In recent years there has been a rapid increase in the use of single-cell sequencing (scRNA-seq) approaches in the field of immunology. With the wide range of technologies available, it is becoming ...harder for users to select the best scRNA-seq protocol/platform to address their biological questions of interest. Here, we compared the advantages and limitations of four commonly used scRNA-seq platforms in order to clarify their suitability for different experimental applications. We also address how the datasets generated by different scRNA-seq platforms can be integrated, and how to identify unknown populations of single cells using unbiased bioinformatics methods.
Large-scale single-cell transcriptomic datasets generated using different technologies contain batch-specific systematic variations that present a challenge to batch-effect removal and data ...integration. With continued growth expected in scRNA-seq data, achieving effective batch integration with available computational resources is crucial. Here, we perform an in-depth benchmark study on available batch correction methods to determine the most suitable method for batch-effect removal.
We compare 14 methods in terms of computational runtime, the ability to handle large datasets, and batch-effect correction efficacy while preserving cell type purity. Five scenarios are designed for the study: identical cell types with different technologies, non-identical cell types, multiple batches, big data, and simulated data. Performance is evaluated using four benchmarking metrics including kBET, LISI, ASW, and ARI. We also investigate the use of batch-corrected data to study differential gene expression.
Based on our results, Harmony, LIGER, and Seurat 3 are the recommended methods for batch integration. Due to its significantly shorter runtime, Harmony is recommended as the first method to try, with the other methods as viable alternatives.
Microglia, the resident immune cells of the brain, rapidly change states in response to their environment, but we lack molecular and functional signatures of different microglial populations. Here, ...we analyzed the RNA expression patterns of more than 76,000 individual microglia in mice during development, in old age, and after brain injury. Our analysis uncovered at least nine transcriptionally distinct microglial states, which expressed unique sets of genes and were localized in the brain using specific markers. The greatest microglial heterogeneity was found at young ages; however, several states—including chemokine-enriched inflammatory microglia—persisted throughout the lifespan or increased in the aged brain. Multiple reactive microglial subtypes were also found following demyelinating injury in mice, at least one of which was also found in human multiple sclerosis lesions. These distinct microglia signatures can be used to better understand microglia function and to identify and manipulate specific subpopulations in health and disease.
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•Mouse microglia are heterogenous cells that are most diverse in the developing brain•Unique microglia transcriptional states can be localized to many brain regions•Small subsets of varied inflammatory microglia found in the aged brain•Diverse activated microglia subpopulations found in mouse demyelinated lesions and human MS
Hammond et al. redefine mouse microglia states using single-cell RNA-seq and in situ brain mapping. They find that microglia are most diverse in the developing, aged, and injured brain. Using focal demyelination, they show that microglia activation states are transcriptionally and spatially distinct within the lesion environment.
Macrophages promote both injury and repair after myocardial infarction, but discriminating functions within mixed populations remains challenging. Here we used fate mapping, parabiosis and ...single-cell transcriptomics to demonstrate that at steady state, TIMD4
LYVE1
MHC-II
CCR2
resident cardiac macrophages self-renew with negligible blood monocyte input. Monocytes partially replaced resident TIMD4
LYVE1
MHC-II
CCR2
macrophages and fully replaced TIMD4
LYVE1
MHC-II
CCR2
macrophages, revealing a hierarchy of monocyte contribution to functionally distinct macrophage subsets. Ischemic injury reduced TIMD4
and TIMD4
resident macrophage abundance, whereas CCR2
monocyte-derived macrophages adopted multiple cell fates within infarcted tissue, including those nearly indistinguishable from resident macrophages. Recruited macrophages did not express TIMD4, highlighting the ability of TIMD4 to track a subset of resident macrophages in the absence of fate mapping. Despite this similarity, inducible depletion of resident macrophages using a Cx3cr1-based system led to impaired cardiac function and promoted adverse remodeling primarily within the peri-infarct zone, revealing a nonredundant, cardioprotective role of resident cardiac macrophages.
Spatial transcriptomics technologies generate gene expression profiles with spatial context, requiring spatially informed analysis tools for three key tasks, spatial clustering, multisample ...integration, and cell-type deconvolution. We present GraphST, a graph self-supervised contrastive learning method that fully exploits spatial transcriptomics data to outperform existing methods. It combines graph neural networks with self-supervised contrastive learning to learn informative and discriminative spot representations by minimizing the embedding distance between spatially adjacent spots and vice versa. We demonstrated GraphST on multiple tissue types and technology platforms. GraphST achieved 10% higher clustering accuracy and better delineated fine-grained tissue structures in brain and embryo tissues. GraphST is also the only method that can jointly analyze multiple tissue slices in vertical or horizontal integration while correcting batch effects. Lastly, GraphST demonstrated superior cell-type deconvolution to capture spatial niches like lymph node germinal centers and exhausted tumor infiltrating T cells in breast tumor tissue.
Flow cytometry (FCM) is widely used in both clinical and basic research to characterize cell phenotypes and functions. The latest FCM instruments analyze up to 20 markers of individual cells, ...producing high-dimensional data. This requires the use of the latest clustering and dimensionality reduction techniques to automatically segregate cell sub-populations in an unbiased manner. However, automated analyses may lead to false discoveries due to inter-sample differences in quality and properties.
We present an R package, flowAI, containing two methods to clean FCM files from unwanted events: (i) an automatic method that adopts algorithms for the detection of anomalies and (ii) an interactive method with a graphical user interface implemented into an R shiny application. The general approach behind the two methods consists of three key steps to check and remove suspected anomalies that derive from (i) abrupt changes in the flow rate, (ii) instability of signal acquisition and (iii) outliers in the lower limit and margin events in the upper limit of the dynamic range. For each file analyzed our software generates a summary of the quality assessment from the aforementioned steps. The software presented is an intuitive solution seeking to improve the results not only of manual but also and in particular of automatic analysis on FCM data.
R source code available through Bioconductor: http://bioconductor.org/packages/flowAI/ CONTACTS: mongianni1@gmail.com or Anis_Larbi@immunol.a-star.edu.sg
Supplementary data are available at Bioinformatics online.
Single-cell mass cytometry significantly increases the dimensionality of cytometry analysis as compared to fluorescence flow cytometry, providing unprecedented resolution of cellular diversity in ...tissues. However, analysis and interpretation of these high-dimensional data poses a significant technical challenge. Here, we present cytofkit, a new Bioconductor package, which integrates both state-of-the-art bioinformatics methods and in-house novel algorithms to offer a comprehensive toolset for mass cytometry data analysis. Cytofkit provides functions for data pre-processing, data visualization through linear or non-linear dimensionality reduction, automatic identification of cell subsets, and inference of the relatedness between cell subsets. This pipeline also provides a graphical user interface (GUI) for ease of use, as well as a shiny application (APP) for interactive visualization of cell subpopulations and progression profiles of key markers. Applied to a CD14-CD19- PBMCs dataset, cytofkit accurately identified different subsets of lymphocytes; applied to a human CD4+ T cell dataset, cytofkit uncovered multiple subtypes of TFH cells spanning blood and tonsils. Cytofkit is implemented in R, licensed under the Artistic license 2.0, and freely available from the Bioconductor website, https://bioconductor.org/packages/cytofkit/. Cytofkit is also applicable for flow cytometry data analysis.
Mononuclear phagocytes (MNPs) encompass dendritic cells, monocytes, and macrophages (MoMac), which exhibit antimicrobial, homeostatic, and immunoregulatory functions. We integrated 178,651 MNPs from ...13 tissues across 41 datasets to generate a MNP single-cell RNA compendium (MNP-VERSE), a publicly available tool to map MNPs and define conserved gene signatures of MNP populations. Next, we generated a MoMac-focused compendium that revealed an array of specialized cell subsets widely distributed across multiple tissues. Specific pathological forms were expanded in cancer and inflammation. All neoplastic tissues contained conserved tumor-associated macrophage populations. In particular, we focused on IL4I1+CD274(PD-L1)+IDO1+ macrophages, which accumulated in the tumor periphery in a T cell-dependent manner via interferon-γ (IFN-γ) and CD40/CD40L-induced maturation from IFN-primed monocytes. IL4I1_Macs exhibited immunosuppressive characteristics through tryptophan degradation and promoted the entry of regulatory T cell into tumors. This integrated analysis provides a robust online-available platform for uniform annotation and dissection of specific macrophage functions in healthy and pathological states.
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•Cross-tissue integration of scRNA from monocytes and macrophages in health and disease•Conserved gene signatures of mononuclear phagocyte populations in human tissues•IL4I1+PD-L1+IDO1+ and TREM2+ TAM subsets accumulate in human tumors•IL4I1+PD-L1+IDO1+ TAM in the tumor periphery exhibit immunosuppressive characteristics
Mulder et al. integrate 178,651 human mononuclear phagocytes (MNPs) from 13 tissues across 41 datasets to generate a MNP single-cell RNA compendium (MNP-VERSE) that enables the definition of conserved gene signatures of MNP populations. This integrated approach provides a robust, online-available platform (https://gustaveroussy.github.io/FG-Lab/) for uniform annotation and dissection of specific macrophage functions in healthy and pathological states.
Human mononuclear phagocytes comprise phenotypically and functionally overlapping subsets of dendritic cells (DCs) and monocytes, but the extent of their heterogeneity and distinct markers for subset ...identification remains elusive. By integrating high-dimensional single-cell protein and RNA expression data, we identified distinct markers to delineate monocytes from conventional DC2 (cDC2s). Using CD88 and CD89 for monocytes and HLA-DQ and FcεRIα for cDC2s allowed for their specific identification in blood and tissues. We also showed that cDC2s could be subdivided into phenotypically and functionally distinct subsets based on CD5, CD163, and CD14 expression, including a distinct subset of circulating inflammatory CD5−CD163+CD14+ cells related to previously defined DC3s. These inflammatory DC3s were expanded in systemic lupus erythematosus patients and correlated with disease activity. These findings further unravel the heterogeneity of DC subpopulations in health and disease and may pave the way for the identification of specific DC subset-targeting therapies.
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•InfinityFlow protein expression analysis reveals DC- and monocyte-specific markers•Monocytes are CD88+CD89+, while cDC2s are HLA-DQ+FcεRIα+•cDC2s comprise CD5+ DC2s and CD5−CD163+/−CD14+/− DC3s•Pro-inflammatory CD14+ DC3 expansion correlates with disease activity in SLE patients
Using high-dimensional protein and RNA single-cell analyses, Dutertre et al. analyze human dendritic cell and monocyte subsets and identify markers that delineate them and unravel their heterogeneity. They also reveal the presence of inflammatory CD14+ DC3s, a subset of cDC2s, that correlate with disease progression and may be functionally involved in systemic lupus erythematosus immunopathology.
Mouse conventional dendritic cells (cDCs) can be classified into two functionally distinct lineages: the CD8α(+) (CD103(+)) cDC1 lineage, and the CD11b(+) cDC2 lineage. cDCs arise from a cascade of ...bone marrow (BM) DC-committed progenitor cells that include the common DC progenitors (CDPs) and pre-DCs, which exit the BM and seed peripheral tissues before differentiating locally into mature cDCs. Where and when commitment to the cDC1 or cDC2 lineage occurs remains poorly understood. Here we found that transcriptional signatures of the cDC1 and cDC2 lineages became evident at the single-cell level from the CDP stage. We also identified Siglec-H and Ly6C as lineage markers that distinguished pre-DC subpopulations committed to the cDC1 lineage (Siglec-H(-)Ly6C(-) pre-DCs) or cDC2 lineage (Siglec-H(-)Ly6C(+) pre-DCs). Our results indicate that commitment to the cDC1 or cDC2 lineage occurs in the BM and not in the periphery.
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