scholarly journals Identifying temporal and spatial patterns of variation from multimodal data using MEFISTO

2022 ◽  
Author(s):  
Britta Velten ◽  
Jana M. Braunger ◽  
Ricard Argelaguet ◽  
Damien Arnol ◽  
Jakob Wirbel ◽  
...  

AbstractFactor analysis is a widely used method for dimensionality reduction in genome biology, with applications from personalized health to single-cell biology. Existing factor analysis models assume independence of the observed samples, an assumption that fails in spatio-temporal profiling studies. Here we present MEFISTO, a flexible and versatile toolbox for modeling high-dimensional data when spatial or temporal dependencies between the samples are known. MEFISTO maintains the established benefits of factor analysis for multimodal data, but enables the performance of spatio-temporally informed dimensionality reduction, interpolation, and separation of smooth from non-smooth patterns of variation. Moreover, MEFISTO can integrate multiple related datasets by simultaneously identifying and aligning the underlying patterns of variation in a data-driven manner. To illustrate MEFISTO, we apply the model to different datasets with spatial or temporal resolution, including an evolutionary atlas of organ development, a longitudinal microbiome study, a single-cell multi-omics atlas of mouse gastrulation and spatially resolved transcriptomics.

2020 ◽  
Author(s):  
Britta Velten ◽  
Jana M. Braunger ◽  
Damien Arnol ◽  
Ricard Argelaguet ◽  
Oliver Stegle

AbstractFactor analysis is among the most-widely used methods for dimensionality reduction in genome biology, with applications from personalized health to single-cell studies. Existing implementations of factor analysis assume independence of the observed samples, an assumption that fails in emerging spatio-temporal profiling studies. Here, we present MEFISTO, a flexible and versatile toolbox for modelling high-dimensional data when spatial or temporal dependencies between the samples are known. MEFISTO maintains the established benefits of factor analysis for multi-modal data, but enables performing spatio-temporally informed dimensionality reduction, interpolation and separation of smooth from non-smooth patterns of variation. Moreover, MEFISTO can integrate multiple related datasets by simultaneously identifying and aligning the underlying patterns of variation in a data-driven manner. We demonstrate MEFISTO through applications to an evolutionary atlas of mammalian organ development, where the model reveals conserved and evolutionary diverged developmental programs. In applications to a longitudinal microbiome study in infants, birth mode and diet were highlighted as major causes for heterogeneity in the temporally-resolved microbiome over the first years of life. Finally, we demonstrate that the proposed framework can also be applied to spatially resolved transcriptomics.


2021 ◽  
Author(s):  
Mika Sarkin Jain ◽  
Krzysztof Polanski ◽  
Cecilia Dominguez Conde ◽  
Xi Chen ◽  
Jongeun Park ◽  
...  

AbstractMultimodal data is rapidly growing in many fields of science and engineering, including single-cell biology. We introduce MultiMAP, an approach for dimensionality reduction and integration of multiple datasets. MultiMAP recovers a single manifold on which all of the data resides and then projects the data into a single low-dimensional space so as to preserve the structure of the manifold. It is based on a framework of Riemannian geometry and algebraic topology, and generalizes the popular UMAP algorithm1 to the multimodal setting. MultiMAP can be used for visualization of multimodal data, and as an integration approach that enables joint analyses. MultiMAP has several advantages over existing integration strategies for single-cell data, including that MultiMAP can integrate any number of datasets, leverages features that are not present in all datasets (i.e. datasets can be of different dimensionalities), is not restricted to a linear mapping, can control the influence of each dataset on the embedding, and is extremely scalable to large datasets. We apply MultiMAP to the integration of a variety of single-cell transcriptomics, chromatin accessibility, methylation, and spatial data, and show that it outperforms current approaches in preservation of high-dimensional structure, alignment of datasets, visual separation of clusters, transfer learning, and runtime. On a newly generated single-cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq) and single-cell RNA-seq (scRNA-seq) dataset of the human thymus, we use MultiMAP to integrate cells along a temporal trajectory. This enables the quantitative comparison of transcription factor expression and binding site accessibility over the course of T cell differentiation, revealing patterns of transcription factor kinetics.


2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Mika Sarkin Jain ◽  
Krzysztof Polanski ◽  
Cecilia Dominguez Conde ◽  
Xi Chen ◽  
Jongeun Park ◽  
...  

AbstractMultimodal data is rapidly growing in many fields of science and engineering, including single-cell biology. We introduce MultiMAP, a novel algorithm for dimensionality reduction and integration. MultiMAP can integrate any number of datasets, leverages features not present in all datasets, is not restricted to a linear mapping, allows the user to specify the influence of each dataset, and is extremely scalable to large datasets. We apply MultiMAP to single-cell transcriptomics, chromatin accessibility, methylation, and spatial data and show that it outperforms current approaches. On a new thymus dataset, we use MultiMAP to integrate cells along a temporal trajectory. This enables quantitative comparison of transcription factor expression and binding site accessibility over the course of T cell differentiation, revealing patterns of expression versus binding site opening kinetics.


2020 ◽  
Author(s):  
André Figueiredo Rendeiro ◽  
Hiranmayi Ravichandran ◽  
Yaron Bram ◽  
Steven Salvatore ◽  
Alain Borczuk ◽  
...  

SummaryRecent studies have provided insights into the pathology and immune response to coronavirus disease 2019 (COVID-19)1–8. However thorough interrogation of the interplay between infected cells and the immune system at sites of infection is lacking. We use high parameter imaging mass cytometry9 targeting the expression of 36 proteins, to investigate at single cell resolution, the cellular composition and spatial architecture of human acute lung injury including SARS-CoV-2. This spatially resolved, single-cell data unravels the disordered structure of the infected and injured lung alongside the distribution of extensive immune infiltration. Neutrophil and macrophage infiltration are hallmarks of bacterial pneumonia and COVID-19, respectively. We provide evidence that SARS-CoV-2 infects predominantly alveolar epithelial cells and induces a localized hyper-inflammatory cell state associated with lung damage. By leveraging the temporal range of COVID-19 severe fatal disease in relation to the time of symptom onset, we observe increased macrophage extravasation, mesenchymal cells, and fibroblasts abundance concomitant with increased proximity between these cell types as the disease progresses, possibly as an attempt to repair the damaged lung tissue. This spatially resolved single-cell data allowed us to develop a biologically interpretable landscape of lung pathology from a structural, immunological and clinical standpoint. This spatial single-cell landscape enabled the pathophysiological characterization of the human lung from its macroscopic presentation to the single-cell, providing an important basis for the understanding of COVID-19, and lung pathology in general.


2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Karoline Holler ◽  
Anika Neuschulz ◽  
Philipp Drewe-Boß ◽  
Janita Mintcheva ◽  
Bastiaan Spanjaard ◽  
...  

AbstractEarly stages of embryogenesis depend on subcellular localization and transport of maternal mRNA. However, systematic analysis of these processes is hindered by a lack of spatio-temporal information in single-cell RNA sequencing. Here, we combine spatially-resolved transcriptomics and single-cell RNA labeling to perform a spatio-temporal analysis of the transcriptome during early zebrafish development. We measure spatial localization of mRNA molecules within the one-cell stage embryo, which allows us to identify a class of mRNAs that are specifically localized at an extraembryonic position, the vegetal pole. Furthermore, we establish a method for high-throughput single-cell RNA labeling in early zebrafish embryos, which enables us to follow the fate of individual maternal transcripts until gastrulation. This approach reveals that many localized transcripts are specifically transported to the primordial germ cells. Finally, we acquire spatial transcriptomes of two xenopus species and compare evolutionary conservation of localized genes as well as enriched sequence motifs.


2021 ◽  
Author(s):  
Shani Ben-Moshe ◽  
Tamar Veg ◽  
Rita Manco ◽  
Stav Dan ◽  
Aleksandra A. Kolodziejczyk ◽  
...  

The liver carries a remarkable ability to regenerate rapidly after acute zonal damage. Single-cell approaches are necessary to study this process, given the spatial heterogeneity of multiple liver cell types. Here, we use spatially-resolved single cell RNA sequencing (scRNAseq) to study the dynamics of mouse liver regeneration after acute acetaminophen (APAP) intoxication. We find that hepatocytes proliferate throughout the liver lobule, creating the mitotic pressure required to repopulate the necrotic pericentral zone rapidly. A subset of hepatocytes located at the regenerating front transiently up-regulate fetal-specific genes, including Afp and Cdh17, as they reprogram to a pericentral state. Zonated endothelial, hepatic-stellate cell (HSC) and macrophage populations are differentially involved in immune recruitment, proliferation and matrix remodeling. We observe massive transient infiltration of myeloid cells, yet stability of lymphoid cell abundance, in accordance with global decline in antigen presentation. Our study provides a resource for understanding the coordinated programs of zonal liver regeneration.


2020 ◽  
Author(s):  
Karoline Holler ◽  
Anika Neuschulz ◽  
Philipp Drewe-Boß ◽  
Janita Mintcheva ◽  
Bastiaan Spanjaard ◽  
...  

SummaryEarly stages of embryogenesis depend heavily on subcellular localization and transport of maternally deposited mRNA. However, systematic analysis of these processes is currently hindered by a lack of spatio-temporal information in single-cell RNA sequencing. Here, we combined spatially-resolved transcriptomics and single-cell RNA labeling to study the spatio-temporal dynamics of the transcriptome during the first few hours of zebrafish development. We measured spatial localization of mRNA molecules with sub-single-cell resolution at the one-cell stage, which allowed us to identify a class of mRNAs that are specifically localized at an extraembryonic position, the vegetal pole. Furthermore, we established a method for high-throughput single-cell RNA labeling in early zebrafish embryos, which enabled us to follow the fate of individual maternal transcripts until gastrulation. This approach revealed that many localized transcripts are specifically transported to the primordial germ cells. Finally, we acquired spatial transcriptomes of two xenopus species, and we compared evolutionary conservation of localized genes as well as enriched sequence motifs. In summary, we established sub-single-cell spatial transcriptomics and single-cell RNA labeling to reveal principles of mRNA localization in early vertebrate development.


Author(s):  
Andrew E. Teschendorff ◽  
Andrew P. Feinberg

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