bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2020.10.14.337642

A Boolean Model of the Formation of Tumour Associated Macrophages in an in-vitro Model of Chronic Lymphocytic Leukaemia

Marku, M.·Raynal, F.·Verstraete, N.·Domagala, M.·Madrid-Mencia, M.·Poupot, M.·Fournie, J.-J.·Ysebaert, L.·Pancaldi, V.
View original source ↗Export metadata ↓

Abstract

The tumour microenvironment is the collection of cells in and surrounding cancer cells in a tumour including a variety of immune cells, especially neutrophils and monocyte-derived macrophages. In a tumour setting, macrophages encompass a spectrum between a tumour-suppressive (M1) or tumour-promoting (M2) state. The biology of macrophages found in tumours (Tumour Associated Macrophages) remains unclear, but understanding their impact on tumour progression is highly important. In this paper, we perform a comprehensive analysis of a macrophage polarization network, following two lines of enquiry: (i) we reconstruct the macrophage polarization network based on literature, extending it to include important stimuli in a tumour setting, and (ii) we build a dynamical model able to reproduce macrophage polarization in the presence of different stimuli, including the contact with cancer cells. Our simulations recapitulate the documented macrophage phenotypes and their dependencies on specific receptors and transcription factors, while also elucidating the formation of a special type of tumour associated macrophages in an in-vitro model of chronic lymphocytic leukaemia. This model constitutes the first step towards elucidating the cross-talk between immune and cancer cells inside tumours, with the ultimate goal of identifying new therapeutic targets that could control the formation of tumour associated macrophages in patients.

Explore related subjects

systems biologyPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Marku, M., Raynal, F., Verstraete, N., Domagala, M., Madrid-Mencia, M., Poupot, M., Fournie, J.-J., Ysebaert, L., Pancaldi, V.. 2020-10-15. A Boolean Model of the Formation of Tumour Associated Macrophages in an in-vitro Model of Chronic Lymphocytic Leukaemia. https://doi.org/10.1101/2020.10.14.337642

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

A single-cell transcriptomic atlas of the pigtail macaque lung

Background: The lungs undergo substantial physiological and immunological changes during respiratory illness to meet the host's increased metabolic and oxygen demands. These responses require coordination among epithelial, endothelial, stromal, and immune cells throughout the respiratory zone, interstitium, and vasculature. Nonhuman primates (NHPs) are a valuable model for studying pulmonary biology as they have similar anatomic, physiologic, and immunologic correspondence to humans. Despite the widespread use of NHPs in pulmonary research, the cellular diversity and molecular characteristics of the pigtail macaque lung remain incompletely defined. Methods: Parenchymal lung tissue from five adult female pigtail macaques (Macaca nemestrina) was dissociated into single cells and profiled using 10x Genomics 3' single-cell RNA sequencing (scRNA-Seq). Next, ambient RNA was removed and quality control analyses performed. Samples were then integrated using scVI and scANVI. Cell types were labeled after unsupervised clustering and subclustering, followed by manual annotation using canonical marker genes. We compared lineages and cell types with the Level 4 Human Lung Cell Atlas (HLCA) using scArches reference mapping and with the Rhesus Immune Reference Atlas (RIRA) using Seurat label transfer. Results: The atlas includes 61,323 cells representing 37 cell types in epithelial, mesenchymal, endothelial, lymphoid, and myeloid populations. Annotated populations included both major and rare lung cell types, including alveolar type I and II cells, club and multiciliated cells, aerocyte capillary, and lymphatic endothelial cells, pre-terminal bronchiole secretory cells, CD4+ and CD8+ T cells, and several innate-like lymphocyte populations (gamma delta T cells, MAIT-like cells, innate-like CD8+ T cells, and group 3 innate lymphoid cells). There was strong concordance at HLCA annotation level 4, including AT1 and AT2 cells (96% and 93%), B cells (94 to 97%), CD4+ T cells (99 to 100%), classical and non-classical monocytes (84% and 85%), alveolar macrophages (80%), and mast cells (96%). Immune populations mapped to the RIRA with similar fidelity (alveolar macrophages and B cells, 99%). Conclusions: While studies of pandemic respiratory infections, vaccines, and therapeutics rely on NHP disease models, this atlas provides a unique reference for epithelial, endothelial, stromal, and immune cell states in the pigtail macaque lung. We found that single-cell populations from the pigtail macaque lung are highly similar to those in the human lung at the lineage level. This reference will enable studies of pathogenesis and vaccine and drug efficacy in a highly translational model of the human lung.

systems biology↗
bioRxiv2026

Cell cycling throttles epithelial--mesenchymal transition

Cell cycle phase is an understudied source of variation in gene expression. EMT is a case in point: this cell state transition that drives metastasis has been studied widely across tissue, stimulus, and microenvironmental conditions, yet cell cycle effects are routinely regressed out of single-cell EMT analyses. Here, we compare EMT between G1 and cycling (S/G2/M) cell states in two independent scRNA-seq datasets of TGF-{beta}-stimulated MCF10A mammary epithelial cells (varying dose and timepoint). G1 cells consistently show higher expression of EMT genes and upregulated RNA velocities than cycling cells, with the strongest upregulation in G1 EMT intermediate states. Consistent with the go-or-grow hypothesis, EMT and cell cycle programs inversely correlate; moreover, this anticorrelation extends to RNA velocity and strengthens with TGF-{beta} dose. We identify a 77-gene G1-specific EMT signature enriched for actin cytoskeletal and extracellular matrix genes that would otherwise be lost by regressing out cell cycle effects. This gene set prompts the hypothesis that, unconstrained by the cytoskeletal demands of mitosis, G1 cells have greater capacity to execute EMT. Further, we developed an EMT gene regulatory model coupled with a cell cycle module, and found through simulation that the model recapitulated this inverse EMT--cell cycle relationship. Using this model, we predict that cycle-selective chemotherapy enriches for the G1 intermediate state, which has high metastatic potential. Cell cycle phase thus ought not be regressed out of EMT states, given its influence on EMT.

systems biology↗
bioRxiv2026

Multi-Enzyme Data-Independent Acquisition Mass Spectrometry for Proteoform-Aware Single-shot Proteomics

Biological regulation does not operate at the level of the protein but across ensembles of protein variants shaped by alternative splicing, post-translational modification and endogenous proteolytic processing. Yet most conventional bottom-up discovery proteomics workflows remain optimized for a canonical protein census, leaving much of the sequence-level variation invisible. Multi-enzyme digestion has been recognized as an effective strategy to expand sequence coverage and proteoform sensitivity, but existing implementations rely on resource-heavy per-enzyme liquid chromatography coupled to mass spectrometry (LC-MS) analysis, dedicated measurements for spectral library construction, and semi-manual reconciliation across multiple software tools, restricting the use to specialized laboratories and limited sample cohorts. Here we introduce mE-DIA, a multi-enzyme, proteoform-resolved data independent acquisition (DIA)-MS workflow that multiplexes complementary protease digested samples into a single DIA injection and processes them through a library-free pipeline within a single analysis engine, without supplemental data dependent acquisition (DDA)-MS measurements. The mE-DIA Pipeline graphical user interface orchestrates the analysis, and a dedicated proteoform inference algorithm and the interactive mE-DIA Peptide Mapper viewer make the sequence-level evidence explorable. Comparing the workflow to prevailing DDA-library-based multi-enzyme DIA on the same raw data and within shared search space showed increased identified protein groups by 30 % and protein groups quantified at [≤] 20 % CV by 47 %. A three-enzyme multiplex of the human pathogen Streptococcus pyogenes resolved 990 protein-backbone variants among 309 canonical protein entries, 1.8-fold more than the most productive single-enzyme analysis, including an H69 C-terminal proteoform of the ribosomal protein uS19 that the trypsin-only workflow did not resolve. By consolidating multi-enzyme acquisition, analysis and proteoform interpretation into one accessible toolset, mE-DIA brings sub-gene-resolved evidence within reach of routine single-shot discovery DIA proteomics.

systems biology↗