Biology subjects

Theis, F. J.

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

At least 109 records · Page 6Linked to original sources

bioRxiv2021

anndata: Annotated data

anndata is a Python package for handling annotated data matrices in memory and on disk (github.com/theislab/anndata), positioned between pandas and xarray. anndata offers a broad range of computationally efficient features including, among others, sparse data support, lazy operations, and a PyTorch interface. Statement of needGenerating insight from high-dimensional data matrices typically works through training models that annotate observations and variables via low-dimensional representations. In exploratory data analysis, this involves iterative training and analysis using original and learned annotations and task-associated representations. anndata offers a canonical data structure for book-keeping these, which is neither addressed by pandas (McKinney, 2010), nor xarray (Hoyer & Hamman, 2017), nor commonly-used modeling packages like scikit-learn (Pedregosa et al., 2011).

bioinformatics↗

bioRxiv2021

Multiple trajectory alignment reconstructs disease dynamics for discovery and clinical benefit

Diseases change over time, both phenotypically and in underlying driving molecular processes. Though understanding disease progression dynamics is critical for diagnostics and treatment, capturing these dynamics is difficult, due to their complexity and the high heterogeneity between individuals. We developed TimeAx, an algorithm which builds a comparative framework for capturing disease dynamics using high-dimensional short time-series data. We demonstrate TimeAx utility by studying disease progression dynamics for multiple diseases and data types. Notably, for urothelial bladder cancer tumorigenesis, we identified a stromal pro-invasion point on the disease progression axis, characterized by massive immune cell infiltration to the tumor microenvironment and increased mortality. Moreover, the continuous TimeAx model differentiated between early and late tumors within the same tumor subtype, uncovering novel molecular transitions and potential targetable pathways. Overall, we present a powerful approach for studying disease progression dynamics, providing improved molecular interpretability and clinical benefits for patient stratification and outcome prediction.

systems biology↗

bioRxiv2021

Proteomics of spatially identified tissues in whole organs

Spatial molecular profiling of complex tissues is essential to investigate cellular function in physiological and pathological states. However, methods for molecular analysis of biological specimens imaged in 3D as a whole are lacking. Here, we present DISCO-MS, a technology combining whole-organ imaging, deep learning-based image analysis, and ultra-high sensitivity mass spectrometry. DISCO-MS yielded qualitative and quantitative proteomics data indistinguishable from uncleared samples in both rodent and human tissues. Using DISCO-MS, we investigated microglia activation locally along axonal tracts after brain injury and revealed known and novel biomarkers. Furthermore, we identified initial individual amyloid-beta plaques in the brains of a young familial Alzheimers disease mouse model, characterized the core proteome of these aggregates, and highlighted their compositional heterogeneity. Thus, DISCO-MS enables quantitative, unbiased proteome analysis of target tissues following unbiased imaging of entire organs, providing new diagnostic and therapeutic opportunities for complex diseases, including neurodegeneration. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=141 SRC="FIGDIR/small/466753v1_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@1e37035org.highwire.dtl.DTLVardef@dbaa98org.highwire.dtl.DTLVardef@19cece1org.highwire.dtl.DTLVardef@183c032_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIDISCO-MS combines tissue clearing, whole-organ imaging, deep learning-based image analysis, and ultra-high sensitivity mass spectrometry C_LIO_LIDISCO-MS yielded qualitative and quantitative proteomics data indistinguishable from fresh tissues C_LIO_LIDISCO-MS enables identification of rare pathological regions & their subsequent molecular analysis C_LIO_LIDISCO-MS revealed core proteome of plaques in 6 weeks old Alzheimer s disease mouse model Supplementary Video can be seen at: http://discotechnologies.org/DISCO-MS/ C_LI

bioengineering↗

bioRxiv2021

T cells modulate the microglial response to brain ischemia

Neuroinflammation after stroke is characterized by the activation of resident microglia and the invasion of circulating leukocytes into the brain. Although lymphocytes infiltrate the brain in small number, they have been consistently demonstrated to be the most potent leukocyte subpopulation contributing to secondary inflammatory brain injury. However, the exact mechanism how this minimal number of lymphocytes can profoundly affect stroke outcome is still largely elusive. Here, using a mouse model for ischemic stroke, we demonstrated that early activation of microglia in response to stroke is differentially regulated by distinct T cell subpopulations. Acute treatment with engineered T cells overexpressing IL-10 administered into the cisterna magna after stroke induces a switch of microglial gene expression to a profile associated with pro-regenerative functions. These findings substantiate the role of T cells in stroke with large impact on the cerebral inflammatory milieu by polarizing the microglial phenotype. Targeting T cell-microglia interactions can have direct translational relevance for further development of immune-targeted therapies for stroke and other neuroinflammatory conditions. SummaryThe crosstalk between brain infiltrating T cells and microglia in response to stroke remains elusive. Benakis et al. report that transcriptional signature of the stroke-associated microglia is reprogrammed by distinct T cell subpopulations. Engineered T cells overexpressing IL-10 administered four hours after stroke reinitiate microglial function inducing a pro-regenerative environment.

immunology↗

bioRxiv2021

Out-of-distribution prediction with disentangled representations for single-cell RNA sequencing data

Learning robust representations can help uncover underlying biological variation in scRNA-seq data. Disentangled representation learning is one approach to obtain such informative as well as interpretable representations. Here, we learn disentangled representations of scRNA-seq data using {beta} variational autoencoder ({beta}-VAE) and apply the model for out-of-distribution (OOD) prediction. We demonstrate accurate gene expression predictions for cell-types absent from training in a perturbation and a developmental dataset. We further show that {beta}-VAE outperforms a state-of-the-art disentanglement method for scRNA-seq in OOD prediction while achieving better disentanglement performance.

bioinformatics↗

bioRxiv2021

Over 1000 tools reveal trends in the single-cell RNA-seq analysis landscape

Recent years have seen a revolution in single-cell technologies, particularly single-cell RNA-sequencing (scRNA-seq). As the number, size and complexity of scRNA-seq datasets continue to increase, so does the number of computational methods and software tools for extracting meaning from them. Since 2016 the scRNA-tools database has catalogued software tools for analysing scRNA-seq data. With the number of tools in the database passing 1000, we take this opportunity to provide an update on the state of the project and the field. Analysis of five years of analysis tool tracking data clearly shows the evolution of the field, and that the focus of developers has moved from ordering cells on continuous trajectories to integrating multiple samples and making use of reference datasets. We also find evidence that open science practices reward developers with increased recognition and help accelerate the field.

bioinformatics↗

bioRxiv2021

Learning cell communication from spatial graphs of cells

Tissue niches are sources of cellular variation and key to understanding both single-cell and tissue phenotypes. The interaction of a cell with its niche can be described through cell communication events. These events cannot be directly observed in molecular profiling assays of single cells and have to be inferred. However, computational models of cell communication and variance attribution defined on data from dissociated tissues suffer from multiple limitations with respect to their ability to define and to identify communication events. We address these limitations using spatial molecular profiling data with node-centric expression modeling (NCEM), a computational method based on graph neural networks which reconciles variance attribution and communication modeling in a single model of tissue niches. We use these models in varying complexity across spatial assays, such as immunohistochemistry and MERFISH, and biological systems to demonstrate that the statistical cell-cell dependencies discovered by NCEM are plausible signatures of known molecular processes underlying cell communication. We identify principles of tissue organisation as cell communication events across multiple datasets using interpretation mechanisms. In the primary motor cortex, we found gene expression variation that is due to niche composition variation across cortical depth. Using the same approach, we also identified niche-dependent cell state variation in CD8 T cells from inflamed colon and colorectal cancer. Finally, we show that NCEMs can be extended to mixed models of explicit cell communication events and latent intrinsic sources of variation in conditional variational autoencoders to yield holistic models of cellular variation in spatial molecular profiling data. Altogether, this graphical model of cellular niches is a step towards understanding emergent tissue phenotypes.

bioinformatics↗

bioRxiv2021

Low numbers of cytokine transcripts drive inflammatory skin diseases by initiating amplification cascades in localized epidermal clusters

Abundant polyclonal T cells infiltrate chronic inflammatory diseases and characterization of these cells is needed to distinguish disease-driving from bystander immune cells. Here, we investigated 52,000 human cutaneous transcriptomes of non-communicable inflammatory skin diseases (ncISD) using spatial transcriptomics. Despite the expected T cell infiltration, we observed only 1-10 pathogenic T cell cytokine per skin section. Cytokine expression was limited to lesional skin and presented in a disease-specific pattern. In fact, we identified responder signatures in direct proximity of cytokines, and showed that single cytokine transcripts initiate amplification cascades of thousands of specific responder transcripts forming localized epidermal clusters. Thus, within the abundant and polyclonal T cell infiltrates of ncISD, only a few T cells drive disease by initiating an inflammatory amplification cascade in their local microenvironment.

immunology↗

bioRxiv2021

scvi-tools: a library for deep probabilistic analysis of single-cell omics data

AO_SCPLOWBSTRACTC_SCPLOWProbabilistic models have provided the underpinnings for state-of-the-art performance in many single-cell omics data analysis tasks, including dimensionality reduction, clustering, differential expression, annotation, removal of unwanted variation, and integration across modalities. Many of the models being deployed are amenable to scalable stochastic inference techniques, and accordingly they are able to process single-cell datasets of realistic and growing sizes. However, the community-wide adoption of probabilistic approaches is hindered by a fractured software ecosystem resulting in an array of packages with distinct, and often complex interfaces. To address this issue, we developed scvi-tools (https://scvi-tools.org), a Python package that implements a variety of leading probabilistic methods. These methods, which cover many fundamental analysis tasks, are accessible through a standardized, easy-to-use interface with direct links to Scanpy, Seurat, and Bioconductor workflows. By standardizing the implementations, we were able to develop and reuse novel functionalities across different models, such as support for complex study designs through nonlinear removal of unwanted variation due to multiple covariates and reference-query integration via scArches. The extensible software building blocks that underlie scvi-tools also enable a developer environment in which new probabilistic models for single cell omics can be efficiently developed, benchmarked, and deployed. We demonstrate this through a code-efficient reimplementation of Stereoscope for deconvolution of spatial transcriptomics profiles. By catering to both the end user and developer audiences, we expect scvi-tools to become an essential software dependency and serve to formulate a community standard for probabilistic modeling of single cell omics.

bioinformatics↗

bioRxiv2021

Compositional perturbation autoencoder for single-cell response modeling

Recent advances in multiplexed single-cell transcriptomics experiments are facilitating the high-throughput study of drug and genetic perturbations. However, an exhaustive exploration of the combinatorial perturbation space is experimentally unfeasible, so computational methods are needed to predict, interpret, and prioritize perturbations. Here, we present the compositional perturbation autoencoder (CPA), which combines the interpretability of linear models with the flexibility of deep-learning approaches for single-cell response modeling. CPA encodes and learns transcriptional drug responses across different cell type, dose, and drug combinations. The model produces easy-to-interpret embeddings for drugs and cell types, which enables drug similarity analysis and predictions for unseen dosage and drug combinations. We show that CPA accurately models single-cell perturbations across compounds, doses, species, and time. We further demonstrate that CPA predicts combinatorial genetic interactions of several types, implying that it captures features that distinguish different interaction programs. Finally, we demonstrate that CPA can generate in-silico 5,329 missing genetic combination perturbations (97.6% of all possibilities) with diverse genetic interactions. We envision our model will facilitate efficient experimental design and hypothesis generation by enabling in-silico response prediction at the single-cell level, and thus accelerate therapeutic applications using single-cell technologies.

bioinformatics↗

bioRxiv2021

Single cell trajectory analysis of human pluripotent stem cells differentiating towards lung and hepatocyte progenitors

Understanding the development of human respiratory tissues is crucial for modeling and treating lung disorders. The molecular details for the specification of lung progenitors from human pluripotent stem cells (hPSCs) are unclear. Here, we use single cell RNA-sequencing with high temporal resolution along an optimized differentiation protocol to determine the transcriptional hierarchy of lung specification from human hPSCs and map out the underlying single cell trajectories. We show that Sonic hedgehog, TGF-{beta} and Notch activation are required in an ISL1/NKX2-1 trajectory that gives rise to lung progenitors during the foregut endoderm stage. Induction of HHEX marks an alternative trajectory at the early definitive endoderm stage, which precedes the lung trajectory and generates a major hepatoblast population. Moreover, neither KDR+ nor mesendoderm progenitors are apparent intermediate states of lung and hepatic lineages. Our hierarchical multistep model predicts mechanisms leading to lung organogenesis, and creates a basis for studying early human lung development, as well as hPSC based disease and drug research. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=132 SRC="FIGDIR/small/432413v2_ufig1.gif" ALT="Figure 1"> View larger version (51K): org.highwire.dtl.DTLVardef@12143b7org.highwire.dtl.DTLVardef@e4aa1dorg.highwire.dtl.DTLVardef@5c5766org.highwire.dtl.DTLVardef@6e2432_HPS_FORMAT_FIGEXP M_FIG C_FIG

developmental biology↗

bioRxiv2021

Squidpy: a scalable framework for spatial single cell analysis

Spatial omics data are advancing the study of tissue organization and cellular communication at an unprecedented scale. Here, we present Squidpy, a Python framework that brings together tools from omics and image analysis to enable scalable description of spatial molecular data, such as transcriptome or multivariate proteins. Squidpy provides both infrastructure and numerous analysis methods that allow to efficiently store, manipulate and interactively visualize spatial omics data.

bioinformatics↗

bioRxiv2020

Ultra-high sensitivity mass spectrometry quantifies single-cell proteome changes upon perturbation

Single-cell technologies are revolutionizing biology but are today mainly limited to imaging and deep sequencing1-3. However, proteins are the main drivers of cellular function and in-depth characterization of individual cells by mass spectrometry (MS)-based proteomics would thus be highly valuable and complementary4,5. Chemical labeling-based single-cell approaches introduce hundreds of cells into the MS, but direct analysis of single cells has not yet reached the necessary sensitivity, robustness and quantitative accuracy to answer biological questions6,7. Here, we develop a robust workflow combining miniaturized sample preparation, very low flow-rate chromatography and a novel trapped ion mobility mass spectrometer, resulting in a more than ten-fold improved sensitivity. We accurately and robustly quantify proteomes and their changes in single, FACS-isolated cells. Arresting cells at defined stages of the cell cycle by drug treatment retrieves expected key regulators such as CDK2NA, the E2 ubiquitin ligase UBE2S, DNA topoisomerases TOP2A/B and the chromatin regulator HMGA1. Furthermore, it highlights potential novel ones and allows cell phase prediction. Comparing the variability in more than 430 single-cell proteomes to transcriptome data revealed a stable core proteome despite perturbation, while the transcriptome appears volatile. This emphasizes substantial regulation of translation and sets the stage for its elucidation at the single cell level. Our technology can readily be applied to ultra-high sensitivity analyses of tissue material8, posttranslational modifications and small molecule studies to gain unprecedented insights into cellular heterogeneity in health and disease.

systems biology↗

bioRxiv2020

Sfaira accelerates data and model reuse in single cell genomics

Exploratory analysis of single-cell RNA-seq data sets is currently based on statistical and machine learning models that are adapted to each new data set from scratch. A typical analysis workflow includes a choice of dimensionality reduction, selection of clustering parameters, and mapping of prior annotation. These steps typically require several iterations and can take up significant time in many single-cell RNA-seq projects. Here, we introduce sfaira, which is a single-cell data and model zoo which houses data sets as well as pre-trained models. The data zoo is designed to facilitate the fast and easy contribution of data sets, interfacing to a large community of data providers. Sfaira currently includes 233 data sets across 45 organs and 3.1 million cells in both human and mouse. Using these data sets we have trained eight different example model classes, such as autoencoders and logistic cell type predictors: The infrastructure of sfaira is model agnostic and allows training und usage of many previously published models. Sfaira directly aids in exploratory data analysis by replacing embedding and cell type annotation workflows with end-to-end pre-trained parametric models. As further example use cases for sfaira, we demonstrate the extraction of gene-centric data statistics across many tissues, improved usage of cell type labels at different levels of coarseness, and an application for learning interpretable models through data regularization on extremely diverse data sets.

bioinformatics↗

bioRxiv2020

scCODA: A Bayesian model for compositional single-cell data analysis

Compositional changes of cell types are main drivers of biological processes. Their detection through single-cell experiments is difficult due to the compositionality of the data and low sample sizes. We introduce scCODA (https://github.com/theislab/scCODA), a Bayesian model addressing these issues enabling the study of complex cell type effects in disease, and other stimuli. scCODA demonstrated excellent detection performance and identified experimentally verified cell type changes that were missed in original analyses.

bioinformatics↗

bioRxiv2020

Learning interpretable latent autoencoder representations with annotations of feature sets

Existing methods for learning latent representations for single-cell RNA-seq data are based on autoencoders and factor models. However, representations learned by autoencoders are hard to interpret and representations learned by factor models have limited flexibility. Here, we introduce a framework for learning interpretable autoencoders based on regularized linear decoders. It decomposes variation into interpretable components using prior knowledge in the form of annotated feature sets obtained from public databases. Through this, it provides an alternative to enrichment techniques and factor models for the task of explaining observed variation with biological knowledge. Benchmarking our model on two single-cell RNA-seq datasets, we demonstrate how our model outperforms an existing factor model regarding scalability while maintaining interpretability.

bioinformatics↗

bioRxiv2020

CellRank for directed single-cell fate mapping

Computational trajectory inference enables the reconstruction of cell-state dynamics from single-cell RNA sequencing experiments. However, trajectory inference requires that the direction of a biological process is known, largely limiting its application to differentiating systems in normal development. Here, we present CellRank (https://cellrank.org) for mapping the fate of single cells in diverse scenarios, including perturbations such as regeneration or disease, for which direction is unknown. Our approach combines the robustness of trajectory inference with directional information from RNA velocity, derived from ratios of spliced to unspliced reads. CellRank takes into account both the gradual and stochastic nature of cellular fate decisions, as well as uncertainty in RNA velocity vectors. On data from pancreas development, we show that it automatically detects initial, intermediate and terminal populations, predicts fate potentials and visualizes continuous gene expression trends along individual lineages. CellRank also predicts a novel dedifferentiation trajectory during regeneration after lung injury, which we follow up experimentally by confirming the existence of previously unknown intermediate cell states.

bioinformatics↗

bioRxiv2020

Identification and characterization of distinct murine brown adipocyte lineages

Brown adipose tissue (BAT) plays an important role in the regulation of body weight and glucose homeostasis. While increasing evidence supports white adipose tissue heterogeneity, little is known about heterogeneity within murine BAT. Using single cell RNA sequencing of the stromal vascular fraction of murine BAT and analysis of 67 brown preadipocyte and adipocyte clones we unravel heterogeneity within brown preadipocytes. Statistical analysis of gene expression profiles from these clones identifies markers distinguishing brown adipocyte lineages. We confirm the presence of distinct brown adipocyte populations in vivo using three identified markers; Eif5, Tcf25, and Bin1. Functionally, we demonstrate that loss of Bin1 enhances UCP1 expression and mitochondrial respiration, suggesting that Bin1 marks a dormant brown adipocyte type. The existence of multiple brown adipocyte lineages suggests distinct functional properties of BAT depending on its cellular composition, with potentially distinct function in thermogenesis and the regulation of whole body energy homeostasis.

cell biology↗