Biology subjects

Velten, L.

Publications and source records attributed to Velten, L..

17 recordsLinked to original sources

bioRxiv2026

Perturb-seq identifies co-regulated gene programs shaping hematopoietic stem and progenitor cell function

To sustain blood formation, hematopoietic stem and progenitor cells (HSPCs) coordinate a multitude of cell biological processes, from cell cycle control and stress responses to lineage priming. While many genetic regulators of high-level HSPC function have been identified, how HSPCs coordinate more basal cell biological programs, and how such programs relate to stem cell function, remains incompletely understood. Here we use Perturb-seq to profile the transcriptional consequences of targeting 520 genes by CRISPRi in primary mouse HSPC cultures. We developed an analytical strategy to separate perturbation-induced changes in cell-state abundance and clonal heterogeneity from cell-state-local transcriptional effects. From these local perturbation signatures, we identified 19 gene regulatory programs (GRPs) that are defined by co-regulation in response to genetic perturbation, in contrast to co-expression or human curation, and align well with cell biological processes. By decomposing gene expression data from functional and clinical studies into program activity, we show that GRP activities associate with, and predict, phenotypes such as clonal output after transplantation, as well as survival and drug response in retrospective acute myeloid leukemia (AML) cohorts. Together, our study establishes perturbation-derived co-regulation programs as an interpretable framework for linking genetic regulators, cell-biological processes and stem-cell-associated phenotypes.

genomics↗

bioRxiv2026

Multiscale biological interactions define clinical trajectories in acute myeloid leukemia

Cancer is characterized by complex interactions across genetic, cellular, and microenvironmental scales. However, a quantitative understanding of how these interactions shape clinical trajectories remains limited. Here, we present a multi-scale single-cell dataset from 184 treatment-naive acute myeloid leukemia (AML) patients spanning all major genetic subtypes, together with an analytical framework to dissect interactions across biological scales. We show that distinct clinical outcomes are encoded by specific cross-scale, cross-compartment interactions present at diagnosis: response to induction therapy is governed by interactions between genetic alterations and leukemic differentiation state; relapse following chemotherapy is associated with non-genetic programs linked to metabolism; and relapse after allogeneic stem cell transplantation is driven by interactions between the immune microenvironment and residual healthy hematopoiesis. Together, our study provides a framework to resolve intra- and inter-patient heterogeneity in cancer and supports a model in which clinical trajectories in AML emerge from defined interactions across biological scales.

cancer biology↗

bioRxiv2026

Spatially resolved, multimodal in vivo Perturb-seq using antibody-based cell hashing

Large-scale perturbation screens have begun to map cell intrinsic gene-function relationships, yet how genes shape tissue architecture remains largely unexplored. To address this gap, we developed PerturbSpace, a novel approach that integrates CRISPR perturbations with spatially hashed single-cell multiomics. This approach enables the first high-throughput, spatially resolved Perturb-seq analysis across complex tissue architecture in vivo. Notably, PerturbSpace enables spatial transcriptome-wide perturbation readouts at organ scale and can be seamlessly integrated with orthogonal modalities. We combine PerturbSpace with surface proteomics and expressed lineage tracing barcodes to demonstrate multimodal compatibility. We use PerturbSpace to study the genetic determinants of tissue architecture. First, we map how 40 transcriptional regulators determine the size and lineage composition of colonies in the spleen during regenerative hematopoiesis. Second, we characterize immune-niche interactions in the liver by dissecting the extrinsic effects mediated by cytokine-secreting immune cells on their neighboring cells. Collectively, our work establishes PerturbSpace as a scalable and cost-effective approach for transcriptome-wide spatial profiling of whole cells while remaining compatible with the single-cell multiomics workflows that the field has already adopted at scale.

genomics↗

bioRxiv2026

Lentiviral single-cell MPRA of synthetic enhancers reveals motif affinity-based encoding of cell type specificity

Cell-state-specific gene expression programs emerge from the interplay between cis-regulatory elements (CREs), such as enhancers, and transcription factors (TFs). Massively parallel reporter assays (MPRAs) have enabled large-scale dissection of CRE design principles, but bulk approaches cannot resolve cell state-specific regulatory logic on continuous trajectories of cellular differentiation, and existing single-cell MPRAs are not readily applicable to primary cell differentiation models. Here, we developed a single-cell lentiviral Massively Parallel Reporter Assay (sc-lentiMPRA) that overcomes these limitations and enables parallel quantification of enhancer activity and cellular transcriptome. Applying sc-lentiMPRA in blood stem differentiation, we profiled the activity and specificity of ~160 fully synthetic enhancers with controlled motif composition and affinities across ~190,000 single cells. Focusing on Trp53 and Cebpa, we show that enhancers with high and low affinity motifs differ qualitatively and quantitatively in their responses to TF expression gradients. For Trp53, low-affinity motifs exhibited near-linear correlation with TF expression, whereas high-affinity motifs showed reduced sensitivity to TF levels and a potential contribution of cofactor availability. In contrast, Cebpa-associated enhancers displayed nonlinear behaviors. Together, sc-lentiMPRA establishes a powerful framework for systematically relating enhancer architecture and TF expression to regulatory output at single-cell resolution during cellular differentiation.

genomics↗

bioRxiv2024

Synthetic enhancers reveal design principles of cell state specific regulatory elements in hematopoiesis

During cellular differentiation, enhancers transform overlapping gradients of transcription factors (TFs) to highly specific gene expression patterns. However, the vast complexity of regulatory DNA impedes the identification of the underlying cis-regulatory rules. Here, we have characterized 62,126 fully synthetic DNA sequences to bottom-up dissect design principles of cell-state specific enhancers in the context of the differentiation of blood stem cells to seven myeloid lineages. Focusing on binding sites for 38 TFs and their pairwise interactions, we found that identical sites displayed both repressive and activating function, as a consequence of cellular context, site combinatorics, or simply predicted occupancy of a TF on an enhancer. Surprisingly, we found that combinations of activating sites frequently neutralized each other or even gained repressive function. These negative synergies convert quantitative imbalances in transcription factor expression into binary downstream activity patterns, a principle that can be exploited to build differentiation-state specific enhancers from scratch.

molecular biology↗

bioRxiv2024

Emergence of activation or repression in transcriptional control under a fixed molecular context

For decades, studies have noted that transcription factors (TFs) can behave as either activators or repressors of different target genes. More recently, evidence suggests TFs can act on transcription simultaneously in positive and negative ways. Here we use biophysical models of gene regulation to define, conceptualize and explore these two aspects of TF action: "duality", where TFs can be overall both activators and repressors at the level of the transcriptional response, and "coherent and incoherent" modes of regulation, where TFs act mechanistically on a given target gene either as an activator or a repressor (coherent) or as both (incoherent). For incoherent TFs, the overall response depends on three kinds of features: the TFs mechanistic effects, the dynamics and effects of additional regulatory molecules or the transcriptional machinery, and the occupancy of the TF on DNA. Therefore, activation or repression can be tuned by just the TF-DNA binding affinity, or the number of TF binding sites, given an otherwise fixed molecular context. Moreover, incoherent TFs can cause non-monotonic transcriptional responses, increasing over a certain concentration range and decreasing outside the range, and we clarify the relationship between non-monotonicity and common assumptions of gene regulation models. Using the mammalian SP1 as a case study and well controlled, synthetically designed target sequences, we find experimental evidence for incoherent action and activation, repression or non-monotonicity tuned by affinity. Our work highlights the importance of moving from a TF-centric view to a systems view when reasoning about transcriptional control.

biophysics↗

bioRxiv2024

Somatic epimutations enable single-cell lineage tracing in native hematopoiesis across the murine and human lifespan

Current approaches to lineage tracing of stem cell clones require genetic engineering or rely on sparse somatic DNA variants, which are difficult to capture at single-cell resolution. Here, we show that targeted single-cell measurements of DNA methylation at single-CpG resolution deliver joint information about cellular differentiation state and clonal identities. We develop EPI-clone, a droplet-based method for transgene-free lineage tracing, and apply it to study hematopoiesis, capturing hundreds of clonal trajectories across almost 100,000 single-cells. Using ground-truth genetic barcodes, we demonstrate that EPI-clone accurately identifies clonal lineages throughout hematopoietic differentiation. Applied to unperturbed hematopoiesis, we describe an overall decline of clonal complexity during murine ageing and the expansion of rare low-output stem cell clones. In aged human donors, we identified expanded hematopoietic clones with and without genetic lesions, and various degrees of clonal complexity. Taken together, EPI-clone enables accurate and transgene-free single-cell lineage tracing at scale.

genomics↗

bioRxiv2024

Identifying cancer cells from calling single-nucleotide variants in scRNA-seq data

Single cell RNA sequencing (scRNA-seq) data is widely used to study cancer cell states and their heterogeneity. However, the tumour microenvironment is usually a mixture of healthy and cancerous cells and it can be difficult to fully separate these two populations based on transcriptomics alone. If available, somatic single nucleotide variants (SNVs) observed in the scRNA-seq data could be used to identify the cancer population. However, calling somatic SNVs in scRNA-seq data is a challenging task, as most variants seen in the short read data are not somatic, but can instead be germline variants, RNA edits or transcription, sequencing or processing errors. Additionally, only variants present in actively transcribed regions for each individual cell will be seen in the data. To address these challenges, we develop CCLONE (Cancer Cell Labelling On Noisy Expression), an interpretable tool adapted to handle the uncertainty and sparsity of SNVs called from scRNA-seq data. CCLONE jointly identifies cancer clonal populations, and their associated variants. We apply CCLONE on two acute myeloid leukaemia datasets and one lung adenocarcinoma dataset and show that CCLONE captures both genetic clones and somatic events for multiple patients. These results show how CCLONE can be used to gather insight into the course of the disease and the origin of cancer cells in scRNA-seq data.

bioinformatics↗

bioRxiv2023

Cell division tracing combined with single-cell transcriptomics reveals new cell types and differentiation paths in the regenerating mouse lung

Understanding the molecular and cellular processes involved in lung epithelial regeneration may fuel the development of new therapeutic approaches for lung diseases. We combined new mouse models that allow diphtheria toxin (DTA)-mediated depletion of specific epithelial cell types and GFP-labeling of dividing cells with single-cell transcriptomics to characterize the regeneration of the distal lung. We uncovered new cell types, some of which likely represent epithelial precursors, propose goblet cells as progenitor cells, and provide evidence that adventitial fibroblasts act as supporting cells in epithelial regeneration. We also found that DTA-expressing cells can persist in the lung, express specific inflammatory factors, and resemble a previously undescribed population in the lungs of COVID-19 patients. Our study provides a comprehensive single-cell atlas of the distal lung that characterizes early transcriptional and cellular responses to defined epithelial injury, encompassing proliferation, differentiation, and cell-to-cell interactions.

cell biology↗

bioRxiv2022

Clonally resolved single-cell multi-omics identifies routes of cellular differentiation in acute myeloid leukemia

Inter-patient variability and the similarity of healthy and leukemic stem cells have impeded the characterization of leukemic stem cells (LSCs) in acute myeloid leukemia (AML), and their differentiation landscape. Here, we introduce CloneTracer, a novel method that adds clonal resolution to single-cell RNA-seq datasets. Applied to samples from 19 AML patients, CloneTracer revealed routes of leukemic differentiation. While residual healthy cells dominated the dormant stem cell compartment, active leukemic stem cells resembled their healthy counterpart and retained erythroid capacity. By contrast, downstream myeloid progenitors were highly aberrant and constituted the disease-defining compartment: Their gene expression and differentiation state determined both chemotherapy response and the leukemias ability to differentiate to transcriptomically normal monocytes. Finally, we demonstrated the potential of CloneTracer to identify surface markers mis-regulated specifically in leukemic cells by intra-patient comparisons. Taken together, CloneTracer revealed a differentiation landscape that mimics its healthy counterpart and determines biology and therapy response in AML.

cancer biology↗

bioRxiv2022

Decoding gene regulation in the mouse embryo using single-cell multi-omics

Following gastrulation, the three primary germ layers develop into the major organs in a process known as organogenesis. Single-cell RNA sequencing has enabled the profiling of the gene expression dynamics of these cell fate decisions, yet a comprehensive map of the interplay between transcription factors and cis-regulatory elements is lacking, as are the underlying gene regulatory networks. Here we generate a multi-omics atlas of mouse early organogenesis by simultaneously profiling gene expression and chromatin accessibility from tens of thousands of single cells. We develop a computational method to leverage the multimodal readouts to predict transcription factor binding events in cis-regulatory elements, which we then use to infer gene regulatory networks that underpin lineage commitment events. Finally, we show that these models can be used to generate in silico predictions of the effect of transcription factor perturbations. We validate this experimentally by showing that Brachyury is essential for the differentiation of neuromesodermal progenitors to somitic mesoderm fate by priming cis-regulatory elements. The data set can be interactively explored at https://www.bioinformatics.babraham.ac.uk/shiny/shiny_multiome_organogenesis/

developmental biology↗

bioRxiv2022

scTAM-seq enables targeted high-confidence analysis of DNA methylation in single cells

Single-cell DNA methylation profiling currently suffers from excessive noise and/or limited cellular throughput. We developed scTAM-seq, a targeted bisulfite-free method for profiling up to 650 CpGs in up to 10,000 cells per experiment, with a dropout rate of less than seven percent. scTAM-seq focuses sequencing coverage on informative, variably methylated CpGs that in many tissues and cell types make up a minor fraction of all CpGs. By applying scTAM-seq to B cells from blood and bone marrow, we demonstrate that it can resolve DNA methylation dynamics across B-cell differentiation at unprecedented resolution, identifying intermediate differentiation states that were previously masked. scTAM-seq additionally queries surface protein expression and somatic mutations, thus enabling integration of single-cell DNA methylation information with cell atlas data, and opening applications in tumor profiling. In summary, scTAM-seq is the first high-throughput, high-confidence method for analyzing DNA methylation at single-CpG resolution across thousands of single cells.

genomics↗

bioRxiv2022

A comprehensive single-cell expression atlas of human AML leukemia-initiating cells unravels the contribution of HIF pathway and its therapeutic potential

Relapse remains a major challenge in the clinical management of acute myeloid leukemia (AML), and is driven by rare therapy-resistant leukemia-initiating stem cells (LSCs) that reside in specific bone marrow niches. Hypoxia signaling keeps cells in a quiescent and metabolically relaxed state, desensitizing them to chemotherapy. This suggests the hypothesis that hypoxia contributes to AML-LSC function and chemoresistance and is a therapeutic target to sensitize AML-LSCs to chemotherapy. Here, we provide a comprehensive single-cell expression atlas (119,000 cells) of AML cells and AML-LSCs in paired diagnostic-relapse samples from risk-stratified patients with AML. The HIF/hypoxia pathway is attenuated in AML-LSCs compared with differentiated AML cells, but is enhanced when compared with healthy hematopoietic cells. Accordingly, chemical inhibition cooperates with standard-of-care chemotherapy to impair leukemogenesis, substantially eliminating AML-LSCs. These findings support the HIF pathway as a stem cell regulator in human AML, and reveal avenues for combinatorial targeted and chemotherapy-based approaches to specifically eliminate AML-LSCs.

cancer biology↗

bioRxiv2021

Single-cell proteo-genomic reference maps of the hematopoietic system enable the purification and massive profiling of precisely defined cell states

Single-cell genomics has transformed our understanding of complex cellular systems. However, excessive costs and a lack of strategies for the purification of newly identified cell types impede their functional characterization and large-scale profiling. Here, we have generated high content single-cell proteo-genomic reference maps of human blood and bone marrow that quantitatively link the expression of up to 197 surface markers to cellular identities and biological processes across all major hematopoietic cell types in healthy aging and leukemia. These reference maps enable the automatic design of cost-effective high-throughput cytometry schemes that outperform state-of-the-art approaches, accurately reflect complex topologies of cellular systems, and permit the purification of precisely defined cell states. The systematic integration of cytometry and proteo-genomic data enables measuring the functional capacities of precisely mapped cell states at the single-cell level. Our study serves as an accessible resource and paves the way for a data-driven era in cytometry.

immunology↗

bioRxiv2020

The molecular logic of synaptic wiring at the single cell level

The correct wiring of neuronal circuits is one of the most complex processes in development, since axons form highly specific connections out of a vast number of possibilities. Circuit structure is genetically determined in vertebrates and invertebrates, but the mechanism guiding each axon to precisely innervate a unique pre-specified target cell is poorly understood. Here, we used single cell genomics, imaging and genetics to show that single-cell specific connections between motoneurons and target muscles are specified through a combinatorial code of immunoglobulin domain proteins. These programs are orchestrated by a homeodomain transcription factor code that specifies cellular identities down to the level of biologically unique cells. Using spatial mapping, we show that this code follows spatial patterns already observed at early embryonic stages, while acting at a much later stage to specify motor circuits. Taken together, our data suggest that a relatively simple homeo-immunoglobulin-code determines neuronal circuit structure.

developmental biology↗

bioRxiv2019

Combined single-cell and spatial transcriptomics reveals the molecular, cellular and spatial bone marrow niche organization

The bone marrow (BM) constitutes the primary site for life-long blood production and skeletal regeneration. However, its cellular composition and the spatial organization into distinct niches remains controversial. Here, we combine single-cell and spatially resolved transcriptomics to systematically map the molecular and cellular composition of the endosteal, sinusoidal, and arteriolar BM niches. This allowed us to transcriptionally profile all major BM resident cell types, determine their localization, and clarify the cellular and spatial sources of key growth factors and cytokines. Our data demonstrate that previously unrecognized Cxcl12-abundant reticular (CAR) cell subsets (i.e. Adipo- and Osteo-CAR cells) differentially localize to sinusoidal or arteriolar surfaces, locally act as professional cytokine secreting cells, and thereby establish distinct peri-vascular micro-niches. Importantly, we also demonstrate that the 3-dimensional organization of the BM can be accurately inferred from single-cell gene expression data using the newly developed RNA-Magnet algorithm. Together, our study reveals the cellular and spatial organization of BM niches, and offers a novel strategy to dissect the complex organization of whole organs in a systematic manner.\n\nOne Sentence SummaryIntegration of single-cell and spatial transcriptomics reveals the molecular, cellular and spatial organization of bone marrow niches

developmental biology↗

bioRxiv2018

MutaSeq reveals the transcriptomic consequences of clonal evolution in acute myeloid leukemia

Cancer stem cells drive disease progression and relapse in many types of cancer. Despite this, a thorough characterization of these cells remains elusive and with i the ability to eradicate cancer at its source. In acute myeloid leukemia (AML), leukemic stem cells (LSCs) underlie mortality but are difficult to isolate due to their low abundance and high similarity to healthy hematopoietic stem cells (HSCs). Here, we demonstrate that LSCs, HSCs, and pre-leukemic stem cells can be identified and molecularly profiled by combining single-cell transcriptomics with lineage tracing using both nuclear and mitochondrial somatic variants. While mutational status discriminates between healthy and cancerous cells, gene expression distinguishes stem cells and progenitor cell populations. Our approach enables the identification of LSC-specific gene expression programs and the characterization of differentiation blocks induced by leukemic mutations. Taken together, we demonstrate the power of single-cell multi-omic approaches in characterizing cancer stem cells.

genomics↗