Integrated analysis of multimodal single-cell data

The simultaneous measurement of multiple modalities, known as multimodal analysis, represents an exciting frontier for single-cell genomics and necessitates new computational methods that can define cellular states based on multiple data types. Here, we introduce ‘weighted-nearest neighbor’ analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of hundreds of thousands of human white blood cells alongside a panel of 228 antibodies to construct a multimodal reference atlas of the circulating immune system. We demonstrate that integrative analysis substantially improves our ability to resolve cell states and validate the presence of previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets, and to interpret immune responses to vaccination and COVID-19. Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets, including paired measurements of RNA and chromatin state, and to look beyond the transcriptome towards a unified and multimodal definition of cellular identity. Availability Installation instructions, documentation, tutorials, and CITE-seq datasets are available at http://www.satijalab.org/seurat

/pdf/integrated-analysis-of-multimodal-single-cell-data-4rbq9nr64n.pdf

The recent advent of methods for high-throughput single-cell molecular profiling has catalyzed a growing sense in the scientific community that the time is ripe to complete the 150-year-old effort to identify all cell types in the human body. The Human Cell Atlas Project is an international collaborative effort that aims to define all human cell types in terms of distinctive molecular profiles (such as gene expression profiles) and to connect this information with classical cellular descriptions (such as location and morphology). An open comprehensive reference map of the molecular state of cells in healthy human tissues would propel the systematic study of physiological states, developmental trajectories, regulatory circuitry and interactions of cells, and also provide a framework for understanding cellular dysregulation in human disease. Here we describe the idea, its potential utility, early proofs-of-concept, and some design considerations for the Human Cell Atlas, including a commitment to open data, code, and community.

The Human Cell Atlas

Trajectory inference approaches analyze genome-wide omics data from thousands of single cells and computationally infer the order of these cells along developmental trajectories. Although more than 70 trajectory inference tools have already been developed, it is challenging to compare their performance because the input they require and output models they produce vary substantially. Here, we benchmark 45 of these methods on 110 real and 229 synthetic datasets for cellular ordering, topology, scalability and usability. Our results highlight the complementarity of existing tools, and that the choice of method should depend mostly on the dataset dimensions and trajectory topology. Based on these results, we develop a set of guidelines to help users select the best method for their dataset. Our freely available data and evaluation pipeline ( https://benchmark.dynverse.org ) will aid in the development of improved tools designed to analyze increasingly large and complex single-cell datasets.

/pdf/a-comparison-of-single-cell-trajectory-inference-methods-2cwu8xnatn.pdf

A comparison of single-cell trajectory inference methods.

Landscape and Dynamics of Single Immune Cells in Hepatocellular Carcinoma.

INTRODUCTION Dendritic cells (DC) are professional antigen-presenting cells that orchestrate immune responses. The human DC population comprises multiple subsets, including plasmacytoid DC (pDC) and two functionally specialized lineages of conventional DC (cDC1 and cDC2), whose origins and differentiation pathways remain incompletely defined. RATIONALE As DC are essential regulators of the immune response in health and disease, potential intervention strategies aiming at manipulation of these cells will require in-depth insights of their origins, the mechanisms that govern their homeostasis, and their functional properties. Here, we employed two unbiased high-dimensional technologies to characterize the human DC lineage from bone marrow to blood. RESULTS We isolated the DC-containing population (Lineage − HLA − DR + CD135 + cells) from human blood and defined the transcriptomes of 710 individual cells using massively parallel single-cell mRNA sequencing. By combining complementary bioinformatic approaches, we identified a small cluster of cells within this population as putative DC precursors (pre-DC). We then confirmed this finding using cytometry by time-of-flight (CyTOF) to simultaneously measure the expression of a panel of 38 different proteins at the single-cell level on Lineage − HLA − DR + cells and found that pre-DC possessed a CD123 + CD33 + CD45RA + phenotype. We confirmed the precursor potential of pre-DC by establishing their potential to differentiate in vitro into cDC1 and cDC2, but not pDC, in the known proportions found in vivo . Interestingly, pre-DC also express classical pDC markers, including CD123, CD303, and CD304. Thus, any previous studies using these markers to identify or isolate pDC will have inadvertently included CD123 + CD33 + pre-DC. We provide here new markers that can be used to identify unambiguously pre-DC from pDC, including CD33, CX3CR1, CD2, CD5, and CD327. When CD123 + CD33 + pre-DC and CD123 + CD33 − pDC were isolated separately, we observed that pre-DC have unique functional properties that were previously attributed to pDC. Although pDC remain bona fide interferon-α–producing cells, their reported interleukin-12 (IL-12) production and CD4 T cell allostimulatory capacity can likely be attributed to “contaminating” pre-DC. We then asked whether the pre-DC population contained both uncommitted and committed pre-cDC1 and pre-cDC2 precursors, as recently shown in mice. Using microfluidic single-cell mRNA sequencing (scmRNAseq), we showed that the human pre-DC population contains cells exhibiting transcriptomic priming toward cDC1 and cDC2 lineages. Flow cytometry and in vitro DC differentiation experiments further identified CD123 + CADM1 − CD1c − putative uncommitted pre-DC, alongside CADM1 + CD1c − pre-cDC1 and CADM1 − CD1c + pre-cDC2. Finally, we found that pre-DC subsets expressed T cell costimulatory molecules and induced comparable proliferation and polarization of naive CD4 T cells as adult DC. However, exposure to the Toll-like receptor 9 (TLR9) ligand CpG triggered IL-12p40 and tumor necrosis factor–α production by early pre-DC, pre-cDC1, and pre-cDC2, in contrast to differentiated cDC1 and cDC2, which do not express TLR9. CONCLUSION Using unsupervised scmRNAseq and CyTOF analyses, we have unraveled the complexity of the human DC lineage at the single-cell level, revealing a continuous process of differentiation that starts in the bone marrow (BM) with common DC progenitors (CDP), diverges at the point of emergence of pre-DC and pDC potential, and culminates in maturation of both lineages in the blood and spleen. The pre-DC compartment contains functionally and phenotypically distinct lineage-committed subpopulations, including one early uncommitted CD123 + pre-DC subset and two CD45RA + CD123 lo lineage-committed subsets. The discovery of multiple committed pre-DC populations with unique capabilities opens promising new avenues for the therapeutic exploitation of DC subset-specific targeting.

/pdf/mapping-the-human-dc-lineage-through-the-integration-of-high-3u0v4m48do.pdf

Mapping the human DC lineage through the integration of high-dimensional techniques

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Mapping the human DC lineage through the integration of high-dimensional techniques