Nils Blüthgen’s research while affiliated with Charité Universitätsmedizin Berlin and other places

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Publications (282)


Diagnosing recipient- vs. donor-derived posttransplant myelodysplastic neoplasm via targeted single-cell mutational profiling
  • Article

December 2024

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12 Reads

Med

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Lam Giang Vuong

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Samantha D. Praktiknjo

RUCova: R emoval of U nwanted Cova riance in mass cytometry data

November 2024

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3 Reads

Bioinformatics

Motivation High dimensional single-cell mass cytometry data are confounded by unwanted covariance due to variations in cell size and staining efficiency, making analysis and interpretation challenging. Results We present RUCova, a novel method designed to address confounding factors in mass cytometry data. RUCova removes unwanted covariance from measured markers applying multivariate linear regression based on Surrogates of sources Unwanted Covariance (SUCs) and principal component analysis (PCA). We exemplify the use of RUCova and show that it effectively removes unwanted covariance while preserving genuine biological signals. Our results demonstrate the efficacy of RUCova in elucidating complex data patterns, facilitating the identification of activated signalling pathways, and improving the classification of important cell populations such as apoptotic cells. By providing a robust framework for data normalization and interpretation, RUCova enhances the accuracy and reliability of mass cytometry analyses, contributing to advances in our understanding of cellular biology and disease mechanisms. Availability and implementation The R package is available on https://github.com/molsysbio/RUCova. Detailed documentation, data, and the code required to reproduce the results are available on https://doi.org/10.5281/zenodo.10913464. Supplementary information Available at Bioinformatics online (PDF).


Subcellular mRNA kinetic modeling reveals nuclear retention as rate-limiting

November 2024

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7 Reads

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2 Citations

Molecular Systems Biology

Eukaryotic mRNAs are transcribed, processed, translated, and degraded in different subcellular compartments. Here, we measured mRNA flow rates between subcellular compartments in mouse embryonic stem cells. By combining metabolic RNA labeling, biochemical fractionation, mRNA sequencing, and mathematical modeling, we determined the half-lives of nuclear pre-, nuclear mature, cytosolic, and membrane-associated mRNAs from over 9000 genes. In addition, we estimated transcript elongation rates. Many matured mRNAs have long nuclear half-lives, indicating nuclear retention as the rate-limiting step in the flow of mRNAs. In contrast, mRNA transcripts coding for transcription factors show fast kinetic rates, and in particular short nuclear half-lives. Differentially localized mRNAs have distinct rate constant combinations, implying modular regulation. Membrane stability is high for membrane-localized mRNA and cytosolic stability is high for cytosol-localized mRNA. mRNAs encoding target signals for membranes have low cytosolic and high membrane half-lives with minor differences between signals. Transcripts of nuclear-encoded mitochondrial proteins have long nuclear retention and cytoplasmic kinetics that do not reflect co-translational targeting. Our data and analyses provide a useful resource to study spatiotemporal gene expression regulation.


Parallel Sequencing of Transposons and Transcriptomes in Single Cells for Genome-Wide Cancer Gene Discovery

November 2024

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9 Reads

Blood

Introduction Genome-wide transposon mutagenesis screens in mice are powerful tools for identifying genomically, transcriptionally, and epigenetically deregulated cancer genes in a single experimental approach. A major limitation, however, is the need for large cohort sizes and substantial sequencing and bioinformatic resources to differentiate causal genetic changes from silent transposon insertions. We address these challenges with single cell PiggyBac Transposon and Transcriptome Sequencing (scPB&T-seq), a novel dual-assay that enables the identification of disrupted molecular networks at the single cell level, thus improving precision and efficacy of cancer gene studies. Methods A murine PiggyBac (PB) transposition screen was performed to generate Asxl1-mutated (Asxl1mut) acute myeloid leukemias (AMLs), a subgroup with particularly poor prognosis. For scPB&T-seq, single cells (sc) from three AMLs were isolated, and genomic DNA was physically separated from mRNA. Sc transposon insertions were amplified via multiple displacement followed by a custom PCR amplification and sequenced alongside sc transcriptomes derived by Smart-Seq2. The data was then integrated with high-throughput droplet-based scRNAseq from the same samples. Results Activating transposon mutagenesis in an Asxl1mut background led to the identification of a large candidate gene catalogue for Asxl1mut AML pathogenesis. Using scPB&T-seq we identified an average of 10-25 transposons per cell, aligning with expected frequencies. All insertions were confirmed in concurrent bulk insertion analyses. About 60% of the insertions impacted gene expression and were retained for further analysis. By integrating scRNAseq data, the insertions were assigned to specific cellular clusters, allowing the reconstruction of clonal evolution and identification of intratumoral heterogeneity. This approach confirmed known AML drivers co-occurring with ASXL1 mutations, such as PRDM16 and MECOM, and revealed novel genomic loci linked to AML pathogenesis. These findings were validated in whole genome and whole transcriptome sequencing data from 774 human AML patients generated within the Munich Leukemia Laboratory 5K Genomes Project. Interestingly, 45 (5.9%) patients from this dataset exhibited a transcriptional profile similar to the main clone of an Asxl1mut PB-AML, showcasing an upregulation of e.g., DOCK5, TCF4, and CSF1. Furthermore, this subgroup of patients displayed a significant enrichment for ASXL1 (31.1% vs. 14.5%, P = 0.005), NRAS (28.9% vs. 15.6%, P = 0.035) and concurrent ASXL1 and NRAS (13.3% vs. 3%, P = 0.004) mutations compared to the remaining 724 AML patients. Of note, an insertion in Nras was an activating insertion in a monocytic-like subclone of this Asxl1mut PB-AML. Conclusion In this study we developed scPB&T-seq, an innovative method to analyze transposon screens that uncovers functional mechanisms behind individual transposon insertions in single cells of heterogenous tumors. This enables the assessment of clonal evolution and oncogenic networks in unprecedented detail, thereby enhancing our understanding of AML pathogenesis and potentially guiding the development of targeted therapies.


Rewired type I IFN signaling is linked to age-dependent differences in COVID-19

October 2024

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36 Reads

Advanced age is the most important risk factor for severe disease or death from COVID-19, but a thorough mechanistic understanding of the molecular and cellular underpinnings is lacking. Multi-omics analysis of samples from SARS-CoV-2 infected persons aged 1 to 84 years, revealed a rewiring of type I interferon (IFN) signaling with a gradual shift from signal transducer and activator of transcription 1 (STAT1) to STAT3 activation in monocytes, CD4 ⁺ T cells and B cells with increasing age. Diversion of interferon IFN signaling was associated with increased expression of inflammatory markers, enhanced release of inflammatory cytokines, and delayed contraction of infection-induced CD4 ⁺ T cells. A shift from IFN-responsive germinal center B (GCB) cells towards CD69 high GCB and atypical B cells corresponded to the formation of IgA in children while complement fixing IgG was dominant in adults. Our data provide a mechanistic basis for inflammation-prone responses to infections and associated pathology during aging. Abstract Figure


Discovery of novel feedback and crosstalk structures in a BCR-driven signaling network by perturbation data-based modeling of BL-2 cells
(A) Systematic perturbation data shown as log2 fold changes to solvent control DMSO. Data was generated by pre-treating BL-2 cells with inhibitors targeting key effectors downstream of BCR for 3h with subsequent BCR stimulation using α-IgM for 30 min (upper panel) or no stimulation as consistency check (lower panel). Phosphorylation of indicated signaling proteins (cf. D) was measured using bead-based ELISAs (mean, n = 3). (B) Modeling workflow using the Modular Response Analysis-based method STASNet to derive a semi-quantitative directed network. The model requires systematic perturbation data (depicted in A) and a curated literature network as starting network (cf. D). In order to avoid overfitting, the data was split into two parts: (1) α-IgM stimulated data was used for parameter fitting and network adjustment and (2) unstimulated inhibitor data was used for verifying model consistency. After each network adjustment step the unseen data part was simulated and compared. If the error reduction as compared to the null model was not significantly worse, the new network adjustment was upheld otherwise the next best solution was simulated and tested. (C) Model performance for each modeling step from the literature-derived starting model to the final model: (TOP) goodness of fit as weighted sum squared residuals divided by number of free parameters and (BOTTOM) consistency check step as percentage of error reduction compared to unperturbed control as null model (see S1 Text BL-2_network_model.html: Tab ‘Network derivation BL-2’). (D) Literature network adjusted to the final signaling network for BL-2 cells derived by the modeling pipeline depicted in B. grey line/text—removed links/nodes; green line/text—added links.
BL-2-derived modeling structure can be transferred to cell line BL-41
(A) Systematic perturbation data for BL-41 cells generated alike the procedure described in Fig 1A (mean, n = 3). (B) Model development statistics (TOP) goodness of fit as reduced chi-square and (BOTTOM) unseen data consistency check as percentage of error reduction compared to unperturbed control as null model for each modeling step from the literature-derived starting model (black and grey arrows in Fig 1D) to the final model (grey–reduction, green—extension). See S1 Text BL-41_network_model.html: Tab ‘Network derivation BL-41’. (C) Venn diagram indicating the shared and not shared structural adjustments in the development of BL-2 and BL-41 cells starting from the same literature network (cf. S3 Fig). (D) Model fit and consistency check statistics for fitted models on BL-2 and BL-41 perturbation data for three different network structures: literature, cell-specific adjusted network (adjusted) and for the best-found structure of the respective other cell line (transfer). See also S2 and S4 Figs. (E) Network coefficients heatmap from models fitted to the BL-2 learned structure for the indicated cell lines. Comparability was ensured by fixing the inhibitor coefficients to BL-2-learned values as both cells received the same inhibitor doses. Stars denote coefficients that are significantly different (i.e., 95%-point wise confidence intervals do not overlap, see S1 Table). (F) Data excerpt for the model-derived negative crosstalk prediction from p38 to RAF/MEK/ERK pathway in BL-2 and BL-41 cells showing the upregulation of α-IgM-induced activation of pERK and pMEK by the p38 inhibitor SB203580 (mean ± s.e.m., n = 3), but no upregulation by p38 inhibitor alone.
Increased MEK/ERK-pathway activity in BCR-activated B cell lines after p38 intervention
(A) Changes in the phosphorylation of MEK and ERK in BL-2 cells after treatment with α-IgM in the presence or absence of the p38 inhibitor SB203580. (B) Phosphorylation of ERK is further increased in α-IgM-treated BL-2 cells after 24h of p38α (MAPK14) knockdown. (C) Phosphorylation of ERK is enriched within the nucleus of α-IgM treated BL-2 which is further enhanced by inhibiting p38. Tubulin and HDAC1 were used as reference for the cytosolic and nuclear fraction, respectively. (D) (TOP) Phosphorylation of c-RAF at serine-residues 289/296/301 is increased after 30 min α-IgM treatment in BL-41 cells but not affected by p38 inhibition. The inhibition of MEK using AZD6244 interrupts the phosphorylation of RAF. Representative Western blot. (BOTTOM) Bar plots quantifying c-RAF phosphorylation measurements for n = 2 replicates. (E) p38 affects the MEK/ERK pathway in a comparable way in different BL cell lines after α-IgM treatment. Shown are phosphorylations of Raf1 at serine 338 (activatory site) and 289/296/301 (ERK feedback sites) as well as of ERK and p38 in Burkitt lymphoma cells BL-2, BL-41 and CA-46.
Phosphoproteomics analysis supports the BCR-signaling model and reveals a dominant effect of the PI3K pathway inhibition onto BCR-signaling in BL-2 cells
Analysis of Tandem-Mass-Tag (TMT) Mass spectrometry measurements for BL-2 cells treated with α-IgM and inhibitor solvent DMSO or inhibitors of PI3K (BKM120), MTORC1 (Rapamycin) or p38 (SB203580) (n = 2). (A) Hierarchical clustering of 3000 most varying phosphosites demonstrates a global effect of α-IgM and PI3K inhibitor BKM120 on the phosphoproteome and subtle effects of the remaining inhibitors. (B) Principal component analysis shows that α-IgM effect is governing the principal components 1 and 2. Only PI3Ki treatment is able to partly revert the α-IgM effect. (C) Overlap of differentially regulated phosphosites (limma, FDR≤5%) for indicated selected comparisons. (D) Upstream kinase activity assessment on base of log2 fold changes (vs. α-IgM+DMSO) in PhosphoSitePlus-annotated target sites (Nov 2021) for selected kinases. Significance asserted by two-sided t-test: ns—not significant; * - 0.05; ** - 0.01; *** - 0.001; **** - 0.0001); Average value indicated. (E). Phosphosites significantly regulated by p38 inhibitor SB203580 (limma, FDR≤5%). Left panel denotes which site was found to be significantly regulated (blue—down; red–up) by the indicated comparison. Sites are annotated as follows: ‘HGNC symbol’_’amino acid’_’position’_’number. of phosphosites’; ERK activation sites and known target sites of ERK [55] are indicated by green and orange circles, respectively.
BL-2-derived network structure sets a veritable starting base to develop networks for DLBCL cell lines HBL-1 and OCI-LY3
(A) Systematic perturbation data of bead-based ELISA measurements of the DLBCL cell lines HBL-1 and OCI-LY3 quantified as log2 fold changes to solvent control (DMSO); mean of n = 3. (B) Goodness of fit expressed as reduced chi-square statistic Xr on selected network structures for the two DLBCL cell lines HBL-1 and OCI-LY3. literature–network from Fig 1B; BL-2 –BL-2 network derived from Fig 1C; adjusted from BL-2 –BL-2 network locally adjusted to respective DLBCL cell line; OCI-LY3/HBL-1 –final adjusted network of respective other DLBCL cell line (see C). (C) Network structures of BL-2 derived starting network and final DLBC-specific networks trained on HBL-1 and OCI-LY3 data (see A). (D) Side-by-side comparison of the model coefficient (path)s (log scale) with fixed inhibitor strengths set to mean of both cell line models. Empty tiles indicate missing links in one of the cell lines (individual links). Asterisks point to non-overlapping confidence intervals as estimated by STASNet profile likelihood function (see S2 Table).

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Quantitative modeling of signaling in aggressive B cell lymphoma unveils conserved core network
  • Article
  • Full-text available

October 2024

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30 Reads

B cell receptor (BCR) signaling is required for the survival and maturation of B cells and is deregulated in B cell lymphomas. While proximal BCR signaling is well studied, little is known about the crosstalk of downstream effector pathways, and a comprehensive quantitative network analysis of BCR signaling is missing. Here, we semi-quantitatively modelled BCR signaling in Burkitt lymphoma (BL) cells using systematically perturbed phosphorylation data of BL-2 and BL-41 cells. The models unveiled feedback and crosstalk structures in the BCR signaling network, including a negative crosstalk from p38 to MEK/ERK. The relevance of the crosstalk was verified for BCR and CD40 signaling in different BL cells and confirmed by global phosphoproteomics on ERK itself and known ERK target sites. Compared to the starting network, the trained network for BL-2 cells was better transferable to BL-41 cells. Moreover, the BL-2 network was also suited to model BCR signaling in Diffuse large B cell lymphoma cells lines with aberrant BCR signaling (HBL-1, OCI-LY3), indicating that BCR aberration does not cause a major downstream rewiring.

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High‐confidence calling of normal epithelial cells allows identification of a novel stem‐like cell state in the colorectal cancer microenvironment

July 2024

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45 Reads

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2 Citations

Single‐cell analyses can be confounded by assigning unrelated groups of cells to common developmental trajectories. For instance, cancer cells and admixed normal epithelial cells could adopt similar cell states thus complicating analyses of their developmental potential. Here, we develop and benchmark CCISM (for Cancer Cell Identification using Somatic Mutations) to exploit genomic single nucleotide variants for the disambiguation of cancer cells from genomically normal non‐cancer cells in single‐cell data. We find that our method and others based on gene expression or allelic imbalances identify overlapping sets of colorectal cancer versus normal colon epithelial cells, depending on molecular characteristics of individual cancers. Further, we define consensus cell identities of normal and cancer epithelial cells with higher transcriptome cluster homogeneity than those derived using existing tools. Using the consensus identities, we identify significant shifts of cell state distributions in genomically normal epithelial cells developing in the cancer microenvironment, with immature states increased at the expense of terminal differentiation throughout the colon, and a novel stem‐like cell state arising in the left colon. Trajectory analyses show that the new cell state extends the pseudo‐time range of normal colon stem‐like cells in a cancer context. We identify cancer‐associated fibroblasts as sources of WNT and BMP ligands potentially contributing to increased plasticity of stem cells in the cancer microenvironment. Our analyses advocate careful interpretation of cell heterogeneity and plasticity in the cancer context and the consideration of genomic information in addition to gene expression data when possible.



RUCova: Removal of Unwanted Covariance in mass cytometry data

May 2024

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30 Reads

High dimensional mass cytometry is confounded by unwanted covariance due to variations in cell size and staining efficiency, making analysis and interpretation challenging. We present RUCova, a novel method designed to address confounding factors in mass cytometry data. RUCova removes unwanted covariance using multivariate linear regression on Surrogates of Unwanted Covariance (SUCs), and Principal Component Analysis (PCA). We exemplify the use of RUCova and show that it effectively removes unwanted covariance while preserving genuine biological signals. Our results demonstrate the efficacy of RUCova in elucidating complex data patterns, facilitating the identification of activated signalling pathways, and improving the classification of important cell populations. By providing a robust framework for data normalization and interpretation, RUCova enhances the accuracy and reliability of mass cytometry analyses, contributing to advancements in our understanding of cellular biology and disease mechanisms. The R package is available on https://github.com/molsysbio/RUCova . Detailed documentation, data, and the code required to reproduce the results are available on https://doi.org/10.5281/zenodo.10913464 . Supplementary material: Available at bioRxiv.


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Spike-in enhanced phosphoproteomics uncovers synergistic signaling responses to MEK inhibition in colon cancer cells

May 2024

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21 Reads

Targeted kinase inhibitors are a cornerstone of cancer therapy, but their success is often hindered by the complexity of cellular signaling networks that can lead to resistance. Overcoming this challenge necessitates a deep understanding of cellular signaling responses. While standard global phosphoproteomics offers extensive insights, lengthy processing times, the complexity of data interpretation, and frequent omission of crucial phosphorylation sites limit its utility. Here, we combine data-independent acquisition (DIA) with spike-in of synthetic heavy stable isotope-labeled phosphopeptides to facilitate the targeted detection of particularly informative phosphorylation sites. Our spike-in enhanced detection in DIA (SPIED-DIA) approach integrates the improved sensitivity of spike-in-based targeted detection with the discovery potential of global phosphoproteomics into a simple workflow. We employed this method to investigate synergistic signaling responses in colorectal cancer cell lines following MEK inhibition. Our findings highlight that combining MEK inhibition with growth factor stimulation synergistically activates JNK signaling in HCT116 cells. This synergy emphasizes the therapeutic potential of concurrently targeting MEK and JNK pathways, as evidenced by the significantly impaired growth of HCT116 cells when treated with both inhibitors. Our results demonstrate that SPIED-DIA effectively identifies synergistic signaling responses in colorectal cancer cells, presenting a valuable tool for uncovering new therapeutic targets and strategies in cancer treatment.


Citations (55)


... For example, transient transcriptome sequencing (Schwalb, et al., 2016) has been extended to improve studies of nascent and short-lived RNAs (Reichholf, et al., 2019;Schofield, et al., 2018). Similarly, nucleotide recoding has been combined with Start-seq to study the kinetics of promoter-proximal pausing (Nechaev, et al., 2010;Zimmer, et al., 2021), Ribo-seq to study the kinetics of translation initiation and elongation (Ingolia, et al., 2009;Schott, et al., 2021), PacBio long read sequencing to improve studies of transcript isoform dynamics (Rahmanian, et al., 2020;Rhoads and Au, 2015), subcellular fractionation to better understand RNA flow between subcellular compartments (Ietswaart, et al., 2024;Müller, et al., 2024;Steinbrecht, et al., 2024), and tethering experiments to probe small molecule binding sites in the transcriptome (Moon, et al., 2024). Nucleotide recoding has also been combined with a number of scRNA-seq protocols to facilitate probing synthesis and degradation kinetics in individual cells (Cao, et al., 2020;Erhard, et al., 2019;Hendriks, et al., 2019;Lin, et al., 2023;Maizels, et al., 2024;Qiu, et al., 2020) and promises to significantly improve analyses of RNA velocity by providing an alternative to using the potentially biased or non-existent intronic coverage at key regulatory genes (Maizels, et al., 2024;Qiu, et al., 2022;Weiler, et al., 2024). ...

Reference:

Expanding and improving analyses of nucleotide recoding RNA-seq experiments with the EZbakR suite
Subcellular mRNA kinetic modeling reveals nuclear retention as rate-limiting
  • Citing Article
  • November 2024

Molecular Systems Biology

... In application of the 3R principle, no separate animals were sacrificed for the purpose of this study. Instead, we re-used data (Syrian hamsters 35 ) and blood samples (Roborovski hamsters 36,37 ) from animals studied in our previously published research. As part of these experiments, 10-to 12-week-old female and male Syrian hamsters (M. ...

Single-cell-resolved interspecies comparison shows a shared inflammatory axis and a dominant neutrophil-endothelial program in severe COVID-19
  • Citing Article
  • June 2024

Cell Reports

... While genetic screens with exon inclusion resolution are limited and often heterogeneous, the availability of large-scale perturbation screens with single-cell transcriptomics outputs, such as Perturb-seq, is rapidly expanding [10][11][12] . These assays provide the throughput and causal connections necessary for systematically investigating splicing factor regulation 13 . ...

scPerturb: harmonized single-cell perturbation data

Nature Methods

... These studies also connect experimental observations to dynamic parameters, giving an insight even when detailed mechanisms remain unclear. Recent work has extended these approaches to a cancer context, though different from the case of persisters we address here [20], highlighting the potential of these tools in guiding clinical treatment strategies. This framework raises several key considerations regarding the design of optimal treatment protocols. ...

On minimising tumoural growth under treatment resistance
  • Citing Article
  • December 2023

Journal of Theoretical Biology

... To get a more fine-grained semi-quantitative understanding of the signaling network, we therefore decided to employ a modeling approach on the more information rich readout of phosphorylation data after systematic pathway perturbations which contains more predictive power [37]. We have previously developed an approach termed STeady-STate Analysis of Signaling Networks (STASNet) that is based on Modular Response Analysis (MRA) [38] and applied this tool to decipher EGFR/RAS signaling in different tumors [26,[39][40][41][42] as well as to compare mouse embryonic stem cells with different sex chromosome compositions [43]. The main concept of MRA is that the measurable global response matrix R (e.g., log fold changes of steady state measurements before and after systematic single node perturbations for every node of the network) theoretically contains the information to derive the so-called local response r, a matrix whose non-zero entries quantify the edges of the underlying network structure which are called local response coefficients (see Material and Methods). ...

Modeling unveils sex differences of signaling networks in mouse embryonic stem cells
  • Citing Article
  • September 2023

Molecular Systems Biology

... These findings imply that elevated levels of IL1B expression may be closely associated with DL-BCL progression and adverse outcomes. Given previous studies identifying macrophages as primary sources of IL1B [45], we obtained macrophages from the blood of both patients with DLBCL and healthy individuals. qPCR experiments confirmed the heightened expression of IL1B in DL-BCL patients (Fig. 5D), further highlighting its significance in DLBCL. ...

In Silico Analysis Predicts Nuclear Factors NR2F6 and YAP1 as Mesenchymal Subtype-Specific Therapeutic Targets for Ovarian Cancer Patients

... It is used widely in immunology research to quantify surface proteins and classify immune cells [Spitzer and Nolan, 2016;Bendall et al., 2011;Horowitz et al., 2013;Giesen et al., 2014;Georg et al., 2022]. Mass cytometry is also increasingly used to study intracellular signalling pathways by measuring phospho-protein abundance, providing insights into diverse cellular processes such as the differentiation pathways of colorectal cancer [Brandt et al., 2019;Sell et al., 2023], organoid heterogeneity [Sufi et al., 2021], acute myeloid leukaemia stem/progenitor cells [Han et al., 2015] and prediction of drug sensitivity in breast cancer [Tognetti et al., 2021]. While the distributions of surface proteins typi-cally show a bimodal pattern, those of intracellular signalling markers show a unimodal distribution with rather small quantitative shifts in response to perturbations. ...

Oncogenic signaling is coupled to colorectal cancer cell differentiation state

... Syrian hamsters (7-9 weeks old) were vaccinated intramuscularly (i.m.) with 2 µg (n = 10) or 5 µg (n = 6) Raxtozinameran (formulated in 20 µL and 50 µL of Comirnaty ® , respectively) on day 0 and received a booster vaccination of the same dosing on day 20. Actual vaccine doses were chosen according to a previously established range in small animal models [22,23]. Control cohorts received 2 doses of 10 4 PFU YF-S0*-vaccine [21] (n = 3, 9 weeks old; n = 3, 20 weeks old) or MEM (Gibco) supplemented with 2% FCS (HyClone TM ) (Sham) (n = 2, 9 weeks old; n = 3, 20 weeks old) intraperitoneally (i.p.) on the same days. ...

Live-attenuated vaccine sCPD9 elicits superior mucosal and systemic immunity to SARS-CoV-2 variants in hamsters

Nature Microbiology

... Missing values were set to (0, 0) 50 . This method has been used previously in comparable biomedical settings 51,52 . Feature expansion was only applied to variables that had missing values. ...

Single-cell gene regulatory network prediction by explainable AI

Nucleic Acids Research