Fluorescence - Organ

Two distinct populations of double positive cells — characterized by different GFP expression levels — were collected as indicated by gates 3 and 4 Figures 1G and S1B. In addition, we sorted two populations that expressed GFP exclusively but again at different intensity levels as shown in gate 2 and 7.

We hypothesized that cells from these two gates correspond to supporting cell subtypes outside the Fgfr3-domain, namely GER, inner border and inner phalangeal cells. Finally, we sampled 12 cells expressing tdTomato alone that served as a negative control and define cell types from regions outside the organ of Corti gate 8, Figure 1G and S1B. Applying all five gates we collected in total single cells from this mouse line. The fourth mouse line featured GFP knocked into the Lgr5 locus.

As previously reported Chai et al. In total we collected single cells, representative for all hair cell and supporting cell types of the organ of Corti.

Materials and Methods

For quality control purposes and downstream analyses each cell is associated to a string of metadata that is of non-transcriptional information including multi-well plate position, date of experiment, mouse line, FACS gate and whether it came from the cochlear apex or base.

A quarter of the assayed genes represented previously known markers associated with cellular identities Table S2. The remaining primer pairs monitored the activity of signaling pathway related genes that have been implicated in early inner ear development or were detectable in previously conducted bulk RNA analyses of neonatal cochlea-derived cells Sajan et al. Data was normalized as previously reported Durruthy-Durruthy et al.

We hypothesized that a grouping strategy, applied in successively ranked steps would allow us to classify all cells and identify the major organ of Corti cell types Figure 2A,B. For this purpose we employed k -means clustering MacQueen, , an unsupervised learning algorithm that associates similar objects of a given data set here cells to an a priori defined number of clusters.

To visualize the multivariate data and recognize patterns, we performed principal component analysis PCA, Jolliffe, and projected the data onto a compressed two-dimensional coordinate system. Sequential clustering of organ of Corti cell types into cell type specific groups. A Legend and color key for panels C-K. B Subcluster tree of identified populations of all sorted cells across ten levels I — X ; grey boxes indicate intermediate groups and black boxes indicate final groups.

C Principal component analysis PCA and k -means clustering of all cells. Shown are projections onto lower-dimensional data space principal component scores PC1 and PC2 visualizing different color-coded parameters. Each data point represents a single cell. FACS-Gates 1 — 8. Variable factor map indicates six representative genes that are most contributive for data separation. This representation shows the correlation between the first two components and the original variables genes , which are called component loadings.

For each population expression levels of one representative marker is projected onto data, here Jag2 and Jag1. Gene expression levels range from grey absent , green low expression to red high expression. Highest gate contribution for each group is indicated. D-K Analogous data representation for subsequently clustered subpopulations.

L Table summarizing total cell number determined for each identified population. Related to Figures S2, S3 and S6. We hypothesized that the cells could be initially subdivided into major cell identities, comprising the presumptive hair cells and supporting cell lineages. In contrast, the cells of the other group lacked expression of hair cell markers and predominantly expressed supporting cell associated genes like Jag1, Cdkn1b p27 Kip1 and Sox9 Chen and Segil, ; Lanford et al. To further discriminate within each of the identified groups of presumptive hair cells and supporting cells we iteratively applied our integrative approach to dissect the dataset by successive bifurcations into smaller subpopulations Figure 2B.

The non-hair cell group of cells was bisected by k -means into two groups that were notably characterized by either absence or presence of Fgfr3 expression Figures 2E and S3C. According to our sorting strategy, we expected these gates to contain inner border, inner phalangeal and GER cells. Consistent with this assumption those cells were characterized by high Gjb2 and Gjb6 Connexin 26 and 30 expression Zhang et al.

Reciprocal Etv4 Mansour et al. Lgr5 Chai et al. Based on the opposing expression patterns of Lgr5 and Kcnj10 Kir4. Cells associated with the smaller group stem mainly from gate 6, gate 7 and the negative control gate 8. The fact that all negative control cells constituted this population Figure S3J , prompted us to consider these cells as off target Figure S3K.

Contemplating all previously identified cell populations we expected the remaining Fgfr3-negative cells, which were all Sox2-positive, to represent either GER cells, inner border cells or inner phalangeal cells. We were able to separate 96 presumptive inner border and inner phalangeal cells from the GER population based on characteristic Lfng Kiernan et al. Different levels of Otol1 Deans et al. The groups of cells are not characterized by single marker gene expression, but rather by the aggregate of all genes assayed. Likewise, our analysis does not simply apply binary codes i.

Compared with published expression studies, which were often not conducted at exactly the same age P2 as our study, we generally confirmed previous reports Table S2. We contend that the identified 9 organ of Corti derived groups represent a quantitative and high-resolution blueprint of the neonatal organ and we utilized this platform to reconstruct some of the fundamental spatial features of the organ of Corti in silico. The morphology of the cochlea resembles a snail shell-like structure in which organ of Corti cells spiral in rows along the longitudinal apex-to-base axis.

Our experimental design allowed for the identification of apical- and basal-derived cells as we encoded their origin during the procedural steps of dissection and FACS Figures 3A and S1. This result agrees with our expectations because all cells originated from a contiguous structure and most differentially expressed genes occur in gradients along the apex-to-base axis of the cochlear duct Lelli et al. Calculating the centroid locations for apical and basal-derived cells within the 2D coordinate system allowed us to determine a vector along which an apex-to-base gradient might be represented most accurately.

PC1 was contributing most to the presumed spatial separation throughout all cell populations, only the second contributor varied Figure S4A,B. For the purpose of data visualization we aligned all nine cell populations in their respective coordinate space such that the apex-to-base axis defined by the centroid-centroid vector was presented vertically Figure 3D. This geometric modification resulted in the data being rotated into an apical-to-basal orientation. Finally, we assembled the nine cell populations side-by-side in an order that reflects the medial-to-lateral anatomical axis Figure 3E.

Prediction of the apex-to-base accuracy was confirmed by projecting data of cochlear duct cells purposely isolated from the middle turn or from the whole organ, showing that their location in the map was in agreement with the region they were dissected from Figure S5. The resulting digitally reconstructed 2D single cell map of the organ of Corti can be interrogated to display expression patterns for each of the genes Figure S6. Spatial trajectory analysis of organ of Corti longitudinal rows. A Schematic illustration and color code. Tissue was dissected into apical and basal halves prior to cell sorting.

No obvious segregation of apical versus basal derived cells is apparent. This distance measurement was repeated across all ten combinatorial 2-dimension representations for PC1 to PC5, and the representation with largest centroid distance was selected for subsequent analysis see Figure S4A,B. D Data space was rotated such that the centroid-centroid-vector was orientated parallel to PC1, apex facing up. To accomplish row-format appearance of the data, it was constricted along the x-axis and visualized in a rank normalized format.

Cells are projected onto 2D subspace and colored based on apical blue or basal orange origin. This control was done to validate the accuracy of the methodology and is illustrated in detail in Figure S5. Related to Figures S1, S4 and S5. The accuracy of the organ of Corti map was assessed by comparing patterns of fluorescent reporter expression of the mouse lines used in this study with measured transcript expression levels.

Neither inner phalangeal cells nor inner border cells showed significantly elevated Atoh1 mRNA expression levels compared to the remaining supporting cell populations Figure 4G. Next we projected only gate 1 sorted cells on the map to highlight that all gate 1 sorted Atoh1-nGFP cells were allocated to IHCs and OHCs as well as inner border and inner phalangeal cells. This analysis effectively exemplifies the high level of accuracy of the in silico map. Comparative validation of the organ of Corti map.

G Statistical analyses of mean expression level differences pairwise among all nine cell type groups. Related to Figures S6 and S7. High GFP intensity was detectable in all supporting cell populations whereas the hair cell populations showed less intense fluorescence Figure 4B.

Assuming that gate 2 sorted cells would correspond to Fgfr3-negative supporting cell types, they were correctly linked with GER, inner border and inner phalangeal cells. Combining the Glast- and the Sox2 alleles allowed us to extract the target populations at high purity, as validated by the computed in situ data projection. Strong tdTomato fluorescence was detected in both cell types Figure 4E. Projecting gates 3 and 4 sorted cells onto the map showed that both supporting cell populations were associated with gate 3, whereas preferentially apical OHCs originated in gate 4 only.

Imaging of organ of Corti whole mount preparations confirmed higher rates of recombination in apical versus basal OHCs, potentially a reflection of the higher levels of Fgfr3 mRNA expression in the apex at P2 Figure 4F. FACS, qRT-PCR and microscopy data concurred with each other such that tdTomato intensity was detected at equivalent levels for both hair cell and supporting cell populations. In conclusion, the expression analysis of reporter gene-based marker activity validated the spatial accuracy of the organ of Corti map.

Figure S7B illustrates the expression patterns of 16 marker genes previously identified to be associated with specific cell populations, further illustrating a high level of prediction accuracy. The established organ of Corti map reveals mRNA expression patterns of individual genes, similar to conventional mRNA in situ hybridization assays. The novelty lies in the fact that expression pattern evaluation can be carried out in parallel for many genes, at single cell resolution, and in readily quantitative format that is digitalized.

Successive PCA and k -means clustering resulted in 9 subpopulations associated with different organ of Corti cell types. To exclude a potential bias introduced by this approach, we employed hierarchical clustering as an additional unsupervised statistical approach and visualized the data in conventional heatmap format. Eleven different hierarchical clusters were found Figure 5A. Hierarchical clusters 1 and 2 corresponded to inner and outer hair cell populations, respectively.

Cluster 6 matched inner border and inner phalangeal cells and cluster 7 contained the GER cell population. Hierarchical clusters 8, 9, 10 and 11 corresponded to the off target cell populations Figure S3J,K , which did not contribute to the organ of Corti map. The 7 organ of Corti groups identified by hierarchical clustering matched the previously determined 9 cell populations to a very high degree In addition, clustering was not guided by confounding factors such as the date of experiment, different FACS gates or different mouse lines as indicated by the unbiased spread of cells across and within the hierarchical clusters Figure 5A,B.

Hierarchical clustering of all single cells.

Differential expression of fluorescence is observed. Analysis of the images with the Image-J software allowed quantification of the fluorescence intensity, which was then converted into a bar graph Figure 5 where a clear difference is discernable.

The pancreas displays much greater fluorescence than the other organs of the GI tract although not reaching statistical significance due to the low number of animals and the high variability observed among the different mice; its fluorescence intensity, on average, was By comparison, the fluorescence intensity for the spleen was 7. Cyan fluorescent protein fluorescence intensity by organ. Fluorescence intensity was quantified using Image-J software. The pancreas expressed the cyan fluorescent protein most strongly, particularly in comparison to the rest of the GI tract and the lungs. Using an inverted fluorescence microscope, we imaged the pancreas, along with the peritoneum and the muscle as well as the liver and the spleen Figure 6.

The findings from macro fluorescence imaging were confirmed at the microscopic level. The staining demonstrated that the acinar cells of the exocrine pancreas expressed blue fluorescence under fluorescence microscopy, while islet cells did not. Fluorescence microscopy of selected tissue.

Associated Data

The microscopic images were obtained in parallel with whole organ images, clearly demonstrating cyan fluorescence in the pancreas a. By contrast, microscopic images of the liver b. The image shows the microscopic fluorescence of pancreatic tissue. The adjoining picture is a blow-up of the inset box and focuses on an area of non-fluorescence. The vein v , collapsed in this picture, and an artery a do not display cyan fluorescence either. Although not shown in this figure, the pancreatic duct cells did not display cyan fluorescence. Fluorescent proteins offer the advantages of color-coding cells and proteins, permitting identification of the different cellular components of tissues as well as proteins, and organelles within cells.

With fluorescent proteins, even single molecules can be imaged in cells [ 5 ]. Up to now, fluorescence imaging has been limited by a fairly narrow range of color choices, relying mainly on the GFP and RFP, which can, for example, be combined in a single cell to develop dual colored cells, with each protein coloring either the cytoplasm or the nucleus [ 6 ] or be used to color code cell-cell interaction [ 7 , 8 ]. Although CFP has been available, its use has been limited due to its short wavelength.

These transgenic mice were bred to generate homozygotes and reported to express fluorescence in all cell types, except erythrocytes and adipocytes. Of particular interest, the pancreas was by far the most fluorescent of all intra-abdominal organs. The basis of the enhanced fluorescence of the pancreas is as yet unclear and should be the subject of further study.

It is possible that the activity of the beta-actin promoter is highest in the pancreas. Nevertheless, our serendipitous finding should allow for the development of color-coded imaging studies of pancreatic function and pathology by breeding our model with currently available color-coded imaging models. For instance, Hara et al. The application of this technology to the CFP mouse would yield an ideal tool to specifically study differences in function and responses to pathology between the endocrine and exocrine components of the pancreas.

Likewise, our laboratory has previously published models for color-coded imaging of the tumor microenvironment and tumor-host interactions [ 7 , 8 , 11 , 12 ]. In this context, the CFP mouse would add yet another color to our armamentarium for color-coding tumor-host interactions in the case of pancreatic cancer.

Recent advances on in vivo imaging with fluorescent proteins. Methods Cell Biol ; GNPs-normal rat demonstrated that normal hepatocyte is shown in Figure 4. Cloudy swelling with pale cytoplasm and poorly delineated and displaced nuclei in all GNPs-treated rats observed Figure 5.

Cellular swelling might be accompanied by leakage of lysosomal hydrolytic enzymes that lead to cytoplasmic degeneration and macromolecular crowding [ 13 — 15 ]. The vacuolated swelling of the cytoplasm of the hepatocytes of the GNPs-treated rats might indicate acute and subacute liver injury induced by these NPs. Variable nuclei sizes were observed in some hepatocytes. The GNPs-normal rat demonstrated normal hepatocyte Figure 4. The sinusoidal Kupffer cells became prominent and increased in number due to GNPs exposure.

Kupffer cells activation might indicate that GNPs activate the phagocytic activity of the sinusoidal cells by increasing the number of kupffer cells to help in removing the accumulated GNPs where lysosomes are involved in the intracellular breakdown into small metabolic products. The produced Kupffer cells hyperplasia might be correlated with the amount of injurious to the hepatic tissue induced by GNPs intoxication and represent a defense mechanism of detoxification.

Kupffer cell hyperplasia contributes to hepatic oxidative stress [ 13 , 14 ]. Sporadic spotty well-defined necrosis was noticed in some hepatocytes of GNPs-treated rats. The insulted cells exhibited highly eosinophilic amorphous cytoplasm with occasional apoptotic characterization Figure 7. Apoptotic alteration might be followed by organelles swelling, specially mitochondria, endoplasmic reticulum, and rupture of lysosomes which might lead to amorphous eosinophilic cytoplasm as an initial sign in the sequence of hepatocytes necrosis before shrinking and dissolution of nuclei [ 13 , 14 ].

The seen hepatocytes necrosis due to GNPs exposure might indicate oxidative stress on these cells by glutathione depletion [ 16 , 17 ]. More vacuolar degeneration was observed in the renal cells of rats exposed to 7 days than ones exposed to 3 days [ 18 , 19 ] Figure Droplets appearance is associated with protein metabolism disturbances. Microscopic pictures show that GNPs-normal rat demonstrated benign, blunt-looking heart muscle with various heart muscle orientations and with no pathological findings [ 18 , 19 ] Figure The SOD which catalyzes the dismutation of the superoxide anion into hydrogen peroxide and molecular oxygen is one of the most important antioxidative enzymes.

This indicates the increased production of free radicals or ROS in these organs as a result of intraperitoneal administration of GNPs into rats, concomitant with the increased production of MDA. Haseeb Khan et al. The smaller particles tend to be more toxic than the larger ones. It has been found that GNPs with a long blood circulation time can accumulate in the liver and spleen and significantly affect the gene expression [ 36 ]. The liver and spleen are considered two dominant organs for biodistribution and metabolism of GNPs [ 27 , 34 — 36 ].

If GNPs are larger than renal filtration cutoff, they are not excreted in urine; instead they are eliminated from the blood by the reticuloendothelial system and thus tend to accumulate in the spleen and liver [ 34 , 37 ]. This result demonstrates that the fluorescence intensity is GNPs size and exposure duration dependent, while the fluorescence peak intensity in the lung organ of rats for G1A was higher than G1B and for G2A than G2B. The results of this study indicate that decrease in GNPs size produces an exponential increase in surface area relative to volume, which may make the GNPs more self-reactive i.

Moreover, increased uptake of NPs may lead to accumulation in certain tissues, where the particles may interfere with critical biological functions [ 9 , 24 ]. The smaller nanoparticles size imparts physical and chemical properties that are very different from those of the same material in bulk form. They have a larger surface area to volume ratio compared to bulk materials; they may thus exhibit an enhanced or hindered tendency to aggregate depending on the surface chemistry , enhanced photoemission, high electrical or heat conductivity, or improved surface catalytic activity [ 22 , 24 ].

The nanoparticle surfaces can interact with biological components, and nanoparticles may be more reactive than larger particles toward biomolecules. GNPs were ultimately trapped by macrophages in the spleen and liver and remained in these tissues until 4 weeks after the single injection [ 10 ]. Nanoparticles for therapeutic use need to have a long retention time in order to encounter and interact with the desired target. However, a long retention time can result in toxic effects in vivo.