Optimal sequencing budget allocation for trajectory reconstruction of single cells.

BACKGROUND: Charting cellular trajectories over gene expression is key to understanding dynamic cellular processes and their underlying mechanisms. While advances in single-cell RNA-sequencing technologies and computational methods have pushed forward the recovery of such trajectories, trajectory inference remains a challenge due to the noisy, sparse, and high-dimensional nature of single-cell data. This challenge can be alleviated by increasing either the number of cells sampled along the trajectory (breadth) or the sequencing depth, i.e. the number of reads captured per cell (depth). Generally, these two factors are coupled due to an inherent breadth-depth tradeoff that arises when the sequencing budget is constrained due to financial or technical limitations. RESULTS: Here we study the optimal allocation of a fixed sequencing budget to optimize the recovery of trajectory attributes. Empirical results reveal that reconstruction accuracy of internal cell structure in expression space scales with the logarithm of either the breadth or depth of sequencing. We additionally observe a power law relationship between the optimal number of sampled cells and the corresponding sequencing budget. For linear trajectories, non-monotonicity in trajectory reconstruction across the breadth-depth tradeoff can impact downstream inference, such as expression pattern analysis along the trajectory. We demonstrate these results for five single-cell RNA-sequencing datasets encompassing differentiation of embryonic stem cells, pancreatic beta cells, hepatoblast and multipotent hematopoietic cells, as well as induced reprogramming of embryonic fibroblasts into neurons. By addressing the challenges of single-cell data, our study offers insights into maximizing the efficiency of cellular trajectory analysis through strategic allocation of sequencing resources.

Gene expression cartography.

Multiplexed RNA sequencing in individual cells is transforming basic and clinical life sciences1-4. Often, however, tissues must first be dissociated, and crucial information about spatial relationships and communication between cells is thus lost. Existing approaches to reconstruct tissues assign spatial positions to each cell, independently of other cells, by using spatial patterns of expression of marker genes5,6-which often do not exist. Here we reconstruct spatial positions with little or no prior knowledge, by searching for spatial arrangements of sequenced cells in which nearby cells have transcriptional profiles that are often (but not always) more similar than cells that are farther apart. We formulate this task as a generalized optimal-transport problem for probabilistic embedding and derive an efficient iterative algorithm to solve it. We reconstruct the spatial expression of genes in mammalian liver and intestinal epithelium, fly and zebrafish embryos, sections from the mammalian cerebellum and whole kidney, and use the reconstructed tissues to identify genes that are spatially informative. Thus, we identify an organization principle for the spatial expression of genes in animal tissues, which can be exploited to infer meaningful probabilities of spatial position for individual cells. Our framework ('novoSpaRc') can incorporate prior spatial information and is compatible with any single-cell technology. Additional principles that underlie the cartography of gene expression can be tested using our approach.

Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram.

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.

Robust reconstruction of single-cell RNA-seq data with iterative gene weight updates.

MOTIVATION: Single-cell RNA-sequencing technologies have greatly enhanced our understanding of heterogeneous cell populations and underlying regulatory processes. However, structural (spatial or temporal) relations between cells are lost during cell dissociation. These relations are crucial for identifying associated biological processes. Many existing tissue-reconstruction algorithms use prior information about subsets of genes that are informative with respect to the structure or process to be reconstructed. When such information is not available, and in the general case when the input genes code for multiple processes, including being susceptible to noise, biological reconstruction is often computationally challenging. RESULTS: We propose an algorithm that iteratively identifies manifold-informative genes using existing reconstruction algorithms for single-cell RNA-seq data as subroutine. We show that our algorithm improves the quality of tissue reconstruction for diverse synthetic and real scRNA-seq data, including data from the mammalian intestinal epithelium and liver lobules. AVAILABILITY AND IMPLEMENTATION: The code and data for benchmarking are available at github.com/syq2012/iterative_weight_update_for_reconstruction.

Revealing lineage-related signals in single-cell gene expression using random matrix theory.

Gene expression profiles of a cellular population, generated by single-cell RNA sequencing, contains rich information about biological state, including cell type, cell cycle phase, gene regulatory patterns, and location within the tissue of origin. A major challenge is to disentangle information about these different biological states from each other, including distinguishing from cell lineage, since the correlation of cellular expression patterns is necessarily contaminated by ancestry. Here, we use a recent advance in random matrix theory, discovered in the context of protein phylogeny, to identify differentiation or ancestry-related processes in single-cell data. Qin and Colwell [C. Qin, L. J. Colwell, Proc. Natl. Acad. Sci. U.S.A. 115, 690-695 (2018)] showed that ancestral relationships in protein sequences create a power-law signature in the covariance eigenvalue distribution. We demonstrate the existence of such signatures in scRNA-seq data and that the genes driving them are indeed related to differentiation and developmental pathways. We predict the existence of similar power-law signatures for cells along linear trajectories and demonstrate this for linearly differentiating systems. Furthermore, we generalize to show that the same signatures can arise for cells along tissue-specific spatial trajectories. We illustrate these principles in diverse tissues and organisms, including the mammalian epidermis and lung, Drosophila whole-embryo, adult Hydra, dendritic cells, the intestinal epithelium, and cells undergoing induced pluripotent stem cells (iPSC) reprogramming. We show how these results can be used to interpret the gradual dynamics of lineage structure along iPSC reprogramming. Together, we provide a framework that can be used to identify signatures of specific biological processes in single-cell data without prior knowledge and identify candidate genes associated with these processes.

Programming cell growth into different cluster shapes using diffusible signals.

Advances in genetic engineering technologies have allowed the construction of artificial genetic circuits, which have been used to generate spatial patterns of differential gene expression. However, the question of how cells can be programmed, and how complex the rules need to be, to achieve a desired tissue morphology has received less attention. Here, we address these questions by developing a mathematical model to study how cells can collectively grow into clusters with different structural morphologies by secreting diffusible signals that can influence cellular growth rates. We formulate how growth regulators can be used to control the formation of cellular protrusions and how the range of achievable structures scales with the number of distinct signals. We show that a single growth inhibitor is insufficient for the formation of multiple protrusions but may be achieved with multiple growth inhibitors, and that other types of signals can regulate the shape of protrusion tips. These examples illustrate how our approach could potentially be used to guide the design of regulatory circuits for achieving a desired target structure.

Evidence for Functional Networks within the Human Brain's White Matter.

Investigation of the functional macro-scale organization of the human cortex is fundamental in modern neuroscience. Although numerous studies have identified networks of interacting functional modules in the gray-matter, limited research was directed to the functional organization of the white-matter. Recent studies have demonstrated that the white-matter exhibits blood oxygen level-dependent signal fluctuations similar to those of the gray-matter. Here we used these signal fluctuations to investigate whether the white-matter is organized as functional networks by applying a clustering analysis on resting-state functional MRI (RSfMRI) data from white-matter voxels, in 176 subjects (of both sexes). This analysis indicated the existence of 12 symmetrical white-matter functional networks, corresponding to combinations of white-matter tracts identified by diffusion tensor imaging. Six of the networks included interhemispheric commissural bridges traversing the corpus callosum. Signals in white-matter networks correlated with signals from functional gray-matter networks, providing missing knowledge on how these distributed networks communicate across large distances. These findings were replicated in an independent subject group and were corroborated by seed-based analysis in small groups and individual subjects. The identified white-matter functional atlases and analysis codes are available at http://mind.huji.ac.il/white-matter.aspx Our results demonstrate that the white-matter manifests an intrinsic functional organization as interacting networks of functional modules, similarly to the gray-matter, which can be investigated using RSfMRI. The discovery of functional networks within the white-matter may open new avenues of research in cognitive neuroscience and clinical neuropsychiatry.SIGNIFICANCE STATEMENT In recent years, functional MRI (fMRI) has revolutionized all fields of neuroscience, enabling identifications of functional modules and networks in the human brain. However, most fMRI studies ignored a major part of the brain, the white-matter, discarding signals from it as arising from noise. Here we use resting-state fMRI data from 176 subjects to show that signals from the human white-matter contain meaningful information. We identify 12 functional networks composed of interacting long-distance white-matter tracts. Moreover, we show that these networks are highly correlated to resting-state gray-matter networks, highlighting their functional role. Our findings enable reinterpretation of many existing fMRI datasets, and suggest a new way to explore the white-matter role in cognition and its disturbances in neuropsychiatric disorders.

A defense-offense multi-layered regulatory switch in a pathogenic bacterium.

Cells adapt to environmental changes by efficiently adjusting gene expression programs. Staphylococcus aureus, an opportunistic pathogenic bacterium, switches between defensive and offensive modes in response to quorum sensing signal. We identified and studied the structural characteristics and dynamic properties of the core regulatory circuit governing this switch by deterministic and stochastic computational methods, as well as experimentally. This module, termed here Double Selector Switch (DSS), comprises the RNA regulator RNAIII and the transcription factor Rot, defining a double-layered switch involving both transcriptional and post-transcriptional regulations. It coordinates the inverse expression of two sets of target genes, immuno-modulators and exotoxins, expressed during the defensive and offensive modes, respectively. Our computational and experimental analyses show that the DSS guarantees fine-tuned coordination of the inverse expression of its two gene sets, tight regulation, and filtering of noisy signals. We also identified variants of this circuit in other bacterial systems, suggesting it is used as a molecular switch in various cellular contexts and offering its use as a template for an effective switching device in synthetic biology studies.

Reversible functional connectivity disturbances during transient global amnesia.

OBJECTIVE: Transient global amnesia (TGA), an abrupt occurrence of severe anterograde episodic amnesia accompanied by repetitive questioning, has been known for more than 50 years. Despite extensive research, there is no clear evidence for the underlying pathophysiological basis of TGA. Moreover, there is no neuroimaging method to evaluate TGA in real time. METHODS: Here we used resting-state functional magnetic resonance imaging recorded in 12 patients during the acute phase of TGA together with connectivity and cluster analyses to detect changes in the episodic memory network in TGA. RESULTS: Our results show a significant reduction in functional connectivity of the episodic memory network during TGA, which is more pronounced in the hyperacute phase than in the postacute phase. This disturbance is bilateral, and reversible after recovery. Although the hippocampus and its connections are significantly impaired, other parts of the episodic memory network are also impaired. Similar results were obtained for the analysis of the episodic memory network whether it was defined in a data-driven or literature-based manner. INTERPRETATION: These results suggest that TGA is related to a functional disturbance in the episodic memory network, and supply a neuroimaging correlate of TGA during the acute phase.

Global regulation of transcription by a small RNA: a quantitative view.

Small RNAs are integral regulators of bacterial gene expression, the majority of which act posttranscriptionally by basepairing with target mRNAs, altering translation or mRNA stability. 6S RNA, however, is a small RNA that is a transcriptional regulator, acting by binding directly to σ(70)-RNA polymerase (σ(70)-RNAP) and preventing its binding to gene promoters. At the transition from exponential to stationary phase, 6S RNA accumulates and globally downregulates the transcription of hundreds of genes. At the transition from stationary to exponential phase (outgrowth), 6S RNA is released from σ(70)-RNAP, resulting in a fast increase in free σ(70)-RNAP and transcription of many genes. The transition from stationary to exponential phase is sharp, and is thus accessible for experimental study. However, the transition from exponential to stationary phase is gradual and complicated by changes in other factors, making it more difficult to isolate 6S RNA effects experimentally at this transition. Here, we use mathematical modeling and simulation to study the dynamics of 6S RNA-dependent regulation, focusing on transitions in growth mediated by altered nutrient availability. We first show that our model reproduces the sharp increase in σ(70)-RNAP at outgrowth, as well as the behavior of two experimentally tested mutants, thus justifying its use for characterizing the less accessible dynamics of the transition from exponential to stationary phase. We characterize the dynamics of the two transitions for Escherichia coli wild-type, as well as for mutants with various 6S RNA-RNAP affinities, demonstrating that the 6S RNA regulation mechanism is generally robust to a wide range of such mutations, although the level of regulation at single promoters and their resulting expression fold change will be altered with changes in affinity. Our results provide insight into the potential advantage of transcription regulation by 6S RNA, as it enables storage and efficient release of σ(70)-RNAP during transitions in nutrient availability, which is likely to give a competitive advantage to cells encountering diverse environmental conditions.

Interactions between distant ceRNAs in regulatory networks.

Competing endogenous RNAs (ceRNAs) were recently introduced as RNA transcripts that affect each other's expression level through competition for their microRNA (miRNA) coregulators. This stems from the bidirectional effects between miRNAs and their target RNAs, where a change in the expression level of one target affects the level of the miRNA regulator, which in turn affects the level of other targets. By the same logic, miRNAs that share targets compete over binding to their common targets and therefore also exhibit ceRNA-like behavior. Taken together, perturbation effects could propagate in the posttranscriptional regulatory network through a path of coregulated targets and miRNAs that share targets, suggesting the existence of distant ceRNAs. Here we study the prevalence of distant ceRNAs and their effect in cellular networks. Analyzing the network of miRNA-target interactions deciphered experimentally in HEK293 cells, we show that it is a dense, intertwined network, suggesting that many nodes can act as distant ceRNAs of one another. Indeed, using gene expression data from a perturbation experiment, we demonstrate small, yet statistically significant, changes in gene expression caused by distant ceRNAs in that network. We further characterize the magnitude of the propagated perturbation effect and the parameters affecting it by mathematical modeling and simulations. Our results show that the magnitude of the effect depends on the generation and degradation rates of involved miRNAs and targets, their interaction rates, the distance between the ceRNAs and the topology of the network. Although demonstrated for a miRNA-mRNA regulatory network, our results offer what to our knowledge is a new view on various posttranscriptional cellular networks, expanding the concept of ceRNAs and implying possible distant cross talk within the network, with consequences for the interpretation of indirect effects of gene perturbation.

SiFT: uncovering hidden biological processes by probabilistic filtering of single-cell data.

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.

Disentanglement of single-cell data with biolord.

Biolord is a deep generative method for disentangling single-cell multi-omic data to known and unknown attributes, including spatial, temporal and disease states, used to reveal the decoupled biological signatures over diverse single-cell modalities and biological systems. By virtually shifting cells across states, biolord generates experimentally inaccessible samples, outperforming state-of-the-art methods in predictions of cellular response to unseen drugs and genetic perturbations. Biolord is available at https://github.com/nitzanlab/biolord .

scPrisma infers, filters and enhances topological signals in single-cell data using spectral template matching.

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.

TACCO unifies annotation transfer and decomposition of cell identities for single-cell and spatial omics.

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).

Emergence of division of labor in tissues through cell interactions and spatial cues.

Most cell types in multicellular organisms can perform multiple functions. However, not all functions can be optimally performed simultaneously by the same cells. Functions incompatible at the level of individual cells can be performed at the cell population level, where cells divide labor and specialize in different functions. Division of labor can arise due to instruction by tissue environment or through self-organization. Here, we develop a computational framework to investigate the contribution of these mechanisms to division of labor within a cell-type population. By optimizing collective cellular task performance under trade-offs, we find that distinguishable expression patterns can emerge from cell-cell interactions versus instructive signals. We propose a method to construct ligand-receptor networks between specialist cells and use it to infer division-of-labor mechanisms from single-cell RNA sequencing (RNA-seq) and spatial transcriptomics data of stromal, epithelial, and immune cells. Our framework can be used to characterize the complexity of cell interactions within tissues.

NovoSpaRc: flexible spatial reconstruction of single-cell gene expression with optimal transport.

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.

ChIP-seq of plasma cell-free nucleosomes identifies gene expression programs of the cells of origin.

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.

Selective flexible packaging pathways of the segmented genome of influenza A virus.

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 × 105 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.

Single-cell analysis of germinal-center B cells informs on lymphoma cell of origin and outcome.

In response to T cell-dependent antigens, mature B cells are stimulated to form germinal centers (GCs), the sites of B cell affinity maturation and the cell of origin (COO) of most B cell lymphomas. To explore the dynamics of GC B cell development beyond the known dark zone and light zone compartments, we performed single-cell (sc) transcriptomic analysis on human GC B cells and identified multiple functionally linked subpopulations, including the distinct precursors of memory B cells and plasma cells. The gene expression signatures associated with these GC subpopulations were effective in providing a sc-COO for ∼80% of diffuse large B cell lymphomas (DLBCLs) and identified novel prognostic subgroups of DLBCL.