Guidelines for the use of flow cytometry and cell sorting in immunological studies (second edition)
TL;DR: These guidelines are a consensus work of a considerable number of members of the immunology and flow cytometry community providing the theory and key practical aspects offlow cytometry enabling immunologists to avoid the common errors that often undermine immunological data.
Abstract: 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.
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TL;DR: Compared with healthy controls, COVID-19 patients’ T cell compartment displays several alterations involving naïve, central memory, effector memory and terminally differentiated cells, as well as regulatory T cells and PD1 + CD57 + exhausted T cells, and significant alterations exist also in several lineage-specifying transcription factors and chemokine receptors.
Abstract: The immune system of patients infected by SARS-CoV-2 is severely impaired Detailed investigation of T cells and cytokine production in patients affected by COVID-19 pneumonia are urgently required Here we show that, compared with healthy controls, COVID-19 patients’ T cell compartment displays several alterations involving naive, central memory, effector memory and terminally differentiated cells, as well as regulatory T cells and PD1+CD57+ exhausted T cells Significant alterations exist also in several lineage-specifying transcription factors and chemokine receptors Terminally differentiated T cells from patients proliferate less than those from healthy controls, whereas their mitochondria functionality is similar in CD4+ T cells from both groups Patients display significant increases of proinflammatory or anti-inflammatory cytokines, including T helper type-1 and type-2 cytokines, chemokines and galectins; their lymphocytes produce more tumor necrosis factor (TNF), interferon-γ, interleukin (IL)-2 and IL-17, with the last observation implying that blocking IL-17 could provide a novel therapeutic strategy for COVID-19 COVID-19 is a serious pandemic threat to public health, but insights on the pathophysiological and immunological conditions are only emerging Here the authors use multi-color flow cytometry to characterize CD4+ and CD8+ T cells in peripheral blood from 39 COVID-19 patients in Italy to report altered T cell activation, function and polarization
597 citations
TL;DR: The association between IL-6 serum levels and the impairment of cytotoxic activity suggests the possibility that targeting this cytokine may restore anti-viral mechanisms.
Abstract: BACKGROUNDCoronavirus disease 19 (COVID-19) is an emerging infectious disease caused by SARS-CoV-2. Antiviral immune response is crucial to achieve pathogen clearance; however, in some patients an excessive and aberrant host immune response can lead to an acute respiratory distress syndrome. The comprehension of the mechanisms that regulate pathogen elimination, immunity, and pathology is essential to better characterize disease progression and widen the spectrum of therapeutic options.METHODSWe performed a flow cytometric characterization of immune cell subsets from 30 patients with COVID-19 and correlated these data with clinical outcomes.RESULTSPatients with COVID-19 showed decreased numbers of circulating T, B, and NK cells and exhibited a skewing of CD8+ T cells toward a terminally differentiated/senescent phenotype. In agreement, CD4+ T and CD8+ T, but also NK cells, displayed reduced antiviral cytokine production capability. Moreover, a reduced cytotoxic potential was identified in patients with COVID-19, particularly in those who required intensive care. The latter group of patients also showed increased serum IL-6 levels that inversely correlated to the frequency of granzyme A-expressing NK cells. Off-label treatment with tocilizumab restored the cytotoxic potential of NK cells.CONCLUSIONThe association between IL-6 serum levels and the impairment of cytotoxic activity suggests the possibility that targeting this cytokine may restore antiviral mechanisms.FUNDINGThis study was supported by funds from the Department of Experimental and Clinical Medicine of University of Florence (the ex-60% fund and the "Excellence Departments 2018-2022 Project") derived from Ministero dell'Istruzione, dell'Universita e della Ricerca (Italy).
383 citations
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Journal Article•
TL;DR: Two independent MRTMs populations exist across tissues with specific niche-dependent phenotype and functional programming, and it is shown that monocyte-derived RTMs (MRTMs) are two separate lineages, rather than representing points along a developmental or maturation continuum.
Abstract: INTRODUCTION Resident tissue macrophages (RTMs) are a heterogeneous population of immune cells occupying multiple tissue niches and exhibiting microenvironment-specific phenotypes and functions. In certain tissues such as the brain, lung, and liver, embryonically derived RTMs maintain themselves by self-renewal, whereas others, including those in the gut, dermis, and pancreas, are replaced by monocytes, at levels that are tissue specific. Once they arrive in their tissue of residence, monocytes undergo extensive differentiation according to molecular cues provided by their distinct tissue-specific niches, enabling their development into specialized RTMs that support local tissue function. RATIONALE As a result of this ontogenetic and tissue niche heterogeneity, each tissue contains multiple populations of macrophages. For example, in the murine lung, alveolar macrophages are the major embryonically derived population in the alveolar spaces, whereas a minor population named interstitial macrophages (IMs) resides within the lung parenchyma. Previous results reported several phenotypically distinct IM subpopulations, whose relationship remained unknown. Do they represent independent populations or, rather, different points on the spectrum of maturation and activation states? How do these differences relate to their localization in tissue or roles in tissue function in health and disease? Does such macrophage heterogeneity also exist in other tissues? RESULTS Here, using single-cell mRNA sequencing, we unbiasedly identified two independent populations exhibiting distinct gene expression profiles and phenotypes: Lyve1loMHCIIhiCX3CR1hi (Lyve1loMHCIIhi) and Lyve1hiMHCIIloCX3CR1lo (Lyve1hiMHCIIlo) RTMs. We uncovered evidence of parallel populations in multiple others tissues, including the heart, fat, and dermis, as well as in human lung and omental and subcutaneous fat tissues, suggesting that a similar dichotomy is observed in human tissues. We further demonstrated that both populations are slowly replaced by Ly6Chi monocytes. Importantly, using complementary fate-mapping models, we showed that monocyte-derived RTMs (MRTMs) are two separate lineages, rather than representing points along a developmental or maturation continuum. Notably, these distinct MRTM populations preferentially reside within different, but conserved, subtissular niches, located either adjacent to nerve bundles and fibers (Lyve1loMHCIIhi) or blood vessels (Lyve1hiMHCIIlo) across tissues. Finally, by acutely depleting Lyve1hiMHCIIlo MRTMs using a mouse model of inducible macrophage depletion during the induction of fibrosis, we found that the absence of Lyve1hiMHCIIlo IMs exacerbated experimental lung and heart fibrosis, demonstrating their critical role in tissue inflammation. CONCLUSION Two independent MRTMs populations exist across tissues with specific niche-dependent phenotype and functional programming. Their different roles in homeostasis, immune regulation, and fibrosis renders them attractive and separate cellular targets for the therapeutic exploitation of RTM subsets.
374 citations
Journal Article•
TL;DR: It is shown that the transcription factor Hobit is specifically up-regulated in Trm cells and, together with related Blimp1, mediates the development of Trms cells in skin, gut, liver, and kidney in mice.
Abstract: Transcription factors define tissue T cells The immune system fights microbial invaders by maintaining multiple lines of defense. For instance, specialized memory T cells [resident memory T cells (Trms)] colonize portals of pathogen entry, such as the skin, lung, and gut, to quickly halt reinfections. Mackay et al. now report that in mice, Trms as well as other tissue-dwelling lymphocyte populations such as natural killer cells share a common transcriptional program driven by the related transcription factors Hobit and Blimp1. Tissue residency and retention of lymphocytes require expression of Hobit and Blimp1, which, among other functions, suppress genes that promote tissue exit. Science, this issue p. 459 Tissue-dwelling lymphocyte populations share a common transcriptional signature. Tissue-resident memory T (Trm) cells permanently localize to portals of pathogen entry, where they provide immediate protection against reinfection. To enforce tissue retention, Trm cells up-regulate CD69 and down-regulate molecules associated with tissue egress; however, a Trm-specific transcriptional regulator has not been identified. Here, we show that the transcription factor Hobit is specifically up-regulated in Trm cells and, together with related Blimp1, mediates the development of Trm cells in skin, gut, liver, and kidney in mice. The Hobit-Blimp1 transcriptional module is also required for other populations of tissue-resident lymphocytes, including natural killer T (NKT) cells and liver-resident NK cells, all of which share a common transcriptional program. Our results identify Hobit and Blimp1 as central regulators of this universal program that instructs tissue retention in diverse tissue-resident lymphocyte populations.
373 citations
Journal Article•
TL;DR: It is shown that the GC response undergoes a temporal switch in its output as it matures, revealing that the reaction engenders both MBC subsets with different immune effector function and, ultimately, LLPCs at largely separate points in time.
Abstract: Though memory B cells (MBCs) and long-lived plasma cells (LLPCs) are both thought to derive from the germinal center (GC) reaction, there is little insight into or agreement about the signals that control differentiation to one cell type or another. By performing BrdU pulse-labeling studies, GC disruption experiments and V gene sequencing, we found that the generation of these cell types is actually temporally controlled and separated during the immune response. We report that MBCs mainly derive from early GCs (much before GC peak size), while more affinity matured LLPCs are predominantly formed during late GCs - long after its peak in size. Based on these findings, we propose a new model that the GC response undergoes a temporal switch, functioning quite differently at early and late stages. Therefore the generation of MBCs and LLPCs is the consequence of a general shift in GC output over time rather than the result of specific instructive signals that are selectively delivered to GC B cells at any given time during the response. We also present direct evidence that a large fraction of long-lived IgM+ MBC, and even some IgG+ MBC, are formed at very early time points, prior to the existence of detectable GCs. The knowledge of when specific long-lived immune-effector cells are generated during an immune response has strong implications for vaccine design and understanding long-term pathogen immunity.
228 citations
References
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TL;DR: In this paper, a different approach to problems of multiple significance testing is presented, which calls for controlling the expected proportion of falsely rejected hypotheses -the false discovery rate, which is equivalent to the FWER when all hypotheses are true but is smaller otherwise.
Abstract: SUMMARY The common approach to the multiplicity problem calls for controlling the familywise error rate (FWER). This approach, though, has faults, and we point out a few. A different approach to problems of multiple significance testing is presented. It calls for controlling the expected proportion of falsely rejected hypotheses -the false discovery rate. This error rate is equivalent to the FWER when all hypotheses are true but is smaller otherwise. Therefore, in problems where the control of the false discovery rate rather than that of the FWER is desired, there is potential for a gain in power. A simple sequential Bonferronitype procedure is proved to control the false discovery rate for independent test statistics, and a simulation study shows that the gain in power is substantial. The use of the new procedure and the appropriateness of the criterion are illustrated with examples.
83,420 citations
TL;DR: Recognition of the widespread applicability of these concepts will increasingly affect the development of new means to treat human cancer.
51,099 citations
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TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
Abstract: Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.
46,982 citations
07 Jun 2015
TL;DR: Inception as mentioned in this paper is a deep convolutional neural network architecture that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14).
Abstract: We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. By a carefully crafted design, we increased the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for ILSVRC14 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection.
40,257 citations
Book•
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TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
Abstract: Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
38,208 citations