Christoph Hafemeister

• PhD
• PostDoc at Children's Cancer Research Institute

Ewing sarcoma (EwS) is a highly aggressive pediatric cancer driven by the EWS::FLI1 (EF) fusion oncoprotein. Emerging evidence suggests that variations in EF expression levels may contribute to tumor cell plasticity, promoting treatment resistance and relapse. Both tumor intrinsic and extrinsic influences such as microenvironmental and therapy rela...

Metastasis is the primary cause of mortality in pediatric cancer, with the lung being a common site for secondary tumor growth. A major challenge in treatment is the lack of models that accurately replicate the interactions between tumors and the lung metastatic niche observed in patients. This limitation hinders the investigation of the molecular...

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...

Langerhans cell histiocytosis (LCH) is a clonal hematopoietic disorder defined by tumorous lesions containing CD1a+/CD207+ cells. Two severe complications of LCH are systemic hyperinflammation and progressive neurodegeneration. The scarcity of primary samples and lack of appropriate models limit our mechanistic understanding of LCH pathogenesis and...

Secondary lymphoid organs (SLOs) provide the confined microenvironment required for stromal cells to interact with immune cells to initiate adaptive immune responses resulting in B cell differentiation. Here, we studied three patients from two families with functional hyposplenism, absence of tonsils, and complete lymph node aplasia, leading to rec...

Ewing sarcoma is a highly aggressive pediatric cancer driven by the EWS::FLI1 (EF) fusion oncogene. Evidence suggests that EF expression levels dictate tumor cell plasticity contributing to treatment resistance and relapse. Our study aims to elucidate the transcriptional programs and phenotypes associated with distinct EF thresholds. To dynamically...

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 analys...

Ewing sarcoma (ES) is an aggressive pediatric bone tumor in adolescents primarily driven by the expression of the fusion oncogene EWS::FLI1. Although the etiology of ES remains controversial, human mesenchymal stem cells (hMSCs) are considered to be the likely cell of origin. This is not only due to their ability to tolerate ectopic EWS::FLI1 expre...

Metastasis remains the main cause of death in pediatric cancer patients with the lung being one of the most common affected organs. Our inability to cure these patients is largely due to insufficient knowledge about tumor and metastatic niche plasticity, while existing models are not fully effective in mirroring the response seen in clinical settin...

The NFAT family of transcription factors plays central roles in adaptive immunity in murine models, however, their contribution to human immune homeostasis remains poorly defined. In a multigenerational pedigree, we identified three patients carrying germline biallelic missense variants in NFATC1, presenting with recurrent infections, hypogammaglob...

Early childhood malignancies are driven by sparse genetic aberrations in oncogenes that often co-occur with large copy number variants (CNVs). The combination of these mutations is thought to transform developmentally pliant embryonic cells to initiate tumorigenesis. However, the mechanistic interactions between CNVs, oncogenes, and differentiation...

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 compar...

Early childhood tumours are thought to arise from transformed embryonic cells, which often carry large copy number variants (CNVs). However, it remains unclear how CNVs contribute to embryonic tumourigenesis due to a lack of suitable models. Here we employ human embryonic stem cell (hESC) differentiation to assess the effects of chromosome 17q/1q g...

Single-cell RNA-sequencing (scRNA-Seq) is a compelling approach to directly and simultaneously measure cellular composition and state, which can otherwise only be estimated by applying deconvolution methods to bulk RNA-Seq estimates. However, it has not yet become a widely used tool in population-scale analyses, due to its prohibitively high cost....

Individual bacterial genomes can have dozens of small noncoding 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 i...

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 d...

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 sp...

Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets to better understand cellular identity and function. Here,...

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 know...

Single-cell RNA-seq (scRNA-seq) data exhibits significant cell-to-cell variation due to technical factors, including the number of detected molecules 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 d...

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 posi...

Diverse subsets of cortical interneurons have vital roles in higher-order brain functions. To investigate how this diversity is generated, here we used single-cell RNA sequencing to profile the transcriptomes of mouse cells collected along a developmental time course. Heterogeneity within mitotic progenitors in the ganglionic eminences is driven by...

We report the results of a DREAM challenge designed to predict relative genetic essentialities based on a novel dataset testing 98,000 shRNAs against 149 molecularly characterized cancer cell lines. We analyzed the results of over 3,000 submissions over a period of 4 months. We found that algorithms combining essentiality data across multiple genes...

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 interneu...

High-throughput single-cell RNA sequencing has transformed our understanding of complex cell populations, but it does not provide phenotypic information such as cell-surface protein levels. Here, we describe cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq), a method in which oligonucleotide-labeled antibodies are used to in...

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...

Environmental Gene Regulatory Influence Networks (EGRINs) coordinate the timing and rate of gene expression in response to environmental signals. EGRINs encompass many layers of regulation, which culminate in changes in accumulated transcript levels. Here, we inferred EGRINs for the response of five tropical Asian rice (Oryza sativa) cultivars to h...

Environmental Gene Regulatory Influence Networks (EGRINs) coordinate the timing and rate of gene expression in response to environmental and developmental signals. EGRINs encompass many layers of regulation, which culminate in changes in the level of accumulated transcripts. Here we infer EGRINs for the response of five tropical Asian rice cultivar...

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silic...

Detailed characteristics and models for all the clusters in the two-season analysis; results used for the comparison of the partial Nagano dataset with our analysis. DOI: http://dx.doi.org/10.7554/eLife.08411.011

Weather data for the dry and wet season experiments. DOI: http://dx.doi.org/10.7554/eLife.08411.012

Clustering, model selection for the two-season analysis and analysis of the partial Nagano dataset. DOI: http://dx.doi.org/10.7554/eLife.08411.013

Plants rely on transcriptional dynamics to respond to multiple climatic fluctuations and contexts in nature. We analyzed genome-wide gene expression patterns of rice (Oryza sativa) growing in rainfed and irrigated fields during two distinct tropical seasons and determined simple linear models that relate transcriptomic variation to climatic fluctua...

Organisms from all domains of life use gene regulation networks to control cell growth, identity, function, and responses to environmental challenges. Although accurate global regulatory models would provide critical evolutionary and functional insights, they remain incomplete, even for the best studied organisms. Efforts to build comprehensive net...

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Elucidation of transcriptional regulatory networks (TRNs) is a fundamental goal in biology, and one of the most important components of TRNs are transcription factors (TFs), proteins that specifically bind to gene promoter and enhancer regions to alter target gene expression patterns. Advances in genomic technologies as well as advances in computat...

Motivation: Experiments in animal models are often conducted to infer how humans will respond to stimuli by assuming that the same biological pathways will be affected in both organisms. The limitations of this assumption were tested in the IMPROVER Species Translation Challenge, where 52 stimuli were applied to both human and rat cells and pertur...

Motivation: Inferring global regulatory networks (GRNs) from genome-wide data is a computational challenge central to the field of systems biology. Although the primary data currently used to infer GRNs consist of gene expression and proteomics measurements, there is a growing abundance of alternate data types that can reveal regulatory interaction...

Post-transcriptional regulation of gene expression contributes to the protein output of a cell, however, methods for measuring translational regulation in complex in vivo systems are lacking. Here, we describe a sensitive method for measuring translational regulation in defined cell populations from heterogeneous tissue in vivo. We adapted the tran...

TE measurements in normal brain OPCs. (A) Distribution of mRNA expression in ribosome-bound and total RNA pools from normal brain OPCs identified differential ribosome recruitment efficiencies. (B) TE values for each biological replicate (black points) plotted with the average of the other three replicates (red line) demonstrated reproducibility of...

TE measurements in PDGF-driven glioma with PTEN deleted. (A) Immunohistochemical staining of tumor sections demonstrated the absence of PTEN protein and increased p-S6 positivity in mice receiving Cre virus in addition to PDGF. (B) Western blot of tumor lysates from mouse glioma demonstrates increased phosphorylation of Akt (T308) and S6 (S235/236)...

Hierachical clustering of normalized microarray expression data from (A) total cellular RNA and (B) ribosome-bound RNA extracted from Olig2+cells in normal brain and PDGF-driven glioma demonstrates the high degree of similarity of PTEN-expressing and PTEN-deleted tumors compared to normal brain OPCs. Hierachical clustering performed on transcripts...

PDGF-driven glioma; IP, Total and TE values. (XLS)

PTEN+/+ PDGF-driven glioma compared to PTEN−/− PDGF-driven glioma; IP, Total and TE values. (XLS)

In vivo quantification of ribosome-bound and total RNA levels in PDGF-driven mouse glioma. (A) Olig2-eGFP-L10a expression faithfully reported endogenous Olig2 expression in tumor. (B) Xist mRNA was abundant in total RNA from tumors generated in female mice, but absent from the ribosome-bound fraction (SD bars shown). (TIF)

Multiple probes against the same gene identified similar TE measurements across the full range of expression values in total RNA. Average percent difference in measures TE between probe and gene average were binned by expression value in total RNA for (A) PDGF-driven mouse glioma, (B) normal brain OPCs and (C) PDGF-driven mouse glioma with PTEN del...

PDGF-driven glioma compared to normal brain OPCs; IP, Total and TE values. (XLS)

Analyzing short time-courses is a frequent and relevant problem in molecular biology, as, for example, 90% of gene expression time-course experiments span at most nine time-points. The biological or clinical questions addressed are elucidating gene regulation by identification of co-expressed genes, predicting response to treatment in clinical, tri...

For designing oligonucleotide tiling arrays popular, current methods still rely on simple criteria like Hamming distance or longest common factors, neglecting base stacking effects which strongly contribute to binding energies. Consequently, probes are often prone to cross-hybridization which reduces the signal-to-noise ratio and complicates downst...

Personalized medicine based on molecular aspects of diseases, such as gene expression profiling, has become increasingly popular. However, one faces multiple challenges when analyzing clinical gene expression data; most of the well-known theoretical issues such as high dimension of feature spaces versus few examples, noise and missing data apply. S...

The regulation of proliferation and differentiation of embryonic and adult stem cells into mature cells is central to developmental biology. Gene expression measured in distinguishable developmental stages helps to elucidate underlying molecular processes. In previous work we showed that functional gene modules, which act distinctly in the course o...

The use of molecular aspects of diseases for clinical diagnosis, has be-come increasingly popular. There are several difficulties in analyzing clinical gene expression data; high dimension of feature spaces vs. few examples, noise and miss-ing data. We use constrained estimation of mixtures of hidden Markov models as a methodology to classify Multi...