During metastasis, cancer cells hijack blood vessels and travel via the circulation to colonize distant sites. Due to the rarity of these events, the immediate cell fate decisions of arrested circulating tumour cells (aCTC) are poorly understood and the role of the endothelium, as the interface of dissemination, remains elusive. Here, we developed a novel strategy to specifically enrich for aCTC subpopulations capturing all cell states of the extravasation process and, in combination with single cell RNA-sequencing, provide a first blueprint of the transcriptional basis of early aCTC decisions. Upon their arrest at the metastatic site, tumour cells either started proliferating intravascularly or extravasated and preferably reached a state of quiescence. Endothelial-derived angiocrine Wnt factors were found to drive this bifurcation by inducing a mesenchymal-like phenotype in aCTCs instructing them to follow the extravasation-dormancy branch. Surprisingly, homogenous tumour cell pools showed an unexpected baseline heterogeneity in Wnt signalling activity and epithelial-to-mesenchyme-transition (EMT) states. This heterogeneity was established at the epigenetic level and served as the driving force of aCTC behaviour. Hypomethylation enabled high baseline Wnt and EMT activity in tumour cells leading them to preferably follow the extravasation-dormancy route, whereas methylated tumour cells had low activity and proliferated intravascularly. The data identify the pre-determined methylation status of disseminated tumour cells as a key regulator of aCTC behaviour in the metastatic niche. While metastatic niche-derived factors per default instruct the acquisition of quiescence, aCTCs unwind a default proliferation program and only deviate from it if hypomethylation in key gene families renders them responsive towards the microenvironment.

Throughout the current SARS-CoV-2 pandemic, limited diagnostic testing capacity prevented sentinel testing of the population, demonstrating the need for novel testing strategies and infrastructures. Here, we describe the set-up of an alternative testing platform, which allows scalable surveillance testing as an acute pandemic response tool and for pandemic preparedness purposes, exemplified by SARS-CoV-2 diagnostics in an academic environment. The testing strategy involves self-sampling based on gargling saline, pseudonymized sample handling, automated 96-well plate-based RNA extraction, and viral RNA detection using a semi-quantitative multiplexed colorimetric reverse transcription loop-mediated isothermal amplification (RT-LAMP) assay with an analytical sensitivity comparable to RT-quantitative polymerase chain reaction (RT-qPCR). We provide standard operating procedures and an integrated software solution for all workflows, including sample logistics, LAMP assay analysis by colorimetry or by sequencing (LAMP-seq), and communication of results to participants and the health authorities. Using large sample sets including longitudinal sample series we evaluated factors affecting the viral load and the stability of gargling samples as well as the diagnostic sensitivity of the RT-LAMP assay. We performed >35,000 tests during the pandemic, with an average turnover time of fewer than 6 hours from sample arrival at the test station to result announcement. Altogether, our work provides a blueprint for fast, sensitive, scalable, cost- and labor-efficient RT-LAMP diagnostics. As RT-LAMP-based testing requires advanced, but non-specialized laboratory equipment, it is independent of potentially limiting clinical diagnostics supply chains.

In any research project involving data-rich assays, exploratory data analysis is a crucial step. Typically, this involves jumping back and forth between visualizations that provide overview of the whole data and others that dive into details. In data quality assessment, for example, it might be very helpful to have one chart showing a summary statistic for all samples, and clicking on one of the data points would display details on this sample in a second plot. Setting up such interactively linked charts is usually too cumbersome and time-consuming to use them in ad hoc analysis. We present R/LinkedCharts, a framework that renders this task radically simple: Producing linked charts is as quickly done as is producing conventional static plots in R, requiring a data scientist to write only very few lines of simple R code to obtain complex and general visualization. We expect that the convenience of our new tool will enable data scientists and bioinformaticians to perform much deeper and more thorough EDA with much less effort. Furthermore, R/LinkedCharts apps, typically first written as quick-and-dirty hacks, can also later be polished to provide interactive data access in publication quality, thus contributing to open science.