Biology subjects

Teichmann, S.

Publications and source records attributed to Teichmann, S..

At least 19 recordsLinked to original sources

bioRxiv2026

A human developmental and adult brain atlas benchmarks dopaminergic stem cell models and cell therapy candidates

Parkinsons disease (PD) is characterized by the progressive loss of midbrain dopaminergic (mDA) neurons1. Stem cell-derived mDA neurons hold promise for disease modelling2,3 and are currently in clinical trials for cell replacement therapy4-6. However, systematic benchmarking has been limited by the lack of a unified high-resolution reference and methods that quantify incomplete or mixed lineage specification in vitro7. We establish a single-cell and spatial atlas of the human developing diencephalon-midbrain-hindbrain axis resolving 93 cell subtypes, including 39 lacking prior single-cell characterization and 4 entirely novel populations. Using this atlas as a reference, we integrate 19 hPSC-derived mDA datasets, both published2,8-25 and unpublished, to build the Human Dopaminergic Neural Atlas (HDNA) spanning 2D, 3D, and graft models, including those used in clinical trials. To classify cells and quantify lineage fidelity, we develop CapybaraBrain, a marker-driven non-negative decomposition framework that assigns each cell continuous identity scores across all 93 developmental programs, enabling systematic discrimination of discrete, transitioning, and cross-lineage hybrid states26. We uncover a pervasive landscape of off-target populations reflecting relaxed transcriptional boundaries in vitro, including a previously unrecognized TH-PITX2 midbrain neuronal population, and we validate atlas-predicted latent lineage plasticity through inducible genetic fate mapping in mouse models. We further define maturation-associated transcriptional programs by harmonizing adult mDA subtype atlases, revealing that dopaminergic identity and maturation are partially decoupled across protocols. Finally, projecting PD patient-derived tri-cultures onto the HDNA uncovers genotype- and cell-type-specific transcriptional dysregulation. Together, these integrated atlases and computational framework establish a unified standard for benchmarking differentiation fidelity, exposing off-target states, and guiding next-generation PD models and cell therapies.

Developmental Biology↗

bioRxiv2026

Multimodal Hypersampling of the adult human heart resolves tissue-specific cell states and disease-associated variants

Cardiovascular diseases often arise in anatomically specialised regions of the heart that remain poorly represented in existing single-cell and spatial atlas references. Here we profiled 28 regions of the adult human heart from 36 donors, combining paired single-nucleus RNA and chromatin accessibility assays with spatial transcriptomics. The resulting atlas integrates 1,059,175 expression profiles and 452,614 chromatin accessibility profiles, and defines spatial niches across the heart. In cardiac valves, we identify a valve fibroblast-macrophage niche that is polarised to the non-fibrosa surface and expands with age. Aortic stenosis genetic risk was enriched in inflammatory regulatory programmes of the Valve Fibroblast Immune cell state. In pulmonary veins, we define PITX2+ Myocardial Sleeve Cells, a region-restricted cardiomyocyte population in which a fine-mapped 4q25 atrial fibrillation variant intersects a cell type-specific open chromatin region. In coronary arteries, we resolve a RUNX1-marked synthetic smooth muscle layer in the subintima that provides a molecular identity for diffuse intimal thickening, a constitutive feature of the healthy vessel wall and a substrate for atherosclerosis, and show by perturbation in human smooth muscle cells that RUNX1 promotes cell-cycle entry while restraining inflammatory cytokine gene expression. Finally, allele-specific accessibility together with deep learning models fine-tuned to predict regulatory activity from DNA sequence identify cell type-specific regulatory effects at GWAS loci for atrial fibrillation, coronary artery disease and calcific aortic valve stenosis, including a variant whose Valve Fibroblast-specific effect on the polyamine transporter ATP13A3 is opposite in direction to the effect seen in bulk-tissue eQTL references that lack valve cells. Together with these cardiac sequence models and CardioSleuth, an AI-powered exploration engine for cardiac regulatory effects, this atlas links cardiac anatomy, gene regulation and disease genetics at single-cell resolution. By placing genetic risk in its precise cellular and anatomical context, it provides a foundation for mechanistic disease understanding, cell-state-specific therapeutic target nomination, and improved interpretation of inherited cardiovascular risk to inform future therapy and management.

Cell Biology↗

bioRxiv2025

Temporal Dynamics of Antigen-Specific T Cell Expansion in Primary SARS-CoV-2 Infection

Quantifying T cell response during primary infection in humans is crucial for understanding adaptive immunity. Leveraging a controlled human challenge to SARS-CoV-2, we characterized antigen-specific T cell response within and across individuals. Notably, individual clones reached similar maximum frequencies despite differences in the timing of their peak expansion. Mathematical modeling showed that this observation is consistent with precursor frequency, but not TCR signal strength, as the source of inter-clonal variability. Single-cell profiling revealed distinct temporal programs for CD4+ and CD8+ T cells, with CD4+ cells expanding earlier but contracting to a lower frequency. Clones with similar receptors, likely recognizing the same antigen, expanded at similar times. Together, these findings highlight how clone-intrinsic properties such as precursor frequency and lineage shape T cell clonal kinetics. These insights provide a quantitative framework for understanding T cell response in humans, with implications for vaccine design.

biophysics↗

bioRxiv2024

The Immunoregulatory Architecture of the Adult Oral Cavity

The immunoregulatory architecture of human oral tissues remains poorly defined despite their central role as barrier interfaces. We present the first integrated single-cell and dual-platform spatial-proteotranscriptomic atlas of oral tissues, profiling >250,000 single-cell transcriptomes and >4 million spatially-resolved cells across 13 niches. Using our AI-enabled AstroSuite (TACIT, Constellation, STARComm, hist2omics), we defined tissue cellular neighborhoods (TCNs) and multicellular interaction modules (MCIMs) in health, revealing peri-epithelial fibroblast-centered hubs enriched for effector cytokines. We harmonized eight fibroblast subtypes (universal, immune, peri-epithelial, peri-vascular, peri-neural, APC-like, stress-responsive, and myofibroblasts) with stress-responsive subtypes partitioning between mucosae (Type I) and glands (Type II). Spatial multiomics mapped receptor-ligand circuits and showed mucosal stress-responsive fibroblasts as immunoregulatory hubs. In chronic periodontitis, niche-aware integration of healthy and diseased datasets revealed rewiring of fibroblast phenotypes and ligand::receptor networks into interdigitated inflammatory and reparative niches. Disease neighborhoods exhibited fragmentation, expansion of MHC-I, MHC-II, and PD-L1 fibroblasts, and predicted spatial engagement with T cells at ectopic lymphoid structures. Drug2Cell analysis highlighted druggable stromal::immune networks. Together, this proteotranscriptomic atlas positions fibroblasts as central architects of structural immunity in human oral tissues and establishes a scalable framework for precision targeting of stromal::immune ecosystems across other barrier organs in health and chronic disease.

cell biology↗

bioRxiv2024

Multiomic analysis reveals developmental dynamics of the human heart in health and disease

Developmental dynamics involve the specification of diverse cell types and their spatial organization into multicellular niches. Here, we combine single-cell and spatial multiomics to define 19 distinct tissue niches in the developing heart, leading to the development of a context-aware, resolution-agnostic niche classification tool (TissueTypist). Applying high-resolution spatial profiling to the developing sinoatrial node, we resolve three pacemaker cell subtypes arrayed along a linear axis. First trimester subpopulations, such as the pacemaker cells in the sinus horn and sinoatrial node head region, display neuro-attractant programmes and interact with parasympathetic neurons via interactions including Semaphorin-Plexin signalling. Temporal trajectories map maturation of atrial and ventricular cardiomyocytes, uncovering a lipid-metabolic switch and potential key regulators of cell type identity. In the ventricle, we identify cellular and transcriptional gradients along both pseudotime and transmural axes, offering new molecular insights into myocardial compaction and maturation. Comparative profiling of euploid and trisomy 21 hearts shows a depletion of compact cardiomyocytes and heightened apoptosis, validated in isogenic-matched trisomy 21 and euploid iPSC-derived cardiomyocytes. This implicates disrupted myocardial growth may be a mechanism for Downs syndrome-associated congenital heart disease. Overall, we deliver a spatially resolved framework of human cardiac development, enabling systematic exploration of developmental niches in health and disease.

developmental biology↗

bioRxiv2024

High-resolution atlas of the developing human heart and the great vessels

The human heart and adjoining great vessels consist of multiple cell types essential for life, yet many remain uncharacterised molecularly during development. Here, we performed a high-resolution profiling of the developing heart and great vessels between 4 and 20 post-conception weeks using single-cell and spatial transcriptomics defining 63 cell types with distinct identity and location-specific signatures. We reveal previously unreported molecular identities in cell types, including the pericardium and the ductus arteriosus. In the cardiomyocytes, we identify signatures of the trabeculated-compact, and right-left axes of ventricular cardiomyocytes. In vessels, we distinguish the constituents belonging to either coronary or great vessels. We confirm our transcriptional findings spatially, revealing nuanced signatures with specific zonation patterns and validating this atlas as a curated transcriptional reference for future studies. We leverage the temporal scope of the presented atlas to build CMageR, a predictive pipeline for scRNA-seq combining cardiac cell annotation with a transcriptional cardiac clock of single-cell developmental age for each cell type. Our cardiomyocyte clock captures dynamic biology, revealing core functional changes and novel markers of maturity during the first and second trimester. Finally, we benchmark in vitro models, suggesting a transcriptional right-chamber bias in stem cell derived cardiomyocytes with the oldest model age-matched to 12 post-conception weeks. Collectively, our work provides a high-resolution atlas of human cardiac development to enhance our understanding of function in development, health, and disease, and a foundation for building a rich reference to benchmark and improve in vitro models.

developmental biology↗

bioRxiv2023

Single cell transcriptomics reveals chondrocyte differentiation dynamics in vivo and in vitro

The consistent production of in vitro chondrocytes that faithfully recapitulate in vivo development would be of great benefit for musculoskeletal disease modelling and regenerative medicine. Current efforts are often limited by off-target differentiation, resulting in a heterogeneous product. Furthermore, the lack of comparison to human embryonic tissue, precludes detailed evaluation of in vitro cells. Here, we perform single-cell RNA sequencing of embryonic long bones dissected from first trimester hind limbs from a range of gestational ages. We combine this with publicly available data to form a detailed atlas of endochondral ossification, which we then use to evaluate a series of published in vitro chondrogenesis protocols, finding substantial variability in cell states produced by each. We apply single-nuclear RNA sequencing to one protocol to enable direct comparison between in vitro and in vivo, and perform trajectory alignment between the two to reveal differentiation dynamics at the single-cell level, shedding new light on off-target differentiation in vitro. Using this information, we inhibit the activity of FOXO1, a transcription factor predicted to be active in embryonic bone development and in chondrogenic cells in vitro, and increase chondrocyte transcripts in vitro. This study therefore presents a new framework for evaluating tissue engineering protocols, using single-cell data from human development to drive improvement and bring the prospect of true engineered cartilage closer to reality.

bioengineering↗

bioRxiv2023

Multifocal, multiphenotypic tumours arising from an MTOR mutation acquired in early embryogenesis

Embryogenesis is a vulnerable time. Mutations in developmental cells can result in the wide dissemination of cells predisposed to disease within mature organs. We characterised the evolutionary history of four synchronous renal tumours from a 14-year-old girl, timing their shared origin to a multipotent embryonic cell committed to the right kidney, around 4 weeks post-conception. Their shared MTOR mutation, absent from normal tissues, enhances protein flexibility, which enables a FAT domain hinge to dramatically increase activity of mTORC1 and mTORC2. Developmental mutations, not usually detected in traditional genetic screening, have vital clinical importance in guiding prognosis, targeted treatment, and family screening decisions for paediatric tumours.

cancer biology↗

bioRxiv2023

Pan-cancer profiling of tumor-infiltrating natural killer cells through transcriptional reference mapping

The functional diversity of natural killer (NK) cell repertoires stems from differentiation, homeostatic receptor-ligand interactions, and adaptive-like responses to viral infections. Here, we generated a single-cell transcriptional reference map of healthy human blood and tissue-derived NK cells, with temporal resolution and fate-specific expression of gene regulator networks defining NK cell differentiation. Using transfer learning, transcriptomes of tumor-infiltrating NK cells from seven solid tumor types (427 patients), combined from 39 datasets, were incorporated into the reference map and interrogated for tumor microenvironment (TME)-induced perturbations. We identified six functionally distinct NK cellular states in healthy and malignant tissues, two of which were commonly enriched for across tumor types: a dysfunctional stressed CD56bright state susceptible to TME-induced immunosuppression and a cytotoxic TME-resistant effector CD56dim state. The ratio of stressed CD56bright and effector CD56dim was predictive of patient outcome in malignant melanoma and osteosarcoma. This resource may inform the design of novel NK cell therapies and can be extended endlessly through transfer learning to interrogate new datasets from experimental perturbations or disease conditions.

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

Childhood-onset asthma is characterized by airway epithelial hillock-to-squamous differentiation in early life

Childhood-onset asthma is characterized by Type 2-inflammation and airway wall remodeling, but mechanisms of asthma development in the first years of life remain unclear. Here, we investigate transcriptional changes in airway wall biopsies of 22 symptomatic one year old children and relate these to asthma at school age. We demonstrate that pre-asthmatic children (n = 10) overexpressed a gene signature characteristic for an airway epithelial differentiation trajectory via hillock cells towards squamous cells (adjusted p-value 8.06e-16), whilst there was no association with gene signatures of Type 2-inflammation or eosinophil activation. Genes expressed along this trajectory are linked to an altered epithelial barrier function, innate immune activation and extracellular matrix remodeling. Functional GWAS analysis supports a causal link between childhood-onset, but not adult-onset asthma, and the hillock-squamous cell differentiation trajectory. Next, we confirmed the presence of hillock-like cells at the RNA and protein level in pediatric upper and lower airway samples. These findings identify a novel mechanism by which an aberrant airway epithelial differentiation trajectory may contribute to a pre-asthmatic state, highlighting the difference between the early origins of childhood-onset asthma and adult asthma, and point to possible new targets for the early diagnosis and treatment of asthma in the first two years of life. One Sentence SummaryRNA sequencing in bronchial biopsies from wheezing infants and children < 2 years shows evidence for an airway epithelial hillock-to-squamous differentiation pathway that marks the development of asthma.

genomics↗

bioRxiv2023

WebAtlas pipeline for integrated single cell and spatial transcriptomic data

Single cell and spatial transcriptomics illuminate complementary features of tissues. However, online dissemination and exploration of integrated datasets is challenging due to the heterogeneity and scale of data. We introduce the WebAtlas pipeline for user-friendly sharing and interactive navigation of integrated datasets. WebAtlas unifies commonly used atlassing technologies into the cloud-optimised Zarr format and builds on Vitessce to enable remote data navigation. We showcase WebAtlas on the developing human lower limb to cross-query cell types and genes across single cell, sequencing- and imaging-based spatial transcriptomic data.

bioinformatics↗

bioRxiv2023

Automatic cell type harmonization and integration across Human Cell Atlas datasets

Harmonizing cell types across the single-cell community and assembling them into a common framework is central to building a standardized Human Cell Atlas. Here we present CellHint, a predictive clustering tree-based tool to resolve cell type differences in annotation resolution and technical biases across datasets. CellHint accurately quantifies cell-cell transcriptomic similarities and places cell types into a relationship graph that hierarchically defines shared and unique cell subtypes. Application to multiple immune datasets recapitulates expert-curated annotations. CellHint also reveals underexplored relationships between healthy and diseased lung cell states in eight diseases. Furthermore, we present a workflow for fast cross-dataset integration guided by harmonized cell types and cell hierarchy, which uncovers underappreciated cell types in adult human hippocampus. Finally, we apply CellHint to 12 tissues from 38 datasets, providing a deeply curated cross-tissue database with [~]3.7 million cells and various machine learning models for automatic cell annotation across human tissues.

bioinformatics↗

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

Multimodal decoding of human liver regeneration

The liver has a unique ability to regenerate1,2, however in the setting of acute liver failure (ALF) this regenerative capacity is often overwhelmed and emergency liver transplantation is the only curative option3-5. To advance our understanding of human liver regeneration and to inform design of pro-regenerative therapies, we use paired single-nuclei RNA sequencing (snRNA-seq) combined with spatial profiling of healthy and ALF explant human livers to generate the first single-cell, pan-lineage atlas of human liver regeneration. We uncover a novel ANXA2+ migratory hepatocyte subpopulation which emerges during human liver regeneration, and a corollary migratory hepatocyte subpopulation in a mouse model of acetaminophen (APAP)-induced liver regeneration. Importantly, interrogation of necrotic wound closure and hepatocyte proliferation across multiple timepoints following APAP-induced liver injury in mice demonstrates that wound closure precedes hepatocyte proliferation. 4-D intravital imaging of APAP-induced mouse liver injury identifies motile hepatocytes at the edge of the necrotic area, enabling collective migration of the hepatocyte sheet to effect wound closure. Depletion of hepatocyte ANXA2 expression reduces HGF-induced human and mouse hepatocyte migration in vitro, and abrogates necrotic wound closure following APAP-induced mouse liver injury. Taken together, our work dissects unanticipated aspects of liver regeneration, demonstrating an uncoupling of wound closure and hepatocyte proliferation and uncovering a novel migratory hepatocyte subpopulation which mediates wound closure following liver injury. Therapies designed to promote rapid reconstitution of normal hepatic microarchitecture and reparation of the gut-liver barrier may open up new areas of therapeutic discovery in regenerative medicine.

cell biology↗

bioRxiv2023

spinDrop: a droplet microfluidic platform to maximise single-cell sequencing information content

Droplet microfluidic methods have massively increased the throughput of single-cell sequencing campaigns. The benefit of scale-up is, however, accompanied by increased background noise when processing challenging samples and the overall RNA capture efficiency is lower. These drawbacks stem from the lack of strategies to enrich for high-quality material or specific cell types at the moment of cell encapsulation and the absence of implementable multi-step enzymatic processes that increase capture. Here we alleviate both bottlenecks using fluorescence-activated droplet sorting to enrich for droplets that contain single viable cells, intact nuclei, fixed cells or target cell types and use reagent addition to droplets by picoinjection to perform multi-step lysis and reverse transcription. Our methodology increases gene detection rates fivefold, while reducing background noise by up to half. We harness these unique properties to deliver a high-quality molecular atlas of mouse brain development, despite starting with highly damaged input material, and provide an atlas of nascent RNA transcription during mouse organogenesis. Our method is broadly applicable to other droplet-based workflows to deliver sensitive and accurate single-cell profiling at a reduced cost.

developmental biology↗

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↗