to C57BL/6 hosts. We conclude that the defective presentation of B16.OVA tumor antigen by tumor-infiltrating dendritic cells and in the tumor-draining lymph node is not due to the presence of "natural" CD4(+)CD25(+) Treg.

Side population is not necessary or sufficient for a cancer stem cell phenotype in glioblastoma multiforme.

There is strong evidence for the existence of cancer stem cells (CSCs) in the aggressive brain tumor glioblastoma multiforme (GBM). These cells have stem-like self-renewal activity and increased tumor initiation capacity and are believed to be responsible for recurrence due to their resistance to therapy. Several techniques have been used to enrich for CSC, including growth in serum-free defined media to induce sphere formation, and isolation of a stem-like cell using exclusion of the fluorescent dye Hoechst 33342, the side population (SP). We show that sphere formation in GBM cell lines and primary GBM cells enriches for a CSC-like phenotype of increased self-renewal gene expression in vitro and increased tumor initiation in vivo. However, the SP was absent from all sphere cultures. Direct isolation of the SP from the GBM lines did not enrich for stem-like activity in vitro, and tumor-initiating activity was lower in sorted SP compared with non-SP and parental cells. Transient exposure to doxorubicin enhanced both CSC and SP frequency. However, doxorubicin treatment altered the cytometric profile and obscured the SP demonstrating the difficulty of identifying SP in cells under stress. Doxorubicin-exposed cells showed a transient increase in SP, but the doxorubicin-SP cells were still not enriched for a stem-like self-renewal phenotype. These data demonstrate that the GBM SP does not necessarily contribute to self-renewal or tumor initiation, key properties of a CSC, and we advise against using SP to enumerate or isolate CSC.