Biology subjects

Theis, F. J.

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

At least 91 records · Page 5Linked to original sources

bioRxiv2023

Optimizing Xenium In Situ data utility by quality assessment and best practice analysis workflows

The Xenium In Situ platform is a new spatial transcriptomics product commercialized by 10X Genomics capable of mapping hundreds of transcripts in situ at a subcellular resolution. Given the multitude of commercially available spatial transcriptomics technologies, recommendations in choice of platform and analysis guidelines are increasingly important. Herein, we explore eight preview Xenium datasets of the mouse brain and two of human breast cancer by comparing scalability, resolution, data quality, capacities and limitations with eight other spatially resolved transcriptomics technologies. In addition, we benchmarked the performance of multiple open source computational tools when applied to Xenium datasets in tasks including cell segmentation, segmentation-free analysis, selection of spatially variable genes and domain identification, among others. This study serves as the first independent analysis of the performance of Xenium, and provides best-practices and recommendations for analysis of such datasets.

bioinformatics↗

bioRxiv2023

Ex vivo tissue perturbations coupled to single cell RNA-seq reveal multi-lineage cell circuit dynamics in human lung fibrogenesis

Pulmonary fibrosis develops as a consequence of failed regeneration after injury. Analyzing mechanisms of regeneration and fibrogenesis directly in human tissue has been hampered by the lack of organotypic models and analytical techniques. In this work, we coupled ex vivo cytokine and drug perturbations of human precision-cut lung slices (hPCLS) with scRNAseq and induced a multi-lineage circuit of fibrogenic cell states in hPCLS, which we show to be highly similar to the in vivo cell circuit in a multi-cohort lung cell atlas from pulmonary fibrosis patients. Using micro-CT staged patient tissues, we characterized the appearance and interaction of myofibroblasts, an ectopic endothelial cell state and basaloid epithelial cells in the thickened alveolar septum of early-stage lung fibrosis. Induction of these states in the ex vivo hPCLS model provides evidence that the basaloid cell state was derived from alveolar type-2 cells, whereas the ectopic endothelial cell state emerged from capillary cell plasticity. Cell-cell communication routes in patients were largely conserved in the hPCLS model and anti-fibrotic drug treatments showed highly cell type specific effects. Our work provides an experimental framework for perturbational single cell genomics directly in human lung tissue that enables analysis of tissue homeostasis, regeneration and pathology. We further demonstrate that hPCLS offers novel avenues for scalable, high-resolution drug testing to accelerate anti-fibrotic drug development and translation.

cell biology↗

bioRxiv2022

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

Multiple pancreatic islet single-cell RNA sequencing (scRNA-seq) datasets have been generated to study development, homeostasis, and diabetes. However, there is no consensus on cell states and pathways across conditions as well as the value of preclinical mouse models. Since these challenges can only be resolved by jointly analyzing multiple datasets, we present a scRNA-seq cross-condition mouse islet atlas (MIA). We integrated over 300,000 cells from nine datasets with 56 samples, varying in age, sex, and diabetes models, including an autoimmune type 1 diabetes (T1D) model (NOD), a gluco-/lipotoxicity T2D model (db/db), and a chemical streptozotocin (STZ) {beta}-cell ablation model. MIA is a curated resource for interactive exploration and computational querying, providing new insights inaccessible from individual datasets. The {beta}-cell landscape of MIA revealed new disease progression cell states and cross-publication differences between previously suggested marker genes. We show that in the STZ model {beta}-cells transcriptionally correlate to human T2D and mouse db/db, but are less similar to human T1D and mouse NOD. We observe different pathways shared between immature, aged, and diabetes model {beta}-cells. In conclusion, our work presents the first comprehensive analysis of {beta}-cell responses to different stressors, providing a roadmap for the understanding of {beta}-cell plasticity, compensation, and demise.

bioinformatics↗

bioRxiv2022

Graph neural networks learn emergent tissue properties from spatial molecular profiles

Tissue phenotypes such as metabolic states, inflammation, and tumor properties are functions of molecular states of cells that constitute the tissue. Recent spatial molecular profiling assays measure tissue architecture motifs in a molecular and often unbiased way and thus can explain some aspects of emergence of these phenotypes. Here, we characterize the ability of graph neural networks to model tissue-level emergent phenotypes based on spatial data by evaluating phenotype prediction across model complexities. First, we show that immune cell dispersion in colorectal tumors, which is known to be predictive of disease outcome, can be captured by graph neural networks. Second, we show that breast cancer tumor classes can be predicted from gene expression alone without spatial information and are thus too simplistic a phenotype to require a complex model of emergence. Third, we show that representation learning approaches for spatial graphs of molecular profiles are limited by overfitting in the prevalent regime of up to 100s of images per study. We address overfitting with within-graph self-supervision and illustrate its promise for tissue representation learning as a constraint for node representations.

bioinformatics↗

bioRxiv2022

Robust dimethyl-based multiplex-DIA workflow doubles single-cell proteome depth via a reference channel

Single-cell proteomics aims to characterize biological function and heterogeneity at the level of proteins in an unbiased manner. It is currently limited in proteomic depth, throughput and robustness, a challenge that we address here by a streamlined multiplexed workflow using data-independent acquisition (mDIA). We demonstrate automated and complete dimethyl labeling of bulk or single-cell samples, without losing proteomic depth. In single runs of mammalian cells, a three-plex analysis of tryptic peptides quantified 7,700 proteins per channel. The Lys-N enzyme enables five-plex quantification at MS1 and MS2 level. Because the multiplex channels are quantitatively isolated from each other, mDIA accommodates a reference channel that does not interfere with the target channels. Our algorithm RefQuant takes advantage of this feature and confidently quantifies close to 4,000 proteins in single cells with excellent reproducibility, while our workflow currently allows routine analysis of 80 single cells per day. The concept of a stable proteome still holds at this deeper proteome coverage.

systems biology↗

bioRxiv2022

Population-level integration of single-cell datasets enables multi-scale analysis across samples

The increasing generation of population-level single-cell atlases with hundreds or thousands of samples has the potential to link demographic and technical metadata with high-resolution cellular and tissue data in homeostasis and disease. Constructing such comprehensive references requires large-scale integration of heterogeneous cohorts with varying metadata capturing demographic and technical information. Here, we present single-cell population level integration (scPoli), a semi-supervised conditional deep generative model for data integration, label transfer and query-to-reference mapping. Unlike other models, scPoli learns both sample and cell representations, is aware of cell-type annotations and can integrate and annotate newly generated query datasets while providing an uncertainty mechanism to identify unknown populations. We extensively evaluated the method and showed its advantages over existing approaches. We applied scPoli to two population-level atlases of lung and peripheral blood mononuclear cells (PBMCs), the latter consisting of roughly 8 million cells across 2,375 samples. We demonstrate that scPoli allows atlas-level integration and automatic reference mapping with label transfer. It can explain sample-level biological and technical variations such as disease, anatomical location and assay by means of its novel sample embeddings. We use these embeddings to explore sample-level metadata, enable automatic sample classification and guide a data integration workflow. scPoli also enables simultaneous sample-level and cell-level analysis of gene expression patterns, revealing genes associated with batch effects and the main axes of between-sample variation. We envision scPoli becoming an important tool for population-level single-cell data integration facilitating atlas use but also interpretation by means of multi-scale analyses.

genomics↗

bioRxiv2022

scifAI: Explainable machine learning for profiling the immunological synapse and functional characterization of therapeutic antibodies

Therapeutic antibodies are widely used to treat severe diseases. Most of them alter immune cells and act within the immunological synapse; an essential cell-to-cell interaction to direct the humoral immune response. Although many antibody designs are generated and evaluated, a high-throughput tool for systematic antibody characterization and prediction of function is lacking. Here, we introduce the first comprehensive open-source framework, scifAI (single-cell imaging flow cytometry AI), for preprocessing, feature engineering and explainable, predictive machine learning on imaging flow cytometry (IFC) data. Additionally, we generate the largest publicly available IFC data set of the human immunological synapse containing over 2.8 million images. Using scifAI, we analyze class frequency- and morphological changes under different immune stimulation. T cell cytokine production across multiple donors and therapeutic antibodies is quantitatively predicted in vitro, linking morphological features with function and demonstrating the potential to significantly impact antibody design. scifAI is universally applicable to IFC data. Given its modular architecture it is straightforward to incorporate into existing workflows and analysis pipelines, e.g. for rapid antibody screening and functional characterization.

bioinformatics↗

bioRxiv2022

Probe set selection for targeted spatial transcriptomics

Targeted spatial transcriptomics methods capture the topology of cell types and states in tissues at single cell- and subcellular resolution by measuring the expression of a predefined set of genes. The selection of an optimal set of probed genes is crucial for capturing and interpreting the spatial signals present in a tissue. However, current selections often rely on marker genes, precluding them from detecting continuous spatial signals or novel states. We present Spapros, an end-to-end probe set selection pipeline that optimizes both probe set specificity for cell type identification and within-cell-type expression variation to resolve spatially distinct populations while taking into account prior knowledge, as well as probe design and expression constraints. To facilitate data analysis and interpretation, Spapros also provides rules for cell type identification. We evaluated Spapros by selecting probes on 6 different data sets and built an evaluation pipeline with 12 quality metrics to find that Spapros outperforms other selection approaches in both cell type recovery and recovering expression variation beyond cell types. Furthermore, we used Spapros to design a SCRINSHOT experiment of adult lung tissue to demonstrate how probes selected with Spapros identify cell types of interest and detect spatial variation even within cell types. Spapros enables optimal probe set selection, probe set evaluation, and probe design, as a freely available Python package.

bioinformatics↗

bioRxiv2022

Deep generative modeling of transcriptional dynamics for RNA velocity analysis in single cells

RNA velocity has been rapidly adopted to guide the interpretation of transcriptional dynamics in snapshot single-cell transcriptomics data. Current approaches for estimating and analyzing RNA velocity can empirically reveal complex dynamics but lack effective strategies for quantifying the uncertainty of the estimate and its overall applicability to the system of interest. Here, we present veloVI (velocity variational inference), a deep generative modeling framework for estimating RNA velocity. veloVI learns a gene-specific dynamical model of RNA metabolism and provides a transcriptome-wide quantification of velocity uncertainty. We show in a series of examples that veloVI compares favorably to previous approaches for inferring RNA velocity with improvements in fit to the data, consistency across transcriptionally similar cells, and stability across preprocessing pipelines for quantifying RNA abundance. Further, we demonstrate that properties unique to veloVI, such as posterior velocity uncertainty, can be used to assess the appropriateness of analysis with velocity to the data at hand. Finally, we highlight veloVI as a flexible framework for modeling transcriptional dynamics by adapting the underlying dynamical model to use time-dependent transcription rates.

bioinformatics↗

bioRxiv2022

CIARA: a cluster-independent algorithm for the identification of markers of rare cell types from single-cell RNA seq data

A powerful feature of single-cell RNA-sequencing data analysis is the possibility to identify novel rare cell types. However, rare cell types are often missed by standard clustering approaches. We have developed CIARA (Cluster Independent Algorithm for the identification of markers of RAre cell types), a computational tool available in R and Python that outperforms existing methods for rare cell type detection. With CIARA, we found a small group of precursor cells among mouse embryonic stem cells and previously uncharacterized rare populations of cells in a human gastrula.

bioinformatics↗

bioRxiv2022

Autocrine Sfrp1 inhibits lung fibroblast invasion during transition to injury induced myofibroblasts

Fibroblast to myofibroblast conversion is a major driver of tissue remodeling in organ fibrosis. Several distinct lineages of fibroblasts support homeostatic tissue niche functions, yet, specific activation states and phenotypic trajectories of fibroblasts during injury and repair have remained unclear. Here, we combined spatial transcriptomics, longitudinal single-cell RNA-seq and genetic lineage tracing to study fibroblast fates during mouse lung regeneration. We discovered a transitional fibroblast state characterized by high Sfrp1 expression, derived from both Tcf21-Cre lineage positive and negative cells. Sfrp1+ cells appeared early after injury in peribronchiolar, adventitial and alveolar locations and preceded the emergence of myofibroblasts. We identified lineage specific paracrine signals and inferred converging transcriptional trajectories towards Sfrp1+ transitional fibroblasts and Cthrc1+ myofibroblasts. Tgf{beta}1 downregulated Sfrp1 in non-invasive transitional cells and induced their switch to an invasive Cthrc1+ myofibroblast identity. Finally, using loss of function studies we showed that autocrine Sfrp1 directly inhibits fibroblast invasion by regulating the RhoA pathway. In summary, our study reveals the convergence of spatially and transcriptionally distinct fibroblast lineages into transcriptionally uniform myofibroblasts and identifies Sfrp1 as an autocrine inhibitor of fibroblast invasion during early stages of fibrogenesis.

cell biology↗

bioRxiv2022

MultiCPA: Multimodal Compositional Perturbation Autoencoder

Single-cell multimodal profiling provides a high-resolution view of cellular information. Recently, multimodal profiling approaches have been coupled with CRISPR technologies to perform pooled screens of single or combinatorial perturbations. This opens the possibility of exploring the massive space of combinatorial perturbations and their regulatory effects computationally from the extrapolation of a few experimentally feasible combinations. Here, we propose MultiCPA, an end-to-end generative architecture to predict multimodal perturbation response at single cell level. Two mixing strategies to integrate multiple modalities are introduced and compared with existing methods. MultiCPA was also shown to accurately predict unseen combinatorial perturbation responses for multiple modalities. The code to reproduce the results is available on GitHub, theislab/multicpa.

systems biology↗

bioRxiv2022

Single-cell reference mapping to construct and extend cell type hierarchies

Single-cell genomics is now producing an ever-increasing amount of datasets that, when integrated, could provide large-scale reference atlases of tissue in health and disease. Such atlases increase the scale and generalizability of analyses and enable combining knowledge generated by individual studies. Specifically, individual studies often differ regarding cell annotation terminology and depth, with different groups often using distinct terminology. Understanding how annotations are related and complement each other would mark a major step towards a consensus-based cell-type annotation reflecting the latest knowledge. Whereas recent computational techniques, referred to as "reference mapping" methods, facilitate the usage and expansion of existing reference atlases by mapping new datasets (i.e., queries) onto an atlas; a systematic approach towards harmonizing dataset-specific cell-type terminology and annotation depth is still lacking. Here, we present "treeArches", a framework to automatically build and extend reference atlases while enriching them with an updatable hierarchy of cell-type annotations across different datasets. We demonstrate various use cases, from automatically resolving relations between reference and query cell types to identifying unseen cell types absent in the reference, such as disease-associated cell states. We envision treeArches enabling data-driven construction of consensus atlas-level cell-type hierarchies and facilitating efficient usage of reference atlases.

bioinformatics↗

bioRxiv2022

Learning consistent subcellular landmarks to quantify changes in multiplexed protein maps

Highly multiplexed quantitative subcellular imaging holds enormous promise for understanding how spatial context shapes the activity of our genome and its products at multiple scales. Yet unbiased analysis of subcellular organisation across experimental conditions remains challenging, because differences in molecular profiles between conditions confound differences in molecular profiles across space. Here, we introduce a deep-learning framework called CAMPA (Conditional Autoencoder for Multiplexed Pixel Analysis), which uses a variational autoencoder conditioned on cellular states and perturbations to learn consistent molecular signatures. Clustering the learned representations into subcellular landmarks allows quantitative comparisons of landmark sizes, shapes, molecular compositions and relative spatial organisation between conditions. By performing high-resolution multiplexed immunofluorescence on human cells, we use CAMPA to reveal how subnuclear organisation changes upon different perturbations of RNA production or processing, and how different membraneless organelles scale with cell size. Furthermore, by integrating information across the cellular and subcellular scales, we uncover new links between the molecular composition of membraneless organelles and bulk RNA synthesis rates of single cells. We anticipate that CAMPA will greatly accelerate the systematic mapping of multiscale atlases of biological organisation to identify the rules by which context shapes physiology and disease.

systems biology↗

bioRxiv2022

Modeling fragment counts improves single-cell ATAC-seq analysis

Single-cell ATAC-sequencing (scATAC-seq) coverage in regulatory regions is typically binarized as an indicator of open chromatin. However, the implications of scATAC-seq data binarization have not systematically been assessed. Here, we show that the goodness-of-fit of existing models and their applications, including clustering, cell type identification, and batch integration, are improved by a quantitative treatment of the fragment counts. These results have immediate implications for scATAC-seq analysis.

bioinformatics↗

bioRxiv2022

Multimodal single cell data integration challenge: results and lessons learned

Biology has become a data-intensive science. Recent technological advances in single-cell genomics have enabled the measurement of multiple facets of cellular state, producing datasets with millions of single-cell observations. While these data hold great promise for understanding molecular mechanisms in health and disease, analysis challenges arising from sparsity, technical and biological variability, and high dimensionality of the data hinder the derivation of such mechanistic insights. To promote the innovation of algorithms for analysis of multimodal single-cell data, we organized a competition at NeurIPS 2021 applying the Common Task Framework to multimodal single-cell data integration. For this competition we generated the first multimodal benchmarking dataset for single-cell biology and defined three tasks in this domain: prediction of missing modalities, aligning modalities, and learning a joint representation across modalities. We further specified evaluation metrics and developed a cloud-based algorithm evaluation pipeline. Using this setup, 280 competitors submitted over 2600 proposed solutions within a 3 month period, showcasing substantial innovation especially in the modality alignment task. Here, we present the results, describe trends of well performing approaches, and discuss challenges associated with running the competition.

bioinformatics↗

bioRxiv2022

Multigrate: single-cell multi-omic data integration

Constructing joint representations from multimodal single-cell datasets is crucial for understanding cellular heterogeneity and function. Traditional methods, such as factor analysis and kNN-based approaches, face computational limitations with scalability across large datasets and multiple modalities. In this work, we demonstrate the product-of-experts VAE-based model, which offers a flexible, scalable solution for integrating multimodal data, allowing for the seamless mapping of both unimodal and multimodal queries onto a reference atlas. We evaluate how different strategies for combining modalities in the VAE framework impact query-to-reference mapping across diverse datasets, including CITE-seq and spatial metabolomics. Our benchmarks assess batch effect correction, biological signal preservation, and imputation of missing modalities. We showcase our approach in a mosaic setting, integrating CITE-seq and multiome data to accurately map unimodal and multimodal queries into the joint latent space. We extend this to spatial data by integrating gene expression and metabolomics from paired Visium and MALDI-MSI slides, achieving a high correlation in metabolite predictions from spatial gene expression. Our results demonstrate that this VAE-based framework is scalable, robust, and easily applicable across multiple modalities, providing a powerful tool for data imputation, querying, and biological discovery.

bioinformatics↗

bioRxiv2022

Biologically informed deep learning to infer gene program activity in single cells

The increasing availability of large-scale single-cell datasets has enabled the detailed description of cell states across multiple biological conditions and perturbations. In parallel, recent advances in unsupervised machine learning, particularly in transfer learning, have enabled fast and scalable mapping of these new single-cell datasets onto reference atlases. The resulting large-scale machine learning models however often have millions of parameters, rendering interpretation of the newly mapped datasets challenging. Here, we propose expiMap, a deep learning model that enables interpretable reference mapping using biologically understandable entities, such as curated sets of genes and gene programs. The key concept is the substitution of the uninterpretable nodes in an autoencoders bottleneck by labeled nodes mapping to interpretable lists of genes, such as gene ontologies, biological pathways, or curated gene sets, for which activities are learned as constraints during reconstruction. This is enabled by the incorporation of predefined gene programs into the reference model, and at the same time allowing the model to learn de novo new programs and refine existing programs during reference mapping. We show that the model retains similar integration performance as existing methods while providing a biologically interpretable framework for understanding cellular behavior. We demonstrate the capabilities of expiMap by applying it to 15 datasets encompassing five different tissues and species. The interpretable nature of the mapping revealed unreported associations between interferon signaling via the RIG-I/MDA5 and GPCRs pathways, with differential behavior in CD8+ T cells and CD14+ monocytes in severe COVID-19, as well as the role of annexins in the cellular communications between lymphoid and myeloid compartments for explaining patient response to the applied drugs. Finally, expiMap enabled the direct comparison of a diverse set of pancreatic beta cells from multiple studies where we observed a strong, previously unreported correlation between the unfolded protein response and asparagine N-linked glycosylation. Altogether, expiMap enables the interpretable mapping of single cell transcriptome data sets across cohorts, disease states and other perturbations.

bioinformatics↗