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

Factor 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.

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 CSF from patients might be enriched for cells relevant in pathogenesis, we have completed a high-resolution single-cell analysis of 96 732 CSF 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, characterized 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 CSF 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.

MUON: multimodal omics analysis framework.

Advances in multi-omics have led to an explosion of multimodal datasets to address questions from basic biology to translation. While these data provide novel opportunities for discovery, they also pose management and analysis challenges, thus motivating the development of tailored computational solutions. Here, we present a data standard and an analysis framework for multi-omics, MUON, designed to organise, analyse, visualise, and exchange multimodal data. MUON stores multimodal data in an efficient yet flexible and interoperable data structure. MUON enables a versatile range of analyses, from data preprocessing to flexible multi-omics alignment.

MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data.

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.

A Single-Cell Transcriptomics CRISPR-Activation Screen Identifies Epigenetic Regulators of the Zygotic Genome Activation Program.

Zygotic genome activation (ZGA) is an essential transcriptional event in embryonic development that coincides with extensive epigenetic reprogramming. Complex manipulation techniques and maternal stores of proteins preclude large-scale functional screens for ZGA regulators within early embryos. Here, we combined pooled CRISPR activation (CRISPRa) with single-cell transcriptomics to identify regulators of ZGA-like transcription in mouse embryonic stem cells, which serve as a tractable, in vitro proxy of early mouse embryos. Using multi-omics factor analysis (MOFA+) applied to ∼200,000 single-cell transcriptomes comprising 230 CRISPRa perturbations, we characterized molecular signatures of ZGA and uncovered 24 factors that promote a ZGA-like response. Follow-up assays validated top screen hits, including the DNA-binding protein Dppa2, the chromatin remodeler Smarca5, and the transcription factor Patz1, and functional experiments revealed that Smarca5's regulation of ZGA-like transcription is dependent on Dppa2. Together, our single-cell transcriptomic profiling of CRISPRa-perturbed cells provides both system-level and molecular insights into the mechanisms that orchestrate ZGA.