Biology subjects

Domcke, S.

Publications and source records attributed to Domcke, S..

9 recordsLinked to original sources

bioRxiv2026

High-throughput identification of chromatin-opening transcription factors

Cell fate transitions involve major changes to the chromatin landscape, some of which are thought to deepen the basin around a newly adopted cell state, stabilizing gene expression programs that might otherwise be transient. Transcription factors (TFs) are central to this process, binding a subset of their genome-wide motif occurrences to create accessible regions that can act as cell type-specific enhancers. However, apart from a few well-studied cases, it is not clear which of the ~1,600 mammalian TFs, when newly expressed, can directly or indirectly initiate newly accessible sites, nor why only some motif occurrences gain accessibility. Here, we applied pooled CRISPR activation (CRISPRa) to upregulate endogenous TFs to physiological levels in mouse embryonic stem cells (mESCs). After screening guides targeting 240 developmental TFs for transcriptional response, we selected 60 and jointly profiled gene expression and chromatin accessibility in ~140,000 single cells. Some TFs (e.g. Cdx2, Sox10) appeared to initiate new accessibility on their own, predominantly at distal sites that are closed in mESCs but coincide with cell type-specific enhancers later in development. Others appeared to partner with "co-TFs", either by leveraging preexisting mESC heterogeneity (e.g. {uparrow}Grhl1 in Tfap2c+ mESCs [->] opening of sites bearing motifs for both), or by hierarchical activation (e.g. {uparrow}Sox7 [->] {uparrow}Gata6 [->] opening of sites bearing either or both motifs). Such dependencies shape which sites are used and which fates are reachable, and may explain the differential behavior of TFs with nearly identical consensus motifs. Thus, the capacity of a newly expressed TF to open chromatin strongly depends on the cell type, cell state and gene regulatory network it lands in. Charting these dependencies will inform how cell fate transitions are initiated in development, homeostasis and disease, and how to engineer them in silico, in vitro and in vivo.

genomics↗

bioRxiv2026

Addressing technical variations in ATAC-seq data and improving motif accessibility analyses

Tagmentation-based methods such as ATAC-seq and Cut&Tag have provided easy ways to profile the epigenome in low-input samples and even single cells. In this contribution, we discuss forms of bias (i.e. technical variations) in tagmentation-based data, in particular ATAC-seq, and introduce three R/bioconductor packages to facilitate bulk and single-cell epigenomic data analysis, with a special focus on motif accessibility analysis. The weightedMotifAccess package uses weight models to enable motif accessibility analysis, including transcription factor footprint information. The betterChromVAR package provides a novel, analytical re-implementation of the popular chromVAR method that offers substantial speed improvements, eliminates stochasticity, and offers additional features. Based on this, we also propose a method, CVnorm, that outperforms alternatives in normalizing technical bias in peak count data. The computational efficiency of these tools further enables a new framework for systematically investigating synergistic and antagonistic interactions between transcription factor motifs. Finally, the epiwraps package streamlines the visualization, normalization, and summarization of epigenomic data.

bioinformatics↗

bioRxiv2026

Evolutionary transfer learning enables organism-wide inference of mammalian enhancer landscapes

Understanding and modeling how a single human genome concurrently encodes gene regulatory programs for thousands of cell types remains a central challenge in genomics and machine learning. Most human cell types emerge during embryonic, fetal, and pediatric development which are inaccessible to comprehensive molecular profiling. To circumvent this, we hypothesized that the mismatch in evolutionary rates between cis-acting enhancers that modulate gene expression (fast) and the trans-acting regulatory factors that specify cell types (slow) creates an opportunity for evolutionary transfer learning. Specifically, models trained to predict cell type-specific enhancers in one species should generalize to the orthologous cell types and enhancers of related species. To test this, we generated a single-cell atlas of chromatin accessibility spanning mouse embryonic day 10 (E10) to birth (P0). Using combinatorial indexing1, we profiled 3.9 million nuclei from 36 staged embryos, resolving genome-wide accessibility in 36 cell classes and 140 cell types. We then trained a series of multi-output deep learning models (CREsted2), each addressing limitations of the preceding approach, towards the goal of genome-wide prediction of distal enhancers across major developmental lineages. An evolution-naive model achieved strong performance on heldout peaks, but exhibited two failure modes during genome-wide inference: overprediction at tandem repeats and conflation of promoter and distal enhancer grammars. An evolution-aware model resolved these by regrouping accessible regions based on their retention and functional coherence across mammalian evolution, but failed to generalize across species. Finally, an evolution-augmented model, STEAM (Synteny-aware Transfer learning for Enhancer Activity Modeling), incorporated enhancer orthologs from 241 mammalian genomes (Zoonomia3) in a synteny-supervised manner. This increased the effective data scale by as much as 195-fold, markedly improving generalization across mammals despite greater label noise. We applied STEAM to the genome-wide inference of cell class-specific distal developmental enhancers in humans, mice (HumMus) and 239 additional mammals3 (BabaGanoush), i.e. 32 x 241 = 7,712 genome-wide distal enhancer prediction tracks. Together, our results unify advances in single-cell profiling, deep learning, and comparative genomics into a framework for the evolutionary transfer learning of noncoding regulatory grammars. More broadly, our work supports the view that model organisms and evolutionarily diverse genomes are indispensable resources for accelerating and enhancing the AI-enabled exploration of human biology. NoteAn interactive version of this preprint, together with count matrices, CREsted models, prediction tracks, code and reproducible figures, is available at this link (ref 4).

genomics↗

bioRxiv2025

Lineage recording in monoclonal gastruloids reveals heritable modes of early development

Mammalian stem cells possess a remarkable capacity for self-organization, a property that underlies increasingly sophisticated in vitro models of early development. However, even under carefully controlled conditions, stem cell-derived models exhibit substantial "inter-individual" heterogeneity. Focusing on gastruloids, a powerful model of the early posterior embryo1, we sought to investigate the origins of this heterogeneity. To this end, we developed a scalable protocol for generating gastruloids that are monoclonal, i.e. derived from a single mouse embryonic stem cell (mESC). Single cell transcriptional profiling of monoclonal gastruloids revealed extensive inter-individual heterogeneity, with some hardly progressing, others resembling conventional gastruloids but biased towards mesodermal or neural lineages, and yet others bearing cell types rare or absent from conventional polyclonal gastruloids. To investigate this further, we leveraged DNA Typewriter2 to record the cell lineage relationships among the mESCs from which monoclonal gastruloids originate. Early in the expansion of "founder" mESCsprior to induction of the resulting aggregates to form gastruloidswe observe clear examples of fate bias or fate restriction, i.e. sister clades that exhibit markedly different cell type compositions. In a separate experiment with DNA Typewriter, we reconstructed a monophyletic "tree of trees", composed of [~]50,000 cells derived from [~]100 gastruloids, all descended from a single "founder of founders" stem cell. From these data, we find that founder mESCs that are more closely related are more likely to give rise to monoclonal gastruloids with similar cell type compositions. Our results suggest that fluctuations in the intrinsic states of mESCs are heritable, and shape their descendants fates across many cell divisions. Our study also showcases how DNA Typewriter can be used to reconstruct high-resolution, monophyletic cell lineage trees in stem cell models of early development.

developmental biology↗

bioRxiv2025

Barcoded monoclonal embryoids are a potential solution to confounding bottlenecks in mosaic organoid screens

Genetic screens in organoids hold tremendous promise for accelerating discoveries at the intersection of genomics and developmental biology. Embryoid bodies (EBs) are self-organizing multicellular structures that recapitulate aspects of early mammalian embryogenesis. We set out to perform a CRISPR screen perturbing all transcription factors (TFs) in murine EBs. Specifically, a library of TF-targeting guide RNAs (gRNAs) was used to generate mouse embryonic stem cells (mESCs) bearing single TF knockouts. Aggregates of these mESCs were induced to form mouse EBs, such that each resulting EB was mosaic with respect to the TF perturbations represented among its constituent cells. Upon performing single cell RNA-seq (scRNA-seq) on cells derived from mosaic EBs, we found many TF perturbations exhibiting large and seemingly significant effects on the likelihood that individual cells would adopt certain fates, suggesting roles for these TFs in lineage specification. However, to our surprise, these results were not reproducible across biological replicates. Upon further investigation, we discovered cellular bottlenecks during EB differentiation that dramatically reduce clonal complexity, curtailing statistical power and confounding interpretation of mosaic screens. Towards addressing this challenge, we developed a scalable protocol in which each individual EB is monoclonally derived from a single mESC and genetically barcoded. In a proof-of-concept experiment, we show how these monoclonal EBs enable us to better quantify the consequences of TF perturbations as well as inter-individual heterogeneity across EBs harboring the same genetic perturbation. Looking forward, monoclonal EBs and EB-derived organoids may be powerful tools not only for genetic screens, but also for modeling Mendelian disorders, as their underlying genetic lesions are overwhelmingly constitutional (i.e. present in all somatic cells), yet give rise to phenotypes with incomplete penetrance and variable expressivity.

developmental biology↗

bioRxiv2023

Multiplex, single-cell CRISPRa screening for cell type specific regulatory elements

CRISPR-based gene activation (CRISPRa) is a promising therapeutic approach for gene therapy, upregulating gene expression by targeting promoters or enhancers in a tissue/cell-type specific manner. Here, we describe an experimental framework that combines highly multiplexed perturbations with single-cell RNA sequencing (sc-RNA-seq) to identify cell-type-specific, CRISPRa-responsive cis-regulatory elements and the gene(s) they regulate. Random combinations of many gRNAs are introduced to each of many cells, which are then profiled and partitioned into test and control groups to test for effect(s) of CRISPRa perturbations of both enhancers and promoters on the expression of neighboring genes. Applying this method to a library of 493 gRNAs targeting candidate cis-regulatory elements in both K562 cells and iPSC-derived excitatory neurons, we identify gRNAs capable of specifically upregulating intended target genes and no other neighboring genes within 1 Mb, including gRNAs yielding upregulation of six autism spectrum disorder (ASD) and neurodevelopmental disorder (NDD) risk genes in neurons. A consistent pattern is that the responsiveness of individual enhancers to CRISPRa is restricted by cell type, implying a dependency on either chromatin landscape and/or additional trans-acting factors for successful gene activation. The approach outlined here may facilitate large-scale screens for gRNAs that activate therapeutically relevant genes in a cell type-specific manner.

genomics↗

bioRxiv2023

Multiplex single-cell chemical genomics reveals the kinase dependence of the response to targeted therapy

Chemical genetic screens are a powerful tool for exploring how cancer cells response to drugs is shaped by their mutations, yet they lack a molecular view of the contribution of individual genes to the response to exposure. Here, we present sci-Plex-Gene-by-Environment (sci-Plex-GxE), a platform for combined single-cell genetic and chemical screening at scale. We highlight the advantages of large-scale, unbiased screening by defining the contribution of each of 522 human kinases to the response of glioblastoma to different drugs designed to abrogate signaling from the receptor tyrosine kinase pathway. In total, we probed 14,121 gene-by-environment combinations across 1,052,205 single-cell transcriptomes. We identify an expression signature characteristic of compensatory adaptive signaling regulated in a MEK/MAPK-dependent manner. Further analyses aimed at preventing adaptation revealed promising combination therapies, including dual MEK and CDC7/CDK9 or NF-kB inhibitors, as potent means of preventing transcriptional adaptation of glioblastoma to targeted therapy.

genomics↗

bioRxiv2022

Multiplex profiling of developmental enhancers with quantitative, single-cell expression reporters

The inability to scalably and precisely measure the activity of developmental enhancers in multicellular systems is a bottleneck in genomics. Here, we develop a dual RNA cassette that decouples the detection and quantification tasks inherent to multiplex single-cell reporter assays, resulting in accurate measurement of reporter expression over a >10,000-fold range of activity with a precision approaching the limit set by Poisson counting noise. Together with RNA barcode circularization, these single-cell quantitative expression reporters (scQers) provide high-contrast readouts analogous to classic in situ assays, but entirely from sequencing. Screening >200 enhancers in a multicellular in vitro model of early mammalian development, we identified numerous autonomous and cell-type-specific elements, including constituents of the Sox2 control region exclusively active in pluripotent cells, endoderm-specific enhancers, including near Foxa2 and Gata4, and a compact pleiotropic enhancer at the Lamc1 locus. scQers can be mobilized in developmental systems to quantitatively characterize native, perturbed, and synthetic enhancers at scale, with high sensitivity and at single-cell resolution.

genomics↗

bioRxiv2021

A temporally resolved, multiplex molecular recorder based on sequential genome editing

DNA is naturally well-suited to serve as a digital medium for in vivo molecular recording. However, DNA-based memory devices described to date are constrained in terms of the number of distinct signals that can be concurrently recorded and/or by a failure to capture the precise order of recorded events1. Here we describe DNA Ticker Tape, a general system for in vivo molecular recording that largely overcomes these limitations. Blank DNA Ticker Tape consists of a tandem array of partial CRISPR-Cas9 target sites, with all but the first site truncated at their 5 ends, and therefore inactive. Signals of interest are coupled to the expression of specific prime editing guide RNAs2. Editing events are insertional, and record the identity of the guide RNA mediating the insertion while also shifting the position of the "write head" by one unit along the tandem array, i.e. sequential genome editing. In this proof-of-concept of DNA Ticker Tape, we demonstrate the recording and decoding of complex event histories or short text messages; evaluate the performance of dozens of orthogonal tapes; and construct "long tape" potentially capable of recording the order of as many as 20 serial events. Finally, we demonstrate how DNA Ticker Tape simplifies the decoding of cell lineage histories.

genomics↗