Biology subjects

Hafemeister, C.

Publications and source records attributed to Hafemeister, C..

10 recordsLinked to original sources

bioRxiv2026

A human lung organoid co-culture model of early bone sarcoma metastasis reveals contact-dependent epithelial remodeling at the metastatic interface

Lung metastasis drives mortality across cancer types, yet how infiltrating tumor cells remodel the lung epithelium to establish metastatic niches remains poorly understood. Here we establish MESCUL (MEtastatic Sarcoma Co-CULture), a co-culture platform combining human lung organoids with patient-derived bone sarcoma cells to model early tumor-lung epithelial interactions in a physiologically relevant 3D system. MESCUL reveals that direct tumor-epithelial contact induces rapid, reproducible lung epithelial remodeling across Ewing sarcoma (ES) and osteosarcoma (OS) models and multiple organoid donor backgrounds, which is contact-dependent and not recapitulated by paracrine signaling. Single-cell RNA sequencing identifies LIMES (Lung Interface Metastasis Signature), a shared transcriptional program encompassing focal adhesion assembly, matrix metalloprotease (MMP) upregulation, and emergence of a damage-associated transitional cell state, in both ES and OS. Mechanistically, tumor-derived fibronectin (FN1) engages epithelial integrin receptors to activate focal adhesion kinase (FAK), driving amphiregulin (AREG) induction and MMP-mediated remodeling; FN1 alone phenocopies this response, and FAK inhibition attenuates it, nominating the FN1-integrin-FAK-AREG axis as a candidate therapeutic vulnerability. The LIMES program, identified through the MESCUL co-culture model, is spatially confined to the tumor-lung interface in patient metastases of both ES and OS, as demonstrated by spatial transcriptomics across nine patients. Massons trichrome staining of matched patient sections reveals pronounced collagen deposition in the peri-tumoral lung parenchyma, consistent with LIMES acting upstream of a wound-healing cascade that proceeds to structural fibrotic remodeling in patient tissue. Together, these findings establish the lung epithelium as an active participant in metastatic colonization, characterize a pharmacologically targetable, spatially restricted epithelial remodeling response at the bone sarcoma-lung interface across OS and ES, and introduce MESCUL as a tractable 3D platform for investigating lung metastasis, with possible implications for tumor types beyond bone sarcomas.

cancer biology↗

bioRxiv2025

Dynamic modelling of EWS::FLI1 fluctuations reveals molecular determinants of phenotypic tumor plasticity and prognosis in Ewing sarcoma

The mechanisms underlying tumor cell plasticity driving drug resistance and disease progression remain poorly understood. In Ewing sarcoma (EwS), variations in EWS::FLI1 (EF) activity have been associated with epithelial-mesenchymal plasticity (EMP). Using degron technology, we titrated endogenous EF in an EwS cell line and linked phenotypic states to distinct EF thresholds. Strikingly, modest EF depletion promoted a pro-metastatic phenotype, that diminished upon near-complete EF loss. Nascent RNA sequencing revealed distinct gene clusters with heterogenous response patterns to varying EF dosage. Target genes most sensitive to subtle EF depletion contained GGAA microsatellites in EF-bound enhancers. Furthermore, we identified Kruppel-like zinc-finger transcription factors associated with EF-repressed EMP genes. Transient EF depletion followed by rapid restoration to simulate oncoprotein fluctuations identified persistently dysregulated genes associated with poor prognosis. This study underscores the therapeutic challenge of insufficient EF inhibition and provides a foundation for exploiting oncoprotein dynamics to uncover therapeutic vulnerabilities in fusion-driven cancers. Beyond EwS, our results underscore the broader impact of oncoprotein dosage dynamics in cancers with otherwise quiet genomes. SIGNIFICANCEWe report EwS as a paradigm for the importance of oncogene fluctuations in tumor cell plasticity and disease progression. Effective therapeutic strategies must ensure complete EF depletion to prevent inadvertent metastasis.

cancer biology↗

bioRxiv2023

Single-cell RNA-seq differential expression tests within a sample should use pseudo-bulk data of pseudo-replicates

Single-cell RNA sequencing (scRNA-seq) has become a standard approach to investigate molecular differences between cell states. Comparisons of bioinformatics methods for the count matrix transformation (normalization) and differential expression (DE) analysis of these data have already highlighted recommendations for effective between-sample comparisons and visualization. Here, we examine two remaining open questions: (i) What are the best combinations of data transformations and statistical test methods, and (ii) how do pseudo-bulk approaches perform in single-sample designs? We evaluated the performance of 343 DE pipelines (combinations of eight types of count matrix transformations and ten statistical tests) on simulated and real-world data, in terms of precision, sensitivity, and false discovery rate. We confirm superior performance of pseudo-bulk approaches without prior transformation. For within-sample comparisons, we advise the use of three pseudo-replicates, and provide a simple R package DElegate to facilitate application of this approach.

bioinformatics↗

bioRxiv2022

Neuroblastoma-associated chromosomal aberrations drive cell identity loss in human neural crest via disruption of developmental regulators

Early childhood tumours arise from transformed embryonic cells, which often carry large copy number alterations (CNA). However, it remains unclear how CNAs contribute to embryonic tumourigenesis due to a lack of suitable models. Here we employ female human embryonic stem cell (hESC) differentiation and single-cell transcriptome and epigenome analysis to assess the effects of chromosome 17q/1q gains, which are prevalent in the embryonal tumour neuroblastoma (NB). We show that CNAs impair the specification of trunk neural crest (NC) cells and their sympathoadrenal derivatives, the putative cells-of-origin of NB. This effect is exacerbated upon overexpression of MYCN, whose amplification co-occurs with CNAs in NB. Moreover, CNAs potentiate the pro-tumourigenic effects of MYCN and mutant NC cells resemble NB cells in tumours. These changes correlate with a stepwise aberration of developmental transcription factor networks. Together, our results sketch a mechanistic framework for the CNA-driven initiation of embryonal tumours.

cancer biology↗

bioRxiv2019

Predicting cellular position in the Drosophila embryo from Single-Cell Transcriptomics data

Single-cell RNA-seq technologies are rapidly evolving but while very informative, in standard scRNAseq experiments the spatial organization of the cells in the tissue of origin is lost. Conversely, spatial RNA-seq technologies designed to keep the localization of the cells have limited throughput and gene coverage. Mapping scRNAseq to genes with spatial information increases coverage while providing spatial location. However, methods to perform such mapping have not yet been benchmarked. To bridge the gap, we organized the DREAM Single-Cell Transcriptomics challenge focused on the spatial reconstruction of cells from the Drosophila embryo from scRNAseq data, leveraging as gold standard genes with in situ hybridization data from the Berkeley Drosophila Transcription Network Project reference atlas. The 34 participating teams used diverse algorithms for gene selection and location prediction, while being able to correctly localize rare subpopulations of cells. Selection of predictor genes was essential for this task and such genes showed a relatively high expression entropy, high spatial clustering and the presence of prominent developmental genes such as gap and pair-ruled genes and tissue defining markers.

systems biology↗

bioRxiv2019

Inference of bacterial small RNA regulatory networks and integration with transcription factor driven regulatory networks

Small non-coding RNAs (sRNAs) are key regulators of bacterial gene expression. Through complementary base pairing, sRNAs affect messenger RNA stability and translation efficiency. Here, we describe a network inference approach designed to identify sRNA-mediated regulation of transcript levels. We use existing transcriptional datasets and prior knowledge to infer sRNA regulons using our network inference tool, the Inferelator. This approach produces genome-wide gene regulatory networks that include contributions by both transcription factors and sRNAs. We show the benefits of estimating and incorporating sRNA activities into network inference pipelines. We comprehensively assess the accuracy of inferred sRNA regulons using available experimental data. We uncover 30 novel experimentally supported sRNA-mRNA interactions in Escherichia coli, outperforming previous network-based efforts. Our findings expand the role of sRNAs in the regulation of chemotaxis, oxidation-reduction processes, galactose intake, and generation of pyruvate. Additionally, our pipeline complements sequence-based sRNA-mRNA interaction prediction methods by adding a data-driven filtering step. Finally, we show the general applicability of our approach by identifying novel, experimentally supported, sRNA-mRNA interactions in Pseudomonas aeruginosa and Bacillus subtilis. Overall, our strategy generates novel insights into the functional implications of sRNA regulation in multiple bacterial species.\n\nIMPORTANCEIndividual bacterial genomes can have dozens of small non-coding RNAs with largely unexplored regulatory functions. Although bacterial sRNAs influence a wide range of biological processes, including antibiotic resistance and pathogenicity, our current understanding of sRNA-mediated regulation is far from complete. Most of the available information is restricted to a few well-studied bacterial species; and even in those species, only partial sets of sRNA targets have been characterized in detail. To close this information gap, we developed a computational strategy that takes advantage of available transcriptional data and knowledge about validated and putative sRNA-mRNA interactions. Our approach facilitates the identification of experimentally supported novel interactions while filtering out false positives. Due to its data-driven nature, our method emerges as an ideal strategy to identify biologically relevant interactions among lists of candidate sRNA-target pairs predicted in silico from sequence analysis or derived from sRNA-mRNA binding experiments.

systems biology↗

bioRxiv2019

Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression

Single-cell RNA-seq (scRNA-seq) data exhibits significant cell-to-cell variation due to technical factors, including the number of molecules detected in each cell, which can confound biological heterogeneity with technical effects. To address this, we present a modeling framework for the normalization and variance stabilization of molecular count data from scRNA-seq experiments. We propose that the Pearson residuals from regularized negative binomial regression, where cellular sequencing depth is utilized as a covariate in a generalized linear model, successfully remove the influence of technical characteristics from downstream analyses while preserving biological heterogeneity. Importantly, we show that an unconstrained negative binomial model may overfit scRNA-seq data, and overcome this by pooling information across genes with similar abundances to obtain stable parameter estimates. Our procedure omits the need for heuristic steps including pseudocount addition or log-transformation, and improves common downstream analytical tasks such as variable gene selection, dimensional reduction, and differential expression. Our approach can be applied to any UMI-based scRNA-seq dataset and is freely available as part of the R package sctransform, with a direct interface to our single-cell toolkit Seurat.

genomics↗

bioRxiv2018

Comprehensive integration of single cell data

Single cell transcriptomics (scRNA-seq) has transformed our ability to discover and annotate cell types and states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, including high-dimensional immunophenotypes, chromatin accessibility, and spatial positioning, a key analytical challenge is to integrate these datasets into a harmonized atlas that can be used to better understand cellular identity and function. Here, we develop a computational strategy to \"anchor\" diverse datasets together, enabling us to integrate and compare single cell measurements not only across scRNA-seq technologies, but different modalities as well. After demonstrating substantial improvement over existing methods for data integration, we anchor scRNA-seq experiments with scATAC-seq datasets to explore chromatin differences in closely related interneuron subsets, and project single cell protein measurements onto a human bone marrow atlas to annotate and characterize lymphocyte populations. Lastly, we demonstrate how anchoring can harmonize in-situ gene expression and scRNA-seq datasets, allowing for the transcriptome-wide imputation of spatial gene expression patterns, and the identification of spatial relationships between mapped cell types in the visual cortex. Our work presents a strategy for comprehensive integration of single cell data, including the assembly of harmonized references, and the transfer of information across datasets.\n\nAvailability: Installation instructions, documentation, and tutorials are available at: https://www.satijalab.org/seurat

genomics↗

bioRxiv2017

Large-scale simultaneous measurement of epitopes and transcriptomes in single cells.

Recent high-throughput single-cell sequencing approaches have been transformative for understanding complex cell populations, but are unable to provide additional phenotypic information, such as protein levels of cell-surface markers. Using oligonucleotide-labeled antibodies, we integrate measurements of cellular proteins and transcriptomes into an efficient, sequencing-based readout of single cells. This method is compatible with existing single-cell sequencing approaches and will readily scale as the throughput of these methods increase.

genomics↗

bioRxiv2017

Inhibitory neuron diversity originates from cardinal classes shared across germinal zones.

Diverse subsets of cortical interneurons play a particularly important role in the stability of the neural circuits underlying cognitive and higher order brain functions, yet our understanding of how this diversity is generated is far from complete. We applied massively parallel single-cell RNA-seq to profile a developmental time course of interneuron development, measuring the transcriptomes of over 60,000 progenitors during their maturation in the ganglionic eminences and embryonic migration into the cortex. While diversity within mitotic progenitors is largely driven by cell cycle and differentiation state, we observed sparse eminence-specific transcription factor expression, which seeds the emergence of later cell diversity. Upon becoming postmitotic, cells from all eminences pass through one of three precursor states, one of which represents a cortical interneuron ground state. By integrating datasets across developmental timepoints, we identified transcriptomic heterogeneity in interneuron precursors representing the emergence of four cardinal classes (Pvalb, Sst, Id2 and Vip), which further separate into subtypes at different timepoints during development. Our analysis revealed that the ASD-associated transcription factor Mef2c discriminates early Pvalb-precursors in E13.5 cells, and removal of Mef2c confirms its essential role for Pvalb interneuron development. These findings shed new light on the molecular diversification of early inhibitory precursors, and suggest gene modules that may link developmental specification with the etiology of neuropsychiatric disorders.

neuroscience↗