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.

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The 3D Genome Structure of Single Cells

Annual Review of Biomedical Data Science ◽

10.1146/annurev-biodatasci-020121-084709 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Tianming Zhou ◽

Ruochi Zhang ◽

Jian Ma

Keyword(s):

Single Cell ◽

Data Science ◽

Spatial Organization ◽

Method Development ◽

Single Cells ◽

Genome Structure ◽

Computational Method ◽

Publication Date ◽

3D Genome ◽

Genome Features

The spatial organization of the genome in the cell nucleus is pivotal to cell function. However, how the 3D genome organization and its dynamics influence cellular phenotypes remains poorly understood. The very recent development of single-cell technologies for probing the 3D genome, especially single-cell Hi-C (scHi-C), has ushered in a new era of unveiling cell-to-cell variability of 3D genome features at an unprecedented resolution. Here, we review recent developments in computational approaches to the analysis of scHi-C, including data processing, dimensionality reduction, imputation for enhancing data quality, and the revealing of 3D genome features at single-cell resolution. While much progress has been made in computational method development to analyze single-cell 3D genomes, substantial future work is needed to improve data interpretation and multimodal data integration, which are critical to reveal fundamental connections between genome structure and function among heterogeneous cell populations in various biological contexts. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 4 is July 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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Hebbian plasticity in parallel synaptic pathways: A circuit mechanism for systems memory consolidation

PLoS Computational Biology ◽

10.1371/journal.pcbi.1009681 ◽

2021 ◽

Vol 17 (12) ◽

pp. e1009681

Author(s):

Michiel W. H. Remme ◽

Urs Bergmann ◽

Denis Alevi ◽

Susanne Schreiber ◽

Henning Sprekeler ◽

...

Keyword(s):

Memory Consolidation ◽

Computational Models ◽

Hippocampal Formation ◽

Spatial Scales ◽

Single Cells ◽

Brain Regions ◽

Quantitative Agreement ◽

Long Term Memory ◽

Hebbian Plasticity ◽

Mathematical Analyses

Systems memory consolidation involves the transfer of memories across brain regions and the transformation of memory content. For example, declarative memories that transiently depend on the hippocampal formation are transformed into long-term memory traces in neocortical networks, and procedural memories are transformed within cortico-striatal networks. These consolidation processes are thought to rely on replay and repetition of recently acquired memories, but the cellular and network mechanisms that mediate the changes of memories are poorly understood. Here, we suggest that systems memory consolidation could arise from Hebbian plasticity in networks with parallel synaptic pathways—two ubiquitous features of neural circuits in the brain. We explore this hypothesis in the context of hippocampus-dependent memories. Using computational models and mathematical analyses, we illustrate how memories are transferred across circuits and discuss why their representations could change. The analyses suggest that Hebbian plasticity mediates consolidation by transferring a linear approximation of a previously acquired memory into a parallel pathway. Our modelling results are further in quantitative agreement with lesion studies in rodents. Moreover, a hierarchical iteration of the mechanism yields power-law forgetting—as observed in psychophysical studies in humans. The predicted circuit mechanism thus bridges spatial scales from single cells to cortical areas and time scales from milliseconds to years.

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Multiplexed Profiling of Single-cell Extracellular Vesicles Secretion

10.1101/459438 ◽

2018 ◽

Author(s):

Yahui Ji ◽

Dongyuan Qi ◽

Linmei Li ◽

Haoran Su ◽

Xiaojie Li ◽

...

Keyword(s):

Single Cell ◽

Cell Lines ◽

Extracellular Vesicles ◽

Single Cells ◽

Cell Communication ◽

Deep Understanding ◽

Surface Markers ◽

Cell Heterogeneity ◽

Health And Disease ◽

Cytokines Secretion

AbstractExtracellular vesicles (EVs) are important intercellular mediators regulating health and disease. Conventional EVs surface marker profiling, which was based on population measurements, masked the cell-to-cell heterogeneity in the quantity and phenotypes of EVs secretion. Herein, by using spatially patterned antibodies barcode, we realized multiplexed profiling of single-cell EVs secretion from more than 1000 single cells simultaneously. Applying this platform to profile human oral squamous cell carcinoma (OSCC) cell lines led to deep understanding of previously undifferentiated single cell heterogeneity underlying EVs secretion. Notably, we observed the decrement of certain EV phenotypes (e.g. CD63+EVs) were associated with the invasive feature of both OSCC cell lines and primary OSCC cells. We also realized multiplexed detection of EVs secretion and cytokines secretion simultaneously from the same single cells to investigate multidimensional spectrum of intercellular communications, from which we resolved three functional subgroups with distinct secretion profiles by visualized clustering. In particular, we found EVs secretion and cytokines secretion were generally dominated by different cell subgroups. The technology introduced here enables comprehensive evaluation of EVs secretion heterogeneity at single cell level, which may become an indispensable tool to complement current single cell analysis and EV research.SignificanceExtracellular vesicles (EVs) are cell derived nano-sized particles medicating cell-cell communication and transferring biology information molecules like nucleic acids to regulate human health and disease. Conventional methods for EV surface markers profiling can’t tell the differences in the quantity and phenotypes of EVs secretion between cells. To address this need, we developed a platform for profiling an array of surface markers on EVs from large numbers of single cells, enabling more comprehensive monitoring of cellular communications. Single cell EVs secretion assay led to previously unobserved cell heterogeneity underlying EVs secretion, which might open up new avenues for studying cell communication and cell microenvironment in both basic and clinical research.

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Haplotype-aware single-cell multiomics uncovers functional effects of somatic structural variation

10.1101/2021.11.11.468039 ◽

2021 ◽

Author(s):

Hyobin Jeong ◽

Karen Grimes ◽

Peter-Martin Bruch ◽

Tobias Rausch ◽

Patrick Hasenfeld ◽

...

Keyword(s):

Single Cell ◽

Nucleosome Occupancy ◽

Single Cells ◽

Chromosomal Rearrangements ◽

Regulatory Elements ◽

Computational Method ◽

Tumour Heterogeneity ◽

Cancer Genomes ◽

Functional Consequences ◽

Oncogenic Transcription Factor

Somatic structural variants (SVs) are widespread in cancer genomes, however, their impact on tumorigenesis and intra-tumour heterogeneity is incompletely understood, since methods to functionally characterize the broad spectrum of SVs arising in cancerous single-cells are lacking. We present a computational method, scNOVA, that couples SV discovery with nucleosome occupancy analysis by haplotype-resolved single-cell sequencing, to systematically uncover SV effects on cis-regulatory elements and gene activity. Application to leukemias and cell lines uncovered SV outcomes at several loci, including dysregulated cancer-related pathways and mono-allelic oncogene expression near SV breakpoints. At the intra-patient level, we identified different yet overlapping subclonal SVs that converge on aberrant Wnt signaling. We also deconvoluted the effects of catastrophic chromosomal rearrangements resulting in oncogenic transcription factor dysregulation. scNOVA directly links SVs to their functional consequences, opening the door for single-cell multiomics of SVs in heterogeneous cell populations.

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A computational method to aid the design and analysis of single cell RNA-seq experiments for cell type identification

10.1101/247114 ◽

2018 ◽

Cited By ~ 1

Author(s):

Douglas Abrams ◽

Parveen Kumar ◽

R. Krishna Murthy Karuturi ◽

Joshy George

Keyword(s):

Experimental Design ◽

Single Cell ◽

Single Cells ◽

Cell Types ◽

Cell Number ◽

Fold Change ◽

Computational Method ◽

Marker Genes ◽

Cell Type ◽

Estimate Sample Size

AbstractBackgroundThe advent of single cell RNA sequencing (scRNA-seq) enabled researchers to study transcriptomic activity within individual cells and identify inherent cell types in the sample. Although numerous computational tools have been developed to analyze single cell transcriptomes, there are no published studies and analytical packages available to guide experimental design and to devise suitable analysis procedure for cell type identification.ResultsWe have developed an empirical methodology to address this important gap in single cell experimental design and analysis into an easy-to-use tool called SCEED (Single Cell Empirical Experimental Design and analysis). With SCEED, user can choose a variety of combinations of tools for analysis, conduct performance analysis of analytical procedures and choose the best procedure, and estimate sample size (number of cells to be profiled) required for a given analytical procedure at varying levels of cell type rarity and other experimental parameters. Using SCEED, we examined 3 single cell algorithms using 48 simulated single cell datasets that were generated for varying number of cell types and their proportions, number of genes expressed per cell, number of marker genes and their fold change, and number of single cells successfully profiled in the experiment.ConclusionsBased on our study, we found that when marker genes are expressed at fold change of 4 or more than the rest of the genes, either Seurat or Simlr algorithm can be used to analyze single cell dataset for any number of single cells isolated (minimum 1000 single cells were tested). However, when marker genes are expected to be only up to fC 2 upregulated, choice of the single cell algorithm is dependent on the number of single cells isolated and proportion of rare cell type to be identified. In conclusion, our work allows the assessment of various single cell methods and also aids in examining the single cell experimental design.

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Quantifying exosome secretion from single cells reveals a modulatory role for GPCR signaling

The Journal of Cell Biology ◽

10.1083/jcb.201703206 ◽

2018 ◽

Vol 217 (3) ◽

pp. 1129-1142 ◽

Cited By ~ 69

Author(s):

Frederik Johannes Verweij ◽

Maarten P. Bebelman ◽

Connie R. Jimenez ◽

Juan J. Garcia-Vallejo ◽

Hans Janssen ◽

...

Keyword(s):

Total Internal Reflection ◽

Single Cells ◽

Cell Communication ◽

Exosome Biogenesis ◽

Real Time Visualization ◽

Target Membrane ◽

Syntaxin 4 ◽

Optical Reporters ◽

Druggable Targets ◽

Insight Into

Exosomes are small endosome-derived extracellular vesicles implicated in cell–cell communication and are secreted by living cells when multivesicular bodies (MVBs) fuse with the plasma membrane (PM). Current techniques to study exosome physiology are based on isolation procedures after secretion, precluding direct and dynamic insight into the mechanics of exosome biogenesis and the regulation of their release. In this study, we propose real-time visualization of MVB–PM fusion to overcome these limitations. We designed tetraspanin-based pH-sensitive optical reporters that detect MVB–PM fusion using live total internal reflection fluorescence and dynamic correlative light–electron microscopy. Quantitative analysis demonstrates that MVB–PM fusion frequency is reduced by depleting the target membrane SNAREs SNAP23 and syntaxin-4 but also can be induced in single cells by stimulation of the histamine H1 receptor (H1HR). Interestingly, activation of H1R1 in HeLa cells increases Ser110 phosphorylation of SNAP23, promoting MVB–PM fusion and the release of CD63-enriched exosomes. Using this single-cell resolution approach, we highlight the modulatory dynamics of MVB exocytosis that will help to increase our understanding of exosome physiology and identify druggable targets in exosome-associated pathologies.

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Adaptive multi-source multi-view latent feature learning for inferring potential disease-associated miRNAs

Briefings in Bioinformatics ◽

10.1093/bib/bbaa028 ◽

2020 ◽

Cited By ~ 2

Author(s):

Qiu Xiao ◽

Ning Zhang ◽

Jiawei Luo ◽

Jianhua Dai ◽

Xiwei Tang

Keyword(s):

Computational Models ◽

Geometric Characteristic ◽

Feature Learning ◽

Cost Effective ◽

Disease Diagnosis ◽

Computational Method ◽

Data Sources ◽

Superior Performance ◽

Graph Regularization ◽

Latent Features

Abstract Accumulating evidence has shown that microRNAs (miRNAs) play crucial roles in different biological processes, and their mutations and dysregulations have been proved to contribute to tumorigenesis. In silico identification of disease-associated miRNAs is a cost-effective strategy to discover those most promising biomarkers for disease diagnosis and treatment. The increasing available omics data sources provide unprecedented opportunities to decipher the underlying relationships between miRNAs and diseases by computational models. However, most existing methods are biased towards a single representation of miRNAs or diseases and are also not capable of discovering unobserved associations for new miRNAs or diseases without association information. In this study, we present a novel computational method with adaptive multi-source multi-view latent feature learning (M2LFL) to infer potential disease-associated miRNAs. First, we adopt multiple data sources to obtain similarity profiles and capture different latent features according to the geometric characteristic of miRNA and disease spaces. Then, the multi-modal latent features are projected to a common subspace to discover unobserved miRNA-disease associations in both miRNA and disease views, and an adaptive joint graph regularization term is developed to preserve the intrinsic manifold structures of multiple similarity profiles. Meanwhile, the Lp,q-norms are imposed into the projection matrices to ensure the sparsity and improve interpretability. The experimental results confirm the superior performance of our proposed method in screening reliable candidate disease miRNAs, which suggests that M2LFL could be an efficient tool to discover diagnostic biomarkers for guiding laborious clinical trials.

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MODELING OF ILIAC ARTERY ANEURYSM USING FLUID–STRUCTURE INTERACTION

Journal of Mechanics in Medicine and Biology ◽

10.1142/s0219519415500414 ◽

2015 ◽

Vol 15 (01) ◽

pp. 1550041 ◽

Cited By ~ 2

Author(s):

HATEF SABOONI ◽

KAMRAN HASSANI ◽

HAMIDREZA GHASEMI BAHRASEMAN

Keyword(s):

Iliac Artery ◽

Computational Models ◽

Fluid Structure Interaction ◽

Artery Aneurysm ◽

Computational Method ◽

Discussion Section ◽

Iliac Artery Aneurysm ◽

Fluid Structure ◽

Shear Rates ◽

Structure Interaction

The aneurysm of iliac artery is a rare entity and there are few computational models that have studied the disease. In this study, we have presented the flow patterns in the aneurysmal artery using Fluid–structure interaction method. The blood was assumed Newotonian, pulsatile, laminar, incompressible, and homogenous. The geometry of the model was made based on CT images of clinical cases. Using the computational method, we have obtained the velocity and pressure contours, shear rates and vortices for the healthy and aneurysmal artery. The results show that a pressure maximum was found at the midpoint of the dilation. The vortices are formed in the aneurysmal area26 and shear rates do not change much. However, the rate increased in the neck of aneurysms. Furthermore, the aneurysm with bigger dilation tend to rupture due to more shear rates in the neck and the velocity at peak systole decreases in the aneurysmal area due to increase of the artery diameter. We have compared our results with some available relevant clinical data in discussion section.

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