Biology subjects

Dann, E.

Publications and source records attributed to Dann, E..

At least 19 recordsLinked to original sources

bioRxiv2026

Systematic identification of seed-driven off-target effects in Perturb-seq experiments

Genome-wide Perturb-seq (GWPS) has emerged as a powerful approach for unbiased mapping of gene regulatory networks. A key assumption underlying many Perturb-seq analyses is that each guide RNA exclusively perturbs a single target locus. Without methods to identify and filter off-target events, erroneous gene-pathway associations driven by off-target activity can propagate into downstream analyses. Here, we present a workflow for the systematic identification of candidate off-target events in CRISPRi Perturb-seq experiments. Our approach exploits the observation that cells harboring a guide which represses an off-target gene display transcriptional similarity to cells in which that gene is directly targeted by an on-target guide. We apply our workflow to multiple GWPS datasets and nominate off-target events in which a guide nominally targeting one gene also represses a distinct gene producing a phenotype likely attributable to the off-target perturbation. We use both off-target gene repression and guide seed sequence alignments at the off-target promoter locus as evidence for off-target effects and find independent evidence of putative off-target events in separate GWPS datasets. Together, these results establish a principled framework for the identification and filtering of off-target guide effects in Perturb-seq experiments.

genomics↗

bioRxiv2026

The modular nature of long-association pathways in the human brain

Human brain functions are organized within large-scale networks, yet the long-range structural pathways that support them remain only partly understood. Diffusion MRI tractography has enabled non-invasive mapping of these connections but its low resolution has also contributed to a view of white matter association bundles as monolithic entities linking distant cortical regions. This contrasts with anatomical studies in non-human primates and humans, which show these bundles act as conduits for complex systems of short- and long-range projections. Here we investigate the dorsal branch of the superior longitudinal fasciculus (SLF-I), the proposed structural backbone of the dorsal attention network, whose anatomy in humans is debated. Using anatomic tracing, polarization-sensitive optical coherence tomography, and in vivo and ex vivo diffusion MRI, we reconstruct the SLF-I across species, modalities, and scales. High resolution tractography (<750 m), consistent with tracer and PS-OCT data, reveals multiple fiber systems of varying lengths rather than uniform parieto-frontal connections observed at more conventional resolution (1.5 mm). Comparative analyses show a consistent organization: the SLF-I carries short-range cortico-cortical projections rostral to area 6 and longer, layered connections extending to parietal regions, while direct parieto-frontal connectivity is mostly supported by the adjacent cingulum bundle. These findings reconcile our understanding of the SLF-I across the scales of non-invasive neuroimaging and invasive anatomic studies and provide an updated framework for future investigations of large-scale networks in health and disease.

neuroscience↗

bioRxiv2025

Genome-scale perturb-seq in primary human CD4+ T cells maps context-specific regulators of T cell programs and human immune traits

Gene regulatory networks encode the fundamental logic of cellular functions, but systematic network mapping remains challenging, especially in cell states relevant to human biology and disease. Here, we perturbed all expressed genes across 22 million primary human CD4+ T cells from four donors and developed a probe-based perturb-seq platform to measure the transcriptome effects in cells at rest and after stimulation. These data allow us to map genes that regulate known and novel pathways, including novel regulators of cytokine production. Importantly, active regulators and the gene programs they control change dramatically across stimulation conditions. Perturbation signatures enabled us to model T cell states observed in population-scale transcriptomic atlases, nominating regulators of Th1 and Th2 polarization and of age-related T cell phenotypes. Finally, we leveraged perturb-seq to implicate context-specific gene regulatory pathways in autoimmune disease risk. Our data provide a foundational resource to decode human immune function and genetic variation and for new approaches to study gene regulatory networks.

genetics↗

bioRxiv2025

An atlas of TF driven gene programs across human cells

Combinations of transcription factors (TFs) regulate gene expression and determine cell fate. Much effort has been devoted to understanding TF activity in different tissues and how tissue-specificity is achieved. However, ultimately gene regulation occurs at the single cell level and the recent explosion in the availability of single cell gene expression data now makes it possible to understand TF activity at this granular level of resolution. Here, we leverage a large collection of Human Cell Atlas (HCA) single cell data to explore TF activity by examining cell-type and tissue-specific sets of target genes, or regulons. We compile a regulon atlas, CellRegulon, and map the activity of TFs in an extensive set of healthy adult and foetal tissues spanning hundreds of cell types. Using CellRegulon, we describe dynamic patterns of co-regulation, associate TF-modules with different cellular functions and characterise the distribution of active TFs and TF families across cell types. We show that CellRegulon can link disease gene expression signatures to cell types and TFs relevant to the disease. Finally, using a newly generated multiome dataset of the adult lung, we show how CellRegulon can be extended into an enhancer-gene regulatory network (eGRN) to improve cell-type associations with genetic risk loci for diseases, such as childhood onset asthma, COPD and IPF, and to identify high risk gene modules. Our database for easy download and interactive exploration allows researchers to understand key gene modules activated at cell type transitions and will therefore be valuable for tasks such as cell type engineering (https://www.cellregulondb.org).

genetics↗

bioRxiv2025

Causal modeling of gene effects from regulators to programs to traits: integration of genetic associations and Perturb-seq

AbstractGenetic association studies provide a unique tool for identifying causal links from genes to human traits and diseases. However, it is challenging to determine the biological mechanisms underlying most associations, and we lack genome-scale approaches for inferring causal mechanistic pathways from genes to cellular functions to traits. Here we propose new approaches to bridge this gap by combining quantitative estimates of gene-trait relationships from loss-of-function burden tests with gene-regulatory connections inferred from Perturb-seq experiments in relevant cell types. By combining these two forms of data, we aim to build causal graphs in which the directional associations of genes with a trait can be explained by their regulatory effects on biological programs or direct effects on the trait. As a proof-of-concept, we constructed a causal graph of the gene regulatory hierarchy that jointly controls three partially co-regulated blood traits. We propose that perturbation studies in trait-relevant cell types, coupled with gene-level effect sizes for traits, can bridge the gap between genetics and biology.

genetics↗

bioRxiv2024

Single Cell Integration Characterises Metaplasia in Inflammatory Intestinal Diseases

The gastrointestinal (GI) tract consists of connected organs, from the oral cavity to rectum, which function to ensure efficient nutrient uptake and barrier immunity. Diseases of the GI tract affect millions worldwide and as such there are now over 25 published single cell RNA-sequencing (scRNAseq) datasets surveying the GI tract, profiling specific anatomical regions, cell lineages, ages and diseases. To consolidate these efforts, we harmonised and integrated scRNAseq datasets across the whole GI tract from developing and adult human tissues, as well as newly generated data from preterm gut. We uniformly processed 385 samples from 189 healthy controls using a newly developed automated QC approach (scAutoQC). In total, our healthy reference contains [~]1.1 million cells which we annotated to a total of 137 fine-grained cell states. We anchor 13 published and 1 unpublished GI disease datasets covering gastric and colorectal (CRC) cancers, celiac disease, ulcerative colitis (UC) and Crohns disease (CD) to this reference, taking our atlas to a total of 1.6 million cells. We provide our atlas as a valuable resource to the community (available at gutcellatlas.org). Using this resource, we discover epithelial cell metaplasia arising from stem cells across intestinal inflammatory diseases (celiac, UC and CD) and CRC with transcriptional similarity to cells of the gastric and Brunners glands. Whilst previously linked to mucosal healing, we now implicate these cells in inflammation through recruitment of immune cells including T cells and neutrophils, and through direct interactions with T cells. Overall, we discover a shift in paradigm whereby changes in stem cells during inflammation lead to altered mucosal tissue architecture, which in turn contributes to ongoing inflammation. These findings highlight that in addition to barrier function, epithelial cells actively contribute to progression of inflammation which may be a function applicable to other tissues and diseases.

genomics↗

bioRxiv2024

Pertpy: an end-to-end framework for perturbation analysis

Advances in single-cell technology have enabled the measurement of cell-resolved molecular states across a variety of cell lines and tissues under a plethora of genetic, chemical, environmental, or disease perturbations. Current methods focus on differential comparison or are specific to a particular task in a multi-condition setting with purely statistical perspectives. The quickly growing number, size, and complexity of such studies requires a scalable analysis framework that takes existing biological context into account. Here, we present pertpy, a Python-based modular framework for the analysis of large-scale perturbation single-cell experiments. Pertpy provides access to harmonized perturbation datasets and metadata databases along with numerous fast and user-friendly implementations of both established and novel methods such as automatic metadata annotation or perturbation distances to efficiently analyze perturbation data. As part of the scverse ecosystem, pertpy interoperates with existing libraries for the analysis of single-cell data and is designed to be easily extended.

bioinformatics↗

bioRxiv2024

Translation of monosynaptic circuits underlying amygdala fMRI neurofeedback training

BackgroundfMRI neurofeedback targeting the amygdala is a promising therapeutical tool in psychiatry. It induces resting-state functional connectivity (rsFC) changes between the amygdala and regions of the salience and default mode networks (SN and DMN, respectively). We hypothesize these rsFC changes happen on the amygdalas underlying anatomical circuits. MethodsWe used the coordinates from regions of interest (ROIs) from studies showing pre-to-post-neurofeedback changes in rsFC with the left amygdala. Using a cross-species brain parcellation, we identified the homologous locations in non-human primates. We injected bidirectional tracers in the amygdala of adult macaques and used bright- and dark-field microscopy to identify cells and axon terminals in each ROI. We also performed additional injections in specific ROIs to validate the results following amygdala injections and delineate potential disynaptic pathways. Finally, we used high-resolution diffusion MRI data from four post-mortem macaque brains and one in vivo human brain to translate our findings to the neuroimaging domain. ResultsThe amygdala had significant monosynaptic connections with all the SN and DMN ipsilateral ROIs. Amygdala connections with the DMN contralateral ROIs are disynaptic through the hippocampus and parahippocampal gyrus. Diffusion MRI in both species benefitted from using the ground-truth tracer data to validate its findings, as we identified false-negative ipsilateral and false-positive contralateral connectivity results. ConclusionsAmygdala neurofeedback modulates the SN and DMN through monosynaptic connections and disynaptic pathways - including hippocampal structures involved in the neurofeedback task. Neurofeedback may be a tool for rapid modulation and reinforcement of these anatomical connections, leading to clinical improvement.

neuroscience↗

bioRxiv2023

A spatial human thymus cell atlas mapped to a continuous tissue axis

T cells develop from circulating precursors, which enter the thymus and migrate throughout specialised sub-compartments to support maturation and selection. This process starts already in early fetal development and is highly active until the involution of the thymus in adolescence. To map the micro-anatomical underpinnings of this process in pre- vs. post-natal states, we undertook a spatially resolved analysis and established a new quantitative morphological framework for the thymus, the Cortico-Medullary Axis. Using this axis in conjunction with the curation of a multimodal single-cell, spatial transcriptomics and high-resolution multiplex imaging atlas, we show that canonical thymocyte trajectories and thymic epithelial cells are highly organised and fully established by post-conception week 12, pinpoint TEC progenitor states, find that TEC subsets and peripheral tissue genes are associated with Hassalls Corpuscles and uncover divergence in the pace and drivers of medullary entry between CD4 vs. CD8 T cell lineages. These findings are complemented with a holistic toolkit for spatial analysis and annotation, providing a basis for a detailed understanding of T lymphocyte development.

immunology↗

bioRxiv2023

Molecular control of the lymphocyte death timer

When stimulated, individual lymphocytes program times for division and death that are inherited within families, revealing a common timing mechanism transmitted over generations. Here we describe a threshold-based mechanism for the time to die. By comparing protein levels in control and apoptosis disabled cells, we show that death can be predicted by a cooperating ensemble of BCL-2 family proteins falling below a critical threshold. Single cell measurements predict the time of death with a simple formula, where an additional inhibition factor explains accelerated death induced by BH3 mimetic compounds. Thus, we identify the death timer as a protein-threshold device that underlies signal integration machinery. Together these results reveal that predicting lymphocyte behavior at single cell level, in complex environments, is possible with modular multiscale models that incorporate timers and heritability features of critical proteins.

immunology↗

bioRxiv2023

multiDGD: A versatile deep generative model for multi-omics data

Recent technological advancements in single-cell genomics have enabled joint profiling of gene expression and alternative modalities at unprecedented scale. Consequently, the complexity of multi-omics data sets is increasing massively. Existing models for multi-modal data are typically limited in functionality or scalability, making data integration and downstream analysis cumbersome. We present multiDGD, a scalable deep generative model providing a probabilistic framework to learn shared representations of transcriptome and chromatin accessibility. It shows outstanding performance on data reconstruction without feature selection. We demonstrate on several data sets from human and mouse that multiDGD learns well-clustered joint representations. We further find that probabilistic modelling of sample covatiates enables post-hoc data integration without the need for fine-tuning. Additionally, we show that multiDGD can detect statistical associations between genes and regulatory regions conditioned on the learned representations. multiDGD is available as an scverse-compatible package (https://github.com/Center-for-Health-Data-Science/multiDGD).

bioinformatics↗

bioRxiv2023

Knockdown of Atg1 or Atg18 in adult adipocytes impairs lipophagy and reduces lifespan of fruit flies (Drosophila melanogaster)

Autophagy, a lysosome-based eukaryotic cellular degradation system, has previously been implicated in lifespan regulation in different animal models. In this report, we show that expression of the RNAi transgenes targeting the transcripts of the key autophagy genes such as Atg1 or Atg18 in adult fly muscle or glia does not affect the overall levels of autophagosomes in those tissues and does not change the lifespan of the tested flies, but lifespan reduction phenotype has become apparent when Atg1 RNAi or Atg18 RNAi is expressed in a non-tissue-specific manner through a Tub-Gal4 in adult flies or after lipophagy is eradicated through the knockdown of Atg1 or Atg18 in adult fly adipocytes. Lifespan reduction was also observed when Atg1 or Atg18 was knocked down in adult fly enteroblasts and middle gut stem cells. Over-expression of wildtype Atg1 in adult fly muscle or adipocytes reduces lifespan. High levels of ubiquitinated protein aggregates could be the culprit of the reduced lifespan of Atg1 over-expression flies. Our research data presented here have highlighted the important functions of the key autophagy genes in adult fly adipocytes, enteroblasts, and midgut stem cells for lifespan regulation and their undetermined functions in adult fly muscle and glia. SummaryIn this research, we have demonstrated that the key autophagy genes play important roles in fly lifespan regulation through adult adipose tissues, enteroblasts, and middle gut stem cells.

genetics↗

bioRxiv2023

Gene-level alignment of single cell trajectories informs the progression of in vitro T cell differentiation

Single-cell data analysis can infer dynamic changes in cell populations, for example across time, space or in response to perturbation. To compare these dynamics between two conditions, trajectory alignment via dynamic programming (DP) optimization is frequently used, but is limited by assumptions such as a definite existence of a match. Here we describe Genes2Genes, a Bayesian information-theoretic DP framework for aligning single-cell trajectories. Genes2Genes overcomes current limitations and is able to capture sequential matches and mismatches between a reference and a query at single gene resolution, highlighting distinct clusters of genes with varying patterns of expression dynamics. Across both real world and simulated datasets, Genes2Genes accurately captured different alignment patterns, demonstrated its utility in disease cell state trajectory analysis, and revealed that T cells differentiated in vitro matched to an immature in vivo state while lacking expression of genes associated with TNF[a] signaling. This use case demonstrates that precise trajectory alignment can pinpoint divergence from the in vivo system, thus guiding the optimization of in vitro culture conditions.

bioinformatics↗

bioRxiv2023

Sensitive cluster-free differential expression testing.

Comparing molecular features, including the identification of genes with differential expression (DE) between conditions, is a powerful approach for characterising disease-specific phenotypes. When testing for DE in single-cell RNA sequencing data, current pipelines first assign cells into discrete clusters (or cell types), followed by testing for differences within each cluster. Consequently, the sensitivity and specificity of DE testing are limited and ultimately dictated by the granularity of the cell type annotation, with discrete clustering being especially suboptimal for continuous trajectories. To overcome these limitations, we present miloDE - a cluster-free framework for differential expression testing. We build on the Milo approach, introduced for differential cell abundance testing, which leverages the graph representation of single-cell data to assign relatively homogenous, neighbouring cells into overlapping neighbourhoods. We address key differences between differential abundance and expression testing at the level of neighbourhood assignment, statistical testing, and multiple testing correction. To illustrate the performance of miloDE we use both simulations and real data, in the latter case identifying a transient haemogenic endothelia-like state in chimeric mouse embryos lacking Tal1 as well as uncovering distinct transcriptional programs that characterise changes in macrophages in patients with Idiopathic Pulmonary Fibrosis. miloDE is available as an open-source R package at https://github.com/MarioniLab/miloDE.

bioinformatics↗

bioRxiv2022

Early human lung immune cell development and its role in epithelial cell fate

During human development, lungs develop their roles of gas exchange and barrier function. Recent single cell studies have focused on epithelial and mesenchymal cell types, but much less is known about the developing lung immune cells, although the airways are a major site of mucosal immunity after birth. An open question is whether tissue-resident immune cells play a role in shaping the tissue as it develops in utero. In order to address this, we profiled lung immune cells using scRNAseq, smFISH and immunohistochemistry. At the embryonic stage, we observed an early wave of innate immune cells, including ILCs, NK, myeloid cells and lineage progenitors. By the canalicular stage, we detected naive T lymphocytes high in cytotoxicity genes, and mature B lymphocytes, including B1 cells. Our analysis suggests that fetal lungs provide a niche for full B cell maturation. Given the abundance of immune cells, we investigated their possible effect on epithelial maturation and found that IL-1{beta} drives epithelial progenitor exit from self-renewal and differentiation to basal cells in vitro. In vivo, IL-1{beta}-producing myeloid cells were found adjacent to epithelial tips, suggesting that immune cells may direct the developing lung epithelium.

immunology↗

bioRxiv2022

Single cell antigen receptor analysis reveals lymphocyte developmental origins

Assessment of single-cell gene expression (scRNA-seq) and adaptive immune receptor sequencing (scVDJ-seq) has been invaluable in studying lymphocyte biology. Here, we introduce Dandelion, a computational pipeline for scVDJ-seq analysis. It enables the application of standard V(D)J analysis workflows to single-cell datasets, delivering improved V(D)J contig annotation and the identification of non-productive and partially spliced contigs. We devised a novel strategy to create an adaptive immune receptor feature space that can be used for both differential V(D)J usage analysis and pseudotime trajectory inference. The application of Dandelion improved the alignment of human thymic development trajectories of double positive T cells to mature single-positive CD4/CD8 T cells, with important new predictions of factors regulating lineage commitment. Dandelion analysis of other cell compartments provided novel insights into the origins of human B1 cells and ILC/NK cell development, illustrating the power of our approach. Dandelion is an open access resource (https://www.github.com/zktuong/dandelion) that will enable future discoveries.

immunology↗

bioRxiv2022

X-inactivation states of single cell transcriptomes reveal cellular phylogenies in human females

Human females undergo X-inactivation (Xi), whereby one copy of X is randomly inactivated early in development, then propagated through cell division. Because Xi state is inherited, its measurement in populations of cells encodes information about the phylogeny that created them and their relationships to other cells. We present a method, inactiveXX, to determine the Xi state of single cell transcriptomes, and demonstrate its accuracy using cancer and gold standard reference data. We apply inactiveXX to single cell transcriptomes from 190 human females, revealing that Xi in humans likely occurs around the 16 cell blastocyst stage and affects both embryonic and extra-embryonic tissues. We further find significant cell type specific variability in Xi skew, only detectable with cell type specific resolution, with certain cell types exhibiting strong population bottlenecks across tissues and disease state.

genomics↗

bioRxiv2022

Precise identification of cell states altered indisease with healthy single-cell references

Single cell genomics is a powerful tool to distinguish altered cell states in disease tissue samples, through joint analysis with healthy reference datasets. Collections of data from healthy individuals are being integrated in cell atlases that provide a comprehensive view of cellular phenotypes in a tissue. However, it remains unclear whether atlas datasets are suitable references for disease-state identification, or whether matched control samples should be employed, to minimise false discoveries driven by biological and technical confounders. Here we quantitatively compare the use of atlas and control datasets as references for identification of disease-associated cell states, on simulations and real disease scRNA-seq datasets. We find that reliance on a single type of reference dataset introduces false positives. Conversely, using an atlas dataset as reference for latent space learning followed by differential analysis against a matched control dataset leads to precise identification of disease-associated cell states. We show that, when an atlas dataset is available, it is possible to reduce the number of control samples without increasing the rate of false discoveries. Using a cell atlas of blood cells from 12 studies to contextualise data from a case-control COVID-19 cohort, we sensitively detect cell states associated with infection, and distinguish heterogeneous pathological cell states associated with distinct clinical severities. Our analysis provides guiding principles for design of disease cohort studies and efficient use of cell atlases within the Human Cell Atlas.

genomics↗