Biology subjects

Sturm, G.

Publications and source records attributed to Sturm, G..

At least 19 recordsLinked to original sources

bioRxiv2026

The Energetic Cost of Adrenergic Signaling in Primary Human Fibroblasts

Stress involves the activation of cellular, physiological, and emotional processes that cost energy--nothing is free in biology. In mammals, the stress response involves hormone release, including norepinephrine (NE), which increases energy expenditure. To quantify the energetic cost of NE signaling in a simple cellular system, we interrogated the dose (0 - 10 M NE) and time-dependent (up to 10 hours) effects of adrenergic signaling in primary human fibroblasts. Oxygen consumption rates (OCR, reflecting ATP generated by mitochondria) and extracellular acidification rate (ECAR, reflecting ATP generated by glycolysis) were measured continuously using extracellular flux analysis, allowing us to estimate the ATP turnover rates, and thus cellular energy expenditure. Within the first 18 minutes (early phase), glycolysis increases up to 47% whereas respiration decreased 2-5%. Both parameters normalized within 1-2 hours for low NE concentrations. This was followed by an increase in oxidative phosphorylation (OxPhos), peaking around 9-12% by 2-6 hours (mid or late-phase). These minutes-to-hours data reveal the temporal dynamics whereby NE increases cellular energy expenditure in fibroblasts. Blocking OxPhos with oligomycin or piericidin A abolished OxPhos changes post-NE addition while conserving the glycolytic response. Withdrawal of glucose from the media significantly dampened the absolute rise in ECAR in response to NE, and instead increased OxPhos, revealing the metabolic flexibility in fibroblasts. Finally, cells with genetic defects impairing OxPhos exhibited a 50% blunted NE-driven metabolic response, consistent with the existence of an energy constraint in mitochondrial diseases. In summary, we have resolved the dynamics and flexible bioenergetic recalibrations associated with NE-driven hypermetabolism in primary human fibroblasts. Mapping the nature and magnitude of these recalibrations in humans would advance our understanding of the potential energetic forces underlying the damage to health by chronic stress.

cell biology↗

bioRxiv2026

Rectangle: robust and scalable multiscale deconvolution informed by single-cell RNA sequencing data

Bulk RNA-seq enables effective profiling of large cohorts and complex experimental designs, but current single-cell-informed deconvolution methods incompletely resolve closely related cell phenotypes, do not scale efficiently to large single-cell datasets, or fail to account for cellular content not represented in the reference. Here, we present Rectangle, an scverse Python framework for single-cell-informed deconvolution of bulk RNA-seq data. Rectangle combines multiscale deconvolution, capturing cellular composition across multiple resolution levels, with explicit modeling of unknown cellular content. In a diverse, cross-method benchmark, Rectangle achieved consistently strong performance across all evaluated metrics, demonstrating high accuracy, high resolution, low spillover, strong scalability and efficiency, and robustness to unknown cellular content. By bridging the resolution of single-cell transcriptomics with the scale and cost-efficiency of bulk RNA-seq, Rectangle enables cell-type and cell-state profiling at scale, supporting population-scale cellular biomarker discovery and tracking of cellular dynamics in settings impractical for comprehensive single-cell sequencing.

bioinformatics↗

bioRxiv2026

A multimodal perturbation atlas defines the phenotypic resolution of cellular morphology.

Modeling cellular behavior requires measurements that capture how cells evolve across time, environments, and interventions. Microscopy is uniquely suited to this goal: it is non-destructive and can be applied to living cells in their native context. Yet its phenotypic resolving power remains incompletely characterized relative to molecular assays. Here, we present a multimodal perturbation atlas of 1,000 pooled CRISPR knockouts in A549 cells, profiled by fluorescence microscopy (42 live, 13 fixed markers), label-free quantitative phase imaging of the same live cells (at single timepoints), and single-cell RNA sequencing (scRNA-seq). We develop deep learning frameworks to interpret the rich cell-biological signatures in these ~65M single-cell profiles. At matched reagent cost, phase imaging exceeds the phenotypic resolution of both fluorescence imaging and scRNA-seq, and more reliably recovers higher-order pathway organization. These results establish intrinsic morphology as a high-precision readout of cellular state, and lay a foundation for live-cell profiling of phenotypic trajectories.

systems biology↗

bioRxiv2026

Chlorophyll a degradation in Prokaryotes

Chlorophyll is one of the most abundant pigments on Earth. Although its degradation is well understood in plants, the role of prokaryotes in this process - despite their vast metabolic capabilities - remains unknown. Recent developments in the field of AI-predicted protein structures have opened new avenues for investigating functional homologies between evolutionary-distant organisms previously inaccessible through traditional sequence- or profile-based methods. Here, we present the first evidence of Chlorophyll a (Chl a) degradation by prokaryotes, discovered through a novel bioinformatic framework which bridges the gap across the domains of life via structural alignments of functionally characterised plant proteins, followed by structure similarity graph-based clustering. Metagenomic sequencing data was assembled and binned, yielding over 70,000 medium- to high-quality genomes in total, furthermore publicly available datasets containing genomes from prokaryotic isolates, metagenome-assembled genomes, as well as single-cell genomes were then mined for prokaryotic homologues of Chl a degradation genes. Our analysis revealed over 400 genomes from diverse taxonomic groups and habitats that possess a complete pathway, more than 50% stemming from isolates. Additionally, many other genomes harbour partial pathways, suggesting that Chl a degradation capabilities are globally widespread across diverse ecosystems. We then validated our in silico findings using the model organism Shewanella acanthi and confirmed its Chl a degradation capability via growth experiments, fluorescence spectroscopy and HPLC analyses. Our findings reveal a previously unrecognised pathway in prokaryotes, highlighting the power of structure-based remote homology detection for uncovering metabolic capabilities and evolutionary relationships.

microbiology↗

bioRxiv2025

Health or disease: a question of rhizomicrobial ecology? The case of Grapevine Trunk Disease

AimsThe incidence of the apoplectic breakdown associated with grapevine trunk diseases (GTDs) is promoted by climate change, which has become a challenge for viticulture worldwide. Outbreak of these conditional diseases is expected to depend on the rhizomicrobiome. However, the impact of the rhizomicrobiome on grapevine resilience has remained poorly understood, particularly regarding its ecological aspects. This study explores the link between GTDs, the rhizomicrobiome, and soil chemistry in vineyards along the Upper Rhine. MethodsUsing amplicon sequencing for both fungal and prokaryotic communities, we show that around half of the fungal rhizosphere community is endowed with pathotrophic potential, independently of the health status of the plant, including seventeen previously reported GTD-associated taxa, predominantly Black Foot Disease. ResultsIn contrast to fungi, bacterial diversity is shifted depending on the micronutrients Fe, Cu, Mn, and Zn. Moreover, taxa enriched in the rhizosphere of asymptomatic vines, such as Pseudophialocephala and Collarina for the mycobiome, and Caulobacter, Kitasatospora, and Entotheonellaceae for the bacteriome, showed correlations with soil properties. The most prominent feature associated with disease outbreaks was drastic changes of microbial co-occurrence networks. These were significantly increased in the fungi, especially for GTDs taxa, such as Fomitoporia, Stereum, Phaeomoniella, and Neofusicoccum. By contrast, there was a depletion of many bacteria and their microbial interactions under disease outbreak such as Isoptericola, Caulobacter, Rhodomicrobium and Thioprofundum. ConclusionThus, likely microbial interactions and not the mere presence of GTDs taxa explains disease outbreak. This finding opens new strategies for sustainable management of GTDs.

ecology↗

bioRxiv2025

IBDome: An integrated molecular, histopathological, and clinical atlas of inflammatory bowel diseases

Multi-omic and multimodal datasets with detailed clinical annotations offer significant potential to advance our understanding of inflammatory bowel diseases (IBD), refine diagnostics, and enable personalized therapeutic strategies. In this multi-cohort study, we performed an extensive multi-omic and multimodal analysis of 1,002 clinically annotated patients with IBD and non-IBD controls, incorporating whole-exome and RNA sequencing of normal and inflamed gut tissues, serum proteomics, and histopathological assessments from images of H&E-stained tissue sections. Transcriptomic profiles of normal and inflamed tissues revealed distinct site-specific inflammatory signatures in Crohns disease (CD) and ulcerative colitis (UC). Leveraging serum proteomics, we developed an inflammatory protein severity signature that reflects underlying intestinal molecular inflammation. Furthermore, foundation model-based deep learning accurately predicted histologic disease activity scores from images of H&E-stained intestinal tissue sections, offering a robust tool for clinical evaluation. Our integrative analysis highlights the potential of combining multi-omics and advanced computational approaches to improve our understanding and management of IBD.

molecular biology↗

bioRxiv2025

Representing Mitochondrial Dynamics with Abstract Algebra

This paper addresses the increasing need for comprehensive mathematical descriptions of cell organization by examining the algebraic structure of mitochondrial network dynamics. Mitochondria are cellular structures involved in metabolism that take the form of a network of membrane-based tubes that undergo continuous re-arrangement by a set of morphological processes, including fission and fusion, carried out by protein-based machinery. Because of their network structure, mitochondria can be represented as graphs, and the morphological operations that take place in the cell, referred to as mitochondrial dynamics, can be represented by changes to the graphs. Prior studies have classified mitochondrial graphs based on graph-theoretic features, but an alternative approach is to focus not on the graphs themselves but on the set of morphological operations inducing mitochondrial dynamics, since this may provide a simpler representation. Moreover, the operations are what determine the graphs that will be generated in a biological system. Here we show that mitochondrial dynamics constitute a groupoid that includes the automorphism group of each mitochondria graph. For multi-component mitochondria we define a graph structure that encapsulates the structure of mitochondrial dynamics. Using one of the morphological operations we can define an equivalence relation among mitochondrial graphs that allows us to replace the complex structure of the full groupoid with a vastly simpler groupoid representation based on equivalence classes. Using these formalisms we define a distance metric for similarity between mitochondrial structures based on an edit distance. In the course of defining these structures we provide a mathematical motivation for new experimental questions regarding mitochondrial fusion, the impacts of cell division on mitochondrial morphology, and the presence of a single giant component in some cell types. This work points to a general strategy for formulating a cell structure state-space, based not on the shapes of cellular structures, but on relations between the dynamic operations that produce them.

cell biology↗

bioRxiv2024

The biophysical mechanism of mitochondrial pearling

Mitochondrial networks exhibit remarkable dynamics that are driven in part by fission and fusion events. However, there are other reorganizations of the network that do not involve fission and fusion. One such exception is the elusive, "beads-on-a-string" morphological transition of mitochondria. During such transitions, the cylindrical tubes of the mitochondrial membrane transiently undergo shape changes to a string of "pearls" connected along thin tubes. These dynamics have been anecdotally observed in many contexts and given disparate explanations. Here we unify these observations by proposing a common underlying mechanism based on the biophysical properties of tubular fluid membranes for which it is known that, under particular regimes of tension and pressure, membranes reach an instability and undergo a shape transition to a string of connected pearls. First, we use high-speed light-sheet microscopy to show that transient, short-lived pearling events occur spontaneously in the mitochondrial network in every cell type we have examined, including primary fibroblasts, T-cells, neurons, and budding yeast. We present evidence that transient mitochondrial pearling occurs during important biological events, particularly during T cell activation, neuronal firing, and replicative senescence. Using our high-temporal resolution data, we identify two distinct categories of spontaneous pearling, i) internal pressure-driven pearling generated by ionic flux, and ii) external tension-driven pearling generated by the cytoskeleton. By applying live-cell STED and FIB-SEM imaging we document the structural reorganization of inner cristae membranes during mitochondrial pearling and the role of the MICOS complex in regulating the frequency of pearling events. We then establish numerous methods for inducing pearling, including the ability to induce these dynamics with single mitochondrion precision. These methods include ionophores, channel activators, osmotic shock, detergents, laser stimulation, membrane intercalating molecules, chemical fixation, and micro-needle force. These disparate inducers establish three main physical causes of pearling, i) ionic flux producing internal osmotic pressure, ii) membrane packing lowering bending elasticity, and iii) external mechanical force increasing membrane tension. Pearling dynamics thereby reveal a fundamental biophysical facet of mitochondrial biology. We suggest that pearling should take its place beside fission and fusion as a key process of mitochondrial dynamics, with implications for physiology, disease, and aging.

biophysics↗

bioRxiv2024

Pearling Drives Mitochondrial DNA Nucleoid Distribution

The mitochondria of most eukaryotes carry an indispensable second genome (mtDNA), encoding genes engaged in oxidative phosphorylation1. The regular positioning and segregation of mtDNA-containing nucleoids is essential for mitochondrial function and inheritance, as well as cellular health2-5. However, the underlying mechanism driving nucleoid distribution and disaggregation remains unknown6,7. Our data reveal that mitochondria frequently undergo reversible pearling, a biophysical instability that undulates tubules into regularly spaced beads 8, typically triggered by calcium influx. We discovered that physiological pearling imposes a characteristic length scale, simultaneously mediating nucleoid disaggregation and establishing inter-nucleoid distancing with near-maximally achievable precision. We found that lamellar cristae invaginations of the inner mitochondrial membrane play a dual role, determining pearling frequency and duration, and preserving the resulting nucleoid spacing after organelle recovery to a tubular form. Thus, disrupting cristae ultrastructure resulted in more frequent pearling, but also aberrant nucleoid clustering. Our results demonstrate that the distribution of mitochondrial genomes is governed by the interplay between rapid and reversible pearling and cristae ultrastructure, establishing a mechanism for this long-puzzling yet fundamental feature of eukaryotic life, and offering insights into its potential modulation.

cell biology↗

bioRxiv2024

Dissecting heterogeneity of tumor microenvironment in colorectal cancer using high-resolution single-cell atlas

The immune composition of the tumor microenvironment (TME) has a major impact on the therapeutic response and clinical outcome in patients with colorectal cancer (CRC). Here, we comprehensively characterize the TME at the single-cell level by first building a large-scale atlas that integrates 4.27 million single cells from 1,670 patient samples. We then complemented the atlas with single-cell profiles from four CRC cohorts with 266 patients, including cells with low mRNA content, spatial transcriptional profiles from 3.7 million cells, and protein profiles from 0.7 million cells. The analysis of the atlas allows refined tumor classification into four immune phenotypes: immune desert, B cell enriched, T cell enriched, and myeloid cell enriched subtypes. Within the myeloid compartment we uncover distinct subpopulations of neutrophils that acquire new functional properties in blood and in the TME, including anti-tumorigenic capabilities. Further, spatial multimodal single-cell profiling reveals that neutrophils are organized in clusters within distinct functional niches. Finally, using an orthotopic mouse model we show that cancer-derived systemic signals modify neutrophil production in the bone marrow, providing evidence for tumor-induced granulopoiesis. Our study provides a big data resource for the CRC and suggests novel therapeutic strategies targeting neutrophils.

bioinformatics↗

bioRxiv2024

Pertpy: an end-to-end framework for perturbation analysis

Advances in single-cell technology have enabled the measurement of cell-resolved molecular states across a variety of cell lines and tissues under a plethora of genetic, chemical, environmental, or disease perturbations. Current methods focus on differential comparison or are specific to a particular task in a multi-condition setting with purely statistical perspectives. The quickly growing number, size, and complexity of such studies requires a scalable analysis framework that takes existing biological context into account. Here, we present pertpy, a Python-based modular framework for the analysis of large-scale perturbation single-cell experiments. Pertpy provides access to harmonized perturbation datasets and metadata databases along with numerous fast and user-friendly implementations of both established and novel methods such as automatic metadata annotation or perturbation distances to efficiently analyze perturbation data. As part of the scverse ecosystem, pertpy interoperates with existing libraries for the analysis of single-cell data and is designed to be easily extended.

bioinformatics↗

bioRxiv2024

Single-cell differential expression analysis between conditions within nested settings

Differential expression analysis provides insights into fundamental biological processes and with the advent of single-cell transcriptomics, gene expression can now be studied at the level of individual cells. Many analyses treat cells as samples and assume statistical independence. As cells are pseudoreplicates, this assumption does not hold, leading to reduced robustness, reproducibility, and an inflated type 1 error rate. In this study, we investigate various methods for differential expression analysis on single-cell data, conduct extensive benchmarking and give recommendations for method choice. The tested methods include DESeq2, MAST, DREAM, scVI, the Permutation Test and distinct. We additionally adapt Hierarchical Bootstrapping to differential expression analysis on single-cell data and include it in our benchmark. We found that differential expression analysis methods designed specifically for single-cell data do not offer performance advantages over conventional pseudobulk methods such as DESeq2 when applied to individual data sets. In addition, they mostly require significantly longer run times. For atlas-level analysis, permutation-based methods excel in performance but show poor runtime, suggesting to use DREAM as a compromise between quality and runtime. Overall, our study offers the community a valuable benchmark of methods across diverse scenarios and offers guidelines on method selection.

bioinformatics↗

bioRxiv2024

Benchmarking second-generation methods for cell-type deconvolution of transcriptomic data

BackgroundIn silico cell-type deconvolution from bulk transcriptomics data is a powerful technique to gain insights into the cellular composition of complex tissues. While first-generation methods used precomputed expression signatures covering limited cell types and tissues, second-generation tools use single-cell RNA sequencing data to build custom signatures for deconvoluting arbitrary cell types, tissues, and organisms. This flexibility poses significant challenges in assessing their deconvolution performance. ResultsHere, we comprehensively benchmark second-generation tools, disentangling different sources of variation and bias using a diverse panel of real and simulated data. Our results reveal substantial differences in accuracy, scalability, and robustness across methods, depending on factors such as cell-type similarity, reference composition, and dataset origin. Conclusions.Our study highlights the strengths, limitations, and complementarity of state-of-the-art tools, shedding light on how different data characteristics and confounders impact deconvolution performance. We provide the scientific community with an ecosystem of tools and resources, omnideconv, simplifying the application, benchmarking, and optimization of deconvolution methods.

bioinformatics↗

bioRxiv2023

Multimodal analysis unveils tumor microenvironment heterogeneity linked to immune activity and evasion

The cellular and molecular heterogeneity of tumors is a major obstacle to cancer immunotherapy. Here, we use a systems biology approach to derive a signature of the main sources of heterogeneity in the tumor microenvironment (TME) from lung cancer transcriptomic data. We demonstrate that this signature, which we called iHet, is conserved in different cancers and associated with antitumor immunity. Through the analysis of single-cell and spatial transcriptomics data, we trace back the cellular origin of the variability that explains the iHet signature. Finally, we demonstrate that iHet has predictive value for cancer immunotherapy, which can be further improved by disentangling three major determinants of anticancer immune responses: activity of immune cells, immune infiltration or exclusion, and cancer-cell foreignness. This work shows how transcriptomics data can be integrated to derive a holistic representation of the phenotypic heterogeneity of the TME, and ultimately to determine its unfolding and fate during immunotherapy with immune checkpoint blockers.

bioinformatics↗

bioRxiv2023

Accelerated physiology and increased energy expenditure in animals and humans with mitochondrial defects: A meta-analysis

Mitochondria are key energy transforming organelles in mammalian cells. However, how defects in oxidative phosphorylation (OxPhos) and other mitochondrial functions influence whole-body energy expenditure (EE) has not been rigorously studied. Cellular and organismal responses to OxPhos defects likely involve a combination of functional downregulation to conserve energy and compensatory upregulation of stress responses. If the energy cost of compensatory responses exceeds the potential energy savings of functional downregulation, as recent work suggests, the result would be an increase in total EE. To address the hypothesis that OxPhos defects increase the energetic cost of living, we performed a meta-analysis of available studies reporting EE in animal models with mitochondrial gene defects. Of all reported experimental conditions (n = 91, from 29 studies), 51% reported a >10% elevation in EE relative to control animals, compared to 11% reporting <10% reduction in EE (p<0.0001, Chi-square). Of the experimental conditions where locomotor activity was also quantified, 39% showed that OxPhos-deficient animals had elevated EE despite reduced locomotor activity, which would be expected to decrease EE. To extend this finding in humans, we re-analyzed a high-quality clinical and multi-omics dataset (Sharma et al. 2021) of mitochondrial disease patients with the m.3243A>G mutation. This analysis similarly indicates an upregulation of energetically costly physiological, immune, and metabolic parameters in people with OxPhos deficiency. These results suggest that animals and humans with mitochondrial defects must expend more energy to sustain life, a state clinically called hypermetabolism. High-quality human energetics studies are needed to understand the magnitude, mechanisms, and modifiability of hypermetabolism in mitochondrial disorders.

cell biology↗

bioRxiv2023

Somatic nuclear mitochondrial DNA insertions are prevalent in the human brain and accumulate in aging fibroblasts

The transfer of mitochondrial DNA into the nuclear genomes of eukaryotes (Numts) has been linked to lifespan in non-human species 1-3 and recently demonstrated to occur in rare instances from one human generation to the next 4. Here we investigated numtogenesis dynamics in humans in two ways. First, we quantified Numts in 1,187 post-mortem brain and blood samples from different individuals. Compared to circulating immune cells (n=389), post-mitotic brain tissue (n=798) contained more Numts, consistent with their potential somatic accumulation. Within brain samples we observed a 5.5-fold enrichment of somatic Numt insertions in the dorsolateral prefrontal cortex compared to cerebellum samples, suggesting that brain Numts arose spontaneously during development or across the lifespan. Moreover, more brain Numts was linked to earlier mortality. The brains of individuals with no cognitive impairment who died at younger ages carried approximately 2 more Numts per decade of life lost than those who lived longer. Second, we tested the dynamic transfer of Numts using a repeated-measures WGS design in a human fibroblast model that recapitulates several molecular hallmarks of aging 5. These longitudinal experiments revealed a gradual accumulation of one Numt every [~]13 days. Numtogenesis was independent of large-scale genomic instability and unlikely driven cell clonality. Targeted pharmacological perturbations including chronic glucocorticoid signaling or impairing mitochondrial oxidative phosphorylation (OxPhos) only modestly increased the rate of numtogenesis, whereas patient-derived SURF1-mutant cells exhibiting mtDNA instability accumulated Numts 4.7- fold faster than healthy donors. Combined, our data document spontaneous numtogenesis in human cells and demonstrate an association between brain cortical somatic Numts and human lifespan. These findings open the possibility that mito-nuclear horizontal gene transfer among human post-mitotic tissues produce functionally-relevant human Numts over timescales shorter than previously assumed.

genomics↗

bioRxiv2023

MitoQuicLy: a high-throughput method for quantifying cell-free DNA from human plasma, serum, and saliva

Circulating cell-free mitochondrial DNA (cf-mtDNA) is an emerging biomarker of psychobiological stress and disease which predicts mortality and is associated with various disease states. To evaluate the contribution of cf-mtDNA to health and disease states, standardized high-throughput procedures are needed to quantify cf-mtDNA in relevant biofluids. Here, we describe MitoQuicLy: Mitochondrial DNA Quantification in cell-free samples by Lysis. We demonstrate high agreement between MitoQuicLy and the commonly used column-based method, although MitoQuicLy is faster, cheaper, and requires a smaller input sample volume. Using 10 {micro}L of input volume with MitoQuicLy, we quantify cf-mtDNA levels from three commonly used plasma tube types, two serum tube types, and saliva. We detect, as expected, significant inter-individual differences in cf-mtDNA across different biofluids. However, cf-mtDNA levels between concurrently collected plasma, serum, and saliva from the same individual differ on average by up to two orders of magnitude and are poorly correlated with one another, pointing to different cf-mtDNA biology or regulation between commonly used biofluids in clinical and research settings. Moreover, in a small sample of healthy women and men (n=34), we show that blood and saliva cf-mtDNAs correlate with clinical biomarkers differently depending on the sample used. The biological divergences revealed between biofluids, together with the lysis-based, cost-effective, and scalable MitoQuicLy protocol for biofluid cf-mtDNA quantification, provide a foundation to examine the biological origin and significance of cf-mtDNA to human health.

molecular biology↗

bioRxiv2022

Functional precision profiling reveals non-mutational rewiring of kinase signaling networks in colorectal cancer

BackgroundDespite major advances in the development of targeted therapies, precision (immuno)oncology approaches for patients with colorectal cancer continue to lag behind other solid cancers. Functional precision oncology - a strategy that is based on perturbing primary tumor cells from cancer patients with drugs - could provide an alternate road forward to personalize treatment. MethodsWe extend here the functional precision oncology paradigm to measuring phosphoproteome landscapes using patient-derived organoids (PDOs). We first employed steady-state multi-omics (exome sequencing, RNA sequencing, and proteomics) and single-cell characterization of the PDOs. The PDOs were then perturbed with kinase inhibitors (MEKi, PI3Ki, mTORi, TBKi, BRAFi, and TAKi), and large-scale phosphoproteomics profiling using data-independent acquisition was carried out. Further, we used imaging mass-cytometry-based single-cell proteomic profiling of the primary tumors to characterize cellular composition of the tumor-microenvironment (TME) and to quantify heterocellular signaling crosstalk. ResultsWe show that kinase inhibitors induce profound off-target effects resulting in a crosstalk with oncogenic and immune-related pathways. Reconstruction of the topologies of the kinase networks revealed that the patient-specific rewiring of the central EGFR-RAS-MAPK network is unaffected by mutations. Moreover, we show non-genetic heterogeneity of the PDOs and patient- and inhibitor-specific upregulation of stemness and differentiation genes by kinase inhibitors. We complemented our functional profiling by spatial proteomics profiling of the primary tumors using imaging mass cytometry. We quantify spatial heterocellular crosstalk and tumor-immune cell interactions, showing an avoidance of PD1+ immune cells and PD-L1+ tumor cells. ConclusionsCollectively, we provide a multi-modal framework for inferring tumor cell intrinsic signaling and external signaling from the TME to inform precision (immuno)-oncology in colorectal cancer.

cancer biology↗