Signalling dynamics, cell decisions, and homeostatic control in health and disease

Valls, Pablo Oriol; Esposito, Alessandro

doi:10.1016/j.ceb.2022.01.011

Cited by 22 publications

(15 citation statements)

References 93 publications

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“…We combined live cell imaging and machine learning to infer the differentiation state of single cells during the process of muscle precursor cell differentiation. Many studies highlight the rich information encapsulated in single-cell dynamics that, with the aid of supervised or unsupervised machine learning, enable effective identification of sub-populations and discrimination of perturbations (Choi et al, 2021; Goglia et al, 2020; Jacques et al, 2021; Jena et al, 2022; Kimmel et al, 2018; Valls & Esposito, 2022), that cannot be inferred from static snapshot images (Copperman et al, 2021; Wang et al, 2020; Wu et al, 2022). For example, approaches that rely on static snapshots make it extremely hard to infer trajectories that deviate from the mainstream cell state progression because they are confounded by cell-to-cell variability.…”

Section: Discussionmentioning

confidence: 99%

Machine learning inference of continuous single-cell state transitions during myoblast differentiation and fusion

Shakarchy

Zarfati

Hazak

et al. 2023

Preprint

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Cells dynamically change their internal organization via continuous cell state transitions to mediate a plethora of physiological processes. Understanding such continuous processes is severely limited due to a lack of tools to measure the holistic physiological state of single cells undergoing a transition. We combined live-cell imaging and machine learning to quantitatively monitor skeletal muscle precursor cell (myoblast) differentiation during multinucleated muscle fiber formation. Our machine learning model predicted the continuous differentiation state of single primary murine myoblasts over time and revealed that inhibiting ERK1/2 leads to a gradual transition from an undifferentiated to a terminally differentiated state 7.5-14.5 hours post inhibition. Myoblast fusion occurred ~3 hours after predicted terminal differentiation. Moreover, we showed that our model could predict that cells have reached terminal differentiation under conditions where fusion was stalled, demonstrating potential applications in screening. This method can be adapted to other biological processes to reveal connections between the dynamic single-cell state and virtually any other functional readout.

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Section: Discussionmentioning

confidence: 99%

Machine learning inference of continuous single-cell state transitions during myoblast differentiation and fusion

Shakarchy

Zarfati

Hazak

et al. 2023

Preprint

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“…Cell-cell communication is crucial for maintaining healthy physiological functions in tissues. In disease, the immune and non-immune niches, as well as the resident cell types, change their abundance and function, which results in an altered interaction among them 1–4 . Discovering these changes is important to better understand disease and identify new treatment strategies.…”

Section: Introductionmentioning

confidence: 99%

Communityassesses differential cell communication using large multi-sample case-control scRNAseq datasets

Solovey,

Celik,

Salcher

et al. 2024

Preprint

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Cell-cell communication is essential for physiological tissue function. In disease, this communication often gets disbalances by changes in the tissue cell type composition, fraction of cell engaged in communication and the rising or dropping expression levels of ligands, receptors and adhesion molecules. The changes in all these components of communication can be studied using single cell RNA-sequencing (scRNAseq) methods. With dropping sequencing costs, it is now possible to perform scRNAseq studies in larger cohorts of case and control samples to better address the heterogeneity of diseases. Here we present community, an R-based tool that is designed to perform differential communication analysis using scRNAseq between large cohorts of cases and controls. Community is able to reconstruct communication between different cell types both in the case and the control cohort of a dataset, and subsequently analyze which communication channels are affected in disease. Community is the first tool that integrates cell type abundance into the calculation of an interaction strength. Community is also able to disentangle the mechanisms underlying these changes, as well as detect interactions that are kept compensated by a sender or a receiver despite the disbalanced signaling from the counterpart. We tested community on two disease entities, ulcerative colitis and acute myeloid leukemia, using published scRNAseq datasets. We compared the performance of our tool to other differential communication pipelines, which community outperformed in speed and robustness. Overall, community is a fast, well-scalable, user-friendly R tool to assess differential cell-cell communication using large case-control scRNAseq datasets disentangling the driving mechanisms of communication shifts in disease.

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“…A complex network of interactions between cells is required for the maintenance of tissue homeostasis 1 . In these processes, extracellular vesicles (EVs) represent one key player in cell-to-cell communication because they convey complicated signaling between cells, including membrane proteins and intravesicular RNA or proteins 2–4 .…”

Section: Introductionmentioning

confidence: 99%

Extracellular vesicles isolated from frozen and fresh human melanoma tissue are similar in purity and protein composition

D’Arrigo,

Lässer,

Urzì

et al. 2024

Preprint

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Extracellular vesicles (EVs) isolated from tumor tissues represent a precious source of EVs because they are enriched in disease-associated biomarkers and are highly concentrated. However, a fundamental question is whether freezing of tissues influences the EVs` integrity and function and whether non-EVs are co-isolated with the EVs. In this work, we isolated EVs from metastatic melanoma tissue both immediately after tissue resection and after being frozen for at least two weeks. Specifically, the samples were divided into two parts: one was immediately processed for EV isolation (hereafter called fresh), and the other was frozen on dry ice and stored for two weeks before being processed for EV isolation (hereafter called frozen). Large and small EVs were isolated through ultracentrifugation, pooled, and further purified on an iodixanol density cushion. The EVs were analyzed by transmission electron microscopy, nanoparticle tracking analysis, and protein quantification as well as by quantitative mass spectrometry. The results did not show any significant difference between EVs isolated from fresh and frozen tissue. Importantly, there was no enrichment of either intracellular proteins or mitochondrial proteins in EVs isolated from frozen tissues vs. fresh tissues. Moreover, there were no significant differences in the quantity of proteins like MT-CO2, Cox6, SLC24A22, HLA-DR, and Erlin2, which were previously identified as potential markers of melanoma, ovarian cancer, and breast cancer. Overall, this study supports the use of frozen tissues as a source of EVs for research purposes because frozen tissue-derived EVs do not differ in purity or protein composition compared to their fresh counterparts.

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Signalling dynamics, cell decisions, and homeostatic control in health and disease

Cited by 22 publications

References 93 publications

Machine learning inference of continuous single-cell state transitions during myoblast differentiation and fusion

Machine learning inference of continuous single-cell state transitions during myoblast differentiation and fusion

Communityassesses differential cell communication using large multi-sample case-control scRNAseq datasets

Extracellular vesicles isolated from frozen and fresh human melanoma tissue are similar in purity and protein composition

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