The earliest intracellular signals determined in T cell activation are local, sub-second Ca2+ microdomains (1). Here we identify a Ca2+ entry component involved in Ca2+ microdomain formation in both non-stimulated and stimulated cells. In non-stimulated cells, spontaneous small Ca2+ microdomains depend on expression of ORAI1, STIM1, and STIM2. Using T cells stably transfected with ORAI1 fused to a genetically encoded Ca2+ indicator for optical imaging spontaneous Ca2+ microdomains depending on ORAI1 were also detected. Super resolution microscopy of non-stimulated T cells resulted in identification of a circular subplasmalemmal region with a diameter of approx. 300 nm with preformed patches of co-localized ORAI1, ryanodine receptors (RYR), and STIM1. Preformed complexes of STIM1 and ORAI1 in non-stimulated cells were confirmed by co-immunoprecipitation and Förster resonance energy transfer studies. Furthermore, within the first second of T cell receptor (TCR) stimulation, Ca2+ microdomain numbers increase in the subplasmalemmal space, an effect not observed upon genetic deletion of Orai1, Stim2 or Ryr1 or upon antagonism of the Ca2+ mobilizing second messenger nicotinic acid adenine dinucleotide phosphate (NAADP). Taken together, while preformed clusters of STIM and ORAI1 allow for local Ca2+ entry events in non-stimulated cells, upon TCR activation, NAADP-evoked Ca2+ release via RYR1, in tight interplay with Ca2+ entry via ORAI1 and STIM, rapidly increases the number of Ca2+ microdomains, thereby initiating spread of Ca2+ signals deeper into the cytoplasm to promote full T cell activation.

Transcription factors (TFs) are key regulators of intrinsic cellular processes, such as differentiation and development, and of the cellular response to external perturbation through signaling pathways. In this review we focus on the role of TFs as a link between signaling pathways and gene regulation. Cell signaling tends to result in the modulation of a set of TFs that then lead to changes in the cell's transcriptional programme. We highlight the molecular layers at which TF activity can be measured and the associated technical and conceptual challenges. These layers include post-translational modifications of the TF, regulation of TF binding to DNA through chromatin accessibility and epigenetics, and expression of target genes. We highlight that a large number of TFs are understudied in both signaling and gene regulation studies, and that our knowledge about known TF targets has a strong literature bias. We argue that TFs serve as a perfect bridge between the fields of gene regulation and signaling, and that separating these fields hinders our understanding of cell functions. Multiomics approaches that measure multiple dimensions of TF activity are ideally suited to study the interplay of cell signaling and gene regulation using TFs as the anchor to link the two fields.

An emerging theme from large‐scale genetic screens that identify genes essential for cell fitness is that essentiality of a given gene is highly context‐specific. Identification of such contexts could be the key to defining gene function and also to develop novel therapeutic interventions. Here, we present Context‐specific Essentiality Network‐tools (CEN‐tools), a website and python package, in which users can interrogate the essentiality of a gene from large‐scale genome‐scale CRISPR screens in a number of biological contexts including tissue of origin, mutation profiles, expression levels and drug responses. We show that CEN‐tools is suitable for the systematic identification of genetic dependencies and for more targeted queries. The associations between genes and a given context are represented as dependency networks (CENs), and we demonstrate the utility of these networks in elucidating novel gene functions. In addition, we integrate the dependency networks with existing protein–protein interaction networks to reveal context‐dependent essential cellular pathways in cancer cells. Together, we demonstrate the applicability of CEN‐tools in aiding the current efforts to define the human cellular dependency map.

BackgroundTo find the best individual chemotherapy for cancer patients, the efficacy of different chemotherapeutic drugs can be predicted by pretesting tumor samples in vitro via the chemotherapy-resistance (CTR)-Test®. Although drug combinations are widely used among cancer therapy, so far only single drugs are tested by this and other tests. However, several first line chemotherapies are combining two or more chemotherapeutics, leading to the necessity of drug combination testing methods.MethodsWe established a system to measure and predict the efficacy of chemotherapeutic drug combinations with the help of the Loewe additivity concept in combination with the CTR-test. A combination is measured by using half of the monotherapy’s concentration of both drugs simultaneously. With this method, the efficacy of a combination can also be calculated based on single drug measurements.ResultsThe established system was tested on a data set of ovarian carcinoma samples using the combination carboplatin and paclitaxel and confirmed by using other tumor species and chemotherapeutics. Comparing the measured and the calculated values of the combination testings revealed a high correlation. Additionally, in 70% of the cases the measured and the calculated values lead to the same chemotherapeutic resistance category of the tumor.ConclusionOur data suggest that the best drug combination consists of the most efficient single drugs and the worst drug combination of the least efficient single drugs. Our results showed that single measurements are sufficient to predict combinations in specific cases but there are exceptions in which it is necessary to measure combinations, which is possible with the presented system.