Biology subjects

Buttner, M.

Publications and source records attributed to Buttner, M..

10 recordsLinked to original sources

bioRxiv2026

Dependencies in heterogeneous, lineage plastic patient-derived prostate cancer organoids revealed through integrated single-cell multiomics and CRISPR screening

Lineage plasticity and tumor heterogeneity limit the effectiveness of targeted therapies, yet the functional dependencies used to nominate therapeutic targets are often derived from homogeneous systems that fail to capture this complexity. Here, we establish a framework to resolve state-specific genetic vulnerabilities by integrating single-cell multiomics (RNA and ATAC) with pooled CRISPR-Cas9 screening across a large panel of patient-derived organoids (PDOs) from castrate-resistant prostate cancer (CRPC) and neuroendocrine prostate cancer (NEPC). We generate a single-cell multiome atlas spanning >190,000 cells across 22 PDOs, defining seven lineage states--including intermediate and plastic populations not resolved by bulk profiling--and demonstrate that these lineage programs robustly classify independent transcriptomic datasets from prostate cancer patient tumors. By systematically coupling this atlas to subtype-resolved CRISPR screens, we construct a functional dependency map linking cell state in heterogeneous 3D human tumor models. We show that intratumoral heterogeneity fundamentally reshapes the interpretation of gene essentiality, whereby gene-level depletion reflects the composite behavior of co-existing subpopulations, and identify a general principle in which resistant "limiting" populations disproportionately determine aggregate fitness effects. This framework reveals both canonical and previously unrecognized lineage-restricted dependencies within highly plastic tumor and NEPC states, including a therapeutically targetable dependency on the aryl hydrocarbon receptor (AHR) in a novel hybrid stem-like/ASCL1 population. Together, these data establish an extensive multi-dimensional prostate cancer resource, identify novel lineage-resolved biology, and provide a generalizable strategy for interpreting functional genomics in heterogeneous human tumors.

cancer biology↗

bioRxiv2025

Extracellular matrix regulates lineage plasticity in prostate cancer through YAP/TEAD

Treatment-related neuroendocrine prostate cancer (NEPC) is an increasingly frequent mechanism of resistance to androgen receptor pathway inhibitor (ARPI) therapy in prostate adenocarcinoma (PRAD). This lineage transition is dependent on upregulation of the NE-specifying transcription factor ASCL1, typically in a genetic background of RB1 and TP53 loss. Here we identify extracellular matrix-integrin-YAP1/TEAD signaling as a critical brake on NEPC lineage transition. Deletion of Itgb1, the shared B1 subunit required for collagen and laminin-mediated integrin activation, is sufficient to induce ASCL1 and NE lineage gene expression, by activating LATS1/2 kinases with subsequent inactivation of YAP1/TEAD signaling. Conversely, restoration of YAP1/TEAD signaling by pharmacological LATS1/2 inhibition, or by expression of constitutively active YAP1/TAZ mutants, prevents or reverts NEPC lineage transition. NOTCH and AR cooperate with YAP/TEAD to repress ASCL1, such that combined inhibition leads to complete reprograming of PRAD into NEPC in vitro, providing a dynamic platform to dissect the molecular events responsible for lineage transition over time. We find that lineage transition is accompanied by a redistribution of FOXA1 and TEAD cistromes from PRAD to NEPC-specific enhancers and requires the pioneering activity of FOXA1. Thus, extracellular matrix/integrin signaling in the PRAD tumor microenvironment restrains NE lineage plasticity, highlighting a potential path for pharmacological inhibitors in modulating treatment-induced lineage change.

cancer biology↗

bioRxiv2024

ConvexGating infers gating strategies from clusters in single cell cytometry data

Manual expert gating remains common practice for the definition of specific cell populations in the analysis of flow cytometry data. The increasing number of measured parameters per individual cell and high inter-rater variability makes manual gating inconsistent in many scenarios such as multi-center studies. Here, we propose ConvexGating, an AI tool that automatically learns gating strategies in an unbiased, fully data-driven, yet interpretable manner. ConvexGating scales efficiently with increasing parameter space, creating proficient strategies with low-contamination in the extracted population for previously known and so far unknown or ill-defined cell populations. The inferred strategies are independent of parent populations, for instance, plasmacytoid dendritic cells (pDCs) can be fully identified as CD45RA- CD123+. In addition to flow cytometry data, ConvexGating derives gating strategies for cyTOF (Cytometry by Time of Flight) and CITEseq (Cellular Indexing of Transcriptomes and Epitopes by Sequencing) data and supports optimal design of marker panels for cell sorting.

immunology↗

bioRxiv2022

Delineating mouse β-cell identity during lifetime and in diabetes with a single cell atlas

Multiple pancreatic islet single-cell RNA sequencing (scRNA-seq) datasets have been generated to study development, homeostasis, and diabetes. However, there is no consensus on cell states and pathways across conditions as well as the value of preclinical mouse models. Since these challenges can only be resolved by jointly analyzing multiple datasets, we present a scRNA-seq cross-condition mouse islet atlas (MIA). We integrated over 300,000 cells from nine datasets with 56 samples, varying in age, sex, and diabetes models, including an autoimmune type 1 diabetes (T1D) model (NOD), a gluco-/lipotoxicity T2D model (db/db), and a chemical streptozotocin (STZ) {beta}-cell ablation model. MIA is a curated resource for interactive exploration and computational querying, providing new insights inaccessible from individual datasets. The {beta}-cell landscape of MIA revealed new disease progression cell states and cross-publication differences between previously suggested marker genes. We show that in the STZ model {beta}-cells transcriptionally correlate to human T2D and mouse db/db, but are less similar to human T1D and mouse NOD. We observe different pathways shared between immature, aged, and diabetes model {beta}-cells. In conclusion, our work presents the first comprehensive analysis of {beta}-cell responses to different stressors, providing a roadmap for the understanding of {beta}-cell plasticity, compensation, and demise.

bioinformatics↗

bioRxiv2022

Pytometry: Flow and mass cytometry analytics in Python

Flow and mass cytometry data are commonly analyzed via manual gating strategies which requires prior knowledge, expertise and time. With increasingly complex experiments with many parameters and samples, traditional manual flow and mass cytometry data analysis becomes cumbersome if not inefficient. At the same time, computational tools developed for the analysis of single-cell RNA-sequencing data have made single cell genomics analysis highly efficient, yet they are mostly inaccessible for the analysis of flow and mass cytometry data due to different data formats, noise assumptions and scales. To bring the advantages of both fields together, we developed Pytometry as an extension to the popular scanpy framework for the analysis of flow and mass cytometry data. We showcase a standard analysis workflow on healthy human bone marrow data, illustrating the applicability of tools developed for the larger feature space of single cell genomics data. Pytometry combines joint analysis of multiple samples and advanced computational applications, ranging from automated pre-processing, cell type annotation and disease classification.

systems biology↗

bioRxiv2021

Multi-omics and 3D-imaging reveal bone heterogeneity and unique calvaria cells in neuroinflammation

The meninges of the brain are an important component of neuroinflammatory response. Diverse immune cells move from the calvaria marrow into the dura mater via recently discovered skull-meninges connections (SMCs). However, how the calvaria bone marrow is different from the other bones and whether and how it contributes to human diseases remain unknown. Using multi-omics approaches and whole mouse transparency we reveal that bone marrow cells are highly heterogeneous across the mouse body. The calvaria harbors the most distinct molecular signature with hundreds of differentially expressed genes and proteins. Acute brain injury induces skull-specific alterations including increased calvaria cell numbers. Moreover, TSPO-positron-emission-tomography imaging of stroke, multiple sclerosis and neurodegenerative disease patients demonstrate disease-associated uptake patterns in the human skull, mirroring the underlying brain inflammation. Our study indicates that the calvaria is more than a physical barrier, and its immune cells may present new ways to control brain pathologies. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=103 SRC="FIGDIR/small/473988v1_ufig1.gif" ALT="Figure 1"> View larger version (36K): org.highwire.dtl.DTLVardef@978194org.highwire.dtl.DTLVardef@bc45e8org.highwire.dtl.DTLVardef@91afdborg.highwire.dtl.DTLVardef@b06bc6_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIBone marrow across the mouse body display heterogeneity in their molecular profile C_LIO_LICalvaria cells have a distinct profile that is relevant to brain pathologies C_LIO_LIBrain native proteins are identified in calvaria in pathological states C_LIO_LITSPO-PET imaging of the human skull can be a proxy of neuroinflammation in the brain C_LI Supplementary Videos can be seen at: http://discotechnologies.org/Calvaria/

cell biology↗

bioRxiv2020

Query to reference single-cell integration with transfer learning

Large single-cell atlases are now routinely generated with the aim of serving as reference to analyse future smaller-scale studies. Yet, learning from reference data is complicated by batch effects between datasets, limited availability of computational resources, and sharing restrictions on raw data. Leveraging advances in machine learning, we propose a deep learning strategy to map query datasets on top of a reference called single-cell architectural surgery (scArches, https://github.com/theislab/scarches). It uses transfer learning and parameter optimization to enable efficient, decentralized, iterative reference building, and the contextualization of new datasets with existing references without sharing raw data. Using examples from mouse brain, pancreas, and whole organism atlases, we showcase that scArches preserves nuanced biological state information while removing batch effects in the data, despite using four orders of magnitude fewer parameters compared to de novo integration. To demonstrate mapping disease variation, we show that scArches preserves detailed COVID-19 disease variation upon reference mapping, enabling discovery of new cell identities that are unseen during training. We envision our method to facilitate collaborative projects by enabling the iterative construction, updating, sharing, and efficient use of reference atlases.

bioinformatics↗

bioRxiv2020

Benchmarking atlas-level data integration in single-cell genomics

Cell atlases often include samples that span locations, labs, and conditions, leading to complex, nested batch effects in data. Thus, joint analysis of atlas datasets requires reliable data integration. Choosing a data integration method is a challenge due to the difficulty of defining integration success. Here, we benchmark 38 method and preprocessing combinations on 77 batches of gene expression, chromatin accessibility, and simulation data from 23 publications, altogether representing >1.2 million cells distributed in nine atlas-level integration tasks. Our integration tasks span several common sources of variation such as individuals, species, and experimental labs. We evaluate methods according to scalability, usability, and their ability to remove batch effects while retaining biological variation. Using 14 evaluation metrics, we find that highly variable gene selection improves the performance of data integration methods, whereas scaling pushes methods to prioritize batch removal over conservation of biological variation. Overall, BBKNN, Scanorama, and scVI perform well, particularly on complex integration tasks; Seurat v3 performs well on simpler tasks with distinct biological signals; and methods that prioritize batch removal perform best for ATAC-seq data integration. Our freely available reproducible python module can be used to identify optimal data integration methods for new data, benchmark new methods, and improve method development.

bioinformatics↗

bioRxiv2019

Expansion and re-classification of the extracytoplasmic function (ECF) σ factor family

Extracytoplasmic function {sigma} factors (ECFs) represent one of the major bacterial signal transduction mechanisms in terms of abundance, diversity and importance, particularly in mediating stress responses. Here, we performed a comprehensive phylogenetic analysis of this protein family by scrutinizing all proteins in the NCBI database. As result, we identified [~]10 ECFs per bacterial genome on average and classified them into 157 phylogenetic ECF groups that feature a conserved genetic neighborhood and a similar regulation mechanism. Our analysis expands the number of unique ECF sequences [~]50-fold relative to previous classification efforts, enriches many original ECF groups with previously unclassified proteins and identifies 22 entirely new ECF groups. The ECF groups are hierarchically related to each other and are further composed of subgroups with closely related sequences. This two-tiered classification allows for the accurate prediction of common promoter motifs and the inference of putative regulatory mechanisms across subgroups composing an ECF group. This comprehensive, high-resolution description of the phylogenetic distribution of the ECF family, together with the massive expansion of classified ECF sequences, enables the application of in silico tools for the prediction of important functional residues, and serves as a powerful hypothesis-generator to guide future research in the field.

bioinformatics↗

bioRxiv2017

Assessment of batch-correction methods for scRNA-seq data with a new test metric

Single-cell transcriptomics is a versatile tool for exploring heterogeneous cell populations. As with all genomics experiments, batch effects can hamper data integration and interpretation. The success of batch effect correction is often evaluated by visual inspection of dimension-reduced representations such as principal component analysis. This is inherently imprecise due to the high number of genes and non-normal distribution of gene expression. Here, we present a k-nearest neighbour batch effect test (kBET, https://github.com/theislab/kBET) to quantitatively measure batch effects. kBET is easier to interpret, more sensitive and more robust than visual evaluation and other measures of batch effects. We use kBET to assess commonly used batch regression and normalisation approaches, and quantify the extent to which they remove batch effects while preserving biological variability. Our results illustrate that batch correction based on log-transformation or scran pooling followed by ComBat reduced the batch effect while preserving structure across data sets. Finally we show that kBET can pinpoint successful data integration methods across multiple data sets, in this case from different publications all charting mouse embryonic development. This has important implications for future data integration efforts, which will be central to projects such as the Human Cell Atlas where data for the same tissue may be generated in multiple locations around the world.\n\n[Before final publication, we will upload the R package to Bioconductor]

bioinformatics↗