Biology subjects

Hemberg, M.

Publications and source records attributed to Hemberg, M..

At least 37 records · Page 2Linked to original sources

bioRxiv2023

Therapy-associated remodeling of pancreatic cancer revealed by single-cell spatial transcriptomics and optimal transport analysis

In combination with cell intrinsic properties, interactions in the tumor microenvironment modulate therapeutic response. We leveraged high-plex single-cell spatial transcriptomics to dissect the remodeling of multicellular neighborhoods and cell-cell interactions in human pancreatic cancer associated with specific malignant subtypes and neoadjuvant chemotherapy/radiotherapy. We developed Spatially Constrained Optimal Transport Interaction Analysis (SCOTIA), an optimal transport model with a cost function that includes both spatial distance and ligand-receptor gene expression. Our results uncovered a marked change in ligand-receptor interactions between cancer-associated fibroblasts and malignant cells in response to treatment, which was supported by orthogonal datasets, including an ex vivo tumoroid co-culture system. Overall, this study demonstrates that characterization of the tumor microenvironment using high-plex single-cell spatial transcriptomics allows for identification of molecular interactions that may play a role in the emergence of chemoresistance and establishes a translational spatial biology paradigm that can be broadly applied to other malignancies, diseases, and treatments.

cancer biology↗

bioRxiv2023

Integrative single-cell characterization of frugivory adaptations in the bat kidney and pancreas

Frugivory evolved multiple times in mammals, including bats. However, the cellular and molecular components driving it remain largely unknown. Here, we used integrative single-cell sequencing on insectivorous and frugivorous bat kidneys and pancreases and identified key cell population, gene expression and regulatory element differences associated with frugivorous adaptation that also relate to human disease, particularly diabetes. We found an increase in collecting duct cells and differentially active genes and regulatory elements involved in fluid and electrolyte balance in the frugivore kidney. In the frugivorous pancreas, we observed an increase in endocrine and a decrease in exocrine cells and differences in genes and regulatory elements involved in insulin regulation. Combined, our work provides novel insights into frugivorous adaptation that also could be leveraged for therapeutic purposes.

genomics↗

bioRxiv2022

Integrative single-cell characterization of hypothalamus sex-differential and obesity-associated genes and regulatory elements

Over 500 noncoding genomic loci are associated with obesity. The majority of these loci reside near genes that are expressed in the hypothalamus in specific neuronal subpopulations that regulate food intake, hindering the ability to identify and functionally characterize them. Here, we carried out integrative single-cell analysis (RNA/ATAC-seq) on both mouse and human male and female hypothalamus to characterize genes and regulatory elements in specific cell subpopulations. Utilizing both transcriptome and regulome data, we identify over 30 different neuronal and non-neuronal cell subpopulations and a shared core of transcription factors that regulate cell cluster-specific genes between mice and humans. We characterize several sex-specific differentially expressed genes and the regulatory elements that control them in specific cell subpopulations. Overlapping cell-specific scATAC peaks with obesity-associated GWAS variants, identifies potential obesity-associated regulatory elements. Using reporter assays and CRISPR editing, we show that many of these sequences, including the top obesity-associated loci (FTO and MC4R), are functional enhancers whose activity is altered due to the obesity-associated variant and regulate known obesity genes. Combined, our work provides a catalog of genes and regulatory elements in hypothalamus cell subpopulations and uses obesity to showcase how integrative single-cell sequencing can identify functional variants associated with hypothalamus-related phenotypes.

neuroscience↗

bioRxiv2022

scHumanNet: a single-cell network analysis platform for the study of cell-type specificity of disease genes

A major challenge in single-cell biology is identifying cell-type-specific gene functions, which may substantially improve precision medicine. Differential expression analysis of genes is a popular, yet insufficient approach, and complementary methods that associate function with cell type are required. Here, we describe scHumanNet (https://github.com/netbiolab/scHumanNet), a single-cell network analysis platform for resolving cellular heterogeneity across gene functions in humans. Based on cell-type-specific networks (CSNs) constructed under the guidance of the HumanNet reference interactome, scHumanNet displayed higher functional relevance to the cellular context than CSNs built by other methods on single-cell transcriptome data. Cellular deconvolution of gene signatures based on network compactness across cell types revealed breast cancer prognostic markers associated with T cells. scHumanNet could also prioritize genes associated with particular cell types using CSN centrality and identified the differential hubness of CSNs between disease and healthy conditions. We demonstrated the usefulness of scHumanNet by uncovering T-cell-specific functional effects of GITR, a prognostic gene for breast cancer, and functional defects in autism spectrum disorder genes specific for inhibitory neurons. These results suggest that scHumanNet will advance our understanding of cell-type specificity across human disease genes.

bioinformatics↗

bioRxiv2022

Demuxafy: Improvement in droplet assignment by integrating multiple single-cell demultiplexing and doublet detection methods

Recent innovations in droplet-based single-cell RNA-sequencing (scRNA-seq) have provided the technology necessary to investigate biological questions at cellular resolution. With the ability to assay thousands of cells in a single capture, pooling cells from multiple individuals has become a common strategy. Droplets can subsequently be assigned to a specific individual by leveraging their inherent genetic differences, and numerous computational methods have been developed to address this problem. However, another challenge implicit with droplet-based scRNA-seq is the occurrence of doublets - droplets containing two or more cells. The inaccurate assignment of cells to individuals or failure to remove doublets contribute unwanted noise to the data and result in erroneous scientific conclusions. Therefore, it is essential to assign cells to individuals and remove doublets accurately. We present a new framework to improve individual singlet classification and doublet removal through a multi-method intersectional approach. We developed a framework to evaluate the enhancement in donor assignment and doublet removal through the consensus intersection of multiple demultiplexing and doublet detecting methods. The accuracy was assessed using scRNA-seq data of [~]1.4 million peripheral blood mononucleated cells from 1,034 unrelated individuals and [~]90,000 fibroblast cells from 81 unrelated individuals. We show that our approach significantly improves droplet assignment by separating singlets from doublets and classifying the correct individual compared to any single method. We show that the best combination of techniques varies under different biological and experimental conditions, and we present a framework to optimise cell assignment for a given experiment. We offer Demuxafy (https://demultiplexing-doublet-detecting-docs.readthedocs.io/en/latest/index.html) - a framework built-in Singularity to provide clear, consistent documentation of each method and additional tools to simplify and improve demultiplexing and doublet removal. Our results indicate that leveraging multiple demultiplexing and doublet detecting methods improves accuracy and, consequently, downstream analyses in multiplexed scRNA-seq experiments.

genomics↗

bioRxiv2021

Droplet-based Single-cell Total RNA-seq Reveals Differential Non-Coding Expression and Splicing Patterns during Mouse Development

In recent years, single-cell transcriptome sequencing has revolutionized biology, allowing for the unbiased characterization of cellular subpopulations. However, most methods amplify the termini of polyadenylated transcripts capturing only a small fraction of the total cellular transcriptome. This precludes the detection of many long non-coding, short non-coding and non-polyadenylated protein-coding transcripts. Additionally, most workflows do not sequence the full transcript hindering the analysis of alternative splicing. We therefore developed VASA-seq to detect the total transcriptome in single cells. VASA-seq is compatible with both plate-based formats and droplet microfluidics. We applied VASA-seq to over 30,000 single cells in the developing mouse embryo during gastrulation and early organogenesis. The dynamics of the total single-cell transcriptome result in the discovery of novel cell type markers many based on non-coding RNA, an in vivo cell cycle analysis and an improved RNA velocity characterization. Moreover, it provides the first comprehensive analysis of alternative splicing during mammalian development.

molecular biology↗

bioRxiv2021

SC3s - efficient scaling of single cell consensus clustering to millions of cells

Technological advances have paved the way for single cell RNAseq (scRNAseq) datasets containing several million cells 1. Such large datasets require highly efficient algorithms to enable analyses at reasonable times and hardware requirements 2. A crucial step in single cell workflows is unsupervised clustering, which aims to delineate putative cell types or cell states based on transcriptional similarity 3. Here, we present a highly efficient k-means based approach, and we demonstrate that it scales linearly with the number of cells with regards to time and memory.

bioinformatics↗

bioRxiv2021

TransposonUltimate: software for transposon classification, annotation and detection

MotivationMost genomes harbor a large number of transposons, and they play an important role in evolution and gene regulation. They are also of interest to clinicians as they are involved in several diseases, including cancer and neurodegeneration. Although several methods for transposon identification are available, they are often highly specialised towards specific tasks or classes of transposons, and they lack common standards such as a unified taxonomy scheme and output file format. Moreover, many methods are difficult to install, poorly documented, and difficult to reproduce. ResultsWe present TransposonUltimate, a powerful bundle of three modules for transposon classification, annotation, and detection of transposition events. TransposonUltimate comes as a Conda package under the GPL-3.0 licence, is well documented and it is easy to install. We benchmark the classification module on the large TransposonDB covering over 891,051 sequences to demonstrate that it outperforms the currently best existing solutions. The annotation and detection modules combine sixteen existing softwares, and we illustrate its use by annotating Caenorhabditis elegans, Rhizophagus irregularis and Oryza sativa subs. japonica genomes. Finally, we use the detection module to discover 29,554 transposition events in the genomes of twenty wild type strains of Caenorhabditis elegans. AvailabilityRunning software and source code available on https://github.com/DerKevinRiehl/TransposonClassifierRFSB. Databases, assemblies, annotations and further findings can be downloaded from https://cellgeni.cog.sanger.ac.uk/browser.html?shared=transposonultimate.

bioinformatics↗

bioRxiv2021

Transcriptome-wide spatial RNA profiling maps the cellular architecture of the developing human neocortex

Spatial genomic technologies can map gene expression in tissues, but provide limited potential for transcriptome-wide discovery approaches and application to fixed tissue samples. Here, we introduce the GeoMX Whole Transcriptome Atlas (WTA), a new technology for transcriptome-wide spatial profiling of tissues with cellular resolution. WTA significantly expands the Digital Spatial Profiling approach to enable in situ hybridisation against 18,190 genes at high-throughput using a sequencing readout. We applied WTA to generate the first spatial transcriptomic map of the fetal human neocortex, validating transcriptome-wide spatial profiling on formalin-fixed tissue material and demonstrating the spatial enrichment of autism gene expression in deep cortical layers. To demonstrate the value of WTA for cell atlasing, we integrated single-cell RNA-sequencing (scRNA-seq) and WTA data to spatially map dozens of neural cell types and showed that WTA can be used to directly measure cell type specific transcriptomes in situ. Moreover, we developed computational tools for background correction of WTA data and accurate integration with scRNA-seq. Our results present WTA as a versatile transcriptome-wide discovery tool for cell atlasing and fixed tissue spatial transcriptomics.

genomics↗

bioRxiv2021

Single paternal Dexamethasone challenge programs offspring metabolism and reveals circRNAs as novel candidates in RNA-mediated inheritance

Single traumatic events that elicit an exaggerated stress response can lead to the development of neuropsychiatric conditions. Studies in mice suggests germline RNA as a mediator of effects of chronic environmental exposures to the progeny. The effects of an acute paternal stress exposure on the germline and their potential consequences on offspring remain unknown. We find that acute administration of an agonist for the stress- sensitive Glucocorticoid receptor, using the common corticosteroid Dexamethasone, affects the RNA payload of post-meiotic transcriptionally silent, mature sperm as soon as 3 hours post exposure. It further impacts early embryonic transcriptional trajectories, as determined by single embryo sequencing, and metabolism in the offspring. Importantly, we show persistent regulation of tRNA fragments in sperm and the descendant 2-cell- embryos, suggesting actual transmission from sperm to embryo. Lastly, we unravel environmentally induced alterations in the previously underconsidered class of sperm circRNAs, and their targets in the early embryo, highlighting this class as a novel candidate in RNA-mediated inheritance.

developmental biology↗

bioRxiv2020

Predicting the impact of sequence motifs on gene regulation using single-cell data

BackgroundBinding of transcription factors (TFs) at proximal promoters and distal enhancers is central to gene regulation. Yet, identification of TF binding sites, also known as regulatory motifs, and quantification of their impact on gene expression remains challenging. ResultsHere we infer putative regulatory motifs along with their cell type-specific importance using a convolutional neural network trained on single-cell data. Comparison of the importance score to expression levels across cells allows us to identify the TFs most likely to be binding at a given motif. Using multiple mouse tissues we obtain a model with cell type resolution which explains 29% of the variance in gene expression. Finally, by applying scover to distal enhancers identified using scATAC-seq from the mouse cerebral cortex we characterize changes in distal regulatory motifs during development. ConclusionsIt is possible to identify regulatory motifs as well as their importance from single-cell data using a neural network model where all of the parameters and outputs are easily interpretable to the user.

bioinformatics↗

bioRxiv2020

Molecular and cellular pathology of monogenic Alzheimer's disease at single cell resolution

Cell and molecular biology analyses of sporadic Alzheimers disease brain are confounded by clinical variability, ageing and genetic heterogeneity. Therefore, we used single-nucleus RNA sequencing to characterize cell composition and gene expression in the cerebral cortex in early-onset, monogenic Alzheimers disease. Constructing a cellular atlas of frontal cortex from 8 monogenic AD individuals and 8 matched controls, provided insights into which neurons degenerate in AD and responses of different cell types to AD at the cellular and systems level. Such responses are a combination of positively adaptive and deleterious changes, including large-scale changes in synaptic transmission and marked metabolic reprogramming in neurons. The nature and scale of the transcriptional changes in AD emphasizes the global impact of the disease across all brain cell types. One Sentence SummaryAlzheimers disease brain atlas provides insights into disease mechanisms

neuroscience↗

bioRxiv2020

Flexible comparison of batch correction methods for single-cell RNA-seq using BatchBench

As the cost of single-cell RNA-seq experiments has decreased, an increasing number of datasets are now available. Combining newly generated and publicly accessible datasets is challenging due to non-biological signals, commonly known as batch effects. Although there are several computational methods available that can remove batch effects, evaluating which method performs best is not straightforward. Here we present BatchBench (https://github.com/cellgeni/batchbench), a modular and flexible pipeline for comparing batch correction methods for single-cell RNA-seq data. We apply BatchBench to eight methods, highlighting their methodological differences and assess their performance and computational requirements through a compendium of well-studied datasets. This systematic comparison guides users in the choice of batch correction tool, and the pipeline makes it easy to evaluate other datasets.

bioinformatics↗

bioRxiv2020

Absent from DNA and protein: genomic characterization of nullomers and nullpeptides across functional categories and evolution

Nullomers and nullpeptides are short DNA or amino acid sequences that are absent from a genome or proteome, respectively. One potential cause for their absence could be that they have a detrimental impact on an organism. Here, we identified all possible nullomers and nullpeptides in the genomes and proteomes of over thirty species and show that a significant proportion of these sequences are under negative selection. We assign nullomers to different functional categories (coding sequences, exons, introns, 5UTR, 3UTR and promoters) and show that nullomers from coding sequences and promoters are most likely to be selected against. Utilizing variants in the human population, we annotate variant-associated nullomers, highlighting their potential use as DNA fingerprints. Phylogenetic analyses of nullomers and nullpeptides across evolution shows that they could be used to build phylogenetic trees. Our work provides a catalog of genomic and proteome derived absent k-mers, together with a novel scoring function to determine their potential functional importance. In addition, it shows how these unique sequences could be used as DNA fingerprints or for phylogenetic analyses.

genomics↗

bioRxiv2020

MicroExonator enables systematic discovery and quantification of microexons across mouse embryonic development

Microexons, exons that are [≤]30 nucleotides, were shown to play key roles in neuronal development, but are difficult to detect and quantify using standard RNA-Seq alignment tools. Here, we present MicroExonator, a novel pipeline for reproducible de novo discovery and quantification of microexons. We processed 289 RNA-seq datasets from eighteen mouse tissues corresponding to nine embryonic and postnatal stages, providing the most comprehensive survey of microexons available for mouse. We detected 2,984 microexons, 332 of which are differentially spliced throughout mouse embryonic brain development, including 29 that are not present in mouse transcript annotation databases. Unsupervised clustering of microexons alone segregates brain tissues by developmental time and further analysis suggest a key function for microexon inclusion in axon growth and synapse formation. Finally, we analysed single-cell RNA-seq data from the mouse visual cortex and we report differential inclusion between neuronal subpopulations, suggesting that some microexons could be cell-type specific.

genomics↗

bioRxiv2019

Obstacles to Studying Alternative Splicing Using scRNA-seq

BackgroundEarly single-cell RNA-seq (scRNA-seq) studies suggested that it was unusual to see more than one isoform being produced from a gene in a single cell, even when multiple isoforms were detected in matched bulk RNA-seq samples. However, these studies generally did not consider the impact of dropouts or isoform quantification errors, potentially confounding the results of these analyses.\n\nResultsIn this study, we take a simulation based approach in which we explicitly account for dropouts and isoform quantification errors. We use our simulations to ask to what extent it is possible to study alternative splicing using scRNA-seq. Additionally, we ask what limitations must be overcome to make splicing analysis feasible. We find that the high rate of dropouts associated with scRNA-seq is a major obstacle to studying alternative splicing. In mice and other well established model organisms, the relatively low rate of isoform quantification errors poses a lesser obstacle to splicing analysis. We find that different models of isoform choice meaningfully change our simulation results.\n\nConclusionsTo accurately study alternative splicing with single-cell RNA-seq, a better understanding of isoform choice and the errors associated with scRNA-seq is required. An increase in the capture efficiency of scRNA-seq would also be beneficial. Until some or all of the above are achieved, we do not recommend attempting to resolve isoforms in individual cells using scRNA-seq.

bioinformatics↗

bioRxiv2019

Fast searches of large collections of single cell data using scfind

Single cell technologies have made it possible to profile millions of cells, but for these resources to be useful they must be easy to query and access. To facilitate interactive and intuitive access to single cell data we have developed scfind, a search engine for cell atlases. Using transcriptome data from mouse cell atlases we show how scfind can be used to evaluate marker genes, to perform in silico gating, and to identify both cell-type specific and housekeeping genes. Moreover, we have developed a subquery optimization routine to ensure that long and complex queries return meaningful results. To make scfind more user friendly and accessible, we use indices of PubMed abstracts and techniques from natural language processing to allow for arbitrary queries. Finally, we show how scfind can be used for multi-omics analyses by combining single-cell ATAC-seq data with transcriptome data.

bioinformatics↗

bioRxiv2019

Sandwich cortical lamination and single-cell analysis decodes the developing spatial processing system.

Classic studies investigating how and when the entorhinal cortex (component of the memory processing system of the brain) develops have been based on traditional thymidine autoradiography and histological techniques. In this study, we take advantage of modern technologies to trace at a high resolution, the cellular complexity of the developing porcine medial entorhinal cortex by using single-cell profiling. The postnatal medial entorhinal cortex comprises 4 interneuron, 3 pyramidal neuron and 2 stellate cell populations which emerge from intermediate progenitor and immature neuron populations. We discover four MGE-derived interneurons and one CGE-derived interneuron population as well as several IN progenitors. We also identify two oligodendrocyte progenitor populations and three populations of oligodendrocytes. We perform a proof-of-concept experiment demonstrating that porcine scRNA-seq data can be used to develop novel protocols for producing human entorhinal cells in-vitro. We identified six transcription factors (RUNX1A1, SOX5, FOXP1, MEF2C, TCF4, EYA2) important in neurodevelopment and differentiation from one RELN+ stellate cell population. Using a lentiviral vector approach, we reprogrammed human induced pluripotent stem cells into stellate cell-like cells which expressed RELN, SATB2, LEF1 and BCL11B. Our findings contribute to the understanding of the formation of the brains cognitive memory and spatial processing system and provides proof-of-concept for the production of entorhinal cells from human pluripotent stem cells in-vitro.

developmental biology↗