Distinct molecular profiles of skull bone marrow in health and neurological disorders.

The bone marrow in the skull is important for shaping immune responses in the brain and meninges, but its molecular makeup among bones and relevance in human diseases remain unclear. Here, we show that the mouse skull has the most distinct transcriptomic profile compared with other bones in states of health and injury, characterized by a late-stage neutrophil phenotype. In humans, proteome analysis reveals that the skull marrow is the most distinct, with differentially expressed neutrophil-related pathways and a unique synaptic protein signature. 3D imaging demonstrates the structural and cellular details of human skull-meninges connections (SMCs) compared with veins. Last, using translocator protein positron emission tomography (TSPO-PET) imaging, we show that the skull bone marrow reflects inflammatory brain responses with a disease-specific spatial distribution in patients with various neurological disorders. The unique molecular profile and anatomical and functional connections of the skull show its potential as a site for diagnosing, monitoring, and treating brain diseases.

Polyploidisation pleiotropically buffers ageing in hepatocytes.

BACKGROUND & AIMS: Polyploidy in hepatocytes has been proposed as a genetic mechanism to buffer against transcriptional dysregulation. Here, we aim to demonstrate the role of polyploidy in modulating gene regulatory networks in hepatocytes during ageing. METHODS: We performed single-nucleus RNA sequencing in hepatocyte nuclei of different ploidy levels isolated from young and old wild-type mice. Changes in the gene expression and regulatory network were compared to three independent strains that were haploinsufficient for HNF4A, CEBPA or CTCF, representing non-deleterious perturbations. Phenotypic characteristics of the liver section were additionally evaluated histologically, whereas the genomic allele composition of hepatocytes was analysed by BaseScope. RESULTS: We observed that ageing in wild-type mice results in nuclei polyploidy and a marked increase in steatosis. Haploinsufficiency of liver-specific master regulators (HFN4A or CEBPA) results in the enrichment of hepatocytes with tetraploid nuclei at a young age, affecting the genomic regulatory network, and dramatically suppressing ageing-related steatosis tissue wide. Notably, these phenotypes are not the result of subtle disruption to liver-specific transcriptional networks, since haploinsufficiency in the CTCF insulator protein resulted in the same phenotype. Further quantification of genotypes of tetraploid hepatocytes in young and old HFN4A-haploinsufficient mice revealed that during ageing, tetraploid hepatocytes lead to the selection of wild-type alleles, restoring non-deleterious genetic perturbations. CONCLUSIONS: Our results suggest a model whereby polyploidisation leads to fundamentally different cell states. Polyploid conversion enables pleiotropic buffering against age-related decline via non-random allelic segregation to restore a wild-type genome. IMPACT AND IMPLICATIONS: The functional role of hepatocyte polyploidisation during ageing is poorly understood. Using single-nucleus RNA sequencing and BaseScope approaches, we have studied ploidy dynamics during ageing in murine livers with non-deleterious genetic perturbations. We have identified that hepatocytes present different cellular states and the ability to buffer ageing-associated dysfunctions. Tetraploid nuclei exhibit robust transcriptional networks and are better adapted to genomically overcome perturbations. Novel therapeutic interventions aimed at attenuating age-related changes in tissue function could be exploited by manipulation of ploidy dynamics during chronic liver conditions.

Concepts and limitations for learning developmental trajectories from single cell genomics.

Single cell genomics has become a popular approach to uncover the cellular heterogeneity of progenitor and terminally differentiated cell types with great precision. This approach can also delineate lineage hierarchies and identify molecular programmes of cell-fate acquisition and segregation. Nowadays, tens of thousands of cells are routinely sequenced in single cell-based methods and even more are expected to be analysed in the future. However, interpretation of the resulting data is challenging and requires computational models at multiple levels of abstraction. In contrast to other applications of single cell sequencing, where clustering approaches dominate, developmental systems are generally modelled using continuous structures, trajectories and trees. These trajectory models carry the promise of elucidating mechanisms of development, disease and stimulation response at very high molecular resolution. However, their reliable analysis and biological interpretation requires an understanding of their underlying assumptions and limitations. Here, we review the basic concepts of such computational approaches and discuss the characteristics of developmental processes that can be learnt from trajectory models.

A test metric for assessing single-cell RNA-seq batch correction.

Single-cell transcriptomics is a versatile tool for exploring heterogeneous cell populations, but as with all genomics experiments, batch effects can hamper data integration and interpretation. The success of batch-effect correction is often evaluated by visual inspection of low-dimensional embeddings, which are inherently imprecise. Here we present a user-friendly, robust and sensitive k-nearest-neighbor batch-effect test (kBET; https://github.com/theislab/kBET ) for quantification of batch effects. We used kBET to assess commonly used batch-regression and normalization approaches, and to quantify the extent to which they remove batch effects while preserving biological variability. We also demonstrate the application of kBET to data from peripheral blood mononuclear cells (PBMCs) from healthy donors to distinguish cell-type-specific inter-individual variability from changes in relative proportions of cell populations. This has important implications for future data-integration efforts, central to projects such as the Human Cell Atlas.

Aldh1b1 expression defines progenitor cells in the adult pancreas and is required for Kras-induced pancreatic cancer.

The presence of progenitor or stem cells in the adult pancreas and their potential involvement in homeostasis and cancer development remain unresolved issues. Here, we show that mouse centroacinar cells can be identified and isolated by virtue of the mitochondrial enzyme Aldh1b1 that they uniquely express. These cells are necessary and sufficient for the formation of self-renewing adult pancreatic organoids in an Aldh1b1-dependent manner. Aldh1b1-expressing centroacinar cells are largely quiescent, self-renew, and, as shown by genetic lineage tracing, contribute to all 3 pancreatic lineages in the adult organ under homeostatic conditions. Single-cell RNA sequencing analysis of these cells identified a progenitor cell population, established its molecular signature, and determined distinct differentiation pathways to early progenitors. A distinct feature of these progenitor cells is the preferential expression of small GTPases, including Kras, suggesting that they might be susceptible to Kras-driven oncogenic transformation. This finding and the overexpression of Aldh1b1 in human and mouse pancreatic cancers, driven by activated Kras, prompted us to examine the involvement of Aldh1b1 in oncogenesis. We demonstrated genetically that ablation of Aldh1b1 completely abrogates tumor development in a mouse model of KrasG12D-induced pancreatic cancer.

Diffusion pseudotime robustly reconstructs lineage branching.

The temporal order of differentiating cells is intrinsically encoded in their single-cell expression profiles. We describe an efficient way to robustly estimate this order according to diffusion pseudotime (DPT), which measures transitions between cells using diffusion-like random walks. Our DPT software implementations make it possible to reconstruct the developmental progression of cells and identify transient or metastable states, branching decisions and differentiation endpoints.

destiny: diffusion maps for large-scale single-cell data in R.

UNLABELLED: : Diffusion maps are a spectral method for non-linear dimension reduction and have recently been adapted for the visualization of single-cell expression data. Here we present destiny, an efficient R implementation of the diffusion map algorithm. Our package includes a single-cell specific noise model allowing for missing and censored values. In contrast to previous implementations, we further present an efficient nearest-neighbour approximation that allows for the processing of hundreds of thousands of cells and a functionality for projecting new data on existing diffusion maps. We exemplarily apply destiny to a recent time-resolved mass cytometry dataset of cellular reprogramming. AVAILABILITY AND IMPLEMENTATION: destiny is an open-source R/Bioconductor package "bioconductor.org/packages/destiny" also available at www.helmholtz-muenchen.de/icb/destiny A detailed vignette describing functions and workflows is provided with the package. CONTACT: carsten.marr@helmholtz-muenchen.de or f.buettner@helmholtz-muenchen.de SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Testing the diffusing boundary model for the helix-coil transition in peptides.

The dynamics of peptide α-helices have been studied extensively for many years, and the kinetic mechanism of the helix-coil dynamics has been discussed controversially. Recent experimental results have suggested that equilibrium helix-coil dynamics are governed by movement of the helix/coil boundary along the peptide chain, which leads to slower unfolding kinetics in the helix center compared with the helix ends and position-independent helix formation kinetics. We tested this diffusion of boundary model in helical peptides of different lengths by triplet-triplet energy transfer measurements and compared the data with simulations based on a kinetic linear Ising model. The results show that boundary diffusion in helical peptides can be described by a classical, Einstein-type, 1D diffusion process with a diffusion coefficient of 2.7⋅10(7) (amino acids)(2)/s or 6.1⋅10(-9) cm(2)/s. In helices with a length longer than about 40 aa, helix unfolding by coil nucleation in a helical region occurs frequently in addition to boundary diffusion. Boundary diffusion is slowed down by helix-stabilizing capping motifs at the helix ends in agreement with predictions from the kinetic linear Ising model. We further tested local and nonlocal effects of amino acid replacements on helix-coil dynamics. Single amino acid replacements locally affect folding and unfolding dynamics with a ϕf-value of 0.35, which shows that interactions leading to different helix propensities for different amino acids are already partially present in the transition state for helix formation. Nonlocal effects of amino acid replacements only influence helix unfolding (ϕf = 0) in agreement with a diffusing boundary mechanism.

Transition state and ground state properties of the helix-coil transition in peptides deduced from high-pressure studies.

Volume changes associated with protein folding reactions contain valuable information about the folding mechanism and the nature of the transition state. However, meaningful interpretation of such data requires that overall volume changes be deconvoluted into individual contributions from different structural components. Here we focus on one type of structural element, the α-helix, and measure triplet-triplet energy transfer at high pressure to determine volume changes associated with the helix-coil transition. Our results reveal that the volume of a 21-amino-acid alanine-based peptide shrinks upon helix formation. Thus, helices, in contrast with native proteins, become more stable with increasing pressure, explaining the frequently observed helical structures in pressure-unfolded proteins. Both helix folding and unfolding become slower with increasing pressure. The volume changes associated with the addition of a single helical residue to a preexisting helix were obtained by comparing the experimental results with Monte Carlo simulations based on a kinetic linear Ising model. The reaction volume for adding a single residue to a helix is small and negative (-0.23 cm(3) per mol = -0.38 Å(3) per molecule) implying that intrahelical hydrogen bonds have a smaller volume than peptide-water hydrogen bonds. In contrast, the transition state has a larger volume than either the helical or the coil state, with activation volumes of 2.2 cm(3)/mol (3.7 Å(3) per molecule) for adding and 2.4 cm(3)/mol (4.0 Å(3) per molecule) for removing one residue. Thus, addition or removal of a helical residue proceeds through a transitory high-energy state with a large volume, possibly due to the presence of unsatisfied hydrogen bonds, although steric effects may also contribute.

Longitudinal single-cell data informs deterministic modelling of inflammatory bowel disease.

Single-cell-based methods such as flow cytometry or single-cell mRNA sequencing (scRNA-seq) allow deep molecular and cellular profiling of immunological processes. Despite their high throughput, however, these measurements represent only a snapshot in time. Here, we explore how longitudinal single-cell-based datasets can be used for deterministic ordinary differential equation (ODE)-based modelling to mechanistically describe immune dynamics. We derived longitudinal changes in cell numbers of colonic cell types during inflammatory bowel disease (IBD) from flow cytometry and scRNA-seq data of murine colitis using ODE-based models. Our mathematical model generalised well across different protocols and experimental techniques, and we hypothesised that the estimated model parameters reflect biological processes. We validated this prediction of cellular turnover rates with KI-67 staining and with gene expression information from the scRNA-seq data not used for model fitting. Finally, we tested the translational relevance of the mathematical model by deconvolution of longitudinal bulk mRNA-sequencing data from a cohort of human IBD patients treated with olamkicept. We found that neutrophil depletion may contribute to IBD patients entering remission. The predictive power of IBD deterministic modelling highlights its potential to advance our understanding of immune dynamics in health and disease.

Delineating mouse ß-cell identity during lifetime and in diabetes with a single cell atlas.

Although multiple pancreatic islet single-cell RNA-sequencing (scRNA-seq) datasets have been generated, a consensus on pancreatic cell states in development, homeostasis and diabetes as well as the value of preclinical animal models is missing. Here, we present an scRNA-seq cross-condition mouse islet atlas (MIA), a curated resource for interactive exploration and computational querying. We integrate over 300,000 cells from nine scRNA-seq datasets consisting of 56 samples, varying in age, sex and diabetes models, including an autoimmune type 1 diabetes model (NOD), a glucotoxicity/lipotoxicity type 2 diabetes model (db/db) and a chemical streptozotocin ß-cell ablation model. The ß-cell landscape of MIA reveals new cell states during disease progression and cross-publication differences between previously suggested marker genes. We show that ß-cells in the streptozotocin model transcriptionally correlate with those in human type 2 diabetes and mouse db/db models, but are less similar to human type 1 diabetes and mouse NOD ß-cells. We also report pathways that are shared between ß-cells in immature, aged and diabetes models. MIA enables a comprehensive analysis of ß-cell responses to different stressors, providing a roadmap for the understanding of ß-cell plasticity, compensation and demise.

Scaffold-Based (Matrigel™) 3D Culture Technique of Glioblastoma Recovers a Patient-like Immunosuppressive Phenotype.

Conventional 2D cultures are commonly used in cancer research though they come with limitations such as the lack of microenvironment or reduced cell heterogeneity. In this study, we investigated in what respect a scaffold-based (Matrigel™) 3D culture technique can ameliorate the limitations of 2D cultures. NGS-based bulk and single-cell sequencing of matched pairs of 2D and 3D models showed an altered transcription of key immune regulatory genes in around 36% of 3D models, indicating the reoccurrence of an immune suppressive phenotype. Changes included the presentation of different HLA surface molecules as well as cellular stressors. We also investigated the 3D tumor organoids in a co-culture setting with tumor-infiltrating lymphocytes (TILs). Of note, lymphocyte-mediated cell killing appeared less effective in clearing 3D models than their 2D counterparts. IFN-γ release, as well as live cell staining and proliferation analysis, pointed toward an elevated resistance of 3D models. In conclusion, we found that the scaffold-based (Matrigel™) 3D culture technique affects the transcriptional profile in a subset of GBM models. Thus, these models allow for depicting clinically relevant aspects of tumor-immune interaction, with the potential to explore immunotherapeutic approaches in an easily accessible in vitro system.

Early life adversity shapes social subordination and cell type-specific transcriptomic patterning in the ventral hippocampus.

Adverse events in early life can modulate the response to additional stressors later in life and increase the risk of developing psychiatric disorders. The underlying molecular mechanisms responsible for these effects remain unclear. Here, we uncover that early life adversity (ELA) in mice leads to social subordination. Using single-cell RNA sequencing (scRNA-seq), we identified cell type-specific changes in the transcriptional state of glutamatergic and GABAergic neurons in the ventral hippocampus of ELA mice after exposure to acute social stress in adulthood. These findings were reflected by an alteration in excitatory and inhibitory synaptic transmission induced by ELA in response to acute social stress. Finally, enhancing the inhibitory network function through transient diazepam treatment during an early developmental sensitive period reversed the ELA-induced social subordination. Collectively, this study significantly advances our understanding of the molecular, physiological, and behavioral alterations induced by ELA, uncovering a previously unknown cell type-specific vulnerability to ELA.

Heterogeneous Development of ß-Cell Populations in Diabetes-Resistant and -Susceptible Mice.

Progressive dysfunction and failure of insulin-releasing ß-cells are a hallmark of type 2 diabetes (T2D). To study mechanisms of ß-cell loss in T2D, we performed islet single-cell RNA sequencing of two obese mouse strains differing in their diabetes susceptibility. With mice on a control diet, we identified six ß-cell clusters with similar abundance in both strains. However, after feeding of a diabetogenic diet for 2 days, ß-cell cluster composition markedly differed between strains. Islets of diabetes-resistant mice developed into a protective ß-cell cluster (Beta4), whereas those of diabetes-prone mice progressed toward stress-related clusters with a strikingly different expression pattern. Interestingly, the protective cluster showed indications of reduced ß-cell identity, such as downregulation of GLUT2, GLP1R, and MafA, and in vitro knockdown of GLUT2 in ß-cells-mimicking its phenotype-decreased stress response and apoptosis. This might explain enhanced ß-cell survival of diabetes-resistant islets. In contrast, ß-cells of diabetes-prone mice responded with expression changes indicating metabolic pressure and endoplasmic reticulum stress, presumably leading to later ß-cell loss. In conclusion, failure of diabetes-prone mice to adapt gene expression toward a more dedifferentiated state in response to rising blood glucose levels leads to ß-cell failure and diabetes development.

Mapping single-cell data to reference atlases by transfer learning.

Large single-cell atlases are now routinely generated to serve as references for analysis of smaller-scale studies. Yet learning from reference data is complicated by batch effects between datasets, limited availability of computational resources and sharing restrictions on raw data. Here we introduce a deep learning strategy for mapping query datasets on top of a reference called single-cell architectural surgery (scArches). scArches uses transfer learning and parameter optimization to enable efficient, decentralized, iterative reference building and contextualization of new datasets with existing references without sharing raw data. Using examples from mouse brain, pancreas, immune and whole-organism atlases, we show that scArches preserves biological state information while removing batch effects, despite using four orders of magnitude fewer parameters than de novo integration. scArches generalizes to multimodal reference mapping, allowing imputation of missing modalities. Finally, scArches retains coronavirus disease 2019 (COVID-19) disease variation when mapping to a healthy reference, enabling the discovery of disease-specific cell states. scArches will facilitate collaborative projects by enabling iterative construction, updating, sharing and efficient use of reference atlases.

A pulmonologist's guide to perform and analyse cross-species single lung cell transcriptomics.

Single-cell ribonucleic acid sequencing is becoming widely employed to study biological processes at a novel resolution depth. The ability to analyse transcriptomes of multiple heterogeneous cell types in parallel is especially valuable for cell-focused lung research where a variety of resident and recruited cells are essential for maintaining organ functionality. We compared the single-cell transcriptomes from publicly available and unpublished datasets of the lungs in six different species: human (Homo sapiens), African green monkey (Chlorocebus sabaeus), pig (Sus domesticus), hamster (Mesocricetus auratus), rat (Rattus norvegicus) and mouse (Mus musculus) by employing RNA velocity and intercellular communication based on ligand-receptor co-expression, among other techniques. Specifically, we demonstrated a workflow for interspecies data integration, applied a single unified gene nomenclature, performed cell-specific clustering and identified marker genes for each species. Overall, integrative approaches combining newly sequenced as well as publicly available datasets could help identify species-specific transcriptomic signatures in both healthy and diseased lung tissue and select appropriate models for future respiratory research.

Epithelial cell plasticity drives endoderm formation during gastrulation.

It is generally accepted that epiblast cells ingress into the primitive streak by epithelial-to-mesenchymal transition (EMT) to give rise to the mesoderm; however, it is less clear how the endoderm acquires an epithelial fate. Here, we used embryonic stem cell and mouse embryo knock-in reporter systems to combine time-resolved lineage labelling with high-resolution single-cell transcriptomics. This allowed us to resolve the morphogenetic programs that segregate the mesoderm from the endoderm germ layer. Strikingly, while the mesoderm is formed by classical EMT, the endoderm is formed independent of the key EMT transcription factor Snail1 by mechanisms of epithelial cell plasticity. Importantly, forkhead box transcription factor A2 (Foxa2) acts as an epithelial gatekeeper and EMT suppressor to shield the endoderm from undergoing a mesenchymal transition. Altogether, these results not only establish the morphogenetic details of germ layer formation, but also have broader implications for stem cell differentiation and cancer metastasis.

CD81 marks immature and dedifferentiated pancreatic ß-cells.

OBJECTIVE: Islets of Langerhans contain heterogeneous populations of insulin-producing ß-cells. Surface markers and respective antibodies for isolation, tracking, and analysis are urgently needed to study ß-cell heterogeneity and explore the mechanisms to harness the regenerative potential of immature ß-cells. METHODS: We performed single-cell mRNA profiling of early postnatal mouse islets and re-analyzed several single-cell mRNA sequencing datasets from mouse and human pancreas and islets. We used mouse primary islets, iPSC-derived endocrine cells, Min6 insulinoma, and human EndoC-ßH1 ß-cell lines and performed FAC sorting, Western blotting, and imaging to support and complement the findings from the data analyses. RESULTS: We found that all endocrine cell types expressed the cluster of differentiation 81 (CD81) during pancreas development, but the expression levels of this protein were gradually reduced in ß-cells during postnatal maturation. Single-cell gene expression profiling and high-resolution imaging revealed an immature signature of ß-cells expressing high levels of CD81 (CD81high) compared to a more mature population expressing no or low levels of this protein (CD81low/-). Analysis of ß-cells from different diabetic mouse models and in vitro ß-cell stress assays indicated an upregulation of CD81 expression levels in stressed and dedifferentiated ß-cells. Similarly, CD81 was upregulated and marked stressed human ß-cells in vitro. CONCLUSIONS: We identified CD81 as a novel surface marker that labels immature, stressed, and dedifferentiated ß-cells in the adult mouse and human islets. This novel surface marker will allow us to better study ß-cell heterogeneity in healthy subjects and diabetes progression.

Non-canonical Wnt/PCP signalling regulates intestinal stem cell lineage priming towards enteroendocrine and Paneth cell fates.

A detailed understanding of intestinal stem cell (ISC) self-renewal and differentiation is required to treat chronic intestinal diseases. However, the different models of ISC lineage hierarchy1-6 and segregation7-12 are subject to debate. Here, we have discovered non-canonical Wnt/planar cell polarity (PCP)-activated ISCs that are primed towards the enteroendocrine or Paneth cell lineage. Strikingly, integration of time-resolved lineage labelling with single-cell gene expression analysis revealed that both lineages are directly recruited from ISCs via unipotent transition states, challenging the existence of formerly predicted bi- or multipotent secretory progenitors7-12. Transitory cells that mature into Paneth cells are quiescent and express both stem cell and secretory lineage genes, indicating that these cells are the previously described Lgr5+ label-retaining cells7. Finally, Wnt/PCP-activated Lgr5+ ISCs are molecularly indistinguishable from Wnt/ß-catenin-activated Lgr5+ ISCs, suggesting that lineage priming and cell-cycle exit is triggered at the post-transcriptional level by polarity cues and a switch from canonical to non-canonical Wnt/PCP signalling. Taken together, we redefine the mechanisms underlying ISC lineage hierarchy and identify the Wnt/PCP pathway as a new niche signal preceding lateral inhibition in ISC lineage priming and segregation.