Pigmentation level of human iPSC-derived RPE does not indicate a specific gene expression profile.

Retinal pigment epithelium (RPE) cells show heterogeneous levels of pigmentation when cultured in vitro. To know whether their color in appearance is correlated with the function of the RPE, we analyzed the color intensities of human-induced pluripotent stem cell-derived RPE cells (iPSC-RPE) together with the gene expression profile at the single-cell level. For this purpose, we utilized our recent invention, Automated Live imaging and cell Picking System (ALPS), which enabled photographing each cell before RNA-sequencing analysis to profile the gene expression of each cell. While our iPSC-RPE were categorized into four clusters by gene expression, the color intensity of iPSC-RPE did not project any specific gene expression profiles. We reasoned this by less correlation between the actual color and the gene expressions that directly define the level of pigmentation, from which we hypothesized the color of RPE cells may be a temporal condition not strongly indicating the functional characteristics of the RPE.

The backs of our eyes are lined with retinal pigment epithelial cells (or RPE cells for short). These cells provide nutrition to surrounding cells and contain a pigment called melanin that absorbs excess light that might interfere with vision. By doing so, they support the cells that receive light to enable vision. However, with age, RPE cells can become damaged and less able to support other cells. This can lead to a disease called age-related macular degeneration, which can cause blindness. One potential way to treat this disease is to transplant healthy RPE cells into eyes that have lost them. These healthy cells can be grown in the laboratory from human pluripotent stem cells, which have the capacity to turn into various specialist cells. Stem cell-derived RPE cells growing in a dish contain varying amounts of melanin, resulting in some being darker than others. This raised the question of whether pigment levels affect the function of RPE cells. However, it was difficult to compare single cells containing various amounts of pigment as most previous studies only analyzed large numbers of RPE cells mixed together. Nakai-Futatsugi et al. overcame this hurdle using a technique called Automated Live imaging and cell Picking System (also known as ALPS). More than 2300 stem cell-derived RPE cells were photographed individually and the color of each cell was recorded. The gene expression of each cell was then measured to investigate whether certain genes being switched on or off affects pigment levels and cell function. Analysis did not find a consistent pattern of gene expression underlying the pigmentation of RPE cells. Even gene expression related to the production of melanin was only slightly linked to the color of the cells. These findings suggests that the RPE cell color fluctuates and is not primarily determined by which genes are switched on or off. Future experiments are required to determine whether the findings are the same for RPE cells grown naturally in the eyes and whether different pigment levels affect their capacity to protect the rest of the eye.

High-throughput identification and quantification of bacterial cells in the microbiota based on 16S rRNA sequencing with single-base accuracy using BarBIQ.

Bacteria often function as a community, called the microbiota, consisting of many different bacterial species. The accurate identification of bacterial types and the simultaneous quantification of the cells of each bacterial type will advance our understanding of microbiota; however, this cannot be performed by conventional 16S rRNA sequencing methods as they only identify and quantify genes, which do not always represent cells. Here, we present a protocol for our developed method, barcoding bacteria for identification and quantification (BarBIQ). In BarBIQ, the 16S rRNA genes of single bacterial cells are amplified and attached to a unique cellular barcode in a droplet. Sequencing the tandemly linked cellular barcodes and 16S rRNA genes from many droplets (representing many cells with unique cellular barcodes) and clustering the sequences using the barcodes determines both the bacterial type for each cell based on 16S rRNA gene and the number of cells for each bacterial type based on the quantity of barcode types sequenced. Single-base accuracy for 16S rRNA sequencing is achieved via the barcodes and by avoiding chimera formation from 16S rRNA genes of different bacteria using droplets. For data processing, an easy-to-use bioinformatic pipeline is available ( https://github.com/Shiroguchi-Lab/BarBIQ_Pipeline_V1_2_0 ). This protocol allows researchers with experience in molecular biology but without bioinformatics experience to perform the process in ~2 weeks. We show the application of BarBIQ in mouse gut microbiota analysis as an example; however, this method is also applicable to other microbiota samples, including those from the mouth and skin, marine environments, soil and plants, as well as those from other terrestrial environments.

Sensory neuronal STAT3 is critical for IL-31 receptor expression and inflammatory itch.

IL-31 receptor blockade suppresses pruritus of atopic dermatitis. However, cell-type-specific contributions of IL-31 receptor to itch, its expression mechanism, and the downstream signaling pathway to induce itch remain unknown. Here, using conditional knockout mice, we demonstrate that IL-31-induced itch requires sensory neuronal IL-31 receptor and STAT3. We find that IL-31 receptor expression is dependent on STAT3 in sensory neurons. In addition, pharmacological experiments suggest that STAT3 activation is important for the itch-inducing signaling downstream of the IL-31 receptor. A cutaneous IL-31 injection induces the nuclear accumulation of activated STAT3 first in sensory neurons that abundantly express IL-31 receptor and then in other itch-transmitting neurons. IL-31 enhances itch induced by various pruritogens including even chloroquine. Finally, pruritus associated with dermatitis is partially dependent on sensory neuronal IL-31 receptor and strongly on sensory neuronal STAT3. Thus, sensory neuronal STAT3 is essential for IL-31-induced itch and further contributes to IL-31-independent inflammatory itch.

CD45 alleviates airway inflammation and lung fibrosis by limiting expansion and activation of ILC2s.

Group 2 innate lymphoid cells (ILC2s) are critical for the immune response against parasite infection and tissue homeostasis and involved in the pathogenesis of allergy and inflammatory diseases. Although multiple molecules positively regulating ILC2 development and activation have been extensively investigated, the factors limiting their population size and response remain poorly studied. Here, we found that CD45, a membrane-bound tyrosine phosphatase essential for T cell development, negatively regulated ILC2s in a cell-intrinsic manner. ILC2s in CD45-deficient mice exhibited enhanced proliferation and maturation in the bone marrow and hyperactivated phenotypes in the lung with high glycolytic capacity. Furthermore, CD45 signaling suppressed the type 2 inflammatory response by lung ILC2s and alleviated airway inflammation and pulmonary fibrosis. Finally, the interaction with galectin-9 influenced CD45 signaling in ILC2s. These results demonstrate that CD45 is a cell-intrinsic negative regulator of ILC2s and prevents lung inflammation and fibrosis via ILC2s.

Identifying a Lung Stem Cell Subpopulation by Combining Single-Cell Morphometrics, Organoid Culture, and Transcriptomics.

Single-cell RNA sequencing is a valuable tool for dissecting cellular heterogeneity in complex systems. However, it is still challenging to estimate the proliferation and differentiation potentials of subpopulations within dormant tissue stem cells. Here, we established a new single-cell analysis method for profiling the organoid-forming capacity and differentiation potential of tissue stem cells to disclose stem cell subpopulations by integrating single-cell morphometrics, organoid-forming assay, and RNA sequencing, a method named scMORN. To explore lung epithelial stem cells, we initially developed feeder-free culture system, which could expand all major lung stem cells, including basal, club, and alveolar type 2 (AT2) cells, and found that club cells contained a subpopulation, which showed better survival rate and high proliferation capacity and could differentiate into alveolar cells. Using the scMORN method, we discovered a club cell subpopulation named Muc5b+ and large club (ML-club) cells that efficiently formed organoids than other club or AT2 cells in our feeder-free organoid culture and differentiated into alveolar cells in vitro. Single-cell transcriptome profiling and immunohistochemical analysis revealed that ML-club cells localized at the intrapulmonary proximal airway and distinct from known subpopulations of club cells such as BASCs. Furthermore, we identified CD14 as a cell surface antigen of ML-club cells and showed that purified CD14+ club cells engrafted into injured mouse lungs had better engraftment rate and expansion than other major lung stem cells, reflecting the observations in organoid culture systems. The scMORN method could be adapted to different stem cell tissues to discover useful stem-cell subpopulations.

Robotic data acquisition with deep learning enables cell image-based prediction of transcriptomic phenotypes.

Single-cell whole-transcriptome analysis is the gold standard approach to identifying molecularly defined cell phenotypes. However, this approach cannot be used for dynamics measurements such as live-cell imaging. Here, we developed a multifunctional robot, the automated live imaging and cell picking system (ALPS) and used it to perform single-cell RNA sequencing for microscopically observed cells with multiple imaging modes. Using robotically obtained data that linked cell images and the whole transcriptome, we successfully predicted transcriptome-defined cell phenotypes in a noninvasive manner using cell image-based deep learning. This noninvasive approach opens a window to determine the live-cell whole transcriptome in real time. Moreover, this work, which is based on a data-driven approach, is a proof of concept for determining the transcriptome-defined phenotypes (i.e., not relying on specific genes) of any cell from cell images using a model trained on linked datasets.

A circulating subset of iNKT cells mediates antitumor and antiviral immunity.

Invariant natural killer T (iNKT) cells are a group of innate-like T lymphocytes that recognize lipid antigens. They are supposed to be tissue resident and important for systemic and local immune regulation. To investigate the heterogeneity of iNKT cells, we recharacterized iNKT cells in the thymus and peripheral tissues. iNKT cells in the thymus were divided into three subpopulations by the expression of the natural killer cell receptor CD244 and the chemokine receptor CXCR6 and designated as C0 (CD244-CXCR6-), C1 (CD244-CXCR6+), or C2 (CD244+CXCR6+) iNKT cells. The development and maturation of C2 iNKT cells from C0 iNKT cells strictly depended on IL-15 produced by thymic epithelial cells. C2 iNKT cells expressed high levels of IFN-γ and granzymes and exhibited more NK cell-like features, whereas C1 iNKT cells showed more T cell-like characteristics. C2 iNKT cells were influenced by the microbiome and aging and suppressed the expression of the autoimmune regulator AIRE in the thymus. In peripheral tissues, C2 iNKT cells were circulating that were distinct from conventional tissue-resident C1 iNKT cells. Functionally, C2 iNKT cells protected mice from the tumor metastasis of melanoma cells by enhancing antitumor immunity and promoted antiviral immune responses against influenza virus infection. Furthermore, we identified human CD244+CXCR6+ iNKT cells with high cytotoxic properties as a counterpart of mouse C2 iNKT cells. Thus, this study reveals a circulating subset of iNKT cells with NK cell-like properties distinct from conventional tissue-resident iNKT cells.

High-throughput identification and quantification of single bacterial cells in the microbiota.

The bacterial microbiota works as a community that consists of many individual organisms, i.e., cells. To fully understand the function of bacterial microbiota, individual cells must be identified; however, it is difficult with current techniques. Here, we develop a method, Barcoding Bacteria for Identification and Quantification (BarBIQ), which classifies single bacterial cells into taxa-named herein cell-based operational taxonomy units (cOTUs)-based on cellularly barcoded 16S rRNA sequences with single-base accuracy, and quantifies the cell number for each cOTU in the microbiota in a high-throughput manner. We apply BarBIQ to murine cecal microbiotas and quantify in total 3.4 × 105 bacterial cells containing 810 cOTUs. Interestingly, we find location-dependent global differences in the cecal microbiota depending on the dietary vitamin A deficiency, and more differentially abundant cOTUs at the proximal location than the distal location. Importantly, these location differences are not clearly shown by conventional 16S rRNA gene-amplicon sequencing methods, which quantify the 16S rRNA genes, not the cells. Thus, BarBIQ enables microbiota characterization with the identification and quantification of individual constituent bacteria, which is a cornerstone for microbiota studies.

Different cell imaging methods did not significantly improve immune cell image classification performance.

Developments in high-throughput microscopy have made it possible to collect huge amounts of cell image data that are difficult to analyse manually. Machine learning (e.g., deep learning) is often employed to automate the extraction of information from these data, such as cell counting, cell type classification and image segmentation. However, the effects of different imaging methods on the accuracy of image processing have not been examined systematically. We studied the effects of different imaging methods on the performance of machine learning-based cell type classifiers. We observed lymphoid-primed multipotential progenitor (LMPP) and pro-B cells using three imaging methods: differential interference contrast (DIC), phase contrast (Ph) and bright-field (BF). We examined the classification performance of convolutional neural networks (CNNs) with each of them and their combinations. CNNs achieved an area under the receiver operating characteristic (ROC) curve (AUC) of ~0.9, which was significantly better than when the classifier used only cell size or cell contour shape as input. However, no significant differences were found between imaging methods and focal positions.

B cell-derived GABA elicits IL-10+ macrophages to limit anti-tumour immunity.

Small, soluble metabolites not only are essential intermediates in intracellular biochemical processes, but can also influence neighbouring cells when released into the extracellular milieu1-3. Here we identify the metabolite and neurotransmitter GABA as a candidate signalling molecule synthesized and secreted by activated B cells and plasma cells. We show that B cell-derived GABA promotes monocyte differentiation into anti-inflammatory macrophages that secrete interleukin-10 and inhibit CD8+ T cell killer function. In mice, B cell deficiency or B cell-specific inactivation of the GABA-generating enzyme GAD67 enhances anti-tumour responses. Our study reveals that, in addition to cytokines and membrane proteins, small metabolites derived from B-lineage cells have immunoregulatory functions, which may be pharmaceutical targets allowing fine-tuning of immune responses.

Mitochondrial reactive oxygen species trigger metformin-dependent antitumor immunity via activation of Nrf2/mTORC1/p62 axis in tumor-infiltrating CD8T lymphocytes.

BACKGROUND: Metformin (Met) is the first-line treatment for type 2 diabetes mellitus and plays an effective role in treating various diseases, such as cardiovascular disease, neurodegenerative disease, cancer, and aging. However, the underlying mechanism of Met-dependent antitumor immunity remains to be elucidated. METHODS: MitoTEMPO, a scavenger of mitochondrial superoxide, abolished the antitumor effect of Met, but not antiprogrammed cell death (PD-1) antibody (Ab) treatment. Consequently, we studied the mechanism of the Met-induced antitumor effect. Expressions of glucose transporter (Glut)-1, mitochondrial reactive oxygen species (mtROS), interferon (IFN)-γ, Ki67, autophagy markers, activation markers for NF-E2-related factor 2 (Nrf2), and mammalian target of rapamaycin complex 1 (mTORC1) in CD8+ tumor-infiltrating T lymphocytes (CD8TILs) were examined by flow cytometry analysis. In addition, conditional knockout mice for Nrf2 and p62 were used to detect these markers, together with the monitoring of in vivo tumor growth. RNA sequencing was performed for CD8TILs and tumor cells. Melanoma cells containing an IFN-γ receptor (IFNγR) cytoplasmic domain deletion mutant was overexpressed and used for characterization of the metabolic profile of those tumor cells using a Seahorse Flux Analyzer. RESULTS: Met administration elevates mtROS and cell surface Glut-1, resulting in the production of IFN-γ in CD8TILs. mtROS activates Nrf2 in a glycolysis-dependent manner, inducing activation of autophagy, glutaminolysis, mTORC1, and p62/SQSTM1. mTORC1-dependent phosphorylation of p62 at serine 351 (p-p62(S351)) is also involved in activation of Nrf2. Conditional deletion of Nrf2 in CD8TILs abrogates mTORC1 activation and antitumor immunity by Met. In synergy with the effect of anti-PD-1 Ab, Met boosts CD8TIL proliferation and IFN-γ secretion, resulting in decreased glycolysis and oxidative phosphorylation in tumor cells. Consequently, Glut-1 is elevated in CD8TILs, together with the expansion of activated dendritic cells. Moreover, tumor cells lacking in IFNγR signaling abolish IFN-γ production and proliferation of CD8TILs. CONCLUSIONS: We found that Met stimulates production of mtROS, which triggers Glut-1 elevation and Nrf2 activation in CD8TILs. Nrf2 activates mTORC1, whereas mTORC1 activates Nrf2 in a p-p62(S351)-dependent manner, thus creating a feedback loop that ensures CD8TILs' proliferation. In combination with anti-PD-1 Ab, Met stimulates robust proliferation of CD8TILs and IFN-γ secretion, resulting in an IFN-γ-dependent reprogramming of the tumor microenvironment.

Multiomics Investigation Revealing the Characteristics of HIV-1-Infected Cells In Vivo.

For eradication of HIV-1 infection, it is important to elucidate the detailed features and heterogeneity of HIV-1-infected cells in vivo. To reveal multiple characteristics of HIV-1-producing cells in vivo, we use a hematopoietic-stem-cell-transplanted humanized mouse model infected with GFP-encoding replication-competent HIV-1. We perform multiomics experiments using recently developed technology to identify the features of HIV-1-infected cells. Genome-wide HIV-1 integration-site analysis reveals that productive HIV-1 infection tends to occur in cells with viral integration into transcriptionally active genomic regions. Bulk transcriptome analysis reveals that a high level of viral mRNA is transcribed in HIV-1-infected cells. Moreover, single-cell transcriptome analysis shows the heterogeneity of HIV-1-infected cells, including CXCL13high cells and a subpopulation with low expression of interferon-stimulated genes, which can contribute to efficient viral spread in vivo. Our findings describe multiple characteristics of HIV-1-producing cells in vivo, which could provide clues for the development of an HIV-1 cure.

Osteoprotegerin-dependent M cell self-regulation balances gut infection and immunity.

Microfold cells (M cells) are responsible for antigen uptake to initiate immune responses in the gut-associated lymphoid tissue (GALT). Receptor activator of nuclear factor-κB ligand (RANKL) is essential for M cell differentiation. Follicle-associated epithelium (FAE) covers the GALT and is continuously exposed to RANKL from stromal cells underneath the FAE, yet only a subset of FAE cells undergoes differentiation into M cells. Here, we show that M cells express osteoprotegerin (OPG), a soluble inhibitor of RANKL, which suppresses the differentiation of adjacent FAE cells into M cells. Notably, OPG deficiency increases M cell number in the GALT and enhances commensal bacterium-specific immunoglobulin production, resulting in the amelioration of disease symptoms in mice with experimental colitis. By contrast, OPG-deficient mice are highly susceptible to Salmonella infection. Thus, OPG-dependent self-regulation of M cell differentiation is essential for the balance between the infectious risk and the ability to perform immunosurveillance at the mucosal surface.

HaloTag-based conjugation of proteins to barcoding-oligonucleotides.

Highly sensitive protein quantification enables the detection of a small number of protein molecules that serve as markers/triggers for various biological phenomena, such as cancer. Here, we describe the development of a highly sensitive protein quantification system called HaloTag protein barcoding. The method involves covalent linking of a target protein to a unique molecule counting oligonucleotide at a 1:1 conjugation ratio based on an azido-cycloalkyne click reaction. The sensitivity of the HaloTag-based barcoding was remarkably higher than that of a conventional luciferase assay. The HaloTag system was successfully validated by analyzing a set of protein-protein interactions, with the identification rate of 44% protein interactions between positive reference pairs reported in the literature. Desmoglein 3, the target antigen of pemphigus vulgaris, an IgG-mediated autoimmune blistering disease, was used in a HaloTag protein barcode assay to detect the anti-DSG3 antibody. The dynamic range of the assay was over 104-times wider than that of a conventional enzyme-linked immunosorbent assay (ELISA). The technology was used to detect anti-DSG3 antibody in patient samples with much higher sensitivity compared to conventional ELISA. Our detection system, with its superior sensitivity, enables earlier detection of diseases possibly allowing the initiation of care/treatment at an early disease stage.

Author Correction: Runx/Cbfß complexes protect group 2 innate lymphoid cells from exhausted-like hyporesponsiveness during allergic airway inflammation.

The original version of this Article contained an error in the author affiliations. Affiliation 5 incorrectly read 'Laboratory for Prediction of Cell Systems Dynamics, RIKEN Center for Biosystems Dynamics Research (BDR), Suite, Hyogo 565-0874, Japan.' This has now been corrected in both the PDF and HTML versions of the Article.

Runx/Cbfß complexes protect group 2 innate lymphoid cells from exhausted-like hyporesponsiveness during allergic airway inflammation.

Group 2 innate lymphoid cells (ILC2s) have tissue-resident competence and contribute to the pathogenesis of allergic diseases. However, the mechanisms regulating prolonged ILC2-mediated TH2 cytokine production under chronic inflammatory conditions are unclear. Here we show that, at homeostasis, Runx deficiency induces excessive ILC2 activation due to overly active GATA-3 functions. By contrast, during allergic inflammation, the absence of Runx impairs the ability of ILC2s to proliferate and produce effector TH2 cytokines and chemokines. Instead, functional deletion of Runx induces the expression of exhaustion markers, such as IL-10 and TIGIT, on ILC2s. Finally, these 'exhausted-like' ILC2s are unable to induce type 2 immune responses to repeated allergen exposures. Thus, Runx confers competence for sustained ILC2 activity at the mucosa, and contributes to allergic pathogenesis.

T Follicular Helper Cell-Germinal Center B Cell Interaction Strength Regulates Entry into Plasma Cell or Recycling Germinal Center Cell Fate.

Higher- or lower-affinity germinal center (GC) B cells are directed either to plasma cell or GC recycling, respectively; however, how commitment to the plasma cell fate takes place is unclear. We found that a population of light zone (LZ) GC cells, Bcl6loCD69hi expressing a transcription factor IRF4 and higher-affinity B cell receptors (BCRs) or Bcl6hiCD69hi with lower-affinity BCRs, favored the plasma cell or recycling GC cell fate, respectively. Mechanistically, CD40 acted as a dose-dependent regulator for Bcl6loCD69hi cell formation. Furthermore, we found that expression of intercellular adhesion molecule 1 (ICAM-1) and signaling lymphocytic activation molecule (SLAM) in Bcl6loCD69hi cells was higher than in Bcl6hiCD69hi cells, thereby affording more stable T follicular helper (Tfh)-GC B cell contacts. These data support a model whereby commitment to the plasma cell begins in the GC and suggest that stability of Tfh-GC B cell contacts is key for plasma cell-prone GC cell formation.

Cbfß2 controls differentiation of and confers homing capacity to prethymic progenitors.

Multipotent hematopoietic progenitors must acquire thymus-homing capacity to initiate T lymphocyte development. Despite its importance, the transcriptional program underlying this process remains elusive. Cbfß forms transcription factor complexes with Runx proteins, and here we show that Cbfß2, encoded by an RNA splice variant of the Cbfb gene, is essential for extrathymic differentiation of T cell progenitors. Furthermore, Cbfß2 endows extrathymic progenitors with thymus-homing capacity by inducing expression of the principal thymus-homing receptor, Ccr9. This occurs via direct binding of Cbfß2 to cell type-specific enhancers, as is observed in Rorγt induction during differentiation of lymphoid tissue inducer cells by activation of an intronic enhancer. As in mice, an alternative splicing event in zebrafish generates a Cbfß2-specific mRNA, important for ccr9 expression. Thus, despite phylogenetically and ontogenetically variable sites of origin of T cell progenitors, their robust thymus-homing capacity is ensured by an evolutionarily conserved mechanism emerging from functional diversification of Runx transcription factor complexes by acquisition of a novel splice variant.

The efficacy and further functional advantages of random-base molecular barcodes for absolute and digital quantification of nucleic acid molecules.

Accurate quantification of biomolecules in system-wide measurements is in high demand, especially for systems with limited sample amounts such as single cells. Because of this, digital quantification of nucleic acid molecules using molecular barcodes has been developed, making, e.g., transcriptome analysis highly reproducible and quantitative. This counting scheme was shown to work using sequence-restricted barcodes, and non-sequence-restricted (random-base) barcodes that may provide a much higher dynamic range at significantly lower cost have been widely used. However, the efficacy of random-base barcodes is significantly affected by base changes due to amplification and/or sequencing errors and has not been investigated experimentally or quantitatively. Here, we show experimentally that random-base barcodes enable absolute and digital quantification of DNA molecules with high dynamic range (from one to more than 104, potentially up to 1015 molecules) conditional on our barcode design and variety, a certain range of sequencing depths, and computational analyses. Moreover, we quantitatively show further functional advantages of the molecular barcodes: the molecular barcodes enable one to find contaminants and misidentifications of target sequences. Our scheme here may be generally used to confirm that the digital quantification works in each platform.