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Integrating single cell omics and single cell imaging allows for a more effective characterisation of the underlying mechanisms that drive a phenotype at the tissue level, creating a comprehensive profile at the cellular level. Although the use of imaging data is well established in biomedical research, its primary application has been to observe phenotypes at the tissue or organ level, often using medical imaging techniques such as MRI, CT, and PET. These imaging technologies complement omics-based data in biomedical research because they are helpful for identifying associations between genotype and phenotype, along with functional changes occurring at the tissue level. Single cell imaging can act as an intermediary between these levels. Meanwhile new technologies continue to arrive that can be used to interrogate the genome of single cells and its related omics datasets. As these two areas, single cell imaging and single cell omics, each advance independently with the development of novel techniques, the opportunity to integrate these data types becomes more and more attractive. This review outlines some of the technologies and methods currently available for generating, processing, and analysing single-cell omics- and imaging data, and how they could be integrated to further our understanding of complex biological phenomena like ageing. We include an emphasis on machine learning algorithms because of their ability to identify complex patterns in large multidimensional data.

Many scenarios in cellular communication requires cells to interpret multiple dynamic signals. It is unclear how exposure to immune stimuli alters transcriptional responses to subsequent stimulus under inflammatory conditions. Using high-throughput microfluidic live cell analysis, we systematically profiled the NF-κB response to different signal sequences in single cells. We found that NF-κB dynamics stores the history of signals received by cells: depending on the dose and type of prior pathogenic and cytokine signal, the NF-κB response to subsequent stimuli varied widely, from no response to full activation. Using information theory, we revealed that these stimulus-dependent changes in the NF-κB response encode and reflect information about the identity and dose of the prior stimulus. Small-molecule inhibition, computational modeling, and gene expression profiling show that this encoding is driven by stimulus-dependent engagement of negative feedback modules. These results provide a model for how signal transduction networks process sequences of inflammatory stimuli to coordinate cellular responses in complex dynamic environments.

Motivation: Cell-cell communications regulate internal cellular states of the cell, e.g., gene expression and cell functions, and play pivotal roles in normal development and disease states. Furthermore, single-cell RNA sequencing methods have revealed cell-to-cell expression variability of highly variable genes (HVGs), which is also crucial. Nevertheless, the regulation on cell-to-cell expression variability of HVGs via cell-cell communications is still unexplored. The recent advent of spatial transcriptome measurement methods has linked gene expression profiles to the spatial context of single cells, which has provided opportunities to reveal those regulations. The existing computational methods extract genes with expression levels that are influenced by neighboring cell types based on the spatial transcriptome data. However, limitations remain in the quantitativeness and interpretability: it neither focuses on HVGs, considers cooperation of neighboring cell types, nor quantifies the degree of regulation with each neighboring cell type. Results: Here, we propose CCPLS (Cell-Cell communications analysis by Partial Least Square regression modeling), which is a statistical framework for identifying cell-cell communications as the effects of multiple neighboring cell types on cell-to-cell expression variability of HVGs, based on the spatial transcriptome data. For each cell type, CCPLS performs PLS regression modeling and reports coefficients as the quantitative index of the cell-cell communications. Evaluation using simulated data showed our method accurately estimated effects of multiple neighboring cell types on HVGs. Furthermore, by applying CCPLS to the two real datasets, we demonstrate CCPLS can be used to extract biologically interpretable insights from the inferred cell-cell communications.

Chlamydomonas reinhardtii is a model organism of increasing biotechnological importance, yet, the evaluation of its life cycle processes and photosynthesis on a single-cell level is largely unresolved. To facilitate the study of the relationship between morphology and photochemistry, we established microfluidics in combination with chlorophyll a fluorescence induction measurements. We developed two types of microfluidic platforms for single-cell investigations: (i) The traps of the “Tulip” device are suitable for capturing and immobilizing single cells, enabling the assessment of their photosynthesis for several hours without binding to a solid support surface. Using this “Tulip” platform, we performed high-quality non-photochemical quenching measurements and confirmed our earlier results on bulk cultures that non-photochemical quenching is higher in ascorbate-deficient mutants (Crvtc2-1) than in the wild-type. (ii) The traps of the “Pot” device were designed for capturing single cells and allowing the growth of the daughter cells within the traps. Using our most performant “Pot” device, we could demonstrate that the FV/FM parameter, an indicator of photosynthetic efficiency, varies considerably during the cell cycle. Our microfluidic devices, therefore, represent versatile platforms for the simultaneous morphological and photosynthetic investigations of C. reinhardtii on a single-cell level.

In the field of cell and tissue engineering, there is an increasing demand for techniques to spatially control the adhesion of cells to substrates of desired sizes and shapes. Here, we describe two novel methods for fabricating a substrate for adhesion of cells to a defined area. In the first method, the surface of the coverslip or plastic dish was coated with Lipidure, a non-adhesive coating material, and air plasma was applied through a mask with holes, to confer adhesiveness to the surface. In the second method, after the surface of the coverslip was coated with gold by sputtering and then with Lipidure; the Lipidure coat was locally removed using a novel scanning laser ablation method. These methods efficiently confined cells within the adhesive area and enabled us to follow individual cells for a longer duration, compared to the currently available commercial substrates. By following single cells within the confined area, we were able to observe several new aspects of cell behavior in terms of cell division, cell–cell collisions, and cell collision with the boundary between adhesive and non-adhesive areas.

Abstract Malignant melanoma is among the tumor entities with the highest increase of incidence worldwide. To elucidate melanoma progression and develop new effective therapies, rodent models are commonly used. While these do not adequately reflect human physiology, two-dimensional cell cultures lack crucial elements of the tumor microenvironment. To address this shortcoming, we have developed a melanoma skin equivalent based on an open-source epidermal model. Melanoma cell lines with different driver mutations were incorporated into these models forming distinguishable tumor aggregates within a stratified epidermis. Although barrier properties of the skin equivalents were not affected by incorporation of melanoma cells, their presence resulted in a higher metabolic activity indicated by an increased glucose consumption. Furthermore, we re-isolated single cells from the models to characterize the proliferation state within the respective model. The applicability of our model for tumor therapeutics was demonstrated by treatment with a commonly used v-raf murine sarcoma viral oncogene homolog B (BRAF) inhibitor vemurafenib. This selective BRAF inhibitor successfully reduced tumor growth in the models harboring BRAF-mutated melanoma cells. Hence, our model is a promising tool to investigate melanoma development and as a preclinical model for drug discovery.

Peritoneal metastases are the leading cause of morbidity and mortality in ovarian cancer. Cancer cells float in peritoneal fluid, named ascites, together with a definitely higher number of non neo-neoplastic cells, as single cells or multicellular aggregates. The aim of this work is to uncover the features that make these aggregates the metastasizing units. Immunofluorescence revealed that aggregates are made almost exclusively of ovarian cancer cells expressing the specific nuclear PAX8 protein. The same cells expressed epithelial and mesenchymal markers, such as EPCAM and αSMA, respectively. Expression of fibronectin further supported a hybrid epithelia-mesenchymal phenotype, that is maintained when aggregates are cultivated and proliferate. Hematopoietic cells as well as macrophages are negligible in the aggregates, while abundant in the ascitic fluid confirming their prominent role in establishing an eco-system necessary for the survival of ovarian cancer cells. Using ovarian cancer cell lines, we show that cells forming 3D structures neo-expressed thoroughly fibronectin and αSMA. Functional assays showed that αSMA and fibronectin are necessary for the compaction and survival of 3D structures. Altogether these data show that metastasizing units display a hybrid phenotype that allows maintenance of the 3D structures and the plasticity necessary for implant and seeding into peritoneal lining.

Abstract Background In breast cancer (BC), overexpression of HER2 on the primary tumor (PT) is determined by immunohistochemistry (IHC) or fluorescence in situ hybridization (FISH) to stratify samples as negative, equivocal and positive to identify patients (pts) for anti-HER2 therapy. CAP/ASCO guidelines recommend FISH for analyzing HER2/neu (ERBB2) gene amplification and for resolving equivocal HER2 IHC results. However, pre-analytical and analytical aspects are often confounded by sample related limitations and tumor heterogeneity and HER2 expression may differ between the PT and circulating tumor cells (CTCs), the precursors of metastasis. We used a validation cohort of BC patients to establish a new DEPArray™-PT-HER2-FISH workflow for further application in a development cohort, characterized as PT-HER2-negative but CTC-HER2/neu-positive, to identify patients with PT-HER2 amplified cells not detected by routine pathology. Methods 50 µm FFPE tumor curls from the validation cohort (n = 49) and the development cohort (n = 25) underwent cutting, deparaffinization and antigen retrieval followed by dissociation into a single-cell suspension. After staining for cytokeratin, vimentin, DAPI and separation via DEPArray™, single cells were processed for HER2-FISH analysis to assess the number of chromosome 17 and HER2 loci signals for comparison, either with available IHC or conventional tissue section FISH. CTC-HER2/neu status was determined using the AdnaTest BreastCancer (QIAGEN, Hilden, Germany). Results Applying CAP/ASCO guidelines for HER2 evaluation of single PT cells, the comparison of routine pathology and DEPArray™-HER2-FISH analysis resulted in a concordance rate of 81.6% (40/49 pts) in the validation cohort and 84% (21/25 pts) in the development cohort, respectively. In the latter one, 4/25 patients had single HER2-positive tumor cells with 2/25 BC patients proven to be HER2-positive, despite being HER2-negative in routine pathology. The two other patients showed an equivocal HER2 status in the DEPArray™-HER2-FISH workflow but a negative result in routine pathology. Whereas all four patients with discordant HER2 results had already died, 17/21 patients with concordant HER2 results are still alive. Conclusions The DEPArray™ system allows pure tumor cell recovery for subsequent HER2/neu FISH analysis and is highly concordant with conventional pathology. For PT-HER2-negative patients, harboring HER2/neu-positive CTCs, this approach might allow caregivers to more effectively offer anti-HER2 treatment.

The goal of this study was to quantify the variability of confocal laser scanning microscopy (CLSM) time-lapse images of early colonizing biofilms to aid in the design of future imaging experiments. To accomplish this a large imaging dataset consisting of 16 independent CLSM microscopy experiments was leveraged. These experiments were designed to study interactions between human neutrophils and single cells or aggregates of Staphylococcus aureus (S. aureus) during the initial stages of biofilm formation. Results suggest that in untreated control experiments, variability differed substantially between growth phases (i.e., lag or exponential). When studying the effect of an antimicrobial treatment (in this case, neutrophil challenge), regardless of the inoculation level or of growth phase, variability changed as a frown-shaped function of treatment efficacy (i.e., the reduction in biofilm surface coverage). These findings were used to predict the best experimental designs for future imaging studies of early biofilms by considering differing (i) numbers of independent experiments; (ii) numbers of fields of view (FOV) per experiment; and (iii) frame capture rates per hour. A spreadsheet capable of assessing any user-specified design is included that requires the expected mean log reduction and variance components from user-generated experimental results. The methodology outlined in this study can assist researchers in designing their CLSM studies of antimicrobial treatments with a high level of statistical confidence.

Accurate annotation of transcript isoforms is crucial for functional genomics research, but automated methods for reconstructing full-length transcripts from RNA sequencing (RNA-seq) data are imprecise. We developed a generalized transcript assembly framework called Bookend that incorporates data from multiple modes of RNA-seq, with a focus on identifying, labeling, and deconvoluting RNA 5′ and 3′ ends. Through end-guided assembly with Bookend we demonstrate that correctly modeling transcript start and end sites is essential for precise transcript assembly. Furthermore, we discover that reads from full-length single-cell RNA-seq (scRNA-seq) methods are sparsely end-labeled, and that these ends are sufficient to dramatically improve precision of assembly in single cells. Finally, we show that hybrid assembly across short-read, long-read, and end-capture RNA-seq in the model plant Arabidopsis and meta-assembly of single mouse embryonic stem cells (mESCs) are both capable of producing tissue-specific end-to-end transcript annotations of comparable or superior quality to existing reference isoforms.