Protective role for kidney TREM2high macrophages in obesity- and diabetes-induced kidney injury.

Diabetic kidney disease (DKD), the most common cause of kidney failure, is a frequent complication of diabetes and obesity, and yet to date, treatments to halt its progression are lacking. We analyze kidney single-cell transcriptomic profiles from DKD patients and two DKD mouse models at multiple time points along disease progression-high-fat diet (HFD)-fed mice aged to 90-100 weeks and BTBR ob/ob mice (a genetic model)-and report an expanding population of macrophages with high expression of triggering receptor expressed on myeloid cells 2 (TREM2) in HFD-fed mice. TREM2high macrophages are enriched in obese and diabetic patients, in contrast to hypertensive patients or healthy controls in an independent validation cohort. Trem2 knockout mice on an HFD have worsening kidney filter damage and increased tubular epithelial cell injury, all signs of worsening DKD. Together, our studies suggest that strategies to enhance kidney TREM2high macrophages may provide therapeutic benefits for DKD.

Tim-3 adapter protein Bat3 acts as an endogenous regulator of tolerogenic dendritic cell function.

Dendritic cells (DCs) sense environmental cues and adopt either an immune-stimulatory or regulatory phenotype, thereby fine-tuning immune responses. Identifying endogenous regulators that determine DC function can thus inform the development of therapeutic strategies for modulating the immune response in different disease contexts. Tim-3 plays an important role in regulating immune responses by inhibiting the activation status and the T cell priming ability of DC in the setting of cancer. Bat3 is an adaptor protein that binds to the tail of Tim-3; therefore, we studied its role in regulating the functional status of DCs. In murine models of autoimmunity (experimental autoimmune encephalomyelitis) and cancer (MC38-OVA-implanted tumor), lack of Bat3 expression in DCs alters the T cell compartment-it decreases TH1, TH17 and cytotoxic effector cells, increases regulatory T cells, and exhausted CD8+ tumor-infiltrating lymphocytes, resulting in the attenuation of autoimmunity and acceleration of tumor growth. We found that Bat3 expression levels were differentially regulated by activating versus inhibitory stimuli in DCs, indicating a role for Bat3 in the functional calibration of DC phenotypes. Mechanistically, loss of Bat3 in DCs led to hyperactive unfolded protein response and redirected acetyl-coenzyme A to increase cell intrinsic steroidogenesis. The enhanced steroidogenesis in Bat3-deficient DC suppressed T cell response in a paracrine manner. Our findings identified Bat3 as an endogenous regulator of DC function, which has implications for DC-based immunotherapies.

COVID-19 tissue atlases reveal SARS-CoV-2 pathology and cellular targets.

COVID-19, which is caused by SARS-CoV-2, can result in acute respiratory distress syndrome and multiple organ failure1-4, but little is known about its pathophysiology. Here we generated single-cell atlases of 24 lung, 16 kidney, 16 liver and 19 heart autopsy tissue samples and spatial atlases of 14 lung samples from donors who died of COVID-19. Integrated computational analysis uncovered substantial remodelling in the lung epithelial, immune and stromal compartments, with evidence of multiple paths of failed tissue regeneration, including defective alveolar type 2 differentiation and expansion of fibroblasts and putative TP63+ intrapulmonary basal-like progenitor cells. Viral RNAs were enriched in mononuclear phagocytic and endothelial lung cells, which induced specific host programs. Spatial analysis in lung distinguished inflammatory host responses in lung regions with and without viral RNA. Analysis of the other tissue atlases showed transcriptional alterations in multiple cell types in heart tissue from donors with COVID-19, and mapped cell types and genes implicated with disease severity based on COVID-19 genome-wide association studies. Our foundational dataset elucidates the biological effect of severe SARS-CoV-2 infection across the body, a key step towards new treatments.

Single-cell meta-analysis of SARS-CoV-2 entry genes across tissues and demographics.

Angiotensin-converting enzyme 2 (ACE2) and accessory proteases (TMPRSS2 and CTSL) are needed for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cellular entry, and their expression may shed light on viral tropism and impact across the body. We assessed the cell-type-specific expression of ACE2, TMPRSS2 and CTSL across 107 single-cell RNA-sequencing studies from different tissues. ACE2, TMPRSS2 and CTSL are coexpressed in specific subsets of respiratory epithelial cells in the nasal passages, airways and alveoli, and in cells from other organs associated with coronavirus disease 2019 (COVID-19) transmission or pathology. We performed a meta-analysis of 31 lung single-cell RNA-sequencing studies with 1,320,896 cells from 377 nasal, airway and lung parenchyma samples from 228 individuals. This revealed cell-type-specific associations of age, sex and smoking with expression levels of ACE2, TMPRSS2 and CTSL. Expression of entry factors increased with age and in males, including in airway secretory cells and alveolar type 2 cells. Expression programs shared by ACE2+TMPRSS2+ cells in nasal, lung and gut tissues included genes that may mediate viral entry, key immune functions and epithelial-macrophage cross-talk, such as genes involved in the interleukin-6, interleukin-1, tumor necrosis factor and complement pathways. Cell-type-specific expression patterns may contribute to the pathogenesis of COVID-19, and our work highlights putative molecular pathways for therapeutic intervention.

Emergence of a High-Plasticity Cell State during Lung Cancer Evolution.

Tumor evolution from a single cell into a malignant, heterogeneous tissue remains poorly understood. Here, we profile single-cell transcriptomes of genetically engineered mouse lung tumors at seven stages, from pre-neoplastic hyperplasia to adenocarcinoma. The diversity of transcriptional states increases over time and is reproducible across tumors and mice. Cancer cells progressively adopt alternate lineage identities, computationally predicted to be mediated through a common transitional, high-plasticity cell state (HPCS). Accordingly, HPCS cells prospectively isolated from mouse tumors and human patient-derived xenografts display high capacity for differentiation and proliferation. The HPCS program is associated with poor survival across human cancers and demonstrates chemoresistance in mice. Our study reveals a central principle underpinning intra-tumoral heterogeneity and motivates therapeutic targeting of the HPCS.

Single cell census of human kidney organoids shows reproducibility and diminished off-target cells after transplantation.

Human iPSC-derived kidney organoids have the potential to revolutionize discovery, but assessing their consistency and reproducibility across iPSC lines, and reducing the generation of off-target cells remain an open challenge. Here, we profile four human iPSC lines for a total of 450,118 single cells to show how organoid composition and development are comparable to human fetal and adult kidneys. Although cell classes are largely reproducible across time points, protocols, and replicates, we detect variability in cell proportions between different iPSC lines, largely due to off-target cells. To address this, we analyze organoids transplanted under the mouse kidney capsule and find diminished off-target cells. Our work shows how single cell RNA-seq (scRNA-seq) can score organoids for reproducibility, faithfulness and quality, that kidney organoids derived from different iPSC lines are comparable surrogates for human kidney, and that transplantation enhances their formation by diminishing off-target cells.

A Cellular Taxonomy of the Bone Marrow Stroma in Homeostasis and Leukemia.

Stroma is a poorly defined non-parenchymal component of virtually every organ with key roles in organ development, homeostasis, and repair. Studies of the bone marrow stroma have defined individual populations in the stem cell niche regulating hematopoietic regeneration and capable of initiating leukemia. Here, we use single-cell RNA sequencing (scRNA-seq) to define a cellular taxonomy of the mouse bone marrow stroma and its perturbation by malignancy. We identified seventeen stromal subsets expressing distinct hematopoietic regulatory genes spanning new fibroblastic and osteoblastic subpopulations including distinct osteoblast differentiation trajectories. Emerging acute myeloid leukemia impaired mesenchymal osteogenic differentiation and reduced regulatory molecules necessary for normal hematopoiesis. These data suggest that tissue stroma responds to malignant cells by disadvantaging normal parenchymal cells. Our taxonomy of the stromal compartment provides a comprehensive bone marrow cell census and experimental support for cancer cell crosstalk with specific stromal elements to impair normal tissue function and thereby enable emergent cancer.

Reference-free inference of tumor phylogenies from single-cell sequencing data.

BACKGROUND: Effective management and treatment of cancer continues to be complicated by the rapid evolution and resulting heterogeneity of tumors. Phylogenetic study of cell populations in single tumors provides a way to delineate intra-tumoral heterogeneity and identify robust features of evolutionary processes. The introduction of single-cell sequencing has shown great promise for advancing single-tumor phylogenetics; however, the volume and high noise in these data present challenges for inference, especially with regard to chromosome abnormalities that typically dominate tumor evolution. Here, we investigate a strategy to use such data to track differences in tumor cell genomic content during progression. RESULTS: We propose a reference-free approach to mining single-cell genome sequence reads to allow predictive classification of tumors into heterogeneous cell types and reconstruct models of their evolution. The approach extracts k-mer counts from single-cell tumor genomic DNA sequences, and uses differences in normalized k-mer frequencies as a proxy for overall evolutionary distance between distinct cells. The approach computationally simplifies deriving phylogenetic markers, which normally relies on first aligning sequence reads to a reference genome and then processing the data to extract meaningful progression markers for constructing phylogenetic trees. The approach also provides a way to bypass some of the challenges that massive genome rearrangement typical of tumor genomes presents for reference-based methods. We illustrate the method on a publicly available breast tumor single-cell sequencing dataset. CONCLUSIONS: We have demonstrated a computational approach for learning tumor progression from single cell sequencing data using k-mer counts. k-mer features classify tumor cells by stage of progression with high accuracy. Phylogenies built from these k-mer spectrum distance matrices yield splits that are statistically significant when tested for their ability to partition cells at different stages of cancer.

Novel multisample scheme for inferring phylogenetic markers from whole genome tumor profiles.

Computational cancer phylogenetics seeks to enumerate the temporal sequences of aberrations in tumor evolution, thereby delineating the evolution of possible tumor progression pathways, molecular subtypes, and mechanisms of action. We previously developed a pipeline for constructing phylogenies describing evolution between major recurring cell types computationally inferred from whole-genome tumor profiles. The accuracy and detail of the phylogenies, however, depend on the identification of accurate, high-resolution molecular markers of progression, i.e., reproducible regions of aberration that robustly differentiate different subtypes and stages of progression. Here, we present a novel hidden Markov model (HMM) scheme for the problem of inferring such phylogenetically significant markers through joint segmentation and calling of multisample tumor data. Our method classifies sets of genome-wide DNA copy number measurements into a partitioning of samples into normal (diploid) or amplified at each probe. It differs from other similar HMM methods in its design specifically for the needs of tumor phylogenetics, by seeking to identify robust markers of progression conserved across a set of copy number profiles. We show an analysis of our method in comparison to other methods on both synthetic and real tumor data, which confirms its effectiveness for tumor phylogeny inference and suggests avenues for future advances.

Inference of tumor phylogenies from genomic assays on heterogeneous samples.

Tumorigenesis can in principle result from many combinations of mutations, but only a few roughly equivalent sequences of mutations, or "progression pathways," seem to account for most human tumors. Phylogenetics provides a promising way to identify common progression pathways and markers of those pathways. This approach, however, can be confounded by the high heterogeneity within and between tumors, which makes it difficult to identify conserved progression stages or organize them into robust progression pathways. To tackle this problem, we previously developed methods for inferring progression stages from heterogeneous tumor profiles through computational unmixing. In this paper, we develop a novel pipeline for building trees of tumor evolution from the unmixed tumor data. The pipeline implements a statistical approach for identifying robust progression markers from unmixed tumor data and calling those markers in inferred cell states. The result is a set of phylogenetic characters and their assignments in progression states to which we apply maximum parsimony phylogenetic inference to infer tumor progression pathways. We demonstrate the full pipeline on simulated and real comparative genomic hybridization (CGH) data, validating its effectiveness and making novel predictions of major progression pathways and ancestral cell states in breast cancers.

Robust unmixing of tumor states in array comparative genomic hybridization data.

MOTIVATION: Tumorigenesis is an evolutionary process by which tumor cells acquire sequences of mutations leading to increased growth, invasiveness and eventually metastasis. It is hoped that by identifying the common patterns of mutations underlying major cancer sub-types, we can better understand the molecular basis of tumor development and identify new diagnostics and therapeutic targets. This goal has motivated several attempts to apply evolutionary tree reconstruction methods to assays of tumor state. Inference of tumor evolution is in principle aided by the fact that tumors are heterogeneous, retaining remnant populations of different stages along their development along with contaminating healthy cell populations. In practice, though, this heterogeneity complicates interpretation of tumor data because distinct cell types are conflated by common methods for assaying the tumor state. We previously proposed a method to computationally infer cell populations from measures of tumor-wide gene expression through a geometric interpretation of mixture type separation, but this approach deals poorly with noisy and outlier data. RESULTS: In the present work, we propose a new method to perform tumor mixture separation efficiently and robustly to an experimental error. The method builds on the prior geometric approach but uses a novel objective function allowing for robust fits that greatly reduces the sensitivity to noise and outliers. We further develop an efficient gradient optimization method to optimize this 'soft geometric unmixing' objective for measurements of tumor DNA copy numbers assessed by array comparative genomic hybridization (aCGH) data. We show, on a combination of semi-synthetic and real data, that the method yields fast and accurate separation of tumor states. CONCLUSIONS: We have shown a novel objective function and optimization method for the robust separation of tumor sub-types from aCGH data and have shown that the method provides fast, accurate reconstruction of tumor states from mixed samples. Better solutions to this problem can be expected to improve our ability to accurately identify genetic abnormalities in primary tumor samples and to infer patterns of tumor evolution. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Retinoblastoma: expression of HLA-G.

PURPOSE: Human leukocyte antigen (HLA) mediates interactions of tumor cells with cytotoxic T lymphocytes (CTLs) and natural killer (NK) cells. Retinoblastoma (RB) is the most common intraocular malignant tumor in childhood and is characterized by direct spread to the optic nerve and orbit as well as hematogenous and lymphatic spread. Earlier, we observed that invasive RB showed reduced HLA, which could contribute to its escape from the immune system. Little is known about the role of the nonclassical HLA molecule, HLA-G, in RB and its role in tumor escape mechanisms in RB. METHODS: Forty archival paraffin-embedded RB tumors were analyzed for the expression of HLA-G by immunohistochemistry using a monoclonal antibody; fresh tumor samples were also subjected to Western blot analysis. There were 22 tumors with no invasion and 18 with invasion of the choroid/optic nerve. Immunoanalysis was performed based on the International Histocompatibility Working Group Project Description. RESULTS: HLA-G was negative in the non-neoplastic retina, reduced in 22/22 tumors with no invasion, and positive in 15/18 with invasion. The immunohistochemistry results were confirmed by Western blot analysis. The difference in expression between the two groups was significant ( p < 0.001). There was no correlation of HLA-G expression with differentiation of the tumors. CONCLUSION: Increased expression of HLA-G was observed in invasive RB. This preliminary observation deserves further investigation and may shed more light on the immune escape mechanisms of this tumor and thus enable novel therapeutic strategies.