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The topology of interactions in network dynamical systems fundamentally underlies their function. Accelerating technological progress creates massively available data about collective nonlinear dynamics in physical, biological, and technological systems. Detecting direct interaction patterns from those dynamics still constitutes a major open problem. In particular, current nonlinear dynamics approaches mostly require to know a priori a model of the (often high dimensional) system dynamics. Here we develop a model-independent framework for inferring direct interactions solely from recording the nonlinear collective dynamics generated. Introducing an explicit dependency matrix in combination with a block-orthogonal regression algorithm, the approach works reliably across many dynamical regimes, including transient dynamics toward steady states, periodic and non-periodic dynamics, and chaos. Together with its capabilities to reveal network (two point) as well as hypernetwork (e.g., three point) interactions, this framework may thus open up nonlinear dynamics options of inferring direct interaction patterns across systems where no model is known. Network dynamical systems can represent the interactions involved in the collective dynamics of gene regulatory networks or metabolic circuits. Here Casadiego et al. present a method for inferring these types of interactions directly from observed time series without relying on their model.

Cellular populations simultaneously encode multiple biological attributes, including spatial configuration, temporal trajectories, and cell-cell interactions. Some of these signals may be overshadowed by others and harder to recover, despite the great progress made to computationally reconstruct biological processes from single-cell data. To address this, we present SiFT, a kernel-based projection method for filtering biological signals in single-cell data, thus uncovering underlying biological processes. SiFT applies to a wide range of tasks, from the removal of unwanted variation in the data to revealing hidden biological structures. We demonstrate how SiFT enhances the liver circadian signal by filtering spatial zonation, recovers regenerative cell subpopulations in spatially-resolved liver data, and exposes COVID-19 disease-related cells, pathways, and dynamics by filtering healthy reference signals. SiFT performs the correction at the gene expression level, can scale to large datasets, and compares favorably to state-of-the-art methods. Cells simultaneously encode multiple signals, some harder to recover. Here, authors introduce SiFT (Signal FilTering), a kernel-based projection method, revealing underlying biological processes in single-cell data.

Single-cell RNA-sequencing (scRNA-seq) technologies have revolutionized modern biomedical sciences. A fundamental challenge is to incorporate spatial information to study tissue organization and spatial gene expression patterns. Here, we describe a detailed protocol for using novoSpaRc, a computational framework that probabilistically assigns cells to tissue locations. At the core of this framework lies a structural correspondence hypothesis, that cells in physical proximity share similar gene expression profiles. Given scRNA-seq data, novoSpaRc spatially reconstructs tissues based on this hypothesis, and optionally, by including a reference atlas of marker genes to improve reconstruction. We describe the novoSpaRc algorithm, and its implementation in an open-source Python package ( https://pypi.org/project/novosparc ). NovoSpaRc maps a scRNA-seq dataset of 10,000 cells onto 1,000 locations in <5 min. We describe results obtained using novoSpaRc to reconstruct the mouse organ of Corti de novo based on the structural correspondence assumption and human osteosarcoma cultured cells based on marker gene information, and provide a step-by-step guide to Drosophila embryo reconstruction in the Procedure to demonstrate how these two strategies can be combined. This protocol describes novoSpaRc, a computational pipeline for de novo reconstruction of spatial gene expression from single-cell RNA sequencing with the potential to incorporate spatial atlas data to improve the reconstruction.

Single-cell RNA sequencing has been instrumental in uncovering cellular spatiotemporal context. This task is challenging as cells simultaneously encode multiple, potentially cross-interfering, biological signals. Here we propose scPrisma, a spectral computational method that uses topological priors to decouple, enhance and filter different classes of biological processes in single-cell data, such as periodic and linear signals. We apply scPrisma to the analysis of the cell cycle in HeLa cells, circadian rhythm and spatial zonation in liver lobules, diurnal cycle in Chlamydomonas and circadian rhythm in the suprachiasmatic nucleus in the brain. scPrisma can be used to distinguish mixed cellular populations by specific characteristics such as cell type and uncover regulatory networks and cell–cell interactions specific to predefined biological signals, such as the circadian rhythm. We show scPrisma’s flexibility in incorporating prior knowledge, inference of topologically informative genes and generalization to additional diverse templates and systems. scPrisma can be used as a stand-alone workflow for signal analysis and as a prior step for downstream single-cell analysis. scPrisma deconvolutes different biological processes in single-cell RNA-sequencing data.

Charting an organs’ biological atlas requires us to spatially resolve the entire single-cell transcriptome, and to relate such cellular features to the anatomical scale. Single-cell and single-nucleus RNA-seq (sc/snRNA-seq) can profile cells comprehensively, but lose spatial information. Spatial transcriptomics allows for spatial measurements, but at lower resolution and with limited sensitivity. Targeted in situ technologies solve both issues, but are limited in gene throughput. To overcome these limitations we present Tangram, a method that aligns sc/snRNA-seq data to various forms of spatial data collected from the same region, including MERFISH, STARmap, smFISH, Spatial Transcriptomics (Visium) and histological images. Tangram can map any type of sc/snRNA-seq data, including multimodal data such as those from SHARE-seq, which we used to reveal spatial patterns of chromatin accessibility. We demonstrate Tangram on healthy mouse brain tissue, by reconstructing a genome-wide anatomically integrated spatial map at single-cell resolution of the visual and somatomotor areas.Tangram is a versatile tool for aligning single-cell and single-nucleus RNA-seq data to spatially resolved transcriptomics data using deep learning.

Transferring annotations of single-cell-, spatial- and multi-omics data is often challenging owing both to technical limitations, such as low spatial resolution or high dropout fraction, and to biological variations, such as continuous spectra of cell states. Based on the concept that these data are often best described as continuous mixtures of cells or molecules, we present a computational framework for the transfer of annotations to cells and their combinations (TACCO), which consists of an optimal transport model extended with different wrappers to annotate a wide variety of data. We apply TACCO to identify cell types and states, decipher spatiomolecular tissue structure at the cell and molecular level and resolve differentiation trajectories using synthetic and biological datasets. While matching or exceeding the accuracy of specialized tools for the individual tasks, TACCO reduces the computational requirements by up to an order of magnitude and scales to larger datasets (for example, considering the runtime of annotation transfer for 1 M simulated dropout observations). Annotation transfer from reference to new datasets is improved with a probabilistic approach.

Cell-free DNA (cfDNA) in human plasma provides access to molecular information about the pathological processes in the organs or tumors from which it originates. These DNA fragments are derived from fragmented chromatin in dying cells and retain some of the cell-of-origin histone modifications. In this study, we applied chromatin immunoprecipitation of cell-free nucleosomes carrying active chromatin modifications followed by sequencing (cfChIP-seq) to 268 human samples. In healthy donors, we identified bone marrow megakaryocytes, but not erythroblasts, as major contributors to the cfDNA pool. In patients with a range of liver diseases, we showed that we can identify pathology-related changes in hepatocyte transcriptional programs. In patients with metastatic colorectal carcinoma, we detected clinically relevant and patient-specific information, including transcriptionally active human epidermal growth factor receptor 2 (HER2) amplifications. Altogether, cfChIP-seq, using low sequencing depth, provides systemic and genome-wide information and can inform diagnosis and facilitate interrogation of physiological and pathological processes using blood samples. Circulating cell-free DNA from patients is analyzed by ChIP-seq to reconstruct gene expression in human organs and tumors.

The genome of influenza A viruses (IAV) is encoded in eight distinct viral ribonucleoproteins (vRNPs) that consist of negative sense viral RNA (vRNA) covered by the IAV nucleoprotein. Previous studies strongly support a selective packaging model by which vRNP segments are bundling to an octameric complex, which is integrated into budding virions. However, the pathway(s) generating a complete genome bundle is not known. We here use a multiplexed FISH assay to monitor all eight vRNAs in parallel in human lung epithelial cells. Analysis of 3.9 × 10 5 spots of colocalizing vRNAs provides quantitative insights into segment composition of vRNP complexes and, thus, implications for bundling routes. The complexes rarely contain multiple copies of a specific segment. The data suggest a selective packaging mechanism with limited flexibility by which vRNPs assemble into a complete IAV genome. We surmise that this flexibility forms an essential basis for the development of reassortant viruses with pandemic potential. The mechanism underlying packaging of the 8 segments of the influenza virus genome into virions is not well understood. Here, the authors use a multiplexed FISH assay to monitor the 8 segments in parallel in infected cells suggesting bundling routes during the packaging process.

Simultaneous profiling of single-cell gene expression and lineage history holds enormous potential for studying cellular decision-making. Recent computational approaches combine both modalities into cellular trajectories; however, they cannot make use of all available lineage information in destructive time-series experiments. Here, we present moslin, a Gromov-Wasserstein-based model to couple cellular profiles across time points based on lineage and gene expression information. We validate our approach in simulations and demonstrate on Caenorhabditis elegans embryonic development how moslin predicts fate probabilities and putative decision driver genes. Finally, we use moslin to delineate lineage relationships among transiently activated fibroblast states during zebrafish heart regeneration.

While advances in single-cell genomics have helped to chart the cellular components of tumor ecosystems, it has been more challenging to characterize their specific spatial organization and functional interactions. Here, we combine single-cell RNA-seq, spatial transcriptomics by Slide-seq, and in situ multiplex RNA analysis to create a detailed spatial map of healthy and dysplastic colon cellular ecosystems and their association with disease progression. We profiled inducible genetic CRC mouse models that recapitulate key features of human CRC, assigned cell types and epithelial expression programs to spatial tissue locations in tumors, and computationally used them to identify the regional features spanning different cells in the same spatial niche. We find that tumors were organized in cellular neighborhoods, each with a distinct composition of cell subtypes, expression programs, and local cellular interactions. Comparing to scRNA-seq and bulk RNA-seq data from human CRC, we find that both cell composition and layout features were conserved between the species, with mouse neighborhoods correlating with malignancy and clinical outcome in human patient tumors, highlighting the relevance of our findings to human disease. Our work offers a comprehensive framework that is applicable across various tissues, tumors, and disease conditions, with tools for the extrapolation of findings from experimental mouse models to human diseases.