Single cell versus large population analysis: cell variability in elemental intracellular concentration and distribution.

Depending on age, phase in the cell cycle, nutrition, and environmental factors, individual cells exhibit large metabolic diversity. To explore metabolic variations in cell populations, laser ablation electrospray ionization (LAESI) mass spectrometry (MS) was used for the in situ analysis of individual cells at atmospheric pressure. Single cell ablation was achieved by delivering mid-IR laser pulses through the etched tip of a GeO(2)-based glass fiber. Metabolic analysis was performed from single cells and small cell populations of Allium cepa and Narcissus pseudonarcissus bulb epidermis, as well as single eggs of Lytechinus pictus. Of the 332 peaks detected for A. cepa, 35 were assigned to metabolites with the help of accurate ion masses and tandem MS. The metabolic profiles from single cells of the two plant species included a large variety of oligosaccharides including possibly fructans in A. cepa, and alkaloids, e.g., lycorine in N. pseudonarcissus. Analysis of adjacent individual cells with a difference in pigmentation showed that, in addition to essential metabolites found in both variants, the pigmented cells contained anthocyanidins, other flavonoids, and their glucosides. Analysis of single epidermal cells from different scale leaves in an A. cepa bulb showed metabolic differences corresponding to their age. Our results indicate the feasibility of using LAESI-MS for the in situ analysis of metabolites in single cells with potential applications in studying cell differentiation, changes due to disease states, and response to xenobiotics.

Article Figures and data Abstract Editor's evaluation Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics Abstract Oral inflammatory diseases such as apical periodontitis are common bacterial infectious diseases that may affect the periapical alveolar bone tissues. A protective process occurs simultaneously with the inflammatory tissue destruction, in which mesenchymal stem cells (MSCs) play a primary role. However, a systematic and precise description of the cellular and molecular composition of the microenvironment of bone affected by inflammation is lacking. In this study, we created a single-cell atlas of cell populations that compose alveolar bone in healthy and inflammatory disease states. We investigated changes in expression frequency and patterns related to apical periodontitis, as well as the interactions between MSCs and immunocytes. Our results highlight an enhanced self-supporting network and osteogenic potential within MSCs during apical periodontitis-associated inflammation. MSCs not only differentiated toward osteoblast lineage cells but also expressed higher levels of osteogenic-related markers, including Sparc and Col1a1. This was confirmed by lineage tracing in transgenic mouse models and human samples from oral inflammatory-related alveolar bone lesions. In summary, the current study provides an in-depth description of the microenvironment of MSCs and immunocytes in both healthy and disease states. We also identified key apical periodontitis-associated MSC subclusters and their biomarkers, which could further our understanding of the protective process and the underlying mechanisms of oral inflammatory-related bone disease. Taken together, these results enhance our understanding of heterogeneity and cellular interactions of alveolar bone cells under pathogenic and inflammatory conditions. We provide these data as a tool for investigators not only to better appreciate the repertoire of progenitors that are stress responsive but importantly to help design new therapeutic targets to restore bone lesions caused by apical periodontitis and other inflammatory-related bone diseases. Editor's evaluation Data from scRNA-Seq analysis demonstrated that acute inflammation stimulates periodontal stem cells to differentiate into osteoblast lineage cells to protect the alveolar bone. In murine models and patients with apical periodontitis, the genes and proteins associated with osteogenesis were enriched. The studies help us understand how MSCs respond to inflammation during apical periodontitis disease progression. https://doi.org/10.7554/eLife.82537.sa0 Decision letter Reviews on Sciety eLife's review process Introduction Oral diseases, particularly dental caries and periodontal diseases, affect 3.5 billion people worldwide (Disease et al., 2018). Untreated dental caries can directly lead to pulp necrosis and periapical lesions, resulting in apical periodontitis (AP). Individuals with at least one tooth affected by AP comprise up to 52% of cases, indicating that AP is a highly prevalent disease (Tibúrcio-Machado et al., 2021). Inflammation in the oral cavity can lead to destruction of surrounding periapical tissues and resorption of hard tissues, a consequence of the unbalanced interaction between infection and the immune response (Gazivoda et al., 2009; Márton and Kiss, 2014). Restoring and regenerating the destroyed periapical alveolar bone structures have always been a challenging task in clinical practice. Active inflammation, the tissue injury and the protective process all occur simultaneously in the setting of chronic AP (Márton and Kiss, 2014). Importantly, there is a complex assemblage of immune cell types involved in the pathogenesis, highlighting the importance of polymorphonuclear leukocytes, lymphocytes, and monocyte/macrophages in periapical defense (Braz-Silva et al., 2019; Nair, 2004). Notably, an increasing number of studies report the involvement of mesenchymal stem cells (MSCs) in the protective action that occurs during oral inflammatory diseases, whereby MSCs exert immunomodulatory effects and have regenerative potential (Li et al., 2014; Márton and Kiss, 2000; Nair, 2004). MSC markers such as CD44, CD73, CD90, CD106, and STRO-1 have been observed in human periapical inflammatory tissues (Estrela et al., 2019; Liao et al., 2011). Cells isolated from the inflamed periapical region were able to produce colony-forming unit-fibroblasts (CFU-Fs) with high-osteogenic capacity. It is also reported that interference with MSC mobilization toward the periapex region in an AP mouse model led to enlargement of lesions, accompanied by decreased wound healing markers and increased inflammatory cytokines (Araujo-Pires et al., 2014). These findings indicate the involvement of MSCs in the repair and regeneration of oral inflammatory-related bone lesions. They also suggest that MSCs present promising targets for treating bone lesions, with great potential for modulating inflammation and promoting tissue regeneration. However, most studies have surveyed whole tissues to understand the transcriptomic and cellular profile of these diseases. Specific cell populations and their regulatory molecules, as well as the interaction among different cell populations, remain far from clear. The advances in single-cell technologies offer an unbiased approach for identifying heterogeneous cell subsets, tracking the trajectories of distinct cell clusters and uncovering regulatory relationships between genes (Hwang et al., 2018; Tang et al., 2009). In this study, we collected mandibular alveolar bone samples from control and AP in mice and subjected them to single-cell RNA sequencing (scRNA-seq). The atlas of the mandibular alveolar bone explored the distinct cell subsets and their expression profiles relevant to AP. We also investigated the relationship between MSCs and immune cell subsets. The results reveal the role of a subset of MSCs in inflammation, which showed increased frequency and which formed a self-supporting network. Moreover, MSCs exhibited upregulated osteogenic potential, which was confirmed in transgenic mouse models and human patients with chronic AP. These results advance our understanding of heterogeneity and interactions of alveolar bone cells in the pathogenesis of inflammatory-related bone diseases. Defining key cellular subsets such as MSCs and their biomarkers in inflamed tissue will be important for identifying new therapeutic targets for oral inflammatory-related bone diseases. Results Single-cell transcriptional profiling identified 15 discrete populations in homeostasis and chronic AP samples Individual cells were isolated from alveolar bone of healthy mice and mice with AP. We modeled AP using a well-established AP mouse model in which the mandibular first molar pulp was exposed and subsequently developed chronic AP over a 3 wk period (Taira et al., 2019). Bar-coded cDNA libraries from individual cells were obtained using the 10× Genomics Chromium Controller platform (Zheng et al., 2017; Figure 1A). The combined libraries from healthy and AP alveolar bone contained 15,148 individual cells. The median value of feature_RNA was between 1000 and 2000 (Figure 1—figure supplement 1B). After quality control filtering and removal of the batch effect between batches, the t-stochastic neighbor embedding (t-SNE) method was applied to reduce the dimensionality. Seurat's unbiased cluster detection algorithm identified 15 distinct cell populations (Figure 1B and C). Cluster-specific transcripts were utilized to annotate cell types with classic markers as described in a previous study (Lin et al., 2021). These included B cell (Cd79a), hematopoietic stem cell (HSC) (Cd34), MSC (Col1a1), natural killer (NK) cell (Klrd1), T cell (Cd3g), dendritic cell (Siglech), epithelial cell (Epcam), erythrocyte (Hbb-bt), macrophage (Adgre1), mast cell (Fcer1a), megakaryocyte (Gp1bb), monocyte (Ly6c2), myeloid progenitor (Mpo), neutrophil (S100a8), and pre-B cell (Vpreb1; Figure 1E and F). The top 20 enriched genes in each defined cluster were identified and compared (Figure 1G). Figure 1 with 1 supplement see all Download asset Open asset Identification of the single-cell atlas of alveolar bone using scRNA-sequencing (scRNA-seq) and unbiased clustering. (A) Schematic diagram of the experimental design. (B–C) t-Stochastic neighbor embedding (t-SNE) representation of aligned gene expression data in single cells extracted from mandibles of control mice (n=8340) and apical periodontitis (AP) mice (n=6808) showing 15 distinct clusters and cellular origin. (D) Relative abundance of 15 cell populations composing alveolar bone under healthy and AP conditions. (E) Expression of gene markers in distinct cell types. (F) Gene expression patterns projected onto t-SNE plots of marker genes. Scale: log-transformed gene expression. (G) Heatmap showing the 20 most upregulated genes (ordered by decreasing Padj value) in each cluster defined in B. Scale: log2 fold change. AP led to significant changes in frequency and transcriptional expression of cell populations All the identified cell clusters were present in both AP and control samples, but there were significant differences in the cellular compositions of particular clusters. T cell, B cell, NK cell, macrophage, epithelial cell, and MSC had significantly increased frequency in AP samples. Neutrophil, myeloid progenitor, monocyte, megakaryocyte, mast cell, HSC, and dendritic cell were markedly decreased (Figures 2A and 1D). Figure 2 Download asset Open asset Changes in frequency and transcriptional expression pattern in each cell population from control and apical periodontitis (AP) groups. (A) Bar plot of cells per cluster (AP versus control). Normalization to overall number of inputs per condition. Fisher's exact test with Bonferroni correction was used. *p<0.05, **p<0.01, and ****p<0.0001. All data were exhibited as mean ± SEM. (B) Violin plots of cluster-specific expression of representative genes. (C–G) Violin plots showing genes that significantly changed in each cluster from control and AP. AP is a complex inflammatory process involving innate and adaptive immune responses (Cotti et al., 2014). A variety of inflammatory cells such as neutrophils, mast cells, monocytes, macrophages, and lymphocytes are involved in periapical lesions, highlighting the direct involvement of the immune response in the pathogenesis of AP (Nair, 2004). Neutrophils are important components in the acute phase of AP as a first line of defense. But they are also important in the progression of AP by interacting with microorganisms, leading to tissue damage and chemotaxis (Braz-Silva et al., 2019). Single-cell differential expression analysis revealed that the most significantly enriched genes in neutrophils were various proinflammatory chemokines and cytokines. These included C-X-C motif chemokine ligand 2 (Cxcl2), C-C motif chemokine ligand 6 (Ccl6), NLR family pyrin domain containing 3 (Nlrp3), and Interleukin-1β (Il1b). Notably, we found that C-C motif chemokine receptor like 2 (Ccrl2) was upregulated in neutrophils during AP (Figure 2D). It is responsible for the innate defense against pathogens and is also involved in the regulation of neutrophil migration (Del Prete et al., 2017; Kolaczkowska and Kubes, 2013; Mantovani et al., 2011). Mast cells, monocytes, and macrophages have critical roles in the inflammatory infiltrate during chronic AP (Braz-Silva et al., 2019). The production of Interleukin-6 (Il6) was present in these cell populations with the highest expression level in mast cells. The pro-inflammatory cytokine IL-1β is a key mediator of host response to microbial infection and is associated with the persistence of AP (Morsani et al., 2011; Ng et al., 2008). We found Il1b transcripts in a series of cell types, such as monocyte, macrophage, mast cell, and neutrophil. Of these, macrophages had the highest Il1b expression. Another major cytokine, tumor necrosis factor (Tnf; Cotti et al., 2014), was detected in immunoresponsive cell clusters, such as monocyte, macrophage, mast cell, myeloid progenitor, neutrophil, and HSC, with the highest expression observed in the monocyte population (Figure 2B). Furthermore, gene signatures from monocytes showed that the interferon-induced transmembranes (IFITMs) protein 1 and 2 (Ifitm1 and Ifitm2; Figure 2E) were upregulated the most during AP. These factors have been associated with signal transduction of anti-inflammation activity in the immune system (Yánez et al., 2020). We also detected upregulated expression levels of Ccl9 in the monocyte population from AP. Ccl9 is an important cytokine and is involved in the survival of osteoclasts during the destruction of the periapical bone (Silva et al., 2007). Also, genes coding for pro-inflammatory calcium-binding S100 family proteins such as S100a9 and S100a11 had increased expression in the AP monocyte cluster (Figure 2E). Previous reports demonstrated that macrophages are capable of secreting pro- and anti-inflammatory substances which act on the development and repair of the AP lesions (Italiani and Boraschi, 2014; Shapouri-Moghaddam et al., 2018). Indeed, several genes encoding pro-inflammatory mediators, including Cxcl2, Cxcl16, Il1a, and Ptgs2, were upregulated in macrophages from AP samples (Figure 2C). Expression of anti-inflammatory-associated genes such as Ifitm1 and Ifitm2 was significantly increased in the AP macrophage cluster compared to control cells. Furthermore, Fcγ receptor IIB (Fcgr2b) was markedly upregulated in macrophages from AP samples. Fcgr2b is expressed in most tissue-resident macrophages (Gautier et al., 2012) and functions to inhibit Fcγ-dependent phagocytosis. It also inhibits release of cytokines such as IL-6, TNF-α, IL-1α, as well as neutrophil chemotactants (Clatworthy and Smith, 2004; Espéli et al., 2016). In addition, expression of Apolipoprotein E (Apoe), which can suppress the pro-inflammatory response (Jofre-Monseny et al., 2007), was significantly increased in the macrophage population (Figure 2C). These data indicated the activation of anti-inflammatory factors by macrophages during local inflammation by AP. The major classes of lymphocytes are T lymphocytes, B lymphocytes, and the NK cells. T and B lymphocytes comprise the majority of the inflammatory infiltrate in AP (Graunaite et al., 2012). A significant increase in the expression of inflammatory-associated genes, such as Srgn, Emb, Ctla4, and Il7r could be observed in the AP T cell population (Figure 2F). In the AP B lymphocytes cluster, inflammation-responsive genes (Fth1, Ftl1, Ebf1, and mt-Nd5) were upregulated (Figure 2G). Interestingly, Lars2, the gene encoding a mitochondrial leucyl tRNA synthase (Carminho-Rodrigues et al., 2020; 't Hart et al., 2005), was significantly upregulated in T and B lymphocytes, indicating changes in mitochondrial metabolism in both clusters. Inflammation induces osteoclasts differentiation leading to periapical alveolar bone destruction Bone destruction is a major pathological factor in chronic inflammatory diseases such as AP. Various cytokines including TNF-α, IL-1α, and IL-6 were released by immunocytes to recruit the osteoclast precursors and induce the maturation of osteoclasts (Lyu et al., 2022). We have detected osteoclast markers including Ctsk, Acp5, Mmp9, and Nfatc1 by scRNA-seq. Moreover, Csfr1, Cx3cr1, Itgam, and Tnfrs11a were used to identify osteoclast precursors. Markers of osteoclast and osteoclast precursors were highly expressed in the clusters of monocyte and macrophage (Figure 3A and B). Gene Ontology (GO) analysis showed that inflammation related immune reactions and bone resorption activity were significantly enriched in macrophage cluster (Figure 3C). To further study the differential trajectory of osteoclasts, pseudotime analysis was performed for the clusters of macrophage and monocyte. Two independent branch points were determined, and five monocyte/macrophage subclusters were scattered at different branches in the developmental tree (Figure 3D and G). The results showed that the monocyte cluster differentiated into the macrophage cluster (Figure 3E). During this trajectory, the gene expression pattern across pseudotime showed that osteoclastic genes, such as Ctsk, Acp5, Mmp9, Atp6v0d2, and Dcstamp, were progressively elevated (Figure 3F). Of note, we have observed a branch which was highly positive for Ctsk and Acp5 (Figure 3H), indicating the mature osteoclasts were differentiated from monocyte/macrophage lineage and contributed to inflammatory bone resorption during AP. We have also analyzed the expression of osteoclast related genes using the bulk RNA-seq library built on mandibular samples extracted from mice with AP. Markers of osteoclast and osteoclast precursors were significantly upregulated, confirming the osteoclasts activity in the inflammatory-related bone lesion (Figure 3I). Figure 3 with 1 supplement see all Download asset Open asset Inflammatory-related bone resorption under apical periodontitis (AP) situation. (A) The expression levels of markers of osteoclasts and osteoclast precursors. (B) Violin plots of the expression of osteoclastogenesis genes. (C) Gene Ontology (GO) enrichment analysis of the biological functions of macrophage cluster. (D) Trajectory order of the monocyte/macrophage populations by pseudotime value. (E) The differentiation trajectory of monocyte and macrophage clusters presented on a t-stochastic neighbor embedding (t-SNE) visualization. (F) The expression patterns of osteoclast markers during the trajectory of monocyte/macrophage populations. (G) Distribution of monocytes/macrophages on the developmental tree by clusters. (H) Heatmap of differential genes of three states. (I) Heatmap of genes associated with osteoclastogenesis in bulk RNA-seq analysis. AP leads to reduced transcriptionally inferred cellular interactions with an increased self-supporting network in MSCs We next sought to characterize the cell-cell communication related to the perturbation of signaling pathways detected in the AP samples by employing CellphoneDB (Efremova et al., 2020; Nagai et al., 2021). We identified a close interaction among MSC, macrophage, and dendritic cells under homeostasis conditions (Figure 4A). A similarly close communication was found among MSC, macrophage, and dendritic cells under inflammatory conditions (Figure 4B). Next, we compared the differential cell-cell interaction (CCI) network between AP and control samples using CrossTalkeR (Nagai et al., 2021). The results suggested that AP is associated with an overall decrease in cellular interactions. However, MSC intercellular communication with mast cells and monocytes is upregulated and accompanied by the highest number of interactions within MSCs themselves (Figure 4C). These results indicate that, although cell populations lose their normal physiological interactions, MSCs were able to establish a self-interacting network and coordinate with certain types of cells during chronic AP. Next, we ranked the individual ligands by the number of their interactions. Inflammatory-related proteins (Lgals9, Tnf, and Ccl4), extracellular matrix protein (Fn1), and protein involved in biomineralization (Spp1) were among the highest interactions. Also, Tgfb1, Vegfb, and Vegfa were in the top 10 most abundant ligands (Figure 4D). Bar plots were also generated to display the top 10 upregulated gene/cell pairs, showing that inflammation associated genes (Tnf/neutrophil, Ccl3/mast cell, Ccl3/monocyte, and Il1b/macrophage) and matrix related genes (Sele/MSC, Fn1/MSC, and Fn1/monocyte) were the most influential ligands during AP when compared to control state (Figure 4E). We used a Sankey plot to further focus on MSC-related interactions (Figure 4F). The results indicated that Sele was primarily directed by MSC toward the MSC cluster via multiple receptors including Glg1, Selplg, and Cd44. Moreover, Fn1 was secreted by MSC, monocyte, macrophage, and mast cells toward MSCs (Figure 4G). Of note, Sele and Fn1 are important cell adhesion molecules that mediate cell homing and migration (Frenette et al., 1998; To and Midwood, 2011). This was in accordance with the increased frequency of the MSC cluster (Figure 2A) as well as the upregulated cellular interaction among MSCs and other cell populations. Figure 4 Download asset Open asset Apical periodontitis (AP) suppressed transcriptionally inferred cellular interactions and increased a self-supporting network within the mesenchymal stem cell (MSC) cluster. (A–C) Network plot of ligand-receptor activity in control (A), AP (B), and AP versus control (C). (D) Bar plot of top 10 most abundant ligands in all inferred ligand-receptor interactions. (E) Ranking of ligand/source regarding communication gains in AP state. (F and G) Sankey plot listing all predicted source, receptor, and receiver interactions associated with Fn1 and Sele. scRNA-seq based identification of AP-associated MSC population MSCs represented the non-immune cell population in alveolar bone, constituting 1.76% of total identified cells. This cell population could be decomposed into four subclusters (Figure 5A). The most dominant subcluster was characterized by high expression of Prrx1, platelet-derived growth factor receptor β (Pdgfrb), and hematopoiesis supporting factors such as C-X-C motif chemokine 12 (Cxcl12) and angiopoietin (Angpt1). It was also characterized by osteogenic-related markers, such as Runt-related transcription factor 2 (Runx2), Sp7, and was thus classified as the MSC_osteolineage cells (OLCs). The other subclusters were identified as MSC_endothelial and Figure and B). The cell composition of the MSC cluster indicated an of subcluster during the subcluster was reduced (Figure Of note, we performed lineage tracing using and transgenic mouse models to identify stem cells in alveolar bone and the surrounding periodontal tissues et al., 2020; et al., et al., 2020; et al., 2020). The results suggested increased of and as well as upregulated and periodontal stem cells in AP lesions (Figure supplement 1A). We compared the MSC marker genes, identified among the four enrichment analysis confirmed that there are four MSC populations. were enriched for tissue system and osteoblast MSC_endothelial cells enrichment for and cells were enriched for of and suggested highly of cells, including regulation of protein from mitochondrial and (Figure supplement Figure with 3 see all Download asset Open asset Identification and of apical periodontitis mesenchymal stem cell (MSC) population and (A) t-Stochastic neighbor embedding (t-SNE) representation an of single cells within the MSC cluster. (B) Violin plots of MSC expression of representative genes. (C) The of four subclusters of MSC population were in control and AP groups. (D) Violin plots the changes in the expression of top upregulated genes in the MSC cluster. and of Sparc (E) and (F) in (G) of in periodontal stem cells (H) tracing analysis of and AP stimulates MSC differentiation toward osteoblast lineage cells We next compared the data from the MSC cluster between AP and control groups. the top upregulated genes, we observed a significant in the single cell expression levels of such as secreted protein that is and in and accompanied by a toward increased expression (Figure We their by and bulk RNA analysis. The results revealed that and were significantly upregulated, and to increase during AP (Figure supplement B). In confirmed the upregulated expression of osteogenic-related markers, such as Sparc and in the AP were increased of cells in the bone the protective of MSC to differentiate into and under AP conditions (Figure and F). tracing analysis further revealed that to AP lesions and differentiated into (Figure Moreover, using mouse we identified cells in the alveolar bone, confirming that inflammation could the progenitor cells in the bone and toward thus to the protective during AP (Figure We investigated the heterogeneity of MSCs by branch expression analysis and pseudotime analysis in 2 et al., The results revealed three distinct states. Of these, state 2 and state 3 represented differentiated cell populations Figure We the of the MSCs with to their states. We the pseudotime of each cluster cell in the state and the results indicate that with MSC_endothelial cells, MSCs were able to differentiate into and cells. cells may to an state (Figure Interestingly, in state as osteogenic exhibited the highest toward differentiation with upregulated of and (Figure Notably, this MSC population increased significantly in the AP (Figure A of the of the gene expression pattern across pseudotime revealed of osteogenic genes such as and during inflammation when compared to the state (Figure the MSC subcluster exhibited gene expression levels of osteogenic markers that increased during AP. These results were in accordance with our previous single-cell analysis of gene expression in the AP (Figure Figure 6 Download asset Open asset Apical periodontitis (AP) stimulates mesenchymal stem cell (MSC) differentiation toward osteoblast lineage cells. (A) analysis of the MSC (B) cell differentiation trajectory of MSC populations in control and AP groups. (C) Heatmap of differential genes of three states. (D) Bar plot of changes between AP and control in as identified in pseudotime analysis. Fisher's exact test with Bonferroni correction was used. ****p<0.0001. All data were exhibited as mean ± SEM. (E) between the pseudotime gene trajectories of MSC showed of and line and indicate marker levels were identified in alveolar bone from patients with AP We next osteogenesis within the MSC population was in human patients with AP. The of and were significantly enhanced in alveolar bone from patients with accompanied by in and (Figure Moreover, detected a of and in AP alveolar bone confirming higher osteogenic potential under AP conditions (Figure of markers in AP lesions from patients is with our previous an of osteogenic within the MSC subcluster in alveolar bone under AP conditions. Figure Download asset Open asset alveolar bone in apical periodontitis (AP) is associated with higher subcluster (A) Gene expression of and in samples of healthy and AP patients showed an upregulated expression in AP in healthy and in AP All data are as the mean ± SEM. (B–C) of and in bone of human alveolar bone exhibited high osteogenic protein levels in AP. 10 In summary, the current study the of MSCs and immunoresponsive cells under healthy and chronic AP including heterogeneity in their of We that this analysis a

Recent advances in single-cell ultra-trace analysis

GATA1 mutations Outside the Blast Cell Population in Transient Leukemia of Down Syndrome

Single cell analytics allows quantitative investigation of single biological cells from a structural, functional and proteomics point of view and opens possibilities to a novel unamplified cell analysis inherently insensitive to ensemble-averaging, cell-cycle or cell-population effects. We report on three different experimental methods and their application to cellular systems with single molecule sensitivity at the single cell level. Firstly, atomic force microscopy (AFM) can be used to elucidate the surface structure of living bacteria down to the nanometer scale where identification of irregular surface areas and 2D-arrays of regular protein s-layers is possible. Secondly, single cell manipulation and probing experiments with optical tweezers (OT) force spectroscopy allows quantitative identification of individual recognition events of membrane bound receptors. And thirdly, a novel, single cell analysis for protein fingerprinting in structured microfluidic device format will allow a future (label-free) on-chip electrophoretical protein separation of single cells without preamplification.

In adipocytes, insulin triggers the redistribution of Glut4 from intracellular compartments to the plasma membrane. Two models have been proposed to explain the effect of insulin on Glut4 localization. In the first, termed dynamic exchange, Glut4 continually cycles between the plasma membrane and intracellular compartments in basal cells, and the major effect of insulin is through changes in the exocytic and endocytic rate constants, k(ex) and k(en). In the second model, termed static retention, Glut4 is packaged in specialized storage vesicles (GSVs) in basal cells and does not traffic through the plasma membrane or endosomes. Insulin triggers GSV exocytosis, increasing the amount of Glut4 in the actively cycling pool. Using a flow cytometry-based assay, we found that Glut4 is regulated by both static and dynamic retention mechanisms. In basal cells, 75-80% of the Glut4 is packaged in noncycling GSVs. Insulin increased the amount of Glut4 in the actively cycling pool 4-5-fold. Insulin also increased k(ex) in the cycling pool 3-fold. After insulin withdrawal, Glut4 is rapidly cleared from the plasma membrane (t((1/2)) of 20 min) by rapid adjustments in k(ex) and k(en) and recycled into static compartments. Complete recovery of the static pool required more than 3 h, however. We conclude that in fully differentiated confluent adipocytes, both the dynamic and static retention mechanisms are important for the regulation of plasma membrane Glut4 content. However, cell culture conditions affect Glut4 trafficking. For example, replating after differentiation inhibited the static retention of Glut4, which may explain differences in previous reports.

Electrochemical quantification of four reactive oxygen and nitrogen species (e.g., H2O2, ONOO−, NO and NO2−) emitted from macrophages populations was completed using a four‐chambers microchip. This microchip was fabricated from a glass substrate equipped with four sets of microband electrodes. Each set consisted of a platinum‐black coated working electrode combined to an Ag/AgCl reference and a Pt counter electrodes. A cover made of polydimethylsiloxane was designed with four wells whose open bottoms fitted over each set. Loading, culture and stimulation of cells were conducted within each well. After stimulation of macrophages by a calcium ionophore, amperometric responses were then monitored using a multipotentiostat to detect one species per well simultaneously. The results showed good reproducibility and were quite consistent with averaged amperometric detections obtained from single cell measurements. This methodology allowed drastically diminishing the number of experiments necessary to reach a statistical significance from n=100–160 experiments required for single cell detection compared to n=5 experiments to obtain the mean behavior of the cell population. This work demonstrated that this simple and versatile microchip could be used for quantification of species in oxidative stress investigations on cell populations as well as a complementary tool to compare behaviors between single cell and cell population for investigations involving many experimental variables.

The notion that most cancers are ecosystems of evolving clones is now widely accepted. Hence, residual neoplastic cells that persist following treatment constitute an ideal evolutionary niche from which recurrent cancers arise and are a major obstacle to the successful treatment of human cancers. We have identified that TGFβ signaling contribute to drug resistance by coordinately repressing transcription and translation of p53 in precancerous and cancer cells. However, the molecular events accompanying the evolution towards a drug tolerant phenotype are not well understood. Indeed, severe cellular stress might generate a subpopulation of reversibly stress-tolerant cells under conditions that are lethal to the majority of the population. Drug tolerance attributed to heterogeneity of gene expression within the population to ensure survival of a minority under stress. To address these questions, we developed an experimental paradigm to generate and propagate populations of paclitaxel tolerant precancerous (MCF10A) and metastatic cancer (MDA-MB-231) cells that can resume proliferation after 1 week of consecutive treatment with lethal doses of the drug . We found that drug-tolerant cells reacquired Paclitaxel-sensitivity after expansion. Then we performed whole transcriptome sequencing to identify single nucleotide variants (SNVs) and gene expression profiles at the single-cell level associated with the evolution towards a reversible drug tolerant state in cancer cells. We evaluated untreated, stress-arrested and proliferating drug-tolerant cells from a small (n&amp;lt;64) clonal population. In addition to sequencing the mRNA of single cells, we also performed DNA and RNA sequencing of populations of untreated and drug-tolerant cells to aid in the identification of RNA variants. We performed differential gene expression profiling for single cells and population cells of the three groups to identify the transcriptional stress response and cytotoxic effects of Paclitaxel on gene expression. We find that within untreated, stress-arrested and drug-tolerant cell groups, specific transcriptional programs were enacted while generating high heterogeneity between single cells within and between groups. Drug-tolerant cells contained specific RNA variants encoded by genes involved in microtubule organization and stabilization as well as cell adhesion and cell surface signaling. Unexpectedly, Drug-tolerant cells from a single progenitor cell rapidly reacquired high degree of heterogeneity in gene expression similar to untreated cells, within a few doublings, while uniquely expressing SNVs and genes such as Integrin alpha 6, the histone demethylase KDM5A, and the IGF-1 receptor. Thus, single cell analyses reveal the dynamics of the response to stress in terms of cell-specific SNVs driving heterogeneity, the survival of a minority population through generation of specific genes and RNA variants and the efficient reconversion of drug-tolerant cells back to normalcy, suggesting that such heterogeneity within cancer cell populations might provide evolutionary advantage to increase the pool of drug tolerant cells poising a prospective path towards drug resistance. This abstract is also presented as poster B68. Citation Format: Fernando J. Lopez-Diaz, Mei-Chong Wendy Lee, Muhammad A. Tariq, Yelena Dayn, Shahid Y. Khan, Charlie Vaske, Nader Pourmand, Beverly M. Emerson. Single-cell RNA sequencing reveals phenotypic plasticity of drug tolerant clonal populations of cancer cells. [abstract]. In: Proceedings of the Third AACR International Conference on Frontiers in Basic Cancer Research; Sep 18-22, 2013; National Harbor, MD. Philadelphia (PA): AACR; Cancer Res 2013;73(19 Suppl):Abstract nr PR05.

Metal/Matrix Enhanced Time-of-flight Secondary Ion Mass Spectrometry for Single Cell Lipids Analysis

BackgroundTherapeutic decisions in cancer are generally guided by molecular biomarkers or, for some newer therapeutics, primary tumor genotype. However, because biomarkers or genotypes may change as new metastases emerge, circulating tumor cells (CTCs) from blood are being investigated for a role in guiding real-time drug selection during disease progression, expecting that CTCs will comprehensively represent the full spectrum of genomic changes in metastases. However, information is limited regarding mutational heterogeneity among CTCs and metastases in breast cancer as discerned by single cell analysis. The presence of disseminated tumor cells (DTCs) in bone marrow also carry prognostic significance in breast cancer, but with variability between CTC and DTC detection. Here we analyze a series of single tumor cells, CTCs, and DTCs for PIK3CA mutations and report CTC and corresponding metastatic genotypes.MethodsWe used the MagSweeper, an immunomagnetic separation device, to capture live single tumor cells from breast cancer patients’ primary and metastatic tissues, blood, and bone marrow. Single cells were screened for mutations in exons 9 and 20 of the PIK3CA gene. Captured DTCs grown in cell culture were also sequenced for PIK3CA mutations.ResultsAmong 242 individual tumor cells isolated from 17 patients and tested for mutations, 48 mutated tumor cells were identified in three patients. Single cell analyses revealed mutational heterogeneity among CTCs and tumor cells in tissues. In a patient followed serially, there was mutational discordance between CTCs, DTCs, and metastases, and among CTCs isolated at different time points. DTCs from this patient propagated in vitro contained a PIK3CA mutation, which was maintained despite morphological changes during 21 days of cell culture.ConclusionsSingle cell analysis of CTCs can demonstrate genotypic heterogeneity, changes over time, and discordance from DTCs and distant metastases. We present a cautionary case showing that CTCs from any single blood draw do not always reflect metastatic genotype, and that CTC and DTC analyses may provide independent clinical information. Isolated DTCs remain viable and can be propagated in culture while maintaining their original mutational status, potentially serving as a future resource for investigating new drug therapies.

Microfluidic-based single cells analysis has been of great interest in recent years, promising disease diagnosis and personalized medicine. Current technologies are challenging in bioselectively isolating specific single cells from complex matrices. Herein, a novel microfluidic platform integrated with cell-recognizable aptamer-encoded microwells was specifically developed to isolate single tumor cells with satisfied single-cell occupancy and unique bioselectivity. In this work, the designed microwell-structures enable us to encourage strong 3D local topographic interactions of the target cell surface with biomolecules and regulate the single-cell resolution. Under the optimized size of microwells, the single-cell occupancy was significantly enhanced from 0.5% to 88.2% through the introduction of the aptamer. Analysis of the target cells was directly performed in short time periods (<5.0 min) with small volumes (4.5 μL). Importantly, such an aptamer-enabled microfluidic device shows an excellent selectivity for target single cells isolation compared with three control cells. Subsequently, targeted isolation and analysis of single tumor cells were demonstrated by using artificial complex cell samples at simulated conditions, and various cellular carboxylesterases were studied by time-course measurements of cellular fluorescence kinetics at individual-cell level. Thus, our technique will open up a new opportunity in single-cell level-based disease diagnosis and personalize medicine screening.

Quantifying silver nanoparticle association and elemental content in single cells using dual mass mode in quadrupole-based inductively coupled plasma-mass spectrometry

A microfluidic system was developed for the analysis of single biological cells, with functional integration of cell sampling, single cell loading, docking, lysing, and capillary electrophoretic (CE) separation with laser induced fluorescence (LIF) detection in microfabricated channels of a single glass chip. Channels were 12 microm deep and 48 microm wide, with a simple crossed-channel design. The effective separation channel length was 35 mm. During sampling with a cell suspension (cell population 1.2 x 10(5) cells per mL in physiological salt solution), differential hydrostatic pressure (created by adjusting liquid levels in the four reservoirs) was used to control cell flow exclusively through the channel crossing. Single cell loading into the separation channel was achieved by electrophoretic means by applying a set of potentials at the four reservoirs, counteracting the hydrostatic flow. A special docking (adhering) procedure for the loaded cell was applied before lysis by repeatedly connecting and disconnecting a set of low potentials, allowing precise positioning of the cell within the separation channel. Cell lysis was then effected within 40 ms under an applied CE separation voltage of 1.4 kV (280 V cm(-1)) within the working electrolyte (pH 9.2 borate buffer) without additional lysates. The docked lysing approach reduced dispersion of released intracellular constituents, and significantly improved the reproducibility of CE separations. Glutathione (GSH) was used as a model intracellular component in single human erythrocyte cells. NDA derivatized GSH was detected using LIF. A throughput of 15 samples h(-1), a retention time precision of 2.4% RSD was obtained for 14 consecutively injected cells. The average cellular concentration of GSH in human erythrocytes was found to be 7.2 [times] 10(-4)+/- 3.3 x 10(-4) M (63 +/- 29 amol per cell). The average separation efficiency for GSH in lysed cells was 2.13 x 10(6)+/- 0.4 x 10(6) plates per m, and was about a factor of 5 higher than those obtained with GSH standards using pinched injection.

Our understanding of the diversity of cells that escape the primary tumor and seed micrometastases remains rudimentary, and approaches for studying circulating and disseminated tumor cells have been limited by low throughput and sensitivity, reliance on single parameter sorting, and a focus on enumeration rather than phenotypic and genetic characterization. Here, we utilize a highly sensitive microfluidic and dielectrophoretic approach for the isolation and genetic analysis of individual tumor cells. We employed fluorescence labeling to isolate 208 single cells from spiking experiments conducted with 11 cell lines, including 8 neuroblastoma cell lines, and achieved a capture sensitivity of 1 tumor cell per 106 white blood cells (WBCs). Sample fixation or freezing had no detectable effect on cell capture. Point mutations were accurately detected in the whole genome amplification product of captured single tumor cells but not in negative control WBCs. We applied this approach to capture 144 single tumor cells from 10 bone marrow samples of patients suffering from neuroblastoma. In this pediatric malignancy, high-risk patients often exhibit wide-spread hematogenous metastasis, but access to primary tumor can be difficult or impossible. Here, we used flow-based sorting to pre-enrich samples with tumor involvement below 0.02%. For all patients for whom a mutation in the Anaplastic Lymphoma Kinase gene had already been detected in their primary tumor, the same mutation was detected in single cells from their marrow. These findings demonstrate a novel, non-invasive, and adaptable method for the capture and genetic analysis of single tumor cells from cancer patients.

Cell annotation is a crucial methodological component to interpreting single cell and spatial omics data. These approaches were developed for single cell analysis but are often biased, manually curated and yet unproven in spatial omics. Here we apply a stemness model for assessing oncogenic states to single cell and spatial omic cancer datasets. This one-class logistic regression machine learning algorithm is used to extract transcriptomic features from non-transformed stem cells to identify dedifferentiated cell states in tumors. We found this method identifies single cell states in metastatic tumor cell populations without the requirement of cell annotation. This machine learning model identified stem-like cell populations not identified in single cell or spatial transcriptomic analysis using existing methods. For the first time, we demonstrate the application of a ML tool across five emerging spatial transcriptomic and proteomic technologies to identify oncogenic stem-like cell types in the tumor microenvironment.