Explore the vast range of titles available.

scRNA-seq dataset integration occurs in different contexts, such as the identification of cell type-specific differences in gene expression across conditions or species, or batch effect correction. We present scAlign, an unsupervised deep learning method for data integration that can incorporate partial, overlapping, or a complete set of cell labels, and estimate per-cell differences in gene expression across datasets. scAlign performance is state-of-the-art and robust to cross-dataset variation in cell type-specific expression and cell type composition. We demonstrate that scAlign reveals gene expression programs for rare populations of malaria parasites. Our framework is widely applicable to integration challenges in other domains.

Multimodal single-cell assays profile multiple sets of features in the same cells and are widely used for identifying and mapping cell states between chromatin and mRNA and linking regulatory elements to target genes. However, the high dimensionality of input features and shallow sequencing depth compared to unimodal assays pose challenges in data analysis. Here we present scPair, a multimodal single-cell data framework that overcomes these challenges by employing an implicit feature selection approach. scPair uses dual encoder-decoder structures trained on paired data to align cell states across modalities and predict features from one modality to another. We demonstrate that scPair outperforms existing methods in accuracy and execution time, and facilitates downstream tasks such as trajectory inference. We further show scPair can augment smaller multimodal datasets with larger unimodal atlases to increase statistical power to identify groups of transcription factors active during different stages of neural differentiation. Multimodal single-cell analysis faces challenges due to high feature dimensionality and shallow sequencing depth. Here, authors present scPair for aligning cell states across modalities with implicit feature selection and enhancing data analysis tasks such as identifying key transcription factors in neural differentiation.

Neural networks such as variational autoencoders (VAE) perform dimensionality reduction for the visualization and analysis of genomic data, but are limited in their interpretability: it is unknown which data features are represented by each embedding dimension. We present siVAE, a VAE that is interpretable by design, thereby enhancing downstream analysis tasks. Through interpretation, siVAE also identifies gene modules and hubs without explicit gene network inference. We use siVAE to identify gene modules whose connectivity is associated with diverse phenotypes such as iPSC neuronal differentiation efficiency and dementia, showcasing the wide applicability of interpretable generative models for genomic data analysis.

Technical variation in feature measurements, such as gene expression and locus accessibility, is a key challenge of large-scale single-cell genomic datasets. We show that this technical variation in both scRNA-seq and scATAC-seq datasets can be mitigated by analyzing feature detection patterns alone and ignoring feature quantification measurements. This result holds when datasets have low detection noise relative to quantification noise. We demonstrate state-of-the-art performance of detection pattern models using our new framework, scBFA, for both cell type identification and trajectory inference. Performance gains can also be realized in one line of R code in existing pipelines.

Analysis of transcriptional and epigenomic changes in the hippocampus of a mouse model of Alzheimer’s disease shows that immune function genes and regulatory regions are upregulated, whereas genes and regulatory regions involved in synaptic plasticity, learning and memory are downregulated; genetic variants associated with Alzheimer’s disease are only enriched in orthologues of upregulated immune regions, suggesting that dysregulation of immune processes may underlie Alzheimer’s disease predisposition. Immune basis of Alzheimer's disease Recent genome-wide association studies have shown substantial genetic variation in non-coding regions associated with Alzheimer's disease, suggesting the involvement of aberrant gene regulation. However, the functional significance of these variants remained unclear. By profiling transcriptional and chromatin state dynamics in a mouse model, Elizabeta Gjoneska and colleagues now show that the immune response genes and their regulatory regions are upregulated, whereas those involved in synaptic plasticity and learning and memory are downregulated. These changes are highly conserved between the mouse model and the human disease. Surprisingly, Alzheimer's disease-associated genetic variants are mainly enriched in higher-activity, immune-related enhancers, and are depleted in lower-activity, neural enhancers. This suggests that genetic predisposition to Alzheimer's may be primarily associated with immune functions, while neuronal plasticity may be affected primarily by non-genetic effects. Alzheimer’s disease (AD) is a severe 1 age-related neurodegenerative disorder characterized by accumulation of amyloid-β plaques and neurofibrillary tangles, synaptic and neuronal loss, and cognitive decline. Several genes have been implicated in AD, but chromatin state alterations during neurodegeneration remain uncharacterized. Here we profile transcriptional and chromatin state dynamics across early and late pathology in the hippocampus of an inducible mouse model of AD-like neurodegeneration. We find a coordinated downregulation of synaptic plasticity genes and regulatory regions, and upregulation of immune response genes and regulatory regions, which are targeted by factors that belong to the ETS family of transcriptional regulators, including PU.1. Human regions orthologous to increasing-level enhancers show immune-cell-specific enhancer signatures as well as immune cell expression quantitative trait loci, while decreasing-level enhancer orthologues show fetal-brain-specific enhancer activity. Notably, AD-associated genetic variants are specifically enriched in increasing-level enhancer orthologues, implicating immune processes in AD predisposition. Indeed, increasing enhancers overlap known AD loci lacking protein-altering variants, and implicate additional loci that do not reach genome-wide significance. Our results reveal new insights into the mechanisms of neurodegeneration and establish the mouse as a useful model for functional studies of AD regulatory regions.

Multi-modal single cell RNA assays capture RNA content as well as other data modalities, such as spatial cell position or the electrophysiological properties of cells. Compared to dedicated scRNA-seq assays however, they may unintentionally capture RNA from multiple adjacent cells, exhibit lower RNA sequencing depth compared to scRNA-seq, or lack genome-wide RNA measurements. We present scProjection, a method for mapping individual multi-modal RNA measurements to deeply sequenced scRNA-seq atlases to extract cell type-specific, single cell gene expression profiles. We demonstrate several use cases of scProjection, including identifying spatial motifs from spatial transcriptome assays, distinguishing RNA contributions from neighboring cells in both spatial and multi-modal single cell assays, and imputing expression measurements of un-measured genes from gene markers. scProjection therefore combines the advantages of both multi-modal and scRNA-seq assays to yield precise multi-modal measurements of single cells. Many expression deconvolution approaches have been developed to estimate % RNA contributions of diverse cell types to mixed RNA measurements. Here, the authors have developed a complementary approach called scProjection to recover cell type-specific expression profiles from mixed RNA measurements.

In this study, the authors used epigenetics, allelic activity, motif conservation, and other techniques to dissect the regulatory circuitry and mechanistic basis of the association between the FTO region and obesity. An adipocyte thermogenesis pathway that appears important was found. Obesity affects more than 500 million people worldwide and contributes to type 2 diabetes, cardiovascular disorders, and cancer. 1 Obesity is the result of a positive energy balance, whereby energy intake exceeds expenditure, resulting in the storage of energy, primarily as lipids in white adipocytes. Energy balance is modulated by food consumption and physical activity, as well as by the dissipation of energy as heat through constitutive thermogenesis in mitochondria-rich brown adipocytes in brown fat and through inducible thermogenesis in beige adipocytes in white fat. 2 – 6 Thermogenesis is triggered by mechanisms within the cells themselves or by the sympathetic nervous system . . .

Gene regulatory elements are central drivers of phenotypic variation and thus of critical importance towards understanding the genetics of complex traits. The Functional Annotation of Animal Genomes consortium was formed to collaboratively annotate the functional elements in animal genomes, starting with domesticated animals. Here we present an expansive collection of datasets from eight diverse tissues in three important agricultural species: chicken ( Gallus gallus ), pig ( Sus scrofa ), and cattle ( Bos taurus ). Comparative analysis of these datasets and those from the human and mouse Encyclopedia of DNA Elements projects reveal that a core set of regulatory elements are functionally conserved independent of divergence between species, and that tissue-specific transcription factor occupancy at regulatory elements and their predicted target genes are also conserved. These datasets represent a unique opportunity for the emerging field of comparative epigenomics, as well as the agricultural research community, including species that are globally important food resources. In order to interpret non-coding variants, information about regulatory elements in the genome is essential. Here, the authors annotate regulatory elements in chicken, pig and cattle, and characterize conservation of these elements between species.

Following publication of the original article [1], the following two errors were found in formulae:

The RAS/ERK pathway plays a central role in diagnosis and therapy for many cancers. ERK activity is highly dynamic within individual cells and drives cell proliferation, metabolism, and other processes through effector proteins including c-Myc, c-Fos, Fra-1, and Egr-1. These proteins are sensitive to the dynamics of ERK activity, but it is not clear to what extent the pattern of ERK activity in an individual cell determines effector protein expression, or how much information about ERK dynamics is embedded in the pattern of effector expression. Here, we evaluate these relationships using live-cell biosensor measurements of ERK activity, multiplexed with immunofluorescence staining for downstream target proteins of the pathway. Combining these datasets with linear regression, machine learning, and differential equation models, we develop an interpretive framework for immunofluorescence data, wherein Fra-1 and pRb levels imply long-term activation of ERK signaling, while Egr-1 and c-Myc indicate more recent activation. Analysis of multiple cancer cell lines reveals a distorted relationship between ERK activity and cell state in malignant cells. We show that this framework can infer various classes of ERK dynamics from effector protein stains within a heterogeneous population, providing a basis for annotating ERK dynamics within fixed cells. ERK signaling involves complex spatiotemporal dynamics, making it difficult to quantify in many systems. In this study, live-cell activity measurements are combined with multiplexed immunofluorescence in a quantitative framework, allowing ERK dynamics to be quantified within fixed-cell samples.