Biology subjects

Geiger-Schuller, K.

Publications and source records attributed to Geiger-Schuller, K..

10 recordsLinked to original sources

bioRxiv2026

Perturb-ME: Scalable mechanism discovery from phenotype-enriched genome-wide screens

Perturb-seq enables pooled genetic screens with rich single-cell profiling readouts, but genome-scale profiling remains costly and may not be associated with other established functional characteristics. Moreover, as screens grow in size and complexity, interpreting the resulting data comprehensively is challenging and slow. Here, we introduce Perturb-seq with Marker Enrichment (Perturb-ME), which combines genome-scale CRISPR screening, phenotype-based enrichment and multimodal single-cell profiling. Applied to MHC-I cell surface protein expression in melanoma, Perturb-ME profiled HLA-low and HLA-high cells with matched RNA, surface-protein and guide measurements. A regulatory model with 221 impactful regulators affecting 1,998 responsive genes recovered seven coherent co-functional regulatory modules governing nine gene programs, including the canonical IFN{gamma}-MHC-I axis regulating an antigen-presentation and interferon-response program. Agentic interpretation of the entire model with an AI co-scientist linked additional modules to trafficking, proteostasis and chromatin regulation. Perturb-ME, along with agentic interpretation, provide a scalable framework for comprehensive functional discovery from phenotype-enriched genetic screens.

cell biology↗

bioRxiv2026

Scalable longitudinal imaging and transcriptomics of cells in dynamic enclosures

Dynamic transitions between cell states underlie both normal physiology and disease. However, most single-cell technologies capture only static snapshots. To address this gap, we developed a platform that integrates light-guided hydrogel polymerization with computer vision to generate on-demand compartments around live cells, enabling longitudinal imaging of cellular behavior paired with whole-transcriptome profiling of the same cells at scale. These data link dynamic phenotypes with molecular programs, enabling deeper characterization of cellular states. This approach revealed an adaptive, drug-resistant state in lung cancer cells characterized by potassium channel upregulation and p53-dependent quiescence. In models of adipogenesis and microglial phagocytosis, joint analysis of imaging and transcriptomic data identified key drivers of cellular function that were missed by transcriptomic clustering alone. These results establish the value of paired functional and transcriptomic analysis to resolve molecular drivers of complex cellular behaviors.

systems biology↗

bioRxiv2026

Linking live-cell behavior to transcriptional responses across perturbations using dynamic caging

Single-cell technologies, encompassing molecular, morphological, and functional assays, have emerged as cornerstones of modern biological research and discovery. However, current experimental methods often fail to explicitly link these omic modalities, especially in live cells or longitudinally through time, impeding the study of multi-scale interactions and mechanisms of regulation. CellCage Enclosure (CCE) technology overcomes these limitations by dynamically compartmentalizing cells, allowing for scalable, live-cell, longitudinal exploration and simultaneous analysis of transcriptomic, proteomic, and morphological profiles. Using this novel technology, we generate previously inaccessible insights across various in vitro cellular systems under a diverse set of perturbations, including the discovery of morphological and proteomic features linked to immune suppressive gene set expression in human primary regulatory T cells (Tregs), as well as direct association of morphological and proteomic features with inflammatory gene modules in human colonic fibroblasts. We then develop a novel pooled CRISPR genetic screening technology using CCEs, PERTURB-LINK (PERTURBational LINKage of transcriptomics and imaging in single cells via enclosure-based screening) and apply this approach in murine bone marrow derived macrophages (BMDMs), enabling multiomic dissection of NF-{kappa}B pathway regulation in response to lipopolysaccharide (LPS) stimulation. Together, these findings demonstrate the broad impact that advancements in live-cell, paired multimodal technologies, especially upon perturbation, may offer in deepening our understanding of cellular biology.

cell biology↗

bioRxiv2025

Intratumoral cDC1-T Cell Clusters Serve as Sites of Local Costimulation to Enhance CTL-Mediated Tumor Rejection

T cells are essential for anti-tumor immunity, but their ability to eliminate tumors depends on coordinated interactions with type 1 conventional dendritic cells (cDC1s). While cDC1s are known for cross-presenting tumor-derived antigens in lymph nodes to prime CD8+ T cells, their role within the tumor itself remains less well understood. Here, we use the Skin Tumor Array by Micro-Poration (STAMP) model to investigate how cDC1-T cell interactions shape immune responses and influence tumor fate. Our data reveal that it is the spatial distribution of both cDC1s and T cells that determines whether a tumor can be rejected. We defined three primary immunotypes based on the spatial distribution of T cells and cDC1s: T cell-inflamed/dendritic cell-inflamed (TC-In/DC-In) tumors, where T cells and cDC1s co-infiltrate the tumor; T cell-inflamed/dendritic cell-excluded (TC-In/DC-Ex) tumors, where T cells infiltrate but cDC1s remain at the periphery; and T cell-excluded/dendritic cell-excluded (TC-Ex/DC-Ex) tumors, which lack both cDC1 and T cell infiltration. Notably, TC-In/DC-In tumors are more likely to undergo rejection, whereas TC-In/DC-Ex tumors persist despite T cell infiltration. Within TC-In/DC-In tumors, cDC1s engage in direct interactions with T cells, upregulate co-stimulatory molecules, and sustain effector T cell responses, while cDC1s in TC-In/DC-Ex tumors express higher migration-associated genes, suggesting a propensity to exit the tumor. We further show that chemokine modulation, particularly through CXCL9, CCL5, and XCL1, can reshape immune infiltration patterns to promote intra-tumoral cDC1-T cell clustering and improve tumor rejection. These findings underscore the unexpectedly important role of cDC1 positioning and function in sustaining effective anti-tumor immunity and highlight spatially organized cDC1-T cell clusters as critical hubs for local T cell activation.

immunology↗

bioRxiv2024

Systematic perturbation screens decode regulators of inflammatory macrophage states and identify a role for TNF mRNA m6A modification

Macrophages adopt dynamic cell states with distinct effector functions to maintain tissue homeostasis and respond to environmental challenges. During chronic inflammation, macrophage polarization is subverted towards sustained inflammatory states which contribute to disease, but there is limited understanding of the regulatory mechanisms underlying these disease-associated states. Here, we describe a systematic functional genomics approach that combines genome-wide phenotypic screening in primary murine macrophages with transcriptional and cytokine profiling of genetic perturbations in primary human monocyte-derived macrophages (hMDMs) to uncover regulatory circuits of inflammatory macrophage states. This process identifies regulators of five distinct inflammatory states associated with key features of macrophage function. Among these, the mRNA m6A writer components emerge as novel inhibitors of a TNF-driven cell state associated with multiple inflammatory pathologies. Loss of m6A writer components in hMDMs enhances TNF transcript stability, thereby elevating macrophage TNF production. A PheWAS on SNPs predicted to impact m6A installation on TNF revealed an association with cystic kidney disease, implicating an m6A-mediated regulatory mechanism in human disease. Thus, systematic phenotypic characterization of primary human macrophages describes the regulatory circuits underlying distinct inflammatory states, revealing post-transcriptional control of TNF mRNA stability as an immunosuppressive mechanism in innate immunity.

immunology↗

bioRxiv2023

Compressed Perturb-seq: highly efficient screens for regulatory circuits using random composite perturbations

Pooled CRISPR screens with single-cell RNA-seq readout (Perturb-seq) have emerged as a key technique in functional genomics, but are limited in scale by cost and combinatorial complexity. Here, we reimagine Perturb-seqs design through the lens of algorithms applied to random, low-dimensional observations. We present compressed Perturb-seq, which measures multiple random perturbations per cell or multiple cells per droplet and computationally decompresses these measurements by leveraging the sparse structure of regulatory circuits. Applied to 598 genes in the immune response to bacterial lipopolysaccharide, compressed Perturb-seq achieves the same accuracy as conventional Perturb-seq at 4 to 20-fold reduced cost, with greater power to learn genetic interactions. We identify known and novel regulators of immune responses and uncover evolutionarily constrained genes with downstream targets enriched for immune disease heritability, including many missed by existing GWAS or trans-eQTL studies. Our framework enables new scales of interrogation for a foundational method in functional genomics.

systems biology↗

bioRxiv2022

Single cell RNA-seq by mostly-natural sequencing by synthesis

Massively parallel single cell RNA-seq (scRNA-seq) for diverse applications, from cell atlases to functional screens, is increasingly limited by sequencing costs, and large-scale low-cost sequencing can open many additional applications, including patient diagnostics and drug screens. Here, we adapted and systematically benchmarked a newly developed, mostly-natural sequencing by synthesis method for scRNA-seq. We demonstrate successful application in four scRNA-seq case studies of different technical and biological types, including 5 and 3 scRNA-seq, human peripheral blood mononuclear cells from a single individual and in multiplex, as well as Perturb-Seq. Our data show comparable results to existing technology, including compatibility with state-of-the-art scRNA-seq libraries independent of the sequencing technology used - thus providing an enhanced cost-effective path for large scale scRNA-seq.

genomics↗

bioRxiv2020

A collection of programs for one-dimensional Ising analysis of linear repeat proteins with point substitutions

A collection of programs is presented to analyze the thermodynamics of folding of linear repeat proteins using a 1D Ising model to determine intrinsic folding and interfacial coupling free energies. Expressions for folding transitions are generated for a series of constructs with different repeat numbers and are globally fitted to transitions for these constructs. These programs are designed to analyze Ising parameters for capped homopolymeric consensus repeat constructs as well as heteropolymeric constructs that contain point substitutions, providing a rigorous framework for analysis of the effects of mutation on intrinsic and directional (i.e., N- versus C-terminal) interfacial coupling free-energies. A bootstrap analysis is provided to estimate parameter uncertainty as well as correlations among fitted parameters. Rigorous statistical analysis is essential for interpreting fits using the complex models required for Ising analysis of repeat proteins, especially heteropolymeric repeat proteins. Programs described here are available at https://github.com/barricklab-at-jhu/Ising_programs.

biophysics↗

bioRxiv2019

Multiplexed single-cell profiling of post-perturbation transcriptional responses to define cancer vulnerabilities and therapeutic mechanism of action

Assays to study cancer cell responses to pharmacologic or genetic perturbations are typically restricted to using simple phenotypic readouts such as proliferation rate or the expression of a marker gene. Information-rich assays, such as gene-expression profiling, are generally not amenable to efficient profiling of a given perturbation across multiple cellular contexts. Here, we developed MIX-Seq, a method for multiplexed transcriptional profiling of post-perturbation responses across a mixture of samples with single-cell resolution, using SNP-based computational demultiplexing of single-cell RNA-sequencing data. We show that MIX-Seq can be used to profile responses to chemical or genetic perturbations across pools of 100 or more cancer cell lines, and combine it with Cell Hashing to further multiplex additional experimental conditions, such as multiple post-treatment time points or drug doses. Analyzing the high-content readout of scRNA-seq reveals both shared and context-specific transcriptional response components that can identify drug mechanism of action and can be used to predict long-term cell viability from short-term transcriptional responses to treatment.

cancer biology↗

bioRxiv2018

Conformational heterogeneity allows access to DNA in longer Transcription Activator-Like Effector (TALE) arrays.

Transcription activator-like effectors (TALEs) bind DNA through an array of tandem 34-residue repeats. Here, we examine the kinetics of DNA binding for a set of TALE arrays with varying numbers of identical repeats using single molecule microscopy. Using a new deterministic modeling approach, we find evidence for conformational heterogeneity in both the free- and DNA-bound TALE arrays. Combined with previous work demonstrating populations of partly folded TALE states, our findings reveal a functional instability in TALE-DNA binding. For TALEs forming less than one superhelical turn around DNA, partly folded open states inhibit DNA binding. In contrast, for TALEs forming more than one turn, the partly folded open states facilitate DNA binding. Overall, we find that increasing repeat number results in significantly slower interconversion between the various DNA-free and DNA-bound states. These findings highlight the role of conformational heterogeneity and dynamics in facilitating macromolecular complex assembly.\n\nImpact StatementSingle molecule DNA-binding trajectories and deterministic modeling analyses demonstrate a functional role for high energy partly folded states in Transcription Activator-Like Effectors (TALEs) that could improve future TALEN design.

biophysics↗