Unlocking autofluorescence in the era of full spectrum analysis: Implications for immunophenotype discovery projects

Unbiased method for spectral analysis of cells with great diversity of autofluorescence spectra

Autofluorescence is an intrinsic feature of cells, caused by the natural emission of light by photo-excitatory molecular content, which can complicate analysis of flow cytometry data. Different cell types have different autofluorescence spectra and, even within one cell type, heterogeneity of autofluorescence spectra can be present, for example, as a consequence of activation status or metabolic changes. By using full spectrum flow cytometry, the emission spectrum of a fluorochrome is captured by a set of photo detectors across a range of wavelengths, creating an unique signature for that fluorochrome. This signature is then used to identify, or unmix, that fluorochrome's unique spectrum from a multicolor sample containing different fluorescent molecules. Importantly, this means that this technology can also be used to identify intrinsic autofluorescence signal of an unstained sample, which can be used for unmixing purposes and to separate the autofluorescence signal from the fluorophore signals. However, this only works if the sample has a singular, relatively homogeneous and bright autofluorescence spectrum. To analyze samples with heterogeneous autofluorescence spectral profiles, we setup an unbiased workflow to more quickly identify differing autofluorescence spectra present in a sample to include as "autofluorescence signatures" during the unmixing of the full stained samples. First, clusters of cells with similar autofluorescence spectra are identified by unbiased dimensional reduction and clustering of unstained cells. Then, unique autofluorescence clusters are determined and are used to improve the unmixing accuracy of the full stained sample. Independent of the intensity of the autofluorescence and immunophenotyping of cell subsets, this unbiased method allows for the identification of most of the distinct autofluorescence spectra present in a sample, leading to less confounding autofluorescence spillover and spread into extrinsic phenotyping markers. Furthermore, this method is equally useful for spectral analysis of different biological samples, including tissue cell suspensions, peripheral blood mononuclear cells, and in vitro cultures of (primary) cells.

Label-Free Assessment of Key Biological Autofluorophores: Material Characteristics and Opportunities for Clinical Applications

Autofluorophores are endogenous fluorescent compounds that naturally occur in the intra and extracellular spaces of all tissues and organs. Most have vital biological functions - like the metabolic cofactors NAD(P)H and FAD+, as well as the structural protein collagen. Others are considered to be waste products - like lipofuscin and advanced glycation end products - which accumulate with age and are associated with cellular dysfunction. Due to their natural fluorescence, these materials have great utility for enabling non-invasive, label-free assays with direct ties to biological function. Numerous technologies, with different advantages and drawbacks, are applied to their assessment, including fluorescence lifetime imaging microscopy, hyperspectral microscopy, and flow cytometry. Here, the applications of label-free autofluorophore assessment are reviewed for clinical and health-research applications, with specific attention to biomaterials, disease detection, surgical guidance, treatment monitoring, and tissue assessment - fields that greatly benefit from non-invasive methodologies capable of continuous, in vivo characterization.

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.

Spatial immune composition of tumor microenvironment in patients with pancreatic cancer

This study examined the composition of the immune microenvironment at different sites within resected pancreas specimens from patients with pancreatic ductal adenocarcinoma (PDAC). Therefore, single-cell suspensions were made from fresh tumor and non-tumorous tissue. Fourteen patients were included from whom twelve PDAC and five non-tumorous samples were obtained. These samples were analyzed with a nineteen marker panel on the Aurora spectral flow cytometer. Furthermore, slides from formalin-fixed paraffine PDACs of eight additional patients were stained with eight markers and analyzed by multispectral imaging. These corresponded to central tumor, periphery of the tumor, i.e., invasive front and resected lymph node and were divided into tumor and adjacent tissue. In the single-cell suspension, a decreased ratio between lymphoid and myeloid cells and between M1 and M2 macrophages was observed in the tumor tissue compared to non-tumorous tissue. Furthermore, an increase in CD169 + macrophages in patients undergoing neoadjuvant therapy was found. Using immunofluorescence, more macrophages compared to T cells were observed, as well as a lower ratio of CD8 to M2 macrophage, a higher ratio of CD4-CD8 T cells and a higher ratio of immune-suppressive cells to pro-inflammatory cells in the PDAC area compared to the adjacent non-tumorous tissue. Finally, there were more immune-suppressive cells in the central tumor area compared to the invasive front. In conclusion, we show a gradient in the immune-suppressive environment in PDAC from most suppressive in the central tumor to least suppressive in distant non-tumorous tissue.

OMIP-095: 40-Color spectral flow cytometry delineates all major leukocyte populations in murine lymphoid tissues

High-dimensional immunoprofiling is essential for studying host response to immunotherapy, infection, and disease in murine model systems. However, the difficulty of multiparameter panel design combined with a lack of existing murine tools has prevented the comprehensive study of all major leukocyte phenotypes in a single assay. Herein, we present a 40-color flow cytometry panel for deep immunophenotyping of murine lymphoid tissues, including the spleen, blood, Peyer's patches, inguinal lymph nodes, bone marrow, and thymus. This panel uses a robust set of surface markers capable of differentiating leukocyte subsets without the use of intracellular staining, thus allowing for the use of cells in downstream functional experiments or multiomic analyses. Our panel classifies T cells, B cells, natural killer cells, innate lymphoid cells, monocytes, macrophages, dendritic cells, basophils, neutrophils, eosinophils, progenitors, and their functional subsets by using a series of co-stimulatory, checkpoint, activation, migration, and maturation markers. This tool has a multitude of systems immunology applications ranging from serial monitoring of circulating blood signatures to complex endpoint analysis, especially in pre-clinical settings where treatments can modulate leukocyte abundance and/or function. Ultimately, this 40-color panel resolves a diverse array of immune cells on the axes of time, tissue, and treatment, filling the niche for a modern tool dedicated to murine immunophenotyping.

Unraveling the potential of 3D bioprinted immunomodulatory materials for regulating macrophage polarization: State-of-the-art in bone and associated tissue regeneration

Macrophage-assisted immunomodulation is an alternative strategy in tissue engineering, wherein the interplay between pro-inflammatory and anti-inflammatory macrophage cells and body cells determines the fate of healing or inflammation. Although several reports have demonstrated that tissue regeneration depends on spatial and temporal regulation of the biophysical or biochemical microenvironment of the biomaterial, the underlying molecular mechanism behind immunomodulation is still under consideration for developing immunomodulatory scaffolds. Currently, most fabricated immunomodulatory platforms reported in the literature show regenerative capabilities of a particular tissue, for example, endogenous tissue (e.g., bone, muscle, heart, kidney, and lungs) or exogenous tissue (e.g., skin and eye). In this review, we briefly introduced the necessity of the 3D immunomodulatory scaffolds and nanomaterials, focusing on material properties and their interaction with macrophages for general readers. This review also provides a comprehensive summary of macrophage origin and taxonomy, their diverse functions, and various signal transduction pathways during biomaterial-macrophage interaction, which is particularly helpful for material scientists and clinicians for developing next-generation immunomodulatory scaffolds. From a clinical standpoint, we briefly discussed the role of 3D biomaterial scaffolds and/or nanomaterial composites for macrophage-assisted tissue engineering with a special focus on bone and associated tissues. Finally, a summary with expert opinion is presented to address the challenges and future necessity of 3D bioprinted immunomodulatory materials for tissue engineering.

A normalized differential sequence feature encoding method based on amino acid sequences

Protein interactions are the foundation of all metabolic activities of cells, such as apoptosis, the immune response, and metabolic pathways. In order to optimize the performance of protein interaction prediction, a coding method based on normalized difference sequence characteristics (NDSF) of amino acid sequences is proposed. By using the positional relationships between amino acids in the sequences and the correlation characteristics between sequence pairs, NDSF is jointly encoded. Using principal component analysis (PCA) and local linear embedding (LLE) dimensionality reduction methods, the coded 174-dimensional human protein sequence vector is extracted using sequence features. This study compares the classification performance of four ensemble learning methods (AdaBoost, Extra trees, LightGBM, XGBoost) applied to PCA and LLE features. Cross-validation and grid search methods are used to find the best combination of parameters. The results show that the accuracy of NDSF is generally higher than that of the sequence matrix-based coding method (MOS) coding method, and the loss and coding time can be greatly reduced. The bar chart of feature extraction shows that the classification accuracy is significantly higher when using the linear dimensionality reduction method, PCA, compared to the nonlinear dimensionality reduction method, LLE. After classification with XGBoost, the model accuracy reaches 99.2%, which provides the best performance among all models. This study suggests that NDSF combined with PCA and XGBoost may be an effective strategy for classifying different human protein interactions.

Label-free macrophage phenotype classification using machine learning methods

Macrophages are heterogeneous innate immune cells that are functionally shaped by their surrounding microenvironment. Diverse macrophage populations have multifaceted differences related to their morphology, metabolism, expressed markers, and functions, where the identification of the different phenotypes is of an utmost importance in modelling immune response. While expressed markers are the most used signature to classify phenotypes, multiple reports indicate that macrophage morphology and autofluorescence are also valuable clues that can be used in the identification process. In this work, we investigated macrophage autofluorescence as a distinct feature for classifying six different macrophage phenotypes, namely: M0, M1, M2a, M2b, M2c, and M2d. The identification was based on extracted signals from multi-channel/multi-wavelength flow cytometer. To achieve the identification, we constructed a dataset containing 152,438 cell events each having a response vector of 45 optical signals fingerprint. Based on this dataset, we applied different supervised machine learning methods to detect phenotype specific fingerprint from the response vector, where the fully connected neural network architecture provided the highest classification accuracy of 75.8% for the six phenotypes compared simultaneously. Furthermore, by restricting the number of phenotypes in the experiment, the proposed framework produces higher classification accuracies, averaging 92.0%, 91.9%, 84.2%, and 80.4% for a pool of two, three, four, five phenotypes, respectively. These results indicate the potential of the intrinsic autofluorescence for classifying macrophage phenotypes, with the proposed method being quick, simple, and cost-effective way to accelerate the discovery of macrophage phenotypical diversity.

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.

iCoreDrop: A robust immune monitoring spectral cytometry assay with six open channels for biomarker flexibility

Recent advances in spectral cytometry have extended our ability to monitor immune cell subsets and activation status while simultaneously improving rare population detection. However, technical challenges in reference control selection and autofluorescence extraction serve as barriers to broad application of spectral flow cytometry. Furthermore, the complexity of spectral cytometry panel development limits the adaptation of established assays. Here, we describe the development of a spectral immunophenotyping assay with robust drop-in capability to enable biomarker interrogation flexibility. The immune monitoring core (iCore), which can be used in part or total, captures broad and granular immune subsets across T cells, B cells, NK cells, monocytes, dendritic cells, and granulocytes in peripheral whole blood. Additional user-selected biomarkers can be dropped in (Drop) using channels BV421, Alexa Fluor 488, PE, PE-Cy7, APC, and APC-Cy7. A comprehensive assessment of reagent and panel performance was conducted, including reference control comparison and optimal autofluorescence (AF) extraction on the 5-laser Cytek Aurora system for healthy donor blood. Assay precision and stability analyses revealed robust intra-assay precision, with 95% of 83 distinct population gates having <20% CV. In the presence of additional drop-in markers in two different settings, a T cell module and a myeloid/B cell module, the drop-in channels themselves achieved <20% CV across 12 out of 13 additionally queried population gates. Overall, establishment of optimal unmixing practices will enable widespread adoption of spectral cytometry assays.