These guidelines are a consensus work of a considerable number of members of the immunology and flow cytometry community. They provide the theory and key practical aspects of flow cytometry enabling immunologists to avoid the common errors that often undermine immunological data. Notably, there are comprehensive sections of all major immune cell types with helpful Tables detailing phenotypes in murine and human cells. The latest flow cytometry techniques and applications are also described, featuring examples of the data that can be generated and, importantly, how the data can be analysed. Furthermore, there are sections detailing tips, tricks and pitfalls to avoid, all written and peer-reviewed by leading experts in the field, making this an essential research companion.

Guidelines for the use of flow cytometry and cell sorting in immunological studies (second edition)

Single-cell transcriptomics yields ever growing data sets containing RNA expression levels for thousands of genes from up to millions of cells. Common data analysis pipelines include a dimensionality reduction step for visualising the data in two dimensions, most frequently performed using t-distributed stochastic neighbour embedding (t-SNE). It excels at revealing local structure in high-dimensional data, but naive applications often suffer from severe shortcomings, e.g. the global structure of the data is not represented accurately. Here we describe how to circumvent such pitfalls, and develop a protocol for creating more faithful t-SNE visualisations. It includes PCA initialisation, a high learning rate, and multi-scale similarity kernels; for very large data sets, we additionally use exaggeration and downsampling-based initialisation. We use published single-cell RNA-seq data sets to demonstrate that this protocol yields superior results compared to the naive application of t-SNE. t-SNE is widely used for dimensionality reduction and visualization of high-dimensional single-cell data. Here, the authors introduce a protocol to help avoid common shortcomings of t-SNE, for example, enabling preservation of the global structure of the data.

/pdf/the-art-of-using-t-sne-for-single-cell-transcriptomics-pw74mkm2c4.pdf

The art of using t-SNE for single-cell transcriptomics.

SARS-CoV-2 messenger RNA vaccination in healthy individuals generates immune protection against COVID-19. However, little is known about SARS-CoV-2 mRNA vaccine-induced responses in immunosuppressed patients. We investigated induction of antigen-specific antibody, B cell and T cell responses longitudinally in patients with multiple sclerosis (MS) on anti-CD20 antibody monotherapy (n = 20) compared with healthy controls (n = 10) after BNT162b2 or mRNA-1273 mRNA vaccination. Treatment with anti-CD20 monoclonal antibody (aCD20) significantly reduced spike-specific and receptor-binding domain (RBD)-specific antibody and memory B cell responses in most patients, an effect ameliorated with longer duration from last aCD20 treatment and extent of B cell reconstitution. By contrast, all patients with MS treated with aCD20 generated antigen-specific CD4 and CD8 T cell responses after vaccination. Treatment with aCD20 skewed responses, compromising circulating follicular helper T (TFH) cell responses and augmenting CD8 T cell induction, while preserving type 1 helper T (TH1) cell priming. Patients with MS treated with aCD20 lacking anti-RBD IgG had the most severe defect in circulating TFH responses and more robust CD8 T cell responses. These data define the nature of the SARS-CoV-2 vaccine-induced immune landscape in aCD20-treated patients and provide insights into coordinated mRNA vaccine-induced immune responses in humans. Our findings have implications for clinical decision-making and public health policy for immunosuppressed patients including those treated with aCD20.

/pdf/cellular-and-humoral-immune-responses-following-sars-cov-2-17nj8vz1ro.pdf

Cellular and humoral immune responses following SARS-CoV-2 mRNA vaccination in patients with multiple sclerosis on anti-CD20 therapy.

Understanding innate immune responses in COVID-19 is important to decipher mechanisms of host responses and interpret disease pathogenesis. Natural killer (NK) cells are innate effector lymphocytes that respond to acute viral infections but might also contribute to immunopathology. Using 28-color flow cytometry, we here reveal strong NK cell activation across distinct subsets in peripheral blood of COVID-19 patients. This pattern was mirrored in scRNA-seq signatures of NK cells in bronchoalveolar lavage from COVID-19 patients. Unsupervised high-dimensional analysis of peripheral blood NK cells furthermore identified distinct NK cell immunotypes that were linked to disease severity. Hallmarks of these immunotypes were high expression of perforin, NKG2C, and Ksp37, reflecting increased presence of adaptive NK cells in circulation of patients with severe disease. Finally, arming of CD56bright NK cells was observed across COVID-19 disease states, driven by a defined protein-protein interaction network of inflammatory soluble factors. This study provides a detailed map of the NK cell activation landscape in COVID-19 disease.

Natural killer cell immunotypes related to COVID-19 disease severity.

Metformin Enhances Autophagy and Normalizes Mitochondrial Function to Alleviate Aging-Associated Inflammation

Accurate and comprehensive extraction of information from high-dimensional single cell datasets necessitates faithful visualizations to assess biological populations. A state-of-the-art algorithm for non-linear dimension reduction, t-SNE, requires multiple heuristics and fails to produce clear representations of datasets when millions of cells are projected. We develop opt-SNE, an automated toolkit for t-SNE parameter selection that utilizes Kullback-Leibler divergence evaluation in real time to tailor the early exaggeration and overall number of gradient descent iterations in a dataset-specific manner. The precise calibration of early exaggeration together with opt-SNE adjustment of gradient descent learning rate dramatically improves computation time and enables high-quality visualization of large cytometry and transcriptomics datasets, overcoming limitations of analysis tools with hard-coded parameters that often produce poorly resolved or misleading maps of fluorescent and mass cytometry data. In summary, opt-SNE enables superior data resolution in t-SNE space and thereby more accurate data interpretation. Visualisation tools that use dimensionality reduction, such as t-SNE, provide poor visualisation on large data sets of millions of observations. Here the authors present opt-SNE, that automatically finds data set-tailored parameters for t-SNE to optimise visualisation and improve analysis.

/pdf/automated-optimized-parameters-for-t-distributed-stochastic-56ce4eky5r.pdf

Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets.

Compensating in flow cytometry is an unavoidable challenge in the data analysis of fluorescence-based flow cytometry. Even the advent of spectral cytometry cannot circumvent the spillover problem, with spectral unmixing an intrinsic part of such systems. The calculation of spillover coefficients from single-color controls has remained essentially unchanged since its inception, and is increasingly limited in its ability to deal with high-parameter flow cytometry. Here, we present AutoSpill, an alternative method for calculating spillover coefficients. The approach combines automated gating of cells, calculation of an initial spillover matrix based on robust linear regression, and iterative refinement to reduce error. Moreover, autofluorescence can be compensated out, by processing it as an endogenous dye in an unstained control. AutoSpill uses single-color controls and is compatible with common flow cytometry software. AutoSpill allows simpler and more robust workflows, while reducing the magnitude of compensation errors in high-parameter flow cytometry. Flow cytometry allows the simultaneous quantification of many markers in and on a cell, but the analysis of such data is complicated. Here, the authors propose AutoSpill, a framework that facilitates the analysis of such data by automating parts of the analysis and requiring fewer controls.

/pdf/autospill-is-a-principled-framework-that-simplifies-the-2dl44b9vcn.pdf

AutoSpill is a principled framework that simplifies the analysis of multichromatic flow cytometry data

Accurate and comprehensive extraction of information from high-dimensional single cell datasets necessitates faithful visualizations to assess biological populations. A state-of-the-art algorithm for non-linear dimension reduction, t-SNE, requires multiple heuristics and fails to produce clear representations of datasets when millions of cells are projected. We developed opt-SNE, an automated toolkit for optimal t-SNE parameter selection that utilizes Kullback-Liebler divergence evaluation in real time to tailor the early exaggeration and overall number of gradient descent iterations in a dataset-specific manner. The precise calibration of early exaggeration together with opt-SNE adjustment of gradient descent learning rate dramatically improves computation time and enables high-quality visualization of large cytometry and transcriptomics datasets, overcoming limitations of analysis tools with hard-coded parameters that often produce poorly resolved or misleading maps of fluorescent and mass cytometry data. In summary, opt-SNE enables optimal data resolution in t-SNE space and more precise data interpretation.

/pdf/automated-optimal-parameters-for-t-distributed-stochastic-4isny73f8m.pdf

Automated Optimal Parameters for T-Distributed Stochastic Neighbor Embedding Improve Visualization and Allow Analysis of Large Datasets

Compensating in classical flow cytometry or unmixing in spectral systems is an unavoidable challenge in the data analysis of fluorescence-based flow cytometry. In both cases, spillover coefficients are estimated for each fluorophore using single-color controls. This approach has remained essentially unchanged since its inception, and is increasingly limited in its ability to deal with high-parameter flow cytometry. Here, we present AutoSpill, a novel approach for calculating spillover coefficients or spectral signatures of fluorophores. The approach combines automated gating of cells, calculation of an initial spillover matrix based on robust linear regression, and iterative refinement to reduce error. Moreover, autofluorescence can be compensated out, by processing it as an endogenous dye in an unstained control. AutoSpill uses single-color controls and is compatible with common flow cytometry software, but it differs in two key aspects from current methods: (1) it is much less demanding in the preparation of controls, as it does not require the presence of well-defined positive and negative populations, and (2) it does not require manual tuning of the spillover matrix, as the algorithm iteratively computes the tuning, producing an optimal compensation matrix. Another algorithm, AutoSpread, complements this approach, providing a robust estimate of the Spillover Spreading Matrix (SSM), while avoiding the need for well-defined positive and negative populations. Together, AutoSpill and AutoSpread provide a superior solution to the problem of fluorophore spillover, allowing simpler and more robust workflows in high-parameter flow cytometry.

https://www.biorxiv.org/content/biorxiv/early/2020/08/10/2020.06.29.177196.full.pdf

Richard Halpert

Papers

Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets.

AutoSpill is a principled framework that simplifies the analysis of multichromatic flow cytometry data

Automated Optimal Parameters for T-Distributed Stochastic Neighbor Embedding Improve Visualization and Allow Analysis of Large Datasets

AutoSpill: A method for calculating spillover coefficients to compensate or unmix high-parameter flow cytometry data