Multiscale topology classifies cells in subcellular spatial transcriptomics.

Spatial transcriptomics measures in situ gene expression at millions of locations within a tissue1, hitherto with some trade-off between transcriptome depth, spatial resolution and sample size2. Although integration of image-based segmentation has enabled impactful work in this context, it is limited by imaging quality and tissue heterogeneity. By contrast, recent array-based technologies offer the ability to measure the entire transcriptome at subcellular resolution across large samples3-6. Presently, there exist no approaches for cell type identification that directly leverage this information to annotate individual cells. Here we propose a multiscale approach to automatically classify cell types at this subcellular level, using both transcriptomic information and spatial context. We showcase this on both targeted and whole-transcriptome spatial platforms, improving cell classification and morphology for human kidney tissue and pinpointing individual sparsely distributed renal mouse immune cells without reliance on image data. By integrating these predictions into a topological pipeline based on multiparameter persistent homology7-9, we identify cell spatial relationships characteristic of a mouse model of lupus nephritis, which we validate experimentally by immunofluorescence. The proposed framework readily generalizes to new platforms, providing a comprehensive pipeline bridging different levels of biological organization from genes through to tissues.

standR: spatial transcriptomic analysis for GeoMx DSP data.

To gain a better understanding of the complexity of gene expression in normal and diseased tissues it is important to account for the spatial context and identity of cells in situ. State-of-the-art spatial profiling technologies, such as the Nanostring GeoMx Digital Spatial Profiler (DSP), now allow quantitative spatially resolved measurement of the transcriptome in tissues. However, the bioinformatics pipelines currently used to analyse GeoMx data often fail to successfully account for the technical variability within the data and the complexity of experimental designs, thus limiting the accuracy and reliability of the subsequent analysis. Carefully designed quality control workflows, that include in-depth experiment-specific investigations into technical variation and appropriate adjustment for such variation can address this issue. Here, we present standR, an R/Bioconductor package that enables an end-to-end analysis of GeoMx DSP data. With four case studies from previously published experiments, we demonstrate how the standR workflow can enhance the statistical power of GeoMx DSP data analysis and how the application of standR enables scientists to develop in-depth insights into the biology of interest.

High-fidelity imaging of intracellular microRNA via a bioorthogonal nanoprobe.

The spatiotemporal visualization of intracellular microRNA (miRNA) plays a critical role in the diagnosis and treatment of malignant disease. Although DNAzyme-based biosensing has been regarded as the most promising candidate, inefficient analytical resolution is frequently encountered. Here, we propose a bioorthogonal approach toward high-fidelity imaging of intracellular miRNA by designing a multifunctional nanoprobe that integrates MnO2 nanosheet-mediated intracellular delivery and activation by a fat mass and obesity-associated protein (FTO)-switched positive feedback. MnO2 nanosheets facilitate nanoprobe delivery and intracellular DNAzyme cofactors are released upon glutathione-triggered reduction. Meanwhile, an m6A-caged DNAzyme probe could be bioorthogonally activated by intracellular FTO to eliminate potential off-target activation. Therefore, the activated DNAzyme probe and substrate probe could recognize miRNA to perform cascade signal amplification in the initiation of the release of Mn2+ from MnO2 nanosheets. This strategy realized high-fidelity imaging of intracellular aberrant miRNA within tumor cells with a satisfactory detection limit of 9.7 pM, paving the way to facilitate clinical tumor diagnosis and prognosis monitoring.

Activation of Intracellular Complement in Lungs of Patients With Severe COVID-19 Disease Decreases T-Cell Activity in the Lungs.

A novel coronavirus, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), arose late in 2019, with disease pathology ranging from asymptomatic to severe respiratory distress with multi-organ failure requiring mechanical ventilator support. It has been found that SARS-CoV-2 infection drives intracellular complement activation in lung cells that tracks with disease severity. However, the cellular and molecular mechanisms responsible remain unclear. To shed light on the potential mechanisms, we examined publicly available RNA-Sequencing data using CIBERSORTx and conducted a Ingenuity Pathway Analysis to address this knowledge gap. In complement to these findings, we used bioinformatics tools to analyze publicly available RNA sequencing data and found that upregulation of complement may be leading to a downregulation of T-cell activity in lungs of severe COVID-19 patients. Thus, targeting treatments aimed at the modulation of classical complement and T-cell activity may help alleviate the proinflammatory effects of COVID-19, reduce lung pathology, and increase the survival of COVID-19 patients.

Steric interactions and out-of-equilibrium processes control the internal organization of bacteria.

Despite the absence of a membrane-enclosed nucleus, the bacterial DNA is typically condensed into a compact body-the nucleoid. This compaction influences the localization and dynamics of many cellular processes including transcription, translation, and cell division. Here, we develop a model that takes into account steric interactions among the components of the Escherichia coli transcriptional-translational machinery (TTM) and out-of-equilibrium effects of messenger RNA (mRNA) transcription, translation, and degradation, to explain many observed features of the nucleoid. We show that steric effects, due to the different molecular shapes of the TTM components, are sufficient to drive equilibrium phase separation of the DNA, explaining the formation and size of the nucleoid. In addition, we show that the observed positioning of the nucleoid at midcell is due to the out-of-equilibrium process of mRNA synthesis and degradation: mRNAs apply a pressure on both sides of the nucleoid, localizing it to midcell. We demonstrate that, as the cell grows, the production of these mRNAs is responsible for the nucleoid splitting into two lobes and for their well-known positioning to 1/4 and 3/4 positions on the long cell axis. Finally, our model quantitatively accounts for the observed expansion of the nucleoid when the pool of cytoplasmic mRNAs is depleted. Overall, our study suggests that steric interactions and out-of-equilibrium effects of the TTM are key drivers of the internal spatial organization of bacterial cells.

Speckled 100 kDa gene in pigs: Alternative splicing, subcellular localization, and response to interferon-α stimulation.

Speckled 100 kDa (Sp100) plays an important role in the antiviral immune response, however, little is known about porcine Sp100. In this study, porcine Sp100 was cloned and its response to interferon (IFN) α was identified. We obtained the cDNA (V1) of the gene, SP100, and seven alternative splicing variants (V2-8). Isoform V1 encoded a 386 amino acid protein and contained a homogeneously-staining region (HSR) domain. Isoforms V3, 4, 6 and 7 were deletion/insertion variants and contained HSR domain as V1. The splicing of porcine SP100 was very complicated and many transcripts existed as revealed by cloning and minigene analyses. Using GFP-fusion constructs isoforms V1, 3, 4, 6 and 7 were localized to nucleus and the nuclear localization signal was identified as PSNRKRR at positions 331-337 of V1. Porcine SP100 was unevenly distributed in all tissues studied and differentially expressed between pigs with different disease-resistance/susceptibilities. Porcine SP100 was strongly increased by IFNα due to the existence of an IFN-stimulated response element in the promoter. A single nucleotide - 70A > C polymorphism enhanced promoter activity. The results provided the basis for determining the role of Sp100 in antiviral responses and may assist in breeding pigs with high disease-resistance.

Dual transcriptional analysis reveals adaptation of host and pathogen to intracellular survival of Pseudomonas aeruginosa associated with urinary tract infection.

Long-term survival of bacterial pathogens during persistent bacterial infections can be associated with antibiotic treatment failure and poses a serious public health problem. Infections caused by the Gram-negative pathogen Pseudomonas aeruginosa, which can cause both acute and chronic infections, are particularly challenging due to its high intrinsic resistance to antibiotics. The ineffectiveness of antibiotics is exacerbated when bacteria reside intracellularly within host cells where they can adopt a drug tolerant state. While the early steps of adherence and entry of P. aeruginosa into mammalian cells have been described, the subsequent fate of internalized bacteria, as well as host and bacterial molecular pathways facilitating bacterial long-term survival, are not well defined. In particular, long-term survival within bladder epithelial cells has not been demonstrated and this may have important implications for the understanding and treatment of UTIs caused by P. aeruginosa. Here, we demonstrate and characterize the intracellular survival of wild type (WT) P. aeruginosa inside bladder epithelial cells and a mutant with a disruption in the bacterial two-component regulator AlgR that is unable to survive intracellularly. Using simultaneous dual RNA-seq transcriptional profiling, we define the transcriptional response of intracellular bacteria and their corresponding invaded host cells. The bacterial transcriptional response demonstrates that WT bacteria rapidly adapt to the stress encountered in the intracellular environment in contrast to ΔalgR bacteria. Analysis of the host transcriptional response to invasion suggests that the NF-κB signaling pathway, previously shown to be required for extracellular bacterial clearance, is paradoxically also required for intracellular bacterial survival. Lastly, we demonstrate that intracellular survival is important for pathogenesis of P. aeruginosa in vivo using a model of murine urinary tract infection. We propose that the unappreciated ability of P. aeruginosa to survive intracellularly may play an important role in contributing to the chronicity and recurrence of P. aeruginosa in urinary tract infections.

Cytoplasmic-translocated Ku70 senses intracellular DNA and mediates interferon-lambda1 induction.

We have previously identified that human Ku70, a nuclear protein, serves as a cytosolic DNA sensor. Upon transfection with DNA or infection with DNA virus, Ku70 translocates from the nucleus into the cytoplasm and then predominately induces interferon lambda1 (IFN-λ1) rather than IFN-alpha or IFN-beta, through a STING-dependent signalling pathway. However, a detailed mechanism for Ku70 cytoplasmic translocation and its correlation with IFN-λ1 induction have not been fully elucidated. Here, we observed that cytoplasmic translocation of Ku70 only occurred in DNA-triggered IFN-λ1-inducible cells. Additionally, infection by Herpes simplex virus type-1 (HSV-1), a DNA virus, induces cytoplasmic translocation of Ku70 and IFN-λ1 induction in a strain-dependent manner: the translocation and IFN-λ1 induction were detected upon infection by HSV-1 McKrae, but not MacIntyre, strain. A kinetic analysis indicated that cytoplasmic translocation of Ku70 was initiated right after DNA transfection and was peaked at 6 hr after DNA stimulation. Furthermore, treatment with leptomycin B, a nuclear export inhibitor, inhibited both Ku70 translocation and IFN-λ1 induction, suggesting that Ku70 translocation is an essential and early event for its cytosolic DNA sensing. We further confirmed that enhancing the acetylation status of the cells promotes Ku70's cytoplasmic accumulation, and therefore increases DNA-mediated IFN-λ1 induction. These findings provide insights into the molecular mechanism by which the versatile sensor detects pathogenic DNA in a localization-dependent manner.

MirLocPredictor: A ConvNet-Based Multi-Label MicroRNA Subcellular Localization Predictor by Incorporating k-Mer Positional Information.

MicroRNAs (miRNA) are small noncoding RNA sequences consisting of about 22 nucleotides that are involved in the regulation of almost 60% of mammalian genes. Presently, there are very limited approaches for the visualization of miRNA locations present inside cells to support the elucidation of pathways and mechanisms behind miRNA function, transport, and biogenesis. MIRLocator, a state-of-the-art tool for the prediction of subcellular localization of miRNAs makes use of a sequence-to-sequence model along with pretrained k-mer embeddings. Existing pretrained k-mer embedding generation methodologies focus on the extraction of semantics of k-mers. However, in RNA sequences, positional information of nucleotides is more important because distinct positions of the four nucleotides define the function of an RNA molecule. Considering the importance of the nucleotide position, we propose a novel approach (kmerPR2vec) which is a fusion of positional information of k-mers with randomly initialized neural k-mer embeddings. In contrast to existing k-mer-based representation, the proposed kmerPR2vec representation is much more rich in terms of semantic information and has more discriminative power. Using novel kmerPR2vec representation, we further present an end-to-end system (MirLocPredictor) which couples the discriminative power of kmerPR2vec with Convolutional Neural Networks (CNNs) for miRNA subcellular location prediction. The effectiveness of the proposed kmerPR2vec approach is evaluated with deep learning-based topologies (i.e., Convolutional Neural Networks (CNN) and Recurrent Neural Network (RNN)) and by using 9 different evaluation measures. Analysis of the results reveals that MirLocPredictor outperform state-of-the-art methods with a significant margin of 18% and 19% in terms of precision and recall.

Effects of spatial heterogeneity on bacterial genetic circuits.

Intracellular spatial heterogeneity is frequently observed in bacteria, where the chromosome occupies part of the cell's volume and a circuit's DNA often localizes within the cell. How this heterogeneity affects core processes and genetic circuits is still poorly understood. In fact, commonly used ordinary differential equation (ODE) models of genetic circuits assume a well-mixed ensemble of molecules and, as such, do not capture spatial aspects. Reaction-diffusion partial differential equation (PDE) models have been only occasionally used since they are difficult to integrate and do not provide mechanistic understanding of the effects of spatial heterogeneity. In this paper, we derive a reduced ODE model that captures spatial effects, yet has the same dimension as commonly used well-mixed models. In particular, the only difference with respect to a well-mixed ODE model is that the association rate constant of binding reactions is multiplied by a coefficient, which we refer to as the binding correction factor (BCF). The BCF depends on the size of interacting molecules and on their location when fixed in space and it is equal to unity in a well-mixed ODE model. The BCF can be used to investigate how spatial heterogeneity affects the behavior of core processes and genetic circuits. Specifically, our reduced model indicates that transcription and its regulation are more effective for genes located at the cell poles than for genes located on the chromosome. The extent of these effects depends on the value of the BCF, which we found to be close to unity. For translation, the value of the BCF is always greater than unity, it increases with mRNA size, and, with biologically relevant parameters, is substantially larger than unity. Our model has broad validity, has the same dimension as a well-mixed model, yet it incorporates spatial heterogeneity. This simple-to-use model can be used to both analyze and design genetic circuits while accounting for spatial intracellular effects.

A large-scale binding and functional map of human RNA-binding proteins.

Many proteins regulate the expression of genes by binding to specific regions encoded in the genome1. Here we introduce a new data set of RNA elements in the human genome that are recognized by RNA-binding proteins (RBPs), generated as part of the Encyclopedia of DNA Elements (ENCODE) project phase III. This class of regulatory elements functions only when transcribed into RNA, as they serve as the binding sites for RBPs that control post-transcriptional processes such as splicing, cleavage and polyadenylation, and the editing, localization, stability and translation of mRNAs. We describe the mapping and characterization of RNA elements recognized by a large collection of human RBPs in K562 and HepG2 cells. Integrative analyses using five assays identify RBP binding sites on RNA and chromatin in vivo, the in vitro binding preferences of RBPs, the function of RBP binding sites and the subcellular localization of RBPs, producing 1,223 replicated data sets for 356 RBPs. We describe the spectrum of RBP binding throughout the transcriptome and the connections between these interactions and various aspects of RNA biology, including RNA stability, splicing regulation and RNA localization. These data expand the catalogue of functional elements encoded in the human genome by the addition of a large set of elements that function at the RNA level by interacting with RBPs.

Breast Cancer Candidate Gene Detection Through Integration of Subcellular Localization Data With Protein-Protein Interaction Networks.

Due to technological advances the quality and availability of biological data has increased dramatically in the last decade. Analysing protein-protein interaction networks (PPINs) in an integrated way, together with subcellular compartment data, provides such biological context, helps to fill in the gaps between a single type of biological data and genes causing diseases and can identify novel genes related to disease. In this study, we present BCCGD, a method for integrating subcellular localization data with PPINs that detects breast cancer candidate genes in protein complexes. We achieve this by defining the significance of the compartment, constructing edge-weighted PPINs, finding protein complexes with a non-negative matrix factorization approach, generating disease-specific networks based on the known disease genes, prioritizing disease candidate genes with a WDC method. As a case study, we investigate the breast cancer but the techniques described here are applicable to other disorders. For the top genes scored by BCCGD approach, we utilize the literature retrieving method to test the correlations of them with the breast cancer. The results show that BCCGD discover some novel breast cancer candidate genes which are valuable references for the biomedical scientists.

Distinct Processing of lncRNAs Contributes to Non-conserved Functions in Stem Cells.

Long noncoding RNAs (lncRNAs) evolve more rapidly than mRNAs. Whether conserved lncRNAs undergo conserved processing, localization, and function remains unexplored. We report differing subcellular localization of lncRNAs in human and mouse embryonic stem cells (ESCs). A significantly higher fraction of lncRNAs is localized in the cytoplasm of hESCs than in mESCs. This turns out to be important for hESC pluripotency. FAST is a positionally conserved lncRNA but is not conserved in its processing and localization. In hESCs, cytoplasm-localized hFAST binds to the WD40 domain of the E3 ubiquitin ligase ß-TrCP and blocks its interaction with phosphorylated ß-catenin to prevent degradation, leading to activated WNT signaling, required for pluripotency. In contrast, mFast is nuclear retained in mESCs, and its processing is suppressed by the splicing factor PPIE, which is highly expressed in mESCs but not hESCs. These findings reveal that lncRNA processing and localization are previously under-appreciated contributors to the rapid evolution of function.

An Entropy-Based Method for Identifying Mutual Exclusive Driver Genes in Cancer.

Cancer in essence is a complex genomic alteration disease which is caused by the somatic mutations during the lifetime. According to previous researches, the first step to overcome cancer is to identify driver genes which can promote carcinogenesis. However, it is still a big challenge to precisely and efficiently extract the cancer related driver genes because the nature of cancer is heterogeneous and there exists tremendously irrelevant passenger mutations which have no function impact on the cancer's development. In this work, we proposed a novel entropy-based method namely EntroRank to identify driver genes by integrating the subcellular localization information and mutual exclusive of variation frequency into the network. EntroRank can take into full consideration different properties of driver genes. Considering the modularity of driver genes, the mutated genes in the network were first clustered into different subgroups according to their located compartments. After that, the structural entropy of the gene in the subgroup was employed to measure its indispensability. Considering mutual exclusive property between driver genes in the modules, relative entropy was utilized to measure the degree of mutual exclusive between two mutated genes in terms of their variation frequency. We applied our method to three different cancers including lung, prostate, and breast cancer. The results show our method not only detect the well-known important drivers but also prioritiz the rare unknown driver genes. Besides, EntroRank can identify driver genes having mutual exclusive property. Compared with other existing methods, our method achieves a better performance for most of cancer types in terms of Precision, Recall, and Fscore.

Wnt Signaling in Cancer: Not a Binary ON:OFF Switch.

In the March 1 issue of Cancer Research, we identified the Wnt receptor Fzd7 as an attractive therapeutic target for the treatment of gastric cancer. In summary, we showed that pharmacological inhibition of Wnt receptors, or genetic deletion of Fzd7, blocks the initiation and growth of gastric tumors. Inhibiting Fzd receptors, specifically Fzd7, inhibits the growth of gastric cancer cells even in the presence of adenomatous polyposis coli (Apc) mutation. Apc is located in the cytoplasm downstream of Fzd7 in the Wnt signaling cascade and APC mutations activate Wnt/ß-catenin signaling, therefore, this result seems counterintuitive. Here, we analyze this result in greater detail in the context of current knowledge of Wnt signaling and discuss the wider implications of this aspect of Wnt signaling in other cancers.

Internal gate mutants of the GABA transporter GAT1 are capable of substrate exchange.

GAT1 is a member of the neurotransmitter:sodium: symporter family and mediates transport of GABA together with sodium and chloride in an electrogenic process enabling efficient synaptic transmission. Biochemical and modelling studies based on the structure of the bacterial homologue LeuT are consistent with a transport mechanism whereby the binding pocket is alternately accessible to either side of the membrane. This is achieved by the sequential opening and closing of extracellular and intracellular gates. The amino acid residues participating in the formation of these gates are highly conserved within the neurotransmitter:sodium: symporter family. Net flux requires that the gating mechanism is operative regardless if the binding pocket is loaded with substrate or empty. On the other hand, exchange of labelled for non-labelled substrate across the membrane only requires gating in the presence of substrate. To address the question if the gating requirements of the substrate-bound and empty transporters are similar or different, we analyzed the impact of mutation of intra- and extra-cellular gate residues on net GABA influx and on exchange by liposomes inlaid with the mutant transporters. Whereas net flux by all four internal gate mutants tested was severely abrogated, each exhibited significant levels of exchange. In contrast, two external gate mutants were impaired in both processes. Our results indicate that perturbation of the internal gate of GAT1 selectively impairs the gating mechanism of the empty transporter. This article is part of the issue entitled 'Special Issue on Neurotransmitter Transporters'.

Tissue-Specific Subcellular Localization Prediction Using Multi-Label Markov Random Fields.

The understanding of subcellular localization (SCL) of proteins and proteome variation in the different tissues and organs of the human body are two crucial aspects for increasing our knowledge of the dynamic rules of proteins, the cell biology, and the mechanism of diseases. Although there have been tremendous contributions to these two fields independently, the lack of knowledge of the variation of spatial distribution of proteins in the different tissues still exists. Here, we proposed an approach that allows predicting protein SCL on tissue specificity through the use of tissue-specific functional associations and physical protein-protein interactions (PPIs). We applied our previously developed Bayesian collective Markov random fields (BCMRFs) on tissue-specific protein-protein interaction network (PPI network) for nine types of tissues focusing on eight high-level SCL. The evaluated results demonstrate the strength of our approach in predicting tissue-specific SCL. We identified 1,314 proteins that their SCL were previously proven cell line dependent. We predicted 549 novel tissue-specific localized candidate proteins while some of them were validated via text-mining.

Optogenetic Tools for Subcellular Applications in Neuroscience.

The ability to study cellular physiology using photosensitive, genetically encoded molecules has profoundly transformed neuroscience. The modern optogenetic toolbox includes fluorescent sensors to visualize signaling events in living cells and optogenetic actuators enabling manipulation of numerous cellular activities. Most optogenetic tools are not targeted to specific subcellular compartments but are localized with limited discrimination throughout the cell. Therefore, optogenetic activation often does not reflect context-dependent effects of highly localized intracellular signaling events. Subcellular targeting is required to achieve more specific optogenetic readouts and photomanipulation. Here we first provide a detailed overview of the available optogenetic tools with a focus on optogenetic actuators. Second, we review established strategies for targeting these tools to specific subcellular compartments. Finally, we discuss useful tools and targeting strategies that are currently missing from the optogenetics repertoire and provide suggestions for novel subcellular optogenetic applications.

Widening Spectrum of Cellular and Subcellular Expression of Human GLUD1 and GLUD2 Glutamate Dehydrogenases Suggests Novel Functions.

Mammalian glutamate dehydrogenase1 (GDH1) (E.C. 1.4.1.3) is a mitochondrial enzyme that catalyzes the reversible oxidative deamination of glutamate to α-ketoglutarate and ammonia while reducing NAD+ and/or NADP+ to NADH and/or NADPH. It links amino acid with carbohydrate metabolism, contributing to Krebs cycle anaplerosis, energy production, ammonia handling and redox homeostasis. Although GDH1 was one of the first major metabolic enzymes to be studied decades ago, its role in cell biology is still incompletely understood. There is however growing interest in a novel GDH2 isoenzyme that emerged via duplication in primates and underwent rapid evolutionary selection concomitant with prefrontal human cortex expansion. Also, the anaplerotic function of GDH1 and GDH2 is currently under sharp focus as this relates to the biology of glial tumors and other neoplasias. Here we used antibodies specific for human GDH1 (hGDH1) and human GDH2 (hGDH2) to study the expression of these isoenzymes in human tissues. Results revealed that both hGDH1 and hGDH2 are expressed in human brain, kidney, testis and steroidogenic organs. However, distinct hGDH1 and hGDH2 expression patterns emerged. Thus, while the Sertoli cells of human testis were strongly positive for hGDH2, they were negative for hGDH1. Conversely, hGDH1 showed very high levels of expression in human liver, but hepatocytes were virtually devoid of hGDH2. In human adrenals, both hGDHs were densely expressed in steroid-producing cells, with hGDH2 expression pattern matching that of the cholesterol side chain cleavage system involved in steroid synthesis. Similarly in human ovaries and placenta, both hGDH1 and hGDH2 were densely expressed in estrogen producing cells. In addition, hGDH1, being a housekeeping enzyme, was also expressed in cells that lack endocrine function. Regarding human brain, study of cortical sections using immunofluorescence (IF) with confocal microscopy revealed that hGDH1 and hGDH2 were both expressed in the cytoplasm of gray and white matter astrocytes within coarse structures resembling mitochondria. Additionally, hGDH1 localized to the nuclear membrane of a subpopulation of astrocytes and of the vast majority of oligodendrocytes and their precursors. Remarkably, hGDH2-specific staining was detected in human cortical neurons, with different expression patterns having emerged. One pattern, observed in large cortical neurons (some with pyramidal morphology), was a hGDH2-specific labeling of cytoplasmic structures resembling mitochondria. These were distributed either in the cell body-axon or on the cell surface in close proximity to astrocytic end-feet that encircle glutamatergic synapses. Another pattern was observed in small cortical neurons with round dense nuclei in which the hGDH2-specific staining was found in the nuclear membrane. A detailed description of these observations and their functional implications, suggesting that the GDH flux is used by different cells to serve some of their unique functions, is presented below.

In vitro and ex vivo strategies for intracellular delivery.

Intracellular delivery of materials has become a critical component of genome-editing approaches, ex vivo cell-based therapies, and a diversity of fundamental research applications. Limitations of current technologies motivate development of next-generation systems that can deliver a broad variety of cargo to diverse cell types. Here we review in vitro and ex vivo intracellular delivery approaches with a focus on mechanisms, challenges and opportunities. In particular, we emphasize membrane-disruption-based delivery methods and the transformative role of nanotechnology, microfluidics and laboratory-on-chip technology in advancing the field.