Biology subjects

Theis, F. J.

Publications and source records attributed to Theis, F. J..

At least 127 records · Page 7Linked to original sources

bioRxiv2020

Wnt/PCP-primed intestinal stem cells directly differentiate into enteroendocrine or Paneth cells

A detailed understanding of intestinal stem cell (ISC) self-renewal and differentiation is required to better treat chronic intestinal diseases. However, different models of ISC lineage hierarchy1-6 and segregation7-12 are debated. Here we report the identification of Lgr5+ ISCs that express Flattop (Fltp), a Wnt/planar cell polarity (PCP) reporter and effector gene. Lineage labelling revealed that Wnt/PCP-activated Fltp+ ISCs are primed either towards the enteroendocrine or the Paneth cell lineage in vivo. Integration of time-resolved lineage labelling with genome-wide and targeted single-cell gene expression analysis allowed us to delineate the ISC differentiation path into enteroendocrine and Paneth cells at the molecular level. Strikingly, we found 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+ labelretaining cells7. Wnt/PCP-activated Lgr5+ ISCs are indistinguishable from Wnt/{beta}-catenin-activated Lgr5+ ISCs based on the expression of stem-cell signature or secretory lineagespecifying genes but possess less self-renewal activity. This suggests 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 identified the Wnt/PCP pathway as a new niche signal and polarity cue regulating stem cell fate. Active Wnt/PCP signalling represents one of the earliest events in ISC lineage priming towards the Paneth and enteroendocrine cell fate, preceding lateral inhibition and expression of secretory lineagespecifying genes. Thus, our findings provide a better understanding of the niche signals and redefine the mechanisms underlying ISC lineage hierarchy and segregation.

developmental biology↗

bioRxiv2020

Query to reference single-cell integration with transfer learning

Large single-cell atlases are now routinely generated with the aim of serving as reference to analyse future 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. Leveraging advances in machine learning, we propose a deep learning strategy to map query datasets on top of a reference called single-cell architectural surgery (scArches, https://github.com/theislab/scarches). It uses transfer learning and parameter optimization to enable efficient, decentralized, iterative reference building, and the contextualization of new datasets with existing references without sharing raw data. Using examples from mouse brain, pancreas, and whole organism atlases, we showcase that scArches preserves nuanced biological state information while removing batch effects in the data, despite using four orders of magnitude fewer parameters compared to de novo integration. To demonstrate mapping disease variation, we show that scArches preserves detailed COVID-19 disease variation upon reference mapping, enabling discovery of new cell identities that are unseen during training. We envision our method to facilitate collaborative projects by enabling the iterative construction, updating, sharing, and efficient use of reference atlases.

bioinformatics↗

bioRxiv2020

Swarm Learning as a privacy-preserving machine learning approach for disease classification

Identification of patients with life-threatening diseases including leukemias or infections such as tuberculosis and COVID-19 is an important goal of precision medicine. We recently illustrated that leukemia patients are identified by machine learning (ML) based on their blood transcriptomes. However, there is an increasing divide between what is technically possible and what is allowed because of privacy legislation. To facilitate integration of any omics data from any data owner world-wide without violating privacy laws, we here introduce Swarm Learning (SL), a decentralized machine learning approach uniting edge computing, blockchain-based peer-to-peer networking and coordination as well as privacy protection without the need for a central coordinator thereby going beyond federated learning. Using more than 14,000 blood transcriptomes derived from over 100 individual studies with non-uniform distribution of cases and controls and significant study biases, we illustrate the feasibility of SL to develop disease classifiers based on distributed data for COVID-19, tuberculosis or leukemias that outperform those developed at individual sites. Still, SL completely protects local privacy regulations by design. We propose this approach to noticeably accelerate the introduction of precision medicine.

bioinformatics↗

bioRxiv2020

Alterations of multiple alveolar macrophage states in chronic obstructive pulmonary disease

Despite the epidemics of chronic obstructive pulmonary disease (COPD), the cellular and molecular mechanisms of this disease are far from being understood. Here, we characterize and classify the cellular composition within the alveolar space and peripheral blood of COPD patients and control donors using a clinically applicable single-cell RNA-seq technology corroborated by advanced computational approaches for: machine learning-based cell-type classification, identification of differentially expressed genes, prediction of metabolic changes, and modeling of cellular trajectories within a patient cohort. These high-resolution approaches revealed: massive transcriptional plasticity of macrophages in the alveolar space with increased levels of invading and proliferating cells, loss of MHC expression, reduced cellular motility, altered lipid metabolism, and a metabolic shift reminiscent of mitochondrial dysfunction in COPD patients. Collectively, single-cell omics of multi-tissue samples was used to build the first cellular and molecular framework for COPD pathophysiology as a prerequisite to develop molecular biomarkers and causal therapies against this deadly disease.

systems biology↗

bioRxiv2020

Benchmarking atlas-level data integration in single-cell genomics

Cell atlases often include samples that span locations, labs, and conditions, leading to complex, nested batch effects in data. Thus, joint analysis of atlas datasets requires reliable data integration. Choosing a data integration method is a challenge due to the difficulty of defining integration success. Here, we benchmark 38 method and preprocessing combinations on 77 batches of gene expression, chromatin accessibility, and simulation data from 23 publications, altogether representing >1.2 million cells distributed in nine atlas-level integration tasks. Our integration tasks span several common sources of variation such as individuals, species, and experimental labs. We evaluate methods according to scalability, usability, and their ability to remove batch effects while retaining biological variation. Using 14 evaluation metrics, we find that highly variable gene selection improves the performance of data integration methods, whereas scaling pushes methods to prioritize batch removal over conservation of biological variation. Overall, BBKNN, Scanorama, and scVI perform well, particularly on complex integration tasks; Seurat v3 performs well on simpler tasks with distinct biological signals; and methods that prioritize batch removal perform best for ATAC-seq data integration. Our freely available reproducible python module can be used to identify optimal data integration methods for new data, benchmark new methods, and improve method development.

bioinformatics↗

bioRxiv2020

Network reconstruction for trans acting genetic loci using multi-omics data and prior information

BackgroundMolecular multi-omics data provide an in-depth view on biological systems, and their integration is crucial to gain insights in complex regulatory processes. These data can be used to explain disease related genetic variants by linking them to intermediate molecular traits (quantitative trait loci, QTL). Molecular networks regulating cellular processes leave footprints in QTL results as so-called trans -QTL hotspots. Reconstructing these networks is a complex endeavor and use of biological prior information has been proposed to alleviate network inference. However, previous efforts were limited in the types of priors used or have only been applied to model systems. In this study, we reconstruct the regulatory networks underlying trans -QTL hotspots using human cohort data and data-driven prior information. ResultsWe devised a strategy to integrate QTL with human population scale multi-omics data and comprehensively curated prior information from large-scale biological databases. State-of-the art network inference methods applied to these data and priors were used to recover the regulatory networks underlying trans -QTL hotspots. We benchmarked inference methods and showed, that Bayesian strategies using biologically-informed priors outperform methods without prior data in simulated data and show better replication across datasets. Application of our approach to human cohort data highlighted two novel regulatory networks related to schizophrenia and lean body mass for which we generated novel functional hypotheses. ConclusionWe demonstrate, that existing biological knowledge can be leveraged for the integrative analysis of networks underlying trans associations to deduce novel hypotheses on cell regulatory mechanisms.

bioinformatics↗

bioRxiv2020

Deep learning the collisional cross sections of the peptide universe from a million training samples

The size and shape of peptide ions in the gas phase are an under-explored dimension for mass spectrometry-based proteomics. To explore the nature and utility of the entire peptide collisional cross section (CCS) space, we measure more than a million data points from whole-proteome digests of five organisms with trapped ion mobility spectrometry (TIMS) and parallel accumulation - serial fragmentation (PASEF). The scale and precision (CV <1%) of our data is sufficient to train a deep recurrent neural network that accurately predicts CCS values solely based on the peptide sequence. Cross section predictions for the synthetic ProteomeTools library validate the model within a 1.3% median relative error (R > 0.99). Hydrophobicity, position of prolines and histidines are main determinants of the cross sections in addition to sequence-specific interactions. CCS values can now be predicted for any peptide and organism, forming a basis for advanced proteomics workflows that make full use of the additional information.

systems biology↗

bioRxiv2020

In vivo identification of apoptotic and extracellular vesicle-bound live cells using image-based deep learning

The in vivo detection of dead cells remains a major challenge due to technical hurdles. Here we present a novel method, where injection of fluorescent Milk fat globule-EGF factor 8 protein (MFG-E8) in vivo combined with imaging flow cytometry and deep learning allows the identification of dead cells based on their surface exposure of phosphatidylserine (PS) and other image parameters. A convolutional autoencoder (CAE) was trained on defined pictures and successfully used to identify apoptotic cells in vivo. However, unexpectedly, these analyses also revealed that the great majority of PS+ cells were not apoptotic, but rather live cells associated with PS+ extracellular vesicles (EVs). During acute viral infection apoptotic cells increased slightly, while up to 30% of lymphocytes were decorated with PS+ EVs of Dendritic cell exosomal origin. The combination of recombinant fluorescent MFG-E8 and the CAE-method will greatly facilitate analyses of cell death and EVs in vivo.

cell biology↗

bioRxiv2020

Design and power analysis for multi-sample single cell genomics experiments

BackgroundThe identification of genes associated with specific experimental conditions, genotypes or phenotypes through differential expression analysis has long been the cornerstone of transcriptomic analysis. Single cell RNA-seq is revolutionizing transcriptomics and is enabling interindividual differential gene expression analysis and identification of genetic variants associated with gene expression, so called expression quantitative trait loci at cell-type resolution. Current methods for power analysis and guidance of experimental design either do not account for the specific characteristics of single cell data or are not suitable to model interindividual comparisons. ResultsHere we present a statistical framework for experimental design and power analysis of single cell differential gene expression between groups of individuals and expression quantitative trait locus analysis. The model relates sample size, number of cells per individual and sequencing depth to the power of detecting differentially expressed genes within individual cell types. Power analysis is based on data driven priors from literature or pilot experiments across a wide range of application scenarios and single cell RNA-seq platforms. Using these priors we show that, for a fixed budget, the number of cells per individual is the major determinant of power. ConclusionOur model is general and allows for systematic comparison of alternative experimental designs and can thus be used to guide experimental design to optimize power. For a wide range of applications, shallow sequencing of high numbers of cells per individual leads to higher overall power than deep sequencing of fewer cells. The model is implemented as an R package scPower.

bioinformatics↗

bioRxiv2020

Automatic identification of relevant genes from low-dimensional embeddings of single cell RNAseq data

Dimensionality reduction is a key step in the analysis of single-cell RNA sequencing data and produces a low-dimensional embedding for visualization and as a calculation base for downstream analysis. Nonlinear techniques are most suitable to handle the intrinsic complexity of large, heterogeneous single cell data. With no linear relation between genes and embedding however, there is no way to extract the identity of genes most relevant for any cells position in the low-dimensional embedding, and thus the underlying process. In this paper, we introduce the concepts of global and local gene relevance to compute an equivalent of principal component analysis loadings for non-linear low-dimensional embeddings. While global gene relevance identifies drivers of the overall embedding, local gene relevance singles out genes that change in small, possibly rare subsets of cells. We apply our method to single-cell RNAseq datasets from different experimental protocols and to different low dimensional embedding techniques, shows our methods versatility to identify key genes for a variety of biological processes. To ensure reproducibility and ease of use, our method is released as part of destiny 3.0, a popular R package for building diffusion maps from single-cell transcriptomic data. It is readily available through Bioconductor.

bioinformatics↗

bioRxiv2020

AutoGeneS: Automatic gene selection using multi-objective optimization for RNA-seq deconvolution

Tissues are complex systems of interacting cell types. Knowing cell-type proportions in a tissue is very important to identify which cells or cell types are targeted by a disease or perturbation. When measuring such responses using RNA-seq, bulk RNA-seq masks cellular heterogeneity. Hence, several computational methods have been proposed to infer cell-type proportions from bulk RNA samples. Their performance with noisy reference profiles highly depends on the set of genes undergoing deconvolution. These genes are often selected based on prior knowledge or a single-criterion test that might not be useful to dissect closely correlated cell types. In this work, we introduce AutoGeneS, a tool that automatically extracts informative genes and reveals the cellular heterogeneity of bulk RNA samples. AutoGeneS requires no prior knowledge about marker genes and selects genes by simultaneously optimizing multiple criteria: minimizing the correlation and maximizing the distance between cell types. It can be applied to reference profiles from various sources like single-cell experiments or sorted cell populations. Results from human samples of peripheral blood illustrate that AutoGeneS outperforms other methods. Our results also highlight the impact of our approach on analyzing bulk RNA samples with noisy single-cell reference profiles and closely correlated cell types. Ground truth cell proportions analyzed by flow cytometry confirmed the accuracy of the predictions of AutoGeneS in identifying cell-type proportions. AutoGeneS is available for use via a standalone Python package (https://github.com/theislab/AutoGeneS).

bioinformatics↗

bioRxiv2020

Cell-type specific impact of glucocorticoid receptor activation on the developing brain

A fine-tuned balance of glucocorticoid receptor (GR) activation is essential for organ formation, with disturbances influencing health outcomes. Excess GR-activation in utero has been linked to brain-related negative outcomes, with unclear underlying mechanisms, especially regarding cell-type specific effects. To address this, we used an in vitro model of fetal human brain, induced pluripotent-stem-cell-derived cerebral organoids, and mapped GR-activation effects using single-cell transcriptomics across development. Interestingly, neurons showed targeted regulation of differentiation- and maturation-related transcripts, suggesting a delay of these processes upon GR-activation. Uniquely in neurons, differentially-expressed transcripts were significantly enriched for genes associated with behavior-related phenotypes and disorders. This suggests that aberrant GR-activation could impact proper neuronal maturation, leading to increased disease susceptibility, through neurodevelopmental processes at the interface of genetic susceptibility and environmental exposure.

neuroscience↗

bioRxiv2019

Self-supervised retinal thickness prediction enables deep learning from unlabeled data to boost classification of diabetic retinopathy

Access to large, annotated samples represents a considerable challenge for training accurate deep-learning models in medical imaging. While current leading-edge transfer learning from pre-trained models can help with cases lacking data, it limits design choices, and generally results in the use of unnecessarily large models. We propose a novel, self-supervised training scheme for obtaining high-quality, pre-trained networks from unlabeled, cross-modal medical imaging data, which will allow for creating accurate and efficient models. We demonstrate this by accurately predicting optical coherence tomography (OCT)-based retinal thickness measurements from simple infrared (IR) fundus images. Subsequently, learned representations outperformed advanced classifiers on a separate diabetic retinopathy classification task in a scenario of scarce training data. Our cross-modal, three-staged scheme effectively replaced 26,343 diabetic retinopathy annotations with 1,009 semantic segmentations on OCT and reached the same classification accuracy using only 25% of fundus images, without any drawbacks, since OCT is not required for predictions. We expect this concept will also apply to other multimodal clinical data-imaging, health records, and genomics data, and be applicable to corresponding sample-starved learning problems.

bioinformatics↗

bioRxiv2019

A Sparse Deep Learning Approach for Automatic Segmentation of Human Vasculature in Multispectral Optoacoustic Tomography

Multispectral Optoacoustic Tomography (MSOT) resolves oxy- (HbO2) and deoxy-hemoglobin (Hb) to perform vascular imaging. MSOT suffers from gradual signal attenuation with depth due to light-tissue interactions: an effect that hinders the precise manual segmentation of vessels. Furthermore, vascular assessment requires functional tests, which last several minutes and result in recording thousands of images. Here, we introduce a deep learning approach with a sparse UNET (S-UNET) for automatic vascular segmentation in MSOT images to avoid the rigorous and time-consuming manual segmentation. We evaluated the S-UNET on a test-set of 33 images, achieving a median DICE score of 0.88. Apart from high segmentation performance, our method based its decision on two wavelengths with physical meaning for the task-at-hand: 850 nm (peak absorption of oxy-hemoglobin) and 810 nm (isosbestic point of oxy-and deoxy-hemoglobin). Thus, our approach achieves precise data-driven vascular segmentation for automated vascular assessment and may boost MSOT further towards its clinical translation.

bioengineering↗

bioRxiv2019

Generalizing RNA velocity to transient cell states through dynamical modeling

The introduction of RNA velocity in single cells has opened up new ways of studying cellular differentiation. The originally proposed framework obtains velocities as the deviation of the observed ratio of spliced and unspliced mRNA from an inferred steady state. Errors in velocity estimates arise if the central assumptions of a common splicing rate and the observation of the full splicing dynamics with steady-state mRNA levels are violated. With scVelo (https://scvelo.org), we address these restrictions by solving the full transcriptional dynamics of splicing kinetics using a likelihood-based dynamical model. This generalizes RNA velocity to a wide variety of systems comprising transient cell states, which are common in development and in response to perturbations. We infer gene-specific rates of transcription, splicing and degradation, and recover the latent time of the underlying cellular processes. This latent time represents the cells internal clock and is based only on its transcriptional dynamics. Moreover, scVelo allows us to identify regimes of regulatory changes such as stages of cell fate commitment and, therein, systematically detects putative driver genes. We demonstrate that scVelo enables disentangling heterogeneous subpopulation kinetics with unprecedented resolution in hippocampal dentate gyrus neurogenesis and pancreatic endocrinogenesis. We anticipate that scVelo will greatly facilitate the study of lineage decisions, gene regulation, and pathway activity identification.

bioinformatics↗

bioRxiv2019

Predicting antigen-specificity of single T-cells based on TCR CDR3 regions

It has recently become possible to assay T-cell specificity with respect to large sets of antigens as well as T-cell receptor sequence in high-throughput single-cell experiments. We propose multiple sequence-data specific deep learning approaches to impute TCR to epitope specificity to reduce the complexity of new experiments. We found that models that treat antigens as categorical variables outperform those which model the TCR and epitope sequence jointly. Moreover, we show that variability in single-cell immune repertoire screens can be mitigated by modeling cell-specific covariates.

bioinformatics↗

bioRxiv2019

Longitudinal single cell transcriptomics reveals Krt8+ alveolar epithelial progenitors in lung regeneration

Lung injury activates quiescent stem and progenitor cells to regenerate alveolar structures. The sequence and coordination of transcriptional programs during this process has largely remained elusive. Using single cell RNA-seq, we first generated a whole-organ birds-eye view on cellular dynamics and cell-cell communication networks during mouse lung regeneration from [~]30,000 cells at six timepoints. We discovered an injury-specific progenitor cell state characterized by Krt8 in flat epithelial cells covering alveolar surfaces. The number of these cells peaked during fibrogenesis in independent mouse models, as well as in human acute lung injury and fibrosis. Krt8+ progenitors featured a highly distinct connectome of receptor-ligand pairs with endothelial cells, fibroblasts, and macrophages. To sky dive into epithelial differentiation dynamics, we sequenced >30,000 sorted epithelial cells at 18 timepoints and computationally derived cell state trajectories that were validated by lineage tracing genetic reporter mice. Airway stem cells within the club cell lineage and alveolar type-2 cells underwent transcriptional convergence onto the same Krt8+ progenitor cell state, which later resolved by terminal differentiation into alveolar type-1 cells. We derived distinct transcriptional regulators as key switch points in this process and show that induction of TNF-alpha/NFkappaB, p53, and hypoxia driven gene expression programs precede a Sox4, Ctnnb1, and Wwtr1 driven switch towards alveolar type-1 cell fate. We show that epithelial cell plasticity can induce non-gradual transdifferentiation, involving intermediate progenitor cell states that may persist and promote disease if checkpoint signals for terminal differentiation are perturbed.

systems biology↗

bioRxiv2019

Deep learning does not outperform classical machine learning for cell-type annotation

Deep learning has revolutionized image analysis and natural language processing with remarkable accuracies in prediction tasks, such as image labeling and semantic segmentation or named-entity recognition and semantic role labeling. Specifically, the combination of algorithmic and hardware advances with the appearance of large and well-labeled datasets has led up to seminal contributions in these fields. The emergence of large amounts of data from single-cell RNA-seq and the recent global effort to chart all cell types in the Human Cell Atlas has attracted an interest in deep-learning applications. However, all current approaches are unsupervised, i.e., learning of latent spaces without using any cell labels, even though supervised learning approaches are often more powerful in feature learning and the most popular approach in the current AI revolution by far. Here, we ask why this is the case. In particular we ask whether supervised deep learning can be used for cell annotation, i.e. to predict cell-type labels from single-cell gene expression profiles. After evaluating 10 classification methods across 14 datasets, we notably find that deep learning does not outperform classical machine-learning methods in the task. Thus, cell-type prediction based on gene-signature derived cell-type labels is potentially too simplistic a task for complex non-linear methods, which demands better labels of functional single-cell readouts.

bioinformatics↗