single cell imaging
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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.

We present a multiomic cell atlas of human lung development that combines single cell RNA and ATAC sequencing, high throughput spatial transcriptomics and single cell imaging. Coupling single cell methods with spatial analysis has allowed a comprehensive cellular survey of the epithelial, mesenchymal, endothelial and erythrocyte/leukocyte compartments from 5-22 post conception weeks. We identify new cell states in all compartments. These include developmental-specific secretory progenitors that resemble cells in adult fibrotic lungs and a new subtype of neuroendocrine cell related to human small cell lung cancer; observations which strengthen the connections between development and disease/regeneration. Our datasets are available for the community to download and interact with through our web interface (https://fetal-lung.cellgeni.sanger.ac.uk). Finally, to illustrate its general utility, we use our cell atlas to generate predictions about cell-cell signalling and transcription factor hierarchies which we test using organoid models.

Mycobacterium tuberculosis (Mtb) segregates within multiple subcellular niches with different biochemical and biophysical properties that, upon treatment, may impact antibiotic distribution, accumulation, and efficacy. However, it remains unclear whether fluctuating intracellular microenvironments alter mycobacterial homeostasis and contribute to antibiotic enrichment and efficacy. Here, we describe a live dual-imaging approach to monitor host subcellular acidification and Mtb intrabacterial pH. By combining this approach with pharmacological and genetic perturbations, we show that Mtb can maintain its intracellular pH independently of the surrounding pH in human macrophages. Importantly, unlike bedaquiline (BDQ), isoniazid (INH) or rifampicin (RIF), the drug pyrazinamide (PZA) displays antibacterial efficacy by acting as protonophore which disrupts intrabacterial pH homeostasis in cellulo. By using Mtb mutants, we confirmed that intracellular acidification is a prerequisite for PZA efficacy in cellulo. We anticipate this imaging approach will be useful to identify host cellular environments that affect antibiotic efficacy against intracellular pathogens.

In the age of high-throughput, single-cell biology, single-cell imaging has evolved not only in terms of technological advancements but also in its translational applications. The synchronous advancements of imaging and computational biology have produced opportunities of merging the two, providing the scientific community with tools towards observing, understanding, and predicting cellular and tissue phenotypes and behaviors. Furthermore, multiplexed single-cell imaging and machine learning algorithms now enable patient stratification and predictive diagnostics of clinical specimens. Here, we provide an overall summary of the advances in single-cell imaging, with a focus on high-throughput microscopy phenomics and multiplexed proteomic spatial imaging platforms. We also review various computational tools that have been developed in recent years for image processing and downstream applications used in biomedical sciences. Finally, we discuss how harnessing systems biology approaches and data integration across disciplines can further strengthen the exciting applications and future implementation of single-cell imaging on precision medicine.

SummaryChromatin instability and loss of protein homeostasis (proteostasis) are two well-established hallmarks of aging, which have been considered largely independent of each other. Using microfluidics and single-cell imaging approaches, we observed that, during the replicative aging of S.cerevisiae, proteostasis decline occurred specifically in the fraction of cells with decreased stability at the ribosomal DNA (rDNA) region. A screen of 170 yeast RNA-binding proteins identified ribosomal RNA (rRNA)- binding proteins as the most enriched group that aggregate upon a decrease in rDNA stability. We further found that loss of rDNA stability contributes to age-dependent aggregation of rRNA-binding proteins through aberrant overproduction of rRNAs. These aggregates negatively impact nucleolar integrity and global proteostasis and hence limit cellular lifespan. Our findings reveal a mechanism underlying the interconnection between chromatin instability and proteostasis decline and highlight the importance of cell-to-cell variability in aging processes.

AbstractTumour progression within the tissue microenvironment is accompanied by complex biomechanical alterations of the extracellular environment. While histopathology images provide robust biochemical markers for tumor progression in clinical settings, a quantitative single cell score using nuclear morphology and chromatin organization integrated with the long range mechanical coupling within the tumor microenvironment is missing. We propose that the spatial chromatin organization in individual nuclei characterises the cell state and their alterations during tumor progression. In this paper, we first built an image analysis pipeline and implemented it to classify nuclei from patient derived breast tissue biopsies of various cancer stages based on their nuclear and chromatin features. Replacing H&E with DNA binding dyes such as Hoescht stained tissue biopsies, we improved the classification accuracy. Using the nuclear morphology and chromatin organization features, we constructed a pseudo-time model to identify the chromatin state changes that occur during tumour progression. This enabled us to build a single-cell mechano-genomic score that characterises the cell state during tumor progression from a normal to a metastatic state. To gain further insights into the alterations in the local tissue microenvironments, we also used the nuclear orientations to identify spatial neighbourhoods that have been posited to drive tumor progression. Collectively, we demonstrate that image-based single cell chromatin and nuclear features are important single cell biomarkers for phenotypic mapping of tumor progression.

AbstractCircadian clocks are self-sustained and cell-autonomous oscillators. They respond to various extracellular cues depending on the time-of-day and the signal intensity. Phase Transition Curves (PTCs) are instrumental in uncovering the full repertoire of responses to a given signal. However, the current methodologies for reconstructing PTCs are low-throughput, laborious, and resource- and time-consuming. We report here the development of an efficient and high throughput assay, dubbed Circadian Single-Cell Oscillators PTC Extraction (Circa-SCOPE) for generating high-resolution PTCs. This methodology relies on continuous monitoring of single-cell oscillations to reconstruct a full PTC from a single culture, upon a one-time intervention. Using Circa-SCOPE, we characterize the effects of various pharmacological and blood-borne resetting cues, at high temporal resolution and a wide concentration range. Thus, Circa-SCOPE is a powerful tool for comprehensive analysis and screening for circadian clocks’ resetting cues, and can be valuable for basic as well as translational research.