Functional analysis of cell plasticity using single-cell technologies

Metazoan organisms are heterocellular systems composed of hundreds of different cell types, which arise from an isogenic genome through differentiation. Cellular 'plasticity' further enables cells to alter their fate in response to exogenous cues and is involved in a variety of processes, such as wound healing, infection, and cancer. Recent advances in cellular model systems, high-dimensional single-cell technologies, and lineage tracing have sparked a renaissance in plasticity research. Here, we discuss the definition of cell plasticity, evaluate state-of-the-art model systems and techniques to study cell-fate dynamics, and explore the application of single-cell technologies to obtain functional insights into cell plasticity in healthy and diseased tissues. The integration of advanced biomimetic model systems, single-cell technologies, and high-throughput perturbation studies is enabling a new era of research into non-genetic plasticity in metazoan systems

Anti-tumour effects of a specific anti-ADAM17 antibody in an ovarian cancer model in vivo.

ADAM 17 (TNF-α converting enzyme, TACE) is a potential target for cancer therapy, but the small molecule inhibitors reported to date are not specific to this ADAM family member. This membrane-bound metalloproteinase is responsible for ectodomain shedding of pathologically significant substrates including TNF-α and EGFR ligands. The aim of this study was to evaluate the pharmacokinetics, pharmacodynamics and anti-tumour efficacy of the first specific inhibitor, an anti-human ADAM17 IgG antibody, clone D1(A12). We used intraperitoneal xenografts of the human ovarian cancer cell line IGROV1-Luc in Balb/c nude mice, chosen because it was previously reported that growth of these xenografts is inhibited by knock-down of TNF-α. In vitro, 200 nM D1(A12) inhibited shedding of ADAM17 substrates TNF-α, TNFR1-α, TGF-α, amphiregulin (AREG), HB-EGF and IL-6Rα, from IGROV1-Luc cells, (4.7 nM IC(50) for TNF-α shedding). In IGROV1-Luc xenografts in vivo, D1(A12) IgG showed pharmacokinetic properties suitable for efficacy studies, with a single i.p. dose of 10 mg/kg D1(A12) sufficient to maintain IgG plasma and ascites fluid concentrations above 100 nM for more than 7 days. The plasma half life was 8.6 days. Next, an efficacy study was performed, dosing D1(A12) or anti-human TNF-α antibody infliximab at 10 mg/kg q7d, quantifying IGROV1-Luc tumour burden by bioluminescence. D1(A12) IgG showed a significant reduction in tumour growth (p = 0.005), 56% of vehicle control. Surprisingly, D1(A12) did not reduce the concentration of circulating human TNF-α, suggesting that another enzyme may compensate for inhibition of ADAM17 in vivo (but not in vitro). However, D1(A12) did show clear pharmacodynamic effects in the mice, with significant inhibition of shedding from tumour of ADAM17 substrates TNFR1-α, AREG, and TGF-α (4-15-fold reductions, p<0.0001 for all three). Thus, D1(A12) has anti-ADAM17 activity in vivo, inhibits shedding of EGFR ligands and has potential for use in EGF ligand-dependent tumours

Directed Scattering for Knowledge Graph-based Cellular Signaling Analysis

Directed graphs are a natural model for many phenomena, in particular scientific knowledge graphs such as molecular interaction or chemical reaction networks that define cellular signaling relationships. In these situations, source nodes typically have distinct biophysical properties from sinks. Due to their ordered and unidirectional relationships, many such networks also have hierarchical and multiscale structure. However, the majority of methods performing node- and edge-level tasks in machine learning do not take these properties into account, and thus have not been leveraged effectively for scientific tasks such as cellular signaling network inference. We propose a new framework called Directed Scattering Autoencoder (DSAE) which uses a directed version of a geometric scattering transform, combined with the non-linear dimensionality reduction properties of an autoencoder and the geometric properties of the hyperbolic space to learn latent hierarchies. We show this method outperforms numerous others on tasks such as embedding directed graphs and learning cellular signaling networks.Comment: 5 pages, 3 figure

SIGNAL-seq:Multimodal Single-cell Inter- and Intra-cellular Signalling Analysis

We present SIGNAL-seq (Split-pool Indexing siG-Nalling AnaLysis by sequencing): a multiplexed splitpool combinatorial barcoding method that simultaneously measures RNA and post-translational modifications (PTMs) in fixed single cells from 3D models. SIGNAL-seq PTM measurements are equivalent to mass cytometry and RNA gene detection is analogous to split-pool barcoding scRNA-seq. By measuring both mRNA ligand-receptor pairs and PTMs in single cells, SIGNAL-seq can simultaneously uncover inter- and intra-cellular regulation of tumour microenvironment plasticity

Single-Cell Signalling Analysis of Heterocellular Organoids

Organoids are powerful biomimetic tissue models. Despite their widespread adoption, methods to analyse cell-type specific post-translational modification (PTM) signalling networks in organoids are absent. Here we report multivariate single-cell analysis of cell-type specific signalling networks in organoids and organoid co-cultures. Simultaneous measurement of 28 PTMs in >1 million single small intestinal organoid cells by mass cytometry reveals cell-type and cell-state specific signalling networks in stem, Paneth, enteroendocrine, tuft, goblet cells, and enterocytes. Integrating single-cell PTM analysis with Thiol-reactive Organoid Barcoding in situ (TOBis) enables high-throughput comparison of signalling networks between organoid cultures. Multivariate cell-type specific PTM analysis of colorectal cancer tumour microenvironment organoids reveals that shApc, KrasG12D, and Trp53R172H cell-autonomously mimic signalling states normally induced by stromal fibroblasts and macrophages. These results demonstrate how standard mass cytometry workflows can be modified to perform high-throughput multivariate cell-type specific signalling analysis of healthy and cancerous organoids

Tundra be dammed: Beaver colonization of the Arctic

Increasing air temperatures are changing the arctic tundra biome. Permafrost is thawing, snow duration is decreasing, shrub vegetation is proliferating, and boreal wildlife is encroaching. Here we present evidence of the recent range expansion of North American beaver (Castor canadensis) into the Arctic, and consider how this ecosystem engineer might reshape the landscape, biodiversity, and ecosystem processes. We developed a remote sensing approach that maps formation and disappearance of ponds associated with beaver activity. Since 1999, 56 new beaver pond complexes were identified, indicating that beavers are colonizing a predominantly tundra region (18,293 km2) of northwest Alaska. It is unclear how improved tundra stream habitat, population rebound following overtrapping for furs, or other factors are contributing to beaver range expansion. We discuss rates and likely routes of tundra beaver colonization, as well as effects on permafrost, stream ice regimes, and freshwater and riparian habitat. Beaver ponds and associated hydrologic changes are thawing permafrost. Pond formation increases winter water temperatures in the pond and downstream, likely creating new and more varied aquatic habitat, but specific biological implications are unknown. Beavers create dynamic wetlands and are agents of disturbance that may enhance ecosystem responses to warming in the Arctic

Proteomics profiling of interactome dynamics by colocalisation analysis (COLA)

Localisation and protein function are intimately linked in eukaryotes, as proteins are localised to specific compartments where they come into proximity of other functionally relevant proteins. Significant co-localisation of two proteins can therefore be indicative of their functional association. We here present COLA, a proteomics based strategy coupled with a bioinformatics framework to detect protein–protein co-localisations on a global scale. COLA reveals functional interactions by matching proteins with significant similarity in their subcellular localisation signatures. The rapid nature of COLA allows mapping of interactome dynamics across different conditions or treatments with high precision.Cancer Research UK; BBSRC

The Autism - Tics, AD/HD and other Comorbidities inventory (A-TAC): further validation of a telephone interview for epidemiological research

<p>Abstract</p> <p>Background</p> <p>Reliable, valid, and easy-to-administer instruments to identify possible caseness and to provide proxies for clinical diagnoses are needed in epidemiological research on child and adolescent mental health.</p> <p>The aim of this study is to provide further validity data for a parent telephone interview focused on Autism - Tics, Attention-deficit/hyperactivity disorder (AD/HD), and other Comorbidities (A-TAC), for which reliability and preliminary validation data have been previously reported.</p> <p>Methods</p> <p>Parents of 91 children clinically diagnosed at a specialized Child Neuropsychiatric Clinic, 366 control children and 319 children for whom clinical diagnoses had been previously assigned were interviewed by the A-TAC over the phone. Interviewers were blind to clinical information. Different scores from the A-TAC were compared to the diagnostic outcome.</p> <p>Results</p> <p>Areas under ROC curves for interview scores as predictors of clinical diagnoses were around 0.95 for most disorders, including autism spectrum disorders (ASDs), attention deficit/hyperactivity disorder (AD/HD), tic disorders, developmental coordination disorders (DCD) and learning disorders, indicating excellent screening properties. Screening cut-off scores with sensitivities above 0.90 (0.95 for ASD and AD/HD) were established for most conditions, as well as cut-off scores to identify proxies to clinical diagnoses with specificities above 0.90 (0.95 for ASD and AD/HD).</p> <p>Conclusions</p> <p>The previously reported validity of the A-TAC was supported by this larger replication study using broader scales from the A-TAC-items and a larger number of diagnostic categories. Short versions of algorithms worked as well as larger. Different cut-off levels for screening versus identifying proxies for clinical diagnoses are warranted. Data on the validity for mood problems and oppositional defiant/conduct problems are still lacking. Although the A-TAC is principally intended for epidemiological research and general investigations, the instrument may be useful as a tool to collect information in clinical practice as well.</p

Pan-cancer deconvolution of tumour composition using DNA methylation

The nature and extent of immune cell infiltration into solid tumours are key determinants of therapeutic response. Here, using a DNA methylation-based approach to tumour cell fraction deconvolution, we report the integrated analysis of tumour composition and genomics across a wide spectrum of solid cancers. Initially studying head and neck squamous cell carcinoma, we identify two distinct tumour subgroups: ‘immune hot’ and ‘immune cold’, which display differing prognosis, mutation burden, cytokine signalling, cytolytic activity and oncogenic driver events. We demonstrate the existence of such tumour subgroups pan-cancer, link clonal-neoantigen burden to cytotoxic T-lymphocyte infiltration, and show that transcriptional signatures of hot tumours are selectively engaged in immunotherapy responders. We also find that treatment-naive hot tumours are markedly enriched for known immune-resistance genomic alterations, potentially explaining the heterogeneity of immunotherapy response and prognosis seen within this group. Finally, we define a catalogue of mediators of active antitumour immunity, deriving candidate biomarkers and potential targets for precision immunotherapy