6 research outputs found
Structured data abstractions and interpretable latent representations for single-cell multimodal genomics
Single-cell multimodal genomics involves simultaneous measurement of multiple types of molecular data, such as gene expression, epigenetic marks and protein abundance, in individual cells. This allows for a comprehensive and nuanced understanding of the molecular basis of cellular identity and function. The large volume of data generated by single-cell multimodal genomics experiments requires specialised methods and tools for handling, storing, and analysing it.
This work provides contributions on multiple levels. First, it introduces a single-cell multimodal data standard — MuData — designed to facilitate the handling, storage and exchange of multimodal data. MuData provides interfaces that enable transparent access to multimodal annotations as well as data from individual modalities. This data structure has formed the foundation for the multimodal integration framework, which enables complex and composable workflows that can be naturally integrated with existing omics-specific analysis approaches.
Joint analysis of multimodal data can be performed using integration methods. In order to enable integration of single-cell data, an improved multi-omics factor analysis model (MOFA+) has been designed and implemented building on the canonical dimensionality reduction approach for multi-omics integration. Inferring later factors that explain variation across multiple modalities of the data, MOFA+ enables the modelling of latent factors with cell group-specific patterns of activity. MOFA+ model has been implemented as part of the respective multi-omics integration framework, and its utility has been extended by software solutions that facilitate interactive model exploration and interpretation.
The newly improved model for multi-omics integration of single cells has been applied to the study of gene expression signatures upon targeted gene activation. In a dataset featuring targeted activation of candidate regulators of zygotic genome activation (ZGA) — a crucial transcriptional event in early embryonic development, — modelling expression of both coding and non-coding loci with MOFA+ allowed to rank genes by their potency to activate a ZGA-like transcriptional response. With identification of Patz1, Dppa2 and Smarca5 as potent inducers of ZGA-like transcription in mouse embryonic stem cells, these findings have contributed to the understanding of molecular mechanisms behind ZGA and laid the foundation for future research of ZGA in vivo.
In summary, this work’s contributions include the development of data handling and integration methods as well as new biological insights that arose from applying these methods to studying gene expression regulation in early development. This highlights how single-cell multimodal genomics can aid to generate valuable insights into complex biological systems
CaO-SiO<sub>2</sub>-B<sub>2</sub>O<sub>3</sub> Glass as a Sealant for Solid Oxide Fuel Cells
Solid oxide fuel cells (SOFCs) are promising devices for electrical power generation from hydrogen or hydrocarbon fuels. The paper reports our study of CaO-SiO2-B2O3 material with composition 36 mol.% SiO2, 26 mol.% B2O3, and 38 mol.% CaO as a high-temperature sealant for SOFCs with an operating temperature of 850 °C. The material was studied as an alternative to presently existing commercial glass and glass-ceramics sealants for SOFCs with operating temperature of 850 °C. Many of these sealants have limited adhesion to the surface of Crofer 22APU steel, commonly used in these SOFCs. The present study included X-ray diffraction, dilatometric, thermal, and microstructural analysis The study has shown that the softening point of the CaO-SiO2-B2O3 glass is around 900 °C, allowing sealing of the SOFCs with this glass at convenient temperature of 925 °C. The CaO-SiO2-B2O3 glass sealant has shown excellent adhesion to the surface of Crofer 22APU steel; SEM images demonstrated evidences of chemical reaction and formation of strong interface on sealant–steel contact surface. Furthermore, the glass has shown a coefficient of thermal expansion about 8.4 × 10−6 1/K after sealing, making it thermomechanically compatible with the existing SOFC materials
CaO-SiO2-B2O3 Glass as a Sealant for Solid Oxide Fuel Cells
Solid oxide fuel cells (SOFCs) are promising devices for electrical power generation from hydrogen or hydrocarbon fuels. The paper reports our study of CaO-SiO2-B2O3 material with composition 36 mol.% SiO2, 26 mol.% B2O3, and 38 mol.% CaO as a high-temperature sealant for SOFCs with an operating temperature of 850 °C. The material was studied as an alternative to presently existing commercial glass and glass-ceramics sealants for SOFCs with operating temperature of 850 °C. Many of these sealants have limited adhesion to the surface of Crofer 22APU steel, commonly used in these SOFCs. The present study included X-ray diffraction, dilatometric, thermal, and microstructural analysis The study has shown that the softening point of the CaO-SiO2-B2O3 glass is around 900 °C, allowing sealing of the SOFCs with this glass at convenient temperature of 925 °C. The CaO-SiO2-B2O3 glass sealant has shown excellent adhesion to the surface of Crofer 22APU steel; SEM images demonstrated evidences of chemical reaction and formation of strong interface on sealant–steel contact surface. Furthermore, the glass has shown a coefficient of thermal expansion about 8.4 × 10−6 1/K after sealing, making it thermomechanically compatible with the existing SOFC materials
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MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data
Abstract: Technological advances have enabled the profiling of multiple molecular layers at single-cell resolution, assaying cells from multiple samples or conditions. Consequently, there is a growing need for computational strategies to analyze data from complex experimental designs that include multiple data modalities and multiple groups of samples. We present Multi-Omics Factor Analysis v2 (MOFA+), a statistical framework for the comprehensive and scalable integration of single-cell multi-modal data. MOFA+ reconstructs a low-dimensional representation of the data using computationally efficient variational inference and supports flexible sparsity constraints, allowing to jointly model variation across multiple sample groups and data modalities
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MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data
Abstract: Technological advances have enabled the profiling of multiple molecular layers at single-cell resolution, assaying cells from multiple samples or conditions. Consequently, there is a growing need for computational strategies to analyze data from complex experimental designs that include multiple data modalities and multiple groups of samples. We present Multi-Omics Factor Analysis v2 (MOFA+), a statistical framework for the comprehensive and scalable integration of single-cell multi-modal data. MOFA+ reconstructs a low-dimensional representation of the data using computationally efficient variational inference and supports flexible sparsity constraints, allowing to jointly model variation across multiple sample groups and data modalities
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Expression profiling of cerebrospinal fluid identifies dysregulated antiviral mechanisms in multiple sclerosis.
Despite the overwhelming evidence that multiple sclerosis is an autoimmune disease, relatively little is known about the precise nature of the immune dysregulation underlying the development of the disease. Reasoning that the cerebrospinal fluid from patients might be enriched for cells relevant in pathogenesis, we have completed a high-resolution single-cell analysis of 96,732 cerebrospinal fluid cells collected from 33 patients with multiple sclerosis (n = 48,675) and 48 patients with other neurological diseases (n = 48,057). Completing comprehensive cell type annotation, we identified a rare population of CD8+ T cells, characterised by the upregulation of inhibitory receptors, increased in patients with multiple sclerosis. Applying a Multi-Omics Factor Analysis to these single-cell data further revealed that activity in pathways responsible for controlling inflammatory and type 1 interferon responses are altered in multiple sclerosis in both T cells and myeloid cells. We also undertook a systematic search for expression quantitative trait loci in the cerebrospinal fluid cells. Of particular interest were two expression quantitative trait loci in CD8+ T cells that were fine mapped to multiple sclerosis susceptibility variants in the viral control genes ZC3HAV1 (rs10271373) and IFITM2 (rs1059091). Further analysis suggests that these associations likely reflect genetic effects on RNA splicing and cell-type specific gene expression respectively. Collectively our study suggests that alterations in viral control mechanisms might be important in the development of multiple sclerosis.Multiple Sclerosis Society in the U