STATegra, a comprehensive multi-omics dataset of B-cell differentiation in mouse

Multi-omics approaches use a diversity of high-throughput technologies to profile the different molecular layers of living cells. Ideally, the integration of this information should result in comprehensive systems models of cellular physiology and regulation. However, most multi-omics projects still include a limited number of molecular assays and there have been very few multi-omic studies that evaluate dynamic processes such as cellular growth, development and adaptation. Hence, we lack formal analysis methods and comprehensive multi-omics datasets that can be leveraged to develop true multi-layered models for dynamic cellular systems. Here we present the STAT egra multi-omics dataset that combines measurements from up to 10 different omics technologies applied to the same biological system, namely the well-studied mouse pre-B-cell differentiation. STATegra include

Single cell RNA-seq and ATAC-seq analysis of cardiac progenitor cell transition states and lineage settlement.

Formation and segregation of cell lineages forming the heart have been studied extensively but the underlying gene regulatory networks and epigenetic changes driving cell fate transitions during early cardiogenesis are still only partially understood. Here, we comprehensively characterize mouse cardiac progenitor cells (CPCs) marked by Nkx2-5 and Isl1 expression from E7.5 to E9.5 using single-cell RNA sequencing and transposase-accessible chromatin profiling (ATAC-seq). By leveraging on cell-to-cell transcriptome and chromatin accessibility heterogeneity, we identify different previously unknown cardiac subpopulations. Reconstruction of developmental trajectories reveal that multipotent Isl1+ CPC pass through an attractor state before separating into different developmental branches, whereas extended expression of Nkx2-5 commits CPC to an unidirectional cardiomyocyte fate. Furthermore, we show that CPC fate transitions are associated with distinct open chromatin states critically depending on Isl1 and Nkx2-5. Our data provide a model of transcriptional and epigenetic regulations during cardiac progenitor cell fate decisions at single-cell resolution

Exploring Inter-Species Regulatory Differences Through Single Cell Analysis of Drosophila Embryogenesis

Variationen in der Genexpression spielen eine zentrale Rolle bei der evolutionären Divergenz, die zur Speziation führt. Dies wird durch Veränderungen sowohl in nicht-kodierenden cis-wirkenden regulatorischen Elementen (CREs) wie Promotoren und Enhancern als auch in trans-wirkenden regulatorischen Elementen bestimmt. Veränderungen in den regulatorischen Sequenzen können Entwicklungsmuster verändern und wirken als eine der treibenden Kräfte der Evolution der Genexpression. Hier untersuche ich die Anwendung der Einzelzell-Multiomik in der evolutionären vergleichenden Genomik, wobei der Schwerpunkt auf den funktionellen Auswirkungen der Divergenz bei cis-regulatorischen Elementen liegt. Unter Verwendung von Hybrid-Embryonen von Drosophila melanogaster und sechellia generiere ich ein diploides Referenzgenom und führe allelspezifische Einzelzellanalysen von scRNA-seq und scATAC-seq durch. Zusammen können diese beiden komplimentären Ansätze einen integrativen Überblick über die Transkription und die Zugänglichkeit des Chromatins liefern, wodurch CREs identifiziert und mit allelspezifischen Veränderungen in den Genen, die sie regulieren, in Verbindung gebracht werden können. Die computergestützte Rekonstruktion verschiedener Zellidentitäten durch Clustering einzelner Zellen ermöglicht es uns auch zu untersuchen, wie sich das Allel-Ungleichgewicht während der Zelltyp-Spezifikation räumlich verändern kann. Im Gegensatz zu früheren Forschungsarbeiten stelle ich fest, dass Gene, die an der Entwicklung und Musterbildung beteiligt sind, ein unterschiedliches allelisches Ungleichgewicht in der Expression und Zugänglichkeit über die Zelltypen hinweg aufweisen. Diese Arbeit zeigt das Potenzial der Kombination von Einzelzell-Multiomik und artübergreifenden Vergleichen in der vergleichenden Genomik und wirft ein neues Licht auf die Rolle von cis-regulatorischen Elementen in der adaptiven Evolution.Variation in gene expression plays a pivotal role in the evolutionary divergence that leads to speciation. This is determined by changes in both non-coding cis-acting regulatory elements (CREs) like promoters and enhancers, as well as trans-acting regulatory elements. Changes in regulatory sequences can alter developmental patterns, acting as one of the driving forces behind gene expression evolution. However, poor sequence conservation of CREs makes it challenging to identify them and link changes in regulatory sequences to new phenotypes. Here, I explore the application of single cell multiomics in evolutionary comparative genomics, with a focus on functional effects of divergence in cis-regulatory elements. Using hybrid embryos of Drosophila melanogaster and Drosophila sechellia, I generate a diploid reference genome and conduct single cell allele-specific analysis of scRNA-seq and scATAC-seq data. Together, these two assays can provide an integrative read-out of transcription and chromatin accessibility, allowing CREs to be identified and linked to allele-specific changes (allelic imbalance) in the genes they regulate. The computational reconstruction of different cell identities via single cell clustering also allows us to investigate how allelic imbalance may vary spatially during cell-type specification. In contrast to previous research, I find that genes involved in development and patterning display differential allelic imbalance in expression and accessibility across cell types. In addition, I investigate the role of neurodevelopmental allelic imbalance in the sechellia lineage and identify candidate genes for sechellia-specific adaptations. While highlighting current computational limitations, this thesis demonstrates the potential of combining single cell multiomics and cross-species comparisons in comparative genomics and shedding new light on the role of cis-regulatory elements and mechanisms of adaptive evolution

STATegra, a comprehensive multi-omics dataset of B-cell differentiation in mouse

Multi-omics approaches use a diversity of high-throughput technologies to profile the different molecular layers of living cells. Ideally, the integration of this information should result in comprehensive systems models of cellular physiology and regulation. However, most multi-omics projects still include a limited number of molecular assays and there have been very few multi-omic studies that evaluate dynamic processes such as cellular growth, development and adaptation. Hence, we lack formal analysis methods and comprehensive multi-omics datasets that can be leveraged to develop true multi-layered models for dynamic cellular systems. Here we present the STATegra multi-omics dataset that combines measurements from up to 10 different omics technologies applied to the same biological system, namely the well-studied mouse pre-B-cell differentiation. STATegra includes high-throughput measurements of chromatin structure, gene expression, proteomics and metabolomics, and it is complemented with single-cell data. To our knowledge, the STATegra collection is the most diverse multi-omics dataset describing a dynamic biological system

Understanding Gene Regulation In Development And Differentiation Using Single Cell Multi-Omics

Transcriptional regulation is a major determinant of tissue-specific gene expression during development. My thesis research leverages powerful single-cell approaches to address this fundamental question in two developmental systems, C. elegans embryogenesis and mouse embryonic hematopoiesis. I have also developed much-needed computational algorithms for single-cell data analysis and exploration. C. elegans is an animal with few cells, but a striking diversity of cell types. In this thesis, I characterize the molecular basis for their specification by analyzing the transcriptomes of 86,024 single embryonic cells. I identified 502 terminal and pre-terminal cell types, mapping most single cell transcriptomes to their exact position in C. elegans’ invariant lineage. Using these annotations, I find that: 1) the correlation between a cell’s lineage and its transcriptome increases from mid to late gastrulation, then falls dramatically as cells in the nervous system and pharynx adopt their terminal fates; 2) multilineage priming contributes to the differentiation of sister cells at dozens of lineage branches; and 3) most distinct lineages that produce the same anatomical cell type converge to a homogenous transcriptomic state. Next, I studied the development of hematopoietic stem cells (HSCs). All HSCs come from a specialized type of endothelial cells in the major arteries of the embryo called hemogenic endothelium (HE). To examine the cellular and molecular transitions underlying the formation of HSCs, we profiled nearly 40,000 rare single cells from the caudal arteries of embryonic day 9.5 (E9.5) to E11.5 mouse embryos using single-cell RNA-Seq and single-cell ATAC-Seq. I identified a continuous developmental trajectory from endothelial cells to early precursors of HSCs, and several critical transitional cell types during this process. The intermediate stage most proximal to HE, which we termed pre-HE, is characterized by increased accessibility of chromatin enriched for SOX, FOX, GATA, and SMAD binding motifs. I also identified a developmental bottleneck separates pre-HE from HE, and RUNX1 dosage regulates the efficiency of the pre-HE to HE transition. A distal enhancer of Runx1 shows high accessibility in pre-HE cells at the bottleneck, but loses accessibility thereafter. Once cells pass the bottleneck, they follow distinct developmental trajectories leading to an initial wave of lympho-myeloid-biased progenitors, followed by precursors of HSCs. During the course of both projects, I have developed novel computational methods for analyzing single-cell multi-omics data, including VERSE, PIVOT and VisCello. Together, these tools constitute a comprehensive single cell data analysis suite that facilitates the discovery of novel biological mechanisms

Computational Stem Cell Biology: Open Questions and Guiding Principles

Computational biology is enabling an explosive growth in our understanding of stem cells and our ability to use them for disease modeling, regenerative medicine, and drug discovery. We discuss four topics that exemplify applications of computation to stem cell biology: cell typing, lineage tracing, trajectory inference, and regulatory networks. We use these examples to articulate principles that have guided computational biology broadly and call for renewed attention to these principles as computation becomes increasingly important in stem cell biology. We also discuss important challenges for this field with the hope that it will inspire more to join this exciting area

The role of non-genetic variability in Acute Myeloid Leukaemia

Acute myeloid leukaemia (AML) is a heterogeneous clonal disorder of haematopoietic progenitor cells with a dismal survival. It has a strong reliance on epigenetic and transcriptional factors for disease progression. Accordingly, my lab has previously identified KAT2A, a histone acetyl-transferase, as a requirement for AML maintenance, where chemical inhibition of KAT2A promotes differentiation of AML cell lines (Tzelepis et al., 2016). More recently, using a conditional knockout mouse model for Kat2a our lab showed that it sustains KMT2A/MLLT3 AML stem cells. Kat2a is a classical regulator of transcriptional variability, its loss leading to cell-to-cell heterogeneity in transcription levels, including from genes involved in ribosomal biogenesis and translation (Domingues et al., 2020). No recurrent mutations in the KAT2A gene have been described in AML, and it is unclear if and how it participates in pre-leukaemia-to-AML progression. In this thesis, I studied Kat2a loss in 2 mouse models of AML representing forms of human disease with a prolonged pre-leukaemia phase which typically require additional mutations for leukaemia progression. Specifically, I analysed the biology of RUNX1RUNX1T1(9a) and Idh1R132H-initiated AML in a conditional Kat2aKO background and observed consistent acceleration of leukaemia initiation and progression with perpetuation of transformed Kat2aKO cells in vivo. Single-cell RNA sequencing (scRNA-seq) of early-stage Kat2aWT and Kat2aKO RUNX1-RUNX1T1(9a) pre-leukaemia, suggested an increase in transcriptional variability upon Kat2a loss, which was accompanied by diversification of cell fates towards B-lymphocytes and monocytes. Furthermore, pseudo-temporal ordering of single Kat2aKO cells revealed a highly branched trajectory populated with intermediate stages of transformation, including accumulation of leukaemia progenitors with RUNX1-RUNX1T1 signature. In contrast, Kat2aWT cells displayed a linear haematopoiesis trajectory with minimal branching, and an abrupt transition towards the candidate leukaemia progenitor state. Pathway analysis combined with functional studies indicate a mechanistic contribution of cytoplasmic translation and ribosomal biogenesis-associated genes towards leukaemia progression in both models of pre-leukaemia. Taken together, my work suggests that loss of Kat2a results in accelerated pre-leukaemia transformation accompanied with diversification of cell fate transitions including with increased accessibility to cell states prone to transformation. Furthermore, transformation-prone cells may benefit from low biosynthetic activity to progress to a leukaemic state. I hypothesize that Kat2a loss may function similarly in the context of other malignancies. In the future, this knowledge may aid in the development of early diagnostic tools and suggest bespoke therapeutic interventions

The Human Cell Atlas White Paper

The Human Cell Atlas (HCA) will be made up of comprehensive reference maps of all human cells - the fundamental units of life - as a basis for understanding fundamental human biological processes and diagnosing, monitoring, and treating disease. It will help scientists understand how genetic variants impact disease risk, define drug toxicities, discover better therapies, and advance regenerative medicine. A resource of such ambition and scale should be built in stages, increasing in size, breadth, and resolution as technologies develop and understanding deepens. We will therefore pursue Phase 1 as a suite of flagship projects in key tissues, systems, and organs. We will bring together experts in biology, medicine, genomics, technology development and computation (including data analysis, software engineering, and visualization). We will also need standardized experimental and computational methods that will allow us to compare diverse cell and tissue types - and samples across human communities - in consistent ways, ensuring that the resulting resource is truly global. This document, the first version of the HCA White Paper, was written by experts in the field with feedback and suggestions from the HCA community, gathered during recent international meetings. The White Paper, released at the close of this yearlong planning process, will be a living document that evolves as the HCA community provides additional feedback, as technological and computational advances are made, and as lessons are learned during the construction of the atlas

SITC cancer immunotherapy resource document: a compass in the land of biomarker discovery.

Since the publication of the Society for Immunotherapy of Cancer\u27s (SITC) original cancer immunotherapy biomarkers resource document, there have been remarkable breakthroughs in cancer immunotherapy, in particular the development and approval of immune checkpoint inhibitors, engineered cellular therapies, and tumor vaccines to unleash antitumor immune activity. The most notable feature of these breakthroughs is the achievement of durable clinical responses in some patients, enabling long-term survival. These durable responses have been noted in tumor types that were not previously considered immunotherapy-sensitive, suggesting that all patients with cancer may have the potential to benefit from immunotherapy. However, a persistent challenge in the field is the fact that only a minority of patients respond to immunotherapy, especially those therapies that rely on endogenous immune activation such as checkpoint inhibitors and vaccination due to the complex and heterogeneous immune escape mechanisms which can develop in each patient. Therefore, the development of robust biomarkers for each immunotherapy strategy, enabling rational patient selection and the design of precise combination therapies, is key for the continued success and improvement of immunotherapy. In this document, we summarize and update established biomarkers, guidelines, and regulatory considerations for clinical immune biomarker development, discuss well-known and novel technologies for biomarker discovery and validation, and provide tools and resources that can be used by the biomarker research community to facilitate the continued development of immuno-oncology and aid in the goal of durable responses in all patients